Rozdział 2.1
import pandas as pd
auta = pd.read_table("http://www.biecek.pl/R/auta.csv",sep=";",header=0)
print(auta.head())
## Marka Model Cena KM Pojemnosc Przebieg Paliwo Produkcja
## 0 Peugeot 206 8799.0 60 1100 85000.0 benzyna 2003
## 1 Peugeot 206 15500.0 60 1124 114000.0 benzyna 2005
## 2 Peugeot 206 11900.0 90 1997 215000.0 diesel 2003
## 3 Peugeot 206 10999.0 70 1868 165000.0 diesel 2003
## 4 Peugeot 206 11900.0 70 1398 146000.0 diesel 2005
Rozdział 2.2.1
from scipy import *
print(array([2, 3, 5, 7, 11, 13, 17]))
## [ 2 3 5 7 11 13 17]
from scipy import *
print(arange(-3, 3+1, 1))
## [-3 -2 -1 0 1 2 3]
from scipy import *
print(arange(0, 100+1, 11))
## [ 0 11 22 33 44 55 66 77 88 99]
import datetime
sol = [ datetime.date(2017, i + 1, 1).strftime('%B') for i in range(12) ]
print(sol)
## ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
import string
LETTERS = list(string.ascii_uppercase)
print(LETTERS)
## ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
Rozdział 2.2.1.1*
import string
LETTERS = list(string.ascii_uppercase)
print(LETTERS[5-1:10])
## ['E', 'F', 'G', 'H', 'I', 'J']
import string
LETTERS = list(string.ascii_uppercase)
sol = [ LETTERS[i] for i in [0,1,8,9,10,11,12,13]]
print(sol)
## ['A', 'B', 'I', 'J', 'K', 'L', 'M', 'N']
import string
LETTERS = list(string.ascii_uppercase)
print(LETTERS[0:len(LETTERS):2])
## ['A', 'C', 'E', 'G', 'I', 'K', 'M', 'O', 'Q', 'S', 'U', 'W', 'Y']
import datetime
sol = [ datetime.date(2017, i + 1, 1).strftime('%B') for i in range(12) ]
del sol[4:9]
print(sol)
## ['January', 'February', 'March', 'April', 'October', 'November', 'December']
from scipy import *
wartosc = array([(1, 'pion'), (3, 'skoczek'),(3, 'goniec'),(5, 'wieza'),(9, 'hetman'),(float('inf'), 'krol')])
print(wartosc[::-1],'\n')
print(wartosc[2])
## [['inf' 'krol']
## ['9' 'hetman']
## ['5' 'wieza']
## ['3' 'goniec']
## ['3' 'skoczek']
## ['1' 'pion']]
##
## ['3' 'goniec']
Rozdział 2.2.2
import warnings
warnings.filterwarnings("ignore")
import pandas.rpy.common as com
koty_ptaki = com.load_data('koty_ptaki', package='Przewodnik')
print(koty_ptaki.head())
## gatunek waga dlugosc predkosc habitat zywotnosc druzyna
## 1 Tygrys 300.0 2.5 60 Azja 25 Kot
## 2 Lew 200.0 2.0 80 Afryka 29 Kot
## 3 Jaguar 100.0 1.7 90 Ameryka 15 Kot
## 4 Puma 80.0 1.7 70 Ameryka 13 Kot
## 5 Leopard 70.0 1.4 85 Azja 21 Kot
Rozdział 2.2.2.1
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import pandas.rpy.common as com
koty_ptaki = com.load_data('koty_ptaki', package='Przewodnik')
print(koty_ptaki.ix[[1,3,5]].head(),'\n')
print(koty_ptaki.ix[[1,3,5],[1,2,3,4]],'\n')
print(koty_ptaki.loc[koty_ptaki["predkosc"] > 100, ["gatunek","predkosc","dlugosc"]])
## gatunek waga dlugosc predkosc habitat zywotnosc druzyna
## 1 Tygrys 300.0 2.5 60 Azja 25 Kot
## 3 Jaguar 100.0 1.7 90 Ameryka 15 Kot
## 5 Leopard 70.0 1.4 85 Azja 21 Kot
##
## waga dlugosc predkosc habitat
## 1 300.0 2.5 60 Azja
## 3 100.0 1.7 90 Ameryka
## 5 70.0 1.4 85 Azja
##
## gatunek predkosc dlugosc
## 6 Gepard 115 1.4
## 8 Jerzyk 170 0.2
## 10 Orzel przedni 160 0.9
## 11 Sokol wedrowny 110 0.5
## 13 Albatros 120 0.8
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import pandas.rpy.common as com
koty_ptaki = com.load_data('koty_ptaki', package='Przewodnik')
print(koty_ptaki["predkosc"],'\n')
koty_ptaki['predkosc_mile'] = koty_ptaki["predkosc"] * 1.6
print(koty_ptaki.head(2))
## 1 60
## 2 80
## 3 90
## 4 70
## 5 85
## 6 115
## 7 65
## 8 170
## 9 70
## 10 160
## 11 110
## 12 100
## 13 120
## Name: predkosc, dtype: int32
##
## gatunek waga dlugosc predkosc habitat zywotnosc druzyna predkosc_mile
## 1 Tygrys 300.0 2.5 60 Azja 25 Kot 96.0
## 2 Lew 200.0 2.0 80 Afryka 29 Kot 128.0
Rozdział 2.2.3
import string
LETTERS = list(string.ascii_uppercase)
trojka = {'liczby':[1,2,3,4,5],'litery':LETTERS,'logika':[True,True,True,False]}
print(trojka,'\n')
print(trojka['logika'])
## {'liczby': [1, 2, 3, 4, 5], 'logika': [True, True, True, False], 'litery': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']}
##
## [True, True, True, False]
Rozdział 2.3
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(daneSoc.head(4))
## wiek wyksztalcenie st.cywilny plec praca \
## 1 70 zawodowe w zwiazku mezczyzna uczen lub pracuje
## 2 66 zawodowe w zwiazku kobieta uczen lub pracuje
## 3 71 zawodowe singiel kobieta uczen lub pracuje
## 4 57 srednie w zwiazku mezczyzna uczen lub pracuje
##
## cisnienie.skurczowe cisnienie.rozkurczowe
## 1 143 83
## 2 123 80
## 3 167 80
## 4 150 87
Rozdział 2.3.1
import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(daneSoc['wiek'].min(),daneSoc['wiek'].max())
## 22 75
import warnings
warnings.filterwarnings("ignore")
from scipy import stats
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(stats.trim_mean(daneSoc['wiek'], 0.2))
## 42.5806451613
import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(daneSoc['wiek'].median())
## 45.0
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(daneSoc['wiek'].describe())
## count 204.000000
## mean 43.161765
## std 13.847100
## min 22.000000
## 25% 30.000000
## 50% 45.000000
## 75% 53.000000
## max 75.000000
## Name: wiek, dtype: float64
import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(std(daneSoc['wiek'], ddof=1))
## 13.8470999195
import warnings
warnings.filterwarnings("ignore")
from scipy import stats
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(stats.kurtosis(daneSoc['wiek']))
## -0.935658875845816
import warnings
warnings.filterwarnings("ignore")
from scipy import stats
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(stats.skew(daneSoc['wiek']))
## 0.23487589610214818
import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(percentile(daneSoc['wiek'],[10,25,50,75,90]))
## [ 26. 30. 45. 53. 62.4]
import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(daneSoc.iloc[:,[0,5,6]].apply(lambda x: (x-mean(x))/std(x,ddof=1)).cov())
## wiek cisnienie.skurczowe cisnienie.rozkurczowe
## wiek 1.000000 -0.027652 -0.083137
## cisnienie.skurczowe -0.027652 1.000000 0.678527
## cisnienie.rozkurczowe -0.083137 0.678527 1.000000
Rozdział 2.3.2
import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(daneSoc['wyksztalcenie'].value_counts())
## podstawowe 93
## srednie 55
## wyzsze 34
## zawodowe 22
## Name: wyksztalcenie, dtype: int64
import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(pd.crosstab(daneSoc['wyksztalcenie'],daneSoc['praca']))
## praca nie pracuje uczen lub pracuje
## wyksztalcenie
## podstawowe 22 71
## srednie 16 39
## wyzsze 6 28
## zawodowe 8 14
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import pandas.rpy.common as com
daneSoc = com.load_data('daneSoc', package='Przewodnik')
print(daneSoc.iloc[:,[0,1,2,3]].describe(include='all'))
## wiek wyksztalcenie st.cywilny plec
## count 204.000000 204 204 204
## unique NaN 4 2 2
## top NaN podstawowe singiel mezczyzna
## freq NaN 93 120 149
## mean 43.161765 NaN NaN NaN
## std 13.847100 NaN NaN NaN
## min 22.000000 NaN NaN NaN
## 25% 30.000000 NaN NaN NaN
## 50% 45.000000 NaN NaN NaN
## 75% 53.000000 NaN NaN NaN
## max 75.000000 NaN NaN NaN
Rozdział 2.4.1
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
daneSoc = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/daneSoc.csv",header=0)
fig = plt.figure(figsize=(14,6))
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
sns.countplot(y="wyksztalcenie",color='C0', data=daneSoc,ax=ax1,order=daneSoc.wyksztalcenie.value_counts().iloc[:4].index)
sns.countplot(x="wyksztalcenie", hue="plec", data=daneSoc,ax=ax2,order=daneSoc.wyksztalcenie.value_counts().iloc[:4].index)
fig.tight_layout()
fig.savefig('rys_2.1.png')
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Rozdział 2.4.2
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
daneSoc = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/daneSoc.csv",header=0)
fig = plt.figure(figsize=(14,6))
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
ax1.hist(daneSoc['wiek'],alpha=0.5)
ax1.set_xlabel('Wiek')
ax1.set_ylabel('Liczba')
ax2.hist(daneSoc['wiek'],alpha=0.5,normed=1)
ax2.set_xlabel('Wiek')
ax2.set_ylabel('Częstość')
fig.tight_layout()
fig.savefig('rys_2.2.png')
![](data:image/png;base64,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)
Rozdział 2.4.3
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
daneSoc = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/daneSoc.csv",header=0)
daneUn = daneSoc[['cisnienie.skurczowe','cisnienie.rozkurczowe']].unstack().reset_index()
daneUn.columns = ['grupa', 'level','cisnienie']
fig = plt.figure(figsize=(14,6))
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
sns.boxplot(x="cisnienie", y="grupa", color="C0",data=daneUn, sym='k.',flierprops=dict(marker='.', markersize=10),ax=ax1)
sns.boxplot(x="wyksztalcenie", y="wiek", color="C0",data=daneSoc,ax=ax2)
fig.tight_layout()
fig.savefig('rys_2.3.png')
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Rozdział 2.4.4
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.distributions.empirical_distribution import *
daneSoc = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/daneSoc.csv",header=0)
fig = plt.figure(figsize=(14,6))
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
sns.kdeplot(daneSoc['wiek'], shade=False, ax= ax1)
ax1.set_title('Rozkład wieku')
ax1.set_xlabel('bw = "scott"')
ax1.set_ylabel('Density')
sns.kdeplot(daneSoc['wiek'], shade=True, bw= 1.5, ax= ax2)
ax2.set_title('Rozkład wieku')
ax2.set_xlabel('bw = 1.5')
ax2.set_ylabel('Density')
fig.tight_layout()
fig.savefig('rys_2.4.png')
![](data:image/png;base64,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import warnings
warnings.filterwarnings("ignore")
from scipy import *
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.distributions.empirical_distribution import *
daneSoc = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/daneSoc.csv",header=0)
X = daneSoc['wiek']
Y = ECDF(X)(X)
Xm = daneSoc[(daneSoc.plec == 'mezczyzna')]['wiek']
Ym = ECDF(Xm)(Xm)
Xk = daneSoc[(daneSoc.plec == 'kobieta')]['wiek']
Yk = ECDF(Xk)(Xk)
fig = plt.figure(figsize=(14,6))
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
ax1.step(ECDF(daneSoc['wiek']).x, ECDF(daneSoc['wiek']).y,color='C0')
ax1.plot(X,Y,'.',color='C0')
ax1.set_title('Dystrybuanta wieku')
ax2.step(ECDF(Xm).x, ECDF(Xm).y,color='C1')
ax2.plot(Xm,Ym,'.',color='C1')
ax2.step(ECDF(Xk).x, ECDF(Xk).y,color='C2')
ax2.plot(Xk,Yk,'.',color='C2')
ax2.set_title('Wiek / plec')
fig.tight_layout()
fig.savefig('rys_2.5.png')
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)
Rozdział 2.4.5*
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
daneSoc = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/daneSoc.csv",header=0)
fig = plt.figure(figsize=(14,6))
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
sns.regplot(x="cisnienie.skurczowe", y="cisnienie.rozkurczowe", data=daneSoc, ax= ax1)
sns.regplot(x="cisnienie.skurczowe", y="cisnienie.rozkurczowe",marker="^", data=daneSoc[(daneSoc.plec == 'mezczyzna')], label='mezczyzna', ax= ax2)
sns.regplot(x="cisnienie.skurczowe", y="cisnienie.rozkurczowe", data=daneSoc[(daneSoc.plec == 'kobieta')], label='kobieta', ax= ax2)
legend = ax2.legend(frameon = True, ncol=2)
frame = legend.get_frame()
frame.set_color('white')
fig.tight_layout()
fig.savefig('rys_2.6.png')
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btk4vbfJZTmOeOykhnXXUdTSud8xQJYc8yAABQWKHq3UHVXMVb2z5DkZaWEcdEWltVO729TDPCaBpqx3DwoIKcW/Qhamk3raPZXuWDvBy7uMeEBQEvAAAlYFmWVi2ZruE+nW5b0qol06uyx1OY547ycj1f3bG0YsmcbNuiHQMAAGNMoerdQdVcxWtZllpWrZaG269allre8lb2syHkp1LKd3UpSKZlRSJFfQ3zfl5HMr1Kualj7RjG3tedgBcAgBKZM6NFa1deeko1bGtTrdauvFRzZoxcVVBJYZ47Si8wRrFETt29GRkjOUWcTgwAAMJlpOrdQdVcxTtu9hy1rb1dkdbWE26PtLaqbe3tGjd7ToVmhrMx2GfX64vJsm1ZRew/AxOoL9envlyfZEnWGGnHUMjYqkcGAKDKzJnRotntE9TZnVQik1dTfY3aJzeG4rfGYZ47SieZyas/6cqypUiEYBcAgLEq6+e04qLlRY1rrNKPu4+bPUf1s2Yr17lPfjIpp6lJtdPb2c+GiAkCeX0x+ZmMbCdS1CFqxhil3LTSXkqW5ciu0vU5msb+OwQAoMIsy9KMKeE8wCHMc8foyru+YsmcPC+QwwFqAACMeY3RBs2bGP4qV8uyVDfj4kpPA2fBTybl9vfLsqyi++xmvKyS+ZRkmfMi2B3E7hwAAADDCgKjo/0ZHe471o6BcBcAAAAlFLiu8t3d8vr7ZDtOUe0YvMBVbzamRD4hy7ZkWeHZs/a5CT0T23JOz3H+RNkAAAAomjFGiXRe8ZQrx7EUIdgFAABACRlj5PX3K0gmZEWiRbVjCEygRD6prJ+VY0dkh6jPbp+b0K/7tmprYpcCGd2m68/6uQh4AQAAcIJ01lVfMifJos8uAAAASs7PZOT19UrGkhWJFvWYtJtS0k3Lsmw5IWrHEDsW7G47FuyOhvC8ewAAAJTUUJ9dP5BTxEfhAAAAgHNhgkDu0aMK8nnZjiMVcf5dzs8rkU8qMH6oKnZjx1XsmlEKdgcR8AIAAJzngsCoN5FVJu8r6tihCXd741lt2Nql/d3Q4JpvAAAgAElEQVQJfecvf6/S0wEAAMAZ8BIJefF+WbYzEO6ehm98xXMJuYEr23ZkW+EId2NuXM/Etmpb8rWCwW69Xatl4684p9cg4AUAADhPndxnNxqSPrvdvWk91XFQW187omB0ix8AAABQYn4+L7+3VybwZRfRZ9cYo6SbVNrNynGc0FTtFhPsXjPhCi1unqNau7i2FMMh4AUAADgPpbJ5xZOujBSaPrsHepJ6cvMBvbg3VumpAAAA4AyZIJDX36cglZYVicgqIqjNeFkl3aQkySmiyrca9Objeqq34zTB7jwtbp59zsHuIAJeAEBJGGO0tyuuPYdTsnxf0yY2yLKKaKgUUkEQ6Pmdh3UkntXE5jotnTNJdkg+5o5zY4xRZ3dSiUxeTfU1ap/cWNVrfajPrhfIcexi2pxV3J6uuNZvPqBXX+8veH99bTg2+wCA8Mq42UpPoaz6sv2aUDe+0tNABWS9rOoidaP+vH46La8vJsmSFTl9HOkFnuL5hLzAC03Fbm8+rof2bdMLva8UDHbHHRfs1oxSsDuIgBcAMOp27otp3cb9iiVziji2PD9QS2OtVi2ZrjkzWio9vVH36POdeuS5TmVy3tBt9z6xS2uWteumpe0VnBlKbXCt9yZyQ7e1NlXnWh/ss5vN+4o4tpwqb8dgjNGrr/dr/eYD2nsoUXBMU31U1y+4UIsuayvz7AAA5xMv8PQvm36md132DknV/ffnaMh6Wf39C9/XZ5d+oiRBH6qXF3i695Wf632z1ypij05kGLiuvFhMxnVlFVGBO9iOIeNlZNuRUIS7vW5cz8S2aHty9wjB7pVa3Dxr1IPdQQS8AIBRtXNfTPet36XASMcXMfYmcrpv/S6tXXlp1QVf5+LR5zv1wIbdOvnv8UzOG7hdIuQdo45f68ertrV+cp/dSJUHu4Ex2rk3pvUdB3SgJ1VwzITGGt24cKoWXz5J0Ygt1wvKPEsAwPlkc/c2vR7v0ububVrUtqDS0ym5B3c/okQ+oQd3P6LbL397paeDMuro2a5DqW519GzXkskLz+m5jDHy4v0KkklZTqSocPf4dgz2KAXMpXQ0369f920dIdit07UT5umqEga7g6r/agEAQsMYo3Ub9w976FFgpHUb92t2+4Sq/gh7sYIg0CPPdZ4S7g4x0iPPdeqtSy6iXcMYE5a1nsrm1Z90JVV/n10/CLT5lR498cIBHY5lCo6ZOL5OKxdN04JLL5DD9xQAoAy8wNNvD26UJP324EZdecEVo1bZWI2yXlYbu7dIkjZ2b9FtM9dQxXue8AJPz3VtkiQ917VJC9vmnfVa95NJefG4JMkq4hA1L3DVn0/KD9zQBLvP9G3RjuSegsFug1Ona8aXJ9gdVP1XDQAQGp3dyRM+ql5IbyKnzu6kZkxpKtOsSuf5nYdPaMtQSCbn6fmdh3XNFVPKNCuUQ7Wv9ZzrK5bIyvdN1bdi8PxAG18+og1butTTVzjYvfCCcVq5aJquuLhVth3+Xw4BAMKjo2e7Em5KEcdWwk2NSmVjNXtw9yNy/bwkyfXzVPGeRzp6tg9Vzybd5FmtdT+XkxfrlXy/qGA3MIES+aSyfk6O7VR9uFtMsPvmyYt0Zd2lslXe1hLVfeUAAKGSyORHdVy1OxIv7rCNYschPKp1rftBoFgid1yf3eoNQ/Oer9/tPKwNW7sUTxW+Tu2TG7Vy0TTNmj42qv4BAOFyfEXjoHOtbKxmx1fvDqKK9/xwrmvdeJ7cWK+CXF52JCIVEe6m3ZSSblqWZcup8j67R/J9+nXf1hGD3WvHX6mlE+aoZXyDkomcvKC8LcTG3k8kAEDFNNXXjOq4ajexubiNbrHjEB7VttaNMYqn8kqkq7/Pbjbv6dkd3fr1ti6lsoUr4C+Z1qyVi6Zp5oXNBLsAgIoZqmg87u+is61sDIPjq3cHUcV7fji+endQMWv95D67duT0MWPezyueT8oYv+oPUDuS79MzfVv14mmC3auaZylqRxSpYAsxAl4AwKhpn9yo1qbaET+63tpUq/bJjWWcVeksnTNJ9z6xa8Q2DfW1ES2dM6mMs0I5VNNaT2YGDlCTqrvPbirr6tfbDunZHYeUzfsFx8y9uEUrFk7V9Enhb+ECAAi3QhWNg8ZiFW+h6t1BVPGObWe71v1sdqAdgymuz65vfCXyCeU8V47jyLKqN9w9ku/TM7Et2pHaU/D+BqdeyyfM06KmgWC3GlTHLAAAY4JlWVq1ZLruW7+r4OFTtiWtWjJ9zFTk2batNcva9cCG3YUPWrOkNcvaOWBtDKqGtZ5zPcUSuarvsxtP5bVh60E9v/OwXO/Uj6pZljT/kgt0642XqKnWke8Pd2ohAADlU6iicdBYrOItVL07iCrese1M17oJArm9vQqy2YGK3dNsd40xSrlppb2ULMuR41RvsNtzLNh9ccRg90otarq8aoLdQdU1GwBA6M2Z0aK1Ky/Vuo37FUu+Ud3Y2lSrVUuma86MlgrObvTdtLRdkvTIc50nVPLW10a0Zln70P0Ye45f68dX8pZ6rYelz25vPKuntxzUppd75BdIwW3L0qLLJ2rFwqma3DpO48ePU39/ugIzBQDgRCNVNA4aS1W8I1XvDqKKd2w607XuJ5Ny+/tl2XZR7RgyXlbJfEqyTFUfoNaTjx0LdvcWvL/Rqde1VRrsDqrOWQEAQm3OjBbNbp+gA0dSMo4jK/A17YKGMVO5e7KblrbrrUsu0vM7D+tIPKuJzXVaOmcSlbvngcG13tmdVCKTV1N9jdonN5ZkrYelz+7hWEZPdRzQll1HClY3RxxLS2ZP0o0LpmpCY235JwgAwGlk/ZxWXLR86M+OY6uxsU7JZFa+H5wwrrFKw54z0Z9PaMW0a4saR8A7tpy81oeTTsdVl8zL+J7sItoxeIGnRD4hN/CO9dmtzn8H9uRj2hDbop0jBLvLJ8zXwqbLqjbYHVTdswMAhJZlWbr4wma1tDQoFkvJK/DR7LHEtm1dc8WUSk8DFWBZlmZMKW3P2FQ2r/5kdffZPXgkpfWbD2jHnt6CHUtqoraumTtZ1115oZrGjY2DFgEAY1NjtEHzJs4Z+nMkYo/pPe3kcW26ZebqSk8DFXDyWj+Z8X25sZiC/rTkRE7ba9cYo6SbVNrNyHEiVXuI2uFjFbvDB7vjjrViuCw0VfrhmCUAAMB5KOf66kvm5HlB1fbZ3XcoofWbD+jl/X0F76+vdbR83oW69oopGlfH1hMAAKDaGWPkJfoVxJOS4xRVtZtxM0p6SUmWnCLGV8Lpgt0mZ9yxVgzlDXaN7yt4/aDUMvesn6M6rzgAAMB5LAiMYomsMkN9dqsr3DXGaNeBfq3ffEB7uhIFxzTWR3X9/Au1bM5k1dZUZ/UGAAAATuSn0/L6Y5KxZBXRZ9cLXMXzSXmBW7V9dg/nY9oQ69BLqX0F7286VrG7sMzBbhBPyN28Rd7mLTLJlLTw7876uarzygMAAJyHjDFKpF0l0nnZdvX12Q2M0Uv7Ylq/+YBe70kVHDOhsUY3LJiqJbMmKVql7SQAAABwosB15fX2ynieLMc5bdvcwXYMGS8j245UZbjbnevVM31bThPsztfC5ssUscpTkGCMkb+3U+6mzfJfflUyhZqbnbnqu/oAAADnoWzOU28yJ2NUdRW7QWC0bfdRPdVxUId60wXHXDC+TisXTtXCyybK4YBBAACAUDBBIK8vpiCdkRWJDIS7p5Hzc4rnBz7FVa3B7obYFr2cHj7Yva5lvhY0lTHYzWblbt0ud1OHzNHeUX/+6vsqAAAAnEf8IFBvPKe86w8Eu1V0yLDnB+p49Yie6jioo/FswTFTWsdp5aKpmvemC2TbVTR5AAAAjMiLx+UlErJsu6h2DL7xFc8l5AZuVR6gdih3VM/EtujldGfB+5udBi1vubKswa7fdUjups3ytu+UPK/woEhEkbmzz+l1CHgBAAAqwBijeCqveDpfdX12XS/Q7146rA1bDqo/lS84ZvqkRq1cNE2z2yfIsgh2AQAAwsLPZuXFYlIQyC6iYtcYo5SbVtpLybKcqgt3D+WOakNsi16pkmDXeJ7c7S/K3bRZwYGuYcdZLRMUXbxI0QXzZNXXn9NrEvACAACUWTKTV3/SlWVJ0Uj1bJCzeU/PvditZ7YdUirjFhwzc2qzVi6apkumNhPsAgAAhEjguvJiMQWuOxDsFhHu5v284vmkjIKqa8cwEOx26JX0/oL3N0cadN2E+ZrfdGlZgl2/N6a+p7Yp9fxmmUym8CDLknPZJYouXiRn5sWjtp+urq8MAGDMCIJAv9nWrZQbqCFqa8msNtn05cR5Lpv3FEvmFPimqip201lXv9l+SL/ZfkjZvF9wzKz2CXrzomlqn9xU5tkBAFA5QRCo7zfPKpmOyxvXrHFLlrKnReiYIJDX36cglZIViRZVtRuYQIl8Qjk/J9uOyFL1rPuuY8Huq8MEu+MjDVo+Yb4WNF0qp8TBrgkC+bt2Dxya9tqeYcdZDQ2KLJqv6KIFssc3j/o8CHgBAKPu0ec79chzncrkPVmyZGT0n+te1Zpl7bppaXulpweUnev5iiVzct1AjmPLcaqj8jWRzuuZrV167sVu5b3glPstSVfMbNXKhdM0dWJD+ScIAEAF9T72iGKP/FJBNiPLsmSMkX3Pf6plzc1qXb2m0tMDiuInk3L7+2VZlqxItKjHpN2Mkm5SlmVXVdVuV+6INsS2jBjsXjdhgeY3XVLyYDdIpeR1bJP7QodMf3zYcXb7RYouXqTI7MuLOsDubFXPVwkAMCY8+nynHtiwWzI64bCoTM4buF0i5MV5IzBG/Ym8UllXkUj19NmNJXJ6estBbXr5sDzfnHK/bUkLL5uoGxdO06QJ59YPDACAMOp97BEd/fn9p9weZDNDtxPyopr5uZy8WG/RfXYlyQtc9eUTMoFfVX12qyXYNcYoeP2A3I2b5e18WQpOLZCQJNXUKHrlFYosXihnUlvJ5nM8Al4AwKgJgkCPPNc5EO4WYqRHnuvUW5dcxEfbMOYN9dm1pUikOtZ7T19GT3UcVMerRxSYU79RI46lxbMm6cYFF6qlqa4CMwQAoPKCIFDskV+OOCb26C81YdVq9rSoOsb35fb2KsjlZEciUhFB7UA7hpSyflaO7ciqknD3YPaINvR1aFf69YL3j4806voJ83Vl06VyrNJ9L5p8Xt62Y4emHe4Zdpw9qU3N1y+Vuexy+dHiqqVHCwEvAGDUPL/zsDI5b8QxmZyn53ce1jVXTCnTrIDyyrm+Yoms/Crqs9t1NKX1mw9o++7egr9/qYnYWjZ3sq6bf6Gax9WUfX4AAFST5PPPKsgOc0DSMUEmo+TvnlfzsmvKNCtgZMYYeYl+BfGk5DgD4W4RMm5GSS8pyZZTJcHugWyPNsS26LVM4WB3QqRR101YoCubLilpsOv3HJG3abPcrTukfL7wINtWZM4sRRcvUs2M6WpsrlMykRu+urdECHgBAKPmSDw7quOAMAkCo1giq0zeV6RK+ux2die0fvMBvdTZV/D+uhpH186bouvmTdG4uvJWGQAAUK3c3t7ixh09UuKZAMXx02l5/THJWLKKDHa9wFM8n5AXuFXTZ/f0wW6Trm+Zr3mNpQt2je/Le/lVuRs3K+gs3BJCkqzmZkWvWqDIwvmyGwfOqrCsyu3/q+MrCAAYEyY2F/eR7mLHAWFgjFEinVc85cpxLEUqXLVrjNHug3E9ufmAdh8sfOBDQ11E18+/UMvmTlZdDdtBAACOF21tLW7cBRNLPBNgZIHryuvtlfG8gQO8isgXB9oxJJXzs7LtSFWEuwPBbodeyxwoeH9LpEnXlTjYDeIJuS90yOvYKpNMDTvOueRNii5eJOfSmbKqqEVL5b+KAIAxY+mcSbr3iV0jtmmor41o6ZxJZZwVUDrJzECwK1W+z64xRi939unJzQe0/3Cy4JjxDTW6YcFULZndpppIdXwEDwCAatO49Br1/PTeEds02PX1arx6aRlnBbzBBIG8vpiCdEZWJDIQ7p7uMcYo5aaV8dKSZVdJsHtYT8e2aPcIwe71LQs0r3Gm7BIEu8YY+Xv2yd20Wf4ru6QCZ1RIkurrFF1wpaJXLZTd2jLq8xgNlf9qAgDGDNu2tWZZux7YsLvwQWuWtGZZO4dRIPRyrqdYIlcVfXaDwGj7nl491XFAXUfTBcdc0FynFQunauFlEyteYQwAQLWzbVsta27W0Z/fP+yYlptuZk+LivDicXmJhCzbLrodQ9pNK+WlJFlVcYDa69nD2hDr0O7MwYL3lzzYzWTlbt0md1OHTG9s2HH21AsVXbxIkbmzZJX50LQzRcALABhVNy1tlyQ98lynMvk3KnnrayNas6x96H4gjDw/UCyRVS7vKxJxKtpn1w8Cdbx6RE91HNSR/sJ9rSe31GvlommaN/MCOXblewIDABAWravXSJJij/5SQeaNSl67vl4tN908dD9QLn42Ky8Wk4JAdhEVu5KU83NK5FMyxifYleR3HZK7cbO8HTslb5hPnUYiisybM9CG4cLwHAxOwAsAkDTw8ZTO7qQSmbya6mvUPrnxrJvE37S0XW9dcpE2vtyjlBuoscbW4svbqHIoYDSvO0onMEZ9iZzSWVeRiKNIBdsbuF6gTS8f1tNbDqovWfg034vaGrRy0TTNntEiewyvp8AYBUGguhqn4pXUAIDqkfWyqouc+5kPravXaMKq1UpvfF6RdFxe43iNu+pq9rTDGK3rjhMFrjvQjiHvDgS7RYS7XuAqkU/KDTzZtiPLqmy4+3r2sJ6OdWjPMMFua7RZ109YoCsa3zTqwa5xXXkvviR302YFBw8NO85qbVF08SJF58+TVR++dUzACwDQzn0xrdu4X72J3NBtrU21WrVkuubMOLseQ7Zta/mVF6qlpUGxWEqeF4zWdMeMUlx3jK6BA9RcJdJ5WbZV0WA35/p6/sVuPbO1S4mMW3DMmy5s0spF03TptPFj+hcFrucr4jhqrIuosb5Gtm1RoQwAkCR5gad7X/m53jd7rSKj0GPUtm1NWL6cPe1pjPZ1x7E+u/39ClJJWZFoUVW7px6gVtlgd3+2WxtiHdqT6Sp4fymD3aA3JveFDrkd26Rs4U+7ybLkXH7pQLXum2ZUdP/sB56sc9jP8l0HAOe5nftium/9LgUn9cztTeR03/pdWrvyUsLGEuC6V7901lVfKi9jVNHq0HTW0293HNJvth8a9gDDy6eP15sXXaQZU5rKPLvy8QMjGaO6GketTfWqibKNBQCcqqNnuw6lutXRs11LJi+s9HTOG1z30eUnk/Li/ZIsWZHier+m3JRSblpWFRygVqlg1wSB/F2vyd3YIX/3nmHHWY0NiixaoOiiBbKbK7d/DgJPlmWpxq7V+NomXVB/9v/+Y2cMAOcxY4zWbdx/Ssg4KDDSuo37Nbt9wpiuBiw3rnt1cz1fvYmcXC8YOJCsQl+CRDqvX2/r0rMvdivvnlotZEma+6ZWrVw0TdMmNpR/gmVgjJEXGNU4tsY3RNVQF+V7AgAwLC/w9FzXJknSc12btLBtHtWkZcB1Hz1+Lic/FpPxPVlOcdcw5+eVyCdlFFS8Yrcz060NfR3aO0ywe8GxYHfuKAe7QTIlr2Or3Be2yMTjw45zZkwfqNaddZmsIvsYj7Yg8GVZlqJ2VPW1jap1akblefmOA4DzWGd38oT2AIX0JnLq7E6O6crAcuO6V6c3+ux6ikTsgXC3AvqSOT295aA2vnRYnn/qbwFsS1pw6UStWDhNk1rqKzDD0vP9QJKl+lpHbQ01FftaAADCpaNnu5JuUpKUdJNUk5YJ1/3cmSCQG+tVkM3KdiJFhbu+8ZXIJ5TzXDmOI0uV2y91Zg5pQ2yL9maHC3bH6/qWBZrbcPGoBbvGGAX7Xx84NO2lV6RgmPYptTWKzp+n6FULZbdNHJXXPlPGBDImUNSuUWNtg2rtmlEvWiDgBYDzWCJT+ICmsx2H4nDdq08yk1d/0pVlS5FIZTbHR/ozeqrjoDpePTLQjuAkjm1p8aw23bhgqlqbw3fww+kExsjzAzmWpeamWtXXRqjWBQAU7fgq0kFUk5Ye1/3cGGPkJfoVxJOS48guItg1xijlppX2UrIsR06FKlElaV/mkDbEOrQvW/jwspIEu7mcvO0vyt24WUHPkWHH2ZPaFF2ySJF5c2XVjE6V7JkwJlAQGEWdqOoi9aqL1I56n+Hj8d0GAOexpvri/qIrdhyKw3WvHtm8p1gyp8A3Feuze6g3rfWbD2jb7qMyBdp2RCO2ls6ZpOvnT9X4hrG3JjwvkG3baqiLqn5igwxn1wAAzsLxVaSDqCYtPa772fMzGXl9MSkwsiLFxXMZL6tkPiVZpqJ9dk8X7E48FuzOGcVg1z/cI29Th9xt26V84QOH5TiKzJml6OJFsi+aWpFiAS/wZYxRfaRe4yL1JQ11j0fACwDnsfbJjWptqh2xXUBrU63aJzeWcVZjH9e98lzPVyyZUz7vKxJx5Djl3/y9fjipJzcf0M59sYL310YdXTtvipbPm6LG+uIO1wiLIDAKgoED0yZMqFPjuBq1NNcpFvPlDffxOgAAhlGoinQQ1aSlw3U/O4HryovFFOTzsiMRqYgCXC9wFc8n5QXesT67lfmU075Ml56Odagz213w/onRCbqhZYFmN8wYlWDT+L68l16Ru2mzgs7Xhx1njW9W9KqFiiy8UnZD+c+mCAJfkhSJRNU2rlVp48r3hjlwpUT4TgOA85hlWVq1ZLruW7+r4IFftiWtWjKdj0mPMq575QSBUSyRVSbnD/TZjZT3I23GGO3pimv95oPadaC/4JhxdRFdf+WFuuaKyaqrGVtbNc8bCNQb66NqHBeVzRoHAIyCQlWkg6gmLR2u+5kxQSCvv09BOiXLiQ6Eu6cRmECJfFJZPyfHdipyiJoxRvuyAxW7pwt25zRcPCr/hgn643Jf2CKvY6tMKjXsOOeSmYouWSjnkpmy7PJ+Gs8Yo8D4ithRNUUbVRepUyRqK+pEJQ1TYVxCY+tfDQCAMzZnRovWrrxU6zbuP6GitLWpVquWTNecGS0VnN3YxXUvL2OM4qm8EmlXjmOVvc+uMUav7O/Tk5sPqLO78D+EmsdFdcOCqbp69iTVRCt7AvJo8oNAMlJdjaPWpnFj6r0BACpvpCrSQVSTjj6u+5nxk0m58bgsSZZz+k9mGWOU9tJKuWlZli2nSoPdtugE3dCyULMbZpxzsGuMkb97r9xNm+W/+poK9i6TpPp6RRdeOXBoWsuEc3rNsxEEvizLUq1Tq4boODlWdext+S4DAGjOjBbNbp+gzu6kEpm8mupr1D65kQrSEuO6l8fQAWpW+Q9QC4zRjj29emrzAR08mi44prWpVjcunKqrLm9TpEJ9gEebMUa+b1QTtdXUUKNxdVHWNQCgJLJ+TisuWl7UuEaCxlHDdS+On80e67MbFF19m/NziueTkkxFK3afjnVofzmC3UxG7pbtcjdtlon1DTvOnjZV0cWLFJk7q+iexaOlULVute1tz9/vMgDACSzL0owpTZWexnmH61462dzAAWrGlP8ANT8ItGXXUT3VcUA9fdmCYya11GvFwqmaf8lEOXZ1bRDPlu8HsixL9bURNY2LjpnAGgBQvRqjDZo3cU6lp3He4bqPzPi+3N5eBbncQCuGIoJa3/iK5xLKB3k5dkTl7rNrjNHebJc2xLaUJdj1D3bJ3bhZ3osvSZ5XeFA0qsi8OYouXiRnyuRzer2zYUwgY8yxat1mRezqPReDgBcAAIwpQweouYEijl3W3657fqBNL/fo6S0HFRvmEL2pExu0ctE0zb24ZUz0oA0CIz8IVFfjaHxDnepr2V4CAIDzkzFGXn+/glRSsp2i+uwaY5RyB9ox2LZ9LNwtH2OM9ma6tKGvQ/uzhwuOmVTTohsmLNCscwx2jevK2/HSwKFpXYeGHWdd0Kro4kWKzr9CVl3dWb/e2fJ9TxEnqnGRBtVXYbVuIezAAQDAmHDKAWplrB7Nu76e33lYz2w9qHi68KEKM6Y06c2Lpumyi8aHYpN4OiccmFYflT1GqpABAADOhp9MyovHJUmWU1zclvNziueS0v/P3psGx3Gdd7+/3mbFABgsBECQ4E6REkUCIiXRskRJtmTJr3NVTqR4iW/sLK6b1ZWUUilXva6KnaQqKed1Jc69Tmx/uLGdKM5NosRvHMsSbUkmRYkWKVAESErcN5AgiIWYwWD27j7nfhgCBIgZYAAMgBnw/L6oNHPm9NPdpzkP/vOc/6NJDGMJmv+m+jgQ6eJqZhphN9zOXYG2eeWvYngY+0gXdvcJSOff3YamYdy1KVetu3Z+x5sLt6p1PdT6Q2VdrZsPJfAqFAqFQqGoaKSUjCZzDdR0fXEbqKUyDj9/7zoHj18nmcm/tWzTqhoe62hlXUv1osW1ULiuADT8Xp366gCWWR5NJRQKhUKhUCiWCjeTwY1EkK5TtLA7ZsdgC/umz+7iiZk5Yfcab0S66M0M5h1TCmFXCoF79jx251Hci5cKjtNCVVgdOzDbt6NXL751neu6GIZB0PTjNwMVW4ihBF6FQqFQKBQVSzJtMxLPImBRfXbjKZu3jvfx9nv9ZGw375i714Z5rKOVVY1VixbXQuAKCVLitXRqAj58XqNiE1+FQqFQKBSKUiFdl+zQEHYiiW6YRYm7UkridpLkTTuGxWyiJqXkQuoaB6YRdps8dTwS3sHmeQi7YjSO03UM+91u5OhowXHGmjasXR0YmzeiLXr1sgApsQwPNf4QVoVV6+ZDCbwKhUKhUCgqjqyd89l1XIGh6yxWSjgSz3DgWB/vnBzAdsWU9zUNdmxo4NH2lTTVBRYpqtIjpES4Ao9lUBW0CPisZeEXrFAoFAqFQjFfpJTY0SjJmIuws+hlbsdQvLDbzubA6jkJu1JKRM8V7CNHcU6dBTE1TwbA68XavguHEdoAACAASURBVA1rZzt6Q/2sjzNfXOFg6CYB04/f9KNry6chsBJ4FQqFQqFQVAxjPrvJrItl6Bj64iRlN2Jp3ui6xrtnBnMVrbdh6Br3bW5kT/tK6qsXvxFEKZBS4giJZehU+ZSvrkKhUCgUCsXtuIkEzsgIhqmh11ahaVlgam44EUc4jGZHsYWzqHYMOWG3lzciXVzLDOUdM29hN5PBPv4eTudRxNCNguP0phVYuzow79mK5vHM+jjzIeetK/AYHmq8YSyj8qt186EEXoVCoVAoFGXP7T671iLZMfQPJ9nfdY3u80PIPLm7Zejcv3UFj2xvoabKuygxlRrbcTENA5/HJBSwFrU5nUKhUCgUCkUl4KZSOCNRcF00w0Qr4kfwiXYMhmEsmh2DlJLzqV4OLKCw6/YP5Kp1j78Pdv4GwxgG5ta7sHbdh97asugWX7lqXQO/6SewzKp181FxAu+JEyf4q7/6K9577z0CgQC/9mu/xm/+5m8C8OMf/5hvfvObXL16lXXr1vH888/z8MMPL3HECoVCMTNSSnr644ymsoT8HtqaqtA0reDryxUhBIdPDjAUS9NQ7eOBrSvQ51GhWer5KplKvhbxZIYDx64zHE9TH/KxfUP9gj8HvYNxfna0l/cvRfK+b5k6D21r5oP3tlDlr7wqgInN0upCATyWapa2mKh8VqFQLEeklGR6LuPG4xhVVXjb1kz5vk47aXxmZe50mQvR9Ai1vpqyna+SWaxr4WYyuNEowrbRTRNKYMeQyCYIeoIlj3VM2H0j0kVfAWG32VPPI+F21npW4LFmV5wgHQfn1BnszqOIq70Fx2m1NVg727F23IsWWFzLMiklQrh4TQ8hby1eY3GrhZeSihJ4o9Eon//85/nlX/5lvv3tb3P16lV+67d+i5UrV7J27Vq++MUv8o1vfIPdu3ezd+9efv/3f59XXnmF5ubmpQ5doVAoCnLycoRXO68wPJoZf60u5GXz6lrOXIlOef2JXavZuia8FKEuKHsP9/DKoR5SGWf8tX99/RxPP9jGUw+0Lfl8lUylXous7fLDty6yv+sa2QmNzH78dg97drTw8PaVJT/mxb4Y+472cvbqSN73NQ2q/BZBn8VAJEX/cJKq1sr4Q8sVAiR4LYOaKh9+T0WlgcsGlc8qFIrlSPLUSSKv/gQncuuHUTMcJvzERwhs2Qrktqn/65n/zWe2PIepL//voLST5q/f/Sb/84E/LImoXer5KpnFuBbCtnGiEUQmi26aOXG3CFzpEsuMkhVZDN3kdjsG28nyn2d/xKfu+kUsszTio5SSc6mrHIh00ZfJb5PQ4q3nkdp2NgZWIaXgtSsH+PDqR4qqKhbREex3u3G6jyETyYLjjE0bsHZ2YGxYt+hFSUI4aJqB3/AR8C3/at18GF/5yle+stRBFMvbb7/Nf/3Xf/Hd734Xj8dDfX09pmny0ksv0dvbS2NjI7/7u7+LaZrcddddvP7669i2zc6dO4uaP5nMLvAZLB90XcPv95BO24g8XoSKOw+1JubGycsRXtx3jmTGnfR6JJ7h6NkhHEdgmre+nFJZl5OXh1kRDtBY61/scGdNseti7+EefnDgAo4z2YzfcQWneiJYps7GWYhopZ6vkinHazHTuhBCMhxL8+O3L/Pakau47uQxriu40BfDMnTamkLzjkdKydmrI7y4/zyvv9vLcCwzZYyua4QCHsIhLz6PiaZpZGyXC9dGqKv2UVemvrtCSFwhsEydUMCirtpH0G8tmsVFsZTiOyQYrAyLjIXOZ0HltMWichdFPtS6mD3JUycZfPHfEKnUpNdFOk3y5Pt4VjRhNTTy7sAxjg+9R8AKsLKqsn60msu6+M9zP+J89CJJJ8U99VvmHUOp56tkFvJaSCFwohHcSARN09EK7HjTdQ2f1yKTsZEil08m7CSx7AgSCgqnb107RF+in7TIsKZ69fxilZJzyav81+AbHBp5j7ibmjKmxVvP/2h4iA/X7aLeU4OmaZyLXuTiyGW8ppd6f13Bud3zF8n89HWyr/wUceVqfiuGgB/r/p34nvkYnl33odeFF03cHavWtXSTkKeKak8Ij+FZ0h2vt6+L2eIPeLFCVXM6dsX9bHb7jaqpqeHkyZPE43EeffTRSe/dfffdHD9+fDHDUygUiqKRUvJq5xXy5YixhA0SYkkbr8eY9G+fkPBq5xW2tNUuC7sGIQSvHOop3JtAwiuHenhy16qiLAVKPV8lU2nXYsxnN5aw0TTJW8evTxv7G919PLStec6xCyk5eSnCvqO99A4l8o4JV3lvPoNTc5DcHHDwxHXWr6wum+dRSonrynFRN+i30MskNkUOlc8qFIrlgpSSyKs/Ia9RfW4Akdd+irVpI4f6jgBwqO8I7Y3blnUVb9pJ09nfDUBnfzfPrH96XpWmpZ6vklnIa5FroBYFNDSzeAuujJsllh0FJPo069p2spyNXgDgbPQCH2jeNacqXiklZ5O5it3r2fwVuyu9DTwSbmeDv3Xy35LC5dTwWQBODZ9lQ83aSWK0TCaxu49jH+lGRqMFY9BXtWLtbMfcehdakdXNpWKsWtdn+AjeodW6+aiof1E7Ojrw+/387d/+Lb/zO7/D4OAg3//+9xkZGSEUClFTM7kCqaamhnPnzhU9v65rqlt0kRg3K3+MMqsAUiwdak3Mnkt9MSLxDLfrLlnbzW2l1nJbqm1X4L3NHzMSz9B7I8Ha5upFjHj2FLMuDh7vJ5V1pm0mm8o6HDkzyAe2tcx4zFLPV8mU67XIty4S6Swjo1kk4PUaHD0zSMaePvaM7fDepWHaNzXO6viukBw7N8Tr7/YyEJla6QDQWOvn8ftaaaz18b8PXJx2vlgyS380SWvD3H5tLwUTRV2f1yIUsDDKQLQvljvpO2Sh81lQOW2x3EnrTlE8al3MjtSlS7jRyJR8diJuZJj3T+wj7iRA04g7CY7feI/7WzoWL9B5Mtt18aOze7Hd3G4K283yo4t7+dTWX5zz8Us9XyWzENdC2DbO8DDStjGLtLAydB1XuIxkY2ScDFYR3rxv9h/BcXOWaY7rcKj/CI+t/mDRcUopOZO8wv7howWtGFq9jTxal7NiyFd8cDp6gZSTBjRSTpqLsctsDm/A7e0j0/ku9nsnwXWnTgxgWXjuvQfvrg6M5qai4y4FUkocXDy6RdAXwlsie4tSM5Z/G7oO5hxKeOdBRQm8NTU1/N3f/R1f/epXeeGFF9i0aRO/9Eu/xIkTJ4DcDZ8PdXXBsqm+qRSqq8t/i7hicVFronguDiTydqvP2gJtgqqlQd5xUjcIh0tvzr8QTLcuEredbyHiWVHU+ZZ6vkqm3K9FdbWfrO0wFE3jYlA7IYaUnWsCNhPJrKCmprjmDbYjePtEH3vfvsxQNL+wu7opxNMfWEPH5hXousZ7F25gFNMlWTOKjqOUOI6LrusEvCahoKfim6XdCd8hC53PgsppZ8udsO4Us0etiyK5ZE9pIDUVydne45gttwSZzsGjfOiu3ZhFNqwqF4pZF2k7TWd/FxNV787+Ln79/ufwWbOvNC31fJVMqa+FlJJsJIKTiqOHfEBxc0gpGc0mGEjE8PoMfP6Z8+isk+VM5Pyk9PZM5Dwf2fwwnhnESikl78cu8dO+TnpT+ZuntQVW8GTzLu6qbiuYA7hCcObiufEfKnRHMNJ5iMSVN3F6rxc8vrmigaqH7idw33Z0/+KuOVcINMBn+Qh5ghhFeAaXA1VVi/9sVta/psCuXbv493//9/H/37t3L01NTYTDYaK3lY9Ho1Hq6vL7ieRjeDihqh2KxDB0qqv9xGKpm524FXc6ak3MHs11cfJeK4mcsC9dQt5xmnCJRPJvKy8XilkXQUufdL6FqPLoRZ1vqeerZMr1WhiGTrDKx+XeCPFkNu8PGH5Lp7A/wy0CHp2RkcLNHiBXFX/45AD7u64RS+T3Jl3bHOLx+1q566b1yehoTgDWhItbhNeeJt0Z4ygVjivQNA2fx6DKb+Hz6CAFiXiaSl3RpfgOqaQfbBYynwWV0xaLyl0U+VDrYnaksHALVfvdJGEnGdI9OO4t+WE4GeP1029XTBXvbNbF/3fyB2ScyflGxsnynXdenFOlaannq2RKeS3cZBI7GgXkTZ/d4vzrM06W0UwcDAgFfSSSGeT0jwAA+668he06k16zXYefnHmzYBWvlJLTiR72R7q4XqBid5W3kUfrOtgQyFkxJOKFz+P08DlG00mCo1nazkdZdSmGZQucfIN1HWvLZjy7OjDXtIGmkXSA0am9KkqNlBJXOliGh6AVyFXrZiGeXfhjzxdD16mq8hGPp3O7cmdJTe3cdwRWlMCbyWT48Y9/zJNPPklVVe6k33rrLTo6Oqiurh6vfBjj+PHjfOxjHyt6fiGkMtKfJa4rpjTuUdzZqDVRPK0NQcJVXoZv+5K0TCO35ccVGIaOZehTbM3qQl5a64MVc62nWxe77mrkX149SyqTN7UAwO812bm5sajzLfV8lUw5XgspJbFElkjCJpW20dCmNFED2Laujh8dvEwmWzh2r8fknrV1eT8PkM46vP1eP28d7yORzj/PxtYaHutoZV1LCE3TyOVht+ZrCgeoDngYKSAMA9QEPTTVBgrGUQrGLBg8lk5NwIPfa45XZyyndXwnfIcsdD4LKqedLXfCulPMHrUuisNsXY1RG8aJRPK+L5Hc8LqM1Pmm+PQe7O3k3vp7KsqLd6Z1kXbSHL7elfe9w9e7+IV1T83KL7bU81UypboWwrZxIhFENotumoBG3qYot+FKl9HsKFnXRtcNTJErUJAuODMIebaT5XTkfN73TkfO82DTzklevDkrhh4ORLrpzw7n/Vyrt5E94XbW+VeiaRqulIW9sAHXsRnoeocHzgzQMJB/JxuAFqrCuq8ds307+s1mXzPNXSqkcEEDr+Gj2lONoRk3Y6+gnOamLYMrxKLHXVHGQpZl8Y1vfINvfvObOI7Dm2++yQ9/+EM+97nP8YlPfIKDBw+yb98+MpkML774IpcuXeKZZ55Z6rAVCoUiL5qm8cSu1eQrsqoOWqBBdcCassVG1+CJXauXzfZbXdd5+sG2wrvxNXj6wbaiG2mVer5KptyuRSrj0DecJJbMYpr6tGtY13X27GiZNvY9O1ryxp5I2/zknSv81feP8pN3ruQVd7euCfM7H9/Gb3xs67QN0jRNyzVyKxCHrsFD25oX7Hl0XYEUEr/HpKUhwIpwgIBv6r8LispB5bMKhWI5oWka4Sc+QiET3pST5vTd4bzvx+04XYMn8nyqcvnhhVfG/WFvx3az/PDCK0s6XyUz32shpcQeiWIP9IMQN8XdmZFSEs8muJG6gS3cSQ3JiuXn1zvHvXdvx3Edfn69c/xYpxKX+X97/5sX+3+WV9xt9Tby6eYn+dzK/8H6QOuMOaEYHSX7xlvE/59vce+blwuKu5lVK/A993ECX/htPI88NC7uLjRSSlzhoKFRZVXR4Gug2hMaF3cVxVPyn8ps2+b69eusXr261FOj6zpf//rX+fKXv8wLL7xAc3Mz/+t//S/uueceAL72ta/xl3/5l/T29rJx40a+/e1v09g4u8YrCoVCsZhsXRPmucc28mrnlUmVvCvrgzzW3sqZK9FJr9eFvDyxazVb14SXItwF46kH2gB45VDPpGpTv9fk6Qfbxt9fqvkqmYW6Fq7r8vLbPfRHUzTV+vno7raCHnyOK4iMpsnauar0YjxtAR7evhKAN7r7JlXyej0me3a0jL8/RiyR5cCxaxw+OYCdp7pG0+De9fU81tFKc13xfrkbWmt46oE2Dp64PqmStybo4aFtzWxorZnm07NHSIkQAp9lUFPlw19ksw9F6VD5rEKhUBRPYMtWGp/7BJHXfoozfEuQMsJhjq4NMdRU+HvsUN8R2hu3VVQVbyHSTprO/u5px3T2d/PM+qeLqjQt9XyVzHyvhZtK4UQjIEG7zfdZSkm2r5fRkRuEaurxtNwSTTNullh2FJDoc1yjtpPlbPTCtGPORM9TX93EwdgJBrL5q+FXeVfwSLiddf6WGUVdKSXu5R7sI124p8+CEOTL0m1L5+raano21CDCNXxs3YabdhULjxAumqbhNbwErYASdEuAJkvRyQFIp9N8+ctf5qWXXgLgxIkTxGIxnn/+ef76r/+a6ury7vQOMDg4utQhVAymqRMOB4lEEmrrkgJQa2K+SCnp6Y8zmsoS8ntoa6pC07SCr1cKs10XQggOnxxgKJamodrHA1tXzKu6tNTzVTKlvBb/uPcUbx7rm2RJYBgaD29v4bNPbRl/TUrJSDxLPJ3FnCD+GoZGTU2AkZFkUbYGQgiOnb9BJJ4hXOVl+4b6SbEPx9K80X2NI6cH8/rl6ppGx+YGHm1fSUPN3JvmSCnpG0qSyNgEvRYtDYGSPo+OKzB0naDPoMrvuaM8VEvxHdLYGJp3HMshnwWV0xaLyl0U+VDrYu5IKcn0XMaNxzFCIezmei7Hrsz4ubU1bVRZ5e2jXsy66E8O0nn96Ixz7WruoCkw8w93pZ6vkpnrtZCuiz18A5G10fMUIqQunCd28E2ckSgDqRus8Ndj1tQS+sBDZFc1jtsx5MPUdapCXuKjmWktGqLpaK65Wh6klAyIGBfsAeIynXfMKu8K9oTbWVuMsJvOYB8/gX2kCzmU37MXwGkMk753I5nNa8C6JVw3BVcs6I8FUkqEdDF1i4Dhw2f6Kupv22IwTI2a6gAjseScLBrqGkL4W5rndOySCbx//ud/zjvvvMPv/u7v8sUvfpHu7m5isRh/8Ad/wOrVq/mzP/uzUhxmQVHJcPGoxEdxO2pNKPKh1sXy4x/3nmJ/17X8/c80eLR9JZ99aguJdJaRuA0wRaicrcBbiIFIiv1dvXSfG8prnWYaGvdvaeKRHS3UVnnnfJyFREiJ6wr8HpOqgIXvDq3WLReBdznks6By2mJR31GKfKh1ociHWheVhZQSZ2QEkYiDbuQVEVMXzjO892WQklgmRtJJETB9VHmqEEi8H3oUc93agscoVuAtFN+pxGUORLoYtKN5x6z25Sp21/pmFnbd/gHszqM4J94H284/yDAw79mKtbMDfeXC2YvlY6K37nKv1l1Kgbdkf0Xs3buXF154gbVr1/LFL34RgOrqav7yL/+Sj3/84xWTECsUCoVCociP67q8eawvv7gLIOGNrms8ubMVITUMY2GqpXuHEuw72sv7F4fzhuKxdHbf3cwH720mFPDkGbH0jFXrVvnMO65at5xR+axCoVAoFJWNm0zijERAalPsGMaQUhI7+GaucZgUpNxc9WzKyeA3A+iaht35LsbaNSUVQosTdpvYE25njW96EVY6Ds7J09hHuhBXewuO02prsXa2Y+24Fy0w951ss2WsWtfSPfg9AXyGd9lV65YbJRN4E4kEa9eunfJ6XV0dyWSyVIdRKBQKhUKxRLz8dk/BituxV6WEfV19PH7fqpIf/9L1GPuO9nLmykje9/1ek4e2NfPQtmb83vKrhBVCIoTE5zEIh3x4rfKL8U5H5bMKhUKhUFQm0nWxb9xAZLO5BmrTaInZvl6cWAyAkewoYxvbJZKEnSDkqUKOjCKvD6C1NM0/tiKE3TZfE48UIeyKSBT7aDd21zFI5m+YhqZhbNqAtbMdY/26RRVWhXDQNAOf4SVg+Zd1tW65UbK/LNra2jh06BAPPvggE10fXnnlFVauXDnNJxUKhUKhUFQC/dGpSWQ+ufdGLL+H2FyQUnKud4R9R3u52Jd/23nIb/Hw9hYeuLsJr1V+SaTjuJiGQZXfoipgoavqhbJF5bMKhUKhUFQWUkqc2AgiHkczzJy4OwNuMglIHOGQdjKT3suILEEp0TUNkUwyn/1oUkpOJi7xZqR7WmF3T7idNf6WwvMIgXvhInbnUdxzhRu2aYEAZsd2rI4d6LWlbQI8HVIKhBB4DA8hbw1eozyt0ZY7JRN4f+VXfoUvfOELPPvsswgh+M53vsOJEyfYu3cvX/rSl0p1GIVCoVAoFEtEU+3kbV2FnBrqq+ffnEFIyanLEfYd7eXqYCLvmNoqD3t2rGTnXSuwzPJqnue6AtDweXTqQgE8ZSg8K6ai8lmFQqFQKCoHN53GiQyDkAXtGG5HSknG0nGEQzybQN6W0Up5q4pXDwTmFJeQglOJy9MKu2t8zTwS3jG9sJtIYncfx363CxnNv4MNQF+9CmtnO+aWzWhFCNylwnVdDMPAb/oJmH50rbzy8TuNkt35T37yk5imyQsvvIBhGHzrW99i3bp1fO1rX+Ppp58u1WEUCoVCoVAsER/d3cYPD17CmaYxmqHDnh2FE9WZEEJy/MIN9h3tpT+Sf9tZQ42PR9tX0r6pAUMvn0RSCImQAq9lUBPw4fPmb+qhKF9UPqtQKBQKRfkjhcjZMWQyuYrdIn9HT9opEk4C2VgDoSoyg5G84zIiS1UoiNa8YlZxCSluVuweY2iOwq6UEtF7Ldc07eRpcN38B/NYmNvuydkwNM0uzvkgpUBKidfwUOMPYenWoh1bMT0lE3jfeustnnnmGZ599tlSTalQKBQKhaKc0DR23tXIofcHCg7ZedcKDGP21aqOK+g6O8T+rmsFLR5a6gM82t7KtnV1ZdOUTEqJIySWoecsGPxW2cSmmD0qn1UoFAqForxxYjHc0RjoRlF2DABZN0ssG0dKF0030DQ4vT5A3aDMa9UrkJxeH+C+In+ovyXsdjNk56+0zQm77azxN+d9X2azOCdOYh85iugvnGvrDfVYu+7DvPduNO/iWCGMNUwzdYuAGcRv+lQRQxlSMoH3t37rt7AsiwcffJDHHnuMPXv2KK8yhUKhUCiWAUJKRuJZEuksz3xwHVLCkdMDuOLWGEPPibsff2T9rOa2HcE7pwY40H2NkUQ275jVK6p4vKOVu9pqyyaZdByBrmv4PSahoIVplE8lsWLuqHxWoVAoFIryJGfHEAEhirZjcKXLaHaUjGNjGAbazYZftpPlqH+Yuq1VbLmQJJC+ldQmfTqn1gcY9g9zr5PFMj0F5xdScHz0PPuHu7gxjbC7J9xOWwFhVwzdwD5yFPvYCcjkz4XRdcwtm7F2dqC3rVq0fFgKFzTwGj6CVkA1TCtzNDmxg8Q8SKVSHD58mLfffpu3336bU6dOsX79evbs2cOePXv4wAc+UIrDLCiDg/mbtyimYpo64XCQSCSB44iZP6BY9qg1sTBIKenpjzOayhLye2hrqiobgWsmpJT0DiWQhoHmurQ2BOcVuxCCwycHGIqlaaj28cDWFehLsD2/Uu/JXOOOp7KMxG00nUnNwVzX5Y2uPm6Mpqmv9rFnR0tRlbtSSvojSTIuHD87SPf5IRIpJ+/Y9SurebyjlfUrq8viGgshc1vSLIOqgIXPs3geZ5XCXJ/TUnyHNDaG5vS5iSyHfBZUTlssKndR5EOti4Ul7aTxmfP36l9MpJQ4vVfwY5PCwmxdXZK8JJoeoda3eI2w8lEJ90O6Lvbw8Lgdg5SSbF8vbjKJEQjgaWmdcj+EFCTsBEk7nTc/jaajnImcv3kAiXd4FDNl4/gtMnUhuDnf5vAGan21Uz4vpOB08jIHol0MZvILu2v9LTxS206bvynvOblnzuWapl3uKXjuWiiEdd8OzI7t6FVVBceVEiklQrhYhoeA5cere8oiD18qYplRqr3F55iGqVFTHWAklsR1Zi+31jWE8Lfk/zFgJkom8N5OJBJh//79fPe73+X06dOcPHlyIQ5TUlQyXDwq8VHcjloTpefk5Qivdl5hePRWZ9e6kJcndq1m65rwEkY2M2OxR+IZTEPHcQXhqrnHvvdwD68c6iGVuSUE+r0mTz/YxlMPtJUy9Gmp1Hsyl7jTGYdIPIOUsmRC+vneEd7ovsb14STxlE2hDGRLWy2PdbTS1jR/wW6+jFkweAydgM8k6LcmCd2KW8znOS0Xgfd2KjGfBZXTFovKXRT5UOti4XCEwz+fepHPbHkOU6+MH0mTp04SefUnuNEIhmHkmkrVhgk/8RECW7bOed60k+YvDn+d//nAHy6ZwFru90NKiTM6gojFwcj1NUhdOE/s4Js4sdj4OLO6muqHHsa/fgMASTtJwkkAesmFSSEF78cv8ma0mxt2LO+Ydf4WHgm3s9o3VdgVsVHso904R7uR8fxNhAGM9WuxdnZgbNqAtkgFLUI4aJqBz/ASsPyqWhfIuBm+dex7/Pb2z+E1irPDmKvAK10XpKSxbQXe+vo5xVvSp9i2bbq7u8erHo4dO8b69ev51V/91VIeRqFQKJY9Jy9HeHHfOcRt3wnDoxle3HeO5x7bWLaC4sTYJ+ZUc4197+EefnDgArc1uCWVcXKvw6KIvJV6T2Ybt+24ROMZsrbAMEqXGB8/P8R/H7xMImXffivHuXd9HY91tNJSHyzJMeeDKwRIDb/XoCFgYZkqyZ2OcnlOS4HKZxUKhWJh6Bo8wfVEP12DJ9jV1L7U4cxI8tRJBl/8N5ByUk7rRCIMvvhvND73iTmLvD+88Aqj2VF+eOEVPrH54yWKeHaU8/1w43Gc0RgIiXbTZzd14TzDe1/m9goBJxZjeO/LhJ58gmzrinGf3VIipOC9m8Lu8CyFXSkl7qUe7CNHcU+fnRL/OD4f1o5tWDvb0evqShp/IcaqdT2Gh5C3pmgR807h9SsHSNhxXr9ygI+ufaKkc0spka6LpmtoHi9mTQ26z4e3vnrOc5ZM4P2N3/gNuru7Wbt2Lbt27eLXf/3Xuf/++6munntwCoVCcScipeTVzitTBLkxhIRXO6+wpYz8SMcodexCCF451DNFNLp1QHjlUA9P7lq1oHYNlXpPZhO3BEZGcz67pmlglMhTNjKa4Y3uXg6/P1DwNvq9Ji31AT714U1Lev3GqnW9pk6oykPAa5XV/SxXyuU5LQUqn1UoFIqFwREOh/qOAHCo7wjtjdvKsmp0DCklkVd/0p3isgAAIABJREFUUliMk5LIaz/Ff9eWWecKaSdNZ383AJ393Tyz/ulFr+It1/sxLuxKmRNpb+q0UkpiB9/Mfz+kxBUO0YMH8P/yL5VU3C1G2K3Cwy82P05boGVyWOk09rET2Ee6kDeGCx5Db2nG2tmOec9WNMsqWezT4boOhmHiN3wEfH50rbzzs6Ug42Y4PnQKgONDp/jQ6kfmLYBLIUC4YFroPh96MIjhKezxPFtK9gR3d3fT1NTEfffdx/3336+SYYVCoZgjPf3xSVvp8zE8mqGnP86a5qXfwj6RUsd++OTApO3e+UhlHA6fHGD3PXPzKiqGSr0nxcR9I5bm1KUI1VVeNB3MElWqDkZT7O+6RtfZIUSBP46CNy0PTEMnnXXpG0qysnHxq3ddIUHKm9W6HlWtO0vK5TktBSqfVSgUioWha/AEcTsOQNyOl2XV6EQyPZdzDb2mwRkeJnOlB1/bmlnN/cMLr2C7uWZatptdkirecrsfbjKJMzICws01ULtNM8/29U6yZciRqz4VSDQ0iMWR1wfQWqZaI8wWIQUn4hd4M9JNxMlvexQQJo348bgGmUwSAjfPpe869pEunBPvg1MgPzJNzHu2Yu1sx1jZkn9MiZFS5HpJGB5q/CEsfXHE5Erl9SsHcETuOXVEds5VvPLmGtA8HoxAACMYRCuib8lcKJnAe/jwYY4dO8bBgwf57ne/y/PPP8+mTZvYvXs3u3fv5pFHHinVoRQKhWJZM5oq0D11juMWk1LHPhRLl3TcXKnUezJTPK4QCFcyMJIiXFOaypFrQwn2dfXy3oXhvAWdGhD0W9QELdC0SYUYiYxdkhiKxXFcTNOgJmgR9Klq3blSLs9pKVD5rEKhUJSeidWiY5RT1Wg+3Hi8uHGjs/M8n1i9O8ZiV/GW0/1wUymckSi4N4VdI//x3WRy0v8L6SJkziNbm6AGi2SS+dSiFiPsrvevREum0d2cnZmL4Mzgadb0JHDePYbovVZwfi1ci7WzA2vHNjS/fx6RFoeUEiFdTN0iYAbxmz6V7xbBxOrdMYqt4pVC4Gazua2SljluvbAY171kT69hGHR0dNDR0cHv/d7vkUqleO211/j2t7/NP/zDP1RMUwqFQqFYakL+4rZpFDtuMSl17A3VxSW6xY6bK5V6TwrFI4TAFbmkT9O0ksTd0z/Kz472cronmvd9TYOgz6LKb2EYGoau5SpnJxD0LnwlgRASKSU+j0FdKIDHUtW686VcntNSoPJZhUKhKD0Tq0XHKIeq0ekwqqqKGxea3c6tidW7Yyx2FW853A83ncaNRhGOg24WFnbHMAK58lgpBa50gcnC7hj6zXGzRUjB8fh53oocm0bYbWVPeAfJ1ChHY8cADX88S+vZCKsvjpDNnsg/uaZhbN6Ya5q2bs2iCH25hmn6zYZpAdUwbZZMrN4do1AVb85L1wFNR/d4MAI+AqtWYI9mFr1RZ0l/nonH43R2dnLo0CEOHTrEqVOn2Lx5M5///OdLeRiFQqFY1rQ1VVEX8k67tb4u5KWtqbjEczEpdewPbF3Bv75+btrt336vyQNbV8w61tlQqffk9rillDiuREiJrmlomkZN0ENLw9ySYSkl56/F2He0lwvX8vuSBf0WD29r5uL1UeKpwhW684mjGBzHxTQMQgGLoN9CV9ULJaNcntNSofJZhUKhKB35qkXHKOcqXm/bGsxweFqbBrOuDu/q4huI5qveHWOxqniX+n64mcwtYdcwcuJuMaxoRFT5EbFYXmEXQKsJoTXPLtdwpeDEDMLuBn8rj4TbafU1IoTLS1eOsOJanLbzIzReTxaIBrRgELNjO1bHDvSahbd7klIghMRjWKph2jzIV707xvGhUzy+8iG8ugWGgWZZ6B4L3RdAt3K7AU1Tv7mup7fJWwhK9uQ+++yznD59mmAwyAc/+EF+9Vd/lYcffpjGxsZSHUKhUCjuCDRN44ldq3lx37m8zbF0DZ7Ytbost9eUOnZd13n6wTZ+cOBC/gZOGjz9YNuCN26q1HsyFve//+wstiMRQqLp2ri4qWvw0LbmWcctpeRUT5R9R3u5MpB/C2NN0MMjO1aya0sjHtNgVe8Iew/3FLx+c4ljJlxXoGkaXktX1boLSLk8p6VA5bMKhUJRWvJVi45RzlW8mqYRfuIjDL74b/kbe2ka4Q8/OavcJV/17hiLVcW7VPfDzWRwR0YQ2Sy6aaIX6UHqCIfRbJysa+O5fyeZ1/cXvB+eXTuLvh+uFBwfPc9b0WNEixB2AUQiwfWf7+fBY6cIJAv/qK23rcLa2YG5ZfOCea2OMWbBYOgmAdOP31QN0+bLxOpdTUo0oSE1cA1I6TY/iR7h2W3PopVhXlsygffxxx/nT/7kT9i+fXvZ/YGrUCgUhZBS0tMfZzSVJeT30NZUVRb/hm1dE+a5xzby03d6uB5JIYRE1zWaw36evL+NrWvCSx1iQcZif7XzCpH4rV8u60Jenti1etrY892Ppx7IVUa8cqhnUoWg32vy9INt4+8XO18x99dxHP7ltXP0R1I0hf18+sMbJ53X9eHkrXtSF5jxvFzX5eW3e+iPpmiq9fPR3W0YC5zwQe78WxuCPH7fKg4e7+PGaAbh5OJuqPbxwXtb2NBaM+McfUNJEhkbv8fkRizN/q5rXB9O5h1fX+3j0faVtG9qwDRuJT4bWmt46oE2Dp64Tix564+bmqCHh7Y1s35lNdcGEyQyNkGvRUtDYE7PohA5Idvn0akJ+PB5jbJ4ppc783lOywmVzyoUikpFSkmm5zJuPI5RVYW3bXG2gk9HvmpRIcUkAaqcq3gDW7bS+NwniLz2U9zI8PjrZl0d4Q8/SWDL1hnnSDtpfKZv2urdMaar4p3L/XVdl+jLL5Hq78Pf1ELVU0/luR9y0q6m6e7HWAwjw/3U1DUVF0MqhTs6ekvYLbJiV0hB3E6QdlLoupnLm9etwfuhR8l2HkGOjN7akVYTwrNrJ8a6ws3upJTI6/3YyQTveyIclJeIOvmF7o2BVTxcu4NWXyNSStwrV7E7j+KcPE21yL/l3jZ1eteEGNjcxKPtv4A2MIR7qQc94EdrbirpsyilRIic/2/OgsGvLBhKgJSSTCbBewM5Oy5H13BMHdvSEcat+3d45H1+Qfwf+PTysx7TpCzQ2noOdHZ28oMf/ICenh40TWPdunV84hOf4J577inVIRaUwcHZGaTfyZimTjgcJBJJLLqviKI8qcQ1cfJyhFc7r0zadl+MCLlYnLwc4aedV+ifICY21QV4skzimwkpJb1DCaRhoAmX1vrgtMnNTPdDCMHhkwMMxdI0VPt4YOuKaSsC53p///bFbo6duzGpEFEDtm+s5yP3t00S3Q1do2kG0f0f957izWN9uO6tGQ1D4+HtLXz2qS0F45gvybRNNJ5BonGpL8ZbJ65zY+TWjwX1NX4+uK15WoH3fO8IB09cJxrPkMo4jKbsSecxEV2D7RvqefaxjRh64fsspaQ/kkTqBpp0aaoNcOFajIMnrjOSmCr8ziRAj83pCInH0An4TGXBsITM9jkdoxTfIY2Ns/NBLESl57OgctpiqcTcRbHwVOK6SJ46SeTVn0yyEzDDYcJPfKQoEXKhiNsJLo30jP+/K1z2XX2Lx1Z9EEO/JUitrWmjygouRYhFIaXE6b2CH5uUZmGuLG7XliMc/vnUi3xmy3PcSEfovH50xs/sau6gKTB518hc7u/1F75H7M0D4LrjPRcwDNyd23B/8SkgV8W67+qbPLbqYYwJonu++zEWgz18g77EAC3BFVh19XljkFIiEgmceBxcJ9c8bRYk7SQJJwloea+zlBLnWh9dPe/QvuZ+zJaWae+He/Eyqc5O3guMcngtxAr0NvO5Or+88knagi3IbBbn+PvYR44iBgYLzi0aw6S2bSS9uQ08FubVfmqOX0aPJ8bHaNUhPPdPL0AXw5ivrkf3ErB8WPrC965YzkjHASSYBpppoXkshkSCd2+cyDUPmYZ8z+kY8/0OmU8+WzKB96WXXuKP/uiP2Lp1Kxs2bEBKydmzZ7lw4QLf+c53uP/++0txmAVFJcPFU4mJj2JhqbQ1cfJyZNrt9s89tnFJRdRyj69Yil0XpT7fuc73ty92033uRsF5/R6DFXVTfWILzfmPe0+xv+tawW3rj7avLLnIm846ROMZXFdiGDrnZ7BGeOqBtrwi6vneEV45dJl4yiGesqc0RJtwGuh67r+arvHkrtU8vH3ltDEahkZNTYCRkSRneqJzig/AcQWGpuHzmoQC1qSKYUVlUS4C73LIZ0HltMVSabmLYnGotHWRPHVyWhuBxuc+saQi70Q6+7t4rWc/H257tCxtGaZjLuuiFOc7l/t7/YXvEdu/DwCJzOWh2q2mZNWPPkbz//m5ouObGEMkHSVuJ6iygoR9tVNicONxnNEYSImmz66qNOtmiWXjSOnO+NmzkfMcHThGx4rtbApvKDznhYscPfUGh9fIgsLuxsAqqrI6VyM9dBir2HEV7GMnIJvfTgNdx9yyGd8DO6m5ewOJeBZHCNyLl6e1kPB+6NFZi7xSuKBpWLqF3/TjNcqroXOlIIVACgF6zh9Xsyx0nw/d6y251cJSCrwl2wfx7W9/mz/90z/lk5/85KTXv/e97/E3f/M3fP/73y/VoRQKhWJeSCl5tfNKXkEJQEh4tfMKW9pql2RrW7nHV2pKfb5znc9xHI5NI+4CpLIuwnWn+Iblm9N1Xd481pdf3AWQ8OaxPj7zxKaS2DVkbZdoIkM262KaBoahIaXk4Inr016Lgyeus35l9aRrkc44/PdblxgaSSOm+R3YGBN2xz4r4Y3uPh7a1lyc3+rN488mPldIkBKfx6Au5MNjld+WTkXlovJZhUJRSUgpibz6k/yCUm4Akdd+iv+uLUueM060ayhnW4ZSUYrzncv9dV03V7k7PmbCf28ugdibBwh/8lNFxTcxBinFzcpaSDhJamU1GjqR136Kd/0G3EgE6di5it1ZLDdXuoxmR8k4NoZhoM1gNyCEy6nhswCcGj7Lhpq16LcJwq506Y6d463M28S25L9+6wfhA0NBWp54iP37/pkPX4jTfGOAQi2BtepqrPt2YLZvR68KYur6+HWXUpJ958i09yrbeQTf2rYZn0UpBUiJqVv4vUG8umfJn99KQkqJdB3QNDTLRDctDI8X3edDK7apX4VSMqm6p6eHZ599dsrrn/70pzl37lypDqNQKBTzpqc/Pmnbfj6GRzP09Of3ZVpoyj2+UlPq853rfP/y2rmCWuxEIvH8v+bfPufLb/cUtDMYw3UlLx/qmXbMTDiuYDCaoj+aQggwzVsJbt9QcpLtQT5GEln6hnLJejLt8NqRq3z1+0cZiKamFXchVwlye8KZyTocOz+9UD5G71C8qPiuDSawXYEG1AQtVjYEqa/xK3FXUXJUPqtQKCqJTM/lSdv28+EMD5O5Mr9coxRMbO411sxrOVOK853L/Y2+/BK4LnCzencC4//vupz6z+8VFd/EGKKZGGMbwKWU4/+fHRggcawrV7U7CzsGKSWj2ThDyWFs4RZd8HB+5BIpJwVAyklxfuTS+HuudHk3dpq/v/KfvHzj58S8U3PZ9YPwK+/AM12ChjMDpP7vb/HQOxGab+SXdo0N6/B94pcI/P7/hefhD6BXTbUTkdf7kbHpd8/IkVHk9YH870mB6zro6ATNIPX+esK+WnyGV4m7MyAdB+nYSASYBnowgLWiCe/KVrwrmrHq6jGqqpa9uAslrOANh8PcuHGDpqamSa9HIhF8vvIzH1YoFHcuo6npBaXZjis15R5fqSn1+c51XH8kVdTn7Gm22kycsz9a3HzFHvd2hJBE42mSaRfT1LHy2BMkMoVqECYzFEtx7MIQb7/fT9bOf34+j4GuayTTtxpoSeT4dr+JTGyuNx2JVOEOxMD4HxEZx6U57McyVQMJxcKi8lmFQlFJuPHifvx2R5fWtiVfs7XlXMVbqvOdy/3NDk4QEG/XNidU8d64cg7Wt84Y31gME6t3x0hk41TrfkBDpNNFxTpGyk4Rd3I+tbPZyTaxeneMU8NnWVu9muOJi7wVPUbMSeT97PpB2H1BsmLYRiZTyEwuX817R3w+rPZ7se5rR6+b2SZOJIvL50UyOV5lKaVESDdXqWv68Zv+SQ0IFVORrouUAs0w0Iyc1YJRU7MgVguVSMn+Nd29ezfPP/88f/zHf8ymTZsAOH36NF/72tfYtWtXqQ6jUCgU8ybkL867qNhxpabc4ys1pT7fuY5rCvt5/9L0VRIAllk4eZg4Z1NtAaOv22gKFzduDCElsXiWeMrGMDTMaeIJeqdvvuC4gkTK5t9/dr6gx67fa1Llt7BMnVTaZmJqn0/cBQhXeWc8D4CgP/9WQAnomoaha+i6RmtDlRJ3FYuCymcVCkUlYVRVFTcuVJomlHNlYjXrGGNVo5XmxVsMpTrfudxfT+MKYGr17hhjr0erJ+dVheIbi2Fi9S5CoIucVjyajVPtrcYIFtckzxY2sUwcIZ1Ze/TC5OpdAIGkT0T5xpX/ICnyFxhsGIQPnBM0DKVzwu7NCud8DNWanFnrx79tG4+sfaTouPRAcfm8Hgjgug6GYeAzPASsAMYMlhR3KuO+uRo3rRY8GF4Pus+PVgJ7u+VIyQTeL37xi3zhC1/gU5/61KQS8nvvvZcvfelLpTqMQqFQzJu2pirqQt5pt/HXhby0NRWXVJWaco+v1JT6fOc636c/vJF9R6/NaNMQrsovIN8+50d3t/HDg5emtWkwDI2PPtg2wxFzSCmJp2xiCRtNY1phd4yWhgA1Qc8UGwTHFcSTNslM/gpaQ9cIBSw8ljGpcZnPa6IlsuP2Yvl2jHk9Jts31Bd1Tq0NVePxjf3RoGsahnHL+mE5rXVF+aPyWYVCUUl429ZghsPTbuM36+rwri4u11gI8lWzjrEcq3hLeb5zub+1H/0YN370Q3AK7JKSIAyNs9sbi4rP27YGvbaGxNVrOQsGIdEl40lgyk1TW92Kp3n6BrtCipzPrptB180ZfXbzzjGhelcgielZInoGR5OQZwPa5kAbH9h/jdqrw5BKF8zxHR0utfo4u9bPcG2uOMKMX2a38yCWWVzhiNbchFYdKmjTIJHo1SE8LS0EPUEsffoijDuRnKDroJkmmulB91gY/kDu/5VNRVGU7F/S2tpa/umf/omzZ89y+fJlstksa9eu5e677y7VIRQKhaIkaJrGE7tW8+K+c3mbO+kaPLFr9ZJ9kZR7fKWm1Oc71/lM02T7xnq6p2m05vcYUxqsFZrTMAwe3t7C/q5r+RutafDw9paitqUl0zbRm8KqYRR/3zVN46Ftzew93IOQOXuJeDJLKpu/csEydR7c2sQHt7cwFE2Nf27ifKGARSxhk8cRAjTYs6OluAZr5JLdB+9u4tXOK6DlqnUnXsPlttYV5Y/KZxUKRSWhaRrhJz7C4Iv/lr+5k6YR/vCTS/o9mq+adYzlWMVbyvOdy/01DIPMfVvxHD6ed04JnNtUDXnyz3zxaZrG8S1VhHpcTHf8xfH3BZIz6wO0FFhjUkoSdpKUkwRNR5+HmH9+5BIJJzlZ2M3DVs8qHu2vwr/vLOJaX8H5hK5xdrWXY1uqyHom566O6/Dz653sWfVQUbFpmobn/p1kXt8/fq/GqqU1NEzdJPzBxwn4aoua705Aui7InG+uZnkxvV50v6rOnQ/GV77yla+UYqK+vj5CoRD19fWsX7+eTZs20diY+1XoW9/6VkVsa0sml4ef5WKg6xp+v4d02kYUan+uuKOotDXRWOtnRThA343EJLGrLuTlFx5ax9Y1M3stLSTlHl+xFLsuSn2+c51v993NXLoeY2B4so+WBuzYWM+nn9g8qzl3bGxgJJHh6mB8Ul5uGBp7dqzks09tmfY8MrbD0EiKZNrB0HX0OfyBVlftwxWSUz1RovEMTp6KYp/H4OHtK/n0E5u4Z10dPo9BXbWPumofg9EUGfvW+TbW+lm/sppoPIvr3iqX8HpMPnRfKw9vn76CA3LJvsdjYgDrV1azsqGK/uEk6Qpe64r5UYrvkGCwOGuQ6VgO+SyonLZYKi13USwOlbYurIZGPCuayF7vQ6Ru5S9mXR31v/AMgS1blyw2Rzj81/mXyYrC/yYNJm9w34rtZe89Wsy6WIjzne39dYTDf5qnYTRBTSTNRA1UGBonNwU58EAtATOQdyfW7fElU6P8x7kfEfdqVCdcrAmFwUm/znubqjhXY7OraceUyuS0myGaHsGRNppuzOuHhqyT5aXrb3BNjxPXHUSeqVbFdD5zuZHNPzuNefI8crSAh7HXC82N/GybhzPr/LgFiiei2RHurduCUcBKQtc0PF6TbNZFSIkerkUP1yJu3EBmMuhoGJqJJ1xH7eMfJrBh41xPf1kgXReEC7qGZnkwg0HMcB1mdQ1GIIDu8SwLH935fofMJ5/VpJyhRXaRbN26lY985CN89atfndKEYseOHXR3d5fiMAvK4ODSms9XEqapEw4HiUQSONM0HFLcOVTqmpBS0tMfZzSVJeT30NZUVVbVguUe30zMdl2U+nznOp/jOPzLa+foj6RorvPzqQ9txLzZedV1XV5+u4f+aIqmWj8f3d02YxXupM+E/Xz0wek/4wrBcCxDxnYnWSTMBiklF/ti7Dt6jXO9I3nHBHwmD9/bwu57mvB58ldUSCnpG0qSyNgEvRYtDQE0TUMIwbHzN4jEM4SrvGzfUD9t5a6UEseVeC2dmpCH1uZaotHk+Lqo9LWumB+l+A5pbJy/x+RyyGdB5bTFUqm5i2JhqdR1IaUk03MZNx7HCIXwrm5b8u/RuJ3g0kjPjOPW1rRRZRXn4bpUFLMuFvJ8i72/k2JwXbR9h9CHI4iGOlIPbac/MwxAU6ARf38UZ3QUMxRCrmwar8xdW9NGAA9ObIShyDVORM6MBYHnRgwjlcX1e8jWV49/Zlvj3TT46gBwhE0sG8cRDvocfHYn4giHrtGzvBU9Rtyd2shME5L7B3xsP5smeGWw4DxaMIixeQNG22qMhgZitV7ORM/PePzN4Q3UFqi6NXWdqpCX+GgGRwhc4WDoBj7dhzEYQaaSGMEgnuaVS/4sLgW3KnRNNMuD4fOi+wPLQsSdjvl+h8wnny2ZwLtt2zbuvvtuMpkMf//3f09r662OjNu3b+fYsWOlOMyCopLh4qnUxEexcKg1ocjHclsXew/38MqhHlITvGv9XpOnH2zjqQfm728npGQkniWRzmLo+pySQSklZ65E+dnRXnr681cuVAc9PLK9hfu3rsCzwM3LclW+GgGvSShoYRr6slsXivlTLgLvcshnQeW0xaL+LVLkQ60LRT6W07pInjpJ5NWfYEeGGU5HqPOFscJ1hJ/4CL4NG3GiUWQmjWbOzic257ObIO2mMObpq+wIh6OjZ/l59DijbnLK+4GU4LErXjacGUGPFajUBYw1q7F2dmDctankW/9NXccftIjHM5gYBMwAlnHneutOFXR9OcuFZS7o3s5SCrwl8+A1DIMXXniBr3zlKzz77LN8/etfZ/fu3QB35K8VCoVCoVhe7D3cww8OXJjip5vKOLnXYV4i72gym2ugpoM5hwRUSMl7F4fZf7SXazemJsKQsz3Y076S+zY3zrkyuBiklLiuxGPphEIeAl5L5QKKikDlswqFQqFYziRPnRz39E3ZKWzXJmWnkDdu0P/9Fwg/+RSBTZtnLe6m7BRxJw7o8xJ3x4Tdg9HjxG8XdqVk5aDNwxd1mi9G0UQB8czjwdq+DWtnO3pjw5xjKYSUAiEEluGl1leNXwjENE2VlysTBV3d48Hwhu5IQbecKGm7So/Hw1/8xV/wz//8z/z2b/82zz//PJ/97GcpUZGwQqFQKBRLghCCVw715G+WBiDhlUM9PLlrVdENxsZIZxwi8QxSSow5iK6uEHSfu8H+rl4Go+m8Y1aE/TzavpLtGxow9IUTqVwh0NDwe0xCtdaCisgKxUKh8lmFQqFQLEeklERe/QlIiSTX/AwhSKTieL06mm4wevht/Bs3Ff2jpiNsRrJxXGHPq4GaLRy6Rs/cFHYnWzFYtmDLpTT3n3MJRabaNIyhr2jE2tmOee89aB7PnGPJx5ioaxoWAdOPz/RjWQZ+y0dWy19Ysdy4Jeha6B4Tw1eN7vMpQbeMKKnAO8ZnPvMZ7rrrLv7wD/+QkydPLsQhFAqFQqFYNA6fHJhky5CPVMbh8MkBdt/TXNSctuMSiWewbYFhzN6OwXYE754Z5I3ua0RGM3nHrGwI8lhHK3evDc+pQVuxOI6LZRrUBD0EfapaV7E8UPmsQqFQKJYTmZ7LOJEIAKlsEmHb6Eik5pIRWXy6H2dkhOz1a3hbWqed63Y7hrmKu7ZwOHpT2E3cJuzWRx3uPZvinksZTLtAta6uY269C2tXB/qq1pLmoFJKhHQxdQu/6cdv+su+GWApka4LSDDMnKDrr0H3epWgW8aUTOC9vaph165d/Md//Adf+MIXsG27VIdRKBQKhWLRGYrlr4ydyzghJdHRDMm0g2nqs67azdouh08OcODYNUaT+b9f1zaHeKyjlU2rahZMbBVCIoTE7zWoCwXwWAvr5atQLAYqn1UoFArFcsWNx3OipeuQTMfRkONN0lJ2Gp/pAzTcRKLgHFJKkk6SpJNkPnYMOWH3NAejJyYJu7or2Xglw/azKVoHC3/vajXVWPe1Y7bfix4sXYO+MVHX0E38hg+/5cPQ7owcVzoOIMGy0C1LCboVSMkE3nxNJ5qamnjhhRcqpuOwQqFQKBT5aKj2lWRcPJXz2UXLGfDPhlTG4efvXefg8eskC1QTb1pVw2MdraxrqZ7V3LPBcVxM06DKb1EVsBa0MlihWGxUPqtQKBSK5UjuB0yJtG0yTgqBGBd3AVxc0k4an+nHKCCYpuwUcTsJmkSbo+hpC4d3Y6f5+cheMfVCAAAgAElEQVRkYTeUcNl27v9n786D40rrg99/z9abutWSrN2WvNuSd3tkeZgZPB7iYYYkww1kAknxBlJwbwUuSSoh1BuWpIrUWzdkmYQkRSAJue8LhCRABpJLeGFmMLPZM9gee7xb8jq2PLasxdp673PO89w/2i1rOa21ZUvy86miGHef5dHpR90//fp3fk+KTZdShNKFWyIZa1ZhPbANY/WqoiYeXdfFMHQCho+QFbovkrrDCV2fhW76MKIqobvQFS3Bm81m+bM/+zPe9a538fDDDwPw3e9+l7Nnz/LZz362WKdRFEVRlLuutbma77x4ccI2DUG/SWtztedzGduhL5ZBCjntHr3xlM1rpzo5eKaLjO16brNhRTl7ti9lWVV4WseeKlcIkBpBv86S0hCWufiDXuX+pOJZRVEUZbFx43GcoSGMsgqMaCmprgHP7VJ2mpKKGny19aMez7hZYtk4UrpougFM/8v9O4ndUyTc23e8SUnjzSxbLqRYeT2LXiivGwxibduMtWMbennZtM9diBAumgY+3U80GMHSp7ew3EKTS+gCPhPd8quE7iJUtATvX/zFX/D666/z9NNPDz/W3NzMv/3bv/HMM8/w+c9/vlinUhRFUZS7Std1ntzVyH/sv+y90JoGT+5qHJe8dYWgbyhDJutgmgbaNBY4G4xnePVkJ0faurHd8X3HNA22rq7k0W311FSEpvsjTUpKietKfJZOJOwj5Fe9dZXFT8WziqIoymLhptM4A/3gumiGiQb0bWrE6brimaJ1EPRtaqD2drznCIdYNoYtHHTdmFHVrldi158RbLicZsuFFGVx7+IFAH1ZPdaO7Zgb1qOZxUldjeyrG7HCBMzAooxvpRBIIUDX0U0TzbJyCd3A4vx5lZyiJXhfeOEFvv3tb1NXVzf82ObNm/nqV7/KBz/4QRUQK4qiKAvaE62NADx3qGNUJW/Qb/Lkrsbh5yEXPA4lssSSNoahYU6j4vXWYJpXTtzg2PkeXDE+m2zoGjvWVbF7Wz1Lptg6YjpcIdGAgM+gtMyHOc0ewYqykKl4VlEURVnohG3j9Pcjsll00wQjl/ZxpMMx/y38W6tpuNBHYMRaDumQxbW1FWT8t1gnbFJOmpSTQtdNdH36id2ssHlz6BwHB08PJ3ZrbtlsuZBi3dU0ZqG8rmVhbmrGemA7Rm3NtM9biJQCKSV+w0+JVYq5yKp1peuCFLfbLVjolg8jGCxaYlxZGIr2asdiMZYsWTLu8UgkwtDQULFOoyiKoiwQUkqudA7xVncCzXVZWlkyZ98YSynp6IoTS2WJBH001oTRNK3g4xPtM5EnWht5vGUZh9u66R1KU1kaoLW5elTlbiKdZTCeC5in02f3Zl+SV45f5+SlW0iPKmHL0NnZXM07t9QRDfunfNypyvfWjZZYlASsSa+TEGLC66AoC5GKZxVFUZRCUvbUFt2drVwvXO8v8Sd6LpVNYsZTiGQKzTRzyd0Rsm6WnbU7oBbYIdG6eyGVgWCAYPUSyjWNrMhyI9lF0Aigz2ABtbGJXdORbLiaq9at6Svc6kxbUoH1wHasLRvRAsUrYHBdB9OwCJklBM0AWZEtmNwdysQo9UeKdu65JKVEui6arqP5fZiq3YJCERO8mzZt4p/+6Z/4+Mc/PvwHXjab5ctf/jJNTU3FOo2iKIqyALRd7WffkWv0xzOYho7jCsrDfva2NNC8vHxOztUXyww/VhHxs66hjPPXBsY9vrelAcBzn6mMT9d1HtxYO+7xjO3QH8vguhJjGlWvb/fEefnYdc5e6fd83m8ZPLixhoc31xEOFrfawBUSpCTgM6iIBPFZo8OCQtc2GvZx7HzvqErm77x4cVwls6IsNCqeVRRFUbw4wuH/PfrvfHDt+4C5S6I5wuE75/+TDzU9jTkmwVroOSkl2cF+/uvk9/iFNU9imd7xYsgMsa589Z0HKtaMOLbNUDaOc7sdw3Rlhc3RoXMcHDhNUqQpG3J454UUG95KE8gWaK6raRjr1+aqdVc0Fq0Q5E61ro+yYGQ4oetIhx9d2cdTq57A1EZf24yb4X+d/Tc+vuUj+I3iF1LMVi6h64CmoVs+9IAPMxRWFbrKKEWbDX/wB3/ARz/6Ub7xjW9QX1+PEIK3334bwzD413/912KdRlEURZnn2q728+zLFxFy1OK89MUyPPvyRZ7es6ZoSd6R5xrpRm+CM1f6KI/4CfjufNT1xTL88/PtgIbfNzp4nen4hvvs2i6moWMYUwtO3+oc4uVj17nw9qDn8yG/yUOba3nHxlqC/uIFbyN764bHVOuOVOjaXrk5xEA8i6Fr6CP2S2WcXI9iUEleZcFS8ayiKIri5VjXKd4e6uRY1ym2V22ds/Mc7znNzUQXx3tO01KzbdLn3FQKZ6CfM71t9GT6ONd/kU1Lmqd8PiklcTs+43YMucRuOwcHzpBycoulbbmQZPlNu+A+WiSMuW0L1vat6KXFqZiVUiKEi2GYlJghgmZwXHzb3neB3mQv7X0Xxl2jF6/tJ2HHefHaft6zYm9RxjQb+QrdXELXQvf50EMhdEutiaEUVrS/GDdu3MiPfvQjfvjDH9LR0YGu63zgAx/gqaeeorS0tFinURRFUeYxKSX7jlwblxTMEzJXOdvUWDbr4KTQuaSUDCVtkDCUsEcleKWUDCZyAWe1Lzir8QkpGYxnSaSzGLo+pV61UkouvD3IS8euc/VmzHObSMjinVvq2dlcjd+afgVFIa4r0DSNoN+ktMTCmOAWromu7WAid21dIdHHJrNlrkfx4y3LVLsGZUFS8ayiKIoyliMcfnbjCAA/u3GEzUs2jquuLdZ5DnUeBeBQ51G2VW0aPs/Y57aUrYfBGCKbRRhwovcsACe6T9NUsXZchaqXlJMmbscBpt2OIStsjgy1c2jgNCSSbLyUZvPFFJHk+IWB84wVjblq3XVr0IzixLhCuGiahk/3UeIv3FvXkQ4nuk8D469Rxs1wqrcdgFO97byr4Z13vYpXOg4gwTTQTB+6z0IPBDF8vrs6DmVhK9q70te//nV+4zd+g4985CPFOqSiKIqywHR0xUfdzu+lL5ahoyvO8trZfWNf6FxZR+C6ueDSdQVZ28V3O1E68rmRj093fLFklqGEjaaDOYUAVUjJ2Sv9vHLsOtd7E57blEf87N5az451VVjT6N072XmFK/D7DKIlgSlXAhe6tsm0g8xnfXPdHRibB09lHA63dXu2sVCU+U7Fs4qiKMpYx3tOE7MTmIZOzE54VtcW6zz5hGvcjo86T/45zRW4Q7c41f4am2s2oZsmZ2+1kXSSACSdpGeF6kiOcIhlY9gzaMeQETZHh9o42H+aiptxdl9IseZaBqNAcQd+H9aWzVgPbEOvHN/jfiaklAjpYuk+SqwgATMwaWFGe9+FgtfoxWv7cUQWAEdk57yKV7ouUrpohpn7n2VhqB66ShEULcH71a9+lQ984AOEQqFiHVJRFEVZYGKpbFG3m8kxxJiy05H/LvTfUz12KuMwEM8g5dT67LpCcvJSL68cv0F3f8pzm6qyAI9uW8rWNUsmrKqdDtcV6JpGyG9SWuJD16dXLV3o53fc0VUZEonG+GP3Dt2dRUgUpdhUPKsoiqKMNLJyNm9sde1cnwfg8PUjBGMZTNtF6jon+9pors71hs9XpuYVquLNtWNIkLSTGMb02jFkhM2RwTbe7DnFisuDvP9CispBt+D2ek01Vst2zI3NaEWqQhXCQdMMAoaPkBXC0KY2/pHVu3n5a+QKd7h6N6+YVbxSiFyrBV1Ht0w008Lw+9EDgaJVMStKXtHekT796U/zP/7H/+AjH/kIDQ0NWNbo0nifKi1XFEVZ9CLBqb3XT3W7mRxjbDJz5L8L/fdkx7Ydl75YBtsRmIY+aZWA4wrePN/Dq8dvFKxorl8S4tHtS9m4smJUL9uZyvXWnX61rpdC13ZsGwqv5C5AZWnxVj9WlLtJxbOKoijKSMNVtSNitbHVtUU9zwhxO87xrpMQSyB7ejE0kLeLAfJVqPn/HsmrindkOwbDmHqMmBFZjgy2c+HKMdadG+JDV9L4nAJFEoaBuaEJ64Ht6EvritIrVkqBEAKf4SPij84o6Tqyejcvf42uxa4PV+/mzaaKV7pO7hY300SzfBg+H0YwqBZDU+6Kos2yP//zPyebzfKf//mfns+3tbUV61SKoijKPNVYE6Yi4p+wTUNFxE9jTXjOzuUzdQxDx3UFhqGPasOQfw7wbM8wdnxCSPpjaVLZ3AJqk/XZzdoub7R3s/9kJ0MJ7yrYxpowj21fyrqG2fchhun11p2qQtc2FDDpi2m5Ng3a+PYMAEG/SWtz9azHoCj3gopnFUVRlDyvqtq8Ylbxep1HExJ/MsupM6/mkroehQnHx1SljpSvUJVSMpSN4wp7Wn12MyLL0b6z9Jw6QvP5GFt7Jlg0rSyK9cA2rK2b0Yp0B4wrHAzdJGgGCZlBdG1m8a1X9W7ese5TdCa6PJ+bahWvdF2k7SB1A93nxwiW5VotqIXQlHugaAnez372s2oSK4qi3Oc0TWNvSwPPvnzRc6E1XYO9LQ1F+bwodC5N0ygNWfTHM5SWWOP2iZZYUKDyND8+gKFEhljSRte1SRO76azDwTNdHDjVSTLteG6zZmmUPduXsrIuUpQF5hwh8Zs6pRE/oYD3ghIzNdG1jZZYDMSzGF4V0Bo8uatRLbCmLFgqnlUURVHyvKpq84pZxTvyPJorCCTtXCsGTaPXHQQ0Qub4ROOtdF/B5xJ2gqNdJ1hR2oihG1NO7mZEluPX3iT75jHWX4yzJeNdrSsBc80qrJYdGKtXFq1aFymxDB9RfwTLmH1861W9m3cz0U3WzXreSVeoilcKgRQCTdfQzAC+inL8JWW4hdeWU5S7pmgJ3ve///3FOpSiKIqygDUvL+fpPWvYd+Qa/fE7FaAVET97WxpoXl4+o+NKKenoihNLZYkEfTTWhEeda2S1aX1lCXu2L+X8tYFRj+fGsAZg3D758S2vCXPzVhIJk/bZTaRtXjt1k4NnbpLOevcha15ezp7tS2monn3VshASKSUBn0FV2D9p4nk2Cl3bFbWlRMM+jp3vJZW5k8wO+k2e3NXIE62NczYmRZlrKp5VFEVRYOLq3bzZVvGmnTSmbnKo8yia4xJIOrd77GpIXUdKScrJreEQNPyj7pwq9FxuATKJi8uZ3nZWlk4tLku7Gc6d2o9x7CwbrqcLlEKADAbwbd+KtWMbell0Rj/3WK7rYhgGJWaQwCyqdceaqHpXCEnaSSEBTTM8f95Tve08tvRhfJhgGOiWhR4KYoZK0EwT09QxwyVodgKEyvAq915RK3gn8sUvfrFYp1IURVHmuebl5TQ1lnG9N4E0DDThsnRJyYy/3W+72l8wIZs/19jkr6ZpvHtng+fjwLh9aioCDCZs+mMZDEMvGNgCDCWy7D95g8Nt3djO+IBO02DzqiXs2b6U2orZ36rmugJd1wkHLcIhqyg9e6diomv73x4XHG7rpncoTWVpgNbmalW5qyx4Kp5VFEVRIJfwfHTZQ8P/NgydcDhAPJ7GHVGumXYzhGeQ4HWEw3fO/yfvXfEEj4Y2gpGFstHtw7Juht5UHwCVoSX4dN+Ez2XcLGknDcjhJGlW2AQmWEwtFR+k4/BLhE9eYnW88KJpcmktgZYHMJvXF6WfrJQCKQU+w080GMHSi3s3GkDWzbKzdofnc7FsnCtDHQCU+iJYI19DV6BpIEyDhCWJVNapHrrKglC0WXr58uVR/3Zdl2vXrqHrOtu3by/WaRRFUZQFQtM0VtSVUl5eQn9/AscjEToVbVf7PVs+9MUyPPvyRZ7es4bm5eUsr414jsHr8ZHPZW2XgUSG3oE0pmlMWLXbN5Tm1RM3OHquB9ejB4Wha2xfW8nubfVURoPT+0HHKOaiabNR6Brqus6DG2vvwYgUZe6oeFZRFEUBCFslbKq8s0iZaeqzjmlHOt51koGuDi47R9lUtRGtdOZf3qfdDPFsAildtAmSuXlSStJvd9B9aD/R8zdYWuDHcU0dbeN6Sna2YtTWzHh8o44pHAzdIGQGCRaxWtdLyAyxrnx1wecfqNkK5K6HdBw0w0Dz+zBCJeiBgGrZpCw4Rftr8Tvf+c64x1zX5Utf+hLLli0r1mkURVGU+4iUkn1Hrnn28wUQMtdqoalx+guW2Y7LQDxDxha5BdTMwgFxd3+KV45f58TFXs+xmIZGS1M1u7fWUxae/uq+I83FommKokyNimcVRVGUuSSFIN1/i9Nn92O6gpO32miuasKcQWom62aJZRO40kHXDTRt4uSutG1Sp04Sf+MwoZ4YSwpslygLEmx5gNJtD6AFZhfXwu22EULgNy0i/jL8hm/yneaYcGw0TUOzLHTLj15SguG79+NSlNmY03IgwzD45Cc/yS/8wi/wq7/6q3N5KkVRFGUR6uiKj2rL4KUvlqGjK16wUnesXGI3S9p2sQx9wj6213sTvHzsOmff6sMrx+yzdB7cUMvDm2uJhGYeFAopEfOgWldRlPFUPKsoiqLMlhQCJzaEG0/Q1n+OhEiDppF0krT3XWDTkubJD3KbI2xi2ThZ18EwDPRJErvi1i1SbxzFPnkaM+vg1TzM1aBvZSUVux6metW6olSvCuGgaQYBI0BJYG6rdScipUS6DmgaumWhWT6s8nJ0n09V6SqLypz/BZlMJunv75/r0yiKoiiLUCyVLdp2+cRuJutimjrWBIndKzeHePnYdc5fG/R8Pug3eWhTLQ9tqp1VMjZfrRvym0RUta6izFsqnlUURVFmQgqBMziIm0yiaRpCl5zoOTNqmxPdp2mqWIupTRxTCimIZWOk3QyGbmIYhRO7Ugjc8xfJHDmKvHIN8E7+xIM6tzYuY9mux1hZPvs2DLlF3lws3UfYHyVgzL4CeNpjcF2QAgwDzbTQ/RZ6IJRL7qqErrKIFS3B+1d/9VfjHkulUhw4cICmpqZinUZRFEW5j0SCU6uKnWi7sYld0/ROokopuXh9kJeOXedKZ6zAeSwe2VJH64Ya/NbkPc4KnWc+9NZVFGU8Fc8qiqIoxZBP7IpkAjQd/XYytv3WOZJOctS2k1XxSimJ20lSTgJNMzAmWNRNxOI4x06QPXYCYvGC23XU+ohvXc36rY+y0Vc6g59wzHmFi6aB3whQYoUwJqkqLiYpBNJ10UwD3e/HCEbR/X40VTih3GeK9lflD3/4w3GPBQIB1qxZw6c+9alinUZRFEW5jzTWhKmI+Cds01AR8dNYEx73+FQTu0JK2q/28/Kx67zdk/DcpizsY/fWeh5YX41V4DiTcV2Bfru3rqrWVZT5ScWziqIoymwMJ3YTCdB1NONOysWRDie6T3vuV6iKN2knSTgJQEMvkNiVUuJevYZz9BjOuQsgvFdNy1gabauCuNs3sH3FLkrNkpn9kCO4roNpWESsMAHz7i1Mlq/S1Xx+9FAAMxRGM1XRhHJ/K9pvwIsvvljwOdd1i3UaRVEU5T6iaRp7Wxp49uWLnoub6RrsbWkYFUy6QjAQy5DKTJzYdYXk1OVbvHLsOl39Kc9tKqMBHt1Wz7a1lTNKyIrb1bpBVa2rKAuCimcVRVGUmZBC4AwNIRJx0HTPZGN734Vx1bt5Y6t4M26GWDaBlC6a7l0NK9MZ7