From 9d9730fb53b90839ac2381922154640f8282af2d Mon Sep 17 00:00:00 2001 From: sbrugman Date: Sat, 16 May 2020 18:24:58 +0200 Subject: [PATCH] Code review --- MANIFEST.in | 3 + docs/source/introduction.rst | 2 +- example.py | 34 + python/phik/betainc.py | 30 +- python/phik/binning.py | 117 ++-- python/phik/bivariate.py | 37 +- python/phik/data_quality.py | 62 +- python/phik/decorators/pandas.py | 8 +- .../notebooks/phik_tutorial_advanced.ipynb | 634 ++++++++---------- .../phik/notebooks/phik_tutorial_basic.ipynb | 549 ++++++++------- .../phik/notebooks/phik_tutorial_spark.ipynb | 28 +- python/phik/outliers.py | 315 ++++----- python/phik/phik.py | 175 +++-- python/phik/report.py | 28 +- python/phik/significance.py | 191 +++--- python/phik/simulation.py | 114 ++-- python/phik/statistics.py | 62 +- python/phik/utils.py | 32 + python/phik/version.py | 4 +- setup.py | 32 +- tests/phik_python/bases.py | 2 +- tests/phik_python/test_phik.py | 58 +- 22 files changed, 1226 insertions(+), 1291 deletions(-) create mode 100644 example.py create mode 100644 python/phik/utils.py diff --git a/MANIFEST.in b/MANIFEST.in index 4e80c20..710b66a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -3,3 +3,6 @@ include LICENSE global-include README.rst global-exclude *.py[cod] __pycache__ *.so +exclude docs tests .readthedocs.yml +recursive-exclude tests *.py +recursive-exclude docs * diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst index 515f7b4..3f03029 100644 --- a/docs/source/introduction.rst +++ b/docs/source/introduction.rst @@ -31,7 +31,7 @@ The ``PhiK`` correlation analyzer library contains several useful functions to h * Visualizing the dependency between variables can be tricky, especially when dealing with (unordered) categorical variables. To help interpret any variable relationship found, we provide a method for the detection of significant excesses or deficits of records with respect to the expected values in a contingency table, so-called outliers, - using a statistically independent evaluation for expected frequency of records, accouncting for the uncertainty on the expectation. + using a statistically independent evaluation for expected frequency of records, accounting for the uncertainty on the expectation. We evaluate the significance of each outlier frequency in a table, and normalize and visualize these accordingly. The resulting plots we find to be very valuable to help interpret variable dependencies, and work alike for interval, ordinal and categorical variables. diff --git a/example.py b/example.py new file mode 100644 index 0000000..810cd29 --- /dev/null +++ b/example.py @@ -0,0 +1,34 @@ +import pandas as pd + +import phik +from phik import resources, report + +# open fake car insurance data +df = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') ) +df.head() + +# Pearson's correlation matrix between numeric variables (pandas functionality) +df.corr() + +# get the phi_k correlation matrix between all variables +df.phik_matrix() + +# get global correlations based on phi_k correlation matrix +df.global_phik() + +# get the significance matrix (expressed as one-sided Z) +# of the hypothesis test of each variable-pair dependency +df.significance_matrix() + +# contingency table of two columns +cols = ['mileage', 'car_size'] +df[cols].hist2d() + +# normalized residuals of contingency test applied to cols +df[cols].outlier_significance_matrix() + +# show the normalized residuals of each variable-pair +df.outlier_significance_matrices() + +# generate a phik correlation report and save as test.pdf +report.correlation_report(df, pdf_file_name='test.pdf') diff --git