Pi puffer life - rule balanced beetwen stability and expansion

[BokaBB]

Why is Pi puffer life called Pilife2 on Catalogue?
Please use valid names!
Any news from here?
Have a good day!

BokaBB

[BokaBB]

I am not retiring at all!
Have a good day!

BokaBB

[yujh]

I am not retiring at all!
Have a good day!

BokaBB

Please, can you post useless things less?(No double posts, pls)

[BokaBB]

I am not retiring at all!
Have a good day!

BokaBB

Please, can you post useless things less?(No double posts, pls)

I want to restore the exploration of my rules.
Have a good day!

BokaBB

[yujh]

I am not retiring at all!
Have a good day!

BokaBB

Please, can you post useless things less?(No double posts, pls)

I want to restore the exploration of my rules.
Have a good day!

BokaBB

Do people stop exploringYOURrules if you are gone? what about CGOL?
If it's interesting enough or people just want to investigate it, they will.
If not, they won't.
Just keep rule-golfing if you want.
You can definetly find more noteable rules-and we want to investigate them.

Edit:I'll apgsearch it.

[BokaBB]

Do you remember that I can't apgsearch?
Have a good day!

BokaBB

[yujh]

Do you remember that I can't apgsearch?
Have a good day!

BokaBB

That's something else.
People will apgsearch(not always for you)
if they want to!

(I hope you wilol have it in the near future!)






OK.
to stop myself from mposting useless things, I will post my apgsearch results here.

Code: Select all

x = 351, y = 297, rule = B34c/S234y8
65b2o$65b2o4$94b2o$94b2o3$64b2o$49b2o13b2o$49b2o5$o$b2o191b2o$2o192b2o
10$39b2o$39b2o20$36b2o$36b2o5$172b2o$172b2o4$207b2o$207b2o5$73b2o$73b
2o16$158b2o32b2o$158b2o32b2o30$221b2o$221b2o9$123b2o$123b2o$233b2o$
233b2o15$185b2o$185b2o31$349b2o$349b2o$289b2o$289b2o3$193b2o$193b2o10$
116b2o$116b2o15$266b2o$266b2o35$222b2o$222b2o21$266b2o$227b2o37b2o$
227b2o28$254b2o$254b2o!

(the most common s4y still life in the blocksoup)P3(bad soup)

Code: Select all

x = 331, y = 286, rule = B34c/S234y8
o$b2o$2o5$51b2o$51b2o23$21b2o$21b2o2$60b2o$60b2o16$85b2o$85b2o67b2o$
154b2o2$51b2o$51b2o2$72b2o$72b2o14$73b2o$73b2o2$140b2o$140b2o7$28b2o$
28b2o20$185b2o$185b2o50$138b2o$138b2o33b2o$173b2o10$86b2o$86b2o14$329b
2o$329b2o2$318b2o$318b2o4$123b2o$123b2o$155b2o$155b2o10$142b2o$127b2o
13b2o$127b2o4$162b2o$162b2o33$158b2o120b2o$158b2o120b2o14$245b2o$245b
2o24$204b2o$204b2o!

boredom pd soup

Code: Select all

x = 331, y = 286, rule = B34c/S234y8
o$b2o$2o5$51b2o$51b2o23$21b2o$21b2o2$60b2o$60b2o16$85b2o$85b2o67b2o$
154b2o2$51b2o$51b2o2$72b2o$72b2o14$73b2o$73b2o2$140b2o$140b2o7$28b2o$
28b2o20$185b2o$185b2o50$138b2o$138b2o33b2o$173b2o10$86b2o$86b2o14$329b
2o$329b2o2$318b2o$318b2o4$123b2o$123b2o$155b2o$155b2o10$142b2o$127b2o
13b2o$127b2o4$162b2o$162b2o33$158b2o120b2o$158b2o120b2o14$245b2o$245b
2o24$204b2o$204b2o!

rarer xs10

Code: Select all

x = 311, y = 291, rule = B34c/S234y8
105b2o$105b2o31$135b2o$135b2o4$128b2o$128b2o$14bo$15b2o$14b2o6$70b2o$
70b2o6$199b2o$199b2o4$64b2o$64b2o2$33b2o$33b2o15$13b2o$13b2o8$2o$2o6$
7b2o$7b2o3$91b2o$91b2o28$212b2o4b2o$212b2o4b2o2$83b2o$83b2o28$120b2o$
120b2o10$309b2o$309b2o11$163b2o$163b2o10$90b2o$90b2o9$280b2o$241b2o37b
2o$241b2o39$229b2o$229b2o13$195b2o$195b2o11$273b2o$273b2o7$211b2o$211b
2o10$230b2o$230b2o!

I need to switch it to 10 blocks.
The worst MWSS seed I've ever seen!!!!

Code: Select all

x = 246, y = 250, rule = B34c/S234y8
o$b2o$2o65$9b2o$9b2o72$70b2o$70b2o6$115b2o$115b2o96$240b2o$240b2o3$
244b2o$244b2o!

[Hunting]

to stop myself from mposting useless things, I will post my apgsearch results here.
<results>

Did you use the Python version of apgsearch (but modified)? If so, can you publish the soup-generating part? This symmetry looks very useful for LeapLife.

By the way, I figured out how to do gutter now! (And dense soups!)

[yujh]

to stop myself from mposting useless things, I will post my apgsearch results here.
<results>

Did you use the Python version of apgsearch (but modified)? If so, can you publish the soup-generating part? This symmetry looks very useful for LeapLife.

By the way, I figured out how to do gutter now! (And dense soups!)

Here(sorry,can't upload)

Code: Select all

# apgsearch-blocksoup v0.3
# Search program which collides a glider into a random field of blonks
# (blocks). Only tested with Conway's Life, but should work for
# any rule which supports the glider, block and blinker.
#
# == Based on apgsearch v0.4 by Adam P. Goucher ==
# 
# Optimised soup searcher, v0.4 (beta release).
#
# -- Processes roughly 100 soups per (second . core . GHz).
#
# -- Can perfectly identify oscillators with period < 1000, well-separated
#    spaceships of low period, and certain infinite-growth patterns (such
#    guns and puffers, including both naturally-occurring types of switch
#    engine).
#
# -- Separates most pseudo-objects into their constituent parts, including
#    all pseudo-still-lifes of 18 or fewer live cells (which is the maximum
#    theoretically possible, given there is a 19-cell pseudo-still-life
#    with two distinct decompositions).
#
# -- Correctly separates non-interacting standard spaceships, irrespective
#    of their proximity. In particular, a LWSS-on-LWSS is registered as two
#    LWSSes, whereas an LWSS-on-HWSS is registered as a single spaceship
#    (since they interact by suppressing sparks).
#
# -- At least 99.9999999% reliable at identifying objects.
#
# -- Scores soups based on the total excitement of the ash objects.
#
# -- Preliminary support for other outer-totalistic rules.
#
# By Adam P. Goucher, with contributions by Andrew Trevorrow,
# Tom Rokicki, Nathaniel Johnston and Dave Greene.
#
# == Modifications by Dave Green ==
# apgsearch v0.4.5
# 
# - Extend list of common names of life objects
# - Store all soupids for rare objects as well as first occurrence for others
# - Save rare-object soupids to the progress folder:
#   new sections in search*.txt, @SAMPLE_SOUPIDS and @RARE_OBJ_SOUPIDS
#
# == Modifications by Arie Paap ==
# apgsearch-blocksoup v0.3
# 
# - Replace hashsoup() with blocksoup() to search result of glider collisions
#   with random blonk fields.
# - Run soup for sufficient time to have first collision.
# - Increase security in stabilise3() to prevent escaping gliders colliding
#   with surrounding blonks after periodicity is detected.
# - Update save_progress() and display_census() to show blocksoup starting
#   conditions.
# - Add @ SEARCH and @RULE lines to progress file when saving
# - Save all soupids for any infinite growth pattern even if included in list
#   of common names (i.e. switch engines).
# - Add diehard detection - starting conditions which result in an empty universe
#   (extremely unlikely for blocksoup)
# - TODO: Add method to check if the intersection of two cell lists is empty, and
#   modify blocksoup() to return a cell list instead of pasting into layer
# - TODO: refactor apgsearch code into separate libraries

import golly as g
from glife import rect, pattern, flip
import glife.base as glb
import time
import math
import operator
import hashlib
import datetime
import os

# Creates a random ash field and runs a glider into it. Ash can consist of
# blonks: blocks or blinkers with sufficient spacing between them to prevent
# interaction. Incoming glider placed to increase probability of an interesting
# reaction occuring.
# Blonks are distributed on a diagonal rectangle of size 128 x 256
#
# Ideally should create pattern and return it instead of placing directly 
# in the universe, but there's no function to perform cell list intersection
# which is needed when checking the blonk mask for ON cells.
# Tried "len(a) + len(b) == golly.join(a, b)", but this always evaluates to true
# because golly.join simply appends b to a
def blonksoup(instring):
    
    # Glider lanes which give interesting reactions
    block_offsets = [-4, -3, -3, -3, 4, 4, 4, 5]
    blinker_offsets = [-4, -5, -2, 1, 0, 3, 6, 5] # Even - phase0; Odd - phase1
    buffer = 10
    
    # Masks to check that region around blonk location is empty
    block_mask = g.parse("3b2o$2b4o$b6o$8o$8o$b6o$2b4o$3b2o!", -3, -3)
    blinker_mask = g.parse("3b2o$2b4o$b6o$8o$8o$b6o$2b4o$3b2o!", -3, -3)
    
    # This gives an array of 64 random bytes to be used in construction
    s = hashlib.sha256(instring).digest()
    s += hashlib.sha256(instring + instring).digest()
    ii = 0 # Index into list of random bytes
    
    # Place a collection of up to twenty five blonks and incoming glider
    # Blonks are placed within a shifted and rotated rectangle <u,v> -> <x,y>
    # u E [ 0, 255 ], v E [ 0, 128 ]
    # x = u - v + 64
    # y = u + v - 64
    # This transform always places an object on cells of a single colour, 
    # so adjust colour as required

    # Place the first blonk and glider
    obj = ord(s[ii])
    u = ord(s[ii+1])
    v = ord(s[ii+2])
    ii += 3
    
    colour = v & 1
    v = v >> 1
    offset = obj & 7
    
    # Location of blonk
    xblonk = u - v + 64 + colour
    yblonk = u + v - 64
    
    if (obj & 8):
        glb.block.put(xblonk, yblonk)
        lane = block_offsets[offset] + xblonk - yblonk
    else:
        # Blinker phase determined by LSB of offset
        glb.block[offset & 1].put(xblonk, yblonk)
        lane = block_offsets[offset] + xblonk - yblonk

    # Add glider to soup
    xglider = (lane + 1) // 2 - buffer
    yglider = - (lane//2) - buffer
    glb.glider.put(xglider, yglider, flip)
    
    # Use next 54 bytes to place the next twenty four blonks.
    
    # First 6 bytes determine object types: 1/2 block; 1/2 blinker (of either phase)
    # Use two bits for each blonk
    objects = []
    for jj in xrange(0, 6):
        obj = ord(s[ii])
        ii += 1
        for kk in xrange(0, 4):
            objects.append(obj & 3)
            obj >> 2
        
    # Remaining 48 bytes determine location of each blonk
    for jj in xrange(0, blocknumber):
        u = ord(s[ii])
        v = ord(s[ii+1])
        color = v & 1
        v = v >> 1
        obj = objects[jj]
        ii += 2
        
        xblonk = u - v + 64 + colour
        yblonk = u + v - 64
        if (obj & 2):
            blonk = glb.block
            mask = g.transform(block_mask, xblonk, yblonk)
        else:
            blonk = glb.block[obj & 1]
            mask = g.transform(blinker_mask, xblonk, yblonk)
            
        occupied = False
        for kk in xrange(0, len(mask), 2):
            if g.getcell(mask[kk], mask[kk+1]):
                occupied = True
        
        if not occupied:
            blonk.put(xblonk, yblonk)
            
    # return thesoup

# Obtains a canonical representation of any oscillator/spaceship that (in
# some phase) fits within a 40-by-40 bounding box. This representation is
# alphanumeric and lowercase, and so much more compact than RLE. Compare:
#
# Common name: pentadecathlon
# Canonical representation: 4r4z4r4
# Equivalent RLE: 2bo4bo$2ob4ob2o$2bo4bo!
#
# It is a generalisation of a notation created by Allan Weschler in 1992.
def canonise(duration):

    representation = "#"

    # We need to compare each phase to find the one with the smallest
    # description:
    for t in xrange(duration):

        rect = g.getrect()
        if (len(rect) == 0):
            return "0"

        if ((rect[2] <= 40) & (rect[3] <= 40)):
            # Fits within a 40-by-40 bounding box, so eligible to be canonised.
            # Choose the orientation which results in the smallest description:
            representation = compare_representations(representation, canonise_orientation(rect[2], rect[3], rect[0], rect[1], 1, 0, 0, 1))
            representation = compare_representations(representation, canonise_orientation(rect[2], rect[3], rect[0]+rect[2]-1, rect[1], -1, 0, 0, 1))
            representation = compare_representations(representation, canonise_orientation(rect[2], rect[3], rect[0], rect[1]+rect[3]-1, 1, 0, 0, -1))
            representation = compare_representations(representation, canonise_orientation(rect[2], rect[3], rect[0]+rect[2]-1, rect[1]+rect[3]-1, -1, 0, 0, -1))
            representation = compare_representations(representation, canonise_orientation(rect[3], rect[2], rect[0], rect[1], 0, 1, 1, 0))
            representation = compare_representations(representation, canonise_orientation(rect[3], rect[2], rect[0]+rect[2]-1, rect[1], 0, -1, 1, 0))
            representation = compare_representations(representation, canonise_orientation(rect[3], rect[2], rect[0], rect[1]+rect[3]-1, 0, 1, -1, 0))
            representation = compare_representations(representation, canonise_orientation(rect[3], rect[2], rect[0]+rect[2]-1, rect[1]+rect[3]-1, 0, -1, -1, 0))

        g.run(1)

    return representation

# A subroutine used by canonise:
def canonise_orientation(length, breadth, ox, oy, a, b, c, d):

    representation = ""

    chars = "0123456789abcdefghijklmnopqrstuvwxyz"

    for v in xrange(int((breadth-1)/5)+1):
        zeroes = 0
        if (v != 0):
            representation += "z"
        for u in xrange(length):
            baudot = 0
            for w in xrange(5):
                x = ox + a*u + b*(5*v + w)
                y = oy + c*u + d*(5*v + w)
                baudot = (baudot >> 1) + 16*g.getcell(x, y)
            if (baudot == 0):
                zeroes += 1
            else:
                if (zeroes > 0):
                    if (zeroes == 1):
                        representation += "0"
                    elif (zeroes == 2):
                        representation += "w"
                    elif (zeroes == 3):
                        representation += "x"
                    else:
                        representation += "y"
                        representation += chars[zeroes - 4]
                zeroes = 0
                representation += chars[baudot]
    return representation

# Compares strings first by length, then by lexicographical ordering.
# A hash character is worse than anything else.
def compare_representations(a, b):

    if (a == "#"):
        return b
    elif (b == "#"):
        return a
    elif (len(a) < len(b)):
        return a
    elif (len(b) < len(a)):
        return b
    elif (a < b):
        return a
    else:
        return b

# Gets the period of an interleaving of degree-d polynomials:
def deepperiod(sequence, maxperiod, degree):

    for p in xrange(1, maxperiod, 1):

        good = True

        for i in xrange(maxperiod):

            diffs = [0] * (degree + 2)
            for j in xrange(degree + 2):

                diffs[j] = sequence[i + j*p]

            # Produce successive differences:
            for j in xrange(degree + 1):
                for k in xrange(degree + 1):
                    diffs[k] = diffs[k] - diffs[k + 1]

            if (diffs[0] != 0):
                good = False
                break

        if (good):
            return p
    return -1

# Analyses a linear-growth pattern, returning a hash:
def linearlyse(maxperiod):

    poplist = [0]*(3*maxperiod)

    for i in xrange(3*maxperiod):

        g.run(1)
        poplist[i] = int(g.getpop())

    p = deepperiod(poplist, maxperiod, 1)

    if (p == -1):
        return "unidentified"

    difflist = [0]*(2*maxperiod)

    for i in xrange(2*maxperiod):

        difflist[i] = poplist[i + p] - poplist[i]

    q = deepperiod(difflist, maxperiod, 0)

    moments = [0, 0, 0]

    for i in xrange(p):

        moments[0] += (poplist[i + q] - poplist[i])
        moments[1] += (poplist[i + q] - poplist[i]) ** 2
        moments[2] += (poplist[i + q] - poplist[i]) ** 3

    prehash = str(moments[1]) + "#" + str(moments[2])

    # Linear-growth patterns with growth rate zero are clearly errors!
    if (moments[0] == 0):
        return "unidentified"

    return "yl" + str(p) + "_" + str(q) + "_" + str(moments[0]) + "_" + hashlib.md5(prehash).hexdigest()

    
# This explodes pseudo-still-lifes and pseudo-oscillators into their
# constituent parts.
#
# -- Requires the period (if oscillatory) and graph-theoretic diameter
#    to not exceed 4096.
# -- Never mistakenly separates a true object.
# -- Correctly separates most pseudo-still-lifes, including the famous:
#    http://www.conwaylife.com/wiki/Quad_pseudo_still_life
# -- Works perfectly for all still-lifes of up to 17 bits.
# -- Doesn't separate 'locks', of which the smallest example has 18
#    bits and is unique:
#
#     ** **
#     ** **
#
#    * *** *
#    ** * **
#
# To use this function (standalone), merely copy it into a script of
# the following form:
#
#   import golly as g
#
#   def pseudo_bangbang():
#
#   [...]
#
#   pseudo_bangbang()
#
# and execute it in Golly with a B3/S23 universe containing any still-
# lifes or oscillators you want to separate. Pure objects correspond to
# connected components in the final state of the universe.
#
# This has dependencies on the rules ContagiousLife, PercolateInfection
# and EradicateInfection.
#
# Not to be confused with the Unix shell instruction for repeating the
# previous instruction as a superuser (sudo !!), or indeed with any
# parodies of this song: https://www.youtube.com/watch?v=YswhUHH6Ufc
#
# Adam P. Goucher, 2014-08-25
def pseudo_bangbang(alpharule):

    g.setrule("APG_ContagiousLife_" + alpharule)
    g.setbase(2)
    g.setstep(12)
    g.step()

    celllist = g.getcells(g.getrect())

    for i in xrange(0, len(celllist)-1, 3):
        
        # Only infect cells that haven't yet been infected:
        if (g.getcell(celllist[i], celllist[i+1]) <= 2):

            # Seed an initial 'infected' (red) cell:
            g.setcell(celllist[i], celllist[i+1], g.getcell(celllist[i], celllist[i+1]) + 2)

            prevpop = 0
            currpop = int(g.getpop())

            # Continue infecting until the entire component has been engulfed:
            while (prevpop != currpop):

                # Percolate the infection to every cell in the island:
                g.setrule("APG_PercolateInfection")
                g.setbase(2)
                g.setstep(12)
                g.step()

                # Transmit the infection across any bridges.
                g.setrule("APG_ContagiousLife_" + alpharule)
                g.setbase(2)
                g.setstep(12)
                g.step()

                prevpop = currpop
                currpop = int(g.getpop())
                
            g.fit()
            g.update()

            # Red becomes green:
            g.setrule("APG_EradicateInfection")
            g.step()


# Counts the number of live cells of each degree:
def degreecount():

    celllist = g.getcells(g.getrect())
    counts = [0,0,0,0,0,0,0,0,0]

    for i in xrange(0, len(celllist), 2):

        x = celllist[i]
        y = celllist[i+1]

        degree = -1

        for ux in xrange(x - 1, x + 2):
            for uy in xrange(y - 1, y + 2):

                degree += g.getcell(ux, uy)

        counts[degree] += 1

    return counts

# Counts the number of live cells of each degree in generations 1 and 2:
def degreecount2():

    g.run(1)
    a = degreecount()
    g.run(1)
    b = degreecount()

    return (a + b)

# If the universe consists only of disjoint *WSSes, this will return
# a triple (l, w, h) giving the quantities of each *WSS. Otherwise,
# this function will return (-1, -1, -1).
#
# This should only be used to separate period-4 moving objects which
# may contain multiple *WSSes.
def countxwsses():

    degcount = degreecount2()
    if (degreecount2() != degcount):
        # Degree counts are not period-2:
        return (-1, -1, -1)

    # Degree counts of each standard spaceship:
    hwssa = [1,4,6,2,0,0,0,0,0,0,0,0,4,4,6,1,2,1]
    mwssa = [2,2,5,2,0,0,0,0,0,0,0,0,4,4,4,1,2,0]
    lwssa = [1,2,4,2,0,0,0,0,0,0,0,0,4,4,2,2,0,0]
    hwssb = [0,0,0,4,4,6,1,2,1,1,4,6,2,0,0,0,0,0]
    mwssb = [0,0,0,4,4,4,1,2,0,2,2,5,2,0,0,0,0,0]
    lwssb = [0,0,0,4,4,2,2,0,0,1,2,4,2,0,0,0,0,0]

    # Calculate the number of standard spaceships in each phase:
    hacount = degcount[17]
    macount = degcount[16]/2 - hacount
    lacount = (degcount[15] - hacount - macount)/2
    hbcount = degcount[8]
    mbcount = degcount[7]/2 - hbcount
    lbcount = (degcount[6] - hbcount - mbcount)/2

    # Determine the expected degcount given the calculated quantities:
    pcounts = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
    pcounts = map(lambda x, y: x + y, pcounts, map(lambda x: hacount*x, hwssa))
    pcounts = map(lambda x, y: x + y, pcounts, map(lambda x: macount*x, mwssa))
    pcounts = map(lambda x, y: x + y, pcounts, map(lambda x: lacount*x, lwssa))
    pcounts = map(lambda x, y: x + y, pcounts, map(lambda x: hbcount*x, hwssb))
    pcounts = map(lambda x, y: x + y, pcounts, map(lambda x: mbcount*x, mwssb))
    pcounts = map(lambda x, y: x + y, pcounts, map(lambda x: lbcount*x, lwssb))

    # Compare the observed and expected degcounts (to eliminate nonstandard spaceships):
    if (pcounts != degcount):
        # Expected and observed values do not match:
        return (-1, -1, -1)

    # Return the combined numbers of *WSSes:
    return(lacount + lbcount, macount + mbcount, hacount + hbcount)


# Generates the helper rules for apgsearch, given a base outer-totalistic rule.
class RuleGenerator:

    # Unless otherwise specified, assume standard B3/S23 rule:
    bee = [False, False, False, True, False, False, False, False, False]
    ess = [False, False, True, True, False, False, False, False, False]
    alphanumeric = "B3S23"
    slashed = "B3/S23"
    ruletype = True
    notationdict = {
        "0"  : [0,0,0,0,0,0,0,0],   #    
        "1e" : [1,0,0,0,0,0,0,0],   #   N
        "1c" : [0,1,0,0,0,0,0,0],   #   NE
        "2a" : [1,1,0,0,0,0,0,0],   #   N,  NE
        "2e" : [1,0,1,0,0,0,0,0],   #   N,  E
        "2k" : [1,0,0,1,0,0,0,0],   #   N,  SE
        "2i" : [1,0,0,0,1,0,0,0],   #   N,  S
        "2c" : [0,1,0,1,0,0,0,0],   #   NE, SE
        "2n" : [0,1,0,0,0,1,0,0],   #   NE, SW
        "3a" : [1,1,1,0,0,0,0,0],   #   N,  NE, E
        "3n" : [1,1,0,1,0,0,0,0],   #   N,  NE, SE
        "3r" : [1,1,0,0,1,0,0,0],   #   N,  NE, S
        "3q" : [1,1,0,0,0,1,0,0],   #   N,  NE, SW
        "3j" : [1,1,0,0,0,0,1,0],   #   N,  NE, W
        "3i" : [1,1,0,0,0,0,0,1],   #   N,  NE, NW
        "3e" : [1,0,1,0,1,0,0,0],   #   N,  E,  S
        "3k" : [1,0,1,0,0,1,0,0],   #   N,  E,  SW
        "3y" : [1,0,0,1,0,1,0,0],   #   N,  SE, SW
        "3c" : [0,1,0,1,0,1,0,0],   #   NE, SE, SW
        "4a" : [1,1,1,1,0,0,0,0],   #   N,  NE, E,  SE
        "4r" : [1,1,1,0,1,0,0,0],   #   N,  NE, E,  S
        "4q" : [1,1,1,0,0,1,0,0],   #   N,  NE, E,  SW
        "4i" : [1,1,0,1,1,0,0,0],   #   N,  NE, SE, S
        "4y" : [1,1,0,1,0,1,0,0],   #   N,  NE, SE, SW
        "4k" : [1,1,0,1,0,0,1,0],   #   N,  NE, SE, W
        "4n" : [1,1,0,1,0,0,0,1],   #   N,  NE, SE, NW
        "4z" : [1,1,0,0,1,1,0,0],   #   N,  NE, S,  SW
        "4j" : [1,1,0,0,1,0,1,0],   #   N,  NE, S,  W
        "4t" : [1,1,0,0,1,0,0,1],   #   N,  NE, S,  NW
        "4w" : [1,1,0,0,0,1,1,0],   #   N,  NE, SW, W
        "4e" : [1,0,1,0,1,0,1,0],   #   N,  E,  S,  W
        "4c" : [0,1,0,1,0,1,0,1],   #   NE, SE, SW, NW
        "5i" : [1,1,1,1,1,0,0,0],   #   N,  NE, E,  SE, S
        "5j" : [1,1,1,1,0,1,0,0],   #   N,  NE, E,  SE, SW
        "5n" : [1,1,1,1,0,0,1,0],   #   N,  NE, E,  SE, W
        "5a" : [1,1,1,1,0,0,0,1],   #   N,  NE, E,  SE, NW
        "5q" : [1,1,1,0,1,1,0,0],   #   N,  NE, E,  S,  SW
        "5c" : [1,1,1,0,1,0,1,0],   #   N,  NE, E,  S,  W
        "5r" : [1,1,0,1,1,1,0,0],   #   N,  NE, SE, S,  SW
        "5y" : [1,1,0,1,1,0,1,0],   #   N,  NE, SE, S,  W
        "5k" : [1,1,0,1,0,1,1,0],   #   N,  NE, SE, SW, W
        "5e" : [1,1,0,1,0,1,0,1],   #   N,  NE, SE, SW, NW
        "6a" : [1,1,1,1,1,1,0,0],   #   N,  NE, E,  SE, S,  SW
        "6c" : [1,1,1,1,1,0,1,0],   #   N,  NE, E,  SE, S,  W
        "6k" : [1,1,1,1,0,1,1,0],   #   N,  NE, E,  SE, SW, W
        "6e" : [1,1,1,1,0,1,0,1],   #   N,  NE, E,  SE, SW, NW
        "6n" : [1,1,1,0,1,1,1,0],   #   N,  NE, E,  S,  SW, W
        "6i" : [1,1,0,1,1,1,0,1],   #   N,  NE, SE, S,  SW, NW
        "7c" : [1,1,1,1,1,1,1,0],   #   N,  NE, E,  SE, S,  SW, W
        "7e" : [1,1,1,1,1,1,0,1],   #   N,  NE, E,  SE, S,  SW, NW
        "8"  : [1,1,1,1,1,1,1,1],   #   N,  NE, E,  SE, S,  SW, W,  NW
        }
    
    allneighbours = [  
        ["0"],
        ["1e", "1c"],
        ["2a", "2e", "2k", "2i", "2c", "2n"],
        ["3a", "3n", "3r", "3q", "3j", "3i", "3e", "3k", "3y", "3c"],
        ["4a", "4r", "4q", "4i", "4y", "4k", "4n", "4z", "4j", "4t", "4w", "4e", "4c"],
        ["5i", "5j", "5n", "5a", "5q", "5c", "5r", "5y", "5k", "5e"],
        ["6a", "6c", "6k", "6e", "6n", "6i"],
        ["7c", "7e"],
        ["8"],
        ]
        
    allneighbours_flat = [n for x in allneighbours for n in x]
    ntbee = {}
    ntess = {}

    # Save all helper rules:
    def saveAllRules(self):
        
        self.saveClassifyObjects()
        self.saveCoalesceObjects()
        self.saveExpungeObjects()
        self.saveExpungeGliders()
        self.saveIdentifyGliders()
        self.saveHandlePlumes()
        self.savePercolateInfection()
        self.saveEradicateInfection()
        self.saveContagiousLife()
        self.savePropagateClassifications()
        self.saveDecayer()
        self.saveTreeMaker()
        if self.t:
            self.saveIdentifyTs()
            self.saveAdvanceTs()
            self.saveAssistTs()
            self.saveExpungeTs()
        
    def testPattern(self, clist, period, moving):
        g.new("Test pattern")
        g.setalgo("QuickLife")
        g.setrule(self.slashed)
        g.putcells(clist)
        r = g.getrect()
        h = g.hash(r)
        g.run(period)
        f = g.getrect()
        if int(g.getpop()) == 0:
            return False
        return h == g.hash(f) and (moving and f != r) or (not moving and f == r)
    
    #Assumes that blinkers exist and are p1 or p2, and that we still don't support B0 rules
    def findBlinkerApgcode(self):
        g.new("Test pattern")
        g.setalgo("QuickLife")
        g.setrule(self.slashed)
        g.setcell(0,0,1)
        g.setcell(0,1,1)
        g.setcell(0,2,1)
        g.step()
        if int(g.getpop()) == 2:
            return "xp2_5"
        elif int(g.getpop()) == 3 and g.getcell(0,0):
            return "xs3_7"
        elif int(g.getpop()) == 3:
            return "xp2_7"
        else:
            return None
    
    #To use this standalone, just copy this into a separate file and add the lines
    '''import golly as g
class Foo:
    slashed = g.getstring("Enter name of rule to test", "Life")'''
    #before it and the lines
    '''foo = Foo()
g.show(foo.testHensel())'''
    #after it, and run it in Golly.
    def testHensel(self):
        #Dict containing all possible transitions:
        dict = { 
                 "0"  : "0,0,0,0,0,0,0,0",
                 "1e" : "1,0,0,0,0,0,0,0",  #   N 
                 "1c" : "0,1,0,0,0,0,0,0",  #   NE
                 "2a" : "1,1,0,0,0,0,0,0",  #   N,  NE
                 "2e" : "1,0,1,0,0,0,0,0",  #   N,  E 
                 "2k" : "1,0,0,1,0,0,0,0",  #   N,  SE
                 "2i" : "1,0,0,0,1,0,0,0",  #   N,  S 
                 "2c" : "0,1,0,1,0,0,0,0",  #   NE, SE
                 "2n" : "0,1,0,0,0,1,0,0",  #   NE, SW
                 "3a" : "1,1,1,0,0,0,0,0",  #   N,  NE, E
                 "3n" : "1,1,0,1,0,0,0,0",  #   N,  NE, SE 
                 "3r" : "1,1,0,0,1,0,0,0",  #   N,  NE, S      
                 "3q" : "1,1,0,0,0,1,0,0",  #   N,  NE, SW
                 "3j" : "1,1,0,0,0,0,1,0",  #   N,  NE, W
                 "3i" : "1,1,0,0,0,0,0,1",  #   N,  NE, NW
                 "3e" : "1,0,1,0,1,0,0,0",  #   N,  E,  S
                 "3k" : "1,0,1,0,0,1,0,0",  #   N,  E,  SW
                 "3y" : "1,0,0,1,0,1,0,0",  #   N,  SE, SW     
                 "3c" : "0,1,0,1,0,1,0,0",  #   NE, SE, SW 
                 "4a" : "1,1,1,1,0,0,0,0",  #   N,  NE, E,  SE
                 "4r" : "1,1,1,0,1,0,0,0",  #   N,  NE, E,  S  
                 "4q" : "1,1,1,0,0,1,0,0",  #   N,  NE, E,  SW
                 "4i" : "1,1,0,1,1,0,0,0",  #   N,  NE, SE, S
                 "4y" : "1,1,0,1,0,1,0,0",  #   N,  NE, SE, SW
                 "4k" : "1,1,0,1,0,0,1,0",  #   N,  NE, SE, W
                 "4n" : "1,1,0,1,0,0,0,1",  #   N,  NE, SE, NW 
                 "4z" : "1,1,0,0,1,1,0,0",  #   N,  NE, S,  SW
                 "4j" : "1,1,0,0,1,0,1,0",  #   N,  NE, S,  W
                 "4t" : "1,1,0,0,1,0,0,1",  #   N,  NE, S,  NW
                 "4w" : "1,1,0,0,0,1,1,0",  #   N,  NE, SW, W
                 "4e" : "1,0,1,0,1,0,1,0",  #   N,  E,  S,  W
                 "4c" : "0,1,0,1,0,1,0,1",  #   NE, SE, SW, NW
                 "5a" : "0,0,0,1,1,1,1,1",  #   SE, S,  SW, W,  NW
                 "5n" : "0,0,1,0,1,1,1,1",  #   E,  S,  SW, W,  NW
                 "5r" : "0,0,1,1,0,1,1,1",  #   E,  SE, SW, W,  
                 "5q" : "0,0,1,1,1,0,1,1",  #   E,  SE, S,  W,  NW
                 "5j" : "0,0,1,1,1,1,0,1",  #   E,  SE, S,  SW, NW 
                 "5i" : "0,0,1,1,1,1,1,0",  #   E,  SE, S,  SW, W 
                 "5e" : "0,1,0,1,0,1,1,1",  #   NE, SE, SW, W,  NW, 
                 "5k" : "0,1,0,1,1,0,1,1",  #   NE, SE, S,  W,  NW
                 "5y" : "0,1,1,0,1,0,1,1",  #   NE, E,  S,  W, NW 
                 "5c" : "1,0,1,0,1,0,1,1",  #   N,  E,  S,  W,  NW
                 "6a" : "0,0,1,1,1,1,1,1",  #   E,  SE, S,  SW, W,  NW
                 "6e" : "0,1,0,1,1,1,1,1",  #   NE, SE, S,  SW, W,  NW
                 "6k" : "0,1,1,0,1,1,1,1",  #   NE, E,  S,  SW, W,  NW
                 "6i" : "0,1,1,1,0,1,1,1",  #   NE, E,  SE, SW, W,  NW
                 "6c" : "1,0,1,0,1,1,1,1",  #   N,  E,  S,  SW, W,  NW
                 "6n" : "1,0,1,1,1,0,1,1",  #   N,  E,  SE, S,  W,  NW
                 "7e" : "0,1,1,1,1,1,1,1",  #   NE, E,  SE, S,  SW, W,  NW 
                 "7c" : "1,0,1,1,1,1,1,1",  #   N,  E,  SE, S,  SW, W,  NW
                 "8"  : "1,1,1,1,1,1,1,1",
                }
        
        #Represents the encoding in dict:
        neighbors = [(-1,0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1)]
        
        #Will store transitions temporarily:
        d2 = [{},{}]
        
        #Used to help a conversion later:
        lnums = []
        for i in xrange(9):
            lnums.append([j for j in dict if int(j[0]) == i])
        
        #Self-explanatory:
        g.setrule(self.slashed)
        
        #Test each transition in turn:
        for i in xrange(2):
            for j in dict:
                j2 = dict[j].split(",")
                g.new("Testing Hensel notation...")
                for k in xrange(len(j2)):
                    k2 = int(j2[k])
                    g.setcell(neighbors[k][0], neighbors[k][1], k2)
                g.setcell(0, 0, i)
                g.run(1)
                d2[i][j] = int(g.getcell(0, 0)) == 1
        
        #Will become the main table of transitions:
        trans_ = [[],[]]
        
        #Will become the final output string:
        not_ = "B"
        for i in xrange(2):
            #Convert d2 to a more usable form
            for j in xrange(9):
                trans_[i].append({})
                for k in lnums[j]:
                    trans_[i][j][k] = d2[i][k]
                    
            #Make each set of transitions:
            for j in xrange(9):
                
                #Number of present transitions for B/S[[j]]
                sum = 0
                for k in trans_[i][j]:
                    if trans_[i][j][k]:
                        sum += 1
                
                #No transitions present:
                if sum == 0:
                    continue
                
                #All transitions present:
                if sum == len(trans_[i][j]):
                    not_ += str(j)
                    continue
                    
                str_ = str(j) #Substring for current set of transitions
                
                #Minus sign needed if more than half of 
                #current transition set is present.
                minus = (sum >= len(trans_[i][j])/2)
                if minus:
                    str_ += "-"
                
                str2 = "" #Another substring for current transition set
                
                #Write transitions:
                for k in trans_[i][j]:
                    if trans_[i][j][k] != minus:
                        str2 += k[1:]
                
                #Append transitions:
                not_ += str_ + "".join(sorted(str2))
                
            if i == 0:
                not_ += "S"
                
        g.new("Test finished.")
        return not_
    
    # Interpret birth or survival string
    def ruleparts(self, part):

        inverse = False
        nlist = []
        totalistic = True
        rule = {}
        for k in self.notationdict:
            rule[k] = False
        
        # Reverse the rule string to simplify processing
        part = part[::-1]
        
        for c in part:
            if c.isdigit():
                d = int(c)
                if totalistic:
                    # Add all the neighbourhoods for this value
                    for neighbour in self.allneighbours[d]:
                        rule[neighbour] = True
                elif inverse:
                    # Add all the neighbourhoods not in nlist for this value
                    for neighbour in self.allneighbours[d]:
                        if neighbour[1] not in nlist:
                            rule[neighbour] = True
                else:
                    # Add all the neighbourhoods in nlist for this value
                    for n in nlist:
                        neighbour = c + n
                        if neighbour in rule:
                            rule[neighbour] = True
                        else:
                            # Error
                            return {}
                    
                inverse = False
                nlist = []
                totalistic = True

            elif (c == '-'):
                inverse = True

            else:
                totalistic = False
                nlist.append(c)
        
        return rule

    # Set isotropic, non-totalistic rule
    # Adapted from something adapted from Eric Goldstein's HenselNotation->Ruletable(1.3).py
    def nt_setrule(self, rulestring):
    
        # neighbours_flat = [n for x in neighbours for n in x]
        b = {}
        s = {}
        sep = ''
        birth = ''
        survive = ''
        
        rulestring = rulestring.lower()
        
        if '/' in rulestring:
            sep = '/'
        elif '_' in rulestring:
            sep = '_'
        elif (rulestring[0] == 'b'):
            sep = 's'
        else:
            sep = 'b'
        
        survive, birth = rulestring.split(sep)
        if (survive[0] == 'b'):
            survive, birth = birth, survive
        survive = survive.replace('s', '')
        birth = birth.replace('b', '')
        
        b = self.ruleparts(birth)
        s = self.ruleparts(survive)

        if b and s:
            self.alphanumeric = 'B' + birth + 'S' + survive
            self.slashed = 'B' + birth + 'S' + survive
            self.hensel = 'B' + birth + 'S' + survive
            self.ntbee = b
            self.ntess = s
            self.rulepath = g.getdir("rules") + self.alphanumeric + ".rule"
        else:
            # Error
            g.note("Unable to process rule definition.\n" +
                    "b = " + str(b) + "\ns = " + str(s))
            g.exit()
    
    # Set outer-totalistic or isotropic non-totalistic rule:
    def setrule(self, rulestring):
        
        # Prevent annoying Golly warnings that pause the script and make it nearly
        # impossible to exit.
        rulestring = rulestring.replace("b", "B").replace("s", "S")
        
        mode = 0 #
        s = [False]*9
        b = [False]*9
        
        #Outer-totalistic
        #if '/' in rulestring:
        if not len(filter(lambda c: c in "acdefghijklmnopqrtuvwxyz", rulestring)):
            for c in rulestring:

                if ((c == 's') | (c == 'S')):
                    mode = 0

                if ((c == 'b') | (c == 'B')):
                    mode = 1

                if (c == '/'):
                    mode = 1 - mode

                if ((ord(c) >= 48) & (ord(c) <= 56)):
                    d = ord(c) - 48
                    if (mode == 0):
                        s[d] = True
                    else:
                        b[d] = True

            prefix = "B"
            suffix = "S"

            for i in xrange(9):
                if (b[i]):
                    prefix += str(i)
                if (s[i]):
                    suffix += str(i)

            self.alphanumeric = prefix + suffix
            self.slashed = prefix + "/" + suffix
            self.hensel = self.alphanumeric
            self.bee = b
            self.ess = s
            self.t = False
            self.g = self.ess[2] & self.ess[3] & (not self.ess[1]) & (not self.ess[4])
            self.g = self.g & (not (self.bee[4] | self.bee[5]))
            self.bl = True #Maybe not, but we can get away with assuming so.
            self.blcode = "xp2_7"
        #Non-totalistic
        else:
            rulestring = rulestring.replace("/", "_")
            self.ruletype = False
            self.t = self.testPattern([1,0,0,1,1,1,2,1], 5, True)
            self.g = self.testPattern([0,0,1,0,2,0,0,1,1,2], 4, True)
            self.bl = self.testPattern([0,0,0,1,0,2], 2, False)
            if self.bl:
                self.blcode = self.findBlinkerApgcode()
                if self.blcode is None:
                    self.bl = False
            if os.path.exists(g.getdir("app") + "Rules/" + rulestring + ".rule"):
                self.rulepath = g.getdir("app") + "Rules/" + rulestring + ".rule"
            elif os.path.exists(g.getdir("rules") + rulestring + ".rule"):
                self.rulepath = g.getdir("rules") + rulestring + ".rule"
            else:
                self.nt_setrule(rulestring)
                self.saveIsotropicRule()
                return
            self.alphanumeric = rulestring
            self.slashed = rulestring
            self.hensel = self.testHensel()
            #Leave bee and ess alone; we don't know what we're dealing with, so default to Life.
    # Save a rule file:
    def saverule(self, name, comments, table, colours):

        ruledir = g.getdir("rules")
        filename = ruledir + name + ".rule"

        results = "@RULE " + name + "\n\n"
        results += "*** File autogenerated by saverule. ***\n\n"
        results += comments
        results += "\n\n@TABLE\n\n"
        results += table
        results += "\n\n@COLORS\n\n"
        results += colours

        # Only create a rule file if it doesn't already exist; this avoids
        # concurrency issues when booting an instance of apgsearch whilst
        # one is already running.
        if not os.path.exists(filename):
            try:
                f = open(filename, 'w')
                f.write(results)
                f.close()
            except:
                g.warn("Unable to create rule table:\n" + filename)

    # Defines a variable:
    def newvar(self, name, vallist):

        line = "var "+name+"={"
        for i in xrange(len(vallist)):
            if (i > 0):
                line += ','
            line += str(vallist[i])
        line += "}\n"

        return line

    # Defines a block of equivalent variables:
    def newvars(self, namelist, vallist):

        block = ""

        for name in namelist:
            block += self.newvar(name, vallist)

        block += "\n"

        return block

    def scoline(self, chara, charb, left, right, amount):     #Second and third parameters not to be confused with Beta Canum Venaticorum and the main victim of a 2015 Paris terrorist attack, respectively.

        line = str(left) + ","

        for i in xrange(8):
            if (i < amount):
                line += chara
            else:
                line += charb
            line += chr(97 + i)
            line += ","

        line += str(right) + "\n"

        return line
    
    def saveIsotropicRule(self):
    
        comments = """
This is a two state, isotropic, non-totalistic rule on the Moore neighbourhood.
The notation used to define the rule was originally proposed by Alan Hensel.
See http://www.ibiblio.org/lifepatterns/neighbors2.html for details
"""

        table = """
n_states:2
neighborhood:Moore
symmetries:rotate4reflect
"""

        table += self.newvars(["a","b","c","d","e","f","g","h"], [0, 1])

        table += "\n# Birth\n"
        for n in self.allneighbours_flat:
            if self.ntbee[n]:
                table += "0,"
                table += str(self.notationdict[n])[1:-1].replace(' ','')
                table += ",1\n"
        
        table += "\n# Survival\n"
        for n in self.allneighbours_flat:
            if self.ntess[n]:
                table += "1,"
                table += str(self.notationdict[n])[1:-1].replace(' ','')
                table += ",1\n"

        table += "\n# Death\n"
        table += self.scoline("","",1,0,0)
        
        colours = ""
        self.saverule(self.alphanumeric, comments, table, colours)
    
    def saveHandlePlumes(self):

        comments = """
This post-processes the output of ClassifyObjects to remove any
unwanted clustering of low-period objects appearing in puffer
exhaust.

state 0:  vacuum

state 7:  ON, still-life
state 8:  OFF, still-life

state 9:  ON, p2 oscillator
state 10: OFF, p2 oscillator

state 11: ON, higher-period object
state 12: OFF, higher-period object
"""
        table = """
n_states:18
neighborhood:Moore
symmetries:permute

var da={0,2,4,6,8,10,12,14,16}
var db={0,2,4,6,8,10,12,14,16}
var dc={0,2,4,6,8,10,12,14,16}
var dd={0,2,4,6,8,10,12,14,16}
var de={0,2,4,6,8,10,12,14,16}
var df={0,2,4,6,8,10,12,14,16}
var dg={0,2,4,6,8,10,12,14,16}
var dh={0,2,4,6,8,10,12,14,16}

var a={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var b={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var c={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var d={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var e={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var f={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var g={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var h={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}


8,da,db,dc,dd,de,df,dg,dh,0
10,da,db,dc,dd,de,df,dg,dh,0

9,a,b,c,d,e,f,g,h,1
10,a,b,c,d,e,f,g,h,2
"""
        colours = """
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
"""
        self.saverule("APG_HandlePlumesCorrected", comments, table, colours)

    def saveExpungeGliders(self):

        comments = """
This removes unwanted gliders.
It is mandatory that one first runs the rules CoalesceObjects,
IdentifyGliders and ClassifyObjects.

