content stringlengths 7 1.05M | fixed_cases stringlengths 1 1.28M |
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#default parameters
#you can make default parameter(last_name= 'unknown') in last like after age
#if we make all parameters defalut than ouput is unknowndef user_info(first_name='unknown', last_name= 'unknown',age = None)
# None is used for numbers
#'unknown' is used for string
def user_info(first_name, last_name,age): #None is a special in python
print(f"Your First name is: {first_name} ")
print(f"Your last name is: {last_name} ")
print(f"Your age is: {age} ")
user_info("Beenash","Pervaiz", 20) | def user_info(first_name, last_name, age):
print(f'Your First name is: {first_name} ')
print(f'Your last name is: {last_name} ')
print(f'Your age is: {age} ')
user_info('Beenash', 'Pervaiz', 20) |
def total(lists):
array = []
sum = 0
for i in lists:
sum += i
array.append(sum)
return array
listss = [1,2,3,5,5,4]
print(total(listss))
| def total(lists):
array = []
sum = 0
for i in lists:
sum += i
array.append(sum)
return array
listss = [1, 2, 3, 5, 5, 4]
print(total(listss)) |
description = 'Setup for the ma11 dom motor'
devices = dict(
dom = device('nicos_ess.devices.epics.motor.EpicsMotor',
description = 'Sample stick rotation',
motorpv = 'SQ:SANS:ma11:dom',
errormsgpv = 'SQ:SANS:ma11:dom-MsgTxt',
precision = 0.1,
),
)
| description = 'Setup for the ma11 dom motor'
devices = dict(dom=device('nicos_ess.devices.epics.motor.EpicsMotor', description='Sample stick rotation', motorpv='SQ:SANS:ma11:dom', errormsgpv='SQ:SANS:ma11:dom-MsgTxt', precision=0.1)) |
with open("input.txt", "r") as f:
values = [int(e) for e in f.readlines()]
windowsSum = list()
index = 0
for value in values:
if(index+2 < len(values)):
sum = value
sum += values[index+1]
sum += values[index+2]
windowsSum.append(sum)
index += 1
isFirst = True
cValue = 0
counter = 0
for value in windowsSum :
if isFirst: isFirst = False
else:
if cValue < value :
counter += 1
cValue = value
print("Total increment = ", counter)
| with open('input.txt', 'r') as f:
values = [int(e) for e in f.readlines()]
windows_sum = list()
index = 0
for value in values:
if index + 2 < len(values):
sum = value
sum += values[index + 1]
sum += values[index + 2]
windowsSum.append(sum)
index += 1
is_first = True
c_value = 0
counter = 0
for value in windowsSum:
if isFirst:
is_first = False
elif cValue < value:
counter += 1
c_value = value
print('Total increment = ', counter) |
TOKEN = "<Your Bot Token Here>"
LOG_LEVEL_STDOUT = "DEBUG" # Console log level
LOG_LEVEL_FILE = "INFO" # File log level
| token = '<Your Bot Token Here>'
log_level_stdout = 'DEBUG'
log_level_file = 'INFO' |
_base_ = './retinanet_r50_fpn_1x_cityscapes.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='checkpoints/resnet101-63fe2227.pth')))
# load_from="checkpoints/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth" | _base_ = './retinanet_r50_fpn_1x_cityscapes.py'
model = dict(backbone=dict(depth=101, init_cfg=dict(type='Pretrained', checkpoint='checkpoints/resnet101-63fe2227.pth'))) |
class Player:
def __init__(self, name, life_value, attack_value):
self.name = name
self.life_value = life_value
self.attack_value = attack_value
def attack(self, enemy_player: 'Player'):
enemy_player.life_value = enemy_player.life_value - self.attack_value
def is_alive(self):
return self.life_value > 0 | class Player:
def __init__(self, name, life_value, attack_value):
self.name = name
self.life_value = life_value
self.attack_value = attack_value
def attack(self, enemy_player: 'Player'):
enemy_player.life_value = enemy_player.life_value - self.attack_value
def is_alive(self):
return self.life_value > 0 |
# Define physical constants
P0 = 1000. # Ground pressure level. Unit: hPa
SCALE_HEIGHT = 7000. # Unit: m
CP = 1004. # specific heat at constant pressure for air (cp) = 1004 J/kg-K
DRY_GAS_CONSTANT = 287.
EARTH_RADIUS = 6.378e+6 # Unit: m
EARTH_OMEGA = 7.29e-5
| p0 = 1000.0
scale_height = 7000.0
cp = 1004.0
dry_gas_constant = 287.0
earth_radius = 6378000.0
earth_omega = 7.29e-05 |
with open('./input.txt') as input:
lines = [int(s.strip()) for s in input.readlines()]
last = 999999999999
counter = 0
for (a, b, c) in zip(lines[0:-2], lines[1:-1], lines[2:]):
current = a + b + c
if (current > last):
counter += 1
last = current
print(counter) # 1378
| with open('./input.txt') as input:
lines = [int(s.strip()) for s in input.readlines()]
last = 999999999999
counter = 0
for (a, b, c) in zip(lines[0:-2], lines[1:-1], lines[2:]):
current = a + b + c
if current > last:
counter += 1
last = current
print(counter) |
# This program saves a list of numbers to a file.
def main():
# Create a list of numbers.
numbers = [1, 2, 3, 4, 5, 6, 7]
# Open a file for writing.
outfile = open('numberlist.txt', 'w')
# Write the list to the file.
for item in numbers:
outfile.write(str(item) + '\n')
# Close the file.
outfile.close()
# Call the main function.
main()
| def main():
numbers = [1, 2, 3, 4, 5, 6, 7]
outfile = open('numberlist.txt', 'w')
for item in numbers:
outfile.write(str(item) + '\n')
outfile.close()
main() |
x = int(input('Enter your Age: '))
print('****************')
for i in range(0, 1):
if x >= 18:
print('You can watch content with R-rating')
elif x >= 13:
print('You can watch movies under parental guidance ')
else:
print('Cartoons permitted')
print(' Thanks! ')
| x = int(input('Enter your Age: '))
print('****************')
for i in range(0, 1):
if x >= 18:
print('You can watch content with R-rating')
elif x >= 13:
print('You can watch movies under parental guidance ')
else:
print('Cartoons permitted')
print(' Thanks! ') |
def internal_consistency_check(Reports_dict, reportnos=None):
return_dict = {}
if reportnos:
search_list = reportnos
else:
search_list = list(Reports_dict.keys())
for reportno in search_list:
rdf = pd.DataFrame()
rdf = Reports_dict[reportno].copy()
print('REPORT', reportno)
if not rdf.empty:
if reportno == '1':
add_down_dont_match = check_add_down(rdf=rdf, tot_col='GEO', columns_to_add=['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL'], groupby_vars=['YEAR', 'DRUG_CODE', 'STATE'])
add_across_dont_match = check_add_across(rdf=rdf, columns_to_add=['Q1', 'Q2', 'Q3', 'Q4'], col_tot=['TOTAL'])
return_dict[reportno] = (add_down_dont_match, add_across_dont_match)
assert return_dict[reportno] == ({}, {})
elif reportno == '2':
add_down_dont_match = check_add_down(rdf=rdf, tot_col='GEO', columns_to_add=['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL'], groupby_vars=['YEAR', 'DRUG_CODE'])
add_across_dont_match = check_add_across(rdf=rdf, columns_to_add=['Q1', 'Q2', 'Q3', 'Q4'], col_tot=['TOTAL'])
return_dict[reportno] = (add_down_dont_match, add_across_dont_match)
assert return_dict[reportno] == ({}, {})
elif reportno == '3':
add_across_dont_match = check_add_across(rdf=rdf, columns_to_add=['Q1', 'Q2', 'Q3', 'Q4'], col_tot=['TOTAL'])
return_dict[reportno] = (add_across_dont_match)
assert return_dict[reportno] == {}
elif reportno == '4':
# 4 is the only report with unexplainable internal inconsistencies, for American Samoa in 2002
add_down_dont_match = check_add_down(rdf=rdf, tot_col='STATE', columns_to_add=['TOTAL GRAMS'], groupby_vars=['YEAR', 'DRUG_CODE'])
rdf['POP'] = np.nan
if '2000 POP' in list(rdf.columns):
rdf['POP'][rdf['2000 POP'].notnull()] = rdf['2000 POP']
if '2010 POP' in list(rdf.columns):
rdf['POP'][rdf['2010 POP'].notnull()] = rdf['2010 POP']
divisor_dont_match = check_divide(rdf, 'TOTAL GRAMS', 'POP', 'GRAMS/100K POP', 100000)
assert all(divisor_dont_match[('TOTAL GRAMS', 'POP', 'GRAMS/100K POP')]['STATE'] == 'AMERICAN SAMOA')
return_dict[reportno] = (add_down_dont_match, divisor_dont_match)
elif reportno == '5' or reportno == '7':
divisor_dont_match = check_divide(rdf, 'TOTAL GRAMS', 'BUYERS', 'AVG GRAMS')
return_dict[reportno] = (divisor_dont_match)
assert return_dict[reportno] == {}
return return_dict
def across_consistency_check(Reports_dict, reportlist):
returndict = {}
if reportlist == ['5', '7']:
# There are some errors, almost entirely in 2011 but a few in buyers in 2014
rdf5, rdf7 = pd.DataFrame(), pd.DataFrame()
rdf5, rdf7 = Reports_dict['5'].copy(), Reports_dict['7'].copy()
assert check_divide(rdf5, 'TOTAL GRAMS', 'BUYERS', 'AVG GRAMS') == {}
assert check_divide(rdf7, 'TOTAL GRAMS', 'BUYERS', 'AVG GRAMS') == {}
returndict[('5', '7')] = groupby_across_sheets(big_df=rdf5[list(set(rdf5.columns) - {'AVG GRAMS'})], small_df=rdf7[list(set(rdf7.columns) - {'AVG GRAMS'})], groupby_vars=['YEAR', 'DRUG CODE', 'BUSINESS ACTIVITY'], compare_cols=['TOTAL GRAMS', 'BUYERS'])
returndict2, returnlist_unmatch = returndict[('5', '7')]
assert all(returnlist_unmatch['YEAR'] == '2011')
for key in returndict2:
assert sum(returndict2[key]['YEAR'] == '2011') + sum(returndict2[key]['YEAR'] == '2014') == len(returndict2[key])
if reportlist == ['2', '3', '4']:
# All errors are in US totals only. But there
rdf2, rdf3, rdf4 = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
rdf2, rdf3, rdf4 = Reports_dict['2'].copy(), Reports_dict['3'].copy(), Reports_dict['4'].copy()
target_us_row_title = list(set(rdf3['GEO']) - set(statelist))[0]
for item in set(rdf2['GEO']) - set(statelist):
rdf2['GEO'].loc[rdf2['GEO'] == item] = target_us_row_title
for item in set(rdf3['GEO']) - set(statelist):
rdf3['GEO'].loc[rdf3['GEO'] == item] = target_us_row_title
for item in set(rdf4['STATE']) - set(statelist):
rdf4['STATE'].loc[rdf4['STATE'] == item] = target_us_row_title
assert all(rdf3.columns == rdf2.columns)
for col in ['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL']:
rdf3[col] = rdf3[col].apply(lambda x: float(str(x).replace(',', '')))
rdf3[col][rdf3['YEAR'] == '2011'] = rdf3[col] / 1000
rdf4['POP'] = np.nan
if '2000 POP' in list(rdf4.columns):
rdf4['POP'][rdf4['2000 POP'].notnull()] = rdf4['2000 POP']
if '2010 POP' in list(rdf4.columns):
rdf4['POP'][rdf4['2010 POP'].notnull()] = rdf4['2010 POP']
df_pop = pd.DataFrame([(float(str(x[0]).replace(',', '')), x[1], x[2]) for x in list(set([tuple(x) for x in rdf4[['POP', 'YEAR', 'STATE']].values]))])
df_pop.columns = ['POP', 'YEAR', 'GEO']
rdf2 = pd.merge(rdf2, df_pop, on=['GEO', 'YEAR'])
merged = pd.merge(rdf2, rdf3, how='outer', on=['DRUG_CODE', 'GEO', 'YEAR'], indicator=True)
missing_entries = merged[merged['_merge'] == 'right_only']
returndict[('5', '7', 'missing_entries')] = missing_entries
merged2 = pd.merge(rdf2, rdf3, how='inner', on=['DRUG_CODE', 'GEO', 'YEAR'], )
for s1 in range(1, 5):
d = check_divide(merged2, 'Q%s_x' % s1, 'POP', 'Q%s_y' % s1, 100000)
assert set(list(d.values())[0]['GEO']) == {target_us_row_title}
returndict[('5', '7', 'Q%s' % s1)] = d
if reportlist == ['1', '2']:
rdf1, rdf2 = pd.DataFrame(), pd.DataFrame()
rdf1, rdf2 = Reports_dict['1'].copy(), Reports_dict['2'].copy()
for tot in [x for x in set(rdf1['GEO']) if x.isdigit() is False]:
rdf1 = rdf1.loc[rdf1['GEO'] != tot]
for us in [x for x in set(rdf2['GEO']) if x not in statelist]:
rdf2 = rdf2.loc[rdf2['GEO'] != us]
rdf2['STATE'] = rdf2['GEO']
returndict[('1', '2')] = groupby_across_sheets(big_df=rdf1, small_df=rdf2, groupby_vars=['YEAR', 'DRUG_CODE', 'STATE'], compare_cols=['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL'])
if reportlist == ['2', '5']:
rdf2, rdf5 = pd.DataFrame(), pd.DataFrame()
rdf2, rdf5 = Reports_dict['2'].copy(), Reports_dict['5'].copy()
rdf2 = rdf2.loc[rdf2['GEO'] != 'UNITED STATES']
rdf2['STATE'] = rdf2['GEO']
rdf2['TOTAL GRAMS'] = rdf2['TOTAL']
rdf2['DRUG CODE'] = rdf2['DRUG_CODE']
returndict[('2', '5')] = groupby_across_sheets(big_df=rdf5, small_df=rdf2, groupby_vars=['YEAR', 'DRUG CODE', 'STATE'], compare_cols=['TOTAL GRAMS'])
return returndict
def groupby_across_sheets(bigdf, smalldf, groupby_vars, compare_cols):
big_df = bigdf.copy()
small_df = smalldf.copy()
returndict2 = {}
for col in compare_cols:
big_df[col] = big_df[col].apply(lambda x: float(str(x).replace(',', '')))
small_df[col] = small_df[col].apply(lambda x: float(str(x).replace(',', '')))
big_df_test = big_df.groupby(groupby_vars).sum()
merged_rdf = pd.merge(big_df_test, small_df, right_on=groupby_vars, left_index=True, how='outer', indicator=True)
returnlist_unmatch = merged_rdf[merged_rdf['_merge'] != 'both']
merged_rdf = pd.merge(big_df_test, small_df, right_on=groupby_vars, left_index=True, how='inner', indicator=True)
for col in compare_cols:
colx = col + '_x'
coly = col + '_y'
df_nonmatch = merged_rdf[merged_rdf.apply(lambda x: are_close(x[colx], x[coly], 0.015) is False, axis=1)]
if len(df_nonmatch) > 0:
returndict2[col] = df_nonmatch
return returndict2, returnlist_unmatch
def check_add_down(rdf, tot_col, columns_to_add, groupby_vars):
rdfa = pd.DataFrame()
rdfa = rdf.copy()
tot_loc = {tot_col: [x for x in list(set(rdfa[tot_col].tolist())) if x in totallist]}
add_down_dont_match = {}
tot_strings = list(tot_loc.values())[0]
for col in columns_to_add:
rdfa[col] = rdfa[col].apply(lambda x: float(x.replace(',', '')))
rdfa['bin'] = rdfa[list(tot_loc.keys())[0]].apply(lambda x: x in tot_strings)
rdf_test = rdfa.groupby(groupby_vars + ['bin']).sum()
pctc = pd.DataFrame(round(abs(rdf_test.groupby(groupby_vars).pct_change())))
totdiv = 0
for year in set(rdfa['YEAR']):
div = len(set(rdfa['bin'][rdfa['YEAR'] == year]))
for v in groupby_vars:
div = div * len(set(rdfa[v][rdfa['YEAR'] == year]))
totdiv = totdiv + div
entries = len(pctc) / totdiv
assert 0.6 <= entries <= 1
for column_to_add in columns_to_add:
r = rdf_test.loc[pctc[column_to_add].notnull() & pctc[column_to_add] != 0]
if len(r) > 0:
add_down_dont_match[column_to_add] = r
return add_down_dont_match
def check_add_across(rdf, columns_to_add, col_tot):
rdfa = pd.DataFrame()
rdfa = rdf.copy()
add_across_dont_match = {}
for col in columns_to_add + col_tot:
rdfa[col] = rdfa[col].apply(lambda x: float(x.replace(',', '')))
r = rdfa.loc[round(rdfa[columns_to_add].sum(axis=1) - rdfa[col_tot[0]], 1) != 0]
if len(r) > 0:
add_across_dont_match[[tuple(columns_to_add + col_tot)]] = r
return add_across_dont_match
def check_divide(rdf, top_divisor, bot_divisor, equals_to, multiplier=1, tolerance=0.02):
rdfa = pd.DataFrame()
rdfa = rdf.copy()
divide_dont_match = {}
for col in [top_divisor, bot_divisor, equals_to]:
rdfa[col] = rdfa[col].apply(lambda x: float(str(x).replace(',', '')))
rdfa['CALCULATED'] = rdfa.apply(lambda x: custom_lambda(x, top_divisor, bot_divisor, equals_to, multiplier, 'calc'), axis=1)
rdfa['BOOL'] = rdfa.apply(lambda x: custom_lambda(x, top_divisor, bot_divisor, equals_to, multiplier, 'bool'), axis=1)
rdfa['CLOSE'] = rdfa.apply(lambda x: custom_lambda(x, top_divisor, bot_divisor, equals_to, multiplier, tolerance), axis=1)
if len(rdfa.loc[-rdfa['CLOSE']]) > 0:
columns_list = [x for x in list(rdfa.columns) if ('_x' not in x and '_y' not in x) or x in [top_divisor, bot_divisor, equals_to]]
divide_dont_match[(top_divisor, bot_divisor, equals_to)] = rdfa[columns_list].loc[-rdfa['CLOSE']]
return divide_dont_match
def custom_lambda(df, top_divisor, bot_divisor, equals_to, multiplier, returntype):
calculated = multiplier * df[top_divisor] / df[bot_divisor]
res = are_equal(calculated, df[equals_to])
if returntype == 'bool':
return res[0]
if type(returntype) is float:
return are_close(calculated, df[equals_to], returntype)
return res[1]
def are_equal(val_compare, reference_val):
r = return_round(reference_val)
val = round(val_compare, r)
comp = round(reference_val, r)
val1 = round(val_compare, r + 1)
comp1 = round(reference_val, r + 1)
return (val == comp or val1 == comp1), (val, comp, val1, comp1)
def are_close(val_compare, reference_val, tolerance):
b, (val, comp, val1, comp1) = are_equal(val_compare, reference_val)
if not b:
if reference_val != 0:
return (abs(val_compare - reference_val) / reference_val <= tolerance) or (abs(val - comp) <= tolerance)
else:
return (abs(val - comp) <= tolerance)
return b
def return_round(x):
if x > 0:
if int(math.log10(x)) == math.log10(x) and int(math.log10(x)) < 0:
magn = int(math.log10(x)) + 1
else:
magn = int(math.log10(x))
if magn < 0:
return -(magn - 1)
elif magn == 0 or magn == 1 or magn == 2:
return 2
elif magn == 3 or magn == 4:
return 1
else:
return 0
elif x == 0:
return 2
else:
raise Exception("shouldn't be less than zero")
| def internal_consistency_check(Reports_dict, reportnos=None):
return_dict = {}
if reportnos:
search_list = reportnos
else:
search_list = list(Reports_dict.keys())
for reportno in search_list:
rdf = pd.DataFrame()
rdf = Reports_dict[reportno].copy()
print('REPORT', reportno)
if not rdf.empty:
if reportno == '1':
add_down_dont_match = check_add_down(rdf=rdf, tot_col='GEO', columns_to_add=['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL'], groupby_vars=['YEAR', 'DRUG_CODE', 'STATE'])
add_across_dont_match = check_add_across(rdf=rdf, columns_to_add=['Q1', 'Q2', 'Q3', 'Q4'], col_tot=['TOTAL'])
return_dict[reportno] = (add_down_dont_match, add_across_dont_match)
assert return_dict[reportno] == ({}, {})
elif reportno == '2':
add_down_dont_match = check_add_down(rdf=rdf, tot_col='GEO', columns_to_add=['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL'], groupby_vars=['YEAR', 'DRUG_CODE'])
add_across_dont_match = check_add_across(rdf=rdf, columns_to_add=['Q1', 'Q2', 'Q3', 'Q4'], col_tot=['TOTAL'])
return_dict[reportno] = (add_down_dont_match, add_across_dont_match)
assert return_dict[reportno] == ({}, {})
elif reportno == '3':
add_across_dont_match = check_add_across(rdf=rdf, columns_to_add=['Q1', 'Q2', 'Q3', 'Q4'], col_tot=['TOTAL'])
return_dict[reportno] = add_across_dont_match
assert return_dict[reportno] == {}
elif reportno == '4':
add_down_dont_match = check_add_down(rdf=rdf, tot_col='STATE', columns_to_add=['TOTAL GRAMS'], groupby_vars=['YEAR', 'DRUG_CODE'])
rdf['POP'] = np.nan
if '2000 POP' in list(rdf.columns):
rdf['POP'][rdf['2000 POP'].notnull()] = rdf['2000 POP']
if '2010 POP' in list(rdf.columns):
rdf['POP'][rdf['2010 POP'].notnull()] = rdf['2010 POP']
divisor_dont_match = check_divide(rdf, 'TOTAL GRAMS', 'POP', 'GRAMS/100K POP', 100000)
assert all(divisor_dont_match['TOTAL GRAMS', 'POP', 'GRAMS/100K POP']['STATE'] == 'AMERICAN SAMOA')
return_dict[reportno] = (add_down_dont_match, divisor_dont_match)
elif reportno == '5' or reportno == '7':
divisor_dont_match = check_divide(rdf, 'TOTAL GRAMS', 'BUYERS', 'AVG GRAMS')
return_dict[reportno] = divisor_dont_match
assert return_dict[reportno] == {}
return return_dict
def across_consistency_check(Reports_dict, reportlist):
returndict = {}
if reportlist == ['5', '7']:
(rdf5, rdf7) = (pd.DataFrame(), pd.DataFrame())
(rdf5, rdf7) = (Reports_dict['5'].copy(), Reports_dict['7'].copy())
assert check_divide(rdf5, 'TOTAL GRAMS', 'BUYERS', 'AVG GRAMS') == {}
assert check_divide(rdf7, 'TOTAL GRAMS', 'BUYERS', 'AVG GRAMS') == {}
returndict['5', '7'] = groupby_across_sheets(big_df=rdf5[list(set(rdf5.columns) - {'AVG GRAMS'})], small_df=rdf7[list(set(rdf7.columns) - {'AVG GRAMS'})], groupby_vars=['YEAR', 'DRUG CODE', 'BUSINESS ACTIVITY'], compare_cols=['TOTAL GRAMS', 'BUYERS'])
(returndict2, returnlist_unmatch) = returndict['5', '7']
assert all(returnlist_unmatch['YEAR'] == '2011')
for key in returndict2:
assert sum(returndict2[key]['YEAR'] == '2011') + sum(returndict2[key]['YEAR'] == '2014') == len(returndict2[key])
if reportlist == ['2', '3', '4']:
(rdf2, rdf3, rdf4) = (pd.DataFrame(), pd.DataFrame(), pd.DataFrame())
(rdf2, rdf3, rdf4) = (Reports_dict['2'].copy(), Reports_dict['3'].copy(), Reports_dict['4'].copy())
target_us_row_title = list(set(rdf3['GEO']) - set(statelist))[0]
for item in set(rdf2['GEO']) - set(statelist):
rdf2['GEO'].loc[rdf2['GEO'] == item] = target_us_row_title
for item in set(rdf3['GEO']) - set(statelist):
rdf3['GEO'].loc[rdf3['GEO'] == item] = target_us_row_title
for item in set(rdf4['STATE']) - set(statelist):
rdf4['STATE'].loc[rdf4['STATE'] == item] = target_us_row_title
assert all(rdf3.columns == rdf2.columns)
for col in ['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL']:
rdf3[col] = rdf3[col].apply(lambda x: float(str(x).replace(',', '')))
rdf3[col][rdf3['YEAR'] == '2011'] = rdf3[col] / 1000
rdf4['POP'] = np.nan
if '2000 POP' in list(rdf4.columns):
rdf4['POP'][rdf4['2000 POP'].notnull()] = rdf4['2000 POP']
if '2010 POP' in list(rdf4.columns):
rdf4['POP'][rdf4['2010 POP'].notnull()] = rdf4['2010 POP']
df_pop = pd.DataFrame([(float(str(x[0]).replace(',', '')), x[1], x[2]) for x in list(set([tuple(x) for x in rdf4[['POP', 'YEAR', 'STATE']].values]))])
df_pop.columns = ['POP', 'YEAR', 'GEO']
rdf2 = pd.merge(rdf2, df_pop, on=['GEO', 'YEAR'])
merged = pd.merge(rdf2, rdf3, how='outer', on=['DRUG_CODE', 'GEO', 'YEAR'], indicator=True)
missing_entries = merged[merged['_merge'] == 'right_only']
returndict['5', '7', 'missing_entries'] = missing_entries
merged2 = pd.merge(rdf2, rdf3, how='inner', on=['DRUG_CODE', 'GEO', 'YEAR'])
for s1 in range(1, 5):
d = check_divide(merged2, 'Q%s_x' % s1, 'POP', 'Q%s_y' % s1, 100000)
assert set(list(d.values())[0]['GEO']) == {target_us_row_title}
returndict['5', '7', 'Q%s' % s1] = d
if reportlist == ['1', '2']:
(rdf1, rdf2) = (pd.DataFrame(), pd.DataFrame())
(rdf1, rdf2) = (Reports_dict['1'].copy(), Reports_dict['2'].copy())
for tot in [x for x in set(rdf1['GEO']) if x.isdigit() is False]:
rdf1 = rdf1.loc[rdf1['GEO'] != tot]
for us in [x for x in set(rdf2['GEO']) if x not in statelist]:
rdf2 = rdf2.loc[rdf2['GEO'] != us]
rdf2['STATE'] = rdf2['GEO']
returndict['1', '2'] = groupby_across_sheets(big_df=rdf1, small_df=rdf2, groupby_vars=['YEAR', 'DRUG_CODE', 'STATE'], compare_cols=['Q1', 'Q2', 'Q3', 'Q4', 'TOTAL'])
if reportlist == ['2', '5']:
(rdf2, rdf5) = (pd.DataFrame(), pd.DataFrame())
(rdf2, rdf5) = (Reports_dict['2'].copy(), Reports_dict['5'].copy())
rdf2 = rdf2.loc[rdf2['GEO'] != 'UNITED STATES']
rdf2['STATE'] = rdf2['GEO']
rdf2['TOTAL GRAMS'] = rdf2['TOTAL']
rdf2['DRUG CODE'] = rdf2['DRUG_CODE']
returndict['2', '5'] = groupby_across_sheets(big_df=rdf5, small_df=rdf2, groupby_vars=['YEAR', 'DRUG CODE', 'STATE'], compare_cols=['TOTAL GRAMS'])
return returndict
def groupby_across_sheets(bigdf, smalldf, groupby_vars, compare_cols):
big_df = bigdf.copy()
small_df = smalldf.copy()
returndict2 = {}
for col in compare_cols:
big_df[col] = big_df[col].apply(lambda x: float(str(x).replace(',', '')))
small_df[col] = small_df[col].apply(lambda x: float(str(x).replace(',', '')))
big_df_test = big_df.groupby(groupby_vars).sum()
merged_rdf = pd.merge(big_df_test, small_df, right_on=groupby_vars, left_index=True, how='outer', indicator=True)
returnlist_unmatch = merged_rdf[merged_rdf['_merge'] != 'both']
merged_rdf = pd.merge(big_df_test, small_df, right_on=groupby_vars, left_index=True, how='inner', indicator=True)
for col in compare_cols:
colx = col + '_x'
coly = col + '_y'
df_nonmatch = merged_rdf[merged_rdf.apply(lambda x: are_close(x[colx], x[coly], 0.015) is False, axis=1)]
if len(df_nonmatch) > 0:
returndict2[col] = df_nonmatch
return (returndict2, returnlist_unmatch)
def check_add_down(rdf, tot_col, columns_to_add, groupby_vars):
rdfa = pd.DataFrame()
rdfa = rdf.copy()
tot_loc = {tot_col: [x for x in list(set(rdfa[tot_col].tolist())) if x in totallist]}
add_down_dont_match = {}
tot_strings = list(tot_loc.values())[0]
for col in columns_to_add:
rdfa[col] = rdfa[col].apply(lambda x: float(x.replace(',', '')))
rdfa['bin'] = rdfa[list(tot_loc.keys())[0]].apply(lambda x: x in tot_strings)
rdf_test = rdfa.groupby(groupby_vars + ['bin']).sum()
pctc = pd.DataFrame(round(abs(rdf_test.groupby(groupby_vars).pct_change())))
totdiv = 0
for year in set(rdfa['YEAR']):
div = len(set(rdfa['bin'][rdfa['YEAR'] == year]))
for v in groupby_vars:
div = div * len(set(rdfa[v][rdfa['YEAR'] == year]))
totdiv = totdiv + div
entries = len(pctc) / totdiv
assert 0.6 <= entries <= 1
for column_to_add in columns_to_add:
r = rdf_test.loc[pctc[column_to_add].notnull() & pctc[column_to_add] != 0]
if len(r) > 0:
add_down_dont_match[column_to_add] = r
return add_down_dont_match
def check_add_across(rdf, columns_to_add, col_tot):
rdfa = pd.DataFrame()
rdfa = rdf.copy()
add_across_dont_match = {}
for col in columns_to_add + col_tot:
rdfa[col] = rdfa[col].apply(lambda x: float(x.replace(',', '')))
r = rdfa.loc[round(rdfa[columns_to_add].sum(axis=1) - rdfa[col_tot[0]], 1) != 0]
if len(r) > 0:
add_across_dont_match[[tuple(columns_to_add + col_tot)]] = r
return add_across_dont_match
def check_divide(rdf, top_divisor, bot_divisor, equals_to, multiplier=1, tolerance=0.02):
rdfa = pd.DataFrame()
rdfa = rdf.copy()
divide_dont_match = {}
for col in [top_divisor, bot_divisor, equals_to]:
rdfa[col] = rdfa[col].apply(lambda x: float(str(x).replace(',', '')))
rdfa['CALCULATED'] = rdfa.apply(lambda x: custom_lambda(x, top_divisor, bot_divisor, equals_to, multiplier, 'calc'), axis=1)
rdfa['BOOL'] = rdfa.apply(lambda x: custom_lambda(x, top_divisor, bot_divisor, equals_to, multiplier, 'bool'), axis=1)
rdfa['CLOSE'] = rdfa.apply(lambda x: custom_lambda(x, top_divisor, bot_divisor, equals_to, multiplier, tolerance), axis=1)
if len(rdfa.loc[-rdfa['CLOSE']]) > 0:
columns_list = [x for x in list(rdfa.columns) if '_x' not in x and '_y' not in x or x in [top_divisor, bot_divisor, equals_to]]
divide_dont_match[top_divisor, bot_divisor, equals_to] = rdfa[columns_list].loc[-rdfa['CLOSE']]
return divide_dont_match
def custom_lambda(df, top_divisor, bot_divisor, equals_to, multiplier, returntype):
calculated = multiplier * df[top_divisor] / df[bot_divisor]
res = are_equal(calculated, df[equals_to])
if returntype == 'bool':
return res[0]
if type(returntype) is float:
return are_close(calculated, df[equals_to], returntype)
return res[1]
def are_equal(val_compare, reference_val):
r = return_round(reference_val)
val = round(val_compare, r)
comp = round(reference_val, r)
val1 = round(val_compare, r + 1)
comp1 = round(reference_val, r + 1)
return (val == comp or val1 == comp1, (val, comp, val1, comp1))
def are_close(val_compare, reference_val, tolerance):
(b, (val, comp, val1, comp1)) = are_equal(val_compare, reference_val)
if not b:
if reference_val != 0:
return abs(val_compare - reference_val) / reference_val <= tolerance or abs(val - comp) <= tolerance
else:
return abs(val - comp) <= tolerance
return b
def return_round(x):
if x > 0:
if int(math.log10(x)) == math.log10(x) and int(math.log10(x)) < 0:
magn = int(math.log10(x)) + 1
else:
magn = int(math.log10(x))
if magn < 0:
return -(magn - 1)
elif magn == 0 or magn == 1 or magn == 2:
return 2
elif magn == 3 or magn == 4:
return 1
else:
return 0
elif x == 0:
return 2
else:
raise exception("shouldn't be less than zero") |
N = int(input())
X = 1
K = 0
while X <= N:
X *= 2
K += 1
print(max(0, K - 1)) | n = int(input())
x = 1
k = 0
while X <= N:
x *= 2
k += 1
print(max(0, K - 1)) |
data_in = [3.0,
1.0,
0.0,
0.0,
1.0,
6.0,
1.0,
0.0,
1.0,
0.0,
3280.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
1.0] | data_in = [3.0, 1.0, 0.0, 0.0, 1.0, 6.0, 1.0, 0.0, 1.0, 0.0, 3280.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0] |
# Parsers
# Parse initial fields name to normalized form.
parse_name = lambda name: str(name).replace(' ', '_').lower()
| parse_name = lambda name: str(name).replace(' ', '_').lower() |
# Oct 2021
# Class for extraction.py
class FoundExpression:
def __init__(self, expression: str,
file: str,
language: str,
line_no: int):
self.expression = expression
self.language = language
self.file = file
self.line_no = line_no | class Foundexpression:
def __init__(self, expression: str, file: str, language: str, line_no: int):
self.expression = expression
self.language = language
self.file = file
self.line_no = line_no |
class Solution:
def minOperations(self, nums: List[int]) -> int:
n = len(nums)
ans = n
nums = sorted(set(nums))
for i, start in enumerate(nums):
end = start + n - 1
index = bisect_right(nums, end)
uniqueLength = index - i
ans = min(ans, n - uniqueLength)
return ans
| class Solution:
def min_operations(self, nums: List[int]) -> int:
n = len(nums)
ans = n
nums = sorted(set(nums))
for (i, start) in enumerate(nums):
end = start + n - 1
index = bisect_right(nums, end)
unique_length = index - i
ans = min(ans, n - uniqueLength)
return ans |
data = (
'jun', # 0x00
'junj', # 0x01
'junh', # 0x02
'jud', # 0x03
'jul', # 0x04
'julg', # 0x05
'julm', # 0x06
'julb', # 0x07
'juls', # 0x08
'jult', # 0x09
'julp', # 0x0a
'julh', # 0x0b
'jum', # 0x0c
'jub', # 0x0d
'jubs', # 0x0e
'jus', # 0x0f
'juss', # 0x10
'jung', # 0x11
'juj', # 0x12
'juc', # 0x13
'juk', # 0x14
'jut', # 0x15
'jup', # 0x16
'juh', # 0x17
'jweo', # 0x18
'jweog', # 0x19
'jweogg', # 0x1a
'jweogs', # 0x1b
'jweon', # 0x1c
'jweonj', # 0x1d
'jweonh', # 0x1e
'jweod', # 0x1f
'jweol', # 0x20
'jweolg', # 0x21
'jweolm', # 0x22
'jweolb', # 0x23
'jweols', # 0x24
'jweolt', # 0x25
'jweolp', # 0x26
'jweolh', # 0x27
'jweom', # 0x28
'jweob', # 0x29
'jweobs', # 0x2a
'jweos', # 0x2b
'jweoss', # 0x2c
'jweong', # 0x2d
'jweoj', # 0x2e
'jweoc', # 0x2f
'jweok', # 0x30
'jweot', # 0x31
'jweop', # 0x32
'jweoh', # 0x33
'jwe', # 0x34
'jweg', # 0x35
'jwegg', # 0x36
'jwegs', # 0x37
'jwen', # 0x38
'jwenj', # 0x39
'jwenh', # 0x3a
'jwed', # 0x3b
'jwel', # 0x3c
'jwelg', # 0x3d
'jwelm', # 0x3e
'jwelb', # 0x3f
'jwels', # 0x40
'jwelt', # 0x41
'jwelp', # 0x42
'jwelh', # 0x43
'jwem', # 0x44
'jweb', # 0x45
'jwebs', # 0x46
'jwes', # 0x47
'jwess', # 0x48
'jweng', # 0x49
'jwej', # 0x4a
'jwec', # 0x4b
'jwek', # 0x4c
'jwet', # 0x4d
'jwep', # 0x4e
'jweh', # 0x4f
'jwi', # 0x50
'jwig', # 0x51
'jwigg', # 0x52
'jwigs', # 0x53
'jwin', # 0x54
'jwinj', # 0x55
'jwinh', # 0x56
'jwid', # 0x57
'jwil', # 0x58
'jwilg', # 0x59
'jwilm', # 0x5a
'jwilb', # 0x5b
'jwils', # 0x5c
'jwilt', # 0x5d
'jwilp', # 0x5e
'jwilh', # 0x5f
'jwim', # 0x60
'jwib', # 0x61
'jwibs', # 0x62
'jwis', # 0x63
'jwiss', # 0x64
'jwing', # 0x65
'jwij', # 0x66
'jwic', # 0x67
'jwik', # 0x68
'jwit', # 0x69
'jwip', # 0x6a
'jwih', # 0x6b
'jyu', # 0x6c
'jyug', # 0x6d
'jyugg', # 0x6e
'jyugs', # 0x6f
'jyun', # 0x70
'jyunj', # 0x71
'jyunh', # 0x72
'jyud', # 0x73
'jyul', # 0x74
'jyulg', # 0x75
'jyulm', # 0x76
'jyulb', # 0x77
'jyuls', # 0x78
'jyult', # 0x79
'jyulp', # 0x7a
'jyulh', # 0x7b
'jyum', # 0x7c
'jyub', # 0x7d
'jyubs', # 0x7e
'jyus', # 0x7f
'jyuss', # 0x80
'jyung', # 0x81
'jyuj', # 0x82
'jyuc', # 0x83
'jyuk', # 0x84
'jyut', # 0x85
'jyup', # 0x86
'jyuh', # 0x87
'jeu', # 0x88
'jeug', # 0x89
'jeugg', # 0x8a
'jeugs', # 0x8b
'jeun', # 0x8c
'jeunj', # 0x8d
'jeunh', # 0x8e
'jeud', # 0x8f
'jeul', # 0x90
'jeulg', # 0x91
'jeulm', # 0x92
'jeulb', # 0x93
'jeuls', # 0x94
'jeult', # 0x95
'jeulp', # 0x96
'jeulh', # 0x97
'jeum', # 0x98
'jeub', # 0x99
'jeubs', # 0x9a
'jeus', # 0x9b
'jeuss', # 0x9c
'jeung', # 0x9d
'jeuj', # 0x9e
'jeuc', # 0x9f
'jeuk', # 0xa0
'jeut', # 0xa1
'jeup', # 0xa2
'jeuh', # 0xa3
'jyi', # 0xa4
'jyig', # 0xa5
'jyigg', # 0xa6
'jyigs', # 0xa7
'jyin', # 0xa8
'jyinj', # 0xa9
'jyinh', # 0xaa
'jyid', # 0xab
'jyil', # 0xac
'jyilg', # 0xad
'jyilm', # 0xae
'jyilb', # 0xaf
'jyils', # 0xb0
'jyilt', # 0xb1
'jyilp', # 0xb2
'jyilh', # 0xb3
'jyim', # 0xb4
'jyib', # 0xb5
'jyibs', # 0xb6
'jyis', # 0xb7
'jyiss', # 0xb8
'jying', # 0xb9
'jyij', # 0xba
'jyic', # 0xbb
'jyik', # 0xbc
'jyit', # 0xbd
'jyip', # 0xbe
'jyih', # 0xbf
'ji', # 0xc0
'jig', # 0xc1
'jigg', # 0xc2
'jigs', # 0xc3
'jin', # 0xc4
'jinj', # 0xc5
'jinh', # 0xc6
'jid', # 0xc7
'jil', # 0xc8
'jilg', # 0xc9
'jilm', # 0xca
'jilb', # 0xcb
'jils', # 0xcc
'jilt', # 0xcd
'jilp', # 0xce
'jilh', # 0xcf
'jim', # 0xd0
'jib', # 0xd1
'jibs', # 0xd2
'jis', # 0xd3
'jiss', # 0xd4
'jing', # 0xd5
'jij', # 0xd6
'jic', # 0xd7
'jik', # 0xd8
'jit', # 0xd9
'jip', # 0xda
'jih', # 0xdb
'jja', # 0xdc
'jjag', # 0xdd
'jjagg', # 0xde
'jjags', # 0xdf
'jjan', # 0xe0
'jjanj', # 0xe1
'jjanh', # 0xe2
'jjad', # 0xe3
'jjal', # 0xe4
'jjalg', # 0xe5
'jjalm', # 0xe6
'jjalb', # 0xe7
'jjals', # 0xe8
'jjalt', # 0xe9
'jjalp', # 0xea
'jjalh', # 0xeb
'jjam', # 0xec
'jjab', # 0xed
'jjabs', # 0xee
'jjas', # 0xef
'jjass', # 0xf0
'jjang', # 0xf1
'jjaj', # 0xf2
'jjac', # 0xf3
'jjak', # 0xf4
'jjat', # 0xf5
'jjap', # 0xf6
'jjah', # 0xf7
'jjae', # 0xf8
'jjaeg', # 0xf9
'jjaegg', # 0xfa
'jjaegs', # 0xfb
'jjaen', # 0xfc
'jjaenj', # 0xfd
'jjaenh', # 0xfe
'jjaed', # 0xff
)
| data = ('jun', 'junj', 'junh', 'jud', 'jul', 'julg', 'julm', 'julb', 'juls', 'jult', 'julp', 'julh', 'jum', 'jub', 'jubs', 'jus', 'juss', 'jung', 'juj', 'juc', 'juk', 'jut', 'jup', 'juh', 'jweo', 'jweog', 'jweogg', 'jweogs', 'jweon', 'jweonj', 'jweonh', 'jweod', 'jweol', 'jweolg', 'jweolm', 'jweolb', 'jweols', 'jweolt', 'jweolp', 'jweolh', 'jweom', 'jweob', 'jweobs', 'jweos', 'jweoss', 'jweong', 'jweoj', 'jweoc', 'jweok', 'jweot', 'jweop', 'jweoh', 'jwe', 'jweg', 'jwegg', 'jwegs', 'jwen', 'jwenj', 'jwenh', 'jwed', 'jwel', 'jwelg', 'jwelm', 'jwelb', 'jwels', 'jwelt', 'jwelp', 'jwelh', 'jwem', 'jweb', 'jwebs', 'jwes', 'jwess', 'jweng', 'jwej', 'jwec', 'jwek', 'jwet', 'jwep', 'jweh', 'jwi', 'jwig', 'jwigg', 'jwigs', 'jwin', 'jwinj', 'jwinh', 'jwid', 'jwil', 'jwilg', 'jwilm', 'jwilb', 'jwils', 'jwilt', 'jwilp', 'jwilh', 'jwim', 'jwib', 'jwibs', 'jwis', 'jwiss', 'jwing', 'jwij', 'jwic', 'jwik', 'jwit', 'jwip', 'jwih', 'jyu', 'jyug', 'jyugg', 'jyugs', 'jyun', 'jyunj', 'jyunh', 'jyud', 'jyul', 'jyulg', 'jyulm', 'jyulb', 'jyuls', 'jyult', 'jyulp', 'jyulh', 'jyum', 'jyub', 'jyubs', 'jyus', 'jyuss', 'jyung', 'jyuj', 'jyuc', 'jyuk', 'jyut', 'jyup', 'jyuh', 'jeu', 'jeug', 'jeugg', 'jeugs', 'jeun', 'jeunj', 'jeunh', 'jeud', 'jeul', 'jeulg', 'jeulm', 'jeulb', 'jeuls', 'jeult', 'jeulp', 'jeulh', 'jeum', 'jeub', 'jeubs', 'jeus', 'jeuss', 'jeung', 'jeuj', 'jeuc', 'jeuk', 'jeut', 'jeup', 'jeuh', 'jyi', 'jyig', 'jyigg', 'jyigs', 'jyin', 'jyinj', 'jyinh', 'jyid', 'jyil', 'jyilg', 'jyilm', 'jyilb', 'jyils', 'jyilt', 'jyilp', 'jyilh', 'jyim', 'jyib', 'jyibs', 'jyis', 'jyiss', 'jying', 'jyij', 'jyic', 'jyik', 'jyit', 'jyip', 'jyih', 'ji', 'jig', 'jigg', 'jigs', 'jin', 'jinj', 'jinh', 'jid', 'jil', 'jilg', 'jilm', 'jilb', 'jils', 'jilt', 'jilp', 'jilh', 'jim', 'jib', 'jibs', 'jis', 'jiss', 'jing', 'jij', 'jic', 'jik', 'jit', 'jip', 'jih', 'jja', 'jjag', 'jjagg', 'jjags', 'jjan', 'jjanj', 'jjanh', 'jjad', 'jjal', 'jjalg', 'jjalm', 'jjalb', 'jjals', 'jjalt', 'jjalp', 'jjalh', 'jjam', 'jjab', 'jjabs', 'jjas', 'jjass', 'jjang', 'jjaj', 'jjac', 'jjak', 'jjat', 'jjap', 'jjah', 'jjae', 'jjaeg', 'jjaegg', 'jjaegs', 'jjaen', 'jjaenj', 'jjaenh', 'jjaed') |
def calculate_area(side_length=10):
print(f"The area of a square with sides of length {side_length} is {side_length**2}.")
length=int(input("Enter side length: "))
if length<=0:
calculate_area(10)
else:
calculate_area(length) | def calculate_area(side_length=10):
print(f'The area of a square with sides of length {side_length} is {side_length ** 2}.')
length = int(input('Enter side length: '))
if length <= 0:
calculate_area(10)
else:
calculate_area(length) |
def find_max(num1, num2):
max_num=-1
if num2> num1:
data = range(num1,num2+1)
main_list = []
for x in data:
b = str(x)
if x < 0:
b = str(x*-1)
sx = list(map(int,list(b)))
if len(sx)==2 and sum(sx)%3==0 and x%5==0:
main_list.append(x)
if len(main_list)!=0:
return max(main_list)
return max_num
#Provide different values for num1 and num2 and test your program.
max_num=find_max(10,100)
print(max_num) | def find_max(num1, num2):
max_num = -1
if num2 > num1:
data = range(num1, num2 + 1)
main_list = []
for x in data:
b = str(x)
if x < 0:
b = str(x * -1)
sx = list(map(int, list(b)))
if len(sx) == 2 and sum(sx) % 3 == 0 and (x % 5 == 0):
main_list.append(x)
if len(main_list) != 0:
return max(main_list)
return max_num
max_num = find_max(10, 100)
print(max_num) |
def calc():
numOne = int(input("What is the first number of your problem?"))
numTwo = int(input("What is the second number of your problem?"))
numThree = input("What type of Math Problem is it, Addition, Subtraction, Multiplication, Division, Remainder, or Exponents? Type exactly.")
if numThree == 'Addition':
print(numOne + numTwo)
elif numThree == 'Subtraction':
print(numOne - numTwo)
elif numThree == 'Multiplication':
print(numOne * numTwo)
elif numThree == 'Division':
print(numOne / numThree)
elif numThree == 'Remainder':
print(numOne % numTwo)
elif numThree == 'Exponents':
print(numOne ** numTwo)
else:
print("Not acceptable format. Restart program and run again.")
while True:
calc()
| def calc():
num_one = int(input('What is the first number of your problem?'))
num_two = int(input('What is the second number of your problem?'))
num_three = input('What type of Math Problem is it, Addition, Subtraction, Multiplication, Division, Remainder, or Exponents? Type exactly.')
if numThree == 'Addition':
print(numOne + numTwo)
elif numThree == 'Subtraction':
print(numOne - numTwo)
elif numThree == 'Multiplication':
print(numOne * numTwo)
elif numThree == 'Division':
print(numOne / numThree)
elif numThree == 'Remainder':
print(numOne % numTwo)
elif numThree == 'Exponents':
print(numOne ** numTwo)
else:
print('Not acceptable format. Restart program and run again.')
while True:
calc() |
x_min = -2
y_min = (-(modelparams['weights'][0] * x_min) / modelparams['weights'][1] -
(modelparams['bias'][0] / model_params['weights'][1]))
x_max = 2
y_max = (-(modelparams['weights'][0] * x_max) / modelparams['weights'][1] -
(modelparams['bias'][0] / modelparams['weights'][1]))
fig, ax = plt.subplots(1, 2, sharex=True, figsize=(7, 3))
ax[0].plot([x_min, x_max], [y_min, y_max])
ax[1].plot([x_min, x_max], [y_min, y_max])
ax[0].scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1],
label='class 0', marker='o')
ax[0].scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1],
label='class 1', marker='s')
ax[1].scatter(X_test[y_test == 0, 0], X_test[y_test == 0, 1],
label='class 0', marker='o')
ax[1].scatter(X_test[y_test == 1, 0], X_test[y_test == 1, 1],
label='class 1', marker='s')
ax[1].legend(loc='upper left')
plt.show()
# The TensorFlow model performs better on the test set just by random chance.
# Remember, the perceptron algorithm stops learning as soon as it classifies
# the training set perfectly.
# Possible explanations why there is a difference between the NumPy and
# TensorFlow outcomes could thus be numerical precision, or slight differences
# in our implementation.
| x_min = -2
y_min = -(modelparams['weights'][0] * x_min) / modelparams['weights'][1] - modelparams['bias'][0] / model_params['weights'][1]
x_max = 2
y_max = -(modelparams['weights'][0] * x_max) / modelparams['weights'][1] - modelparams['bias'][0] / modelparams['weights'][1]
(fig, ax) = plt.subplots(1, 2, sharex=True, figsize=(7, 3))
ax[0].plot([x_min, x_max], [y_min, y_max])
ax[1].plot([x_min, x_max], [y_min, y_max])
ax[0].scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1], label='class 0', marker='o')
ax[0].scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1], label='class 1', marker='s')
ax[1].scatter(X_test[y_test == 0, 0], X_test[y_test == 0, 1], label='class 0', marker='o')
ax[1].scatter(X_test[y_test == 1, 0], X_test[y_test == 1, 1], label='class 1', marker='s')
ax[1].legend(loc='upper left')
plt.show() |
for i in range(0, 201, 2):
print(i)
for i in range(0, 100, 3):
print(i)
| for i in range(0, 201, 2):
print(i)
for i in range(0, 100, 3):
print(i) |
# This is all about using strings
stg_1 = "this is the first message without a tab"
print(stg_1)
stg_2 = "\t this is the second message with a tab"
print(stg_2)
stg_3 = "this is another message with a newline\n"
print(stg_3)
| stg_1 = 'this is the first message without a tab'
print(stg_1)
stg_2 = '\t this is the second message with a tab'
print(stg_2)
stg_3 = 'this is another message with a newline\n'
print(stg_3) |
#!/usr/bin/python
# unicode.py
text = u'\u041b\u0435\u0432 \u041d\u0438\u043a\u043e\u043b\u0430\
\u0435\u0432\u0438\u0447 \u0422\u043e\u043b\u0441\u0442\u043e\u0439: \n\
\u0410\u043d\u043d\u0430 \u041a\u0430\u0440\u0435\u043d\u0438\u043d\u0430'
print (text)
| text = u'Лев Николаевич Толстой: \nАнна Каренина'
print(text) |
#to have some interaction
#we need an loop to look for actions
def setup():
size(400, 400)
#executed once
println("This is the setup. Executed once. Initiate things here")
#executed all the time waiting for infos
def draw():
#do the bakcground color transformation
noStroke()
fill(map(mouseX, width, 0, 0, width), 100)
rect(0, 0, width, height)
#do the circle color transformation
fill(mouseX)
#draw an ellipse
ellipse(mouseX, mouseY, 10, 10)
#print some infos
println("Frame number: " + frameCount)
print("mouse x: " + mouseX )
println(" mouse y: " + mouseY )
| def setup():
size(400, 400)
println('This is the setup. Executed once. Initiate things here')
def draw():
no_stroke()
fill(map(mouseX, width, 0, 0, width), 100)
rect(0, 0, width, height)
fill(mouseX)
ellipse(mouseX, mouseY, 10, 10)
println('Frame number: ' + frameCount)
print('mouse x: ' + mouseX)
println(' mouse y: ' + mouseY) |
##########################################################################
# NSAp - Copyright (C) CEA, 2013
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
options = (
("documentation_folder", {
"type": "string",
"default": None,
"help": "the folder containing the documentation of the project.",
"group": "piws",
"level": 1,
}),
("show_user_status", {
"type": "yn",
"default": True,
"help": "Show or not the user status link on the website.",
"group": "piws",
"level": 1,
}),
("ldap_groups_dn", {
"type": "string",
"default": None,
"help": "LDAP groups dn for LDAP groups synchronisation in CW <= 3.20,"
"otherwise not required.",
"group": "piws",
"level": 1,
}),
('apache-cleanup-session-time',
{'type': 'time',
'default': None,
'help': ('Duration of inactivity after which an apache de-authentication'
'will be triggered'),
'group': 'piws',
'level': 1,
}),
('deauth-redirect-url',
{'type': 'string',
'default': None,
'help': 'Redirection url after apache deauthentication occured.',
'group': 'piws',
'level': 1,
}),
("enable-cwusers-watcher", {
"type": "string",
"default": 'no',
"help": ("If 'yes', an email is sent (this email address has to be "
"set in the [MAIL] all-in-one section) when a CW user is "
"created or deleted."),
"group": "piws",
"level": 1,
}),
('enable-apache-logout',
{'type': 'yn',
'default': False,
'help': 'Enable Apache logout',
'group': 'piws',
'level': 1,
}),
('logo',
{'type': 'string',
'default': 'images/nsap.png',
'help': 'Navigation bar logo',
'group': 'piws',
'level': 1,
}),
('enable-upload',
{'type' : 'yn',
'default': False,
'help': ('If true enable the upload, ie relax security on user and '
'group entities. The database must be regenerated if this '
'option is modified.'),
'group': 'piws',
'level': 1,
}),
('authorized-upload-groups',
{'type': 'csv',
'default': 'users',
'help': 'A list of groups that will be able to upload data.',
'group': 'piws',
'level': 1,
}),
('share_group_uploads',
{'type': 'yn',
'default': False,
'help': 'If true, share uploads between the memebers of a group.',
'group': 'piws',
'level': 1,
}),
("metagen_url", {
"type": "string",
"default": None,
"help": "the URL to the metagen bioresource.",
"group": "piws",
"level": 1,
}),
("allow-inline-relations", {
"type": "yn",
"default": True,
"help": ("if False remove inline relations from the schema: inline "
"relations are not compatible with the massive store."),
"group": "piws",
"level": 1,
}),
)
| options = (('documentation_folder', {'type': 'string', 'default': None, 'help': 'the folder containing the documentation of the project.', 'group': 'piws', 'level': 1}), ('show_user_status', {'type': 'yn', 'default': True, 'help': 'Show or not the user status link on the website.', 'group': 'piws', 'level': 1}), ('ldap_groups_dn', {'type': 'string', 'default': None, 'help': 'LDAP groups dn for LDAP groups synchronisation in CW <= 3.20,otherwise not required.', 'group': 'piws', 'level': 1}), ('apache-cleanup-session-time', {'type': 'time', 'default': None, 'help': 'Duration of inactivity after which an apache de-authenticationwill be triggered', 'group': 'piws', 'level': 1}), ('deauth-redirect-url', {'type': 'string', 'default': None, 'help': 'Redirection url after apache deauthentication occured.', 'group': 'piws', 'level': 1}), ('enable-cwusers-watcher', {'type': 'string', 'default': 'no', 'help': "If 'yes', an email is sent (this email address has to be set in the [MAIL] all-in-one section) when a CW user is created or deleted.", 'group': 'piws', 'level': 1}), ('enable-apache-logout', {'type': 'yn', 'default': False, 'help': 'Enable Apache logout', 'group': 'piws', 'level': 1}), ('logo', {'type': 'string', 'default': 'images/nsap.png', 'help': 'Navigation bar logo', 'group': 'piws', 'level': 1}), ('enable-upload', {'type': 'yn', 'default': False, 'help': 'If true enable the upload, ie relax security on user and group entities. The database must be regenerated if this option is modified.', 'group': 'piws', 'level': 1}), ('authorized-upload-groups', {'type': 'csv', 'default': 'users', 'help': 'A list of groups that will be able to upload data.', 'group': 'piws', 'level': 1}), ('share_group_uploads', {'type': 'yn', 'default': False, 'help': 'If true, share uploads between the memebers of a group.', 'group': 'piws', 'level': 1}), ('metagen_url', {'type': 'string', 'default': None, 'help': 'the URL to the metagen bioresource.', 'group': 'piws', 'level': 1}), ('allow-inline-relations', {'type': 'yn', 'default': True, 'help': 'if False remove inline relations from the schema: inline relations are not compatible with the massive store.', 'group': 'piws', 'level': 1})) |
class Command:
def __init__(self, name, desc="", args=[]):
self.name = name
self.desc = desc
self.args = args
| class Command:
def __init__(self, name, desc='', args=[]):
self.name = name
self.desc = desc
self.args = args |
n1,n2=map(int,input().split())
a=[]
for i in range(n2):
a.append(list(map(float,input().split())))
for i in zip(*a):
print(sum(i)/n2) | (n1, n2) = map(int, input().split())
a = []
for i in range(n2):
a.append(list(map(float, input().split())))
for i in zip(*a):
print(sum(i) / n2) |
def read_txt_file_str(filename):
f=open('text_files/'+filename, "r")
contents=f.read()
f.close()
return contents
def read_txt_file_list(filename):
f=open('text_files/'+filename, "r")
contents=f.readlines()
f.close()
return contents | def read_txt_file_str(filename):
f = open('text_files/' + filename, 'r')
contents = f.read()
f.close()
return contents
def read_txt_file_list(filename):
f = open('text_files/' + filename, 'r')
contents = f.readlines()
f.close()
return contents |
# Author: Jocelino F.G.
n = int(input())
vetor = [n]
dobro = n
for i in range(0, 10):
dobro = dobro * 2
vetor.append(dobro)
print("N[{}] = {}".format(i, vetor[i]))
| n = int(input())
vetor = [n]
dobro = n
for i in range(0, 10):
dobro = dobro * 2
vetor.append(dobro)
print('N[{}] = {}'.format(i, vetor[i])) |
def is_even(number):
return number % 2 == 0
| def is_even(number):
return number % 2 == 0 |
class InvalidOperationError(BaseException):
pass
class Node():
def __init__(self, value, next=None):
self.value = value
self.next = next
class Stack():
def __init__(self, node=None):
self.top = node
def __len__(self):
count = 0
curr = self.top
while curr:
count += 1
curr = curr.next
return count
def push(self, value):
node = Node(value)
node.next = self.top
self.top = node
def pop(self):
if self.is_empty():
raise InvalidOperationError("Method not allowed on empty collection")
else:
node = self.top.value
self.top = self.top.next
# node.next = None
return node
def peek(self):
if self.is_empty():
raise InvalidOperationError("Method not allowed on empty collection")
return self.top.value
def is_empty(self):
return self.top is None
class Queue():
def __init__(self):
self.front = None
self.rear = None
def enqueue(self, value):
node = Node(value)
if not self.front:
self.front, self.rear = node, node
else:
self.rear.next = node
self.rear = node
def dequeue(self):
if self.is_empty():
raise InvalidOperationError("Method not allowed on empty collection")
node = self.front
self.front = self.front.next
return node.value
def peek(self):
if self.is_empty():
raise InvalidOperationError("Method not allowed on empty collection")
return self.front.value
def is_empty(self):
if not self.front:
return True
| class Invalidoperationerror(BaseException):
pass
class Node:
def __init__(self, value, next=None):
self.value = value
self.next = next
class Stack:
def __init__(self, node=None):
self.top = node
def __len__(self):
count = 0
curr = self.top
while curr:
count += 1
curr = curr.next
return count
def push(self, value):
node = node(value)
node.next = self.top
self.top = node
def pop(self):
if self.is_empty():
raise invalid_operation_error('Method not allowed on empty collection')
else:
node = self.top.value
self.top = self.top.next
return node
def peek(self):
if self.is_empty():
raise invalid_operation_error('Method not allowed on empty collection')
return self.top.value
def is_empty(self):
return self.top is None
class Queue:
def __init__(self):
self.front = None
self.rear = None
def enqueue(self, value):
node = node(value)
if not self.front:
(self.front, self.rear) = (node, node)
else:
self.rear.next = node
self.rear = node
def dequeue(self):
if self.is_empty():
raise invalid_operation_error('Method not allowed on empty collection')
node = self.front
self.front = self.front.next
return node.value
def peek(self):
if self.is_empty():
raise invalid_operation_error('Method not allowed on empty collection')
return self.front.value
def is_empty(self):
if not self.front:
return True |
# Given
x = 10000.0
y = 3.0
print(x / y)
print(10000 / 3)
# What is happening?
# Given
print(x - 1 / y)
print((x - 1) / y)
# What is happening?
# Given
x = 'foo'
y = 'bar'
# Create 'foobar' using x and y
s = x + y
print(s)
# Create 'foo -> bar' using x and y
print(x + " -> " + y)
# Given
x = 'hello world'
# from x create 'HELLO WORLD'
print(x.upper())
# from x create 'hellX wXrld'
print(x.replace('o', 'X'))
# Given
x = 10000.0
y = 3.0
# print "10000 / 3 = 3333" using x and y
print("{x} / {y} = {z}".format(x=x, y=y, z=x/y))
# Given
s = ['hello', 'world']
# print 'helloworld'
print(s[0] + s[1])
# print 'hello world'
print(s[0] , s[1])
# print 'hello
print(s[0])
# world'
print(s[1])
# Given
x = "Monty Python and the Holy Grail"
# create the list ['Monty', 'Python', 'and', 'the', 'Holy', 'Grail']
print(x.split())
y = "one,two,three,four"
# create the list ['one', 'two', 'three', 'four'
print(y.split(','))
| x = 10000.0
y = 3.0
print(x / y)
print(10000 / 3)
print(x - 1 / y)
print((x - 1) / y)
x = 'foo'
y = 'bar'
s = x + y
print(s)
print(x + ' -> ' + y)
x = 'hello world'
print(x.upper())
print(x.replace('o', 'X'))
x = 10000.0
y = 3.0
print('{x} / {y} = {z}'.format(x=x, y=y, z=x / y))
s = ['hello', 'world']
print(s[0] + s[1])
print(s[0], s[1])
print(s[0])
print(s[1])
x = 'Monty Python and the Holy Grail'
print(x.split())
y = 'one,two,three,four'
print(y.split(',')) |
class Config:
def __init__(self):
self.data_dir = './data/'
self.data_path = self.data_dir + 'peot.txt'
self.pickle_path = self.data_dir + 'tang.npz'
self.load_path = './checkpoints/peot9.pt'
self.save_path = './checkpoints/peot9.pt'
self.do_train = False
self.do_test = False
self.do_predict = True
self.do_load_model = True
self.num_epoch = 40
self.batch_size = 128
self.lr = 1e-3
self.weight_decay = 1e-4
self.max_gen_len = 200
self.max_len = 125
self.embedding_dim = 300
self.hidden_dim = 256
| class Config:
def __init__(self):
self.data_dir = './data/'
self.data_path = self.data_dir + 'peot.txt'
self.pickle_path = self.data_dir + 'tang.npz'
self.load_path = './checkpoints/peot9.pt'
self.save_path = './checkpoints/peot9.pt'
self.do_train = False
self.do_test = False
self.do_predict = True
self.do_load_model = True
self.num_epoch = 40
self.batch_size = 128
self.lr = 0.001
self.weight_decay = 0.0001
self.max_gen_len = 200
self.max_len = 125
self.embedding_dim = 300
self.hidden_dim = 256 |
'''
@Author: Ofey Chan
@Date: 2020-03-03 19:23:15
@LastEditors: Ofey Chan
@LastEditTime: 2020-03-03 20:07:31
@Description: General permutation group class.
@Reference:
'''
| """
@Author: Ofey Chan
@Date: 2020-03-03 19:23:15
@LastEditors: Ofey Chan
@LastEditTime: 2020-03-03 20:07:31
@Description: General permutation group class.