FOnsY8eR/beKji2rnKT0+tCBDdt5sGqbbNO7EopkFLiN3yUBSOYujWr403pnEKAcHNtF3w+zEgEPRhUVbqKMkLR/tJ85pln+PSnPz3u8c7OTn73d3/Xc1ViRVEURZlM8/Jynt6zhn1Hro2q5K2I+Nnb0kDz8nIAhJAMxNMkMw6GXjix67iCYxd6eeX4dfqGvCuD65aE2LN9KRtXVKDr069EcFyBoeuEAybhoG9Gx1AU5e5T8ayiKIoyHVJKnNggbiyBpmmjKnZHmqh6N+9E92nWRFeSdFI4wkHXDTSPVgduVzf2kWM4p8+CbXufT4fzywOcXhti6cqNPFa+ZdaJXdd1MQyDEjNIwJzbRdOkEEgh0HQ9l9ANBHIJ3Qn6DivK/a6oLRpSqRR/9Ed/NPzYSy+9xGc+8xk2bNhQrNMoiqLcNVJKOrrixFJZIkEfjTXhSW87msk+ExFCcLitm96hNJWlAVqbq9Gn8E31fBj7TBQaQ/PyclbXlfA33ztN72CaymiA33yqCZ/Ph5CSwXiWRMrGMDTMAoFf1nE50t7N/hOdDCa8F2VrqA7z2I6lrG8oK/izCyE4eekW/fEM5WE/W1YvQdd1hJAIIQn4DMojAfyWOWqfmbyO88F8mBcTme/jUxYWFc8qirJYpZ00ATMw5e2llGQ6ruLG4xjhMP7G5UX5fB1ID1IWiE5rn+mOfab7TIeUEjcWw4nF0DRtuMfuWBk3g9/wk3Wz7KzdcXtngdZ+iWzfLXwVS5BNq5FA0k5xM9lL2BdCH1O1Kx0Hp+0c9tHjiLevFxzXQFjn1Jog59aU0FzZzNNlmzwTu1JK5M0u7PgQVrgUrbbG8/WVUoCUWIaPiD+MfeY02YFBRFmU0JZtRU3ySscBQPP5it52Ya7mc7HN9bxVFjdNSulx0+v0dXZ28rGPfYytW7fyx3/8x3zpS1/im9/8Jp/4xCf45Cc/OS9/ecbq6fFeVEcZzzR1ystL6O9P4DjePX6U+8timxNtV/snrRgtxj4Tef5wB88d6iCVcYYfC/pNntzVyBOtjfN67HnTmRcTjeH7r1zi0o2hcfssrwnzf/7iBjRdQy/wOZPOOhw628WBk50k0o7nNquXlrJn+1JW1ZVO+Hl14OQNXj3RSSZ75zg+y2D3tnqe2NlIOGSNG8dMX8f5YD7Mi3sxPuXuK8acqKqKzHociyGeBRXTTtVii12U4liM88IRDv/S/iwfanoac4IFuvKS7W3073sBp79/+DGzvJzyve8m1OS9ENhUpJ00f3L4r/lc6+9OOYk13bHPdJ/J5OdFX1+cdP8AIh4HtAlbBDjS4b8uP89Tq54Y7q07+NqrDL76KiKbxnYdLMMEnx/zwRb8D7aO+5wRA4PYbx7HPn4Skt4txYQGb9X7OLU2yNv1AbaXNvGOAoldAPetq2TfOIoYGiKWjRPxhdFLS/HtfABj5fLcMW9XEAeNAEEzSOz1A7fHfSfm0n1+ort3E31493Qu5TApZW5xNENH9/vQgyXogeL38Z2r+Vzs94q5mLfK3TfbeTGbeNb4whe+8IUZ7z1CJBLhPe95D9/85jf527/9Wy5evMjf/d3f8f73v3/BBMPJpHdFlzKermsEgz7SaRvh1RhTue8spjnRdrWfZ1++SDIzut9iKuvSdrWP6vIQVWXBWe8zkecPd/Af+y+P+1BwXEF7Rz+WqbNm6fjqh/kw9pGmOi8mGsNPjlyjdzDtud9gIsvF6wO0NteMey6Ztnn5+A2+89OLnOsYwPb4gG1qLOfpPat5bPsyKiITB5QHTt7gJ0eu4bq5vmN5riu4cjNGOGixdlnZqH1m+jrOB/NhXtyr8Sl3XzHmREmJf9bjWAzxLKiYdqoWU+yiFM9inBdvdp/kVO8ZQlaI+nDthNsm29voefa7iNToZKJIp0m2ncVXXYNVWTWjcXz/4g+5NPAWSSfFxiVNRR/7bPaZjKaBkUkRu96JyNhoujHp58LZvnOc77tA0AxSHapi8LVX6d/3E6Tr4ohc/CKR6K5AdLyNZhoYy5YihcC9dJnMCy+SfW4f4tp1sMcXKSQDGsfWh3jhHaW0r4uwZulG3l/zGBvCK/HrPs8xuW9dJfPiK5DJkHGzZEQGXdMxbRfnylUoj2CVLyHiC1Pqi+AzfAy9vn943CNJ1yV9+TKaZRJoXD6l6yhdF4RAM030UABfeQVmNIoRDKFb1pwkd+dqPhf7vWIu5q1y9812Xswmni3q/aEVFRV84xvfYMOGDezYsYNdu3YV8/CKoihzTkrJviPXPBf0AhAS9h25NirBN5N9JiKE4LlDHVBocwnPHepAjFkhdz6MfSYmGoPjOJ6J2ZGudSdwRvQfG0pm+dHBq/z5vx7jpTevk86ODkY1YPOqJfz2L2/mw0+up7Fm8m9JhRC8eqITKeTwtdA0DV3Xc60WPF6Tmb6O88F8mBcTme/jUxY2Fc8qirKYOMLhUOdRAA51HsUR3nczQe7ztX/fC1Do81NK+n/6kxl9vqadNEe6TgBwpOsEacf7y/uZjn02+0xECoE9NEDmxvVcOwbDmNLCXiN77p7oPo3t2gy++urtg96JJ4evpZRkXz9M5rWDJL/yNdLf/h7uxcuex75eZfHjh0r5n/9HJW9si9Jct5n/u+GXeXflLiJmqPDPIiXZN47mzo8k7eSqcdNOGiElugTj6BnK/VH8Ri7JJKS4M+4CBve/ipDe8awUAmHbSARYJmZFBb76enzV1VilZUVrweB57jmcz8VW7Hmr3J9m9dv0yCOPeD7uOA6Dg4M8/PDDw9/AHDhwYDanUhRFuSs6uuKjbvf20hfL0NEVZ3ltZMb7TORwW/eo2/m9pDIOh9u6eXDjnW9358PYZ2KiMfTFplaF9u+vXObJXct59UQnR89147jjAzVd09i+tpJHt9VTOc3KzuMXeklncklkTdM8qwvGviYzfR3ng/kwLyYy38enLCwqnlUUZTE73nOauB0HIG7HOd5zmpaabZ7bZjqujrqN3YvT10fmWseUKzbzfnD5OWw3F9fZbpYfXH6OD6z7paKNfTb7eBG2jRsbRKTSoOmYvqkldvPa+y6QdJIAJJ0kF372PKFsGiQ4ckTxgcwl90ypQTKJ/ZJ3MjVrarSvCHBqbZDechNTM3ggsp4HyzZNmNQdSd7sQg7lWvhk3CyC3DgEEkc6WEYQZ2iI7M0b+OuW5sZ+8viotgxeRCZD8uRJwltz11k4Nmg6us+HHgpihkrmNJFbyFzO52Ir1rxV7m+z+i374Ac/uKBuV1MURZlMLDW1hOR8J1oAACAASURBVOLI7Wayz0R6hyavaPDabj6MfSYKHVtKSXZM9W0hF68P8ZffPu5Z0WkaGi3rq3nn1nrKI1O/5UVKiesK/D6DjONOaVG0ka/JTF/H+WA+zItinPdejU9ZWFQ8qyjKYjWyKjDvUOdRtlVt8uzx6cbjUzquG5ten++R1bt5R7pO8N5VTxbsxTvdsc90n7HcZBI3HkfYWXTDRDOmnzIZWb0rpURISef1C6weLtaVIHN3lRkSNAmFbvnqjRqcWhukfWWArKVjagatpet5R3QT4SkmdvNEMoVEIiW3q3c18p9+aTtN0AwAGm4icednGRic/MBSYvf1IqVEDwSwysvRfb57/tk6V/O52IoxbxUFZpng/e3f/u1ijUNRFGVeiAS9+1VNtN1M9plIZenUFp0Yu918GPtMeB3bdQWukBiGhutMfttUKjM+EeyzdHY11/DIljoioamP33UFmqYR9JuUllgYuk5dhfciFWONfE1m+jrOB/NhXhTjvPdqfMrCouJZRVEWq5FVgXkTVQca4fCUjmtEpnd3zMjq3bzJqninO/aZ7gO5vrBOLIZIJkEKNMNEn0FiNy9fvesKgUAgkQyFQCAQQqCLXK/MQulPV4OLDX5Orgtyo8oCTcPUDHaVNvFgdOO0E7sAQrhowQC6ppMRWSRi1PldXNJOmoAZxCi5E/eaZQXWihjT1iCwYiX+urppj2suzdV8LraZzltFGatoPXg/97nPcfr0ac/ntm7dWqzTKIqizKnGmjAVk1R5VkT8NNbcCRhmss9EWpurCfonDiqDfpPW5upRj82Hsc/EyDG4rsC2XVwh0TSNssj0E3RBv8G7dizlv//aDt7z4PIpJXellNiuQNegIhKgvrKE8ogf43bV7kxek5m+jvPBfJgXE5nv41MWLhXPKoqyWHhVBeYV6vHpb1yOWV4+4XHNigr8DY1THodX9W5eoV68Mxn7dPeRUuImEmS7u8nevIFIpdB0fUYVu6PGIR3e7DqJLRxccn1pdQFSA1yBKcDAO7kbC+m8vqWE//lLS3jukSg3qn2Yusmu6EY+2fDL7F2yc9rJXff2zx2xwlStbMaKlpG2ve8eS9lpjNJSfLX1w4+FtmxD992OuaS88z8ATUPTdYySEiK73jGtcd0NczGfi20mc11RCilagvf73/8+v/7rv84PfvCDcc/Nh6bViqIoU6FpGntbGtALfKWua7C3pWHULUcz2Wciuq7z5K7Gwl/ra/DkrsZxLQPmw9hnQtM0Ht5ch+vkEruM6HFrGiamMbVzlwQtnmxt5L//2g72tjQQCkweoLvidhsGS6euIkR1eYigx34zeU1m+jrOB/NhXkxkvo9PWbhUPKsoymLhVRWYl68OHEvTNMr3vhsKfX5qGuU/9/i0Pl+9qnfz8lW8xRj7VPcRto3d10f2ZifOQD8IgWZYRYkZMm6GN24eI3Z7HIGUw9ozt3jsf7/FjoNd6AU+Rq7W+viv3VH+13uX8MamEpJBI1exG93IJxuennZiV0qJEA4GOuX+MioDFQStILqm07epEQfvBdEcBH2bxvytIASRR945Lqmr6frwduVPvGfexrPFns/FNpO5riiFGF/4whe+UIwD/eM//iPPPPMMf/iHf8jAwMCoBSn+/u//nk984hPFOM2cSiZVr76p0nWNYNBHOm0jCi1jrtxXijknpJR0dMV5uydOKuMSLbm7PZyqyoJUl4fovJUgNaIHbEXEzy8+tJLm5eO/CZ7JPhNZszSKZepcvRnDce8EYUG/yVMPreCJVu9vmmcz9hu9cbr6ksSTNrYjqKsI8dTDuX1s2+YffnCG/33wKmev3GLbmgoMw0AIwaGzXRy/1EvfUJr6ytDwazX83MVeunoT1C0JjXodpZQk0ja3BtOEgyZLokF6BtJk7DvjLg1ZbFlTydvd8Vzy10O0xMe7dzbwK4+tYVV9KaYxhZWNXYGuaURCFkuiAUIBC10fPbaxc3DNsrJpvyYzfR3ng/y8uN4To3coQzLj4LiS6rIATz28atpzeqRivF8U+3dOubeKMSdKSqbeY7uQxRDPgoppp0rFs4qXuZgXaSd9V3tpOsLh/7v0Y7Ki8HtBT/IWO6q3oGuj4yarsgpfdQ3Zm52IVGr4cbOigiW/+F5CTc1THkfaSfMv7d9DyMJrKnQle3nn0geHr89Mxu61T9a1MXRj+N+9Q91s0GuJvbafnhNHMOIJrNq6cX9jDGVi+M3xnye6rpGWKTRXR47Jj2bcLIOZIeLZBAc73yB6c4jmkz1serObyp4Upke7sbRP4+TaIC88WMrxphD9peZwK4bW6AbeV72HpvByfLpV8DqMJaVASoHf8FEWiBKyQhi6QcbNYOomjnTYN/AmQyGNklgW077zg6RDFlc2VPJ2GTRHV6PrOrrfwiiNEt6yFd3vJ9NxFZw7FaV6MEjFL76Xinc/OeUx3m35+ZzpvIE7OJhbME5IzKoqKqc5n8ea7XvFbH5PlflrtvNiNvGsJotUjrB161ZOnDjBxYsX+eQnP8myZcv40pe+RGlp6fBz811Pz71trr2QmKZOeXkJ/f0JHMf7G0Dl/lKsOdF2tZ99R67RF7uzWmtFxM/eloa7nrDJJ/liqSyRoI/GmvCkieaZ7DMRIQSH27rpHUpTWRqgtbl6St+QT3cc33y+nQMnO3HcOx8JpqHxyJY6rnXFuXRjaNw+S0r9OK4klbkT6AX9Zq5qFXjuUAeprIOGhkQS9OWee/fOBuIpm1jSRgLGmMRqZ2+SWDpLZ2+SE5d66epLjT01AJap8+CGGh7f2TClpG5+0bSAzyAc8hHwef+hNdkcnMlrMtPX8V57/nAHPz7UQSrtIJFoaAQDJu/Z1Tir5HQxP0OK/Tun3BvFmBNVVbPvobcY4llQMe1UqXhW8VLseeEIh39pf5YPNT1915K8cTvBlcGOSbdbEW0kbHmvMyClJNNxFTcex4hE8Dc0TvvztSvZw5GbxybdrqV2OzWhqhmPfew+aSfNP7f9O7/e/CsEpAFDCfSDb2K9eRaZzWC7DpZhovsCRHfvJvrwbiBXgfv3J7/Bx7d8BL8xOsniYPP3J77Ox7f+Bia5pGvWzRK3EzjCQcvapI4dxz56DGOg8HtwV4XJybVBzi8P4Jh3rqeORjVh3lXVyspIw6Q//0hCuLn1I4wgoduVusPjlg7/dfl5nlr1BFk3y9uxztwTUqJ190IqA34fsqoCLBP8PpbXriMSHN97VwhB/PBB7L4+rCWVhHe2Loh4NtneRv++F8jevMlQepDSYBRfTS3le989qwTvbN8rivF7qsw/s50Xs4lni57gBYjH43zqU5/irbfe4itf+QpPP/30ggiIVTA8dSogVsYqxpxou9rPsy9fxOuLLl2Dp/esUVV5c+Cbz7fzyvEbnov3TuUDwmeODuzylbaGroHGcIJXColEsrelgYc3149K7OY5ruD4hV5eOXGDW4Pe/cFMQyMS8hHwGRi6xhOtjaxeWmABiNvj0YCgz6Q0bA331fWi5uAdzx/u4D/2X/aeBBq8752rZpzkVZ8hyljzLcELCzeeBRXTTpV6L1K8FHteHOk6zk87XuHnGh9VCybdJV8+/k9c6D7HZl8j/23N+4gdep3+fT8Bcgl3KXNrPeQT7uV7Hyf68G5+fGUfx7pPsr16C+9ZsXfUMZ+7uo83u0+xo3ozjy3bTcJOYLsOdPdgHz2Oc/rsqOrWkYShc265nxNrA3QtGV2Ra6KzM7qBXWWbKDGmt/CuEC66ZhAygwTNgGcC/vStNn524zDvqG9l05I7yUzh2KAb6D4fRjCAHgyhLYBk7XQl29voefa7ICVJO8lQNkapL0LICoGmUfX0B2ac5FWfIYqXe5ngLdpXiCPzxOFwmH/4h3/gr//6r/ngBz+IU+CNTlEUJU9Kyb4j1zwTawBCwr4j12hqLFPVeUXkui4HTnbOOLkLIKQYrhSQ3Enw6lqu95UQudWD8wc8cPImj2yuY2RzWtsRHGnvZv/JGwzEvW9TskydSNDC7zPutIGQ8Prpm6yqLx03LxxXYOo6pSGLcHDy3mpqDt4hhOC5Qx2FJ4HMVWg/3rJsQVRuKMpUqXhWUZRiGrmA0qHOo2yr2nRXWzXcj4aGernZcY6o7dJhdpB00wy++urtZ+Xw+3zu/yWgMbj/VXytOznV2w7Aqd523tXwzuEq3oyb4VRPO0JITnafZWN0Leb5q9hHjyOu3yg4llRpkDfXmJxa6SfjHx0v6VIjKnzU6VH2lG9HH9FOYjKucDB1i6i/dFyl8UiOdDjRnevheqLrFOsjKzEtP3oggFVRgeGb/mLGC4mUkv59L4DMFZkk7CQACTtJ0AqiSej/6U8Irm9a9LG9cn8o2qfL2J5kmqbxe7/3e2zatIl//ud/LtZpFEVZpDq64qNuiffSF8vQ0RVnee3sq7SUnB8f7MB1Z3cjh+tC/m8VMSI54roS/fZt/SODpkzW4eSlW2xbW0Um63LobBcHTnUST9mex/dZOpGgD5+lewZfg4lcO4f6qtytS44rsEydJdEAwQJtGLyoOXjH4bbuUa03vKQyDofbunlwY+1dGpWizD0VzyqKUkwjF1DKL5ikqniLT0qJSCRw4nG+d+Y7IATC0EAKDvz0WzRlc/GdI0b3AnaEi6mbiEyGo688i1Oavf14lhev7R+u4n3hyktknAzhpGDd1TiZ576GWyBOkppG//IKDqySvFVjjFvgy9JMVvtrycYGMdCxRYZLg1dYW7560p8zn9gt85fhNyZPzrb3niOVSSBMnQEzS5vZR0vdjkn3WywyHVdx+vsBSNkp3Nu9oF3pkrJThKwQTl8fmWsdBBqX38uhKkpRFD3Be/PmTTo6OtA0jRUrVvD444/z+OOPF+s0iqIsUrHU1BaEmep2ytR0DXj3t52OkelhOaL8VQCGppGr1B2dRO4eSLHvyDV+duYmqYz3whvrG8pYvayU4xduTTqGeDqL4wQI+k2WlAaxzKlXQeSpOXhH75B3e4yZbqcoC4WKZxVFKZaR1bt5qoq3uKQQOPEYIp4AKUmJNG/F3x61TbKvh3wcOrY75cgq3r6uDigNDj93qreNB2tbSGaT9J89wWNvJajvzlKozlOWhHh7fSUvNqYZCI1/3qeZtESb2Rlp4qWO/bjcqeht77vA6uiKglW800nsStdBoiEsgzcSF4hVBIaTzIe6j7Gtdst9M//ceO7LlZHVu3nDVbxouDHV1khZHIr2m93X18enPvUpDh06NPzGqWka73rXu3jmmWcIBoOTHEFRlPtZJDi1W4Smup0yNTVls39v1sgFyFLKUWlcwyMCllIiJOMWdBt5rI0rK9izfSn1lSXc6ElMmODNf96Uh/3UVZZM2F93MmoO3lFZOrUecFPdTlEWChXPKopSLCOrd/NUFe/sSSkR6TRuPI7MZEDXh3vHfv/8jxBydM/LeGB85e5IjnDR0IgF88fPnUNLpTnzo3+j6txNdicK3+ElG+o5vz7KTyv7sPXxX3znErsb2BXdQMgIcKH/EilndIFFykl5VvEK4WDqPiKTJHaFY6PpOprfjxmNogcCHO0+wYCRYWRLtPtt/hnhMDC6ejdvZBWvEVncd+Yp94+iJXi/+MUvMjQ0xJe//GXWrFmDEILz58/zla98hb/5m7/hM5/5TLFOpSjKItRYE6Yi4p/wFvmKiJ/GmvBdHNXi954HG/nB61dm1aZB0+4kWg1dG07cjrwjTUqJEAz3txVjzqdrsG1tJbu3LaV6RNK5rjJEtMTHYGJ01ezI81VGA2xatWTWvbPUHLyjtbma77x4ccI2DUG/SWtz9V0claLMPRXPKopSDF7Vu3mqindm3HQaNxFHpDNoUqKZJpp55xom7QRXhjrG7Xel3kfL2SSW4x3rSinJWnClzo90BVX9DuuupGi8mcEQ3kUGtqHxVmOQ2APr+Zm/E1eO386nWeyMNtN6O7ELuUXR2vsueB4zX8WraTpCuPhNH+FAOaZueW4vbBvNNND9/nH9dNX8y/E3LscoKyNxo9fz+YSdJFK9FH/DzBYNVpT5pmi/1QcOHOB73/se9fX1w4+tXLmSpqYmPvaxj6mAWFGUCWmaxt6WBp59+aLnIle6BntbGlQD/CIzDINHttTxyvEb4xbUGt9YwVt+MbWR/4bcY1JKXCGRBQ5kGhoPrK9m99Y6yiPjq0E1TeOhTbU8f7gDka+oIJfY1XUNQ9d4fGdjUeaFmoN36LrOk7sa+Y/9l70ngQZP7mpUC6wpi46KZxVFKQav6t28+62KcjbcbBYRjyHSGaRw0U0L3fBuY/D9i+OrdwHQdc6sDrD1XMqzvYIE2lb4WdORZt2VFOWxwtW+AxGD9hVBzq4KEAvpoF0fFyflE7u7ohsJjlkA7dLglXHVu3lJJ8mFgctsWtJMOFCGoY3/OYeTuoHghIukqfmXo2kaPS2rcX5w3vO1dxD0PLCKZfdBbK/cH4qW4M1ms1RXj6/kWbp0Kf23G1sriqJMpHl5OU/vWcO+I9dGVVFWRPzsbWmgeXn5PRzd4vXhJ5qAXNuEkZW8hqHxyJY6rnXFuXRjaNx+ZSUWjsgtmpbn95ns3lrHUCLLwbNdOAViZMvU2bWhhke21FEamrjlweqlUfa2NHDwzE3iKRvDyCUV52JeqDl4xxOtuWqG5w51jKrkDfpNntzVOPy8oiwmKp5VFGW2JqqezLufqiinSzoOTiyGSKeQjotuWbn2AxN8qVyoehdy/VfPrs7dHbbhUgrfiJuTbANiYYPNl9KjHh/J1aCjzs+5lUGu1lqkfRpe2UKfZtEa3UBrdMO4xC4Urt6Vt3sAGxi8NdjBrroHhpO7Ukqk49yu1A1MmNTNU/PvDkc4vB7sJrCrntWnewjG79wRmAr7uLSpinSwm83CWfTXQrk/FG0Wr1ixgh//+Mc89dRTox7/0Y9+RENDQ7FOoyjKIte8vJymxjI6uuLEUlkiQR+NNeFFUTUphOBwWze9Q2kqSwO0NlfPmwrIDz/RxIf2ruXHBzvoGkhRUx7kPbsaMQwDISS3BhN84/lz9A1lqSoL8CuPrsK0LIQQnLx0i/54hvKwn/KIn1eOd9Le4Z0ICfgM3rGxloc211IS8L7lbCTHcbEsg51N1bxzS91dmReLeQ5O1xOtjTzesmzeztu7TUqp5sUip+JZRVFmK+1meHTZQ1PaLrwAk0pSSjIdV0kN9hGMVuBvXD7rz0LpurnF0lLpXELTMNA0Hc2aWrzRlxmkuWKd53NJO0Vvup+3m6OktpZSe2WIshsDhAdShIbShPq9qxEyJX46VkW5ujJCX6nOTTGE9LityaeZtEY3Fkzs5mWFzZbKDcP/dqWLjoHfsPAb/uHeZhk7jaH5wWeh+wPoJSWTJnVHWuzzbzqGr8Uy4GGJdqMLEikoCVFSX03l7Wt+P1wLL2knTcBU62ksJpocu5TkDP3kJz/hd37nd3jooYdYty735nru3DkOHjzIn/zJn/BLv/RLxTjNnOrpUasnTpVp6pSXl9Dfn8BxPG6FUe47ak5M7PnDHQuuEjJruwwlsqRtB13X0QsE71JKLncO8fKx61y6Pr7SF6AkYPLw5joe3FhDwDd5AGW7Ar+lEy3x4bfuv4BrsVuI7xdtV/tVZfccKsacqKqa/SIpiyGeBRXTTtVCfC9S5p6aF4Ul29vo3/cCdn8ffel+KgLlWOUVlO99N6Gm5mkdSwqBm0ggkkmknQXdmLBKdzaybpbYrU5SR9/EPX4amUgU3NZYtRKrZTvJFTW8NnSaE0MXcBk/D/yaxc4JKnYLcYWDqVuEzCDB28k16bpIJLrPjx4IYJSUzNm1UIpnIb9XOMLhX9qf5UNNT6vq5SKb7byYTTxbtAQvwOHDh/nWt75FR0cHmUyGFStW8Gu/9mvs3r27WKeYUyoYnrqF/GamzA01Jwp7/nDHhL1M3/fOVfMmySulJJmxiScdbMfFNL37nOW3PXdtgJePXaejy7vPV7TExxPvWMHmFWUYkwSqUkpcVxLwGUTDPqwJzq0sbAvt/aLtav+EvZmf3rNGJXlnab4keGHhx7OgYtqpWmjvRcrdoeaFt2R7Gz3PfhekJGknGcrGKPVFCFkh0DSqnv7ApEleKQQimcRNpZCZ9JwmdaWUJO0UsYvtZI+8ibhwiYKLQgQDWFs3Y+3YRqzU4vX+UxyPXUB4JXZ1H62lzeycZmJXCAdLtyixSvAZvjtJXb8fIxhCD4XUXUELzEJ+rzjSdZyfdrzCzzU+el/0Y76b7mWCt2ip+tOnT9Pa2kpra2uxDqkoirLgCSF47lBH4dXKZK7H6eMty+7pbe9CSGLJLPGUA5rE0PWCyV0hJGeu9PHyset03kp6blNR6ufRbUtpaapiSUWYwcHkqP6+o44nJVJIgn6DaLl/0kSwotxNUkr2HbnmmdwFEBL2HblGU2OZ+sNsEVDxrKIoynhSSvr3vQBSIpEk7Fz8l7CTBK0gmoT+n/6E4PqmcZ+F0nVxEjFkOpur1EXLtWAwJ2/VNRNCCmJDfSSOHcF+8wSyr3D/dL2+DuuB7Zgb1jNEhp8OnOREx8WCid3d1VvYEVyPqU197K5w8Ok+ooFyTKkDEgwDMxJRSV3lnhjZp/l+6cd8vyjaq/jhD3+YN954A6PAqpaKoij3o8Nt3aPaMnhJZRwOt3Xz4MbauzSqO2wn14YhmXEwDB3D0PBcOQJwheDExVu8fOw6vYNpz22qy4Ps2b6UzauWYOja7eN5c4UAqREJmYRDvoItIBTlXuroio9qy+ClL5ahoyvO8triVJAq946KZxVFUcbLdFzFub3QZMpO4cpc31pXuqTsFCErhNPXR+ZaB4HG5bjZLCKRQGQySMdGu12pqxlzl0RyhM1gxyWSb7yBe+YcOAXib9PE3NiM9cA2jPo6Bu04rw28wYlYocSuRWt0Iw+VbaSyrJR4LIMjJq7Kk1IihIvf9BP1hTE1Hc30YYRuV+qqYgblHjrec5q4nbv7Mm7HOd5zWlXxLhJFe4f9+Z//eb7+9a/z0Y9+VH0LpSiKclvvkHcidKbbFUsq7RBLZcnauTYME7VDsB3B0fPd7D/RSX+BRNfSqhIe276UpuXlkyZqXVdg6Ln+uiUBS31mKPNaLJWdfKNpbKfMbyqeVRRFGc+N55JBI6t38/JVvFJIMtevo1kW0hW3F0rT5qxSNy+djjNw4k0yR44iO7sKbqdVlGM9sB1ryya0YCCX2O15vWBiN6D7aI1uYGdpMwHDjzmFpGwusSvwGxZhXxjTF0APBjHCYZXUVeaFkdW7eaqKd/Eo2ivY39/PSy+9xNe+9jXq6+vxjVnp8dvf/naxTqUoirJgVJZObWXSqW43G64QDCVsUhkHKSWGUbgNA0DGdjnc1sWBk53EkrbnNivqIjy2fSlrlkYnTYY4rsA0NCoiAYIBFUAoC0MkOLWVq6e6nTK/qXhWURRlPCMcBkZX7yIl3E5oppJDBMwgesCPpulo5twmM6WUxLrfJvbGIezjpyBd4E4bTcNYvxZrxzaMlcvRNI0BO8ZrPW9yMnYB4dFDLZfY3cjOaDMBfWqf7cMVu5iEg+VYJWHMcARN3Q2izDMjq3fzVBXv4lG0v7BLS0sX1OITiqIod0NrczXfefHihG0agn6T1ubqORtDKusQT2bJZF0MQ0fXC7dhgFzLiJ+duclrp24WHPe6hih7ti9lRW3ppOe3HYGuaVRGAwR8KrGrLCyNNWEqIv4J2zRURPw01oTv4qiUuaLiWUVRlPH8jcsxyspIvN2NJgSahOEFJjSNlJOhZEktvtr6OR2H6zoMnD2Ra8Nw+UrB7bRwCeb2rVjbt6KX5tonDdgxXhs4ycnYxeImdp0sfjNEtKwGM1KK4VNf+Crzk1f1bp6q4l0civbqffGLXyzWoRRFURYNXdd5clcj/7H/svdCaxo8uaux6AusCSmJJ7Mk0i6uKzDNiat1AeIpm9dOdXLwTBcZ2/XcZuOKCvZsr2dp1eTJLMcV+EyT2ooQqYCx4FaXVRQATdPY29LAsy9f9FxoTddgb0uDup1/kVDxrKIoyh1SSkQyiZtK0bu6CnG1DQ0J2uhiAQdB36YGaufoszA91M/A4Z+RefMYcihWcDtjeQPWA9sx1q8drp4dsGMcGDjJqQkSu7tuJ3b9U0zsCtcF4RIIlRKtWYEZVIulKfOfV/VunqriXRzmPD3vOA5CiHG3uCmKotwvnmhtBOC5Qx2jKmKDfpMndzUOP18MWdsllsySyrjouoaua5iT3CY3EM+w/0Qnb7R34bjjA19dgy2rK3l0ez015aFJx2C7goClUxEJEgpaBPwmqeTEi1QpynzWvLycp/esYd+Ra6MqeSsifva2NNC8vPwejk65G1Q8qyjK/UIKMZzUldkMoOHqkmPBPvzbami40EdgROuudMji2toKMv5brJMOplacFIMQgqHL54kfPoTTfg4KLWzm92Ft3oT1wDb0qsrhh/tvV+wWSuwGdT+7ohtpiTZNKbErpcSxs7gIQmVLiJZVY8zhonGKUkwTVe/mqSrehW/OX7mnnnqKK1eu0NbWVpTjnT17lj/90z/l7Nmz+P1+3vGOd/C5z32OiooKfvazn/GXf/mXXL58mbq6On7zN3+T9773vUU5r6LcL6SUdHTFiaWyRII+GmvCd/0baSEEh9u66R1KU1kaoLW5uugVrnnF/nld1+XHBzvoGkhRUxbkPQ82YhgGT7Q28njLsmn/XLZt87UfttE9kKa6LMD/9YvNWNboxSqklCTSNvGUg+PkFk3TdTh5qZf+eIbysJ8tq5eMO9etwTSvHL/OsQu9uB6liYausWNdFY9uq6c84qezN8mFtwco8VvUVY6vVMgndpeUBidctG2m5sPcnMjdHF+heabMnebl5axviN619yZlflHxrKIsTGknTcCc+3UOChFCED98ELuvD6uignDrChpm3QAAIABJREFUg3Ma02Y6ruLG4xjhMP7G5VOOQ6Tj4CTjyHQWaWcBLbdImmHSm7pFyAqys3YH1AI7JFp3L6QyEAwQrF5C+e3zZN0spumdYpBSku28Ts+tt6lasgxf3VLP8dnpJP1vHib1xhFkT2/BMevVVVgt2zE3bUAb8eVbvz3Egf6TnIpfQhZI7LZEmthVvgm/PvkCcNJxkBroJUEqlq+gLGshvG90m7Z7PT8nc7fG15XopqZk7trVKZB2Mzy67KHcP6REu9EFiRSUBJH1Nber8nPbhVWCd8HSpJReNw0Xzb59+4jFYrzvfe+b9bEcx2HPnj28//3v57d+67dIJBL8/u//PuFwmD/8wz/kiSee4POf/zxPPfUUR48e5ROf+ATf+ta32Lx585SO39NT+HYPZTTT1CkvL6G/P6Fuu15E2q72z7hCrVhz4vnDHXel0hVm9/N6+ebz7Rw42Yk7ogrWMDQe2VLHh59omvbx/p9vHuHSjaFxj6+uL+XzH27JLZoWt0llHSS5hCzAgZM3ePVEJ5nsnWvo95ns3lrH/8/em0fHcd13vp9aeu8G0Ni4Aty0kBTFRaSojbJpm5Ko8RpbURLnjcdzTk6eZ16SybPzZpLJeUkmTsZvYp0cZ3QSz5uJXyZOfGI5juXESUxJlEcLTYkUJW6QQFHiBpIAsTaWXmu59/3RbBCNrm40Gg0QoO7nHB2BXffeulX311W//tW3fr89W1dybSTNS8evcvr8MF53AJ+hs3tTO3u2raQx4ufc1TEOd11jLGVNtmmM+Hlwy3I2rGrEcQUBn0FT1F8S2K2XXdR7rerNQs6v3nZ2M1iK95DFboNLnXrYRFtbrM6zusFS8mdB+bTVshSvRYrqcYTDd858n1/c+MSsFGn1souR5w+QOPBjRDYz+ZkeDBHf/zjNj+6veVwv0me6SRx8HieRmPzMjMeJ73uU8MZNnn3cXA6RSiJyFtJ10HQDbVrwOW2n+Mbx/86v7/hlwr5IzfPLnD/H+OFDWKMJ+jODLAu14W+K0/DgHkLrNwCQ7L3M+JHXsE6fBsu7uC8aGGvW4P/wHvTVK4sCxNUEdu9vuosd0Ts4dPV1PtbxMLru/XBcSonmuki/CZEwDbFWmkIxmpujdbte1GqfC8VCzS9pJfm/D/8/fPXB3yTqX1r1DJbiPaSWa4VidszVLubizxq/93u/93s1966C9evXs2lTfQylv7+fP/uzP+OrX/0qbW1thEIh0uk0L7zwAn6/n4GBAX7/938f0zTp6Ojg7NmzvP/++3zkIx+pavx02pq5kQIAXdcIhfxkszbCKymhYsnRfSnB9196n3Su+JF0xnLpvjRCezxMW1OobP962MRzR3t49tXzJRdCxxWc6UngM3VuW9VY09jTmevxTufbz53h5RO9yGnXcCnhUv8EY6kc225r9e7sQbngLkBiIsdbZwe5Y3UTQsp8KgbtRnD3hWOXcd3iibiu4P3eMU6fG+Ynb12lP5EpGTfgM3h46wp+ft/tbFnfQtBvcO7qGM8d7SFrFZ+nnO3y/pVR2uMhbl/dSCzsx/BQpNTDLuq9VvVmIedXbzu7WSy1e8hit8FbgXrYRCQSqPOsbrCU/FlQPm21LLVrkWJ2vDVwitNDbxP2hVkZXV51v3rYxcjzBxj+4Q+QTnGxWuk4ZM50o/l8hDbcVtPY00mf6Wbw+99DZIp9O5HNku5+B3/7MnytbQC42SzO2Ch2IoFIJdFkPte8phueatpnzv49w9kRrqUH2dp2V03zy5w/x8hzP0bkcgxlRxBSkBMWIWGQfv89siNDDL/4PKkXX8Tt6wO3NCAidHDDQYzGOEiBuaYTPd4EwIg9zsHhN/inocP0WyMlfUN6gIfj2/l0+4dYF1rJxbFLXBi7RMAM0BJqLmorHSefWTgYRGtuItbYRmu0nYAZwDD0ul4varXPhWKh5vffT3+bgfQgV5J97F5+z7ztZz5YaveQ2VwrFLUzV7uYiz9b1/dDRkdH+cEPfsDTTz89+dnVq1frNv6yZcvYtGkTzzzzDKlUiuHhYZ5//nn27t3L22+/zebNm4vab968ma6urrrtX6G4VZFScvDYZc8CQgBCwsFjl5lPwb8QggNHerwLkQHIfA5bUS7/1iyo9/G6rsuhU30V555XXFb3Ppdt22WDuwUuDyRBukXOuBCCV04Wz0NKiZASx5W4Lp6B3XDA5JFdHfz7z+/g0d2dREO+yb6Hu64VnScpJVJINPKq0TfODGAa8/eK+mKwzUos5PzqbWeK6ljsNqioP8qfVSiWPlPzTR7pexNHODP0qB9CCBIHflyxTeK5H9fNp00cfB7PV7LyDRh+/gDW8BC53l7soUGwHXTDQDcrpydI2ykujvcAcHG8h7Sdqml+44cPgZQI4U6ug3Bs7LEEzrVe0q++irh8xXuAgJ90xM94g4+0XwNDAymxjr3JsDXGjwYO8d8uP8up5Pslqt2QHuAjzTv5lc4neLDpbgK6DyFczoy8B8CZkfcQws3fvx0HdB0t3gTtrURblrE8uoyGQGxe0m3dTPushoWaX9JKcjZxDoCziXMkLe8CYIq5U821IvHiC8qfXeLUTWv/zjvv8K//9b9GCEEmk+FXf/VXuXz5Mp/85Cf51re+xc6dO+e8D13Xefrpp/niF7/IX/7lXwKwe/duvvKVr/Bv/+2/ZdmyZUXtm5qaSEyRns88fr4gkWJmjOsBHWMeAzuKheNi3ziJZI5K/ksimePqcIq1yxs8t8/VJg6f7idjOVML8paQsRzePDvIA1tW1LSPAvU43qn802sX8zlsK4znCslzb1zmU3vWzzjeN394ZsY2AH/78nn+tymv5J86N0LOzp9DKSVS5utRlLtNx8I+Prx9JfdtXobfV/qK2tXBFONpC23KeIauYRjapLM733ZR77WqNws5v3rb2c1kKd1DFrsN3iosFpu4FfzZ/D6UT1sNi8XuFPXneN/bJJ0UaBpJJ8Xp4be5d8WOqvrO1S5GD79elJbBC5HJkH7rDZruf6CmfRTIXLyIO5oouUdJKZGuC0LgDA3iXOkhsGoVUH2+/mfP/DPi+itDQgqePffPfGHLk7OaX+5KL+7EOJoGw9kRfLYkaAn8DkDau1MgAKaBFg6RQ2A5aUBDIMi5NpmYjyMrxzlz5YeeqRjCepAH41u4t3ET/mk5dt8dPU/GyQIaGTvDhcQF7lx5F1pDFKlD2BemIRBD10rXvp7Xi7nY50KwUPP7y5PfLbKxv+z+Lv9u5y/XfT/zxVK6h5S7VkzFTYzg9F4mtGbtgs3rVuRm2kXdArx/9Ed/xGc/+1l+4zd+gx078l/+jo4Ofv3Xf50//uM/5jvf+c6c92FZFl/60pfYv38/X/rSl0in0/yn//Sf+I3f+I05jw3Q3BxZVAV7lgINDeqV1FuBCwOpqlSYUjeIxyvn36rVJlK2QKsUubpO0hIzzmEm6nm8AKPpMnnCppFI2VWNNzxR3au1iaRNY2N48t8ZWyBl/sGsx9ttk4SDJp/58AYeuHslPrP8ebgynCEfH9DQDR1zSmB3KvNpF/Veq3qzkPOrt50tBpbCPWSx2+Ctxs22iVvBnwXl086Wm213ivriuA7HTh0vunYfGzzOR++8H9Oo/udvrXaRTI9X9f0zk2Nzv29ctCeLrObfshLguvkiSoB2fVtAOjQ2hCsMVEzaSnNhrKfoswtjPfiCEPZXP84YDrqUWMkJYkkLo4J/6l+zmsgDu9BCQSZeeBmkZDwzNvnrYCysc+g2OL8SpAbTJQwRM8je9u080LqFgFGqTnaF4OyF9/EBQtewgiYnzF62r95DQyBGc6ipqgJ4c71e1Ms+54uFml8yl+TdkXNFn707cg5fWBINLK1cvEviHjLlWlGJkFw6vyMWOzfDLur2DT158iTf/OY3MYzi/D2f//zn+cY3vlGXfbz22mtcuXKFL3/5yxiGQSwW49d+7df49Kc/zcMPP8zo6GhR+0QiQXNzc5nRShkZSSm1Q5UYhk5DQ4jx8UxJrk/F0kNzXZwq1lETLomE9+tZc7WJiE/3fAo/nahfLzuHaqnH8U6lKTxzBV6AeMRXcTxXCMZTFiF/ddeheNTH2Fj6el/JtaEkjlv5HBo6fPLBNWxb30w6lS3bTkiJsJ28atfIF1Z1hcRLDzyfdlHvtao3Czm/etnZYmAp3UMWuw3eKtTDJurxg+RW8GdB+bTVspSuRYrqeaPvOCPp4lRXI+lxfvLu61WpEOdqF064oarXnJ1o45zvG2lhYFsWhde2JBQHl6/PI6eZjI2XUcx68O2u700qKwsIKfjWse9VreLN9PQwdPAgVm8v4K0dloAWChF6fB+BLXchAbvvGq4ryDoWrhSMRXRObQhwYaUP6RE4DxtBHmq6m12NG/HrPuy0wCZXvB8peW/oLGNWhlzExPUZSCCbS/Nu70XuX7WLMauy6rpe14u52ud8s1Dz+5M3/4enjT316v9YMirepXQPyeCrKo1bRvOB8mfnxFztYi7+bN0CvKFQyPNJZTKZrJuCwHVdhBBFN0zLyivdHnzwQZ599tmi9l1dXWzbtq3q8YWQSyI59mLCdcWSqRipKM+q1gjxaKCoOvx0mmMBVrVEZlzvWm1i151t/M3B98jkyud4CgVMdt7RNmebq+fxAuzf3cHfH7qAWyG4ahgaj93b4TleJuuQzFrkLIFhaPzc3g38zoU3Z9zvz354PTnL5c13B3nlZC+JCsdj6Pk3+4MBH3evbyk7VyEkUkrCQR/bb2vlcNc1RiZyZdM1zbdd1Hut6s1Czm+udrYYWQr3kMVug7caN9smbgV/FpRPO1tutt0p6ocjHA5fPeaZZ/Lw1WPc3XIXpl7dT+Ba7SK8azf6d/+mYpoGPRQifM+9sx5fCoHIZBC5LMKykJqBHo7gTkxQyOE0/dDNxkaM9hW4TnXXhLSdKlHvFrgw1sNEJknY5x2AcHM5xk69ReroEdy+a2X34eiQ9Wvk/Bot7cvRN23EuZ6TWLa3QTRKf6aPE5tD5QO7epAHmrZwT8Odk6kYnGl5jaXjoBk6IhDgqLhKusGHlKAhMTHQNZ0jfcfZ3rZ13u0iP7/62ed8sFDzS1ql6t0C746cYzQ9TtS/dFS8S+EeYq7qwGiK41RI+WQ2N2OuXDq/IxY7N8Mu6pYUYsuWLfzpn/5p0WcTExP84R/+IffcU59qiDt27CAcDvP000+TyWRIJBJ885vf5N577+XTn/40V69e5W//9m/J5XK8/PLLvPzyyzz55OzyBCkUH0Q0TWPfrg7KiX10Dfbt6pjX1z11XWf/fZ3l84tqsP++zqpenZqJeh+vYRjs2bqi4tz3bF1R9FqMKwSjEzl6h1KMTGRxBZimjqZpmD4fHe2Vn9ytagvzevcQT/3Ncf7+0AXP4K5GPrBr6qBrGpqu8aFtKzzPoSskriuJhkxWtEaIx/LVgm+2XSwG26zEQs6vFjtTzJ3FboOK+qL8WYViaXNisIuk7V2oKWknOTE4/wULdV0nvv/xim3ijz1elU8rpcTNZLBHhsj19WH1XsUZHUVaNhoaus9H40MPUzaxpqbR8MBDs7pH/eD9fy5RVhYQUvCD9/+55PPMQB99P/oBl7/+nxn/+7/3DO5KIGfCWERnNKqTDegITePwylzR/IbtMZ7dofHswxHOr/KXBHeDOcE9uTb+j87PcX/TlpI8u4WiaZpuoDfHMZYt412nj5SbBgmGpuPTfZPnf6HsAhaHfVZioeb3P9/5bkUb+5/vfLcu+1HcQNM04vserXitiH/sEeXPLnE0WacyeWfPnuULX/gCuq6TSCS47bbbuHz5MtFolD//8z9n48aNMw9SBV1dXfyX//JfOHPmDH6/n927d/Obv/mbLFu2jDfeeIM/+IM/4Ny5c6xatYqvfOUrPProo1WPPTg4UZc5fhAwTZ14PEIikVJPeG4hui8lOHjscpFSrTkWYN+uDjatiVfsWy+beO5oDweO9BQpeUMBk/33dfLY7s6ax/ViLsfrxbefO8OhU31FCkvD0NizdQVfuF4MLWM5JNMWOcvFMPSKN9Fv/vA0lwdKX5FpjPiwXUk66612bm0MksrYRa+VB/wmH9q2gj1bVxa1dUU+93E05CMW9nnOp9J5urOjkaPdAwyNZ2ltCLJ7U3vRD5Z62UW916reLOT8qrGzxc5SvIcsdhtc6tTDJtraYnOex63gz4LyaatlKV6LFOVxhMP/e+ovywaoAKK+KP/71n9VUYVYL7sYef4Aied+jMjcUPLqoRDxxx6n+dH9ZftJx8FJJ5FZC3ld3a+ZlVWTmfPnGH/tpzhjYzeOo7GRhgceIrR+Q9VzTtsp/vit/1Y2+AagazpfvudLBDQ/492nSB59A/fCxfJjBjR623wEs4KgfcN3SQV1zqwPcq3Fxxc3/wIZbA6NnuLt5HnPcYI5wcbLNmY4grWilY+vewRdv/FQWwqBJiVaKIQWi07mH7Zdm++9+0OybhZD934IHvVF+eW7v4B75SqZsRFCjc0EOtcU+cVztYt62ed8sVDzS1pJ/uNP/3BGG/vPD/32olfxLsV7SPpMN4kXX8AZGZn8zGxuJv6xRwhv3HQTZ3brMFe7mIs/W7cAL8DIyAg/+tGPuHDhAsFgkHXr1vGJT3yCSGRpJGlWznD1LMWLmaI6pJT09CeZyFjEQn46l0WrKxRRR5sQQlQMGtaTWo+3HK7r8uPXe+gfzbAsHuLx+zrRNJ1kxiKVcXCFxKxQ2Gwqh0718vKJq6SyM+dLAti8Ns7e7atY3R5FCMGpc8Mkkjni0QBbN7QUnUPXFei6TixsEgl6B3an4nWenn/j8ozB+HraRb3Xqt4s5Py87GwpKXeX6j1ksdvgUmaxBHhh6fuzoHzaalmq1yKFN0k7xcUyqQWmsraxk2iZFANQf582efR17JERfC2tRO/dXeLTSsfBTacRVg5pWUgh0ablAa8GKSVW31XcdBojEsG/fOWsx7iS7OVIX+U0YXoqy5Zeif/0WeRE+WCgsbaT0U2dHIkMI3UNpCSayJCaSBCOxUnFQ6Bp5HBwIwEu5vo9xwlJk7utFm73LUdrbZ5UIC6LtBM0g/k0DKaJFg6jRcKTx5wPIEo0dAbTQxXPhXbuErGj7yBGxxjJJmgOxvHFm4nve3Qy6DVXu6iXfc4XCzW/C2M9vHT50Izt9nbsYV1jfcU99Wap3kOklOR6LuEmkxixGIGOTuXP1pFbJsC71FHOcPUs1YuZYv5QNlFK1nJIpm2ylotuaOizuHEeOtXLC8cuI4VESCiXSlHT4O71LezdsYrlzTNXNc4HdjUaIj4iQX/V85nOc0d7ePbV814110CDn3l4PY/t7lR2ofBE2YViOospwHsroHza6lDXIoUX820XUghEOo2bzSAtO6881XW0eRIz1ANXuEyce5fkG0dxzpwFUea8BPz4tt6Nb+d29NaWks2vXDlM9/BZNrXcwab2zRxKnOSd1AXPoSJGiAeb7mZH7A580xSjUko010Xz+yEaRQ8GirYJ6RLxRYj5o+ha5fOaPtPN4Pe/B1KSttOMWxM0+GOEfWHQNNqeeJLwxk3qeqEoQdmEwoubGeCdk/b/X/7Lf8lf/dVfAfDzP//zFdt+97sqj4pCobj1EUIykbZIZx1cKTENvWrF7o0xBC8d78V1ZdnALsDOO9rYu2MVLY3BGcd0XIFpaDTHgoSCc3vtSwjBgSM93sFdAAkHjvTwyK7V1DHVu0KhUMwLyp9VKBTzjXQcnGQSmcshbQt0YzKou5gDu7n0BKNvvUH22JvIoeGy7fRl7fh27sDcsikfdPXAdizeGz2Po8Mb6fd49cpZz3bR64Hd7RUCu3owiNbaMpmGoYArHIJGiKZgw4yB3cJ4iYPPg5RIJCk7DUDKThPyhdAkJF58gdCdSyMFlkKh+GAzp1/5a9eunfx73bp1c52LQqFQLFkyWYdkNp9bVzd0dF3DLFsNqzwDiQzPvnKOdM47vy7kizrpGmxY1TBjcNdxXHw+g9bGIEF/ffJ5He0eKErL4EUm53C0e4A921ZWbKdQKBQ3G+XPKhSK+cC1LERyApHLIV0xmXZBM30zd76JSCmZuHqJiaOvY59+G2zbu6FhYG7eiG/nDvRVK2Z8xftg708Z8TlYJqCVqgTygd2tbI/dXhrYFQINiR4K5/PrTguKu8IhYPhpDbeXzbPrRa7nEk4iAUDGzuDKfFo0V7pk7AxhXxhnZITc5R5869X9QaFQLG7m9Gv/q1/96uTfX/va1+Y8GYVCoVhKuEIwnrLJ5ByklBiGjmnWlgf16mCSl4738s7FkbLC2EJgt+BAJ5K5Mi1vBHbb4iECvvoWahgaz9a1nUKhUNxMlD+rUCjqhZvNItJJRDaHFALd9KFpOtos3+a6GdhWhtHTJ8gcO4a4fLVsO62pEd892/FtuxstMnN6sAErwSsjx3nX6QGP2HbUCF9PxXB7SfEu6ThohokejRbl1y3gCAe/7iMeasVvzD5w7ibzOYSnqncLTKp40XAnVNobhUKx+Knrr/5z585x9uxZcrnSoMNnPvOZeu5KoVAobhqZrMNExsKyBYahoesa1KDWBbjQN87LJ65y9vJY2TbTA7sF4tFASVvHFfhMfV4CuwVaG2ZOCTGbdgqFQrGYUP6sQqGoFiklIpvFTacQOQuEmw/q6gbaLJSkNwspJcmha0y8cQTrxElIZ8q2NW7fgG/ndoz166pKKzFgJTiUOEl36qLndl3AOl8bT3Q8VhLYxXHA70dvjqMHS/1JVziYmo+WYDNBs9QfrhYjGgWK1buT+5ii4jViKse7QqFY/NTt1/+f//mf89RTT3lu0zRNOcQKhWJJ463WrU2NIaXkvStjvHT8KheveSsCNPIF1LwCuwABv8nWDTeKVxQCu/VMxVCO3ZvaeeYn71dM0xAKmOze1D6v81AoFIp6o/xZhUIxE9J1cdNpRCaTz6crQTNNdF2HRZxPdyqWk2P83bfJHDuG+/4FKFd3PRzCt30rvh3b0ONNVY09YCV4NXGCM6lLntt1ASELgjakjARSCNCnFE4LBCHehO4rVeQK4aJrBk2BJsK+UNXHW45A5xqMpiZSvUOe21N2mlj7KgIdnXPel0KhUMw3dYsCfPvb3+a3fuu3+NSnPkUkEqnXsAqFQnHTkFKSyTkkM3Zd1LpCSrovJnjpxFWuDqY82zRF/Xxo+0pylstP3rriXchMgw9tW4Gu67iuQDc0WhqChALzG9gtoOs6++/r5NlXz5ed3/77OvM/dBQKhWIJofxZhUIxHSklwrYRqRQilwPHvlEkzVgY36seSClJjY8w/tYb2G8eR45WeHts9Sp8O7djbroTzazuGPtzIxwaPVlVYLfgSTuuw2t9b/Chlfejh0L5/LpGqfJZSIGGRqO/gbB/5rQQ1aJpGoO7NuD8w1lP795BMLhzPatnyC+sUCgUi4G63ZFSqRRf+MIXZkyurlAoFIsdVwjGk/b1QmdzU+vmx5OcPjfMSyeuMpDwfvWttTHI3h2r2HZbC8b1wKiha7xyso+cdUMpG/CbfGjbCh7YshwhJfFYgHBw4Yt1PLY7r2Q4cKSnSMkbCpjsv69zcrtCoVAsJZQ/q1AoIK/SdSYmsJJphGWBvJ5PF2CRF0mbjiMcxi++T/qNozjvvAuu693Q58PcsjmfhmH5sqrH78+N8GriJO+mqw/sAmhCogFdqYs82PIYAX+pIldIAUhivigRX6Tu12ZHOBwODRC8byUbugYJJa3JbZmon3Nb2siGBrhbOJj467pvhUKhqDd1C/Du2rWLM2fOsGnTpnoNqVAo5gkpJT39SSYyFrGQn85l0UX1Y7bS/IQQHO0eYGg8S2tDkN2b2ieVouX6VdNnPJ3Dbxg0xQLYTkGtC31DGVI5m0jAx4rW0uIOlXBcwfH3hnj5xFVGxr0Loq1oCbN3xyo2r4nTP5LhfO/45L72bF3JA3ct45UTfQxPZGlpCLLn7uUYhkFDxEc0VN7RrGWNK50nLx7b3ckju1bPqs9MLBbbrPc8ahmv1u+BQqGoHeXPKhRLCykluZ5LuMkkRjRKoHNNTfdr6bqITAY3l8V1HdJjPpyUBUJDNwxgbvl0c26OgFGcK1ZIQfrUCZzRMcymRsJbt6NrOlJKrL6ruOk0RjiMf8WqyWMqbMtOjBGMNXpuK/RzmxqYOHmc7JtvIq8NlJ2b1tqCb+cOfHffhRasPp/ttdwwhxIneTfd47m9wYiwI3obZs5Bj9zwUVzHQTcM3KAPNxwATSPpZghwI8ArpURIQcQM4782jEgNkatyfQs2kRkbIdTYXLFP1s3x4dUPwmrgIYnW2w+pDETCRFa203q9X9bNEawxwJt1sgTNm1OXol7fj+nUekzl+o1mx2gKNs55XgrFBx1NynIJd2bHoUOH+JM/+RM+85nP0NHRUfJDc8+ePfXYzbwyOKiqY1aLaerE4xESiRSOI272dBSzoPtSgoPHLjMycSPg2BwLsG9XB5vWxGset142UWl+Pf0TZRWjnctinv0ao36Onx0q2+eFN3oYHM0irl8Km6IBHtyyHIDDXdcYS914kt8Y8fPgluVsWFXZAbEclze6B3j1VB/jU/pPpXNZlL07VnFnRxPne8c997VuRYwLfROMpSwKl+qWWID9969h89rmms5huTV+7mjPvKhxZ2MX82Wbs6Xe86hlvFq/B0tFOa3uIYrp1MMm2trmXgTnVvBnQfm01aKuRUub9JluEgefx0kkJj8z43Hi+x4lvLHyQxopBCKdxs1mkY4FjguajmYYGKZGY0OYsfE0rjP3n8qOdPjR+ef45PrHMLW8vmrsp68w9sorCOvGfV73BwjddRdifBxnfPzGMTU00PBg/tozfvgQzvgYo7kxmgKNmA2NJdtc20KkUpDNgShj17qOeeftmDt3YKzpmFXQ71pumFcTJzlbNrCAzcsQAAAgAElEQVQbplkE+dnO/fiNG0FR6boITfLc4Os8vvmTmB7pLaSUuEIQ9oXwXbjC2IsHZ7W+BZuwEyOMZBM0B+P44s1V2cRM1HK9cITDd858n1/c+ERpIbl5Zi7fj0rUekzl+mWdLP/56Df4j7t//aYFwmtF3UMUXszVLubiz9YtwLtx48byO9E0uru767GbeUU5w9WjLmZLk+5LCb7/0vsIj2+9rsETe2+rOZBWD5uoNL/xlEUybXk6oFJCNGzSEClWHYwnc4ymLAxdQ5/ST0qJEJJI2Ec46Lte0OzGdst2AYnfV+q06FpeueoV5M1aDq+/3c9PT/eRynoXINuwqoG9O1axfkUDmqZx7uoYzx3tKTnmnO0wnrKIhXwE/Ca6rmHoGpqmVVyrWtb4uaM9FfPp/szD62sOHFZrF/Npm7Oh3vOoZbxavwdzXauFRN1DFNNZLAHeW8GfBeXTVou6Fi1d0me6Gfz+97yLg2kabU88WRTEklIiLAuRTufz6No26Lpnvtd6B3i7hrt5rfcoD6zczZaWTYz99BUSB18obSgEuC56QwNGtPh6JnM50DQ0v5+skyFpp4j6IgTNEDKXQ0iBFC4ynQbLLjsXNxIitGsn5vat6LHorI4jH9g9wdn0Zc/tDWaEh5q2EnZ0Tg12saN9K7fHNyBdF0030GJR3klf5LW+ozywYjdbWouDjI5wCBoBmgKN5M6endX6QrFNpO0049YEDf4YYV+4bJ/ZUMv14lj/CV7seZmPdX6YXcu217zv2TLb78dsqPWYyvX73tkf8lrvGzyw8l6evGNpFTJV9xCFFzczwFu3x0gvvvhivYZSKBTzgJSSg8cuewaNAISEg8cus7Gz6aa9El9uflLKSRWrzyidmyMEYymbaMg3qbYSQjCWtkHmc+DqRj5VQwFXwkTaJhL0lRzvRDrvGDc3GCXbhMwre9evbJjclsraHD59jdfevkbW8s5rtrEzzkfuWUlH+40LtpSSw13XPI95ImUjhSSVdYhMOa7CHLzWqpY1FkJw4EiPd3AXQObz7D6ya/W8pQBYLLZZ73nUMt5cvgcLsVYKxa2O8mcVisWPlJLEwee9g1f5BiRefIHghttwM2lkzirNo+tbmDy6jnQ4OdAFwMmBLu5suo2xV17xbnw9N66YSKKHI2iFe7mUuMkJQMNsbiZjZwFI2xkMocHoaPm8utcZWRblwoYYEx2t/IsN96Hr1aec6Lse2H1vhsDutthtaBL+6UI+eP3u4Fk2NK7DiDehB4O4wuXkpbfz52LwbTY134GhGzjCwa/7aAu14jd8Va9v6M6NRf5ToY9EkrLTAKTsNCFfCE1S0me+cYTDkb43ATjS9ybb27YsiIq3lvNXLbUeU7l+WSfLsf6TABzrP8mn1u9fcipehWIxUbcrzKpVqwCwbZtr167R0dFRr6EVCkUd6OlPFr3u7cXIRI6e/iRrls9dBTVbKs0vnXWQ1yNeUsJUX0RKQOadmUzOJRLKO8PpnIsUMh+3lOC6Ak3LK7DE9Q+lhGzOITSlSJntuJOBYMcV+MxSB3gsZdE3lCYa9nHoVB9Hu/uxPJ7OacCW9S3s3bGSFS2l1dj7htJFaRnyxyOxrs9B0zSkBMeV+KfF67zWqpY1Pto9UPSqvxeZnMPR7gHuv2t5xXa1slhss97zqGW8Wr8HBeZ7rRSKWx3lzyoUi59cz6Wi184LSCmRwgUhsa5dI3niLQKrOtB0vS55dGvhzMh7pJ18sDHtpHn/tecJWR73+anBOCmQuSxaKJz/p20jr/um2WwKRzqYjsCfs5F2hrIhOk2DUAgrYPDO3U2Mx4Mgspwbu8jt8Q0zzr0vN8SriZNlA7uNZoSHmraxNbYBQ8uf2/dGz5G1UkhDZzjs8K45wpZgvmBb98hZ0k7q+rlI8fbwGba23kVLMF4U1Cu3vlNxRkbIXe4h2LmmpE/GzuDKfMDblS4ZO0PYFy7pM9+cGOwiaScBSNpJTgx2LYiKt5bzVy21HlO5fv9w/gC2m/8tZLsW/3D+wJJT8SoUi4m6SXyy2Sz/4T/8B3bs2MHjjz8OwPj4OL/0S7/E+JQcQgqF4uYwkfHOBVtru3pTab+OeyN4KqdJTaf+277eTkqJZTvFLbWpaRhubHGmSSXFlH+7ZaSXjit44c3LfP1vjnPodF9JcFfXNHbe0cb/+eQ2fmHf7Z7BXYBUrvgVOiElmqZhaBq6rk/Ot9w8pp+zWtZ4aDxbVZ9q29XCYrHNes+jlna1fg+mMp9rpVDc6ih/VqFY/LjJfKBISolwXYTjICwLaVnIwr1S0xC53A0V7E1gqnq3wLW+c96Np6ktpT3l4XvhDTQhcFITxMYtokkHvy28g7umidbQgNbWhtYQJYuDf8obZmdG3kOI8orf3twQz1w7yP939R89g7uNZpR/0fog/6bjs+xouGMyuOvaFt2J90nF/KQaA7g+g5ODb+MKN6/eHXz7+qFKkPDO8FlaQvESxWZhfWfCnbiRimbSJqaodwuk7PSk3zS1z3wyVbFa4EjfmziisqiiHtRy/qqh1mMq1y9pJSfVuwWO9Z8k6yg/VqGolbrd8b7+9a/T3d3NU089hTEll5Hrujz11FP12o1CoaiRWKi6yq/Vtqs3lfZrGjcuVdo0V3bqvw0NHEdgO6Io525pvxt/m3pxO33Kv41p22xHkJjIMZDI8G7PaEng1TQ07t+8jK/8/HY+t3cDrU0hKhEJ5JXDhVToPkPDZ+oYRvGlefo8Ckw/Z7WscWtDda9BVduuFhaLbdZ7HrW0q/V7MJX5XCuF4lZH+bMKxeLGtSykFAjbRtoW0nVvvNYy5eE4gBHxfsC+UExV7xaYCIKUHjkZp/utPhOkRAgXx7XAcZCuS8ASGOVSOgb8aM3NaC3NaOEQmq6Rcy0ELpb/xvUs42Q4N3axpHshsPsXV/+R99NXSrY3mlE+7hHYlbYNmsZZfZihiIvw3dhX2knRPXKW7pGzpOwkUoKhGfnX890MJwa7SvZjRKvLDWzEbrxNVegzVb1boKDind5nPpmqWC1QUK7ON7Wcv2qo9ZjK9fuf73x3Ur1boKDiVSgUtVG3AO9zzz3Hf/2v/5X9+/dPftbQ0MDXvvY1nn/++XrtRqFQ1EjnsijNsUDFNs2xAJ3LZldwoV5Uml84aKLp2nUV7vSt+WfyEvCZOpK8UjcUMIvaTv27EC/VNAgGijPV+EwDXdfRdX0yoGY7LiPjWQZHM57pDPw+nYe3ruD/+oUdfGrPOuIznOcC7fEgDWEfxvXAbiFvqt9nTAZ5DUPHZ5Zeqr3WqpY13r2pnVCgcraeUMBk96b2qo6pFhaLbdZ7HrWMV/v3IM98r5VCcauj/FmFYvEghcDNZLATCayBAbJXr2IPDGA2t2I2NICml80hajY24l++coFnfAMv9S5Af2cDtulVKLXYaXVNAzuVxB0ehrEJkNLz0a6rg6tr4PehxZvQ/DdqS0gkWSdHNuRjPF7sW0xV8fZmKwd2m8woH299iH/T8Vm2N9yBoV33Sx0HTdfQ29ugJc7J0bOe5+L4wGlO9J9G13R8uomh36hx4aUADXSuwYxXLmhrNjcT6LhRVDbQuQajqalEvVsgZacx4vGiPvOFl2K1wEKoeGs5fzNR6zGV6yeE5GzinOf7aErFq1DUTt0CvKlUirVr15Z83tzcTDrtfaFVKBQLh6Zp7NvVQRkxKLoG+3Z13JQCa1B5fpqm0RjxT6ptpZQIIRBCIKXE0KAh4itSW+m6TiyUV8gaHlc6U4dYuLTAGuQ/j4VNbEcwPJZlcDTrWTwtFDD46D2r+Pe/cA+P37+GWLg6xaYrJFJI4g0BPvnQOnweE2yI+ECDBo85llurWtZY13X239dJWUGoBvvv65zXol2LxTbrPY9axpvN96C0wfyvlUJxq6P8WYXi5iGFwE2lsEeGyV3rw+rrxRkeRuZyIAS6YaCbJrqu0/DgnvJPOzWNhgceumk+LXirdwHQNc7f2eyt4i34saYJQ8MwPg62XdJMArZPIxk1mWjwkQ6b2OFAyds9OdfCRXDx9iamO3oZJ8PR4dN8t+8gf9FbLrAb4xNtD/Gljs+yveH2aYFdHa2tFb21Fd3nK8qvWzRXKRnJjjJqjWHqZsmaeClANU0jvu/Riusb/9gjJf7T4K4NOHjLmx0EgzvXL4hNeClWCyyEireW8zcTtR5TuX6j1hhCCoTH90CpeBWK2qnbr8DOzk6OHDkC3HjdGODAgQOsXHnznp4qFIsNKSWXrk3QdWGYS9cmir4v8z3epjVxnth7W4lCsDkW4Im9t7FpTeWnvfVGCMHrb1/jH1+7yOtvX+POjsbJ+Vm2SzbnYNkuzbEAv/SJzXzyobUEfPrkMWqaRjDg47H7OvnZvbfRGCkOsK5qi15XqBZXSg74TR7d7d2nIexj18Z2fIbB0FiWnF0a2I2GfOzf3cm//4V72Lerg3DwhgJWSknvYIr3rozSO5gqWg8hJa4riIV8LG8JEwn6J9ckHvWTytiMJXOkMjYrmsP8zMPrWdESLvo8HvVXXKty41Xq99juTn7m4fUlSt5QwORnHl7PY7vLP+Gvlz1XY5v1/u7UOo/5Hq9Sn1/6xGY+++ENFddqIc6TQnGrovxZhaI6pJRkL10k9XYX2UsXa7rXTCp0CwHd3qukhvuRlo2GhmaYaKb3W0ah9RtofuxxzMbGos/NxkaaH3uc0PqZi4jVm/FcPqepl3pXTDk/lze2cGHrCjS/P59eQghwp/ibtl2SkxfA9ukkoz4mGnykoj4cn0427Oftne103dMODTfeCJJIxgKCd7e2MtIeLhonqzlcNVK8OHGCc5lKgd2fYVvsdoR7XZ3pOGi6cT2w24Luy/vXU/PrFnBcF9AwMMm5WdJOZtJGnGn5f70UoOGNm2h74knM5ubJfUBeedr2xJOEN24q3p9wOBwa4O37VpKJFvv2maift+9byeHQQFXq2bmoRyspXQsc6XsT27Xn/P2pxPTzV6Dc+atEtcc0/dxWUu+mryuthRQzqniVmlehqJ7K7+XOgs9//vP86q/+Kp/73OcQQvAXf/EXdHV18dxzz/Hbv/3b9dqNQrGk6b6U4OCxy4xM3Kie2xwLsG9XR03B1VrG27QmzsbOJnr6k0xkLGIhP53LoguucnjuaA8HjvQUpTx45ifvs+OO1nwVZPIKhULBtOGxLNtvb+OeO9o4dW6YRDJHPBpg64aWScXi+pUN9A2lSeVsIgEfK1rDaJrGpx4SM/ZJZi0GE1lOnhviwJEezzk3Rvx8aNvKfADYI23CuatjHO66xljKKurz4JblrFkWJRL00xjzl+QH7umf4HzfOOmsC0g0NCzHRQIXPD7v6Z+oaC/lxqvU77HdnTyyazVHuwcYGs/S2hBk96b2imrQettzJdus975qncdCjVepz6Y18bJrtZDnSaG4FVH+rEIxM+kz3SQOPo+TSEx+ZsbjxPc9OmPgyLUsRCqFsHLgOICGZhhoaDiGxo8v/S8+uf4xTG3mn6mh9RsIrluP1XcVN53GiETwL195U5S7OTfHX7zzN3xp67/ClS73Lr9ncpuQgiP9b3Lfsp1o19va7S7y/gDGy6/jnrsI2QpBrNUrmLhrLdaG1aDrGIMJ9GwOEQria23i9uvHq93bRmBoDJHOYAdM9KiY3AYwaI9xOnOBXnvMczdxM8ZD8a1siW6YVOsK4fJyzyE+uv6jGG2tk0Hd4mO32L18x+SxOsLlwMUX+fkNn0Ug6E8NALAs3IauGXzvvR/y5O2fwW/cGCvr5ojqxWse3riJ0J0bmbjwHt9586/4xZ2fJ7buds/1zbo5Prz6QVgNPCTRevshlYFImMjKdlqv9/Haz1Qc4fDM2R/yixufwKzQrhyT86iAdu4SfX/2NHL0RuHOar8/s6Fw/nI9l3CTSYxYjEBH56y/H9UcU6Hd1HNbrt9obpz3EjeKDTYFGotsocCYNYGpm3NaD4Xig4Ym6/i46O/+7u/467/+a86fP08wGGTdunV88YtfLMpjtpgZHFyYqpq3AqapE49HSCRSOE65TP+KqXRfSvD9l95HeHzjdI1ZqwPrPd5cmY1NPHe0h2dfPc/0R7ZCSlwhaYr6iYR8CHFDQWXoGo/t7mTDqkaPEWtHCMnp88O8fKKXayPer9+2NAbZu30l225rLSp0NZVzV8d47mhPyXoIKTF1jSc/ejtb1jWX9Ct3LhwhECJ/3CVF1jTKKmvLjTdTv9lSrf3V41qx2Gx9sbKUzpO6hyimUw+baGurT/Gcpe7PgvJpq0Vdi2ZP+kw3g9//nqfCFE0rUQdKIRDpNG42g7BsECIf0PUIMnUNd/Na71EeWLmbLS31C3TNFsPUaGwIMzaexnWq+6n844sHOT5wih3tW3l87b6ibaeH3uGnV4+wvX0Laxs6obcf580TON3vFit3p+L3YW65C9/O7RjL5pZb/0p2gFcTJzif6fXcHjdj7IlvY0t0Pbp2w8+Vts3Z1GVeS73NfR0PsKW1/JpIKRFSEPNH+ecLL/B63zEeWHkvT97xmaJ23zv7Q17rfcNzWzlq6VMrx/pP8GLPy3ys88PsWra9aFs9rhez/f580Km0HosBdQ9ReDFXu5iLP1vXxyCf+9zn+NznPlfPIRWKWwIpJQePXfYMvAAICQePXWZjZ1NVT1XrPd5CIoTIK2Q95u64+Q8TExYBX77YWWH+QsLhrmusX9lQl2NyXMGJ94Z4+WQvw2PeqonlzWH27ljJlnUt6OXynpJfj8Nd14rWQ0qJpmn4DA1d1/lfb13hrrXxormXOxf5HMP5v10hSwO8Eg4c6eGRXauLFLaVzm2lfrNlNvY3V5ayrS8k6jwpFPVD+bMKhTdSShIHn/cOTuUbMHLwefyda5DZNCJng+uAbqBdL2BLGf9jalqDkwNdbGy+vSoV72Ig5+Y4PXQGgNNDZ/hox8P4dT85YZG0khy9dhwcm9Gjr5PreQXZP1h2LL2tFd/OHZh3b0YLVFe0txw1B3YdB93vQ7Q2c3z8MMI0ODn4Npua78DQjZJxHOEQMkI0BRuwXIs3B04B+dfsP7V+P0EzCORftT/Wf9JzWzlq6VMrU1MKHOl7k+1tW+qqGq3m+5N48QVCd25Uvhrzvx4Kxa3InL4h3//+93niiScAeOaZZyq2/bmf+7m57EqhWNL09CeLXpn2YmQiR09/kjXLZ35iU+/xFpKj3QNFaRmklNef+he3y1kuoWDxj4CxlEXfUJqVbZGa9287gmNnBnjlZG9RKoWprG6L8JF7VlcdDOsbSk+OJa9XOTYMDWPKjxiv9Zh+Lgp4qYCnp3XI5ByOdg9w/13LZxxvpn6zZTb2t2H13BTXS9nWFxJ1nhSK2lH+rEJRHbmeS0VpGeC6H5d/5QqkRPT3k36ni+Dq66+Cm6WvXnsxtShZ2klzZuS9m6rinQ0/ufwqjsj7gbZrceDiizy48l6QcOHiKdYfu8zqi+P4HO98o+g65sY78O3cjt4596KyMwV2m30NPNS0tTSw67popoHe0oweCPDOUPdk4bS0k6J75GyRitcVDn7dT3OoFd/1V+z/4fwBbPfGufiH8wcmVbeVtpWjlj61MrUgWKFwWD1Vo17fn+k4IyPkLvcQ7FxTt/0uVeZ7PRSKW5E5BXh///d/f9Ih/t3f/d2y7TRNUw6x4gPNRMY7kLhY2i0kQ+PZyaDuVDSNIuWpU0aKmMqVVhOuhqzlcOSdfg6dvkYq4z3GuhUNfGTHKjasmp1KOJWzi1JJGGXSOExfj6Fxb+WwnOb+S8n04see/cuNN1O/2bKQ9reUbX0hUedJoagd5c8qFNXhJpPTArr5/0u0G36TpiEq5ZP1wKso2VJR8ebVu92IKX7rmaGz7EzEEMdP037Ju64DgBaL4btnG+aOrejRaNl21XI528+riRNcyPR5bm/2NbCnaRt3RdeVBnY1Hb2xET0cArwLpxVUvFJCwDCJh1qL8+hOUdsWKKhuC397bSunyK00Xr1VvF4FweqtGnWTyeraTagUOwuxHgrFrcicvh2nTp2a/PvMmTNznoxCcasSC/lnbnQT2y0UtuMS9BmTwdCpQdT8RzecY7NMSoRIoDolSIF01uFwVx+Hu66RtbzznN3Z0cTeHatqUjYKKQn6TAxdQ9e1ioHh6evR2uDtnGoUR7vLDTm9f7nxZuo3WxbS/paqrS806jwpFLWj/FmFojzScXDSKWTOws2kkZaF1Ar+jgaaVvIM2ojM7k2rqerdAotdxSulJCcsDlw8iOXkH56Gs4LbejLcdimLk/unsn1zHe003v8Qxu0b0OaQMqvA5Ww/ryROcLFCYPfhpm1snh7YFQINiR6LlgSYu0fOTqp3C6TsFN0j77Fn5W4CZmn6iKlq2wIF1W3hb69t5RS5lcart4p3qlq0QL1Vo0aVQXwjpt60Woj1UChuRdTjD4ViAehcFqU5Fqj4CnVzLEDnsupu/PUebz4RUpJM26RzDo7jsmlNE8GAj5xVnEpA16AQftU0CAZKL0+NET8rWsNV7XcibXHoVB9Huvux7NLk5hpw1/pm9m5fxcrW2lI+OI5LOOjjnjtbef2da7Nej92b2nnmJ++XpFWYei7y/y6N8IYCJrs3FRfdKDfeTP1my0La31Ky9ZuJOk8KhUKhqAdSCNxUCpHLIm0b6Qo0XUfTdQKrOjCbmnDGx8v2Nxsb8S9fWfX+vNS7BRajitd2bdJOGkvYWK7FuyPvs3zI5o5LGVZfy6GXSa9q+XSurGugZ30jMt7Ix9etn3NwtyfTz6uj5QO7LdcVuyWBXSnRXBc9EkGLRUvmMV29K2XebzY0nTMjZ/nw6gdK9uWlti3wRt/xsgKIcorcSuPVW8XrpRYtUE/VaKBzDWY8XjFNg9ncTKBj7sWQlzILtR4Kxa3I3B8ZXqerq4uf/dmfZfv27WzatKnkP4Xig4ymaezb1UG5Ol26Bvt2VZ9zq97jzQeZnMPQaJrewRTJ6ykRTNPAMAw+tG2FZ8qBQmaDWNhXMnddgwe3LJ/xmBITOf7+0AW+/jfHefVUX0lwV9fgnjta+XdPbuPz++6oKbjrOC6GrrG8JUJzQxBD12taD13X2X9fZ8m50DRtsgZJSYE1AA3239dZUiit3Hgz9ZstC2l/S8HWFwPqPCkU9UH5s4oPIq5lYScS5K5dw+q9mn9F3Ln+2r5pTgYANU2j4cE95V8t0jQaHnhoVvcaL/VugYKK92YjpGDCSjGUHWYkl8CWLuQszv/kR+w/OMC+10fp7PMO7o7GA5za1c5PPrGOM9vaSMf8ZJwM58Yu1jyfnsw1vtP7HH/V92PP4G6Lr5FPt3+IX179GbbENkwGd6WU4Djo/gD68mXojQ2eQeaCejefUi0f2PUZPgzdmFRRTsdLbVsg42bJuN5pO6YqfKsdr1yfWvFSixYod7y1oGka8X2PVvz+xD/2yAfeV1uo9VAobkXq9ujjd37ndwgGg/zar/0a4XB1CjuF4oPEpjVxnth7GwePXS5S2TXHAuzb1cGmNfGbOl49cFxBYjzLRDqfk9YwdEyz1HHcszWv7HjlZF+RkjcU8LF5bZyJtF1UAK0x4ufBLcvZsKp8wa6h0QwvnejlxHtDCI/qtKahsfPOdj60bQXxWG1P/F1XYBgabfEQAV/x5bPW9Xhsd/4p/YEjPUXK21jIz9oVMS72TRR9HgqY7L+vc7JftePN1G+2LKT9LUZbX4yo86RQzB3lzyo+CExV6QrLBuGim7788+EZiqKF1m+g+bHHGX/tpzhjY5Ofm42NNDzwEKH1G6qeRyX1boGbpeKVUpJzLCayKSzXQteNfOBtYJjsseM4Xe/Q4Xi/MeXocGGVn7NrgrB8mWc878zIe2xoXIuuG1XPqSdzjVcSJ7iUvea5vcXXyMPxbWyKrPVQ7Ar0UBCtTFC3gCtcTgx0IaVE13QMzSgJOE5XUVZS2wopkeRrb0jN8NQgTFfkVhqvXJ9aqaQWLTB5vMw9zVV44ybanniSxIsv4IyMTH5uNjcT/9gjhDd+sB8kzmo9lIpXoShBk9MrHdXI9u3b+elPf0pkljmXFhODgyqhebWYpk48HiGRSOE4pa+/K8ojpaSnP8lExiIW8tO5LDqnJ7X1Hq+W/aeyNjnbJRgOkEpmEVWahBCCU+eGSSRzxKMBtm5oQdd1pJT0DaVJ5WwiAR8rWsNlj6lvOMVLx6/SdX7EszKx39TZvXkZe7auoCFcm2NWKJzRGPURCVYeo9b1EEJwtHuAofEsrQ1Bdm9qR9f1sp/XOl69mel463mtuNm2vlRYCudJ3UMU06mHTbS1zT1v4a3gz4Lyaavlg3Qtci0LkUohcjlwbNCNOaUIkFJi9V3FTacxIhH8y1fO+l6TdtJcmfBOLzCV1bEVhM2FeeDiCJucyGGGNCaSWZBaPg/xO2ew3zyBuNpbtm+uIURi42qGb2tjTMsLFVbFVhL1ec99WaS9qgDlpcw1Xq0Q2G31NbKnbGDXRQteD+waMweTx3NJBtODRP2RorGms7axk6gvf53sTw9y7Npxz3ZJK01vKj/vVdHlRMqci13Ld7As3DbjeOX61ErSTnFxrHwhvAJrGztpCsXq6tPmei7hJpMYsRiBjs5F56vdDGazHgX7u5l8kO4hiuqZq13MxZ+tW4D34x//OM888wzROlQAvVkoZ7h61MVMkbUckul8YBdNw+/TaWwMMzaWxnXrclmpSE//BC8dv8qZnlHP7UG/wQNblvPQluWEg7MrzFZASonrShoiPmJhv3K8akBdKxReKLtQTGexBHhvBX8WlE9bLbfytUgKgUincbOZIpWuohQhBVknQ8a1cISN3/ARawgy1tNP9thb2CdOQybj3VnTMG7fgG/nDoz1a+vqK17K9PFK4gQ92X7P7a2+Jh6Ob2NjZE1pMNZ10fwBtKbqAruucPHpJo2BRvyGshMvbpXJgh4AACAASURBVOXrhaI2lE0ovLiZAd666dq//OUv87WvfY3f+q3fWvJOsUKh8MYVgomUTSbn4EqJaegYRv1VoeWQUnK+b5yXjl/l3FXvAh+RoMmerSu4b/Mygv7aL3G2Kwj7DeLxIHq5xKYKhUKhuKVQ/qxiKTOp0rVzYN1Q6eq6DvPwFs9Sx3It0k4Gy7XQNC2fcxgd571zDJ04QfbM+2X7apEw5vat+O7Zjt7YULc5SSm5lM0rdmcK7G6KlAaUpeOg+/0Qb0L3zRyoFcJF1wyag011K1qmUCgUiptD3QK8f/qnf8qVK1d49tlnicfjJTebQ4cO1WtXCoViAZFSks7ZpDIOlu2iGzq6rmGWreQ1P3N49/IoLx2/Sk+/d9L9xoifh7etYNfGdvxm9fnMpuO4Ap+ps6wphN9X+zgKhUKhWHoof1axlCin0q0ml+4HFSEFaTtDVmRxhYuhm+i6gUilsE+cxn7rBHLMW0QAoHeuxrdzB+bGO6pSxlZLNYHdNl8TD8e3szGypjSw67popoHe0oweCMy4PyEFGtDgbyDiV/nGFQqF4lagbgHej370o/UaSqFQLAJsx2UibZHJCaBQMG1hA55CSLoujPDyiav0DXtXWG5uCPDh7avYcXsr5hzUxK6QaBq0NAQJBVTSfoVCofggovxZxWJnUqVr5cBWKt1qkFKSExYZO1NUME3XDNzLV7CPHcfpfpeyRST8fnx334W5cztG+9xyvnrN7VI2Xzztci2BXcdBM0z0eBN6cGYFrpQSIQVRX4SYf/Hl6FcoFApF7dQtivErv/Ir9RpKoVDcJISUpDI2qayD4woMXcMwNFhAtS7kU0GceG+Il0/0MjSW9WzTHg+xd8cq7l7fgjGHFApCSqSQxMJ+YmGfcnQVCoXiA4zyZxWLDS+VrmaYeX9FqXQr4kqXlJ0m52ZBgqYbGIaJtCzs0+9gv3kcMTBYtr/e3oZv53bMLZvRqlDFzgYpJRezfbyaOMHl7IBnm3Z/nD1N28ordjUdvakJPRyqap+ucAgaIZqCDRULqCkUCoViaVK3AO/AwABf//rX+frXvw7AN77xDf76r/+aDRs28NRTT9HR0VGvXSkUijpTKJiWtVx0XcunYFjA3LoFbEfw5rsDvHKyl9Gk5dlmVVuEj+xYxcY1cfQ5BGPzBdQEkaCfxph/TmMpFAqF4tZA+bOKm40UApHLITIZhG0ple4skVKSdbKk3SyOsNE1A00zQAMxOIT95gnsU11gefuZ6Dq+zRtp+tB9WC3tuPWpR140v4uZPl4drRzYfbhpG3d6BXZFPrWCHouhRyNV7dNxXQKGj5ZQG6ah3lJTKBSKW5W6XeG/+tWvTt6ATp06xbe+9S1+93d/l66uLv7oj/6Ip59+ul67UigUdcAVgvGUTTbn4AqJaeqY5s350ZCzXY5293PoZB8TGduzzdoVMT6yYxW3rWqcs8rWcQShgEFTPIShfigpFAqF4jrKn1UsNNJxcDMZRC6HdCxwXNB0NMNQuXRngadaVzeRrovz7nvYbx7HvXS5bH+toQHfPdswt2/F3xAjEAtgT+SgTgFeKSUXMnnF7pVchcBufDt3hjtLA7tSorkCPRpFi0Wq8oWFcDE0k9ZQnIBZXwWyQqFQKBYfdQvwHj16lOeffx6AH//4x+zbt48nnniCxx9/nEceeaReu1EoFHOgkIIhnXWwHIFpaGi6hjmHFAdzIZNzONx1jcNd18jkHM82d3Q0sXfHStYun3uFYtsVBH0GLc0hfAucT1ihUCgUix/lzyrmE+m6iGz2ejDXRtgOSImm6/n/0OEmPWxfilRU645PYL91AufEKWQyVXYMY8M6fDt3YNy2Hm0eHvrnA7u9vJo4+f+zd6/BcZ1nYuf/7zl9RzeAxoUgCRK8SgIpXgCRpjSSKNG2bGm8ZY9no52t1GTyYb64MvNhp7Y2SWVTqUxSqeRDkqmaqdpKsh9mJls1O2tbmZmaya5lWbYlS6Z1IQWABAWSIikRFAmCIHHv6znv++6HBkhcTgONewN8flUuU+hz3vP2wUH3208/53lWENjVODUJVCpV0RyNNWAVdZFaEtJATQghHhurFuD1PI+6ujoAPvjgA/7hP/yHANTU1JDNBjdHEkKsj1zRJ5P1KHgalMJ1FOEN/AAxkS3yy4t3+fDTuxS8+Q0tFKVSDK3NNexpSdG2LVnRuNZaBu5nyRQ8aqJhdjQlUErha0M07NBYu7TArjGGj/rucX88T1NtjFOHtpVuj9wErLX0D04ykSuSikdoa1lZI43NfC7E5ifXn1gvsp4Vq+VhqYV8Hut5WO0/rJuq3NJaxHHly+bl8I1HxstRNEWstTjT2brW4t/4opSte/Va+ezbeIzw8aOEn+nAaUiveD7WWuzdQUw2h5OIo7a3AHAjd4f3Rrq5XQiu87stkualdAdPlgnsesUc0WQdqq62osDuwwZqkSSp8Pws39H8GPWxumU+y41hraXQfxM9OYmbTBJtm1+2Yjk247kQW8P0NZ0bGyZe17Bq17QQsIoB3t27d/P+++8Ti8W4evUqL774IlC6va2xsXG1DiOEqJCvDRMZj1zRx1qL6zq4G1BXd6bRyQLv9Qzw8eVBfB286G6si5HL+wwOZxkczvLJlSH+vw/6een4Dl48trPs2Ndvj3G29y5jmUc11WoTYV48toOTT20jEl7ah6gff9TPmx/2z8os/v7PrvHas228eqptSWOtt76bI7x97hbDE4WHP2tIRXnl5G4O7Vn6B5nNfC7E5ifXn1hPsp4Vy2W1xuRy6EIB6xXB9wGFCpU+binloCQ7d9msteT8PDmdRxsPpdzSOVVgc3m8Cxfxzndjh0fKjuHs3EH4RCehw0+hwqtT+kJ/fpPix+ex4xOleWLp35XggydD3GE8cJ9tJHmp5RRPJnYHB3a0xoZD/Dh3gW+3fmvRLzSttWijSYQT1EVTgQ3U8n6eP/rkP/G/n/oDYqHY0p/oBshe7mPk7bfwRx79TkPpNOlXvkmi/dCyx92M50JsDdPXtDcyzHB+hIZYmnC6YcXXtBDTVi3A+73vfY/vfe97GGP4nd/5HZqbmxkbG+P3f//3+Qf/4B+s1mGEEAuYVYJBG0JTDdNKObEb58FYnne7b9P12X20CQ7sJmIhHFXa1nWY1fSsUPT5yblS3bSgIO/122P8+KN+poe21qKAybzHT89/SToVW1Jg88cf9fPX792AOVPNFfzSz6FqA0t9N0d4451rzD3NwxMF3njnGq+fOfjYnAux+cn1J9abrGdFpazW6Gz2Ye1c62vUVDM0hZLauavEMx5ZL0tBF1FKoZSD45Q+wuqBu3jnuvAv9U0F1AOEQoSOHCqVYdixfVXnpj+/SeFn74K1WCw3G+BX++BuXXC2f9O44fkvHPY/yBD7mkXtm7M+931UJALpeq6MXWOoMEzf8FWONJUP/Ew3UGuuacR1yicz/O2NN5koTvC3N97kt5787rKe73rKXu5j6I0fzMvC9kdGGHrjBzS//lvLDohttnMhtoaZ13TOy+Fpj5yXQ63CNS3EtFUL8H7rW9/ixIkTZDIZ9u/fD0BtbS3/5J/8E7797W+v1mGEEAGmSzDkixpnKqgb3uBsXYC7w1ne6brNxRsPAu+SU5QCu8l4GMdR3B0uLYi1gXlrVAu/6Bng+SPbZ2UyWGs523sXY0v/BnCnzoFSCmPh7XO3aG+rr7AhheHND/vnBZRmzuPND/v5xsldVXeLuLWWt8/dmhfcnfY4nQux+cn1JzaCrGdFObMCul6xVG7hYUBXsnNXk7GGnJ8jpwtorXFdF2dqYWg9D//TK3jnuzB3BsqOoRrShE90ED52FBVf/SxNa20pc9cavmiAD/bB3TJ3/DeMaY5fy7HnnqU+mkIBxXPnie0tlWawvo8Kh1HNTTjhMNpoeoYuAdAzdIlDDU/OC94ao3GUS2M8TWyRBmp5P8+5wR4Azg328J39r1V15qq1lpG33ypfYsNaRn76E+JPtS/51vbNdi7E1jDzmrZYMl7pM2/GyxIPx1GWZV/TQsy0ogCv53mEp25vKRaLpNNp0uk0xeKjW6RfffVVisUikUhkZTMVQswSVIIhVCUfLr68N8nPu27TdzP4NjmloCYWpiYexp1q8JbLe7PWcdaWtpupUPS5cP0BHU80P/zZwP0so5OlUgSOU6ovPPeNcXiiQP/gJHu2pxad+0d998o2fJuWK/h81HeP555e3UyQleofnJxVliHI43IuxOYn159YL7KeFUGsMZhsFp3Pz8/QlXILa6Kgi+T8LEVdnCq/4OBO1Sk2wyN4n3Tj9VyEXD54AKVwnzxYytbdt7Z1Lc3AXW6Ex/nVSRgs0wd427DPyc81TfdyKErfVxZ0kZgbxY5NYO7cxW3dgdPQgBN7FKTtG75K1i81hsv6mVlZvNZaLIZUJEUyUlPRXP/2xpt4uvR65uli1WeuFvpvzirLEMQfHqZwq59Y254ljb3ZzoXYGmZe0zkvh7YaAG01OS9HIpxY9jUtxEwrCvCePHmSnp7SN2DHjh1b8E20r69vJYcSQlC9JRigtOC82j/C3/3iOp99ORa4TSIWor2tnjv3s1PzfsSfk3ZqsaVbHOcYmZwdwBzLFlBKEXLnB3ZnmsgVyz420/3xMh8alrndeqr0OT4O50JsfnL9ifUi61kBUw2qCgVMLocpFMD3QDJ015yxhoyXo2DyGGNwHPdhCQZrDPradbxz3egbn5cdQyVrCHUcI9x5HKeuTLR1lVhruZb7kl/kPuLu8eBtto0Znr2QYd+ARzam8MKPXlPyfoGIE0YpB6vA3dY8a9+Z2bvTprN4wRIPla+zG2Rmxuq0as9c1ZOTlW03MbGkcTfjuRBbw/Q1PTN7d9rDLF7Ukq9pIeZaUYD3X//rf/3w3//23/5boJT5EI2WvoGcmJggmVxZ53YhxHQJhiL5oqmqEgwwFdi9Ncq73Xf44m7wm1JtIszp4zv5Svs27o/m+aup2pkzheYEfIOCuwDpZOn1RWuD6yp2NycJV/ChKxWvLOuqqbayBV6l262nSp/j43AuxOYn159YL7KefXxZ36c4Okbx3jDFXAGFRbmh0gpEauiuGWstBVMk5+Uo6iKO46KUeliGwUxm8Lsv4H3Sgx0PblQG4O7ZXcrWfeoJlLu0ZrrLmfO17Je8N9rNQOFBYG5Fyzg89znsvWtgzMNgMMqdOQgGTdF6xMI1hAIaN87M3p2W8TJcHrnGSzufI+Qu7eP7zIzVadWeueomk5Vtl1r8brSZNuO5EFvD9DU9M3t32sws3qVe00LMtaII0W/8xm88/Pdzzz3Hn//5n1NfX89v/uZv8pu/+Zv4vs+f/umfcurUqRVPVIjHja8NI+MF7tzPMDyWxzcQCjnzMl83irGWizce8H/81UX+65tXAoO76VSU757ex//29zt54egOImGXHU0J6mrmBxhj0dCskgxBn6OjkRBH9jVgjKU+FWF7Qw0HWutoSC1ce6whFaWtpbLF4qlD24hHF148x6MhTh3aVtF466mtJSnnQmwZcv2J9SLr2ceHNQady+EN36cwcIfCwAD+xATWaBzXRS0xeCaWxjceY4Vx7ucfMFEYR2Nw3VCpDq216P5b5P/qb8n+yX+i+M57wcHdaITwyWdIfO93if/O3yd0uH1Ng7vWWq5mbvGnt/87Pxj8aSm4O0fLOPxGD/z9c7D/gUKFQ+A4+Aq0+3AgQGFcRdZ6qHQdzq7ZjYPnZu9aa8GCqxz6HlxZ8g17QRmr084N9pD3q/MOmGjbHkLphRsChxoaiO6uvMnqZj0XYmuItu3Bra+fl707LeNlcdPpJV3TQgRZtRTAf/fv/h379u3jmWeeefiz73znOxw5cuRhNoQQYmHGWMYzRe4OZxkYzpL3/FJd2SrJ1gXQxvDJ1SH++Ic9/OXbn3Hnwfw3qub6OP/TVw/wv/7PHZw61EJoxvyVUqVGaXMWqUopUolStkzQ07VYXjy6ndqaCDsaE9TEIg/3e+Xk7nnjTXMUvHJyd8WZV47j8NqzbeUX0Qpee7atKps6ybkQW4lcf2IjyHp26zGehzc6SvHePYp37uA/eIAt+qUar+EQSl5D1lSpBEOG+/lhHuRGKBqvdO6nm6YVCnjnu8j9n39G7v/6S/xPL4Mx88ZxtjUT/dY3qflffo/oa6/gNDet6byttVzJ9POnt/87Pxz8KXeL8wO7O0jx3R71KLA79YalUHiJCIXoVHTXgnEUJuSAUmgMAx3zawRPZ+9aa0v9NZRLyAnhOi6T3iTdQ71Leg5BGavTpjNXq5FSivQr3wzO9ihtQPrr31jSXRWb9VyIrUEpxdDJA/jMf20D8DEMndgvdwqJFVu1r6k//vhjfvazn5FIJB7+rKmpiX/xL/4FX/va11brMEJsOdZa8gXNZL5IvqhxHaeqSjBM8/xSYPcXPXcYKdPIq60lxcsdO3iqLY2zwBvUgdY6Xj3Vxtneu4xlHi22WpuStLeF+fSLEQrFUnMlay2xSIivPtPKb7y4PzCD+dCeNK+fOcjb527NajLWkIryysndHNqzcBbAXK+eKn17+uaH/bOaPMWjIV57tu3h49VIzoXYSuT6E+tN1rObnzUGk8uic3lssYDVBhUqZYmqkGTorpeCLpB9WIJhumHao/Ovh+7jn+vCu9gLRS94ENcldOgpwic6cHa1rkvwYzqw+97tbm7n7gduszPaxOl0BwfirRjVT/HceezYjDvZapP0HkpQ9Avs+nyMeM5/GKzM1oT57EgjmeQw+43GnQp0T2fvWguOcnCVO+/5fjhwno7mI4Scxa/jhTJWp1Vz/dlE+yGaX/8tRn76E/zh4Yc/DzU0kP76N0i0H6p4rM1+LsTm5xufs/F7xJ7dyYHeIeKTjz7/5pIRrh9pJh+/x1HjV/T3LUQ5q3b1WGvxvPlvzplMBq11wB5CPN58bRjPFMkVNBZLyHUIh9a2fthyFD3NR333eO/CHSaywQvwPdtTfP1EK185spPx8Rxa28DtZjrQWsf+nbUM3M+SKXjURMPsaEqglOI7LxguXH/Ag/E82+rjnOncSXiRD2WH9qRpb6unf3CSiVyRVDxCW8vyaya+eqqNb5zcxUd997g/nqepNsapQ9s2RbagnAuxlcj1J9aTrGc3J10sYjIZTLEAngfKQbkuynEfZoqKtaetJuNlKegC1hocJzQrqGu1xr98Fe98F6b/y7LjqLpaws90EOo4ilNTsx5TL5ViyN7i/ZGewGxdmB3YnV5Tufv2ENvbhr07iMnmcBIJ8vUJdtkJbKqmdBfKwH2cbB5TEyeyvZGnp/Yt6CIJJw5Azs9xansnNaEEYbd8/ee8LpCsIAA0Vpzg5dZfq2i7ag1qJtoPEX+qnUL/TfTkJG4qRXR325LXs1vhXIjNLa8LvLzredgFvGBRdwYhk4OaBDU7t9E0dU1X+vctRDnKWrt4JKYC//gf/2MmJib4gz/4A3bt2oUxhuvXr/NHf/RHtLS08B/+w39YjcOsqaEh6VpYqVDIIZ2uYWQkg+8H32og5rPWki14ZHI+Bd8QclTV3oqRK/j86tJdzl68S3ZG5txMT+yq40xnK/t21OK6irq6BGNj2YoCvAvxtSEcckgno0TC8sFsM5PXChFErgsx12pcE83NK29OshXWs7D117RWa3Q2i8nnMcUiWIOzjKZobkhRV5tgbDyL9lflI9Fjx1hD3s+R00V84+EEZJ6asXG8T3rwuy9gM5kyI4F7YD/hkx24B/avW+mMUmC3n/dGehgsDgdu0xpt5nS6g/3xnQuu263v40QjqPr6iusCW2uxGFLhFMnI+gSzxfLI2kXMJdeECLLS62Il69lV+3rgn/2zf8bv//7v893vfnfWG9/Jkyf5l//yX67WYYTYlDxfM5Etki8aLBbXcaquBMO0yZzHLy8O8MGlQQpecLbS4b1pznS0smtbZc26KqW1wXEVTbUxYos0VxJCCCFWm6xnq1Op7EIOnZ8uu6CnsnMdHNcF5Mvg9VbQRXJ+lqIulmrqKgd3RuaZtRZ94wu8813oz65PNRkLEI8T7jhK+JkOnHT9Os1+qhRDtp/3Rrq5VxwJ3GZXbBun64+zr4LArgqFcBobcKILN7udSRufRDhBbSSFo6rzc4EQQojNY9UiKA0NDfzlX/4lly9f5ubNm7iuy969ezl48OBqHUKITcVYSybnkc37eNrgOmqqfmx1ZuyOTRZ478IAH/fdw9Pzv2lSCo4daOTljla2NyQCRlg+bSwOUJ+KPGyeJoQQQqw3Wc9WB2sMplDA5HIYrzi77IJyUCEJhm0EbTVZL0teF7FW4zghnDm3E9tcDq+nF+98F3ZktOxYTutOwic6CR1+al1rI1cS2N0d28arrafYSTN6gZtdrTEowKmvw0lUvjbWRhNxQjTGmwm5ktAghBBidaz6O0p7ezvt7e2rPawQm0bR85nIeuSLGpTCdRShKs3WBXgwnucX3Xf45OoQ2sxfxLqOovPJZl4+vpPGutWtS2WMxVpIJcKkEuGqLVchhBDi8SLr2fU1L6Dr+4AqBXQBllF+QawOYw1ZL0feFNDGf1iCQanZHyP1nQG88934l/qmfn8BwmFCRw4RPtGJu71lHWb/SKl52k3eG+0pn7Eb3cZL6Q4O1rSSqo0xOVEIzDy21qK0xkkmUanKexwYa1AoGmL1UutVCCHEqpOvDIVYBcZYJnNFMnmNNoaQ6+BWcVAXYHA4y7vdd+i5fj/wrrmw63Dy0DZOH9tBfbLy280qYa1Fa0syHqY2GcGRwK4QQgjx2Fg0oCtZjRuqVFe3QE7n8bWH45SCuu7cbF3Pw790udQ0beBu2fFUYwPhE52Ejz2Niq1vYLOSwO7u2DZOpzvYG9sxFbxeYF3q+zjRGKq5qeI6wdZajNUkw0lSkeU3vRVCCCEWIqsnIZZpZsO0oqdxXAdHVXe2LsDtoUl+3nWbT78IXuRGwy7PPd3CC0d3kIyvfsaM7xsSMZf6dGyqZIUQQgghtrIFSy6ABHSrgLWWvJ8nrwt4ZkZd3YDfjRkexjvfjdfTC/l88IBK4T71RClbd2/bugc1rbVcztzk/QUDuy28lO5gT2z7ovOzvo8Kh1HNTTjhytfHvtbEQhHqo424jtSKFkIIsXZkNSXEEhW9UsO0gveoYVooVP0Lts8Hxnmn6zaffTkW+Hg8GuKFo9v5tae3E1+DBme+NkTDLo0NccKb4HwJIYQQYnkeBXSzGM+bH9CVkgtVwVpLwRTJeTl842EBx3Hn1dWF0u9Uf3a91DTtxhdlx1SpJOHO44Q6juHULr8T+HJNB3bfG+lmyAuuAdwWa+F0pYFdrVEhd8kN1IzROMqlKZ4mGlrdO+GEEEKIIBLgFaICpRIMpYZpvtaEQm5VN0ybZq3lsy/HeKfrNl/cnQjcJpUIc/rYTr5yaBvR8OoHXqdLVjTXx9dkfCGEEEJsLKt1KTu3WJCA7ibgGY+sl6NoilhrSyUYHDdwVWsmJvG7L+B90oOdCF5LArh720rZuk8eRLnrv96z1tKX+YL3R3oWDOy+lO5gT3zH4uNpjcXipOtxllBWolSOwVAbSZGM1FS8nxBCCLFSEuAVYgG5gk8mVyRX1LiOg+OoTZGta6yl74sR3um+ze2hTOA26VSUl47v5JknmwmvQTdqbSwKSzoZJRGTD3ZCCCHEVmE8D53LYotFrOdhtY9SrgR0q5ixhqyfI68LaO3juqGpMgzzt7XWYvpv4Z3vwr/8GRgTPGg0QvjYUcInOnCaGtf2CZRhrJnK2O3h/moEdo3BAm5DPaGUwtdlnnsA3/jE3Tj1sVocVd0l24QQQmw9EuAVYg5fGyYyHrmij7UW13XWraSAtZaB+1kyBY+aaJgdTYkl1SzTxnLx+gPe6b7NvZFc4DbN9TFePr6T5voEec9naCS35OMEzfv2UIYvH+RA+zTXxamtiZJKhBcc11pL/+AkE7kiqXiEtpZS4wljDB/13eP+eJ6m2hinDm3DqaCRRbnxlrvPcsYTQgghthJr7cP6udbzShm6xpSCuU6pTqsKRTZ6mqKMgi6UsnV1EccpX1cXwBYKeBcv4Z/rwtx/UHZMp2Ub4ZOdhJ4+hIqU/917fpHwGl0bxpqHGbv3veDyY3ti2zmdPl5RYBcoNVCrSeCm6/HDLniVBXe18Qk7YWrDSVLR2WUpjDFMfvQB3vAw4YYGkqeeq2hNC5D388RCS29KV26/5Y4nhBBic5AArxCUMl4zUyUYitoQctS6l2C4fnuMs713GcsUH/6sribC80e2c6C1bsF9fW3oujrEu913GJ4oBG6zozHBmc5WYhGXDy4NMpYZXPJxFpr3eLaIoxTWWprqYnzjK20c2pMuu1/fzRHePndr1nwbUlHqkhG6rt4nV/Af/vz7P7vGa8+28eqptiWP98rJ3WXnsdA+wJLHE0IIITY7a0yp3EIhj/E9KPqgQE0FBR3XhQ24BV9UTltNxstS0AWsNThOqGxQF0DfG8I714V/8VKpvEYQ1yV0uJ3wiU6c1h2LfuFtjOad22f5+u7TOKvYXGw6sPveSA8PFgzsdrAnvr2iMa3WpS8sphqoaav54aU3+daebwDlg7HWWiyG+kgdkVCEv7j8Br/d/jqhqRrGw2+9ycibP8LkHyVdDP3g+6Rf+3UavvnagnPyjc/3r/7NrPEqUW6/5Y4nhBBi83D/8A//8A83ehLVIpstLr6RAMBxFPF4hHzewxi70dNZFmst+YJmbDLP6GSRgm9QSuE6at2zNK/fHuPHH/WTL+pZPy94mht3xmiojdFQO/8b96Kv+eDSIP/PT6/Rc+0BuTn7A7S1JPnu6X38+nN7mMx5vPXxrSUfZ7F55wo+KEU07KAcRa6o6bs5zLZ0gub6+Lz9+m6O8MY718gWZs9jcCTLlf5RfG1m/Q58bbjcP0I45HAwIAhdFoC2nQAAIABJREFUbryF5rHQPp9cHeLC9ft42s57bKHnJebbCq8VYvXJdSHmWo1roqZGGhlNW8qa1vo+OpPBn5jAHx9Fj41hCgWwFmUpZelWmHG42TiOIhYNUyh42MrvxK9KxhpyfpZxL8OkN4mxdqoEQ/Dvzvo+/qeXKfzoJ3g//wVm4G5gKQZVX0fk+eeIffd/IHz0aZzaVEXr5Gujn/P52E2ioSiN8YZVeX6XMp/z1/fepWviKjkzP6FhW6iO/7Hlq5xu6KA+nFx0TGstSmuc2hRuOv2wdnDf8BUuD18j5kZpjjcF7quNTzwcpzHWQCQU4ZN7F7h4/xKJcIKdye0Mv/UmD/7mr7C+P2s/6/vkLvehwmHiBw6Wndvc8SpVbr/ljicekbWLmEuuCRFkpdfFStaz8vWdeOzkiz6ZnEfBMw9LMLjuxn1wsdZytvcu5f72jYWzvXfZv7P24YI6X/T54NIg718cIJv3A/c70FrLVztb2bejtN9yjrPYvH95cQBjLa6rCLkOjuNgpmqVGVvKgG1vq581nrWWt8/dmjcPay1jGQ9sqdSE486Zg4U3P+znGyd3zbq1rdx4M5/X3Hksts90FnVz2J13Lso9LyGEEKLaWWsxnofJlurnGt8vlVuYCuIqHFiDuvxi7RR0kZyfxdMeKFUqwbBAhqYZHcP7pAe/+wI2my27nXtwP+GTnbj79y05wG+M5vLwZwBcHv6MA3V7l53Fa6zh08nPeX+0hwfeeOA2SRuhXodpsHF2RZsrGtdqjRMOo5oaZzWF00bTda8XgK57vTxZ/wSuM/PxUjmGhngTYbdUa9o3Ph8OnAfgw4HzHGs8zMibP1rw+CM//hH1r3wzsFzD3PE6mo9UlHVbbr/ljieEEGJzkVd28VjwfM1Etki+aDDGEgo5616CoZyB+9lZZRmCjGWKDNzPUpeK8MuLd/ng0t15WbjT2tvSfPWZnezeNrsG2FKOs7N54a6/2lgG7k8ymfMWrE88PFGgf3CSPdsfzaV/cDKwjEQ272OnI64WrGVe449cweejvns89/SjzINy4y00j4X2KXoaPRWk9nxDJDz/+QU9LyGEEKLaWGMe1s81fhGKPhaLckMopUrBpS2anbuVaavJeqWGaaUSDC5qgQCqtRZ943O8c13oazdKi6wgiTjhjmOEO4/jpOuXPb/rY1+Q80tlCXJ+jutjX/BE+sCSxqgksLsvvoO9oWa+HL5Z8bFKWbsGp74WJ5GY93jf8FUyXhbXdch4WfqGr3Kk6RDGGhRQH6kjEZm9X/dQL5PeJACT3iSf/vSvqMkH98J4+PxyOSY//ojaZ5+b99jc8bqHejnZ0rHgeAvtt9zxhBBCbC4S4BVbljGWyZxHtuDj+xrXLQV1S4Hd6pEplKl1NoPWhp93f8nVW2N4fsDtc8CR/Y2c6dzJjsbg4Gwlx1lsO2stxlhSiTDDEbeizOeJXHHB/542t0uxxaICAvD3x/MVjbfQPBbaZ+ZtFHqBWyoqPa4QQgix3rzR0VLdT98H5ZTqiwKEQlXw1bZYDmsteT9PThfwTBFHle4yUmqBwG42h9dzEe98N3Z0tOx2zq5Wwic6CB16ChVa2cfDmdm705aSxWus4dJUYHe4bGB3J6fTx2mNNPH/fv6Tio9lfR8VDuPMydqdpo2mZ+jSrJ/1DF3iibr91MVS1Ebm3+U2Mzt22pe3+nhq0WcK3oP7834WNF4lWbfl9jvS2L6s8YQQQmw+8qoutpxc3mcyXyRf1LhOKagbWiDLdKPVRMNlH/O1KQWp8z6DI/MzARyl6HyiiZc7dtK0SE3YhY5TyXa+NsQjLulUDMdR1CYqC3Cm4pEF/3taaE6wOCi4C9A0p0ZwufEWmsdC+8z8AsBd4MuASo8rhBBCrDeTz5XeR0OVvfeL6uUZj6yXpaBL6y7HcRcswWCtxdwZKDVN+/Qy6OA7vgiHCR09TPhEJ27LtlWb78zs3WmVZNYuJbC7O9YCwGcj1ys61qNau7U4yfJ3qfUNXyXrZ5i+w88COT/LQHaQtrpdgfvMzI6dNhovPR+nTP3jaeHG+fV9g8arJOu23H5/e+PNZY0nhBBi85EAr9gSfG2YyHjkiv7DuroLlQ6oJjuaEtTVRGaVT/D8UmA3VwiurxtyFSfbt/HS8Z3UJysrwh10nLnqaiLsaJp925nWhlDIYVt9fFa5graWJA2p6ILlERpSUdpaZje5KLdfIhZieEKVyjSo+eUZAOLREKcOzf4Qspx5LLRPJPwoMzlcpg5h0PMSQgghhFgNxppSCQZTQGuN67qLZr9az8Pv7cM734W5O1h2O9XUSPhEJ+GjT6Niq9uYMCh7d1q5zFpjDb2TN3h/pIcRfyJw3/3xnZxOd7Ar9mgNWOmxrO+jQmGcluCs3Wkzs3ettVgLrnJwlcO5wW5OtByfl/EalDULcPtgPcc/vEMseBkPgBOPk/zKqYrGg4WzbsvtZy2cG+yhIVo3L/NYsniFEGLrkVd0sWlZa8nmPTJ5n4JnCLmqaurqLoVSiuePbOfHH/VT8DQTWa9sfd1I2OG5wy28cHQHqcTSMkhnHieo8oCj4Pkj2x8uAI2xKCCdipKIzc8AUkrxysndvPHOtbLjvXJy97wFZbn9lFLU1YQZnSwGZ84qeO3ZtnnNKJYzj8X2qauJADawiVq55yWEEEIIsRJ5XSDn5ShqD8dxUErhLhCUBDAPHuCd78br6YVCmS+7HYfQU08QOtGJu2ft1jBB2bvT5mbWLjewW/GxRj/nibp9OPV1gbV25yrV3i1lurpOiIgbKpXqsrZsxmtQ1iwAjsOVY80cPX+vbBZv+tVfn7emLTseC2fdltsv6+fwdJGsn6MmPPscSBavEEJsPRLgFZtO0fOZyHoUPIPF4jpO2UzLzWK6PMHQaD7w8XjU5fkjO/i1p7eTiC3/z/ZAax2vnmrjbO/dWZm8dTURnj+ynQOtdRhjuD2UwWBpqU8Qj5Y/3qE9aV4/c5C3z91iZPLRh4qGVJRXTu7m0J401lr6ByeZyBVJxSO0tSRn7Tczi3bv9lrqkhG6rt6flb0cj4Z47dk2Xj3Vtug8Zo43PY/2tnpu3p2oaA7T+wBlHzu0J13J6RZCCCGEWJBvPLJ+noLOgwXluIsGda0x6KvXSk3TvrhZdjuVShLuPE6o8xhOam0bwy6UUTvt8vBn7Ktt41L2C345cmGBwG4rL6WP0xoQ2K3kWI629E7c4ODBk2gMi6VEaKPpvncRRzm4ykVNBddLRRpKZma85v08ISdUNtsW4PrxFqJulMO9w5jco0C0E4+TfvXXafjma7O2Xyh7t9wcYqHYgtm7GS8DQMbLkgjFJYtXCCG2OGVtuTaqj5+hoeBFhpgvFHJIp2sYGcngBzT9Wm3aGMYzHvmCj7Z2Xr3Wzchay/Xb4/y86zafDwTXGkvGw7x4bAfPHmohGlm9khPWWgbuZ8kUPGqiYXY0JVBKcfXWKB99epfJvP9wEVhJUNNay+37GazrooymtbEGpRR9N0cWDJIGBX+VUhhj+KjvHvfH8zTVxjh1aNu8LIdy85g73uX+0WXNodx4krm7NOv9WiE2B7kuxFyrcU00N69tAGsz+fLi1bK17MUjbkhRV5tgbDyL9tfvI5GxhryfI6sLGOOjphqmLbrfxCR+Vw9eVw92IjjTE8Ddt6dUW/fJg6gK1k+rIe/nGczcK/u4sYbPC3f5tHCLMT8TuM2BeCun0x20xpqXdyxjsI6DqU1CJMT2mhbe7n+X7+x/DbdMiQtjDTk/x0h+jKhbCgW7rkMyGWNyMo+e0QR4b10bMTfKX1x+g+8e+Ba3Jm4vOE+AttQu6LqINzxMuLGJ5FdOBa5pJ70MX4z1LzrezDn8dvvr5HUhcL+8Lsw6Ry2JZmKh2Lzt9ta1kQyXr0ssHpG1i5hLrgkRZKXXxUrWsxLgnUECvJVbjxczYy2ZqQZjnm9wXbUlgmvGWi7fHOGdrtt8ORS8wK1PRjh9fCcnn9q2LtnJxhg+HxjnJ+duoQJuJXMUvH7m4IJB3rnXRN/NkQXLJiw23mqohjk87mThI4LIdSHmkgDv6pIAb2XWO8Bb0EVyfpaiLqKUE7jmmstai755C/98F/6Vz8CU+fuIRgkfP0L4RAdOY+Mqz3z5tDVcnLjOL0d7GPWDg9IH4rs4nT6+aGC3nFITNYNKJXFSj3ok9N7v41cDH/FrO05xpOnQvH20MaQiNaQis7/AX+j16NxgNz/tf5evt728YeUNqmEOjyNZu4i55JoQQTYywCv3Y4iqYq0lX9Bk8kXyRYPjlOrqhjZ5CQYo1bS9eOMB73TdZnAkuGZYY12MMx07OX6waV2ylK21GGNJJcJ8cvV+2Q8axpbKFbS31VcUZLfW8va5W4GB1eWMtxzVMAchhBBCPN601WS9LHldxFqN44RwKrgl3uYLeBcv4Z3vwt5/UHY7Z/s2wieeIfR0OyqytP4Ma6kU2L3GL0cvlA3sHkzs4sX6lQZ2NU5NApVKzcpWntk0rWfoEocannyYxetrTSwUpjneWDazN8jMcggbVd6gGuYghBCiOsm7gagKs+rqWovrOlsiqAvga0P3Z/d5t/sOD8aDa+xub0hwpnMnR/Y1TjWKW595JaIu9akYtwYnZ5UwCDI8UaB/cJI92xf/Rql/lcdbjmqYgxBCCCEeP9Zacn6enM7jGw9nqgSDUot/9NJ3B/HOd+P3fgqeF7yR6xJ6+lApW3fnjqr6ono6sPv+6AXGFgjsnq7vYGesaVnHeBjYjcdRdbWBZSj6hq+SnSoFkfUz9A1f5XDDkzjKpSmeJhqKLvm4M5uZbVSTsmqYgxBCiOokAV6xYbQxTGQ8ckWNNoaQ60wFN6tnkboSnm/4+PI93uu5M6uh2Uy7tyU509m6rlmk0+e6JR0nHCplLUzkguc310ZttxzVMAchhBBCPD487ZF5WIJBoZSDW0m2ru/j913BO9+N+bJ8XVdVX0/4RAfh40dQicRqTn3FtDVcmMrYLRfYfSKxmxfTx9kZXV5gt3QgjROJoJoaUWWa0c3M3oVSQLj7Xi8nt3VQF6td1mGDmpmtdwZtNcxBCCFE9ZJ3ArGuytXV3QpN06bliz4ffjrI+xfvkskFZ17s31nLmc5WDuysXbfArjEWsKSTURKx8KzHUvHKbunbqO2WoxrmIIQQQoitzVhD1suRN3m01rhuCKfC2/7NyCheVw9e9wXIBpfvAnCfOED4ZCfu/n1Vla0LpRIUFyauLxrYPZ0+zo4VBHat5+FEo5CuxwmHF9x2ZvautRZHOXimyGdjNzgZW16268zM2WnrnUFbDXMQQghRvSTAK9bcVq6rO1M273O2d4CzvXfJF3XgNu1t9ZzpbKWtZf1KAkzX2U3Gw9TWRAI/GLS1JGlIRRcsadCQitLWkiz7+FqOtxzVMAchhBBCbD3WWgqmSM7LUdRFHKdUgsF1K8jWNQZ943O8c13oazfKbqcSCUIdxwg/cxynvm41p78qtNX0TFzj7OgFxvzgpsGrEtj1fVQ4jLOtedHALjzK3rWWUhKJcnGmSjgsN9s1KHN22npl0FbDHIQQQlQ3eRcQa2Yr19WdaSJb5P0LA3zYN0jRm98lUQFH9jdwprOVHY016zav6e7AiWiY+mR0wdq+SileObmbN965FtiUzFHwysndFWeNrPZ4y1ENcxBCCCHE1uEbj4yXo2gKYEE5bkVBXQCbzeJ1X8T7pBs7OlZ2O2f3LsInOgi1P4kKVd9HtenA7i9HLzBeJrD7ZGI3L6Y72BFtXPZxrO+jQiGchgacWOX1cj99cIWMl8FVzrwGasvNdg3KnF3pmEtVDXMQQghR3apv1SA2ta1eV3emkYkCv+i5w/kr9/D1/AiioxQdTzTxcsdOmuvj6zo33zfEoy71qThuQOOJIIf2pHn9zEHePndrVtZrQyrKKyd3c2hPeklzWO3xlqMa5iCEEEKIzctYQ87PkdMFtPansnXdipa21lrM7Tt457rw+66ADr7Di0iY0JGnCZ/owG3ZtrpPYJX4VtMz8RlnRy8uENht43T6ONtXEtg1BgU49fU4iaWtn4t+gd77fYSdUNkv8Jea7bpQ5uxyx1yqapiDEEKI6ifvAGLFjLVk86W6ukVva9bVnWloNMe73Xfo/uw+xs4P7IZcxYmntvHS8R2kU7F1nZunDbGwS2PDowZqS3FoT5r2tnr6ByeZyBVJxSO0tSSXneW62uNt1jlUE2utnAshhBBiAfNLMDilhmmVZusWi/i9fXjnuzCD98pu5zQ3ET7RSejoYVS08izV9fQwsDtykXEdHNh9KtHGiysM7JYOpnFqalC1S1ubGKNxVYhkJMkre15edPu8LpCsMBCa1wVe3vX8qo65VNUwh2qW9/PEQuv7mUsIIarR4/cOIFZNLu8xNJojm/NAKdwtWFd3poEHGd7puk3vjWEC7vgnEnJ49nALLxzbQW1ifRt3+b4mEnHZXru8wO5MSin2bF+9GsGrPd5mnUM16Ls5ItnMQgghRBkrKcEAYO4/wDvfjXfhIhSKwRs5DqH2Jwmf6MBpq95SUb7VdI9/xtnRC0zobOA2TyX2cDp9nJZow4qOZX0fJxJBtTSg3MrXsdZajDXURlIkI6UyaOlY/YrmMlcyXMORpkOrOuZmnEO18o3P96/+Db/d/rpkLwshHnvyKiiWpOhpJnNFPG1JpeL42uBu0Uzdaf2DE7zTdZvL/aOBj8ciLs8f2c7zR7aTiC3e/GE1+doQDjk0p+NEw/LnLMrruzkSWI94eKLAG+9c4/UzByXIK4QQ4rFjjGHSy5Ip5JZcggHAao2+eq3UNO1mf9ntVG2K8DMdhDqO4iSrt8Grb3y6p0oxrHlgd7ocQ0MaJ7a0DExtfGJujPpYHY7a2p9FRHndQ73czQxKDWIhhEACvKICs+rqak0o5OK6Wztb11rLjTvj/LzrNjfujAduUxMP8+LR7Tx7uIVYZH3/lLQ2OK6isTZGPCp/xmJh1lrePncrsNkcgLHw9rlbtLfVV20mkRBCCLFapkswFLw8GeWSK5aybZeUrTs+gdfVg9/Vg50MLl0A4O7fS/hEJ+4TB1AV9kXYCJUEdttr9vBi/coDu6UD+jg1yWWXY2iMNxJ11/eOOVFdZtYmlhrEQgghAV5RhrGWTG6qrq5vCE3X1V3h7f/VzlrLlf5R3um+Tf9gcKfaupoIp4/v5GR7M5F1Ph/aWBSW+lSEmpgsakVl+gcnZ5VlCDI8UaB/cFJKWQghhNiyPOOR9bIUjQfWEg6FCTkujnIw1iy6v7UW/UU/3vku9JXPIKAXAwCxGOHjR0plGBpWIRi6hnzj0zUV2J1cILB7Ot3BtsjK7/SxWqNcF9XchBOu/M43ay0WQ2pGOQbxeOse6mXSK31em/QmJYtXCPHYkwCveMhaS67gk8l75Isa13FwHEV4C2fqTjPG0vv5MO9232bgQfDitrE2xssdO+l4omndG8gZa7HGkkpESCXCkmUplmQiV6YO4DK3E0IIITYLYw05P0dOF9Ba47ouSjkVl2AAsPk83oVevPPd2AfDZbdzdm4vNU073I5aQvByI1QS2D1Us5cX08dXJ7BrLcoYnFQKJ7m0AK02PolwgtpISsoxCGB29u40yeIVQjzu5NVPUPR8JrIeBc9grCXkOitu1LVZaGPo/uw+73bf4f5YPnCbbek4X+1s5cj+RlxnfQOr1lq0MdTEItQlIzgS2BXLkIpXlu1d6XZCCCFEtSvoIjk/S1EXUcpBKQd3CQ28APTdQbxzXfiX+sDzgjcKhQg9fYjwiQ7cnTtWYeZryzM+3RNXOTvauy6BXShl7TqRMCrdtKQyFb7xiTphGuPNhJZQPkNsfTOzd6dJFq8Q4nEn75SPKV8bJrIe+YKPngrqOo7CWUo6wybm+YbzV+7xi547jE4GZy3uaq7hTGcr7XvSGxJY9bQhEXGpT8Vxq7hmm6h+bS1JGlLRBcs0NKSitLVUb9MXIYQQYjHGGjJejrzOY63BcVycJWbzWd/H//Qy3vluzO07ZbdT6XrCJzoJHz+CisdXOvU15xmfromr/Gr0IpM6F7jN4anAbvNqBXaNQaFw0vVLaqJmbGm/xliaWGhpzdfE1heUvTtNsniFEI8zeeV7jBhjmcx5ZAs+njaEHIVyFKHHJKgLUPA0H306yPsXBpjIBWdi7NuR4kxnKwdb6zakFIKvDeGQw/Z0/LHJpBZrSynFKyd388Y71wIbrTkKXjm5W0p/CCGE2JTyukDOy1HURRzHRSmFUktbQ5mRUbxPuvG6L0IuOACKUrhPHCg1Tdu/d1O8b04Hds+OXiRTNrC7byqwW78qx7TWorTBSSVRyZqKz5O1FmMNqUiSZLjy/cTjJSh7d5pk8QohHmcS4N3irLVkC6VmaQVP46ipurrrXEN2o+UKPmd773K29y65gh+4zZO76/lqZ+uGNZnSxuAoRWNtjHi08j9Nay39g5NM5Iqk4hHaWhbvRrycfdbCZp77ZnNoT5rXzxzk7XO3ZmXyNqSivHJyN4f2rE62jhBCCLEetNVkvCwFnQcLynFxl3gbvzWG3KdXmXzvQ/xrN8pup2pqCHUeI9x5HKeudqVTXxelwO4Vzo72lg3sPl2zjxdWMbALgO/jxBOoutTSyjFoj/DdYZIFQyiVh7al1enN+/kNy/a11lLov4menMRNJom27VnS2nQj577ZLJS9O02yeIUQj6tN9ar38ccf87u/+7uzfmatxfM8rly5wq9+9Sv+43/8j9y4cYMdO3bwve99j+985zsbNNuNVfB8JrOlZmkocB2H0BLrjm0FE9kiv7x4lw8/HaTg6XmPK+DpfQ283NlKa9PGdOQtNVCD2poIqcTSaqD23RxZcsBuOfushc08983q0J407W31EiAXQogNJOvZ5bPWkvPz5HQe33g4yi1l6i7xbcxkMvjdF/E+6caOjZfdzmnbVWqa1v4kapOsoz3j88n4FX41dpGMDu4v8XRyPy/WH6NpFQO7VmuccBi2NeOEKv+IqY1GXfsC+94H5EZHmQ5Fh9Jp0q98k0T7oUXH8I3P96/+Db/d/vq6B/Wyl/sYefst/JGRhz/bLHPfjPK6wMu7nq9ou6ScTyHEY2ZTvep95Stf4eLFi7N+9p//83/m8uXL3Lt3j9/7vd/jn//zf863v/1tzp8/zz/6R/+Iffv2cfTo0Q2a8foKqqvrPmaZutNGJwv8oucO5y7fw9fz70l3FBw/2MTLHa1sS29M3bSVNlDruzkSeMv98ESBN965xutnDs4Lei5nn7Wwmee+2SmlNixLXQghhKxnl6Ooi2T93FTDNFVqmLbU2rrWYr68g3fuE/y+K2BM8IaRMOGjRwid6MDd1rwKs18fGxbYNQZlwamvw1lCLWJjNI5ySdy8x9h//zHY2Qs8f2SEoTd+QPPrv7VooLR7qJe7mcF1vzU/e7mPoTd+sCnnvlklwzUcaVo8cC6EEI+jTRXgnevOnTv82Z/9GX/913/N3/3d37F3715ef/11AJ5//nm+9rWv8cMf/nBLL4ilru5s98dyvNt9h66r9zF2fmDXdRQnnmrmpeM7aajduFuhfG2IRVy2LbOBmrWWt8/dCqynCmAsvH3uFu1t9Q+zM5ezz1rYzHMXQgghVpusZ4Npq8l6eQomjzYa1wnhOEvPorXFIv7FT/HOd2HuDZXdzmluInyyk9CRw6hodCVTX1dF4/HJ+BU+GOsNDOwqFE8n9/HCKgd2gVI5hpoEqrZ2SXV2LZZUJEVNOMGdn//f8wKkMzZm5Kc/If5Ue9nxZ96yv5635ltrGXn7rU05dyGEEFvTpn4H+eM//mP+3t/7e+zcuZNLly5x+PDhWY8fPnyYH/3oRxWP5zgKx6n+4NB0Xd1MzqfoaZRSuK7CXcdbx6Yzg0v/XyYDYh0NPMjw809uc+H6g8B1Vjjk8OzhFl46voO65MYt2rWxhBxFc7qGaHj5v68vBsYZmSyw0Fp6ZLLA7QcZ9m6vXfY+SzH7mthccxdrp9LrQjxe5LoQcz3O18Rqr2dh86xp57K2VI805+cpGg8Xl5BT+t9S6aH7FM59QrGnF4rF4I1ch/jRw4SeOY7TumtTfUlcNB7nxi5PNU8LDuweTe3ndLqDpkjdqh7bGg2ui9O0rVSWoUKe8akJx6mL1eIoh9wXX6BHRxZc3+mRYfw7t4jv2Rv4eNfAJSb9DCjFpJ/h4oNLfGVH5xKf0WyVvB5V69zF2nmc36dEMLkmRJCNvC42bYD3yy+/5K233uKtt94CYHR0lJaWllnb1NfXMzKjHtJiGhqqu1tr0fMZnSyS9zRWuSRTlS+o1koyubENAT6/M8aPzn7BhWv3Ax+PRV2+emI3Xzu5e8n1bVeTNgYFNNTGqImvfB6f38sQquAFwzou6XTNsvdZjtrahW/Pq+a5i7Wz2HUhHk9yXYi5HrdrYi3WswB1dYkll37aSJ72mCxmKPhFbARqYhFqWPp6yWpNrvcymV+do3D9i7LbufW11Dx3gpqvPINbm1zBzNdfUXucvd/LO/e6yfjBgd3O9BO8sv0EzbHVzdi11oI2uPWNuKnK11u+8Ym6URri9YRnNsL7wqsoQSVuvcD1na99zl3omrVGPDfUxdeeeo7QEhvuBVnw9ajK5y7WzuP2PiUWJ9eECLIR18Wmfff4i7/4C775zW/S3Lx6tbGGhzNVl+1gjGU8WySX9/C1JRSqjm+HXNchmYwxOZlH6/XN4LXWcuPOOD/75DbXvhwL3KYmFuLFYzv4tSPbiUdDGM9nbMxf13nCVAM1C7WJCMlEmGLeo5j3Vjyu0hq/gvOujGZkJLPsfZbCdR1qa+OMj+cWvCaqce5i7VR6XYjHi1wXYq7VuCY24xd8a7GeBRgby1bdmnYuay0ZP0e+mEejCanlfywx4+MUzndT7OrBTpZfB4QO7CN68hlCTxxAOQ6VYdMPAAAgAElEQVR5F2qATLaAnd+Lt6oUjcfHY32cHe0lu0DG7kvpDhojdeDBpFcIGGl5rPYhGsFNp1HGgbHcovv4RhNyHOqidURC0an5PJpTjjBaL37icyoMAeu7jwe6GM7ObpI3nB3nZ1c+WFEmbCWvR9U6d7F2ZO0i5pJrQgRZ6XWxkvXspg3w/vjHP+af/tN/+vC/0+k0o6Ojs7YZGRmhoaGh4jGNsZhyRT7XkbWWfEEzmS9SKGoc18FRCqUUOqBh2MYoXaham3Wbk7WWq7dGeafrDjcHJwK3qa2JcPrYDr7Svo3IVAmEjThn1lq0nmqglio1UCvNY3Xm0tpUQzoZZXii/MK9IRWltbEG3zfL3mc5tDYL7l/NcxdrZ7HrQjye5LoQcz1u18RarGehtKa1VXoaC7pA1stR1B6OU2qYBg7+EidsrUV/fhPvfBf66rXytVDjMcLHjxJ+pgOnodSMVQMYQ4hS4oTV4JdrurbBisbj/PhlPhjtJWvmr4MUiiPJ/byYPk5DuFSmajWfi7UWZS2qrhYnHkdbYJEPrKU6u4ZUOEUyUvqgGvR3HWrdjVufxl8gQz3U0EBo5+55+/vG5+ztc4G/97O3z3G08ekV17Nd6PWo2ucu1s7j9j4lFifXhAiyEdfFpnzn6Ovr4/bt27zwwgsPf3b06FH+23/7b7O26+3t5fjx4+s9vWXzfM1E1iNX0IDFdR1CofWrq1utjLVc+nyYd7tuc+dBNnCbhlSUlzt20vlkc0W38q8l39dEIyG2pZfXQK0SSileObmbN965Fth4zFHwysnds0qOLGeftbCZ5y6EEEKslq26ng2irSbjZSnoAtYaHCe07N4RNpfHu9CLd74LO1w+uObs3EH4RCehw0+hllAntloUjce58ct8uEBg92hyPy/MCOyuOt/HicdRdbWoCta01lq0MSQjcVKRFI5aeB+lFOlXvsnQGz8IDtArRfrr3whc33UP9TLpTQaOO+lN0j3Uy8mWjkXnvFybee5CCCG2pk0Z4P3000+pr68nmXxUM+vb3/42f/Inf8IPf/hDvvOd7/DBBx/w7rvv8v3vf38DZ7o4Yy2ZnEc271PUhpBTapgGEqjSxtBz7QHvdt9maHT+rWgAzfVxznTu5NiBJtwNvhVRa4PrKprTcaLhtf/TOrQnzetnDvL2uVuzMlsbUlFeObmbQ3vSq7KPzF0IIYRYfVtpPRvEWEPez5HTRXzj4SgXpZypjN2l0wN38c534ff2gV+m7FYoROjIIcInOnF3bF/B7DdOwXicH+/jg9FL5DYosGu1RjkuqrEBJ1pZc2Jfa2KhMM3xRtwlNMVLtB+i+fXfYuSnP8EfHn7481BDA+mvf4NE+6H5xzI+Hw6cX3DcDwfO09F8ZE0zYTfz3IUQQmw9m/Jd4/79+/NqlTU2NvJf/st/4d/8m3/Dv/pX/4rW1lb+/b//97S3t2/QLBdW8Hwmsx75ogalcB1FWLovAuD5hk+uDvGLnjuMlLklf2dTDWc6Wzm8N73hTUS0sThAOhUlEVvfDJFDe9K0t9XTPzjJRK5IKh6hrSW5YCbrcvZZC5t57kIIIcRKbYX1bJCCLpLzs3jaA1UqweAuM1BlPQ//0yt457swdwbKbqca0oRPdBI+dgQV39gGwMtVMB7nxvr4cGyhwO4BXkwfI71WgV1jUAqc2lqcmkRF+xhrcHBojKeJhSoLBs+VaD9E/Kl2Cv030ZOTuKkU0d1tZdd3eV3g5V3PLzpuXhdIrnGQdDPPXQghxNairC1XsOrxMzQUXNd1tRhjmcwVyeQ12pgNLyWwEq6rqKtLMDaWXbUatwVP81HfIO9fGGAiG9yIbM/2FF/tbOWJXXUbHtSztlSzOZUIk0pENnw+Gy0UckinaxgZyUgNIvGQXBciiFwXYq7VuCaam1OrPKvN68uLV1HreDeYtpqslyOvC1ircVYYmDLDI3ifdOP1XIRc8F1cKIX75MFStu6+Pctah4Uch2QqyuREYcNq8BZMkXNjlxcM7B5LHeSF+qNrG9jFopJJVE1NRefSWouxmlQkRTJc2T6bhbxHiSByXYi55JoQQVZ6XaxkPStfC66xcg3TNnNwd7XlCj6/unSXsxfvki0E33L3xK46znS2sm/HGtUYWyLfNyRiLvXJWNV3qRZCCCGEWG3WWvJ+nqzOzyjBoFBqmdm6xqCvXcc7142+8XnZ7VSyhlDHsVLTtNrNG9QvmCIfj/Xx0dingYFdB8XR1EFeqD9GOrw2z9NaizIWpyaBSlV+R1SpHEOEdKxp0Tq7QgghhFgfEuBdI742TGS8qYClNEwLMpnz+OXFAT64NEjB04HbHN6b5kxnK7uak4GPrzdfG8Ihh5aGOGH5fQohhBDiMeMZj6yXpaCLADiOu+wSDABmMoPffQHvkx7s+HjZ7dw9u0vZuk89gVpmg7ZqMB3Y/XDsEnlTnPe48zBj9xj1axrYNTiJGlSqpqIGarA65RiEEEIIsTYkwLuKrP3/2bv34Ljq+/7/r3P2vtpd3SXbwjIB3+QLtrGxwATsECfwJRMa+LlMMmkzaZq0udDkm5K0JKEtZZg0Q6CXIZ20SdqkSVPq4JaENAXydRITLsY3LGMZGWMDlm1sWbYu1l17zvn8/hASuqxkSZb2Ij0fM0ziPUe7H4kP0lsvv/f9MeroTqqjiwPTxtLa3qPfvnxae+vOKumObFm3LGnVlSXauHqeyovGN/9rurmekWVJxYmwIiH+swEAALOHZ7y+EQxej1zXkc/nlz2Bg7SGM8bIO3Gq79C0ulel0cYjBIMKXLVCgbWrZZeWTPr1skG31zswY3f0YHeRri9YOb3BruvKjkZlJeLjDnZn8jgGAABmCpKqKdCbdNTGgWkXdf5Ct35b85ZeOtIo1xs5t9dnW7p6caluXD1PxYnsOCCDObsAAGA2Msaox+tVV7JLvW5Stm3Lsiz5fJP/9cH09MipfUXJfTXyzjaOep9dVqrA2jXyr1wmKxic9Otlg+6BUQyjBbu2VsUXakM6gt1IRFZ+YtzBrtQ/jiHAOAYAALIcAe8kpTowzUeom1JDU6eeqXlLB46dU6oj/QI+W9dUlemGq+YqP5Y9b/dKup6iQZ8K48zZBQAAs4PjJdWR7Fav1y0ZybJ98l3iSAT3bKOcfTVKHqyVelMfpCufT/6qJX3dupdV5PxfqvcFu69od+srYwa71xdcpfzA9I0iM44jOxSUVVI8odEWnvFkyWIcAwAAOYKAd4K6up2+A9OSrmybA9PGcrKxXTv2n9IrbzanvB4K+HTt8nJdv3KuYpFAmlc3Otf15PfbKi+IKBjI3RlvAAAA4+EZT91OlzrdHnmeI8vyybJ8lzRlzLiunMNHlNy3X179yVHvs/ITCly9Wv7VK2Xn5U3+BbNEt9uj3RfqtGeMYHd1fJE2FK5Uvn8ag13XlWX7ZBcXyQ6NP6DtH8cQC8QUD47/4DUAAJBZBLzjkPLAtBw+3GG6vXH6gnbsP6XXTramvB4N+bVh5Rxdt3xOVs2z9d4eG1EQDyovnNtvBwQAALiYHrdXXU6net1eWZYty7JlX8KBaZLktV5Q8qUDcmoOyHR0jnqf78orFFi3Wr4rr5jQyIBs1e32aHfrK9p94RX1eCO7lNMW7PYfoBaPy45NLDB3PUchX5hxDAAA5KDsSdeyDAemTYwxRq+dbNVv9p/S8TNtKe+JRwO64ap5uqaqTKFp7Iw1xuj0uU519CSVFwpobkl0zO4DY4xct2/ObiJvdszZ9TxPu+vO6tyFbpUkwlpfVSZ7BvxyBQAAxuYaV53JTnW7vTLGlW37LznUNcbIff1NJfftl/vaMaWcySVJkYgCq1cqcPVq2YUFl/Sa2aLr7WB3T+sr6jEjg12fbK1KLNKGgskFu0mnVwH/OBsPHGfCB6hJkue58ll+FYeLFMqxcQzGGPXUH1dXa5Mi+UUKVS6YFbU8AADDEfAOw4FpE+MZo9o3mvTM/lM6da4j5T2F8ZBuXDVPVy8uVcA/vV/LY6da9ULtGbV2vPOWuPy8oDasmKMrK/JH3N8/Z7egMCTfLAk4n95dr6d21aurxxl4bOuvj+qW6krdvL4ygysDAADTwRijLqdbXW63HC8p2/LJsixZ1iUGu11dSh6oVXLffpnmllHvsyvm9R2atmyJLP/M+PVjPMHu6sRibShYqYR/cqMnPM/VjlMv6L3zb5Btj94cYRxHViAgq7REdmD8Y8+MMTLylAgmlBeMTmqNmdR5uE7N23+pZHOTmrqbVRQuVKCwSIWb36/o0qpMLw8AgLSaGRXWFOnqdtR4oUtBv48D0y7C9Yx21Z7WL55/Q2ebu1LeU1oQ1sbVFVq1sDgt4emxU616ene9vGFNI60dvXp6d71uXl85EPI6rqeA39acwogC/tkzbuPp3fV6/NnXpWFfo64ep+9xiZAXAIAZIukm1TEwgsGSZdnyXWK3riS5b51Wcu9+Oa8clhwn9U1+v/wrqhRYu0a+uXMu+TWzRTqC3X7HWt9Uc3ezjrW+qUWFV464PjCOoSBfdnRiAa3juYr6I8oPxXNyHEPn4To1bvuJZIy6kl1Kukl1JbtkNTercdtPVLrlTkJeAMCsQsA7iCdv1nRxTpbjenrpSKN+e+AtNV3oSXnP3OKoNq2p0PLLi2Tb6XmLlDFGL9SeGRHu9vOM9ELtGS0oj8nnt1WcCGfV/N908DxPT+2qHxHuDjDSU7vq9b51lzGuAQCAHNaR7FK32yXXc+Wz/WN2f46XSSblHDrcd2ja6TOj3mcVFymwdo0CVy2XFQ5f8utmiy63R7taD2lva13qYNeytSa+WNdNQbAr9XXvHm56TZJ0uOk1XZl/+dB/j44jOxKRlZ+Y+DgG26+ySLECvuw55HgijDFq3v5LyRgZGXUk+2Y9dyQ7FQlEZBmp+Vf/T5ElSxnXAACYNWZXwoVJ63Vc7ak7q2dfPq0LHSNPBJakyvKY3rOmQovnF6S9mDp9rnPIWIbhjDFqae9RW1dSVQuK0riy7LG77uyQsQypdPU42l13VtcunzmdNgAAzDbdTvfbo8YuvdT3zjcp+VKNkgdqpe7u1DdZlnxLFvV1615eOaNCtU63++2O3Tr1jhrsLtF1BSumJNjtd6z1TXU5fe+S63K6Brp4jevK8vlklRTLDo7/UOD+cQz5wYSiOTiOYbCe+uNympslSV3JLrnGldQ3X7or2aVoICqnqUk9J+oVrlyQyaUCAJA2BLwYU3evoxcPNei5g6fV2Z06HFxYka9Na+bpXXMTGSvoO3pGFtxSXzErST7bkm1bckdr8Z0Fzl0Y5ZeySd4HAABmJuN5cl87puTe/XLfeHPU+6x4TP7VVymwZpXsRDx9C0yDTrdbu1pf0d7WV9RrRtbAPsvW1fEluq5gpeL+qQ1MB3fv9jt8/oiuiM2XP5EvOz6xw9ocz1HUH83ZcQzDue3tkjSke7ffQBevLLltqQ9+BgBgJiLgRUod3Um9cPCMdh4603fgXApXLSzRDVfNUUXJxE8Enmp5oZFvMTOekW1b8vmsgeA5Hhl/p8NMU5IY39skx3sfAACYWbz2djn7X1bypQMyY4RjvgWVCqxbI9/ihbJ8M+ssg75gt28UQ7qD3X6Du3clyfI8dfh6dCTQohXxinE/zzvjGEpydhxDKr5Y3+8eg7t3+w3u4vXFZ9ZfOgAAMBYCXgxxoaNXz718WrvqGpR0vBHXLUtaeUWxblpboaVXlKq1tVOum/mu2LklUeXnBdXa0SvPGNmWJb/fGjJLtigeUmV55sPoTFlfVaatvz465piGSMiv9VVlaVwVAADIJGOMvPoTSu6rkXP4iOSNrP8kSaGgAletVGDtatklxeldZBr0B7t7WuuUTBHs+i3fwIzd6Qp2pWHdu8bIktQVC8oJ+nTgfJ2qSpbKd5GZyjNpHEMqocoF8hUUqOOtcymvdyQ7FS+rUGg+BwcDAGYPAl5IkpoudOu3B97SvlcbU44x8NmW1iwq0Y2r56kkPyKfL7tmq1mWpWuXl+uXe+oVsK0Rh4TZlrR53fwZNRNuomzb1i3VlXr82ddTH7RmSbdUV3LAGgAAs4Dp6VHy4CE5e/fLO3d+1Pvs8jIF1q6Rf0WVrAnMfM0V7ckubT+3T7vHCHavji/RtQUrpjXY7dffvWt5RsmQT91Rf1+HhaROp0N1TUe0oqRq1I+faeMYUrEsS43rrpTzxBGlquwdeWpce4Uum8V1PwBg9iHgneXONnfpmZpTOnD0nFKNp/X7LF2ztFw3rJqrglgo/QscB88zMkZas6hEJfkRbd97Qk1tPQPXi+IhbV43X1ULCjO4yuxw8/q+ToandtUP6eSNhPy6pbpy4DoAAJiZ3Iazfd26Bw9JydRnGMjnk79qiQLrrpZdMXdG/gV5h9ut3U2HtPdCnXq90YPd6wpWKJaGYFfq6959tfGIjCV1JgLy/CM7dQ80HlJV0eIRXbwzdRxDKo7n6IXIWYWr5+nK2kZF2t85aLkrFtSxFaXqjpzVSs+RfwoOGgQAIBfwE2+Weutch36z/5ReeaMpZTNnMGDr2mVzdP3KOYpHs7Nbwxgj1/MUCweVHwvKsixVLQhqaWWB6hva1dbVq3gkqMryWFb9YmKMyej6bl5fqc1rK/Tki/VqaOlSeWFE/6e6Ur4ZNkMPAAD0MY4j5/ARJffVyDtxctT7rPx8BdauVmDVSll5M++t/ZLU4XbpxZZD2nfh8Kgdu2sTS3Vt/vK0BbtSX33Y09ut5ZdfLZMXGfPeHrdXUTsy8HGZHMfQ7XQr7E/v+Q3dbo82XrZBukzS9UbWWw1y2trkjyeUN69MJW/X1d1uj2IEvACAWYKfeLPM8TNt+s3+UzpyoiXl9UjIrw0r5mjDijmKhLJ3eziup1DAp7LCiHzDRgpYlqUFc7LzUIW6480Z7zAevoaTjR1643QbXc4AAMwwXkurki8dkFPzskxn56j3+RZe0Xdo2hXvkjVDRzWNP9hdoZh/7IB1qhnHkR0KKq+0Ugsn8PV3PFdRfyRj4xgcz9HWIz/VR5duSWunbCyQN2RMhVO8WD8+vE0fXXorHbsAgFmLn4CzgDFGR0+1asf+U3rjdOoTkeORgN591VytrypXKJi9nZyu68nns1RaEFYokFvbt+54s7btODpiFEZTW4+27TiqLZsWTnvAmg1rAAAA08s59rqS+/bLPfq6ZEY5DDcaUWD1VQqsWSW7sCC9C0yjvmC3VvsuvJoy2A1Yfq3LX6r1ieXpD3Y9T5aR7KJC2eHxd8G+M46hOKPjGGoaa3Wmo0E1jbVaV7561q8DAIBMyq2EDBPiGaPDx5u1Y/8pnWzsSHlPQSyoG1fN09olZQr4s7djw3s7kcyPBRWLZOfIiLEYY7R974mUc44lyTPS9r0ntLSyYNrGNWTDGgAAwPTr/s9to16zL6tQYO1q+auWyPLP3F8F2p0uvdhaq30XDssx7ojr/cHu+y5bK6vbJ8fz0rtAx5GdF5WVSIy77sr0OIbBHM/RrtP7JEm7Tu/T6tIVGemezZZ1AACQafz0m4E8z+jg6+e1Y/8pNTR3pbynJD+sjavnafWikhEjDrKJMUaO6ykRDSqRF8zZ4LG+oX3IWIZUmtp6VN/QPm3jJbJhDQAAIAMCAflXLFNg7Wr55pRnejXTqt3p1Iuth8YMdtcmluraguXKD+QpFgipvXvs+mgqGdeV5fPJKi2RHRh/963jOYr6oxkbxzBcTWOt2pPtkqT2ZHvGumezZR0AAGQaAe8M4rieal47p2dq3tL5C90p75lTFNWmNfO04l3Fsu3sDksd11M4mHrObq5p6+q9+E0TuC9X1wAAANLHKilWYO0aBVYulxUOZXo506rd6dTO1lq9dOHV0Tt2E0tVXbBCeb70Hgom9TUtWK4rOx6XHY+N++Ncz5HfDqgsUpLRcQyDDe6a7ZeJ7tlsWQcAANmAn3wzQK/jau/hs3r2wGm1dqQO5+aXxfSeNRVakgNvv39nzm5EoUD2zgOeiPg4x0qM975cXQMAAJh+/tVXyb9imXwL5md93Xep2pxOvdhSq5fasjPYld45RM0qKZblG19t2z+OoSCYn/FxDMMN7prtl4nu2WxZBwAA2YCAN4d19zra9UqDnjt4Rh1dyZT3XDEvoU1rKnTlvPHP98qUXJ+zO5bK8piK4qExRyQUxUOqLB9/R0curgEAAEy/8AdukbK77LtkbU6ndrYc1P62I6MGu9fkV6k6f7mimQp2XVeW7ZNdVCR7Ah3U2TaOYbBUXbP90tk9my3rAAAgW/BTLwd1dif1fO0Z7aw9o+7ekQWtJC2tLNCmNRWqLM/+WarGGLmuUTwayOk5u2OxLEub183Xth1HUx5yZlvS5nXT22WTDWsAAAC4FBcLdoOWX+syHewaI8v1ZCdismPj/4tzz3Pls/1ZNY5huFRds/3S2T2bLesAACBbEPDmkAudvXru5dPa/UqDep2RJ/1aklZcUaRNayo0tzgv/QuchHfm7IZyfs7uxVQtKNSWTQu1fe+JIV20RfGQNq+br6oFhbNiDQAAABPVH+y+1PaqXDOyDu4LdpepOn9ZxoJdSZLjyI5EZOUnZI2ztu0fx5AIJpSXZeMYBhura7ZfOrpns2UdAABkE37i5YDmtm799sBp7Xv1rBx3ZOulbVlavahEG1fPU2lBJAMrnLjBc3aDflv1De1q6+pVPBJUZXlsWrpIjTFpeZ2xVC0o1NLKgoyuIxvWAAAAMB4XnI6Bjt3UwW5A1+RXaX2Gg13jOLICASUL4wpHxt+163iOIr6ICsKJCY9j6Ha6Ffan73Pudnu08bIN47ovNo3BarasAwCAbMJPvCzW2NKlZ2reUs1r5+SZkcGu32dp7ZIy3bhqngrjuXEy8vA5u3XHm9PSTZqu1xkPy7K0YE5mR2dkwxoAAABGMxDsXjgiV6MHu9X5yxXxZa4ONsbIMp7sgnyZcEhPvv6UbrviFvnssQ9T8zxXtuVTSaRYId/Ez55wPEdbj/xUH126JW1dqrFAnlaUVKXltXJhHQAAZBMC3iz01rkO7ag5pUOvNynFqFQF/baql5Xr+qvmKhHNjcPIjDFyPU+xcFD5sb45u3XHm1POg21q69G2HUe1ZdPCKQlf0/U6AAAAuDQXnA690HJQNWMEu+vzl2l9/rKMBruS+sYxRKOyEnFZtq3ac3U613VOdU1HRg0gjTHyjKt4MK54cPIH29Y01upMRwOzZgEAgCQC3qxS39Cm3+w/pVfrW1JeDwd92rBijjasmKNoODsPXkjFcVyFgn6VFUYG5uwaY7R974mUh31Jkmek7XtPaGllwSWNDkjX6wAAAGDyLhbshqyArsmSYLd/HINVWiI70FeTu56rA42HJEkHGg+pqmjxiC5ex3MU9gVVECq+aIfvWAbPoGXWLAAAkAh4M84Yo2NvXdCO/af0+lsXUt6TFwno3SvnqHpZucLB3PlX5nmeLMtSSX5E4dDQddc3tA8Zl5BKU1uP6hvaL2mUQLpeBwAAABOXU8Gu58mSZBcUyI4OPfeirumIOp0OSVKn0zGki9cznmSk4nDhlMzMrWmsVXuyXZLUnmynixcAABDwZooxRofrW7Rj/ymdONue8p78vKBuWDVP1ywtU8A/sUMXMskzRsYzSuQFFR9lhERbV++4nmu8913qx1/q6wAAAGD8Ljgder7lZR248FrqYNcO6JpElgS7xsjyPNl5ebLiIw+mHdy926+/i1cyygtElQgmpuTdYoO7d/vRxQsAAKgC0szzjGrfOK8d+9/SmabOlPcU54e1cdU8rV5UIr8vd4JdSXIcT9GwTwWxsGx79CI2Hhnf7ODx3pfp1wEAAMDFtTrteqH5oGraXpM3SrC7Pn+51ieqFM70jF1Jcl3ZoZCsgnxZduq6fHD3br+OZIcONx3RDRXXKeCbutFqg7t3+9HFCwAACHjTxPU81bx2Ts/UvKVzrd0p75lTFNXG1fO08oriMcPRbOS4noIBW+VFEQX8F58pVlkeU1E8NOb4hKJ4SJXlkz98Ip2vAwAAgNG1Jtv7OnbbjqYMdsN2sG8UQ5YEu8Z1Zfn9skqKB+bspjK8e9cYI8mSz7JV1/Sabrxsw5StKVX3bj+6eAEAmN2oAKZZ0vG099WzevbAW2ppTz0G4LLSPL1nTYWWLCiUnWMHfbmekWeMihNhRULj306WZWnzuvnatuNoygPQbEvavG7+Jb+VLV2vAwAAgJHGE+yuz1+ua/KrFLYz/46qvjm7luz8/BFzdlMZ3L1rjJFt2fJZPlmWNeWdtam6d/vRxQsAwOxGwDtNenpd7apr0HMvn1Z7VzLlPe+am9B71lToyoqpmcmVTp4xcl1PiWhQkaBvUuuvWlCoLZsWavveE0M6bIviIW1eN19VCwqnZK3peh0AAAD0aUm26fmWg3q57TV5Gvm37FkX7Bojy/Vkx2OyYnnjqm37u3eNkSxL8lt+2cPGOExVZ+1Y3btT/VoAACD38NN/inV2O3qh9rR2Hjqjrh435T2L5xfoPWsqtGBOPM2rmxqO4yoRC+mysrhaWzvlOCO7McarakGhllYWqL6hXW1dvYpHgqosH3l4xaVK1+sAAADMZn3B7st6ue1oTgS7kmRcR3YwJCs/Meqc3VS6nR6tK1+tqD+isH/0sRLdbo9ilxi6drs92jiOcQ9T8VoAACD38NN/irR19uq5l09rV12DepMjA09L0vJ3FWnTmgrNK8lL/wKngOO4CgX9KinOUzjkn7I5wZZlpSXsTtfrAAAAzDbjCXar3w52Q1kU7BpfVHZpiWRf/AyJwVzPUVGkQJfnz5dtTf+hyLFAnlaUVE376wAAgNxEwHuJWtp79Nuat7T31bNy3JHFrG1JqxaWaOPqCpUVXnyOVzZyXU8+n6XSwohCAbYMAAAA+jS/HeweHCXYjdghVecv17r8pVkU7LqybOXOvswAACAASURBVJ/soiIFywrU3dolzx3fO9Jcz1XA9qsoUqKAb/TD1wAAANKJtG6SzrV06Zmat7T/tXPyzMhi1mdbWrukVDeumqeiRDgDK7x0nmdkSSqIB5UXzo6CHAAAAJnXnLyg55pf1sH2YzJjBrtVCtnZEYT2HaBmZMfjsmN5sn3j77z1jNdXF4fyFQ3kZtMGAACYuQh4J+j0+Q7t2P+Wat84rxS5rgJ+W9VV5Xr3VXOVyMvNUNQYI88zikcDikeDzKkFAACApBwNdo2R5bqy8/JkJeITrm0d11UsGFUiOPGPBQAASAcC3nE6cbZNv3npLR2ub055PRz06brlc7Rh5RzlhbOjmJ0Mx/UUCfpUGA9P2YxdAAAA5Lam5AU9f5Fg99qCFVqbWJo1wa4kyXVlh0KySksmdICa1DeOIWgHVJJXJN8EZ/QCAACkEwHvGIwxev30Be3Yf0rHTl1IeU9e2K/rV87VtcvLFQ7m7pfTcT0F/LbKCyMK+ClgAQAA0B/sHtDB9tdTBrtRO6TqghVal1iqYBYFu8ZxZAcDUmGx7MDE1uUZTzKWisIFCvtzc9QaAACYXXI3kZxGxhi9eqJFO/afUn1De8p7EnlB3XDVXF1TVaZgDgeirmdkWVJxPKxImO0AAACAvmD3ueYDqh0j2O3v2M2qYHfQAWp2ODThj+8bx5CnRDDGOAYAAJAzSPQG8Tyj2tfP67cH3tLp850p7ymKh7Rx9TytWVwq/wQOZsg278zZDSoeDVDAAgAAQOd7W/V8y8sXCXZXam1iSXYFu8bI8jzZiYTsvOiEP75vHINfxdES+X38igQAAHIL1csgX//RS2po7kp5rawwok2rK7TyymL5cnw2reN4ioZ9KogxZ7ef53naXXdW5y50qyQR1vqqMtkTnNMGAACQq873tuq5lgM61P5GymA3zxfWtfkrdHWWBbuSJMeRHYnKyo9PeM6uZzx5xlNBKF/RQGSaFph+Ld2tKgjnZ3oZAAAgTQh4B0kV7laU5GnTmgpVXV4oO8e7XAfm7BYxZ3ewp3fX66ld9erqcQYe2/rro7qlulI3r6/M4MoAAACmVy4Hu8Z1Zfn9skpLJjxnV5Icz1HUX6BoXlyuO/Jzz1XdTrf+9qVv66vr/y8zhAEAmCUIeEdx+Zy4Nq2p0KLL8nN+fMHAnN1EWJEQ/8oHe3p3vR5/9nUN/32mq8fpe1wi5AUAADPOud4WPd/y8pjB7nX5K3V1YokCdnbVj8bzZEmy8/NlRyfedet6jvx2QKXREhVFC9Tc06ERxWAOe+L1p9TW26YnXn9Kdy7+UKaXAwAA0iC7qrUMC/ptLXg72H3X3ESml3PJPGNkmLM7Ks/z9NSu+tHreSM9tate71t3GeMaAADAjHCut0XPtbysQ+2vp7ye1cGuMbJcV3ZenqxEfMK1rTFGRp4KgvmKBqM5fZ7GaLqdbu1tOCBJ2ttwQLddcQtdvAAAzALZVbVl2EOfu06tHU7Oz9iVmLM7Hrvrzg4Zy5BKV4+j3XVnde3yOWlaFQAAwNQ719ui55oP6FDHGymvZ3OwK6lvzm