Run this for two generations, and observe the population
counts after 1 and 2 generations. This will give the
following data:

number of gliders = (p(1) - p(2))/5
"""
        table = """
n_states:18
neighborhood:Moore
symmetries:rotate4reflect

var a={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var b={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var c={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var d={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var e={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var f={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var g={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var h={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}

13,a,b,c,d,e,f,g,h,14
14,a,b,c,d,e,f,g,h,0
"""
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
"""
        self.saverule("APG_ExpungeGliders", comments, table, colours)

    def saveIdentifyGliders(self):

        comments = """
Run this after CoalesceObjects to find any gliders.

state 0:  vacuum
state 1:  ON
state 2:  OFF
"""
        table = """
n_states:18
neighborhood:Moore
symmetries:rotate4reflect

var a={0,2}
var b={0,2}
var c={0,2}
var d={0,2}
var e={0,2}
var f={0,2}
var g={0,2}
var h={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var i={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var j={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var k={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var l={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var m={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var n={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var o={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var p={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}
var q={3,4}
var r={9,10}
var s={11,12}

1,1,a,1,1,b,1,c,d,3
d,1,1,1,1,a,b,1,c,4

3,i,j,k,l,m,n,o,p,5
4,i,j,k,l,m,n,o,p,6

1,q,i,j,a,b,c,k,l,7
d,q,i,j,a,b,c,k,l,8
1,i,a,b,c,d,e,j,q,7
f,i,a,b,c,d,e,j,q,8

5,7,8,7,7,8,7,8,8,9
6,7,7,7,7,8,8,7,8,10
5,i,j,k,l,m,n,o,p,15
6,i,j,k,l,m,n,o,p,16
15,i,j,k,l,m,n,o,p,1
16,i,j,k,l,m,n,o,p,2

7,i,j,k,l,m,n,o,p,11
8,i,j,k,l,m,n,o,p,12

9,i,j,k,l,m,n,o,p,13
10,i,j,k,l,m,n,o,p,14
11,r,j,k,l,m,n,o,p,13
11,i,r,k,l,m,n,o,p,13
12,r,j,k,l,m,n,o,p,14
12,i,r,k,l,m,n,o,p,14

11,i,j,k,l,m,n,o,p,1
12,i,j,k,l,m,n,o,p,2
"""
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
"""
        self.saverule("APG_IdentifyGliders", comments, table, colours)
    
    def saveIdentifyTs(self):
    
        comments = """
To identify the common spaceship xq4_27, also known as the T.

state 0:  vacuum
state 11:  p3+ on
state 12:  p3+ off
state 13:  T on
state 14:  T off
state 15:  not-T on
state 16:  not-T off
"""
        table = """
n_states:18
neighborhood:Moore
symmetries:rotate4reflect
var a={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17}
var aa=a
var ab=a
var ac=a
var ad=a
var ae=a
var af=a
var ag=a
var o={0,2,12,14}
var oa=o
var ob=o
var oc=o
var od=o
var s={5,6,17}
var sa=s
var sb=s
var sc=s
var n={7,8,9,10,11,15,16}
var xo={2,4,6,14}
var xn={1,3,5,13,17}
var i={11,12}
var io={0,1,2,11,12}
var ioa=io
var b={0,12}
11,11,o,12,11,12,11,12,oa,1
11,11,12,o,oa,io,oc,od,12,1
11,12,11,o,oa,ob,oc,12,12,1
11,12,12,12,12,12,12,12,12,1
11,12,11,o,oa,ob,oc,od,11,1
11,11,o,11,oa,11,io,io,io,1
11,11,11,11,11,12,o,oa,ob,1
11,11,11,o,oa,io,ob,oc,11,1
11,11,11,o,oa,ob,oc,12,12,1
11,11,o,11,oa,11,io,ioa,io,1
11,11,12,11,12,11,12,o,0,1
11,11,11,11,io,ioa,o,oa,ob,1
11,11,11,o,oa,ob,oc,od,12,1
12,11,o,11,oa,11,12,12,12,2
12,11,11,o,oa,ob,oc,11,12,2
12,11,12,12,11,12,11,12,12,2
12,11,12,12,11,i,o,oa,ob,2
12,11,12,12,o,oa,ob,12,12,2
b,11,11,o,io,oa,ioa,ob,11,2
12,11,11,12,o,o,oa,ob,ob,2
12,11,11,11,11,12,o,oa,ob,2
b,11,11,11,o,oa,ob,oc,od,2
1,1,2,1,2,1,2,1,2,15
1,2,1,o,oa,ob,oc,od,1,3
1,1,o,2,1,2,1,2,oa,3
1,1,2,o,oa,io,oc,od,2,3
1,2,1,o,oa,ob,oc,2,2,3
1,2,2,2,2,2,o,oa,ob,3
1,1,o,1,oa,1,io,ioa,io,3
1,1,2,1,2,1,12,2,0,3
1,1,2,1,2,1,2,o,0,3
1,1,1,1,io,ioa,o,oa,ob,3
1,1,1,2,o,oa,ob,2,1,3
1,1,1,o,oa,ob,oc,od,2,3
1,1,1,2,2,1,2,2,1,3
2,1,2,2,1,2,1,2,2,4
2,1,2,2,1,12,12,12,2,12
2,1,12,2,1,2,12,12,12,12
2,1,o,12,a,aa,ab,12,oa,12
2,2,1,2,12,1,o,o,o,12
2,1,o,1,oa,1,2,2,2,4
2,1,1,o,oa,ob,oc,1,2,4
2,1,2,1,2,1,2,2,2,4
2,1,2,2,1,io,o,oa,ob,4
2,1,2,2,o,oa,ob,oc,od,4
2,1,1,o,io,oa,ioa,ob,1,4
2,1,1,2,o,oa,ob,oc,od,4
2,1,1,1,o,oa,ob,oc,od,4
2,1,1,1,1,2,o,oa,ob,4
4,3,3,4,3,4,3,4,3,6
6,3,3,4,3,4,3,4,3,4
4,3,4,3,4,3,4,4,4,6
6,3,4,3,4,3,4,4,4,4
3,3,3,3,3,3,4,3,4,5
5,3,3,3,3,3,4,3,4,3
3,3,4,3,4,3,4,4,4,5
5,3,4,3,4,3,4,4,4,3
3,3,3,4,4,3,4,4,3,5
5,3,3,4,4,3,4,4,3,3
3,5,5,4,12,12,12,4,4,17
3,s,a,aa,ab,ac,ad,ae,af,5
4,s,a,aa,ab,ac,ad,ae,af,6
3,a,s,aa,ab,ac,ad,ae,af,5
4,a,s,aa,ab,ac,ad,ae,af,6
6,12,6,5,5,6,o,oa,ob,12
6,s,sa,sb,a,aa,ab,ac,ad,14
6,s,sa,a,aa,ab,ac,ad,sb,14
5,s,sa,a,aa,ab,ac,ad,sb,13
5,s,sa,sb,a,aa,ac,ac,ad,13
6,13,o,oa,oa,14,13,6,14,12
17,14,14,13,13,14,12,12,12,13
14,17,13,14,o,oa,12,12,12,12
xn,n,a,aa,ab,ac,ad,ae,af,15
xo,n,a,aa,ab,ac,ad,ae,af,16
xn,a,n,aa,ab,ac,ad,ae,af,15
xo,a,n,aa,ab,ac,ad,ae,af,16
1,a,aa,ab,ac,ad,ae,af,ag,15
2,a,aa,ab,ac,ad,ae,af,ag,16
"""
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
17 127    0  127
"""
        self.saverule("APG_IdentifyTs", comments, table, colours)
    
    def saveAdvanceTs(self):
    
        comments = """
To filter out extraneous results from the output of APG_IdentifyTs.

state 0:  vacuum
state 11:  p3+ on
state 12:  p3+ off
state 13:  T on
state 14:  T off
state 15:  not-T on
state 16:  not-T off
"""
        table = """
n_states:18
neighborhood:Moore
symmetries:rotate4reflect
var a={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17}
var aa=a
var ab=a
var ac=a
var ad=a
var ae=a
var af=a
var ag=a
var in={1,3,5,15}
var io={2,4,6,16}
var i={1,2,3,4,5,6,15,16}
var o={0,12,14}
var oa=o
var ob=o
var oc=o
var od=o
var oe=o
var of=o
var og=o
var oo={12,14}
var c={0,12,13,14}
var ca=c
var t={13,14}
in,a,aa,ab,ac,ad,ae,af,ag,11
io,a,aa,ab,ac,ad,ae,af,ag,12
#Birth
o,13,13,13,oa,ob,oc,od,oe,13
o,13,13,oa,ob,13,oc,od,oe,13
o,13,13,oa,ob,oc,od,13,oe,13
o,13,13,oa,ob,oc,od,oe,13,13
o,13,oa,13,ob,13,oc,od,oe,13
o,13,oa,ob,13,oc,13,od,oe,13
#Inert
o,13,13,c,ca,oa,ob,oc,od,o
o,13,oa,c,ob,oc,od,oe,of,o
o,13,13,oa,13,ob,13,oc,13,o
o,oa,13,ob,c,oc,od,oe,of,o
o,13,oa,ob,13,oc,od,oe,of,o
o,13,oa,ob,oc,13,od,oe,of,o
o,13,13,oa,13,13,ob,oc,od,o
o,oa,13,ob,13,oc,13,od,13,o
o,oa,13,ob,oc,od,13,oe,of,o
#Survival
13,13,13,o,oa,ob,oc,od,c,13
13,13,o,13,oa,13,ob,oc,od,13
13,13,13,13,o,oa,ob,oc,od,13
13,c,o,oa,13,ob,13,oc,od,13
#Death
13,13,13,13,13,o,oa,ob,oc,14
13,13,13,13,13,13,o,c,oa,14
13,13,o,oa,ob,c,oc,od,oe,14
13,o,13,oa,ob,oc,od,oe,of,14
13,13,13,o,oa,13,ob,oc,13,14
13,o,oa,ob,oc,od,oe,of,og,14
#Not T
0,o,oa,ob,oc,od,oe,of,og,0
oo,o,oa,ob,oc,od,oe,of,og,12
o,a,aa,ab,ac,ad,ae,af,t,16
o,a,aa,ab,ac,ad,ae,t,af,16
13,a,aa,ab,ac,ad,ae,af,t,15
13,a,aa,ab,ac,ad,ae,t,af,15
"""
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
17 127    0  127
"""
        self.saverule("APG_AdvanceTs", comments, table, colours)

    def saveExpungeTs(self):
    
        comments = """
To filter out extraneous results from the output of APG_IdentifyTs.

state 0:  vacuum
state 11:  p3+ on
state 12:  p3+ off
state 13:  T on
state 14:  T off
state 17:  about to die
"""
        table = """
n_states:18
neighborhood:Moore
symmetries:rotate8reflect
var a={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17}
var aa=a
var ab=a
var ac=a
var ad=a
var ae=a
var af=a
var ag=a
var o={0,12,14}
var oa=o
var ob=o
var oc=o
var od=o
var oe=o
var of=o
var og=o
var t={13,14}
13,o,oa,ob,oc,od,oe,of,og,17
13,13,o,oa,ob,oc,od,oe,of,17
13,13,13,o,oa,ob,oc,od,oe,17
13,13,13,o,oa,ob,oc,od,13,17
13,13,o,13,oa,13,ob,oc,od,17
13,13,13,13,13,13,o,13,oa,17
14,o,oa,ob,oc,od,oe,of,og,12
17,a,aa,ab,ac,ad,ae,af,ag,0
t,17,a,aa,ab,ac,ad,ae,af,17
"""
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
17 127    0  127
"""
        self.saverule("APG_ExpungeTs", comments, table, colours)
        
    def saveAssistTs(self):
    
        comments = """
To help filter out extraneous results from the output of APG_IdentifyTs.

state 0:  vacuum
state 11:  p3+ on
state 12:  p3+ off
state 13:  T on
state 14:  T off
state 15:  not-T on
state 15:  not-T off
"""
        table = """
n_states:18
neighborhood:Moore
symmetries:rotate8reflect
var a={0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17}
var aa=a
var ab=a
var ac=a
var ad=a
var ae=a
var af=a
var ag=a
var t={13,14}
var nt={15,16}
13,nt,a,ab,ac,ad,ae,af,ag,15
14,nt,a,ab,ac,ad,ae,af,ag,16
12,t,a,ab,ac,nt,ad,ae,af,16
"""
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
17 127    0  127
"""
        self.saverule("APG_AssistTs", comments, table, colours)

    def saveEradicateInfection(self):

        comments = """
To run after ContagiousLife to disinfect any cells in states 3, 4, 7, and 8.

state 0:  vacuum
state 1:  ON
state 2:  OFF
"""
        table = """
n_states:7
neighborhood:Moore
symmetries:permute

var a={0,1,2,3,4,5,6}
var b={0,1,2,3,4,5,6}
var c={0,1,2,3,4,5,6}
var d={0,1,2,3,4,5,6}
var e={0,1,2,3,4,5,6}
var f={0,1,2,3,4,5,6}
var g={0,1,2,3,4,5,6}
var h={0,1,2,3,4,5,6}
var i={0,1,2,3,4,5,6}

4,a,b,c,d,e,f,g,h,6
3,a,b,c,d,e,f,g,h,5
"""
        colours = """
0    0    0    0
1    0    0  255
2    0    0  127
3  255    0    0
4  127    0    0
5    0  255    0
6    0  127    0
7  255    0  255
8  127    0  127
"""
        self.saverule("APG_EradicateInfection", comments, table, colours)

    def savePercolateInfection(self):

        comments = """
Percolates any infection to all cells of that particular island.

state 0:  vacuum
state 1:  ON
state 2:  OFF
"""
        table = """
n_states:7
neighborhood:Moore
symmetries:permute

var a={0,1,2,3,4,5,6}
var b={0,1,2,3,4,5,6}
var c={0,1,2,3,4,5,6}
var d={0,1,2,3,4,5,6}
var e={0,1,2,3,4,5,6}
var f={0,1,2,3,4,5,6}
var g={0,1,2,3,4,5,6}
var h={0,1,2,3,4,5,6}
var i={0,1,2,3,4,5,6}

var q={3,4}
var da={2,4,6}
var la={1,3,5}

da,q,b,c,d,e,f,g,h,4
la,q,b,c,d,e,f,g,h,3
"""
        colours = """
0    0    0    0
1    0    0  255
2    0    0  127
3  255    0    0
4  127    0    0
5    0  255    0
6    0  127    0
7  255    0  255
8  127    0  127
"""
        self.saverule("APG_PercolateInfection", comments, table, colours)
        
    def saveExpungeObjects(self):

        comments = """
This removes unwanted monominos, blocks, blinkers and beehives.
It is mandatory that one first runs the rule ClassifyObjects.

Run this for four generations, and observe the population
counts after 0, 1, 2, 3 and 4 generations. This will give the
following data:

number of monominos = p(1) - p(0)
number of blocks = (p(2) - p(1))/4
number of blinkers = (p(3) - p(2))/5
number of beehives = (p(4) - p(3))/8
"""
        table = "n_states:18\n"
        table += "neighborhood:Moore\n"
        table += "symmetries:rotate4reflect\n\n"

        table += self.newvars(["a","b","c","d","e","f","g","h","i"], range(0, 17, 1))

        table += """
# Monomino
6,0,0,0,0,0,0,0,0,0

# Death
6,a,b,c,d,e,f,g,h,0
a,6,b,c,d,e,f,g,h,0

# Block
7,7,7,7,0,0,0,0,0,1
1,1,1,1,0,0,0,0,0,0
1,a,b,c,d,e,f,g,h,7

# Blinker
10,0,0,0,9,9,9,0,0,2
9,9,10,0,0,0,0,0,10,3
2,a,b,c,d,e,f,g,h,10
3,a,b,c,d,e,f,g,h,9
9,2,0,3,0,2,0,3,0,6

# Beehive
7,0,7,8,7,0,0,0,0,1
7,0,0,7,8,8,7,0,0,1
8,7,7,8,7,7,0,7,0,4
4,1,1,4,1,1,0,1,0,5
4,a,b,c,d,e,f,g,h,8
5,5,b,c,d,e,f,g,h,6
5,a,b,c,d,e,f,g,h,15
15,a,b,c,d,e,f,g,h,8
"""

        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
"""
        self.saverule("APG_ExpungeObjects", comments, table, colours)

    def saveCoalesceObjects(self):

        comments = """
A variant of HistoricalLife which separates a field of ash into
distinct objects.

state 0:  vacuum
state 1:  ON
state 2:  OFF
"""
        table = "n_states:3\n"
        table += "neighborhood:Moore\n"
        
        if self.ruletype: #Outer-totalistic
            table += "symmetries:permute\n\n"
    
            table += self.newvars(["a","b","c","d","e","f","g","h","i"], [0, 1, 2])
            table += self.newvars(["da","db","dc","dd","de","df","dg","dh","di"], [0, 2])
            table += self.newvars(["la","lb","lc","ld","le","lf","lg","lh","li"], [1])
    
            minperc = 10
    
            for i in xrange(9):
                if (self.bee[i]):
                    if (minperc == 10):
                        minperc = i
                    table += self.scoline("l","d",0,1,i)
                    table += self.scoline("l","d",2,1,i)
                if (self.ess[i]):
                    table += self.scoline("l","d",1,1,i)
    
            table += "\n# Bridge inductors\n"
    
            for i in xrange(9):
                if (i >= minperc):
                    table += self.scoline("l","d",0,2,i)
    
            table += self.scoline("","",1,2,0)
        else: #Isotropic non-totalistic
            rule1 = open(self.rulepath, "r")
            lines = rule1.read().split("\n")
            lines1 = []
            for i in lines:
                l1 = i.split("\r")
                for j in l1:
                    lines1.append(j)
            rule1.close()
            for q in xrange(len(lines1)-1):
                if lines1[q].startswith("@TABLE"):
                    lines1 = lines1[q:]
                    break
            vars = []
            for q in xrange(len(lines1)-1): #Copy symmetries and vars
                i = lines1[q]
                if i[:2] == "sy" or i[:1] == "sy":
                    table += i + "\n\n"
                if i[:2] == "va" or i[:1] == "va":
                    '''table += self.newvar(i[4:5].replace("=", ""), [0, 1, 2])
                    vars.append(i[4:5].replace("=", ""))'''
                if i != "":
                    if i[0] == "0" or i[0] == "1":
                        break
            
            alpha = "abcdefghijklmnopqrstuvwxyz"
            vars2 = []
            '''for i in alpha: 
                if not i in [n[0] for n in vars]: #Create new set of vars for OFF cells
                    table += self.newvars([i + j for j in alpha[:9]], [0, 2])
                    vars2 = [i + j for j in alpha[:9]]
                    break
                    
            for i in alpha: 
                if not i in [n[0] for n in vars] and not i in [n[0] for n in vars2]:
                    for j in xrange(5-len(vars)):
                        table += self.newvar(i + alpha[j], [0, 1, 2])
                        vars.append(i + alpha[j])
                    break'''
            vars = ["aa", "ab", "ac", "ad", "ae", "af", "ag", "ah"]
            vars2 = ["ba", "bb", "bc", "bd", "be", "bf", "bg", "bh"]
            table += self.newvars(vars, [0, 1, 2])
            table += self.newvars(vars2, [0, 2])
            
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1 and not i.startswith("var"):
                    vn = 0
                    vn2 = 0
                    for j in q[:-1]:
                        if j == "0":
                            table += vars2[vn2]
                            vn2 += 1
                        elif j == "1":
                            table += "1"
                        elif j != "#":
                            table += vars[vn]
                            vn += 1
                        table += ","
                    table += str(2-int(q[len(q)-1]))
                    table += "\n"
                
            for i in xrange(256): #Get all B3+ rules
                ncells = 0
                for j in xrange(8):
                    if (i & 2**j) > 0:
                        ncells += 1
                if ncells == 3:
                    q = "0,"
                    vn = 0
                    for j in xrange(8):
                        if i & 2**j > 0:
                            q += str((i & 2**j)/2**j) + ","
                        else:
                            q += vars[vn] + ","
                            vn += 1
                    q += "2\n"
                    table += q
                
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
"""
        self.saverule("APG_CoalesceObjects_"+self.alphanumeric, comments, table, colours)
    
    def saveDecayer(self):
    
        comments = """
A multipurpose rule used to assist with decomposition.
"""
        table = """
n_states:9
neighborhood:vonNeumann
symmetries:permute
var a={0,1,2,3,4,5,6,7,8}
var aa=a
var ab=a
var ac=a
8,a,aa,ab,ac,0
7,a,aa,ab,ac,0
6,a,aa,ab,ac,2
5,a,aa,ab,ac,1
4,a,aa,ab,ac,2
3,a,aa,ab,ac,1
2,a,aa,ab,ac,0
1,a,aa,ab,ac,0
0,a,aa,ab,ac,0
"""
        colours = ""
        self.saverule("APG_Decayer", comments, table, colours)
        
    def saveTreeMaker(self):
        
        comments = """
A surprisingly simple rule used to prepare objects for decomposition.
"""

        '''table = """
n_states:3
neighborhood:Moore
symmetries:permute
var a={0,1,2}
var aa=a
var ab=a
var ac=a
var ad=a
var ae=a
var af=a
var ag=a
var b={0,2}
var ba=b
var bb=b
var bc=b
var bd=b
var be=b
var bf=b
var bg=b
0,1,1,1,b,ba,bb,bc,bd,2"""'''
        table = "n_states:3\n"
        table += "neighborhood:Moore\n"
        
        if self.ruletype: #Outer-totalistic
            table += "symmetries:permute\n\n"
    
            table += self.newvars(["da","db","dc","dd","de","df","dg","dh","di"], [0,2])
            table += self.newvars(["la","lb","lc","ld","le","lf","lg","lh","li"], [1])
    
            minperc = 10
    
            for i in xrange(9):
                if (self.bee[i]):
                    table += self.scoline("l","d",0,2,i)
    
        else: #Isotropic non-totalistic
            rule1 = open(self.rulepath, "r")
            lines = rule1.read().split("\n")
            lines1 = []
            for i in lines:
                l1 = i.split("\r")
                for j in l1:
                    lines1.append(j)
            rule1.close()
            for q in xrange(len(lines1)-1):
                if lines1[q].startswith("@TABLE"):
                    lines1 = lines1[q:]
                    break
            vars = []
            for q in xrange(len(lines1)-1): #Copy symmetries and vars
                i = lines1[q]
                if i[:2] == "sy" or i[:1] == "sy":
                    table += i + "\n\n"
                if i[:2] == "va" or i[:1] == "va":
                    '''table += self.newvar(i[4:5].replace("=", ""), [0, 1, 2])
                    vars.append(i[4:5].replace("=", ""))'''
                if i != "":
                    if i[0] == "0" or i[0] == "1":
                        break
            
            alpha = "abcdefghijklmnopqrstuvwxyz"
            vars2 = []
            '''for i in alpha: 
                if not i in [n[0] for n in vars]: #Create new set of vars for OFF cells
                    table += self.newvars([i + j for j in alpha[:9]], [0, 2])
                    vars2 = [i + j for j in alpha[:9]]
                    break
                    
            for i in alpha: 
                if not i in [n[0] for n in vars] and not i in [n[0] for n in vars2]:
                    for j in xrange(5-len(vars)):
                        table += self.newvar(i + alpha[j], [0, 1, 2])
                        vars.append(i + alpha[j])
                    break'''
            vars = ["aa", "ab", "ac", "ad", "ae", "af", "ag", "ah"]
            vars2 = ["ba", "bb", "bc", "bd", "be", "bf", "bg", "bh"]
            table += self.newvars(vars, [0, 1, 2])
            table += self.newvars(vars2, [0, 2])
            
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1 and not i.startswith("var") and q[0] != "1":
                    vn = 0
                    vn2 = 0
                    for j in q[:-1]:
                        if j == "0":
                            table += vars2[vn2]
                            vn2 += 1
                        elif j == "1":
                            table += "1"
                        elif j != "#":
                            table += ("0",vars[vn])[j!=0]
                            vn += 1
                        table += ","
                    table += "2\n"
                
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
"""
        self.saverule("APG_TreeMaker_"+self.alphanumeric, comments, table, colours)
    
    def saveClassifyObjects(self):

        comments = """
This passively classifies objects as either still-lifes, p2 oscillators
or higher-period oscillators. It is mandatory that one first runs the
rule CoalesceObjects.

state 0:  vacuum
state 1:  input ON
state 2:  input OFF

state 3:  ON, will die
state 4:  OFF, will remain off
state 5:  ON, will survive
state 6:  OFF, will become alive

state 7:  ON, still-life
state 8:  OFF, still-life

state 9:  ON, p2 oscillator
state 10: OFF, p2 oscillator

state 11: ON, higher-period object
state 12: OFF, higher-period object
"""
        table = "n_states:18\n"
        table += "neighborhood:Moore\n"
        if self.ruletype: #Outer-totalistic
            table += "symmetries:permute\n\n"
    
            table += self.newvars(["a","b","c","d","e","f","g","h","i"], range(0, 17, 1))
            table += self.newvars(["la","lb","lc","ld","le","lf","lg","lh","li"], range(1, 17, 2))
            table += self.newvars(["da","db","dc","dd","de","df","dg","dh","di"], range(0, 17, 2))
            table += self.newvars(["pa","pb","pc","pd","pe","pf","pg","ph","pi"], [0, 3, 4])
            table += self.newvars(["qa","qb","qc","qd","qe","qf","qg","qh","qi"], [5, 6])
    #Serious modifications necessary:
            for i in xrange(9):
                if (self.bee[i]):
                    table += self.scoline("l","d",2,6,i)
                    table += self.scoline("q","p",3,9,i)
                    table += self.scoline("q","p",4,12,i)
                if (self.ess[i]):
                    table += self.scoline("l","d",1,5,i)
                    table += self.scoline("q","p",5,7,i)
                    table += self.scoline("q","p",6,12,i)
            table += self.scoline("","",2,4,0)
            table += self.scoline("","",1,3,0)
            table += self.scoline("","",5,11,0)
            table += self.scoline("","",3,11,0)
            table += self.scoline("","",4,8,0)
            table += self.scoline("","",6,10,0)
        
        else: #Isotropic non-totalistic
            rule1 = open(self.rulepath, "r")
            lines = rule1.read().split("\n")
            lines1 = []
            for i in lines:
                l1 = i.split("\r")
                for j in l1:
                    lines1.append(j)
            rule1.close()
            for q in xrange(len(lines1)-1):
                if lines1[q].startswith("@TABLE"):
                    lines1 = lines1[q:]
                    break
                if lines1[0].startswith("@TREE"):
                    g.warn("apgsearch v.0.54+0.1i does not support rule trees")
            vars = []
            for q in xrange(len(lines1)-1): #Copy symmetries and vars
                i = lines1[q]
                if i[:2] == "sy" or i[:1] == "sy":
                    table += i + "\n\n"
                if i[:2] == "va" or i[:1] == "va":
                    '''table += self.newvar(i[4:5].replace("=", ""), [0, 1, 2, 3, 4, 5, 6])
                    vars.append(i[4:5].replace("=", ""))'''
                if i != "":
                    if i[0] == "0" or i[0] == "1":
                        break
            alpha = "abcdefghijklmnopqrstuvwxyz"
            ovars = []
            '''for i in alpha: 
                if not i in [n[0] for n in vars]: #Create new set of vars for ON cells
                    table += self.newvars([i + j for j in alpha[:9]], [1, 5, 6])
                    ovars = [i + j for j in alpha[:9]]
                    break'''
            
            dvars = []
            
            vars = ["aa", "ab", "ac", "ad", "ae", "af", "ag", "ah"]
            dvars = ["ba", "bb", "bc", "bd", "be", "bf", "bg", "bh"]
            ovars = ["ca", "cb", "cc", "cd", "ce", "cf", "cg", "ch"]
            table += self.newvars(vars, xrange(7))
            table += self.newvars(dvars, [0, 2, 3, 4])
            table += self.newvars(ovars, [1, 5, 6])
            '''for i in alpha: 
                if not i in [n[0] for n in vars] and not i in [n[0] for n in ovars]: #Create new set of vars for OFF cells
                    table += self.newvars([i + j for j in alpha[:9]], [0, 2, 3, 4])
                    dvars = [i + j for j in alpha[:9]]
                    break
                    
            for i in alpha: 
                if not i in [n[0] for n in vars] and not i in [n[0] for n in ovars] and not i in [n[0] for n in dvars]:
                    for j in xrange(8-len(vars)):
                        table += self.newvar(i + alpha[j], [0, 1, 2, 3, 4, 5, 6])
                        vars.append(i + alpha[j])
                    break'''
            
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1:
                    vn = 0
                    ovn = 0
                    dvn = 0
                    if q[0] == "0" or q[0] == "1":
                        if q[0] == "0":
                            table += "2"
                        elif q[0] == "1":
                            table += "1"
                        elif q[0] != "#":
                            table += vars[vn]
                            vn += 1
                        table += ","
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += "1"
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        table += str(4-int(q[0])+2*int(q[len(q)-1]))
                        table += "\n"
                    elif not i.startswith("var"): #Line starts with a variable.
                        table += vars[vn] + ","
                        vn += 1
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += "1"
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        table += str(4+2*int(q[len(q)-1]))
                        table += "\n1,"
                        vn = 0
                        for j in q[1:-1]:
                            if j == "0":
                                table += "2"
                            elif j == "1":
                                table += "1"
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        table += str(3+2*int(q[len(q)-1]))
                        table += "\n"
            table += "2," + ",".join(vars[:8]) + ",4\n"
            table += "1," + ",".join(vars[:8]) + ",5\n"
                    
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1:
                    vn = 0
                    ovn = 0
                    dvn = 0
                    if q[0] == "0" or q[0] == "1":
                        table += str(4+2*int(q[0])) + ","
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += ovars[ovn]
                                ovn += 1
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[0] == "0" and q[len(q)-1] == "0":
                            table += "8"
                        if q[0] == "1" and q[len(q)-1] == "0":
                            table += "10"
                        if q[0] == "0" and q[len(q)-1] == "1":
                            table += "12"
                        if q[0] == "1" and q[len(q)-1] == "1":
                            table += "12"
                        table += "\n"
                    elif not i.startswith("var"): #Line starts with a variable.
                        table += "5,"
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += ovars[ovn]
                                ovn += 1
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[len(q)-1] == "0":
                            table += "7"
                        if q[len(q)-1] == "1":
                            table += "11"
                        table += "\n3,"
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += ovars[ovn]
                                ovn += 1
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[len(q)-1] == "0":
                            table += "9"
                        if q[len(q)-1] == "1":
                            table += "11"
                        table += "\n"
                        
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1:
                    vn = 0
                    ovn = 0
                    dvn = 0
                    if q[0] == "0" or q[0] == "1":
                        table += str(3+2*int(q[0])) + ","
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += ovars[ovn]
                                ovn += 1
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[0] == "0" and q[len(q)-1] == "0":
                            table += "11"
                        if q[0] == "1" and q[len(q)-1] == "0":
                            table += "11"
                        if q[0] == "0" and q[len(q)-1] == "1":
                            table += "9"
                        if q[0] == "1" and q[len(q)-1] == "1":
                            table += "7"
                        table += "\n"
                    elif not i.startswith("var"): #Line starts with a variable.
                        table += "6,"
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += ovars[ovn]
                                ovn += 1
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[len(q)-1] == "0":
                            table += "12"
                        if q[len(q)-1] == "1":
                            table += "10"
                        table += "\n4,"
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += ovars[ovn]
                                ovn += 1
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[len(q)-1] == "0":
                            table += "8"
                        if q[len(q)-1] == "1":
                            table += "12"
                        table += "\n"
            table += "4," + ",".join(vars[:8]) + ",8\n"
            table += "3," + ",".join(vars[:8]) + ",11\n"
            table += "6," + ",".join(vars[:8]) + ",12\n"
            table += "5," + ",".join(vars[:8]) + ",7\n"
                        
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
"""
        self.saverule("APG_ClassifyObjects_"+self.alphanumeric, comments, table, colours)

    def savePropagateClassifications(self):
        
        comments = """This propagates the result of running ClassifyObjects for two generations.
"""
        
        table = "n_states:18\n"
        table += "neighborhood:Moore\n"
        table += "symmetries:permute\n\n"
    
        table += self.newvars(["a","b","c","d","e","f","g","h","i"], range(0, 17, 1))
        
        table += """
7,11,b,c,d,e,f,g,h,11
7,12,b,c,d,e,f,g,h,11
7,9,b,c,d,e,f,g,h,9
7,10,b,c,d,e,f,g,h,9
8,11,b,c,d,e,f,g,h,12
8,12,b,c,d,e,f,g,h,12
8,9,b,c,d,e,f,g,h,10
8,10,b,c,d,e,f,g,h,10

7,13,b,c,d,e,f,g,h,11
7,14,b,c,d,e,f,g,h,11
8,13,b,c,d,e,f,g,h,14
8,14,b,c,d,e,f,g,h,14
9,13,b,c,d,e,f,g,h,11
9,14,b,c,d,e,f,g,h,11
10,13,b,c,d,e,f,g,h,14
10,14,b,c,d,e,f,g,h,14

9,11,b,c,d,e,f,g,h,11
9,12,b,c,d,e,f,g,h,11
10,11,b,c,d,e,f,g,h,12
10,12,b,c,d,e,f,g,h,12

13,11,b,c,d,e,f,g,h,11
13,12,b,c,d,e,f,g,h,11
14,11,b,c,d,e,f,g,h,12
14,12,b,c,d,e,f,g,h,12
13,9,b,c,d,e,f,g,h,11
14,9,b,c,d,e,f,g,h,12
"""
        colours = """
0    0    0    0
1  255  255  255
2  127  127  127
7    0    0  255
8    0    0  127
9  255    0    0
10 127    0    0
11   0  255    0
12   0  127    0
13 255  255    0
14 127  127    0
"""

        self.saverule("APG_PropagateClassification", comments, table, colours)
        #foo = "" + 2
    def saveContagiousLife(self):

        comments = """
A variant of HistoricalLife used for detecting dependencies between
islands.

state 0:  vacuum
state 1:  ON
state 2:  OFF
"""
        table = "n_states:7\n"
        table += "neighborhood:Moore\n"
        
        if self.ruletype:
            table += "symmetries:permute\n\n"

            table += self.newvars(["a","b","c","d","e","f","g","h","i"], range(0, 7, 1))
            table += self.newvars(["la","lb","lc","ld","le","lf","lg","lh","li"], range(1, 7, 2))
            table += self.newvars(["da","db","dc","dd","de","df","dg","dh","di"], range(0, 7, 2))
            table += self.newvar("p",[3, 4])
            table += self.newvars(["ta","tb","tc","td","te","tf","tg","th","ti"], [3])
            table += self.newvars(["qa","qb","qc","qd","qe","qf","qg","qh","qi"], [0, 1, 2, 4, 5, 6])

            for i in xrange(9):
                if (self.bee[i]):
                    table += self.scoline("l","d",4,3,i)
                    table += self.scoline("l","d",2,1,i)
                    table += self.scoline("l","d",0,1,i)
                    table += self.scoline("l","d",6,5,i)
                    table += self.scoline("t","q",0,4,i)
                if (self.ess[i]):
                    table += self.scoline("l","d",3,3,i)
                    table += self.scoline("l","d",5,5,i)
                    table += self.scoline("l","d",1,1,i)

            table += "# Default behaviour (death):\n"
            table += self.scoline("","",1,2,0)
            table += self.scoline("","",5,6,0)
            table += self.scoline("","",3,4,0)
        else:
            rule1 = open(self.rulepath, "r")
            lines = rule1.read().split("\n")
            lines1 = []
            for i in lines:
                l1 = i.split("\r")
                for j in l1:
                    lines1.append(j)
            rule1.close()
            for q in xrange(len(lines1)-1):
                if lines1[q].startswith("@TABLE"):
                    lines1 = lines1[q:]
                    break
            vars = []
            for q in xrange(len(lines1)-1): #Copy symmetries and vars
                i = lines1[q]
                if i[:2] == "sy" or i[:1] == "sy":
                    table += i + "\n\n"
                if i[:2] == "va" or i[:1] == "va":
                    '''table += self.newvar(i[4:5].replace("=", ""), [0, 1, 2, 3, 4, 5, 6])
                    vars.append(i[4:5].replace("=", ""))'''
                if i != "":
                    if i[0] == "0" or i[0] == "1":
                        break
            alpha = "abcdefghijklmnopqrstuvwxyz"
            ovars = []
            '''for i in alpha: 
                if not i in [n[0] for n in vars]: #Create new set of vars for ON cells
                    table += self.newvars([i + j for j in alpha[:9]], [1, 3, 5])
                    ovars = [i + j for j in alpha[:9]]
                    break'''
            dvars = []
            '''for i in alpha: 
                if not i in [n[0] for n in vars] and not i in [n[0] for n in ovars]: #Create new set of vars for OFF cells
                    table += self.newvars([i + j for j in alpha[:9]], [0, 2, 4, 6])
                    dvars = [i + j for j in alpha[:9]]
                    break
                    
            for i in alpha: 
                if not i in [n[0] for n in vars] and not i in [n[0] for n in ovars] and not i in [n[0] for n in dvars]:
                    for j in xrange(8-len(vars)):
                        table += self.newvar(i + alpha[j], [0, 1, 2, 3, 4, 5, 6])
                        vars.append(i + alpha[j])
                    break'''
            
            qvars = []
            '''for i in alpha:
                if not i in [n[0] for n in vars] and not i in [n[0] for n in ovars] and not i in [n[0] for n in dvars]:
                    table += self.newvars([i + j for j in alpha[:9]], [0, 1, 2, 4, 5, 6])
                    qvars = [i + j for j in alpha[:9]]
                    break'''
                    
            vars = ["aa", "ab", "ac", "ad", "ae", "af", "ag", "ah"]
            dvars = ["ba", "bb", "bc", "bd", "be", "bf", "bg", "bh"]
            ovars = ["ca", "cb", "cc", "cd", "ce", "cf", "cg", "ch"]
            qvars = ["da", "db", "dc", "dd", "de", "df", "dg", "dh"]
            table += self.newvars(vars, xrange(7))
            table += self.newvars(dvars, [0, 2, 4, 6])
            table += self.newvars(ovars, [1, 3, 5])
            table += self.newvars(qvars, [0, 1, 2, 4, 5, 6])
            