@Reference:
""" |
# Forcing recursion for no good reason. But it passed so....
def solution_r(n):
if n <= 0:
return n
else:
if not n%3 or not n%5:
return n + solution_r(n-1)
else:
return solution_r(n-1)
def solution(number):
if not number:
return 0
return solution_r(number-1)
assert solution(10) == 23, "Oops, recursion is the devil"
| def solution_r(n):
if n <= 0:
return n
elif not n % 3 or not n % 5:
return n + solution_r(n - 1)
else:
return solution_r(n - 1)
def solution(number):
if not number:
return 0
return solution_r(number - 1)
assert solution(10) == 23, 'Oops, recursion is the devil' |
class Solution:
def minJumps(self, arr: List[int]) -> int:
graph = defaultdict(list)
for i in range(len(arr)):
graph[arr[i]].append(i)
visited = set()
src, dest = 0, len(arr) - 1
queue = deque()
queue.append((src, 0))
visited.add(src)
while queue:
node, dist = queue.popleft()
if node == dest:
return dist
for child in [node - 1, node + 1] + graph[arr[node]][::-1]:
if 0 <= child < len(arr) and child != node and child not in visited:
visited.add(child)
if child == dest:
return dist + 1
queue.append((child, dist + 1))
return -1
| class Solution:
def min_jumps(self, arr: List[int]) -> int:
graph = defaultdict(list)
for i in range(len(arr)):
graph[arr[i]].append(i)
visited = set()
(src, dest) = (0, len(arr) - 1)
queue = deque()
queue.append((src, 0))
visited.add(src)
while queue:
(node, dist) = queue.popleft()
if node == dest:
return dist
for child in [node - 1, node + 1] + graph[arr[node]][::-1]:
if 0 <= child < len(arr) and child != node and (child not in visited):
visited.add(child)
if child == dest:
return dist + 1
queue.append((child, dist + 1))
return -1 |
def transitions(y,x):
yield y+1,x
yield y,x+1
yield y-1,x
yield y,x-1
def valid_transitions(arr):
# print(arr)
Y = len(arr)
X = len(arr[0])
def _f(y0,x0):
for y,x in transitions(y0,x0):
if 0 <= y < Y and 0 <= x < X and arr[y][x] != "-":
yield y,x
return _f
def opp(player):
if player == "W":
return "B"
else:
return "W"
def dfs(board, init, visited, tran_fn, ans):
q = [(init, 'W')]
while q:
(y,x), player = q.pop()
if (y,x) not in visited:
visited.add((y,x))
ans[y][x] = player
for yn,xn in tran_fn(y,x):
# print((y,x), (yn,xn))
item = (yn,xn), opp(player)
q.append(item)
Y,X = [int(x) for x in input().split()]
board = []
for y in range(Y):
s = input()
board.append(s)
def run(board):
tran_fn = valid_transitions(board)
Y = len(board)
X = len(board[0])
ans = [["-" for _ in range(X)] for _ in range(Y)]
visited = set()
for y in range(Y):
for x in range(X):
if board[y][x] == '.':
dfs(board, (y,x), visited, tran_fn, ans)
ans = ["".join(xs) for xs in ans]
print("\n".join(ans))
# print(board)
run(board)
# print(list(valid_transitions(board)(0,0)))
| def transitions(y, x):
yield (y + 1, x)
yield (y, x + 1)
yield (y - 1, x)
yield (y, x - 1)
def valid_transitions(arr):
y = len(arr)
x = len(arr[0])
def _f(y0, x0):
for (y, x) in transitions(y0, x0):
if 0 <= y < Y and 0 <= x < X and (arr[y][x] != '-'):
yield (y, x)
return _f
def opp(player):
if player == 'W':
return 'B'
else:
return 'W'
def dfs(board, init, visited, tran_fn, ans):
q = [(init, 'W')]
while q:
((y, x), player) = q.pop()
if (y, x) not in visited:
visited.add((y, x))
ans[y][x] = player
for (yn, xn) in tran_fn(y, x):
item = ((yn, xn), opp(player))
q.append(item)
(y, x) = [int(x) for x in input().split()]
board = []
for y in range(Y):
s = input()
board.append(s)
def run(board):
tran_fn = valid_transitions(board)
y = len(board)
x = len(board[0])
ans = [['-' for _ in range(X)] for _ in range(Y)]
visited = set()
for y in range(Y):
for x in range(X):
if board[y][x] == '.':
dfs(board, (y, x), visited, tran_fn, ans)
ans = [''.join(xs) for xs in ans]
print('\n'.join(ans))
run(board) |
def insertShiftArray(arr, value):
mid = len(arr) // 2
new_arr = []
for i in range(0, mid):
new_arr.append(arr[i])
new_arr.append(value)
for i in range(mid, len(arr)):
new_arr.append(arr[i])
return new_arr
test = [1, 2, 3, 4, 5]
print(test)
print(insertShiftArray(test, 8))
test2 = [1,2,3,4]
print(test2, 8)
print(insertShiftArray(test2, 8))
| def insert_shift_array(arr, value):
mid = len(arr) // 2
new_arr = []
for i in range(0, mid):
new_arr.append(arr[i])
new_arr.append(value)
for i in range(mid, len(arr)):
new_arr.append(arr[i])
return new_arr
test = [1, 2, 3, 4, 5]
print(test)
print(insert_shift_array(test, 8))
test2 = [1, 2, 3, 4]
print(test2, 8)
print(insert_shift_array(test2, 8)) |
class Hamming:
def distance(self, first, second):
num_of_errors = 0
if type(first) != str or type(second) != str:
return "Wrong type of strands"
if len(first) != len(second):
return "Strands should be the same length"
for i in range(len(first)):
if first[i] != second[i]:
num_of_errors += 1
return num_of_errors
| class Hamming:
def distance(self, first, second):
num_of_errors = 0
if type(first) != str or type(second) != str:
return 'Wrong type of strands'
if len(first) != len(second):
return 'Strands should be the same length'
for i in range(len(first)):
if first[i] != second[i]:
num_of_errors += 1
return num_of_errors |
conditons = True
alcool = 0
gas = 0
disel = 0
while conditons :
T = int(input())
if T == 4:
conditons = False;
else:
if T == 1:
alcool +=1
if T == 2:
gas +=1
if T == 3:
disel +=1
print("MUITO OBRIGADO")
print(f"Alcool: {alcool}")
print(f"Gasolina: {gas}")
print(f"Diesel: {disel}") | conditons = True
alcool = 0
gas = 0
disel = 0
while conditons:
t = int(input())
if T == 4:
conditons = False
else:
if T == 1:
alcool += 1
if T == 2:
gas += 1
if T == 3:
disel += 1
print('MUITO OBRIGADO')
print(f'Alcool: {alcool}')
print(f'Gasolina: {gas}')
print(f'Diesel: {disel}') |
def estimator(data):
output = {'data':data, 'impact': {}, 'severeImpact': {}}
output['impact']['currentlyInfected'] = data['reportedCases'] * 10
output['severeImpact']['currentlyInfected'] = data['reportedCases'] * 50
if data['periodType'] == 'weeks':
data['timeToElapse'] = data['timeToElapse'] * 7
elif data['periodType'] == 'months':
data['timeToElapse'] = data['timeToElapse'] * 30
output['impact']['infectionsByRequestedTime'] = output['impact']['currentlyInfected'] * (2 ** (data['timeToElapse']//3))
output['severeImpact']['infectionsByRequestedTime'] = output['severeImpact']['currentlyInfected'] * (2 ** (data['timeToElapse']//3))
output['impact']['severeCasesByRequestedTime'] = int(15/100 * (output['impact']['infectionsByRequestedTime']))
output['severeImpact']['severeCasesByRequestedTime'] = int(15/100 * (output['severeImpact']['infectionsByRequestedTime']))
output['impact']['hospitalBedsByRequestedTime'] = int((35/100 * (data['totalHospitalBeds'])) - output['impact']['severeCasesByRequestedTime'])
output['severeImpact']['hospitalBedsByRequestedTime'] = int((35/100 * (data['totalHospitalBeds'])) - output['severeImpact']['severeCasesByRequestedTime'])
output['impact']['casesForICUByRequestedTime'] = int(5/100 * output['impact']['infectionsByRequestedTime'])
output['severeImpact']['casesForICUByRequestedTime'] = int(5/100 * output['severeImpact']['infectionsByRequestedTime'])
output['impact']['casesForVentilatorsByRequestedTime'] = int(2/100 * output['impact']['infectionsByRequestedTime'])
output['severeImpact']['casesForVentilatorsByRequestedTime'] = int(2/100 * output['severeImpact']['infectionsByRequestedTime'])
output['impact']['dollarsInFlight'] = int((output['impact']['infectionsByRequestedTime'] * data['region']['avgDailyIncomeInUSD'] * data['region']['avgDailyIncomePopulation']) /data['timeToElapse'])
output['severeImpact']['dollarsInFlight'] = int((output['severeImpact']['infectionsByRequestedTime'] * data['region']['avgDailyIncomeInUSD'] * data['region']['avgDailyIncomePopulation'])/data['timeToElapse'])
return output
| def estimator(data):
output = {'data': data, 'impact': {}, 'severeImpact': {}}
output['impact']['currentlyInfected'] = data['reportedCases'] * 10
output['severeImpact']['currentlyInfected'] = data['reportedCases'] * 50
if data['periodType'] == 'weeks':
data['timeToElapse'] = data['timeToElapse'] * 7
elif data['periodType'] == 'months':
data['timeToElapse'] = data['timeToElapse'] * 30
output['impact']['infectionsByRequestedTime'] = output['impact']['currentlyInfected'] * 2 ** (data['timeToElapse'] // 3)
output['severeImpact']['infectionsByRequestedTime'] = output['severeImpact']['currentlyInfected'] * 2 ** (data['timeToElapse'] // 3)
output['impact']['severeCasesByRequestedTime'] = int(15 / 100 * output['impact']['infectionsByRequestedTime'])
output['severeImpact']['severeCasesByRequestedTime'] = int(15 / 100 * output['severeImpact']['infectionsByRequestedTime'])
output['impact']['hospitalBedsByRequestedTime'] = int(35 / 100 * data['totalHospitalBeds'] - output['impact']['severeCasesByRequestedTime'])
output['severeImpact']['hospitalBedsByRequestedTime'] = int(35 / 100 * data['totalHospitalBeds'] - output['severeImpact']['severeCasesByRequestedTime'])
output['impact']['casesForICUByRequestedTime'] = int(5 / 100 * output['impact']['infectionsByRequestedTime'])
output['severeImpact']['casesForICUByRequestedTime'] = int(5 / 100 * output['severeImpact']['infectionsByRequestedTime'])
output['impact']['casesForVentilatorsByRequestedTime'] = int(2 / 100 * output['impact']['infectionsByRequestedTime'])
output['severeImpact']['casesForVentilatorsByRequestedTime'] = int(2 / 100 * output['severeImpact']['infectionsByRequestedTime'])
output['impact']['dollarsInFlight'] = int(output['impact']['infectionsByRequestedTime'] * data['region']['avgDailyIncomeInUSD'] * data['region']['avgDailyIncomePopulation'] / data['timeToElapse'])
output['severeImpact']['dollarsInFlight'] = int(output['severeImpact']['infectionsByRequestedTime'] * data['region']['avgDailyIncomeInUSD'] * data['region']['avgDailyIncomePopulation'] / data['timeToElapse'])
return output |
# -*- python -*-
load("@drake//tools/workspace:os.bzl", "determine_os")
def _impl(repository_ctx):
os_result = determine_os(repository_ctx)
if os_result.error != None:
fail(os_result.error)
if os_result.is_macos:
repository_ctx.symlink(
"/usr/local/opt/double-conversion/include",
"include",
)
repository_ctx.symlink(
Label(
"@drake//tools/workspace/double_conversion:package-macos.BUILD.bazel", # noqa
),
"BUILD.bazel",
)
elif os_result.is_ubuntu:
repository_ctx.symlink(
"/usr/include/double-conversion",
"include/double-conversion",
)
repository_ctx.symlink(
Label(
"@drake//tools/workspace/double_conversion:package-ubuntu.BUILD.bazel", # noqa
),
"BUILD.bazel",
)
else:
fail("Operating system is NOT supported", attr = os_result)
double_conversion_repository = repository_rule(
local = True,
configure = True,
implementation = _impl,
)
| load('@drake//tools/workspace:os.bzl', 'determine_os')
def _impl(repository_ctx):
os_result = determine_os(repository_ctx)
if os_result.error != None:
fail(os_result.error)
if os_result.is_macos:
repository_ctx.symlink('/usr/local/opt/double-conversion/include', 'include')
repository_ctx.symlink(label('@drake//tools/workspace/double_conversion:package-macos.BUILD.bazel'), 'BUILD.bazel')
elif os_result.is_ubuntu:
repository_ctx.symlink('/usr/include/double-conversion', 'include/double-conversion')
repository_ctx.symlink(label('@drake//tools/workspace/double_conversion:package-ubuntu.BUILD.bazel'), 'BUILD.bazel')
else:
fail('Operating system is NOT supported', attr=os_result)
double_conversion_repository = repository_rule(local=True, configure=True, implementation=_impl) |
def linear_search(array, y):
for i in range(len(array)):
if array[i] == y:
return i
return -1
arrSize=int(input("Enter Array Size"))
array=[]
print("Enter Array Elements")
for i in range(arrSize):
array.append(int(input()))
y = int(input("Enter Number you want to find =:-"))
result = linear_search(array, y)
if(result == -1):
print("Element not found")
else:
print("Element found at index: ", result)
| def linear_search(array, y):
for i in range(len(array)):
if array[i] == y:
return i
return -1
arr_size = int(input('Enter Array Size'))
array = []
print('Enter Array Elements')
for i in range(arrSize):
array.append(int(input()))
y = int(input('Enter Number you want to find =:-'))
result = linear_search(array, y)
if result == -1:
print('Element not found')
else:
print('Element found at index: ', result) |
# https://atcoder.jp/contests/abc194/tasks/abc194_b
N = int(input())
job_list = []
a_min_idx, b_min_idx = 0, 0
a_2nd, b_2nd = 0, 0
for i in range(N):
a, b = list(map(int, input().split()))
job_list.append([a, b])
if job_list[a_min_idx][0] > a:
a_2nd = a_min_idx
a_min_idx = i
if job_list[b_min_idx][1] > b:
b_2nd = b_min_idx
b_min_idx = i
ans = 0
if a_min_idx == b_min_idx:
ans = min(max(job_list[a_min_idx][0], job_list[b_2nd][1]), max(job_list[a_2nd][0], job_list[b_min_idx][1]),
job_list[a_min_idx][0] + job_list[b_min_idx][1])
print(ans)
exit()
ans = max(job_list[a_min_idx][0], job_list[b_min_idx][1])
print(ans)
| n = int(input())
job_list = []
(a_min_idx, b_min_idx) = (0, 0)
(a_2nd, b_2nd) = (0, 0)
for i in range(N):
(a, b) = list(map(int, input().split()))
job_list.append([a, b])
if job_list[a_min_idx][0] > a:
a_2nd = a_min_idx
a_min_idx = i
if job_list[b_min_idx][1] > b:
b_2nd = b_min_idx
b_min_idx = i
ans = 0
if a_min_idx == b_min_idx:
ans = min(max(job_list[a_min_idx][0], job_list[b_2nd][1]), max(job_list[a_2nd][0], job_list[b_min_idx][1]), job_list[a_min_idx][0] + job_list[b_min_idx][1])
print(ans)
exit()
ans = max(job_list[a_min_idx][0], job_list[b_min_idx][1])
print(ans) |
# https://practice.geeksforgeeks.org/problems/get-minimum-element-from-stack/1#
# Approach is to store an array containing stack elements and minEle in separate variable
# For push
# if minEle is None add element to s and assign minEle - element
# if minEle <= element add element to s
# else add 2*x-minEle in s and assign minEle the element value
# For pop
# if s is empty return -1
# if last element of s is >= minEle return and remove last element of s
# else return minEle assign minEle = 2*minEle - last value of S, and also remove last element
# assign minEle None if size of s is 0
# For getMin
# return minEle if not None else -1
class Stack:
def __init__(self):
self.s = []
self.minEle = None
def push(self, x):
if self.minEle is None:
self.minEle = x
self.s.append(x)
else:
if self.minEle <= x:
self.s.append(x)
else:
self.s.append(2 * x - self.minEle)
self.minEle = x
def pop(self):
val = -1
if len(self.s) != 0:
if self.s[-1] >= self.minEle:
val = self.s[-1]
else:
val = self.minEle
self.minEle = 2 * self.minEle - self.s[-1]
del self.s[-1]
if len(self.s) == 0:
self.minEle = None
return val
def getMin(self):
return self.minEle if self.minEle is not None else -1
if __name__ == '__main__':
t = int(input())
for _ in range(t):
q = int(input())
arr = [int(x) for x in input().split()]
stk = Stack()
qi = 0
qn = 1
while qn <= q:
qt = arr[qi]
if qt == 1:
stk.push(arr[qi + 1])
qi += 2
elif qt == 2:
print(stk.pop(), end=' ')
qi += 1
else:
print(stk.getMin(), end=' ')
qi += 1
qn += 1
print()
| class Stack:
def __init__(self):
self.s = []
self.minEle = None
def push(self, x):
if self.minEle is None:
self.minEle = x
self.s.append(x)
elif self.minEle <= x:
self.s.append(x)
else:
self.s.append(2 * x - self.minEle)
self.minEle = x
def pop(self):
val = -1
if len(self.s) != 0:
if self.s[-1] >= self.minEle:
val = self.s[-1]
else:
val = self.minEle
self.minEle = 2 * self.minEle - self.s[-1]
del self.s[-1]
if len(self.s) == 0:
self.minEle = None
return val
def get_min(self):
return self.minEle if self.minEle is not None else -1
if __name__ == '__main__':
t = int(input())
for _ in range(t):
q = int(input())
arr = [int(x) for x in input().split()]
stk = stack()
qi = 0
qn = 1
while qn <= q:
qt = arr[qi]
if qt == 1:
stk.push(arr[qi + 1])
qi += 2
elif qt == 2:
print(stk.pop(), end=' ')
qi += 1
else:
print(stk.getMin(), end=' ')
qi += 1
qn += 1
print() |
# MIT License
#
# Copyright (c) 2017 Matt Boyer
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
VALID_PAGE_SIZES = (1, 512, 1024, 2048, 4096, 8192, 16384, 32768)
SQLITE_TABLE_COLUMNS = {
'sqlite_master': ('type', 'name', 'tbl_name', 'rootpage', 'sql',),
'sqlite_sequence': ('name', 'seq',),
'sqlite_stat1': ('tbl', 'idx', 'stat',),
'sqlite_stat2': ('tbl', 'idx', 'sampleno', 'sample'),
'sqlite_stat3': ('tbl', 'idx', 'nEq', 'nLt', 'nDLt', 'sample'),
'sqlite_stat4': ('tbl', 'idx', 'nEq', 'nLt', 'nDLt', 'sample'),
}
# These are the integers used in ptrmap entries to designate the kind of page
# for which a given ptrmap entry holds a notional "child to parent" pointer
BTREE_ROOT_PAGE = 1
FREELIST_PAGE = 2
FIRST_OFLOW_PAGE = 3
NON_FIRST_OFLOW_PAGE = 4
BTREE_NONROOT_PAGE = 5
PTRMAP_PAGE_TYPES = (
BTREE_ROOT_PAGE,
FREELIST_PAGE,
FIRST_OFLOW_PAGE,
NON_FIRST_OFLOW_PAGE,
BTREE_NONROOT_PAGE,
)
OVERFLOW_PAGE_TYPES = (
FIRST_OFLOW_PAGE,
NON_FIRST_OFLOW_PAGE,
)
# These are identifiers used internally to keep track of page types *before*
# specialised objects can be instantiated
FREELIST_TRUNK_PAGE = 'freelist_trunk'
FREELIST_LEAF_PAGE = 'freelist_leaf'
PTRMAP_PAGE = 'ptrmap_page'
UNKNOWN_PAGE = 'unknown'
FREELIST_PAGE_TYPES = (
FREELIST_TRUNK_PAGE,
FREELIST_LEAF_PAGE,
)
NON_BTREE_PAGE_TYPES = (
FREELIST_TRUNK_PAGE,
FIRST_OFLOW_PAGE,
NON_FIRST_OFLOW_PAGE,
PTRMAP_PAGE,
)
| valid_page_sizes = (1, 512, 1024, 2048, 4096, 8192, 16384, 32768)
sqlite_table_columns = {'sqlite_master': ('type', 'name', 'tbl_name', 'rootpage', 'sql'), 'sqlite_sequence': ('name', 'seq'), 'sqlite_stat1': ('tbl', 'idx', 'stat'), 'sqlite_stat2': ('tbl', 'idx', 'sampleno', 'sample'), 'sqlite_stat3': ('tbl', 'idx', 'nEq', 'nLt', 'nDLt', 'sample'), 'sqlite_stat4': ('tbl', 'idx', 'nEq', 'nLt', 'nDLt', 'sample')}
btree_root_page = 1
freelist_page = 2
first_oflow_page = 3
non_first_oflow_page = 4
btree_nonroot_page = 5
ptrmap_page_types = (BTREE_ROOT_PAGE, FREELIST_PAGE, FIRST_OFLOW_PAGE, NON_FIRST_OFLOW_PAGE, BTREE_NONROOT_PAGE)
overflow_page_types = (FIRST_OFLOW_PAGE, NON_FIRST_OFLOW_PAGE)
freelist_trunk_page = 'freelist_trunk'
freelist_leaf_page = 'freelist_leaf'
ptrmap_page = 'ptrmap_page'
unknown_page = 'unknown'
freelist_page_types = (FREELIST_TRUNK_PAGE, FREELIST_LEAF_PAGE)
non_btree_page_types = (FREELIST_TRUNK_PAGE, FIRST_OFLOW_PAGE, NON_FIRST_OFLOW_PAGE, PTRMAP_PAGE) |
# #### We create a function cleanQ so we can do the cleaning and preperation of our data
# #### INPUT: String
# #### OUTPUT: Cleaned String
def cleanQ(query):
query = query.lower()
tokenizer = RegexpTokenizer(r'\w+')
tokens = tokenizer.tokenize(query)
stemmer=[ps.stem(i) for i in tokens]
filtered_Q = [w for w in stemmer if not w in stopwords.words('english')]
return filtered_Q
# #### We create a function computeTF so we can calculate the tf
# #### INPUT: Dictionary where the keys are the terms_id and the values are the frequencies of this term Id in the document
# #### OUTPUT: TF of the specific Term_id in the corresponding document
def computeTF(doc_words):
bow = 0
for k, v in doc_words.items():
bow = bow + v
tf_word = {}
for word, count in doc_words.items():
tf_word[word] = count / float(bow)
return tf_word
| def clean_q(query):
query = query.lower()
tokenizer = regexp_tokenizer('\\w+')
tokens = tokenizer.tokenize(query)
stemmer = [ps.stem(i) for i in tokens]
filtered_q = [w for w in stemmer if not w in stopwords.words('english')]
return filtered_Q
def compute_tf(doc_words):
bow = 0
for (k, v) in doc_words.items():
bow = bow + v
tf_word = {}
for (word, count) in doc_words.items():
tf_word[word] = count / float(bow)
return tf_word |
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
# OpneWinchPy : a library for controlling the Raspberry Pi's Winch
# Copyright (c) 2020 Mickael Gaillard <mick.gaillard@gmail.com>
__version__ = "0.1.0"
| __version__ = '0.1.0' |
lst = []
count_of_elements = int(input("How many elements want to store in list?"))
for i in range(count_of_elements):
element = input("Enter the element:")
lst.append(element)
print(lst)
| lst = []
count_of_elements = int(input('How many elements want to store in list?'))
for i in range(count_of_elements):
element = input('Enter the element:')
lst.append(element)
print(lst) |
class Solution:
def groupAnagrams(self, strs: List[str]) -> List[List[str]]:
ans = []
one_hot = {}
for word in strs:
mapping = [0 for _ in range(26)]
for char in word:
representation = ord(char)
mapping[representation % 26] += 1
mapping = tuple(mapping)
if mapping not in one_hot:
one_hot[mapping] = []
one_hot[mapping].append(word)
for key, values in one_hot.items():
ans.append(values)
return ans
| class Solution:
def group_anagrams(self, strs: List[str]) -> List[List[str]]:
ans = []
one_hot = {}
for word in strs:
mapping = [0 for _ in range(26)]
for char in word:
representation = ord(char)
mapping[representation % 26] += 1
mapping = tuple(mapping)
if mapping not in one_hot:
one_hot[mapping] = []
one_hot[mapping].append(word)
for (key, values) in one_hot.items():
ans.append(values)
return ans |
# -*- coding: utf-8 -*-
__author__ = 'Tommy Stallings'
__email__ = 'tommy.stallings2@gmail.com'
__version__ = '1.0'
| __author__ = 'Tommy Stallings'
__email__ = 'tommy.stallings2@gmail.com'
__version__ = '1.0' |
def GetChargeLevel():
return {'data': 42, 'error': 'NO_ERROR'}
def GetBatteryTemperature():
return {'data': 25.4, 'error': 'NO_ERROR'}
def GetBatteryVoltage():
return {'data': 3111, 'error': 'NO_ERROR'}
def GetBatteryCurrent():
return {'data': 800, 'error': 'NO_ERROR'}
def GetIoVoltage():
return {'data': 5432, 'error': 'NO_ERROR'}
def GetIoCurrent():
return {'data': 300, 'error': 'NO_ERROR'}
def GetStatus():
return {'data': {
'powerInput': 'adapter connected',
'powerInput5vIo': 'powered'
}, 'error': 'NO_ERROR'}
| def get_charge_level():
return {'data': 42, 'error': 'NO_ERROR'}
def get_battery_temperature():
return {'data': 25.4, 'error': 'NO_ERROR'}
def get_battery_voltage():
return {'data': 3111, 'error': 'NO_ERROR'}
def get_battery_current():
return {'data': 800, 'error': 'NO_ERROR'}
def get_io_voltage():
return {'data': 5432, 'error': 'NO_ERROR'}
def get_io_current():
return {'data': 300, 'error': 'NO_ERROR'}
def get_status():
return {'data': {'powerInput': 'adapter connected', 'powerInput5vIo': 'powered'}, 'error': 'NO_ERROR'} |
def message_replier(messages):
for message in messages:
userid = message.from_user.id
banlist = redisserver.sismember('zigzag_banlist', '{}'.format(userid))
if banlist:
return
if userid in messanger_list:
bot.reply_to(message, MESSANGER_LEAVE_MSG, parse_mode="Markdown")
messanger_list.remove(userid)
bot.send_message("-" + str(SUPPORT_GP), "New feedback!:")
bot.forward_message("-" + str(SUPPORT_GP), message.chat.id, message.message_id)
return
if REPLIER:
if message.text in reply_message_list:
bot.reply_to(message, reply_message_list.get(message.text), parse_mode="Markdown")
if message.text == "Send feedback":
bot.reply_to(message, MESSANGER_JOIN_MSG, parse_mode="Markdown")
messanger_list.append(userid)
return
if userid in in_chat_with_support:
bot.forward_message("-" + str(SUPPORT_GP), message.chat.id, message.message_id)
return
if message.from_user.id in ADMINS_IDS:
if message.chat.id == -SUPPORT_GP:
try:
bot.forward_message(message.reply_to_message.forward_from.id, message.chat.id, message.message_id)
bot.reply_to(message, "REPLY SENT")
except:
bot.reply_to(message, "ERROR SENDING?")
| def message_replier(messages):
for message in messages:
userid = message.from_user.id
banlist = redisserver.sismember('zigzag_banlist', '{}'.format(userid))
if banlist:
return
if userid in messanger_list:
bot.reply_to(message, MESSANGER_LEAVE_MSG, parse_mode='Markdown')
messanger_list.remove(userid)
bot.send_message('-' + str(SUPPORT_GP), 'New feedback!:')
bot.forward_message('-' + str(SUPPORT_GP), message.chat.id, message.message_id)
return
if REPLIER:
if message.text in reply_message_list:
bot.reply_to(message, reply_message_list.get(message.text), parse_mode='Markdown')
if message.text == 'Send feedback':
bot.reply_to(message, MESSANGER_JOIN_MSG, parse_mode='Markdown')
messanger_list.append(userid)
return
if userid in in_chat_with_support:
bot.forward_message('-' + str(SUPPORT_GP), message.chat.id, message.message_id)
return
if message.from_user.id in ADMINS_IDS:
if message.chat.id == -SUPPORT_GP:
try:
bot.forward_message(message.reply_to_message.forward_from.id, message.chat.id, message.message_id)
bot.reply_to(message, 'REPLY SENT')
except:
bot.reply_to(message, 'ERROR SENDING?') |
def maior_E_menor(x, y):
if x > y:
return x, y
return y, x
x = int(input())
y = int(input())
if(x == y):
print("0")
else:
maior, menor = maior_E_menor(x, y)
soma = 0
menor +=1
while(menor < maior):
if menor % 2 != 0:
soma += menor
menor += 1
print(soma) | def maior_e_menor(x, y):
if x > y:
return (x, y)
return (y, x)
x = int(input())
y = int(input())
if x == y:
print('0')
else:
(maior, menor) = maior_e_menor(x, y)
soma = 0
menor += 1
while menor < maior:
if menor % 2 != 0:
soma += menor
menor += 1
print(soma) |
# Want to extract domain hotmail.com
data = 'From ritchie_ng@hotmail.com Tues May 31'
at_position = data.find('@')
print(at_position)
space_position = data.find(' ', at_position)
# Starting from at_position, where's the next space
print(space_position)
host = data[at_position + 1: space_position]
print(host) | data = 'From ritchie_ng@hotmail.com Tues May 31'
at_position = data.find('@')
print(at_position)
space_position = data.find(' ', at_position)
print(space_position)
host = data[at_position + 1:space_position]
print(host) |
def stable_sorted_copy(alist, _indices=xrange(sys.maxint)):
# the 'decorate' step: make a list such that each item
# is the concatenation of sort-keys in order of decreasing
# significance -- we'll sort this auxiliary-list
decorated = zip(alist, _indices)
# the 'sort' step: just builtin-sort the auxiliary list
decorated.sort()
# the 'undecorate' step: extract the items from the
# decorated, and now correctly sorted, auxiliary list
return [ item for item, index in decorated ]
def stable_sort_inplace(alist):
# if "inplace" sorting is desired, simplest is to assign
# to a slice-of-all-items of the original input list
alist[:] = stable_sorted_copy(alist)
| def stable_sorted_copy(alist, _indices=xrange(sys.maxint)):
decorated = zip(alist, _indices)
decorated.sort()
return [item for (item, index) in decorated]
def stable_sort_inplace(alist):
alist[:] = stable_sorted_copy(alist) |
def wellbracketed(s):
c=0
for i in range(0, len(s)):
if s[i] == "(":
c = c + 1
elif s[i] == ")":
c = c - 1
if c == 0:
return(True)
else:
return(False) | def wellbracketed(s):
c = 0
for i in range(0, len(s)):
if s[i] == '(':
c = c + 1
elif s[i] == ')':
c = c - 1
if c == 0:
return True
else:
return False |
#!/usr/bin/env python3
# Get superior triangular matrix a)
n = 3
A = [[1, 1/2, 1/3], [1/2, 1/3, 1/4], [1/3, 1/4, 1/5]]
b = [-1, 1, 1]
for i in range(n):
pivot = A[i][i]
b[i] /= pivot
for j in range(n):
A[i][j] /= pivot
for j in range(i+1, n):
times = A[j][i]
b[j] -= times * b[i]
for k in range(n):
A[j][k] -= times * A[i][k]
print("A:", A)
print("b:", b)
# Get x b)
x = [0, 0, 0]
x[2] = b[2]
x[1] = b[1] - A[1][2] * x[2]
x[0] = b[0] - A[0][2] * x[2] - A[0][1] * x[1]
print("x:", x)
# EXTERNAL STABILITY
# A.dx = db - dA.x
# don't feel like importing copy
A = [[1, 1/2, 1/3], [1/2, 1/3, 1/4], [1/3, 1/4, 1/5]]
# calculate new b
dA = 0.05
db = 0.05
nA = [[dA, dA, dA], [dA, dA, dA], [dA, dA, dA]]
b = [db, db, db]
for i in range(n):
for j in range(n):
b[i] -= nA[i][j] * x[j]
# solve new system
for i in range(n):
pivot = A[i][i]
b[i] /= pivot
for j in range(n):
A[i][j] /= pivot
for j in range(i+1, n):
times = A[j][i]
b[j] -= times * b[i]
for k in range(n):
A[j][k] -= times * A[i][k]
print("\nA:", A)
print("b:", b)
# Get x (external stability)
x = [0, 0, 0]
x[2] = b[2]
x[1] = b[1] - A[1][2] * x[2]
x[0] = b[0] - A[0][2] * x[2] - A[0][1] * x[1]
print("stablity:", x)
# The one with the most sensitivity to erros is x3 (aka x[2] or z) d)
# abs(x[0] < x[1] < x[2])
print("most sensitive:", max(abs(i) for i in x))
| n = 3
a = [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]
b = [-1, 1, 1]
for i in range(n):
pivot = A[i][i]
b[i] /= pivot
for j in range(n):
A[i][j] /= pivot
for j in range(i + 1, n):
times = A[j][i]
b[j] -= times * b[i]
for k in range(n):
A[j][k] -= times * A[i][k]
print('A:', A)
print('b:', b)
x = [0, 0, 0]
x[2] = b[2]
x[1] = b[1] - A[1][2] * x[2]
x[0] = b[0] - A[0][2] * x[2] - A[0][1] * x[1]
print('x:', x)
a = [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]
d_a = 0.05
db = 0.05
n_a = [[dA, dA, dA], [dA, dA, dA], [dA, dA, dA]]
b = [db, db, db]
for i in range(n):
for j in range(n):
b[i] -= nA[i][j] * x[j]
for i in range(n):
pivot = A[i][i]
b[i] /= pivot
for j in range(n):
A[i][j] /= pivot
for j in range(i + 1, n):
times = A[j][i]
b[j] -= times * b[i]
for k in range(n):
A[j][k] -= times * A[i][k]
print('\nA:', A)
print('b:', b)
x = [0, 0, 0]
x[2] = b[2]
x[1] = b[1] - A[1][2] * x[2]
x[0] = b[0] - A[0][2] * x[2] - A[0][1] * x[1]
print('stablity:', x)
print('most sensitive:', max((abs(i) for i in x))) |
class CustomerAddWebsitePermissionDenied(Exception):
pass
class ObjectDoesNotExist(Exception):
pass
| class Customeraddwebsitepermissiondenied(Exception):
pass
class Objectdoesnotexist(Exception):
pass |
#!/bin/python3
def main(person_list):
users = []
for name, email in person_list:
if email.endswith('@gmail.com'):
users.append(name)
print(*sorted(users), sep='\n')
if __name__ == '__main__':
N = int(input())
persons = []
for N_itr in range(N):
firstName, emailID = input().split()
persons.append((firstName, emailID))
main(persons)
| def main(person_list):
users = []
for (name, email) in person_list:
if email.endswith('@gmail.com'):
users.append(name)
print(*sorted(users), sep='\n')
if __name__ == '__main__':
n = int(input())
persons = []
for n_itr in range(N):
(first_name, email_id) = input().split()
persons.append((firstName, emailID))
main(persons) |
# basic example
class MetaSpam(type):
# notice how the __new__ method has the same arguments
# as the type function we used earlier.
def __new__(metaclass, name, bases, namespace):
name = 'SpamCreateByMeta'
bases = (int,) + bases
namespace['eggs'] = 1
return type.__new__(metaclass, name, bases, namespace)
# regular Spam
class Spam(object):
pass
print(Spam.__name__)
print(issubclass(Spam, int))
try:
Spam.eggs
except Exception as e:
print(e)
# meta Spam
class Spam(object, metaclass=MetaSpam):
pass
print(Spam.__name__)
print(issubclass(Spam, int))
print(Spam.eggs)
| class Metaspam(type):
def __new__(metaclass, name, bases, namespace):
name = 'SpamCreateByMeta'
bases = (int,) + bases
namespace['eggs'] = 1
return type.__new__(metaclass, name, bases, namespace)
class Spam(object):
pass
print(Spam.__name__)
print(issubclass(Spam, int))
try:
Spam.eggs
except Exception as e:
print(e)
class Spam(object, metaclass=MetaSpam):
pass
print(Spam.__name__)
print(issubclass(Spam, int))
print(Spam.eggs) |
S = input()
for i in range(len(S)):
print(i + 1)
| s = input()
for i in range(len(S)):
print(i + 1) |
def c_to_f(c):
Farhenheite=(c*9/5)+32
return Farhenheite
n=int(input("Celcius="))
f=c_to_f(n)
print(f,"'F")
| def c_to_f(c):
farhenheite = c * 9 / 5 + 32
return Farhenheite
n = int(input('Celcius='))
f = c_to_f(n)
print(f, "'F") |
class QueueOverflow(BaseException):
pass
# Node of a doubly linkedlist
class Node:
# constructor
def __init__(self, data=None):
self.data = data
self.next = None
self.prev = None
# method for setting the data field of the node
def setData(self, data):
self.data = data
# method for getting the data field of the node
def getData(self):
return self.data
# method for setting the next field of the node
def setNext(self, nextOne):
self.next = nextOne
# method for getting the next field of the node
def getNext(self):
return self.next
# return True if the node has a pointer to the next node
def hasNext(self):
return self.next is not None
# method for setting the next field of the node
def setPrev(self, prevOne):
self.prev = prevOne
# method for getting the prev field of the node
def getPrev(self):
return self.prev
# return True if the node has a pointer to the previous node
def hasPrev(self):
return self.prev is not None
'''
returns a copy of the current Node's data
if include_pointers is set to True, the pointers
next and prev will be added to the returned node
'''
def copy(self, include_pointers=False):
if include_pointers:
to_return = Node(self.data)
to_return.next = self.next
to_return.prev = self.prev
return to_return
return Node(self.data)
# Linked Queue
class Queue:
'''
Initialize method, creates Stack
:param limit, max amount of elements in stack
'''
def __init__(self, limit=None):
self.limit = limit
self.head = None
self.tail = None
'''
Used to convert data to Node if data is not already Node
:param data, the data converted
'''
def __toNode(self, data):
if isinstance(data, Node):
return data.copy()
return Node(data)
'''
Checks if stack has too many elements, and if it does, it raises
StackOverflow
'''
def __isError(self):
if self.limit is not None and self.limit <= self.Size():
raise QueueOverflow
'''
Add elements to end of stack
:param data, element to add to stack
'''
def Push(self, data):
self.__isError()
data = self.__toNode(data)
if self.head is None:
self.head = data
self.tail = data
else:
data.prev = self.tail
self.tail.next = data
self.tail = data
'''
Removes last element in stack, returns the element popped
'''
def Pop(self):
if self.head == self.tail:
to_return = self.head
self.head = None
self.tail = None
else:
to_return = self.head
self.head = self.head.next
self.head.prev = None
return to_return
'''
Returns the last element in stack
'''
def Front(self):
return self.head
'''
Returns the size of the stack
'''
def Size(self):
self.current = self.head
currentNum = 0
if self.current is not None:
while self.current.getNext() is not None:
currentNum += 1
self.current = self.current.getNext()
return currentNum+1
return currentNum
'''
Returns a boolean value if the stack is empty or not
'''
def isEmptyQueue(self):
return self.head is None
'''
Returns a boolean value depending on if the stack is full
'''
def isFullQueue(self):
if isinstance(self.limit, int):
if self.limit == self.Size():
return True
return False
'''
Do not use this, testing only
'''
def showAll(self):
self.current = self.head
currentNum = 0
if self.current is not None:
while self.current.getNext() is not None:
currentNum += 1
yield self.current.data
self.current = self.current.getNext()
yield self.current.data
'''
Returns a copy of the stack
'''
def copy(self):
newStack = Queue()
self.current = self.head
currentNum = 0
if self.current is not None:
while self.current.getNext() is not None:
currentNum += 1
newStack.Push(self.current.data)
self.current = self.current.getNext()
newStack.Push(self.current.data)
return newStack
def load_from_iterable(self, iterable):
for item in iterable:
self.Push(item)
'''
Returns length of Stack
'''
def __len__(self):
return self.Size()
def __repr__(self):
return self
def __str__(self):
return str(list(self.showAll()))
| class Queueoverflow(BaseException):
pass
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
self.prev = None
def set_data(self, data):
self.data = data
def get_data(self):
return self.data
def set_next(self, nextOne):
self.next = nextOne
def get_next(self):
return self.next
def has_next(self):
return self.next is not None
def set_prev(self, prevOne):
self.prev = prevOne
def get_prev(self):
return self.prev
def has_prev(self):
return self.prev is not None
"\n returns a copy of the current Node's data\n if include_pointers is set to True, the pointers\n next and prev will be added to the returned node\n "
def copy(self, include_pointers=False):
if include_pointers:
to_return = node(self.data)
to_return.next = self.next
to_return.prev = self.prev
return to_return
return node(self.data)
class Queue:
"""
Initialize method, creates Stack
:param limit, max amount of elements in stack
"""
def __init__(self, limit=None):
self.limit = limit
self.head = None
self.tail = None
'\n Used to convert data to Node if data is not already Node\n :param data, the data converted\n '
def __to_node(self, data):
if isinstance(data, Node):
return data.copy()
return node(data)
'\n Checks if stack has too many elements, and if it does, it raises\n StackOverflow\n '
def __is_error(self):
if self.limit is not None and self.limit <= self.Size():
raise QueueOverflow
'\n Add elements to end of stack\n :param data, element to add to stack\n '
def push(self, data):
self.__isError()
data = self.__toNode(data)
if self.head is None:
self.head = data
self.tail = data
else:
data.prev = self.tail
self.tail.next = data
self.tail = data
'\n Removes last element in stack, returns the element popped\n '
def pop(self):
if self.head == self.tail:
to_return = self.head
self.head = None
self.tail = None
else:
to_return = self.head
self.head = self.head.next
self.head.prev = None
return to_return
'\n Returns the last element in stack\n '
def front(self):
return self.head
'\n Returns the size of the stack\n '
def size(self):
self.current = self.head
current_num = 0
if self.current is not None:
while self.current.getNext() is not None:
current_num += 1
self.current = self.current.getNext()
return currentNum + 1
return currentNum
'\n Returns a boolean value if the stack is empty or not\n '
def is_empty_queue(self):
return self.head is None
'\n Returns a boolean value depending on if the stack is full\n '
def is_full_queue(self):
if isinstance(self.limit, int):
if self.limit == self.Size():
return True
return False
'\n Do not use this, testing only\n '
def show_all(self):
self.current = self.head
current_num = 0
if self.current is not None:
while self.current.getNext() is not None:
current_num += 1
yield self.current.data
self.current = self.current.getNext()
yield self.current.data
'\n Returns a copy of the stack\n '
def copy(self):
new_stack = queue()
self.current = self.head
current_num = 0
if self.current is not None:
while self.current.getNext() is not None:
current_num += 1
newStack.Push(self.current.data)
self.current = self.current.getNext()
newStack.Push(self.current.data)
return newStack
def load_from_iterable(self, iterable):
for item in iterable:
self.Push(item)
'\n Returns length of Stack\n '
def __len__(self):
return self.Size()
def __repr__(self):
return self
def __str__(self):
return str(list(self.showAll())) |
#!/usr/bin/python
'''
Copyright 2016 Aaron Stephens <aaron@icebrg.io>, ICEBRG
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''
class CodeDirectory(object):
# Constructor
def __init__(self, version=None, flags=None, hash_offset=None,
ident_offset=None, n_special_slots=None, n_code_slots=None,
code_limit=None, hash_size=None, hash_type=None,
platform=None, page_size=None, scatter_offset=None,
team_id_offset=None, identity=None, team_id=None):
self.version = version
self.flags = flags
self.hash_offset = hash_offset
self.ident_offset = ident_offset
self.n_special_slots = n_special_slots
self.n_code_slots = n_code_slots
self.code_limit = code_limit
self.hash_size = hash_size
self.hash_type = hash_type
self._platform = platform
self.page_size = page_size
self._scatter_offset = scatter_offset
self._team_id_offset = team_id_offset
self.identity = identity
self._team_id = team_id
self.hashes = []
# Properties
@property
def platform(self):
if self.version >= 0x20200:
return self._platform
return None
@platform.setter
def platform(self, platform):
self._platform = platform
@property
def scatter_offset(self):
if self.version >= 0x20100:
return self._scatter_offset
return None
@scatter_offset.setter
def scatter_offset(self, scatter_offset):
self._scatter_offset = scatter_offset
@property
def team_id_offset(self):
if self.version >= 0x20200:
return self._team_id_offset
@team_id_offset.setter
def team_id_offset(self, team_id_offset):
self._team_id_offset = team_id_offset
@property
def team_id(self):
if self.version >= 0x20200 and self.team_id_offset != 0:
return self._team_id
return None
@team_id.setter
def team_id(self, team_id):
self._team_id = team_id
# Generators
def gen_hashes(self):
for i in self.hashes:
yield i
# Functions
def add_hash(self, hash): self.hashes.append(hash)
| """
Copyright 2016 Aaron Stephens <aaron@icebrg.io>, ICEBRG
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
class Codedirectory(object):
def __init__(self, version=None, flags=None, hash_offset=None, ident_offset=None, n_special_slots=None, n_code_slots=None, code_limit=None, hash_size=None, hash_type=None, platform=None, page_size=None, scatter_offset=None, team_id_offset=None, identity=None, team_id=None):
self.version = version
self.flags = flags
self.hash_offset = hash_offset
self.ident_offset = ident_offset
self.n_special_slots = n_special_slots
self.n_code_slots = n_code_slots
self.code_limit = code_limit
self.hash_size = hash_size
self.hash_type = hash_type
self._platform = platform
self.page_size = page_size
self._scatter_offset = scatter_offset
self._team_id_offset = team_id_offset
self.identity = identity
self._team_id = team_id
self.hashes = []
@property
def platform(self):
if self.version >= 131584:
return self._platform
return None
@platform.setter
def platform(self, platform):
self._platform = platform
@property
def scatter_offset(self):
if self.version >= 131328:
return self._scatter_offset
return None
@scatter_offset.setter
def scatter_offset(self, scatter_offset):
self._scatter_offset = scatter_offset
@property
def team_id_offset(self):
if self.version >= 131584:
return self._team_id_offset
@team_id_offset.setter
def team_id_offset(self, team_id_offset):
self._team_id_offset = team_id_offset
@property
def team_id(self):
if self.version >= 131584 and self.team_id_offset != 0:
return self._team_id
return None
@team_id.setter
def team_id(self, team_id):
self._team_id = team_id
def gen_hashes(self):
for i in self.hashes:
yield i
def add_hash(self, hash):
self.hashes.append(hash) |
class ProfilePage:
BACK_TO_USERS = "Back to members"
EDIT_USER_BUTTON = "Change role"
USER_EMAIL = "Email"
USER_FIRST_NAME = "First name"
USER_LAST_NAME = "Last name"
USER_ROLE = "Role"
USER_STATUS = "Status"
USER_PENDING = "Pending"
USER_DEACTIVATE = "Deactivate member"
USER_REACTIVATE = "Reactivate member"
USER_NOT_ACTIVATED_YET = "This member hasn't signed in to their export control account."
class UsersPage:
MANAGE_ORGANISATIONS_MEMBERS_TAB = "Members"
USER_EMAIL = "Email"
USER_NAME = "Name"
USER_PENDING = "Pending"
USER_ROLE = "Role"
USER_STATUS = "Status"
class AddUserForm:
USER_ROLE_QUESTION = "Role"
USER_ADD_TITLE = "Add a member"
USER_EMAIL_QUESTION = "Email"
USER_ADD_FORM_BACK_TO_USERS = "Back to members"
ASSIGN_USER_QUESTION = "Assigned sites"
class EditUserForm:
USER_ROLE_QUESTION = "Role"
USER_EDIT_TITLE = "Change role"
USER_EDIT_FORM_BACK_TO_USER = "Back to member"
USER_EDIT_FORM_SAVE = "Save"
class AssignToSitesForm:
ASSIGN_USER_TO_SITES_TITLE = "Assign member to sites"
ASSIGN_USER_TO_SITES_DESCRIPTION = ""
| class Profilepage:
back_to_users = 'Back to members'
edit_user_button = 'Change role'
user_email = 'Email'
user_first_name = 'First name'
user_last_name = 'Last name'
user_role = 'Role'
user_status = 'Status'
user_pending = 'Pending'
user_deactivate = 'Deactivate member'
user_reactivate = 'Reactivate member'
user_not_activated_yet = "This member hasn't signed in to their export control account."
class Userspage:
manage_organisations_members_tab = 'Members'
user_email = 'Email'
user_name = 'Name'
user_pending = 'Pending'
user_role = 'Role'
user_status = 'Status'
class Adduserform:
user_role_question = 'Role'
user_add_title = 'Add a member'
user_email_question = 'Email'
user_add_form_back_to_users = 'Back to members'
assign_user_question = 'Assigned sites'
class Edituserform:
user_role_question = 'Role'
user_edit_title = 'Change role'
user_edit_form_back_to_user = 'Back to member'
user_edit_form_save = 'Save'
class Assigntositesform:
assign_user_to_sites_title = 'Assign member to sites'
assign_user_to_sites_description = '' |
'''Program to find the factorial of a number using recursion'''
def factorial_of_a_number(n):
if n<=1:
return 1
else:
return n * factorial_of_a_number(n-1)
#Taking number from user and passing it to the function
n = int(input())
print(factorial_of_a_number(n)) | """Program to find the factorial of a number using recursion"""
def factorial_of_a_number(n):
if n <= 1:
return 1
else:
return n * factorial_of_a_number(n - 1)
n = int(input())
print(factorial_of_a_number(n)) |
def dec_to_bin(n):
if n < 0:
return bin(n * -1)[2:]
return bin(n)[2:]
def trim_to(number, length):
zeroes = ''
for i in range(int(length) - len(number)):
zeroes += '0'
return zeroes + number
def bit_not(number):
negated = ''
for bit in number:
if bit == '1':
negated += '0'
elif bit == '0':
negated += '1'
return negated
def add_one(number, length):
addone = ''
carryover = ''
if number[len(number) - 1] == '0':
addone += number[:-1]
addone += '1'
return addone
else: #Not the best way to do a bitwise addition but it works
carryover = '1' #Because the number we start with is 1 and 1 + 1 = 0 with carryover 1
#We iterate through the number but in reversed order sind we start the addition from the end.
for bit in number[::-1]:
if bit == '0':
if carryover == '1':
addone += '1'
carryover = '0'
else:
addone += '0'
if bit == '1':
if carryover == '1':
addone += '0'
else:
addone += '1'
carryover = '0'
return trim_to(addone[::-1], length)
if __name__ == '__main__':
number = input('Enter the (decimal) number you want to convert to the Two\'s Complement: ')
bitlength = len(dec_to_bin(int(number)))
length = input('Enter the length to which your number should be trimmed to: ')
if bitlength > int(length):
print('You should use at least a length equal or greater then the binary number itself!')
else:
if int(number) >= 0:
bit = trim_to(dec_to_bin(int(number)), length)
print('\nConverting ' + number + ' using the bit length ' + length + ' to -> ' + bit)
print('Finally we get...')
print(trim_to(dec_to_bin(int(number)), length))
else:
bit = trim_to(dec_to_bin(int(number)), length)
not_bit = bit_not(bit)
print('\nConverting the positive number of ' + number + ' using the bit length ' + length + ' to -> ' + bit)
print('Negating ' + bit + ' to -> ' + not_bit)
print('Finally add 1 to the negated number and we get...')
print (add_one(not_bit, length))
print('\nEnd...') | def dec_to_bin(n):
if n < 0:
return bin(n * -1)[2:]
return bin(n)[2:]
def trim_to(number, length):
zeroes = ''
for i in range(int(length) - len(number)):
zeroes += '0'
return zeroes + number
def bit_not(number):
negated = ''
for bit in number:
if bit == '1':
negated += '0'
elif bit == '0':
negated += '1'
return negated
def add_one(number, length):
addone = ''
carryover = ''
if number[len(number) - 1] == '0':
addone += number[:-1]
addone += '1'
return addone
else:
carryover = '1'
for bit in number[::-1]:
if bit == '0':
if carryover == '1':
addone += '1'
carryover = '0'
else:
addone += '0'
if bit == '1':
if carryover == '1':
addone += '0'
else:
addone += '1'
carryover = '0'
return trim_to(addone[::-1], length)
if __name__ == '__main__':
number = input("Enter the (decimal) number you want to convert to the Two's Complement: ")
bitlength = len(dec_to_bin(int(number)))
length = input('Enter the length to which your number should be trimmed to: ')
if bitlength > int(length):
print('You should use at least a length equal or greater then the binary number itself!')
elif int(number) >= 0:
bit = trim_to(dec_to_bin(int(number)), length)
print('\nConverting ' + number + ' using the bit length ' + length + ' to -> ' + bit)
print('Finally we get...')
print(trim_to(dec_to_bin(int(number)), length))
else:
bit = trim_to(dec_to_bin(int(number)), length)
not_bit = bit_not(bit)
print('\nConverting the positive number of ' + number + ' using the bit length ' + length + ' to -> ' + bit)
print('Negating ' + bit + ' to -> ' + not_bit)
print('Finally add 1 to the negated number and we get...')
print(add_one(not_bit, length))
print('\nEnd...') |
def printSet(set):
sorted(set)
print('{', end=" ")
for x in set :
print(x, end=" ")
print('}')
def main():
a = set("Hi There, Raghuram")
b = set("Hello, Nice to Meet you")
x = set('hello')
y = set('world')
printSet(a)
printSet(b)
printSet(x - y )
if __name__ == "__main__": main() | def print_set(set):
sorted(set)
print('{', end=' ')
for x in set:
print(x, end=' ')
print('}')
def main():
a = set('Hi There, Raghuram')
b = set('Hello, Nice to Meet you')
x = set('hello')
y = set('world')
print_set(a)
print_set(b)
print_set(x - y)
if __name__ == '__main__':
main() |
with open('input.txt') as f:
lines = [int(i) for i in f.readlines()]
depth_increased = 0
for i in range(0, len(lines) - 3):
last_sum = sum(lines[i-1:i+2])
current_sum = sum(lines[i:i+3])
if (current_sum > last_sum):
depth_increased = depth_increased + 1
print(depth_increased) # 1600
| with open('input.txt') as f:
lines = [int(i) for i in f.readlines()]
depth_increased = 0
for i in range(0, len(lines) - 3):
last_sum = sum(lines[i - 1:i + 2])
current_sum = sum(lines[i:i + 3])
if current_sum > last_sum:
depth_increased = depth_increased + 1
print(depth_increased) |
class Bye:
def __init__(self):
self.foo = 'bar'
def is_hello(self):
return type(self) == Hello
class Hello:
def __init__(self):
self.value = 'foobar'
print(Bye().is_hello())
| class Bye:
def __init__(self):
self.foo = 'bar'
def is_hello(self):
return type(self) == Hello
class Hello:
def __init__(self):
self.value = 'foobar'
print(bye().is_hello()) |
# Leetcode 70. Climbing Stairs
#
# Link: https://leetcode.com/problems/climbing-stairs/
# Difficulty: Easy
# Solution using DP
# Complexity:
# O(N) time | where N represent the number of steps of the staircase
# O(1) space
class Solution:
def climbStairs(self, n: int) -> int:
one, two = 1, 1
for i in range(n - 1):
one, two = one + two, one
return one
| class Solution:
def climb_stairs(self, n: int) -> int:
(one, two) = (1, 1)
for i in range(n - 1):
(one, two) = (one + two, one)
return one |
## Set to display confirmation dialog on exit. You can always use 'exit' or
# 'quit', to force a direct exit without any confirmation.
c.JupyterConsoleApp.confirm_exit = False
## Whether to display a banner upon starting the QtConsole.
c.JupyterQtConsoleApp.display_banner = False
## Start the console window with the menu bar hidden.
c.JupyterQtConsoleApp.hide_menubar = True
| c.JupyterConsoleApp.confirm_exit = False
c.JupyterQtConsoleApp.display_banner = False
c.JupyterQtConsoleApp.hide_menubar = True |
#66: Compute a Grade Point Average
a={'A+':4.0,'A':4.0,'A-':3.7,'B+':3.3,'B':3.0,'B-':2.7,'C+':2.3,'C':2.0,'C-':1.7,'D+':1.3,'D':1.0,'F':0}
add=lambda x,y:x+y
c=0
d=0
while True:
b=input("Enter the grade:")
if b=="":
break
c=add(c,a[b])
d+=1
print("Average grade point:",c/d)
| a = {'A+': 4.0, 'A': 4.0, 'A-': 3.7, 'B+': 3.3, 'B': 3.0, 'B-': 2.7, 'C+': 2.3, 'C': 2.0, 'C-': 1.7, 'D+': 1.3, 'D': 1.0, 'F': 0}
add = lambda x, y: x + y
c = 0
d = 0
while True:
b = input('Enter the grade:')
if b == '':
break
c = add(c, a[b])
d += 1
print('Average grade point:', c / d) |
def test_mining_property_tester(web3_tester):
web3 = web3_tester
assert web3.eth.mining is False
def test_mining_property_ipc_and_rpc(web3_empty, wait_for_miner_start,
skip_if_testrpc):
web3 = web3_empty
skip_if_testrpc(web3)
wait_for_miner_start(web3)
assert web3.eth.mining is True
| def test_mining_property_tester(web3_tester):
web3 = web3_tester
assert web3.eth.mining is False
def test_mining_property_ipc_and_rpc(web3_empty, wait_for_miner_start, skip_if_testrpc):
web3 = web3_empty
skip_if_testrpc(web3)
wait_for_miner_start(web3)
assert web3.eth.mining is True |
class Solution:
def checkIfExist(self, arr):
exists = {}
for num in arr:
if (num * 2) in exists or ((num / 2) in exists and num % 2 == 0):
return True
exists[num] = True
return False | class Solution:
def check_if_exist(self, arr):
exists = {}
for num in arr:
if num * 2 in exists or (num / 2 in exists and num % 2 == 0):
return True
exists[num] = True
return False |
#input
# 637 3371 327 67 924 968 2 6 6 77 5464 21813 70 5 67 74225 46 2 310 6156 50 4 623 87675 1702 3947 4927 9628 7 510 31 65 3321 23993 8406 88 -1
array = [int(x) for x in input().split()]
array.remove(-1)
swaps = 0
for i in range(0, len(array) - 1):
if array[i] > array[i+1]:
swaps += 1
t = array[i]
array[i] = array[i+1]
array[i+1] = t
checksum = 0
for num in array:
checksum += num
checksum *= 113
checksum %= 10000007
print(swaps, checksum) | array = [int(x) for x in input().split()]
array.remove(-1)
swaps = 0
for i in range(0, len(array) - 1):
if array[i] > array[i + 1]:
swaps += 1
t = array[i]
array[i] = array[i + 1]
array[i + 1] = t
checksum = 0
for num in array:
checksum += num
checksum *= 113
checksum %= 10000007
print(swaps, checksum) |
DEBUG = True
| debug = True |
# Write a program that reads a word and prints the number of syllables in the word.
# For this exercise, assume that syllables are determined as follows: Each sequence of
# adjacent vowels a e i o u y , except for the last e in a word, is a syllable. However, if
# that algorithm yields a count of 0, change it to 1. For example,
# Word Syllables
# Harry 2
# hairy 2
# hare 1
# the 1
inputWord = str(input("Enter a word: "))
vowels = ('a','e','i','o','u','y','A','E','I','O','U','Y')
lastVowel = False
lastCons = False
currVowel = False
currCons = False
syllablesCount = 0
for i in range(len(inputWord)):
letter = inputWord[i]
if letter in vowels:
currVowel = True
currCons = False
else:
currVowel = False
currCons = True
if currCons == True and lastVowel == True:
syllablesCount += 1
lastVowel = currVowel
lastCons = currCons
lastStart = len(inputWord) - 1
lastEnd = len(inputWord)
last = inputWord[lastStart:lastEnd]
if last == "a" or last == "i" or last == "o" or last == "u" or last == "y" or last == "A" or last == "I" or last == "O" or last == "U" or last == "Y":
syllablesCount += 1
if syllablesCount == 0:
syllablesCount = 1
print("There are %d syllables" % syllablesCount) | input_word = str(input('Enter a word: '))
vowels = ('a', 'e', 'i', 'o', 'u', 'y', 'A', 'E', 'I', 'O', 'U', 'Y')
last_vowel = False
last_cons = False
curr_vowel = False
curr_cons = False
syllables_count = 0
for i in range(len(inputWord)):
letter = inputWord[i]
if letter in vowels:
curr_vowel = True
curr_cons = False
else:
curr_vowel = False
curr_cons = True
if currCons == True and lastVowel == True:
syllables_count += 1
last_vowel = currVowel
last_cons = currCons
last_start = len(inputWord) - 1
last_end = len(inputWord)
last = inputWord[lastStart:lastEnd]
if last == 'a' or last == 'i' or last == 'o' or (last == 'u') or (last == 'y') or (last == 'A') or (last == 'I') or (last == 'O') or (last == 'U') or (last == 'Y'):
syllables_count += 1
if syllablesCount == 0:
syllables_count = 1
print('There are %d syllables' % syllablesCount) |
# Maximum number of images to keep in the database
MAX_FILES = 20
# Size of a JPEG minimum coded unit (MCU)
BLOCK_SIZE = 16
# Images will be resized to this width
IMAGE_WIDTH = 1200
# Image aspect ratio
ASPECT_RATIO = 3
| max_files = 20
block_size = 16
image_width = 1200
aspect_ratio = 3 |
# Source: https://leetcode.com/problems/contains-duplicate-iii/
# Better approach; Current time complexity: O(n * k)
class Solution:
def containsNearbyAlmostDuplicate(self, nums: List[int], k: int, t: int) -> bool:
if t == 0 and len(set(nums)) == len(nums):
return False
for i in range(len(nums)):
for j in range(i + 1, min(i + k + 1, len(nums))):
if abs(nums[i] - nums[j]) <= t:
return True
return False
| class Solution:
def contains_nearby_almost_duplicate(self, nums: List[int], k: int, t: int) -> bool:
if t == 0 and len(set(nums)) == len(nums):
return False
for i in range(len(nums)):
for j in range(i + 1, min(i + k + 1, len(nums))):
if abs(nums[i] - nums[j]) <= t:
return True
return False |
response = sm.sendAskYesNo("Are you sure you want to leave?")
# sm.sendSay("Response was " + str(response) + "\r\rAnswer was " + str(answer))
if response:
sm.clearPartyInfo(401060000)
sm.dispose()
| response = sm.sendAskYesNo('Are you sure you want to leave?')