4oJKukWJZv4qPGHM9VxBdWQTgh25p5wW6/J15/Skm3V5KUdHvp4gUAYJbIwuotc2ZCh6vjegoGmLM7HucudE/pfQAAANmm8e1g95VRg92INhSs0Jp4dga7xnFkBfyySoplB4MT/njPc2VbPpVGihX0ZdcM4ak2uHu3H128AADMDtlXxWFSmLM7cSWJ8RW6470PAAAgWzT2Nr8d7L6Z8npfsLtSa+KLszPY7Z+zW5AvOxqd+McbI894SgTjigXzpn6BWWhw924/ungBAJgdsq+aw4QwZ3fy1leVaeuvj445piES8mt9VVkaVwUAAHBpHj+7Y9RgN+aL6LpsDnb75+zGYrLisUnVtq7nKOwLqyCcP6PHMQyWqnu3H128AADMfLOj4pmhHMdTOODT3JI8JfKChLsTZNu2bqmulEb7slnSLdWVHLAGAABySqpwN+aL6P3F6/XZ+f+f1ucvy8pwV44j2x+QPadc9iQOUfM8VzJScbhIRZHCWRPuSqm7d/v1d/ECAICZKwsrO1zMTJuza4xRfUO72rp6FY8EVVk+uW6Nybh5faUk6ald9UM6eSMhv26prhy4PpU8z9PuurM6d6FbJYmw1leVESIDAIBpEfNF3x7FsEj+bAx1JRnXleX3ySotkR2Y+Jzc/nEM8WBM8WBsGlY4ft1Od9o7Zcfq3u03XV28Ld2tKgjnT+lzAgCAicvOKg8pzcQ5u3XHm7V97wk1tfUMPFYUD2nzuvmqWlCYljXcvL5S71t3WVpC16d3148Ik7f++ui0hckAAGB2ir8d7K7O5mDX82RZkp2fLzsamdRzOK6rsD+ownBBxjt2Hc/R1iM/1UeXbknr17y1t00bK64b131TGfB2O93625e+ra+u/7+MfwAAIMOys9rDEMYYeTNwzm7d8WZt23FUnhn6eFNbj7btOKotmxamLeS1bVvXLp8zra/x9O56Pf7s69Kwz7erx+l7XCLkBQAAl+yOsk1alDdffis73+lljJHlebLz8iY9Z9cznmzZKokUKuQPTcMqJ66msVZnOhpU01irdeWr0/a65dFSfeCK96ft9fo98fpTautt4xA3AACyAO8Lz3KO6ykUsDW3eGbN2TXGaPveEyPC3X6ekbbvPSFjRrkhx3iep6d21Y8IdweYvjERnueldV0AAGDmqcq7PGvDXbmu7EBAdnnZpObsGmPkeq5igTyV55VmTbjreI52nd4nSdp1ep8cb/RDfGeCwWMh9jYcULfTneEVAQAwuxHwZin37beslRdGVJSIyLZnRrDbr76hfchYhlSa2npU39CephVNr911Z4eMZUilq8fR7rqzaVoRAABA+hjXlSxLVkmx7KJCWZMYheV6jgK2X3PyyjI+a3e4msZatSf76tb2ZLtqGmszvKLpNfhQNw5xAwAg8wh4s4znGXmep8JYSOWF0RlxiFoqbV2pT/md7H3Z7tyF8XU1jPc+AACAXGA8T/KM7IIC+SZ5iJrnuZKRisNFKo4UZXzW7nCDu3f7zeQu3lSHutHFCwBAZmVXdTSLGWPkOJ5iEb/mFucpGp548ZtL4pHglN6X7UoS4zt4Yrz3AQAAZDNjjOS4svPyZJeXyo5MvMbpH8cQD8ayahzDcIO7d/vN5C7ewd27/ejiBQAgswh4s4Djegr4bc0tiSqRF5oxc3bHUlkeU1F87CK9KB5SZXl2vf1ustZXlSkSGvtMw0jIr/VVZWlaEQAAwDRxHNnBoOw5ZbIneYia6zkK2gHNyStTLMvGMQyWqnu330zs4k3VvduPLl4AADKHgDeDXNeTZFRaEFZJfkS+Scwiy1WWZWnzuvkabbSwbUmb182fMWG3bdu6pbpSGu3TsaRbqitlz6I9AAAAZhbjOLJsS1ZpiezCgknN2fU8V5axVBwpVlGkMOvGMQyXqnu330zs4k3VvduPLl4AADInuyumGcozRq5rlB8Lak5RnkKBsTs7Z6qqBYXasmnhiE7eonhIWzYtVNWCwgytbHrcvL5St99wxYhO3kjIr9tvuEI3r6/M0MoAAAAmz7h9M3LtokLZJZObs/vOOIa4yvJKFPJl/5iusbp3+82kLt6xunf70cULAEBmzM5kMYMcx1VeOKj8eFD2DOlOvRRVCwq1ZH6+dted1bkL3SpJhLW+qmzGdrLevL5S71t32az5fAEAwMxljJHlebLjMdmxyY9RcDxHEV9EBeFE1nfsDtbt9mjjZRuGPNbrJhX0BUbcF7Nz/9eu1t42bay4blz3hf2cKwEAQDrlfqWRI1zXUzBgq7goqoDfl+nlZI26483avveEmtp6Bh7bc/isNq+bP+M6ePvZtq1rl8/J9DIAAAAmz3FkR6OyEvFJjWKQ+sYx+Cy/SiMlI0LRXBAL5GlFSdXAnx3P0Y8Pb9NHl26RfwYEusOVR0v1gSven+llAACAFHLnr8hzlOsZGWNUlAirtIBwd7C6483atuPokHBXkpraerRtx1HVHW/O0MoAAACQSt+cXVtWWansgvxJhbvDxzHkYribSk1jrc50NMy4ubsAACD7EfBOk745u57ikYDmFueNmLs62xljtH3vCXkm9XXPSNv3npAxo9wAAACAtHlnzm6R7JJi2f7J1bau5yhoBzQnr0yxYN4UrzJzBs/jnUlzdwEAQG4g4J0GjuMpHLA1tzhPibzsPyAiE+ob2kd07g7X1Naj+obUpxIDAABg+hnPkzxXdiIhX3mp7HDo4h+Uguf1BcTF4SIVRQpzatbueNQ01qo92Ve3tifb6eIFAABpNbMqqwxzXU+2LZUXRVSUiMi2OURtNG1dvVN6HwAAAKaOMUZyXNmRsOyyMtl50Uk/j+s5igVjKs8rVcg/uYA4mw3u3u1HFy8AAEgnAt4p4LqejIyKE2GVMWd3XOKR8XU2j/c+AAAATBHHke33yS4vlZ0/uTm7kuR6rvyWT+XRMsWDsSleZPYY3L3bjy5eAACQTgS8l8DzjFzXKD8W1NyiPIWZsztuleUxFc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Rozdział 2.4.6
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.graphics.mosaicplot import mosaic
daneSoc = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/daneSoc.csv",header=0)
fig = plt.figure(figsize=(14,6))
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
mosaic(daneSoc, ['wyksztalcenie'], ax= ax1)
mosaic(daneSoc, ['wyksztalcenie', 'praca'], ax= ax2,)
fig.tight_layout()
fig.savefig('rys_2.7.png')
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)
Rozdział 2.5.1
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
tylkoCorsa = auta >> sift(X.Model=="Corsa") >> head()
print(tylkoCorsa)
## Marka Model Cena KM Pojemnosc Przebieg Paliwo Produkcja
## 1000 Opel Corsa 13450.0 70 1248 190000.0 diesel 2004
## 1001 Opel Corsa 25990.0 75 1300 84000.0 diesel 2008
## 1002 Opel Corsa 17990.0 80 1229 48606.0 benzyna 2007
## 1003 Opel Corsa 23999.0 60 998 63000.0 benzyna 2009
## 1004 Opel Corsa 16500.0 101 1700 118000.0 diesel 2006
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
tylkoCorsa = auta >> sift((X.Model=="Corsa") & (X.Produkcja==2010) & (X.Paliwo == "diesel"))
print(tylkoCorsa)
## Marka Model Cena KM Pojemnosc Przebieg Paliwo Produkcja
## 1035 Opel Corsa 49050.0000 75 1300 100.0 diesel 2010
## 1060 Opel Corsa 47202.3416 76 1300 53000.0 diesel 2010
## 1109 Opel Corsa 37900.0000 95 1248 8300.0 diesel 2010
## 1144 Opel Corsa 12200.0000 75 1248 11378.0 diesel 2010
## 1158 Opel Corsa 34900.0000 95 1300 24000.0 diesel 2010
## 1178 Opel Corsa 37900.0000 75 1248 18500.0 diesel 2010
Rozdział 2.5.2
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
trzyKolumny = select(auta, X.Marka, X.Model, X.Cena)
print(trzyKolumny.head())
## Marka Model Cena
## 0 Peugeot 206 8799.0
## 1 Peugeot 206 15500.0
## 2 Peugeot 206 11900.0
## 3 Peugeot 206 10999.0
## 4 Peugeot 206 11900.0
import pandas as pd
auta = pd.read_csv("/home/krz/Pulpit/Przewodnik_R2/auta.csv",header=0)
u = [auta.columns.values[i].startswith('M') for i in range(auta.shape[1])]
nam = auta.columns.values
kol = nam[u]
print(auta[kol].head())
## Marka Model
## 0 Peugeot 206
## 1 Peugeot 206
## 2 Peugeot 206
## 3 Peugeot 206
## 4 Peugeot 206
Rozdział 2.5.3
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
autaZWiekiem = auta >> mutate(Wiek = 2013 - X.Produkcja)
autaZWiekiem = autaZWiekiem >> mutate(PrzebiegNaRok = X.Przebieg/X.Wiek)
print(select(autaZWiekiem, X.Marka,X.Model,X.Cena,X.Paliwo,X.Wiek,X.PrzebiegNaRok).head())
## Marka Model Cena Paliwo Wiek PrzebiegNaRok
## 0 Peugeot 206 8799.0 benzyna 10 8500.0
## 1 Peugeot 206 15500.0 benzyna 8 14250.0
## 2 Peugeot 206 11900.0 diesel 10 21500.0
## 3 Peugeot 206 10999.0 diesel 10 16500.0
## 4 Peugeot 206 11900.0 diesel 8 18250.0
Rozdział 2.4.5
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
posortowaneAuta = arrange(auta, X.Model, X.Cena)
print(posortowaneAuta.head())
## Marka Model Cena KM Pojemnosc Przebieg Paliwo Produkcja
## 163 Peugeot 206 6330.70 90 1997 90000.0 diesel 2004
## 23 Peugeot 206 6599.00 70 1398 277000.0 diesel 2004
## 55 Peugeot 206 6900.00 90 1997 132000.0 diesel 2004
## 193 Peugeot 206 7900.00 88 1360 114000.0 benzyna 2004
## 135 Peugeot 206 8469.45 60 1124 77800.0 benzyna 2005
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
posortowaneAuta = arrange(auta, X.Model, -X.Cena)
print(posortowaneAuta.head())
## Marka Model Cena KM Pojemnosc Przebieg Paliwo Produkcja
## 16 Peugeot 206 36115.00 60 1100 100.0 benzyna 2010
## 60 Peugeot 206 33600.00 65 1100 380.0 benzyna 2011
## 88 Peugeot 206 26990.00 75 1400 14000.0 benzyna 2009
## 189 Peugeot 206 26000.00 70 1398 56000.0 diesel 2007
## 72 Peugeot 206 24723.95 102 1360 90000.0 benzyna 2005
Rozdział 2.5.5
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
tylkoKia = sift(auta, X.Marka=="Kia")
posortowane = arrange(tylkoKia, X.Cena)
czteryKolumny = select(posortowane, X.Model,X.Cena,X.Przebieg,X.Produkcja)
print(czteryKolumny.head())
## Model Cena Przebieg Produkcja
## 905 Cee'd 1026.6 28000.0 2008
## 957 Cee'd 13900.0 129000.0 2009
## 985 Cee'd 14700.0 34000.0 2009
## 936 Cee'd 14900.0 158500.0 2005
## 999 Cee'd 16900.0 9900.0 2010
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
print(
select(
arrange(
sift(auta,
X.Marka == "Kia"),
X.Cena),
X.Model, X.Cena, X.Przebieg, X.Produkcja).head()
)
## Model Cena Przebieg Produkcja
## 905 Cee'd 1026.6 28000.0 2008
## 957 Cee'd 13900.0 129000.0 2009
## 985 Cee'd 14700.0 34000.0 2009
## 936 Cee'd 14900.0 158500.0 2005
## 999 Cee'd 16900.0 9900.0 2010
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
posortowane = auta >> \
sift(X.Marka=="Kia") >> \
arrange(X.Cena) >> \
select(X.Model,X.Cena,X.Przebieg,X.Produkcja)
print(posortowane.head())
## Model Cena Przebieg Produkcja
## 905 Cee'd 1026.6 28000.0 2008
## 957 Cee'd 13900.0 129000.0 2009
## 985 Cee'd 14700.0 34000.0 2009
## 936 Cee'd 14900.0 158500.0 2005
## 999 Cee'd 16900.0 9900.0 2010
Rozdział 2.5.6
from dplython import *
from scipy import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
print(
auta >> \
summarize(sredniaCena = mean(X.Cena), \
medianaPrzebiegu = X.Przebieg.median(), \
sredniWiek = (2013 - X.Produkcja).mean(), \
liczba = X.Cena.count())
)
## liczba medianaPrzebiegu sredniWiek sredniaCena
## 0 2400 122000.0 6.638333 33340.382256
from dplython import *
from scipy import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
print(
auta >> \
group_by(X.Marka) >> \
summarize(sredniaCena = X.Cena.mean(), \
medianaPrzebiegu = X.Przebieg.median(), \
#sredniWiek = (2013 - X.Produkcja).mean(), \
liczba = X.Marka.count()) >> \
arrange(X.sredniaCena)
)
## Marka liczba medianaPrzebiegu sredniaCena
## 1 Fiat 200 75000.0 15772.328710
## 4 Peugeot 400 127750.0 17388.505657
## 3 Opel 800 120550.0 33590.744556
## 2 Kia 200 42850.0 36982.635041
## 5 Volkswagen 600 146448.0 39432.688580
## 0 Audi 200 150452.5 59891.568039
import pandas as pd
from dplython import *
auta = DplyFrame(pandas.read_csv('/home/krz/Pulpit/Przewodnik_R2/auta.csv'))
df = auta >> \
select(X.Marka,X.Cena,X.Przebieg,X.Model) >> \
group_by(X.Marka) >> \
mutate(Przebieg=X.Przebieg/X.Przebieg.mean())
print(df.head(4))
## Marka Cena Przebieg Model
## 0 Peugeot 8799.0 0.671202 206
## 1 Peugeot 15500.0 0.900200 206
## 2 Peugeot 11900.0 1.697745 206
## 3 Peugeot 10999.0 1.302921 206
Rozdział 2.5.7
import pandas as pd
from pandasdmx import Request
estat = Request('ESTAT')
tsdtr210 = estat.data('tsdtr210', params={'startPeriod': '1990'})
tsdtr210 = tsdtr210.write(tsdtr210.data.series)
tsdtr210 = tsdtr210.unstack()
lista = tsdtr210.index.tolist()
d = pd.DataFrame(lista)
d = d.drop(d.columns[[3]], axis=1)
d['values'] = [tsdtr210[i] for i in range(len(tsdtr210))]
d.columns.values[0] = 'unit'
d.columns.values[1] = 'vehicle'
d.columns.values[2] = 'geo'
d.columns.values[3] = 'time'
print(d.head(),'\n\n\tRozciągnij na kolumny - time:\n')
szeroka = d.pivot_table(index=['unit','vehicle','geo'], columns='time')
print(szeroka.query('geo=="PL"'),'\n\n\tRozciągnij na kolumny - geo:\n')
szeroka2 = d.pivot_table(index=['unit','vehicle','time'], columns='geo')
print(szeroka2.head())
## unit vehicle geo time values
## 0 PC BUS_TOT AT 1990 11.0
## 1 PC BUS_TOT AT 1991 10.6
## 2 PC BUS_TOT AT 1992 10.5
## 3 PC BUS_TOT AT 1993 10.7
## 4 PC BUS_TOT AT 1994 10.6
##
## Rozciągnij na kolumny - time:
##
## values \
## time 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
## unit vehicle geo
## PC BUS_TOT PL 28.1 25.6 24.4 23.2 20.9 19.9 19.4 17.9 17.4 16.8
## CAR PL 41.3 49.8 55.3 57.9 62.3 64.6 69.3 71.3 72.1 72.3
## TRN PL 30.6 24.6 20.3 18.9 16.8 15.5 11.3 10.8 10.5 10.9
##
## ... \
## time ... 2005 2006 2007 2008 2009 2010 2011 2012 2013
## unit vehicle geo ...
## PC BUS_TOT PL ... 22.4 21.8 20.8 19.9 17.9 16.8 15.7 14.8 14.1
## CAR PL ... 69.4 70.1 70.7 71.9 74.7 76.1 77.4 78.5 79.6
## TRN PL ... 8.2 8.2 8.5 8.2 7.4 7.1 6.9 6.7 6.2
##
##
## time 2014
## unit vehicle geo
## PC BUS_TOT PL 14.3
## CAR PL 79.9
## TRN PL 5.8
##
## [3 rows x 25 columns]
##
## Rozciągnij na kolumny - geo:
##
## values ... \
## geo AT BE BG CH CY CZ DE DK EE EL ...
## unit vehicle time ...
## PC BUS_TOT 1990 11.0 10.6 NaN 3.7 NaN NaN 9.1 11.3 NaN 32.4 ...
## 1991 10.6 10.7 NaN 3.9 NaN NaN 8.5 11.1 NaN 31.8 ...
## 1992 10.5 10.7 NaN 4.0 NaN NaN 8.3 11.1 NaN 32.2 ...
## 1993 10.7 10.9 NaN 3.9 NaN 19.1 8.1 11.2 NaN 31.7 ...
## 1994 10.6 11.8 NaN 3.9 NaN 17.0 7.3 11.3 NaN 31.1 ...
##
##
## geo NL NO PL PT RO SE SI SK TR UK
## unit vehicle time
## PC BUS_TOT 1990 NaN NaN 28.1 NaN NaN 9.5 30.6 NaN 67.4 7.0
## 1991 NaN NaN 25.6 NaN NaN 9.5 29.3 NaN 67.4 6.8
## 1992 NaN NaN 24.4 22.7 NaN 9.4 23.0 NaN 67.2 6.7
## 1993 NaN NaN 23.2 19.3 NaN 9.3 21.1 41.4 64.0 6.6
## 1994 NaN NaN 20.9 17.9 NaN 9.3 20.4 39.8 60.3 6.6
##
## [5 rows x 35 columns]
import pandas as pd
szeroka = pd.read_csv("szeroka.csv")
tidy = pd.melt(szeroka, id_vars = ['vehicle','geo'], var_name=['rok'],
value_name='wartosc')
print(tidy.tail())
## vehicle geo rok wartosc
## 2725 TRN MK 2014 0.9
## 2726 TRN EU27 2014 7.6
## 2727 TRN EU28 2014 7.6
## 2728 TRN LT 2014 1
## 2729 TRN NL 2014 9.7
Rozdział 2.5.8
import pandas as pd
from pandasdmx import Request
estat = Request('ESTAT')
tsdtr210 = estat.data('tsdtr210', params={'startPeriod': '1990'})
tsdtr210 = tsdtr210.write(tsdtr210.data.series)
tsdtr210 = tsdtr210.unstack()
lista = tsdtr210.index.tolist()
d = pd.DataFrame(lista)
d = d.drop(d.columns[[3]], axis=1)
d['values'] = [tsdtr210[i] for i in range(len(tsdtr210))]
d.columns.values[0] = 'unit'
d.columns.values[1] = 'vehicle'
d.columns.values[2] = 'geo'
d.columns.values[3] = 'time'
d['time'] = d['time'].astype(str)
d['geo_time'] = d['geo'] + ':' + d['time']
d = d.drop(['time','geo'], axis=1)
print(d.head())
## unit vehicle values geo_time
## 0 PC BUS_TOT 11.0 AT:1990
## 1 PC BUS_TOT 10.6 AT:1991
## 2 PC BUS_TOT 10.5 AT:1992
## 3 PC BUS_TOT 10.7 AT:1993
## 4 PC BUS_TOT 10.6 AT:1994
import pandas as pd
df = pd.DataFrame()
df['daty'] = ["2004-01-01","2012-04-15","2006-10-29","2010-03-03"]
df['id'] = [1,2,3,4]
df = df.join(df['daty'].str.split('-', 2, expand=True).\
rename(columns={0:'rok', 1:'miesiac', 2:'dzien'}))
print(df.head())
## daty id rok miesiac dzien
## 0 2004-01-01 1 2004 01 01
## 1 2012-04-15 2 2012 04 15
## 2 2006-10-29 3 2006 10 29
## 3 2010-03-03 4 2010 03 03
Rozdział 2.6.2
import pandas as pd
koty_ptaki = pd.read_table("http://www.biecek.pl/R/koty_ptaki.csv",sep=';',decimal=',',header=0)
print(koty_ptaki.head())
## gatunek waga dlugosc predkosc habitat zywotnosc druzyna
## 0 Tygrys 300.0 2.5 60 Azja 25 Kot
## 1 Lew 200.0 2.0 80 Afryka 29 Kot
## 2 Jaguar 100.0 1.7 90 Ameryka 15 Kot
## 3 Puma 80.0 1.7 70 Ameryka 13 Kot
## 4 Leopard 70.0 1.4 85 Azja 21 Kot
Rozdział 2.6.4
import pandas as pd
d = '{"0":{"rok":"2010","wartosc":"15.5"}, "1":{"rok":"2011","wartosc":"10.1"}, "2":{"rok":"2012","wartosc":"12.2"}}'
print(pd.read_json(d, orient='index'))
## rok wartosc
## 0 2010 15.5
## 1 2011 10.1
## 2 2012 12.2
import pandas as pd
koty_ptaki = pd.read_csv('koty_ptaki.csv')
print(koty_ptaki.iloc[0:2].to_json(orient='index'))
## {"0":{"gatunek":"Tygrys","waga":300.0,"dlugosc":2.5,"predkosc":60,"habitat":"Azja","zywotnosc":25,"druzyna":"Kot"},"1":{"gatunek":"Lew","waga":200.0,"dlugosc":2.0,"predkosc":80,"habitat":"Afryka","zywotnosc":29,"druzyna":"Kot"}}
Rozdział 2.6.5
from astropy.extern.six.moves.urllib import request
from urllib.request import urlopen
from bs4 import BeautifulSoup
url = "http://www.filmweb.pl/premiere"
soup = BeautifulSoup(request.urlopen(url), "lxml")
tags = soup.find_all('h3')
print('\tPierwsze %.0f tytułów:' % len(tags),'\n')
for t in tags:
print(t.text)
## Pierwsze 31 tytułów:
##
## Jackie (2016)
## McImperium (2016)
## Rings (2017)
## Boy 7 (2015)
## Fukushima, moja miłość (2016)
## Skazana (2015)
## LEGO® BATMAN: FILM (2017)
## Ciemniejsza strona Greya (2017)
## John Wick 2 (2017)
## To tylko koniec świata (2016)
## Szwedzka teoria miłości (2015)
## Cmentarz wspaniałości (2015)
## Nauczycielka (2016)
## Soy Nero (2016)
## Moonlight (2016)
## Milczenie (2016)
## Lekarstwo na życie (2016)
## Był sobie pies (2017)
## PolandJa (2017)
## Zerwany kłos (2016)
## Ostatnie tango (2015)
## Wściekły Nick (2016)
## Alpejska przygoda (2015)
## Ukryte działania (2016)
## Pokot (2017)
## xXx: Reaktywacja (2017)
## Planetarium (2016)
## Porady na zdrady (2016)
## Praktykant (2016)
## Barbie w świecie gier (2017)
## Wielka wyprawa Molly (2016)
Rozdział 2.6.6
import pandas as pd
data = pd.read_fwf('/home/krz/Pulpit/Przewodnik_R2/daneFWF.txt',
colspecs = [(0,1),(1,3),(3,7),(7,12),(12,14)],names = ['V1','V2','V3','V4','V5'])
print(data)
## V1 V2 V3 V4 V5
## 0 1 10 ALA STOP 13
## 1 1 11 OLA STOP 5
Rozdział 2.6.7
from astropy.extern.six.moves.urllib import request
import pandas as pd
from scipy import *
url = "http://biecek.pl/R/dane/GUS_LUDN.xlsx"
open('GUS_LUDN.xlsx', 'wb').write(request.urlopen(url).read())
ludnosc = pd.read_excel('GUS_LUDN.xlsx',sheetname='DATA')
ludnosc['VALUE'] = ludnosc['VALUE'].apply(pd.to_numeric, args=('coerce',))
print(ludnosc.head())
## TERYT_CODE TERYT_NAME AGE SEX YEAR MEASURE UNIT VALUE \
## 0 0 POLAND total total 1995 person 38609399.0
## 1 0 POLAND total total 1996 person 38639341.0
## 2 0 POLAND total total 1997 person 38659979.0
## 3 0 POLAND total total 1998 person 38666983.0
## 4 0 POLAND total total 1999 person 38263303.0
##
## ATTRIBUTE
## 0
## 1
## 2
## 3
## 4
Rozdział 2.6.8
from astropy.extern.six.moves.urllib import request
import savReaderWriter as spss
import pandas as pd
url = "http://www.biecek.pl/R/dane/daneSPSS.sav"
open('daneSPSS.sav', 'wb').write(request.urlopen(url).read())
mydata_spss = spss.SavReader('daneSPSS.sav', ioLocale='pl_PL.UTF-8',returnHeader=True)
raw_data_list = list(mydata_spss)
dane = pd.DataFrame(raw_data_list)
data = dane.rename(columns=dane.loc[0]).iloc[1:]
print(data)
## b'wiek' b'waga' b'diagnoza' b'urodzony'
## 1 18 62 b'zdrowy' b'1989-10-07'
## 2 19 66 b'zdrowy' b'1988-01-01'
## 3 21 98 b'chory' b'1986-11-06'
## 4 28 74 b'chory' b'1979-12-01'
## 5 16 55 b'chory' b'1991-06-18'
## 6 25 71 b'zdrowy' b'1982-01-30'
## 7 27 70 b'zdrowy' b'1980-04-22'
## 8 21 59 b'chory' b'1986-05-18'
## 9 24 65 b'zdrowy' b'1983-03-29'
## 10 24 86 b'chory' b'1983-03-27'
## 11 20 82 b'zdrowy' b'1987-09-19'
Rozdział 2.6.9
import scipy.io as sc
from astropy.extern.six.moves.urllib import request
url = "http://www.biecek.pl/R/dane/daneMatlab.MAT"
open('daneMatlab.MAT', 'wb').write(request.urlopen(url).read())
daneMat = sc.loadmat('daneMatlab.MAT')
print(daneMat['normalny'].shape)
## (10, 10)
Rozdział 2.6.10
import pandas as pd
#dane = pd.read_sas('daneSAS',format='sas7bdat')