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1 and not i.startswith("var"):
                    vn = 0
                    ovn = 0
                    dvn = 0
                    qvn = 0
                    table += str(2-int(q[0])) + ","
                    for j in q[1:-1]:
                        if j == "0":
                            table += dvars[dvn]
                            dvn += 1
                        elif j == "1":
                            table += ovars[ovn]
                            ovn += 1
                        elif j != "#":
                            table += vars[vn]
                            vn += 1
                        table += ","
                    if q[len(q)-1] == "0":
                        table += "2"
                    if q[len(q)-1] == "1":
                        table += "1"
                    table += "\n"
                    vn = 0
                    ovn = 0
                    dvn = 0
                    qvn = 0
                    table += str(4-int(q[0])) + ","
                    for j in q[1:-1]:
                        if j == "0":
                            table += dvars[dvn]
                            dvn += 1
                        elif j == "1":
                            table += ovars[ovn]
                            ovn += 1
                        elif j != "#":
                            table += vars[vn]
                            vn += 1
                        table += ","
                    if q[len(q)-1] == "0":
                        table += "4"
                    if q[len(q)-1] == "1":
                        table += "3"
                    table += "\n"
                    vn = 0
                    ovn = 0
                    dvn = 0
                    qvn = 0
                    table += str(6-int(q[0])) + ","
                    for j in q[1:-1]:
                        if j == "0":
                            table += dvars[dvn]
                            dvn += 1
                        elif j == "1":
                            table += ovars[ovn]
                            ovn += 1
                        elif j != "#":
                            table += vars[vn]
                            vn += 1
                        table += ","
                    if q[len(q)-1] == "0":
                        table += "6"
                    if q[len(q)-1] == "1":
                        table += "5"
                    table += "\n"
                    
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1:
                    vn = 0
                    ovn = 0
                    dvn = 0
                    qvn = 0
                    if q[0] == "0":
                        table += "0,"
                        for j in q[1:-1]:
                            if j == "0":
                                table += dvars[dvn]
                                dvn += 1
                            elif j == "1":
                                table += ovars[ovn]
                                ovn += 1
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[len(q)-1] == "1":
                            table += "1"
                        else:
                            table += "0"
                        table += "\n"
                        
            for i in lines1:
                q = i.split("#")[0].replace(" ", "").split(",")
                if len(q[0]) > 1:
                    if len(q) == 1 and (q[0][1] == "0" or q[0][1] == "1"):
                        q = list(q[0])
                if len(q) > 1:
                    vn = 0
                    ovn = 0
                    dvn = 0
                    qvn = 0
                    if q[0] == "0":
                        table += "0,"
                        for j in q[1:-1]:
                            if j == "0":
                                table += qvars[qvn]
                                qvn += 1
                            elif j == "1":
                                table += "3"
                            elif j != "#":
                                table += vars[vn]
                                vn += 1
                            table += ","
                        if q[len(q)-1] == "1":
                            table += "4"
                        else:
                            table += "0"
                        table += "\n"

        colours = """
0    0    0    0
1    0    0  255
2    0    0  127
3  255    0    0
4  127    0    0
5    0  255    0
6    0  127    0
7  255    0  255
8  127    0  127
"""
        self.saverule("APG_ContagiousLife_"+self.alphanumeric, comments, table, colours)


class Soup:

    # The rule generator:
    rg = RuleGenerator()

    # Should we skip error-correction:
    skipErrorCorrection = False

    # A dict mapping binary representations of small possibly-pseudo-objects
    # to their equivalent canonised representation.
    #
    # This is many-to-one, as (for example) all of these will map to
    # the same pseudo-object (namely the beacon on block):
    #
    # ..**.**  ..**.**  **.....                           **.....
    # ..**.**  ...*.**  **.....                           *......
    # **.....  *......  ..**...                           ...*.**
    # **.....  **.....  ..**... [...12 others omitted...] ..**.**
    # .......  .......  .......                           .......
    # .......  .......  ..**...                           .......
    # .......  .......  ..**...                           .......
    #
    # The first few soups are much slower to process, as objects are being
    # entered into the cache.
    cache = {}

    # A dict to store memoized decompositions of possibly-pseudo-objects
    # into constituent parts. This is initialised with the unique minimal
    # pseudo-still-life (two blocks on lock) that cannot be automatically
    # separated by the routine pseudo_bangbang(). Any larger objects are
    # ambiguous, such as this one:
    #
    #     *
    #    * * **
    #     ** **
    #
    #    * *** *
    #    ** * **
    #
    # Is it a (block on (lock on boat)) or ((block on lock) on boat)?
    # Ahh, the joys of non-associativity.
    #
    # See http://paradise.caltech.edu/~cook/Workshop/CAs/2DOutTot/Life/StillLife/StillLifeTheory.html
    decompositions = {"xs18_3pq3qp3": ["xs14_3123qp3", "xs4_33"]}

    # A dict of objects in the form {"identifier": ("common name", points)}
    #
    # As a rough heuristic, an object is worth 15 + log2(n) points if it
    # is n times rarer than the pentadecathlon.
    #
    # Still-lifes are limited to 10 points.
    # p2 oscillators are limited to 20 points.
    # p3 and p4 oscillators are limited to 30 points.
    commonnames = {"xp2_7": ("blinker", 0),
                   "xp2_7e": ("toad", 0),
                   "xp2_318c": ("beacon", 0),
                   "xp2_xrhewehrz253y4352": ("spark coil on distant-boats", 10),
                   "xp2_o8031u0u1308ozol555o0o555lozx6430346": ("[4-fold diags]", 10),
                   "xp2_rhewehr": ("spark coil", 15),
                   "xp2_c8b8acz31d153" :("symmetric griddles", 15),
                   "xp2_2a54": ("clock", 16),
                   "xp2_31ago": ("bipole", 17),
                   "xp2_0g0k053z32": ("quadpole", 18),
                   "xp2_g8gid1e8z1226": ("great on-off", 19),
                   "xp2_jlg4glj": ("[2-fold odd] pole variant", 20),
                   "xp2_318c0f9": ("cis-beacon and table", 20),
                   "xp2_wbq23z32": ("trans-beacon and table", 20),
                   "xp2_31egge13": ("test tubebaby", 20),
                   "xp2_31ago0ui": ("cis-bipole and table", 20),
                   "xp2_318c0fho": ("cis-beacon-down and long hook", 20),
                   "xp2_318c0f96": ("cis-beacon and cap", 20),
                   "xp2_3hu0og26": ("cis-beacon-up and long hook", 20),
                   "xp2_jlg44glj": ("[2-fold even] pole variant", 20),
                   "xp2_wbq2sgz32": ("beacon and two tails", 20),
                   "xp2_g0k053z11": ("tripole", 20),
                   "xp2_g45h1sz03": ("fox", 20),
                   "xp2_33gv1og26": ("Z_and_cis-beacon_and_block", 20),
                   "xp2_4alhewehr": ("030c029200f50292030c", 20),
                   "xp2_31egge1da": ("01860149007a004b0030", 20),
                   "xp2_253gv1og26": ("?-Z-and_beacon_and_boat", 20),
                   "xp2_xoga13z253": ("bipole on boat", 20),
                   "xp2_xoga13z653": ("ship on bipole", 20),
                   "xp2_4k1u1k4zx1": ("by flops", 20),
                   "xp2_c8b8aczw33": ("griddle and block", 20),
                   "xp2_w8o0uh3z32": ("trans-beacon-down and long hook", 20),
                   "xp2_mlhewehrz1": ("01860149007b014801870001", 20),
                   "xp2_4aarhewehr": ("spark_coil siamese hat", 20),
                   "xp2_318czw3553": ("trans-beacon and cap", 20),
                   "xp2_c813z31178c": ("?-eater_siamese_table and ?-beacon", 20),
                   "xp2_gbhewehrz11": ("0186014a0078014a01850003", 20),
                   "xp2_25acz31d153": ("griddle and long_boat", 20),
                   "xp2_wgbq2sgz642": ("bipole and 2_tails", 20),
                   "xp2_gbhewehrz01": ("?21?004", 20),
                   "xp2_j1u062goz11": ("cis-beacon and dock", 20),
                   "xp2_c88b9iczw32": ("001800280041005f0030000c000c", 20),
                   "xp2_318cz311dic": ("beehive_with_long_leg and cis-beacon", 20),
                   "xp2_35acz31d153": ("griddle and long_ship", 20),
                   "xp2_c8b8acz3553": ("griddle and cap", 20),
                   "xp2_318cz315b8o": ("0318031000d400da00020003", 20),
                   "xp2_c813z311dio": ("0300024001a30023002c006c", 20),
                   "xp2_c813z311dic": ("[[cis-beacon-up and beehive_fuse_longhook]]", 20),
                   "xp2_318cz3115a4": ("tub_with_long_leg and cis-beacon", 20),
                   "xp2_4k1v0f9zw11": ("griddle and table", 20),
                   "xp2_ca9baiczw32": ("1 beacon", 20),
                   "xp2_318cz311da4": ("cis-boat_with_long_leg and ?-beacon", 20),
                   "xp2_c813z3115a4": ("tub_with_long_leg and ?-beacon", 20),
                   "xp2_8e1t2gozw23": ("21P2", 20),
                   "xp2_31egge132ac": ("?-test_tubebaby siamese eater", 20),
                   "xp2_c813z311da4": ("cis-boat_with_long_leg and ?-beacon", 20),
                   "xp2_03lk46z6401": ("trans-beacon-up and long hook", 20),
                   "xp2_gk2t2sgzw23": ("00180028000b006a000a00140008", 20),
                   "xp2_rb88czx318c": ("?19?036", 20),
                   "xp2_c8b8acz0253": ("griddle and boat", 20),
                   "xp2_rb88brz0103": ("griddle and blocks", 20),
                   "xp2_c813z319lic": ("cis-loaf_with_long_leg and ?-beacon", 20),
                   "xp2_caabaiczw32": ("18P2.471", 20),
                   "xp2_wg0k053z642": ("pentapole", 20),
                   "xp2_35453z255dic": ("0308029400f4029603090006", 20),
                   "xp2_xg8o652zca02": ("tripole on boat", 20),
                   "xp2_0mlhewehrz32": ("030c029200f60290030e00010003", 20),
                   "xp2_03544oz4a511": ("odd test tubebaby", 20),
                   "xp2_0318cz69d596": ("00c0012c00ac01a3012300c0", 20),
                   "xp2_03544ozca511": ("trans-boat test tubebaby", 20),
                   "xp2_318cz39d1d96": ("018c0189006b0068000b00090006", 20),
                   "xp2_0318cz255d96": ("racetrack and ?-beacon", 20),
                   "xp2_03544ozcid11": ("beehive test tubebaby", 20),
                   "xp2_og2t1egozx32": ("00180024003400d500d300100018", 20),
                   "xp2_318czw3115a4": ("tub_with_long_leg and ?-beacon", 20),
                   "xp2_318czw3115ac": ("trans-boat_with_long_leg and trans-beacon", 20),
                   "xp2_04a96z31d153": ("[[loaf on griddle]]", 20),
                   "xp2_03lk453z6401": ("trans-beacon and dock", 20),
                   "xp2_6a88baiczx32": ("24P2", 20),
                   "xp2_069b8b5zc813": ("018001860069006b0008000b0005", 20),
                   "xp2_39u0og26z023": ("long_and_cis-beacon", 20),
                   "xp2_69b8baiczx32": ("22P2", 20),
                   "xp2_c813z311dik8": ("trans-loaf_with_long_leg and ?-beacon", 20),
                   "xp2_c8b8q4oz33x1": ("006c006a0008000b001800240018", 20),
                   "xp2_039c8a6zc813": ("?-hook_siamese_carrier and beacon", 20),
                   "xp2_69e0ehhe0e96": ("04620a950af506960060", 20),
                   "xp2_03544oz4ad11": ("cis-boat test tubebaby", 20),
                   "xp2_69b8b9iczx32": ("26P2", 20),
                   "xp2_ciab8brzw103": ("006c006a0008006b002800240018", 20),
                   "xp2_04aab8ozc813": ("01800184006a006a000b00080018", 20),
                   "xp2_og2t1e8z6221": ("00d800580046003d0001000e0008", 20),
                   "xp2_31agozca22ac": ("cis-bipole and dock", 20),
                   "xp2_318czw31178c": ("?-eater_siamese_table and ?-beacon", 20),
                   "xp2_g318cz1pl552": ("004000ac00ac02a303230030", 20),
                   "xp2_xrhewehrz253": ("boat on spark coil", 20),
                   "xp2_0318cz69d552": ("00c0012301a300ac00ac0040", 20),
                   "xp2_4k1v0ciczw11": ("griddle and beehive", 20),
                   "xp2_06996z31d153": ("griddle and pond", 20),
                   "xp2_318czcid1d96": ("0306030900d600d000160012000c", 20),
                   "xp2_04aab96zc813": ("racetrack and ortho-beacon", 20),
                   "xp2_wo44871z6221": ("eater plug", 20),
                   "xp2_xg8o653zca02": ("tripole on ship", 20),
                   "xp2_ci9b8brzw103": ("006c006a0008006b004800240018", 20),
                   "xp2_318cz311dik8": ("loaf_with_long_leg and cis-beacon-down", 20),
                   "xp2_og2t1acz6221": ("00d800580046003d0001000a000c", 20),
                   "xp2_09v0c813z321": ("23P2", 20),
                   "xp2_4k1u1k4zw121": ("why not", 20),
                   "xp2_rhewe44ewehr": ("long piston", 20),
                   "xp2_31agozwca22ac": ("trans-bipole and dock", 20),
                   "xp2_06a88a52zc813": ("tub_with_long_nine and ?-beacon", 20),
                   "xp2_8kaabaiczw103": ("[[?19?037]]", 20),
                   "xp2_0g04q23z345lo": ("03000100016300d5003400240018", 20),
                   "xp2_0c813z69d1dic": ("0180024001a3002301ac012c00c0", 20),
                   "xp2_0cimgm96zog26": ("0300030c00d200d60010001600090006", 20),
                   "xp2_04aab871zc813": ("anvil and ?-beacon", 20),
                   "xp2_w8o0uh32acz32": ("long_hook_siamese_eater and trans-beacon-down", 20),
                   "xp2_0ggm952z344ko": ("loaf test tubebaby", 20),
                   "xp2_0c813z255d1e8": ("cis-beacon and anvil", 20),
                   "xp2_0c813z65115a4": ("tub_with_long_nine and ?-beacon", 20),
                   "xp2_318c0s26zw311": ("00c000c300350034000400380020", 20),
                   "xp2_xohf0318cz253": ("long_hook_on_boat and cis-beacon-up", 20),
                   "xp2_651u8z318nge2": ("01980298020601fd0041000e0008", 20),
                   "xp2_03lkmzojc0cjo": ("lightbulb_and_cis-hook", 20),
                   "xp2_0318cz65115a4": ("tub_with_long_nine and ?-beacon", 20),
                   "xp2_25a88gz8ka221": ("tubs test tubebaby", 20),
                   "xp2_8ki1688gzx3421": ("4 boats", 20),
                   "xp2_wca9e0eiczc813": ("?-loaf_siamese_table_and_R-bee_and_beacon", 20),
                   "xp2_31a08zy0123cko": ("quadpole on ship", 20),
                   "xp2_356o8gz318nge3": ("031802980186007d0041002e0018", 20),
                   "xp2_0318cz8kid1d96": ("01000283024301ac002c01a0012000c0", 20),
                   "xp2_256o8gzy120ago": ("boat on quadpole", 20),
                   "xp2_ca1n0brz330321": ("fore and back", 20),
                   "xp2_c88b96z330318c": ("worm and cis-beacon_and_block", 20),
                   "xp2_c8b8acz3303552": ("?-griddle and R-bee_and_block", 20),
                   "xp2_0318cz259d1d96": ("?22?047", 20),
                   "xp2_0c8b8acz651156": ("griddle and dock", 20),
                   "xp2_3hu062goz01226": ("?-long_hook_siamese_eater and beacon", 20),
                   "xp2_03lkl3zojc0cjo": ("lightbulb_and_cis-C", 20),
                   "xp2_3p6gmkkozw1046": ("00c000a8002e0041005f0030000c000c", 20),
                   "xp2_62go0u1u066zy21": ("very_long_beehive and ?-beacon and block", 20),
                   "xp2_4a960u2kgzx6952": ("004000ac012a00c8000b0018002400280010", 20),
                   "xp2_c813z35t1eozx11": ("030005c0042303a300ac006c", 20),
                   "xp2_6t1ege11egozw11": ("031804a406bc02a502430180", 20),
                   "xp2_xrhewehrz653y4356": ("spark coil on distant-ships", 20),
                   "xp2_4a4owo4a96zx1221": ("loaf tub test tubebaby", 20),
                   "xp2_wgj1u0og26z25421": ("20P2", 20),
                   "xp2_318c0s48gzw359611": ("R-loaf_at_R-loaf and beacon", 20),
                   "xp2_g8o6hewehrz121011": ("0610052801e80526061500350002", 20),
                   "xp2_bh2882hbzd841148d": ("[4-fold] pole variant", 20),
                   "xp2_318czw35t1eozy111": ("0600060001b001a8002e0021001d0006", 20),
                   "xp2_3p606p3z0o555oz6430346": ("lightbulbs", 20),
                   "xp2_25a4owo4a52z4a521w125a4": ("04020a05050a029400f00294050a0a050402", 20),
                   "xp2_256o808ozxg999gzx110116a4": ("spark coil on diagnal_boats", 20),
                   "xp2_25ic0ci52zwhaaahz8k96069k8": ("tub_lightbulbs", 20),
                   "xp3_co9nas0san9oczgoldlo0oldlogz1047210127401": ("pulsar", 8),
                   "xp3_025qzraaahz08kb": ("[[long_hooks eating tubs]]", 10),
                   "xp3_03p606p3z252x252": ("[[short_hooks eating tubs]]", 10),
                   "xp3_0gs46364sgz13e8ogo8e31zy11": ("star", 15),
                   "xp3_4s3ia4oggzw12e": ("pulsar quadrant", 20),
                   "xp3_695qc8zx33": ("jam", 24),
                   "xp3_025qzrq221": ("trans-block tub eater", 28),
                   "xp3_4hh186z07": ("caterer", 29),
                   "xp3_025qz32qq1": ("cis-block tub eater", 30),
                   "xp3_3560uh224a4": ("longhook_eating_tub and trans-ship-up", 30),
                   "xp3_4a422hu06a4": ("cis-boat and long_hook_eating_tub", 30),
                   "xp3_2530fh884a4": ("longhook_eating_tub and cis-boat-up", 30),
                   "xp3_4a422hu0696": ("cis-beehive tub eater", 30),
                   "xp3_2560uh224a4": ("longhook_eating_tub and trans-boat-up", 30),
                   "xp3_2524sws4252": ("bent keys", 30),
                   "xp3_2524sws44a4": ("odd keys", 30),
                   "xp3_2522ewe2252": ("short keys", 30),
                   "xp3_4a422hu069a4": ("longhook_eating_tub and ortho-loaf-up", 30),
                   "xp3_4a422hu06996": ("longhook_eating_tub and cis-pond", 30),
                   "xp3_31ecsggsce13": ("keys variant", 30),
                   "xp3_6a889b8ozx33": ("00300033000900fe008000180018", 30),
                   "xp3_04qi3s4z33w1": ("trice tongs", 30),
                   "xp3_0g31u0ooz345d": ("0060009000b301a9001e000000180018", 30),
                   "xp3_6a889b8ozx356": ("0060005000100056009500d300100018", 30),
                   "xp3_co9na4oggzw12e": ("2 pulsar quadrants", 30),
                   "xp3_g8o0uh224a4z01": ("longhook_eating_tub and trans-boat-down", 30),
                   "xp3_g8o0uh224a4z11": ("longhook_eating_tub and trans-boat-down", 30),
                   "xp3_j1u0uh224a4z11": ("dock tub eater", 30),
                   "xp3_g31u0ooz11078c": ("00300033000900fe010001980018", 30),
                   "xp3_o4o0uh224a4z01": ("longhook_eating_tub and trans-beehive", 30),
                   "xp3_g88rb88gz11w8kb": ("longhook siamese hook_eating_tub and ?-block", 30),
                   "xp3_8k4o0uh224a4zw1": ("longhook_eating_tub and ortho-loaf-down", 30),
                   "xp3_g31u0o8zd5430fio": ("0300024801f80000007e008900b301b0", 30),
                   "xp3_0ggmmggs252z32w46": ("020005180298008000fe000900330030", 30),
                   "xp3_s471174sz11744711": ("cross", 30),
                   "xp3_g31u0u13gz1pgf0fgp1": ("00c600aa002800aa012901ab0028002800aa00c6", 30),
                   "xp3_ca66311366acz3566c88c6653": ("sym boat keys", 30),
                   "xp3_3js46364sj3zhje8ogo8ejhz11x1x11": ("4 blocks on star", 30),
                   "xp3_69ba99b606b99ab96z69d599d606d995d96": ("[4-fold]?p3", 30),
                   "xp3_s47117471174szt57y375tz1174471744711": ("cross 2", 30),
                   "xp3_3jc42s0s24cj3z08l55o0o55l8z6611210121166": ("[4-fold] trice tongs", 30),
                   "xp3_3jc42s0s24cj3z08l55t0t55l8z6611210121166": ("[4-fold] trice tongs variant", 30),
                   "xp3_co9na4o0o4an9oczwhaaah0haaahz63ita43034ati36": ("[4-fold] pulsar quadrants", 30),
                   "xp3_y43p606p3zo80gg8ka52x25ak8gg08ozol588g8gy3g8g885lozy2125qxq521zy46430346": ("[4-fold] short_hooks eating tubs", 30),
                   "xp4_y1g8bb8gzcc0u1y21u0cczy0124kk421zy311": ("octagon IV", 20),
                   "xp4_y1g8bb8gzcc0u1wccw1u0cczy0124kk421zy311": ("octagon IV w block", 20),
                   "xp4_37bkic": ("mold", 21),
                   "xp4_ssj3744zw3": ("mazing", 23),
                   "xp4_8eh5e0e5he8z178a707a871": ("cloverleaf", 25),
                   "xp4_hv4a4vh": ("monogram", 30),
                   "xp4_3lk453z34ats": ("cis-mold and dock", 30),
                   "xp4_199aaooaa991": ("?p4", 30),
                   "xp4_ciq3k3qiczcimgagmiczx343": ("cloverleaf siamese hat", 30),
                   "xp4_g8eh5e0e5he8gz0178a707a871": ("cloverleaf siamese beehives", 30),
                   "xp4_17f8a606a8f71z8ef1560651fe8": ("4 tails", 30),
                   "xp4_y38eh5e0e5he8zg88cgc89n8a707a8n98cgc88gzhara0arahy3hara0arahz1226162it2as0sa2ti2616221zy32ehke0ekhe2": ("[4-fold] cloverleafs", 30),
                   "xp5_idiidiz01w1": ("octagon II", 26),
                   "xp5_y131u0u13z3lkkk21x12kkkl3z32x12s0s21x23zy13210123": ("harbor", 30),
                   "xp5_3pmwmp3zx11": ("fumarole", 33),
                   "xp5_4aarahrrharaa4": ("hats ?p5", 40),
                   "xp5_25a84cgz8ka2461": ("tubs-fumarole", 40),
                   "xp5_35a84cgzoka2461": ("boats-fumarole", 40),
                   "xp5_39u0u93z0fm0mfzc97079c": ("bookends_pair ?p5", 40),
                   "xp5_356o8gy0g8o653zy09cvwvc9": ("ship-tie-boats fumarole", 40),
                   "xp5_oca71v0v17acoztll55o0o55lltz012747074721": ("[4-fold] hearts", 40),
                   "xp6_ccb7w66z066": ("unix", 20),
                   "xp6_3jc42sws24cj3z0g999gwg999gzcc3243w3423cc": ("[4-fold] pseudo trice tongs", 25),
                   "xp6_co9na4owo4an9oczw1iii1w1iii1zc65qk87w78kq56czw11y411": ("[4-fold] pseudo pulsar quadrants", 25),
                   "xp6_g80e55e08gz120ekke021": ("A for all", 45),
                   "xp8_gk2gb3z11": ("figure-8", 20),
                   "xp8_g3jgz1ut": ("blocker", 24),
                   "xp8_wgovnz234z33": ("Tim Coe's p8", 31),
                   "xp8_2erore2z07x7": ("smiley", 45),
                   "xp14_j9d0d9j": ("tumbler", 25),
                   "xp15_4r4z4r4": ("pentadecathlon", 15),
                   "xp15_sksy0ohf0fhoy0skszvnvy0c4o0o4cy0vnvzy423032": ("bi-pentadecathlon w sym-hooks_pair", 25),
                   "xp30_w33z8kqrqk8zzzw33": ("cis-queen-bee-shuttle", 24),
                   "xp30_w33z8kqrqk8zzzx33": ("trans-queen-bee-shuttle", 24),
                   "xp30_w33z8kqrqk8zzzz25brb52zwoo": ("symmetric queen-bee-shuttle 1", 24),
                   "xp46_330279cx1aad3y833zx4e93x855bc": ("cis-twin-bees-shuttle", 35),
                   "xp46_330279cx1aad3zx4e93x855bcy8cc": ("trans-twin-bees-shuttle", 35),
                   "xq3_16614e8o4aa82cc2": ("edge-repair spaceship 1", 75),
                   "xq3_km92z1d9czxor33zy11": ("edge-repair spaceship 2", 75),
                   "xq3_mhqkzarahh0heezdhb5": ("dart", 75),
                   "xq3_3u0228mc53bgzof0882d6koq1": ("turtle", 75),
                   "xq4_153": ("glider", 0),
                   "xq4_6frc": ("lightweight spaceship", 6),
                   "xq4_27dee6": ("middleweight spaceship", 8),
                   "xq4_27deee6": ("heavyweight spaceship", 12),
                   "xq4_27dee6z4eb776": ("MWSS on MWSS 1", 20),
                   "xq4_27deee6z4eb7776": ("HWSS on HWSS 1", 20),
                   "xq4_27de6z4eb776": ("LWSS on MWSS 1", 40),
                   "xq4_27du6ze98885": ("LWSS on MWSS 3", 40),
                   "xq4_0791h1az8smec": ("LWSS on MWSS 2", 40),
                   "xq4_27de6z4eb7776": ("LWSS on HWSS 1", 40),
                   "xq4_6ed72z6777be4": ("LWSS on HWSS 2", 40),
                   "xq4_27de6z8smeeec": ("LWSS on HWSS 5", 40),
                   "xq4_27dumze988885": ("LWSS on HWSS 7", 40),
                   "xq4_27due6ze98885": ("MWSS on MWSS 5", 40),
                   "xq4_27de6zw8smeeec": ("LWSS on HWSS 3", 40),
                   "xq4_027deee6z8smec": ("LWSS on HWSS 4", 40),
                   "xq4_06eeed72zcems8": ("LWSS on HWSS 6", 40),
                   "xq4_27dee6zw4eb776": ("MWSS on MWSS 2", 40),
                   "xq4_677be4zx48889e": ("MWSS on MWSS 3", 40),
                   "xq4_06eed72zaghgis": ("MWSS on MWSS 4", 40),
                   "xq4_27dee6z4eb7776": ("MWSS on HWSS 2", 40),
                   "xq4_6eeed72zaghgis": ("MWSS on HWSS 10", 40),
                   "xq4_27duee6ze98885": ("MWSS on HWSS 11", 40),
                   "xq4_27duu6ze988885": ("MWSS on HWSS 13", 40),
                   "xq4_6eed72zaghhgis": ("MWSS on HWSS 14", 40),
                   "xq4_27dee6zw4eb7776": ("MWSS on HWSS 1", 40),
                   "xq4_06eeed72z677be4": ("MWSS on HWSS 3", 40),
                   "xq4_27dee6zx8smeeec": ("MWSS on HWSS 4", 40),
                   "xq4_aghgiszza1hh197": ("MWSS on HWSS 7", 40),
                   "xq4_02111197zcssqe4": ("MWSS on HWSS 9", 40),
                   "xq4_0791hh1az8smeec": ("MWSS on HWSS 12", 40),
                   "xq4_06eeed72zaghgis": ("MWSS on HWSS 15", 40),
                   "xq4_27duue6ze988885": ("HWSS on HWSS 7", 40),
                   "xq4_0sighhgazz791h1a": ("MWSS on HWSS 5", 40),
                   "xq4_sighhgazz791hh1a": ("MWSS on HWSS 6", 40),
                   "xq4_0aghhgiszza1h197": ("MWSS on HWSS 8", 40),
                   "xq4_27deee6zw4eb7776": ("HWSS on HWSS 2", 40),
                   "xq4_027deee6zsighhga": ("HWSS on HWSS 6", 40),
                   "xq4_06eeed72zaghhgis": ("HWSS on HWSS 8", 40),
                   "xq4_a1hh197zx6777be4": ("HWSS on HWSS 9", 40),
                   "xq4_27deee6zwsighhga": ("HWSS on HWSS 10", 40),
                   "xq4_0aghhgiszza1hh197": ("HWSS on HWSS 4", 40),
                   "xq4_aghhgiszzwa1hh197": ("HWSS on HWSS 5", 40),
                   "xq4_027deee6z4eqscc6": ("sidecar", 45),
                   "xq4_a1hhpnzy03mvo2ms8zy211": ("MWSS on pushalong", 55),
                   "xq4_82gfxossz34d72w37d6": ("HWSS on semi-X66", 60),
                   "xq4_0oorxroozdjio404oijd": ("X66", 66),
                   "xq7_3nw17862z6952": ("loafer", 70),
                   "xq7_7dfxg88gxfd7zw123u2222u321": ("weekender", 70),
                   "xq12_fh1i0i1hfzw8sms8zxfjf": ("lightweight schick engine", 50),
                   "xq12_xupuzw27d72zu10109ghuz3221": ("asymmetric schick engine", 50),
                   "xq12_v1120211vz01gpcpg1zxv7vzy01": ("middleweight schick engine", 50),
                   "xq12_v1120211vz120ioi021zw1vev1zx121": ("heavyweight schick engine", 50),
                   "xq16_gcbgzvgg826frc": ("Coe ship", 50),
                   "xs4_33": ("block", 0),
                   "xs4_252": ("tub", 0),
                   "xs5_253": ("boat", 0),
                   "xs6_bd": ("snake", 0),
                   "xs6_696": ("beehive", 0),
                   "xs6_356": ("ship", 0),
                   "xs6_39c": ("carrier", 0),
                   "xs6_25a4": ("barge", 0),
                   "xs7_25ac": ("long boat", 0),
                   "xs7_2596": ("loaf", 0),
                   "xs7_178c": ("eater", 0),
                   "xs7_3lo": ("long_snake", 2),
                   "xs8_3pm": ("shillelagh", 0),
                   "xs8_69ic": ("mango", 0),
                   "xs8_6996": ("pond", 0),
                   "xs8_35ac": ("long ship", 0),
                   "xs8_178k8": ("twit", 0),
                   "xs8_25ak8": ("long barge", 0),
                   "xs8_312ko": ("canoe", 0),
                   "xs8_31248c": ("very long snake", 3),
                   "xs8_32qk": ("hook with tail", 4),
                   "xs9_4aar": ("hat", 0),
                   "xs9_31ego": ("integral sign", 0),
                   "xs9_25ako": ("very long boat", 0),
                   "xs9_178ko": ("trans boat with tail", 0),
                   "xs9_178kc": ("cis boat with tail", 2),
                   "xs9_312453": ("long shillelagh", 4),
                   "xs9_25a84c": ("tub with long tail", 4),
                   "xs9_g0g853z11": ("long canoe", 4),
                   "xs9_178426": ("long_hook_with_tail", 6),
                   "xs9_31248go": ("very very long Snake", 8),
                   "xs10_35ako": ("very long ship", 0),
                   "xs10_g8o652z01": ("boat-tie", 0),
                   "xs10_32qr": ("block on table", 1),
                   "xs10_178kk8": ("beehive with tail", 1),
                   "xs10_69ar": ("loop", 2),
                   "xs10_358gkc": ("cis-shillelagh", 3),
                   "xs10_0drz32": ("broken snake", 3),
                   "xs10_g0s252z11": ("integral with tub", 3),
                   "xs10_3542ac": ("long integral", 4),
                   "xs10_1784ko": ("claw with tail", 4),
                   "xs10_3215ac": ("boat with long tail", 4),
                   "xs10_ggka52z1": ("trans barge with tail", 5),
                   "xs10_g8ka52z01": ("very long barge", 5),
                   "xs10_0j96z32": ("?10?006", 6),
                   "xs10_0cp3z32": ("?10?007", 6),
                   "xs10_4al96": ("barge siamese loaf", 7),
                   "xs10_178ka4": ("cis-barge with tail", 7),
                   "xs10_31eg8o": ("?10?010", 7),
                   "xs10_xg853z321": ("very long canoe", 8),
                   "xs10_drz32": ("?10?004", 9),
                   "xs10_25a8426": ("?10?009", 10),
                   "xs10_ggka23z1": ("?10?008", 10),
                   "xs10_2eg853": ("?10?011", 11),
                   "xs10_1784213": ("?10?012", 11),
                   "xs10_wg853z65": ("very^3 long Snake", 14),
                   "xs11_g8o652z11": ("boat tie ship", 0),
                   "xs11_g0s453z11": ("elevener", 2),
                   "xs11_ggm952z1": ("trans loaf with tail", 3),
                   "xs11_69lic": ("loaf siamese loaf", 4),
                   "xs11_178jd": ("11-loop", 4),
                   "xs11_178kic": ("cis loaf with tail", 4),
                   "xs11_2530f9": ("cis boat and table", 5),
                   "xs11_ggka53z1": ("trans-longboat with tail", 5),
                   "xs11_g0s253z11": ("trans boat with nine", 5),
                   "xs11_2ege13": ("?11?011", 6),
                   "xs11_2560ui": ("trans-boat and table", 6),
                   "xs11_178b52": ("?11-boat bend tail", 6),
                   "xs11_3586246": ("[11-snake]", 6),
                   "xs11_g0s256z11": ("cis-boat with nine", 6),
                   "xs11_31e853": ("?11?015", 7),
                   "xs11_31461ac": ("?11?004", 7),
                   "xs11_g8ka52z11": ("very very long boat", 7),
                   "xs11_25icz65": ("?11?013", 8),
                   "xs11_178c4go": ("?11?002", 8),
                   "xs11_32132ac": ("?11?005", 8),
                   "xs11_256o8go": ("boat on snake", 8),
                   "xs11_354c826": ("?11?010", 8),
                   "xs11_69jzx56": ("?11?008", 8),
                   "xs11_08o652z32": ("boat on aircraft", 8),
                   "xs11_178ka6": ("cis-longboat with tail", 9),
                   "xs11_25a84ko": ("?11?014", 9),
                   "xs11_3542156": ("?11?018", 9),
                   "xs11_69jzx123": ("?11?017", 9),
                   "xs11_25akg8o": ("?11?016", 10),
                   "xs11_178c48c": ("?11?022", 10),
                   "xs11_0cp3z65": ("?11?029", 10),
                   "xs11_31eg84c": ("?11?007", 10),
                   "xs11_358gka4": ("?11?006", 10),
                   "xs11_4ai3zx123": ("?11?021", 10),
                   "xs11_17842ac": ("?11?020", 11),
                   "xs11_35a8426": ("?11?019", 11),
                   "xs11_0drz65": ("?11?027", 12),
                   "xs11_25iczx113": ("?11?025", 12),
                   "xs11_wg84213z65": ("very very long canoe", 12),
                   "xs11_0g0s252z121": ("?11?028", 12),
                   "xs11_g88a52z23": ("?11?030", 13),
                   "xs11_3215a8o": ("?11?031", 14),
                   "xs11_17842sg": ("?11?023", 15),
                   "xs11_03ia4z65": ("very_long_hook_with_tail", 15),
                   "xs11_321eg8o": ("005000680008000b0005", 17),
                   "xs11_xg853zca1": ("very^4 long Snake", 18),
                   "yl12_1_8_c7310da81295b6611e6e4e34a80a5523": ("pufferfish", 75),
                   "yl144_1_16_afb5f3db909e60548f086e22ee3353ac": ("block-laying switch engine", 16),
                   "yl384_1_59_7aeb1999980c43b4945fb7fcdb023326": ("glider-producing switch engine", 17),
                   "xp10_9hr": ("[HighLife] p10", 6),
                   "xp7_13090c8": ("[HighLife] p7", 9),
                   "xq48_07z8ca7zy1e531": ("[HighLife] bomber", 9),
                   "xq8_2je4": ("[2x2] crawler", 0),
                   "yl8_1_1_aae0a4678d7caeb6b463f7c082d8bd1a": ("crawler wick", 20),
                   "yl4_1_1_38bc1dca7a1fb43eaade7bc292acedb5": ("crawler double wick", 50),
                   "xs8_33cc": ("block-tie", 0),
                   "xs12_33cc33": ("tri-block I", 3),
                   "xs12_ggcc33z11": ("tri-block II", 5),
                   "xs16_ccjjcczw11": ("quad-block", 8),
                   "xs16_ciddiczw11": ("16_big_honeycomb", 1),
                   "xs18_04ak8a53z6521": ("?-backwards-unix-boats-18", 3),
                   "xs9_256oo": ("block-tie-boat", 2),
                   "xs10_33cko": ("block-tie-ship", 0),
                   "xs11_g8kc33z01": ("block-tie-long-boat", 4),
                   "xs12_g8kc33z11": ("block-tie-long-ship", 5),
                   "xs10_3146oo": ("block-tie-carrier", 7),
                   "xs11_33c453": ("block-tie-eater I", 8),
                   "xs11_ggkc33z1": ("block-tie-eater II", 10),
                   "xs10_3123cc": ("block-tie-snake", 8),
                   "xs13_33cj96": ("t-eater", 10),
                   "xs14_o4o7poz01": ("t-eater", 10),
                   "xs14_3p6o66zw1": ("t-eater", 10),
                   "xs15_32134bjo": ("t-eater", 10),
                   "xs15_3pq4og4c": ("t-eater", 10),
                   "xs15_4ap6ooz32": ("t-eater", 10),
                   "xs15_8k4o7pozw1": ("t-eater", 10),
                   "xs15_gbbk46z121": ("t-eater", 10),
                   "xs15_i5p6ooz11": ("t-eater", 10),
                   "xs18_rr44rr": ("t-eater", 10),
                   "xs15_32qjc33": ("t-eater+.1", 12),
                   "xs16_8ehla4z33": ("t-eater+.1", 12),
                   "xs16_3js3pm": ("t-eater+.2", 12),
                   "xs17_3js3qic": ("t-eater++", 14),
                   "xq5_27": ("t", 0),
                   "xq4_1ba4": ("ant", 0),
                   "xq3_4aar": ("hat ship", 6),
                   "xq2_4ear": ("hat ship", 6),
                   "xq4_36bsk": ("double glider", 5),
                   "xq4_xkgp47z247": ("triple glider", 16),
                   "xq6_2ju0uj2": ("mirrored 2c/6", 14), 
                   "xq2_1fcgcf1": ("c/2 #1", 13),
                   "xq2_0ki313ikz12": ("c/2 #2", 26),
                   "xq2_2l4x4l2zw13231": ("c/2 #3", 26),
                   "xq2_1feice0ecief1": ("c/2 #4", 36),
                   "xq2_523a6m8zxl5dgz526233": ("c/2 #5", 42),
                   "xq4_v91e0e19v": ("symmetric 2c/4", 33),
                   "xq4_1b997zw8d99e": ("skew 2c/4", 39),
                   "xq5_227x4ee": ("double t", 13),
                   "xq5_72202a0ew8s": ("trans triple-t", 26),
                   "xq5_72202a0ew27": ("cis triple-t", 26),
                   "xq6_4rrwe465": ("2c/6 #1", 22),
                   "xq4_0g8e71f6cz707": ("c/4 #1", 30),
                   "xp4_69f": ("cap", 0),
                   "xp4_0diczpi62ac": ("cap variant I", 34),
                   "xp4_39c8a6z0f96zc93156": ("cap variant II", 34),
                   "xp4_9f0ciu": ("oscillating cap on table", 23),
                   "xp4_69f0ciu": ("oscillating cap on cap", 23),
                   "xp4_08o69fz321": ("cap on bookend", 22),
                   "xp4_g8o69fz121": ("cap on bun", 22),
                   "xp4_0ggca96z7w7": ("cap on R-loaf", 23),
                   "xp4_8g0si96zb43": ("cap on R-mango", 22),
                   "xp6_1e4278": ("[[tlife]] p6", 7),
                   "xp8_44hrrhrrh44": ("semi-octagon", 25),
                   "xp160_3v": ("[[tlife]] p160", 0),
                   "xs8_rr": ("bi-block", 0),
                   "xs10_660696": ("block-on-beehive", 0),
                   "xs9_253033": ("block-on-boat", 0),
                   "xs12_6960696": ("beehive-on-beehive", 0),
                   "xs11_2596066": ("block-on-loaf", 0),
                   "xp2_rbzw23": ("block-on-beacon", 5),
                   "xs12_3560653": ("cis-ship-on-ship", 0),
                   "xs10_2530352": ("cis-boat-on-boat", 0),
                   "xs12_6606996": ("block-on-pond", 0),
                   "xs11_2560696": ("boat-on-beehive", 0),
                   "xs13_25960696": ("loaf-on-beehive", 0),
                   "xs14_259606952": ("cis-loaf-on-loaf", 0),
                   "xs10_330356": ("block-on-ship", 0),
                   "xs14_69606996": ("beehive-on-pond", 0),
                   "xs11_25ac0cc": ("block-on-long-boat", 0),
                   "xs10_25606a4": ("trans-boat-on-boat", 0),
                   "xs16_699606996": ("pond-on-pond", 0),
                   "xs13_25ac0cic": ("beehive-on-longboat", 0),
                   "xs12_3560696": ("beehive-on-ship", 0),
                   "xs11_33032ac": ("tail block-on-eater", 0),
                   "xp2_2530318c": ("cis-beacon-on-boat", 8),
                   "xs12_25606952": ("cis-boat-on-loaf", 1),
                   "xs12_256069a4": ("trans-boat-on-loaf", 1),
                   "xp2_rbzw23033": ("cis-block-on-beacon-on-block", 9),
                   "xs13_25606996": ("boat-on-pond", 2),
                   "xs16_rr0rr": ("tetra-block I", 2),
                   "xs16_rrzmmz11": ("tetra-block II", 2),
                   "xs15_259606996": ("loaf-on-pond", 3),
                   "xs12_3303pm": ("head block-on-shillelagh", 3),
                   "xs14_25ac0ca52": ("cis-longboat-on-longboat", 3),
                   "xs12_rrz66": ("[[pseudo]] tri-block", 3),
                   "xp2_318c0cic": ("beehive-on-beacon", 11),
                   "xs14_2596069a4": ("trans-loaf-on-loaf", 3),
                   "xs12_66069ic": ("block-on-mango", 3),
                   "xs11_2530356": ("cis-boat-on-ship", 3),
                   "xs12_33035ac": ("block-on-long-ship", 3),
                   "xs11_25606ac": ("trans-boat-on-ship", 3),
#added on 20200820 when apgsearching B23n3/S23-q(leaplife)
                   "xp3_334oo":("[[leaplife]] p3",0),
                   "xp6_ehhhe":("[[leaplife]] p6",0)}