if response:
sm.clearPartyInfo(401060000)
sm.dispose() |
VISIT_DETAIL_FORM_CONSTANTS = {
'visit_date':{
'max_length': 100,
"data-dojo-type": "dijit.form.DateTextBox",
"data-dojo-props": r"'required' :true"
},
'op_surgeon':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' : true"
},
'referring_doctor':{
'max_length': 100,
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-props": r"'required' :false"
},
'consult_nature':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
'status':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
'remarks':{
'max_length': 150,
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' :false"
}
}
VISIT_COMPLAINTS_FORM_CONSTANTS = {
#{"field" : 'visit_detail',
#'max_length' : '100' ,
#"data-dojo-type" : "dijit.form.Select",
# "data-dojo-props": r"'required' : false ,'readOnly':true,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
#'style':r"display:none;"
#},
#{"field" : 'parent_clinic',
#'max_length' : '100' ,
#"data-dojo-type" : "dijit.form.Select",
# "data-dojo-props": r"'required' : false ,'readOnly':true,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
#'style':r"display:none;"
#},
#{"field" : 'base_model',
#'max_length' : '100' ,
#"data-dojo-type" : "dijit.form.ValidationTextBox",
# "data-dojo-props": r"'required' : false ,'readOnly':true,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
#'style':r"display:none;"
#},
'complaint':{
'max_length': '100',
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-props": r"'required' : false ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
},
'duration':{
'max_length': '100',
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-props": r"'required' : false ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
}
}
VISIT_HPI_FORM_CONSTANTS = {
'hpi':{
'max_length': '1000',
"data-dojo-type": "dijit/form/SimpleTextarea",
"data-dojo-id": "visit_hpi",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-_:0-9#]+','invalidMessage' : 'Invalid Character'",
"style" : r"width: 70%;min-width:50%;"
}
}
VISIT_PAST_HISTORY_FORM_CONSTANTS = {
'past_history':{
'max_length': '1000',
"data-dojo-type": "dijit.form.SimpleTextarea",
"data-dojo-id": "visit_past_history",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-_:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_IMAGING_FORM_CONSTANTS = {
'modality':{
'max_length': '100',
"data-dojo-type": "dijit.form.Select",
"data-dojo-id": "visit_imaging_imaging",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'finding':{
'max_length': '1000',
"data-dojo-type": "dijit.form.SimpleTextarea",
"data-dojo-id": "visit_imaging_finding",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_INVESTIGATION_FORM_CONSTANTS = {
'investigation':{
'max_length': '100',
"data-dojo-type": "dijit.form.Select",
"data-dojo-id": "visit_investigation_investigation",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'value':{
'max_length': '100',
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-id": "visit_investigation_value",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_ROS_FORM_CONSTANTS = {
'const_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'eye_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'ent_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'cvs_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'resp_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'gi_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'gu_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'ms_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'integ_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'neuro_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'psych_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'endocr_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'immuno_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'hemat_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_FOLLOW_UP_FORM_CONSTANTS = {
'visit_date':{
'max_length': 100,
"data-dojo-type": "dijit.form.DateTextBox",
"data-dojo-props": r"'required' :true"
},
'op_surgeon':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' : true"
},
'consult_nature':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
'status':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
"subjective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"objective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"assessment":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"plan":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_SOAP_FORM_CONSTANTS = {
"subjective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"objective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"assessment":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"plan":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
} | visit_detail_form_constants = {'visit_date': {'max_length': 100, 'data-dojo-type': 'dijit.form.DateTextBox', 'data-dojo-props': "'required' :true"}, 'op_surgeon': {'max_length': 100, 'data-dojo-type': 'dijit.form.Select', 'data-dojo-props': "'required' : true"}, 'referring_doctor': {'max_length': 100, 'data-dojo-type': 'dijit.form.ValidationTextBox', 'data-dojo-props': "'required' :false"}, 'consult_nature': {'max_length': 100, 'data-dojo-type': 'dijit.form.Select', 'data-dojo-props': "'required' :true"}, 'status': {'max_length': 100, 'data-dojo-type': 'dijit.form.Select', 'data-dojo-props': "'required' :true"}, 'remarks': {'max_length': 150, 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' :false"}}
visit_complaints_form_constants = {'complaint': {'max_length': '100', 'data-dojo-type': 'dijit.form.ValidationTextBox', 'data-dojo-props': "'required' : false ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'duration': {'max_length': '100', 'data-dojo-type': 'dijit.form.ValidationTextBox', 'data-dojo-props': "'required' : false ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}}
visit_hpi_form_constants = {'hpi': {'max_length': '1000', 'data-dojo-type': 'dijit/form/SimpleTextarea', 'data-dojo-id': 'visit_hpi', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-_:0-9#]+','invalidMessage' : 'Invalid Character'", 'style': 'width: 70%;min-width:50%;'}}
visit_past_history_form_constants = {'past_history': {'max_length': '1000', 'data-dojo-type': 'dijit.form.SimpleTextarea', 'data-dojo-id': 'visit_past_history', 'data-dojo-props': "'required' : 'true' ,'regExp':'[a-zA-Z /-_:0-9#]+','invalidMessage' : 'Invalid Character'"}}
visit_imaging_form_constants = {'modality': {'max_length': '100', 'data-dojo-type': 'dijit.form.Select', 'data-dojo-id': 'visit_imaging_imaging', 'data-dojo-props': "'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'finding': {'max_length': '1000', 'data-dojo-type': 'dijit.form.SimpleTextarea', 'data-dojo-id': 'visit_imaging_finding', 'data-dojo-props': "'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}}
visit_investigation_form_constants = {'investigation': {'max_length': '100', 'data-dojo-type': 'dijit.form.Select', 'data-dojo-id': 'visit_investigation_investigation', 'data-dojo-props': "'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'value': {'max_length': '100', 'data-dojo-type': 'dijit.form.ValidationTextBox', 'data-dojo-id': 'visit_investigation_value', 'data-dojo-props': "'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}}
visit_ros_form_constants = {'const_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'eye_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'ent_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'cvs_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'resp_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'gi_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'gu_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'ms_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'integ_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'neuro_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'psych_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'endocr_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'immuno_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'hemat_symp': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}}
visit_follow_up_form_constants = {'visit_date': {'max_length': 100, 'data-dojo-type': 'dijit.form.DateTextBox', 'data-dojo-props': "'required' :true"}, 'op_surgeon': {'max_length': 100, 'data-dojo-type': 'dijit.form.Select', 'data-dojo-props': "'required' : true"}, 'consult_nature': {'max_length': 100, 'data-dojo-type': 'dijit.form.Select', 'data-dojo-props': "'required' :true"}, 'status': {'max_length': 100, 'data-dojo-type': 'dijit.form.Select', 'data-dojo-props': "'required' :true"}, 'subjective': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'objective': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'assessment': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'plan': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}}
visit_soap_form_constants = {'subjective': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'objective': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'assessment': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}, 'plan': {'max_length': '500', 'data-dojo-type': 'dijit.form.Textarea', 'data-dojo-props': "'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"}} |
def _merge_mro(seqs):
res = []
i = 0
while 1:
nonemptyseqs = [seq for seq in seqs if seq]
if not nonemptyseqs:
return res
i += 1
for seq in nonemptyseqs:
cand = seq[0]
nothead = [s for s in nonemptyseqs if cand in s[1:]]
if nothead:
cand = None
else:
break
if not cand:
raise GIError("Inconsistent hierarchy")
res.append(cand)
for seq in nonemptyseqs:
if seq[0] == cand:
del seq[0]
def _calc_mro(C):
return _merge_mro([[C]] + map(_calc_mro, C.__bases__) + [list(C.__bases__)])
def _mro(bases):
segs = []
for base in bases:
segs.append(_calc_mro(base))
return _merge_mro(segs)
if __name__ == '__main__':
class F(object): pass
class E(object): pass
class D(object): pass
class C(D, F): pass
class B(D, E): pass
class A(B, C): pass
print(_mro([A, D, E, F]))
| def _merge_mro(seqs):
res = []
i = 0
while 1:
nonemptyseqs = [seq for seq in seqs if seq]
if not nonemptyseqs:
return res
i += 1
for seq in nonemptyseqs:
cand = seq[0]
nothead = [s for s in nonemptyseqs if cand in s[1:]]
if nothead:
cand = None
else:
break
if not cand:
raise gi_error('Inconsistent hierarchy')
res.append(cand)
for seq in nonemptyseqs:
if seq[0] == cand:
del seq[0]
def _calc_mro(C):
return _merge_mro([[C]] + map(_calc_mro, C.__bases__) + [list(C.__bases__)])
def _mro(bases):
segs = []
for base in bases:
segs.append(_calc_mro(base))
return _merge_mro(segs)
if __name__ == '__main__':
class F(object):
pass
class E(object):
pass
class D(object):
pass
class C(D, F):
pass
class B(D, E):
pass
class A(B, C):
pass
print(_mro([A, D, E, F])) |
def Skew(Genome):
res = [0]
for nuc in Genome:
to_app = res[-1]
if nuc == 'C':
to_app -= 1
elif nuc == 'G':
to_app += 1
res.append(to_app)
return res
def MinimumSkew(Genome):
min_val = 0
current_val = 0
min_pos = [0]
for i, nuc in enumerate(Genome):
if nuc == 'C':
current_val -= 1
elif nuc == 'G':
current_val += 1
if current_val < min_val:
min_pos = [i + 1]
min_val = current_val
elif current_val == min_val:
min_pos.append(i + 1)
return min_pos
Genome = 'GATGCAAAGGGCGCGGCCTGAATCATCTTTAAAACAAAACAATTCGGGTTTTTCTGGGGCAGCCGTCCTGACAACTCAATATCATTATTATTACGCGACCGCTAGGCCCTTTGCGATTGTAGGCGTTTCCAAGCTACAGGAGTACAACAAGTATGATCGGTTTAGATTATCTCGATGGACTGCGTCTACGTCTCTTGACGTCACCCTCCTTTGGGGCGTGCTCCTGCTCCGGCGTTTTGGCGTATGCAGCGGTTGACCGACCACCACACAGATATGCCGATATTTGAACCGAGCTGCACAAAATCGACTTATGTTGGAGTGGTCTACTACGCTATGCCACCGTGATCGTTATTCCTAAAAGTCTCAGACTTTCCTCTCGAGGGTTACAGGGGAAACACTTATCAGTCTCCCAGGGGCCCAACATTAGCTTCATCTATAGGTTTAAGCGGCGATTTGGCTTTGGCTAGGGACACTGTCAAAGTTGCGGTATGATGATCTCAGATTGTACCATCTAGGTAAGAGTGCGTAGAAGAATTGGAAAGTACGGAACGTGTTCATGAGGAACTTTCATTGCACGGTGGTTACCTCCACGCTGCCACAAGAGGACCGCTGTATCGTAGGGCGGATCGCGACACCGTCTTCTAGCTCCATATAGCAGAAGGTCCTTCGGGAGGTCTATCTACACTCAAAAAAAAGATTTCTCGACATCTAAGGGCCCATGAAACTTCGAAGGTAACGCACTCCCTTGCGCAGACACCTACAGCAAATCGCTTAAGTTGCGGAGATTGCTAATCTAGCTGTGCTTAGGGGGCTATACAGAGGGGATAATGCGGCCAACGTTCACACTTTGAAAAGTACACGATGCGGCCGGAAGGAACTTAGTTTAGGATATTACGTTTTTGACGTAACTAAAACTGATTTTGGCGAACCGAATCCTGCGTATTACTGCAGTTTCGTAACAGCCCCTTAGTCTCGGAGCGATATGCGTGTACAATGCAAGGCTAGAATTGTGACTAATGTCCAAGTATGTCACATGATCGGTGTGTCAAAGGGCGGGCAACACGTACGGACTTTACAAACAGGGTATTGAGCGTTGATCCCTCAAGCTTGACCGACGGCACGTTTACGCGCATTAGCTAGACACATAATAGCGTCAACGACCCCCACACTCGAGTCATCAACGCCGAGAGAACAAGCAGTGTACAGCACGCGGACGGATGCGGCTGTCGGGGAAGGATCGGACTTCAAGTAGTGTCGTCTGATTTACGGATTTAGCTGCTCCCCGAAATTCGCTAGTGAGAAACAGCGAGAAATCGAGCTGATTAGTGAGGGGGCACGACGGGGTCCGTTGAGTGCACACAAAGGGTCCCCGTGCGTCGGTTTGCACGCGATACGTCTTAGACCCATAGTACAGGTGACCCTACGCAAGGCGTGCACTTTGTCGTCTTACCCCCCAGTTAGAAGGCCTCTACAGAAGACAGCTTCAAGTTGCACAACACGATGTTTCGACGCGTAATAAAACGAGCTATATTGGAATACAGACTCTGCACGTGCTACGTATACCAGGACAGGGGACCTAGATCCGTGTCAATTCCCGGCTTCTCTTTTCAGTGACGACCAGTTCTCCAAAGGGCGAGGGTTGGCCAACGAACACTTAGGTTAGTATGGGACACGCTTTCTGTTCTTCTCCGTTATGCCTCTCAGCGGTAGTCTCGATGGACTCGCCACAGACGCATCTGACTTTTCGGATAAATCACATGAACAGTGGAGAAATAGAATAAGCCGGGTTTCTGATAGTGGAATTAGAGTCTCCAACGAGCTAAAGTCTGTTGCGTTCCATTGGCCTCCCTTTGTGGTACCCACGGGCTGGTGTTCCCACGATAGCTCAGCAAGCCGATGGTGACTAAGCTTGGCATACCAATGACAAGGCCGTCATGAAAATCATCCCTGGGCGCCGAGGGAAGGAGCGCTCTCCATGAAATGAGAGCGTTGGGAAGACATTATGCGACGCTCTGCGCAAAATACGCATGAGGGGATACCCAACTGGTTCGGCCGGGCTAACTAAAGTCTTATAGTACGCTCCCGAAGTTAGCCTCCAGCTTGTTCGTCGCTTGCAGGAGCATTATTTCCAAGCTCCAATTTCTTAAGCGCGTCAAGAGCACTGATACGGTGCAGCGTAGTTGTTGACATCCCGAGCCCTTCATCAACAGGTAACTCCAGTTGAGTCGTACCGAGGGCATGTAATCCAAACATCAACCGAAATAAATAAACTTCCCCCAGTCGCTAGTGTCCATATGTAGCATCCACAAGTCCTGCGCGCCAATAAGAGCATAATGGCATGTCTACTAATCGGGATGAAGTGCAGAGGAACACTAAGATGATAAATTAACCCCGTCGAATACGATTTCGCTGGAACATCTTAGTTGAGGTCTAAATCTCTATAGGGTCCGGGAAGAGTACGAAAAAGGGGGCTGATCGGAGCACCTGAGAAAACCGTAAGCGGTTTCGGTGAAATGAGCGATGTTTGTGACTGTTGCTCCGTCGAATGCTCCGAGTAGAATACAACGCCCGTTTAGACACGCGTTGGGGAAAGTATAAAGGGCGATGTATCACCTGTCCGCTCGCAAAGTTGGACGACACTGTTGAATGAAAACTTGCACAGGCACGACTGACCGGGTCTATTACCGCGAGCGAAATACGCCGTGTCCTGCACTGCATTATGAAACGCACGGACAGACATCAGTTGACATACTGAACCACTGAGTAAGCAATTTGCATCCCATCTGGGATAAATCAACACCCAGTACGCTACTTACCAAGAGGCGTCAGACCGACAATAAGCCTCACGGGGCCTTAAGTATCGCTCTCTTAAAATTCACTACACCGCCAGCTAGGGTAGGACACTCATCCCCAGGCATGGGGGTAATCGCTCTGTCCAGGGGTGAACTCGAGTCACGATTCGATTGTACAGAATCGCGAGATACTTCCTATCAACTCCAAAGACCTAATCGGTTCAGTCAAAGCCATGTCTAACTGACACACAGGCAGTATGTGAGAGGGCCCGCGTCTGGACGCCGATCTTGGTGAGGCGGCTCATGCATTGGGCCACATGAAAGGACAGTTCAACCGGATAAAGAACCCGACCGTTCCTTAAACCGCTAACTCAACAGGGCGAGTCCAAACGTAGACAAGACCAAACCGCATCACCAGGTGAAAATTGGAAGCGTCTACTCGTATGTTAGGAACAAATGAGCTTCGTATGAACTCCGATCGATCAAATAACCTGCATCGTACTGGGGGTGCTGTAGGTTTGCCTAGAGGGTCAGACGAAAACCAAATGGCGATGCTCTGGTTCCGTAGCGGGCCCCTCTTGCGGATGACATTGCAGGGAGCGGATCCTGCGCGTTTATATAACAGCGATCCTTGCAGGGCATTGACAGAGCAAATCAATCAACTGAACCGAACGCGTGCCCACTCATTGAGTCGACTTCGTATGGTCCTTCGGTCACAGTACTTGACGCGGGAATGATAAGGCCCGTGTGCACTCCCCTAGGTCAACATCTGTATCTACCTAGGAAGTAGGACGATTGCAAATTGGCGCATCAAAATCCGATGTAACTCTTAGATCTCGAGGATCGAGCTCGACCGTCGCGAGTGGAAAATACAAATGCCTCAGTGGCCCTTGTTCGGCTCAGTATGAGCTCCTTGTTTCCGCGACCGCATCTAATGTGATCCAACATCTTACCTCCTCTGGAGACGGAGGCTACAGATAGATGAGTCTAATTTTCTTACAACTAATAAATCGAAAATACGGCTCATGGCGTTGAGCAAGCAAAGTACGACACGTCCGTAAAAGTCGGATTCCTCAGCAAGTGTCTTGACCATCACAGAGAATATTTCAGCAAGCTGGCCCATGTAAAAACTACTGACACTTTCGACCTGTCACACCAGAACTTACAGAAAAAACTCTCCTGAGGGAGCCCCTAACTCCGGAAGATCTTAATGATCGATCGTGCAACCTGGATGCCGTGTGAGTGTGGCTACCTCATTCCTACGGGGCTGTCCGCAGGTTCGGTCGCGTAGGTCTGGCTGGCCAGTGTACTTGGATGGAAAAAGATTTACCAGCTCAGGACGCGCCCCTTAGAGACCTGCAAATCCGTATCAAATTCACTCGTCGAAAAACTCGCCTTGTAATTTTTGTGATCACATTATATTTACGATGCTGTCCAAGGTAATAGGGTATATCCCAGTATAACTGGTACACGCTCGCACACGTGCCGAGTACCAACTCCCTCGCTTGGTTGATCTATCAGGGGTAGGGAAGCTTCGAGGGCCAGGCGTAGCTCCAAATCTCAAGAACGGTTCTAATCCTCAACCCGTAGGATCTCTCGTTCATACGTCGCCCTACGACTCTCCGAAAGTATGGGCACACTCGCCATAAATATCTAAAAACGATCTTCGCGCACGTGCAACGGTCTGATGGCCTTATGCTGTAATTCGGTTTCTGTGGGAATTCGGTAACGGATAGTCACTGAGCTAGTGCCGGTCGCTGCTGGTTTAGATGAATGCAGGAGATCCGCATCAGCGCCGAGCGTTGCGGGCGTCTCGGCTGTAAGTTGTGGCCAATGCGGGCGTACTGTTATACATACAGGGGAATTGGCGTCAAACCGTTTATGCGAAATGCTGTCTATGCCAGGTGAGCATTACACACGTGAGATCGAATCGAACGTAGGAAAGGTGGTCGTATGGGAAACGTCATGTTGATGAGACTTCCATTAACGGGCCGAAACTAACGTCTTAGCGTCGTTGGTAAAGTTCAGCTGGGATACTTTTGAGGTCGCTCATCTCCCCGCGTCACTTAGCATCTACTCTAGCCTTTCTAGAGGGGTTAGTACTCCTGGGAGCCGCTCTGCAGATATTCAATGAAAGCCCAATTGAGGCGGCATGTGGATTGTAATTCACCCTCCGCCTACAGTAGACATAGAGCTGTGCTCAAATAGGTTGAGAGGGGCGCCATTAACCATCGTGGTACGGACCTATTTAGAGCGAATATCTAACCAGCAATTTCTCTCAGAGGATACATCCTACTCAAGATGCTGTACAGGCATTGCCAAATTACCGTGATTCCAAACTTTTGGGAGGCTTTCCCTTTTCTAGGTGGATAGTCACGTCCATACTATCAAGCTAAGAGCCACGCTGCTTTCTACTCCTAAATTTCTTAAAGAACTCTCTGTCCTAAGTTTGCCACAACGACTGCTACGCGCCAAAAAAACCGACCAGCACACACGGTTATCGACCTTTGATAACTGGCCGCCGGACAGATCAAATGGACCCCAGTCAAGCCCGGTAACTTATGCGCAAGTTTGCACAGGGGAACGACACGGACTCTGCCAATTATATACTACATGTAACTCGCGCAAATTGGCAGCATACGTACGTCTTCGTATGTCCTGCACCCGCATGCCGTAACGGACCTCAATTCGGCACCTTGAAGTTGCGTCTTTCTTCCCTATCGCCATTCTGTAGACAATCAGACTTGGTATTAGGTCCCGCTATTCAAACCCCCAACCGATCGCGCAGAGTAACGATTCCTGCAATTATGCGGAATGGTCGCCCACGGGCGGATTGGGTTCGTTGCAGACATTCAGTAGTTCTTCTGCATGACGTCGGGCTAATAGCACGACTTCCTTCAATACCGCAATCATCGGTCACCCCTCAGACCACTCTTTCCCAATAGCAGGACAGAACCCCATCTGCATTGGACTCAGGTTACTCTAACCGAAAGTTCCTTTCTTTAGTGACAGCGAGATGGGTATGCGGAGGGCGTGCAATCACTGTGACAAATTCCTCGTTTTTACCTGAAACTTTCCTTTGTAGGCGTCTCGGAAGTAGATTTCACCAACGTGATACCAACAGAATGGGTCAGACGTGGTGTATTCCAGGGTGAGGAAACCTCCCCTGAACCGAGGCGACGAACCTTAGATTACGTGTTGAGATTTCAAATCGTTTCTAGTGTGCGCACAATTACCTTGGCAGACCTAACTCTCACGCATACATAGGACACCTCACCATAGCCATTTGCGATGTTCCCCCGTACTGCCCTAGACAAGCTATACGACACCCCCTACGACATGACTCGTAACCCGTTTCCATGTAGGCCAGTTTATCATGATACATGAGTAAACACAATGTATAGTGAGCTTGTTAGTACTATCTCAGTGGGTGACGACTGCTAAAGCTCTCATAAAAAGTCGGGCGAAAACGACACTCGCATCCGATGGTTTCCGGTCTTTCGGGCTGCCCATGAAGGGGCCCCTATAACCGACAGTTAGTGAAGCGAATAGTGACTGTGCGCCGCAAAACGCGCGTACAAAGCGTAACCCATAACCTGGTGGTCCTCACATGATTCCAGAAGTGACAAACCAACAAGAATAGTTTATTCTGCTTTGCCCCGGGCGGTCTGTTCTCACTTTGTCCATATGCCGGCCGCGTTGGTCTCAGAACAGTGAAGATCTTTTTGAAAGTAAACCCATTCCGGATTTCGTGTCTATCCCGATTCATACCTCCGTCGGGCACTTGTTAAGGGGGACGATCCGCATATTGTACTGTAAGCTTAGATCACTTCGCGAGAAATGATAGATCGCAGTACACTTCCGTCTATACCAATACTTTGACTGCTGCAACCATCTAACGGCCCTGCGGGCCATAGCCGAAAATTATCAATGGTTGGTCCTGCGTGGGCACCCAGTACAAGATTTCTTCTAACCCCTGAGCAGTAACTATTGACCCTAGGTACAAAAACATACAGGACCTTGATCGATGATCAGAGAAGGACACAGATCCGGATTATATGCCTCTATAGTAACTCCGGAATAAGCGAAGGAACCTGGCTGACCCCTGAACAATCAGAGCGGAGGGCGATCTGTAAACTATAGAAAGCGTTCAATTCAATCGAAGACAGGCCGTATCCCATTTTGTACAAGGGTTGCCGGGACAGAAAATAGCCGGTCGAAGTTCCGGCATTCCAAAAGCAGTTTAGCAACGATACAATCAAGCGAGAGAGACTATATAGACAGGTAGTCCGGTCGTGGTTATCCGATTTCGTAGACACGGTACGGCCAAGCTGGGAAACGAACGGTGTGTTTGGGCATAAGTCTGTTGAGTCAGAAGGTCGCGGTTTCTAGATCGGTGAGGTCTGTGAAAAGGAGGTGTGACATGCTTAGGCATATTAAGAGTCTTCGAACACAATTGTGTTGACACGTTGCATACCGCTCTCTACTGAACGCACAGGCCGTGAGCGTTATATATACTGAACCAGCGCATAGGCCGTACCTTCCCCCAATAGTTCTTTGGCGAGGGAGAACCTATGAACAGGCCGAGTTTCTCTTTAATTAGTCTCGCGTCTAAACTCGGCCAGTTCTAGGCATTTATATTTACCAGAATCGGCAAAAGGGAAAATTAACATAGCAAAAACACATGGGTTCTATCGTTAGTTATAGGCGTATTTAATCGAGCCGTGCACGGTTCTCATTGCTATAGGAGGGTACTGCTATGCGACAATTACCGACAACAGCTATAACTGCTTATTATCATAACTGAGCAGTCAGCTTCACGTACGTAAGAACAGAAAGCCTCTTACGGACATACCATCCCTTTCGGAAAGACGTGCATAAGCATAGTGCGCTGGGCCTGGTCCACTTAGCGTCTAGTCCTCTAATCATCACCATACACACTATAATACGCCTCCCGAGCAAGATGGAATACACTGGTGAGGCTAATGCGCCAGCGGACGAGAGGGATGGTTCGCCATCGTATTATGACTATGCCGGTAGATAGGCCCTGGCCGACTAGGATAAGCCGCAAAAAGCTACGATGCGAAACTCCGAGACGAGCCGAAAGCGTATAACAGACGACGTCCAGGAACTCATGTACCCCCTCACGGGTACGTGCATGATGCTCACCGCGATCATCCCTCCCTTTAGGGCTGCCATTTATTGTTTGTGAATTCAAATCACAGTACACCTTATTTTACAGAAAGCCAGGCACCTCAGATGTGGCTGTTGCGTCGACTAGTGCAGGTCTAGAATGTGGTGTTACGCTAGGAAGACCCCGTCGGTAACAACCACAATGTGGGGTAGGTACATTGGGGCGTACCGTAGTGAAGCTGCATGCGAACTCCTTTGTCGCGTGAATTCAAACCCTGCGGACCGTTTTTTGGGGTGTATCCATGTCGAGAAGCTGCTAGGCTTGGTCGTATAAGGATAGAGTCACCTCCAATCTCTGGTCTAGTTTGGAGCTTACCGCATTGTTCGAGTGGTCATGCTCCGGTCGTTCGTACGAATATATACAGGAAAGCTTCTCATCGCGTATCTACTTGCCTCGTCGTCATTGCGTGATTCCAATCGTGCTTTACGCGAAAAGCAAGCAGCCCTCTCGAAGGCTTAAGAGGGGGTACTGATACCCTGGGATCCAAGATTGTGCGCGTTCGGTTTCAGAAATCATCTTTGCCTGCTTGGGTAAATGGCAAACCTAAAGCGAATATAATCTGCGCGACACTTTCTCCGACACTTCGTATTGCACGACCAAAGGATCCTCGGTCCTTTATTCTTTTGACTGAGGAATGCCACTCGAGCTTGTTTCTACAGCGACACCTTGAAGAGTTCTGCAATATCACGGAGTGTTGTTATAGGATGCCACCCTGTCTCTAATCCACACCCCACCGCTTGTTTCTATCTCAATCATCTGTGGGTTGCTGCTGGAGCAGCTACTTCTGCATGGGCCGCGAACTCCACAGTCTTGGGTAGCCCAGGAACTGCAACGGAATAACGCACGCGTCGGTTCTCCAGCAGTGATTTTATAGTTATGACTGGAGGTTTTAAAGAGGACCGGGCTGAGCGGGCTATCGTTTGACTGCTATGCAAGGGCTTAATCGAATCTAGCTAGAGGTCACCTGGAAAAACTGCTCTTTGTAGACAAAATCCGCGTCGGCGTAAATTGATATGGGAGCCAATTTGGGTCAACTTCGACAAACTACAATCGGGACCTAACAGACGCAGCGTATCTGTGCACATAGCTTTAGACACGTTGTCTTGAGCATTAGATGCGCAACTAATTATTCAACCACTGAATTCCTAAATCGCCGAGAAGACTCCTGTGAGCAGTTACAAATGAGAGGCATCGCCCTGCTGAACCGCCGTGCAAAGTATTAGCAGACGATTCGCGACTCAACATCGGGGCGGCGCGGGAAAAGCTCCGCCAATGCTACCGGAATAGATTAACGCAGAGGCTGCAGTGACTATAGTTTCCGTGGAAGGTTTACGCGATTCGATGGCTGGCCACAGGACTGCCGACAAAACGGAGCCGCATTCCGCATGCTACCAGGCATAATAGTGAAACGTCGATTTTTCTACATCCGGACCTGCTTTCGTTGAACCTCAGCCTTCGCAGGGCAGAAGTTCTGAGAAAAGCTAAAATGAGGTGTTTGACTAGCCTTTTGTGTAAGTGCGTGAGAGTTTTTATCCGTAACTGAGAGCGATACACGGCCATACCTAAACTAGAGGAGCGCCTTACCGTTTTACGTGCAGAGCGACAAACCAATTAGATCGGTGGATATCCAATTCATTGACTGCGTCGTCCAGTACAGTCAAGAGACGTGAATACCGTTCGAATTTCGTGCATAACTCCTGGGAGAAGAACTTACACGATAGCCATTCGGGTGGCCACTATAAGTTTGATAGCTGAGTTCAGTCCTGCCACACTAGGGGGTCGCTAACGAGCGACCTGCTACTGCGCTTGTCCGGCTGAACTTGTGGTCTGCGATCGCTCTTGCTCGTACCCCTACGGTTCGGTAGGAGCTTTGGGCTAGGTAGTACAGATGACTTATGCGAGCCCTCAAGCACATAGAGAAGGATGTTCGTAATCCACATAACTACTATACCTTCTAAGGGATCCCATCTTATCTCCCGGCGTAATGTTATCGTCTTGGCAAAAGTTCGACCGTGACTAAAAGAGACTCGATCGGTTTCAGCGTTTTGAGACATCCTCTTAGTGAGATGGTGACTCCACGAGGTAACGAAGACCCAGGGTTCTATCGTCCGTAGCGCTGGTACCGTTATTCACTTCAGTCTTCTATCGTGATTCTTATGTAAGCTAGTTAACCTGTTCAATTGGTAGAGCGCGCGCGAATCCTACGTATAAACGTTAACGTGGCGACATGTCAGGGCTGATTGCCATCGGCTTCCGCGGTTGCTACTCGGCCCACACGTCCTTTCGCGACGGATTATACAAAGGACGATCTCGCGCACGTGTATTCCGGAGGTAGTGCACCCAAGGAGGTATAAGTTTTATCAATGAGTGGGAATGCGGACTGGTGCACTAGGTTGCGGGTGGTGGTACACAAGAGTATGGTCCCTGTGGCTAGTACATTTACAGAGACAGCCTCAGGCGGGGGTCCCTCTCGCCTGTATGGAGCAGACTAACTCGGATTATCCTGCCAAGAGGCCCAGACGTGTGCGTTTTCAAACACTTAATATCCGGAGAACCGGCCAGTGCTAGTACCGGAACAGACACTGCACACATTGGAGGGGAGTACCAATAGAAACAATTCGCGTAAAGTGCCAAGTCAGGTCCACTCGAGCGCTCCAGAGACCAAGGTCATGCTTCGATTGGCTATTCATTGCCTTCTCCCACTCGTTACCTTGACTAGATCACGCTGTAACGGACGATTATATCGCGTTGTGTATCGAGTGCAACGGTTCAGTCCACCCGGAATACCATTTTACTCGGCGCCATGGGACATCCGGGGGCGAGCTGAGACGACTAAATTTCTAACCGGCCGCGCTCCAGAAAAAGGTCCGATCAGAAGACGGTTCTCCTCTCGACTGCCTTGATCTGGCCAAATCCGTTCACCTCTCAGCATTATAGGGGCACAAAGGACCTGTCTCTCAAACTACATCCGCCATGTTTGATGGCCAACTACCAACGTAAAAAGAGGTTCTCTCGAGTCGGCTCATACTTTGGTCGAACACCCACACCTTTGTTAGAAACTTGCATGCCACTACCTTCTGTCATCTAGCAAGAAACTACTGTCCAGCGAGCGCAGGCCCAATGCGAAGACCAGGATCTTTACCGCTATGGCCTGTCGCGGCGTCGTCTCAAGCAAAGCGTTAGTCTGCACTGGCTACGTAACTAAAGTGCGTGGAACGCGCCACCTCGGACGGGTTTTAGTGCTGGCCACGGAAGCATCTGGCATTGGTCGTGCCCATGCAATACGCGTAATTGAATCCATAAAACACGAACTTTTAAGCAGCGTACGGAACCTTTTTTCAGTCTCCCCCAATCCCGATTCTGTGGTACGGATGCCCGGGGGGGCGGTGTACCGTGCAGGCAGCAGGATAATGGGCAAATAAGAGAGCTACCATGCCGAGAGACCTAGCCTCCGACGTTATTTCGGAAGAATCGGATCATTGGACTGAGTAGAGCCCGTTGAATGGCTTGGCGTACGATCTGCCAATTGCGTAATCCTGCATTTCATGATTGAAAGCATCCCCTAAAACTTTGCTATCGGAGACTGCTAGACTACGTTATGGAGAGGAAGCTGACGGCGGTGCTAGGTCCTACGTCATAATATTGCCTGGATTACTGTTTAAGGATGGTCACTCCTATTGTAGCTCGTTCCTTTCGCACAGACCACGATCGGAGTATAGTATGCGGGAATGAAGATATATAAGTGCCTCTCTTGCTCTAATGGGACCGTTCATGCTACGTGAGTGAGTTTTATTAAGCCATACGAAATGCCGTGTGATCGTGCATAAGCTAGGAGTCTCGTGGAGCGCCTCCATGTCCGTAGGTGGGTACGATCTTTTCCTGTTTACTTAATCTATAACCACACCTGGTGCCACTCCTGAATAGATAACGTTTGTGTACCAGACGGCGCGCCCTTTGGCATGTCTTTCGGCCCCGAAAGAGCGGCTGGTTCGCTCTTGACTGGTGAAGTGGGAAATTGTGTTAAGAAGTGCCACGAGCTCCGTCTTTTCAATGCCCGTACGTGCTGTTGACGCGTTCAGACCCGGACGCTTTGTTGTTATACAGTGCTTCGTCCTAGGCCTCACTGTCCCGCGAGATAATCTTTACTGTGTGACTCAACCTTCGACTCGCCCTCTCATCCTTCCACATACTTGCACCTCAAACATCCTGATGTTACAGGCTAAAGAGAACTTGCTTCGCGTAACTCCGCACGCAACTGGTATCTACTTCGTGGGCTCGGCGGTGTCGCTTTTGAACCCCCCCCTCGCATGGCCCAGAAACCAGCCACCGCCCTTTACCCCACTAACCGATTTCTGGTAACTTCACCTGAAAAATACTCATACTATTCACCCAACACCTACCGGGTAATAACCTCGGGAGTAATCGCGTATCTGTACCTTGCAGGCAAATCAAGAACCTGCAAGAGACATGAGTACCAGCAGTTTCGCGCTGCAGGTCGGCCCAATTACGATCACATTGTCAAACTCCCCCGGTTTAATTACTCGGAGTGCTTATTAACCCTAATGTCGACCGCTGGCGTAGCTCTGCTTGACTAGAGCAGAAAGGCACCTACTACGCCAGATGAACCCGATCTCAATGCTTTCACCAAATCCTCCGTGATCATGTCCACAGGAAGGGCAAGGCCTTAGTCCAAGGCTATGAAGGGGCTATCAAAAAACCCTTACACGACAGCTCCGGCAAACATGCGGCCCAGGACGTAAGCCTGTACTAACAAGTCCCGTGAGCGGTCTCGTTGGTTGGTTGTATGTGTGTCGCCACGCGTACTGCGTGGAGCGGTCGATATGACATAAGCAGCAGGGACCGTTAAAATAAGCCTCGAGTTGGGGTTCCTCCCATTCCTGATTTTGACCTCGTAGTTTATCAGGTTATCATTACTTAGCTTATACGCCCACGAGTACGGAGTAAGTCGCCATGGCTATCCTTATCGTTTACCTACCCGTCCTCCGGTTCACACTACGGGATGGGACGCCAGGAAACACATATGGTGCAGCTGTGCTCTAGTAGGGTTCTGAACGGATTTCATGGGAGTTAGACATTGGGGATCACATGTCCTGTACAGACATGATAACGCTCCAAGCTGTGTGTAACAGGTGCGACTTAAAGCTATGGATCTTAGAGTTCGGTTGCCATGCTATTAGGGGTTCACATGTCTTAAATCTAGGGGATCCTAATTTCAAAACAGGACGTAAATGCTGTAAAGTGGTATAATCCTTCTCATACAAGATGTGGGAAAAAGGCCGATCCAGTTCCACCCTCGGATCTGCGTGAATTTAGAATTATGTATGTAGCAAATCAAGCTCCAGGCCACGCCCATGTCGACAGCTGAGGACGATGCCCGGCTAACCATTTAGGTGATCTCCGTTGAAGCGATCCGAAAAATAGGTGGGTCGGTTCCGGGCGAGACGAGTGATTCAATTCCCGTTAAAAAGTTCCTCGCGGTACGAGCCGGTCGCTCAGGCCCATGAAAGCTAAGACGGCACGTCGCGGTAAACTCGAATGGCTGTCTCGGAAAATACTAGAACCATTCCCAATGCTGCCTAGTGTTATACATGCTCACCTGGTCCTTAGATACAAGGTACCACAGCCGCATGGTGCAAGAGGAGCAGCCTCGTGGTGCTCATCACCGCAATCTTCGTATCGTCATATTCCCGTCGTGAAGGAAAGCGACTCGACTACCTAACCCTGTGCTCCGAGCAGCAACTTTTTTGCATTGCGTCAGGCATCTCTCCGACGAGAAGCCGGAGCGTAAGGGAAGCCATTACGGGCATGTGAGGCAGTTATTTCTGACTACACGATAGCGTCCATAGGCAAGTAGACACTGGCTTATCTCGATATGGGGTATAGCGAAGCTACCATCCATGGAGTCCTCAGTTAGGTCCCGCTACACTCGACTACCGACAGGAGCTAGCTCGTGTCTATACCCTGAACGGGAGCCGTCTTATGCCCAAGAATATATCCGTTATAACCGGCAGCCACGTGAATTACAGGACCAATGAGACTTATTCGTAGAACAACCGTCTGGTAGTACCCCTATGTATTGGCAGTGACAGGTCAACATATCTAGACATCTTGAGGAAGGGATCAAGAGGAGAGCAAAGTGCGTTCGATCTACTGTCCAATACATTCCACGTCACGAAGTGGACAATGGGTACGGACACCTTGGGACTCAAGCGGGCAACTATTCCATTCTATACATGATCGCAACTATTGTCAGTCTCGCACTCGAGGGTACGAACTCGGCGGGGAAGGGCTGTCAGTGCCGCATTTGCACTCACTTATCCCCGCCACCGTTAACAGGGTAACGTTTGCAAAATTGGTGAGTAAATCGGTCGATTGCATATCAGACGTGCGTGGCAGAATATAAGCCGATAGCTTCGACTATTGATTCCTCATCGTCCAGGGTTGTTGACTGGGGAGAGCTCATATCATGGGGCACCTACGTTGCTGGAACTGTTGGATACGCACTATAAACCTCATAGAGCTCTGCCCACGTCATAAGCCTTAAGCATGAAGCTCGCGTTTTGTGGCAGCCCAGGGTAACAAGCACATATGGTTGCCCGCGATACCTGAGATAGATGTTATCGCTATAACTATAGCTCCTATCAAGTCATTAATATCTATGCTACAGAAGAAAAGGTAAAGCATACTACCTCACTTAAACTACCTAGTCTTTGAAATGCCACTCCTGTCAAGGGGAGGTGTTACTCCACGCAGTATAGACTCCAGCATGATGGACGCCGGGAAACGCCTCGTCGTCTCTTAACGATAACGTTATTAGAAAGGGGACAGACGTTACGTTCGTGACGTGTTAGATAATGCGTGAGAAATTGTGTTCATCGTCAAAGTTAGTATATCGTATATCCCTGTTACCATTGCGTTTTTTAGTGGGAGGCTTAGCCCAGCCGTGTGACCACCGGCAGGTACATAACAAGTGTGACTCGGCGTGTGCATACACTGGCATCAGCTGGCTGACTTGCTCCATGACGAGCCACCCCTGAAACCTAGCAACTGACCTCATGCGGAATTTACGGGGTATTGAGTTCTTCACCCGAAGCTGCTGGTTAGTGTGTCCGATATGCGAGCTGCGAGGTCATTCTTGCTGAAACTAATATGCTTCGAACTTAATCGCTTTGAGTAGCTGGATGATTTGCTCATGCCGAGATGCGCGTTGATCGTTGTCGGCTCAGGTTTTCCCTTTACAACATCTTCTGGGATGAATCTGGAACACTCGCACGGTTCATACTAGAAATTCCTCGCAAGGAGAGAGGGCCTCCGTTCGGAAGAGTCCGATCGTTAGACTATGTTGTTCAGGTCGTTCCTGCATTGAAGAAAACCTACGAACCGGAAGGGCTACCCTCCTTCGTCTCATAGGTGCCTAAACCGTCCGGATTGCACTAGGCACGATACTGCGCATTTTACTTCTGCCTGGGTGCTATAGGAAGTGCCCGGTAGAGATCACGCCGAAGATTGATGTAATCAATAAGGCTGCTGAAATCTGTACAGACGGCCGTCAGGACATCAGGGGAAAACTCTCGTGAGTGAGTACAAAATTAAATGGCCCTGGGGAGACTGAACATTTAGACTAACGAAGTCACGGTCGTCGCGTCATAAGAAATTCGAACAGTGGCCTGGGCGACTAGGATCTCGGTAGCGATTAACACGGTATCGAATCGAACGTAACTATTGATCGGTAAATCCCCCGGGCTCTAGGGGTCGGGGGTATACTTTCCTTTTTAATAGGCCCGACCCCCGAAACTGGCTCATCGCATATCCCGTTTGCAATGGAAGCGGTAGTTTTGTATAATTACGATTCGTTATAAAGTACATGCCACTTCGAGCACCGTCATGTGATCACGCCAGAGCACTCCGTTTGGCTGTGGGCCGTGGCACCACCGTAGTTCATATTCATTGCGAGCTCTTAGAGTTCATGTCCTCACCGTAGACCGGAGCAGAACCGCTCACCACGAACGCTAGAAGGCGGCTGTGCTCGGCCAACTGAACCACCAGCAGGGCAGACGATGGTTAGTATGGACCTGCTGGTTATCGGTTTTCAACGGGCTGTCGCTAATATTGTGGCTACCTCGATTAAGCGCCGCCGCAGCATTCGCGGGAGGAAGACCAATAGTCCGTACCTATTGACGCTCCCCGTGTATCCGAAGACGCCAGGGACCCTCTTCTGGTCGCCGACCGAAAGAAAGAAATTACTGGGATTGGTAGCGTCCCGTGGTTGAGGCATCGGCAGGCGGGATCTTCTAGTTAGAACTAGAATTCGGTACCTGCGCCCTCATCATCTTTACCATTCCCGATGACCCCTTAGGCCGAGTCGAGGACGTTCCCTAAGTCAGATTAGCTCTAGCCTGAGAAGCTCTTTACGGGACCAACTACTTGAGTGGATCGTGCCTTTCTATATCCTTGTCGCTTATGAGTTTCGGGACCTCGAGTTTTTCTGCAACCACTTGGCTACGTTAGGACTGTTAAATTGGCCACTTAAGTCCCAGAGCGATGTAGACATAATAATTATCGCCTCTTTTCGAGATATAAAAAGTATTCTGTTCTAACATCGTAACATAATTACGTTGCGTCTGCTCCAGGACGGAGATGGATTACGCGGCTTGCCTTGGCGAATAAACCTGTCCGAAAGCTCCGCGCGTCGAATAGTAGCAGTCATAGTGCACCGTGGGTGATCTGGTACCGCAAACATAGGGGTTCCCTACTCGACTAGACATAGAGGAAAGTACCGACATACAGAAATTACTGGGCCCGGGATGGCTTCCTGATCTACAACGTTAGAAGGATCCTGTGCCCCCGCGGGGAATCCTGTTACGGACTCACGCGCTCAGACACTTGCGATTAAGCTGGAAACCTTCGCTTCGCCCCAATGCGAGTCCTGACTGGGCGGTACCACGACCGAAATGCGTTACGCGTGTGCGTAGGACGTGTCGCTTCCAAAATCGGAGGCATTTCCCAAGGTTCCTGAGGGGGGGTGCTGAGGGAAGCTAAGCAGTAGACGACGCGTGTTACGCGAGCGGATTACTTCCTGGAGTTCTTCGATTCTTCACAGATTCGATAATAGTCCAGCAGCCGATATGCTTAACCTGGAGTTGAGCATCTCGCTACAGCGATGTCTCTAGCCCATTTTCTCGTGTGCTTGAGTCTTCTCACACGCAAACACAGTTTCCTTCTACAGATAGTTGGCTATTTTCGGACCGGTGTGAGTGTCATGAGCCAAGGGCTCGCGCCCAGACCATGAAGTCGGAGAAGTCTGGTCAGGTCCCGCCCGATACGTGCTATGTTCAAGCAATTTGGCTAGCTCTTAGCGCCCCGAGTGAGTATACATTCTTTGACGCTGGGCCTCTGGCGACGCCAACGAGGGTCCAATATGGCTACCGTAAACTGCACCACGGGGTGGAGGTCAAAGATATCGGCCGCCTATAATGTTGCATCACCAACTCAAGATATAACTGCTACGAGCTACTGTGTCCCCCAGTAAATAAGCGACGGCTCGGCACAATTTGTGGGCAACCGAAGTGGCATTCTCTCACGGATTTACGATAATAAGAGCGACAGGCGAGTGCCTACTTAGGCTATCAATTTTAGACAAAGATACGTGGGTGGAGTAGTCTCTCGAAACCGGGAACCTATCGTATAAAGCTATTGACTAATTGACCTACTGCGAAGGTGACAGCAGCCGCCATCTATAAGTCCGCCCAGCAGTGATGTTTGCGCACTATTGACGGACGCACTCTTTCAATCTGCGAGAGACCAGCCCCCATGCATGGCGAGTGACCGACACGAGGATGGATTAACTCCTCGGCTGTATCTTTTGCCTGATGCGCATTCGACACGAAAGAGAGCTTTACTAGCAAAGGATCACCGTCTCAGTGTGTGAAGCCAAGTTCTTGCCTATGGAAAGATCGCGGTAGACAGCTTCTTGGCTATTACGAGGTGTTGCCCCGGCACGAGCCTACAGTCCTAGCCAGAGGCCATCCCTCAGGGTCACAAATGTGCGTCTCGCCGGGAGGGTTCATTAGAGTCCTGTGCCAACGTAAGAGTAAGACTTATCCGGCGAACTTTGTGTGAGTCTTATGGAAACACCATCGACGTTTATTATGTATATGCACGTGGCCTGACCTAGGAGCGTAGCGACATAATGGGCGGTGAGTTACCGCTCACGGTACTATTGCTGACTACAATAGTCATCGTAGAGTGATCAGTCCGAGGCGTATCACGTAAGTTAGATAGGTAAACGACATTAGGGCTACGATCCGGCCGAGGAGATAACTATGACTAAAGTCCTCCGCATAATTCAAGTCTCAACTTAATGATCGGACAAATACGTGGATTTTACTTCCGTTCGCGCGAAACCGTCGGGAACTCATCGTTACCCTGCGAGTTCCCGCCCCTGCGATACATGGAAATCCTGGAACCGTTCTACCACAGTAGAGTATGAAGTAACCGCATCGGTCCCGTGATCAGTCGCCAAACGAATTAGGAGTGGGTTTTTAACGATGCCACCTACGGCACTGCGAGGTGAAGGGGCCGCCCAACTATAGAGAAGAGGAAAGGTTTGACTGAAGGGCGTCCTGATAGCCGGCATATTGTTTAGGGGCAGTCGAGGGACCAGTCCAGCGTTCCAGCGGCACAGAAGGCTTGTAACATGTTAATTCCTGGCGAAAACTGTTACACGACAGCGGTGTAGTATCTAGTGTGGATGATATTGGTGTATATTCGAGTTTGGAGATTATCTATATTACAAGCAACCACAGGGTATCCATGGCAGGACACAACTTGCCTTCTAGTGCTATGTTATATGATTCGCCAGAGGTGCGGTCCTCCTTCAATGCATATGGGGCGATATTGTTTAGTTTAGGCTCGGTAGTAGTATTGTGTGATCTGAGGGCACACTGAGACTTTCATGCTGAACGGTCGTGACTGGCGAGATGAAAGTTAGTGTTTGAGCGGGGACCCGGCGAGATCTAAGATGCTAATGACACTGTGGTCCCGCGCCCGTAAAGAGTACCCCTATTGACCTACGGCCATAAGGATTGCCGTTGAGGTAAGGGTCCATAGGTCTCGAGCGGCACTTACCCTGGCTAGGCTGATGCTTTATCCTATATCCTCTGTGGATGTCACATGACGTTCGTTACTACTTATGACTTGCTTCCTTGAGGGTAGTGGAGTTGATTCGGGCACGTGATGCGCGCGCAGGCGGGCCACAGCAACCACGTCACCTCATCACAATGCCTCTAGACCGTTTTCCCATCATAGTTCAATATACACAATTGGCATGCAAGTCCGCACCTCTGTAGTACGATCGCAAGGGGGGTCCCATTTAGACGCCGCATTATACTAAGCTCATACTTCAGTGATTGATTCGTGGTCCCCACTCGGGACAACGTTTCTTCCCCACGCGAAAGTCTAAGCGGCTTTTTGTTTTCTCATCTAAAGATAGCTCCCATTATCAACAGAGACTGATCCTGCTGTTTGTGTTGTGTTAGACGGGGAAATGCAGTAACGTAGCGATCCTTATGGAATTATAACAGCAGTTAGTCCACCAATGAGCATTAGACCCTACGTAGGTCAACCTGAGATGGGTGATGGTTACCTATTCCTGCACAACAATTCTGTTCTGCACCGCATTCGCATTCTATGACGATTCTAAGGTGTGACCTCTAAAATGTTCTCCCGGGGAGGCTTATCACCGGAATAGTTAGCCTGACCCCCCACAATAGATGAATGGTGAGGATTAACTTTACTGCGATTAGAGTCGCATTGTTTTCCAGGCAATAGCGGCGGACCCCTTACTCCACTCAAGCAGGAGCTCGAGCCATGCGCTTCTCTGCATTCCACGCTAACCGCTGTCGATCTCTGAGAATGGTGCAGTATGCGACTCCAGTCCCTAGATAGTCGCTAAACCAGTGATGAACTCAGCTTAGATGGGTTTTGACTTCATGCAATAAACAAATGCATCAAACGGTGTGACTCGGTCATGATTGCCAGAGAATCGTGGAGGCAAATAATTACAGGAGCCGCACCCGAATTTCGGCAATGCTTCTGTCACAAGAATGGAAACATGACCTGTTTACAGTTATTCGCCTTACTGCCTCGTTCGATTGTACCTGCTTCTGAATGATACACGAGTTCGCTAACCGCGTTTGTTTTCAAGTAGAACGAACGATTGGTCTGTAAGCACAACCACTTCGCTTTGCACATGGCTCACGATGTCTCGTCCGCGTCGGTACGCGCGGTTGCCAGGCCAGTGCCAATCTATTACCCGTTGCCATTTTGGTGTCACTCACGTGTATAATTGCCATGGGATTCACGCCTGCGATACTAGACATCCTAAATTTTGAACTCAACTCATGAGGAGCATTTGCGTAACAGCGTTTCAGTACGCCCTTTTGTGCTTTTAATAGGCGCGTCGCCCATATCACGAGGTATCGGCTTGGGTATCGTCATGGCTCGAGGTGAGGCATCCTATCTGGCCAAGAAACTCAACGAAGACGGAGGCTGGTTTTATCTGTTTCTCTCTAACAGGCCCAGCGCGCTAGCCAAGGCCTCCGTCCCCAGCCATAATCCAAAGGCCTATGTTTATACTGTTGGTCTCTCTATAATTCTCGCTATTCGTATCTTGAGTAATTATTACCGGAGTATTGCTCGCTCAGAAGCCTATTATCTATCCCGTGTTGACAGTGCCACTTCTGTCCTCACTCTGGAGTGGCTTACCATGGAGAGATTGAAACAAATCTGGCTACCCTAGCAGATTATTACTAGTAGCTTAGCGAAGCATCGCGGTGAGGACGAGAGCATTCCGTGTCGCTACCAGGCCTCCCCGTCAGCACGACCTTGGAACGATAAAGCAATTAAATAATGAAGGTGCAAACGTGGATAGAATGAGAGGCCCACGCACTCATGAAAAGGGTCCACTTCGCTTGTCATATGATTTTCGCCCCCTATTTGAGTACACACCACTACTAAGGCCTAAAACCAACCGTACCGCTGGGACGTTTCACGGGACTCATACGGTTGTTGTTAAGAGGCCGTCATAAAAGGATGGCCGGCGATAATAGCTGCACTTGTGTCAACATGTATGTTAACAGTCACGCCGCTGGATAGTTAAAGTTGCCAGTTTAGATTTCGCCCCGTGATCCCATCTCTAACTCCCATCGGAGTTGCCGGATAATCCGGGTAGTCCAAAGCTATGTACATGCTGGATATGAATTAACCGCGATTTCACCTAGAGCTCGAGAGCTGTGGAGATCTGTATCGGAGCGCTGAAACCATTCTTACAATCATTGGAGACAGACATAGGGCATCCAGTCATAACAACACGTTTAATCACCATGACACTTTATCACTTGTCGGCGGGTGCAGCCCACCACGCGCCAAAAGGAGTCATCACAGTGTACTGTGGGTGTGGCGTAAGGTTAACCCCAGTTCAAAACGTGAAGAGACCAGGGGTCTTGCACGTGTAAGGATTTTCCGGTTTCTCATAGGTACTGTTTCCCATATAGGTTTAAACCGCTGACCGACGAAGGCTCGGGGCCCAACGTTACTCCATTGAAATACATTCGAGAGAATGAGATCGACGTACCCCTCTAAACTGGCTAATCTTATGCCCCAATGAAAGATCCCCCGGAATGACTATATTGATAGGTGGCCCGGCGGTAGTGTCAAGTGCCAAAGAGACCACTTAGGGCACAGACGAGATCCTGAAGTGGGTCAACCTCGACACCTTAGCAGAGGACGACGAGCGAGTGCGCCAGCCCTGAGCGCGCCTTCATCTTTGACTATTGATGGCGGCGACGTGTTTATCGGAGCATGCATTACATATGGGAGAGGTCCCGAGACGACGCAGTAACCAATTTAAGAGGCAAAACAAGATGACCGCCTAAGGATCTTACTTGGAGCACATCATCCCATGGTTAGGTGGCCAGAGAATAGGAGGTTTAACCCTGGACCGAGACGATCATATACAGCGCTTTAACTACCACGTTTCACGCGACTTAATAACCATTCTACGGGACCTATTAGGAGGCTATAGCGAGACAAACATGTTTTTTTGTCCTCTCCATCTGACTTGTTAGAGGGTAGACTTAGCGAGCTTACGAAGAAAAGCCGTCACCCTCCAACTGAAGCCGAATGTCCCTAGCCGTGAAGAAGAAGTAGAAACTTATGCGTTCTCCCTTAAGTAGCTGATCATTCGATTTGATCGGGTTGGACGATGTTCTAGCGCAAAAAGCAAGACTAGGGTTAAAATATTGCCGCTCCGGAATGCCGTTGATGCCTCTGCCGCACGATGCCCCTTGAGGCCCTACCAGCATTAGCACACTTGCTTTCCCTTTTGCGAGAACACGTAAAGCTAGACAGAATACTATCGACTGGGCGAATAGGCAGGGGAAATTTCGACGAGTCGGTAAAGAACTAGGGGGACTGAAATTTGTATATATCCCGTGCTTCTAAACCGGTTACTCGGTGAATGTAGTCGGTCTCCTAGGCAGTATTATAACACAGCGGTATATCTGGTCCATTAACCGAAGACTGTAGTAGGCGCCCGTGTTTAGGTACAGAGTCGGTGTGGGTAATCAGATACCTATACTGGGGTGGATTCCATCCCTTGCAGAAGTATTTAGGGTAAATGTAGAGGTGCTGTGTAGTCCTTAATCTTATGACCCTGCCTATGAAATGCTGTCAAATCCTCCGATACGTCTCTAAGGACTCGTACGCAATCCGGCATCTTGAAGCAACGCCTTCCGTATCGGCTTCAACGTAGCCATATGAGGCCTGTCTATAACGTATTCATGCCCCACGATTGGTTCAAGCGGGCCTTTTGATGCCGGTAGATAGTATCAACCCGTACATCTGACAAAAAAATTGTCACACCGGCCCAAAGCGACTGTAATGGACCCTGCTTATCGTGGTTTCAGTGCGGCAGAGATTTCGATTTGTCGTTGCTTTATACCTACAAACCACTGGAGCGTCCTACTACGGTCATCTTTAGTCGGTAGACTACTAAGTTAGATGACTTAAGTTAGGAATAGAGTGTCTCAGCATACATTGTGTGGACTACGTAAGCGCAAGAATAACTTAACAATAAAATGAACGAGCTCGTATCAGTGCGAGCGAACTGGGAGCACTGGCACCGTTCTGATATGGTCCAGTATTTGTACGAGCGTTGTCTAACAGCGATAGGGCTGGAGTACGAGACTGAACCGATCTTCGGTATCGTGCTTCTCACATCCACAGCGAAAACTGAGAGTTGCATCTTGGTGAATGCAGTCACCGGGAGTGACATCCTGCAGAAAATTACCGCTGTACCATCGCTTAACGCCGAGCCCTATACTTATTTACGTTGATAACGTGAGTGGAGAAGCAATCTGAAACGTAACTCCCAAAAACCCCTCATTGAGAATTCAGGCGACCCAAGACCGCCAACTTTCACCCATCTTATCTGATGTCACTTTAAACGAGCGACCTTGACCGAATGAGGGGCTTGCCACCACCTCCAACTCCAAGTCGGAGTTCGCTCGCACAGTGTTCTTCAAGTCGTGATGTTGATCTGTCTGGGGCCAATTATGCTAAATACTGTCGGTCGCCGTTGCTTTAATAAGCCGAAACACCAGCTTAAAAAAAAGCGACGGGCCTAGCAGCGATCAAGCGCGTACCTCGATAGGCTTGTGCATGCCTTCCAATGTTGAGGATCGAGGTCGGCAAATTACAGGCTTTATGGATCATTGTAATTCAAATCAGGTTGGTGAAAACCGTTACCTCTAGGCTCGTCAATGCGATCGGGGTCCAATGGCACGGGACATATAAGGGGGCTATGGGTGTGATTACGCACAAATATTGATTGAAATGGACAGTTTGGGTCATGCGCGCTCACCATGTGCACTGTGGGTAGACGCAGTAAGTCGAATTCCTTAGTTTCCGGCCGGTCCTGTGTCTAGAACACAGATGCAGCGTCTCAAACCGCTACACTAGCAGTGTTCGTAGGTAGACGTTCGACGACTTCACCAGTGCTCACGATAGCCACCGGGGTACACACCTTCACCTCAGGTGTTTAATTGGCAGCCAACCTCGGCTCTGTTCCCTGCTGGTCATGCTGAACTAGCCTAGCACTCCTGGACGAGAAGAGACTTGCGGCCGTCTCCTACACATGTCTACTTAGCAGGGTAACTGCCCATAATGGCCTCCTATTCCTGGAGGTCCATGGGTATACGCTTGGAACCACGCCTTTGTGAATTATGCGAAAGGGCACTACGCAATATCAGTGGGAACAAGTACGGCAATTCCGAATTATGCGATATTCAAGGCCCTCATCAGTTGCTAAACTGGGTACCGGTACGCGCACTCATCGGGAGACTCAGGTAGCGGGTCGCAGCTACCCACCGGTCCGTGAGTCCCAATACACAAACTTCCATCGGGCTCCCCGAAATAGTTGGCCGTCGCACTTTACTACCCCGCTGCGCATCTGAGCAGGATTGTTAGTTTGTTGGCATCAGGTAGTCGTTAGCTCCGCAGTAAACCATCATACCGTACAGAGTGAACCTGTTTATGTGGATTGTTTTACCTGCGATTAACGTTACTAAGAGAGGCGTGGTGTGCCAACTCAGCGAAACTTTAGGGCTGCGGAGTCAAGTTTTCGTGATTTACAGCCACTTCACGCGATGGTCTACTGTCACACTGAAATTACGCTCCAGAGAATGCAGTTAATGTAGGAGGAGGTCTTGCTTTACACTAGCCGACTAACAGGACTCTCGTATATCGATGTGCCATGTGCCACCTTGCCCCCCGTTACCTGTATGCATGGCATCTATGGATGCTGCTCTAGATATTGTGGTAATCTGCGGGCCCAGCACGCCGCACGACCTCCGCCCATTGATAGTGCATCAGGTGGTGCCTTTAAACCTCGATGTACCCGGGGTCGTCTACCTCAGGGCTTCACTAATGTGAGTACACATACAATCAAGCCCCACCACCGAATTTGTTGATATGCTCGACGAAGTTGCTTCAGATGACCGTGAGCAGTCGAATCTGCCCCGTACTCGGAATTGTGACAACTTAGTTGATACTTCGCTCGTGATGGTAGATGGTACGTACGCAATTTAGTAATTGTGAATCCTGGAGTTAGGACAAGTCTGCAAATCACTGCCCGCGAAGAAGTGTCGCAGCGCGAGTGTGTTCTCCGCGGGATAAGCATTGTATCCAGGAGCTTTGACGTTTAAACGCAATGACCCCAGTGTCGTCAGCTCGGCGTGCTTTCACGTATCAGCAAGAGGCTTCAATCTCCCTGCGGCTTAGGCCCAAGAAACCCGAGCACTATTGGGATTGTGCAGCGGCCCGCTCGGGATTTCACTCTTCATGTCCTGTTGTGTGTGTGGTATAGCGGCGAGTTATCAATATATATCGCTCCGAAGAACGGCAAACGATGAATAACGTACAACCGAATAGTGTTCGCTTTATCCCTCAGCTTAACTTGCAGGTGAACAGAGTTCCTCTCAGTCACAAGGTCTACGATACTTACAGTGGGGAATAGCGCGTTGATACCATATGTAAGTTCAGCCCCAGCCGGGGCCGCCGGACCCTCCGATTCGAGTCTGTATTCAGTGAGGGCGTAACGAAGTTCTGGCCGAGAAATCAAGCGGGTGAACGAGCGCTTGCGCGCGTCGAATTGTCAAGGAGAGAGCAAAAACGCTGTATTTAGCCCAATCCCGCAGCTCCTGCAGCGGGAATCTGGAGAGAGTTTGGCAGGAGGCCTTTCGAGTGCAAAGCTTTCACGGCAACGGTGATGATGCTCAGGCGGCCCTAGTCTGCTACAGGCACTCAAATGCACGGCTTACAGAGTTGTCGGAGACTCGCGTCATATACTTCTACCTGCCGTTTTGTGGGGTGAAGATGGGCGGATGACCCTTATCGACTATATCTTGAGGGGCAGTGGACGGCCGTCAGGGGAATTAATGATGAAAAATATTTAAAACGGGGGTGCTCCAGATGAAGCCGGGATTTACAACAGCAGGCCAACCCACCGCAAACTTACAGGAAGAATTTCACCCTTCTCGGGTAGAGTGACGAGTAAGCCGACCGAGACGCTCCTTCCATGGATTAGTACGAGTTCGGAGAAACAGGTATTCTAGAATTGCCACCAAAACGCGTCTCCACCTCCCAAAGATTAACACACTCGCATTGTGAGCCTTTTCAAGGGGCGCCTCACAAGGTCACCCACGTGGAATCGGTGATGACAAAGACGTATCAAAGAATCCTGACTGCTTCGGAGTGCTCTAAAAGAGTTATGATCAAGCAATCCTGGACGAATAGACCGCCGGGGCAGGGGGGACCGGCTCGGGGTAGCACGCACTAGCACTCGTCGTATTGGTCGCTCACAAGAGTCATTTGGCCATTGCACGGAGCGATCAGATGGGGATCGGCGCTTAGGGGATAATATTACTCTCACCAAGGAAAGCAACCTTCGAAAGGAGACGTTTGGAAAGCCATCGACGACGCCTCGGACTATAGAGCTTGAGGGAATACGCCAGCTATCAGCGCGGACCCCGAGCTTTTCGGCACGGTGTGAAGGGTAACAATCGATTACTCATGCGGTACAGACACAGTAGGTCAATCAGCACTTTTAAAGTGTTTCACAGAGCCCATAAAGTCTCTTCAGAGTCTAACGAAAGGAAGGTGTAACTGTGTTAGACTCCGGCCCGACCCCCAAAGCGGCCTCGATCGATCAGTTATCCTGTGATAAATGAGTATGGAATACCATACTTAATAGTAGCCTCAACTTGACGGCACCACCATACTCGACCGATCTCAAACAGGGCTAGGGACGCGCCGGGATACCTGGCCCGTTGTTCTACGGAAGCTTAAAGTGATATGCTGCATTAGGATTTTTGTCTTTTATGGGTACAACCTTTACCTGCGCAGTGTCTAGGTACCTGCGGTCGCCCTCAACAAGGTTGAGCATTTCACGGAAGACCTATCTCTATGACGCTCTGCTTAGTCGCCAAAAGTCGTTATTCTAACATCATACACCGAAAAAGTTGCTACCGGGGCGGCTTTGATCGACATATCGTTTAGGACTAAGAGCATTCATCTTTAGGTGCTGTGTAGAGTCACGTTCGCACCACTCAAACGCAGTCCGCCATAAAACAGCTCGGCAAGATCAGGATCCCCTTTTCGAGCCCCCCAACACTATATAATGCCTCAGGTTGGCATTAGTGTGGGATCATGAGTAGAGAGGAGTGAAGGCTTCGTCAAAGGAGGAACACAGGATTTAGGCGCAGATGTGTCATAAGTTCATTGCATCTGTGATGCCTCGGGGGTCGGGGTGGCTGCCTATGCAAAACACTATAAGAACTTTGTCCTTTTTGTGATTCGACCGCGCGGAGGCCAAACTCATAATCATCTCGAGCATCACGCCAGAATTGATCATCTCTCAAAGCTGGTAGATTGGCTCATCTGCCTACTGTACCGGCTCTATTAGACGGGTGATGACATCATAGGTATGAGAAGCAGTCAGTACAATCGATACTGTCCGATCCTCTTCAGCGAGTCGGCTAGCGCCAAAATTCGCTCAGCTGACCGTAGTGAGACTAATTCCGGTGAGAGCTAGCGCCGATATGTTGGAAACCGTTTTCAGCGGATTCTTAAAAGAACTCCCATGGCTATAGTAGTCACGCCTAACGGGCTGGGCAGAATTTTTCCAACCCGAACGAGATTTCCAGAATTGGCATGGCATGAGGCTATAAACCCGAGTTCTTTTGTGGGGTCTAACGACGAGGTTATCACACGACGACGGGCACACATAGGGATGGTGGATTTGTATGTATCTGCTTCTCAATTTTGGTAGCTGTAATACCATCCGGCAAGTGATCCGATACATCTGGTAGCGAATTGAACGCCCCTGGCTATCACACCATCTGAGCGCTAGTCCGTATGAGTAGTACCATTGATATTGTTGCGGGCGCCACACGGTTTACGCACTAAGCCTATGTTTTACAAGGTAGGAGCCCGCCGTAGAGTAAGATAGCCGCGTCTCCCCCCCACTCCAGTACGCCGCCGGAACGGTTCGTTATGCTGGAAACGACTTCCCGCCACTGGCAATAACTGCTAAGCGTAGTTTCCAGCTCAGCGACGCAGGGCACACGAGAGTGTTTCTGACTGACCGGATCAACGCTAGTTCACACGGCCCTGTACATGACGAGGCGCCCGTTCCTAACACTTTCAGGAAGGCAAGTGGGCGGTTCCAAAGGTTGTTGAGCTCCCCCGGTATGGCGTCTGCGAGCCGCGCCCGCCGCCACGCGCTTGCCAAACGATTACCGCGAGGGAGTGTAACCCGGTTTAGGATGATTTTATTTGATGGATTCAGTGAGTTAAGCTGTACTCAAGCCCCGTATCACTTACGCATATCATAGGGTTCAGTGGAACTAGATATGCGCCGTTTGGCGATGATGGAGATTACTGGGATCCAGGTCTGATTGTCATATCCCTGTGTCGTCAACTAGAGGGTTGAAAAGACCAATTCTCCTCACGTCGGTCCGATCGCCAGGACGAAACTAATGGATCCCTTATTTTTACCCGTAACTACATACGTTTAATACCAGAACCGCCAATCTACTGGCTCTGAGTTAGCGGCAGCCCGTTACTAAAATGACTGTACTTGTAGTACGCTTTCGACACTTTACGGAGAGCCTTTCTTCAGCCTTCCTGAACGAGTTTGCGATTCACGATGTTTGTCGCGTCATATTGGAGCTCTGGAAGAGAAGGGGAAAACATGCTTGGCCATAGAACGGAAACAGCTGGTTGGGCTGTCGTCTTATCGTTGGGAGACGCCATACCCACGCGCAACGCAGTCGGTCCTCATTGGAGTAATAATACCCGAGCGCAACGAGAGGACTTATAAACCATTGGAAAAATACAGAATAAAAAGCCACGAAGCCTGGAACAGCTTACCCCTTATAATCAGTCGCCTCCTCTTATCAACATAGATCAGGCCGTGCATTGATTACGTGAGCGAGGAGGCTAATCGATAATTTCGTTGTACGAGACGAACGGACGTAGCAGTACCTGGGGTCAATTACGCCTATATTGGTCAATAATTCAGGTCACCACGTGATTTGGGGTAGACTACCGGAGGTCCATTCACGGTAAGTGTCGGTTGAATCGCGATTCCGATGTCATCGTTACCGTTCTGCGTTTTTGAAATCGGACCTGCATTGGAGCGTTAACTATCCACTTGTACTGGAACCTTATGAGATCCAGCAGATCCTATCACCCGGGCTGAATCGTTGACAGCTTCTATTAGTGGCGGGGTTCTAGCAACCATTGAGTATTGAGAGGAAATGCAGACACTGATGCCGAAGTCACAGGAGATTCTTACTGCCATTCTCAGCACCAACTGAGGCTCCAGAAAGTTGGGCCAGGCCTAATTTAGCTAAGGTATGGAATACACGGTTCCGTTTAATAGGGGTAAATCTTTCGGAGGAGCTCGAAGTTAAACCTCTGTCGCTCAAATTCCCGGCGTTTCTCCCATTGTTCGACTGTTGATCGGTAGTGCCGTCCCGGGAAGCCCCAGGCATTTCAGCACATGTAACTGCCAATGGTAATTTTGTCTAGGCGCATCACGCAAAACTGTAGGCTCGAGGCACCAAGTTCCTAGTATCAGCAGTTTGTGGAAATGGGACGGTGACCTGAAGGTTACTACCATATAGCATTCAAACATTCTAGATGAAGTTCGTTTCGACCGCTTTTGACAACTGTCTTGGAGAACTAGCAAATTATGAAAGCAGGGTCCCCGCCAACCTCTGTAATCATTATGTACTCTTGGGTTCGAAAATGAAAACCCTGATACCTCAATAATAGCGGTTGGATGAGAGCACCTCCAGCAGTAAATCTATACGGTTCTAGGACGCTTAACACACCAAAATATACCGCTCCTTGCGTTGAAAAATCGACGAAAGCGTCCTCGGCCGGGCTAAGTACCTGTACAATATGTCGATGAAGCGGGACCGGTTTCTATTGACTTAATAAGGACCCCCGCACCTTATGCGGTCAGGCTCCTGACTCAAGCATTACCTAATCTGGTACGTGACGTTGTTTGTTGTGTTATAGAGAGTATTTAGAACACGAGACTGGCTTAACCTCCGGCTCAGCCAGCCAACCTGATTACATCGCCGGTGACATCCGAGCCCGAGAAATTTCCCCATCGGGTGCTTTGTATGGAAATGACTGTTTTAGCGGTTGACGGATTGGCTCAGTGAGCATTTCGCGCTTAGCGGGCAAGGCCGGGTCATTATTTACGCCGGGCTACCGAAGCGGATTTGATTGGGGAAATTTTTATGTAAGGGAATCCTAACGGCTGCCGTTGTGGAGCTTGCCGGATAGTGCGGACAAGCTCCAGCTAGCAGCACTTCCCAGCCTATATGCTACCAGTTGAGCATACCAGAATAGCTTTGCGGTGCACGAGTAGAGAGAAGGGCAGGATTCAGACAATACCTAAAAAGCCGTAAAGCAGCCTAGACGTCTATTGGTTTTCTCCGGTCTGGACGTCTTCCTTGTAGCGAAGTTCTACAAGTTCTAACACGATGATAGGGAGAACAAGAACCACTTTTCGATGTCATGGGAGTGTACTCCAATAAACTCTAGACCGGTTAATGAAGCATCACTGTCGGTTCTGCAGACACCCACCTAATGCGTTCGCTCTGCCAAACCTTCATCTCAGCGACTGTACACAATTCGCTGTTAAGAAACGTTTCATTTCGAAATCACGACGCTTACACCGACCTGAAGTGGAATTACTAGGTGAACAATAGAGTAGGCCAGAACGCGTACGACTAGGGAAGGTTTTCAGGCCATGAGGGCCTAGTTTATTTCACGAAATCTGGAAGTTCCACCATGAGTCTGACGGCTCGTGTCCGGCGTGCTGTAGGTCAATGCCGCTTAGCTGCGCCGAACTGGCGATTTATCCAAGGCAATGTGTAAAGGAAGTGTCTGTGCGGTGACGATGTTGGGGAATTACCCAGAGTTCAATTGGCCGTTAGATCCCCCCGCCGTCCTGACTGAGGACCAGCACTGCAGTTCTGTTGCCTCCCAAAGAGCCCTCCCCGGGTGACTGAGTAGCAGGGGCTGCAAGACAAGGCCCGACTAGCCGTTCTAGTGGCCGAGAACCCGGAGTTTTGTTGAGCCCGGGTCGCGAAGGTTCTTTATAACTGCCTTGGCCATCAATTGGTATAGCGTCCCCGTCATGTCGGTTTTAAGCTCGTCGGACCCTCATAAAGTCGATTCGTAAACCATCATTATATGCGCCACTGGGTCGTACGACTGCAGAGAGCCGGGCGGAACATAATTCCGAGCGTCGATTATCCCAGCACAGCACGCGTCGGGTTTTAAGCTACAGCTAGAGCCGTCACTAGATATTTAATCGGCATCGTATTAGGATGTCATCCATGCCAGTAAACGCCGCTCAGCTCTAATACGATCCTTGTGGTGAAGAGGACGAAGGCCGTTCTGATCTGTGTGGTCTATCTTGTCGAGATGGACATGATGCTCAACCTGAACCTTAGTTTCGTATAACGCTCGGAGCCTTGGGACAGCGAAGGTCAGAGCGTATTCTGTTATTTAGCTGGCAACGTTGCGCTTACCTAGGTGCGGCCTTAATGAAAGAATTTGAGTTCTCGTCGTTACAACTTTTGCACTCCCCAATTAGATACTTAGTGAACCCACTCCTACCAGCATACACACTATCACGATCTGGATTCAGGCCTTCGTTGATGTCTCATCGTTACGATCTTACCAAAACGGGATGGGGCAAACAGAAACTCACCAAACGGGCCCCCCTCCCGAGACCTACATCGGCACAATCATTAGTCCACAAAAGACAGATATGCAGCCAGCTCTTGAAGTTGGGATTTCGTAGTAATTCGTATACCTTCCCGGTATGCCGTACATGTGTGGTTAGCAGATCGGGATAAGAAATCGTCACCGGCAAGAAGGCTGATGGATTTAGGGTCGCGAGGGAAACGGGTAAGTGCTAGTCGTCTACGGCGAACTGTTTCCTAAAGGAGGCTATCGTAATTCCGAATCGGCCACCAAGTTCTATCGTGCTCCGTACGCTGGGTTAACACCAATGATTCGTTGTGTCCCGTCATTCGTTATAGGCCGATCAGCGTCGTTACTGAGTTATAATCGGGCCCGGACTTTCGAACATTCTCAGGGAGATGTGTGTGTAGAGAAGCTATTAGTGCACCAACAGTTCAAGAACTTGAATATCCCAAAGGGCCATAAGTTATGCGTATAAAGACGAGAGTTAATGTGAATGTTAGAGGGTGCTCTCTAGTATGAGGGCATCCTCCTGGCATTAAGTACTGCTGATAATCACCTGATCTAGGGACAACAGTAGCGCTTGGCAGTGGGGTTACACCTACATCGACGGTGCACAATGGAATTGGTAGGCCCTCGAACGTGTACCATATAGTCCTGCACGGACATTTTAATTATCAATTGCTACCTTACGGGACAACTATCCGGTATCAGTGAAGAGACATCCAGTGCATATCAGTAACACCTTCCGGAGGACGCTCACACTATCCCCCCGTTGCAACCTGAATTATCTTAGGAGGCTTACACGACCTGATATGGGGGAGCCCCTTTGCTGTAGGCACTTATTGCACTGTGATTGTAATCATAGACGTCCGAGTCTTCCCCGGCTGCTGCCATAACTCACGTGAGTTTAATGGAGTTGAGCAACTAGTAAGGGCTGGCGACGAGTAAAATGGTTCCTGTTACAGAAGGAGGGGTCTTGTAATAGGGTTGGTCGGCACATGTGGCAGGAACGTCTGGGGGGTTCCTCCAGTATCAAACCAGAATGACGCCGAATCTACACCTAACGTTTTGCGTGTCTACCTCTAATCATCTCAACTGACGCAAGACGACAAGCGCTGGCGTTGTACCTATCTAGACCCCTGCCTCCCAACAGACCGAATGTAAACTAGTATGATCAAAAGCTCGGTCTCATTTACACTTCAGGAACCGTGCACTCATAGCCGGCCTTAGGTCTATTGCGTAAGACCACGGGATAGATCCCTGATCTCCAAGCAGCTAAATGTTTTGCGATACTTGAGACTAGAATTTATGCTAACAATATAGATAGTGGGAAGGTAAAATGACGGCCCGGTAAATCCGACAGGTTTGCATCGGCGTCATTTAACGGAACTGGTCATTGTGGAGGGGTTGTGGTATAGATACGTCCGTTCATTGTTTAATTAGTTGTTTATAGGTTGACATTTCACGGGGACCCGTGAGTCCTAAATTTGGCACACTGTGCGGGCCGACGTCCTTTAGTGTCAAGGTCTAGTTAATTCCACACTTGAGCATCCTTTCACAGAACGAGCGACTGGCGAAGACAGTCTGTCCGTGCTTACTGGTGAAGGTATCCTGGATCTATGTGATGACTGAAGAAGCCTCCAACAGGTAGGACAGTTACAATTCATCGGCCAAACCGTACATGGACGGATTGTCCCCTGGAGTCTGATAGATAATAAAGGTCCCGTCAGCGTCCGAACGGGTTGCTCGCTCAGAGAGACAGATGACACAAGTATCCGGGTTGGCGTCCACTTCTGAGATCCAATGGGCTTCGCCAGCGGAGACATTCGGTTTAATAGGAGCGGCACATCGTTTTTATCCAAACTGACTCCCCCGGATTATAGCACGGCATCTGTTACTTCGTCTTCGTAATTAGCCGGTTATGAAGGTTGGCAGCAACGCCCCATGCAAGAATCAAGCCATCGATAAGCCATGCCTGATAGCCTTTTCTACCGGTGATGAGTTGAAGACTCCCAATCTCCATCCTTCGAGCGCGCCGTCGCTTACGGTGCATGCCGAGGGCTACGGCCTCCTGTACGCGTCCCTAGCTGTCGCGGAGTGTTATTTGTCATGTGGTACTACATTTCGCTGAAACCTACAGCGGGGGAGGCCTGACGTTAACTGGGAAAATCCTTCATCCCCTACGTCTTCTCTTGTGTACGAGTACTCCCCATTTAAGACTGGTTCTAACCCTACTATAGCCTGAGGCAGGACCAAGAGACGCAGCCGACACCAAAACCTACCAAGAGCGGCAGATTCGGTTACCCGCGGAAGCCTGAATTGTGGAAATAACTCAAGCACACACTCAGGGATACTCTTACCCGCCGGTGCGTTGCACGTGCGCCGACACTGTCCATCCCCCCACGGGAGTCGTCCATAATCCTCAAAGATATAATCCCTCGCAGGAGTGAGCCCGCGATATCGGCGTAACAGCGAATTACGTCGCGGGTTCGGGTAGAGACTAGACATGCAGGCGCGAAGACCTCGGTATTTCCCCGCGATGAATAGACGTCGTAGCGGAAATTGAACGATGGACCACGGACTTATACTAAATTTTCCCATATGCGAAAGTATTAGGAATGTCGACAACGCGCCATAGACACATCCCATAGTTTACTGACCTATCGACCCTAATAGATCCGCAGTATCTGATGTTTCTCTTACAAGAGACGCGCAGGTAACACTTGAGCCCTTCGCTTAGCGTGCATCTGAGCTAGGCAACGGCGCAAGCGTCTTTTTTGCTCTCGGGTGGGAAACCAGTTGGAGATCTCCATGACTAGAGACAGGGGTGATGACCTGGTCGGGAGTCCTCACGGGTGGCTGCAAGGACAAGCTTAATTAGGTTCACAACTATGAAACATAGGATTTATGAAGCAGGTGCTATCAGTTTTCATGGCTGGCATTTACTCTAATTCCACCTACTTGTGTGACCTAGGGGAACGTAACGTAGTTCAAAATGGCTTTCGCCCACTCTCAAGGGCAGCGAATCCTCAAATAATACCACGCCCTAACCGTATTGCTGTTTTGTGTAAGCTCTATGTGGCATGCAGGAGGCCTATCCAGTGATGAAATAGATCTGGAAATACTCTGACCATTGGAGGACCTGTCGTTAAAGAATACGGCTACAACCCAAGATACGTACCCGGCTTCGCGAATATGTTATAGATTGCCTGTGCAGCATTAAGTGATCTTCATACGAAATGAGGAGGACCTAGGAACGGATGTCGATAATGACCGCCGTAATTTGACTCCCCGGCGAAGTCACTGTGAGAACATCGCTTGCCCGCGCATCGCGTAGGGATAATATATGCAGTGCCGACTATGTCGCAGATAGTCCTATTTTAGTTGACCACACTACCGAGATAACTCGACAAGCGATGGGGGGGACGGAATGAGTCAGCCATCGTTCAAGATATGATAGAAGGGTATTTGGTCGCTCGCTACATACCGGTCAAATCCTGTCTCAATTCGTCGTCACGCTTCACAACGATGTATGATTAGAATTGATTCGGCACCGACTCAGACTAATAATAATCTGTTTAAGGGGGGGCTAATCTGAAGTGCGATCTAGGTGAGCTTGAGTCGCGAACGCGAGTTAGTGTTTATCGAAGTCTTAACACCGCTTGGTCATAGCAAAATTAGTTCGCTCACCCCTTCCTCCCGTATTCGCCGCACACCCCTTCCGGTCCGCAAGTGATCTCGAGAGGGAGTCGCGTTCAAGTGAGCACGCCGATCTAGGTCGACTAGCTGTCCCTTGGAAGACGTACAGCCAGGGTATTCGTAAGGCACAAAGTCGTGTGCTACTACTCAACCTGCCGAGGGAATGCACGAAATTACCGATCAAGAGCCAATCCTAGACGATTCGCAAATCTTGTAGACCACCCATCGGCCATGGGACAGGCGTGATATGTCTCCACAGATTCGGGGTCTAAGTTGTTGGCACAACATTAAAGTCCTTTCTTTCTTTTTTCCAGCGGGGAACTATTGCTTCTTGCCCCCTGTGATATTGGTTTAGTGAGCTATAGCGTCGATTATTCGTACAAGTCCGACGCTGCGGAGAGTTCGAAAAATAGGATCAATAGAACAGGACTAAGTCCCCTTCAACGCCCCATATGAACCTACGGATTTGTTATACACGGTCCGTCCGCTAAGGCAAACGTCGATAATAACGCATTAGTCGGATACAACCGCGTGGTCGGTGCTCAGATTGCCAGACGTTACAAGGCGGCACCAACAACAAATGCATCCATGATATATTTGAGCCGAATGACGAGCGCCAATAAGTGGCTTGGGTCGTGATGCCGCCCCCTACGGTCCGTTGAAGACTGAGTCTTGGAATTCGAGGGGGCCTGTGAACAGGTGTGTTGCCGATGTTTCACTGTGCACTCTGAGTCTGCTACACGAGCAAACAACAATATGCAACCTTCTTAGCGGCCCAAATCGCTTGGTTAAAGGCAGTTTGAAGTCACCATTGGTGAAGTCACGCCTGTGTTTAGGTGTGGTCCAGGGTATGGATATACCCCTCGTGAGACGCCAAACAAAGGATGTCTACTTTATGGCCCTATTTTCTAATTGAACATCGGCACTATCACGTCCTTCAGTTTGAAGGCAGGAGGGCTTAGTTATTAGGGGCTTCGCCGTTCTCCCGAAGACCTTTAGTGGGATCTATACATGGCGTCGTCTGTTGTTGGTGCTATCCTTTGAAAGTTCGAGTGGCGCCGAAACTTGAGCGGTCTGGAAGATGGAAACGCGACGTAACCAGGGATTTTAATGTATTTGCCGATATTAACAGCCGTAAGTCGGGATGACCTGGCCGAATGTTCGGGGAAATTATGGGGCCCGCCTAATGAAGTGCGCTTATCAGCAAAACCAGGCTTACACGATCCCGGTCGACCGCCCTGGTTATGAGGTGAACGCGGACCTGGCCTTCTTGACGTGTCTCTGCCCCAGGTAGGTTTGCAAAGGCTGATGTTGCGACATAGACTGTGGTGAGTAGTCCACGCGCCGACGTACCGGGCGAGCTATCGTACCCATCAGCTTGAGATAATGTGTCGCCCCTAAGCATAAAGATCTAACGAACAAGGTGGTTCGCACTACCATCTGCGGAGCTGAAGCACCTACCTACTGGCGCAACGGTATCAATGTTGTGGCAACTCCGCACGTATGAAGGAAGGGAGTCCTACCAACGGATTCGGTCATCTGTATACACGCTCATAACTAACCTACTCACCAACGTTAAATAAACGGATTTCGACAATGGACGCACTAAGGCATTGAAGTGACTACCTGGTAGGAGTAATTCGGTTCATTTACGAATATGTAATATTCGTTTATGGCTTATTTACTGTTAGGATGTGGTCGAACTCGCACTAGATATCATAGTAAAGGGCGGACTTTACTGTATCAGTGAGTTGACCTGGCAATTCAACAAAGCGTATGTCTCTAAAGAAATTAGCAACCGAGCCTCAAAACCTTATCGAGATCCTGGCATCCAAGGAGAGTGTCCTCATTGCTCCACAGCGAGACTTGCGACCCTGGACAGCATGCGGTTTAATGTGTTCGGGTACTGTTTATACGGGTGAGTTTACGGTTGATCACTTACAACTTCTATTGTCCCATACAGTTAAGCCCCGACGGTCTCTAAAGCCAGTTCGGAGCCATCTTTCGCTTGTGGGAGGCGCCAAGGGCCTACACACCGGATCCGCGCTCTGCCAAGGTCGCGGTACTGGAGAGTCGTGTTGTCGTAACACATGCTACCAATACCGATCTAAAAAACGCTAATAATTGTGTGCCGCAGGACCTTCTCTTAAGTCGACAGTCCTTGACTCCGTCTATGATCGCGCCGGCGTTGTACCAACGATTCTCCGCCGGGGAATGATCGATCAACATTGTCTGGCGAGCTAGCCAAATCAAGACCTTCAGCATAGACAAGTCGGCATAGTGTGGTATAGATAGCTCACCTCTACGCTTCCCTTCCTAGGCGTTCGGTCATCTCGTCGAGGGAGTGTGTACGTGTGTCTATGAGGCCAACATAGCCCTCTCTATTACTCAAGATGTCTGATACATTTACAATCCCACTATCCCCTCCGAGACGACGGGTACACTAAGTTGCTCGTCCCCGCGAAGACGCATACATTACCTGCGGTTATCCGGGGTTGCAACAGCCTGCAGGCGCTGGCTCATTACCTTTCCAAATGCAGCAATGTCCCACGTGATATCAACATGTGTGGGACAATGGCCTAAAGGTGGTTCCATACTAGCTTATACAGCGTCCAATAGCACCAGTTGCTTCTCAAGGAACGGTCTAGGGTTAGTAGCCCATTTGCCATCTGGACCCACCGTACCCGTTCCGGCAAAGGTCCAAAAAGCACATCGTGTTGAGCACTGTAAAGCCAAAGTGCAGCTGAAGACGAGATGTCCGGTGGACTGACAGAGTAGTCGTAGCAGTCTAGGCGACCGCTGAGACATCAAACAACCCCACGCGACTTTTTTCGCAAGCGCAAGGTTACACCTGTCTCCACATGCCACGCTAGGTCGTGCGAAACTGGTTTGAAGGCCGGGATAGATTACTCAATAACATACACCGCGTGTATCCAACTAGACACGCCGCTATTCCGGACTCAGTTGGGATTCCATAATCTCAGTCTGAATATTACAAGCCACACACCCGGCACCATGGAATTTTTCGGGAGGCAATCGCTATTTTAGCCGTCAACTCTTAGGATAGCAACGTCATGAGAAAGGTCTATCCTGGCGCTGAGCACAGAGTGGGCAACAAGTCGTTGCGGAACTTACCGCTGTCGGGTGCCGCTAGGCTTAGATTAAGTGACCAAGACATTGACCTCTCAACAATGGTCTGCACAGATTCCTGGGCCTCAAAACCAACTGTCGGTTCCTATCCTACATTGGGGGCGGTTCATCCAGTCGAACCGGTGCTTTAGTATCCGCGGTGACAGCGTTTGTTATGCACTATGTAGCGCCACGACTCGCCCGGAACTGGTCCGAAGTGGTTACAAACGAGTATCATTAATGCAATCCTTGTTGACCTAGTCATAAGTCCTAATTCAAGTTATTAAAGATACAGAGGGGATTTGCCTTTTATTTCAGCTTCCCGCCACGGATTTTCATGGGCCCGTTTTACCTAGATGCTGCCGTGTGCCCGTACTGTAATAGGCGGGGTGGAAAACAATTTCCTTTGTATTAAACGTTTATGCTGCTGGAGGCCCAATGGGACACACGACTCCCGTCATGAGAAGTTAGATTAGCGGATCTCGCGATGAACTTTGGGTCAGCCGGCCAAAACGCCCAAGAAGTCGGATTGAGTCAGAATGTCCCTGCACGCCGGGTCTGGCTGCGAAACGATACTGGGTGAGGAGAAACTGGCAAGCCAAACTCGGCTGTGTTCGTTCTAGTCACATATCCCTACTTGAGCTCACCCTTATGTCTATCATTGTCGATCTGTACTACGGCTAATTTATTAGATGAGTTGTTACAAGGCTTCATCATTCACACAGGACAAAGTAAGATGGGTCTTAAGCACCGGCAATGGATGCACATAAGAATAGCGACAGAACAGCAACAGCGCAGGTATGCGATAAGCACGAACCCTGTTATGCAGCATACCAACCCTCATATATGAGGGATCCTCTAGTTGCCGGGTGGCAACGTATGGGAGGTCCATCGCCTAGCGCGTGGTACGGTGAGTAGATCACACGCTTGTCCTGCCGCCATGCGGTAAAACAGAGAGTACTTGCTATTCAAGATGGTCGGTGGTTGTTTGTGTTAGGATGAGTGGATAGCCAAAGACTCGCTATGGGGATTAGTCCAAGCTGGATAGATAATCCGGATAAAGGGAATTTGACGTGGTCCCTTGCATGGCTCGCGGACCGCTAGTGATCTCCTAAATGCCGCCAAGTTCATAGAAGGTAGAGCGATAGACTGGTGGTAGCGGCTACTCAGACAGTAATATCCTCGAGGAAAGAGTAGGTAAGCATGGATACCCACCATTACGCCGTTTTTCTCGTCGGTCTATGGGATATTGTGGGTAGTATGACGTGGGGAGCCAGCGATCTACAACTCATAATTTACCATTAAACGCCTAGTTCGGTGTGGGTTTTCGAAAGGTAGGAATGTGCGATGTAGTAGGAGTGGGTTTAATACGGACAGTGCACACTACTGAGGCCACATACTCAGTATGAGTGGATGTCAGACTGAACTCGGCCTCGTAATCGATGCCAAGTCTGCCAAATTGTTCCTGTTCGCGTACCCGTATCATAGCCCCTTATGCTAGGAGAGTCTCGAGGATCCGTCTCACCAACTTTCGCAGACACTGGTTCTTAACGTAACGAATGAAAAGTTCGGGACATCGTACAATGTTGCGCAACATCCCGGATAAGAGGCACTCAAGTGGTCCAACGTGCGCCTCAAGTACCAGAAATTCTGCTTCGCCTAGCAGAGAACTCACACACACGCCCCTCCCCTGCACACGAGGCAACGGTCTAGGTTAGTTCGCCTGAGCAAAAGAATATCGCGTGGGGATGGTGCTTAGAGCCATGTCTGTTCTCTCATCAACTGTCTCTGAGCCTGCTATGCGGCTAATGAAAGTATCACCAAGCTGTCATATGAAACCCAAAAGTGTATCTGGTACGCAATTTAATCCTCCAAAAATTCCGTCCGTAGGGCTTCCGCTGCCACCGGGCCTTACAATAAGGACCTGGGAACCGAACCCATTGTACACGTCTTACCGGTTGTACAGTAATTGATACTTGGGTGCGTCGGGCACAGAGAGGGCCAGTCTTGGGCCGAATATACGTCTGAGTCCGTCCTTTCCGAGATCGGCGTATTAGATCCTTATATACGCCCCCGGCTGCAATGGAGGCCCGAGGAGGGCCTAAGCTTCCGCATATCAGGAGGAAAGATATTAAACATCATGAGCGCATGACGCCTGACCGCGATTGAAAAGGAATTTCTTCCAGCTAGAATACTACATGCGGCCATGAATACGCCGTTCACTCGCTCACCCGGTCGTATGTGACGCAATACTGCAATTCATCGGTCTGGCTATAGCGTGCGGGCGGGAAGGCCATAAGATCAACTCAAGTTCCAACAGCAAGAGGATTGGTCAGTCGGGTAGGTATGAGGCCAATTATAAGTACAGACGGGAGTCTCCTAAACTCACTCAGCGTATATATCAAGTGTTCGCGTTTTTCCATATCAAGCGAGACTCGGACCACCTCTGTTAATGCTACCATGAGTAAGTATGAGATCGAACCGTTGTAATTGAATAAAGTCGTTTATTCTAAAGCGATTGTGCGCTACACCGGGCGTAAAGAGCCACGTCTAATAACGAGCCGGTGGTTGATGCTCGTGTGGCTGCTATTAGTAATCCCTATCGAAAGGTTCGGCAATAGGTAACGCGTCGAGAGTGGGCCTCCACGATCTCTACTGACTGTACGCTTGATTGTTCGGTGCATATTATTACTAACCGGTTTGACAACCAACGTCATAGGTTGACAGCTAAGAGAATGTGATTCATTGGGCGAGTATGATCTAGGTCAAAGGGTTTAACGTATTTCGCGTAATACCCTTCACGAGTCGTGCTTATAGTCAGTAGGGGTGTGTATACTCCAGCCGCCGCTGAACCAGAGCGTTAACCCGTGGCCTATTAACACCGCCCTACTGTCCGCACCACTCATTTACCGGCGGAACCTAGACGCAGCTATATTAATGAACAGTCTCACGACGACCTTATAACTTGGCAAAATCGTTCTAACAAGATACGCCCTGGTTTATACTATTAGAGGGCGGAATATTCGTATACAACAAATAGGACAAGTCGGGACCTGCTATATTGGTTCATTCGTCACGTCCTATCACCGCTTGATCGATCTGACCATGTGCCGCTAGAACTTTCCGACATGGGCCCGCTTCTCGTACCAGTCTTCGAGTTCCTGTTCGTCCTTCTGACCTCCCCCACTATAGGGCGTCAACATCGGTATGAGGCAGACGTGAAATTTACTCGCGCTTTAGAGCTGGTGCTTGATTATTCGCCCCGTCCGACCTTAGCCAAGGATACTTCACGATTTTTCGTCCGGGCCCTAGGGACACCCCACATAACATCAACTGGAGTTTAGTGCTAACAGTGCGAAGAAAAAGTTGTGCCGACCACCAAGACGGGTCGACGAGATGATAATAGCGCGACACAGTGATAGCCATTAGGATCAATGGCGTTTAGACCGTAATAGCATTGCGGCCAAGCTCGACATTGTTACGTAGCACTCATGGTCGCTGTTGTTGCGGTTACGGACCCTCGACTGGTCATGGTATCGTGCTTACCCCCTGTCCGGGTGAATAACAAGACCGTGGTCAAGATAGCCCGTACGTGGTTGTCTGCTTTTCGGAATTGACCGCGGCTTTAAGAGACAGAGCGATTTGCGCTCTTAACAATGAAAGAGCGCCATAGACAATTGGTTTAGAGCCTGAATATCTTCTCTCGACGTGGGTTTATTCATGTGCGCCGCGCACTGTGCCCGCGGGTCCGCTTTTGGCGGGCTGTCCGGCAATTGGGGAGGGCCTAACTCGAAGGGAGTTCTCTTGAATCCTAGCTGATGATTCAGAACCTGGTTCACCACGTTGAACATTTAACATGGAGCGACGGCAGGAATGGGGCCTCCCAGCAGTGTATTATTGGCGGCCTGGGTACTAAAAAGAGACGCCTACGCGTGTTTGGAAGTCGGGATGAGACTTTCTTCGTTTCGGCGACATGGGCAAACCTCTCCACTTAAAGAATAAATCCAGCTGTTGCGTGAGCATCGTGTCGAACCGCTAGACTCCATTGCCGCTAGCAATTGGCCTGAGCGGAAAGACCCCACTGAAGTAGCAGACCAGTGGCGCGCCATGTATAAGGAGGACACCACTCCCAGTTTAAAGCCTCTTTGCAGTCATGCGTATTCCCTCTGAAGGTTTGCTGAAGCGACTGACGACCAGGTTTTTGGAATTGTATATCCTGTCCACATCAATGGTTCAGTTGACTTGTTTCTAAATTGACACCCCTTGATTTGAGGCTGACAGTATAACAGGTTGTAGCGCACACACAACAAGGAACTGAGTCTATATGGAAGTAGCCTATTTGAACTAGTCTTTGCTGATCTATGTAGCGGCGTACCGACGAATCTGTTGGAATACTATTGCGTCAACTACGGATCGAGCATGCCCATTAGAACCCAGACGAGATTCCCGAAAAGTTATTCAAGTTTGATCCGGTTTCTAGCGTGCTGATTTTATCTGGTGATGGATTTGGGGGTAGCCCACTGGTTTGATATCAGAAGGACAAACTTCTGACCGGTGACAACATTTGTCAAATGCAATCCTGAGATCTGAAGGAAAACTTTAGTTTGTCCCGGGCGTGTTAACCCTGGACCGAGCAGCAGAGATATGTTGCTCCAGGCTCGCGGCTGACATATGTTGGTGAGTAATCGGGCACTATTTATAATTTGCGCATTCATCTGCGCTACCTGGACACCGGCGCATGGTTTGGTGCTCCTAAAGCCTACCTTCCCGACCCCGAAGAAAAACATACCATTTGTGTTCTGGCACAGTTTAACTGACCTATGCGCACAGTCGAACAGAGGATATACCCATTTAACTCAAGGTTCACTATTCTTTATTATCCAGCTCGAGATGTTCTCCGTGTAAGACGCAGACTCGAATTCAAGTCGGGCCGAACTCCAAGGCAGCGGTGTGCACTTCGGCGTTCACCGAAACCGCGGTGACTTCAGGTCATGACGCTGAACCGATGGCTTTTTCCGGACCCCAGATCTAGCTATCCACAAGGGTTTTATTGCAATGGTGCGCATCGCCGCGGCTTGGAAGTTCATCGTAATTCGCGTCAAGGCTTAACTCTCTGCGTCGAACCCTACATTTGAGCAACCTGCGTAGTGTACTTGTCTTGCTCATGGTGCTTCTCCATGTATAATTGTTATACTCCTGTGTCATATGCATAATGCAAGCGATCCAGAGGAGTCGCGGTCGAGTTAAGCAACCATTGGTACCTGTGCGAGCCTGAATTAGTTTTGACAAGACCGGTCTAGTGGTCGGGCGTTTGACTGGAAACAGATACATACTTATCGACAATGTTATAACTGCAGTACGTTACTCGCGTGGACATCTTGTGTGCACTCTTGACGTAGCCAACATGCAGCATTCGTTAGTACGTGAATTCAACTGCATCACGCACATCGACAATGGTCATGAACTGTCCGTTGTCACCGTATCACCTTAGGAGTAGCGAGGATTTTGGGTGCCACTACAGACCCACCTTCTACTTTTAAAAACGCGAAACCCCTCGGTAAGAAGCCACTAAGAAACGACGTTGGTCTGACGAATGACATCTCCTCACATTAAAGTGAGAGTTAACATTCATCTGTATGCCCCTGGTGCCCACATTGCTAGTAACTGAGCCGGCTGTCACGGTTCTGACCTACCGCTTTGCGAGACAGAGTCATTTGGCGCCGAAGGACGTTGCGCCGCCTCCTCTCGATAGTGAGCCACCATCAGAGAGTCCCTTAGCGTCGCGGCGTGGCCGTTCGTACTGTCGCCACACAAAATACTACATTCCCCACTCGAACGGAGTAGGATTAGCACCCTCTATGCACTCTACCACTCATCCTAGGCCCCTCGACTGGACGTGCATCCCGGCAGCTGTTTTGGCGGCCTGCAATTATGCGAGTGGTAAGCTCCATCCAATGACCTCTTATGCAAAATCATAATATAGGCGAAAAGTTCTCTAGCCAGAGTTGGAGACATTGCTGACGGAGACCCCCCGACTGTTGTAACTGCAACTACAACCAGGAACCGGGCAAATTAACTAACGGGGGCAGTCGCGCGTAGGTCTCAGTAGGCGAGACGCTGTAAGGCCTAGCAATATCATAAGCGAGTCCACTCAACTTACACTAGCAAAATGGAGGGTTGATTACTACACCCTGCTTGATACTACGATCTCCTGGCATTTGTCATCGGTAGCTAATCTATTGCATAGAGCCCGACAGCGACCCAACCCGCAAGTAGATCACGGAAAAGCTGAGCCGGCCAAAGTATCCGCGGCTTTGGTTTCAGAGACACCTGCTACCAAGGTAGTGCCGAGTCGAGTCCATCATTACACTATTCCCTTGGCGCGTCATGAGTTCACTGATGGGATTGTCCTGCACGCCTATTGTCCTAACATTCGTTCTTGGGTCACGCAGTTCTACGCAGGTTAGACTAGGCGTTTGACAGCCCGGCCGTCGTCCAGTACACGGTTAAATATACGCTCAATACTAAACACGGGAAGTGGTGGAGCTGGCTCAAGTCACGGTTATTTAGGCCCATAAACACATTACATCAGAAGAGTAAGCACACTGGCCAGAGAGGTTTGGACCAACTCAAACGCCACAGCCGTGCCGTTCTTTTCCCTGGCGACCATATCTTATTTGTCCGGGTAGGGATCTTTGGGCAGCGTCCACCCTTTCCACTCACTCAGTGCCGATCTCGGACGGAGGCGGCTTGACGTTAACAAGCCTATACTAGGCAGAGTGGGGCCAGGTTACCGGTGCGAGACTACCTGAGCAACCCCTGGCTCGTCGTTCGCATCTATCCTGCCGCAAAGTCATAATCTTCATCAGGGGGCGAAGGCCCATCACGGGGTGCTGTTACTATAAGACCTAACGTAGCCTCGTAAGTTTTGGTCGCAGTTGAGAGTGTCACCTTTAGCAAAATATTTTGCCTACTCCTAGCCTGGTTAATGGGAGACCACGCTATTTGAGTGGTAACTGGAGAGACGTCCCAGTCCATACCCCGTCGGCACAATCCCCCGGCGCTCAGGAAACGGAGGCTTTTCAGAGCTCGCTGTATTGCAGCAGCGAGTCACTGCAATAAATGTTCCAACCCGGACCTCATCTGCAAACAGCTTGCAAACCTGCGTTTGCTTACTTCCCAATATTGAGTCCTATTAATACCGCAAGGACCACTGTAGAACAGCGTAAATGGGTACCTCGAAATTACACTTCCAGCCAGTACTTACCCCCCTTGTCCTCCTGACTTTCAGTCCAATATGGACAGATCGCGATTAGTTGCTGCGTCAACCGCGTTGACTCCTCTCAAGATGGATACTCTGGAAGAAACCTTACCGCGCGATAACAGTCCCGTATAGCTAAATTATACGCGCCGGCACATGGCTTTTTGGCTACTTGCCCACGTGGGACTAGTGGCAGCTGATTACTGAGACGATGTACTGGTAATACCTAGGGGATGCCTAGCACCACCTACGGATATCGGGAGTCTCGGCATGCCATCTGTATCGATGAATTAACTCACGTCCTCGCGTCTGACCATGATGCAGGCCAGGCTCTCGATATACAATAAGCATTGGTGTTACATATCAGTACAAAAATTGAGCTAAGGGTAAATTATTGAACCGGGAAAAGCTTCCAAAGTCGGCTCTAAGTAGTGGGCTTGAGGGGGATCTGATGATGCGGCTCTGAATAGTTCAGATCCTGGCCATAACGTGTCCTACGAACCATGCCGTACTGCAGGGCCATTCGCCACGGGGCCAACTGTAGAAGCCCCGGTCCCGCTGTGATATATTAGGGTAACCTAAGGGAGCCTCCGCTAAGCGTCCACCTCTTTGGGGTGAAGTCCGTCATCGGCCAGCGGCAATGGCCTCGAAACTTGTTGCTATTGAGCGGGATTGGCATCGGTTAGCGCGCTTAGTCTGGGATGCAATAGTGTTCAAGCAGGTAAAGAGCTCAAATTATGCAGGATCATGTGGTGCATTGAGACATTTTGAAAATGGGTATCTATTCTAGGGACACTCTATCTTTGTTCCATTATATAGCAGTAGGTTGTTACCACGAGTGGTGTTCTCCCTCGTGAGAAATACCAAGAGGGAGAACATCTTAGTATTTTTATTATACGACTCAATTTCCGCAACTGCCAGGATGAGCGCAGATTCAACCACGTCAAGATTCGGGGCATTACCGGTGGTCGTTAAGCAGCAGAACTGTGCCCGGCGCATTGTCGACCACCCATGCTCGGTAACTCTCTGCGGTCGACTGAAGAGCGTGGGAGGACGGATTGCGCGTAATCGTACGGTTTGCTGGACGTAGTAATTAAGACGCATCGACAGAACAATTACGCGGCATAGCACAGTGGACTCCCTAAAACGTCTATTTTCCTCGGATGGGCTTACATAGACTGTCCCCAGAATACGCACACGATGGGAGGTAGGTGACTCCAGACTAAGTCCCACAGGTTCACGTCCGTTGGGCACGCGAGTACCAGTTCCTCCATGGCCGCATGAACATTTTCCATGCTCGCGTCATCCAGAGCCATTCGAGGTACCGTGCACGATAGCGCTCACGTCCTCGTTGATACTCGGAGAGCTGTCGTGCCTAACGGGTGTGCTGACTTGCAAGAATTGTTGCCCGGGCAAGAAGGCAAATAGGTTCCCGCCGCAGCCGCGAGCACCTAAACCCGGTATCTTATGAAGTGGGTAAGGAAATGCTTTTAGACCACGAATAGTTATAGATCATCCTCCGAACTGATAACGAAAAGCCTACTATGCACGTACAGTCAATACCCAAATCCCCCTAACCGGCAGAAGATTTGTAGCGCATTCAGCAGGTGCACCGGAAGTGTGGGGCTTAGGGTTTGCTCATCAACGAATCTGTGAGATCGTTCCACTATGAAAAAGTAACCCCACAGATTCCGGGAATTCTGTTTATCGACGGAGCGGCATAATAACCATCGCGTGCCAACTCTTGAAAATCGGCTTAACCGACCTGCAAGAAGCTGGAAACCGACTTTGTTTTAAACGCACCCATAATTCCTGGCCGCACGAGGAAGGAGCAGACCCAAAAGGATTGAACGCACAAGCGCGCGACAGCTGACAAGGTAACCTTCGCGGTACGGACTAACACGGTGATCCTATTGGTGTCATGCAGCGCTTTGTAGTGTCAAAGGAGCCGTCGAGGACTTCGGCGTTGCAGAGGGGCATCAGAGGGCTGGAAAACAGGTTCCCTGGACTTGTAGGAATGTGGGTTGATACCAGCATGCTAAGCTAGGCACGTATCAGGTAAAAACCGTACCTTCACGTTAATGGTGCATCGAGCAGGACCGCAACGCTTTTAGTTAAGTTTTGAGGCCGCACGACTGTTGTGGCGTCTATTGATATTGAATTCAGACCGCTACTTAAAATCACAGCGAGGGCACACCGAATAGAGGCGCCGGAATCTGGATCTTACATACTGAAGAAAAAATTGTCATTCCCACTTTCAAACGCTGTTGATAGTAGTTGTGTTTATAGAGGTAACGAGGACGTCGACCGTAAGCCAAGAATGGTTTATCTTGGGATTCAAACCATTAAATCGCTGCTTCTGGACAATGATTCTAATAGCCGCCCCCGTTTAACCGAAAGGTGGTTGTCACGATACGCCTATCGAGGGGACGGATCCTCTCACTTCTTGCGCACCACTCGCGCAGGTATCCGAGCGTGGCCGCTTGTAACCATGAAATTTTCAAATTCATCACCCCTTCGAGATTATACAATGTTCTACTACCCTCCTATCGACAAATAACTCGTGCCATGATCGCACGTTTGTGCACCATGGTGGTCGAGCGGAAGTATCGCGTTCCGGACTTGCTAGGCTCGATTGTGTTGAACTAGGTCGGTGGTAAAGCATTATACCAGCGTCAGAGTTCTGCACAAAAATTTCGGTTCGAGGTCGCACGATAGAGGGGCCAGGAAACCGTAACCCCTATGGAGAACCTTGCCGGTGTCTACCAATGCGTAAGCTCAGACTCAGGCGACGTACCTTCACTCCCCATACTGAAACTCGTCGGGTACGTCGGTTATCAAGAGCCACCCGTGTGCACCGACATTTCAGGGTTCCTATGTTATCACTTTTAACTTCGTTGGAAAACGACAGTGTACCGGTCCTGTAGACAGCTATGACAGATTACGGTTACAACAGAGCATCAATGCTGCAGAGCTTCTAGCTTTCGGTATTCGATTGTGGACCGTTGGGGGCATACGGCCGTTGCATGGGTTTTGCTTCCACGATATGTTGGTGACGCCCACGCTCCCCATAGCGCAGAAATCATATACCCTGTCGCTGGTTCCGCAGTTTGGACACCCTACTCCCTGATCAGATCACTCGACTCGACCTAATGATAACAGCCCTGCCTCCATGAGACCATGCCGACATCGGCTAGTGTACAACAGACCCTCTTAGGCGATTTCATTCGGGTGTATGGGACGCCCCATCTGGAGTGGATCACGTGCCAATCAAAAGTGGTCGCAATGGGCGCCCTAAATACTCTGGCTCACCTCCCCCCAGGCGGGCTATGGCGATGATGACTCCGAGCGTCTGGCTATTTTGGGTCCACTAGCCAAGGTAATTGCCGATATTAAGGGGTCCATACGATATGTTACAAACACCGAGACGTTCGAACCAACAGATCACATTTTTACACGAAGGAGTAGAGTCAACAAGTTCCGGGGCACCATATAACTTTGTATTCAGCCCCAAGCTATGATGCAACTGAAACGTGTAAAAGAGGAAAAGGACCAACTGGAGCTGGGATCTTTTTTACGCTGCGGCTAGGAGCTATAGATCGCAAGGGTACCAAAATATAAAAGACAGGCTGGGGAATGCTGTGCAGTGCGTCCTGAGGTACTGGAACGCGCGGGCGGCAGTGTAGAACTGGAGGGGGAGCCAACAGGGGACCCGTAATCATGTGCTAAAGCACACTCGCGAGCCGTCCGAATCCTTCTCACATGTCTCAAGTGGTCCGGCGCGGCTATGGACACGACTTCTTTTTTGATGTAGGTATCAAACTCATGTGAGGTATGCAGGCCACTCGGCCACTAACATCCCACCCAGCTTAAAGAAAGCACAAAATGTCGCGGAGCTTCCCCCCCTACGATCCAGGTTGTCCTATGTATGATCAAGGCCCATGGGACGATTCACTGAGTCGAGTTTCAACACAGGATGCACAATAAGGTGACCTAACTCGAGAGTGCTCCAGTTTGGATAACCACCGTGCCCCCCCTTACAATGCGAACTATAAACACTTGCGACAAATCCCTCTGAAATGATCGGCGGTATCGCCGCTCCCATGCAGGCTGGGCGAGGACTCCAGCAATGTACACTACAAAGTTGACTTAGACTCCGGTGCGTCCGATCAATCTGGTTGGTCCCTGTGTACCCTAAGGGTGTATTTCCGATTACTATGACACCGTGACAATACTGGAGTGGACGAGATACTACAATAGCGTAACACTTGTGCGTTATAATCAACCGTGGGCAACATTTGTACCAGGCGACGCTGCTGTCCTTCCCCATTCTTTTCATCTTCCGCTAAGGTCCTAATTGTGGTACGTTAAGCGATGCATCGGCGCTCTATACGGATCCTAATCCTAGGTAAGCGAGCAACCTTTTTCTCACTTCTCCAGTTGTCCAAGCCAACCTTAAATGATATATGTAATATATAGTAGACCTGTCTCATTCCGGCAAATTCGGCAGGCTTGCCCTGGAAGTAAACCCAAATTGGTAAACGGGTAGCTGTGGTAATTGTTGGGGGTGGTATTACATGCCAACCCCAAGGTTTTAGTTGTGTCAGTAGAGCCAAACAAGGAGTTAGAATTCGTTGAGAAGTTACGGTCAGGAGAGCGTAGCACCCACGAACAGTTTCCGTCGCCGTACGTCGAGTGCCGTGCTGGCTTCGAGTGGCAAAGGACCATAGAGCTATGGAGACACGCGTGTAAGTGGTACTAGGTGTAGGTCCTGCCCTGTTGGGCCATCTATGGTCTAAGGCAGCATATTGGTGGTTGGTACTCGAGTAATGCCAAGAGAATGACACGCGAATTAATGTAAGAGATCCGTGAGAAGCTCGCGTGGGTTTGTAAGACTTTTCCAGAGCGGTTTCTCTATTCCTGGGAAAACGACTTGTCTAAAGACGAAATAATTCCCTAGTTGCATCGCCAAAATCGCCATGAGCAGTATCGTTAAAACTCCACTACGCGGTTAAGATAGGTGGGTCTCAGAATCCAGATTATTTCATTGGAGTCTGTTAACATCTCCTGGTATTACTCTAATGTATTTAGTTGTCACGATTAGCCACCCGATTCGGTCGATAATCAAATCACTCTAATCCTAAGGTTCAGCTTCACACGAATCCTATAATCAACGTGAATCAAAATGTAGGCTGTTCACAGAACACTGTAGTCGACAATTCAAGAACATCGACATACGGCACGACGGTTCCCATTGCAGATATATGGTGGAGAGACTTCTGAGGAGTACCCAGAAACATAAGATTAGTAGGGTCTGCTCCGAGTACGGAGGATTGCCAACAAGACAGGAATTGTATAGAACGAGGCTTTATGTATACCCTCTTGGCGCTCTATCGAGTGATCCCTGGGACACTAGGTTCAGTTCAACTAGACAAATGTACCGAGGAGGTCGCCGACCAGCTGTATAAGCTCCCGATTATAACGTCATGGTATGTCAAGACATAGTTCCGGTGGGTTGGTGTGTGACATAGCCCAACCGGACCCGGCCTGTGGACGAGCTTTGGTTTCATAGTTCGGTGGAGACACATCCAACTGAGGCGTTTTGGAATCTAAGTAAGGAAGCTATGTTCCCACTCATGTTCCGTGGTGTTGTTCCCCCTTCTAATCTGGTGGTACCAGCCACCGCCGGAGATTTGATGCGCTTGGCCCAGGACGGTAGACCCGACGACTAGCGAGTTCCAAGCTATTCTGCACGACACTAAGCTATGGCTGGATAGCTAATACACCACATATGAAGCGCACGGCCGTGCCGGATTCAGATGTGCGCACCGCGGCTTGGTGGCTGCGACTCAAGCATCCACTCCTACGCCCCTGGGCTACTAGAAGCGGCGCGCAATATATAGCAAGTGCGAACCATCGGATATATACCTGGGTAGGTCAGCTCATGTGAAAAGGCCTCTGGGGACTTACCCATATACTAGTGGTATGGTCTTCCTAGATCATCTGATAATGGGTTGTGGTGTTCAATATAAAATAGGACCGTACACAATCGAGTATCGCATTCCTATTCCTCGAGATCACCACCCCTAGCATCTGCGATTGATGGTACCTATCTGGATAGGAATAACCACCTCCGTTTGGGGTTTCCTTGAAAGCGATCGGCTATCCGCCAGAGCGTAAGTTCGGAAATGTCGTAATGGCGGTCCGTGCTTTGAAGGAAAACCCGACGTACTACATCTTCACTAGGCGGGATTGCGCCGATGCGAGGGTGAAGTATATGCCCGCGACGACGGTGCTAGCCGACTGAAGAGGTTAAGATCACACCGTCGTATAACAGGTTCCTTTTTCTTCTCAACCGACACCGCTGTCCTACCTTACATGCAATAAAGTATTGGCTCCTTCCCCGGTGAGACTCAGCTAGGGCGTAGCCTCCTTGGCTATGACATCAGAAAACGCAGCGTGCATTTGGAGTTATTTGCGCTGTTAGCCTCATATGTATTGTAATCTGCTGTCTTGTCTCACAAGAGCCCACACCTCGTTTAAAGGAGGCAAATCTTGAACTCTCGGCGATCAGATTATGGCTTATGTCTATCAGCCGTTTCGCAGTACTTCCGACCTTTATAGAGAGTACTATAGAACGATGCGGTGAGGATGAGTTTTAACAGATGTGATTTGTTATGCGTGCGGGCAAACCCAGATTATATACAACATTCAGGACAGAGCCTTTATGATTTAATCCAGTTTCTAGGTAGTAGGCTGTTTAATCCTCTCAATAAGTACCGGCTCCTTAATTTAGATAAAGGCAGAGTGCCAGCGTTTGCACAAACACTGGCAATCAAAGACGGCGCTGCATGGTCGCACGGTCGCTCGCTTGTTTTTTTTATTGCTAAGAACCACGTGTTGCTCTGAGAACGCCCCGTGCAAGCCACACAAGCAATTCGGCCGGTCTCTCGCTGATCACGGTCGTCACGTGAAATACGTGCATTTAGTACAGAAGTGTCGCTACCGTGGACACGGGCAATTTTACCGAGTTCCCATACACTATGTGCTGCCGTTACCTTTAGAGAGTAGAGCTAACAGGATCACAAATGTGTCTCACGCCCCAGGCGTTTCCGAGCGGCCTGTGTGCGGACTAGACCCGCCTCGCCTATGGGGCTTAACTTACAACCTCCCCACCGGCCCCTGCATCATATTTACCCCGCACAGCAGTTCTTACTAGAACTCCATTCATGAAGAGATACCTGAACGGTGCCCGGGAGAACCTTTTCAAACGGCCGGGTCGTTGAAAGTAGCCAAATTACACCTGACCTCGGTTGATTTCTGTGTTAGGCGACGAAGTTTTCCCACCTACGACAACAACGTCTACCACGGTGAAGGCTCGCCGGGAGTAGTGCCGTAACTAATGGAGCTGTGTAACTGCACCGTGCCTCATCACGTTACCACCGTGACCCAGACGATGACGATTGAAATTATCCATATGTGCTTAAGCGCCGTATTATGCGTGGAACCGTGTCCCGAAAACGATTGGCAGCTGTACCCACCTGCTAGCTCATACAGCAGCCAATGGTGAGTTAGCTTGGTATTCGACAAATTTGCATTGGAGAGTCACCAACGCCAGTCACCTTCTATCTGGACTCAGAGAGCAGCGTGGAGCTCGCCCTATAGACAGACTCGATCTTCTCTTACCTATGGAGAATCGCCCCGCCGCGAGGCGGTGCAAGATTGCATTACAGACCGGAGGCATGGAGACCTGCTCGAGAAAGGAACCGGGATTGGAAACCGAGTCACTGGCACAGATTATCTAGGACCCTTAGCCGCAGTGCCGCTCCACAAGGTCGTCGTTAATGGAATGTTGTAGCCCGCGTATTGATGAGATTAGCGAGGCTCCTGTGTTCATCACAGGCCTCCCGAATTAATTAAAGATGATGTACGGAGTGATAATATCATCTTCAGTATACAACCAGAAGCCGGAACTCCAACTCATAACCATCCAGAGACTGCCGCCCAGGTTGCTGTTCAGCTCCCCGCTTAAAATGTGGGCATAGGTGTTGACTCGAACCTCCAATGTCAACAAATATTGCTTGCCCCCGTGTTGTATGCGAGCGTGTTCAGTTCTCTCAGGATGAGATTTCGCGGCGATGGTCTTAATGTATTGGGTCATGCACTTTAGAGTGGTTCTCAGCATCTCCTGTTGAGCTTGGATGGCGTGTGACCCCTATCTGTGCAAGTTCTCAATTAGAGTAGGGTTATGAGATAGGTGCGCCCTATTACTTTCAGTCTCAGCAGGTCAGGGGTTGACCGGTGAGAGACCCCACTGACATAGTGCCGCCATAACCGGACAGCGCTGCGAGCAGAAAAAGTACTTCGGGGGCTATTGAAGTTGACGGTACGAGCTTCCAGTATATTGGAATGGGCCCCATTCGAGTCATTCTATTCATGAGGATTTAATTTCGAACCTTATACAGACTTACGCTCCGCTAGTGGCTCAGCTAAGGTTCATCGGAGCCACGTACTTACGATTATCGCAAGATACTCCCCCATTTGATGCCATACTTGCCCACCGCACGTTGCTACTAATGATAACAGCCTCGCCGTTATGGCGCCTAGGGAAGACATCAACCGCTGCAATGGGCAACAGATTTAGACTGTGCGGCACCAATTTCCCTGGATATCCGTTACAACATGCCGGCTCTCCGACAAGATTTGACATCCGCATCTCCTGCTGCTTCGACTGTCTAATACAGAGTAAGGGTCAGGTCATGTGTGACGGTTAATACGAGACGAAGGACAGGGTACGGCTCGGTGTGCTGCTCCTGCCTCTGAGCCCCACCGACTGGCGCAGCTGTCAGCCTTTTCCCATTTAGTCTGATGCTAAGGACGAACTCAGCAATCTACCCATGCATAGTAACCCCAAGATATAGATCCGGGAGACGTCTACTACACCTGATAGAGTGTGATCGTCTGGTTCATGCTAATCAGTGTCCCTCGCAACTGGGGCTATTCTGGTCGTACCAGGCCTAAGTCAGTGAGCTGTGCCTTGGAGCCACCAGGTAAGAGTAGTGTGCTTACACATGTTACGTCCTAGGGGGGACGCATGTCTACTCCTGCTCGCCTAATGAGAAGACCCAGAGACCGGAGTCTAGCGCGACCAAAAGTTGGGTATGCCAGCCCCAAGGGCCTCCTGGAATCTACATGCAAGATCGTTACCTATGAATACCTTCGCAATAATACTCAGTACTCGGTTCCAAACGTCAGACAGTAAAATATGAGACCCACTGGTGTCACTGACGTGGTTGTCTAGAGTAGGCCTCGACCCGCAATGCACGGCCTTCGTGACGTTGGCTGAAGCATATGGACTGTTTCTCCGCAAGTTGGGTGTGATTCCCCTTATAGGGTTAATGTAGGAGAGAAGAGTTGTGCCTTTTGGGTGGGTTTCACGCAAAGTTAGCCTATTATGTAAGGGCCACCCACAAGTCAGATAGTTTGAAAGACCCGAACCTAAATCGCACTCGGCTCACGTTCAAATGCTACCGGCCACCGGGATATTATAAGCGTACAATGCTCCAGACCCCTACGGTAAATAGGGGTTGTTGCACTGCCAGCGCAACTTCGTGATGGGGCTTGGATTTCACATATCTATCGAAGTCTTTGTTGGAATTGTAGTGGGGTCGCGCACCTTCGTCCATCCTCTTTGCCGGAGGAATGTGCTGATGAACGTGGGTGTAGCACATCAGCTGTATGTTCGGCATTACTGGAAAAGTCCGCCTTATAGCGTTGCGCTAGCGCAGAGAGAACGACTCATTAGTGTGGAACCCGGCGAGGAAAGCGGGAGGATAATAGCCACATGTCCGCGCTCCCGAGGGTGTAACTATTCGGGTCGTTGATAACAACTCGATTGAAGACGCAGCAGGAAGGGCGCATCCGTGGGACTGTAAACGCCCGGGGAATAAGCTTTCAGCTGTGAGTGCCTTATCACTTAACGCGTGAAGGTCCGTTTTGTGTAAATACCCGTTCTAAAGAGTGGATGCCTGGACGAGTCTGTCACAGATGCCCAATCACCTTCTCGTAAGTCGCGAATTTGAACTTGGTGAACTGTATCTAAGATGCGCCCTACCACGGTGGGCGCCGGGACTCCGCTAGTTTGACAGCATGTAGATGAGGGGAGACTCTTGACTGACTAAAGCGCTCCACTTCCGGTGGGCCCCTCGCGCCTCGACATATCAGGATAACAAGCCTCACACTGATCATCGGCTGTTAAGAAGCGTCGGTGGCATTAGTGATCCGACATTTGTCCGAGTTTACCTTTGACCTCGTAGGTTACTGAATAAATACAAAATTATGTTAGGTCGATGCAACGCAAGCAGCAAAGAATTCGATACCCAACTGGACGGCGCCATTAACGCTTCATTAGAAAATTTACAGTAGCCGACTTCTATCTCAATGAACGCCCCGGCCGTCACTTCAAGGAGAAACAACCGCTAATCTACAGTGCTTTTACTATCGCACCGGACCCTTCCACGCCGAAACGAAGTCATCTGATGTAAACGTTGGTCCGAGTTTAGCGGTATTGTCTTAAGCGTTCTAGCGGACTTGTAATTTAGGCAAAGCTGGCCGGTAAGGCCTTTCTGTTCACGACCTGTTACCTTCAGTCGCGGCACCCGAACGACGTGGACGTCTCGGAACGGGTATCAGATATAAGCTTATACCTGTAGCAGTCGCGAGTTGACCCTACTTCAAGATCGCATTCCGTTGCCATCCAATACGAAGCTCTCTCAGTCCATATCTCGCACCGTACTATGGGGAATCTACTACTCGGATGTAGTCCATTCAGCTTGTAGGGGGTCATGGTATATTTAATTCTCTACCTCAATCGTACGTTGAACTTACCGGTTGTTTCGTCATCATCGGTTGTACGTGGTAGGTATTTGAGGGCTGTCTGAGTCGCGACCTAAGTTAAGGTTCCGGGCTTTATATTTTACCATGGCTTTTGGAATTTCCGCACCACTAATCGGCTCTCAGGCGTTCGAAAATTCAGTAACGACAACCTAGCAGCGCAGTCTCTACGCTTACCAAGGGGCTGGGACTGCCGCAATTATAGTCGGGTTTTTACATCTTGAAGTGGCGTCATATAGTGTGCCTTGATGATGCTGCTTGCTTTCGCTTATGGTGGAATTATCTCGGGATAGGGCAACAGAATACGTGAGCCCCGTGGGGAAGGCCTCCATGAGAGCATGCGCCTTAAGCCTTCATGGGAATTTGGACTCAAAAAAATATCGCGTAGTTGAGAGTTCCTTTTCCACGTTACAGTGCCGGGGCCTTTCAGACCGGTGCTTCGGAGTGGTAGACGGCTCGAGGCTGACTGACCACGGTCGACCCATATCACAGGTAAATCTGGGGGTATTGAGTGACCTCACGGTTCCTACTAAACGTGGACGCTCTTATAACTGGACGGTGGTCTCAGCTAGAGGGAGTTCGGGGGAGTACGCTTAGGAGTTTTGCACGACAGTTTGTGTTAACTGTGAACGCGGTCGCATGACGGTGCACGCAGCAAGCTCCTATGGGCGATCTCTCTGTGTTTTACTAATTTAAAGCAGAGCTTGACCAACACTAGAGTCATCCCTCGATTCTCACTGTCATAATACGAACTCCATTGACATCTACGTCATCGGTTCCTCGATACATATCTAAACGAGGGACTTTCCAAGAGTCCCAGCCGTAGTCGGCTACTAAGGACCCGCGCTTTCCCGACATGCCAACACATCGACGACGGACTAGGTCCGAGCTTCATGTGTGCCACCAAAGTCGGCTAGACGGTGACCTTAAAAATCCCAACGTCAGTCCGCGGGCCAAAGCCGGCAGTTCAGGGGTGCAACCAGGGCTTGGTGGGATAGCCCCACTTAAGTTATTTTGGATAGCGTCTGAGGCGTAAGTCAACTAGACGTGAGTTCACCCAGGGGTGCTCCTATAACCCAAAACGAGTATGGTGCAACGTCGTGCCTTAAGACATAACTGCTCCGGAACGTCCATAAACTTTGCGTCTGCCCAGTCGTACCGTGGCGTGATCCTCGCAGAACCGAGATCGAAAACTCGTACTGACCGGCAAAACCACTGTGCGTTGTCTATGATATTCGCGACCTCGGACAACCCAGAACCAGCAATAAGGGGGTCCGTGTTGAGAGAGCCCGCATAAGAGCCGCGAATTATGCAGGAGTTTACTCGGGTTACATCGTGCTGTGACGTAAGCTGAGATAGGAGAACGGGCAATATGTTAACCTCCCACGTGCGTTCCATTATAGGGTCTCTCTTCCCGCGCGCGTAATCCAGAGGGGTCCGTGCGCATGGGTAGCACAGGACATTAGGTCGGGAACGATGTTTCAACCACCTATTAAGGCGCAGGAGCGCCGAGACAGCTTTCGAAGCATATCTAATGCTTTACGGGCCCGGGCAGCTACTACTTTACCCCCGTCTAAAAGTCTTCCCTGCCCGGTTCATTCTAACCAATCCCGTACACGAAATGAATGGGAATTAACCTTAGGGAGAATTTAAATTTGAAGGACGCACTAGAGGAGACATCGGGGGGAGGCGATCCGGTGCAAGTGAGTTCGTCCACGCCACTTGATAAGATAGGTTCATTCCATGACTTCCGTATCCGAAGAATGCTTCTTCGAGTCTTTACCGTATTAAAATTATATCTTCCGGGGATCTTCGGACGTGCTATACGTTCTGGGCCCTAAATAGCATCCGTACGGATAGATTTCTCCTTGCTCGACGAGCTAACTTCCTTTAACCCCACCGTAGTCAATGGCAACCAGTTCCTGTGATAGGTAAGCAGTCACCTGTATGGCCCTCTGGTGTCAACGATACATACTTATATCGTTGCAACAAATAAGAAAATGCGTTCCATCCCCTACTAATTCGCCCTAGAACCTTCAGTACGGGACCTCTAGACGGGTGTCAAAGGCTCGGCAAAGTTTTTGGATCCTTATATCAAACAGAGATAGTCAACATGTCGGGGAAGGATTAAGGCTCCATGCTCGAGCGTTTAACTGTCGTTGCGTGATAGTGCAGTCAATGATACATTGCGGTACCCAGTGTCAGGTTGGGATTCCGTTTAACGACTTAGTTCGTTTCCGTGCAGAGCCCACAGTCGATTCGCTAGTGTCAATTCAGATGCGGTGTCGCCTAGCCCCACGGCCCCTATCCGGAATGAACCGAGACTCTTACATGAGGTAGGTACGTTAACCGAATTAGCTCGCTCTCCACGAACACTGTCTTCGCACTTCACCTCGGGTATAAAGACTACTGCGCGACAGTAGCTGAGTCAGGCGTTACTAGTGTGTAGCGATCTTCGAAGTCTATGGTGACTCAGTCACGCTTGTAGCTATGAAATAGGAGAACGGGGTGGATTATATTGAGCCAGCCAGGTCCAATCTGAGGCTTTTCTCATACGACAACGCTGAGTACCACGCCCTAATCCATATCCCCCCCCCATTACTTTGACTGTATCCCTCTTTATGGTTGCATTCGTCAAGTATTAACCGAGTAGCCATACGTCTCCCTGAGAGAAGCGCTTATGCATTTGATATATAAAAAGCGTCCTGGCGCGACAGACCGATACAGGCCCCGTCGCAAGCTGGCGGTGAGAATTGACGGTTCGTCCAGGCATGCCACAAGATAGTGTCGGTCGTATACGGAGCATTAGCGATCACCGAAAATGTATGGTCCTCGCATACCGCGAGACCTAGAACGCACTAGTTCGCAGGCATCTGACAGAAAGGGCTCTTTGTGGGCTTGGCTAACGGTAACTCGGCCCAGCGGTCCGATTCTGACAGCACATACTTGATCCGGGCTATACATCGTCGATCCAGCAATGGTAACCAACACAGGGTTTATCGATATGAATCCCCAAGTACAGTGCCGCAAACTGCGTTATCCACGGAACATGATGGTTAGTAGGTGGCGACCAAGCAACAATGACTGTGAGCAACACATAGGTTAACACGCTAAATTCGAGCAATTCGAGAGCGTGTTCCAAATTTTGTCTTAAGTATATCTCGGAAGTAAATTAAATTTCACGTGATCCCACTATCATATATCTTCACACCCCATCTCAGCTGCCGTGATACCTAATACGCTTGATGGCTAGCGATGTTCTATGACAACCTAACGTGAGAGTTGAGAGTATTCTTGAAGGTGGGAAAGTTGAGGCCCTATCACGATCTTTACCGGGGGCTTATCCAAAAAATGCCTATGAGTATATTGTTGCGAAGGTCTAGGTGTTGAGATTTGTTACAGGCCTCAGCGATATGGTTAGATTAGAGTCCTGTTCATTCTCCCGCCTTGGTGTCCAAAGAAGCGCCTGGAACCGACAAGGAGGGGCACAAGTCTGCGAGGCCGAATCAACCTTCAGGAACATATAGTGTTTATACGTCACCCTGGTACGACATGCAGCCGTCCTGCTGAGCGAAAGGCAGGACCCGCAGCCGGTAACTCAGCGCGTTATGGGAGCGTTAGCTAGTTATAGTACAATTTATTTGATCAGACTACGCCTCCTACGTAGCTCATCTAATGTCCACTTTACATACCCACAACAAGTACATAGTGGGTCTCGCTCAGGTTAATCAAAGTGAGTACGAACGCGTGTACAATGTCAGAGGAAAAGACAATCAGGCAAGCCTGTACCCCCATATGCTTACGGCCAAGGCGGTAGTTTACATCCCTGTGGATAAGGCAGGGGGCCCGCGTAACAGCAAGGTGCCCTCAACGGCAGATCTCTCGGCCTATTGGTCTTGCTAGTTGTGCTGGGTAGTGGTGCCAATTGACTCGCGGCTACAGAGAGAGTGGCGAGTGCAGATCAATCTCGGATCGCGGAGTAACCGTAGCTAAAGGCTCGTCGGTGACGAACCCGTGACCATTGTATACGGGAGTTCCGATAAACTTGTCAAGAAGTTATATGTTACGATAGGCGGGACGCGTATGACTTGCCGATGACCGTCATTCCACCATAGTTGACCGGTTTAGGGCAATGGAGCACACCCCGCGCGGTTGCAAGCTGCCGGCAGCGCACGCGGTAATGGCTCAGGGAAGAATATGGGTGCACTACGCGTAGGTGGATCGTTAAGGTGAAGGCCTACTACAGCCCTAGCTCAGACTAAGAGGCTACGGCGCGGCGCCGCGTCCACTATATCCGCAGGGGTCGTCGTTCCAAACCGGGGGATGACGGACTCAGGAGGGAGCCTGCCCAGACTGGAGGAAGACGGGTTATCAAGGGGGTACCAGAATTCTAATGCGTAGTTTGTAAGAAATCAAGCGAACCCCCACTCAAACTGCCAGGGTGCCGAAGGTTGAAGCATCAGCATATCCCGCATTTTAGTGATAGCTTGGACCTACACCTCCCGAATATCCTCGTCTAGATGAGGGCGAGCTACGCGGCAGTTCAATCGACGCCTCAGCAGTATCGCAAAGCGACTACAAACGTGCTCTAGACAACAGAATGTCGCCTTAAATATAATCGAATGCCGTCTGACCGGAGCTAACGTCGTCCTCTCAAGCTCGCCACACCCCTGACGCGACCCCTAAGTGCTGTCGGGAGGTCGACGCTATGTTAACGTCCGTAACGGCGAAGTTGCATACGAATATTATTTTCGTGCCTTTGTAATCCGGAGTACTTGGAGAACCGGGAAACTTATCGCGCACAGGTCATTTCCCTTATGGGTAACTACGAGCGATGAGCAAGCAGCGTGCTGCCTTGAGTACGTTATCTCCTACATTACTTGAGACTGTCCTTTGCGGGGTCCATCATTGTTATCTCTCTCCAGCTGTGTGTTTCAAAGCAGTTAAAACAGTCTATATCGGGCCTGAGATGTTTTTTCTCGTAACGCAATGGTATTAGGGCTTGACTCTTGACTCGTTCCAAAGAAATTTCACGTGCATGTCGTATGCACAGGGGTATCTGAATTGCGAGCAGGAGAAATCTCGCTATCGGTTGGGAAGGGCACAATATCATCTGCGCCTAATCCGTATGGGATAGTATGACGATCTGGCGCACATTCTTAGCGTGGGATAGGATGTGTCGAATCCAGTTCGAAGACTATACCTGTTCCATTGCCAGTATACTACAAGTAGTCGGCCAACGTTACCTGAACAGGATCGCTCATTCTCTGGTATGTTTTCCCCGATGGCTCGCCCCACTCGGATAAGAAAACCAGAGCGATTTTTGCTTCGAAACGCGGCTCAGCAATTCGCGGTGTTGTACGCATATGGTGAGTCGGTCCAAACATGAGAATTCGTGTGAGGACAGAGATATCCCGTCGGAGGTACGGTTACTTGTATGGACATCCAAAGTAGGCTACCAAGTTCGCATTACGCGTAACGTAGCCACCGCCGCATTTGCGTACTGCATGGCCCAGTTCGAAAGGTTGATTATCCCCATAAACTTAAGTGATCAACTCTCAGGTGTTATTTAACGCTGTTATGGCGACCGACCCCGGCATTGGGTGCCCAAATGTACACGGTAGCCCATGCCTGAGGCGAACATTAAGGGCATCACTGTCAGTTTAGGCTGGGGATACCCCGATTGGGCCAGAAAATTCTTCGCTAAAACCATTCGATGCCTGCTTGGATATTACTAATGCGCAGTTTACCCTTCCACGAGATCGCCGGGCTCAACCGTAGCTGCACGGAGTGGGTGGCTGTTTGGTTTCCGAGGACCCGGCGGTTACCCGCGCGGGCGCTGTGGGAAGCTACGGCGAGCCCCACAGGCGATTAAAATCTTTCTGCTAACGATGCTGGTAGTACTAAATTGCTGAACAGTTGAGTCGGTCGCTACTGCCCTATCCGCATGGGAATTCTGTATAAGTGATTTCGGACGTTTTGTGGCCCGCTGTAGAGCCGAAGGATATGGAGTTAGGTGTCCTGGACTATTTGTATCCAAGCTCGCGTTGACTTCTATTACACACGACCTCTCTGTGTTGATCAACGGGCGTTGACAACGGGTTTCTGTACCCCCTGCTCGGCGGAATCGGGCCCTAGTCCACTTACAGCGAGTGTGGGCTTGGCTCTAGTTCATGATCCGACCTAAGAATGTGTCCATGCCGCCCTCAACTACAATAATGGTCAGTCCGATACACTAGCCTGTGCAGTTAGTCGGTCTAAGGTCTGTTCGCTCAAAACTACCAGTTACGTTAGGGCACCTAGATCCCCGCACGCCGTGGTCGTCCACCCCGCGGTCGGACGAGATCCATATAACGAAAGCATAAAGCATTTAAAGTATGTCTTGCAGGGGTCCGTCGTTGTCGCCTCAACGTTGTTAATTTACATTCAGATGTTAAGGTTAGCGGTATAGCCAGGATAGGAAGGAGATTGTCGGCTTTTGCGTCCACAGCAGTTTACGAGTAGCATGAAGGATTAGCACAATTAAGTCCCAGTGCCATCGAGTAATAGCAAAGGTGCCCAGAATGCATGGTCACTAGTAGTGTTAAGAGGCGCCTATTTAGGTCCGAATTTAAATGACGTTGTGTGAGTGCATGCCTTAATGATTTGTGGTGTTAGGCGATATCCTCCGTAGCCAGCTTGAGGCTCCTGATCAGTGTGTGTCCCCATGTGGCGGGCTATCCACACCGGATCTCTCCTGGCAGGTTTAAATGCTCCGGTTCGTATTTGGTCGCCAGCAGTACAGATATAAGTGTTCTAACATCACGGTCCGCCGATTAAACCCGCTACTCAGTAAATATCCACGTAGCTGCCAGAGACTTCCTAGCGCCAATCTGGTAAGGACCCTAAGCGATCCTACGTGCTAATGGCTAAATCTCAACAACACTCAATGTCTCTTGAGTTGATCACTAGACCCCGGATGCTCCAGGATTGCTAGTTGATGATTCCGCATCCTAGACCGGTTCAAATACATCCCTAAGTACCGGGGTACGGCGCAGATAGCCGAGAATCGATGACCAATAGTGGTCATAATACAGCGAGGAGGGGCAACATGCTTCACTTATAAGTAAACAATGCCGGGGTTCCATATAAACATGCTTTTTTGGTTGGCGCAGTTAAATTTCGTCCGTAGTAGAAACGCTCGTTAGTTATGTTACGCCAGTTCGAGGGTTATGCTCGGTACATGTCCTGGCTCAGTCTCGCTCTTCTATTAAGTGGCAGGGTTCGAACAGGTGCCCTGACAGTGTTGCACATGCCTCGACGCTTCCCTTTATAGCACTACTACTAGGCTGTATAGCATCGGAACCAAGGTGTTCCTCGCCTAATTTGGAGCTCGAGAAGGGCGGCGAAACACCCAATTTGAAATACATCGTGTGAACGTTGTCGAGAGTTCGTAGTGCGAGAAACCGATCAAGATATTGTGTACAACGCCCAATAGCCTCCTTCGCGATTATTCACCATGCCTACTACGCGCCGCTCATAACTTGCAAAGGCGTAATTCTTATGATAGATTGCGCACCGTGACCGGTTCAATCTTCTTGGAGACACATGACTAATAGCTTGTATCATACCACTTTGACTTTCTTGCTTCAGTTTTGTTCCAATTAGGCCTTGTGAAGACGCCTGCAGTATAAATAGTTGCTATCATCCATTTGTGATATATGCCGACGCGACCCAGCTGCAATATTGGCGTGTCGATATCGTAAAGGACTAAGACCATCACGCAAGACCGGTTTATTAAATGAGTACATTGCCTTTCAGTCCCCGTCAGTTCGCGTGATTACTGCCCATTCTTCCATCCTGATCCGCGTCATGTACTCAAGTACCAGAATGTGAACGATATTCCGGTAATCTTAGTGGGGGAGTATAACATGCCGTCACAGCTCGCGTACGGTACAGGCTAAGTACACGTAATGCTAGTGCAAGGGGGCATTATGTCGAGTATCGTTTCGTGGGATTATAAGTCCCTTGTTTCCCTAAATTCTCCGCGGCGTTTCTGTATCATTAACTTCAGAAGAATTCCGCTCGTCCAGGTGAGTGGTCTGGTATAGAGCTCCATTACAGGAGTCTTCATTTAGGTTCTGGATGTCAAGAACGGACCAGGATACTTACTGTAGCATGGTTCAGCACACCCAGAAGAGCCCTAGGTCCCTGCGACGCCTGGTGCAACTAATATAGAGGAACCTAGATTATTTGCGTCCATATGTTGTGAAAAAGATGCTATGAGAAGCTATGGTGCTTGGGGGCGTCGCTAGTCGTGCATAACACTGACGAGATAAGAGCCCGGCGCGAGAGGATCAACAATAATCAACACTCGCGTGCCCCTTGGGACTCAGCAACGACGGGTGGCTATTCTACATCCCGCTGCTAACGCTCCACCGCACCCTCCCGGCCTTCTGGTTCAAGGGTGGCTTGAGCCATTAAAGACTCAACCCCATCTAAAATAAATATCTGCGTAGACACTCTAGCGTAACTGCTCGAGGAGGACTCGTACGGAAGAATACAGATTCGTCTTCTGTCACCCTATTTAAGAAGGATATGGTAAGTGCTAGATTAAGCGTAATAATTCGCCTAAGATCATACGTTAGCCGACCTTCATCAAGGGAACGGGGTCTTGATTGCGTAGTGATTCTCGATTTCACAGTCGACATATAGTCGTGGAGCCCATGTAAAGTCGTTGTCGGCGGTTTTCTCAGCCTCGATCTACTTATCTGGCCCATCTTTCGTAATTACCTGAGCGGCAAGATATATTACCCCTCCCACCCAAACTTTCGTTAACACTAAGGTCCGCCACGTAAGTTAAAGTTATGGGGGCAATCTACGCAGGCGCAGCCACCCAATCGTGACGGGTCTGTCGCCCACGGCCTGCAAAAGGCGTGGGTTAGCTCAGTATTTACCTACATATTGGACGTAGACAGCCCGCCGTGATGCACTCAGGAATTGGGTAGTAAGGTCGATAGCTAGACCGTTACACTGGAAACTGAGATTAGTTTGTGCCAGTGTGTAGTCGACACACACACGTAGATCTACTTCGTCTAGCCGCGCCATTAACAAGCTTCCAAATGTGTCACTGCCCATTGGGCCACGGCATCCTAGTTGCTTTTAAACCGCCACCCAATTTCTCTGTCCACATGCTTAACATGAGAGTACGTAAGTCTTGCCGCCTCACACGCGTGTCCACTGGAATAGAGCTTTTCATCGTTTAAACTATAAAAACATCTGCAGTAATCATCGATGCACCAACCTGCGCAGAGTCAACACGGAGCCTGTTTCGGGCCACGTCACGTATAGGACGCTGACACCTGAGCAGATCCAACTCCTGGCAACCCGCAATATAAATGGCCCCGCCGTGTGAGGTCGTGTCCGGGATAGTAGAGCCGTTTATAGCTATAAATATCGGTACCAATGCAGAAGTCAATCGTCGTCAAAGTATACAATGTTGTGAGTATCACAAAAGCTTCCCTACTAACAGGCACAGCGCCGCGCGCGGCAATAGGGCGTGCACTCGAACGAACCGCATCGCAAGGCACATGCTTTGCGCTCTACCCGTAGCTAACCATTCACACACCGAACTTGTATATCTCCATAACAAATCTGTTGGCAACTACAGCGGTCTCGAGGACTATTAGCCACTACGGGGGAATTCAACGCTTGGAGTCAGGATATACTACCCAAAATGACTACTTTACTCCTCTTTAATAGGCTACATAGAGGGCTTTCGAGTGTCTTAGCTGAACTCGATTTGAGTAACGTTAGGGACTAACCGTCTCAAGCCGGCTCTTGTTTCAAGACTCCCGCGGCAATACACGTCTCACCTTCCTCTGACTCGTGATCAATCAGCTAAGGGCCTCGGGCACAAGAAAAAAACCTATTCGTCTATCCCTCGCGGGGGAGTTGCGTCGCTGCCAGAGTAATGTTTATTGTCTTCCTAGGATAGCGGGTACGAATCTCGCCTATTAAGAACCGTCTAATGATTAGAAACCGTTTTACCTCTATTCGAGCATAGTTATGCAAATCTCGCCCCAATGTCCGGAACGTGCTCAGAAGATCTATGGCGAGAAGGGGAGTTTAATACTGCTGACTATGCGTCGGAATTAATTCTACTCAAACTGGAGTTGCCAGTACTCACTAGACATCTACGCCTCTGGCAGCGGATTAGCTTCTATCAACGCGCACCGCGACCTTGTTACCCTGTAACTGAGTACTTTCGAGCCCGGTGTTTCGCCTGAGGTTAAGTTGACACCAGACAGGAGGTGTTAAGACAAAGTGAATATCCTCAAACCACCTCAATTGCGACGGACAGGGTGTGTGACCAAGAGGGGGGACAAAATAGGAAGCTCTTAGTTCGAGAAAATGCGCTATTCCACACAGGCCTCTAGCGGTGTAGGAGTCTATACCACGCGTGTTCTCAGTATCAGACGAGTTGCATAGAAACGTGATCGGTCAGATAACGAGAAGAACTGAACTCTGGTTAACATGAGTTGGTCGGTCCCACTCTAGGACGGGTAGCACATGCACTGAGACCCGCGGGCGTCTTTACGGTTGTATAAAACAAAAAGGTCTAACGAAGAGCTTCAAGAGATCAAGGCATGTGAACGTAATATGAATATGTGACGAGGCAAAGGTGCCCTAATTGGACAGTTGATCAAAGCATGGACTGAGAGAGGGGCATATAAAACTGGGTAATCGACGCCCTGCAGTAAGCCCTCGGCCCGTTGTAACTGGCCTCGCCGATCAGTTTACCATCTCTCTGCATACTAAAGGCTGGTTCGATTACCTAGCGTTACCCGACCACCTATGTTGCCAATCCTAACCGATTGATGTAAGACAATGGTTAATTAGCCTCAGACGCACAGCTCCGCAAAGAGGCTTCAGAACTGTCATGAGTTCAACGCATTCCGGCCAGGCTCTGAGGAACATCCCTCATGAAGTCCTGGCAGTCTTCGCGACTTTACTGCCATCTCCGCTAACTGGTGCTCGGTACTTCAGTTAGACATGATAGTGGTGTTTTAGAAAAAGTGTTCCGCACCGATATCCGGCAGAACGCGTAAGCTGTACACGACATATTCACAAGATGCGGACGCTGTGGCAATAGTCGCAGAAAGTTTTGAACATGTACAACGACGATCATTAGCGCAGAATGGTGAGCGTCGGACTTAGCTCCCAAGCGATTTGCTAGAAACAAGATGGTCCGATCGACGACACGCACCCCCACAGGGCTGTTCTTTCTGGCGATGCAGCCGTATCGAAGCATATCATATAATTTATCATAGGCACGGGCCTCTCACCAAGGGTCTAACAGATCGACCTTGGCCGACCACGAGTCAATTGGTCTCAAAACGGTCACTGTAAAATAAGGCACTAACAACACGGGGACCGGTAATACGGATGCCTGCGTATACTCGCTATACGATCTCCGGACGCACTGGGATGACACCGTCGTACACTCTTCATCAGGGGCGGTCACCGCAGTGCTACGGTGTGTCTTTTTTCACGATACGTAATGTAGTACCCATTACACGGACTCTTCAAAGGAGAAAATGGAGCGACCGCTTACATTCTGTTAAAATAGTTAGATACTCCCTACCGGTATTATAGCTCACCTACGGCTCTACCACACCGGACTCCCGTTGTTCGGGATGTAAAGAGGGGGGTTGAAGTATTGCTCAATAGTACTTCGGAAACTTAGAATAAAATGAGGTCTTCTCTGATCTGCGCCTAGATCCAATTGATGAGCGGTTCCTGTGAAACGCGGTACAATTCGACAGGTGACCAAGAATGAACGTGGCAACCACTGCTCTGCACCACGAATGCGAAAACATACGGAGTCTCCGACACTCTAGGATGACAACCCATAACTTCAAATACTAATTCACGACACGGTCGGGGGGCGCGTACCGACGGAGAACTTTCTACAGAGAAGCCCTGGCAGACGTTGTGGGGGCCTACTCTGCACGTTGGCGTTAGACAAGGCGGGACCAGACTTAGATCAACTTGTCTCCACTTATCGGTACGATACGGCCGCGTCGCACCAATTAAAATCCTGTTAGCCATCCTCTGGATGGGTGAAAGGGACAATGGCGACTATATGAGGGGCGGCTAGGCGCCGCTTATGTCGGGAGACCGGGGCTTACTCAGAGGATGACTGAACTACAAAATGCTGAGATAGCTTGGCGGTTGCATTTTGAGGTAGTCGTTGGGGAAACCTCTCTTCGTCTTACGGGATGAGTTTTGGGCCCAACCCTATATTGGGGCTCGGTGGTTGCTATTACTATGTCCCTTGTAAAAGCCTTTAGAATCTGACAACCAAACAGCCTGTAGACTCGGTGTGAGCCAACAAGTAGGGCGGCAGTGATCCTAGGTCAGGCTATTGTATGTCAATGCCCAGGAGTTGGGTCCCCCATTTGGTAGACGTTATTTCGACTAGCTCCCGTCGTACTTTGGTAGACGGACGGACGTCTAGTGCGGGTGCAACCGGTCAAAGATTTTCAAACTCCTCTTAAATGGGAGAAGGGGTAACAATCCGCTTTGAAGGCATAATTCGGTACCCGACTTATACGCCACCATTAGCGCGAACTACACATCTAGCTAGCGGGAGCAGCCCTAAGTTAGAGAATCCATCCGCGATTACCCTATCGATCCCCATCATCCATTCTTAGTATGTCCCATCCGCAGACCTTTAACACGGAGAAAGGGCTCTGTCCAACGCATCGCGTACTCAACTCATGCGCGGTCACAGTTTACAGATGACTAACACGAAGTAGGCGTCGCGCCCTTCACGGACATGGGCTGTCAATCAAGAGGTTTTGAGTGCGCATCAAGCGGGTATCACCTTCGCATTTAGCGTAGAGGATTTCTTCGATTATATATCGTCATGCAGTTATCGCCAATGGTGCATGGTGGCCTTTTTTCGTTCAGGTTGATGCTCGCTCACCTTCGTGCATACTTGCTTGACGTTGTGGAATCGAACCGAGTAGGAATAATTTATATTACCTCGTGATACTACCGATTGGACGTCACCGAGCCAACGTGGTGTACGATGTAGCCCCCTTGCGCTGACCTTCCGTTTGACTAGGGTAGATCAAAACGTATGCTGGTTTTGCCTGGCCGTGAACCTACGGATACACACACATTTACGTTGCTTATTAGGGGATTCCAGGTCCACAGCTTGCTCGGAAGGGGCGAGTGAGCGTTCCTTACCCGGTCCCAGGGGGGGCCTACACCTGCGGTACTTCCTACCTCGTCGAGTAGGGCATTTGGCCCGCGAGATTCTGAGAGTGATGAGTTCGCTTCGAGACTAGAACATCCCAAAGGGGGTAAGCCCGAAGTGATCCGCTACCTACACCGCATCATCCGTCGACAACGGGGCGGTTCAGTGGATGGACTTCTGCTCGGATCGTTTTTATGGTGCTACCTTACGTCCTGCAAAACTTCCAAAAGCCGAGCCTCATATGTGCTCAGGACCAGCCGTGACCGACATGTAAGCTGCCCCTGTCCTAGATCTGGGATCCTACCAGACATAGAACATGTTTTAGGTAGCATTGAGGTCGTTACTAATACAAGGTTCGTCCGGGCTTGGCCTTGGTACTTAAAGTGATGTTAGTTTGGTCTAATCCACGTATTGACGATTGTGTGGCTTGACTTAGATCGGCGGTGTTTGCCAGGAAGTTAGATCAGTAGATGTGCAGCTAAACAATGGAGGTGTTCGTGGCCTCTCGCCACTCTAATGTTGCCCACCTAGGGTCACAGGGGTTGAAGCGGGCTTGGATATACGTTGGCACTATACGTCCGGAGGACAGTAATGCCTCTAGATACAACGGTGGGGGGTCCTTGTGATGTATGGTTGATTTTGGCCCACTCACTTCCCTGCACCGGATATAACTGTGGCGATGTCCCAAATTACTGGATTCGTACTGGGATAGCGCGCAGGTTACATAACGCTTGGACGAGTGTGTGTACCCTGAAAGCGCATACAATACTGCAGGCAATTGCTATCAATACCCGTGAAGGTAAACGTTTGCCGAAGGGTGATTCTTCTACCTCGTCTTAGGCGTTTTAGCCGGATCGCGAAGACAAGAAATATAATCGCTTTTTCGAAACGTAGTAGATATGAATGGGGACTTGTCAGTTCAAATTAGACCTACATAGGCGTTATTCACGCAACTGTAAACACTTCCGGCAAACTATACACCCCAACGTTTCAAGTCGTCGAGGTTACACCATCATTCAGCGACTACGGACTCTTGTATCAGCCTAGAGAGATAAGTGCGAACCAGTCTATAAAACGCCCAGCCCAGGAGCTTAGGGCTCTAGGATTCTTCGTTGACACCCGCGGTCACTAAATCTCTGTTTGGCGAATTAGGGTCAATCGCTTCTGCTCGAGTGTCCCGGTGATGTTGCGGCTGCCAACGAGGGGCCCGCCCCGCTCAGGTGAGGTGGGAACTGGGCCGTCCACTTTAGCGCGATACACATCTCTACGATTAGGATCGGTTTTTCATCATCAGATCGCTCGATAAACTATATACGGGGAACCACCTCTCCTGACCATATCCAATAGAACATGTTGCCTAGGATGGCGAAGATTGCATGTTAAATTTTTAGAATACACCCTGATCGGTCTATAGGTATGACTCCCTTGAGACGGAAAGGAGGTGTCCTCGTTGTGCCTCTATCACGCACTAGCAAATAAGACCCCCCGCGTTTCGGGACACTTGGCGGCTTGCGACTCGTAGAAGCTTTGTTTCCGGTTTATCTGCTCTTATTGGAAAGTCTTTGCTGAATCCTGCCGGGTACCATGAATCGCTATTCATAACCACTGGTTACGACACATCAGAGTTACATTGGTCTTGGCATGATATGGTGCGCTCTCGCTGTCGGGATCACTAGCGGCAGAAGCGTGGAGCCACAGCTTAGAAAAAACCACAAGCAGAAGGGCGCTAGCCACGTCGTATCCCTGGTTACACTTCCCTACGTCCCACAGTAAAGTGTAGCCCTATCGCCAAGGCTGGTTTTCTAATTATTAGACGAAGTGCAGGCCTTCGAGGATCCGGAGGCACCTTCTTCTGGTCGTATCCCAGTAAACGTGTCCGGGACTCAGGCATATACTGCCCCTATTAGGTTCTCGGAGGTCTGAAGCTAAGCAAAATATGACCTTGGTTACGTACCGGGCCCGGATTAATGGACATGACTGGTCATGTGATCCCTATGTGACTTCATCTATTTGCGACGGCCAGAAACGGTGGGTCGATAACGAGCATCTGTAAGACGAGCCGACACTCCGGCAATCGATTGTACAAATGGACTATTTTTGCAATGCTATAACCTGTGCGGACAAATGAGTTTGAAACATCAAGATAAGAGTTCATGGTAACTCACAGTTCAACAAGTATCAAGGTCCGTCTGCGCTCGATGGACAATGATTGAGCACGGTCATTTGATTGCCGCTGAAGAACTAGAACTACGTAACGTCATTAAACCACAACCGACACCCTCATTACTGAGGACTTAACTGGTCGTCGAAAATCATCTTATTAAGAGAGAGAGCGTGTGCGGTAGGCCCATCTTTCACAATATTACCTGCCCAAATCTAGTGGTCTATAATCTCACGGAGTAATGTGCGACCGAGCGTCCTTTGCCCTCCTGGTGGTGCCGACGCGTCCACCGGAGCCAACTCCAGCTGTAGAGCACATCAAATTTATGGTAACACAACAGACCACCTCGACAACGGAGGTATAAAGCTAACGTTAGTAATGGACAGATAAGTGTATCAAAGAGACGAAAGTTATACCCTCGGCCATGACTCAACGCTCTCACATGGTAAAGAACTTACTTACATTTGTAGCCCTAAAAGAAATCCACGCCGAGAGGTTAGTGGTAATTTCGAAGTCTGGCTGAATCACTACTCGACATGCCAACGTAGCGAATCACGGTCCGTTCGATGAAGTGGCGCACGGAAAAGTCTTGCCCGGCTTGCGCAAGACCCGCGCTCGACGGAACCGGCTTTCACACATGAACTTAGTTTACGGAAATGCGCAGCAGAGTCCGAATAAGATACAGACCCCGAAGCTCACCAATGTCGAGCTATAGTGCTAAGACAGACCAATCAATCGGTCTCTCTCATGACGATAGGAGGCTGTGCGCACGAAGGGACATCATCGACATGTTGAGAAGAGGTCTTTTTGTGCAATACGACTTCATACGTCAATAACTAGTGTCGGCGTCGAATAGCTTTTACATCTAAGATGTCTGTAGTTCGAATGTATAATCGTACGGCGCTCCGGGCGGAGACTAATAGCAATACTAGCGTCTCCATGTTCTGAATCGTTGCCGCTTCTGTCTAATTAAAATGGAAATTTTATTCGAGGCCCTAAGATGAGCAGTAGTAGCTCATATGGTATCGATAGTCGGTACGAGAATTAATAGTCGTCTATAAAACTACTCGATCGCGTCGGTAGAGCCCTGGGACCTCCGTGACGTGTAGCTGGTTTAGCAAGGCGCAGTATCTGAAACTCCAGTCAAACGTCAGCTTCGTAAGCGACTAACCAGGCAGATGTGAACATCTCAAGATGCTAGAGATAGAATCATGAAATCACAACTATCGCGATTTGTAACAGTAAAGTATAGTGGTAATAGTGTGCTATCCTAACCCAATGTCAGTGTGGGTCTGTCTAAATAACACCATGAAAGGTGTTGGAACCCCCCCTAATATTCTTGCCGGACTGGAATTAGTGTAGTTGTCTCTGAAGAATTCCCCCTAGACTGATAAATCATCGTCGTACTCTAGAATATCTATTAGATTGAAACGCGAGAGCAACGAACAGAGGCTTTATACCGGTAACAGACGCCTCCAATATTGCTCACGACTCGCTAACACCTACCACCCCTCTGTAGATGTGTTCGGCTAGGAGAGTCCGTTTTTCGTCTGGCTGAACGCCTTCAGACACGGATTCCCGAATCGCTAGCAACGCCTTCGCCGAGTCTCTGTGGAATAAAACTACTGTGCCAGTCATAGCACACGCACACATTTAGCATAAGCTTCGAAACCCTAGAGTAGCAAAGGAACCTTTAATTCGCCCAAGGACACGGTGCTTAATGACGAGAGTGAGTGCTCATGAGCCGACGGTTAATGAACCATAAAGCGCCACTCGATCTATTTGATGAGAGCGGGTATGCAGGTGAATGGCCGTGGATAACCAACGCAGAGGGAACTGCATCTGGTTCGTTTTTTCGCGGCCCATTCGACCGATGATAACAGCAAGAGACGGCTGGGACTCCTATACAAGTTCGGCTACACGATGCTTCCTTGGGTTGACTGAGCTCTGCACTATATCAGCGTGACAGCGGCCTCCCGATGAGCAGTTTTAGCTTAATGGATAATGACACTGCTTAATGTGGCACTAACCCATCGCGTCGTACTAATAACCGCTAACTCCGTGTGTTTGCTCCTACGGGATATCCGCAATTAGTTTGAGAAACTCCGGGCGGACGGTCCGGGCAACTAAATACTGGACCGGTTATATCAGTGTGCGGAATGGTAGTGCGATTTCTTCTCAGACACCGAGGTCACCCAATCGTTTACCCTTTCTCGTAACGAAAAAGCAGGTGAGTGGGTCAGTGTCCTTGCCGTAAAAAGACTCGTCTTACATGCAAAAGAGGACACTAACAATGTTCAAGGCGGTCCTGACCCCAACTTTCTGGATTCGTCGGTACTATCGTGCTCATATCCCGGGCCAATTTGGTAGAACGCCTGCGTCCAAGTTTACTCCTGCTTACCCTCCACTAAGGACTGCACGGCGTCTTGCGCTGAGAACGAAATCTTATCAGCCTCTCCTCCCAGCTTAGTGTGGTAGTGCTCGAACAATTGCGTCTGCCAGTATCATTTTGCAAAACGGCATAACACTCAGCCTCGGATAGGATAGGGGTGAATCTGCTGAGAGTGTAGCTTAAATAGGTTGGTTCATGTGGTAATAACCTATTGAGTATATGACGACGTAGCCCAGTGCCAAACAAGTTATTAGGGCATCAACATCGGCCAAGGAAGGTGCGTCCGTCTCCCCTAGGATATGGACGGCGTATATTAGTGCACCTTCTGAGTATACAATCAGTCGGGAGTCCTCGAGAGAATCGGCAAATCAAGAGAGGAGGTCGCAGATGGAATTGCCCCGTAACTCACAGCGTGCCTTCTAAGTGACGCATGTACGTGCGAGTCCGAGAAAATTCGAAAGCATGGAGCAAAGGAGACATGGAGAGTCCTTTTCACAATCTATGACCACGTCCAGATCCAATTGAACTTGCGAGTTGTGTAGTTTCCGGTAAGGTTCCCCCCAATACTAGTTCGTTCATCCAAAGGGCTTCATCTGTGCTGAGAGGGATTACCATGACCGTGAACCAGCAACATGTTACCGCGTGCATAGGAGGTCGAACGATTGACGCCATCTTAATCATCCGGATGTTTGACGCCTTCAGAAAAGAACCCTATGGCGACGTACCATTATCACGGGGGTAACAATCAGGATAGTGGGGTTGGCATAAACCATGCTGCAGTACGCCAGAAAACGCTGGGACTTTGCCGCGGACCTACGTGCATTACGCAAGGAAAAATCGGAGATAGCTATTACCCGCCCTCCAACTAAAAACTTAACCATCTAATATACCTGATCAGATTGAAGGCCAGAAACTAAGTCCCCGATACCGCATGTAACGCAAGGTTAGGTGGCTTAGATAAACAACTAAGTCTTAATGATTCTTCGAGCCCGTTCTTGTAATGACTCCAGTTGCTAGGGGCGATCGGATGACGCCCATCATCGTCCTCGCCCCAGGCACTAATCCTTCCCAACTAAGGGCCGAAACGTATGTCCGGCCTTGACGCTTTGAAAGAGCGCCGTAAGCAAATCATGTTGGCGGGTCTCGATGATATGTTCGTTAACAAGTGGAAGACCGTCTCAATCCGGTAGCATCCCGTTTAGGAAATGGGGAAAGTTCGTGCCGTTGAGCCCTGCGTCCCGGATCTGTATTTCAAACCCTAAAACGACTCCTTTTCTTCGCGCACCTATTGCCGCGATTATATACTCTTTGCGGTTGAACCATAGCAGTAGGTTCACCAGGGCGGTCCGGGCTAGTACCGCATTGCATTGTTTTGGAACGTTGAGTGTGGCCACGTGATGCTCCGGGCATGTCGCCGGCTTGAGGACCTAACCATAAAGTTACGGGCATAACCCCAGTCCTATTGAGTTCGACCGGTTTGTTAATATCAGTAATGACGATCAGTACCAGCTTAACAACCCGACTCGACATCACCCTTTTTTTATATACGGTAACCCATTGCCAGTAATCTACCTCGCACTGTGGCGTGTAGGGGTCCCCTCAGTCAGGTCGATCAACTTTGATCGTGCATCCTTTGCTACCAGCACCGGTGGGGGTCCGCTTAGCACGCATCTCCAGATTTTCCCCATTCTGGTTTGCTTCGTACAGGCCATGGATGGGGTGGATTCTGTCGCACGGACACTCATTAGCCCGAAAGATCCAGGAGACAGGTACGACGTGACATATGATGGGGGCTCCTGCGACACTTGAAACGACAATCGCCCCCCAGATGTTGAAAGTGCAAGTTTGACTGACCCTTCAGGGGAAAATTTGGCTCCGGATGAACAGGAGCCTTCTCCGTTTATTGAAGATACGGTGTTGATTATGAGAAGAGTCCGTCTTCGAACGGTTTGTGAGCAGCGCGGTGAACCGGACTCGGGACGAGGGCCGTCAAGTAGGCTTTCTAAAATTTTACCACGTGATCCTCAGATGGACGTCTTGTATCCTAAAAACAACGCTGCAGAGGCACGCACCGCAAAAGTCCTATGGCGACAGGCGTGAACAATTCCGCTAGCCTTGAACTTGGCGAAGGGTTGGGGTCCTTTCTCTATAGAGAGGGCGCAGGAATGCATCACACAATCCCGCTAGATACCAAGCATCACCGAACCGTTCACCGTGTCTCAACTTTGAAACCCGCCCGAGTGTTCTACGCGTGTACTATTTGTACTACTCTGCTAGAAGCGACCCGAACCTCCCACCGGATGTACATGAACTGGCACCAGCGACTAACGAAAGATACCTATAATCTTGAGATGAAGGGGTCATAGCCACTCAGAGTCTCCCTGCGGATATCTTGCGGTAAGTCGACGCCGGCCTTTAAATGCAGTGCGTGATACCAGCTAAGTGGCACCCGGTTTCAGGAAGGGAGCTTCCGGTCTGGATGGGTGGGGAAGTCTTAAGACTTTGAAGTACTATGTTTTAACGATAATCGACCGGATCAAACCACCGCTCACTGCTCGGACATTCAACAGTAACGTACGCTATCTTCTACGGCGCTGGCTACATAGCCTTATTGCCTGACAAGTTAGGATACTTTTTGCAAAGGCTGAAAAGTTACGAGACCCGCATTTCTAAACTTCTAGAGATATGCTACTGGTAGCATTGCGTGGAACGAAAGATCAAAGTGTGGCATGGAGTCGCCATGCCACCCATTCAACTAAGTATAGCGGGGACCGTTAAATGGTGAAGTCCCTGAAGGCCACCCAATTTGCCCATGTTGATAACTACTCGAGCACCGCTCAGCCTTTCTCCTTGCGACTTGGAAGCACAGTGTGTCCGGTCCCTAGCGAAGGCGGAGCGCGGAGAATGCGAAGAGGCCTACCTGCCAGCGATTTAGAGCCTCGCGGGAGCGGCATTAGCGAGATCGTACCTGTTCAGGTTGCTACTTAGAGCATCTGGAGACAGATGGACGAATTAAACCTTTAATTTCGATGCGTCGTCCCTCGGTATGCCTCAGCCCGGCGTGGGATGGACCCTGCGTTTATCACAAAATGGCAATACCTATGCGGCTCAGGTTATTTTCAAGATAGCCCCCGTTCGTACCCGTACAAACCGCCCAAGTAGTACCGCTCTGGTGAAACGCGCTTCCTCTATGTAATGCTGTACCTTAATCGTCTGCCGGTTAATTATTGGATGTCTCTGAGTAGTCATACCCTGCGTTCAATCCTCGCGTTTTTTCCCGGAGCTTCATCAACAGCAAAAGGAAAAGTACATAGTACTGTACTGAGGGACAGAAATGCTGATACAACCGTGACAAACTGTGGGTTATAAAGAGCCACCACGAAACAGGATTACTAGCCCCAGTGGCGTTTGTACGCTCTCGATATTAGGGATAGAATGTATACCAGTTTGTCTCGTATAGACCAGCATATAATTCGCAATCCATTACATGTGTCAATCCCGTACTTTGCACACTCTGTAGTTTGTAGCAAAGGCTAACCTAGCGTCGCCAAGAGGCACTTAACCGCGATTTGGCGGCATTGACATCTAGAGTCTATCCTCACAATTCTACGCGCTGGCTACGGCTGCTGAGTCAGCAACGCCTTACGTCGTCCAGCGAACCACGGGTTACCCATTCAGATTGCCCGGCTGACTGGAAAACGTGGTAATTGCCGTACTCCACTCGGCATTAAGAAAATGGCGACGAGAGAACTTATGTAACCGTTTCCTTCAACCCTTAGATAAAGTTAGCATCCATCCATATGTTGAAAGGGCCTATAAAGACGCACAATGAGGCCGTGAGTCTTGCGGTATAGCTGACTTGACGGCAAAGTTACGCAATTGAACACGTCTACTCCTAGCTCCTTGTCAGTTAGTGAAGGACGACTCCTGTATTGACACCGGGCAGCTATCTAAACAGAGTAATGCGATCTTGTGTACACATTGGGCAAAATTGATGCGTCTTATGGTCTTCGTCACCTGCCCGTTTGGCTATGTTTAACATGGGCTGCTCCAGTATAGGGCGCCAGTGACAGCAGAAATTAACCAGTCAAACTTGCCTATACAGGTCTGATCGATCAGAGCTGCGTTGAAGTAAGATCCACATTAGCAGTGCAAAGTGCGGTGCTGCGCGGGCAGGTCTGGATTCTTCGACCTTCCACTCTTGACATCAAACAAAGGGCTGCGACAGGGAATCTTAATGGAGTGATCTTTGTACGAGTTTACCAGTATATCCGACCTCCTGATCCTCCCCGATGTGAGTCGCTACTAGACGGTAGGGCAATCGGTCGTATTAGAACCGGACTGGGTTTAGTTTAACTGGCTGACAGCCAGCGGGCCACTGGTACGCTAGAATAGCCGACAAGGTGTCGTAGATTCACTTAACCGGGAGAAAATACGGAGTCATCTTATGCAACATAGATGCACGTCAAGTCCTACACAGAGCCATTCAAATGAATTCCACTACGTGCCAACATAGTCGTTTTGGAACGAGCGTAGCGACGTAAATGGGCTCTCAGATTATAAAGGAATGCGGCCGGACGTACTTCCCCATCCGAACTGCTAACAATCAAGGGAGTCGGCGTCGCTCAAAGGGATCTGACCTCCTACTATAACATCGGGAAGCGGAGCAGTTCTTTGCATTATATGGACTCCCTATGTAGGCACGAGCTAGGAGTACATAACAACGATCTTTCGATGTTCCTGCCCATAAATGGCAAGACCGGGGAGGATATACCGATCACGTCATGGTTTTAGGTAAGATTTACCTGAGCAGCGCTGTACGAGTTGGATGCGCAAATTGTTCACGTCATAAATTGTGCCTGCTTCCAGTGCGGTAGCAATGTCTAACTAAATTTGGCTTGTCTGCCACTGAGCCACACCATCAGTCGATAAAATTTGCAACGAGCATCTTGTACAACAATCTGGTAAGTTACCTCTTCAACCAAGGCGCTACGAGTTACTAACAGCATATGTAGGATATAACCTAAATGTGGTTAAGAGTTCGAAGGTTGCGAGGTTGTGCAATTTATAACTACAGCGTTCGCTACTGAACCTTGTTTTTACTTACCCAGTCGCCTAACCCGGCAGTTGCGACTTGGATATAGATTAACTGCCCACCTACCGATACGGCCGCAGTTCCGGGTAAAATCACCCGTATGGGTATTAAAAAAATGAATAACACGTGACGTGTCCTGGCTTCAAGGTCGATGGGATGGATCCTAGCATTATCCTTATGGCGGTGATTCATATTCACAGCGTGGCCGCATAGTCTCTAACCCGGACGGCAGGCGATTGTCCCAGCCCCTAACCTTGAAATCGTGGGTGGGGGCGGGAATATTCCATCCTTCGCTCCCCGTCGGGGCGGGAGATCGGGCCATATCTGCCGTAAGCGCCACCCGGCACATAATACTTAATGATAGTCTCTAATTACACGCCAGCAGAGCGCTTGAAGCTCTAAGTCTAGAAGTTTGGTCGCGATCTCCTTAGCCGCAATTGGTCGCGCACAGTCTTTCCGGGGCAAACGAATTCTATTTCCACGATATAGAGTGCCAGGGATACAGAGTCACATCACTCGGGCCTTCGATTCGGCTTATAACACGTTAAGACTTGATAACCCCTTCCCCGTTCACCGTGGCCGCACGGTGACCAGTACAGTGACAGCCAACACATATCATCTTACTGTTACTACGCCGACCTTCGTGTGCCCGCTCGATTAAAAATGACTTGAGTGGTATAGTAACGGCGAAAGTAATGTCTGGATTTCTGGTGAGTAGTAGCGGGTGGGTTTCTACCAGCGGAGACACGCGCGGATTGCGGACGGAATGTGTTCTTGATGCAAATGGAAAGTTGCGCGGAAAGAGCTATCCGCTCCAGCTACACGTGGACTGAGTGATGATCATATCAGCATAGCCACAAGCTCTACAACTGTCATAACGACAGGTGGGGTCCGGGTACACTCAACAATACTGGAGGCCTAGGAGGGTCCGCAAGGCCAATTATTGGCCCTCCAGCACAAGCGTCATAAAGCGGTTGGTGACGTTCACATACGAGACGATTGTCCTATCAGACTATTATGGCCCACCTAACTTAAGACACGCATCCTTGGGGGCTAAGTTGGAGTTCGAGCGTCAGACATCGCACACCGAAGCAGTCAGCTCTAAATTTCGAGTAGGGTATCATAATTTCTACGTACCCTATGGTCACGCGCTTTCGAAATGTCTAGTGCAGACCGCTGTATCAGCGGCCTCAAGAGCCGCTGTAACGGTGGCGAGAGTATGCTAGTTTCGCCGGCTGGTCAATTCCAATTAACTAAGTGGGCGTCGAGCAGCGCTGCCACTATAGGCCACATGATTCATACCACATACCATATTCAAAACCTAGCTCGCCCGCGAATGACGAGCCATAACTGGCATACGCAGCGTCGTGTGACGGGGGGCTACATGGTAGACTGCATTGACCGATTCGTAGCGACGCGTGTTCTAGGGACACGGCAGCTAACGGTCTGTCACTCGACTTTGTCAAGAAGCGGTGCGGATCGAGGTATTCTGCTCGATCATAAGCCCCACGCACGAGAGTCTGCGAAAGAGGCGGTACCCTGCACCATACCCGTCCGCACGGGGCGTCCTAAAACTTAATCAATAAACTTGCGCTAGGCTAGTGTGTAAATAGAGAATTGCCGCGGCTCCAAGCTAGAGGACGTTTCAAAGCTTCGTGTTTATGCTTCCGCGCCACAACACGTCGCACGACTAGGTGCGACGGAATTGTACCACCGTCATGAGAGGTGAGGTGTCCTCTAATATTTGAACGTCTATGAAGATTTGCGCCGGTCATCATCGGGAGTGTGTCGCCTCAGGATATGACGTAGTGAGAGATTAGCAGGTCAGTGTGATTAATACCCGGCCGTTGGTGGGCTGTTGAACACAGAAGATGGGATAATATACCACGTAAGGCTGTTAGTCTCTGAGGTGGTCAGGGGAACGTGCCTACCCGTGGACTTCTCATACATCACTACGCGCGGCGAATGCGTTTCTTTACCTTAGATTTAGTCATACAACAAACAAGCATGAAGTGGGGGTGAGTTCTGATTCCGTAAAACCACTTGCGAACGAATGTATGTAGATCCCGATGATGCAATGACTTGTCGTAAACGTTATCATTTTAGCCGGCATCGTTATCTGCTCTACCGCCTGCTGTAATAGCAGTTAACCGCGTGTTACAGAGAATCAAGATGCTGATATGTAGTAACAATGTGACGTGGGGTAGCCGCTCCTTTAAACGGATAAAGTCCCATAGGAGCCCTCGCATTATCGATTTAATAGGGCTACCGAAGCTACAATAGTCTAAGACGGCTTAGGACTGGCATACGGAAAACCCCGAGATCGTTACAACAGGGAATAATATCACTAGCCGTGGTGCTCGGCAAGCGGAACATATTTTCTACCTTTTAGTGAGGTCGACAGCTGCAGCCGCTCAGCAGCATTTGGATTGTCCCCAGCAGTTCAGCGATCGTCATTGTCATCTCCAAATCTGAACTGAAATGTAGACGCTTCTGTGTCGTGACGCCCTGATCCCCCTGATACATCGCCTGGGGTGACGCAGATCGATGTTAAAGAATGAACCAAACAGTGAAACTAGGACCATGCCGTAGGTAGCCTATCGCGCTTTATATAGTAACGGTGTGCCTTCCAATCTATGGGACGTGTACATGGGCTCGTCAGGTTTCTGGTCATGCTGGAAAAGTCCGCGTAGCAAGGTCGCCTGCCGCATGCTGCCGAGTTTTTTGATCCAGACCCTTGTACATGCTAAGGCCTTCCTAGTTCTTCAGATATTTGTAAAGAATTGCTGTGGCAATGAACCCCATGATCCAGTTATCTCCATAAGCACCGTCCCCCACACCTGGTTATTCACAAGAATGCTCAACCCACAAGGACGTCTATAGTAATCGCCGTGGCCGAGGGTCCGTGATGGACTGTTGTACTCAGCACGGTTGGCTGTATTGTCGAGGCCACCTATTCTATCTTTCGAGACTTCTTACCCTCTCATAGTACACACAGGTTGGTCAATTGGGCACTTTCTTTCGCCTGAAGGTCGACAGTTTTTTAGAGCCTTCTAAAAGCCACTAGATTATTGGGCGGACGCTAGCGTCGAACTAGCTCACACTGCATCAGCAGGGATTTTAGAATGATGGATAAAGCCTACCGCACGACTCTCCTCGGGCTTGCCACCGAAGTGAATCGTATAATCACAGCGCATTCCCGAGGCTGCTTGGGGACTGATGGTGATGATTGAATTTGGTAGGGCTCGGCCATCGCCCGCAATCCTGTAATTACGAGTTGGCTAGACATCACAGCTGGGACTAATAGCAACGCGATTTTAGCCCCGCTAGGTTCAAGTATTTGTTGGTCGCACTGGCAATTCTATGCACCGACACAGCGTTGTGCACTAGAGACACTAATTCCCTTAGAGACCATTTCCCGTACTAGGAGGCGCTGCGGTATGATACCACCAGGAGACATTCACTGATGAAAGCCAGACTTTTGAGATTCCATGACTCAGCGAGGACACTACCTAACACCGCTTTTGGGGTCCAGAATACCTTATACTCCTCCTTCGCTCCGGGTCTTGCCGCCCCCCCTCCCTTTGAGTGATTTACGAAAAGGTAACAAGGGAAGCAAGACTTGGACCTAGATTCATCCCTGACCATATATCCAGCCAGCGTTTTATTAATAGTCTATTAGCAACCCTCTTCGCATTTCAGACAATAGCCCCACGGTTGCCACAGGTATAGTGTGCAGTTAATCCCTCCGCACGACTGTACCAAGGCTTGTCATAACTAACCGTCCTTCATACTAGGTGCCCGTAACACGGTGCAGTCATTTCCCATACTTTACTTTGCTGCTGACCAAATAGGTTCGCACATATATAGCTGGGATTGAGGCTTGTGATTGATGATATCTCGTGATCCCTTGGATAAATATGTGTAAGATGAGCTAGGACGCGCAAAAGTTTGAAACTGAAGATCGCCTCTATGCGGGGACAGAGAATCTTCTTAGACAAGGTATAGCTAACTGCTAATGGCTCTGTAGGGTTACAAGATCACCTACGTGGCAGACAGAGTAGTCCTTCGAGGGCAATATTTAGGCCGTTTCTGCCCGAGCTAGGGTACTACCGTGACCTTGTAGCAGATTTATTCTCTGGGTGCTTTTGCACCTGCAGCCGTGTCCCATAACGGCCGTTTGGTAATATAACCCTGTTCTGTCTCTGCCTTGAGTCGGACTGGCATTCTATCCTCACTACCCTTATAGCCAGGTGAGCTATCCCCTAATGCCGACAGCGTTTAGACTGCTTTTGAGAATATGCCGGCCCTTGGGGATTGAATAAGTTTATACTGCGACCCAACGGTGCATCCCAGCATTCCTATTCCTTTGGTATCAGGTGGCCTCCAGATAATCAATGTACAGCTTACCTTCTACGAAATTAGGTGACGGCCAGATCCCGGTTGCAGGATATTCGATGCTCCAGGGCTTGTACAATATTCCGAGAAGCGAGGTCGGACACGGTATACACTTTACCTTCTGTTAGAAACTGTACACATGGGCCTGGAGAAAGGCACAACCGACGTGGGTGCTGTTACGGGTCATAGGAGCACTGACGAGAGTTTGAAAAATCCCATGTAAGGTTCTACTGAGCCTGTCCACCAATACCGCACGGCAATGCGACGGTGTAGCTGCCCCTGATGACCAAGGAGAAGTGACTGTAACATACGGAATACCTTGTCGAAAAGTCTCTTACGTGCCGTAACCATACGTATCATACTAAAAGTAAGCGTATCTATTCTTTATTGACACCAGTACTGAGACGGAAGGGACGTTCGTCCAGGAACTCAGGTCTACCTCAACCGGACTTTGCTAGCTGCAACCTACCGTCTTTGCTATCCACTTTCTGCCGTGGGTGTTCGTAACTCTCACCACCTCACATATGGGGCCATGAGGCCACACCTCCCGCCCCCCGGCGCTTACCCGGAGTGGACATAATCTAGGAATACTGACCCACGGGTGCTTCTTTTGATTTCGAGGACTCTTGTTCTTAGAATAAGTCTAGAAGTCCTTATACCAAAAGCGTCGGATCTGCCAACCGTATACGTAAACTACATCCAGACGCCAGGAAGTTCCTTGCACCAAAATTCAAGATTCCATAATATGTAACGCCACCCAGACTGGGGACAAGTCACCTACTATGTCCACCGACGGGAGGGCCGAGAGGGCCGGTTCGTTAGGAGGTATTCCTTGTCACCCCCGGCGTAAGCTTTTTAGCGCCTTCTACTTTTCGGCAGATCAGCCACGGGTGAGAAGGGGCGTAACGCATTTACCCATATCTGAGAGATAACACATAAAATACTTTTGAACCTTAATATATCGCACATAGTACAACCAGACACCGCTGAATAATCTTACCCACGACGAACGGAATTCGGTTGTGGGATTACCTTGGTTACTGGCCGTAAGCCCCCGCCAAAGACGCTTACACGATCCAAGGAAGTTGGGTTCCCGCGAAACTACAGCTGATCTCATCTTACACGAGCAGGGTGCCTCCAGTTGGTAGGTTATAGGACTAAGCCGCGCCATTGTCGCTGATTCCTGACGAGCGCCCTACCCTCAAGCAAACACACTAAAAGGCATGGATCGTTCTCATGAAAGGAGTTCGAGCGAAGATCGATGTGTATGCACATAGAGGTTCTGTCACACACCTGTAATAAACTTGCATCACGAGTACCCGCATGATAAACTGTCGTAAACGTTCACATTGCCTTCGCAGCCCTGAGCTTCCCTGACTTACATTCCGTACCAGGTTGATAGCAGAAAACCGAGTCGGAGGCTCGGAAATGGGTTAACCCTTACAAAAAGTGTAAATTACGGATTCTTTGCTCGCCTTGGACCTAGACGAGTGGATTCGCCTCGAGACACTAGAGTCAGGACACCAAGCTCAAGAGTGTTTTTCAGTCCCGGGATTAGGGTGGCTCAAGGCTTCCAGCGGGAACTAAGCGTCTGCCTACCTGGTATTCCTTGCACATCGGGATGCTGACCACTCCGATCCGTACCAAGACATCGTGACCGTTTGGTCCTCGTCAGGGTGCCTTCGCGTACCCTCATGAATCCGGACCGCACTGCAACTTTATGTACCGGTATGCTGGTCCCGACGATGCACTTATGAAGATCGTGAACAGGGCGGCGCGCCAACTAAAGTTCCTCACTTGTCCATCTCAAAACTTCTATCCTCGCACAACGTCAGGTGATGCCTATCCGTCGATTTCTGGAACTTATGGACTAAGGCCCGATGCGTCCTAGTAGGCACGCATTTATTAGTGTTAACGAAATTACATCATTTGACAGCTCCATTCACTCAAACCTCCAGGCGACCCCTCTTACCAGCTCTTGTTATGCTAGAGCATCTTAAAAGGACATCTCTTATCCCCACACAAGGGTAAGGCATCTAGCGAGGGAACGGTAATCTGAATTTGATACGGACCTCGTAGACTCTGTTAACAAAAGACTAGGTCCGCCTCGTCCCACCCGTGCTCTACGTGCGTCCCAGTCAATTAATTGTGGGCACGGAGTACGAGCTTAGTAGACCGCAGAACATCCTCGGCGCGGGGCTTGGACAGACCTTACGCTGTGGTTACTAGTGGTCAACACCTGGAAGTACTAACCTCTCACATTGTCCCGAACATAGCTTTCGGACGTGGGCGAGCACGGGGTGGCTGCTTAAATGGACCAGGGTAACGCCCAGAAACACGGTATCATATTATCATCGGCAAGCGCCCTCCACAATATGTAAGATGACGGTCTTTACCGTCACGCGCCCAGTCTTCCCGTCGTGGGTCGTATTTATGCCACAATTCCCGAGTGGTTCTCCATCTCGTCCACGTCGCCGCGCAGGTCTCAACCTAAGACACCCCTCCCTTGCCCCAAGATAAAATTATAGTCATCCGCGCTAATGTCTAGGTATATGCTTGGCGGTAAGCTACTAGCGAACAAAACACTTTTTGCTCACTTCAACCTGGTTACGCGTCGAACAACCTCTCTGCGCGATGCTCGGTAACCGCTTGTAAAACTCCCGGCACACGAATCTGATGCTATTCAGTTGAGATTGAGACAATTCCATAAACACACCTCCTCGCCAGTGCAGATCCGGGTACACGTCATTGTAACTACGTCGGACGCCCCGTATGGATCGACAGACTACCCCTCCAGAGCGTTTCACTTATATCACATGTACCAGAGTGTATATGGTAGCCGACGTCTTAGGAAGGATCTAACCCTCAGTGAGCCCGGTAGCTTCGGGTAACCAAACCGGTCGTCGCGGGGAATCACCAGGCATAATTATAAAGAGGGTATCCCAGTTAAGGTTCCAATGTTGCTATCTGCCGCGCTAGATTTAACGCTGGCAATGTTTAGATTCGACAGTTCGGGTTTTTTCACTGCTTAATGCGGGCTCCTTCTCTAGTCCTCTTCGCGACAGAGCAATATAAATGTTAACCCGTTCACGACTGGGCAGGGGAACGCCGGCCAGTGAGCTTCGCCATGCAGATCCAGGCAGACTGGCATGAGTTAGGGGAGCGTACGTGGAAACGAGTAGCACGGCTTAACCAGTAGTTCCATATAACCAACGGGTTTATGGGTCAGAAGCGCTTTGACCCCGGTGCGGACGAGTGGCTCCCCCGTGACAGGTTCTGAGAGGCGGATCACCTCATCTCCAGAGTGCATATTAGGATTTGGGCCGGGGCGTTAACCGTGTCAGGACTTCCTAGACTTGGAAAACCGAACATGGAAACATCATCCCTCCTCAGTCAAGCTCCTTCCAAACGATTTCGGTACACCATTCAGCTCCATTACCGGTTCCTTTCTCAAATAATTCTTTACAGTGGTCAGTAAAAACACAATATCTAATCGCTCAGAGGGCTCGCCTTCACCTTGCACAAAAACCCGAGTGAGAGAGTGAGGCTGTCGGTGCTTACCTGGAGGGCTTGGTTCTGAGTTCTCACCGGATACAGAGTCTTTAGTCCTGGGGCTCTATCGAGCAGGGAAACGCTCGCACCAACTGGACGCCCTTTTAACCCATTAGGAAGTCATACGTGGGAAGCCGTGAACTGTGGGTAGGACGTGAACGTGAAATACCACAAATGATAATTACGTCGGGATTCGGATGGATGAAGAAAAACGGCCGCCCATCAGAATGGGCGAACGGTAGATTGATTCCAGCGAATGGAACCTCCATGCTAGGAGCGTACGCTTGTGTTGACTATTGATGCTAAGCGATGTTGGAGGCCATCATCCTGACCATCTGAAGATACTAATATGTTACGGGGGAGGCGCTCACGTAGTAACATAGCGCACACGCCCTGGTCCAAGTCGCGGGTCTTACTTTTAGGACCATGATTCGCGAACAAAACGATGTAGATCCTACTGGGGAGTGTAAAGCACCTTAGGTTGCAGCATGAGACCCCGAAAGCGTGAACGGTTCTAAAATAGTGTGGCCCAAGTCATGTGGAGCGCAAGTTATAAGTGTGGAGCGAAGATACGCACGCTGTAATGCGCGAATATAGGCGGCTACAGCTCAGGACCTGCTTACCGTTCGTTCAGGCACGAGCCTCAGCTATGGTCGTACTGAGCAGGGGGCTGTGTCGCAGATATTTGGGAGCAATATTTGCAAATAGTCCTACATAGTAACATTGGCGTCGAAACGGCTAGGAGAGCGGGCCGGATTTGCCATTCAAGCTGGGTTAGCGCAAACGACAATAAGCAGTCCCACGAGCAGAATGCGGGTGGGTACCCTCTCGACTAACATTTGCCCGTCTGTTGCCTACAGAACAACCCCTATTTGCGCAATTTACCTGCGTGTCAGACCGATGAATTTTCAGGTGTATGCTCTATGACGCAGCGAACACCTTAAAATCCCAGAGTAGCAAGCTTCCCCCGCATTATGGTGAGCAAACCTTGATCGCTCACCCGCGATCGCTTCTCCTTAATTAGCGAAACGGTTGCCTTCTCACTCTCTCAAGTCTAAATCCCTCCCCCGCCAAGCAGTCGGCGCTAGGATCTTGCAAAAACGGATTGGATCACTATGGTGAGGACTACTTGATTACAGCTGATTTCTAGCCGATGCCGGGAGATTCCGAAAGTGTTCGCGGGTTATAAGCTTCGAGACTGGTTTCTTCAACCCTAAGACAGTCGTTGCATCGCACAGGGAACCGCTTGCAGCGCCCAGCCTCTCATTGGGCTCATAGCCCGCAGTGAGACCACAGTCGATAACAGAATGGTTATCTGTTTACCGAGTACGGTACTCCGGACGTGATGCCAACTGGCTCCTAATTCGTATCCCTGATCTATGCTGGATCCATTGCGGAGGGTTCCGCCACCAAGCAGCGAGCAGTAGTCGGGATTTGTGTTCTAGACTACCCATTTACGCCAGGGTGTTTCATTTCAATCCACTATCGCTCAAATCCGCGTCAGCTAGTCCCGTCAGCGTGCTCCCACACCGACCGCCGATTCCCTGATATATTGCCAGCTCCGGATTCCATTGCTTGCTCGTCCCCCTATGCGCGGCATTACCGCGCATATTGTGGACCTGAGTCGTCTGCAATCCCGGGCCACTTGGCAATTACATTTTAAAGCGACAGGGTAGTGCAAGAGAGATCCGACAGGATCCTAAATCGTGAGTCTCATGTAGAGGCCCAGTCTTACAGACTATGATGTTCACGCCCGTAAATGACACACGGGAAAGATAGAGACTCAAGAGATGACTGTAACGATAGATGGCTTAGGAGCACGGGCATGGAGTCTACCGGCCGGACAGTGCAGCTGATGGGTAAATCTGTCGTGATATCGAGCCGATCTTGCACAAAGGCCGCACGCGACACCCCGGTCTTGCGCATACGCTCCTCGCCTGATACAGGTCGTGACCTGGTGTAATGCGGGGGTATAGCTTGACTGCGCCTTGTATCAGATCAAACCCAGCGAGTACGGTGAAGAAGTTGTTAAGTACGGATTTCCCGACGAAGCCTTTGTAGTACGTACCGCTAGAGCCAGGCGTTGGAGGAGATCGCTGGCGTTTCGGTCGATCAACTAGCTACCAAACCGGCCAATTAGGGGGAAGCTAATAGTGGCCAAGGGGATCGGAGCGTTGGCTAGGGCCAGCCGAAGGAGCAATCCCACGCGCCGTCCTTTTCTAGTTTGTCCCGCTTTTTAACTTGAACGCGCGGAGTGTCGAAGCTAGTCAGGTTCATAACAGAGGTCTGGGAGAATCCTATCGACACGCACATGCCATGCGAAAACTACAAGATACTTTGTCGCGCTACGAAGCAGAGAAGATGCTTATGTGAGATTTTTAAAGACTCTGTTTCGAATTCGTCTCTTAACACCTGGCGACCGGATTTATGGCGCTGTAAGGGACTGCAGGTGATCTATCAACTATACGTCATAGGGGCCAACGCAGTTTTCAGCTACGCTCGCCAAATACGGGCGTCATGCCGAACAGGCACTTATAAGAGGGCGATAACGTTCATTCCCGACTCCGCGACCAGCTATTAGCGATTTGATGCTGCTATAAGACAACTTATGAGACGGGTACTAGCGGTTGGCCTTTGGTTGAATAAATGCCCGCCACGATGGACTGGCTCAGATCAGCGGAGGCGCCCCTTGCACACGGCCCTATCGTTTGCACGCTTCGGTGATTCCGCGCATCGAACAACGCTTAGCGCGTCAATGTCAAAAGATCTACCCGTAGCCCCAAAGTATATCGTCACATGACAACGACGATGGTATACGCGTTTAAGCATGGTGATCGTTGTAGTACGGGTCCGACGCGCATACTTTGAGTTCCGCCGCATATTGTATTCCGTACGCGTTCTACCCCGCAATTATCTGGAGTTAGTGGGCCCAGTAAGGATATAAGTGGGAATCTCCACATACATCTTCAAATAAGGGGCCATGCCGCTCAGCCGAACTTTGGATCGATGGGATAGGTGAACCGAGGGCAAGTTGCCTACCACGTAGCACTCCGCAGGGCAAGCCTCGGATCGATGCCGACTCCCCAACATGTTTCTCTTAAAGTCTCTATAAACGGCCGTCTCCAGCTTCAGTGTTAAGATCTCATCTGAAATACTCCAAGGTAGATTGATCAAGGGAATGACGCACCGGCATCAATAATCAGACTCCACGCGTATGCAGCTACTAATTACCTGTGGCCTAAGCACGCGAGTCGGAAAGCGCAGCTGTGTCCAACACTGCACCCAGCAGTTATCGGGCCAAAGTCCAGGCCGGAATGGATTAGTGTGGATTTTGCCATTAATGAAATACCGGTTACAAATATCGACTCTCATAGGAAGGTGTATACAATTAGTCCGTCTACCGCCTTAGGTCATCTTTCATTACAAGCACAGCCTTTGCATGTCCGCCACCACCCCAAGATATTTGGTATCTGGAAAAAATTTTCATTATGTCGACTTATTGGGCCATCTTACGACGTACACCTTGTTAGTGCTTAGAATCTCGGAACATAGAGGAAATCTCCGACCTTTAAGAATATGTGTTCACCTTAAAGAGGCTAAAAGCGTGTGTTAGCCAGGTCGTAGTAGAGCGAATCTAAGGCAGTGTACTCGGAATCGATCGTTGACTGTAGGATACGCACGGGCTAACTTAGGAGCGACGATGCGTACTTGTGCACGCTAAAGCCGCGTCCGGCGTCAGAAACACTACGAAGTTGTGTTGCTCCTTTACACCCAGTCCTAGACCCCACTTTACGCACCTGGCCACGTGGGCCGAGCGTAACCGGCTTGCACCGCGAAGCTAGGCACCGCTTTGTCCTTAACCGTACAGCACTCCACGGTTCGACTTATCTTGTTGATGGATCTAAGAGTCATATGTAGGGTGGCGTCAGAATGACCTCCACATGAATTCCGCAGCCTTTAACGGCACTCATTTTGAGCGCAACTAAACGCCCCTGAGGTAATATCGAGCGTTGTGATGACAGCGCATCTTGTGGTAACTCAACCCAAACATATAGCGCGCTATCTTCGTTTGTCTCAGCCGTCATTCTCGGCAATAGCACTCACTCTGGGCGAAAGGGTATCCAGGTAGACTGGCACAGCCTCTACTTGTTGGGTGTTACCCCTGCGCCGGAGTACACAGTAGCACATACTAATCCGGGCCTCAGTGAACCTAGAAGGAGTATGTGTATACACACGAGTGAAACGTGCCAAGAGACTACCCAAGCGCGAATCCGCGTACACATAGGTCAGGGCATCCCCCACACTGTTATCCTAACGGCTCACGGTCATCAAATTACTGTGATGTTGGTCTATGGGTCTTGCTGGTGAGCCGGCACATTCAAGGTAGGACTGACTTAATCCTGCATGAATGCCCTAGCGGCATGCAAACCTAACTCCAAGTGGCGCTGGGGAAACTCATGAACTCGAAAACACTCATGCGAAGCCTACAGGAGCCGATGAGAATCGAAAGTAACCGCAAGCAGACGAGGTACAGCACTCTCAGATAATTTCTCGTTACGTCAAGAAATGACAAATTCCACTAAAGTTGAGTTAGAATCAACGTCCGCGACACCCAATAAGTGTCAAGGGCTCCCCGGAGACTGGGCGATGCTTTCTTGCTTGGGACCACGTTACATCCATGTGCGTTCAGAACCTCTATTAGGTGGCAGTGCCCGTCTGCCAGGAGTCGATAAGATCAGGTTTGATTACGATTTAGAATATTAATTAGAACCTCGGGCCGCTATTGAATCCACTGAATACTTACATCGCTTAACTGCGGCACGCCGCCTGTACGCTCATCCATTACGTCGGAGCTACACGTTTTTGTGACGACATGTTACATACCTGTATACGAGGGCTCAGGATTTCATCCGCCAGAGACATCTATGGGATAGTGTTCACGGGTGGGTACTCTGTAGGGGAGGGTGATGTATGTTGTCTCATCATCGTCAGAATAAATGAAGTTGTTCTCGGTGCTGACGATGCGTCATCTATTACACGCACCAGTAGCAATCTCGTCAGCTCCATTCTGTGCGTGGCAGGTTCCTTTCGCTGGACCAATGCAATCCTTTGATTACGGTGTACACGCATCTACAACGGCTCACTTTGTTGTGACATGCCTCGACCTGGTCTGAGGCTTTTGTACGTCCTATTCATGAGGGTAAGGTGCAATTCGAGGAGGCCGGCCACGGGCGGAACGTAACTCAACTTCGCACAATTGATCCCGTGCGCGGCCTCCAACTTATAGTTAATCCAGCAAGTCCCCTTAGAGTAGAGAAACAAATAGTATTTGATTTTCCCCTCTTCCTCATTTTAACGGCTACATCCCACTGCGGTCGTAATGACCGGACCGCGCGGGTCGTTTCATGACGCCCGCTCCATCCTCAAGGAGGGTGGCTTACTCTCCAGCAAGCGGTCAAGGGATTTCCGAGACGATACTCACTGTCTTTCGTGCTCTTTGGTTAGTGCTTGGCATACCGGCGTTAGAGCTCATCACAGCCGGCTACCCCATGACGAGTGCGATCCAGTCGTTGCGTTCGCTGTGTCGTATTGCTCTCACCCACTTTAGGACAGGCCACAAACCGCCAGGGTCCAACCGCGGCAAACAGAGTTGATTAGTCTAAATATGGTCTATAAGTACCAATCAATCTCAGCTGTCCGGAAATACCCGTGCATTCATTGTCCGGCAAACTTCGGAAGCTTTTCCAAGCGGCGGTTCTTAGGATGTTTTCGCAATAATTAGCGAATAGTGGTGGTTCCACCCCTTGGAGTTTAAACGGGACGCTGCCTTTAAACTCCTTGTCCTCGAGCTTAGGTATTAAACCTAGAAGGTCTCAAGACAAAATATTAGGTAAGTAACCTAACGATAAGGGCAATGATTCGGATTTATCACTCGGTCCATATATCGCTCATCTCTCTAAGCATTATCTGCCGGAGACGAACTAAGAGCTGGCGGGTCATACCACAAGCCGCGCAAATTAGATGCAACAATCTAGTCAACACTTGAAACATTTCACTCCTTACTCACTGCTCTGGCACTATGCGTACCACACATGGCGTAGCGACCGTTTGCCTGTGTCAGCAGGTGGAGTTGGTCTGTCATTCACAGCGCTGCAAGTAGGCTTAACTATAAGAATGCAATTAGTGTCAAGGGAACAGTCAAAGCACCCGTTCATGATAGTAAGTACGTTTCCAACCGCAGATCTTAAAGCCAGGCACCGTAGTATCTTGTCTGGGATCTTCAACTCACCAACTACATCACTCGGTTACTCTCCTTTGCCAGGTATCTAATGAGTGCGTATAGAATACATCAACAACAGGCAGTGTTGTGCACGAGTCCGGGCGCCCCATTGGGATCGTTACGTGCGCGCTGTGAGTGCGTCGGTTACTTTCGATGCACTACTTAACGGAGCCTCTCAAGCCAAACCCGTATGTGGTCCTTACAGTAAGCCATGTTGATATGAATCAAAACCGGCTCGGTTCATTAGCTCTGTATCGCCCTGGTCATCCCCGGTTATATTTCTAGCATTATGCAAGGCGAAATTGGAACTAGTGCCAGGGTTCACTTAGGCAGTATCATTCGTATCGCCAGTCTTCACAACTCTTACTGAAACTAGGCTACGTTAAGACGGAAGAAGCATTTTTTATTATAGGCCACAGTCGAGCCAGGCGCACAGAGGGCCTCCGCAATAGTGGGACAACTACGTGATGCGCCCGTCGGATATGACCCATAAACTGGTACTGTACCGGAGACCCTCGTCGTCCATCGGCGGATCTACGGCTCTCTAGGTCTGGGGTTTTACCATGCAGATGTCAGATTATTCTCTATCCTATGGTCCCCAAGGACTTTTAATTCCTTCGGTTTCAGCGAAACAAGTGTAACTGGCGCATGTCCAGAGCCCTGCTAACCTGGACGTCGTCCTCCCGATCTACTAAGCCACGCACGACTCCCGTCTAACGGGTGGTCTTACGAAGTTTTATAGGTATGTAGTGGCCTTGACTACCGGCGCCCACTCGGTCGAGTTCAAACACTTTCCACCTAGTGCATATTTAGTAGTTATTCAAGCTATCTGGCCCGAACCTGCAATACGCAAGCGTTTGGTTGCGAACTTATTTATGAAAGTCTTCGCGTACGCGCGTAAGTGACAAGATCTCGGGTACATCTAGTACAGAGTCAGCGGTTAGACTCGGTTTTCCATCTGCATAAGTCGCGCATTGTCTGGAATGTCCTTAACGTGCTTCGAACGAGTCCCTATGGTCCGACGGTTCGATCGTATAAATGACATTAGCCGCCAGCTGTCCTCCCGGAGCGACGCCTTAAGTTGCATGCTAATCGTCTATTGGGCCCCGCGCACGTGCCCTGTACGGGAGCACGTTTTCGTACCTGAGCGGTTCGAGACTCCCAAAAGAGACGCTTAGCGTCGTCTTTGTGAAGACCTATGCAGTTGCACGATAGAACATGCTTTACCCCACGCATTCGCGATAGAACGTTTTCCAACCTTTGGAAGATTAAAGCAGTGCACCAGAAAACCGGCCTGCATGGTTCGCTGTCGAGCGGGCTTTTGTTGATAAGAGGCTCAAACGTAACCGGCGGAGTAATAGCGGTGCTTATGGGTCAACCTTGGAGTTCATGGTCCAACCCGCACGACAACCAGTAGACTGGTCGACACAGTCACCCGATCTGTCGAGAAGCTCATAGGGTTCTATATCTAATAGCCCTAAGGTGGCGTACGCAATAGATATTGACTTCATTCTGTGGGACCTTGACTAGGGAGCGATTCGACTCATAGTCGGAATTTAAACCAGTTGCAGGCCTATTCGCCACCGTCGGACGACGGGAGATAGTCTTCTGACTCCTGACAGCAGGAGCCGCCCCTAGCCTATTGCCGTCAAGCATAGACTGGGTTTTGGAATGGTTACGGTGAGCGTCGCTTAAGCAGAGTGCAATGTACATACCAATGGTTGCCCTTACACCTGTGAGGCCATAGAACCAACCTAAATAGCCAGGATTGGACGCCATCGTTTATTCACCTTCTAATTAACACTTGACCACAAGGTAGGTGCTATGAGCGATGACTCGCTTATGAAAAACTGGATGCAGGCAGCAAGCGGTAATATAGGGGCAACACGTAAGGACCCTAGTTTTCTGAGAACGACGCACTGAAGTGGTAACACGCCCATAAGTTATATGGCACCGAAGACTGTCTTAACAAGCTGCATCTTCCTACCTTTTTCTATTAGCCTAGAATTGGCAGATGGAATCTTGATATGCTTGACGCTAGGAAGACGGTATTCTTCATACAAACGAATACAGTCTCGATCTCGCCGAACCATCAGTAGCTATTGACGGCTATCAATCAGGCCGGATTATTGTGGGCCTTTCATACTCCTCCCAGGCAATTTTCATCCTAGAAAAAACCAGTCATTTCAACTCATCTTCTTCGGTGGCCAGCGAGATGGGAATGCTACTATTCTCACCTGTGGTCACAAACAGCTTACGAATGGCTCTTACGCCGCTGTTATCTAGCTCTCTAATTCGCGCTCTTTCTCTACACGGGACAGTTAGAGATTTCTCTAGATGCTTCTCTGAAAATCCTGGCTTCATATGATTAGATCAAGATAGGTGCGTTCCAGGTCACATGAGCATGACACGTGGAGACAACAGATGCTGAGGGCTTGCTCTTGCTTCTACACGGCAAGGCGAGACCAAACAAGAGGGACCGACCGTTCGGATTTGTCCTCGGCGGAGCTCGATTTAGCTGGTACCCTATGATCCGGTCTTTCACCAGTCGCGAGCCGACTGGCGTGCGAGTTTTCAGAGACGAGCGCCCCGAAAGGGCACC'
result = MinimumSkew(Genome)
' '.join(map(str, result))
| def skew(Genome):
res = [0]
for nuc in Genome:
to_app = res[-1]
if nuc == 'C':
to_app -= 1
elif nuc == 'G':
to_app += 1
res.append(to_app)
return res
def minimum_skew(Genome):
min_val = 0
current_val = 0
min_pos = [0]
for (i, nuc) in enumerate(Genome):
if nuc == 'C':
current_val -= 1
elif nuc == 'G':
current_val += 1
if current_val < min_val:
min_pos = [i + 1]
min_val = current_val
elif current_val == min_val:
min_pos.append(i + 1)
return min_pos