a/python/phik/betainc.py b/python/phik/betainc.py index f51b180..be4dc10 100644 --- a/python/phik/betainc.py +++ b/python/phik/betainc.py @@ -14,7 +14,8 @@ """ import numpy as np from scipy.special import gammaln -from typing import Union +from typing import Tuple + def contfractbeta(a: float, b: float, x: float, ITMAX: int = 5000, EPS:float = 1.0e-7) -> float: """Continued fraction form of the incomplete Beta function. @@ -58,7 +59,6 @@ def contfractbeta(a: float, b: float, x: float, ITMAX: int = 5000, EPS:float = 1 return az raise ValueError('a={0:f} or b={1:f} too large, or ITMAX={2:d} too small to compute incomplete beta function.'.format(a,b,ITMAX)) - return 0 def incompbeta(a: float, b: float, x: float) -> float: @@ -79,20 +79,20 @@ def incompbeta(a: float, b: float, x: float) -> float: :rtype: float ''' # special cases - if (x == 0): - return 0; - elif (x == 1): - return 1; + if x == 0: + return 0 + elif x == 1: + return 1 # default lbeta = gammaln(a+b) - gammaln(a) - gammaln(b) + a * np.log(x) + b * np.log(1-x) - if (x < (a+1) / (a+b+2)): + if x < (a + 1) / (a + b + 2): p = np.exp(lbeta) * contfractbeta(a, b, x) / a else: p = 1 - np.exp(lbeta) * contfractbeta(b, a, 1-x) / b return p -def log_incompbeta(a: float, b: float, x: float) -> Union[float,float]: +def log_incompbeta(a: float, b: float, x: float) -> Tuple[float,float]: '''Evaluation of logarithm of incomplete beta function Logarithm of incomplete beta function is implemented to ensure sufficient precision @@ -113,19 +113,19 @@ def log_incompbeta(a: float, b: float, x: float) -> Union[float,float]: :rtype: tuple ''' # special cases - if (x == 0): - return (-np.inf, 0) - elif (x == 1): - return (0, -np.inf) + if x == 0: + return -np.inf, 0 + elif x == 1: + return 0, -np.inf # default lbeta = gammaln(a+b) - gammaln(a) - gammaln(b) + a * np.log(x) + b * np.log(1-x) - if (x < (a+1) / (a+b+2)): + if x < (a + 1) / (a + b + 2): p = np.exp(lbeta) * contfractbeta(a, b, x) / a logp = lbeta + np.log(contfractbeta(a, b, x)) - np.log(a) logq = np.log(1-p) else: - p = 1 - np.exp(lbeta) * ( contfractbeta(b, a, 1-x) / b ) + p = 1 - np.exp(lbeta) * (contfractbeta(b, a, 1-x) / b) logp = np.log(p) logq = lbeta + np.log(contfractbeta(b, a, 1-x)) - np.log(b) - return (logp, logq) + return logp, logq diff --git a/python/phik/binning.py b/python/phik/binning.py index cd73b82..e34ad81 100644 --- a/python/phik/binning.py +++ b/python/phik/binning.py @@ -12,15 +12,17 @@ modification, are permitted according to the terms listed in the file LICENSE. """ +from typing import List, Tuple, Union, Optional import numpy as np import pandas as pd import warnings from phik import definitions as defs +from phik.utils import array_like_to_dataframe, guess_interval_cols -def bin_edges(arr, nbins:int, quantile:bool = False) -> np.ndarray: +def bin_edges(arr: Union[np.ndarray, list, pd.Series], nbins:int, quantile:bool = False) -> np.ndarray: """ Create uniform or quantile bin-edges for the input array. @@ -29,20 +31,18 @@ def bin_edges(arr, nbins:int, quantile:bool = False) -> np.ndarray: :param bool quantile: uniform bins (False) or bins based on quantiles (True) :returns: array with bin edges """ - if not isinstance(arr, (np.ndarray, list, pd.Series)): - raise TypeError('arr is not array like.') if quantile: quantiles = np.linspace(0, 1, nbins + 1) xbins = np.quantile(arr[~np.isnan(arr)], quantiles) - xbins[0] = xbins[0] - 1E-14 + xbins[0] -= 1E-14 else: xbins = np.linspace(min(arr[~np.isnan(arr)]) - 1E-14, max(arr[~np.isnan(arr)]), nbins + 1) return xbins -def bin_array(arr, bin_edges): +def bin_array(arr: Union[np.ndarray, list], bin_edges: Union[np.ndarray, list]) -> Tuple[np.ndarray, list]: """ Index the data given the bin_edges. @@ -52,10 +52,6 @@ def bin_array(arr, bin_edges): :param bin_edges: list with bin edges. :returns: indexed data """ - if not isinstance(arr, (np.ndarray, list)): - raise TypeError('arr is not a list or numpy array.') - if not isinstance(bin_edges, (np.ndarray, list)): - raise TypeError('bin_edges is not a list or numpy array.') # Bin data binned_arr = np.searchsorted(bin_edges, arr).astype(object) @@ -79,35 +75,33 @@ def bin_array(arr, bin_edges): return binned_arr, bin_labels -def bin_data(data, cols:list=[], bins=10, quantile: bool=False, retbins: bool=False): +def bin_data(data: pd.DataFrame, cols: Union[list, np.ndarray, tuple]=(), bins:Union[int,list,np.ndarray,dict]=10, + quantile: bool=False, retbins: bool=False): """ - Index the input dataframe given the bin_edges for the columns specified in cols. + Index the input DataFrame given the bin_edges for the columns specified in cols. :param DataFrame data: input data :param list cols: list of columns with numeric data which needs to be indexed :param bins: number of bins, or a list of bin edges (same for all columns), or a dictionary where per column the bins are specified. (default=10)\ E.g.: bins = {'mileage':5, 'driver_age':[18,25,35,45,55,65,125]} :param quantile: when bins is an integer, uniform bins (False) or bins based on quantiles (True) - :returns: rebinned dataframe + :returns: rebinned DataFrame :rtype: pandas.DataFrame """ - if not isinstance(data, pd.DataFrame): - raise TypeError('data is not a pandas DataFrame.') - if not isinstance(cols, (list,np.ndarray)): - raise TypeError('cols is not array-like.') - if not isinstance(bins, (int,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') + if isinstance(bins, dict): for col in cols: if col not in bins: - raise AssertionError('column {0} is not included in bins dictionary.'.format(col)) + raise ValueError('column {0} is not included in bins dictionary.'.format(col)) # check for numeric bins for col in list(set(data._get_numeric_data().columns) - set(cols)): nuq = data[col].nunique() if (nuq > 0.9 * len(data)) or (nuq > 100): warnings.warn( - "numeric variable {1:s} has {0:d} unique values. Are you sure you don't want to bin it?".format(nuq, str(col)), Warning) + "numeric variable {1:s} has {0:d} unique values. Are you sure you don't want to bin it?".format(nuq, str(col)), + Warning + ) binned_data = data.copy() @@ -133,44 +127,40 @@ def bin_data(data, cols:list=[], bins=10, quantile: bool=False, retbins: bool=Fa return binned_data -def create_correlation_overview_table(vals:dict) -> pd.DataFrame: +def create_correlation_overview_table(vals: List[Tuple[str, str, float]]) -> pd.DataFrame: """ Create overview table of phik/significance data. - :param dict vals: dictionary holding data for each variable pair formatted as {'var1:var2' : value} + :param list vals: list holding tuples of data for each variable pair formatted as ('var1', 'var2', value) :returns: symmetric table with phik/significances of all variable pairs :rtype: pandas.DataFrame """ - if not isinstance(vals, dict): - raise TypeError('vals is not a dict.') ll = [] - for k, v in vals.items(): - ll.append(k.split(':')+[v]) - ll.append(list(reversed(k.split(':')))+[v]) + for c0, c1, v in vals: + ll.append([c0, c1, v]) + ll.append([c1, c0, v]) - corr_matrix = pd.DataFrame(ll, columns=['var1', 'var2', 'vals'])\ - .pivot_table(index='var1', columns='var2', values='vals') - + corr_matrix = pd.DataFrame(ll, columns=['var1', 'var2', 'vals']).pivot_table(index='var1', columns='var2', values='vals') + corr_matrix.columns.name = None + corr_matrix.index.name = None return corr_matrix def hist2d_from_rebinned_df(data_binned:pd.DataFrame, dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: """ - Give binned 2d dataframe of two colums of rebinned input dataframe + Give binned 2d DataFrame of two columns of rebinned input DataFrame - :param df: input data. Dataframe must contain exactly two columns + :param df: input data. DataFrame must contain exactly two columns :param bool dropna: remove NaN values with True :param bool drop_underflow: do not take into account records in underflow bin when True (relevant when binning\ a numeric variable) :param bool drop_overflow: do not take into account records in overflow bin when True (relevant when binning\ a numeric variable) - :returns: histogram dataframe + :returns: histogram DataFrame """ - if not isinstance(data_binned, pd.DataFrame): - raise TypeError('data_binned is not a pandas DataFrame.') - assert len(data_binned.columns) == 2, 'DataFrame should contain only two columns' + c0, c1 = data_binned.columns if not dropna: data_binned.fillna(defs.NaN, inplace=True) @@ -180,19 +170,20 @@ def hist2d_from_rebinned_df(data_binned:pd.DataFrame, dropna:bool=True, drop_und data_binned.replace(defs.OF, np.nan, inplace=True) # create a contingency table - c0, c1 = data_binned.columns df_datahist = data_binned.groupby([c0, c1])[c0].count().to_frame().unstack().fillna(0) df_datahist.columns = df_datahist.columns.droplevel() return df_datahist -def hist2d(df, interval_cols=None, bins=10, quantile:bool=False, dropna:bool=True, drop_underflow:bool=True, - drop_overflow:bool=True, retbins:bool=False) -> pd.DataFrame: +def hist2d(df: pd.DataFrame, interval_cols:Optional[Union[list, np.ndarray]]=None, + bins:Union[int,float,list,np.ndarray,dict]=10, + quantile:bool=False, dropna:bool=True, drop_underflow:bool=True, + drop_overflow:bool=True, retbins:bool=False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, dict]]: """ - Give binned 2d dataframe of two colums of input dataframe + Give binned 2d DataFrame of two columns of input DataFrame - :param df: input data. Dataframe must contain exactly two columns + :param df: input data. DataFrame must contain exactly two columns :param interval_cols: columns with interval variables which need to be binned :param bins: number of bins, or a list of bin edges (same for all columns), or a dictionary where per column the bins are specified. (default=10)\ E.g.: bins = {'mileage':5, 'driver_age':[18,25,35,45,55,65,125]} @@ -202,27 +193,43 @@ def hist2d(df, interval_cols=None, bins=10, quantile:bool=False, dropna:bool=Tru a numeric variable) :param bool drop_overflow: do not take into account records in overflow bin when True (relevant when binning\ a numeric variable) - :returns: histogram dataframe + :returns: histogram DataFrame """ - if not isinstance(df, pd.DataFrame): - raise TypeError('df is not a pandas DataFrame.') - if not isinstance(interval_cols, (type(None), list, np.ndarray)): - raise TypeError('interval_cols is not None or a list.') - if not isinstance(bins, (int,float,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') - assert len(df.columns) == 2, 'DataFrame should contain only two columns' + if len(df.columns) != 2: + raise ValueError('DataFrame should contain only two columns') - if isinstance( interval_cols, type(None) ): - interval_cols = df.select_dtypes(include=[np.number]).columns.tolist() - if interval_cols: - print('interval_cols not set, guessing: {0:s}'.format(str(interval_cols))) - assert isinstance( interval_cols, list ), 'interval_cols is not a list.' + if interval_cols is None: + interval_cols = guess_interval_cols(df) data_binned, binning_dict = bin_data(df, interval_cols, retbins=True, bins=bins, quantile=quantile) - datahist = hist2d_from_rebinned_df(data_binned, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow) + datahist = hist2d_from_rebinned_df( + data_binned, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) if retbins: return datahist, binning_dict return datahist + + +def hist2d_from_array(x: Union[pd.Series, list, np.ndarray], y: [pd.Series, list, np.ndarray], **kwargs) -> Union[pd.DataFrame, Tuple[pd.DataFrame, dict]]: + """ + Give binned 2d DataFrame of two input arrays + + :param x: input data. First array-like. + :param y: input data. Second array-like. + :param interval_cols: columns with interval variables which need to be binned + :param bins: number of bins, or a list of bin edges (same for all columns), or a dictionary where per column the bins are specified. (default=10)\ + E.g.: bins = {'mileage':5, 'driver_age':[18,25,35,45,55,65,125]} + :param bool quantile: when the number of bins is specified, use uniform binning (False) or quantile binning (True) + :param bool dropna: remove NaN values with True + :param bool drop_underflow: do not take into account records in underflow bin when True (relevant when binning\ + a numeric variable) + :param bool drop_overflow: do not take into account records in overflow bin when True (relevant when binning\ + a numeric variable) + :returns: histogram DataFrame + """ + + df = array_like_to_dataframe(x, y) + return hist2d(df, **kwargs) diff --git a/python/phik/bivariate.py b/python/phik/bivariate.py index a185195..a2f61a6 100644 --- a/python/phik/bivariate.py +++ b/python/phik/bivariate.py @@ -4,7 +4,7 @@ Description: Convert Pearson correlation value into a chi2 value of a contingency test - matrix of a bivariate gaussion, and vice-versa. + matrix of a bivariate gaussian, and vice-versa. Calculation uses scipy's mvn library. Authors: @@ -18,8 +18,6 @@ import numpy as np from scipy.stats import mvn from scipy import optimize -# from joblib import Parallel, delayed -# from phik.config import ncores as NCORES import warnings @@ -72,7 +70,6 @@ def _mvn_array(rho: float, sx: np.ndarray, sy: np.ndarray) -> list: odd_odd = True corr = [_calc_mvnun(lower, upper, mu, S) for lower, upper in ranges] - # corr = Parallel(n_jobs=NCORES, prefer="threads")(delayed(_calc_mvnun)(lower, upper, mu, S) for lower, upper in ranges) # add second half, exclude center corr += corr if not odd_odd else corr[:-1] @@ -98,9 +95,9 @@ def bivariate_normal_theory(rho: float, nx:int=-1, ny:int=-1, n:int=1, :param int n: number of entries. default is one. :return: np.ndarray of binned bivariate normal pdf """ - assert n >= 1, 'Number of entries needs to be one or greater.' - assert nx > 1 or sx is not None, 'number of bins along x-axis is unknown' - assert ny > 1 or sy is not None, 'number of bins along y-axis is unknown' + + if n < 1: + raise ValueError('Number of entries needs to be one or greater.') if sx is None: sx = np.linspace(-5,5,nx+1) if sy is None: @@ -109,8 +106,8 @@ def bivariate_normal_theory(rho: float, nx:int=-1, ny:int=-1, n:int=1, bvn = np.zeros((ny, nx)) for i in range(len(sx) - 1): for j in range(len(sy) - 1): - lower = [sx[i], sy[j]] - upper = [sx[i+1], sy[j+1]] + lower = (sx[i], sy[j]) + upper = (sx[i+1], sy[j+1]) p = _mvn_un(rho, lower, upper) bvn[j, i] = p bvn *= n @@ -144,23 +141,24 @@ def chi2_from_phik(rho: float, n: int, subtract_from_chi2:float=0, :param int ny: number of uniform bins on y-axis. alternative to sy. :returns float: chi2 value ''' - assert nx>1 or sx is not None, 'number of bins along x-axis is unknown' - assert