    # First soup to contain a particular object:
    alloccur = {}

    # A tally of objects that have occurred during this run of apgsearch:
    objectcounts = {}

    # Any soups with positive scores, and the number of points.
    soupscores = {}

    # Temporary list of unidentified objects:
    unids = []

    # Things like glider guns and large oscillators belong here:
    superunids = []
    gridsize = 0
    resets = 0

    # For profiling purposes:
    qlifetime = 0.0
    ruletime = 0.0
    gridtime = 0.0

    # Increment object count by given value:
    def incobject(self, obj, incval):
        if (incval > 0):
            if obj in self.objectcounts:
                self.objectcounts[obj] = self.objectcounts[obj] + incval
            else:
                self.objectcounts[obj] = incval

    # Increment soup score by given value:
    def awardpoints(self, soupid, incval):
        if (incval > 0):
            if soupid in self.soupscores:
                self.soupscores[soupid] = self.soupscores[soupid] + incval
            else:
                self.soupscores[soupid] = incval

    # Increment soup score by appropriate value:
    def awardpoints2(self, soupid, obj):
        if obj in self.commonnames:
            self.awardpoints(soupid, self.commonnames[obj][1])
            if not (obj in self.alloccur):
                self.alloccur[obj] = [soupid]
            elif (obj[0] == 'y'):
                # Save all soupids for growing patterns
                self.alloccur[obj] += [soupid]
        else:
            if not (obj in self.alloccur):
                self.alloccur[obj] = [soupid]
            else:
                self.alloccur[obj] += [soupid]
            if (obj[0] == 'x'):
                prefix = obj.split('_')[0]
                prenum = int(prefix[2:])
                if (obj[1] == 's'):
                    self.awardpoints(soupid, 10) # rare still-lifes are limited to 10 points
                elif (obj[1] == 'p'):
                    if (prenum == 2):
                        self.awardpoints(soupid, 20) # p2 oscillators are limited to 20 points
                    elif ((prenum == 3) | (prenum == 4)):
                        self.awardpoints(soupid, 30) # p3 and p4 oscillators are limited to 30 points
                    else:
                        self.awardpoints(soupid, 40)
                else:
                    self.awardpoints(soupid, 50)
            else:
                self.awardpoints(soupid, 50)

    # Assuming the pattern has stabilised, perform a census:
    def census(self, stepsize):
        
        g.setrule("APG_CoalesceObjects_" + self.rg.alphanumeric)
        g.setbase(2)
        g.setstep(stepsize)
        g.step()

        # apgsearch theoretically supports up to 2^14 rules, whereas the Guy
        # glider is only stable in 2^8 rules. Ensure that this is one of these
        # rules by doing some basic Boolean arithmetic.
        #
        # This should be parsed as `gliders exist', not `glider sexist':
        glidersexist = self.rg.ess[2] & self.rg.ess[3] & (not self.rg.ess[1]) & (not self.rg.ess[4])
        glidersexist = glidersexist & (not (self.rg.bee[4] | self.rg.bee[5]))

        if (glidersexist):
            g.setrule("APG_IdentifyGliders")
            g.setbase(2)
            g.setstep(2)
            g.step()

        g.setrule("APG_ClassifyObjects_" + self.rg.alphanumeric)
        g.setbase(2)
        g.setstep(max(8, stepsize))
        g.step()

        # Only do this if we have an infinite-growth pattern:
        if (stepsize > 8):
            g.setrule("APG_HandlePlumes")
            g.setbase(2)
            g.setstep(1)
            g.step()
            g.setrule("APG_ClassifyObjects_" + self.rg.alphanumeric)
            g.setstep(stepsize)
            g.step()

        # Remove any gliders:
        if (glidersexist):
            g.setrule("APG_ExpungeGliders")
            g.run(1)
            pop5 = int(g.getpop())
            g.run(1)
            pop6 = int(g.getpop())
            self.incobject("xq4_153", (pop5 - pop6)/5)

        # Remove any blocks, blinkers and beehives:
        g.setrule("APG_ExpungeObjects")
        pop0 = int(g.getpop())
        g.run(1)
        pop1 = int(g.getpop())
        g.run(1)
        pop2 = int(g.getpop())
        g.run(1)
        pop3 = int(g.getpop())
        g.run(1)
        pop4 = int(g.getpop())

        # Blocks, blinkers and beehives removed by ExpungeObjects:
        self.incobject("xs1_1", (pop0-pop1))
        self.incobject("xs4_33", (pop1-pop2)/4)
        self.incobject("xp2_7", (pop2-pop3)/5)
        self.incobject("xs6_696", (pop3-pop4)/8)

    # Removes an object incident with (ix, iy) and returns the cell list:
    def grabobj(self, ix, iy):

        allcells = [ix, iy, g.getcell(ix, iy)]
        g.setcell(ix, iy, 0)
        livecells = []
        deadcells = []

        marker = 0
        ll = 3

        while (marker < ll):
            x = allcells[marker]
            y = allcells[marker+1]
            z = allcells[marker+2]
            marker += 3

            if ((z % 2) == 1):
                livecells.append(x)
                livecells.append(y)
            else:
                deadcells.append(x)
                deadcells.append(y)

            for nx in xrange(x - 1, x + 2):
                for ny in xrange(y - 1, y + 2):

                    nz = g.getcell(nx, ny)
                    if (nz > 0):
                        allcells.append(nx)
                        allcells.append(ny)
                        allcells.append(nz)
                        g.setcell(nx, ny, 0)
                        ll += 3

        return livecells

    # Command to Grab, Remove and IDentify an OBJect:
    def gridobj(self, ix, iy, gsize, gspacing, pos):

        allcells = [ix, iy, g.getcell(ix, iy)]
        g.setcell(ix, iy, 0)
        livecells = []
        deadcells = []

        # This tacitly assumes the object is smaller than 1000-by-1000.
        # But this is okay, since it is only used by the routing logic.
        dleft = ix + 1000
        dright = ix - 1000
        dtop = iy + 1000
        dbottom = iy - 1000

        lleft = ix + 1000
        lright = ix - 1000
        ltop = iy + 1000
        lbottom = iy - 1000

        lpop = 0
        dpop = 0

        marker = 0
        ll = 3

        while (marker < ll):
            x = allcells[marker]
            y = allcells[marker+1]
            z = allcells[marker+2]
            marker += 3

            if ((z % 2) == 1):
                livecells.append(x)
                livecells.append(y)
                lleft = min(lleft, x)
                lright = max(lright, x)
                ltop = min(ltop, y)
                lbottom = max(lbottom, y)
                lpop += 1
            else:
                deadcells.append(x)
                deadcells.append(y)
                dleft = min(dleft, x)
                dright = max(dright, x)
                dtop = min(dtop, y)
                dbottom = max(dbottom, y)
                dpop += 1

            for nx in xrange(x - 1, x + 2):
                for ny in xrange(y - 1, y + 2):

                    nz = g.getcell(nx, ny)
                    if (nz > 0):
                        allcells.append(nx)
                        allcells.append(ny)
                        allcells.append(nz)
                        g.setcell(nx, ny, 0)
                        ll += 3

        lwidth = max(0, 1 + lright - lleft)
        lheight = max(0, 1 + lbottom - ltop)
        dwidth = max(0, 1 + dright - dleft)
        dheight = max(0, 1 + dbottom - dtop)

        llength = max(lwidth, lheight)
        lbreadth = min(lwidth, lheight)
        dlength = max(dwidth, dheight)
        dbreadth = min(dwidth, dheight)

        self.gridsize = max(self.gridsize, llength)

        objid = "unidentified"
        bitstring = 0

        if (lpop == 0):
            objid = "nothing"
        else:
            if ((lwidth <= 7) & (lheight <= 7)):
                for i in xrange(0, lpop*2, 2):
                    bitstring += (1 << ((livecells[i] - lleft) + 7*(livecells[i + 1] - ltop)))

                if bitstring in self.cache:
                    objid = self.cache[bitstring]

        if (objid == "unidentified"):
            # This has passed through the routing logic without being identified,
            # so save it in a temporary list for later identification:
            self.unids.append(bitstring)
            self.unids.append(livecells)
            self.unids.append(lleft)
            self.unids.append(ltop)
        elif (objid != "nothing"):
            # The object is non-empty, so add it to the census:
            ux = int(0.5 + float(lleft)/float(gspacing))
            uy = int(0.5 + float(ltop)/float(gspacing))
            soupid = ux + (uy * gsize) + pos
            
            for comp in self.decompositions[objid]:
                self.incobject(comp, 1)
                self.awardpoints2(soupid, comp)


    # Tests for population periodicity:
    def naivestab(self, period, security, length):

        depth = 0
        prevpop = 0
        for i in xrange(length):
            g.run(period)
            currpop = int(g.getpop())
            if (currpop == prevpop):
                depth += 1
            else:
                depth = 0
            prevpop = currpop
            if (depth == security):
                # Population is periodic.
                return True

        return False

    # This should catch most short-lived soups with few gliders produced:
    def naivestab2(self, period, length):

        for i in xrange(length):
            r = g.getrect()
            if (len(r) == 0):
                return True
            pop0 = int(g.getpop())
            g.run(period)
            hash1 = g.hash(r)
            pop1 = int(g.getpop())
            g.run(period)
            hash2 = g.hash(r)
            pop2 = int(g.getpop())

            if ((hash1 == hash2) & (pop0 == pop1) & (pop1 == pop2)):

                if (g.getrect() == r):
                    return True
                
                g.run((2*int(max(r[2], r[3])/period)+1)*period)
                hash3 = g.hash(r)
                pop3 = int(g.getpop())
                if ((hash2 == hash3) & (pop2 == pop3)):
                    return True

        return False
            
    # Runs a pattern until stabilisation with a 99.99996% success rate.
    # False positives are handled by a later error-correction stage.
    def stabilise3(self):

        # Phase I of stabilisation detection, designed to weed out patterns
        # that stabilise into a cluster of low-period oscillators within
        # about 6000 generations.

        if (self.naivestab2(12, 10)):
            return 4;

        if (self.naivestab(12, 50, 200)):
            return 4;

        if (self.naivestab(60, 30, 200)):
            return 6;

        # Phase II of stabilisation detection, which is much more rigorous
        # and based on oscar.py.

        # Should be sufficient:
        prect = [-2000, -2000, 4000, 4000]

        # initialize lists
        hashlist = []        # for pattern hash values
        genlist = []         # corresponding generation counts

        for j in xrange(4000):

            g.run(30)

            h = g.hash(prect)

            # determine where to insert h into hashlist
            pos = 0
            listlen = len(hashlist)
            while pos < listlen:
                if h > hashlist[pos]:
                    pos += 1
                elif h < hashlist[pos]:
                    # shorten lists and append info below
                    del hashlist[pos : listlen]
                    del genlist[pos : listlen]
                    break
                else:
                    period = (int(g.getgen()) - genlist[pos])

                    prevpop = g.getpop()

                    for i in xrange(20):
                        g.run(period)
                        currpop = g.getpop()
                        if (currpop != prevpop):
                            period = max(period, 4000)
                            break
                        prevpop = currpop
                        
                    return max(1 + int(math.log(period, 2)),3)

            hashlist.insert(pos, h)
            genlist.insert(pos, int(g.getgen()))

        g.setalgo("HashLife")
        g.setrule(self.rg.slashed)
        g.setbase(2)
        g.setstep(16)
        g.step()
        stepsize = 12
        g.setalgo("QuickLife")
        g.setrule(self.rg.slashed)

        return 12

    # Differs from oscar.py in that it detects absolute cycles, not eventual cycles.
    def bijoscar(self, maxsteps):

        initpop = int(g.getpop())
        initrect = g.getrect()
        if (len(initrect) == 0):
            return 0
        inithash = g.hash(initrect)

        for i in xrange(maxsteps):

            g.run(1)

            if (int(g.getpop()) == initpop):

                prect = g.getrect()
                phash = g.hash(prect)

                if (phash == inithash):

                    period = i + 1

                    if (prect == initrect):
                        return period
                    else:
                        return -period
        return -1

    # For a non-moving unidentified object, we check the dictionary of
    # memoized decompositions of possibly-pseudo-objects. If the object is
    # not already in the dictionary, it will be memoized.
    #
    # Low-period spaceships are also separated by this routine, although
    # this is less important now that there is a more bespoke prodecure
    # to handle disjoint unions of standard spaceships.
    #
    # @param moving  a bool which specifies whether the object is moving
    def enter_unid(self, unidname, soupid, moving):

        if not(unidname in self.decompositions):

            # Separate into pure components:
            if (moving):
                g.setrule("APG_CoalesceObjects_" + self.rg.alphanumeric)
                g.setbase(2)
                g.setstep(3)
                g.step()
            else:
                pseudo_bangbang(self.rg.alphanumeric)

            listoflists = [] # which incidentally don't contain themselves.

            # Someone who plays the celllo:
            celllist = g.join(g.getcells(g.getrect()), [0])

            for i in xrange(0, len(celllist)-1, 3):
                if (g.getcell(celllist[i], celllist[i+1]) != 0):
                    livecells = self.grabobj(celllist[i], celllist[i+1])
                    if (len(livecells) > 0):
                        listoflists.append(livecells)

            listofobjs = []

            for livecells in listoflists:

                g.new("Subcomponent")
                g.setalgo("QuickLife")
                g.setrule(self.rg.slashed)
                g.putcells(livecells)
                period = self.bijoscar(1000)
                canonised = canonise(abs(period))
                if (period < 0):
                    listofobjs.append("xq"+str(0-period)+"_"+canonised)
                elif (period == 1):
                    listofobjs.append("xs"+str(len(livecells)/2)+"_"+canonised)
                else:
                    listofobjs.append("xp"+str(period)+"_"+canonised)

            self.decompositions[unidname] = listofobjs

        # Actually add to the census:
        for comp in self.decompositions[unidname]:
            self.incobject(comp, 1)
            self.awardpoints2(soupid, comp)

    # This function has lots of arguments (hence the name):
    #
    # @param gsize     the square-root of the number of soups per page
    # @param gspacing  the minimum distance between centres of soups
    # @param ashes     a list of cell lists
    # @param stepsize  binary logarithm of amount of time to coalesce objects
    # @param intergen  binary logarithm of amount of time to run HashLife
    # @param pos       the index of the first soup on the page
    def teenager(self, gsize, gspacing, ashes, stepsize, intergen, pos):

        # For error-correction:
        if (intergen > 0):
            g.setalgo("HashLife")
            g.setrule(self.rg.slashed)

        # If this gets incremented, we panic and perform error-correction:
        pathological = 0

        # Draw the soups:
        for i in xrange(gsize * gsize):

            x = int(i % gsize)
            y = int(i / gsize)

            g.putcells(ashes[3*i], gspacing * x, gspacing * y)

        # Because why not?
        g.fit()
        g.update()

        # For error-correction:
        if (intergen > 0):
            g.setbase(2)
            g.setstep(intergen)
            g.step()

        # Apply rules to coalesce objects and expunge annoyances such as
        # blocks, blinkers, beehives and gliders:
        start_time = time.clock()
        self.census(stepsize)
        end_time = time.clock()
        self.ruletime += (end_time - start_time)

        # Now begin identifying objects:
        start_time = time.clock()
        celllist = g.join(g.getcells(g.getrect()), [0])

        if (len(celllist) > 2):
            for i in xrange(0, len(celllist)-1, 3):
                if (g.getcell(celllist[i], celllist[i+1]) != 0):
                    self.gridobj(celllist[i], celllist[i+1], gsize, gspacing, pos)

        # If we have leftover unidentified objects, attempt to canonise them:
        while (len(self.unids) > 0):
            ux = int(0.5 + float(self.unids[-2])/float(gspacing))
            uy = int(0.5 + float(self.unids[-1])/float(gspacing))
            soupid = ux + (uy * gsize) + pos
            unidname = self.process_unid()
            if (unidname == "PATHOLOGICAL"):
                pathological += 1
            if (unidname != "nothing"):

                if ((unidname[0] == 'U') & (unidname[1] == 'S') & (unidname[2] == 'S')):
                    
                    # Union of standard spaceships:
                    countlist = unidname.split('_')
                    
                    self.incobject("xq4_6frc", int(countlist[1]))
                    for i in xrange(int(countlist[1])):
                        self.awardpoints2(soupid, "xq4_6frc")

                    self.incobject("xq4_27dee6", int(countlist[2]))
                    for i in xrange(int(countlist[2])):
                        self.awardpoints2(soupid, "xq4_27dee6")
                        
                    self.incobject("xq4_27deee6", int(countlist[3]))
                    for i in xrange(int(countlist[3])):
                        self.awardpoints2(soupid, "xq4_27deee6")
                        
                elif ((unidname[0] == 'x') & ((unidname[1] == 's') | (unidname[1] == 'p'))):
                    self.enter_unid(unidname, soupid, False)
                else:
                    if ((unidname[0] == 'x') & (unidname[1] == 'q') & (unidname[3] == '_')):
                        # Separates low-period (<= 9) non-standard spaceships in medium proximity:
                        self.enter_unid(unidname, soupid, True)
                    else:
                        self.incobject(unidname, 1)
                        self.awardpoints2(soupid, unidname)

        end_time = time.clock()
        self.gridtime += (end_time - start_time)

        return pathological

    # This basically orchestrates everything:
    def stabilise_soups_parallel(self, root, pos, gsize):

        ashes = []
        stepsize = 3

        g.new("Random soups")
        g.setalgo("QuickLife")
        g.setrule(self.rg.slashed)

        gspacing = 0

        # Generate and run the soups until stabilisation:
        for i in xrange(gsize * gsize):

            # Generate the soup from the SHA-256 of the concatenation of the
            # seed with the index:
            blonksoup(root + str(pos + i))
            # thesoup.put()

            start_time = time.clock()
            # Ensure glider hits soup or has passed through
            g.run(1000)
            # Run the soup until stabilisation:
            stepsize = max(stepsize, self.stabilise3())
            end_time = time.clock()
            self.qlifetime += (end_time - start_time)

            # Ironically, the spelling of this variable is incurrrect:
            currrect = g.getrect()
            ashes.append(g.getcells(currrect))

            if (len(currrect) == 4):
                ashes.append(currrect[0])
                ashes.append(currrect[1])
                # Choose the grid spacing based on the size of the ash:
                gspacing = max(gspacing, 2*currrect[2])
                gspacing = max(gspacing, 2*currrect[3])
                g.select(currrect)
                g.clear(0)
            else:
                # Stabilized soup is empty
                ashes.append(0)
                ashes.append(0)
                self.incobject("xs1_0_diehard",1)
                self.awardpoints2(pos + i, "xs1_0_diehard")
            g.select([])

        # Account for any extra enlargement caused by running CoalesceObjects:
        gspacing += 2 ** (stepsize + 1) + 1000

        # Remember the dictionary, just in case we have a pathological object:
        prevdict = self.objectcounts.copy()
        prevscores = self.soupscores.copy()
        prevunids = self.superunids[:]

        if (self.teenager(gsize, gspacing, ashes, stepsize, 0, pos) > 0):
            if (self.skipErrorCorrection == False):
                # Arrrggghhhh, there's a pathological object! Usually this means
                # that naive stabilisation detection returned a false positive.
                self.resets += 1
                
                # Reset the object counts:
                self.objectcounts = prevdict
                self.soupscores = prevscores
                self.superunids = prevunids

                # 2^18 generations should suffice. This takes about 30 seconds in
                # HashLife, but error-correction only occurs very infrequently, so
                # this has a negligible impact on mean performance:
                gspacing += 2 ** 19
                stepsize = max(stepsize, 12)
                
                # Clear the universe:
                g.new("Error-correcting phase")
                self.teenager(gsize, gspacing, ashes, stepsize, 18, pos)

        # Erase any ashes. Not least because England usually loses...
        ashes = []

    # Pop the last unidentified object from the stack, and attempt to
    # ascertain its period and classify it.
    def process_unid(self):

        g.new("Unidentified object")
        g.setalgo("QuickLife")
        g.setrule(self.rg.slashed)
        y = self.unids.pop()
        x = self.unids.pop()
        livecells = self.unids.pop()
        bitstring = self.unids.pop()
        g.putcells(livecells, -x, -y, 1, 0, 0, 1, "or")
        period = self.bijoscar(1000)
        
        if (period == -1):
            # Infinite growth pattern, probably. Most infinite-growth
            # patterns are linear-growth (such as puffers, wickstretchers,
            # guns etc.) so we analyse to see whether we have a linear-
            # growth pattern:
            
            descriptor = linearlyse(1000)
            if (descriptor[0] == "y"):
                return descriptor

            # Okay, so it's not linear growth. It may be an unstabilised
            # ember that slipped through the net, but this will be handled
            # by error-correction (unless it persists another 2^18 gens,
            # which is so unbelievably improbable that you are more likely
            # to be picked up by a passing ship in the vacuum of space).
            self.superunids.append(livecells)
            self.superunids.append(x)
            self.superunids.append(y)
            
            return "PATHOLOGICAL"
        elif (period == 0):
            return "nothing"
        else:
            if (period == -4):

                triple = countxwsses()

                if (triple != (-1, -1, -1)):

                    # Union of Standard Spaceships:
                    return ("USS_" + str(triple[0]) + "_" + str(triple[1]) + "_" + str(triple[2]))

            
            canonised = canonise(abs(period))

            if (canonised == "#"):

                # Okay, we know that it's an oscillator or spaceship with
                # a non-astronomical period. But it's too large to canonise
                # in any of its phases (i.e. transcends a 40-by-40 box).
                self.superunids.append(livecells)
                self.superunids.append(x)
                self.superunids.append(y)
                
                return "OVERSIZED"
            
            else:

                # Prepend a prefix according to whether it is a still-life,
                # oscillator or moving object:
                if (period == 1):
                    descriptor = ("xs"+str(len(livecells)/2)+"_"+canonised)
                elif (period > 0):
                    descriptor = ("xp"+str(period)+"_"+canonised)
                else:
                    descriptor = ("xq"+str(0-period)+"_"+canonised)

                if (bitstring > 0):
                    self.cache[bitstring] = descriptor

                return descriptor

    # This doesn't really do much, since unids should be empty and
    # actual pathological/oversized objects will rarely arise naturally.
    def display_unids(self):

        g.new("Unidentified objects")
        g.setalgo("QuickLife")
        g.setrule(self.rg.slashed)

        rowlength = 1 + int(math.sqrt(len(self.superunids)/3))

        for i in xrange(len(self.superunids)/3):

            xpos = i % rowlength
            ypos = int(i / rowlength)

            g.putcells(self.superunids[3*i], xpos * (self.gridsize + 8) - self.superunids[3*i + 1], ypos * (self.gridsize + 8) - self.superunids[3*i + 2], 1, 0, 0, 1, "or")

        g.fit()
        g.update()

    # Saves a machine-readable textual file containing the census:
    def save_progress(self, numsoups, root):

        g.show("Saving progress...")

        # Count the total number of objects:
        totobjs = 0
        censustable = "@CENSUS TABLE\n"
        tlist = sorted(self.objectcounts.iteritems(), key=operator.itemgetter(1), reverse=True)
        for objname, count in tlist:
            totobjs += count
            censustable += objname + " " + str(count) +"\n"

        g.show("Writing header information...")

        # The MD5 hash of the root string:
        md5root = hashlib.md5(root).hexdigest()

        # Header information:
        results = "@SEARCH blonksoup\n"
        results += "@MD5 "+md5root+"\n"
        results += "@ROOT "+root+"\n"
        results += "@NUM_SOUPS "+str(numsoups)+"\n"
        results += "@NUM_OBJECTS "+str(totobjs)+"\n"
        results += "@RULE "+self.rg.slashed+"\n"

        results += "\n"

        # Census table:
        results += censustable

        g.show("Compactifying score table...")

        results += "\n"

        # Number of soups to record:
        highscores = 100

        results += "@TOP "+str(highscores)+"\n"

        ilist = sorted(self.soupscores.iteritems(), key=operator.itemgetter(1), reverse=True)

        # Empty the high score table:
        self.soupscores = {}
        
        for soupnum, score in ilist[:highscores]:
            self.soupscores[soupnum] = score
            results += str(soupnum) + " " + str(score) + "\n"

        # all soupids for rare objects
        results += "\n@SAMPLE_SOUPIDS\n"
        rareobj = ""
        for objname, count in tlist:
            # blinkers and gliders have no alloccur[] entry for some reason,
            # so the line below avoids errors in B3/S23, maybe other rules too?
            if objname in self.alloccur:
                if objname in self.commonnames:
                    results += objname + " "
                    for soup in self.alloccur[objname]: results+=root+str(soup)+" "
                    results=results[:-1]+"\n"
                else:
                    rareobj += objname+" "+root+str(self.alloccur[objname][0])+"\n"
        results += "\n@RARE_OBJ_SOUPIDS\n" + rareobj

        g.show("Writing progress file...")

        dirname = g.getdir("data")
        separator = dirname[-1]
        progresspath = dirname + "apgsearch" + separator + "progress" + separator
        if not os.path.exists(progresspath):
            os.makedirs(progresspath)

        filename = progresspath + "search_" + md5root + ".txt"
        
        try:
            f = open(filename, 'w')
            f.write(results)
            f.close()
        except:
            g.warn("Unable to create progress file:\n" + filename)

    # Save soup RLE:
    def save_soup(self, root, soupnum):

        # Soup pattern will be stored in a temporary directory:
        souphash = hashlib.sha256(root + str(soupnum))
        rlepath = souphash.hexdigest()
        rlepath = g.getdir("temp") + rlepath + ".rle"
        
        results = "<a href=\"open:" + rlepath + "\">"
        results += root + str(soupnum)
        results += "</a>"
        
        prect = g.getrect()
        if prect:
            g.select(prect)
            g.clear(0)
            g.select([])
        blonksoup(root + str(soupnum))
        try:
            g.save(rlepath, "rle")
        except:
            g.warn("Unable to create soup pattern:\n" + rlepath)

        return results
        
    # Display results in Help window:
    def display_census(self, numsoups, root):

        dirname = g.getdir("data")
        separator = dirname[-1]
        apgpath = dirname + "apgsearch" + separator
        objectspath = apgpath + "objects" + separator + self.rg.alphanumeric + separator
        if not os.path.exists(objectspath):
            os.makedirs(objectspath)

        # save_soup() builds the pattern in the current layer
        g.new("Soup regeneration")
        g.setrule(self.rg.slashed)

        results = "<html>\n<title>Census results</title>\n<body bgcolor=\"#FFFFCE\">\n"
        results += "<p>Soup search conducted in " + self.rg.slashed + " with blonksoup() soup generation.\n"
        results += "<p>Census results after processing " + str(numsoups) + " soups:\n"
        
        tlist = sorted(self.objectcounts.iteritems(), key=operator.itemgetter(1), reverse=True)    
        results += "<p><center>\n"
        results += "<table cellspacing=1 border=2 cols=2>\n"
        results += "<tr><td>&nbsp;Object&nbsp;</td><td align=center>&nbsp;Common name&nbsp;</td>\n"
        results += "<td align=right>&nbsp;Count&nbsp;</td><td>&nbsp;First occurrence&nbsp;</td></tr>\n"
        for objname, count in tlist:
            if (objname[0] == 'x'):
                if (objname[1] == 'p'):
                    results += "<tr bgcolor=\"#CECECF\">"
                elif (objname[1] == 'q'):
                    results += "<tr bgcolor=\"#CEFFCE\">"
                else:
                    results += "<tr>"
            else:
                results += "<tr bgcolor=\"#FFCECE\">"
            results += "<td>"
            results += "&nbsp;"
            
            # Using "open:" link enables one to click on the object name to open the pattern in Golly:
            rlepath = objectspath + objname + ".rle"
            if (objname[0] == 'x'):
                results += "<a href=\"open:" + rlepath + "\">"
            # If the name is longer than that of the block-laying switch engine:
            if len(objname) > 51:
                # Contract name and include ellipsis:
                results += objname[:40] + "&#8230;" + objname[-10:]
            else:
                results += objname
            if (objname[0] == 'x'):
                results += "</a>"
            results += "&nbsp;"

            if (objname[0] == 'x'):
                # save object in rlepath if it doesn't exist
                if not os.path.exists(rlepath):
                    # Canonised objects are at most 40-by-40:
                    rledata = "x = 40, y = 40, rule = " + self.rg.slashed + "\n"
                    # http://ferkeltongs.livejournal.com/15837.html
                    compact = objname.split('_')[1] + "z"
                    i = 0
                    strip = []
                    while (i < len(compact)):
                        c = ord2(compact[i])
                        if (c >= 0):
                            if (c < 32):
                                # Conventional character:
                                strip.append(c)
                            else:
                                if (c == 35):
                                    # End of line:
                                    if (len(strip) == 0):
                                        strip.append(0)
                                    for j in xrange(5):
                                        for d in strip:
                                            if ((d & (1 << j)) > 0):
                                                rledata += "o"
                                            else:
                                                rledata += "b"
                                        rledata += "$\n"
                                    strip = []
                                else:
                                    # Multispace character:
                                    strip.append(0)
                                    strip.append(0)
                                    if (c >= 33):
                                        strip.append(0)
                                    if (c == 34):
                                        strip.append(0)
                                        i += 1
                                        d = ord2(compact[i])
                                        for j in xrange(d):
                                            strip.append(0)
                        i += 1
                    # End of pattern representation:
                    rledata += "!\n"
                    try:
                        f = open(rlepath, 'w')
                        f.write(rledata)
                        f.close()
                    except:
                        g.warn("Unable to create object pattern:\n" + rlepath)
            
            results += "</td><td align=center>&nbsp;"
            if (objname in self.commonnames):
                results += self.commonnames[objname][0]
            results += "&nbsp;</td><td align=right>&nbsp;" + str(count) + "&nbsp;"
            results += "</td><td>"
            if objname in self.alloccur:
                results += "&nbsp;" 
                for soup in self.alloccur[objname]:
                    results += self.save_soup(root, soup) + "&nbsp;"
            results += "</td></tr>\n"
        results += "</table>\n</center>\n"

        ilist = sorted(self.soupscores.iteritems(), key=operator.itemgetter(1), reverse=True)
        results += "<p><center>\n"
        results += "<table cellspacing=1 border=2 cols=2>\n"
        results += "<tr><td>&nbsp;Soup number&nbsp;</td><td align=right>&nbsp;Score&nbsp;</td></tr>\n"
        for soupnum, score in ilist[:50]:
            results += "<tr><td>&nbsp;"
            results += self.save_soup(root, soupnum)
            results += "&nbsp;</td><td align=right>&nbsp;" + str(score) + "&nbsp;</td></tr>\n"
        
        results += "</table>\n</center>\n"
        results += "</body>\n</html>\n"
        
        htmlname = apgpath + "latest_census.html"
        try:
            f = open(htmlname, 'w')
            f.write(results)
            f.close()
            g.open(htmlname)
        except:
            g.warn("Unable to create html file:\n" + htmlname)
        

# Converts a base-36 case-insensitive alphanumeric character into a
# numerical value.
def ord2(char):

    x = ord(char)

    if ((x >= 48) & (x < 58)):
        return x - 48

    if ((x >= 65) & (x < 91)):
        return x - 55

    if ((x >= 97) & (x < 123)):
        return x - 87

    return -1


# Obtain the parameters to conduct the search:
number = int(g.getstring("How many soups to search?", "1000000"))
rootstring = g.getstring("What seed to use for this search (make this unique)?", datetime.datetime.now().replace(microsecond=0).isoformat()+"_")
rulestring = g.getstring("Which rule to use?", "B3/S23")
blocknumber = int(g.getstring("How many blocks in a soup? (2-25)", "24"))-1
# initpos = int(g.getstring("Initial position: ", "0"))
initpos = 0

start_time = time.clock()

soup = Soup()
soup.rg.setrule(rulestring)
soup.rg.saveAllRules()

if (soup.rg.slashed == "B36/S23"):
    soup.skipErrorCorrection = True

scount = 0

# We have a lot of soups per page, instead of one. This parallel approach
# was suggested by Tomas Rokicki, and results in approximately a
# fourfold increase in soup-searching speed!
sqrtspp = 10
spp = sqrtspp ** 2

# Do stuff repeatedly:
for i in xrange(int((number-1)/spp)+1):
    soup.stabilise_soups_parallel(rootstring, scount + initpos, sqrtspp)
    scount = spp*(i+1)
    g.show(str(scount) + " soups processed ("+str(int(scount/(time.clock() - start_time)))+" per second) : (type 's' to see latest census or 'q' to quit).")

    # Automatically save progress every 5000 soups:
    if ((scount % 5000) == 0):
        soup.save_progress(scount, rootstring)
    
    event = g.getevent()
    if event.startswith("key"):
        evt, ch, mods = event.split()
        if ch == "s":
            g.setrule(soup.rg.slashed)
            soup.save_progress(scount, rootstring)
            soup.display_census(scount, rootstring)
        elif ch == "q":
            break

end_time = time.clock()

g.setrule(soup.rg.slashed)
soup.save_progress(scount, rootstring)

# Give the number of soups processed together with the amount of time
# elapsed (and indications as to which parts of the script are taking
# the longest).
g.show(str(scount) + " soups processed in " + str(end_time - start_time) +
       "(" + str(soup.qlifetime) + ", " + str(soup.ruletime) + ", " + str(soup.gridtime) + ") secs.")

soup.display_census(scount, rootstring)
soup.display_unids()

deleted xs40~13 part

[Hunting]

to stop myself from mposting useless things, I will post my apgsearch results here.
<results>

Did you use the Python version of apgsearch (but modified)? If so, can you publish the soup-generating part? This symmetry looks very useful for LeapLife.

By the way, I figured out how to do gutter now! (And dense soups!)

Here(sorry,can't upload)

Code: Select all

nice

deleted xs40~13 part

Thank you very much! Now I can explore LeapLife...

[BokaBB]

We are going to need a W110 unit cell. We already have TONS of guns,reflectors and gates,that must be possible!
Also, please apgsearch my rules and post the results to Catalogue if you can.
Have a good day!

BokaBB

[yujh]

We are going to need a W110 unit cell. We already have TONS of guns,reflectors and gates,that must be possible!
Also, please apgsearch my rules and post the results to Catalogue if you can.
Have a good day!

BokaBB

? Tons of them?

[BokaBB]

Maybe tons is too much,but still we have many.
Have a good day!

BokaBB

[Hunting]

We are going to need a W110 unit cell. We already have TONS of guns,reflectors and gates,that must be possible!
Also, please apgsearch my rules and post the results to Catalogue if you can.
Have a good day!

BokaBB

Let FWKS do it, I'm both too lazy and unexperienced on this kind of thing.

[BokaBB]

We are going to need a W110 unit cell. We already have TONS of guns,reflectors and gates,that must be possible!
Also, please apgsearch my rules and post the results to Catalogue if you can.
Have a good day!

BokaBB

Let FWKS do it, I'm both too lazy and unexperienced on this kind of thing.

In that case best go to help with Traffic Flow. I will find a way to help too.
Have a good day!

BokaBB

[FWKnightship]

Let FWKS do it, I'm both too lazy and unexperienced on this kind of thing.