genome = 'GATGCAAAGGGCGCGGCCTGAATCATCTTTAAAACAAAACAATTCGGGTTTTTCTGGGGCAGCCGTCCTGACAACTCAATATCATTATTATTACGCGACCGCTAGGCCCTTTGCGATTGTAGGCGTTTCCAAGCTACAGGAGTACAACAAGTATGATCGGTTTAGATTATCTCGATGGACTGCGTCTACGTCTCTTGACGTCACCCTCCTTTGGGGCGTGCTCCTGCTCCGGCGTTTTGGCGTATGCAGCGGTTGACCGACCACCACACAGATATGCCGATATTTGAACCGAGCTGCACAAAATCGACTTATGTTGGAGTGGTCTACTACGCTATGCCACCGTGATCGTTATTCCTAAAAGTCTCAGACTTTCCTCTCGAGGGTTACAGGGGAAACACTTATCAGTCTCCCAGGGGCCCAACATTAGCTTCATCTATAGGTTTAAGCGGCGATTTGGCTTTGGCTAGGGACACTGTCAAAGTTGCGGTATGATGATCTCAGATTGTACCATCTAGGTAAGAGTGCGTAGAAGAATTGGAAAGTACGGAACGTGTTCATGAGGAACTTTCATTGCACGGTGGTTACCTCCACGCTGCCACAAGAGGACCGCTGTATCGTAGGGCGGATCGCGACACCGTCTTCTAGCTCCATATAGCAGAAGGTCCTTCGGGAGGTCTATCTACACTCAAAAAAAAGATTTCTCGACATCTAAGGGCCCATGAAACTTCGAAGGTAACGCACTCCCTTGCGCAGACACCTACAGCAAATCGCTTAAGTTGCGGAGATTGCTAATCTAGCTGTGCTTAGGGGGCTATACAGAGGGGATAATGCGGCCAACGTTCACACTTTGAAAAGTACACGATGCGGCCGGAAGGAACTTAGTTTAGGATATTACGTTTTTGACGTAACTAAAACTGATTTTGGCGAACCGAATCCTGCGTATTACTGCAGTTTCGTAACAGCCCCTTAGTCTCGGAGCGATATGCGTGTACAATGCAAGGCTAGAATTGTGACTAATGTCCAAGTATGTCACATGATCGGTGTGTCAAAGGGCGGGCAACACGTACGGACTTTACAAACAGGGTATTGAGCGTTGATCCCTCAAGCTTGACCGACGGCACGTTTACGCGCATTAGCTAGACACATAATAGCGTCAACGACCCCCACACTCGAGTCATCAACGCCGAGAGAACAAGCAGTGTACAGCACGCGGACGGATGCGGCTGTCGGGGAAGGATCGGACTTCAAGTAGTGTCGTCTGATTTACGGATTTAGCTGCTCCCCGAAATTCGCTAGTGAGAAACAGCGAGAAATCGAGCTGATTAGTGAGGGGGCACGACGGGGTCCGTTGAGTGCACACAAAGGGTCCCCGTGCGTCGGTTTGCACGCGATACGTCTTAGACCCATAGTACAGGTGACCCTACGCAAGGCGTGCACTTTGTCGTCTTACCCCCCAGTTAGAAGGCCTCTACAGAAGACAGCTTCAAGTTGCACAACACGATGTTTCGACGCGTAATAAAACGAGCTATATTGGAATACAGACTCTGCACGTGCTACGTATACCAGGACAGGGGACCTAGATCCGTGTCAATTCCCGGCTTCTCTTTTCAGTGACGACCAGTTCTCCAAAGGGCGAGGGTTGGCCAACGAACACTTAGGTTAGTATGGGACACGCTTTCTGTTCTTCTCCGTTATGCCTCTCAGCGGTAGTCTCGATGGACTCGCCACAGACGCATCTGACTTTTCGGATAAATCACATGAACAGTGGAGAAATAGAATAAGCCGGGTTTCTGATAGTGGAATTAGAGTCTCCAACGAGCTAAAGTCTGTTGCGTTCCATTGGCCTCCCTTTGTGGTACCCACGGGCTGGTGTTCCCACGATAGCTCAGCAAGCCGATGGTGACTAAGCTTGGCATACCAATGACAAGGCCGTCATGAAAATCATCCCTGGGCGCCGAGGGAAGGAGCGCTCTCCATGAAATGAGAGCGTTGGGAAGACATTATGCGACGCTCTGCGCAAAATACGCATGAGGGGATACCCAACTGGTTCGGCCGGGCTAACTAAAGTCTTATAGTACGCTCCCGAAGTTAGCCTCCAGCTTGTTCGTCGCTTGCAGGAGCATTATTTCCAAGCTCCAATTTCTTAAGCGCGTCAAGAGCACTGATACGGTGCAGCGTAGTTGTTGACATCCCGAGCCCTTCATCAACAGGTAACTCCAGTTGAGTCGTACCGAGGGCATGTAATCCAAACATCAACCGAAATAAATAAACTTCCCCCAGTCGCTAGTGTCCATATGTAGCATCCACAAGTCCTGCGCGCCAATAAGAGCATAATGGCATGTCTACTAATCGGGATGAAGTGCAGAGGAACACTAAGATGATAAATTAACCCCGTCGAATACGATTTCGCTGGAACATCTTAGTTGAGGTCTAAATCTCTATAGGGTCCGGGAAGAGTACGAAAAAGGGGGCTGATCGGAGCACCTGAGAAAACCGTAAGCGGTTTCGGTGAAATGAGCGATGTTTGTGACTGTTGCTCCGTCGAATGCTCCGAGTAGAATACAACGCCCGTTTAGACACGCGTTGGGGAAAGTATAAAGGGCGATGTATCACCTGTCCGCTCGCAAAGTTGGACGACACTGTTGAATGAAAACTTGCACAGGCACGACTGACCGGGTCTATTACCGCGAGCGAAATACGCCGTGTCCTGCACTGCATTATGAAACGCACGGACAGACATCAGTTGACATACTGAACCACTGAGTAAGCAATTTGCATCCCATCTGGGATAAATCAACACCCAGTACGCTACTTACCAAGAGGCGTCAGACCGACAATAAGCCTCACGGGGCCTTAAGTATCGCTCTCTTAAAATTCACTACACCGCCAGCTAGGGTAGGACACTCATCCCCAGGCATGGGGGTAATCGCTCTGTCCAGGGGTGAACTCGAGTCACGATTCGATTGTACAGAATCGCGAGATACTTCCTATCAACTCCAAAGACCTAATCGGTTCAGTCAAAGCCATGTCTAACTGACACACAGGCAGTATGTGAGAGGGCCCGCGTCTGGACGCCGATCTTGGTGAGGCGGCTCATGCATTGGGCCACATGAAAGGACAGTTCAACCGGATAAAGAACCCGACCGTTCCTTAAACCGCTAACTCAACAGGGCGAGTCCAAACGTAGACAAGACCAAACCGCATCACCAGGTGAAAATTGGAAGCGTCTACTCGTATGTTAGGAACAAATGAGCTTCGTATGAACTCCGATCGATCAAATAACCTGCATCGTACTGGGGGTGCTGTAGGTTTGCCTAGAGGGTCAGACGAAAACCAAATGGCGATGCTCTGGTTCCGTAGCGGGCCCCTCTTGCGGATGACATTGCAGGGAGCGGATCCTGCGCGTTTATATAACAGCGATCCTTGCAGGGCATTGACAGAGCAAATCAATCAACTGAACCGAACGCGTGCCCACTCATTGAGTCGACTTCGTATGGTCCTTCGGTCACAGTACTTGACGCGGGAATGATAAGGCCCGTGTGCACTCCCCTAGGTCAACATCTGTATCTACCTAGGAAGTAGGACGATTGCAAATTGGCGCATCAAAATCCGATGTAACTCTTAGATCTCGAGGATCGAGCTCGACCGTCGCGAGTGGAAAATACAAATGCCTCAGTGGCCCTTGTTCGGCTCAGTATGAGCTCCTTGTTTCCGCGACCGCATCTAATGTGATCCAACATCTTACCTCCTCTGGAGACGGAGGCTACAGATAGATGAGTCTAATTTTCTTACAACTAATAAATCGAAAATACGGCTCATGGCGTTGAGCAAGCAAAGTACGACACGTCCGTAAAAGTCGGATTCCTCAGCAAGTGTCTTGACCATCACAGAGAATATTTCAGCAAGCTGGCCCATGTAAAAACTACTGACACTTTCGACCTGTCACACCAGAACTTACAGAAAAAACTCTCCTGAGGGAGCCCCTAACTCCGGAAGATCTTAATGATCGATCGTGCAACCTGGATGCCGTGTGAGTGTGGCTACCTCATTCCTACGGGGCTGTCCGCAGGTTCGGTCGCGTAGGTCTGGCTGGCCAGTGTACTTGGATGGAAAAAGATTTACCAGCTCAGGACGCGCCCCTTAGAGACCTGCAAATCCGTATCAAATTCACTCGTCGAAAAACTCGCCTTGTAATTTTTGTGATCACATTATATTTACGATGCTGTCCAAGGTAATAGGGTATATCCCAGTATAACTGGTACACGCTCGCACACGTGCCGAGTACCAACTCCCTCGCTTGGTTGATCTATCAGGGGTAGGGAAGCTTCGAGGGCCAGGCGTAGCTCCAAATCTCAAGAACGGTTCTAATCCTCAACCCGTAGGATCTCTCGTTCATACGTCGCCCTACGACTCTCCGAAAGTATGGGCACACTCGCCATAAATATCTAAAAACGATCTTCGCGCACGTGCAACGGTCTGATGGCCTTATGCTGTAATTCGGTTTCTGTGGGAATTCGGTAACGGATAGTCACTGAGCTAGTGCCGGTCGCTGCTGGTTTAGATGAATGCAGGAGATCCGCATCAGCGCCGAGCGTTGCGGGCGTCTCGGCTGTAAGTTGTGGCCAATGCGGGCGTACTGTTATACATACAGGGGAATTGGCGTCAAACCGTTTATGCGAAATGCTGTCTATGCCAGGTGAGCATTACACACGTGAGATCGAATCGAACGTAGGAAAGGTGGTCGTATGGGAAACGTCATGTTGATGAGACTTCCATTAACGGGCCGAAACTAACGTCTTAGCGTCGTTGGTAAAGTTCAGCTGGGATACTTTTGAGGTCGCTCATCTCCCCGCGTCACTTAGCATCTACTCTAGCCTTTCTAGAGGGGTTAGTACTCCTGGGAGCCGCTCTGCAGATATTCAATGAAAGCCCAATTGAGGCGGCATGTGGATTGTAATTCACCCTCCGCCTACAGTAGACATAGAGCTGTGCTCAAATAGGTTGAGAGGGGCGCCATTAACCATCGTGGTACGGACCTATTTAGAGCGAATATCTAACCAGCAATTTCTCTCAGAGGATACATCCTACTCAAGATGCTGTACAGGCATTGCCAAATTACCGTGATTCCAAACTTTTGGGAGGCTTTCCCTTTTCTAGGTGGATAGTCACGTCCATACTATCAAGCTAAGAGCCACGCTGCTTTCTACTCCTAAATTTCTTAAAGAACTCTCTGTCCTAAGTTTGCCACAACGACTGCTACGCGCCAAAAAAACCGACCAGCACACACGGTTATCGACCTTTGATAACTGGCCGCCGGACAGATCAAATGGACCCCAGTCAAGCCCGGTAACTTATGCGCAAGTTTGCACAGGGGAACGACACGGACTCTGCCAATTATATACTACATGTAACTCGCGCAAATTGGCAGCATACGTACGTCTTCGTATGTCCTGCACCCGCATGCCGTAACGGACCTCAATTCGGCACCTTGAAGTTGCGTCTTTCTTCCCTATCGCCATTCTGTAGACAATCAGACTTGGTATTAGGTCCCGCTATTCAAACCCCCAACCGATCGCGCAGAGTAACGATTCCTGCAATTATGCGGAATGGTCGCCCACGGGCGGATTGGGTTCGTTGCAGACATTCAGTAGTTCTTCTGCATGACGTCGGGCTAATAGCACGACTTCCTTCAATACCGCAATCATCGGTCACCCCTCAGACCACTCTTTCCCAATAGCAGGACAGAACCCCATCTGCATTGGACTCAGGTTACTCTAACCGAAAGTTCCTTTCTTTAGTGACAGCGAGATGGGTATGCGGAGGGCGTGCAATCACTGTGACAAATTCCTCGTTTTTACCTGAAACTTTCCTTTGTAGGCGTCTCGGAAGTAGATTTCACCAACGTGATACCAACAGAATGGGTCAGACGTGGTGTATTCCAGGGTGAGGAAACCTCCCCTGAACCGAGGCGACGAACCTTAGATTACGTGTTGAGATTTCAAATCGTTTCTAGTGTGCGCACAATTACCTTGGCAGACCTAACTCTCACGCATACATAGGACACCTCACCATAGCCATTTGCGATGTTCCCCCGTACTGCCCTAGACAAGCTATACGACACCCCCTACGACATGACTCGTAACCCGTTTCCATGTAGGCCAGTTTATCATGATACATGAGTAAACACAATGTATAGTGAGCTTGTTAGTACTATCTCAGTGGGTGACGACTGCTAAAGCTCTCATAAAAAGTCGGGCGAAAACGACACTCGCATCCGATGGTTTCCGGTCTTTCGGGCTGCCCATGAAGGGGCCCCTATAACCGACAGTTAGTGAAGCGAATAGTGACTGTGCGCCGCAAAACGCGCGTACAAAGCGTAACCCATAACCTGGTGGTCCTCACATGATTCCAGAAGTGACAAACCAACAAGAATAGTTTATTCTGCTTTGCCCCGGGCGGTCTGTTCTCACTTTGTCCATATGCCGGCCGCGTTGGTCTCAGAACAGTGAAGATCTTTTTGAAAGTAAACCCATTCCGGATTTCGTGTCTATCCCGATTCATACCTCCGTCGGGCACTTGTTAAGGGGGACGATCCGCATATTGTACTGTAAGCTTAGATCACTTCGCGAGAAATGATAGATCGCAGTACACTTCCGTCTATACCAATACTTTGACTGCTGCAACCATCTAACGGCCCTGCGGGCCATAGCCGAAAATTATCAATGGTTGGTCCTGCGTGGGCACCCAGTACAAGATTTCTTCTAACCCCTGAGCAGTAACTATTGACCCTAGGTACAAAAACATACAGGACCTTGATCGATGATCAGAGAAGGACACAGATCCGGATTATATGCCTCTATAGTAACTCCGGAATAAGCGAAGGAACCTGGCTGACCCCTGAACAATCAGAGCGGAGGGCGATCTGTAAACTATAGAAAGCGTTCAATTCAATCGAAGACAGGCCGTATCCCATTTTGTACAAGGGTTGCCGGGACAGAAAATAGCCGGTCGAAGTTCCGGCATTCCAAAAGCAGTTTAGCAACGATACAATCAAGCGAGAGAGACTATATAGACAGGTAGTCCGGTCGTGGTTATCCGATTTCGTAGACACGGTACGGCCAAGCTGGGAAACGAACGGTGTGTTTGGGCATAAGTCTGTTGAGTCAGAAGGTCGCGGTTTCTAGATCGGTGAGGTCTGTGAAAAGGAGGTGTGACATGCTTAGGCATATTAAGAGTCTTCGAACACAATTGTGTTGACACGTTGCATACCGCTCTCTACTGAACGCACAGGCCGTGAGCGTTATATATACTGAACCAGCGCATAGGCCGTACCTTCCCCCAATAGTTCTTTGGCGAGGGAGAACCTATGAACAGGCCGAGTTTCTCTTTAATTAGTCTCGCGTCTAAACTCGGCCAGTTCTAGGCATTTATATTTACCAGAATCGGCAAAAGGGAAAATTAACATAGCAAAAACACATGGGTTCTATCGTTAGTTATAGGCGTATTTAATCGAGCCGTGCACGGTTCTCATTGCTATAGGAGGGTACTGCTATGCGACAATTACCGACAACAGCTATAACTGCTTATTATCATAACTGAGCAGTCAGCTTCACGTACGTAAGAACAGAAAGCCTCTTACGGACATACCATCCCTTTCGGAAAGACGTGCATAAGCATAGTGCGCTGGGCCTGGTCCACTTAGCGTCTAGTCCTCTAATCATCACCATACACACTATAATACGCCTCCCGAGCAAGATGGAATACACTGGTGAGGCTAATGCGCCAGCGGACGAGAGGGATGGTTCGCCATCGTATTATGACTATGCCGGTAGATAGGCCCTGGCCGACTAGGATAAGCCGCAAAAAGCTACGATGCGAAACTCCGAGACGAGCCGAAAGCGTATAACAGACGACGTCCAGGAACTCATGTACCCCCTCACGGGTACGTGCATGATGCTCACCGCGATCATCCCTCCCTTTAGGGCTGCCATTTATTGTTTGTGAATTCAAATCACAGTACACCTTATTTTACAGAAAGCCAGGCACCTCAGATGTGGCTGTTGCGTCGACTAGTGCAGGTCTAGAATGTGGTGTTACGCTAGGAAGACCCCGTCGGTAACAACCACAATGTGGGGTAGGTACATTGGGGCGTACCGTAGTGAAGCTGCATGCGAACTCCTTTGTCGCGTGAATTCAAACCCTGCGGACCGTTTTTTGGGGTGTATCCATGTCGAGAAGCTGCTAGGCTTGGTCGTATAAGGATAGAGTCACCTCCAATCTCTGGTCTAGTTTGGAGCTTACCGCATTGTTCGAGTGGTCATGCTCCGGTCGTTCGTACGAATATATACAGGAAAGCTTCTCATCGCGTATCTACTTGCCTCGTCGTCATTGCGTGATTCCAATCGTGCTTTACGCGAAAAGCAAGCAGCCCTCTCGAAGGCTTAAGAGGGGGTACTGATACCCTGGGATCCAAGATTGTGCGCGTTCGGTTTCAGAAATCATCTTTGCCTGCTTGGGTAAATGGCAAACCTAAAGCGAATATAATCTGCGCGACACTTTCTCCGACACTTCGTATTGCACGACCAAAGGATCCTCGGTCCTTTATTCTTTTGACTGAGGAATGCCACTCGAGCTTGTTTCTACAGCGACACCTTGAAGAGTTCTGCAATATCACGGAGTGTTGTTATAGGATGCCACCCTGTCTCTAATCCACACCCCACCGCTTGTTTCTATCTCAATCATCTGTGGGTTGCTGCTGGAGCAGCTACTTCTGCATGGGCCGCGAACTCCACAGTCTTGGGTAGCCCAGGAACTGCAACGGAATAACGCACGCGTCGGTTCTCCAGCAGTGATTTTATAGTTATGACTGGAGGTTTTAAAGAGGACCGGGCTGAGCGGGCTATCGTTTGACTGCTATGCAAGGGCTTAATCGAATCTAGCTAGAGGTCACCTGGAAAAACTGCTCTTTGTAGACAAAATCCGCGTCGGCGTAAATTGATATGGGAGCCAATTTGGGTCAACTTCGACAAACTACAATCGGGACCTAACAGACGCAGCGTATCTGTGCACATAGCTTTAGACACGTTGTCTTGAGCATTAGATGCGCAACTAATTATTCAACCACTGAATTCCTAAATCGCCGAGAAGACTCCTGTGAGCAGTTACAAATGAGAGGCATCGCCCTGCTGAACCGCCGTGCAAAGTATTAGCAGACGATTCGCGACTCAACATCGGGGCGGCGCGGGAAAAGCTCCGCCAATGCTACCGGAATAGATTAACGCAGAGGCTGCAGTGACTATAGTTTCCGTGGAAGGTTTACGCGATTCGATGGCTGGCCACAGGACTGCCGACAAAACGGAGCCGCATTCCGCATGCTACCAGGCATAATAGTGAAACGTCGATTTTTCTACATCCGGACCTGCTTTCGTTGAACCTCAGCCTTCGCAGGGCAGAAGTTCTGAGAAAAGCTAAAATGAGGTGTTTGACTAGCCTTTTGTGTAAGTGCGTGAGAGTTTTTATCCGTAACTGAGAGCGATACACGGCCATACCTAAACTAGAGGAGCGCCTTACCGTTTTACGTGCAGAGCGACAAACCAATTAGATCGGTGGATATCCAATTCATTGACTGCGTCGTCCAGTACAGTCAAGAGACGTGAATACCGTTCGAATTTCGTGCATAACTCCTGGGAGAAGAACTTACACGATAGCCATTCGGGTGGCCACTATAAGTTTGATAGCTGAGTTCAGTCCTGCCACACTAGGGGGTCGCTAACGAGCGACCTGCTACTGCGCTTGTCCGGCTGAACTTGTGGTCTGCGATCGCTCTTGCTCGTACCCCTACGGTTCGGTAGGAGCTTTGGGCTAGGTAGTACAGATGACTTATGCGAGCCCTCAAGCACATAGAGAAGGATGTTCGTAATCCACATAACTACTATACCTTCTAAGGGATCCCATCTTATCTCCCGGCGTAATGTTATCGTCTTGGCAAAAGTTCGACCGTGACTAAAAGAGACTCGATCGGTTTCAGCGTTTTGAGACATCCTCTTAGTGAGATGGTGACTCCACGAGGTAACGAAGACCCAGGGTTCTATCGTCCGTAGCGCTGGTACCGTTATTCACTTCAGTCTTCTATCGTGATTCTTATGTAAGCTAGTTAACCTGTTCAATTGGTAGAGCGCGCGCGAATCCTACGTATAAACGTTAACGTGGCGACATGTCAGGGCTGATTGCCATCGGCTTCCGCGGTTGCTACTCGGCCCACACGTCCTTTCGCGACGGATTATACAAAGGACGATCTCGCGCACGTGTATTCCGGAGGTAGTGCACCCAAGGAGGTATAAGTTTTATCAATGAGTGGGAATGCGGACTGGTGCACTAGGTTGCGGGTGGTGGTACACAAGAGTATGGTCCCTGTGGCTAGTACATTTACAGAGACAGCCTCAGGCGGGGGTCCCTCTCGCCTGTATGGAGCAGACTAACTCGGATTATCCTGCCAAGAGGCCCAGACGTGTGCGTTTTCAAACACTTAATATCCGGAGAACCGGCCAGTGCTAGTACCGGAACAGACACTGCACACATTGGAGGGGAGTACCAATAGAAACAATTCGCGTAAAGTGCCAAGTCAGGTCCACTCGAGCGCTCCAGAGACCAAGGTCATGCTTCGATTGGCTATTCATTGCCTTCTCCCACTCGTTACCTTGACTAGATCACGCTGTAACGGACGATTATATCGCGTTGTGTATCGAGTGCAACGGTTCAGTCCACCCGGAATACCATTTTACTCGGCGCCATGGGACATCCGGGGGCGAGCTGAGACGACTAAATTTCTAACCGGCCGCGCTCCAGAAAAAGGTCCGATCAGAAGACGGTTCTCCTCTCGACTGCCTTGATCTGGCCAAATCCGTTCACCTCTCAGCATTATAGGGGCACAAAGGACCTGTCTCTCAAACTACATCCGCCATGTTTGATGGCCAACTACCAACGTAAAAAGAGGTTCTCTCGAGTCGGCTCATACTTTGGTCGAACACCCACACCTTTGTTAGAAACTTGCATGCCACTACCTTCTGTCATCTAGCAAGAAACTACTGTCCAGCGAGCGCAGGCCCAATGCGAAGACCAGGATCTTTACCGCTATGGCCTGTCGCGGCGTCGTCTCAAGCAAAGCGTTAGTCTGCACTGGCTACGTAACTAAAGTGCGTGGAACGCGCCACCTCGGACGGGTTTTAGTGCTGGCCACGGAAGCATCTGGCATTGGTCGTGCCCATGCAATACGCGTAATTGAATCCATAAAACACGAACTTTTAAGCAGCGTACGGAACCTTTTTTCAGTCTCCCCCAATCCCGATTCTGTGGTACGGATGCCCGGGGGGGCGGTGTACCGTGCAGGCAGCAGGATAATGGGCAAATAAGAGAGCTACCATGCCGAGAGACCTAGCCTCCGACGTTATTTCGGAAGAATCGGATCATTGGACTGAGTAGAGCCCGTTGAATGGCTTGGCGTACGATCTGCCAATTGCGTAATCCTGCATTTCATGATTGAAAGCATCCCCTAAAACTTTGCTATCGGAGACTGCTAGACTACGTTATGGAGAGGAAGCTGACGGCGGTGCTAGGTCCTACGTCATAATATTGCCTGGATTACTGTTTAAGGATGGTCACTCCTATTGTAGCTCGTTCCTTTCGCACAGACCACGATCGGAGTATAGTATGCGGGAATGAAGATATATAAGTGCCTCTCTTGCTCTAATGGGACCGTTCATGCTACGTGAGTGAGTTTTATTAAGCCATACGAAATGCCGTGTGATCGTGCATAAGCTAGGAGTCTCGTGGAGCGCCTCCATGTCCGTAGGTGGGTACGATCTTTTCCTGTTTACTTAATCTATAACCACACCTGGTGCCACTCCTGAATAGATAACGTTTGTGTACCAGACGGCGCGCCCTTTGGCATGTCTTTCGGCCCCGAAAGAGCGGCTGGTTCGCTCTTGACTGGTGAAGTGGGAAATTGTGTTAAGAAGTGCCACGAGCTCCGTCTTTTCAATGCCCGTACGTGCTGTTGACGCGTTCAGACCCGGACGCTTTGTTGTTATACAGTGCTTCGTCCTAGGCCTCACTGTCCCGCGAGATAATCTTTACTGTGTGACTCAACCTTCGACTCGCCCTCTCATCCTTCCACATACTTGCACCTCAAACATCCTGATGTTACAGGCTAAAGAGAACTTGCTTCGCGTAACTCCGCACGCAACTGGTATCTACTTCGTGGGCTCGGCGGTGTCGCTTTTGAACCCCCCCCTCGCATGGCCCAGAAACCAGCCACCGCCCTTTACCCCACTAACCGATTTCTGGTAACTTCACCTGAAAAATACTCATACTATTCACCCAACACCTACCGGGTAATAACCTCGGGAGTAATCGCGTATCTGTACCTTGCAGGCAAATCAAGAACCTGCAAGAGACATGAGTACCAGCAGTTTCGCGCTGCAGGTCGGCCCAATTACGATCACATTGTCAAACTCCCCCGGTTTAATTACTCGGAGTGCTTATTAACCCTAATGTCGACCGCTGGCGTAGCTCTGCTTGACTAGAGCAGAAAGGCACCTACTACGCCAGATGAACCCGATCTCAATGCTTTCACCAAATCCTCCGTGATCATGTCCACAGGAAGGGCAAGGCCTTAGTCCAAGGCTATGAAGGGGCTATCAAAAAACCCTTACACGACAGCTCCGGCAAACATGCGGCCCAGGACGTAAGCCTGTACTAACAAGTCCCGTGAGCGGTCTCGTTGGTTGGTTGTATGTGTGTCGCCACGCGTACTGCGTGGAGCGGTCGATATGACATAAGCAGCAGGGACCGTTAAAATAAGCCTCGAGTTGGGGTTCCTCCCATTCCTGATTTTGACCTCGTAGTTTATCAGGTTATCATTACTTAGCTTATACGCCCACGAGTACGGAGTAAGTCGCCATGGCTATCCTTATCGTTTACCTACCCGTCCTCCGGTTCACACTACGGGATGGGACGCCAGGAAACACATATGGTGCAGCTGTGCTCTAGTAGGGTTCTGAACGGATTTCATGGGAGTTAGACATTGGGGATCACATGTCCTGTACAGACATGATAACGCTCCAAGCTGTGTGTAACAGGTGCGACTTAAAGCTATGGATCTTAGAGTTCGGTTGCCATGCTATTAGGGGTTCACATGTCTTAAATCTAGGGGATCCTAATTTCAAAACAGGACGTAAATGCTGTAAAGTGGTATAATCCTTCTCATACAAGATGTGGGAAAAAGGCCGATCCAGTTCCACCCTCGGATCTGCGTGAATTTAGAATTATGTATGTAGCAAATCAAGCTCCAGGCCACGCCCATGTCGACAGCTGAGGACGATGCCCGGCTAACCATTTAGGTGATCTCCGTTGAAGCGATCCGAAAAATAGGTGGGTCGGTTCCGGGCGAGACGAGTGATTCAATTCCCGTTAAAAAGTTCCTCGCGGTACGAGCCGGTCGCTCAGGCCCATGAAAGCTAAGACGGCACGTCGCGGTAAACTCGAATGGCTGTCTCGGAAAATACTAGAACCATTCCCAATGCTGCCTAGTGTTATACATGCTCACCTGGTCCTTAGATACAAGGTACCACAGCCGCATGGTGCAAGAGGAGCAGCCTCGTGGTGCTCATCACCGCAATCTTCGTATCGTCATATTCCCGTCGTGAAGGAAAGCGACTCGACTACCTAACCCTGTGCTCCGAGCAGCAACTTTTTTGCATTGCGTCAGGCATCTCTCCGACGAGAAGCCGGAGCGTAAGGGAAGCCATTACGGGCATGTGAGGCAGTTATTTCTGACTACACGATAGCGTCCATAGGCAAGTAGACACTGGCTTATCTCGATATGGGGTATAGCGAAGCTACCATCCATGGAGTCCTCAGTTAGGTCCCGCTACACTCGACTACCGACAGGAGCTAGCTCGTGTCTATACCCTGAACGGGAGCCGTCTTATGCCCAAGAATATATCCGTTATAACCGGCAGCCACGTGAATTACAGGACCAATGAGACTTATTCGTAGAACAACCGTCTGGTAGTACCCCTATGTATTGGCAGTGACAGGTCAACATATCTAGACATCTTGAGGAAGGGATCAAGAGGAGAGCAAAGTGCGTTCGATCTACTGTCCAATACATTCCACGTCACGAAGTGGACAATGGGTACGGACACCTTGGGACTCAAGCGGGCAACTATTCCATTCTATACATGATCGCAACTATTGTCAGTCTCGCACTCGAGGGTACGAACTCGGCGGGGAAGGGCTGTCAGTGCCGCATTTGCACTCACTTATCCCCGCCACCGTTAACAGGGTAACGTTTGCAAAATTGGTGAGTAAATCGGTCGATTGCATATCAGACGTGCGTGGCAGAATATAAGCCGATAGCTTCGACTATTGATTCCTCATCGTCCAGGGTTGTTGACTGGGGAGAGCTCATATCATGGGGCACCTACGTTGCTGGAACTGTTGGATACGCACTATAAACCTCATAGAGCTCTGCCCACGTCATAAGCCTTAAGCATGAAGCTCGCGTTTTGTGGCAGCCCAGGGTAACAAGCACATATGGTTGCCCGCGATACCTGAGATAGATGTTATCGCTATAACTATAGCTCCTATCAAGTCATTAATATCTATGCTACAGAAGAAAAGGTAAAGCATACTACCTCACTTAAACTACCTAGTCTTTGAAATGCCACTCCTGTCAAGGGGAGGTGTTACTCCACGCAGTATAGACTCCAGCATGATGGACGCCGGGAAACGCCTCGTCGTCTCTTAACGATAACGTTATTAGAAAGGGGACAGACGTTACGTTCGTGACGTGTTAGATAATGCGTGAGAAATTGTGTTCATCGTCAAAGTTAGTATATCGTATATCCCTGTTACCATTGCGTTTTTTAGTGGGAGGCTTAGCCCAGCCGTGTGACCACCGGCAGGTACATAACAAGTGTGACTCGGCGTGTGCATACACTGGCATCAGCTGGCTGACTTGCTCCATGACGAGCCACCCCTGAAACCTAGCAACTGACCTCATGCGGAATTTACGGGGTATTGAGTTCTTCACCCGAAGCTGCTGGTTAGTGTGTCCGATATGCGAGCTGCGAGGTCATTCTTGCTGAAACTAATATGCTTCGAACTTAATCGCTTTGAGTAGCTGGATGATTTGCTCATGCCGAGATGCGCGTTGATCGTTGTCGGCTCAGGTTTTCCCTTTACAACATCTTCTGGGATGAATCTGGAACACTCGCACGGTTCATACTAGAAATTCCTCGCAAGGAGAGAGGGCCTCCGTTCGGAAGAGTCCGATCGTTAGACTATGTTGTTCAGGTCGTTCCTGCATTGAAGAAAACCTACGAACCGGAAGGGCTACCCTCCTTCGTCTCATAGGTGCCTAAACCGTCCGGATTGCACTAGGCACGATACTGCGCATTTTACTTCTGCCTGGGTGCTATAGGAAGTGCCCGGTAGAGATCACGCCGAAGATTGATGTAATCAATAAGGCTGCTGAAATCTGTACAGACGGCCGTCAGGACATCAGGGGAAAACTCTCGTGAGTGAGTACAAAATTAAATGGCCCTGGGGAGACTGAACATTTAGACTAACGAAGTCACGGTCGTCGCGTCATAAGAAATTCGAACAGTGGCCTGGGCGACTAGGATCTCGGTAGCGATTAACACGGTATCGAATCGAACGTAACTATTGATCGGTAAATCCCCCGGGCTCTAGGGGTCGGGGGTATACTTTCCTTTTTAATAGGCCCGACCCCCGAAACTGGCTCATCGCATATCCCGTTTGCAATGGAAGCGGTAGTTTTGTATAATTACGATTCGTTATAAAGTACATGCCACTTCGAGCACCGTCATGTGATCACGCCAGAGCACTCCGTTTGGCTGTGGGCCGTGGCACCACCGTAGTTCATATTCATTGCGAGCTCTTAGAGTTCATGTCCTCACCGTAGACCGGAGCAGAACCGCTCACCACGAACGCTAGAAGGCGGCTGTGCTCGGCCAACTGAACCACCAGCAGGGCAGACGATGGTTAGTATGGACCTGCTGGTTATCGGTTTTCAACGGGCTGTCGCTAATATTGTGGCTACCTCGATTAAGCGCCGCCGCAGCATTCGCGGGAGGAAGACCAATAGTCCGTACCTATTGACGCTCCCCGTGTATCCGAAGACGCCAGGGACCCTCTTCTGGTCGCCGACCGAAAGAAAGAAATTACTGGGATTGGTAGCGTCCCGTGGTTGAGGCATCGGCAGGCGGGATCTTCTAGTTAGAACTAGAATTCGGTACCTGCGCCCTCATCATCTTTACCATTCCCGATGACCCCTTAGGCCGAGTCGAGGACGTTCCCTAAGTCAGATTAGCTCTAGCCTGAGAAGCTCTTTACGGGACCAACTACTTGAGTGGATCGTGCCTTTCTATATCCTTGTCGCTTATGAGTTTCGGGACCTCGAGTTTTTCTGCAACCACTTGGCTACGTTAGGACTGTTAAATTGGCCACTTAAGTCCCAGAGCGATGTAGACATAATAATTATCGCCTCTTTTCGAGATATAAAAAGTATTCTGTTCTAACATCGTAACATAATTACGTTGCGTCTGCTCCAGGACGGAGATGGATTACGCGGCTTGCCTTGGCGAATAAACCTGTCCGAAAGCTCCGCGCGTCGAATAGTAGCAGTCATAGTGCACCGTGGGTGATCTGGTACCGCAAACATAGGGGTTCCCTACTCGACTAGACATAGAGGAAAGTACCGACATACAGAAATTACTGGGCCCGGGATGGCTTCCTGATCTACAACGTTAGAAGGATCCTGTGCCCCCGCGGGGAATCCTGTTACGGACTCACGCGCTCAGACACTTGCGATTAAGCTGGAAACCTTCGCTTCGCCCCAATGCGAGTCCTGACTGGGCGGTACCACGACCGAAATGCGTTACGCGTGTGCGTAGGACGTGTCGCTTCCAAAATCGGAGGCATTTCCCAAGGTTCCTGAGGGGGGGTGCTGAGGGAAGCTAAGCAGTAGACGACGCGTGTTACGCGAGCGGATTACTTCCTGGAGTTCTTCGATTCTTCACAGATTCGATAATAGTCCAGCAGCCGATATGCTTAACCTGGAGTTGAGCATCTCGCTACAGCGATGTCTCTAGCCCATTTTCTCGTGTGCTTGAGTCTTCTCACACGCAAACACAGTTTCCTTCTACAGATAGTTGGCTATTTTCGGACCGGTGTGAGTGTCATGAGCCAAGGGCTCGCGCCCAGACCATGAAGTCGGAGAAGTCTGGTCAGGTCCCGCCCGATACGTGCTATGTTCAAGCAATTTGGCTAGCTCTTAGCGCCCCGAGTGAGTATACATTCTTTGACGCTGGGCCTCTGGCGACGCCAACGAGGGTCCAATATGGCTACCGTAAACTGCACCACGGGGTGGAGGTCAAAGATATCGGCCGCCTATAATGTTGCATCACCAACTCAAGATATAACTGCTACGAGCTACTGTGTCCCCCAGTAAATAAGCGACGGCTCGGCACAATTTGTGGGCAACCGAAGTGGCATTCTCTCACGGATTTACGATAATAAGAGCGACAGGCGAGTGCCTACTTAGGCTATCAATTTTAGACAAAGATACGTGGGTGGAGTAGTCTCTCGAAACCGGGAACCTATCGTATAAAGCTATTGACTAATTGACCTACTGCGAAGGTGACAGCAGCCGCCATCTATAAGTCCGCCCAGCAGTGATGTTTGCGCACTATTGACGGACGCACTCTTTCAATCTGCGAGAGACCAGCCCCCATGCATGGCGAGTGACCGACACGAGGATGGATTAACTCCTCGGCTGTATCTTTTGCCTGATGCGCATTCGACACGAAAGAGAGCTTTACTAGCAAAGGATCACCGTCTCAGTGTGTGAAGCCAAGTTCTTGCCTATGGAAAGATCGCGGTAGACAGCTTCTTGGCTATTACGAGGTGTTGCCCCGGCACGAGCCTACAGTCCTAGCCAGAGGCCATCCCTCAGGGTCACAAATGTGCGTCTCGCCGGGAGGGTTCATTAGAGTCCTGTGCCAACGTAAGAGTAAGACTTATCCGGCGAACTTTGTGTGAGTCTTATGGAAACACCATCGACGTTTATTATGTATATGCACGTGGCCTGACCTAGGAGCGTAGCGACATAATGGGCGGTGAGTTACCGCTCACGGTACTATTGCTGACTACAATAGTCATCGTAGAGTGATCAGTCCGAGGCGTATCACGTAAGTTAGATAGGTAAACGACATTAGGGCTACGATCCGGCCGAGGAGATAACTATGACTAAAGTCCTCCGCATAATTCAAGTCTCAACTTAATGATCGGACAAATACGTGGATTTTACTTCCGTTCGCGCGAAACCGTCGGGAACTCATCGTTACCCTGCGAGTTCCCGCCCCTGCGATACATGGAAATCCTGGAACCGTTCTACCACAGTAGAGTATGAAGTAACCGCATCGGTCCCGTGATCAGTCGCCAAACGAATTAGGAGTGGGTTTTTAACGATGCCACCTACGGCACTGCGAGGTGAAGGGGCCGCCCAACTATAGAGAAGAGGAAAGGTTTGACTGAAGGGCGTCCTGATAGCCGGCATATTGTTTAGGGGCAGTCGAGGGACCAGTCCAGCGTTCCAGCGGCACAGAAGGCTTGTAACATGTTAATTCCTGGCGAAAACTGTTACACGACAGCGGTGTAGTATCTAGTGTGGATGATATTGGTGTATATTCGAGTTTGGAGATTATCTATATTACAAGCAACCACAGGGTATCCATGGCAGGACACAACTTGCCTTCTAGTGCTATGTTATATGATTCGCCAGAGGTGCGGTCCTCCTTCAATGCATATGGGGCGATATTGTTTAGTTTAGGCTCGGTAGTAGTATTGTGTGATCTGAGGGCACACTGAGACTTTCATGCTGAACGGTCGTGACTGGCGAGATGAAAGTTAGTGTTTGAGCGGGGACCCGGCGAGATCTAAGATGCTAATGACACTGTGGTCCCGCGCCCGTAAAGAGTACCCCTATTGACCTACGGCCATAAGGATTGCCGTTGAGGTAAGGGTCCATAGGTCTCGAGCGGCACTTACCCTGGCTAGGCTGATGCTTTATCCTATATCCTCTGTGGATGTCACATGACGTTCGTTACTACTTATGACTTGCTTCCTTGAGGGTAGTGGAGTTGATTCGGGCACGTGATGCGCGCGCAGGCGGGCCACAGCAACCACGTCACCTCATCACAATGCCTCTAGACCGTTTTCCCATCATAGTTCAATATACACAATTGGCATGCAAGTCCGCACCTCTGTAGTACGATCGCAAGGGGGGTCCCATTTAGACGCCGCATTATACTAAGCTCATACTTCAGTGATTGATTCGTGGTCCCCACTCGGGACAACGTTTCTTCCCCACGCGAAAGTCTAAGCGGCTTTTTGTTTTCTCATCTAAAGATAGCTCCCATTATCAACAGAGACTGATCCTGCTGTTTGTGTTGTGTTAGACGGGGAAATGCAGTAACGTAGCGATCCTTATGGAATTATAACAGCAGTTAGTCCACCAATGAGCATTAGACCCTACGTAGGTCAACCTGAGATGGGTGATGGTTACCTATTCCTGCACAACAATTCTGTTCTGCACCGCATTCGCATTCTATGACGATTCTAAGGTGTGACCTCTAAAATGTTCTCCCGGGGAGGCTTATCACCGGAATAGTTAGCCTGACCCCCCACAATAGATGAATGGTGAGGATTAACTTTACTGCGATTAGAGTCGCATTGTTTTCCAGGCAATAGCGGCGGACCCCTTACTCCACTCAAGCAGGAGCTCGAGCCATGCGCTTCTCTGCATTCCACGCTAACCGCTGTCGATCTCTGAGAATGGTGCAGTATGCGACTCCAGTCCCTAGATAGTCGCTAAACCAGTGATGAACTCAGCTTAGATGGGTTTTGACTTCATGCAATAAACAAATGCATCAAACGGTGTGACTCGGTCATGATTGCCAGAGAATCGTGGAGGCAAATAATTACAGGAGCCGCACCCGAATTTCGGCAATGCTTCTGTCACAAGAATGGAAACATGACCTGTTTACAGTTATTCGCCTTACTGCCTCGTTCGATTGTACCTGCTTCTGAATGATACACGAGTTCGCTAACCGCGTTTGTTTTCAAGTAGAACGAACGATTGGTCTGTAAGCACAACCACTTCGCTTTGCACATGGCTCACGATGTCTCGTCCGCGTCGGTACGCGCGGTTGCCAGGCCAGTGCCAATCTATTACCCGTTGCCATTTTGGTGTCACTCACGTGTATAATTGCCATGGGATTCACGCCTGCGATACTAGACATCCTAAATTTTGAACTCAACTCATGAGGAGCATTTGCGTAACAGCGTTTCAGTACGCCCTTTTGTGCTTTTAATAGGCGCGTCGCCCATATCACGAGGTATCGGCTTGGGTATCGTCATGGCTCGAGGTGAGGCATCCTATCTGGCCAAGAAACTCAACGAAGACGGAGGCTGGTTTTATCTGTTTCTCTCTAACAGGCCCAGCGCGCTAGCCAAGGCCTCCGTCCCCAGCCATAATCCAAAGGCCTATGTTTATACTGTTGGTCTCTCTATAATTCTCGCTATTCGTATCTTGAGTAATTATTACCGGAGTATTGCTCGCTCAGAAGCCTATTATCTATCCCGTGTTGACAGTGCCACTTCTGTCCTCACTCTGGAGTGGCTTACCATGGAGAGATTGAAACAAATCTGGCTACCCTAGCAGATTATTACTAGTAGCTTAGCGAAGCATCGCGGTGAGGACGAGAGCATTCCGTGTCGCTACCAGGCCTCCCCGTCAGCACGACCTTGGAACGATAAAGCAATTAAATAATGAAGGTGCAAACGTGGATAGAATGAGAGGCCCACGCACTCATGAAAAGGGTCCACTTCGCTTGTCATATGATTTTCGCCCCCTATTTGAGTACACACCACTACTAAGGCCTAAAACCAACCGTACCGCTGGGACGTTTCACGGGACTCATACGGTTGTTGTTAAGAGGCCGTCATAAAAGGATGGCCGGCGATAATAGCTGCACTTGTGTCAACATGTATGTTAACAGTCACGCCGCTGGATAGTTAAAGTTGCCAGTTTAGATTTCGCCCCGTGATCCCATCTCTAACTCCCATCGGAGTTGCCGGATAATCCGGGTAGTCCAAAGCTATGTACATGCTGGATATGAATTAACCGCGATTTCACCTAGAGCTCGAGAGCTGTGGAGATCTGTATCGGAGCGCTGAAACCATTCTTACAATCATTGGAGACAGACATAGGGCATCCAGTCATAACAACACGTTTAATCACCATGACACTTTATCACTTGTCGGCGGGTGCAGCCCACCACGCGCCAAAAGGAGTCATCACAGTGTACTGTGGGTGTGGCGTAAGGTTAACCCCAGTTCAAAACGTGAAGAGACCAGGGGTCTTGCACGTGTAAGGATTTTCCGGTTTCTCATAGGTACTGTTTCCCATATAGGTTTAAACCGCTGACCGACGAAGGCTCGGGGCCCAACGTTACTCCATTGAAATACATTCGAGAGAATGAGATCGACGTACCCCTCTAAACTGGCTAATCTTATGCCCCAATGAAAGATCCCCCGGAATGACTATATTGATAGGTGGCCCGGCGGTAGTGTCAAGTGCCAAAGAGACCACTTAGGGCACAGACGAGATCCTGAAGTGGGTCAACCTCGACACCTTAGCAGAGGACGACGAGCGAGTGCGCCAGCCCTGAGCGCGCCTTCATCTTTGACTATTGATGGCGGCGACGTGTTTATCGGAGCATGCATTACATATGGGAGAGGTCCCGAGACGACGCAGTAACCAATTTAAGAGGCAAAACAAGATGACCGCCTAAGGATCTTACTTGGAGCACATCATCCCATGGTTAGGTGGCCAGAGAATAGGAGGTTTAACCCTGGACCGAGACGATCATATACAGCGCTTTAACTACCACGTTTCACGCGACTTAATAACCATTCTACGGGACCTATTAGGAGGCTATAGCGAGACAAACATGTTTTTTTGTCCTCTCCATCTGACTTGTTAGAGGGTAGACTTAGCGAGCTTACGAAGAAAAGCCGTCACCCTCCAACTGAAGCCGAATGTCCCTAGCCGTGAAGAAGAAGTAGAAACTTATGCGTTCTCCCTTAAGTAGCTGATCATTCGATTTGATCGGGTTGGACGATGTTCTAGCGCAAAAAGCAAGACTAGGGTTAAAATATTGCCGCTCCGGAATGCCGTTGATGCCTCTGCCGCACGATGCCCCTTGAGGCCCTACCAGCATTAGCACACTTGCTTTCCCTTTTGCGAGAACACGTAAAGCTAGACAGAATACTATCGACTGGGCGAATAGGCAGGGGAAATTTCGACGAGTCGGTAAAGAACTAGGGGGACTGAAATTTGTATATATCCCGTGCTTCTAAACCGGTTACTCGGTGAATGTAGTCGGTCTCCTAGGCAGTATTATAACACAGCGGTATATCTGGTCCATTAACCGAAGACTGTAGTAGGCGCCCGTGTTTAGGTACAGAGTCGGTGTGGGTAATCAGATACCTATACTGGGGTGGATTCCATCCCTTGCAGAAGTATTTAGGGTAAATGTAGAGGTGCTGTGTAGTCCTTAATCTTATGACCCTGCCTATGAAATGCTGTCAAATCCTCCGATACGTCTCTAAGGACTCGTACGCAATCCGGCATCTTGAAGCAACGCCTTCCGTATCGGCTTCAACGTAGCCATATGAGGCCTGTCTATAACGTATTCATGCCCCACGATTGGTTCAAGCGGGCCTTTTGATGCCGGTAGATAGTATCAACCCGTACATCTGACAAAAAAATTGTCACACCGGCCCAAAGCGACTGTAATGGACCCTGCTTATCGTGGTTTCAGTGCGGCAGAGATTTCGATTTGTCGTTGCTTTATACCTACAAACCACTGGAGCGTCCTACTACGGTCATCTTTAGTCGGTAGACTACTAAGTTAGATGACTTAAGTTAGGAATAGAGTGTCTCAGCATACATTGTGTGGACTACGTAAGCGCAAGAATAACTTAACAATAAAATGAACGAGCTCGTATCAGTGCGAGCGAACTGGGAGCACTGGCACCGTTCTGATATGGTCCAGTATTTGTACGAGCGTTGTCTAACAGCGATAGGGCTGGAGTACGAGACTGAACCGATCTTCGGTATCGTGCTTCTCACATCCACAGCGAAAACTGAGAGTTGCATCTTGGTGAATGCAGTCACCGGGAGTGACATCCTGCAGAAAATTACCGCTGTACCATCGCTTAACGCCGAGCCCTATACTTATTTACGTTGATAACGTGAGTGGAGAAGCAATCTGAAACGTAACTCCCAAAAACCCCTCATTGAGAATTCAGGCGACCCAAGACCGCCAACTTTCACCCATCTTATCTGATGTCACTTTAAACGAGCGACCTTGACCGAATGAGGGGCTTGCCACCACCTCCAACTCCAAGTCGGAGTTCGCTCGCACAGTGTTCTTCAAGTCGTGATGTTGATCTGTCTGGGGCCAATTATGCTAAATACTGTCGGTCGCCGTTGCTTTAATAAGCCGAAACACCAGCTTAAAAAAAAGCGACGGGCCTAGCAGCGATCAAGCGCGTACCTCGATAGGCTTGTGCATGCCTTCCAATGTTGAGGATCGAGGTCGGCAAATTACAGGCTTTATGGATCATTGTAATTCAAATCAGGTTGGTGAAAACCGTTACCTCTAGGCTCGTCAATGCGATCGGGGTCCAATGGCACGGGACATATAAGGGGGCTATGGGTGTGATTACGCACAAATATTGATTGAAATGGACAGTTTGGGTCATGCGCGCTCACCATGTGCACTGTGGGTAGACGCAGTAAGTCGAATTCCTTAGTTTCCGGCCGGTCCTGTGTCTAGAACACAGATGCAGCGTCTCAAACCGCTACACTAGCAGTGTTCGTAGGTAGACGTTCGACGACTTCACCAGTGCTCACGATAGCCACCGGGGTACACACCTTCACCTCAGGTGTTTAATTGGCAGCCAACCTCGGCTCTGTTCCCTGCTGGTCATGCTGAACTAGCCTAGCACTCCTGGACGAGAAGAGACTTGCGGCCGTCTCCTACACATGTCTACTTAGCAGGGTAACTGCCCATAATGGCCTCCTATTCCTGGAGGTCCATGGGTATACGCTTGGAACCACGCCTTTGTGAATTATGCGAAAGGGCACTACGCAATATCAGTGGGAACAAGTACGGCAATTCCGAATTATGCGATATTCAAGGCCCTCATCAGTTGCTAAACTGGGTACCGGTACGCGCACTCATCGGGAGACTCAGGTAGCGGGTCGCAGCTACCCACCGGTCCGTGAGTCCCAATACACAAACTTCCATCGGGCTCCCCGAAATAGTTGGCCGTCGCACTTTACTACCCCGCTGCGCATCTGAGCAGGATTGTTAGTTTGTTGGCATCAGGTAGTCGTTAGCTCCGCAGTAAACCATCATACCGTACAGAGTGAACCTGTTTATGTGGATTGTTTTACCTGCGATTAACGTTACTAAGAGAGGCGTGGTGTGCCAACTCAGCGAAACTTTAGGGCTGCGGAGTCAAGTTTTCGTGATTTACAGCCACTTCACGCGATGGTCTACTGTCACACTGAAATTACGCTCCAGAGAATGCAGTTAATGTAGGAGGAGGTCTTGCTTTACACTAGCCGACTAACAGGACTCTCGTATATCGATGTGCCATGTGCCACCTTGCCCCCCGTTACCTGTATGCATGGCATCTATGGATGCTGCTCTAGATATTGTGGTAATCTGCGGGCCCAGCACGCCGCACGACCTCCGCCCATTGATAGTGCATCAGGTGGTGCCTTTAAACCTCGATGTACCCGGGGTCGTCTACCTCAGGGCTTCACTAATGTGAGTACACATACAATCAAGCCCCACCACCGAATTTGTTGATATGCTCGACGAAGTTGCTTCAGATGACCGTGAGCAGTCGAATCTGCCCCGTACTCGGAATTGTGACAACTTAGTTGATACTTCGCTCGTGATGGTAGATGGTACGTACGCAATTTAGTAATTGTGAATCCTGGAGTTAGGACAAGTCTGCAAATCACTGCCCGCGAAGAAGTGTCGCAGCGCGAGTGTGTTCTCCGCGGGATAAGCATTGTATCCAGGAGCTTTGACGTTTAAACGCAATGACCCCAGTGTCGTCAGCTCGGCGTGCTTTCACGTATCAGCAAGAGGCTTCAATCTCCCTGCGGCTTAGGCCCAAGAAACCCGAGCACTATTGGGATTGTGCAGCGGCCCGCTCGGGATTTCACTCTTCATGTCCTGTTGTGTGTGTGGTATAGCGGCGAGTTATCAATATATATCGCTCCGAAGAACGGCAAACGATGAATAACGTACAACCGAATAGTGTTCGCTTTATCCCTCAGCTTAACTTGCAGGTGAACAGAGTTCCTCTCAGTCACAAGGTCTACGATACTTACAGTGGGGAATAGCGCGTTGATACCATATGTAAGTTCAGCCCCAGCCGGGGCCGCCGGACCCTCCGATTCGAGTCTGTATTCAGTGAGGGCGTAACGAAGTTCTGGCCGAGAAATCAAGCGGGTGAACGAGCGCTTGCGCGCGTCGAATTGTCAAGGAGAGAGCAAAAACGCTGTATTTAGCCCAATCCCGCAGCTCCTGCAGCGGGAATCTGGAGAGAGTTTGGCAGGAGGCCTTTCGAGTGCAAAGCTTTCACGGCAACGGTGATGATGCTCAGGCGGCCCTAGTCTGCTACAGGCACTCAAATGCACGGCTTACAGAGTTGTCGGAGACTCGCGTCATATACTTCTACCTGCCGTTTTGTGGGGTGAAGATGGGCGGATGACCCTTATCGACTATATCTTGAGGGGCAGTGGACGGCCGTCAGGGGAATTAATGATGAAAAATATTTAAAACGGGGGTGCTCCAGATGAAGCCGGGATTTACAACAGCAGGCCAACCCACCGCAAACTTACAGGAAGAATTTCACCCTTCTCGGGTAGAGTGACGAGTAAGCCGACCGAGACGCTCCTTCCATGGATTAGTACGAGTTCGGAGAAACAGGTATTCTAGAATTGCCACCAAAACGCGTCTCCACCTCCCAAAGATTAACACACTCGCATTGTGAGCCTTTTCAAGGGGCGCCTCACAAGGTCACCCACGTGGAATCGGTGATGACAAAGACGTATCAAAGAATCCTGACTGCTTCGGAGTGCTCTAAAAGAGTTATGATCAAGCAATCCTGGACGAATAGACCGCCGGGGCAGGGGGGACCGGCTCGGGGTAGCACGCACTAGCACTCGTCGTATTGGTCGCTCACAAGAGTCATTTGGCCATTGCACGGAGCGATCAGATGGGGATCGGCGCTTAGGGGATAATATTACTCTCACCAAGGAAAGCAACCTTCGAAAGGAGACGTTTGGAAAGCCATCGACGACGCCTCGGACTATAGAGCTTGAGGGAATACGCCAGCTATCAGCGCGGACCCCGAGCTTTTCGGCACGGTGTGAAGGGTAACAATCGATTACTCATGCGGTACAGACACAGTAGGTCAATCAGCACTTTTAAAGTGTTTCACAGAGCCCATAAAGTCTCTTCAGAGTCTAACGAAAGGAAGGTGTAACTGTGTTAGACTCCGGCCCGACCCCCAAAGCGGCCTCGATCGATCAGTTATCCTGTGATAAATGAGTATGGAATACCATACTTAATAGTAGCCTCAACTTGACGGCACCACCATACTCGACCGATCTCAAACAGGGCTAGGGACGCGCCGGGATACCTGGCCCGTTGTTCTACGGAAGCTTAAAGTGATATGCTGCATTAGGATTTTTGTCTTTTATGGGTACAACCTTTACCTGCGCAGTGTCTAGGTACCTGCGGTCGCCCTCAACAAGGTTGAGCATTTCACGGAAGACCTATCTCTATGACGCTCTGCTTAGTCGCCAAAAGTCGTTATTCTAACATCATACACCGAAAAAGTTGCTACCGGGGCGGCTTTGATCGACATATCGTTTAGGACTAAGAGCATTCATCTTTAGGTGCTGTGTAGAGTCACGTTCGCACCACTCAAACGCAGTCCGCCATAAAACAGCTCGGCAAGATCAGGATCCCCTTTTCGAGCCCCCCAACACTATATAATGCCTCAGGTTGGCATTAGTGTGGGATCATGAGTAGAGAGGAGTGAAGGCTTCGTCAAAGGAGGAACACAGGATTTAGGCGCAGATGTGTCATAAGTTCATTGCATCTGTGATGCCTCGGGGGTCGGGGTGGCTGCCTATGCAAAACACTATAAGAACTTTGTCCTTTTTGTGATTCGACCGCGCGGAGGCCAAACTCATAATCATCTCGAGCATCACGCCAGAATTGATCATCTCTCAAAGCTGGTAGATTGGCTCATCTGCCTACTGTACCGGCTCTATTAGACGGGTGATGACATCATAGGTATGAGAAGCAGTCAGTACAATCGATACTGTCCGATCCTCTTCAGCGAGTCGGCTAGCGCCAAAATTCGCTCAGCTGACCGTAGTGAGACTAATTCCGGTGAGAGCTAGCGCCGATATGTTGGAAACCGTTTTCAGCGGATTCTTAAAAGAACTCCCATGGCTATAGTAGTCACGCCTAACGGGCTGGGCAGAATTTTTCCAACCCGAACGAGATTTCCAGAATTGGCATGGCATGAGGCTATAAACCCGAGTTCTTTTGTGGGGTCTAACGACGAGGTTATCACACGACGACGGGCACACATAGGGATGGTGGATTTGTATGTATCTGCTTCTCAATTTTGGTAGCTGTAATACCATCCGGCAAGTGATCCGATACATCTGGTAGCGAATTGAACGCCCCTGGCTATCACACCATCTGAGCGCTAGTCCGTATGAGTAGTACCATTGATATTGTTGCGGGCGCCACACGGTTTACGCACTAAGCCTATGTTTTACAAGGTAGGAGCCCGCCGTAGAGTAAGATAGCCGCGTCTCCCCCCCACTCCAGTACGCCGCCGGAACGGTTCGTTATGCTGGAAACGACTTCCCGCCACTGGCAATAACTGCTAAGCGTAGTTTCCAGCTCAGCGACGCAGGGCACACGAGAGTGTTTCTGACTGACCGGATCAACGCTAGTTCACACGGCCCTGTACATGACGAGGCGCCCGTTCCTAACACTTTCAGGAAGGCAAGTGGGCGGTTCCAAAGGTTGTTGAGCTCCCCCGGTATGGCGTCTGCGAGCCGCGCCCGCCGCCACGCGCTTGCCAAACGATTACCGCGAGGGAGTGTAACCCGGTTTAGGATGATTTTATTTGATGGATTCAGTGAGTTAAGCTGTACTCAAGCCCCGTATCACTTACGCATATCATAGGGTTCAGTGGAACTAGATATGCGCCGTTTGGCGATGATGGAGATTACTGGGATCCAGGTCTGATTGTCATATCCCTGTGTCGTCAACTAGAGGGTTGAAAAGACCAATTCTCCTCACGTCGGTCCGATCGCCAGGACGAAACTAATGGATCCCTTATTTTTACCCGTAACTACATACGTTTAATACCAGAACCGCCAATCTACTGGCTCTGAGTTAGCGGCAGCCCGTTACTAAAATGACTGTACTTGTAGTACGCTTTCGACACTTTACGGAGAGCCTTTCTTCAGCCTTCCTGAACGAGTTTGCGATTCACGATGTTTGTCGCGTCATATTGGAGCTCTGGAAGAGAAGGGGAAAACATGCTTGGCCATAGAACGGAAACAGCTGGTTGGGCTGTCGTCTTATCGTTGGGAGACGCCATACCCACGCGCAACGCAGTCGGTCCTCATTGGAGTAATAATACCCGAGCGCAACGAGAGGACTTATAAACCATTGGAAAAATACAGAATAAAAAGCCACGAAGCCTGGAACAGCTTACCCCTTATAATCAGTCGCCTCCTCTTATCAACATAGATCAGGCCGTGCATTGATTACGTGAGCGAGGAGGCTAATCGATAATTTCGTTGTACGAGACGAACGGACGTAGCAGTACCTGGGGTCAATTACGCCTATATTGGTCAATAATTCAGGTCACCACGTGATTTGGGGTAGACTACCGGAGGTCCATTCACGGTAAGTGTCGGTTGAATCGCGATTCCGATGTCATCGTTACCGTTCTGCGTTTTTGAAATCGGACCTGCATTGGAGCGTTAACTATCCACTTGTACTGGAACCTTATGAGATCCAGCAGATCCTATCACCCGGGCTGAATCGTTGACAGCTTCTATTAGTGGCGGGGTTCTAGCAACCATTGAGTATTGAGAGGAAATGCAGACACTGATGCCGAAGTCACAGGAGATTCTTACTGCCATTCTCAGCACCAACTGAGGCTCCAGAAAGTTGGGCCAGGCCTAATTTAGCTAAGGTATGGAATACACGGTTCCGTTTAATAGGGGTAAATCTTTCGGAGGAGCTCGAAGTTAAACCTCTGTCGCTCAAATTCCCGGCGTTTCTCCCATTGTTCGACTGTTGATCGGTAGTGCCGTCCCGGGAAGCCCCAGGCATTTCAGCACATGTAACTGCCAATGGTAATTTTGTCTAGGCGCATCACGCAAAACTGTAGGCTCGAGGCACCAAGTTCCTAGTATCAGCAGTTTGTGGAAATGGGACGGTGACCTGAAGGTTACTACCATATAGCATTCAAACATTCTAGATGAAGTTCGTTTCGACCGCTTTTGACAACTGTCTTGGAGAACTAGCAAATTATGAAAGCAGGGTCCCCGCCAACCTCTGTAATCATTATGTACTCTTGGGTTCGAAAATGAAAACCCTGATACCTCAATAATAGCGGTTGGATGAGAGCACCTCCAGCAGTAAATCTATACGGTTCTAGGACGCTTAACACACCAAAATATACCGCTCCTTGCGTTGAAAAATCGACGAAAGCGTCCTCGGCCGGGCTAAGTACCTGTACAATATGTCGATGAAGCGGGACCGGTTTCTATTGACTTAATAAGGACCCCCGCACCTTATGCGGTCAGGCTCCTGACTCAAGCATTACCTAATCTGGTACGTGACGTTGTTTGTTGTGTTATAGAGAGTATTTAGAACACGAGACTGGCTTAACCTCCGGCTCAGCCAGCCAACCTGATTACATCGCCGGTGACATCCGAGCCCGAGAAATTTCCCCATCGGGTGCTTTGTATGGAAATGACTGTTTTAGCGGTTGACGGATTGGCTCAGTGAGCATTTCGCGCTTAGCGGGCAAGGCCGGGTCATTATTTACGCCGGGCTACCGAAGCGGATTTGATTGGGGAAATTTTTATGTAAGGGAATCCTAACGGCTGCCGTTGTGGAGCTTGCCGGATAGTGCGGACAAGCTCCAGCTAGCAGCACTTCCCAGCCTATATGCTACCAGTTGAGCATACCAGAATAGCTTTGCGGTGCACGAGTAGAGAGAAGGGCAGGATTCAGACAATACCTAAAAAGCCGTAAAGCAGCCTAGACGTCTATTGGTTTTCTCCGGTCTGGACGTCTTCCTTGTAGCGAAGTTCTACAAGTTCTAACACGATGATAGGGAGAACAAGAACCACTTTTCGATGTCATGGGAGTGTACTCCAATAAACTCTAGACCGGTTAATGAAGCATCACTGTCGGTTCTGCAGACACCCACCTAATGCGTTCGCTCTGCCAAACCTTCATCTCAGCGACTGTACACAATTCGCTGTTAAGAAACGTTTCATTTCGAAATCACGACGCTTACACCGACCTGAAGTGGAATTACTAGGTGAACAATAGAGTAGGCCAGAACGCGTACGACTAGGGAAGGTTTTCAGGCCATGAGGGCCTAGTTTATTTCACGAAATCTGGAAGTTCCACCATGAGTCTGACGGCTCGTGTCCGGCGTGCTGTAGGTCAATGCCGCTTAGCTGCGCCGAACTGGCGATTTATCCAAGGCAATGTGTAAAGGAAGTGTCTGTGCGGTGACGATGTTGGGGAATTACCCAGAGTTCAATTGGCCGTTAGATCCCCCCGCCGTCCTGACTGAGGACCAGCACTGCAGTTCTGTTGCCTCCCAAAGAGCCCTCCCCGGGTGACTGAGTAGCAGGGGCTGCAAGACAAGGCCCGACTAGCCGTTCTAGTGGCCGAGAACCCGGAGTTTTGTTGAGCCCGGGTCGCGAAGGTTCTTTATAACTGCCTTGGCCATCAATTGGTATAGCGTCCCCGTCATGTCGGTTTTAAGCTCGTCGGACCCTCATAAAGTCGATTCGTAAACCATCATTATATGCGCCACTGGGTCGTACGACTGCAGAGAGCCGGGCGGAACATAATTCCGAGCGTCGATTATCCCAGCACAGCACGCGTCGGGTTTTAAGCTACAGCTAGAGCCGTCACTAGATATTTAATCGGCATCGTATTAGGATGTCATCCATGCCAGTAAACGCCGCTCAGCTCTAATACGATCCTTGTGGTGAAGAGGACGAAGGCCGTTCTGATCTGTGTGGTCTATCTTGTCGAGATGGACATGATGCTCAACCTGAACCTTAGTTTCGTATAACGCTCGGAGCCTTGGGACAGCGAAGGTCAGAGCGTATTCTGTTATTTAGCTGGCAACGTTGCGCTTACCTAGGTGCGGCCTTAATGAAAGAATTTGAGTTCTCGTCGTTACAACTTTTGCACTCCCCAATTAGATACTTAGTGAACCCACTCCTACCAGCATACACACTATCACGATCTGGATTCAGGCCTTCGTTGATGTCTCATCGTTACGATCTTACCAAAACGGGATGGGGCAAACAGAAACTCACCAAACGGGCCCCCCTCCCGAGACCTACATCGGCACAATCATTAGTCCACAAAAGACAGATATGCAGCCAGCTCTTGAAGTTGGGATTTCGTAGTAATTCGTATACCTTCCCGGTATGCCGTACATGTGTGGTTAGCAGATCGGGATAAGAAATCGTCACCGGCAAGAAGGCTGATGGATTTAGGGTCGCGAGGGAAACGGGTAAGTGCTAGTCGTCTACGGCGAACTGTTTCCTAAAGGAGGCTATCGTAATTCCGAATCGGCCACCAAGTTCTATCGTGCTCCGTACGCTGGGTTAACACCAATGATTCGTTGTGTCCCGTCATTCGTTATAGGCCGATCAGCGTCGTTACTGAGTTATAATCGGGCCCGGACTTTCGAACATTCTCAGGGAGATGTGTGTGTAGAGAAGCTATTAGTGCACCAACAGTTCAAGAACTTGAATATCCCAAAGGGCCATAAGTTATGCGTATAAAGACGAGAGTTAATGTGAATGTTAGAGGGTGCTCTCTAGTATGAGGGCATCCTCCTGGCATTAAGTACTGCTGATAATCACCTGATCTAGGGACAACAGTAGCGCTTGGCAGTGGGGTTACACCTACATCGACGGTGCACAATGGAATTGGTAGGCCCTCGAACGTGTACCATATAGTCCTGCACGGACATTTTAATTATCAATTGCTACCTTACGGGACAACTATCCGGTATCAGTGAAGAGACATCCAGTGCATATCAGTAACACCTTCCGGAGGACGCTCACACTATCCCCCCGTTGCAACCTGAATTATCTTAGGAGGCTTACACGACCTGATATGGGGGAGCCCCTTTGCTGTAGGCACTTATTGCACTGTGATTGTAATCATAGACGTCCGAGTCTTCCCCGGCTGCTGCCATAACTCACGTGAGTTTAATGGAGTTGAGCAACTAGTAAGGGCTGGCGACGAGTAAAATGGTTCCTGTTACAGAAGGAGGGGTCTTGTAATAGGGTTGGTCGGCACATGTGGCAGGAACGTCTGGGGGGTTCCTCCAGTATCAAACCAGAATGACGCCGAATCTACACCTAACGTTTTGCGTGTCTACCTCTAATCATCTCAACTGACGCAAGACGACAAGCGCTGGCGTTGTACCTATCTAGACCCCTGCCTCCCAACAGACCGAATGTAAACTAGTATGATCAAAAGCTCGGTCTCATTTACACTTCAGGAACCGTGCACTCATAGCCGGCCTTAGGTCTATTGCGTAAGACCACGGGATAGATCCCTGATCTCCAAGCAGCTAAATGTTTTGCGATACTTGAGACTAGAATTTATGCTAACAATATAGATAGTGGGAAGGTAAAATGACGGCCCGGTAAATCCGACAGGTTTGCATCGGCGTCATTTAACGGAACTGGTCATTGTGGAGGGGTTGTGGTATAGATACGTCCGTTCATTGTTTAATTAGTTGTTTATAGGTTGACATTTCACGGGGACCCGTGAGTCCTAAATTTGGCACACTGTGCGGGCCGACGTCCTTTAGTGTCAAGGTCTAGTTAATTCCACACTTGAGCATCCTTTCACAGAACGAGCGACTGGCGAAGACAGTCTGTCCGTGCTTACTGGTGAAGGTATCCTGGATCTATGTGATGACTGAAGAAGCCTCCAACAGGTAGGACAGTTACAATTCATCGGCCAAACCGTACATGGACGGATTGTCCCCTGGAGTCTGATAGATAATAAAGGTCCCGTCAGCGTCCGAACGGGTTGCTCGCTCAGAGAGACAGATGACACAAGTATCCGGGTTGGCGTCCACTTCTGAGATCCAATGGGCTTCGCCAGCGGAGACATTCGGTTTAATAGGAGCGGCACATCGTTTTTATCCAAACTGACTCCCCCGGATTATAGCACGGCATCTGTTACTTCGTCTTCGTAATTAGCCGGTTATGAAGGTTGGCAGCAACGCCCCATGCAAGAATCAAGCCATCGATAAGCCATGCCTGATAGCCTTTTCTACCGGTGATGAGTTGAAGACTCCCAATCTCCATCCTTCGAGCGCGCCGTCGCTTACGGTGCATGCCGAGGGCTACGGCCTCCTGTACGCGTCCCTAGCTGTCGCGGAGTGTTATTTGTCATGTGGTACTACATTTCGCTGAAACCTACAGCGGGGGAGGCCTGACGTTAACTGGGAAAATCCTTCATCCCCTACGTCTTCTCTTGTGTACGAGTACTCCCCATTTAAGACTGGTTCTAACCCTACTATAGCCTGAGGCAGGACCAAGAGACGCAGCCGACACCAAAACCTACCAAGAGCGGCAGATTCGGTTACCCGCGGAAGCCTGAATTGTGGAAATAACTCAAGCACACACTCAGGGATACTCTTACCCGCCGGTGCGTTGCACGTGCGCCGACACTGTCCATCCCCCCACGGGAGTCGTCCATAATCCTCAAAGATATAATCCCTCGCAGGAGTGAGCCCGCGATATCGGCGTAACAGCGAATTACGTCGCGGGTTCGGGTAGAGACTAGACATGCAGGCGCGAAGACCTCGGTATTTCCCCGCGATGAATAGACGTCGTAGCGGAAATTGAACGATGGACCACGGACTTATACTAAATTTTCCCATATGCGAAAGTATTAGGAATGTCGACAACGCGCCATAGACACATCCCATAGTTTACTGACCTATCGACCCTAATAGATCCGCAGTATCTGATGTTTCTCTTACAAGAGACGCGCAGGTAACACTTGAGCCCTTCGCTTAGCGTGCATCTGAGCTAGGCAACGGCGCAAGCGTCTTTTTTGCTCTCGGGTGGGAAACCAGTTGGAGATCTCCATGACTAGAGACAGGGGTGATGACCTGGTCGGGAGTCCTCACGGGTGGCTGCAAGGACAAGCTTAATTAGGTTCACAACTATGAAACATAGGATTTATGAAGCAGGTGCTATCAGTTTTCATGGCTGGCATTTACTCTAATTCCACCTACTTGTGTGACCTAGGGGAACGTAACGTAGTTCAAAATGGCTTTCGCCCACTCTCAAGGGCAGCGAATCCTCAAATAATACCACGCCCTAACCGTATTGCTGTTTTGTGTAAGCTCTATGTGGCATGCAGGAGGCCTATCCAGTGATGAAATAGATCTGGAAATACTCTGACCATTGGAGGACCTGTCGTTAAAGAATACGGCTACAACCCAAGATACGTACCCGGCTTCGCGAATATGTTATAGATTGCCTGTGCAGCATTAAGTGATCTTCATACGAAATGAGGAGGACCTAGGAACGGATGTCGATAATGACCGCCGTAATTTGACTCCCCGGCGAAGTCACTGTGAGAACATCGCTTGCCCGCGCATCGCGTAGGGATAATATATGCAGTGCCGACTATGTCGCAGATAGTCCTATTTTAGTTGACCACACTACCGAGATAACTCGACAAGCGATGGGGGGGACGGAATGAGTCAGCCATCGTTCAAGATATGATAGAAGGGTATTTGGTCGCTCGCTACATACCGGTCAAATCCTGTCTCAATTCGTCGTCACGCTTCACAACGATGTATGATTAGAATTGATTCGGCACCGACTCAGACTAATAATAATCTGTTTAAGGGGGGGCTAATCTGAAGTGCGATCTAGGTGAGCTTGAGTCGCGAACGCGAGTTAGTGTTTATCGAAGTCTTAACACCGCTTGGTCATAGCAAAATTAGTTCGCTCACCCCTTCCTCCCGTATTCGCCGCACACCCCTTCCGGTCCGCAAGTGATCTCGAGAGGGAGTCGCGTTCAAGTGAGCACGCCGATCTAGGTCGACTAGCTGTCCCTTGGAAGACGTACAGCCAGGGTATTCGTAAGGCACAAAGTCGTGTGCTACTACTCAACCTGCCGAGGGAATGCACGAAATTACCGATCAAGAGCCAATCCTAGACGATTCGCAAATCTTGTAGACCACCCATCGGCCATGGGACAGGCGTGATATGTCTCCACAGATTCGGGGTCTAAGTTGTTGGCACAACATTAAAGTCCTTTCTTTCTTTTTTCCAGCGGGGAACTATTGCTTCTTGCCCCCTGTGATATTGGTTTAGTGAGCTATAGCGTCGATTATTCGTACAAGTCCGACGCTGCGGAGAGTTCGAAAAATAGGATCAATAGAACAGGACTAAGTCCCCTTCAACGCCCCATATGAACCTACGGATTTGTTATACACGGTCCGTCCGCTAAGGCAAACGTCGATAATAACGCATTAGTCGGATACAACCGCGTGGTCGGTGCTCAGATTGCCAGACGTTACAAGGCGGCACCAACAACAAATGCATCCATGATATATTTGAGCCGAATGACGAGCGCCAATAAGTGGCTTGGGTCGTGATGCCGCCCCCTACGGTCCGTTGAAGACTGAGTCTTGGAATTCGAGGGGGCCTGTGAACAGGTGTGTTGCCGATGTTTCACTGTGCACTCTGAGTCTGCTACACGAGCAAACAACAATATGCAACCTTCTTAGCGGCCCAAATCGCTTGGTTAAAGGCAGTTTGAAGTCACCATTGGTGAAGTCACGCCTGTGTTTAGGTGTGGTCCAGGGTATGGATATACCCCTCGTGAGACGCCAAACAAAGGATGTCTACTTTATGGCCCTATTTTCTAATTGAACATCGGCACTATCACGTCCTTCAGTTTGAAGGCAGGAGGGCTTAGTTATTAGGGGCTTCGCCGTTCTCCCGAAGACCTTTAGTGGGATCTATACATGGCGTCGTCTGTTGTTGGTGCTATCCTTTGAAAGTTCGAGTGGCGCCGAAACTTGAGCGGTCTGGAAGATGGAAACGCGACGTAACCAGGGATTTTAATGTATTTGCCGATATTAACAGCCGTAAGTCGGGATGACCTGGCCGAATGTTCGGGGAAATTATGGGGCCCGCCTAATGAAGTGCGCTTATCAGCAAAACCAGGCTTACACGATCCCGGTCGACCGCCCTGGTTATGAGGTGAACGCGGACCTGGCCTTCTTGACGTGTCTCTGCCCCAGGTAGGTTTGCAAAGGCTGATGTTGCGACATAGACTGTGGTGAGTAGTCCACGCGCCGACGTACCGGGCGAGCTATCGTACCCATCAGCTTGAGATAATGTGTCGCCCCTAAGCATAAAGATCTAACGAACAAGGTGGTTCGCACTACCATCTGCGGAGCTGAAGCACCTACCTACTGGCGCAACGGTATCAATGTTGTGGCAACTCCGCACGTATGAAGGAAGGGAGTCCTACCAACGGATTCGGTCATCTGTATACACGCTCATAACTAACCTACTCACCAACGTTAAATAAACGGATTTCGACAATGGACGCACTAAGGCATTGAAGTGACTACCTGGTAGGAGTAATTCGGTTCATTTACGAATATGTAATATTCGTTTATGGCTTATTTACTGTTAGGATGTGGTCGAACTCGCACTAGATATCATAGTAAAGGGCGGACTTTACTGTATCAGTGAGTTGACCTGGCAATTCAACAAAGCGTATGTCTCTAAAGAAATTAGCAACCGAGCCTCAAAACCTTATCGAGATCCTGGCATCCAAGGAGAGTGTCCTCATTGCTCCACAGCGAGACTTGCGACCCTGGACAGCATGCGGTTTAATGTGTTCGGGTACTGTTTATACGGGTGAGTTTACGGTTGATCACTTACAACTTCTATTGTCCCATACAGTTAAGCCCCGACGGTCTCTAAAGCCAGTTCGGAGCCATCTTTCGCTTGTGGGAGGCGCCAAGGGCCTACACACCGGATCCGCGCTCTGCCAAGGTCGCGGTACTGGAGAGTCGTGTTGTCGTAACACATGCTACCAATACCGATCTAAAAAACGCTAATAATTGTGTGCCGCAGGACCTTCTCTTAAGTCGACAGTCCTTGACTCCGTCTATGATCGCGCCGGCGTTGTACCAACGATTCTCCGCCGGGGAATGATCGATCAACATTGTCTGGCGAGCTAGCCAAATCAAGACCTTCAGCATAGACAAGTCGGCATAGTGTGGTATAGATAGCTCACCTCTACGCTTCCCTTCCTAGGCGTTCGGTCATCTCGTCGAGGGAGTGTGTACGTGTGTCTATGAGGCCAACATAGCCCTCTCTATTACTCAAGATGTCTGATACATTTACAATCCCACTATCCCCTCCGAGACGACGGGTACACTAAGTTGCTCGTCCCCGCGAAGACGCATACATTACCTGCGGTTATCCGGGGTTGCAACAGCCTGCAGGCGCTGGCTCATTACCTTTCCAAATGCAGCAATGTCCCACGTGATATCAACATGTGTGGGACAATGGCCTAAAGGTGGTTCCATACTAGCTTATACAGCGTCCAATAGCACCAGTTGCTTCTCAAGGAACGGTCTAGGGTTAGTAGCCCATTTGCCATCTGGACCCACCGTACCCGTTCCGGCAAAGGTCCAAAAAGCACATCGTGTTGAGCACTGTAAAGCCAAAGTGCAGCTGAAGACGAGATGTCCGGTGGACTGACAGAGTAGTCGTAGCAGTCTAGGCGACCGCTGAGACATCAAACAACCCCACGCGACTTTTTTCGCAAGCGCAAGGTTACACCTGTCTCCACATGCCACGCTAGGTCGTGCGAAACTGGTTTGAAGGCCGGGATAGATTACTCAATAACATACACCGCGTGTATCCAACTAGACACGCCGCTATTCCGGACTCAGTTGGGATTCCATAATCTCAGTCTGAATATTACAAGCCACACACCCGGCACCATGGAATTTTTCGGGAGGCAATCGCTATTTTAGCCGTCAACTCTTAGGATAGCAACGTCATGAGAAAGGTCTATCCTGGCGCTGAGCACAGAGTGGGCAACAAGTCGTTGCGGAACTTACCGCTGTCGGGTGCCGCTAGGCTTAGATTAAGTGACCAAGACATTGACCTCTCAACAATGGTCTGCACAGATTCCTGGGCCTCAAAACCAACTGTCGGTTCCTATCCTACATTGGGGGCGGTTCATCCAGTCGAACCGGTGCTTTAGTATCCGCGGTGACAGCGTTTGTTATGCACTATGTAGCGCCACGACTCGCCCGGAACTGGTCCGAAGTGGTTACAAACGAGTATCATTAATGCAATCCTTGTTGACCTAGTCATAAGTCCTAATTCAAGTTATTAAAGATACAGAGGGGATTTGCCTTTTATTTCAGCTTCCCGCCACGGATTTTCATGGGCCCGTTTTACCTAGATGCTGCCGTGTGCCCGTACTGTAATAGGCGGGGTGGAAAACAATTTCCTTTGTATTAAACGTTTATGCTGCTGGAGGCCCAATGGGACACACGACTCCCGTCATGAGAAGTTAGATTAGCGGATCTCGCGATGAACTTTGGGTCAGCCGGCCAAAACGCCCAAGAAGTCGGATTGAGTCAGAATGTCCCTGCACGCCGGGTCTGGCTGCGAAACGATACTGGGTGAGGAGAAACTGGCAAGCCAAACTCGGCTGTGTTCGTTCTAGTCACATATCCCTACTTGAGCTCACCCTTATGTCTATCATTGTCGATCTGTACTACGGCTAATTTATTAGATGAGTTGTTACAAGGCTTCATCATTCACACAGGACAAAGTAAGATGGGTCTTAAGCACCGGCAATGGATGCACATAAGAATAGCGACAGAACAGCAACAGCGCAGGTATGCGATAAGCACGAACCCTGTTATGCAGCATACCAACCCTCATATATGAGGGATCCTCTAGTTGCCGGGTGGCAACGTATGGGAGGTCCATCGCCTAGCGCGTGGTACGGTGAGTAGATCACACGCTTGTCCTGCCGCCATGCGGTAAAACAGAGAGTACTTGCTATTCAAGATGGTCGGTGGTTGTTTGTGTTAGGATGAGTGGATAGCCAAAGACTCGCTATGGGGATTAGTCCAAGCTGGATAGATAATCCGGATAAAGGGAATTTGACGTGGTCCCTTGCATGGCTCGCGGACCGCTAGTGATCTCCTAAATGCCGCCAAGTTCATAGAAGGTAGAGCGATAGACTGGTGGTAGCGGCTACTCAGACAGTAATATCCTCGAGGAAAGAGTAGGTAAGCATGGATACCCACCATTACGCCGTTTTTCTCGTCGGTCTATGGGATATTGTGGGTAGTATGACGTGGGGAGCCAGCGATCTACAACTCATAATTTACCATTAAACGCCTAGTTCGGTGTGGGTTTTCGAAAGGTAGGAATGTGCGATGTAGTAGGAGTGGGTTTAATACGGACAGTGCACACTACTGAGGCCACATACTCAGTATGAGTGGATGTCAGACTGAACTCGGCCTCGTAATCGATGCCAAGTCTGCCAAATTGTTCCTGTTCGCGTACCCGTATCATAGCCCCTTATGCTAGGAGAGTCTCGAGGATCCGTCTCACCAACTTTCGCAGACACTGGTTCTTAACGTAACGAATGAAAAGTTCGGGACATCGTACAATGTTGCGCAACATCCCGGATAAGAGGCACTCAAGTGGTCCAACGTGCGCCTCAAGTACCAGAAATTCTGCTTCGCCTAGCAGAGAACTCACACACACGCCCCTCCCCTGCACACGAGGCAACGGTCTAGGTTAGTTCGCCTGAGCAAAAGAATATCGCGTGGGGATGGTGCTTAGAGCCATGTCTGTTCTCTCATCAACTGTCTCTGAGCCTGCTATGCGGCTAATGAAAGTATCACCAAGCTGTCATATGAAACCCAAAAGTGTATCTGGTACGCAATTTAATCCTCCAAAAATTCCGTCCGTAGGGCTTCCGCTGCCACCGGGCCTTACAATAAGGACCTGGGAACCGAACCCATTGTACACGTCTTACCGGTTGTACAGTAATTGATACTTGGGTGCGTCGGGCACAGAGAGGGCCAGTCTTGGGCCGAATATACGTCTGAGTCCGTCCTTTCCGAGATCGGCGTATTAGATCCTTATATACGCCCCCGGCTGCAATGGAGGCCCGAGGAGGGCCTAAGCTTCCGCATATCAGGAGGAAAGATATTAAACATCATGAGCGCATGACGCCTGACCGCGATTGAAAAGGAATTTCTTCCAGCTAGAATACTACATGCGGCCATGAATACGCCGTTCACTCGCTCACCCGGTCGTATGTGACGCAATACTGCAATTCATCGGTCTGGCTATAGCGTGCGGGCGGGAAGGCCATAAGATCAACTCAAGTTCCAACAGCAAGAGGATTGGTCAGTCGGGTAGGTATGAGGCCAATTATAAGTACAGACGGGAGTCTCCTAAACTCACTCAGCGTATATATCAAGTGTTCGCGTTTTTCCATATCAAGCGAGACTCGGACCACCTCTGTTAATGCTACCATGAGTAAGTATGAGATCGAACCGTTGTAATTGAATAAAGTCGTTTATTCTAAAGCGATTGTGCGCTACACCGGGCGTAAAGAGCCACGTCTAATAACGAGCCGGTGGTTGATGCTCGTGTGGCTGCTATTAGTAATCCCTATCGAAAGGTTCGGCAATAGGTAACGCGTCGAGAGTGGGCCTCCACGATCTCTACTGACTGTACGCTTGATTGTTCGGTGCATATTATTACTAACCGGTTTGACAACCAACGTCATAGGTTGACAGCTAAGAGAATGTGATTCATTGGGCGAGTATGATCTAGGTCAAAGGGTTTAACGTATTTCGCGTAATACCCTTCACGAGTCGTGCTTATAGTCAGTAGGGGTGTGTATACTCCAGCCGCCGCTGAACCAGAGCGTTAACCCGTGGCCTATTAACACCGCCCTACTGTCCGCACCACTCATTTACCGGCGGAACCTAGACGCAGCTATATTAATGAACAGTCTCACGACGACCTTATAACTTGGCAAAATCGTTCTAACAAGATACGCCCTGGTTTATACTATTAGAGGGCGGAATATTCGTATACAACAAATAGGACAAGTCGGGACCTGCTATATTGGTTCATTCGTCACGTCCTATCACCGCTTGATCGATCTGACCATGTGCCGCTAGAACTTTCCGACATGGGCCCGCTTCTCGTACCAGTCTTCGAGTTCCTGTTCGTCCTTCTGACCTCCCCCACTATAGGGCGTCAACATCGGTATGAGGCAGACGTGAAATTTACTCGCGCTTTAGAGCTGGTGCTTGATTATTCGCCCCGTCCGACCTTAGCCAAGGATACTTCACGATTTTTCGTCCGGGCCCTAGGGACACCCCACATAACATCAACTGGAGTTTAGTGCTAACAGTGCGAAGAAAAAGTTGTGCCGACCACCAAGACGGGTCGACGAGATGATAATAGCGCGACACAGTGATAGCCATTAGGATCAATGGCGTTTAGACCGTAATAGCATTGCGGCCAAGCTCGACATTGTTACGTAGCACTCATGGTCGCTGTTGTTGCGGTTACGGACCCTCGACTGGTCATGGTATCGTGCTTACCCCCTGTCCGGGTGAATAACAAGACCGTGGTCAAGATAGCCCGTACGTGGTTGTCTGCTTTTCGGAATTGACCGCGGCTTTAAGAGACAGAGCGATTTGCGCTCTTAACAATGAAAGAGCGCCATAGACAATTGGTTTAGAGCCTGAATATCTTCTCTCGACGTGGGTTTATTCATGTGCGCCGCGCACTGTGCCCGCGGGTCCGCTTTTGGCGGGCTGTCCGGCAATTGGGGAGGGCCTAACTCGAAGGGAGTTCTCTTGAATCCTAGCTGATGATTCAGAACCTGGTTCACCACGTTGAACATTTAACATGGAGCGACGGCAGGAATGGGGCCTCCCAGCAGTGTATTATTGGCGGCCTGGGTACTAAAAAGAGACGCCTACGCGTGTTTGGAAGTCGGGATGAGACTTTCTTCGTTTCGGCGACATGGGCAAACCTCTCCACTTAAAGAATAAATCCAGCTGTTGCGTGAGCATCGTGTCGAACCGCTAGACTCCATTGCCGCTAGCAATTGGCCTGAGCGGAAAGACCCCACTGAAGTAGCAGACCAGTGGCGCGCCATGTATAAGGAGGACACCACTCCCAGTTTAAAGCCTCTTTGCAGTCATGCGTATTCCCTCTGAAGGTTTGCTGAAGCGACTGACGACCAGGTTTTTGGAATTGTATATCCTGTCCACATCAATGGTTCAGTTGACTTGTTTCTAAATTGACACCCCTTGATTTGAGGCTGACAGTATAACAGGTTGTAGCGCACACACAACAAGGAACTGAGTCTATATGGAAGTAGCCTATTTGAACTAGTCTTTGCTGATCTATGTAGCGGCGTACCGACGAATCTGTTGGAATACTATTGCGTCAACTACGGATCGAGCATGCCCATTAGAACCCAGACGAGATTCCCGAAAAGTTATTCAAGTTTGATCCGGTTTCTAGCGTGCTGATTTTATCTGGTGATGGATTTGGGGGTAGCCCACTGGTTTGATATCAGAAGGACAAACTTCTGACCGGTGACAACATTTGTCAAATGCAATCCTGAGATCTGAAGGAAAACTTTAGTTTGTCCCGGGCGTGTTAACCCTGGACCGAGCAGCAGAGATATGTTGCTCCAGGCTCGCGGCTGACATATGTTGGTGAGTAATCGGGCACTATTTATAATTTGCGCATTCATCTGCGCTACCTGGACACCGGCGCATGGTTTGGTGCTCCTAAAGCCTACCTTCCCGACCCCGAAGAAAAACATACCATTTGTGTTCTGGCACAGTTTAACTGACCTATGCGCACAGTCGAACAGAGGATATACCCATTTAACTCAAGGTTCACTATTCTTTATTATCCAGCTCGAGATGTTCTCCGTGTAAGACGCAGACTCGAATTCAAGTCGGGCCGAACTCCAAGGCAGCGGTGTGCACTTCGGCGTTCACCGAAACCGCGGTGACTTCAGGTCATGACGCTGAACCGATGGCTTTTTCCGGACCCCAGATCTAGCTATCCACAAGGGTTTTATTGCAATGGTGCGCATCGCCGCGGCTTGGAAGTTCATCGTAATTCGCGTCAAGGCTTAACTCTCTGCGTCGAACCCTACATTTGAGCAACCTGCGTAGTGTACTTGTCTTGCTCATGGTGCTTCTCCATGTATAATTGTTATACTCCTGTGTCATATGCATAATGCAAGCGATCCAGAGGAGTCGCGGTCGAGTTAAGCAACCATTGGTACCTGTGCGAGCCTGAATTAGTTTTGACAAGACCGGTCTAGTGGTCGGGCGTTTGACTGGAAACAGATACATACTTATCGACAATGTTATAACTGCAGTACGTTACTCGCGTGGACATCTTGTGTGCACTCTTGACGTAGCCAACATGCAGCATTCGTTAGTACGTGAATTCAACTGCATCACGCACATCGACAATGGTCATGAACTGTCCGTTGTCACCGTATCACCTTAGGAGTAGCGAGGATTTTGGGTGCCACTACAGACCCACCTTCTACTTTTAAAAACGCGAAACCCCTCGGTAAGAAGCCACTAAGAAACGACGTTGGTCTGACGAATGACATCTCCTCACATTAAAGTGAGAGTTAACATTCATCTGTATGCCCCTGGTGCCCACATTGCTAGTAACTGAGCCGGCTGTCACGGTTCTGACCTACCGCTTTGCGAGACAGAGTCATTTGGCGCCGAAGGACGTTGCGCCGCCTCCTCTCGATAGTGAGCCACCATCAGAGAGTCCCTTAGCGTCGCGGCGTGGCCGTTCGTACTGTCGCCACACAAAATACTACATTCCCCACTCGAACGGAGTAGGATTAGCACCCTCTATGCACTCTACCACTCATCCTAGGCCCCTCGACTGGACGTGCATCCCGGCAGCTGTTTTGGCGGCCTGCAATTATGCGAGTGGTAAGCTCCATCCAATGACCTCTTATGCAAAATCATAATATAGGCGAAAAGTTCTCTAGCCAGAGTTGGAGACATTGCTGACGGAGACCCCCCGACTGTTGTAACTGCAACTACAACCAGGAACCGGGCAAATTAACTAACGGGGGCAGTCGCGCGTAGGTCTCAGTAGGCGAGACGCTGTAAGGCCTAGCAATATCATAAGCGAGTCCACTCAACTTACACTAGCAAAATGGAGGGTTGATTACTACACCCTGCTTGATACTACGATCTCCTGGCATTTGTCATCGGTAGCTAATCTATTGCATAGAGCCCGACAGCGACCCAACCCGCAAGTAGATCACGGAAAAGCTGAGCCGGCCAAAGTATCCGCGGCTTTGGTTTCAGAGACACCTGCTACCAAGGTAGTGCCGAGTCGAGTCCATCATTACACTATTCCCTTGGCGCGTCATGAGTTCACTGATGGGATTGTCCTGCACGCCTATTGTCCTAACATTCGTTCTTGGGTCACGCAGTTCTACGCAGGTTAGACTAGGCGTTTGACAGCCCGGCCGTCGTCCAGTACACGGTTAAATATACGCTCAATACTAAACACGGGAAGTGGTGGAGCTGGCTCAAGTCACGGTTATTTAGGCCCATAAACACATTACATCAGAAGAGTAAGCACACTGGCCAGAGAGGTTTGGACCAACTCAAACGCCACAGCCGTGCCGTTCTTTTCCCTGGCGACCATATCTTATTTGTCCGGGTAGGGATCTTTGGGCAGCGTCCACCCTTTCCACTCACTCAGTGCCGATCTCGGACGGAGGCGGCTTGACGTTAACAAGCCTATACTAGGCAGAGTGGGGCCAGGTTACCGGTGCGAGACTACCTGAGCAACCCCTGGCTCGTCGTTCGCATCTATCCTGCCGCAAAGTCATAATCTTCATCAGGGGGCGAAGGCCCATCACGGGGTGCTGTTACTATAAGACCTAACGTAGCCTCGTAAGTTTTGGTCGCAGTTGAGAGTGTCACCTTTAGCAAAATATTTTGCCTACTCCTAGCCTGGTTAATGGGAGACCACGCTATTTGAGTGGTAACTGGAGAGACGTCCCAGTCCATACCCCGTCGGCACAATCCCCCGGCGCTCAGGAAACGGAGGCTTTTCAGAGCTCGCTGTATTGCAGCAGCGAGTCACTGCAATAAATGTTCCAACCCGGACCTCATCTGCAAACAGCTTGCAAACCTGCGTTTGCTTACTTCCCAATATTGAGTCCTATTAATACCGCAAGGACCACTGTAGAACAGCGTAAATGGGTACCTCGAAATTACACTTCCAGCCAGTACTTACCCCCCTTGTCCTCCTGACTTTCAGTCCAATATGGACAGATCGCGATTAGTTGCTGCGTCAACCGCGTTGACTCCTCTCAAGATGGATACTCTGGAAGAAACCTTACCGCGCGATAACAGTCCCGTATAGCTAAATTATACGCGCCGGCACATGGCTTTTTGGCTACTTGCCCACGTGGGACTAGTGGCAGCTGATTACTGAGACGATGTACTGGTAATACCTAGGGGATGCCTAGCACCACCTACGGATATCGGGAGTCTCGGCATGCCATCTGTATCGATGAATTAACTCACGTCCTCGCGTCTGACCATGATGCAGGCCAGGCTCTCGATATACAATAAGCATTGGTGTTACATATCAGTACAAAAATTGAGCTAAGGGTAAATTATTGAACCGGGAAAAGCTTCCAAAGTCGGCTCTAAGTAGTGGGCTTGAGGGGGATCTGATGATGCGGCTCTGAATAGTTCAGATCCTGGCCATAACGTGTCCTACGAACCATGCCGTACTGCAGGGCCATTCGCCACGGGGCCAACTGTAGAAGCCCCGGTCCCGCTGTGATATATTAGGGTAACCTAAGGGAGCCTCCGCTAAGCGTCCACCTCTTTGGGGTGAAGTCCGTCATCGGCCAGCGGCAATGGCCTCGAAACTTGTTGCTATTGAGCGGGATTGGCATCGGTTAGCGCGCTTAGTCTGGGATGCAATAGTGTTCAAGCAGGTAAAGAGCTCAAATTATGCAGGATCATGTGGTGCATTGAGACATTTTGAAAATGGGTATCTATTCTAGGGACACTCTATCTTTGTTCCATTATATAGCAGTAGGTTGTTACCACGAGTGGTGTTCTCCCTCGTGAGAAATACCAAGAGGGAGAACATCTTAGTATTTTTATTATACGACTCAATTTCCGCAACTGCCAGGATGAGCGCAGATTCAACCACGTCAAGATTCGGGGCATTACCGGTGGTCGTTAAGCAGCAGAACTGTGCCCGGCGCATTGTCGACCACCCATGCTCGGTAACTCTCTGCGGTCGACTGAAGAGCGTGGGAGGACGGATTGCGCGTAATCGTACGGTTTGCTGGACGTAGTAATTAAGACGCATCGACAGAACAATTACGCGGCATAGCACAGTGGACTCCCTAAAACGTCTATTTTCCTCGGATGGGCTTACATAGACTGTCCCCAGAATACGCACACGATGGGAGGTAGGTGACTCCAGACTAAGTCCCACAGGTTCACGTCCGTTGGGCACGCGAGTACCAGTTCCTCCATGGCCGCATGAACATTTTCCATGCTCGCGTCATCCAGAGCCATTCGAGGTACCGTGCACGATAGCGCTCACGTCCTCGTTGATACTCGGAGAGCTGTCGTGCCTAACGGGTGTGCTGACTTGCAAGAATTGTTGCCCGGGCAAGAAGGCAAATAGGTTCCCGCCGCAGCCGCGAGCACCTAAACCCGGTATCTTATGAAGTGGGTAAGGAAATGCTTTTAGACCACGAATAGTTATAGATCATCCTCCGAACTGATAACGAAAAGCCTACTATGCACGTACAGTCAATACCCAAATCCCCCTAACCGGCAGAAGATTTGTAGCGCATTCAGCAGGTGCACCGGAAGTGTGGGGCTTAGGGTTTGCTCATCAACGAATCTGTGAGATCGTTCCACTATGAAAAAGTAACCCCACAGATTCCGGGAATTCTGTTTATCGACGGAGCGGCATAATAACCATCGCGTGCCAACTCTTGAAAATCGGCTTAACCGACCTGCAAGAAGCTGGAAACCGACTTTGTTTTAAACGCACCCATAATTCCTGGCCGCACGAGGAAGGAGCAGACCCAAAAGGATTGAACGCACAAGCGCGCGACAGCTGACAAGGTAACCTTCGCGGTACGGACTAACACGGTGATCCTATTGGTGTCATGCAGCGCTTTGTAGTGTCAAAGGAGCCGTCGAGGACTTCGGCGTTGCAGAGGGGCATCAGAGGGCTGGAAAACAGGTTCCCTGGACTTGTAGGAATGTGGGTTGATACCAGCATGCTAAGCTAGGCACGTATCAGGTAAAAACCGTACCTTCACGTTAATGGTGCATCGAGCAGGACCGCAACGCTTTTAGTTAAGTTTTGAGGCCGCACGACTGTTGTGGCGTCTATTGATATTGAATTCAGACCGCTACTTAAAATCACAGCGAGGGCACACCGAATAGAGGCGCCGGAATCTGGATCTTACATACTGAAGAAAAAATTGTCATTCCCACTTTCAAACGCTGTTGATAGTAGTTGTGTTTATAGAGGTAACGAGGACGTCGACCGTAAGCCAAGAATGGTTTATCTTGGGATTCAAACCATTAAATCGCTGCTTCTGGACAATGATTCTAATAGCCGCCCCCGTTTAACCGAAAGGTGGTTGTCACGATACGCCTATCGAGGGGACGGATCCTCTCACTTCTTGCGCACCACTCGCGCAGGTATCCGAGCGTGGCCGCTTGTAACCATGAAATTTTCAAATTCATCACCCCTTCGAGATTATACAATGTTCTACTACCCTCCTATCGACAAATAACTCGTGCCATGATCGCACGTTTGTGCACCATGGTGGTCGAGCGGAAGTATCGCGTTCCGGACTTGCTAGGCTCGATTGTGTTGAACTAGGTCGGTGGTAAAGCATTATACCAGCGTCAGAGTTCTGCACAAAAATTTCGGTTCGAGGTCGCACGATAGAGGGGCCAGGAAACCGTAACCCCTATGGAGAACCTTGCCGGTGTCTACCAATGCGTAAGCTCAGACTCAGGCGACGTACCTTCACTCCCCATACTGAAACTCGTCGGGTACGTCGGTTATCAAGAGCCACCCGTGTGCACCGACATTTCAGGGTTCCTATGTTATCACTTTTAACTTCGTTGGAAAACGACAGTGTACCGGTCCTGTAGACAGCTATGACAGATTACGGTTACAACAGAGCATCAATGCTGCAGAGCTTCTAGCTTTCGGTATTCGATTGTGGACCGTTGGGGGCATACGGCCGTTGCATGGGTTTTGCTTCCACGATATGTTGGTGACGCCCACGCTCCCCATAGCGCAGAAATCATATACCCTGTCGCTGGTTCCGCAGTTTGGACACCCTACTCCCTGATCAGATCACTCGACTCGACCTAATGATAACAGCCCTGCCTCCATGAGACCATGCCGACATCGGCTAGTGTACAACAGACCCTCTTAGGCGATTTCATTCGGGTGTATGGGACGCCCCATCTGGAGTGGATCACGTGCCAATCAAAAGTGGTCGCAATGGGCGCCCTAAATACTCTGGCTCACCTCCCCCCAGGCGGGCTATGGCGATGATGACTCCGAGCGTCTGGCTATTTTGGGTCCACTAGCCAAGGTAATTGCCGATATTAAGGGGTCCATACGATATGTTACAAACACCGAGACGTTCGAACCAACAGATCACATTTTTACACGAAGGAGTAGAGTCAACAAGTTCCGGGGCACCATATAACTTTGTATTCAGCCCCAAGCTATGATGCAACTGAAACGTGTAAAAGAGGAAAAGGACCAACTGGAGCTGGGATCTTTTTTACGCTGCGGCTAGGAGCTATAGATCGCAAGGGTACCAAAATATAAAAGACAGGCTGGGGAATGCTGTGCAGTGCGTCCTGAGGTACTGGAACGCGCGGGCGGCAGTGTAGAACTGGAGGGGGAGCCAACAGGGGACCCGTAATCATGTGCTAAAGCACACTCGCGAGCCGTCCGAATCCTTCTCACATGTCTCAAGTGGTCCGGCGCGGCTATGGACACGACTTCTTTTTTGATGTAGGTATCAAACTCATGTGAGGTATGCAGGCCACTCGGCCACTAACATCCCACCCAGCTTAAAGAAAGCACAAAATGTCGCGGAGCTTCCCCCCCTACGATCCAGGTTGTCCTATGTATGATCAAGGCCCATGGGACGATTCACTGAGTCGAGTTTCAACACAGGATGCACAATAAGGTGACCTAACTCGAGAGTGCTCCAGTTTGGATAACCACCGTGCCCCCCCTTACAATGCGAACTATAAACACTTGCGACAAATCCCTCTGAAATGATCGGCGGTATCGCCGCTCCCATGCAGGCTGGGCGAGGACTCCAGCAATGTACACTACAAAGTTGACTTAGACTCCGGTGCGTCCGATCAATCTGGTTGGTCCCTGTGTACCCTAAGGGTGTATTTCCGATTACTATGACACCGTGACAATACTGGAGTGGACGAGATACTACAATAGCGTAACACTTGTGCGTTATAATCAACCGTGGGCAACATTTGTACCAGGCGACGCTGCTGTCCTTCCCCATTCTTTTCATCTTCCGCTAAGGTCCTAATTGTGGTACGTTAAGCGATGCATCGGCGCTCTATACGGATCCTAATCCTAGGTAAGCGAGCAACCTTTTTCTCACTTCTCCAGTTGTCCAAGCCAACCTTAAATGATATATGTAATATATAGTAGACCTGTCTCATTCCGGCAAATTCGGCAGGCTTGCCCTGGAAGTAAACCCAAATTGGTAAACGGGTAGCTGTGGTAATTGTTGGGGGTGGTATTACATGCCAACCCCAAGGTTTTAGTTGTGTCAGTAGAGCCAAACAAGGAGTTAGAATTCGTTGAGAAGTTACGGTCAGGAGAGCGTAGCACCCACGAACAGTTTCCGTCGCCGTACGTCGAGTGCCGTGCTGGCTTCGAGTGGCAAAGGACCATAGAGCTATGGAGACACGCGTGTAAGTGGTACTAGGTGTAGGTCCTGCCCTGTTGGGCCATCTATGGTCTAAGGCAGCATATTGGTGGTTGGTACTCGAGTAATGCCAAGAGAATGACACGCGAATTAATGTAAGAGATCCGTGAGAAGCTCGCGTGGGTTTGTAAGACTTTTCCAGAGCGGTTTCTCTATTCCTGGGAAAACGACTTGTCTAAAGACGAAATAATTCCCTAGTTGCATCGCCAAAATCGCCATGAGCAGTATCGTTAAAACTCCACTACGCGGTTAAGATAGGTGGGTCTCAGAATCCAGATTATTTCATTGGAGTCTGTTAACATCTCCTGGTATTACTCTAATGTATTTAGTTGTCACGATTAGCCACCCGATTCGGTCGATAATCAAATCACTCTAATCCTAAGGTTCAGCTTCACACGAATCCTATAATCAACGTGAATCAAAATGTAGGCTGTTCACAGAACACTGTAGTCGACAATTCAAGAACATCGACATACGGCACGACGGTTCCCATTGCAGATATATGGTGGAGAGACTTCTGAGGAGTACCCAGAAACATAAGATTAGTAGGGTCTGCTCCGAGTACGGAGGATTGCCAACAAGACAGGAATTGTATAGAACGAGGCTTTATGTATACCCTCTTGGCGCTCTATCGAGTGATCCCTGGGACACTAGGTTCAGTTCAACTAGACAAATGTACCGAGGAGGTCGCCGACCAGCTGTATAAGCTCCCGATTATAACGTCATGGTATGTCAAGACATAGTTCCGGTGGGTTGGTGTGTGACATAGCCCAACCGGACCCGGCCTGTGGACGAGCTTTGGTTTCATAGTTCGGTGGAGACACATCCAACTGAGGCGTTTTGGAATCTAAGTAAGGAAGCTATGTTCCCACTCATGTTCCGTGGTGTTGTTCCCCCTTCTAATCTGGTGGTACCAGCCACCGCCGGAGATTTGATGCGCTTGGCCCAGGACGGTAGACCCGACGACTAGCGAGTTCCAAGCTATTCTGCACGACACTAAGCTATGGCTGGATAGCTAATACACCACATATGAAGCGCACGGCCGTGCCGGATTCAGATGTGCGCACCGCGGCTTGGTGGCTGCGACTCAAGCATCCACTCCTACGCCCCTGGGCTACTAGAAGCGGCGCGCAATATATAGCAAGTGCGAACCATCGGATATATACCTGGGTAGGTCAGCTCATGTGAAAAGGCCTCTGGGGACTTACCCATATACTAGTGGTATGGTCTTCCTAGATCATCTGATAATGGGTTGTGGTGTTCAATATAAAATAGGACCGTACACAATCGAGTATCGCATTCCTATTCCTCGAGATCACCACCCCTAGCATCTGCGATTGATGGTACCTATCTGGATAGGAATAACCACCTCCGTTTGGGGTTTCCTTGAAAGCGATCGGCTATCCGCCAGAGCGTAAGTTCGGAAATGTCGTAATGGCGGTCCGTGCTTTGAAGGAAAACCCGACGTACTACATCTTCACTAGGCGGGATTGCGCCGATGCGAGGGTGAAGTATATGCCCGCGACGACGGTGCTAGCCGACTGAAGAGGTTAAGATCACACCGTCGTATAACAGGTTCCTTTTTCTTCTCAACCGACACCGCTGTCCTACCTTACATGCAATAAAGTATTGGCTCCTTCCCCGGTGAGACTCAGCTAGGGCGTAGCCTCCTTGGCTATGACATCAGAAAACGCAGCGTGCATTTGGAGTTATTTGCGCTGTTAGCCTCATATGTATTGTAATCTGCTGTCTTGTCTCACAAGAGCCCACACCTCGTTTAAAGGAGGCAAATCTTGAACTCTCGGCGATCAGATTATGGCTTATGTCTATCAGCCGTTTCGCAGTACTTCCGACCTTTATAGAGAGTACTATAGAACGATGCGGTGAGGATGAGTTTTAACAGATGTGATTTGTTATGCGTGCGGGCAAACCCAGATTATATACAACATTCAGGACAGAGCCTTTATGATTTAATCCAGTTTCTAGGTAGTAGGCTGTTTAATCCTCTCAATAAGTACCGGCTCCTTAATTTAGATAAAGGCAGAGTGCCAGCGTTTGCACAAACACTGGCAATCAAAGACGGCGCTGCATGGTCGCACGGTCGCTCGCTTGTTTTTTTTATTGCTAAGAACCACGTGTTGCTCTGAGAACGCCCCGTGCAAGCCACACAAGCAATTCGGCCGGTCTCTCGCTGATCACGGTCGTCACGTGAAATACGTGCATTTAGTACAGAAGTGTCGCTACCGTGGACACGGGCAATTTTACCGAGTTCCCATACACTATGTGCTGCCGTTACCTTTAGAGAGTAGAGCTAACAGGATCACAAATGTGTCTCACGCCCCAGGCGTTTCCGAGCGGCCTGTGTGCGGACTAGACCCGCCTCGCCTATGGGGCTTAACTTACAACCTCCCCACCGGCCCCTGCATCATATTTACCCCGCACAGCAGTTCTTACTAGAACTCCATTCATGAAGAGATACCTGAACGGTGCCCGGGAGAACCTTTTCAAACGGCCGGGTCGTTGAAAGTAGCCAAATTACACCTGACCTCGGTTGATTTCTGTGTTAGGCGACGAAGTTTTCCCACCTACGACAACAACGTCTACCACGGTGAAGGCTCGCCGGGAGTAGTGCCGTAACTAATGGAGCTGTGTAACTGCACCGTGCCTCATCACGTTACCACCGTGACCCAGACGATGACGATTGAAATTATCCATATGTGCTTAAGCGCCGTATTATGCGTGGAACCGTGTCCCGAAAACGATTGGCAGCTGTACCCACCTGCTAGCTCATACAGCAGCCAATGGTGAGTTAGCTTGGTATTCGACAAATTTGCATTGGAGAGTCACCAACGCCAGTCACCTTCTATCTGGACTCAGAGAGCAGCGTGGAGCTCGCCCTATAGACAGACTCGATCTTCTCTTACCTATGGAGAATCGCCCCGCCGCGAGGCGGTGCAAGATTGCATTACAGACCGGAGGCATGGAGACCTGCTCGAGAAAGGAACCGGGATTGGAAACCGAGTCACTGGCACAGATTATCTAGGACCCTTAGCCGCAGTGCCGCTCCACAAGGTCGTCGTTAATGGAATGTTGTAGCCCGCGTATTGATGAGATTAGCGAGGCTCCTGTGTTCATCACAGGCCTCCCGAATTAATTAAAGATGATGTACGGAGTGATAATATCATCTTCAGTATACAACCAGAAGCCGGAACTCCAACTCATAACCATCCAGAGACTGCCGCCCAGGTTGCTGTTCAGCTCCCCGCTTAAAATGTGGGCATAGGTGTTGACTCGAACCTCCAATGTCAACAAATATTGCTTGCCCCCGTGTTGTATGCGAGCGTGTTCAGTTCTCTCAGGATGAGATTTCGCGGCGATGGTCTTAATGTATTGGGTCATGCACTTTAGAGTGGTTCTCAGCATCTCCTGTTGAGCTTGGATGGCGTGTGACCCCTATCTGTGCAAGTTCTCAATTAGAGTAGGGTTATGAGATAGGTGCGCCCTATTACTTTCAGTCTCAGCAGGTCAGGGGTTGACCGGTGAGAGACCCCACTGACATAGTGCCGCCATAACCGGACAGCGCTGCGAGCAGAAAAAGTACTTCGGGGGCTATTGAAGTTGACGGTACGAGCTTCCAGTATATTGGAATGGGCCCCATTCGAGTCATTCTATTCATGAGGATTTAATTTCGAACCTTATACAGACTTACGCTCCGCTAGTGGCTCAGCTAAGGTTCATCGGAGCCACGTACTTACGATTATCGCAAGATACTCCCCCATTTGATGCCATACTTGCCCACCGCACGTTGCTACTAATGATAACAGCCTCGCCGTTATGGCGCCTAGGGAAGACATCAACCGCTGCAATGGGCAACAGATTTAGACTGTGCGGCACCAATTTCCCTGGATATCCGTTACAACATGCCGGCTCTCCGACAAGATTTGACATCCGCATCTCCTGCTGCTTCGACTGTCTAATACAGAGTAAGGGTCAGGTCATGTGTGACGGTTAATACGAGACGAAGGACAGGGTACGGCTCGGTGTGCTGCTCCTGCCTCTGAGCCCCACCGACTGGCGCAGCTGTCAGCCTTTTCCCATTTAGTCTGATGCTAAGGACGAACTCAGCAATCTACCCATGCATAGTAACCCCAAGATATAGATCCGGGAGACGTCTACTACACCTGATAGAGTGTGATCGTCTGGTTCATGCTAATCAGTGTCCCTCGCAACTGGGGCTATTCTGGTCGTACCAGGCCTAAGTCAGTGAGCTGTGCCTTGGAGCCACCAGGTAAGAGTAGTGTGCTTACACATGTTACGTCCTAGGGGGGACGCATGTCTACTCCTGCTCGCCTAATGAGAAGACCCAGAGACCGGAGTCTAGCGCGACCAAAAGTTGGGTATGCCAGCCCCAAGGGCCTCCTGGAATCTACATGCAAGATCGTTACCTATGAATACCTTCGCAATAATACTCAGTACTCGGTTCCAAACGTCAGACAGTAAAATATGAGACCCACTGGTGTCACTGACGTGGTTGTCTAGAGTAGGCCTCGACCCGCAATGCACGGCCTTCGTGACGTTGGCTGAAGCATATGGACTGTTTCTCCGCAAGTTGGGTGTGATTCCCCTTATAGGGTTAATGTAGGAGAGAAGAGTTGTGCCTTTTGGGTGGGTTTCACGCAAAGTTAGCCTATTATGTAAGGGCCACCCACAAGTCAGATAGTTTGAAAGACCCGAACCTAAATCGCACTCGGCTCACGTTCAAATGCTACCGGCCACCGGGATATTATAAGCGTACAATGCTCCAGACCCCTACGGTAAATAGGGGTTGTTGCACTGCCAGCGCAACTTCGTGATGGGGCTTGGATTTCACATATCTATCGAAGTCTTTGTTGGAATTGTAGTGGGGTCGCGCACCTTCGTCCATCCTCTTTGCCGGAGGAATGTGCTGATGAACGTGGGTGTAGCACATCAGCTGTATGTTCGGCATTACTGGAAAAGTCCGCCTTATAGCGTTGCGCTAGCGCAGAGAGAACGACTCATTAGTGTGGAACCCGGCGAGGAAAGCGGGAGGATAATAGCCACATGTCCGCGCTCCCGAGGGTGTAACTATTCGGGTCGTTGATAACAACTCGATTGAAGACGCAGCAGGAAGGGCGCATCCGTGGGACTGTAAACGCCCGGGGAATAAGCTTTCAGCTGTGAGTGCCTTATCACTTAACGCGTGAAGGTCCGTTTTGTGTAAATACCCGTTCTAAAGAGTGGATGCCTGGACGAGTCTGTCACAGATGCCCAATCACCTTCTCGTAAGTCGCGAATTTGAACTTGGTGAACTGTATCTAAGATGCGCCCTACCACGGTGGGCGCCGGGACTCCGCTAGTTTGACAGCATGTAGATGAGGGGAGACTCTTGACTGACTAAAGCGCTCCACTTCCGGTGGGCCCCTCGCGCCTCGACATATCAGGATAACAAGCCTCACACTGATCATCGGCTGTTAAGAAGCGTCGGTGGCATTAGTGATCCGACATTTGTCCGAGTTTACCTTTGACCTCGTAGGTTACTGAATAAATACAAAATTATGTTAGGTCGATGCAACGCAAGCAGCAAAGAATTCGATACCCAACTGGACGGCGCCATTAACGCTTCATTAGAAAATTTACAGTAGCCGACTTCTATCTCAATGAACGCCCCGGCCGTCACTTCAAGGAGAAACAACCGCTAATCTACAGTGCTTTTACTATCGCACCGGACCCTTCCACGCCGAAACGAAGTCATCTGATGTAAACGTTGGTCCGAGTTTAGCGGTATTGTCTTAAGCGTTCTAGCGGACTTGTAATTTAGGCAAAGCTGGCCGGTAAGGCCTTTCTGTTCACGACCTGTTACCTTCAGTCGCGGCACCCGAACGACGTGGACGTCTCGGAACGGGTATCAGATATAAGCTTATACCTGTAGCAGTCGCGAGTTGACCCTACTTCAAGATCGCATTCCGTTGCCATCCAATACGAAGCTCTCTCAGTCCATATCTCGCACCGTACTATGGGGAATCTACTACTCGGATGTAGTCCATTCAGCTTGTAGGGGGTCATGGTATATTTAATTCTCTACCTCAATCGTACGTTGAACTTACCGGTTGTTTCGTCATCATCGGTTGTACGTGGTAGGTATTTGAGGGCTGTCTGAGTCGCGACCTAAGTTAAGGTTCCGGGCTTTATATTTTACCATGGCTTTTGGAATTTCCGCACCACTAATCGGCTCTCAGGCGTTCGAAAATTCAGTAACGACAACCTAGCAGCGCAGTCTCTACGCTTACCAAGGGGCTGGGACTGCCGCAATTATAGTCGGGTTTTTACATCTTGAAGTGGCGTCATATAGTGTGCCTTGATGATGCTGCTTGCTTTCGCTTATGGTGGAATTATCTCGGGATAGGGCAACAGAATACGTGAGCCCCGTGGGGAAGGCCTCCATGAGAGCATGCGCCTTAAGCCTTCATGGGAATTTGGACTCAAAAAAATATCGCGTAGTTGAGAGTTCCTTTTCCACGTTACAGTGCCGGGGCCTTTCAGACCGGTGCTTCGGAGTGGTAGACGGCTCGAGGCTGACTGACCACGGTCGACCCATATCACAGGTAAATCTGGGGGTATTGAGTGACCTCACGGTTCCTACTAAACGTGGACGCTCTTATAACTGGACGGTGGTCTCAGCTAGAGGGAGTTCGGGGGAGTACGCTTAGGAGTTTTGCACGACAGTTTGTGTTAACTGTGAACGCGGTCGCATGACGGTGCACGCAGCAAGCTCCTATGGGCGATCTCTCTGTGTTTTACTAATTTAAAGCAGAGCTTGACCAACACTAGAGTCATCCCTCGATTCTCACTGTCATAATACGAACTCCATTGACATCTACGTCATCGGTTCCTCGATACATATCTAAACGAGGGACTTTCCAAGAGTCCCAGCCGTAGTCGGCTACTAAGGACCCGCGCTTTCCCGACATGCCAACACATCGACGACGGACTAGGTCCGAGCTTCATGTGTGCCACCAAAGTCGGCTAGACGGTGACCTTAAAAATCCCAACGTCAGTCCGCGGGCCAAAGCCGGCAGTTCAGGGGTGCAACCAGGGCTTGGTGGGATAGCCCCACTTAAGTTATTTTGGATAGCGTCTGAGGCGTAAGTCAACTAGACGTGAGTTCACCCAGGGGTGCTCCTATAACCCAAAACGAGTATGGTGCAACGTCGTGCCTTAAGACATAACTGCTCCGGAACGTCCATAAACTTTGCGTCTGCCCAGTCGTACCGTGGCGTGATCCTCGCAGAACCGAGATCGAAAACTCGTACTGACCGGCAAAACCACTGTGCGTTGTCTATGATATTCGCGACCTCGGACAACCCAGAACCAGCAATAAGGGGGTCCGTGTTGAGAGAGCCCGCATAAGAGCCGCGAATTATGCAGGAGTTTACTCGGGTTACATCGTGCTGTGACGTAAGCTGAGATAGGAGAACGGGCAATATGTTAACCTCCCACGTGCGTTCCATTATAGGGTCTCTCTTCCCGCGCGCGTAATCCAGAGGGGTCCGTGCGCATGGGTAGCACAGGACATTAGGTCGGGAACGATGTTTCAACCACCTATTAAGGCGCAGGAGCGCCGAGACAGCTTTCGAAGCATATCTAATGCTTTACGGGCCCGGGCAGCTACTACTTTACCCCCGTCTAAAAGTCTTCCCTGCCCGGTTCATTCTAACCAATCCCGTACACGAAATGAATGGGAATTAACCTTAGGGAGAATTTAAATTTGAAGGACGCACTAGAGGAGACATCGGGGGGAGGCGATCCGGTGCAAGTGAGTTCGTCCACGCCACTTGATAAGATAGGTTCATTCCATGACTTCCGTATCCGAAGAATGCTTCTTCGAGTCTTTACCGTATTAAAATTATATCTTCCGGGGATCTTCGGACGTGCTATACGTTCTGGGCCCTAAATAGCATCCGTACGGATAGATTTCTCCTTGCTCGACGAGCTAACTTCCTTTAACCCCACCGTAGTCAATGGCAACCAGTTCCTGTGATAGGTAAGCAGTCACCTGTATGGCCCTCTGGTGTCAACGATACATACTTATATCGTTGCAACAAATAAGAAAATGCGTTCCATCCCCTACTAATTCGCCCTAGAACCTTCAGTACGGGACCTCTAGACGGGTGTCAAAGGCTCGGCAAAGTTTTTGGATCCTTATATCAAACAGAGATAGTCAACATGTCGGGGAAGGATTAAGGCTCCATGCTCGAGCGTTTAACTGTCGTTGCGTGATAGTGCAGTCAATGATACATTGCGGTACCCAGTGTCAGGTTGGGATTCCGTTTAACGACTTAGTTCGTTTCCGTGCAGAGCCCACAGTCGATTCGCTAGTGTCAATTCAGATGCGGTGTCGCCTAGCCCCACGGCCCCTATCCGGAATGAACCGAGACTCTTACATGAGGTAGGTACGTTAACCGAATTAGCTCGCTCTCCACGAACACTGTCTTCGCACTTCACCTCGGGTATAAAGACTACTGCGCGACAGTAGCTGAGTCAGGCGTTACTAGTGTGTAGCGATCTTCGAAGTCTATGGTGACTCAGTCACGCTTGTAGCTATGAAATAGGAGAACGGGGTGGATTATATTGAGCCAGCCAGGTCCAATCTGAGGCTTTTCTCATACGACAACGCTGAGTACCACGCCCTAATCCATATCCCCCCCCCATTACTTTGACTGTATCCCTCTTTATGGTTGCATTCGTCAAGTATTAACCGAGTAGCCATACGTCTCCCTGAGAGAAGCGCTTATGCATTTGATATATAAAAAGCGTCCTGGCGCGACAGACCGATACAGGCCCCGTCGCAAGCTGGCGGTGAGAATTGACGGTTCGTCCAGGCATGCCACAAGATAGTGTCGGTCGTATACGGAGCATTAGCGATCACCGAAAATGTATGGTCCTCGCATACCGCGAGACCTAGAACGCACTAGTTCGCAGGCATCTGACAGAAAGGGCTCTTTGTGGGCTTGGCTAACGGTAACTCGGCCCAGCGGTCCGATTCTGACAGCACATACTTGATCCGGGCTATACATCGTCGATCCAGCAATGGTAACCAACACAGGGTTTATCGATATGAATCCCCAAGTACAGTGCCGCAAACTGCGTTATCCACGGAACATGATGGTTAGTAGGTGGCGACCAAGCAACAATGACTGTGAGCAACACATAGGTTAACACGCTAAATTCGAGCAATTCGAGAGCGTGTTCCAAATTTTGTCTTAAGTATATCTCGGAAGTAAATTAAATTTCACGTGATCCCACTATCATATATCTTCACACCCCATCTCAGCTGCCGTGATACCTAATACGCTTGATGGCTAGCGATGTTCTATGACAACCTAACGTGAGAGTTGAGAGTATTCTTGAAGGTGGGAAAGTTGAGGCCCTATCACGATCTTTACCGGGGGCTTATCCAAAAAATGCCTATGAGTATATTGTTGCGAAGGTCTAGGTGTTGAGATTTGTTACAGGCCTCAGCGATATGGTTAGATTAGAGTCCTGTTCATTCTCCCGCCTTGGTGTCCAAAGAAGCGCCTGGAACCGACAAGGAGGGGCACAAGTCTGCGAGGCCGAATCAACCTTCAGGAACATATAGTGTTTATACGTCACCCTGGTACGACATGCAGCCGTCCTGCTGAGCGAAAGGCAGGACCCGCAGCCGGTAACTCAGCGCGTTATGGGAGCGTTAGCTAGTTATAGTACAATTTATTTGATCAGACTACGCCTCCTACGTAGCTCATCTAATGTCCACTTTACATACCCACAACAAGTACATAGTGGGTCTCGCTCAGGTTAATCAAAGTGAGTACGAACGCGTGTACAATGTCAGAGGAAAAGACAATCAGGCAAGCCTGTACCCCCATATGCTTACGGCCAAGGCGGTAGTTTACATCCCTGTGGATAAGGCAGGGGGCCCGCGTAACAGCAAGGTGCCCTCAACGGCAGATCTCTCGGCCTATTGGTCTTGCTAGTTGTGCTGGGTAGTGGTGCCAATTGACTCGCGGCTACAGAGAGAGTGGCGAGTGCAGATCAATCTCGGATCGCGGAGTAACCGTAGCTAAAGGCTCGTCGGTGACGAACCCGTGACCATTGTATACGGGAGTTCCGATAAACTTGTCAAGAAGTTATATGTTACGATAGGCGGGACGCGTATGACTTGCCGATGACCGTCATTCCACCATAGTTGACCGGTTTAGGGCAATGGAGCACACCCCGCGCGGTTGCAAGCTGCCGGCAGCGCACGCGGTAATGGCTCAGGGAAGAATATGGGTGCACTACGCGTAGGTGGATCGTTAAGGTGAAGGCCTACTACAGCCCTAGCTCAGACTAAGAGGCTACGGCGCGGCGCCGCGTCCACTATATCCGCAGGGGTCGTCGTTCCAAACCGGGGGATGACGGACTCAGGAGGGAGCCTGCCCAGACTGGAGGAAGACGGGTTATCAAGGGGGTACCAGAATTCTAATGCGTAGTTTGTAAGAAATCAAGCGAACCCCCACTCAAACTGCCAGGGTGCCGAAGGTTGAAGCATCAGCATATCCCGCATTTTAGTGATAGCTTGGACCTACACCTCCCGAATATCCTCGTCTAGATGAGGGCGAGCTACGCGGCAGTTCAATCGACGCCTCAGCAGTATCGCAAAGCGACTACAAACGTGCTCTAGACAACAGAATGTCGCCTTAAATATAATCGAATGCCGTCTGACCGGAGCTAACGTCGTCCTCTCAAGCTCGCCACACCCCTGACGCGACCCCTAAGTGCTGTCGGGAGGTCGACGCTATGTTAACGTCCGTAACGGCGAAGTTGCATACGAATATTATTTTCGTGCCTTTGTAATCCGGAGTACTTGGAGAACCGGGAAACTTATCGCGCACAGGTCATTTCCCTTATGGGTAACTACGAGCGATGAGCAAGCAGCGTGCTGCCTTGAGTACGTTATCTCCTACATTACTTGAGACTGTCCTTTGCGGGGTCCATCATTGTTATCTCTCTCCAGCTGTGTGTTTCAAAGCAGTTAAAACAGTCTATATCGGGCCTGAGATGTTTTTTCTCGTAACGCAATGGTATTAGGGCTTGACTCTTGACTCGTTCCAAAGAAATTTCACGTGCATGTCGTATGCACAGGGGTATCTGAATTGCGAGCAGGAGAAATCTCGCTATCGGTTGGGAAGGGCACAATATCATCTGCGCCTAATCCGTATGGGATAGTATGACGATCTGGCGCACATTCTTAGCGTGGGATAGGATGTGTCGAATCCAGTTCGAAGACTATACCTGTTCCATTGCCAGTATACTACAAGTAGTCGGCCAACGTTACCTGAACAGGATCGCTCATTCTCTGGTATGTTTTCCCCGATGGCTCGCCCCACTCGGATAAGAAAACCAGAGCGATTTTTGCTTCGAAACGCGGCTCAGCAATTCGCGGTGTTGTACGCATATGGTGAGTCGGTCCAAACATGAGAATTCGTGTGAGGACAGAGATATCCCGTCGGAGGTACGGTTACTTGTATGGACATCCAAAGTAGGCTACCAAGTTCGCATTACGCGTAACGTAGCCACCGCCGCATTTGCGTACTGCATGGCCCAGTTCGAAAGGTTGATTATCCCCATAAACTTAAGTGATCAACTCTCAGGTGTTATTTAACGCTGTTATGGCGACCGACCCCGGCATTGGGTGCCCAAATGTACACGGTAGCCCATGCCTGAGGCGAACATTAAGGGCATCACTGTCAGTTTAGGCTGGGGATACCCCGATTGGGCCAGAAAATTCTTCGCTAAAACCATTCGATGCCTGCTTGGATATTACTAATGCGCAGTTTACCCTTCCACGAGATCGCCGGGCTCAACCGTAGCTGCACGGAGTGGGTGGCTGTTTGGTTTCCGAGGACCCGGCGGTTACCCGCGCGGGCGCTGTGGGAAGCTACGGCGAGCCCCACAGGCGATTAAAATCTTTCTGCTAACGATGCTGGTAGTACTAAATTGCTGAACAGTTGAGTCGGTCGCTACTGCCCTATCCGCATGGGAATTCTGTATAAGTGATTTCGGACGTTTTGTGGCCCGCTGTAGAGCCGAAGGATATGGAGTTAGGTGTCCTGGACTATTTGTATCCAAGCTCGCGTTGACTTCTATTACACACGACCTCTCTGTGTTGATCAACGGGCGTTGACAACGGGTTTCTGTACCCCCTGCTCGGCGGAATCGGGCCCTAGTCCACTTACAGCGAGTGTGGGCTTGGCTCTAGTTCATGATCCGACCTAAGAATGTGTCCATGCCGCCCTCAACTACAATAATGGTCAGTCCGATACACTAGCCTGTGCAGTTAGTCGGTCTAAGGTCTGTTCGCTCAAAACTACCAGTTACGTTAGGGCACCTAGATCCCCGCACGCCGTGGTCGTCCACCCCGCGGTCGGACGAGATCCATATAACGAAAGCATAAAGCATTTAAAGTATGTCTTGCAGGGGTCCGTCGTTGTCGCCTCAACGTTGTTAATTTACATTCAGATGTTAAGGTTAGCGGTATAGCCAGGATAGGAAGGAGATTGTCGGCTTTTGCGTCCACAGCAGTTTACGAGTAGCATGAAGGATTAGCACAATTAAGTCCCAGTGCCATCGAGTAATAGCAAAGGTGCCCAGAATGCATGGTCACTAGTAGTGTTAAGAGGCGCCTATTTAGGTCCGAATTTAAATGACGTTGTGTGAGTGCATGCCTTAATGATTTGTGGTGTTAGGCGATATCCTCCGTAGCCAGCTTGAGGCTCCTGATCAGTGTGTGTCCCCATGTGGCGGGCTATCCACACCGGATCTCTCCTGGCAGGTTTAAATGCTCCGGTTCGTATTTGGTCGCCAGCAGTACAGATATAAGTGTTCTAACATCACGGTCCGCCGATTAAACCCGCTACTCAGTAAATATCCACGTAGCTGCCAGAGACTTCCTAGCGCCAATCTGGTAAGGACCCTAAGCGATCCTACGTGCTAATGGCTAAATCTCAACAACACTCAATGTCTCTTGAGTTGATCACTAGACCCCGGATGCTCCAGGATTGCTAGTTGATGATTCCGCATCCTAGACCGGTTCAAATACATCCCTAAGTACCGGGGTACGGCGCAGATAGCCGAGAATCGATGACCAATAGTGGTCATAATACAGCGAGGAGGGGCAACATGCTTCACTTATAAGTAAACAATGCCGGGGTTCCATATAAACATGCTTTTTTGGTTGGCGCAGTTAAATTTCGTCCGTAGTAGAAACGCTCGTTAGTTATGTTACGCCAGTTCGAGGGTTATGCTCGGTACATGTCCTGGCTCAGTCTCGCTCTTCTATTAAGTGGCAGGGTTCGAACAGGTGCCCTGACAGTGTTGCACATGCCTCGACGCTTCCCTTTATAGCACTACTACTAGGCTGTATAGCATCGGAACCAAGGTGTTCCTCGCCTAATTTGGAGCTCGAGAAGGGCGGCGAAACACCCAATTTGAAATACATCGTGTGAACGTTGTCGAGAGTTCGTAGTGCGAGAAACCGATCAAGATATTGTGTACAACGCCCAATAGCCTCCTTCGCGATTATTCACCATGCCTACTACGCGCCGCTCATAACTTGCAAAGGCGTAATTCTTATGATAGATTGCGCACCGTGACCGGTTCAATCTTCTTGGAGACACATGACTAATAGCTTGTATCATACCACTTTGACTTTCTTGCTTCAGTTTTGTTCCAATTAGGCCTTGTGAAGACGCCTGCAGTATAAATAGTTGCTATCATCCATTTGTGATATATGCCGACGCGACCCAGCTGCAATATTGGCGTGTCGATATCGTAAAGGACTAAGACCATCACGCAAGACCGGTTTATTAAATGAGTACATTGCCTTTCAGTCCCCGTCAGTTCGCGTGATTACTGCCCATTCTTCCATCCTGATCCGCGTCATGTACTCAAGTACCAGAATGTGAACGATATTCCGGTAATCTTAGTGGGGGAGTATAACATGCCGTCACAGCTCGCGTACGGTACAGGCTAAGTACACGTAATGCTAGTGCAAGGGGGCATTATGTCGAGTATCGTTTCGTGGGATTATAAGTCCCTTGTTTCCCTAAATTCTCCGCGGCGTTTCTGTATCATTAACTTCAGAAGAATTCCGCTCGTCCAGGTGAGTGGTCTGGTATAGAGCTCCATTACAGGAGTCTTCATTTAGGTTCTGGATGTCAAGAACGGACCAGGATACTTACTGTAGCATGGTTCAGCACACCCAGAAGAGCCCTAGGTCCCTGCGACGCCTGGTGCAACTAATATAGAGGAACCTAGATTATTTGCGTCCATATGTTGTGAAAAAGATGCTATGAGAAGCTATGGTGCTTGGGGGCGTCGCTAGTCGTGCATAACACTGACGAGATAAGAGCCCGGCGCGAGAGGATCAACAATAATCAACACTCGCGTGCCCCTTGGGACTCAGCAACGACGGGTGGCTATTCTACATCCCGCTGCTAACGCTCCACCGCACCCTCCCGGCCTTCTGGTTCAAGGGTGGCTTGAGCCATTAAAGACTCAACCCCATCTAAAATAAATATCTGCGTAGACACTCTAGCGTAACTGCTCGAGGAGGACTCGTACGGAAGAATACAGATTCGTCTTCTGTCACCCTATTTAAGAAGGATATGGTAAGTGCTAGATTAAGCGTAATAATTCGCCTAAGATCATACGTTAGCCGACCTTCATCAAGGGAACGGGGTCTTGATTGCGTAGTGATTCTCGATTTCACAGTCGACATATAGTCGTGGAGCCCATGTAAAGTCGTTGTCGGCGGTTTTCTCAGCCTCGATCTACTTATCTGGCCCATCTTTCGTAATTACCTGAGCGGCAAGATATATTACCCCTCCCACCCAAACTTTCGTTAACACTAAGGTCCGCCACGTAAGTTAAAGTTATGGGGGCAATCTACGCAGGCGCAGCCACCCAATCGTGACGGGTCTGTCGCCCACGGCCTGCAAAAGGCGTGGGTTAGCTCAGTATTTACCTACATATTGGACGTAGACAGCCCGCCGTGATGCACTCAGGAATTGGGTAGTAAGGTCGATAGCTAGACCGTTACACTGGAAACTGAGATTAGTTTGTGCCAGTGTGTAGTCGACACACACACGTAGATCTACTTCGTCTAGCCGCGCCATTAACAAGCTTCCAAATGTGTCACTGCCCATTGGGCCACGGCATCCTAGTTGCTTTTAAACCGCCACCCAATTTCTCTGTCCACATGCTTAACATGAGAGTACGTAAGTCTTGCCGCCTCACACGCGTGTCCACTGGAATAGAGCTTTTCATCGTTTAAACTATAAAAACATCTGCAGTAATCATCGATGCACCAACCTGCGCAGAGTCAACACGGAGCCTGTTTCGGGCCACGTCACGTATAGGACGCTGACACCTGAGCAGATCCAACTCCTGGCAACCCGCAATATAAATGGCCCCGCCGTGTGAGGTCGTGTCCGGGATAGTAGAGCCGTTTATAGCTATAAATATCGGTACCAATGCAGAAGTCAATCGTCGTCAAAGTATACAATGTTGTGAGTATCACAAAAGCTTCCCTACTAACAGGCACAGCGCCGCGCGCGGCAATAGGGCGTGCACTCGAACGAACCGCATCGCAAGGCACATGCTTTGCGCTCTACCCGTAGCTAACCATTCACACACCGAACTTGTATATCTCCATAACAAATCTGTTGGCAACTACAGCGGTCTCGAGGACTATTAGCCACTACGGGGGAATTCAACGCTTGGAGTCAGGATATACTACCCAAAATGACTACTTTACTCCTCTTTAATAGGCTACATAGAGGGCTTTCGAGTGTCTTAGCTGAACTCGATTTGAGTAACGTTAGGGACTAACCGTCTCAAGCCGGCTCTTGTTTCAAGACTCCCGCGGCAATACACGTCTCACCTTCCTCTGACTCGTGATCAATCAGCTAAGGGCCTCGGGCACAAGAAAAAAACCTATTCGTCTATCCCTCGCGGGGGAGTTGCGTCGCTGCCAGAGTAATGTTTATTGTCTTCCTAGGATAGCGGGTACGAATCTCGCCTATTAAGAACCGTCTAATGATTAGAAACCGTTTTACCTCTATTCGAGCATAGTTATGCAAATCTCGCCCCAATGTCCGGAACGTGCTCAGAAGATCTATGGCGAGAAGGGGAGTTTAATACTGCTGACTATGCGTCGGAATTAATTCTACTCAAACTGGAGTTGCCAGTACTCACTAGACATCTACGCCTCTGGCAGCGGATTAGCTTCTATCAACGCGCACCGCGACCTTGTTACCCTGTAACTGAGTACTTTCGAGCCCGGTGTTTCGCCTGAGGTTAAGTTGACACCAGACAGGAGGTGTTAAGACAAAGTGAATATCCTCAAACCACCTCAATTGCGACGGACAGGGTGTGTGACCAAGAGGGGGGACAAAATAGGAAGCTCTTAGTTCGAGAAAATGCGCTATTCCACACAGGCCTCTAGCGGTGTAGGAGTCTATACCACGCGTGTTCTCAGTATCAGACGAGTTGCATAGAAACGTGATCGGTCAGATAACGAGAAGAACTGAACTCTGGTTAACATGAGTTGGTCGGTCCCACTCTAGGACGGGTAGCACATGCACTGAGACCCGCGGGCGTCTTTACGGTTGTATAAAACAAAAAGGTCTAACGAAGAGCTTCAAGAGATCAAGGCATGTGAACGTAATATGAATATGTGACGAGGCAAAGGTGCCCTAATTGGACAGTTGATCAAAGCATGGACTGAGAGAGGGGCATATAAAACTGGGTAATCGACGCCCTGCAGTAAGCCCTCGGCCCGTTGTAACTGGCCTCGCCGATCAGTTTACCATCTCTCTGCATACTAAAGGCTGGTTCGATTACCTAGCGTTACCCGACCACCTATGTTGCCAATCCTAACCGATTGATGTAAGACAATGGTTAATTAGCCTCAGACGCACAGCTCCGCAAAGAGGCTTCAGAACTGTCATGAGTTCAACGCATTCCGGCCAGGCTCTGAGGAACATCCCTCATGAAGTCCTGGCAGTCTTCGCGACTTTACTGCCATCTCCGCTAACTGGTGCTCGGTACTTCAGTTAGACATGATAGTGGTGTTTTAGAAAAAGTGTTCCGCACCGATATCCGGCAGAACGCGTAAGCTGTACACGACATATTCACAAGATGCGGACGCTGTGGCAATAGTCGCAGAAAGTTTTGAACATGTACAACGACGATCATTAGCGCAGAATGGTGAGCGTCGGACTTAGCTCCCAAGCGATTTGCTAGAAACAAGATGGTCCGATCGACGACACGCACCCCCACAGGGCTGTTCTTTCTGGCGATGCAGCCGTATCGAAGCATATCATATAATTTATCATAGGCACGGGCCTCTCACCAAGGGTCTAACAGATCGACCTTGGCCGACCACGAGTCAATTGGTCTCAAAACGGTCACTGTAAAATAAGGCACTAACAACACGGGGACCGGTAATACGGATGCCTGCGTATACTCGCTATACGATCTCCGGACGCACTGGGATGACACCGTCGTACACTCTTCATCAGGGGCGGTCACCGCAGTGCTACGGTGTGTCTTTTTTCACGATACGTAATGTAGTACCCATTACACGGACTCTTCAAAGGAGAAAATGGAGCGACCGCTTACATTCTGTTAAAATAGTTAGATACTCCCTACCGGTATTATAGCTCACCTACGGCTCTACCACACCGGACTCCCGTTGTTCGGGATGTAAAGAGGGGGGTTGAAGTATTGCTCAATAGTACTTCGGAAACTTAGAATAAAATGAGGTCTTCTCTGATCTGCGCCTAGATCCAATTGATGAGCGGTTCCTGTGAAACGCGGTACAATTCGACAGGTGACCAAGAATGAACGTGGCAACCACTGCTCTGCACCACGAATGCGAAAACATACGGAGTCTCCGACACTCTAGGATGACAACCCATAACTTCAAATACTAATTCACGACACGGTCGGGGGGCGCGTACCGACGGAGAACTTTCTACAGAGAAGCCCTGGCAGACGTTGTGGGGGCCTACTCTGCACGTTGGCGTTAGACAAGGCGGGACCAGACTTAGATCAACTTGTCTCCACTTATCGGTACGATACGGCCGCGTCGCACCAATTAAAATCCTGTTAGCCATCCTCTGGATGGGTGAAAGGGACAATGGCGACTATATGAGGGGCGGCTAGGCGCCGCTTATGTCGGGAGACCGGGGCTTACTCAGAGGATGACTGAACTACAAAATGCTGAGATAGCTTGGCGGTTGCATTTTGAGGTAGTCGTTGGGGAAACCTCTCTTCGTCTTACGGGATGAGTTTTGGGCCCAACCCTATATTGGGGCTCGGTGGTTGCTATTACTATGTCCCTTGTAAAAGCCTTTAGAATCTGACAACCAAACAGCCTGTAGACTCGGTGTGAGCCAACAAGTAGGGCGGCAGTGATCCTAGGTCAGGCTATTGTATGTCAATGCCCAGGAGTTGGGTCCCCCATTTGGTAGACGTTATTTCGACTAGCTCCCGTCGTACTTTGGTAGACGGACGGACGTCTAGTGCGGGTGCAACCGGTCAAAGATTTTCAAACTCCTCTTAAATGGGAGAAGGGGTAACAATCCGCTTTGAAGGCATAATTCGGTACCCGACTTATACGCCACCATTAGCGCGAACTACACATCTAGCTAGCGGGAGCAGCCCTAAGTTAGAGAATCCATCCGCGATTACCCTATCGATCCCCATCATCCATTCTTAGTATGTCCCATCCGCAGACCTTTAACACGGAGAAAGGGCTCTGTCCAACGCATCGCGTACTCAACTCATGCGCGGTCACAGTTTACAGATGACTAACACGAAGTAGGCGTCGCGCCCTTCACGGACATGGGCTGTCAATCAAGAGGTTTTGAGTGCGCATCAAGCGGGTATCACCTTCGCATTTAGCGTAGAGGATTTCTTCGATTATATATCGTCATGCAGTTATCGCCAATGGTGCATGGTGGCCTTTTTTCGTTCAGGTTGATGCTCGCTCACCTTCGTGCATACTTGCTTGACGTTGTGGAATCGAACCGAGTAGGAATAATTTATATTACCTCGTGATACTACCGATTGGACGTCACCGAGCCAACGTGGTGTACGATGTAGCCCCCTTGCGCTGACCTTCCGTTTGACTAGGGTAGATCAAAACGTATGCTGGTTTTGCCTGGCCGTGAACCTACGGATACACACACATTTACGTTGCTTATTAGGGGATTCCAGGTCCACAGCTTGCTCGGAAGGGGCGAGTGAGCGTTCCTTACCCGGTCCCAGGGGGGGCCTACACCTGCGGTACTTCCTACCTCGTCGAGTAGGGCATTTGGCCCGCGAGATTCTGAGAGTGATGAGTTCGCTTCGAGACTAGAACATCCCAAAGGGGGTAAGCCCGAAGTGATCCGCTACCTACACCGCATCATCCGTCGACAACGGGGCGGTTCAGTGGATGGACTTCTGCTCGGATCGTTTTTATGGTGCTACCTTACGTCCTGCAAAACTTCCAAAAGCCGAGCCTCATATGTGCTCAGGACCAGCCGTGACCGACATGTAAGCTGCCCCTGTCCTAGATCTGGGATCCTACCAGACATAGAACATGTTTTAGGTAGCATTGAGGTCGTTACTAATACAAGGTTCGTCCGGGCTTGGCCTTGGTACTTAAAGTGATGTTAGTTTGGTCTAATCCACGTATTGACGATTGTGTGGCTTGACTTAGATCGGCGGTGTTTGCCAGGAAGTTAGATCAGTAGATGTGCAGCTAAACAATGGAGGTGTTCGTGGCCTCTCGCCACTCTAATGTTGCCCACCTAGGGTCACAGGGGTTGAAGCGGGCTTGGATATACGTTGGCACTATACGTCCGGAGGACAGTAATGCCTCTAGATACAACGGTGGGGGGTCCTTGTGATGTATGGTTGATTTTGGCCCACTCACTTCCCTGCACCGGATATAACTGTGGCGATGTCCCAAATTACTGGATTCGTACTGGGATAGCGCGCAGGTTACATAACGCTTGGACGAGTGTGTGTACCCTGAAAGCGCATACAATACTGCAGGCAATTGCTATCAATACCCGTGAAGGTAAACGTTTGCCGAAGGGTGATTCTTCTACCTCGTCTTAGGCGTTTTAGCCGGATCGCGAAGACAAGAAATATAATCGCTTTTTCGAAACGTAGTAGATATGAATGGGGACTTGTCAGTTCAAATTAGACCTACATAGGCGTTATTCACGCAACTGTAAACACTTCCGGCAAACTATACACCCCAACGTTTCAAGTCGTCGAGGTTACACCATCATTCAGCGACTACGGACTCTTGTATCAGCCTAGAGAGATAAGTGCGAACCAGTCTATAAAACGCCCAGCCCAGGAGCTTAGGGCTCTAGGATTCTTCGTTGACACCCGCGGTCACTAAATCTCTGTTTGGCGAATTAGGGTCAATCGCTTCTGCTCGAGTGTCCCGGTGATGTTGCGGCTGCCAACGAGGGGCCCGCCCCGCTCAGGTGAGGTGGGAACTGGGCCGTCCACTTTAGCGCGATACACATCTCTACGATTAGGATCGGTTTTTCATCATCAGATCGCTCGATAAACTATATACGGGGAACCACCTCTCCTGACCATATCCAATAGAACATGTTGCCTAGGATGGCGAAGATTGCATGTTAAATTTTTAGAATACACCCTGATCGGTCTATAGGTATGACTCCCTTGAGACGGAAAGGAGGTGTCCTCGTTGTGCCTCTATCACGCACTAGCAAATAAGACCCCCCGCGTTTCGGGACACTTGGCGGCTTGCGACTCGTAGAAGCTTTGTTTCCGGTTTATCTGCTCTTATTGGAAAGTCTTTGCTGAATCCTGCCGGGTACCATGAATCGCTATTCATAACCACTGGTTACGACACATCAGAGTTACATTGGTCTTGGCATGATATGGTGCGCTCTCGCTGTCGGGATCACTAGCGGCAGAAGCGTGGAGCCACAGCTTAGAAAAAACCACAAGCAGAAGGGCGCTAGCCACGTCGTATCCCTGGTTACACTTCCCTACGTCCCACAGTAAAGTGTAGCCCTATCGCCAAGGCTGGTTTTCTAATTATTAGACGAAGTGCAGGCCTTCGAGGATCCGGAGGCACCTTCTTCTGGTCGTATCCCAGTAAACGTGTCCGGGACTCAGGCATATACTGCCCCTATTAGGTTCTCGGAGGTCTGAAGCTAAGCAAAATATGACCTTGGTTACGTACCGGGCCCGGATTAATGGACATGACTGGTCATGTGATCCCTATGTGACTTCATCTATTTGCGACGGCCAGAAACGGTGGGTCGATAACGAGCATCTGTAAGACGAGCCGACACTCCGGCAATCGATTGTACAAATGGACTATTTTTGCAATGCTATAACCTGTGCGGACAAATGAGTTTGAAACATCAAGATAAGAGTTCATGGTAACTCACAGTTCAACAAGTATCAAGGTCCGTCTGCGCTCGATGGACAATGATTGAGCACGGTCATTTGATTGCCGCTGAAGAACTAGAACTACGTAACGTCATTAAACCACAACCGACACCCTCATTACTGAGGACTTAACTGGTCGTCGAAAATCATCTTATTAAGAGAGAGAGCGTGTGCGGTAGGCCCATCTTTCACAATATTACCTGCCCAAATCTAGTGGTCTATAATCTCACGGAGTAATGTGCGACCGAGCGTCCTTTGCCCTCCTGGTGGTGCCGACGCGTCCACCGGAGCCAACTCCAGCTGTAGAGCACATCAAATTTATGGTAACACAACAGACCACCTCGACAACGGAGGTATAAAGCTAACGTTAGTAATGGACAGATAAGTGTATCAAAGAGACGAAAGTTATACCCTCGGCCATGACTCAACGCTCTCACATGGTAAAGAACTTACTTACATTTGTAGCCCTAAAAGAAATCCACGCCGAGAGGTTAGTGGTAATTTCGAAGTCTGGCTGAATCACTACTCGACATGCCAACGTAGCGAATCACGGTCCGTTCGATGAAGTGGCGCACGGAAAAGTCTTGCCCGGCTTGCGCAAGACCCGCGCTCGACGGAACCGGCTTTCACACATGAACTTAGTTTACGGAAATGCGCAGCAGAGTCCGAATAAGATACAGACCCCGAAGCTCACCAATGTCGAGCTATAGTGCTAAGACAGACCAATCAATCGGTCTCTCTCATGACGATAGGAGGCTGTGCGCACGAAGGGACATCATCGACATGTTGAGAAGAGGTCTTTTTGTGCAATACGACTTCATACGTCAATAACTAGTGTCGGCGTCGAATAGCTTTTACATCTAAGATGTCTGTAGTTCGAATGTATAATCGTACGGCGCTCCGGGCGGAGACTAATAGCAATACTAGCGTCTCCATGTTCTGAATCGTTGCCGCTTCTGTCTAATTAAAATGGAAATTTTATTCGAGGCCCTAAGATGAGCAGTAGTAGCTCATATGGTATCGATAGTCGGTACGAGAATTAATAGTCGTCTATAAAACTACTCGATCGCGTCGGTAGAGCCCTGGGACCTCCGTGACGTGTAGCTGGTTTAGCAAGGCGCAGTATCTGAAACTCCAGTCAAACGTCAGCTTCGTAAGCGACTAACCAGGCAGATGTGAACATCTCAAGATGCTAGAGATAGAATCATGAAATCACAACTATCGCGATTTGTAACAGTAAAGTATAGTGGTAATAGTGTGCTATCCTAACCCAATGTCAGTGTGGGTCTGTCTAAATAACACCATGAAAGGTGTTGGAACCCCCCCTAATATTCTTGCCGGACTGGAATTAGTGTAGTTGTCTCTGAAGAATTCCCCCTAGACTGATAAATCATCGTCGTACTCTAGAATATCTATTAGATTGAAACGCGAGAGCAACGAACAGAGGCTTTATACCGGTAACAGACGCCTCCAATATTGCTCACGACTCGCTAACACCTACCACCCCTCTGTAGATGTGTTCGGCTAGGAGAGTCCGTTTTTCGTCTGGCTGAACGCCTTCAGACACGGATTCCCGAATCGCTAGCAACGCCTTCGCCGAGTCTCTGTGGAATAAAACTACTGTGCCAGTCATAGCACACGCACACATTTAGCATAAGCTTCGAAACCCTAGAGTAGCAAAGGAACCTTTAATTCGCCCAAGGACACGGTGCTTAATGACGAGAGTGAGTGCTCATGAGCCGACGGTTAATGAACCATAAAGCGCCACTCGATCTATTTGATGAGAGCGGGTATGCAGGTGAATGGCCGTGGATAACCAACGCAGAGGGAACTGCATCTGGTTCGTTTTTTCGCGGCCCATTCGACCGATGATAACAGCAAGAGACGGCTGGGACTCCTATACAAGTTCGGCTACACGATGCTTCCTTGGGTTGACTGAGCTCTGCACTATATCAGCGTGACAGCGGCCTCCCGATGAGCAGTTTTAGCTTAATGGATAATGACACTGCTTAATGTGGCACTAACCCATCGCGTCGTACTAATAACCGCTAACTCCGTGTGTTTGCTCCTACGGGATATCCGCAATTAGTTTGAGAAACTCCGGGCGGACGGTCCGGGCAACTAAATACTGGACCGGTTATATCAGTGTGCGGAATGGTAGTGCGATTTCTTCTCAGACACCGAGGTCACCCAATCGTTTACCCTTTCTCGTAACGAAAAAGCAGGTGAGTGGGTCAGTGTCCTTGCCGTAAAAAGACTCGTCTTACATGCAAAAGAGGACACTAACAATGTTCAAGGCGGTCCTGACCCCAACTTTCTGGATTCGTCGGTACTATCGTGCTCATATCCCGGGCCAATTTGGTAGAACGCCTGCGTCCAAGTTTACTCCTGCTTACCCTCCACTAAGGACTGCACGGCGTCTTGCGCTGAGAACGAAATCTTATCAGCCTCTCCTCCCAGCTTAGTGTGGTAGTGCTCGAACAATTGCGTCTGCCAGTATCATTTTGCAAAACGGCATAACACTCAGCCTCGGATAGGATAGGGGTGAATCTGCTGAGAGTGTAGCTTAAATAGGTTGGTTCATGTGGTAATAACCTATTGAGTATATGACGACGTAGCCCAGTGCCAAACAAGTTATTAGGGCATCAACATCGGCCAAGGAAGGTGCGTCCGTCTCCCCTAGGATATGGACGGCGTATATTAGTGCACCTTCTGAGTATACAATCAGTCGGGAGTCCTCGAGAGAATCGGCAAATCAAGAGAGGAGGTCGCAGATGGAATTGCCCCGTAACTCACAGCGTGCCTTCTAAGTGACGCATGTACGTGCGAGTCCGAGAAAATTCGAAAGCATGGAGCAAAGGAGACATGGAGAGTCCTTTTCACAATCTATGACCACGTCCAGATCCAATTGAACTTGCGAGTTGTGTAGTTTCCGGTAAGGTTCCCCCCAATACTAGTTCGTTCATCCAAAGGGCTTCATCTGTGCTGAGAGGGATTACCATGACCGTGAACCAGCAACATGTTACCGCGTGCATAGGAGGTCGAACGATTGACGCCATCTTAATCATCCGGATGTTTGACGCCTTCAGAAAAGAACCCTATGGCGACGTACCATTATCACGGGGGTAACAATCAGGATAGTGGGGTTGGCATAAACCATGCTGCAGTACGCCAGAAAACGCTGGGACTTTGCCGCGGACCTACGTGCATTACGCAAGGAAAAATCGGAGATAGCTATTACCCGCCCTCCAACTAAAAACTTAACCATCTAATATACCTGATCAGATTGAAGGCCAGAAACTAAGTCCCCGATACCGCATGTAACGCAAGGTTAGGTGGCTTAGATAAACAACTAAGTCTTAATGATTCTTCGAGCCCGTTCTTGTAATGACTCCAGTTGCTAGGGGCGATCGGATGACGCCCATCATCGTCCTCGCCCCAGGCACTAATCCTTCCCAACTAAGGGCCGAAACGTATGTCCGGCCTTGACGCTTTGAAAGAGCGCCGTAAGCAAATCATGTTGGCGGGTCTCGATGATATGTTCGTTAACAAGTGGAAGACCGTCTCAATCCGGTAGCATCCCGTTTAGGAAATGGGGAAAGTTCGTGCCGTTGAGCCCTGCGTCCCGGATCTGTATTTCAAACCCTAAAACGACTCCTTTTCTTCGCGCACCTATTGCCGCGATTATATACTCTTTGCGGTTGAACCATAGCAGTAGGTTCACCAGGGCGGTCCGGGCTAGTACCGCATTGCATTGTTTTGGAACGTTGAGTGTGGCCACGTGATGCTCCGGGCATGTCGCCGGCTTGAGGACCTAACCATAAAGTTACGGGCATAACCCCAGTCCTATTGAGTTCGACCGGTTTGTTAATATCAGTAATGACGATCAGTACCAGCTTAACAACCCGACTCGACATCACCCTTTTTTTATATACGGTAACCCATTGCCAGTAATCTACCTCGCACTGTGGCGTGTAGGGGTCCCCTCAGTCAGGTCGATCAACTTTGATCGTGCATCCTTTGCTACCAGCACCGGTGGGGGTCCGCTTAGCACGCATCTCCAGATTTTCCCCATTCTGGTTTGCTTCGTACAGGCCATGGATGGGGTGGATTCTGTCGCACGGACACTCATTAGCCCGAAAGATCCAGGAGACAGGTACGACGTGACATATGATGGGGGCTCCTGCGACACTTGAAACGACAATCGCCCCCCAGATGTTGAAAGTGCAAGTTTGACTGACCCTTCAGGGGAAAATTTGGCTCCGGATGAACAGGAGCCTTCTCCGTTTATTGAAGATACGGTGTTGATTATGAGAAGAGTCCGTCTTCGAACGGTTTGTGAGCAGCGCGGTGAACCGGACTCGGGACGAGGGCCGTCAAGTAGGCTTTCTAAAATTTTACCACGTGATCCTCAGATGGACGTCTTGTATCCTAAAAACAACGCTGCAGAGGCACGCACCGCAAAAGTCCTATGGCGACAGGCGTGAACAATTCCGCTAGCCTTGAACTTGGCGAAGGGTTGGGGTCCTTTCTCTATAGAGAGGGCGCAGGAATGCATCACACAATCCCGCTAGATACCAAGCATCACCGAACCGTTCACCGTGTCTCAACTTTGAAACCCGCCCGAGTGTTCTACGCGTGTACTATTTGTACTACTCTGCTAGAAGCGACCCGAACCTCCCACCGGATGTACATGAACTGGCACCAGCGACTAACGAAAGATACCTATAATCTTGAGATGAAGGGGTCATAGCCACTCAGAGTCTCCCTGCGGATATCTTGCGGTAAGTCGACGCCGGCCTTTAAATGCAGTGCGTGATACCAGCTAAGTGGCACCCGGTTTCAGGAAGGGAGCTTCCGGTCTGGATGGGTGGGGAAGTCTTAAGACTTTGAAGTACTATGTTTTAACGATAATCGACCGGATCAAACCACCGCTCACTGCTCGGACATTCAACAGTAACGTACGCTATCTTCTACGGCGCTGGCTACATAGCCTTATTGCCTGACAAGTTAGGATACTTTTTGCAAAGGCTGAAAAGTTACGAGACCCGCATTTCTAAACTTCTAGAGATATGCTACTGGTAGCATTGCGTGGAACGAAAGATCAAAGTGTGGCATGGAGTCGCCATGCCACCCATTCAACTAAGTATAGCGGGGACCGTTAAATGGTGAAGTCCCTGAAGGCCACCCAATTTGCCCATGTTGATAACTACTCGAGCACCGCTCAGCCTTTCTCCTTGCGACTTGGAAGCACAGTGTGTCCGGTCCCTAGCGAAGGCGGAGCGCGGAGAATGCGAAGAGGCCTACCTGCCAGCGATTTAGAGCCTCGCGGGAGCGGCATTAGCGAGATCGTACCTGTTCAGGTTGCTACTTAGAGCATCTGGAGACAGATGGACGAATTAAACCTTTAATTTCGATGCGTCGTCCCTCGGTATGCCTCAGCCCGGCGTGGGATGGACCCTGCGTTTATCACAAAATGGCAATACCTATGCGGCTCAGGTTATTTTCAAGATAGCCCCCGTTCGTACCCGTACAAACCGCCCAAGTAGTACCGCTCTGGTGAAACGCGCTTCCTCTATGTAATGCTGTACCTTAATCGTCTGCCGGTTAATTATTGGATGTCTCTGAGTAGTCATACCCTGCGTTCAATCCTCGCGTTTTTTCCCGGAGCTTCATCAACAGCAAAAGGAAAAGTACATAGTACTGTACTGAGGGACAGAAATGCTGATACAACCGTGACAAACTGTGGGTTATAAAGAGCCACCACGAAACAGGATTACTAGCCCCAGTGGCGTTTGTACGCTCTCGATATTAGGGATAGAATGTATACCAGTTTGTCTCGTATAGACCAGCATATAATTCGCAATCCATTACATGTGTCAATCCCGTACTTTGCACACTCTGTAGTTTGTAGCAAAGGCTAACCTAGCGTCGCCAAGAGGCACTTAACCGCGATTTGGCGGCATTGACATCTAGAGTCTATCCTCACAATTCTACGCGCTGGCTACGGCTGCTGAGTCAGCAACGCCTTACGTCGTCCAGCGAACCACGGGTTACCCATTCAGATTGCCCGGCTGACTGGAAAACGTGGTAATTGCCGTACTCCACTCGGCATTAAGAAAATGGCGACGAGAGAACTTATGTAACCGTTTCCTTCAACCCTTAGATAAAGTTAGCATCCATCCATATGTTGAAAGGGCCTATAAAGACGCACAATGAGGCCGTGAGTCTTGCGGTATAGCTGACTTGACGGCAAAGTTACGCAATTGAACACGTCTACTCCTAGCTCCTTGTCAGTTAGTGAAGGACGACTCCTGTATTGACACCGGGCAGCTATCTAAACAGAGTAATGCGATCTTGTGTACACATTGGGCAAAATTGATGCGTCTTATGGTCTTCGTCACCTGCCCGTTTGGCTATGTTTAACATGGGCTGCTCCAGTATAGGGCGCCAGTGACAGCAGAAATTAACCAGTCAAACTTGCCTATACAGGTCTGATCGATCAGAGCTGCGTTGAAGTAAGATCCACATTAGCAGTGCAAAGTGCGGTGCTGCGCGGGCAGGTCTGGATTCTTCGACCTTCCACTCTTGACATCAAACAAAGGGCTGCGACAGGGAATCTTAATGGAGTGATCTTTGTACGAGTTTACCAGTATATCCGACCTCCTGATCCTCCCCGATGTGAGTCGCTACTAGACGGTAGGGCAATCGGTCGTATTAGAACCGGACTGGGTTTAGTTTAACTGGCTGACAGCCAGCGGGCCACTGGTACGCTAGAATAGCCGACAAGGTGTCGTAGATTCACTTAACCGGGAGAAAATACGGAGTCATCTTATGCAACATAGATGCACGTCAAGTCCTACACAGAGCCATTCAAATGAATTCCACTACGTGCCAACATAGTCGTTTTGGAACGAGCGTAGCGACGTAAATGGGCTCTCAGATTATAAAGGAATGCGGCCGGACGTACTTCCCCATCCGAACTGCTAACAATCAAGGGAGTCGGCGTCGCTCAAAGGGATCTGACCTCCTACTATAACATCGGGAAGCGGAGCAGTTCTTTGCATTATATGGACTCCCTATGTAGGCACGAGCTAGGAGTACATAACAACGATCTTTCGATGTTCCTGCCCATAAATGGCAAGACCGGGGAGGATATACCGATCACGTCATGGTTTTAGGTAAGATTTACCTGAGCAGCGCTGTACGAGTTGGATGCGCAAATTGTTCACGTCATAAATTGTGCCTGCTTCCAGTGCGGTAGCAATGTCTAACTAAATTTGGCTTGTCTGCCACTGAGCCACACCATCAGTCGATAAAATTTGCAACGAGCATCTTGTACAACAATCTGGTAAGTTACCTCTTCAACCAAGGCGCTACGAGTTACTAACAGCATATGTAGGATATAACCTAAATGTGGTTAAGAGTTCGAAGGTTGCGAGGTTGTGCAATTTATAACTACAGCGTTCGCTACTGAACCTTGTTTTTACTTACCCAGTCGCCTAACCCGGCAGTTGCGACTTGGATATAGATTAACTGCCCACCTACCGATACGGCCGCAGTTCCGGGTAAAATCACCCGTATGGGTATTAAAAAAATGAATAACACGTGACGTGTCCTGGCTTCAAGGTCGATGGGATGGATCCTAGCATTATCCTTATGGCGGTGATTCATATTCACAGCGTGGCCGCATAGTCTCTAACCCGGACGGCAGGCGATTGTCCCAGCCCCTAACCTTGAAATCGTGGGTGGGGGCGGGAATATTCCATCCTTCGCTCCCCGTCGGGGCGGGAGATCGGGCCATATCTGCCGTAAGCGCCACCCGGCACATAATACTTAATGATAGTCTCTAATTACACGCCAGCAGAGCGCTTGAAGCTCTAAGTCTAGAAGTTTGGTCGCGATCTCCTTAGCCGCAATTGGTCGCGCACAGTCTTTCCGGGGCAAACGAATTCTATTTCCACGATATAGAGTGCCAGGGATACAGAGTCACATCACTCGGGCCTTCGATTCGGCTTATAACACGTTAAGACTTGATAACCCCTTCCCCGTTCACCGTGGCCGCACGGTGACCAGTACAGTGACAGCCAACACATATCATCTTACTGTTACTACGCCGACCTTCGTGTGCCCGCTCGATTAAAAATGACTTGAGTGGTATAGTAACGGCGAAAGTAATGTCTGGATTTCTGGTGAGTAGTAGCGGGTGGGTTTCTACCAGCGGAGACACGCGCGGATTGCGGACGGAATGTGTTCTTGATGCAAATGGAAAGTTGCGCGGAAAGAGCTATCCGCTCCAGCTACACGTGGACTGAGTGATGATCATATCAGCATAGCCACAAGCTCTACAACTGTCATAACGACAGGTGGGGTCCGGGTACACTCAACAATACTGGAGGCCTAGGAGGGTCCGCAAGGCCAATTATTGGCCCTCCAGCACAAGCGTCATAAAGCGGTTGGTGACGTTCACATACGAGACGATTGTCCTATCAGACTATTATGGCCCACCTAACTTAAGACACGCATCCTTGGGGGCTAAGTTGGAGTTCGAGCGTCAGACATCGCACACCGAAGCAGTCAGCTCTAAATTTCGAGTAGGGTATCATAATTTCTACGTACCCTATGGTCACGCGCTTTCGAAATGTCTAGTGCAGACCGCTGTATCAGCGGCCTCAAGAGCCGCTGTAACGGTGGCGAGAGTATGCTAGTTTCGCCGGCTGGTCAATTCCAATTAACTAAGTGGGCGTCGAGCAGCGCTGCCACTATAGGCCACATGATTCATACCACATACCATATTCAAAACCTAGCTCGCCCGCGAATGACGAGCCATAACTGGCATACGCAGCGTCGTGTGACGGGGGGCTACATGGTAGACTGCATTGACCGATTCGTAGCGACGCGTGTTCTAGGGACACGGCAGCTAACGGTCTGTCACTCGACTTTGTCAAGAAGCGGTGCGGATCGAGGTATTCTGCTCGATCATAAGCCCCACGCACGAGAGTCTGCGAAAGAGGCGGTACCCTGCACCATACCCGTCCGCACGGGGCGTCCTAAAACTTAATCAATAAACTTGCGCTAGGCTAGTGTGTAAATAGAGAATTGCCGCGGCTCCAAGCTAGAGGACGTTTCAAAGCTTCGTGTTTATGCTTCCGCGCCACAACACGTCGCACGACTAGGTGCGACGGAATTGTACCACCGTCATGAGAGGTGAGGTGTCCTCTAATATTTGAACGTCTATGAAGATTTGCGCCGGTCATCATCGGGAGTGTGTCGCCTCAGGATATGACGTAGTGAGAGATTAGCAGGTCAGTGTGATTAATACCCGGCCGTTGGTGGGCTGTTGAACACAGAAGATGGGATAATATACCACGTAAGGCTGTTAGTCTCTGAGGTGGTCAGGGGAACGTGCCTACCCGTGGACTTCTCATACATCACTACGCGCGGCGAATGCGTTTCTTTACCTTAGATTTAGTCATACAACAAACAAGCATGAAGTGGGGGTGAGTTCTGATTCCGTAAAACCACTTGCGAACGAATGTATGTAGATCCCGATGATGCAATGACTTGTCGTAAACGTTATCATTTTAGCCGGCATCGTTATCTGCTCTACCGCCTGCTGTAATAGCAGTTAACCGCGTGTTACAGAGAATCAAGATGCTGATATGTAGTAACAATGTGACGTGGGGTAGCCGCTCCTTTAAACGGATAAAGTCCCATAGGAGCCCTCGCATTATCGATTTAATAGGGCTACCGAAGCTACAATAGTCTAAGACGGCTTAGGACTGGCATACGGAAAACCCCGAGATCGTTACAACAGGGAATAATATCACTAGCCGTGGTGCTCGGCAAGCGGAACATATTTTCTACCTTTTAGTGAGGTCGACAGCTGCAGCCGCTCAGCAGCATTTGGATTGTCCCCAGCAGTTCAGCGATCGTCATTGTCATCTCCAAATCTGAACTGAAATGTAGACGCTTCTGTGTCGTGACGCCCTGATCCCCCTGATACATCGCCTGGGGTGACGCAGATCGATGTTAAAGAATGAACCAAACAGTGAAACTAGGACCATGCCGTAGGTAGCCTATCGCGCTTTATATAGTAACGGTGTGCCTTCCAATCTATGGGACGTGTACATGGGCTCGTCAGGTTTCTGGTCATGCTGGAAAAGTCCGCGTAGCAAGGTCGCCTGCCGCATGCTGCCGAGTTTTTTGATCCAGACCCTTGTACATGCTAAGGCCTTCCTAGTTCTTCAGATATTTGTAAAGAATTGCTGTGGCAATGAACCCCATGATCCAGTTATCTCCATAAGCACCGTCCCCCACACCTGGTTATTCACAAGAATGCTCAACCCACAAGGACGTCTATAGTAATCGCCGTGGCCGAGGGTCCGTGATGGACTGTTGTACTCAGCACGGTTGGCTGTATTGTCGAGGCCACCTATTCTATCTTTCGAGACTTCTTACCCTCTCATAGTACACACAGGTTGGTCAATTGGGCACTTTCTTTCGCCTGAAGGTCGACAGTTTTTTAGAGCCTTCTAAAAGCCACTAGATTATTGGGCGGACGCTAGCGTCGAACTAGCTCACACTGCATCAGCAGGGATTTTAGAATGATGGATAAAGCCTACCGCACGACTCTCCTCGGGCTTGCCACCGAAGTGAATCGTATAATCACAGCGCATTCCCGAGGCTGCTTGGGGACTGATGGTGATGATTGAATTTGGTAGGGCTCGGCCATCGCCCGCAATCCTGTAATTACGAGTTGGCTAGACATCACAGCTGGGACTAATAGCAACGCGATTTTAGCCCCGCTAGGTTCAAGTATTTGTTGGTCGCACTGGCAATTCTATGCACCGACACAGCGTTGTGCACTAGAGACACTAATTCCCTTAGAGACCATTTCCCGTACTAGGAGGCGCTGCGGTATGATACCACCAGGAGACATTCACTGATGAAAGCCAGACTTTTGAGATTCCATGACTCAGCGAGGACACTACCTAACACCGCTTTTGGGGTCCAGAATACCTTATACTCCTCCTTCGCTCCGGGTCTTGCCGCCCCCCCTCCCTTTGAGTGATTTACGAAAAGGTAACAAGGGAAGCAAGACTTGGACCTAGATTCATCCCTGACCATATATCCAGCCAGCGTTTTATTAATAGTCTATTAGCAACCCTCTTCGCATTTCAGACAATAGCCCCACGGTTGCCACAGGTATAGTGTGCAGTTAATCCCTCCGCACGACTGTACCAAGGCTTGTCATAACTAACCGTCCTTCATACTAGGTGCCCGTAACACGGTGCAGTCATTTCCCATACTTTACTTTGCTGCTGACCAAATAGGTTCGCACATATATAGCTGGGATTGAGGCTTGTGATTGATGATATCTCGTGATCCCTTGGATAAATATGTGTAAGATGAGCTAGGACGCGCAAAAGTTTGAAACTGAAGATCGCCTCTATGCGGGGACAGAGAATCTTCTTAGACAAGGTATAGCTAACTGCTAATGGCTCTGTAGGGTTACAAGATCACCTACGTGGCAGACAGAGTAGTCCTTCGAGGGCAATATTTAGGCCGTTTCTGCCCGAGCTAGGGTACTACCGTGACCTTGTAGCAGATTTATTCTCTGGGTGCTTTTGCACCTGCAGCCGTGTCCCATAACGGCCGTTTGGTAATATAACCCTGTTCTGTCTCTGCCTTGAGTCGGACTGGCATTCTATCCTCACTACCCTTATAGCCAGGTGAGCTATCCCCTAATGCCGACAGCGTTTAGACTGCTTTTGAGAATATGCCGGCCCTTGGGGATTGAATAAGTTTATACTGCGACCCAACGGTGCATCCCAGCATTCCTATTCCTTTGGTATCAGGTGGCCTCCAGATAATCAATGTACAGCTTACCTTCTACGAAATTAGGTGACGGCCAGATCCCGGTTGCAGGATATTCGATGCTCCAGGGCTTGTACAATATTCCGAGAAGCGAGGTCGGACACGGTATACACTTTACCTTCTGTTAGAAACTGTACACATGGGCCTGGAGAAAGGCACAACCGACGTGGGTGCTGTTACGGGTCATAGGAGCACTGACGAGAGTTTGAAAAATCCCATGTAAGGTTCTACTGAGCCTGTCCACCAATACCGCACGGCAATGCGACGGTGTAGCTGCCCCTGATGACCAAGGAGAAGTGACTGTAACATACGGAATACCTTGTCGAAAAGTCTCTTACGTGCCGTAACCATACGTATCATACTAAAAGTAAGCGTATCTATTCTTTATTGACACCAGTACTGAGACGGAAGGGACGTTCGTCCAGGAACTCAGGTCTACCTCAACCGGACTTTGCTAGCTGCAACCTACCGTCTTTGCTATCCACTTTCTGCCGTGGGTGTTCGTAACTCTCACCACCTCACATATGGGGCCATGAGGCCACACCTCCCGCCCCCCGGCGCTTACCCGGAGTGGACATAATCTAGGAATACTGACCCACGGGTGCTTCTTTTGATTTCGAGGACTCTTGTTCTTAGAATAAGTCTAGAAGTCCTTATACCAAAAGCGTCGGATCTGCCAACCGTATACGTAAACTACATCCAGACGCCAGGAAGTTCCTTGCACCAAAATTCAAGATTCCATAATATGTAACGCCACCCAGACTGGGGACAAGTCACCTACTATGTCCACCGACGGGAGGGCCGAGAGGGCCGGTTCGTTAGGAGGTATTCCTTGTCACCCCCGGCGTAAGCTTTTTAGCGCCTTCTACTTTTCGGCAGATCAGCCACGGGTGAGAAGGGGCGTAACGCATTTACCCATATCTGAGAGATAACACATAAAATACTTTTGAACCTTAATATATCGCACATAGTACAACCAGACACCGCTGAATAATCTTACCCACGACGAACGGAATTCGGTTGTGGGATTACCTTGGTTACTGGCCGTAAGCCCCCGCCAAAGACGCTTACACGATCCAAGGAAGTTGGGTTCCCGCGAAACTACAGCTGATCTCATCTTACACGAGCAGGGTGCCTCCAGTTGGTAGGTTATAGGACTAAGCCGCGCCATTGTCGCTGATTCCTGACGAGCGCCCTACCCTCAAGCAAACACACTAAAAGGCATGGATCGTTCTCATGAAAGGAGTTCGAGCGAAGATCGATGTGTATGCACATAGAGGTTCTGTCACACACCTGTAATAAACTTGCATCACGAGTACCCGCATGATAAACTGTCGTAAACGTTCACATTGCCTTCGCAGCCCTGAGCTTCCCTGACTTACATTCCGTACCAGGTTGATAGCAGAAAACCGAGTCGGAGGCTCGGAAATGGGTTAACCCTTACAAAAAGTGTAAATTACGGATTCTTTGCTCGCCTTGGACCTAGACGAGTGGATTCGCCTCGAGACACTAGAGTCAGGACACCAAGCTCAAGAGTGTTTTTCAGTCCCGGGATTAGGGTGGCTCAAGGCTTCCAGCGGGAACTAAGCGTCTGCCTACCTGGTATTCCTTGCACATCGGGATGCTGACCACTCCGATCCGTACCAAGACATCGTGACCGTTTGGTCCTCGTCAGGGTGCCTTCGCGTACCCTCATGAATCCGGACCGCACTGCAACTTTATGTACCGGTATGCTGGTCCCGACGATGCACTTATGAAGATCGTGAACAGGGCGGCGCGCCAACTAAAGTTCCTCACTTGTCCATCTCAAAACTTCTATCCTCGCACAACGTCAGGTGATGCCTATCCGTCGATTTCTGGAACTTATGGACTAAGGCCCGATGCGTCCTAGTAGGCACGCATTTATTAGTGTTAACGAAATTACATCATTTGACAGCTCCATTCACTCAAACCTCCAGGCGACCCCTCTTACCAGCTCTTGTTATGCTAGAGCATCTTAAAAGGACATCTCTTATCCCCACACAAGGGTAAGGCATCTAGCGAGGGAACGGTAATCTGAATTTGATACGGACCTCGTAGACTCTGTTAACAAAAGACTAGGTCCGCCTCGTCCCACCCGTGCTCTACGTGCGTCCCAGTCAATTAATTGTGGGCACGGAGTACGAGCTTAGTAGACCGCAGAACATCCTCGGCGCGGGGCTTGGACAGACCTTACGCTGTGGTTACTAGTGGTCAACACCTGGAAGTACTAACCTCTCACATTGTCCCGAACATAGCTTTCGGACGTGGGCGAGCACGGGGTGGCTGCTTAAATGGACCAGGGTAACGCCCAGAAACACGGTATCATATTATCATCGGCAAGCGCCCTCCACAATATGTAAGATGACGGTCTTTACCGTCACGCGCCCAGTCTTCCCGTCGTGGGTCGTATTTATGCCACAATTCCCGAGTGGTTCTCCATCTCGTCCACGTCGCCGCGCAGGTCTCAACCTAAGACACCCCTCCCTTGCCCCAAGATAAAATTATAGTCATCCGCGCTAATGTCTAGGTATATGCTTGGCGGTAAGCTACTAGCGAACAAAACACTTTTTGCTCACTTCAACCTGGTTACGCGTCGAACAACCTCTCTGCGCGATGCTCGGTAACCGCTTGTAAAACTCCCGGCACACGAATCTGATGCTATTCAGTTGAGATTGAGACAATTCCATAAACACACCTCCTCGCCAGTGCAGATCCGGGTACACGTCATTGTAACTACGTCGGACGCCCCGTATGGATCGACAGACTACCCCTCCAGAGCGTTTCACTTATATCACATGTACCAGAGTGTATATGGTAGCCGACGTCTTAGGAAGGATCTAACCCTCAGTGAGCCCGGTAGCTTCGGGTAACCAAACCGGTCGTCGCGGGGAATCACCAGGCATAATTATAAAGAGGGTATCCCAGTTAAGGTTCCAATGTTGCTATCTGCCGCGCTAGATTTAACGCTGGCAATGTTTAGATTCGACAGTTCGGGTTTTTTCACTGCTTAATGCGGGCTCCTTCTCTAGTCCTCTTCGCGACAGAGCAATATAAATGTTAACCCGTTCACGACTGGGCAGGGGAACGCCGGCCAGTGAGCTTCGCCATGCAGATCCAGGCAGACTGGCATGAGTTAGGGGAGCGTACGTGGAAACGAGTAGCACGGCTTAACCAGTAGTTCCATATAACCAACGGGTTTATGGGTCAGAAGCGCTTTGACCCCGGTGCGGACGAGTGGCTCCCCCGTGACAGGTTCTGAGAGGCGGATCACCTCATCTCCAGAGTGCATATTAGGATTTGGGCCGGGGCGTTAACCGTGTCAGGACTTCCTAGACTTGGAAAACCGAACATGGAAACATCATCCCTCCTCAGTCAAGCTCCTTCCAAACGATTTCGGTACACCATTCAGCTCCATTACCGGTTCCTTTCTCAAATAATTCTTTACAGTGGTCAGTAAAAACACAATATCTAATCGCTCAGAGGGCTCGCCTTCACCTTGCACAAAAACCCGAGTGAGAGAGTGAGGCTGTCGGTGCTTACCTGGAGGGCTTGGTTCTGAGTTCTCACCGGATACAGAGTCTTTAGTCCTGGGGCTCTATCGAGCAGGGAAACGCTCGCACCAACTGGACGCCCTTTTAACCCATTAGGAAGTCATACGTGGGAAGCCGTGAACTGTGGGTAGGACGTGAACGTGAAATACCACAAATGATAATTACGTCGGGATTCGGATGGATGAAGAAAAACGGCCGCCCATCAGAATGGGCGAACGGTAGATTGATTCCAGCGAATGGAACCTCCATGCTAGGAGCGTACGCTTGTGTTGACTATTGATGCTAAGCGATGTTGGAGGCCATCATCCTGACCATCTGAAGATACTAATATGTTACGGGGGAGGCGCTCACGTAGTAACATAGCGCACACGCCCTGGTCCAAGTCGCGGGTCTTACTTTTAGGACCATGATTCGCGAACAAAACGATGTAGATCCTACTGGGGAGTGTAAAGCACCTTAGGTTGCAGCATGAGACCCCGAAAGCGTGAACGGTTCTAAAATAGTGTGGCCCAAGTCATGTGGAGCGCAAGTTATAAGTGTGGAGCGAAGATACGCACGCTGTAATGCGCGAATATAGGCGGCTACAGCTCAGGACCTGCTTACCGTTCGTTCAGGCACGAGCCTCAGCTATGGTCGTACTGAGCAGGGGGCTGTGTCGCAGATATTTGGGAGCAATATTTGCAAATAGTCCTACATAGTAACATTGGCGTCGAAACGGCTAGGAGAGCGGGCCGGATTTGCCATTCAAGCTGGGTTAGCGCAAACGACAATAAGCAGTCCCACGAGCAGAATGCGGGTGGGTACCCTCTCGACTAACATTTGCCCGTCTGTTGCCTACAGAACAACCCCTATTTGCGCAATTTACCTGCGTGTCAGACCGATGAATTTTCAGGTGTATGCTCTATGACGCAGCGAACACCTTAAAATCCCAGAGTAGCAAGCTTCCCCCGCATTATGGTGAGCAAACCTTGATCGCTCACCCGCGATCGCTTCTCCTTAATTAGCGAAACGGTTGCCTTCTCACTCTCTCAAGTCTAAATCCCTCCCCCGCCAAGCAGTCGGCGCTAGGATCTTGCAAAAACGGATTGGATCACTATGGTGAGGACTACTTGATTACAGCTGATTTCTAGCCGATGCCGGGAGATTCCGAAAGTGTTCGCGGGTTATAAGCTTCGAGACTGGTTTCTTCAACCCTAAGACAGTCGTTGCATCGCACAGGGAACCGCTTGCAGCGCCCAGCCTCTCATTGGGCTCATAGCCCGCAGTGAGACCACAGTCGATAACAGAATGGTTATCTGTTTACCGAGTACGGTACTCCGGACGTGATGCCAACTGGCTCCTAATTCGTATCCCTGATCTATGCTGGATCCATTGCGGAGGGTTCCGCCACCAAGCAGCGAGCAGTAGTCGGGATTTGTGTTCTAGACTACCCATTTACGCCAGGGTGTTTCATTTCAATCCACTATCGCTCAAATCCGCGTCAGCTAGTCCCGTCAGCGTGCTCCCACACCGACCGCCGATTCCCTGATATATTGCCAGCTCCGGATTCCATTGCTTGCTCGTCCCCCTATGCGCGGCATTACCGCGCATATTGTGGACCTGAGTCGTCTGCAATCCCGGGCCACTTGGCAATTACATTTTAAAGCGACAGGGTAGTGCAAGAGAGATCCGACAGGATCCTAAATCGTGAGTCTCATGTAGAGGCCCAGTCTTACAGACTATGATGTTCACGCCCGTAAATGACACACGGGAAAGATAGAGACTCAAGAGATGACTGTAACGATAGATGGCTTAGGAGCACGGGCATGGAGTCTACCGGCCGGACAGTGCAGCTGATGGGTAAATCTGTCGTGATATCGAGCCGATCTTGCACAAAGGCCGCACGCGACACCCCGGTCTTGCGCATACGCTCCTCGCCTGATACAGGTCGTGACCTGGTGTAATGCGGGGGTATAGCTTGACTGCGCCTTGTATCAGATCAAACCCAGCGAGTACGGTGAAGAAGTTGTTAAGTACGGATTTCCCGACGAAGCCTTTGTAGTACGTACCGCTAGAGCCAGGCGTTGGAGGAGATCGCTGGCGTTTCGGTCGATCAACTAGCTACCAAACCGGCCAATTAGGGGGAAGCTAATAGTGGCCAAGGGGATCGGAGCGTTGGCTAGGGCCAGCCGAAGGAGCAATCCCACGCGCCGTCCTTTTCTAGTTTGTCCCGCTTTTTAACTTGAACGCGCGGAGTGTCGAAGCTAGTCAGGTTCATAACAGAGGTCTGGGAGAATCCTATCGACACGCACATGCCATGCGAAAACTACAAGATACTTTGTCGCGCTACGAAGCAGAGAAGATGCTTATGTGAGATTTTTAAAGACTCTGTTTCGAATTCGTCTCTTAACACCTGGCGACCGGATTTATGGCGCTGTAAGGGACTGCAGGTGATCTATCAACTATACGTCATAGGGGCCAACGCAGTTTTCAGCTACGCTCGCCAAATACGGGCGTCATGCCGAACAGGCACTTATAAGAGGGCGATAACGTTCATTCCCGACTCCGCGACCAGCTATTAGCGATTTGATGCTGCTATAAGACAACTTATGAGACGGGTACTAGCGGTTGGCCTTTGGTTGAATAAATGCCCGCCACGATGGACTGGCTCAGATCAGCGGAGGCGCCCCTTGCACACGGCCCTATCGTTTGCACGCTTCGGTGATTCCGCGCATCGAACAACGCTTAGCGCGTCAATGTCAAAAGATCTACCCGTAGCCCCAAAGTATATCGTCACATGACAACGACGATGGTATACGCGTTTAAGCATGGTGATCGTTGTAGTACGGGTCCGACGCGCATACTTTGAGTTCCGCCGCATATTGTATTCCGTACGCGTTCTACCCCGCAATTATCTGGAGTTAGTGGGCCCAGTAAGGATATAAGTGGGAATCTCCACATACATCTTCAAATAAGGGGCCATGCCGCTCAGCCGAACTTTGGATCGATGGGATAGGTGAACCGAGGGCAAGTTGCCTACCACGTAGCACTCCGCAGGGCAAGCCTCGGATCGATGCCGACTCCCCAACATGTTTCTCTTAAAGTCTCTATAAACGGCCGTCTCCAGCTTCAGTGTTAAGATCTCATCTGAAATACTCCAAGGTAGATTGATCAAGGGAATGACGCACCGGCATCAATAATCAGACTCCACGCGTATGCAGCTACTAATTACCTGTGGCCTAAGCACGCGAGTCGGAAAGCGCAGCTGTGTCCAACACTGCACCCAGCAGTTATCGGGCCAAAGTCCAGGCCGGAATGGATTAGTGTGGATTTTGCCATTAATGAAATACCGGTTACAAATATCGACTCTCATAGGAAGGTGTATACAATTAGTCCGTCTACCGCCTTAGGTCATCTTTCATTACAAGCACAGCCTTTGCATGTCCGCCACCACCCCAAGATATTTGGTATCTGGAAAAAATTTTCATTATGTCGACTTATTGGGCCATCTTACGACGTACACCTTGTTAGTGCTTAGAATCTCGGAACATAGAGGAAATCTCCGACCTTTAAGAATATGTGTTCACCTTAAAGAGGCTAAAAGCGTGTGTTAGCCAGGTCGTAGTAGAGCGAATCTAAGGCAGTGTACTCGGAATCGATCGTTGACTGTAGGATACGCACGGGCTAACTTAGGAGCGACGATGCGTACTTGTGCACGCTAAAGCCGCGTCCGGCGTCAGAAACACTACGAAGTTGTGTTGCTCCTTTACACCCAGTCCTAGACCCCACTTTACGCACCTGGCCACGTGGGCCGAGCGTAACCGGCTTGCACCGCGAAGCTAGGCACCGCTTTGTCCTTAACCGTACAGCACTCCACGGTTCGACTTATCTTGTTGATGGATCTAAGAGTCATATGTAGGGTGGCGTCAGAATGACCTCCACATGAATTCCGCAGCCTTTAACGGCACTCATTTTGAGCGCAACTAAACGCCCCTGAGGTAATATCGAGCGTTGTGATGACAGCGCATCTTGTGGTAACTCAACCCAAACATATAGCGCGCTATCTTCGTTTGTCTCAGCCGTCATTCTCGGCAATAGCACTCACTCTGGGCGAAAGGGTATCCAGGTAGACTGGCACAGCCTCTACTTGTTGGGTGTTACCCCTGCGCCGGAGTACACAGTAGCACATACTAATCCGGGCCTCAGTGAACCTAGAAGGAGTATGTGTATACACACGAGTGAAACGTGCCAAGAGACTACCCAAGCGCGAATCCGCGTACACATAGGTCAGGGCATCCCCCACACTGTTATCCTAACGGCTCACGGTCATCAAATTACTGTGATGTTGGTCTATGGGTCTTGCTGGTGAGCCGGCACATTCAAGGTAGGACTGACTTAATCCTGCATGAATGCCCTAGCGGCATGCAAACCTAACTCCAAGTGGCGCTGGGGAAACTCATGAACTCGAAAACACTCATGCGAAGCCTACAGGAGCCGATGAGAATCGAAAGTAACCGCAAGCAGACGAGGTACAGCACTCTCAGATAATTTCTCGTTACGTCAAGAAATGACAAATTCCACTAAAGTTGAGTTAGAATCAACGTCCGCGACACCCAATAAGTGTCAAGGGCTCCCCGGAGACTGGGCGATGCTTTCTTGCTTGGGACCACGTTACATCCATGTGCGTTCAGAACCTCTATTAGGTGGCAGTGCCCGTCTGCCAGGAGTCGATAAGATCAGGTTTGATTACGATTTAGAATATTAATTAGAACCTCGGGCCGCTATTGAATCCACTGAATACTTACATCGCTTAACTGCGGCACGCCGCCTGTACGCTCATCCATTACGTCGGAGCTACACGTTTTTGTGACGACATGTTACATACCTGTATACGAGGGCTCAGGATTTCATCCGCCAGAGACATCTATGGGATAGTGTTCACGGGTGGGTACTCTGTAGGGGAGGGTGATGTATGTTGTCTCATCATCGTCAGAATAAATGAAGTTGTTCTCGGTGCTGACGATGCGTCATCTATTACACGCACCAGTAGCAATCTCGTCAGCTCCATTCTGTGCGTGGCAGGTTCCTTTCGCTGGACCAATGCAATCCTTTGATTACGGTGTACACGCATCTACAACGGCTCACTTTGTTGTGACATGCCTCGACCTGGTCTGAGGCTTTTGTACGTCCTATTCATGAGGGTAAGGTGCAATTCGAGGAGGCCGGCCACGGGCGGAACGTAACTCAACTTCGCACAATTGATCCCGTGCGCGGCCTCCAACTTATAGTTAATCCAGCAAGTCCCCTTAGAGTAGAGAAACAAATAGTATTTGATTTTCCCCTCTTCCTCATTTTAACGGCTACATCCCACTGCGGTCGTAATGACCGGACCGCGCGGGTCGTTTCATGACGCCCGCTCCATCCTCAAGGAGGGTGGCTTACTCTCCAGCAAGCGGTCAAGGGATTTCCGAGACGATACTCACTGTCTTTCGTGCTCTTTGGTTAGTGCTTGGCATACCGGCGTTAGAGCTCATCACAGCCGGCTACCCCATGACGAGTGCGATCCAGTCGTTGCGTTCGCTGTGTCGTATTGCTCTCACCCACTTTAGGACAGGCCACAAACCGCCAGGGTCCAACCGCGGCAAACAGAGTTGATTAGTCTAAATATGGTCTATAAGTACCAATCAATCTCAGCTGTCCGGAAATACCCGTGCATTCATTGTCCGGCAAACTTCGGAAGCTTTTCCAAGCGGCGGTTCTTAGGATGTTTTCGCAATAATTAGCGAATAGTGGTGGTTCCACCCCTTGGAGTTTAAACGGGACGCTGCCTTTAAACTCCTTGTCCTCGAGCTTAGGTATTAAACCTAGAAGGTCTCAAGACAAAATATTAGGTAAGTAACCTAACGATAAGGGCAATGATTCGGATTTATCACTCGGTCCATATATCGCTCATCTCTCTAAGCATTATCTGCCGGAGACGAACTAAGAGCTGGCGGGTCATACCACAAGCCGCGCAAATTAGATGCAACAATCTAGTCAACACTTGAAACATTTCACTCCTTACTCACTGCTCTGGCACTATGCGTACCACACATGGCGTAGCGACCGTTTGCCTGTGTCAGCAGGTGGAGTTGGTCTGTCATTCACAGCGCTGCAAGTAGGCTTAACTATAAGAATGCAATTAGTGTCAAGGGAACAGTCAAAGCACCCGTTCATGATAGTAAGTACGTTTCCAACCGCAGATCTTAAAGCCAGGCACCGTAGTATCTTGTCTGGGATCTTCAACTCACCAACTACATCACTCGGTTACTCTCCTTTGCCAGGTATCTAATGAGTGCGTATAGAATACATCAACAACAGGCAGTGTTGTGCACGAGTCCGGGCGCCCCATTGGGATCGTTACGTGCGCGCTGTGAGTGCGTCGGTTACTTTCGATGCACTACTTAACGGAGCCTCTCAAGCCAAACCCGTATGTGGTCCTTACAGTAAGCCATGTTGATATGAATCAAAACCGGCTCGGTTCATTAGCTCTGTATCGCCCTGGTCATCCCCGGTTATATTTCTAGCATTATGCAAGGCGAAATTGGAACTAGTGCCAGGGTTCACTTAGGCAGTATCATTCGTATCGCCAGTCTTCACAACTCTTACTGAAACTAGGCTACGTTAAGACGGAAGAAGCATTTTTTATTATAGGCCACAGTCGAGCCAGGCGCACAGAGGGCCTCCGCAATAGTGGGACAACTACGTGATGCGCCCGTCGGATATGACCCATAAACTGGTACTGTACCGGAGACCCTCGTCGTCCATCGGCGGATCTACGGCTCTCTAGGTCTGGGGTTTTACCATGCAGATGTCAGATTATTCTCTATCCTATGGTCCCCAAGGACTTTTAATTCCTTCGGTTTCAGCGAAACAAGTGTAACTGGCGCATGTCCAGAGCCCTGCTAACCTGGACGTCGTCCTCCCGATCTACTAAGCCACGCACGACTCCCGTCTAACGGGTGGTCTTACGAAGTTTTATAGGTATGTAGTGGCCTTGACTACCGGCGCCCACTCGGTCGAGTTCAAACACTTTCCACCTAGTGCATATTTAGTAGTTATTCAAGCTATCTGGCCCGAACCTGCAATACGCAAGCGTTTGGTTGCGAACTTATTTATGAAAGTCTTCGCGTACGCGCGTAAGTGACAAGATCTCGGGTACATCTAGTACAGAGTCAGCGGTTAGACTCGGTTTTCCATCTGCATAAGTCGCGCATTGTCTGGAATGTCCTTAACGTGCTTCGAACGAGTCCCTATGGTCCGACGGTTCGATCGTATAAATGACATTAGCCGCCAGCTGTCCTCCCGGAGCGACGCCTTAAGTTGCATGCTAATCGTCTATTGGGCCCCGCGCACGTGCCCTGTACGGGAGCACGTTTTCGTACCTGAGCGGTTCGAGACTCCCAAAAGAGACGCTTAGCGTCGTCTTTGTGAAGACCTATGCAGTTGCACGATAGAACATGCTTTACCCCACGCATTCGCGATAGAACGTTTTCCAACCTTTGGAAGATTAAAGCAGTGCACCAGAAAACCGGCCTGCATGGTTCGCTGTCGAGCGGGCTTTTGTTGATAAGAGGCTCAAACGTAACCGGCGGAGTAATAGCGGTGCTTATGGGTCAACCTTGGAGTTCATGGTCCAACCCGCACGACAACCAGTAGACTGGTCGACACAGTCACCCGATCTGTCGAGAAGCTCATAGGGTTCTATATCTAATAGCCCTAAGGTGGCGTACGCAATAGATATTGACTTCATTCTGTGGGACCTTGACTAGGGAGCGATTCGACTCATAGTCGGAATTTAAACCAGTTGCAGGCCTATTCGCCACCGTCGGACGACGGGAGATAGTCTTCTGACTCCTGACAGCAGGAGCCGCCCCTAGCCTATTGCCGTCAAGCATAGACTGGGTTTTGGAATGGTTACGGTGAGCGTCGCTTAAGCAGAGTGCAATGTACATACCAATGGTTGCCCTTACACCTGTGAGGCCATAGAACCAACCTAAATAGCCAGGATTGGACGCCATCGTTTATTCACCTTCTAATTAACACTTGACCACAAGGTAGGTGCTATGAGCGATGACTCGCTTATGAAAAACTGGATGCAGGCAGCAAGCGGTAATATAGGGGCAACACGTAAGGACCCTAGTTTTCTGAGAACGACGCACTGAAGTGGTAACACGCCCATAAGTTATATGGCACCGAAGACTGTCTTAACAAGCTGCATCTTCCTACCTTTTTCTATTAGCCTAGAATTGGCAGATGGAATCTTGATATGCTTGACGCTAGGAAGACGGTATTCTTCATACAAACGAATACAGTCTCGATCTCGCCGAACCATCAGTAGCTATTGACGGCTATCAATCAGGCCGGATTATTGTGGGCCTTTCATACTCCTCCCAGGCAATTTTCATCCTAGAAAAAACCAGTCATTTCAACTCATCTTCTTCGGTGGCCAGCGAGATGGGAATGCTACTATTCTCACCTGTGGTCACAAACAGCTTACGAATGGCTCTTACGCCGCTGTTATCTAGCTCTCTAATTCGCGCTCTTTCTCTACACGGGACAGTTAGAGATTTCTCTAGATGCTTCTCTGAAAATCCTGGCTTCATATGATTAGATCAAGATAGGTGCGTTCCAGGTCACATGAGCATGACACGTGGAGACAACAGATGCTGAGGGCTTGCTCTTGCTTCTACACGGCAAGGCGAGACCAAACAAGAGGGACCGACCGTTCGGATTTGTCCTCGGCGGAGCTCGATTTAGCTGGTACCCTATGATCCGGTCTTTCACCAGTCGCGAGCCGACTGGCGTGCGAGTTTTCAGAGACGAGCGCCCCGAAAGGGCACC'
result = minimum_skew(Genome)
' '.join(map(str, result)) |
load("@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl", "tool_path", "action_config", "tool")
load("@bazel_tools//tools/build_defs/cc:action_names.bzl", "ACTION_NAMES")
def _impl(ctx):
tool_paths = [
tool_path(
name = "gcc",
path = "wrapper_cc.sh",
),
tool_path(
name = "g++",
path = "wrapper_cxx.sh",
),
tool_path(
name = "ld",
path = "/usr/bin/ld",
),
tool_path(
name = "ar",
path = "/usr/bin/ar",
),
tool_path(
name = "cpp",
path = "/usr/bin/cpp",
),
tool_path(
name = "gcov",
path = "/usr/bin/gcov",
),
tool_path(
name = "nm",
path = "/usr/bin/nm",
),
tool_path(
name = "objdump",
path = "/usr/bin/objdump",
),
tool_path(
name = "strip",
path = "/usr/bin/strip",
),
]
action_configs = [
action_config (
action_name = ACTION_NAMES.cpp_link_executable,
enabled = True,
tools = [
tool (path = "wrapper_cc_link.sh"),
]
),
]
return cc_common.create_cc_toolchain_config_info(
ctx = ctx,
toolchain_identifier = "cross-distcc-toolchain",
host_system_name = "i686-unknown-linux-gnu",
target_system_name = "distcc-unknown",
target_cpu = "cpudistcc",
target_libc = "unknown",
compiler = "distcccompile",
abi_version = "unknown",
abi_libc_version = "unknown",
tool_paths = tool_paths,
action_configs = action_configs,
cxx_builtin_include_directories = [
"/usr/lib/gcc/",
"/usr/local/include",
"/usr/include/",
"/usr/lib/gcc/x86_64-redhat-linux/7/include/",
]
)
cc_toolchain_config = rule(
implementation = _impl,
attrs = {},
provides = [CcToolchainConfigInfo],
)
| load('@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl', 'tool_path', 'action_config', 'tool')
load('@bazel_tools//tools/build_defs/cc:action_names.bzl', 'ACTION_NAMES')
def _impl(ctx):
tool_paths = [tool_path(name='gcc', path='wrapper_cc.sh'), tool_path(name='g++', path='wrapper_cxx.sh'), tool_path(name='ld', path='/usr/bin/ld'), tool_path(name='ar', path='/usr/bin/ar'), tool_path(name='cpp', path='/usr/bin/cpp'), tool_path(name='gcov', path='/usr/bin/gcov'), tool_path(name='nm', path='/usr/bin/nm'), tool_path(name='objdump', path='/usr/bin/objdump'), tool_path(name='strip', path='/usr/bin/strip')]
action_configs = [action_config(action_name=ACTION_NAMES.cpp_link_executable, enabled=True, tools=[tool(path='wrapper_cc_link.sh')])]
return cc_common.create_cc_toolchain_config_info(ctx=ctx, toolchain_identifier='cross-distcc-toolchain', host_system_name='i686-unknown-linux-gnu', target_system_name='distcc-unknown', target_cpu='cpudistcc', target_libc='unknown', compiler='distcccompile', abi_version='unknown', abi_libc_version='unknown', tool_paths=tool_paths, action_configs=action_configs, cxx_builtin_include_directories=['/usr/lib/gcc/', '/usr/local/include', '/usr/include/', '/usr/lib/gcc/x86_64-redhat-linux/7/include/'])
cc_toolchain_config = rule(implementation=_impl, attrs={}, provides=[CcToolchainConfigInfo]) |
class Graph():
def __init__(self, vertices):
self.V = vertices
self.graph = [[0 for column in range(vertices)] for row in range(vertices)]
def printSolution(self, dist):
print("Vertex \t Distance from source")
for node in range(self.V):
print(node, "\t", dist[node])
def minDistance(self, dist, sptSet):
minimum = float('inf')
min_index = 0
for v in range(self.V):
if dist[v] < minimum and sptSet[v] == False:
minimum = dist[v]
min_index = v
return min_index
# For a given source node in the graph, the algorithm finds the shortest path between that node and every other
def dijkstra(self, src):
dist = [float('inf')] * self.V
dist[src] = 0
sptSet = [False] * self.V
for cout in range(self.V):
u = self.minDistance(dist, sptSet)
sptSet[u] = True
for v in range(self.V):
if self.graph[u][v] > 0 and sptSet[v] == False \
and dist[v] > dist[u] + self.graph[u][v]:
dist[v] = dist[u] + self.graph[u][v]
self.printSolution(dist)
g = Graph(10)
g.graph = [[0, 4, 0, 0, 8, 0, 0, 0, 0, 0],
[0, 0, 8, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 5, 0, 0, 0, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 10, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 10, 0, 0, 5, 0],
[0, 0, 0, 0, 0, 0, 4, 0, 0, 8],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
g.dijkstra(0)
| class Graph:
def __init__(self, vertices):
self.V = vertices
self.graph = [[0 for column in range(vertices)] for row in range(vertices)]
def print_solution(self, dist):
print('Vertex \t Distance from source')
for node in range(self.V):
print(node, '\t', dist[node])
def min_distance(self, dist, sptSet):
minimum = float('inf')
min_index = 0
for v in range(self.V):
if dist[v] < minimum and sptSet[v] == False:
minimum = dist[v]
min_index = v
return min_index
def dijkstra(self, src):
dist = [float('inf')] * self.V
dist[src] = 0
spt_set = [False] * self.V
for cout in range(self.V):
u = self.minDistance(dist, sptSet)
sptSet[u] = True
for v in range(self.V):
if self.graph[u][v] > 0 and sptSet[v] == False and (dist[v] > dist[u] + self.graph[u][v]):
dist[v] = dist[u] + self.graph[u][v]
self.printSolution(dist)
g = graph(10)
g.graph = [[0, 4, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 10, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 10, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
g.dijkstra(0) |
def setup_animation():
init_figure()
for line in lines:
self.line = self.mplCanvas.canvas.ax.plot([], [], [])
self.stopped = False | def setup_animation():
init_figure()
for line in lines:
self.line = self.mplCanvas.canvas.ax.plot([], [], [])
self.stopped = False |
def count_substring(string, sub_string):
# Init counter
counter = 0
# Find first index
ind = string.find(sub_string)
# Iterate over string
for i in string:
# If not found, return counter
if ind == -1:
return counter
else:
# Increment counter
counter += 1
# Throw the beginning of string until position of next unmatched part
string = string[ind + 1:]
# Try to find the string again
ind = string.find(sub_string)
if __name__ == '__main__':
string = input().strip()
sub_string = input().strip()
count = count_substring(string, sub_string)
print(count) | def count_substring(string, sub_string):
counter = 0
ind = string.find(sub_string)
for i in string:
if ind == -1:
return counter
else:
counter += 1
string = string[ind + 1:]
ind = string.find(sub_string)
if __name__ == '__main__':
string = input().strip()
sub_string = input().strip()
count = count_substring(string, sub_string)
print(count) |
s = input()
if s[0:3] == 'KIH':
s = 'A'+s
elif s[0:4] != 'AKIH':
print('NO')
exit(0)
if s[4:5] == 'B':
s = s[0:4]+'A'+s[4:]
elif s[4:6] != 'AB':
print('NO')
exit(0)
if s[5:7] == 'BR':
s = s[0:6]+'A'+s[6:]
elif s[5:7] != 'BA':
print('NO')
exit(0)
if (s[7:9] == 'RA' and len(s) == 9) or (s[7:8] == 'R' and len(s) == 8):
print('YES')
else:
print('NO')
| s = input()
if s[0:3] == 'KIH':
s = 'A' + s
elif s[0:4] != 'AKIH':
print('NO')
exit(0)
if s[4:5] == 'B':
s = s[0:4] + 'A' + s[4:]
elif s[4:6] != 'AB':
print('NO')
exit(0)
if s[5:7] == 'BR':
s = s[0:6] + 'A' + s[6:]
elif s[5:7] != 'BA':
print('NO')
exit(0)
if s[7:9] == 'RA' and len(s) == 9 or (s[7:8] == 'R' and len(s) == 8):
print('YES')
else:
print('NO') |
def main():
file = open('6.txt')
banks = []
for line in file:
cells = [int(i) for i in line.split()]
banks.extend(cells)
all_banks = []
while banks not in all_banks:
all_banks.append(banks.copy())
i, m = max(enumerate(banks), key=lambda x: x[1])
banks[i] = 0
for x in range(m):
banks[(i + x + 1) % len(banks)] += 1
print(len(all_banks))
i = all_banks.index(banks)
print(len(all_banks) - i)
if __name__ == '__main__':
main()
| def main():
file = open('6.txt')
banks = []
for line in file:
cells = [int(i) for i in line.split()]
banks.extend(cells)
all_banks = []
while banks not in all_banks:
all_banks.append(banks.copy())
(i, m) = max(enumerate(banks), key=lambda x: x[1])
banks[i] = 0
for x in range(m):
banks[(i + x + 1) % len(banks)] += 1
print(len(all_banks))
i = all_banks.index(banks)
print(len(all_banks) - i)
if __name__ == '__main__':
main() |
def get_key(x, y):
return str(x)+"-"+str(y)
#input_line = input("Enter input:\n")
filename = "..\inputs\day_three_input.txt"
f = open(filename)
input_line = f.readline()
x = 0
y = 0
present_locations = {}
present_locations[get_key(x,y)]=1
for c in input_line:
if c == ">":
x += 1
elif c == "<":
x -= 1
elif c == "^":
y += 1
else:
y -= 1
present_locations[get_key(x,y)]=1
print("Unique houses: "+str(len(present_locations.keys())))
| def get_key(x, y):
return str(x) + '-' + str(y)
filename = '..\\inputs\\day_three_input.txt'
f = open(filename)
input_line = f.readline()
x = 0
y = 0
present_locations = {}
present_locations[get_key(x, y)] = 1
for c in input_line:
if c == '>':
x += 1
elif c == '<':
x -= 1
elif c == '^':
y += 1
else:
y -= 1
present_locations[get_key(x, y)] = 1
print('Unique houses: ' + str(len(present_locations.keys()))) |
class PersegiPanjang(object) :
def __init__(self,p,l) :
self.panjang = p
self.lebar = l
def hitungLuas(self) :
return self.panjang * self.lebar
def cetakLuas(self) :
print('Panjang : ',self.panjang)
print('Lebar : ',self.lebar)
print('Luas : ',self.hitungLuas())
def main() :
test = PersegiPanjang(5,6)
test.cetakLuas()
if __name__ == '__main__' :
main() | class Persegipanjang(object):
def __init__(self, p, l):
self.panjang = p
self.lebar = l
def hitung_luas(self):
return self.panjang * self.lebar
def cetak_luas(self):
print('Panjang : ', self.panjang)
print('Lebar : ', self.lebar)
print('Luas : ', self.hitungLuas())
def main():
test = persegi_panjang(5, 6)
test.cetakLuas()
if __name__ == '__main__':
main() |
#this code gives the numbers of integers, floats, and strings present in the list
a= ['Hello',35,'b',45.5,'world',60]
i=f=s=0
for j in a:
if isinstance(j,int):
i=i+1
elif isinstance(j,float):
f=f+1
else:
s=s+1
print('Number of integers are:',i)
print('Number of Floats are:',f)
print("numbers of strings are:",s)
| a = ['Hello', 35, 'b', 45.5, 'world', 60]
i = f = s = 0
for j in a:
if isinstance(j, int):
i = i + 1
elif isinstance(j, float):
f = f + 1
else:
s = s + 1
print('Number of integers are:', i)
print('Number of Floats are:', f)
print('numbers of strings are:', s) |
while True:
a, b = map(int, input().split())
if a == 0 and b == 0: break
print("Yes" if (a > b) else "No")
| while True:
(a, b) = map(int, input().split())
if a == 0 and b == 0:
break
print('Yes' if a > b else 'No') |
class ComplexNumber(object):
def __init__(self, real, imag):
self.real = real
self.imag = imag
def __add__(self, other):
return ComplexNumber(self.real+other.real, self.imag+other.imag)
def __sub__(self, other):
return ComplexNumber(self.real-other.real, self.imag-other.imag)
def __mul__(self, other):
r = self.real*other.real-(self.imag*other.imag)
i = self.real*other.imag+self.imag*other.real
return ComplexNumber(r, i)
def __div__(self, other):
conj = other.conjugate()
numer = self*conj
denom = float((other*conj).real)
return ComplexNumber(numer.real/denom, numer.imag/denom)
def conjugate(self):
return ComplexNumber(self.real, -self.imag)
def __repr__(self):
return "{}{:+}i".format(self.real, self.imag)
def norm(self):
return ComplexNumber((self.real**2+self.imag**2)**.5, 0)
def __eq__(self, other):
return self.real == other.real and self.imag == other.imag
def dist(self, other):
return self.norm() - other.norm() | class Complexnumber(object):
def __init__(self, real, imag):
self.real = real
self.imag = imag
def __add__(self, other):
return complex_number(self.real + other.real, self.imag + other.imag)
def __sub__(self, other):
return complex_number(self.real - other.real, self.imag - other.imag)
def __mul__(self, other):
r = self.real * other.real - self.imag * other.imag
i = self.real * other.imag + self.imag * other.real
return complex_number(r, i)
def __div__(self, other):
conj = other.conjugate()
numer = self * conj
denom = float((other * conj).real)
return complex_number(numer.real / denom, numer.imag / denom)
def conjugate(self):
return complex_number(self.real, -self.imag)
def __repr__(self):
return '{}{:+}i'.format(self.real, self.imag)
def norm(self):
return complex_number((self.real ** 2 + self.imag ** 2) ** 0.5, 0)
def __eq__(self, other):
return self.real == other.real and self.imag == other.imag
def dist(self, other):
return self.norm() - other.norm() |
rec3 = 0
rec4 = 0
def hanoi3num(n, origem, destino, trabalho):
global rec3
rec3 += 1
if n == 1:
return rec3
hanoi3num(n-1, origem, trabalho, destino)
hanoi3num(n-1, trabalho, destino, origem)
return rec3
def hanoi4num(n, origem, destino, trabalho1, trabalho2):
global rec4
if n == 0:
return rec4
if n == 1:
rec4 += 1
return rec4
hanoi4num(n-2, origem, trabalho1, trabalho2, destino)
rec4 += 3
hanoi4num(n-2, trabalho1, destino, origem, trabalho2)
return rec4
def gera_matriz(dim_colunas):
global rec3
global rec4
matriz = []
linha1 = []
linha2 = []
for c in range(1, dim_colunas):
linha1.append('%d' % c)
for c in range(1, dim_colunas):
rec3 = 0
rec4 = 0
qtd_rec3 = hanoi3num(c, 'A', 'C', 'B')
qtd_rec4 = hanoi4num(c, 'A', 'D', 'B', 'C')
dif = qtd_rec3 - qtd_rec4
linha2.append('%d' % dif)
matriz.append(linha1)
matriz.append(linha2)
return matriz
def escreve_matriz(matriz, n):
for x in range(2):
for y in range(n-1):
print(matriz[x][y], end=" ")
print()
discos = int(input())
saida = gera_matriz(discos+1)
escreve_matriz(saida, discos+1)
| rec3 = 0
rec4 = 0
def hanoi3num(n, origem, destino, trabalho):
global rec3
rec3 += 1
if n == 1:
return rec3
hanoi3num(n - 1, origem, trabalho, destino)
hanoi3num(n - 1, trabalho, destino, origem)
return rec3
def hanoi4num(n, origem, destino, trabalho1, trabalho2):
global rec4
if n == 0:
return rec4
if n == 1:
rec4 += 1
return rec4
hanoi4num(n - 2, origem, trabalho1, trabalho2, destino)
rec4 += 3
hanoi4num(n - 2, trabalho1, destino, origem, trabalho2)
return rec4
def gera_matriz(dim_colunas):
global rec3
global rec4
matriz = []
linha1 = []
linha2 = []
for c in range(1, dim_colunas):
linha1.append('%d' % c)
for c in range(1, dim_colunas):
rec3 = 0
rec4 = 0
qtd_rec3 = hanoi3num(c, 'A', 'C', 'B')
qtd_rec4 = hanoi4num(c, 'A', 'D', 'B', 'C')
dif = qtd_rec3 - qtd_rec4
linha2.append('%d' % dif)
matriz.append(linha1)
matriz.append(linha2)
return matriz
def escreve_matriz(matriz, n):
for x in range(2):
for y in range(n - 1):
print(matriz[x][y], end=' ')
print()
discos = int(input())
saida = gera_matriz(discos + 1)
escreve_matriz(saida, discos + 1) |
class Solution:
def strMultiply(self, s, char):
bonus = 0
n = int(char)
newStr = ''
for i in range(len(s) - 1, -1, -1):
m = int(s[i])
result = n * m + bonus
newChar, bonus = str(result % 10), result // 10
newStr = newChar + newStr
if bonus == 0:
return newStr
else:
return str(bonus) + newStr
def strAdd(self, s1, s2, offset):
bonus, loc2 = 0, len(s2) - 1
newStr = s1[len(s1) - offset:]
for i in range(len(s1) - 1 - offset, -1, -1):
result = int(s1[i]) + int(s2[loc2]) + bonus
newChar, bonus = str(result % 10), result // 10
loc2 -= 1
newStr = newChar + newStr
for i in range(loc2, -1, -1):
result = int(s2[i]) + bonus
newChar, bonus = str(result % 10), result // 10
newStr = newChar + newStr
if bonus == 0:
return newStr
else:
return str(bonus) + newStr
def multiply(self, num1, num2):
if num1 == "0" or num2 == "0":
return "0"
multiplySet = dict()
result = self.strMultiply(num1, num2[-1])
multiplySet[num2[-1]] = result
offset = 1
for i in range(len(num2) - 2, -1, -1):
if num2[i] in multiplySet:
subResult = multiplySet[num2[i]]
else:
subResult = self.strMultiply(num1, num2[i])
multiplySet[num2[i]] = subResult
result = self.strAdd(result, subResult, offset)
offset += 1
return result
| class Solution:
def str_multiply(self, s, char):
bonus = 0
n = int(char)
new_str = ''
for i in range(len(s) - 1, -1, -1):
m = int(s[i])
result = n * m + bonus
(new_char, bonus) = (str(result % 10), result // 10)
new_str = newChar + newStr
if bonus == 0:
return newStr
else:
return str(bonus) + newStr
def str_add(self, s1, s2, offset):
(bonus, loc2) = (0, len(s2) - 1)
new_str = s1[len(s1) - offset:]
for i in range(len(s1) - 1 - offset, -1, -1):
result = int(s1[i]) + int(s2[loc2]) + bonus
(new_char, bonus) = (str(result % 10), result // 10)
loc2 -= 1
new_str = newChar + newStr
for i in range(loc2, -1, -1):
result = int(s2[i]) + bonus
(new_char, bonus) = (str(result % 10), result // 10)
new_str = newChar + newStr
if bonus == 0:
return newStr
else:
return str(bonus) + newStr
def multiply(self, num1, num2):
if num1 == '0' or num2 == '0':
return '0'
multiply_set = dict()
result = self.strMultiply(num1, num2[-1])
multiplySet[num2[-1]] = result
offset = 1
for i in range(len(num2) - 2, -1, -1):
if num2[i] in multiplySet:
sub_result = multiplySet[num2[i]]
else:
sub_result = self.strMultiply(num1, num2[i])
multiplySet[num2[i]] = subResult
result = self.strAdd(result, subResult, offset)
offset += 1
return result |
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