Code: Select all

x = 5130, y = 739, rule = B34c/S234y8:T5168,5167+96
4699b2o$4699bo$4700b3o$4702bo4$4895bo$4894bobo$4893bo3bo$4893bo3bo$
4893bobobo$4053b2o838bobobo$4053bo839bo3bo$4054b3o836bo3bo$4056bo837bo
bo$4895bo18b2o$4877b2o35bo$4878bo27bo5bobo$4249bo628bobo6b2o16b4o3b2o$
4248bobo628b3o5bobo14b2obobo$4247bo3bo629b3o6bo12b3obo2bo42bo$4247bo3b
o629bo2bo2bo2bo13b2obobo43b3o$4247bobobo630b2o6bo14b4o47bo$3407b2o838b
obobo635bobo5bo10bo48b2o9b2o$3407bo839bo3bo635b2o5bo70bo2bo$3408b3o
836bo3bo642b3o66bo2bobo18b2o$3410bo837bobo716bo19bo$4249bo18b2o613b3o
77bob2o12bo5bobo$4231b2o35bo612bob3o11b2ob2o62bo13b2o5b2o$4232bo27bo5b
obo611bobobo16bo75b2o$3603bo628bobo6b2o16b4o3b2o612bo2bo12bo2b2o56b3o
16b3o$3602bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o$3601bo
3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o$3601bo3bo629bo2bo2bo
2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo$3601bobobo630b2o6bo14b
4o47bo590bo63b2o3bo17b2o$2761b2o838bobobo635bobo5bo10bo48b2o9b2o564b3o
8b2ob2o62bobo3bo15bo4bo$2761bo839bo3bo635b2o5bo70bo2bo622b6o18bo15bo6b
o$2762b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo$2764bo837bobo
716bo19bo552bo48bo3b2o3bo3bobo25bo8bo$3603bo18b2o613b3o77bob2o12bo5bob
o542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo$3585b2o35bo612bob3o11b2ob
2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo$3586bo27bo5bobo611b
obobo16bo75b2o553b3o67bobo6bo2b2o2bo6bo6bo4bo18bo$2957bo628bobo6b2o16b
4o3b2o612bo2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o$2956bobo
628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo2bobo6b2o
32bo$2955bo3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o
4b2o4b2o6b2o31b2o$2955bo3bo629bo2bo2bo2bo13b2obobo43b3o576b2o8bo2b2o
54b2obob2o3bo13bo621b5o19bobo$2955bobobo630b2o6bo14b4o47bo590bo63b2o3b
o17b2o612b3o20bo36bo$2115b2o838bobobo635bobo5bo10bo48b2o9b2o564b3o8b2o
b2o62bobo3bo15bo4bo611bo58bo$2115bo839bo3bo635b2o5bo70bo2bo622b6o18bo
15bo6bo537b2o129bo$2116b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o
3bo537bo$2118bo837bobo716bo19bo552bo48bo3b2o3bo3bobo25bo8bo534b3o$
2957bo18b2o613b3o77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b
2o3bo534bo129b2o3b2o$2939b2o35bo612bob3o11b2ob2o62bo13b2o5b2o544b5o4b
2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo$2940bo27bo5bobo611bobobo16bo75b2o
553b3o67bobo6bo2b2o2bo6bo6bo4bo18bo544bo$2311bo628bobo6b2o16b4o3b2o
612bo2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo$
2310bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo2b
obo6b2o32bo541b2o48b2o39b2o4b2o6b3o$2309bo3bo629b3o6bo12b3obo2bo42bo
578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b2o6b2o31b2o591bo40bobo2bobo$
2309bo3bo629bo2bo2bo2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo
621b5o19bobo624b3o39b4o$2309bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b2o
612b3o20bo36bo591bo37bo2b2o2bo$1469b2o838bobobo635bobo5bo10bo48b2o9b2o
564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b2o6b2o$1469bo
839bo3bo635b2o5bo70bo2bo622b6o18bo15bo6bo537b2o129bo624bobo5bo2bo13bo
2bo4bo2bo$1470b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo
691b2o61bo15bo7b6o2b6o$1472bo837bobo716bo19bo552bo48bo3b2o3bo3bobo25bo
8bo534b3o692bo76b2o9bo2bo4bo2bo$2311bo18b2o613b3o77bob2o12bo5bobo542b
2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b2o3b2o556bobo77b2o3bo5b
2o6b2o$2293b2o35bo612bob3o11b2ob2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b
2o2bo12bo6bo665bo5bo556b2o83bo$2294bo27bo5bobo611bobobo16bo75b2o553b3o
67bobo6bo2b2o2bo6bo6bo4bo18bo544bo749bobo$1665bo628bobo6b2o16b4o3b2o
612bo2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo
623b2o5bo9b2ob2o$1664bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o
3b2o617b5o5bobo2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo$
1663bo3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b
2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo$1663bo3bo629bo2bo2bo
2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o39b4o
565b2o64bo2bo7b2o7b2o3b2o$1663bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b
2o612b3o20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$823b2o838bobobo635b
obo5bo10bo48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o
2bo12b2o6b2o595b3o4b2o6b3o9bobo$823bo839bo3bo635b2o5bo70bo2bo622b6o18b
o15bo6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo$824b
3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b
6o2b6o592bob2o3bo19bo2bo$826bo837bobo716bo19bo552bo48bo3b2o3bo3bobo25b
o8bo534b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo$1665bo18b2o613b3o
77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b2o
3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo$1647b2o35bo612bob3o11b2ob2o62b
o13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo556b2o83bo636b2o
9b2o$1648bo27bo5bobo611bobobo16bo75b2o553b3o67bobo6bo2b2o2bo6bo6bo4bo
18bo544bo749bobo646bo$1019bo628bobo6b2o16b4o3b2o612bo2bo12bo2b2o56b3o
16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o5bo9b2ob2o646b3o
$1018bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo
2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo$1017bo3bo
629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b2o6b2o31b
2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo$1017bo3bo629bo2bo2bo2bo13b2obo
bo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o39b4o565b2o64bo
2bo7b2o7b2o3b2o$1017bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b2o612b3o
20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$177b2o838bobobo635bobo5bo
10bo48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b
2o6b2o595b3o4b2o6b3o9bobo576bo$177bo839bo3bo635b2o5bo70bo2bo622b6o18bo
15bo6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo576b2o$
178b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo
15bo7b6o2b6o592bob2o3bo19bo2bo576b2o$180bo837bobo716bo19bo552bo48bo3b
2o3bo3bobo25bo8bo534b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo$1019bo
18b2o613b3o77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo
534bo129b2o3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo$1001b2o35bo612bob3o
11b2ob2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo556b2o
83bo636b2o9b2o$1002bo27bo5bobo611bobobo16bo75b2o553b3o67bobo6bo2b2o2bo
6bo6bo4bo18bo544bo749bobo646bo$373bo628bobo6b2o16b4o3b2o612bo2bo12bo2b
2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o5bo9b2ob
2o646b3o$372bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b
5o5bobo2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo$
371bo3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b
2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo$371bo3bo629bo2bo2bo2b
o13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o39b4o
565b2o64bo2bo7b2o7b2o3b2o$371bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b
2o612b3o20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$371bobobo635bobo5bo
10bo48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b
2o6b2o595b3o4b2o6b3o9bobo576bo$371bo3bo635b2o5bo70bo2bo622b6o18bo15bo
6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo576b2o$371b
o3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b6o2b6o
592bob2o3bo19bo2bo576b2o$372bobo716bo19bo552bo48bo3b2o3bo3bobo25bo8bo
534b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo1237bo$373bo18b2o613b3o
77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b2o
3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo1239b2o$355b2o35bo612bob3o11b2o
b2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo556b2o83bo
636b2o9b2o1228b2o23b2o$356bo27bo5bobo611bobobo16bo75b2o553b3o67bobo6bo
2b2o2bo6bo6bo4bo18bo544bo749bobo646bo1254b3o$356bobo6b2o16b4o3b2o612bo
2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o
5bo9b2ob2o646b3o1251b3o$357b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o
3b2o617b5o5bobo2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo
647bo1250bob2o$359b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o
4b2o4b2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo1901b3o$359bo2bo
2bo2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o
39b4o565b2o64bo2bo7b2o7b2o3b2o1899bo$360b2o6bo14b4o47bo590bo63b2o3bo
17b2o612b3o20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$365bobo5bo10bo
48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b2o6b
2o595b3o4b2o6b3o9bobo576bo$365b2o5bo70bo2bo622b6o18bo15bo6bo537b2o129b
o624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo576b2o$372b3o66bo2bobo
18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b6o2b6o592bob2o3bo19bo2bo
576b2o$445bo19bo552bo48bo3b2o3bo3bobo25bo8bo534b3o692bo76b2o9bo2bo4bo
2bo593b3o3b2o18bobobo1237bo$361b3o77bob2o12bo5bobo542b2o3b2o4b2o47bo6b
o4bo10bo2bo13bo3b2o3bo534bo129b2o3b2o556bobo77b2o3bo5b2o6b2o595bo24bo
2bo1239b2o$359bob3o11b2ob2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo
12bo6bo665bo5bo556b2o83bo636b2o9b2o1228b2o23b2o$358bobobo16bo75b2o553b
3o67bobo6bo2b2o2bo6bo6bo4bo18bo544bo749bobo646bo1254b3o633bo$358bo2bo
12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o5bo
9b2ob2o646b3o1251b3o634b2o$359b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo2bo
bo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo1250bob2o633b
2o$364b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b2o6b2o31b2o591bo40bobo2bobo
562bo66bob2o5bo3bo9bo1901b3o$364b2o8bo2b2o54b2obob2o3bo13bo621b5o19bob
o624b3o39b4o565b2o64bo2bo7b2o7b2o3b2o1899bo$379bo63b2o3bo17b2o612b3o
20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$364b3o8b2ob2o62bobo3bo15bo
4bo611bo58bo623b2o4bo2b2o2bo12b2o6b2o595b3o4b2o6b3o9bobo576bo$423b6o
18bo15bo6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo
576b2o$422bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b6o2b6o592bob2o3bo19bo2b
o576b2o$372bo48bo3b2o3bo3bobo25bo8bo534b3o692bo76b2o9bo2bo4bo2bo593b3o
3b2o18bobobo1237bo$362b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b
2o3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo1239b2o$363b5o4b2o49b6o7bo6bo
2b2o2bo12bo6bo665bo5bo556b2o83bo636b2o9b2o1228b2o23b2o$364b3o67bobo6bo
2b2o2bo6bo6bo4bo18bo544bo749bobo646bo1254b3o633bo$365bo67bo3bo7b4o8b2o
7b2o20b3o543b2o101bo3bo623b2o5bo9b2ob2o646b3o1251b3o634b2o$433b5o5bobo
2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo1250bob2o
633b2o$432b2o3b2o4b2o4b2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9b
o1901b3o$433b5o19bobo624b3o39b4o565b2o64bo2bo7b2o7b2o3b2o1899bo$434b3o
20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$435bo58bo623b2o4bo2b2o2bo
12b2o6b2o595b3o4b2o6b3o9bobo576bo$362b2o129bo624bobo5bo2bo13bo2bo4bo2b
o594b3o3bobo7b2o12bo576b2o$363bo691b2o61bo15bo7b6o2b6o592bob2o3bo19bo
2bo576b2o$360b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo$360bo129b2o3b
2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo$490bo5bo556b2o83bo636b2o9b2o
1253b2o$387bo749bobo646bo1254b3o633bo$388b2o101bo3bo623b2o5bo9b2ob2o
646b3o1251b3o634b2o$387b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo
1250bob2o633b2o$437bo40bobo2bobo562bo66bob2o5bo3bo9bo1901b3o$438b3o39b
4o565b2o64bo2bo7b2o7b2o3b2o1899bo$440bo37bo2b2o2bo562b2o53b2o10bob2o$
472b2o4bo2b2o2bo12b2o6b2o595b3o4b2o6b3o9bobo576bo$472bobo5bo2bo13bo2bo
4bo2bo594b3o3bobo7b2o12bo576b2o$409b2o61bo15bo7b6o2b6o592bob2o3bo19bo
2bo576b2o$409bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo1237bo$407bobo77b2o
3bo5b2o6b2o595bo24bo2bo1239b2o$407b2o83bo636b2o9b2o1228b2o23b2o$491bob
o646bo1254b3o633bo$473b2o5bo9b2ob2o646b3o1251b3o634b2o$472b3o4bobo7bo
5bo647bo1250bob2o633b2o$402bo66bob2o5bo3bo9bo1901b3o$403b2o64bo2bo7b2o
7b2o3b2o1899bo$402b2o53b2o10bob2o$457b3o4b2o6b3o9bobo576bo$457b3o3bobo
7b2o12bo576b2o$456bob2o3bo19bo2bo576b2o$456b3o3b2o18bobobo1237bo$457bo
24bo2bo1239b2o$483b2o9b2o1228b2o23b2o$494bo1254b3o633bo$495b3o1251b3o
634b2o$497bo1250bob2o633b2o$1748b3o$1749bo2$417bo$418b2o$417b2o$1078bo
$1079b2o$1078b2o23b2o$1103b3o633bo$1103b3o634b2o$1102bob2o633b2o$1102b
3o$1103bo5$432bo$433b2o$432b2o23b2o$457b3o633bo$457b3o634b2o$456bob2o
633b2o$456b3o$457bo8$447bo$448b2o$447b2o4540b2o$4989b2o4$4990bo$4989bo
bo$4990bo5$4343b2o$4343b2o4$4344bo$4343bobo$4344bo5$3697b2o1325bo$
3697b2o1323b3o$5021bo$5010b2o9b2o$5009bo2bo$3698bo1290b2o18bobobo$
3697bobo1290bo19bob3o$3698bo1291bobo5b2o12b3o4bo$4991b2o5b3o18bo$5000b
2obo14bobo$5000bo2bo13b2ob2o$5000b2obo12bo5bo$3051b2o1325bo619b3o9b2o
7bo$3051b2o1323b3o619b2o10bobo3b2o3b2o$4375bo628bo5bo$4364b2o9b2o625b
2o$4363bo2bo636b2o20b2o6b2o$3052bo1290b2o18bobobo656bo2bo4bo2bo$3051bo
bo1290bo19bob3o654b6o2b6o$3052bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo
2bo$4345b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o$4354b2obo14bobo630bo2b2o2b
o4bo$4354bo2bo13b2ob2o631b4o7bo$4354b2obo12bo5bo628bobo2bobo$2405b2o
1325bo619b3o9b2o7bo631b2o4b2o$2405b2o1323b3o619b2o10bobo3b2o3b2o640b2o
3b2o$3729bo628bo5bo652bo5bo$3718b2o9b2o625b2o$3717bo2bo636b2o20b2o6b2o
629bo3bo$2406bo1290b2o18bobobo656bo2bo4bo2bo599bo29b3o$2405bobo1290bo
19bob3o654b6o2b6o596b2o$2406bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2b
o598b2o$3699b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o$3708b2obo14bobo630bo2b
2o2bo4bo$3708bo2bo13b2ob2o631b4o7bo$3708b2obo12bo5bo628bobo2bobo650b2o
$1759b2o1325bo619b3o9b2o7bo631b2o4b2o651bo$1759b2o1323b3o619b2o10bobo
3b2o3b2o640b2o3b2o637b3o$3083bo628bo5bo652bo5bo637bo$3072b2o9b2o625b2o
$3071bo2bo636b2o20b2o6b2o629bo3bo$1760bo1290b2o18bobobo656bo2bo4bo2bo
599bo29b3o$1759bobo1290bo19bob3o654b6o2b6o596b2o$1760bo1291bobo5b2o12b
3o4bo633bo2bo13bo2bo4bo2bo598b2o$3053b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b
2o1231bo$3062b2obo14bobo630bo2b2o2bo4bo1246b2o$3062bo2bo13b2ob2o631b4o
7bo1246b2o$3062b2obo12bo5bo628bobo2bobo650b2o$1113b2o1325bo619b3o9b2o
7bo631b2o4b2o651bo$1113b2o1323b3o619b2o10bobo3b2o3b2o640b2o3b2o637b3o$
2437bo628bo5bo652bo5bo637bo$2426b2o9b2o625b2o$2425bo2bo636b2o20b2o6b2o
629bo3bo$1114bo1290b2o18bobobo656bo2bo4bo2bo599bo29b3o$1113bobo1290bo
19bob3o654b6o2b6o596b2o$1114bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2b
o598b2o$2407b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o1231bo$2416b2obo14bobo
630bo2b2o2bo4bo1246b2o$2416bo2bo13b2ob2o631b4o7bo1246b2o$2416b2obo12bo
5bo628bobo2bobo650b2o1232bo$467b2o1325bo619b3o9b2o7bo631b2o4b2o651bo
1170b2o20b2o36b2o$467b2o1323b3o619b2o10bobo3b2o3b2o640b2o3b2o637b3o
1172bo20bo38b2o$1791bo628bo5bo652bo5bo637bo1174bobo8bo7bobo$1780b2o9b
2o625b2o2479b2o8bobo5b2o$1779bo2bo636b2o20b2o6b2o629bo3bo1827b2o$468bo
1290b2o18bobobo656bo2bo4bo2bo599bo29b3o1828b2o$467bobo1290bo19bob3o
654b6o2b6o596b2o1861b2o$468bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2bo
598b2o1857bobo$1761b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o1231bo1226bo52bo
$1770b2obo14bobo630bo2b2o2bo4bo1246b2o1278b3o$1770bo2bo13b2ob2o631b4o
7bo1246b2o1276bo$1770b2obo12bo5bo628bobo2bobo650b2o1232bo645b2o$1148bo
619b3o9b2o7bo631b2o4b2o651bo1170b2o20b2o36b2o$1146b3o619b2o10bobo3b2o
3b2o640b2o3b2o637b3o1172bo20bo38b2o$1145bo628bo5bo652bo5bo637bo1174bob
o8bo7bobo670bo$1134b2o9b2o625b2o2479b2o8bobo5b2o396bo272b2o$1133bo2bo
636b2o20b2o6b2o629bo3bo1827b2o401b3o271b2o$1113b2o18bobobo656bo2bo4bo
2bo599bo29b3o1828b2o404bo$1114bo19bob3o654b6o2b6o596b2o1861b2o403b2o$
1114bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2bo598b2o1857bobo$1115b2o5b3o18b
o631bo2b2o2bo4b3o5b2o6b2o1231bo1226bo52bo$1124b2obo14bobo630bo2b2o2bo
4bo1246b2o1278b3o637b2o3b2o$1124bo2bo13b2ob2o631b4o7bo1246b2o1276bo
628b2o4b2o6b3o$1124b2obo12bo5bo628bobo2bobo650b2o1232bo645b2o627bobo2b
obo5bo3bo$502bo619b3o9b2o7bo631b2o4b2o651bo1170b2o20b2o36b2o1277bo2bo
8bobo$500b3o619b2o10bobo3b2o3b2o640b2o3b2o637b3o1172bo20bo38b2o1275bo
4bo8bo$499bo628bo5bo652bo5bo637bo1174bobo8bo7bobo670bo644b6o$488b2o9b
2o625b2o2479b2o8bobo5b2o396bo272b2o375b3o268b4o15bobo2bobo$487bo2bo
636b2o20b2o6b2o629bo3bo1827b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bo
b2o$467b2o18bobobo656bo2bo4bo2bo599bo29b3o1828b2o404bo644bo5bo277bob2o
4bobo2bobo$468bo19bob3o654b6o2b6o596b2o1861b2o403b2o645bo3bo272bo4bo3b
o$468bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2bo598b2o1857bobo1053b3o273bobo
2b5o$469b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o1231bo1226bo52bo1002b3o266b
2o5b2o2b2o3b2o$478b2obo14bobo630bo2b2o2bo4bo1246b2o1278b3o637b2o3b2o
619bo6b2o11b5o$478bo2bo13b2ob2o631b4o7bo1246b2o1276bo628b2o4b2o6b3o
620bobo7bo11b3o$478b2obo12bo5bo628bobo2bobo650b2o1232bo645b2o627bobo2b
obo5bo3bo357b2o173bo84b2o3bo19bo$476b3o9b2o7bo631b2o4b2o651bo1170b2o
20b2o36b2o1277bo2bo8bobo359bo173bo84b2o3bo$476b2o10bobo3b2o3b2o640b2o
3b2o637b3o1172bo20bo38b2o1275bo4bo8bo357b3o173bobo83b2o3bo$482bo5bo
652bo5bo637bo1174bobo8bo7bobo670bo644b6o366bo176bo81b2o3bobo$480b2o
2479b2o8bobo5b2o396bo272b2o375b3o268b4o15bobo2bobo521bo80bobo4bo14b3o$
481b2o20b2o6b2o629bo3bo1827b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bo
b2o517bo80bo19bob3o$502bo2bo4bo2bo599bo29b3o1828b2o404bo644bo5bo277bob
2o4bobo2bobo521bo47bo31b2o18bobobo$501b6o2b6o596b2o1861b2o403b2o645bo
3bo272bo4bo3bo532bobo45bobo50bo2bo$485bo2bo13bo2bo4bo2bo598b2o1857bobo
1053b3o273bobo2b5o533bo100b2o9b2o$483bo2b2o2bo4b3o5b2o6b2o1231bo1226bo
52bo1002b3o266b2o5b2o2b2o3b2o532bo18b2o91bo$483bo2b2o2bo4bo1246b2o
1278b3o637b2o3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o$485b4o7bo1246b2o
1276bo628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo$483bobo2bob
o650b2o1232bo645b2o627bobo2bobo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b
2o17bobo4b2o29bo$483b2o4b2o651bo1170b2o20b2o36b2o1277bo2bo8bobo359bo
173bo84b2o3bo539b2o3bo3bo16b2obo$495b2o3b2o637b3o1172bo20bo38b2o1275bo
4bo8bo357b3o173bobo83b2o3bo543bo5bo15b2ob2o$495bo5bo637bo1174bobo8bo7b
obo670bo644b6o366bo176bo81b2o3bobo543b2obo3bo15b2obo34bo$2315b2o8bobo
5b2o396bo272b2o375b3o268b4o15bobo2bobo521bo80bobo4bo14b3o528bo5bo15bob
o34b3o$496bo3bo1827b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bob2o517bo
80bo19bob3o529bo3bo6bo10bo35b3o$467bo29b3o1828b2o404bo644bo5bo277bob2o
4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo$465b2o1861b2o403b2o645bo3b
o272bo4bo3bo532bobo45bobo50bo2bo542b2o63b2o$466b2o1857bobo1053b3o273bo
bo2b5o533bo100b2o9b2o520b2o49bo7bobo15bo$1098bo1226bo52bo1002b3o266b2o
5b2o2b2o3b2o532bo18b2o91bo520b3o47bobo8bo8bo5bobo$1096b2o1278b3o637b2o
3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o516bob2o15b2o28b2o19bobo4b2o$
1097b2o1276bo628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bo
bo13b3o2bo28b2o18bo3b2o$495b2o1232bo645b2o627bobo2bobo5bo3bo357b2o173b
o84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo4bo10b2o29b2o18bo3b2o$
496bo1170b2o20b2o36b2o1277bo2bo8bobo359bo173bo84b2o3bo539b2o3bo3bo16b
2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo$493b3o1172bo20bo38b2o1275bo
4bo8bo357b3o173bobo83b2o3bo543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo
102bobo$493bo1174bobo8bo7bobo670bo644b6o366bo176bo81b2o3bobo543b2obo3b
o15b2obo34bo601b2o23bo16b3o8bo102bo3bo$1669b2o8bobo5b2o396bo272b2o375b
3o268b4o15bobo2bobo521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo
21b2o130bo3bo$1682b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bob2o517bo
80bo19bob3o529bo3bo6bo10bo35b3o587bobo11b2o52b2o99bobobo$1682b2o404bo
644bo5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b
3o98bobobo$1682b2o403b2o645bo3bo272bo4bo3bo532bobo45bobo50bo2bo542b2o
63b2o569b5o47b2o15b2obo97bo3bo$1679bobo1053b3o273bobo2b5o533bo100b2o9b
2o520b2o49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo3bo$452bo1226bo
52bo1002b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b3o47bobo8bo8bo5bobo570b
5o4b2o42b2obo8bo4bo2bo97bobo$450b2o1278b3o637b2o3b2o619bo6b2o11b5o515b
2o35bo27b3o63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo
$451b2o1276bo628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bo
bo13b3o2bo28b2o18bo3b2o577bo54bo6b2o88b2o$1083bo645b2o627bobo2bobo5bo
3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo4bo10b2o29b
2o18bo3b2o628b2obo98bo22bo$1021b2o20b2o36b2o1277bo2bo8bobo359bo173bo
84b2o3bo539b2o3bo3bo16b2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo
2b2o98bobo8b2o8b4o$1022bo20bo38b2o1275bo4bo8bo357b3o173bobo83b2o3bo
543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo
4b2obobob2o$1022bobo8bo7bobo670bo644b6o366bo176bo81b2o3bobo543b2obo3bo
15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o$1023b2o8bobo5b
2o396bo272b2o375b3o268b4o15bobo2bobo521bo80bobo4bo14b3o528bo5bo15bobo
34b3o588bo8b3o2bo21b2o130bo3bo535bo3bo95bo2bo4bob2obob2o$1036b2o401b3o
271b2o373bo3bo279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo10bo
35b3o587bobo11b2o52b2o99bobobo534bob3obo94bo11b4o9b2o$1036b2o404bo644b
o5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b3o
98bobobo470b2o63b5o96bobo10bo10bobo$1036b2o403b2o645bo3bo272bo4bo3bo
532bobo45bobo50bo2bo542b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23b
o$1033bobo1053b3o273bobo2b5o533bo100b2o9b2o520b2o49bo7bobo15bo569b2o3b
2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o$1033bo52bo1002b3o266b2o5b2o2b
2o3b2o532bo18b2o91bo520b3o47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo
97bobo469bo161bobo$1084b3o637b2o3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o
516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo$1083bo
628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b
2o18bo3b2o577bo54bo6b2o88b2o513bobo132bo2bo5b2o5b2ob2o$437bo645b2o627b
obo2bobo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo
4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo$375b
2o20b2o36b2o1277bo2bo8bobo359bo173bo84b2o3bo539b2o3bo3bo16b2obo618b2o
4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o40bo26b2o
104bo2bo12bo2b2o$376bo20bo38b2o1275bo4bo8bo357b3o173bobo83b2o3bo543bo
5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo4b2obo
bob2o448bo68bo105b2o14b3o$376bobo8bo7bobo670bo644b6o366bo176bo81b2o3bo
bo543b2obo3bo15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o
447bobo82b2o100bo3b3o$377b2o8bobo5b2o396bo272b2o375b3o268b4o15bobo2bob
o521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535bo
3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o$390b2o401b3o271b2o373bo3b
o279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo10bo35b3o587bobo11b
2o52b2o99bobobo534bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo$390b2o404bo
644bo5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b
3o98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o$390b2o
403b2o645bo3bo272bo4bo3bo532bobo45bobo50bo2bo542b2o63b2o569b5o47b2o15b
2obo97bo3bo471bo165b2o23bo437bo$387bobo1053b3o273bobo2b5o533bo100b2o9b
2o520b2o49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o
437bo$387bo52bo1002b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b3o47bobo8bo
8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o$438b3o637b
2o3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o516bob2o15b2o28b2o19bobo4b2o
572b3o5bo47bo5bobo5b2o99bo635bo6bo457bo7bo174bo$437bo628b2o4b2o6b3o
620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b2o18bo3b2o577b
o54bo6b2o88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o$437b2o627bobo2bo
bo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo4bo10b
2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo454bo2bo
171b5o40b2o$1068bo2bo8bobo359bo173bo84b2o3bo539b2o3bo3bo16b2obo618b2o
4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o40bo26b2o
104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o$1067bo4bo8bo357b3o173bobo
83b2o3bo543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b
2o7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o$422bo644b6o366bo176b
o81b2o3bobo543b2obo3bo15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo
12bob3o447bobo82b2o100bo3b3o648bo25b3o$147bo272b2o375b3o268b4o15bobo2b
obo521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535b
o3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo$147b3o271b2o
373bo3bo279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo10bo35b3o
587bobo11b2o52b2o99bobobo534bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo
450bobo179bo$150bo644bo5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b
2o7bobo633bo3bo64b3o98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80b
o102b2ob2o450bo187b3o34bo44bo18bo$149b2o645bo3bo272bo4bo3bo532bobo45bo
bo50bo2bo542b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b
2o177b2o7bo3bo31b3o42b3o18b3o$797b3o273bobo2b5o533bo100b2o9b2o520b2o
49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o437bo671b
4o148bo6bo5bo29bo44bo24bo$797b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b3o
47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o
671b6o144b3o43b2o34b2o7b2o22b2o$432b2o3b2o619bo6b2o11b5o515b2o35bo27b
3o63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo
457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo$420b2o4b2o6b3o620bobo7bo
11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b2o18bo3b2o577bo54bo6b2o
88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo2bo64b
o36bo$420bobo2bobo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o
29bo582bo2bo4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo
6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo$422bo2bo8bobo359bo173bo84b2o
3bo539b2o3bo3bo16b2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o
98bobo8b2o8b4o448b2o40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3o
b2o445b6o154bo5bo26b3o72bo$421bo4bo8bo357b3o173bobo83b2o3bo543bo5bo15b
2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo4b2obobob2o
448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo$421b6o366bo176bo81b2o3bo
bo543b2obo3bo15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o
447bobo82b2o100bo3b3o648bo25b3o609b3o28bobo$151b3o268b4o15bobo2bobo
521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535bo3b
o95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo481bo191b3o5b5o
68b2o3b2o$150bo3bo279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo
10bo35b3o587bobo11b2o52b2o99bobobo534bob3obo94bo11b4o9b2o441bo2b2o78b
3o106bo450bobo179bo482b2o189bo6b2o3b2o67bo5bo$149bo5bo277bob2o4bobo2bo
bo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b3o98bobobo470b2o63b5o96b
obo10bo10bobo437bo2bo2b2o80bo102b2ob2o450bo187b3o34bo44bo18bo373b2o
192bo5b2o3b2o$150bo3bo272bo4bo3bo532bobo45bobo50bo2bo542b2o63b2o569b5o
47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b2o177b2o7bo3bo31b3o42b3o18b
3o373bo272bo3bo$151b3o273bobo2b5o533bo100b2o9b2o520b2o49bo7bobo15bo
569b2o3b2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o437bo671b4o148bo6bo5bo
29bo44bo24bo632b2o4b2o6b3o$151b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b
3o47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o
671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo$412bo6b2o11b5o515b2o35bo
27b3o63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo
6bo457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o$411b
obo7bo11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b2o18bo3b2o577bo54b
o6b2o88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo
2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo$149b2o173bo84b2o3bo19bo518bob
o5b2o17bobo4b2o29bo582bo2bo4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o
25bobo103bobobo6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo114b2o3bobo
435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o$150bo173bo84b2o3bo539b2o3bo3bo
16b2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o
448b2o40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o445b6o154bo
5bo26b3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo$147b3o173bobo83b
2o3bo543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o
7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo662b3o8bo
5b2o46bo15bo7b6o2b6o$147bo176bo81b2o3bobo543b2obo3bo15b2obo34bo601b2o
23bo16b3o8bo102bo3bo536b3o96bo12bob3o447bobo82b2o100bo3b3o648bo25b3o
609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo$324bo80bobo4bo14b3o528bo
5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535bo3bo95bo2bo4bob2obob2o449bo
bo81bo99b2o3bo2b2o452b2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b
2o3bo5b2o6b2o$324bo80bo19bob3o529bo3bo6bo10bo35b3o587bobo11b2o52b2o99b
obobo534bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo450bobo179bo482b2o189bo
6b2o3b2o67bo5bo554b2ob2o15bo66bo$324bo47bo31b2o18bobobo532b2o7bobo633b
o3bo64b3o98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o
450bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob2o66bobo$323bobo45b
obo50bo2bo542b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b
2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo9b2ob
2o$324bo100b2o9b2o520b2o49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo
3bo468b3o191b2o437bo671b4o148bo6bo5bo29bo44bo24bo632b2o4b2o6b3o422bobo
117b2o18bobo58b3o4bobo7bo5bo$324bo18b2o91bo520b3o47bobo8bo8bo5bobo570b
5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o671b6o144b3o43b2o34b2o7b
2o22b2o632bobo2bobo431b3o118bo24bobo49bob2o5bo3bo9bo$306b2o35bo27b3o
63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo
457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o433b3o
118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o$307bo27bo5bobo27b3o65bo515bobo
13b3o2bo28b2o18bo3b2o577bo54bo6b2o88b2o513bobo132bo2bo5b2o5b2ob2o455bo
bo172b3o487b2o6b2o151bo2bobo2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo
431b3o119b2o6bo2bo10b2o52bob2o$307bobo5b2o17bobo4b2o29bo582bo2bo4bo10b
2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo454bo2bo
171b5o40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b
2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo$308b2o3bo3bo16b2obo618b2o4bo7bo
29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o40bo26b2o104bo2b
o12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o445b6o154bo5bo26b3o72bo121bo437b
3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo$312b
o5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo4b2ob
obob2o448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo662b3o8bo5b2o46bo
15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo$311b2obo3bo15b2obo
34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o447bobo82b2o100bo3b3o
648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo389b2o147bo2bo
12bobo6bobo33b2o18bobobo$312bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo
535bo3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo481bo191b
3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b2o110b2o8bo16bo
53bo2bo$313bo3bo6bo10bo35b3o587bobo11b2o52b2o99bobobo534bob3obo94bo11b
4o9b2o441bo2b2o78b3o106bo450bobo179bo482b2o189bo6b2o3b2o67bo5bo554b2ob
2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o$315b2o7bobo633bo3bo64b3o
98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o450bo187b
3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob2o66bobo406b2o4bobo17bo6bobo
119bobobo80bo$324b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo
644b2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo
9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o$312b2o49bo7bobo15bo569b2o3b2o
2bobo41bo2b2o14bobo96bo3bo468b3o191b2o437bo671b4o148bo6bo5bo29bo44bo
24bo632b2o4b2o6b3o422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b
2o124b3o84bo$311b3o47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo
469bo161bobo465b3o671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o
118bo24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo$310bob2o15b2o28b2o
19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo457bo7bo174bo489b2o4b2o
143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o433b3o118bobo6b2o13bo2bo49bo2bo
7b2o7b2o3b2o410bobo15b2o4b2o$309bobo13b3o2bo28b2o18bo3b2o577bo54bo6b2o
88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo2bo64b
o36bo114b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo
9bo7b2o$309bo2bo4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bo
bobo6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2b
o52b2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130b
o$310b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o
40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o445b6o154bo5bo26b
3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bo
bo7b2o12bo429b2o137b3o$316b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b
2o7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo662b3o
8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442bobo
123bobobo$328b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o447bobo82b2o100bo
3b3o648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo389b2o147bo
2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo$316bo8b3o2bo21b2o130bo3b
o535bo3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo481bo191b
3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b2o110b2o8bo16bo
53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o$315bobo11b2o52b2o99bobobo534bob
3obo94bo11b4o9b2o441bo2b2o78b3o106bo450bobo179bo482b2o189bo6b2o3b2o67b
o5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o348bo67bo2b2o
12bobobo63bo59bo$314bo3bo64b3o98bobobo470b2o63b5o96bobo10bo10bobo437bo
2bo2b2o80bo102b2ob2o450bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bo
b2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo$
314b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b2o177b2o7bo3bo31b3o
42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo9b2ob2o409b2o3bo15b2o6b
2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o$313b2o3b2o2bobo41bo2b2o14b
obo96bo3bo468b3o191b2o437bo671b4o148bo6bo5bo29bo44bo24bo632b2o4b2o6b3o
422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o
90b4o26bo$314b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o671b6o144b
3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo24bobo49bob2o5bo3bo9bo411b
2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo$315b3o5bo47bo5bobo5b2o
99bo635bo6bo457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo
61b4o433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo
3bo123bo2b2o9bo88b2o4b2o25bob2o$316bo54bo6b2o88b2o513bobo132bo2bo5b2o
5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo2bo64bo36bo114b2o4bo435bo11bo
60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo9bo7b2o631b2o2bo8b3o86b
2o6b2o25b6o$367b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo454bo2bo171b5o
40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o
409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o
29b3o$366bo2b2o98bobo8b2o8b4o448b2o40bo26b2o104bo2bo12bo2b2o456b2o171b
2o3b2o7b6o23b3ob2o445b6o154bo5bo26b3o72bo121bo437b3o7bobo52bobo5bo2bo
13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o513bo78b3o8b
6o25b2o$366b2o12bo89b2o7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o
447b4o156bo3bo662b3o8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o
35bo19bo2bo442bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo$379b3o96bo12b
ob3o447bobo82b2o100bo3b3o648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9b
o2bo4bo2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b
3o56bo3bo69bobo2bo5bo76bo38b3o$378bo3bo95bo2bo4bob2obob2o449bobo81bo
99b2o3bo2b2o452b2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo
5b2o6b2o391bo35b2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o
430bo5bo68b2o4bo3bo116bo7bo$377bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo
450bobo179bo482b2o189bo6b2o3b2o67bo5bo554b2ob2o15bo66bo406bobo5bo27bo
120b3o15b2o53b2o9b2o348bo67bo2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o
65b2o57bobo$313b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o450bo
187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob2o66bobo406b2o4bobo17bo
6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo430bo3bo57bo145bobo56bo2bo$
314bo165b2o23bo437bo644b2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b
3o138b2o8bo50b2o5bo9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bob
o4b2o65b2o46b2o401b2o39bo3bo143bo59b2o$311b3o191b2o437bo671b4o148bo6bo
5bo29bo44bo24bo632b2o4b2o6b3o422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o
3bo14b2o4b2o124b3o84bo427b2o90b4o26bo376b2o5b3o15bo41b3o143b2o$311bo
161bobo465b3o671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo24b
obo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo
374b2o12bo8bobo42bo160b2o$476bo6bo457bo7bo174bo489b2o4b2o143bo8bo2bobo
2bo64bobo591b2ob2o8bo61b4o433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o
410bobo15b2o4b2o498bo3bo123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o
204bo40b2o$337bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo
2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o
434bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo$
338b2o25bobo103bobobo6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo114b2o3b
obo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o
9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b
2o80bo36bo$296b2o40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o
445b6o154bo5bo26b3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bob
o124bo6bo9b2o46bobo7b2o12bo429b2o137b3o513bo78b3o8b6o25b2o365b2o8b2o4b
2o7b4o52b3o76bo117b2o$297bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo
662b3o8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442b
obo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o$
297bobo82b2o100bo3b3o648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo
4bo2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o
56bo3bo69bobo2bo5bo76bo38b3o365bo24bo138bo$298bobo81bo99b2o3bo2b2o452b
2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b
2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o4bo
3bo116bo7bo358b2o252b3o$300bo2b2o78b3o106bo450bobo179bo482b2o189bo6b2o
3b2o67bo5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o348bo67b
o2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo$
297bo2bo2b2o80bo102b2ob2o450bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o
635bo8bob2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo
63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo$297bo644b2o
177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo9b2ob2o
409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o401b2o39bo
3bo143bo59b2o277bo94b2o15bo2bo102b2o$298bo671b4o148bo6bo5bo29bo44bo24b
o632b2o4b2o6b3o422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o
124b3o84bo427b2o90b4o26bo376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo
4bobobo$295b3o671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo
24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25b
obo374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo$295bo7bo174bo489b
2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o433b3o118bobo6b2o13bo2bo
49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo123bo2b2o9bo88b2o4b2o25bob
2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o$302bobo172b3o487b2o6b2o151bo2b
obo2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bo
b2o434bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo
381bo$301bo2bo171b5o40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b
2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo
373b3o136bo3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo$
302b2o171b2o3b2o7b6o23b3ob2o445b6o154bo5bo26b3o72bo121bo437b3o7bobo52b
obo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o
513bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o$490b3o
25b5o447b4o156bo3bo662b3o8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo
6b2o35bo19bo2bo442bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b
2o3b2o7b4o55bo77b3o494b2o190bo$493bo25b3o609b3o28bobo631b2o2bo12bo2bo
59b2o9bo2bo4bo2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bob
obo371b3o56bo3bo69bobo2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o$298b
2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b
2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o4bo
3bo116bo7bo358b2o252b3o411bobo3bo180bo$297bobo179bo482b2o189bo6b2o3b2o
67bo5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o348bo67bo2b
2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b
2o3bobo178b2o128bo$297bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob
2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo430bo
3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2ob2o305b3o$
296b2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo
9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o401b
2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o
63bo$324b4o148bo6bo5bo29bo44bo24bo632b2o4b2o6b3o422bobo117b2o18bobo58b
3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o90b4o26bo376b2o5b3o15bo
41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo
93bo146bo65b2o$323b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo
24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25b
obo374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo545b5o25b5o25b5o25b
2o3b2o90bobo145bo25bo$322b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b
4o433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo
123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o$321b2o6b2o151bo2bobo2bo64bo36bo114b
2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo9bo7b2o
631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo381bo647bo271bo
$322b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o409b
3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o29b3o
366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o$323b6o154bo5bo26b
3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bo
bo7b2o12bo429b2o137b3o513bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o52b3o76bo
117b2o378bo2b2o612bo22b3o10bo187b5o$324b4o156bo3bo662b3o8bo5b2o46bo15b
o7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442bobo123bobobo431b3o
70bo4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o494b2o190bo424b3o
22bo9bobo185b2o3b2o$485b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo389b
2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o56bo3bo69bob
o2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o176b2o3b2o
117b3o$315bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b
2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o4bo
3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo$316b2o
189bo6b2o3b2o67bo5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b
2o348bo67bo2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo
103bo116bo413b2o3bobo178b2o128bo338b3o296bo5bo$314b2o192bo5b2o3b2o635b
o8bob2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo
430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2ob2o305b
3o111bo4b2o180bo41bo71b2o223b2obob2o$316bo272bo3bo421b3o138b2o8bo50b2o
5bo9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o
401b2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo
237b3o63bo113bobo3b2o180bo28b3o82b2o181b2obob2o$576b2o4b2o6b3o422bobo
117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o90b4o26b
o376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob
3o24b2ob3o28bo93bo146bo65b2o113bo300b2o$576bobo2bobo431b3o118bo24bobo
49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo
374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo545b5o25b5o25b5o25b2o
3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo38bo$503b2ob2o8bo61b4o
433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo
123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo$61b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo
9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo381bo
647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo$61b2o3bobo435b2o2b
o5bo2bo52b2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o
6bo130bo373b3o136bo3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo
379b2obo837bobo80b2o340b3o29bo81bo4bo97b3o76bo5bo41b3o$67bo437b3o7bobo
52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o
513bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o612bo22b
3o10bo187b5o422bo30bo82bo25bo75bo35bo34b2o56b2o$505b3o8bo5b2o46bo15bo
7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442bobo123bobobo431b3o70b
o4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o494b2o190bo424b3o22bo
9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo$504b2o2bo12bo2bo59b2o9bo2bo4bo
2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o56bo
3bo69bobo2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o176b
2o3b2o117b3o438bo111bo38b2o56b3o$503bo15bo2bobo60b2o3bo5b2o6b2o391bo
35b2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o
4bo3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo303b3o
131b2o110b2o42bo54bo$503b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b
2o348bo67bo2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo
103bo116bo413b2o3bobo178b2o128bo338b3o296bo5bo304bo4b2ob2o278bo4b2o6b
2o$510bo8bob2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2b
o63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2o
b2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo129bo
143b2o9b2o4bo2bo$369b3o138b2o8bo50b2o5bo9b2ob2o409b2o3bo15b2o6b2o120bo
bobo81b3o287b3o136bobo4b2o65b2o46b2o401b2o39bo3bo143bo59b2o277bo94b2o
15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o63bo113bobo3b2o180bo28b3o82b
2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo$369bobo117b
2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o90b4o26bo
376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o
24b2ob3o28bo93bo146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o
134bo99bo39b2o18bo$369b3o118bo24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o
125bo372bo3bo124b5o8bo89b6o25bobo374b2o12bo8bobo42bo160b2o323b2o93b2ob
o6b2o5bo2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o
181bo5bo38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o$
369b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo
123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo$369b3o119b
2o6bo2bo10b2o52bob2o434bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o
51b2o158b3o36bobo381bo647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38b
obo171b2ob2o81b2o9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o$
369b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o
29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o340b3o29bo81b
o4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob2o
122bo16bo8bo$369bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o513bo78b3o8b
6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o
422bo30bo82bo25bo75bo35bo34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bob
o27b2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo$369b3o122b2o7bo10bo2b
o6b2o35bo19bo2bo442bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b
2o3b2o7b4o55bo77b3o494b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34b
o2b3o52bo173bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bob
o10b2o$350b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o
56bo3bo69bobo2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o
176b2o3b2o117b3o438bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bob
o130b2o19b2o$351bo35b2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o
58b3o430bo5bo68b2o4bo3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o
7bo300bo3bo303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3b
o17b2o$351bobo5bo27bo120b3o15b2o53b2o9b2o348bo67bo2b2o12bobobo63bo59bo
372bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b2o3bobo178b2o128bo
338b3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo
146b2o16bobo5b2o$352b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo
63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2ob
2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo129bo143b
2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo$356b2o3bo15b2o6b2o
120bobobo81b3o287b3o136bobo4b2o65b2o46b2o401b2o39bo3bo143bo59b2o277bo
94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o63bo113bobo3b2o180bo28b
3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo337bo2bo
4bob2obob2o18bo86b2o2bo157bobo23bo$356b2o3bo14b2o4b2o124b3o84bo427b2o
90b4o26bo376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b
2o24b2ob3o24b2ob3o28bo93bo146bo65b2o113bo300b2o394bobo5bo10bobo67b2o
20b2o32b3o134bo99bo39b2o18bo338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o$
356b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo374b2o12bo8bobo42bo
160b2o323b2o93b2obo6b2o5bo2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo
267b2o100bobo81b3o181bo5bo38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o
98b2ob2o53bob2o331b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo$
358bobo15b2o4b2o498bo3bo123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o
204bo40b2o381bo5bo7b2o546b2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b
2o3b2o27bo82b5o99b2o74b3o45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o
127bobo6bo153bo332bobo8b2o8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo$
359bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo
381bo647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o
9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo
54bo16bobobo107b3o39b3o$370b2o6bo130bo373b3o136bo3bo86b2o4b2o29b3o366b
2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o340b3o29bo81bo4bo97b3o
76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob2o122bo16bo8b
o335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo$369b2o137b3o513bo78b3o8b
6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o
422bo30bo82bo25bo75bo35bo34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bob
o27b2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o
6b2o71bo154b3o$381bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b
2o3b2o7b4o55bo77b3o494b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34b
o2b3o52bo173bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bob
o10b2o348bobo43b4o81bobo151b3o$293bo86bo126bobobo371b3o56bo3bo69bobo2b
o5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o176b2o3b2o117b
3o438bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o
239b2o106bo3bo23bo5bo12bo2bo$293b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo
68b2o4bo3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo
303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b
2o105bo3bo42b4o26bobo39b2o9bo155b3o$296bo67bo2b2o12bobobo63bo59bo372bo
3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b2o3bobo178b2o128bo338b
3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo146b
2o16bobo5b2o234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo$
295b2o68b5o12bo2bo63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo
115bo323b2o2bo88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o
20b2o116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7b
obo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo$237b3o136bobo4b2o65b2o46b
2o401b2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo
237b3o63bo113bobo3b2o180bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b
3o140bo2b3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo
3bo35bo68bo19b2ob2o152b3o$377b2o90b4o26bo376b2o5b3o15bo41b3o143b2o338b
obo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo93bo146bo65b2o
113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18bo338bo12bob
3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo$236bo3bo
124b5o8bo89b6o25bobo374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo
545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo38bo
168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo4b2ob
obob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o$236bo3b
o123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o
8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o
4bo3bo16bo5bobo$364b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bob
o381bo647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b
2o9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19b
o54bo16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo$237b3o136bo
3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o
340b3o29bo81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o
127b5obob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414b
o2bo4bo2bo5b2o6b4o58bo17b3o15b2o$378bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o
52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo
34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obo
b2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o
56b2o18bo$297b3o70bo4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o
494b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo173bo39bo
52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o
81bobo151b3o390bo5bo4bo76bo$237b3o56bo3bo69bobo2bo5bo76bo38b3o365bo24b
o138bo502bo5bo177b3o425bo20bo20b2o176b2o3b2o117b3o438bo111bo38b2o56b3o
264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b2o106bo3bo23bo5bo12b
o2bo635bo2bo77bo$295bo5bo68b2o4bo3bo116bo7bo358b2o252b3o411bobo3bo180b
o423b2o31b3o7bo300bo3bo303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo
13bo129b2o3bo17b2o240b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo
74bobo$236bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b2o3bobo178b
2o128bo338b3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b
2ob2o9bo146b2o16bobo5b2o234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b
2o7bobo155bo396b2o4b2o$236bo3bo57bo145bobo56bo2bo275b2o110bo106bobo
115bo323b2o2bo88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o
20b2o116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7b
obo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o$255b
2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o
63bo113bobo3b2o180bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo
2b3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo
68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bobo$230b2o5b3o15bo41b3o
143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo93bo
146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18bo
338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo
390bo106bo43b5o29bobo56bo$230b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5b
o2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo
38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo
4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o
481b2o9b2o43bobo30bobo56bo$227b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b2o
bo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o45bo
bo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o8b4o
29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o4bo
3bo16bo5bobo479bo2bo91b2o52bo$226b3o16b4o51b2o158b3o36bobo381bo647bo
271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo4b2o
17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo16bobo
bo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b3o31b
2o2b2o51b3o$227b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o340b3o
29bo81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5o
bob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4b
o2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo$220b2o8b2o4b2o7b4o
52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo
34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obo
b2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o
56b2o18bo477bobo5bo12b2obo84bo125b2o$219bobo8b2o3b2o7b4o55bo77b3o494b
2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo173bo39bo52bo
3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o81bo
bo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o$219bo24bo138bo502bo5bo
177b3o425bo20bo20b2o176b2o3b2o117b3o438bo111bo38b2o56b3o264bobo57b2o
70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b2o106bo3bo23bo5bo12bo2bo635bo2bo
77bo486b2o222b3o4b2o$218b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo
303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b
2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b3o16b3o43b2o
158b2o4b2o$249bobo103bo116bo413b2o3bobo178b2o128bo338b3o296bo5bo304bo
4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b2o
234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o
557b2o3b2o16bo3bo43bo149b2o8b2o6b2o$136b2o110bo106bobo115bo323b2o2bo
88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo
129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo341bobobo
36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o573b2o16bo5bo39b3o
149bobo9bo6bobo$137bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o63b
o113bobo3b2o180bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b
3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo68b
o19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bobo484bo13bo3b2obob2o39bo
151bo20bo$137bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo93b
o146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18b
o338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo
390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o$138b2o93b2obo6b2o5b
o2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo
38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo
4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o
481b2o9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo$237bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o
8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o
4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o$237bo647bo
271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo4b2o
17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo16bobo
bo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b3o31b
2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo$233b2obo837bobo80b2o340b3o29bo
81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob
2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4bo2b
o5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b
2o3bo$232bo2b2o612bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo34b2o56b2o
171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obob2o23bobo
7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o56b2o18bo
477bobo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo$
232b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo173bo39bo
52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o
81bobo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b
2o2bo6bo7b6o52bo$240bo5bo177b3o425bo20bo20b2o176b2o3b2o117b3o438bo111b
o38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b2o106bo3b
o23bo5bo12bo2bo635bo2bo77bo486b2o222b3o4b2o237bo55bo3bo83bo18bo4bo6bo
6bo2b2o2bo6bobo63b3o$240bobo3bo180bo423b2o31b3o7bo300bo3bo303b3o131b2o
110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b2o105bo3bo42b
4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b3o16b3o43b2o158b2o4b2o237b
3o55bo83b3o20b2o7b2o8b4o7bo3bo$240b2o3bobo178b2o128bo338b3o296bo5bo
304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b
2o234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o
557b2o3b2o16bo3bo43bo149b2o8b2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o
$151b2o2bo88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o
116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo
341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o573b2o16b
o5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o$152bo
30b2o28b2o28bo5bo237b3o63bo113bobo3b2o180bo28b3o82b2o181b2obob2o206bo
20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo
157bobo23bo341bo3bo35bo68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bo
bo484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o$151b2o3b2o24b2ob
3o24b2ob3o28bo93bo146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o
134bo99bo39b2o18bo338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bo
bo59bo4bo15b2obo390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o289b
3o78bo36bo20b3o$153b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo
81b3o181bo5bo38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob
2o331b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo
19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo593bo58b
o$153b2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o
74b3o45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bob
o8b2o8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo
55b2o4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo$
239bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo
4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo
16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b
3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo$428bobo80b2o340b3o29bo81bo
4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob2o
122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4bo2bo
5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b
2o3bo495b2o182b2o$203bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo34b2o
56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obob2o
23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o56b
2o18bo477bobo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo
52bo444b2o4bo2b2o63b2o3b2o102b2o$203b3o22bo9bobo185b2o3b2o559b3o109b3o
34bo2b3o52bo173bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo
3bobo10b2o348bobo43b4o81bobo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o
4b2o296bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo$206bo
20bo20b2o176b2o3b2o117b3o438bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3b
o6b2o3bobo130b2o19b2o239b2o106bo3bo23bo5bo12bo2bo635bo2bo77bo486b2o
222b3o4b2o237bo55bo3bo83bo18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o$
205b2o31b3o7bo300bo3bo303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo
13bo129b2o3bo17b2o240b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo
74bobo485b3o16b3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448b
o5bo128bo3bo102b3obo$249b3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10b
o10bobo4bo88b2ob2o9bo146b2o16bobo5b2o234bo107bobobo23b2obob2o4b3o6b4o
25b2o32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b2o6b2o
237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bo
bo3bo$209bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo129bo143b2o9b2o4bo2b
o337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo341bobobo36bo9bo26bo30bo3bo5bo
9b2obo154bo235b2o108bo39b2o3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o
51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o
2bo$209bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b3o5bo4bo
2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo68bo19b2ob
2o152b3o235b2o105b3o39b2o3b2o29bo56bobo484bo13bo3b2obob2o39bo151bo20bo
289bo3bo112bobo19b5o449bo3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo$323b2o
394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18bo338bo12bob3o16b2o9b2o
76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo390bo106bo43b5o29bobo
56bo475b2o17bo3b2o200b2o20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo
2b2o74bo2b2o2bo37bo75b3o$136b2o100bobo81b3o181bo5bo38bo168b2o4bobo9b2o
69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo4b2obobob2o27bo2bo
75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o481b2o9b2o43bobo30bob
o56bo363bo22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o
2bo4b2o106bo$137b2o66b2o3b2o27bo82b5o99b2o74b3o45bobo172bob2o80bobo10b
o5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o8b4o29bobo18b2o54b2o2bo
5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o4bo3bo16bo5bobo479bo2bo
91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo630bo2bo4bo2bo13bo2bo5bobo
106b2o$136bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo4b
2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo16bo
bobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b3o
31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo1083b2o81b2o36b6o2b6o7bo15bo$
207b3o29bo81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o
127b5obob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414b
o2bo4bo2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo
25bobo3bo3b2o3bo495b2o182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo$208bo
30bo82bo25bo75bo35bo34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b
2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o
71bo154b3o388bobo5b6o56b2o18bo477bobo5bo12b2obo84bo125b2o297bo2bo102bo
3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o10b
o10bobo39b2o6b2o5bo3b2o128b3o$346b3o109b3o34bo2b3o52bo173bo39bo52bo3b
2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o81bobo
151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b2o2bo
6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo$
345bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b
2o106bo3bo23bo5bo12bo2bo635bo2bo77bo486b2o222b3o4b2o237bo55bo3bo83bo
18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o$
211b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b
2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b3o16b3o43b2o
158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo3bo102b3obo
456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo$213bo4b2ob2o278bo4b2o6b2o339b
obo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b2o234bo107bobobo23b2obob2o4b
3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b
2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b
2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo
$72b2o20b2o116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo
13bo7bobo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o
573b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o4b2o4b2o3b
2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo9bo4bo4bobo66bo9bo3bo
5b2obo66bo41b5obob2o$73bo20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo337bo
2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo68bo19b2ob2o152b3o
235b2o105b3o39b2o3b2o29bo56bobo484bo13bo3b2obob2o39bo151bo20bo289bo3bo
112bobo19b5o449bo3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b
2o7b2o7bo2bo64b2o32bo5bo7b4o$73bobo5bo10bobo67b2o20b2o32b3o134bo99bo
39b2o18bo338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo
15b2obo390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o289b3o78bo36b
o20b3o451b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo10b2o
53b2o27b3o13b2o4b2o$74b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bo
b2o331b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60b
o19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo593bo
58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o106bo524bobo9b3o6b2o4b3o82bo
4b2ob2o6b2o3bobo$79bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo
332bobo8b2o8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo
2bo20bo55b2o4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b
6o575bo630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo
14bo$78b2ob2o81b2o9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o
362bo22bo28b2obo19bo54bo16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b
3o15bo459b2o18bobo2bo51b3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo
1083b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o$79bob2o38b2o
51bo3b2o22bo8b2o5b2ob2o127b5obob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo
54b2ob2o12bo2bo110bo414bo2bo4bo2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o
56b3o66bo297bo105bo8bo25bobo3bo3b2o3bo495b2o182b2o402bo57bo23bo38bo2bo
4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o$80bobo39b2o50bo3b2o23b
2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo351bo26b2o
b2o3b2o9b4o6b2o71bo154b3o388bobo5b6o56b2o18bo477bobo5bo12b2obo84bo125b
2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b
2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o$
81bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348b
obo43b4o81bobo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo
6bo12bo2b2o2bo6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b
2o55bo136bo433b3o69b2o9b2o107bo$175bobo57b2o70bo28bo4bo3bo6b2o3bobo
130b2o19b2o239b2o106bo3bo23bo5bo12bo2bo635bo2bo77bo486b2o222b3o4b2o
237bo55bo3bo83bo18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o632b3o8b2o
3bo64bobo130b5o434b3o70bo118bo4b2o6b2o$176bo33b2o23bo106bobo13bo129b2o
3bo17b2o240b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b
3o16b3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo
3bo102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o
9b2o4bo2bo$209bobo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b2o234bo107bobo
bo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo
3bo43bo149b2o8b2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o
84b3o6b2o4b2o39b2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b
2obob2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo$208bo11b4o9b2o5b3o86bo
161bo2bo13bo7bobo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo
39b2o3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o
4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo9bo4bo4bobo
66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo$208bo2bo4bob2obob2o18bo86b2o
2bo157bobo23bo341bo3bo35bo68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo
56bobo484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o449bo3bo43bob
obo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b
4o633bob2o$208bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo
4bo15b2obo390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o289b3o78bo
36bo20b3o451b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo
10b2o53b2o27b3o13b2o4b2o632bo$200b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b
2o107b2o42bo359bobo43b2o60bo19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo
22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o
106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo$199bobo8b2o8b4o29bob
o18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o4bo3bo16b
o5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo630bo2bo4bo2bo
13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo578b2o40bo8bo$
199bo22bo28b2obo19bo54bo16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b
3o15bo459b2o18bobo2bo51b3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo
1083b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o607b2o32bobo
4b2o7bobo$198b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4bo2bo5b
2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b2o
3bo495b2o182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo18b2o
3b3o74bo2b3o636bo2bo3bobo10b2o$216bo26b2ob2o3b2o9b4o6b2o71bo154b3o388b
obo5b6o56b2o18bo477bobo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo
10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b
2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o638b2o19b2o$215bobo43b4o81bobo
151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b2o2bo
6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo
433b3o69b2o9b2o107bo632b2o3bo17b2o$106b2o106bo3bo23bo5bo12bo2bo635bo2b
o77bo486b2o222b3o4b2o237bo55bo3bo83bo18bo4bo6bo6bo2b2o2bo6bobo63b3o
384bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo4b2o6b2o620b2o16b
obo5b2o$107b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b
3o16b3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo
3bo102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o
9b2o4bo2bo612b2o6bo2bo13bo7bobo$106bo107bobobo23b2obob2o4b3o6b4o25b2o
32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b2o6b2o237bo
51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bobo3bo
446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo429b3o67bo
115bo2b3o5bo4bo2bobo611bobo7bobo23bo$214bobobo36bo9bo26bo30bo3bo5bo9b
2obo154bo235b2o108bo39b2o3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o
51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o
2bo446bobo9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612bo17b
3o15b2o$214bo3bo35bo68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bobo
484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o449bo3bo43bobobo6b
2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o
633bob2o612b2o18bo$214bo3bo41bobo59bo4bo15b2obo390bo106bo43b5o29bobo
56bo475b2o17bo3b2o200b2o20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo
2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo
634bo$215bobo43b2o60bo19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo22b2o
85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o106bo
524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo686b3o$216bo12bo2bo4bo2bo
20bo55b2o4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o
575bo630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo
14bo578b2o40bo8bo$227b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b
3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo1083b2o81b2o36b6o2b6o7bo15b
o550bobo78bo2bo19bo3b2obo74b2o607b2o32bobo4b2o7bobo635b3o$229bo2bo4bo
2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo
3b2o3bo495b2o182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo
18b2o3b3o74bo2b3o636bo2bo3bobo10b2o577bo55b3o$245bobo5b6o56b2o18bo477b
obo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo444b2o
4bo2b2o63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b
3o79bo2bo24bo76b2o638b2o19b2o575b2o$247bo5bo4bo76bo478b2o5b2o13bo210b
2o4b2o296bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo
58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo632b2o3bo17b2o551b2o23b
2o55b3o$254bo2bo77bo486b2o222b3o4b2o237bo55bo3bo83bo18bo4bo6bo6bo2b2o
2bo6bobo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo4b2o
6b2o620b2o16bobo5b2o545b3o81bo$252bobo2bobo74bobo485b3o16b3o43b2o158b
2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo3bo102b3obo456b2o
6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o4bo2bo612b2o6b
o2bo13bo7bobo544b3o81bo$252b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b2o6b2o
237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bo
bo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo429b3o
67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b3o$91b2o108bo39b2o
3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o4b2o4b
2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo9bo4bo4bobo66bo9b
o3bo5b2obo66bo41b5obob2o615b2o18bo612bo17b3o15b2o544b3o$92b2o105b3o39b
2o3b2o29bo56bobo484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o
449bo3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo
64b2o32bo5bo7b4o633bob2o612b2o18bo563bo$91bo106bo43b5o29bobo56bo475b2o
17bo3b2o200b2o20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo2b2o74bo2b
2o2bo37bo75b3o423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo$187b2o
9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo
62b2o6b2o12bo2b2o2bo4b2o106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo
579bo686b3o$186bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo630bo2bo
4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo578b2o40bo
8bo$166b2o18bobo2bo51b3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo1083b
2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o607b2o32bobo4b2o7b
obo635b3o$167bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b2o3bo495b2o
182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b
3o636bo2bo3bobo10b2o577bo55b3o$167bobo5bo12b2obo84bo125b2o297bo2bo102b
o3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o
10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o638b2o19b2o575b
2o$168b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o
54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo
632b2o3bo17b2o551b2o23b2o55b3o442bo$176b2o222b3o4b2o237bo55bo3bo83bo
18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o
434b3o70bo118bo4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo$176b3o16b
3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo3bo
102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o
4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o$171b2o3b2o16bo3b
o43bo149b2o8b2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b
3o6b2o4b2o39b2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obo
b2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b
3o439b3obo2bo124b2o$175b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b
2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo
9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612bo17b3o15b2o544b
3o522b2obobo$175bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o449bo
3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o
32bo5bo7b4o633bob2o612b2o18bo563bo513b2o9b4o3b2o$165b2o17bo3b2o200b2o
20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o
423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo1076bobo10bo5bobo$53bo
22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o
106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo686b3o1075bo20bo$53b
3o21b2o83bo6bo15bo18b6o575bo630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo
12b2o7bobo3b3o83bo5bo14bo578b2o40bo8bo1714b2o20b2o$56bo19bo84bo3b2o3bo
32bo6bo1083b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o607b2o
32bobo4b2o7bobo635b3o$55bo105bo8bo25bobo3bo3b2o3bo495b2o182b2o402bo57b
o23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o636bo2bo3bob
o10b2o577bo55b3o$55bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o
63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2b
o24bo76b2o638b2o19b2o575b2o$59bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o
54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo
632b2o3bo17b2o551b2o23b2o55b3o442bo$o55bo3bo83bo18bo4bo6bo6bo2b2o2bo6b
obo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo4b2o6b2o
620b2o16bobo5b2o545b3o81bo442b4o127bo$3o55bo83b3o20b2o7b2o8b4o7bo3bo
448bo5bo128bo3bo102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bob
o67b3o114b2o9b2o4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o$
3bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bobo
3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo429b3o
67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b3o439b3obo2bo124b2o
$2b2o51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo
47b3o2bo446bobo9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612b
o17b3o15b2o544b3o522b2obobo$56bo3bo112bobo19b5o449bo3bo43bobobo6b2o4b
2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o633bob
2o612b2o18bo563bo513b2o9b4o3b2o$57b3o78bo36bo20b3o451b3o45bob3o5bobo3b
o2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo
634bo1076bobo10bo5bobo$138bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o
106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo686b3o1075bo20bo$139b
o630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo
578b2o40bo8bo1714b2o20b2o$648b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19b
o3b2obo74b2o607b2o32bobo4b2o7bobo635b3o$61b2o182b2o402bo57bo23bo38bo2b
o4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o636bo2bo3bobo10b2o577bo
55b3o$62b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o
128b3o434b3o79bo2bo24bo76b2o638b2o19b2o575b2o$4b3o54bo6b2o2bo63bo5bo
503bo58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo632b2o3bo17b2o551b
2o23b2o55b3o442bo$3bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo
4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$2bo5bo128bo3bo
102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o
4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o1503bo3bo$2bo5bo
43b2o84b3o6b2o4b2o39b2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o
101b2obob2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo
544b2obo79b3o439b3obo2bo124b2o1506bo$5bo45bo2bo12b5o75bobo2bobo40bo47b
3o2bo446bobo9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612bo
17b3o15b2o544b3o522b2obobo1629bo4bo$3bo3bo43bobobo6b2o4b2o2bo76b4o39b
3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o633bob2o612b2o18bo
563bo513b2o9b4o3b2o1629bo$4b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o
423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo1076bobo10bo5bobo1615b
2o7bo3bo4b2o$5bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o106bo524bobo9b3o6b2o4b
3o82bo4b2ob2o6b2o3bobo579bo686b3o1075bo20bo1614bobo7bobo6bobo$124bo2bo
4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo578b2o40bo
8bo1714b2o20b2o1613bo20bo$2b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo
3b2obo74b2o607b2o32bobo4b2o7bobo635b3o2710b2o20b2o$3bo57bo23bo38bo2bo
4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o636bo2bo3bobo10b2o577bo
55b3o$3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o
638b2o19b2o575b2o$o58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo632b
2o3bo17b2o551b2o23b2o55b3o442bo$58b3o8b2o3bo64bobo130b5o434b3o70bo118b
o4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$59b2o6bob2o3bo
63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o4bo2bo612b2o6bo2bo13bo
7bobo544b3o81bo441b2obobo124b2o1503bo3bo$50b2o8b2o2bo2bob2o3bo62bo5bo
7bobo4b3o101b2obob2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo611bobo7bo
bo23bo544b2obo79b3o439b3obo2bo124b2o1506bo$49bobo9bo4bo4bobo66bo9bo3bo
5b2obo66bo41b5obob2o615b2o18bo612bo17b3o15b2o544b3o522b2obobo1629bo4bo
$49bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o633bob2o612b2o18bo563bo
513b2o9b4o3b2o1629bo$48b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo
1076bobo10bo5bobo1615b2o7bo3bo4b2o$146bobo9b3o6b2o4b3o82bo4b2ob2o6b2o
3bobo579bo686b3o1075bo20bo1614bobo7bobo6bobo$65b3o77bo12b2o7bobo3b3o
83bo5bo14bo578b2o40bo8bo1714b2o20b2o1613bo20bo$65bobo78bo2bo19bo3b2obo
74b2o607b2o32bobo4b2o7bobo635b3o2710b2o20b2o$65b3o78bobobo18b2o3b3o74b
o2b3o636bo2bo3bobo10b2o577bo55b3o$65b3o79bo2bo24bo76b2o638b2o19b2o575b
2o$65b3o69b2o9b2o107bo632b2o3bo17b2o551b2o23b2o55b3o442bo$65b3o70bo
118bo4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$65bobo67b3o
114b2o9b2o4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o1503bo
3bo$65b3o67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b3o439b3obo
2bo124b2o1506bo$251b2o18bo612bo17b3o15b2o544b3o522b2obobo1629bo4bo$
267bob2o612b2o18bo563bo513b2o9b4o3b2o1629bo$268bo634bo1076bobo10bo5bob
o1615b2o7bo3bo4b2o$215bo686b3o1075bo20bo1614bobo7bobo6bobo$213b2o40bo
8bo1714b2o20b2o1613bo20bo$214b2o32bobo4b2o7bobo635b3o2710b2o20b2o$247b
o2bo3bobo10b2o577bo55b3o$246b2o19b2o575b2o$244b2o3bo17b2o551b2o23b2o
55b3o442bo$246b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$239b2o6bo2bo
13bo7bobo544b3o81bo441b2obobo124b2o1503bo3bo$238bobo7bobo23bo544b2obo
79b3o439b3obo2bo124b2o1506bo$238bo17b3o15b2o544b3o522b2obobo1629bo4bo$
237b2o18bo563bo513b2o9b4o3b2o1629bo$257bo1076bobo10bo5bobo1615b2o7bo3b
o4b2o$256b3o1075bo20bo1614bobo7bobo6bobo$1333b2o20b2o1613bo20bo$256b3o
2710b2o20b2o$200bo55b3o$198b2o$174b2o23b2o55b3o442bo$173b3o81bo442b4o
127bo1502bobo$173b3o81bo441b2obobo124b2o1503bo3bo$173b2obo79b3o439b3ob
o2bo124b2o1506bo$174b3o522b2obobo1629bo4bo$175bo513b2o9b4o3b2o1629bo$
688bobo10bo5bobo1615b2o7bo3bo4b2o$688bo20bo1614bobo7bobo6bobo$687b2o
20b2o1613bo20bo$2323b2o20b2o3$55bo$54b4o127bo1502bobo$53b2obobo124b2o
1503bo3bo$52b3obo2bo124b2o1506bo$53b2obobo1629bo4bo$43b2o9b4o3b2o1629b
o$42bobo10bo5bobo1615b2o7bo3bo4b2o$42bo20bo1614bobo7bobo6bobo$41b2o20b
2o1613bo20bo$1677b2o20b2o4$1042bobo$1042bo3bo$1046bo$1042bo4bo$1046bo$
1033b2o7bo3bo4b2o$1032bobo7bobo6bobo$1032bo20bo$1031b2o20b2o4$396bobo$
396bo3bo$400bo$396bo4bo$400bo$387b2o7bo3bo4b2o$386bobo7bobo6bobo$386bo
20bo$385b2o20b2o!

[BokaBB]

Let FWKS do it, I'm both too lazy and unexperienced on this kind of thing.

Code: Select all

x = 5130, y = 739, rule = B34c/S234y8:T5168,8191+96
4699b2o$4699bo$4700b3o$4702bo4$4895bo$4894bobo$4893bo3bo$4893bo3bo$
4893bobobo$4053b2o838bobobo$4053bo839bo3bo$4054b3o836bo3bo$4056bo837bo
bo$4895bo18b2o$4877b2o35bo$4878bo27bo5bobo$4249bo628bobo6b2o16b4o3b2o$
4248bobo628b3o5bobo14b2obobo$4247bo3bo629b3o6bo12b3obo2bo42bo$4247bo3b
o629bo2bo2bo2bo13b2obobo43b3o$4247bobobo630b2o6bo14b4o47bo$3407b2o838b
obobo635bobo5bo10bo48b2o9b2o$3407bo839bo3bo635b2o5bo70bo2bo$3408b3o
836bo3bo642b3o66bo2bobo18b2o$3410bo837bobo716bo19bo$4249bo18b2o613b3o
77bob2o12bo5bobo$4231b2o35bo612bob3o11b2ob2o62bo13b2o5b2o$4232bo27bo5b
obo611bobobo16bo75b2o$3603bo628bobo6b2o16b4o3b2o612bo2bo12bo2b2o56b3o
16b3o$3602bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o$3601bo
3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o$3601bo3bo629bo2bo2bo
2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo$3601bobobo630b2o6bo14b
4o47bo590bo63b2o3bo17b2o$2761b2o838bobobo635bobo5bo10bo48b2o9b2o564b3o
8b2ob2o62bobo3bo15bo4bo$2761bo839bo3bo635b2o5bo70bo2bo622b6o18bo15bo6b
o$2762b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo$2764bo837bobo
716bo19bo552bo48bo3b2o3bo3bobo25bo8bo$3603bo18b2o613b3o77bob2o12bo5bob
o542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo$3585b2o35bo612bob3o11b2ob
2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo$3586bo27bo5bobo611b
obobo16bo75b2o553b3o67bobo6bo2b2o2bo6bo6bo4bo18bo$2957bo628bobo6b2o16b
4o3b2o612bo2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o$2956bobo
628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo2bobo6b2o
32bo$2955bo3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o
4b2o4b2o6b2o31b2o$2955bo3bo629bo2bo2bo2bo13b2obobo43b3o576b2o8bo2b2o
54b2obob2o3bo13bo621b5o19bobo$2955bobobo630b2o6bo14b4o47bo590bo63b2o3b
o17b2o612b3o20bo36bo$2115b2o838bobobo635bobo5bo10bo48b2o9b2o564b3o8b2o
b2o62bobo3bo15bo4bo611bo58bo$2115bo839bo3bo635b2o5bo70bo2bo622b6o18bo
15bo6bo537b2o129bo$2116b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o
3bo537bo$2118bo837bobo716bo19bo552bo48bo3b2o3bo3bobo25bo8bo534b3o$
2957bo18b2o613b3o77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b
2o3bo534bo129b2o3b2o$2939b2o35bo612bob3o11b2ob2o62bo13b2o5b2o544b5o4b
2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo$2940bo27bo5bobo611bobobo16bo75b2o
553b3o67bobo6bo2b2o2bo6bo6bo4bo18bo544bo$2311bo628bobo6b2o16b4o3b2o
612bo2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo$
2310bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo2b
obo6b2o32bo541b2o48b2o39b2o4b2o6b3o$2309bo3bo629b3o6bo12b3obo2bo42bo
578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b2o6b2o31b2o591bo40bobo2bobo$
2309bo3bo629bo2bo2bo2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo
621b5o19bobo624b3o39b4o$2309bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b2o
612b3o20bo36bo591bo37bo2b2o2bo$1469b2o838bobobo635bobo5bo10bo48b2o9b2o
564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b2o6b2o$1469bo
839bo3bo635b2o5bo70bo2bo622b6o18bo15bo6bo537b2o129bo624bobo5bo2bo13bo
2bo4bo2bo$1470b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo
691b2o61bo15bo7b6o2b6o$1472bo837bobo716bo19bo552bo48bo3b2o3bo3bobo25bo
8bo534b3o692bo76b2o9bo2bo4bo2bo$2311bo18b2o613b3o77bob2o12bo5bobo542b
2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b2o3b2o556bobo77b2o3bo5b
2o6b2o$2293b2o35bo612bob3o11b2ob2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b
2o2bo12bo6bo665bo5bo556b2o83bo$2294bo27bo5bobo611bobobo16bo75b2o553b3o
67bobo6bo2b2o2bo6bo6bo4bo18bo544bo749bobo$1665bo628bobo6b2o16b4o3b2o
612bo2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo
623b2o5bo9b2ob2o$1664bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o
3b2o617b5o5bobo2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo$
1663bo3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b
2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo$1663bo3bo629bo2bo2bo
2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o39b4o
565b2o64bo2bo7b2o7b2o3b2o$1663bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b
2o612b3o20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$823b2o838bobobo635b
obo5bo10bo48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o
2bo12b2o6b2o595b3o4b2o6b3o9bobo$823bo839bo3bo635b2o5bo70bo2bo622b6o18b
o15bo6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo$824b
3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b
6o2b6o592bob2o3bo19bo2bo$826bo837bobo716bo19bo552bo48bo3b2o3bo3bobo25b
o8bo534b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo$1665bo18b2o613b3o
77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b2o
3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo$1647b2o35bo612bob3o11b2ob2o62b
o13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo556b2o83bo636b2o
9b2o$1648bo27bo5bobo611bobobo16bo75b2o553b3o67bobo6bo2b2o2bo6bo6bo4bo
18bo544bo749bobo646bo$1019bo628bobo6b2o16b4o3b2o612bo2bo12bo2b2o56b3o
16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o5bo9b2ob2o646b3o
$1018bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo
2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo$1017bo3bo
629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b2o6b2o31b
2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo$1017bo3bo629bo2bo2bo2bo13b2obo
bo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o39b4o565b2o64bo
2bo7b2o7b2o3b2o$1017bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b2o612b3o
20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$177b2o838bobobo635bobo5bo
10bo48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b
2o6b2o595b3o4b2o6b3o9bobo576bo$177bo839bo3bo635b2o5bo70bo2bo622b6o18bo
15bo6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo576b2o$
178b3o836bo3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo
15bo7b6o2b6o592bob2o3bo19bo2bo576b2o$180bo837bobo716bo19bo552bo48bo3b
2o3bo3bobo25bo8bo534b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo$1019bo
18b2o613b3o77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo
534bo129b2o3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo$1001b2o35bo612bob3o
11b2ob2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo556b2o
83bo636b2o9b2o$1002bo27bo5bobo611bobobo16bo75b2o553b3o67bobo6bo2b2o2bo
6bo6bo4bo18bo544bo749bobo646bo$373bo628bobo6b2o16b4o3b2o612bo2bo12bo2b
2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o5bo9b2ob
2o646b3o$372bobo628b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o3b2o617b
5o5bobo2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo$
371bo3bo629b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b
2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo$371bo3bo629bo2bo2bo2b
o13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o39b4o
565b2o64bo2bo7b2o7b2o3b2o$371bobobo630b2o6bo14b4o47bo590bo63b2o3bo17b
2o612b3o20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$371bobobo635bobo5bo
10bo48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b
2o6b2o595b3o4b2o6b3o9bobo576bo$371bo3bo635b2o5bo70bo2bo622b6o18bo15bo
6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo576b2o$371b
o3bo642b3o66bo2bobo18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b6o2b6o
592bob2o3bo19bo2bo576b2o$372bobo716bo19bo552bo48bo3b2o3bo3bobo25bo8bo
534b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo1237bo$373bo18b2o613b3o
77bob2o12bo5bobo542b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b2o
3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo1239b2o$355b2o35bo612bob3o11b2o
b2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo12bo6bo665bo5bo556b2o83bo
636b2o9b2o1228b2o23b2o$356bo27bo5bobo611bobobo16bo75b2o553b3o67bobo6bo
2b2o2bo6bo6bo4bo18bo544bo749bobo646bo1254b3o$356bobo6b2o16b4o3b2o612bo
2bo12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o
5bo9b2ob2o646b3o1251b3o$357b3o5bobo14b2obobo617b2o4bo9b3o56bo3bo16b2o
3b2o617b5o5bobo2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo
647bo1250bob2o$359b3o6bo12b3obo2bo42bo578b2o9b3o55bo5bo16b2o620b2o3b2o
4b2o4b2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9bo1901b3o$359bo2bo
2bo2bo13b2obobo43b3o576b2o8bo2b2o54b2obob2o3bo13bo621b5o19bobo624b3o
39b4o565b2o64bo2bo7b2o7b2o3b2o1899bo$360b2o6bo14b4o47bo590bo63b2o3bo
17b2o612b3o20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$365bobo5bo10bo
48b2o9b2o564b3o8b2ob2o62bobo3bo15bo4bo611bo58bo623b2o4bo2b2o2bo12b2o6b
2o595b3o4b2o6b3o9bobo576bo$365b2o5bo70bo2bo622b6o18bo15bo6bo537b2o129b
o624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo576b2o$372b3o66bo2bobo
18b2o601bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b6o2b6o592bob2o3bo19bo2bo
576b2o$445bo19bo552bo48bo3b2o3bo3bobo25bo8bo534b3o692bo76b2o9bo2bo4bo
2bo593b3o3b2o18bobobo1237bo$361b3o77bob2o12bo5bobo542b2o3b2o4b2o47bo6b
o4bo10bo2bo13bo3b2o3bo534bo129b2o3b2o556bobo77b2o3bo5b2o6b2o595bo24bo
2bo1239b2o$359bob3o11b2ob2o62bo13b2o5b2o544b5o4b2o49b6o7bo6bo2b2o2bo
12bo6bo665bo5bo556b2o83bo636b2o9b2o1228b2o23b2o$358bobobo16bo75b2o553b
3o67bobo6bo2b2o2bo6bo6bo4bo18bo544bo749bobo646bo1254b3o633bo$358bo2bo
12bo2b2o56b3o16b3o554bo67bo3bo7b4o8b2o7b2o20b3o543b2o101bo3bo623b2o5bo
9b2ob2o646b3o1251b3o634b2o$359b2o4bo9b3o56bo3bo16b2o3b2o617b5o5bobo2bo
bo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo1250bob2o633b
2o$364b2o9b3o55bo5bo16b2o620b2o3b2o4b2o4b2o6b2o31b2o591bo40bobo2bobo
562bo66bob2o5bo3bo9bo1901b3o$364b2o8bo2b2o54b2obob2o3bo13bo621b5o19bob
o624b3o39b4o565b2o64bo2bo7b2o7b2o3b2o1899bo$379bo63b2o3bo17b2o612b3o
20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$364b3o8b2ob2o62bobo3bo15bo
4bo611bo58bo623b2o4bo2b2o2bo12b2o6b2o595b3o4b2o6b3o9bobo576bo$423b6o
18bo15bo6bo537b2o129bo624bobo5bo2bo13bo2bo4bo2bo594b3o3bobo7b2o12bo
576b2o$422bo6bo32bo3b2o3bo537bo691b2o61bo15bo7b6o2b6o592bob2o3bo19bo2b
o576b2o$372bo48bo3b2o3bo3bobo25bo8bo534b3o692bo76b2o9bo2bo4bo2bo593b3o
3b2o18bobobo1237bo$362b2o3b2o4b2o47bo6bo4bo10bo2bo13bo3b2o3bo534bo129b
2o3b2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo1239b2o$363b5o4b2o49b6o7bo6bo
2b2o2bo12bo6bo665bo5bo556b2o83bo636b2o9b2o1228b2o23b2o$364b3o67bobo6bo
2b2o2bo6bo6bo4bo18bo544bo749bobo646bo1254b3o633bo$365bo67bo3bo7b4o8b2o
7b2o20b3o543b2o101bo3bo623b2o5bo9b2ob2o646b3o1251b3o634b2o$433b5o5bobo
2bobo6b2o32bo541b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo1250bob2o
633b2o$432b2o3b2o4b2o4b2o6b2o31b2o591bo40bobo2bobo562bo66bob2o5bo3bo9b
o1901b3o$433b5o19bobo624b3o39b4o565b2o64bo2bo7b2o7b2o3b2o1899bo$434b3o
20bo36bo591bo37bo2b2o2bo562b2o53b2o10bob2o$435bo58bo623b2o4bo2b2o2bo
12b2o6b2o595b3o4b2o6b3o9bobo576bo$362b2o129bo624bobo5bo2bo13bo2bo4bo2b
o594b3o3bobo7b2o12bo576b2o$363bo691b2o61bo15bo7b6o2b6o592bob2o3bo19bo
2bo576b2o$360b3o692bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo$360bo129b2o3b
2o556bobo77b2o3bo5b2o6b2o595bo24bo2bo$490bo5bo556b2o83bo636b2o9b2o
1253b2o$387bo749bobo646bo1254b3o633bo$388b2o101bo3bo623b2o5bo9b2ob2o
646b3o1251b3o634b2o$387b2o48b2o39b2o4b2o6b3o623b3o4bobo7bo5bo647bo
1250bob2o633b2o$437bo40bobo2bobo562bo66bob2o5bo3bo9bo1901b3o$438b3o39b
4o565b2o64bo2bo7b2o7b2o3b2o1899bo$440bo37bo2b2o2bo562b2o53b2o10bob2o$
472b2o4bo2b2o2bo12b2o6b2o595b3o4b2o6b3o9bobo576bo$472bobo5bo2bo13bo2bo
4bo2bo594b3o3bobo7b2o12bo576b2o$409b2o61bo15bo7b6o2b6o592bob2o3bo19bo
2bo576b2o$409bo76b2o9bo2bo4bo2bo593b3o3b2o18bobobo1237bo$407bobo77b2o
3bo5b2o6b2o595bo24bo2bo1239b2o$407b2o83bo636b2o9b2o1228b2o23b2o$491bob
o646bo1254b3o633bo$473b2o5bo9b2ob2o646b3o1251b3o634b2o$472b3o4bobo7bo
5bo647bo1250bob2o633b2o$402bo66bob2o5bo3bo9bo1901b3o$403b2o64bo2bo7b2o
7b2o3b2o1899bo$402b2o53b2o10bob2o$457b3o4b2o6b3o9bobo576bo$457b3o3bobo
7b2o12bo576b2o$456bob2o3bo19bo2bo576b2o$456b3o3b2o18bobobo1237bo$457bo
24bo2bo1239b2o$483b2o9b2o1228b2o23b2o$494bo1254b3o633bo$495b3o1251b3o
634b2o$497bo1250bob2o633b2o$1748b3o$1749bo2$417bo$418b2o$417b2o$1078bo
$1079b2o$1078b2o23b2o$1103b3o633bo$1103b3o634b2o$1102bob2o633b2o$1102b
3o$1103bo5$432bo$433b2o$432b2o23b2o$457b3o633bo$457b3o634b2o$456bob2o
633b2o$456b3o$457bo8$447bo$448b2o$447b2o4540b2o$4989b2o4$4990bo$4989bo
bo$4990bo5$4343b2o$4343b2o4$4344bo$4343bobo$4344bo5$3697b2o1325bo$
3697b2o1323b3o$5021bo$5010b2o9b2o$5009bo2bo$3698bo1290b2o18bobobo$
3697bobo1290bo19bob3o$3698bo1291bobo5b2o12b3o4bo$4991b2o5b3o18bo$5000b
2obo14bobo$5000bo2bo13b2ob2o$5000b2obo12bo5bo$3051b2o1325bo619b3o9b2o
7bo$3051b2o1323b3o619b2o10bobo3b2o3b2o$4375bo628bo5bo$4364b2o9b2o625b
2o$4363bo2bo636b2o20b2o6b2o$3052bo1290b2o18bobobo656bo2bo4bo2bo$3051bo
bo1290bo19bob3o654b6o2b6o$3052bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo
2bo$4345b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o$4354b2obo14bobo630bo2b2o2b
o4bo$4354bo2bo13b2ob2o631b4o7bo$4354b2obo12bo5bo628bobo2bobo$2405b2o
1325bo619b3o9b2o7bo631b2o4b2o$2405b2o1323b3o619b2o10bobo3b2o3b2o640b2o
3b2o$3729bo628bo5bo652bo5bo$3718b2o9b2o625b2o$3717bo2bo636b2o20b2o6b2o
629bo3bo$2406bo1290b2o18bobobo656bo2bo4bo2bo599bo29b3o$2405bobo1290bo
19bob3o654b6o2b6o596b2o$2406bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2b
o598b2o$3699b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o$3708b2obo14bobo630bo2b
2o2bo4bo$3708bo2bo13b2ob2o631b4o7bo$3708b2obo12bo5bo628bobo2bobo650b2o
$1759b2o1325bo619b3o9b2o7bo631b2o4b2o651bo$1759b2o1323b3o619b2o10bobo
3b2o3b2o640b2o3b2o637b3o$3083bo628bo5bo652bo5bo637bo$3072b2o9b2o625b2o
$3071bo2bo636b2o20b2o6b2o629bo3bo$1760bo1290b2o18bobobo656bo2bo4bo2bo
599bo29b3o$1759bobo1290bo19bob3o654b6o2b6o596b2o$1760bo1291bobo5b2o12b
3o4bo633bo2bo13bo2bo4bo2bo598b2o$3053b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b
2o1231bo$3062b2obo14bobo630bo2b2o2bo4bo1246b2o$3062bo2bo13b2ob2o631b4o
7bo1246b2o$3062b2obo12bo5bo628bobo2bobo650b2o$1113b2o1325bo619b3o9b2o
7bo631b2o4b2o651bo$1113b2o1323b3o619b2o10bobo3b2o3b2o640b2o3b2o637b3o$
2437bo628bo5bo652bo5bo637bo$2426b2o9b2o625b2o$2425bo2bo636b2o20b2o6b2o
629bo3bo$1114bo1290b2o18bobobo656bo2bo4bo2bo599bo29b3o$1113bobo1290bo
19bob3o654b6o2b6o596b2o$1114bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2b
o598b2o$2407b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o1231bo$2416b2obo14bobo
630bo2b2o2bo4bo1246b2o$2416bo2bo13b2ob2o631b4o7bo1246b2o$2416b2obo12bo
5bo628bobo2bobo650b2o1232bo$467b2o1325bo619b3o9b2o7bo631b2o4b2o651bo
1170b2o20b2o36b2o$467b2o1323b3o619b2o10bobo3b2o3b2o640b2o3b2o637b3o
1172bo20bo38b2o$1791bo628bo5bo652bo5bo637bo1174bobo8bo7bobo$1780b2o9b
2o625b2o2479b2o8bobo5b2o$1779bo2bo636b2o20b2o6b2o629bo3bo1827b2o$468bo
1290b2o18bobobo656bo2bo4bo2bo599bo29b3o1828b2o$467bobo1290bo19bob3o
654b6o2b6o596b2o1861b2o$468bo1291bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2bo
598b2o1857bobo$1761b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o1231bo1226bo52bo
$1770b2obo14bobo630bo2b2o2bo4bo1246b2o1278b3o$1770bo2bo13b2ob2o631b4o
7bo1246b2o1276bo$1770b2obo12bo5bo628bobo2bobo650b2o1232bo645b2o$1148bo
619b3o9b2o7bo631b2o4b2o651bo1170b2o20b2o36b2o$1146b3o619b2o10bobo3b2o
3b2o640b2o3b2o637b3o1172bo20bo38b2o$1145bo628bo5bo652bo5bo637bo1174bob
o8bo7bobo670bo$1134b2o9b2o625b2o2479b2o8bobo5b2o396bo272b2o$1133bo2bo
636b2o20b2o6b2o629bo3bo1827b2o401b3o271b2o$1113b2o18bobobo656bo2bo4bo
2bo599bo29b3o1828b2o404bo$1114bo19bob3o654b6o2b6o596b2o1861b2o403b2o$
1114bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2bo598b2o1857bobo$1115b2o5b3o18b
o631bo2b2o2bo4b3o5b2o6b2o1231bo1226bo52bo$1124b2obo14bobo630bo2b2o2bo
4bo1246b2o1278b3o637b2o3b2o$1124bo2bo13b2ob2o631b4o7bo1246b2o1276bo
628b2o4b2o6b3o$1124b2obo12bo5bo628bobo2bobo650b2o1232bo645b2o627bobo2b
obo5bo3bo$502bo619b3o9b2o7bo631b2o4b2o651bo1170b2o20b2o36b2o1277bo2bo
8bobo$500b3o619b2o10bobo3b2o3b2o640b2o3b2o637b3o1172bo20bo38b2o1275bo
4bo8bo$499bo628bo5bo652bo5bo637bo1174bobo8bo7bobo670bo644b6o$488b2o9b
2o625b2o2479b2o8bobo5b2o396bo272b2o375b3o268b4o15bobo2bobo$487bo2bo
636b2o20b2o6b2o629bo3bo1827b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bo
b2o$467b2o18bobobo656bo2bo4bo2bo599bo29b3o1828b2o404bo644bo5bo277bob2o
4bobo2bobo$468bo19bob3o654b6o2b6o596b2o1861b2o403b2o645bo3bo272bo4bo3b
o$468bobo5b2o12b3o4bo633bo2bo13bo2bo4bo2bo598b2o1857bobo1053b3o273bobo
2b5o$469b2o5b3o18bo631bo2b2o2bo4b3o5b2o6b2o1231bo1226bo52bo1002b3o266b
2o5b2o2b2o3b2o$478b2obo14bobo630bo2b2o2bo4bo1246b2o1278b3o637b2o3b2o
619bo6b2o11b5o$478bo2bo13b2ob2o631b4o7bo1246b2o1276bo628b2o4b2o6b3o
620bobo7bo11b3o$478b2obo12bo5bo628bobo2bobo650b2o1232bo645b2o627bobo2b
obo5bo3bo357b2o173bo84b2o3bo19bo$476b3o9b2o7bo631b2o4b2o651bo1170b2o
20b2o36b2o1277bo2bo8bobo359bo173bo84b2o3bo$476b2o10bobo3b2o3b2o640b2o
3b2o637b3o1172bo20bo38b2o1275bo4bo8bo357b3o173bobo83b2o3bo$482bo5bo
652bo5bo637bo1174bobo8bo7bobo670bo644b6o366bo176bo81b2o3bobo$480b2o
2479b2o8bobo5b2o396bo272b2o375b3o268b4o15bobo2bobo521bo80bobo4bo14b3o$
481b2o20b2o6b2o629bo3bo1827b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bo
b2o517bo80bo19bob3o$502bo2bo4bo2bo599bo29b3o1828b2o404bo644bo5bo277bob
2o4bobo2bobo521bo47bo31b2o18bobobo$501b6o2b6o596b2o1861b2o403b2o645bo
3bo272bo4bo3bo532bobo45bobo50bo2bo$485bo2bo13bo2bo4bo2bo598b2o1857bobo
1053b3o273bobo2b5o533bo100b2o9b2o$483bo2b2o2bo4b3o5b2o6b2o1231bo1226bo
52bo1002b3o266b2o5b2o2b2o3b2o532bo18b2o91bo$483bo2b2o2bo4bo1246b2o
1278b3o637b2o3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o$485b4o7bo1246b2o
1276bo628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo$483bobo2bob
o650b2o1232bo645b2o627bobo2bobo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b
2o17bobo4b2o29bo$483b2o4b2o651bo1170b2o20b2o36b2o1277bo2bo8bobo359bo
173bo84b2o3bo539b2o3bo3bo16b2obo$495b2o3b2o637b3o1172bo20bo38b2o1275bo
4bo8bo357b3o173bobo83b2o3bo543bo5bo15b2ob2o$495bo5bo637bo1174bobo8bo7b
obo670bo644b6o366bo176bo81b2o3bobo543b2obo3bo15b2obo34bo$2315b2o8bobo
5b2o396bo272b2o375b3o268b4o15bobo2bobo521bo80bobo4bo14b3o528bo5bo15bob
o34b3o$496bo3bo1827b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bob2o517bo
80bo19bob3o529bo3bo6bo10bo35b3o$467bo29b3o1828b2o404bo644bo5bo277bob2o
4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo$465b2o1861b2o403b2o645bo3b
o272bo4bo3bo532bobo45bobo50bo2bo542b2o63b2o$466b2o1857bobo1053b3o273bo
bo2b5o533bo100b2o9b2o520b2o49bo7bobo15bo$1098bo1226bo52bo1002b3o266b2o
5b2o2b2o3b2o532bo18b2o91bo520b3o47bobo8bo8bo5bobo$1096b2o1278b3o637b2o
3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o516bob2o15b2o28b2o19bobo4b2o$
1097b2o1276bo628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bo
bo13b3o2bo28b2o18bo3b2o$495b2o1232bo645b2o627bobo2bobo5bo3bo357b2o173b
o84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo4bo10b2o29b2o18bo3b2o$
496bo1170b2o20b2o36b2o1277bo2bo8bobo359bo173bo84b2o3bo539b2o3bo3bo16b
2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo$493b3o1172bo20bo38b2o1275bo
4bo8bo357b3o173bobo83b2o3bo543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo
102bobo$493bo1174bobo8bo7bobo670bo644b6o366bo176bo81b2o3bobo543b2obo3b
o15b2obo34bo601b2o23bo16b3o8bo102bo3bo$1669b2o8bobo5b2o396bo272b2o375b
3o268b4o15bobo2bobo521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo
21b2o130bo3bo$1682b2o401b3o271b2o373bo3bo279bo2b2obo2bo2bo2bob2o517bo
80bo19bob3o529bo3bo6bo10bo35b3o587bobo11b2o52b2o99bobobo$1682b2o404bo
644bo5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b
3o98bobobo$1682b2o403b2o645bo3bo272bo4bo3bo532bobo45bobo50bo2bo542b2o
63b2o569b5o47b2o15b2obo97bo3bo$1679bobo1053b3o273bobo2b5o533bo100b2o9b
2o520b2o49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo3bo$452bo1226bo
52bo1002b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b3o47bobo8bo8bo5bobo570b
5o4b2o42b2obo8bo4bo2bo97bobo$450b2o1278b3o637b2o3b2o619bo6b2o11b5o515b
2o35bo27b3o63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo
$451b2o1276bo628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bo
bo13b3o2bo28b2o18bo3b2o577bo54bo6b2o88b2o$1083bo645b2o627bobo2bobo5bo
3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo4bo10b2o29b
2o18bo3b2o628b2obo98bo22bo$1021b2o20b2o36b2o1277bo2bo8bobo359bo173bo
84b2o3bo539b2o3bo3bo16b2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo
2b2o98bobo8b2o8b4o$1022bo20bo38b2o1275bo4bo8bo357b3o173bobo83b2o3bo
543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo
4b2obobob2o$1022bobo8bo7bobo670bo644b6o366bo176bo81b2o3bobo543b2obo3bo
15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o$1023b2o8bobo5b
2o396bo272b2o375b3o268b4o15bobo2bobo521bo80bobo4bo14b3o528bo5bo15bobo
34b3o588bo8b3o2bo21b2o130bo3bo535bo3bo95bo2bo4bob2obob2o$1036b2o401b3o
271b2o373bo3bo279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo10bo
35b3o587bobo11b2o52b2o99bobobo534bob3obo94bo11b4o9b2o$1036b2o404bo644b
o5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b3o
98bobobo470b2o63b5o96bobo10bo10bobo$1036b2o403b2o645bo3bo272bo4bo3bo
532bobo45bobo50bo2bo542b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23b
o$1033bobo1053b3o273bobo2b5o533bo100b2o9b2o520b2o49bo7bobo15bo569b2o3b
2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o$1033bo52bo1002b3o266b2o5b2o2b
2o3b2o532bo18b2o91bo520b3o47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo
97bobo469bo161bobo$1084b3o637b2o3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o
516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo$1083bo
628b2o4b2o6b3o620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b
2o18bo3b2o577bo54bo6b2o88b2o513bobo132bo2bo5b2o5b2ob2o$437bo645b2o627b
obo2bobo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo
4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo$375b
2o20b2o36b2o1277bo2bo8bobo359bo173bo84b2o3bo539b2o3bo3bo16b2obo618b2o
4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o40bo26b2o
104bo2bo12bo2b2o$376bo20bo38b2o1275bo4bo8bo357b3o173bobo83b2o3bo543bo
5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo4b2obo
bob2o448bo68bo105b2o14b3o$376bobo8bo7bobo670bo644b6o366bo176bo81b2o3bo
bo543b2obo3bo15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o
447bobo82b2o100bo3b3o$377b2o8bobo5b2o396bo272b2o375b3o268b4o15bobo2bob
o521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535bo
3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o$390b2o401b3o271b2o373bo3b
o279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo10bo35b3o587bobo11b
2o52b2o99bobobo534bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo$390b2o404bo
644bo5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b
3o98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o$390b2o
403b2o645bo3bo272bo4bo3bo532bobo45bobo50bo2bo542b2o63b2o569b5o47b2o15b
2obo97bo3bo471bo165b2o23bo437bo$387bobo1053b3o273bobo2b5o533bo100b2o9b
2o520b2o49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o
437bo$387bo52bo1002b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b3o47bobo8bo
8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o$438b3o637b
2o3b2o619bo6b2o11b5o515b2o35bo27b3o63b3o516bob2o15b2o28b2o19bobo4b2o
572b3o5bo47bo5bobo5b2o99bo635bo6bo457bo7bo174bo$437bo628b2o4b2o6b3o
620bobo7bo11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b2o18bo3b2o577b
o54bo6b2o88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o$437b2o627bobo2bo
bo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o29bo582bo2bo4bo10b
2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo454bo2bo
171b5o40b2o$1068bo2bo8bobo359bo173bo84b2o3bo539b2o3bo3bo16b2obo618b2o
4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o40bo26b2o
104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o$1067bo4bo8bo357b3o173bobo
83b2o3bo543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b
2o7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o$422bo644b6o366bo176b
o81b2o3bobo543b2obo3bo15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo
12bob3o447bobo82b2o100bo3b3o648bo25b3o$147bo272b2o375b3o268b4o15bobo2b
obo521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535b
o3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo$147b3o271b2o
373bo3bo279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo10bo35b3o
587bobo11b2o52b2o99bobobo534bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo
450bobo179bo$150bo644bo5bo277bob2o4bobo2bobo521bo47bo31b2o18bobobo532b
2o7bobo633bo3bo64b3o98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80b
o102b2ob2o450bo187b3o34bo44bo18bo$149b2o645bo3bo272bo4bo3bo532bobo45bo
bo50bo2bo542b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b
2o177b2o7bo3bo31b3o42b3o18b3o$797b3o273bobo2b5o533bo100b2o9b2o520b2o
49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o437bo671b
4o148bo6bo5bo29bo44bo24bo$797b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b3o
47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o
671b6o144b3o43b2o34b2o7b2o22b2o$432b2o3b2o619bo6b2o11b5o515b2o35bo27b
3o63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo
457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo$420b2o4b2o6b3o620bobo7bo
11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b2o18bo3b2o577bo54bo6b2o
88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo2bo64b
o36bo$420bobo2bobo5bo3bo357b2o173bo84b2o3bo19bo518bobo5b2o17bobo4b2o
29bo582bo2bo4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo
6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo$422bo2bo8bobo359bo173bo84b2o
3bo539b2o3bo3bo16b2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o
98bobo8b2o8b4o448b2o40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3o
b2o445b6o154bo5bo26b3o72bo$421bo4bo8bo357b3o173bobo83b2o3bo543bo5bo15b
2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo4b2obobob2o
448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo$421b6o366bo176bo81b2o3bo
bo543b2obo3bo15b2obo34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o
447bobo82b2o100bo3b3o648bo25b3o609b3o28bobo$151b3o268b4o15bobo2bobo
521bo80bobo4bo14b3o528bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535bo3b
o95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo481bo191b3o5b5o
68b2o3b2o$150bo3bo279bo2b2obo2bo2bo2bob2o517bo80bo19bob3o529bo3bo6bo
10bo35b3o587bobo11b2o52b2o99bobobo534bob3obo94bo11b4o9b2o441bo2b2o78b
3o106bo450bobo179bo482b2o189bo6b2o3b2o67bo5bo$149bo5bo277bob2o4bobo2bo
bo521bo47bo31b2o18bobobo532b2o7bobo633bo3bo64b3o98bobobo470b2o63b5o96b
obo10bo10bobo437bo2bo2b2o80bo102b2ob2o450bo187b3o34bo44bo18bo373b2o
192bo5b2o3b2o$150bo3bo272bo4bo3bo532bobo45bobo50bo2bo542b2o63b2o569b5o
47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b2o177b2o7bo3bo31b3o42b3o18b
3o373bo272bo3bo$151b3o273bobo2b5o533bo100b2o9b2o520b2o49bo7bobo15bo
569b2o3b2o2bobo41bo2b2o14bobo96bo3bo468b3o191b2o437bo671b4o148bo6bo5bo
29bo44bo24bo632b2o4b2o6b3o$151b3o266b2o5b2o2b2o3b2o532bo18b2o91bo520b
3o47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o
671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo$412bo6b2o11b5o515b2o35bo
27b3o63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo
6bo457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o$411b
obo7bo11b3o517bo27bo5bobo27b3o65bo515bobo13b3o2bo28b2o18bo3b2o577bo54b
o6b2o88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo
2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo$149b2o173bo84b2o3bo19bo518bob
o5b2o17bobo4b2o29bo582bo2bo4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o
25bobo103bobobo6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo114b2o3bobo
435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o$150bo173bo84b2o3bo539b2o3bo3bo
16b2obo618b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o
448b2o40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o445b6o154bo
5bo26b3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo$147b3o173bobo83b
2o3bo543bo5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o
7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo662b3o8bo
5b2o46bo15bo7b6o2b6o$147bo176bo81b2o3bobo543b2obo3bo15b2obo34bo601b2o
23bo16b3o8bo102bo3bo536b3o96bo12bob3o447bobo82b2o100bo3b3o648bo25b3o
609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo$324bo80bobo4bo14b3o528bo
5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo535bo3bo95bo2bo4bob2obob2o449bo
bo81bo99b2o3bo2b2o452b2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b
2o3bo5b2o6b2o$324bo80bo19bob3o529bo3bo6bo10bo35b3o587bobo11b2o52b2o99b
obobo534bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo450bobo179bo482b2o189bo
6b2o3b2o67bo5bo554b2ob2o15bo66bo$324bo47bo31b2o18bobobo532b2o7bobo633b
o3bo64b3o98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o
450bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob2o66bobo$323bobo45b
obo50bo2bo542b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b
2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo9b2ob
2o$324bo100b2o9b2o520b2o49bo7bobo15bo569b2o3b2o2bobo41bo2b2o14bobo96bo
3bo468b3o191b2o437bo671b4o148bo6bo5bo29bo44bo24bo632b2o4b2o6b3o422bobo
117b2o18bobo58b3o4bobo7bo5bo$324bo18b2o91bo520b3o47bobo8bo8bo5bobo570b
5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o671b6o144b3o43b2o34b2o7b
2o22b2o632bobo2bobo431b3o118bo24bobo49bob2o5bo3bo9bo$306b2o35bo27b3o
63b3o516bob2o15b2o28b2o19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo
457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o433b3o
118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o$307bo27bo5bobo27b3o65bo515bobo
13b3o2bo28b2o18bo3b2o577bo54bo6b2o88b2o513bobo132bo2bo5b2o5b2ob2o455bo
bo172b3o487b2o6b2o151bo2bobo2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo
431b3o119b2o6bo2bo10b2o52bob2o$307bobo5b2o17bobo4b2o29bo582bo2bo4bo10b
2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo454bo2bo
171b5o40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b
2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo$308b2o3bo3bo16b2obo618b2o4bo7bo
29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o40bo26b2o104bo2b
o12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o445b6o154bo5bo26b3o72bo121bo437b
3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo$312b
o5bo15b2ob2o623b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b2o7bobo4b2ob
obob2o448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo662b3o8bo5b2o46bo
15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo$311b2obo3bo15b2obo
34bo601b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o447bobo82b2o100bo3b3o
648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo389b2o147bo2bo
12bobo6bobo33b2o18bobobo$312bo5bo15bobo34b3o588bo8b3o2bo21b2o130bo3bo
535bo3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo481bo191b
3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b2o110b2o8bo16bo
53bo2bo$313bo3bo6bo10bo35b3o587bobo11b2o52b2o99bobobo534bob3obo94bo11b
4o9b2o441bo2b2o78b3o106bo450bobo179bo482b2o189bo6b2o3b2o67bo5bo554b2ob
2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o$315b2o7bobo633bo3bo64b3o
98bobobo470b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o450bo187b
3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob2o66bobo406b2o4bobo17bo6bobo
119bobobo80bo$324b2o63b2o569b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo
644b2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo
9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o$312b2o49bo7bobo15bo569b2o3b2o
2bobo41bo2b2o14bobo96bo3bo468b3o191b2o437bo671b4o148bo6bo5bo29bo44bo
24bo632b2o4b2o6b3o422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b
2o124b3o84bo$311b3o47bobo8bo8bo5bobo570b5o4b2o42b2obo8bo4bo2bo97bobo
469bo161bobo465b3o671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o
118bo24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo$310bob2o15b2o28b2o
19bobo4b2o572b3o5bo47bo5bobo5b2o99bo635bo6bo457bo7bo174bo489b2o4b2o
143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o433b3o118bobo6b2o13bo2bo49bo2bo
7b2o7b2o3b2o410bobo15b2o4b2o$309bobo13b3o2bo28b2o18bo3b2o577bo54bo6b2o
88b2o513bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo2bo64b
o36bo114b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo
9bo7b2o$309bo2bo4bo10b2o29b2o18bo3b2o628b2obo98bo22bo491b2o25bobo103bo
bobo6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2b
o52b2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130b
o$310b2o4bo7bo29b2o5bobo7bo7bo3b2o101bo525bo2b2o98bobo8b2o8b4o448b2o
40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o445b6o154bo5bo26b
3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bo
bo7b2o12bo429b2o137b3o$316b3o5bo28bobo7bo8bo7bobo102bobo524b2o12bo89b
2o7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo662b3o
8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442bobo
123bobobo$328b2o23bo16b3o8bo102bo3bo536b3o96bo12bob3o447bobo82b2o100bo
3b3o648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo389b2o147bo
2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo$316bo8b3o2bo21b2o130bo3b
o535bo3bo95bo2bo4bob2obob2o449bobo81bo99b2o3bo2b2o452b2o179bo481bo191b
3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b2o110b2o8bo16bo
53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o$315bobo11b2o52b2o99bobobo534bob
3obo94bo11b4o9b2o441bo2b2o78b3o106bo450bobo179bo482b2o189bo6b2o3b2o67b
o5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o348bo67bo2b2o
12bobobo63bo59bo$314bo3bo64b3o98bobobo470b2o63b5o96bobo10bo10bobo437bo
2bo2b2o80bo102b2ob2o450bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bo
b2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo$
314b5o47b2o15b2obo97bo3bo471bo165b2o23bo437bo644b2o177b2o7bo3bo31b3o
42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo9b2ob2o409b2o3bo15b2o6b
2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o$313b2o3b2o2bobo41bo2b2o14b
obo96bo3bo468b3o191b2o437bo671b4o148bo6bo5bo29bo44bo24bo632b2o4b2o6b3o
422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o
90b4o26bo$314b5o4b2o42b2obo8bo4bo2bo97bobo469bo161bobo465b3o671b6o144b
3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo24bobo49bob2o5bo3bo9bo411b
2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo$315b3o5bo47bo5bobo5b2o
99bo635bo6bo457bo7bo174bo489b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo
61b4o433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo
3bo123bo2b2o9bo88b2o4b2o25bob2o$316bo54bo6b2o88b2o513bobo132bo2bo5b2o
5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo2bo64bo36bo114b2o4bo435bo11bo
60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo9bo7b2o631b2o2bo8b3o86b
2o6b2o25b6o$367b2obo98bo22bo491b2o25bobo103bobobo6b2o8bo454bo2bo171b5o
40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o
409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o
29b3o$366bo2b2o98bobo8b2o8b4o448b2o40bo26b2o104bo2bo12bo2b2o456b2o171b
2o3b2o7b6o23b3ob2o445b6o154bo5bo26b3o72bo121bo437b3o7bobo52bobo5bo2bo
13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o513bo78b3o8b
6o25b2o$366b2o12bo89b2o7bobo4b2obobob2o448bo68bo105b2o14b3o645b3o25b5o
447b4o156bo3bo662b3o8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o
35bo19bo2bo442bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo$379b3o96bo12b
ob3o447bobo82b2o100bo3b3o648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9b
o2bo4bo2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b
3o56bo3bo69bobo2bo5bo76bo38b3o$378bo3bo95bo2bo4bob2obob2o449bobo81bo
99b2o3bo2b2o452b2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo
5b2o6b2o391bo35b2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o
430bo5bo68b2o4bo3bo116bo7bo$377bob3obo94bo11b4o9b2o441bo2b2o78b3o106bo
450bobo179bo482b2o189bo6b2o3b2o67bo5bo554b2ob2o15bo66bo406bobo5bo27bo
120b3o15b2o53b2o9b2o348bo67bo2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o
65b2o57bobo$313b2o63b5o96bobo10bo10bobo437bo2bo2b2o80bo102b2ob2o450bo
187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob2o66bobo406b2o4bobo17bo
6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo430bo3bo57bo145bobo56bo2bo$
314bo165b2o23bo437bo644b2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b
3o138b2o8bo50b2o5bo9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bob
o4b2o65b2o46b2o401b2o39bo3bo143bo59b2o$311b3o191b2o437bo671b4o148bo6bo
5bo29bo44bo24bo632b2o4b2o6b3o422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o
3bo14b2o4b2o124b3o84bo427b2o90b4o26bo376b2o5b3o15bo41b3o143b2o$311bo
161bobo465b3o671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo24b
obo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo
374b2o12bo8bobo42bo160b2o$476bo6bo457bo7bo174bo489b2o4b2o143bo8bo2bobo
2bo64bobo591b2ob2o8bo61b4o433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o
410bobo15b2o4b2o498bo3bo123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o
204bo40b2o$337bobo132bo2bo5b2o5b2ob2o455bobo172b3o487b2o6b2o151bo2bobo
2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o
434bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo$
338b2o25bobo103bobobo6b2o8bo454bo2bo171b5o40b2o445b2o4b2o262bo114b2o3b
obo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o
9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b
2o80bo36bo$296b2o40bo26b2o104bo2bo12bo2b2o456b2o171b2o3b2o7b6o23b3ob2o
445b6o154bo5bo26b3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bob
o124bo6bo9b2o46bobo7b2o12bo429b2o137b3o513bo78b3o8b6o25b2o365b2o8b2o4b
2o7b4o52b3o76bo117b2o$297bo68bo105b2o14b3o645b3o25b5o447b4o156bo3bo
662b3o8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442b
obo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o$
297bobo82b2o100bo3b3o648bo25b3o609b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo
4bo2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o
56bo3bo69bobo2bo5bo76bo38b3o365bo24bo138bo$298bobo81bo99b2o3bo2b2o452b
2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b
2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o4bo
3bo116bo7bo358b2o252b3o$300bo2b2o78b3o106bo450bobo179bo482b2o189bo6b2o
3b2o67bo5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o348bo67b
o2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo$
297bo2bo2b2o80bo102b2ob2o450bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o
635bo8bob2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo
63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo$297bo644b2o
177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo9b2ob2o
409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o401b2o39bo
3bo143bo59b2o277bo94b2o15bo2bo102b2o$298bo671b4o148bo6bo5bo29bo44bo24b
o632b2o4b2o6b3o422bobo117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o
124b3o84bo427b2o90b4o26bo376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo
4bobobo$295b3o671b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo
24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25b
obo374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo$295bo7bo174bo489b
2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b4o433b3o118bobo6b2o13bo2bo
49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo123bo2b2o9bo88b2o4b2o25bob
2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o$302bobo172b3o487b2o6b2o151bo2b
obo2bo64bo36bo114b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bo
b2o434bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo
381bo$301bo2bo171b5o40b2o445b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b
2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo
373b3o136bo3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo$
302b2o171b2o3b2o7b6o23b3ob2o445b6o154bo5bo26b3o72bo121bo437b3o7bobo52b
obo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o
513bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o$490b3o
25b5o447b4o156bo3bo662b3o8bo5b2o46bo15bo7b6o2b6o407b3o122b2o7bo10bo2bo
6b2o35bo19bo2bo442bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b
2o3b2o7b4o55bo77b3o494b2o190bo$493bo25b3o609b3o28bobo631b2o2bo12bo2bo
59b2o9bo2bo4bo2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bob
obo371b3o56bo3bo69bobo2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o$298b
2o179bo481bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b
2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o4bo
3bo116bo7bo358b2o252b3o411bobo3bo180bo$297bobo179bo482b2o189bo6b2o3b2o
67bo5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b2o348bo67bo2b
2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b
2o3bobo178b2o128bo$297bo187b3o34bo44bo18bo373b2o192bo5b2o3b2o635bo8bob
2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo430bo
3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2ob2o305b3o$
296b2o177b2o7bo3bo31b3o42b3o18b3o373bo272bo3bo421b3o138b2o8bo50b2o5bo
9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o401b
2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o
63bo$324b4o148bo6bo5bo29bo44bo24bo632b2o4b2o6b3o422bobo117b2o18bobo58b
3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o90b4o26bo376b2o5b3o15bo
41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo
93bo146bo65b2o$323b6o144b3o43b2o34b2o7b2o22b2o632bobo2bobo431b3o118bo
24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25b
obo374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo545b5o25b5o25b5o25b
2o3b2o90bobo145bo25bo$322b2o4b2o143bo8bo2bobo2bo64bobo591b2ob2o8bo61b
4o433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo
123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o$321b2o6b2o151bo2bobo2bo64bo36bo114b
2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo9bo7b2o
631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo381bo647bo271bo
$322b2o4b2o262bo114b2o3bobo435b2o2bo5bo2bo52b2o4bo2b2o2bo12b2o6b2o409b
3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o29b3o
366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o$323b6o154bo5bo26b
3o72bo121bo437b3o7bobo52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bo
bo7b2o12bo429b2o137b3o513bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o52b3o76bo
117b2o378bo2b2o612bo22b3o10bo187b5o$324b4o156bo3bo662b3o8bo5b2o46bo15b
o7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442bobo123bobobo431b3o
70bo4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o494b2o190bo424b3o
22bo9bobo185b2o3b2o$485b3o28bobo631b2o2bo12bo2bo59b2o9bo2bo4bo2bo389b
2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o56bo3bo69bob
o2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o176b2o3b2o
117b3o$315bo191b3o5b5o68b2o3b2o554bo15bo2bobo60b2o3bo5b2o6b2o391bo35b
2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o4bo
3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo$316b2o
189bo6b2o3b2o67bo5bo554b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b
2o348bo67bo2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo
103bo116bo413b2o3bobo178b2o128bo338b3o296bo5bo$314b2o192bo5b2o3b2o635b
o8bob2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo63bobo
430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2ob2o305b
3o111bo4b2o180bo41bo71b2o223b2obob2o$316bo272bo3bo421b3o138b2o8bo50b2o
5bo9b2ob2o409b2o3bo15b2o6b2o120bobobo81b3o287b3o136bobo4b2o65b2o46b2o
401b2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo
237b3o63bo113bobo3b2o180bo28b3o82b2o181b2obob2o$576b2o4b2o6b3o422bobo
117b2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o90b4o26b
o376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob
3o24b2ob3o28bo93bo146bo65b2o113bo300b2o$576bobo2bobo431b3o118bo24bobo
49bob2o5bo3bo9bo411b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo
374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo545b5o25b5o25b5o25b2o
3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo38bo$503b2ob2o8bo61b4o
433b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo
123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo$61b2o4bo435bo11bo60bo2b2o2bo431b3o119b2o6bo2bo10b2o52bob2o434bo
9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo381bo
647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo$61b2o3bobo435b2o2b
o5bo2bo52b2o4bo2b2o2bo12b2o6b2o409b3o131bo7b2o3bo45b2o6b3o9bobo431b2o
6bo130bo373b3o136bo3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo
379b2obo837bobo80b2o340b3o29bo81bo4bo97b3o76bo5bo41b3o$67bo437b3o7bobo
52bobo5bo2bo13bo2bo4bo2bo408bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o
513bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o612bo22b
3o10bo187b5o422bo30bo82bo25bo75bo35bo34b2o56b2o$505b3o8bo5b2o46bo15bo
7b6o2b6o407b3o122b2o7bo10bo2bo6b2o35bo19bo2bo442bobo123bobobo431b3o70b
o4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o494b2o190bo424b3o22bo
9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo$504b2o2bo12bo2bo59b2o9bo2bo4bo
2bo389b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o56bo
3bo69bobo2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o176b
2o3b2o117b3o438bo111bo38b2o56b3o$503bo15bo2bobo60b2o3bo5b2o6b2o391bo
35b2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo68b2o
4bo3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo303b3o
131b2o110b2o42bo54bo$503b2ob2o15bo66bo406bobo5bo27bo120b3o15b2o53b2o9b
2o348bo67bo2b2o12bobobo63bo59bo372bo3bo54bo5bo75b3o65b2o57bobo388bobo
103bo116bo413b2o3bobo178b2o128bo338b3o296bo5bo304bo4b2ob2o278bo4b2o6b
2o$510bo8bob2o66bobo406b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2b
o63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2o
b2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo129bo
143b2o9b2o4bo2bo$369b3o138b2o8bo50b2o5bo9b2ob2o409b2o3bo15b2o6b2o120bo
bobo81b3o287b3o136bobo4b2o65b2o46b2o401b2o39bo3bo143bo59b2o277bo94b2o
15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o63bo113bobo3b2o180bo28b3o82b
2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo$369bobo117b
2o18bobo58b3o4bobo7bo5bo408b2o3bo14b2o4b2o124b3o84bo427b2o90b4o26bo
376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o
24b2ob3o28bo93bo146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o
134bo99bo39b2o18bo$369b3o118bo24bobo49bob2o5bo3bo9bo411b2o3bo13b3o4b2o
125bo372bo3bo124b5o8bo89b6o25bobo374b2o12bo8bobo42bo160b2o323b2o93b2ob
o6b2o5bo2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o
181bo5bo38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o$
369b3o118bobo6b2o13bo2bo49bo2bo7b2o7b2o3b2o410bobo15b2o4b2o498bo3bo
123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo$369b3o119b
2o6bo2bo10b2o52bob2o434bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o
51b2o158b3o36bobo381bo647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38b
obo171b2ob2o81b2o9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o$
369b3o131bo7b2o3bo45b2o6b3o9bobo431b2o6bo130bo373b3o136bo3bo86b2o4b2o
29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o340b3o29bo81b
o4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob2o
122bo16bo8bo$369bobo124bo6bo9b2o46bobo7b2o12bo429b2o137b3o513bo78b3o8b
6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o
422bo30bo82bo25bo75bo35bo34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bob
o27b2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo$369b3o122b2o7bo10bo2b
o6b2o35bo19bo2bo442bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b
2o3b2o7b4o55bo77b3o494b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34b
o2b3o52bo173bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bob
o10b2o$350b2o147bo2bo12bobo6bobo33b2o18bobobo354bo86bo126bobobo371b3o
56bo3bo69bobo2bo5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o
176b2o3b2o117b3o438bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bob
o130b2o19b2o$351bo35b2o110b2o8bo16bo53bo2bo355b3o68b2o2bo12bo2bo63b2o
58b3o430bo5bo68b2o4bo3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o
7bo300bo3bo303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3b
o17b2o$351bobo5bo27bo120b3o15b2o53b2o9b2o348bo67bo2b2o12bobobo63bo59bo
372bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b2o3bobo178b2o128bo
338b3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo
146b2o16bobo5b2o$352b2o4bobo17bo6bobo119bobobo80bo348b2o68b5o12bo2bo
63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo115bo323b2o2bo88b2ob
2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo129bo143b
2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo$356b2o3bo15b2o6b2o
120bobobo81b3o287b3o136bobo4b2o65b2o46b2o401b2o39bo3bo143bo59b2o277bo
94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o63bo113bobo3b2o180bo28b
3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo337bo2bo
4bob2obob2o18bo86b2o2bo157bobo23bo$356b2o3bo14b2o4b2o124b3o84bo427b2o
90b4o26bo376b2o5b3o15bo41b3o143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b
2o24b2ob3o24b2ob3o28bo93bo146bo65b2o113bo300b2o394bobo5bo10bobo67b2o
20b2o32b3o134bo99bo39b2o18bo338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o$
356b2o3bo13b3o4b2o125bo372bo3bo124b5o8bo89b6o25bobo374b2o12bo8bobo42bo
160b2o323b2o93b2obo6b2o5bo2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo
267b2o100bobo81b3o181bo5bo38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o
98b2ob2o53bob2o331b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo$
358bobo15b2o4b2o498bo3bo123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o
204bo40b2o381bo5bo7b2o546b2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b
2o3b2o27bo82b5o99b2o74b3o45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o
127bobo6bo153bo332bobo8b2o8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo$
359bo9bo7b2o631b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bobo
381bo647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o
9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo
54bo16bobobo107b3o39b3o$370b2o6bo130bo373b3o136bo3bo86b2o4b2o29b3o366b
2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o340b3o29bo81bo4bo97b3o
76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob2o122bo16bo8b
o335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo$369b2o137b3o513bo78b3o8b
6o25b2o365b2o8b2o4b2o7b4o52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o
422bo30bo82bo25bo75bo35bo34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bob
o27b2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o
6b2o71bo154b3o$381bobo123bobobo431b3o70bo4bo5bo75bo11b4o27bo364bobo8b
2o3b2o7b4o55bo77b3o494b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34b
o2b3o52bo173bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bob
o10b2o348bobo43b4o81bobo151b3o$293bo86bo126bobobo371b3o56bo3bo69bobo2b
o5bo76bo38b3o365bo24bo138bo502bo5bo177b3o425bo20bo20b2o176b2o3b2o117b
3o438bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o
239b2o106bo3bo23bo5bo12bo2bo$293b3o68b2o2bo12bo2bo63b2o58b3o430bo5bo
68b2o4bo3bo116bo7bo358b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo
303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b
2o105bo3bo42b4o26bobo39b2o9bo155b3o$296bo67bo2b2o12bobobo63bo59bo372bo
3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b2o3bobo178b2o128bo338b
3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo146b
2o16bobo5b2o234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo$
295b2o68b5o12bo2bo63bobo430bo3bo57bo145bobo56bo2bo275b2o110bo106bobo
115bo323b2o2bo88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o
20b2o116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7b
obo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo$237b3o136bobo4b2o65b2o46b
2o401b2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo
237b3o63bo113bobo3b2o180bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b
3o140bo2b3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo
3bo35bo68bo19b2ob2o152b3o$377b2o90b4o26bo376b2o5b3o15bo41b3o143b2o338b
obo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo93bo146bo65b2o
113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18bo338bo12bob
3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo$236bo3bo
124b5o8bo89b6o25bobo374b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5bo2bo
545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo38bo
168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo4b2ob
obob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o$236bo3b
o123bo2b2o9bo88b2o4b2o25bob2o369b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o
8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o
4bo3bo16bo5bobo$364b2o2bo8b3o86b2o6b2o25b6o365b3o16b4o51b2o158b3o36bob
o381bo647bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b
2o9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19b
o54bo16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo$237b3o136bo
3bo86b2o4b2o29b3o366b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o
340b3o29bo81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o
127b5obob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414b
o2bo4bo2bo5b2o6b4o58bo17b3o15b2o$378bo78b3o8b6o25b2o365b2o8b2o4b2o7b4o
52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo
34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obo
b2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o
56b2o18bo$297b3o70bo4bo5bo75bo11b4o27bo364bobo8b2o3b2o7b4o55bo77b3o
494b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo173bo39bo
52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o
81bobo151b3o390bo5bo4bo76bo$237b3o56bo3bo69bobo2bo5bo76bo38b3o365bo24b
o138bo502bo5bo177b3o425bo20bo20b2o176b2o3b2o117b3o438bo111bo38b2o56b3o
264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b2o106bo3bo23bo5bo12b
o2bo635bo2bo77bo$295bo5bo68b2o4bo3bo116bo7bo358b2o252b3o411bobo3bo180b
o423b2o31b3o7bo300bo3bo303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo
13bo129b2o3bo17b2o240b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo
74bobo$236bo3bo54bo5bo75b3o65b2o57bobo388bobo103bo116bo413b2o3bobo178b
2o128bo338b3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b
2ob2o9bo146b2o16bobo5b2o234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b
2o7bobo155bo396b2o4b2o$236bo3bo57bo145bobo56bo2bo275b2o110bo106bobo
115bo323b2o2bo88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o
20b2o116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7b
obo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o$255b
2o39bo3bo143bo59b2o277bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o
63bo113bobo3b2o180bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo
2b3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo
68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bobo$230b2o5b3o15bo41b3o
143b2o338bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo93bo
146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18bo
338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo
390bo106bo43b5o29bobo56bo$230b2o12bo8bobo42bo160b2o323b2o93b2obo6b2o5b
o2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo
38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo
4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o
481b2o9b2o43bobo30bobo56bo$227b2o15b4o5b2o204bo40b2o381bo5bo7b2o546b2o
bo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o45bo
bo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o8b4o
29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o4bo
3bo16bo5bobo479bo2bo91b2o52bo$226b3o16b4o51b2o158b3o36bobo381bo647bo
271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo4b2o
17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo16bobo
bo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b3o31b
2o2b2o51b3o$227b2o6bo9bo2bo51bo79b2o80bo36bo379b2obo837bobo80b2o340b3o
29bo81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5o
bob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4b
o2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo$220b2o8b2o4b2o7b4o
52b3o76bo117b2o378bo2b2o612bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo
34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obo
b2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o
56b2o18bo477bobo5bo12b2obo84bo125b2o$219bobo8b2o3b2o7b4o55bo77b3o494b
2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo173bo39bo52bo
3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o81bo
bo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o$219bo24bo138bo502bo5bo
177b3o425bo20bo20b2o176b2o3b2o117b3o438bo111bo38b2o56b3o264bobo57b2o
70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b2o106bo3bo23bo5bo12bo2bo635bo2bo
77bo486b2o222b3o4b2o$218b2o252b3o411bobo3bo180bo423b2o31b3o7bo300bo3bo
303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b
2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b3o16b3o43b2o
158b2o4b2o$249bobo103bo116bo413b2o3bobo178b2o128bo338b3o296bo5bo304bo
4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b2o
234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o
557b2o3b2o16bo3bo43bo149b2o8b2o6b2o$136b2o110bo106bobo115bo323b2o2bo
88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo
129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo341bobobo
36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o573b2o16bo5bo39b3o
149bobo9bo6bobo$137bo94b2o15bo2bo102b2o441bo30b2o28b2o28bo5bo237b3o63b
o113bobo3b2o180bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b
3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo68b
o19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bobo484bo13bo3b2obob2o39bo
151bo20bo$137bobo92bo2b2o5bobo4bobobo543b2o3b2o24b2ob3o24b2ob3o28bo93b
o146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18b
o338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo
390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o$138b2o93b2obo6b2o5b
o2bo545b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo81b3o181bo5bo
38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo
4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o
481b2o9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo$237bo5bo7b2o546b
2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o74b3o
45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o
8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o
4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o$237bo647bo
271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo4b2o
17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo16bobo
bo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b3o31b
2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo$233b2obo837bobo80b2o340b3o29bo
81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob
2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4bo2b
o5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b
2o3bo$232bo2b2o612bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo34b2o56b2o
171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obob2o23bobo
7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o56b2o18bo
477bobo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo$
232b2o190bo424b3o22bo9bobo185b2o3b2o559b3o109b3o34bo2b3o52bo173bo39bo
52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o
81bobo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b
2o2bo6bo7b6o52bo$240bo5bo177b3o425bo20bo20b2o176b2o3b2o117b3o438bo111b
o38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b2o106bo3b
o23bo5bo12bo2bo635bo2bo77bo486b2o222b3o4b2o237bo55bo3bo83bo18bo4bo6bo
6bo2b2o2bo6bobo63b3o$240bobo3bo180bo423b2o31b3o7bo300bo3bo303b3o131b2o
110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b2o105bo3bo42b
4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b3o16b3o43b2o158b2o4b2o237b
3o55bo83b3o20b2o7b2o8b4o7bo3bo$240b2o3bobo178b2o128bo338b3o296bo5bo
304bo4b2ob2o278bo4b2o6b2o339bobo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b
2o234bo107bobobo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o
557b2o3b2o16bo3bo43bo149b2o8b2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o
$151b2o2bo88b2ob2o305b3o111bo4b2o180bo41bo71b2o223b2obob2o163b2o20b2o
116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo
341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o573b2o16b
o5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o$152bo
30b2o28b2o28bo5bo237b3o63bo113bobo3b2o180bo28b3o82b2o181b2obob2o206bo
20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo337bo2bo4bob2obob2o18bo86b2o2bo
157bobo23bo341bo3bo35bo68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bo
bo484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o$151b2o3b2o24b2ob
3o24b2ob3o28bo93bo146bo65b2o113bo300b2o394bobo5bo10bobo67b2o20b2o32b3o
134bo99bo39b2o18bo338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bo
bo59bo4bo15b2obo390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o289b
3o78bo36bo20b3o$153b5o25b5o25b5o25b2o3b2o90bobo145bo25bo267b2o100bobo
81b3o181bo5bo38bo168b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bob
2o331b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo
19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo593bo58b
o$153b2obo27b3o27b3o22bo100b2o86b3o81b3o268b2o66b2o3b2o27bo82b5o99b2o
74b3o45bobo172bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo332bob
o8b2o8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo
55b2o4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo$
239bo271bo270bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo
4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo
16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b
3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo$428bobo80b2o340b3o29bo81bo
4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o127b5obob2o
122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4bo2bo
5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b
2o3bo495b2o182b2o$203bo22b3o10bo187b5o422bo30bo82bo25bo75bo35bo34b2o
56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obob2o
23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o71bo154b3o388bobo5b6o56b
2o18bo477bobo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo
52bo444b2o4bo2b2o63b2o3b2o102b2o$203b3o22bo9bobo185b2o3b2o559b3o109b3o
34bo2b3o52bo173bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo
3bobo10b2o348bobo43b4o81bobo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o
4b2o296bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo$206bo
20bo20b2o176b2o3b2o117b3o438bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3b
o6b2o3bobo130b2o19b2o239b2o106bo3bo23bo5bo12bo2bo635bo2bo77bo486b2o
222b3o4b2o237bo55bo3bo83bo18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o$
205b2o31b3o7bo300bo3bo303b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo
13bo129b2o3bo17b2o240b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo
74bobo485b3o16b3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448b
o5bo128bo3bo102b3obo$249b3o296bo5bo304bo4b2ob2o278bo4b2o6b2o339bobo10b
o10bobo4bo88b2ob2o9bo146b2o16bobo5b2o234bo107bobobo23b2obob2o4b3o6b4o
25b2o32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b2o6b2o
237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bo
bo3bo$209bo41bo71b2o223b2obob2o163b2o20b2o116bo9bo129bo143b2o9b2o4bo2b
o337bo11b4o9b2o5b3o86bo161bo2bo13bo7bobo341bobobo36bo9bo26bo30bo3bo5bo
9b2obo154bo235b2o108bo39b2o3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o
51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o
2bo$209bo28b3o82b2o181b2obob2o206bo20bo122bo2b2o130b3o140bo2b3o5bo4bo
2bobo337bo2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo68bo19b2ob
2o152b3o235b2o105b3o39b2o3b2o29bo56bobo484bo13bo3b2obob2o39bo151bo20bo
289bo3bo112bobo19b5o449bo3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo$323b2o
394bobo5bo10bobo67b2o20b2o32b3o134bo99bo39b2o18bo338bo12bob3o16b2o9b2o
76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo15b2obo390bo106bo43b5o29bobo
56bo475b2o17bo3b2o200b2o20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo
2b2o74bo2b2o2bo37bo75b3o$136b2o100bobo81b3o181bo5bo38bo168b2o4bobo9b2o
69bo20bo17b2o14b3o129b5o98b2ob2o53bob2o331b2o7bobo4b2obobob2o27bo2bo
75b3o7b2o5b2o107b2o42bo359bobo43b2o60bo19bobo4b2o481b2o9b2o43bobo30bob
o56bo363bo22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o
2bo4b2o106bo$137b2o66b2o3b2o27bo82b5o99b2o74b3o45bobo172bob2o80bobo10b
o5bobo16bo2bo12bo2b2o127bobo6bo153bo332bobo8b2o8b4o29bobo18b2o54b2o2bo
5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o4bo3bo16bo5bobo479bo2bo
91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo630bo2bo4bo2bo13bo2bo5bobo
106b2o$136bo69b5o110bo3b2o100bo74bo4b2ob2o38bobo171b2ob2o81b2o9bobo4b
2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o362bo22bo28b2obo19bo54bo16bo
bobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b3o
31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo1083b2o81b2o36b6o2b6o7bo15bo$
207b3o29bo81bo4bo97b3o76bo5bo41b3o171bob2o38b2o51bo3b2o22bo8b2o5b2ob2o
127b5obob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414b
o2bo4bo2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo
25bobo3bo3b2o3bo495b2o182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo$208bo
30bo82bo25bo75bo35bo34b2o56b2o171bobo39b2o50bo3b2o23b2obo6bo92bobo27b
2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo351bo26b2ob2o3b2o9b4o6b2o
71bo154b3o388bobo5b6o56b2o18bo477bobo5bo12b2obo84bo125b2o297bo2bo102bo
3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o10b
o10bobo39b2o6b2o5bo3b2o128b3o$346b3o109b3o34bo2b3o52bo173bo39bo52bo3b
2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348bobo43b4o81bobo
151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b2o2bo
6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo$
345bo111bo38b2o56b3o264bobo57b2o70bo28bo4bo3bo6b2o3bobo130b2o19b2o239b
2o106bo3bo23bo5bo12bo2bo635bo2bo77bo486b2o222b3o4b2o237bo55bo3bo83bo
18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o$
211b3o131b2o110b2o42bo54bo265bo33b2o23bo106bobo13bo129b2o3bo17b2o240b
2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b3o16b3o43b2o
158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo3bo102b3obo
456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo$213bo4b2ob2o278bo4b2o6b2o339b
obo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b2o234bo107bobobo23b2obob2o4b
3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b
2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b
2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo
$72b2o20b2o116bo9bo129bo143b2o9b2o4bo2bo337bo11b4o9b2o5b3o86bo161bo2bo
13bo7bobo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo39b2o3b2o
573b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o4b2o4b2o3b
2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo9bo4bo4bobo66bo9bo3bo
5b2obo66bo41b5obob2o$73bo20bo122bo2b2o130b3o140bo2b3o5bo4bo2bobo337bo
2bo4bob2obob2o18bo86b2o2bo157bobo23bo341bo3bo35bo68bo19b2ob2o152b3o
235b2o105b3o39b2o3b2o29bo56bobo484bo13bo3b2obob2o39bo151bo20bo289bo3bo
112bobo19b5o449bo3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b
2o7b2o7bo2bo64b2o32bo5bo7b4o$73bobo5bo10bobo67b2o20b2o32b3o134bo99bo
39b2o18bo338bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo4bo
15b2obo390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o289b3o78bo36b
o20b3o451b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo10b2o
53b2o27b3o13b2o4b2o$74b2o4bobo9b2o69bo20bo17b2o14b3o129b5o98b2ob2o53bo
b2o331b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b2o107b2o42bo359bobo43b2o60b
o19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo593bo
58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o106bo524bobo9b3o6b2o4b3o82bo
4b2ob2o6b2o3bobo$79bob2o80bobo10bo5bobo16bo2bo12bo2b2o127bobo6bo153bo
332bobo8b2o8b4o29bobo18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo
2bo20bo55b2o4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b
6o575bo630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo
14bo$78b2ob2o81b2o9bobo4b2o17bobo2bo5b2o8bo126bobo5bobo92bo5bo22b2o
362bo22bo28b2obo19bo54bo16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b
3o15bo459b2o18bobo2bo51b3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo
1083b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o$79bob2o38b2o
51bo3b2o22bo8b2o5b2ob2o127b5obob2o122bo16bo8bo335b2o45bo5b3o11bo6bobo
54b2ob2o12bo2bo110bo414bo2bo4bo2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o
56b3o66bo297bo105bo8bo25bobo3bo3b2o3bo495b2o182b2o402bo57bo23bo38bo2bo
4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o$80bobo39b2o50bo3b2o23b
2obo6bo92bobo27b2o2b2o3b2o7b4o94b2obob2o23bobo7bobo4b2o7bobo351bo26b2o
b2o3b2o9b4o6b2o71bo154b3o388bobo5b6o56b2o18bo477bobo5bo12b2obo84bo125b
2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b
2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o$
81bo39bo52bo3b2o25bo100b2o28bobo3b3o7b2o4b2o123b2o6bo2bo3bobo10b2o348b
obo43b4o81bobo151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo
6bo12bo2b2o2bo6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b
2o55bo136bo433b3o69b2o9b2o107bo$175bobo57b2o70bo28bo4bo3bo6b2o3bobo
130b2o19b2o239b2o106bo3bo23bo5bo12bo2bo635bo2bo77bo486b2o222b3o4b2o
237bo55bo3bo83bo18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o632b3o8b2o
3bo64bobo130b5o434b3o70bo118bo4b2o6b2o$176bo33b2o23bo106bobo13bo129b2o
3bo17b2o240b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b
3o16b3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo
3bo102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o
9b2o4bo2bo$209bobo10bo10bobo4bo88b2ob2o9bo146b2o16bobo5b2o234bo107bobo
bo23b2obob2o4b3o6b4o25b2o32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo
3bo43bo149b2o8b2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o
84b3o6b2o4b2o39b2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b
2obob2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo$208bo11b4o9b2o5b3o86bo
161bo2bo13bo7bobo341bobobo36bo9bo26bo30bo3bo5bo9b2obo154bo235b2o108bo
39b2o3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o
4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo9bo4bo4bobo
66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo$208bo2bo4bob2obob2o18bo86b2o
2bo157bobo23bo341bo3bo35bo68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo
56bobo484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o449bo3bo43bob
obo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b
4o633bob2o$208bo12bob3o16b2o9b2o76b3o7bo158b3o15b2o340bo3bo41bobo59bo
4bo15b2obo390bo106bo43b5o29bobo56bo475b2o17bo3b2o200b2o20b2o289b3o78bo
36bo20b3o451b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo
10b2o53b2o27b3o13b2o4b2o632bo$200b2o7bobo4b2obobob2o27bo2bo75b3o7b2o5b
2o107b2o42bo359bobo43b2o60bo19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo
22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o
106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo$199bobo8b2o8b4o29bob
o18b2o54b2o2bo5bobo4bo2bo106bo43bo360bo12bo2bo4bo2bo20bo55b2o4bo3bo16b
o5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo630bo2bo4bo2bo
13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo578b2o40bo8bo$
199bo22bo28b2obo19bo54bo16bobobo107b3o39b3o370b3o2b6o2b3o73bobo6bobo6b
3o15bo459b2o18bobo2bo51b3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo
1083b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o607b2o32bobo
4b2o7bobo$198b2o45bo5b3o11bo6bobo54b2ob2o12bo2bo110bo414bo2bo4bo2bo5b
2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b2o
3bo495b2o182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo18b2o
3b3o74bo2b3o636bo2bo3bobo10b2o$216bo26b2ob2o3b2o9b4o6b2o71bo154b3o388b
obo5b6o56b2o18bo477bobo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo
10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b
2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o638b2o19b2o$215bobo43b4o81bobo
151b3o390bo5bo4bo76bo478b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b2o2bo
6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo
433b3o69b2o9b2o107bo632b2o3bo17b2o$106b2o106bo3bo23bo5bo12bo2bo635bo2b
o77bo486b2o222b3o4b2o237bo55bo3bo83bo18bo4bo6bo6bo2b2o2bo6bobo63b3o
384bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo4b2o6b2o620b2o16b
obo5b2o$107b2o105bo3bo42b4o26bobo39b2o9bo155b3o395bobo2bobo74bobo485b
3o16b3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo
3bo102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o
9b2o4bo2bo612b2o6bo2bo13bo7bobo$106bo107bobobo23b2obob2o4b3o6b4o25b2o
32bobo6b2o7bobo155bo396b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b2o6b2o237bo
51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bobo3bo
446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo429b3o67bo
115bo2b3o5bo4bo2bobo611bobo7bobo23bo$214bobobo36bo9bo26bo30bo3bo5bo9b
2obo154bo235b2o108bo39b2o3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o
51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o
2bo446bobo9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612bo17b
3o15b2o$214bo3bo35bo68bo19b2ob2o152b3o235b2o105b3o39b2o3b2o29bo56bobo
484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o449bo3bo43bobobo6b
2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o
633bob2o612b2o18bo$214bo3bo41bobo59bo4bo15b2obo390bo106bo43b5o29bobo
56bo475b2o17bo3b2o200b2o20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo
2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo
634bo$215bobo43b2o60bo19bobo4b2o481b2o9b2o43bobo30bobo56bo363bo22b2o
85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o106bo
524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo686b3o$216bo12bo2bo4bo2bo
20bo55b2o4bo3bo16bo5bobo479bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o
575bo630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo
14bo578b2o40bo8bo$227b3o2b6o2b3o73bobo6bobo6b3o15bo459b2o18bobo2bo51b
3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo1083b2o81b2o36b6o2b6o7bo15b
o550bobo78bo2bo19bo3b2obo74b2o607b2o32bobo4b2o7bobo635b3o$229bo2bo4bo
2bo5b2o6b4o58bo17b3o15b2o459bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo
3b2o3bo495b2o182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo
18b2o3b3o74bo2b3o636bo2bo3bobo10b2o577bo55b3o$245bobo5b6o56b2o18bo477b
obo5bo12b2obo84bo125b2o297bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo444b2o
4bo2b2o63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b
3o79bo2bo24bo76b2o638b2o19b2o575b2o$247bo5bo4bo76bo478b2o5b2o13bo210b
2o4b2o296bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o54bo6b2o2bo63bo5bo503bo
58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo632b2o3bo17b2o551b2o23b
2o55b3o$254bo2bo77bo486b2o222b3o4b2o237bo55bo3bo83bo18bo4bo6bo6bo2b2o
2bo6bobo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo4b2o
6b2o620b2o16bobo5b2o545b3o81bo$252bobo2bobo74bobo485b3o16b3o43b2o158b
2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo3bo102b3obo456b2o
6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o4bo2bo612b2o6b
o2bo13bo7bobo544b3o81bo$252b2o4b2o557b2o3b2o16bo3bo43bo149b2o8b2o6b2o
237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bo
bo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo429b3o
67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b3o$91b2o108bo39b2o
3b2o573b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b2o31b2o6b2o4b2o4b
2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo9bo4bo4bobo66bo9b
o3bo5b2obo66bo41b5obob2o615b2o18bo612bo17b3o15b2o544b3o$92b2o105b3o39b
2o3b2o29bo56bobo484bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o
449bo3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo
64b2o32bo5bo7b4o633bob2o612b2o18bo563bo$91bo106bo43b5o29bobo56bo475b2o
17bo3b2o200b2o20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo2b2o74bo2b
2o2bo37bo75b3o423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo$187b2o
9b2o43bobo30bobo56bo363bo22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo
62b2o6b2o12bo2b2o2bo4b2o106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo
579bo686b3o$186bo2bo91b2o52bo363b3o21b2o83bo6bo15bo18b6o575bo630bo2bo
4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo578b2o40bo
8bo$166b2o18bobo2bo51b3o31b2o2b2o51b3o365bo19bo84bo3b2o3bo32bo6bo1083b
2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o607b2o32bobo4b2o7b
obo635b3o$167bo19bo88b2o56b3o66bo297bo105bo8bo25bobo3bo3b2o3bo495b2o
182b2o402bo57bo23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b
3o636bo2bo3bobo10b2o577bo55b3o$167bobo5bo12b2obo84bo125b2o297bo2bo102b
o3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o
10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o638b2o19b2o575b
2o$168b2o5b2o13bo210b2o4b2o296bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o
54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo
632b2o3bo17b2o551b2o23b2o55b3o442bo$176b2o222b3o4b2o237bo55bo3bo83bo
18bo4bo6bo6bo2b2o2bo6bobo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o
434b3o70bo118bo4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo$176b3o16b
3o43b2o158b2o4b2o237b3o55bo83b3o20b2o7b2o8b4o7bo3bo448bo5bo128bo3bo
102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o
4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o$171b2o3b2o16bo3b
o43bo149b2o8b2o6b2o237bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b
3o6b2o4b2o39b2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obo
b2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b
3o439b3obo2bo124b2o$175b2o16bo5bo39b3o149bobo9bo6bobo235b2o51bo5bo79b
2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo47b3o2bo446bobo
9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612bo17b3o15b2o544b
3o522b2obobo$175bo13bo3b2obob2o39bo151bo20bo289bo3bo112bobo19b5o449bo
3bo43bobobo6b2o4b2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o
32bo5bo7b4o633bob2o612b2o18bo563bo513b2o9b4o3b2o$165b2o17bo3b2o200b2o
20b2o289b3o78bo36bo20b3o451b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o
423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo1076bobo10bo5bobo$53bo
22b2o85bo4bo15bo3bobo593bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o
106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo686b3o1075bo20bo$53b
3o21b2o83bo6bo15bo18b6o575bo630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo
12b2o7bobo3b3o83bo5bo14bo578b2o40bo8bo1714b2o20b2o$56bo19bo84bo3b2o3bo
32bo6bo1083b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo3b2obo74b2o607b2o
32bobo4b2o7bobo635b3o$55bo105bo8bo25bobo3bo3b2o3bo495b2o182b2o402bo57b
o23bo38bo2bo4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o636bo2bo3bob
o10b2o577bo55b3o$55bo2bo102bo3b2o3bo13bo2bo10bo4bo6bo52bo444b2o4bo2b2o
63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2b
o24bo76b2o638b2o19b2o575b2o$59bo102bo6bo12bo2b2o2bo6bo7b6o52bo387b3o
54bo6b2o2bo63bo5bo503bo58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo
632b2o3bo17b2o551b2o23b2o55b3o442bo$o55bo3bo83bo18bo4bo6bo6bo2b2o2bo6b
obo63b3o384bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo4b2o6b2o
620b2o16bobo5b2o545b3o81bo442b4o127bo$3o55bo83b3o20b2o7b2o8b4o7bo3bo
448bo5bo128bo3bo102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bob
o67b3o114b2o9b2o4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o$
3bo51bo5bo79bo32b2o6bobo2bobo5b5o448bo5bo43b2o84b3o6b2o4b2o39b2o47bobo
3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o101b2obob2o2bobo5bobo429b3o
67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b3o439b3obo2bo124b2o
$2b2o51bo5bo79b2o31b2o6b2o4b2o4b2o3b2o450bo45bo2bo12b5o75bobo2bobo40bo
47b3o2bo446bobo9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612b
o17b3o15b2o544b3o522b2obobo$56bo3bo112bobo19b5o449bo3bo43bobobo6b2o4b
2o2bo76b4o39b3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o633bob
2o612b2o18bo563bo513b2o9b4o3b2o$57b3o78bo36bo20b3o451b3o45bob3o5bobo3b
o2b2o74bo2b2o2bo37bo75b3o423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo
634bo1076bobo10bo5bobo$138bo58bo453bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o
106bo524bobo9b3o6b2o4b3o82bo4b2ob2o6b2o3bobo579bo686b3o1075bo20bo$139b
o630bo2bo4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo
578b2o40bo8bo1714b2o20b2o$648b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19b
o3b2obo74b2o607b2o32bobo4b2o7bobo635b3o$61b2o182b2o402bo57bo23bo38bo2b
o4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o636bo2bo3bobo10b2o577bo
55b3o$62b2o4bo2b2o63b2o3b2o102b2o399b3o57b2o10bo10bobo39b2o6b2o5bo3b2o
128b3o434b3o79bo2bo24bo76b2o638b2o19b2o575b2o$4b3o54bo6b2o2bo63bo5bo
503bo58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo632b2o3bo17b2o551b
2o23b2o55b3o442bo$3bo3bo59b5o632b3o8b2o3bo64bobo130b5o434b3o70bo118bo
4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$2bo5bo128bo3bo
102b3obo456b2o6bob2o3bo63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o
4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o1503bo3bo$2bo5bo
43b2o84b3o6b2o4b2o39b2o47bobo3bo446b2o8b2o2bo2bob2o3bo62bo5bo7bobo4b3o
101b2obob2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo
544b2obo79b3o439b3obo2bo124b2o1506bo$5bo45bo2bo12b5o75bobo2bobo40bo47b
3o2bo446bobo9bo4bo4bobo66bo9bo3bo5b2obo66bo41b5obob2o615b2o18bo612bo
17b3o15b2o544b3o522b2obobo1629bo4bo$3bo3bo43bobobo6b2o4b2o2bo76b4o39b
3o75bo424bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o633bob2o612b2o18bo
563bo513b2o9b4o3b2o1629bo$4b3o45bob3o5bobo3bo2b2o74bo2b2o2bo37bo75b3o
423b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo1076bobo10bo5bobo1615b
2o7bo3bo4b2o$5bo48b3o5bo62b2o6b2o12bo2b2o2bo4b2o106bo524bobo9b3o6b2o4b
3o82bo4b2ob2o6b2o3bobo579bo686b3o1075bo20bo1614bobo7bobo6bobo$124bo2bo
4bo2bo13bo2bo5bobo106b2o442b3o77bo12b2o7bobo3b3o83bo5bo14bo578b2o40bo
8bo1714b2o20b2o1613bo20bo$2b2o81b2o36b6o2b6o7bo15bo550bobo78bo2bo19bo
3b2obo74b2o607b2o32bobo4b2o7bobo635b3o2710b2o20b2o$3bo57bo23bo38bo2bo
4bo2bo9b2o127bo436b3o78bobobo18b2o3b3o74bo2b3o636bo2bo3bobo10b2o577bo
55b3o$3o57b2o10bo10bobo39b2o6b2o5bo3b2o128b3o434b3o79bo2bo24bo76b2o
638b2o19b2o575b2o$o58b2o4b2o4bobo9b2o55bo136bo433b3o69b2o9b2o107bo632b
2o3bo17b2o551b2o23b2o55b3o442bo$58b3o8b2o3bo64bobo130b5o434b3o70bo118b
o4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$59b2o6bob2o3bo
63b2ob2o9bo5b2o111bobo6bo430bobo67b3o114b2o9b2o4bo2bo612b2o6bo2bo13bo
7bobo544b3o81bo441b2obobo124b2o1503bo3bo$50b2o8b2o2bo2bob2o3bo62bo5bo
7bobo4b3o101b2obob2o2bobo5bobo429b3o67bo115bo2b3o5bo4bo2bobo611bobo7bo
bo23bo544b2obo79b3o439b3obo2bo124b2o1506bo$49bobo9bo4bo4bobo66bo9bo3bo
5b2obo66bo41b5obob2o615b2o18bo612bo17b3o15b2o544b3o522b2obobo1629bo4bo
$49bo22bo64b2o3b2o7b2o7bo2bo64b2o32bo5bo7b4o633bob2o612b2o18bo563bo
513b2o9b4o3b2o1629bo$48b2o110b2obo10b2o53b2o27b3o13b2o4b2o632bo634bo
1076bobo10bo5bobo1615b2o7bo3bo4b2o$146bobo9b3o6b2o4b3o82bo4b2ob2o6b2o
3bobo579bo686b3o1075bo20bo1614bobo7bobo6bobo$65b3o77bo12b2o7bobo3b3o
83bo5bo14bo578b2o40bo8bo1714b2o20b2o1613bo20bo$65bobo78bo2bo19bo3b2obo
74b2o607b2o32bobo4b2o7bobo635b3o2710b2o20b2o$65b3o78bobobo18b2o3b3o74b
o2b3o636bo2bo3bobo10b2o577bo55b3o$65b3o79bo2bo24bo76b2o638b2o19b2o575b
2o$65b3o69b2o9b2o107bo632b2o3bo17b2o551b2o23b2o55b3o442bo$65b3o70bo
118bo4b2o6b2o620b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$65bobo67b3o
114b2o9b2o4bo2bo612b2o6bo2bo13bo7bobo544b3o81bo441b2obobo124b2o1503bo
3bo$65b3o67bo115bo2b3o5bo4bo2bobo611bobo7bobo23bo544b2obo79b3o439b3obo
2bo124b2o1506bo$251b2o18bo612bo17b3o15b2o544b3o522b2obobo1629bo4bo$
267bob2o612b2o18bo563bo513b2o9b4o3b2o1629bo$268bo634bo1076bobo10bo5bob
o1615b2o7bo3bo4b2o$215bo686b3o1075bo20bo1614bobo7bobo6bobo$213b2o40bo
8bo1714b2o20b2o1613bo20bo$214b2o32bobo4b2o7bobo635b3o2710b2o20b2o$247b
o2bo3bobo10b2o577bo55b3o$246b2o19b2o575b2o$244b2o3bo17b2o551b2o23b2o
55b3o442bo$246b2o16bobo5b2o545b3o81bo442b4o127bo1502bobo$239b2o6bo2bo
13bo7bobo544b3o81bo441b2obobo124b2o1503bo3bo$238bobo7bobo23bo544b2obo
79b3o439b3obo2bo124b2o1506bo$238bo17b3o15b2o544b3o522b2obobo1629bo4bo$
237b2o18bo563bo513b2o9b4o3b2o1629bo$257bo1076bobo10bo5bobo1615b2o7bo3b
o4b2o$256b3o1075bo20bo1614bobo7bobo6bobo$1333b2o20b2o1613bo20bo$256b3o
2710b2o20b2o$200bo55b3o$198b2o$174b2o23b2o55b3o442bo$173b3o81bo442b4o
127bo1502bobo$173b3o81bo441b2obobo124b2o1503bo3bo$173b2obo79b3o439b3ob
o2bo124b2o1506bo$174b3o522b2obobo1629bo4bo$175bo513b2o9b4o3b2o1629bo$
688bobo10bo5bobo1615b2o7bo3bo4b2o$688bo20bo1614bobo7bobo6bobo$687b2o
20b2o1613bo20bo$2323b2o20b2o3$55bo$54b4o127bo1502bobo$53b2obobo124b2o
1503bo3bo$52b3obo2bo124b2o1506bo$53b2obobo1629bo4bo$43b2o9b4o3b2o1629b
o$42bobo10bo5bobo1615b2o7bo3bo4b2o$42bo20bo1614bobo7bobo6bobo$41b2o20b
2o1613bo20bo$1677b2o20b2o4$1042bobo$1042bo3bo$1046bo$1042bo4bo$1046bo$
1033b2o7bo3bo4b2o$1032bobo7bobo6bobo$1032bo20bo$1031b2o20b2o4$396bobo$
396bo3bo$400bo$396bo4bo$400bo$387b2o7bo3bo4b2o$386bobo7bobo6bobo$386bo
20bo$385b2o20b2o!

Weird,I cannot see anything....
Have a good day!

BokaBB

[yujh]

Let FWKS do it, I'm both too lazy and unexperienced on this kind of thing.

Code: Select all

boka, please cut this part out.

Weird,I cannot see anything....
Have a good day!

BokaBB

It’s just too big.

[BokaBB]

I will try to install apgsearch soon. Please explore my rules,I will try to help soon.
Have a good day!

BokaBB

[MathAndCode]

This forms a stable puffer.

Code: Select all

x = 22, y = 4, rule = B34c/S234y8
2bo16bo$b3o14b3o$o3bo12bo3bo$2ob2o12b2ob2o!

This one is much cleaner.

Code: Select all

x = 23, y = 4, rule = B34c/S234y8
2bo17bo$b3o15b3o$o3bo13bo3bo$2ob2o13b2ob2o!

(These might already be known.)



Edit: This puffer produces even less ash.

Code: Select all

x = 39, y = 66, rule = B34c/S234y8
10bo17bo$9b3o15b3o$8b5o13b5o$7b2o3b2o11b2o3b2o$6b2ob3ob2o9b2ob3ob2o$7b
7o11b7o$8bo3bo13bo3bo3$3bo31bo$3b2o29b2o$3bobo27bobo$2b2obo9bobo3bobo
9bob2o$2b2o3bo7bob2ob2obo7bo3b2o$3o5bobo5b2o3b2o5bobo5b3o$b4ob2obobo15b
obob2ob4o$2b4o4bo17bo4b4o3$15b3o3b3o$15b3o3b3o$8b3o2b2obobobobob2o2b3o
$7bo3bo2b2ob2ob2ob2o2bo3bo$4bo3bo5bobobobobobo5bo3bo$3b2o3bo3bo2b3o3b
3o2bo3bo3b2o$3bob2obob2o3b3o3b3o3b2obob2obo$6b4o19b4o$6b2o23b2o4$8b2o
8b3o8b2o$8b2o9bo9b2o2$17b5o$14bo9bo$12b2obo2b3o2bob2o$13b2o9b2o$17b5o
8$13b2o4bo4b2o$13b2o3bobo3b2o$19bo5$19bo$18bobo$18bobo$19bo2$14b2o7b2o
$13bo2bo5bo2bo$14b2o7b2o2$19bo$18bobo$18bobo$19bo!

[Hunting]

This forms a stable puffer.

Code: Select all

x = 22, y = 4, rule = B34c/S234y8
2bo16bo$b3o14b3o$o3bo12bo3bo$2ob2o12b2ob2o!

This one is much cleaner.

Code: Select all

x = 23, y = 4, rule = B34c/S234y8
2bo17bo$b3o15b3o$o3bo13bo3bo$2ob2o13b2ob2o!

(These might already be known.)



Edit: This puffer produces even less ash.

Code: Select all

x = 39, y = 66, rule = B34c/S234y8
10bo17bo$9b3o15b3o$8b5o13b5o$7b2o3b2o11b2o3b2o$6b2ob3ob2o9b2ob3ob2o$7b
7o11b7o$8bo3bo13bo3bo3$3bo31bo$3b2o29b2o$3bobo27bobo$2b2obo9bobo3bobo
9bob2o$2b2o3bo7bob2ob2obo7bo3b2o$3o5bobo5b2o3b2o5bobo5b3o$b4ob2obobo15b
obob2ob4o$2b4o4bo17bo4b4o3$15b3o3b3o$15b3o3b3o$8b3o2b2obobobobob2o2b3o
$7bo3bo2b2ob2ob2ob2o2bo3bo$4bo3bo5bobobobobobo5bo3bo$3b2o3bo3bo2b3o3b
3o2bo3bo3b2o$3bob2obob2o3b3o3b3o3b2obob2obo$6b4o19b4o$6b2o23b2o4$8b2o
8b3o8b2o$8b2o9bo9b2o2$17b5o$14bo9bo$12b2obo2b3o2bob2o$13b2o9b2o$17b5o
8$13b2o4bo4b2o$13b2o3bobo3b2o$19bo5$19bo$18bobo$18bobo$19bo2$14b2o7b2o
$13bo2bo5bo2bo$14b2o7b2o2$19bo$18bobo$18bobo$19bo!

Please don't. Like corderpuffers in CGoL, these are not of interest in any way.

[Schiaparelliorbust]

This forms a stable puffer.

Code: Select all

x = 22, y = 4, rule = B34c/S234y8
2bo16bo$b3o14b3o$o3bo12bo3bo$2ob2o12b2ob2o!

This one is much cleaner.

Code: Select all

x = 23, y = 4, rule = B34c/S234y8
2bo17bo$b3o15b3o$o3bo13bo3bo$2ob2o13b2ob2o!

(These might already be known.)



Edit: This puffer produces even less ash.

Code: Select all

x = 39, y = 66, rule = B34c/S234y8
10bo17bo$9b3o15b3o$8b5o13b5o$7b2o3b2o11b2o3b2o$6b2ob3ob2o9b2ob3ob2o$7b
7o11b7o$8bo3bo13bo3bo3$3bo31bo$3b2o29b2o$3bobo27bobo$2b2obo9bobo3bobo
9bob2o$2b2o3bo7bob2ob2obo7bo3b2o$3o5bobo5b2o3b2o5bobo5b3o$b4ob2obobo15b
obob2ob4o$2b4o4bo17bo4b4o3$15b3o3b3o$15b3o3b3o$8b3o2b2obobobobob2o2b3o
$7bo3bo2b2ob2ob2ob2o2bo3bo$4bo3bo5bobobobobobo5bo3bo$3b2o3bo3bo2b3o3b
3o2bo3bo3b2o$3bob2obob2o3b3o3b3o3b2obob2obo$6b4o19b4o$6b2o23b2o4$8b2o
8b3o8b2o$8b2o9bo9b2o2$17b5o$14bo9bo$12b2obo2b3o2bob2o$13b2o9b2o$17b5o
8$13b2o4bo4b2o$13b2o3bobo3b2o$19bo5$19bo$18bobo$18bobo$19bo2$14b2o7b2o
$13bo2bo5bo2bo$14b2o7b2o2$19bo$18bobo$18bobo$19bo!

Please don't. Like corderpuffers in CGoL, these are not of interest in any way.

They might not be immediately interesting, but you never know, they might turn out to be useful in some way.

[Hunting]

They might not be immediately interesting, but you never know, they might turn out to be useful in some way.

This is an year-old thread. Do you really think no one have ever enumerated all combinations of two Pis?

[BokaBB]

They might not be immediately interesting, but you never know, they might turn out to be useful in some way.

This is an year-old thread. Do you really think no one have ever enumerated all combinations of two Pis?

I don't know how to apgsearch and I absolutely know devils will never allow me.
Investigate or not.
Have a good day!

BokaBB

[FWKnightship]

New p10:

Code: Select all

x = 16, y = 16, rule = B34c/S234y8
6b2o$6b2o3$7bo$6b2o$2o3bobo3bo$2o2b3o7bo$9bo5bo$8bob2o3bo$9bo4bo$6bo2b
o3$7bo2bo$8b2o!

Soup(in another rule):

Code: Select all

x = 32, y = 32, rule = B34c/S234y
obo2b4o2b2ob4ob2o2b4o2bobo$2b2ob2obo5bo2bo5bob2ob2o$3o4bo3bo3b2o3bo3bo
4b3o$bo4bobob2ob6ob2obobo4bo$4b3ob3o2bob2obo2b3ob3o$2o2bo2bo3bo3b2o3bo
3bo2bo2b2o$2ob2obo3bo2b2o2b2o2bo3bob2ob2o$obo2bobo3b2o2b2o2b2o3bobo2bo
bo$2ob2o3bo2b4o2b4o2bo3b2ob2o$4bo5bobobo2bobobo5bo$3b2obo2b2o2b6o2b2o
2bob2o$ob2obob2o5bo2bo5b2obob2obo$o6b3o12b3o6bo$3b2obobobo4b2o4bobobob
2o$2obo2bob4o2bo2bo2b4obo2bob2o$ob4obo2bo2bo4bo2bo2bob4obo$ob4obo2bo2b
o4bo2bo2bob4obo$2obo2bob4o2bo2bo2b4obo2bob2o$3b2obobobo4b2o4bobobob2o$
o6b3o12b3o6bo$ob2obob2o5bo2bo5b2obob2obo$3b2obo2b2o2b6o2b2o2bob2o$4bo
5bobobo2bobobo5bo$2ob2o3bo2b4o2b4o2bo3b2ob2o$obo2bobo3b2o2b2o2b2o3bobo
2bobo$2ob2obo3bo2b2o2b2o2bo3bob2ob2o$2o2bo2bo3bo3b2o3bo3bo2bo2b2o$4b3o
b3o2bob2obo2b3ob3o$bo4bobob2ob6ob2obobo4bo$3o4bo3bo3b2o3bo3bo4b3o$2b2o
b2obo5bo2bo5bob2ob2o$obo2b4o2b2ob4ob2o2b4o2bobo!