ny>1 or sy is not None, 'number of bins along y-axis is unknown' + if sx is None: sx = np.linspace(-5,5,nx+1) + if sy is None: sy = np.linspace(-5,5,ny+1) + if corr0 is None: corr0 = _mvn_array(0, sx, sy) if scale is None: # scale ensures that for rho=1, chi2 is the maximum possible value corr1 = _mvn_array(1, sx, sy) - chi2_one = n * sum([((c1-c0)*(c1-c0)) / c0 for c0,c1 in zip(corr0,corr1)]) + chi2_one = n * sum([((c1-c0)*(c1-c0)) / c0 for c0, c1 in zip(corr0, corr1)]) chi2_max = n * min(nx-1, ny-1) scale = (chi2_max - pedestal) / chi2_one corrr = _mvn_array(rho, sx, sy) - chi2 = pedestal + ( n * sum([((cr-c0)*(cr-c0)) / c0 for c0,cr in zip(corr0,corrr)]) ) * scale + chi2 = pedestal + (n * sum([((cr-c0)*(cr-c0)) / c0 for c0, cr in zip(corr0, corrr)])) * scale return chi2 - subtract_from_chi2 @@ -184,19 +182,24 @@ def phik_from_chi2(chi2:float, n:int, nx:int, ny:int, sx:np.ndarray=None, sy:np. :returns float: correlation coefficient ''' + if pedestal < 0: + raise ValueError('noise pedestal should be greater than zero.') - assert nx>1 or sx is not None, 'number of bins along x-axis is unknown' - assert ny>1 or sy is not None, 'number of bins along y-axis is unknown' - assert pedestal>=0, 'noise pedestal should be greater than zero.' if sx is None: sx = np.linspace(-5,5,nx+1) + elif nx <= 1: + raise ValueError('number of bins along x-axis is unknown') + if sy is None: sy = np.linspace(-5,5,ny+1) + elif ny <= 1: + raise ValueError('number of bins along y-axis is unknown') + corr0 = _mvn_array(0, sx, sy) # scale ensures that for rho=1, chi2 is the maximum possible value corr1 = _mvn_array(1, sx, sy) - if 0 in corr0 and len(corr0)>10000: + if 0 in corr0 and len(corr0) > 10000: warnings.warn('Many cells: {0:d}. Are interval variables set correctly?'.format(len(corr0))) chi2_one = n * sum([((c1-c0)*(c1-c0)) / c0 for c0,c1 in zip(corr0,corr1)]) diff --git a/python/phik/data_quality.py b/python/phik/data_quality.py index a97547e..9505031 100644 --- a/python/phik/data_quality.py +++ b/python/phik/data_quality.py @@ -15,12 +15,15 @@ import warnings import copy +from typing import Tuple +import pandas as pd +import numpy as np -def dq_check_nunique_values(df, interval_cols, dropna=True): +def dq_check_nunique_values(df: pd.DataFrame, interval_cols: list, dropna:bool=True) -> Tuple[pd.DataFrame, list]: """ - Basic data quality checks per column in a dataframe. + Basic data quality checks per column in a DataFrame. The following checks are done: @@ -37,7 +40,7 @@ def dq_check_nunique_values(df, interval_cols, dropna=True): a) 1 if dropna=False (NaN is now also considered a valid category), or b) 2 if dropna=True - The function returns a dataframe where all columns with invalid data are removed. Also the list of interval_cols + The function returns a DataFrame where all columns with invalid data are removed. Also the list of interval_cols is updated and returned. :param pd.DataFrame df: input data @@ -49,9 +52,11 @@ def dq_check_nunique_values(df, interval_cols, dropna=True): # check non-interval variable for number of unique values for col in sorted(list(set(df.columns)-set(interval_cols))): if df[col].nunique() > 100: - warnings.warn('The number of unique values of variable {0:s} is very large: {1:d}. Are you sure this is ' - 'not an interval variable? Analysis for pairs of variables including {0:s} might be slow.' - .format(col, df[col].nunique())) + warnings.warn( + 'The number of unique values of variable {0:s} is very large: {1:d}. Are you sure this is ' + 'not an interval variable? Analysis for pairs of variables including {0:s} might be slow.' + .format(col, df[col].nunique()) + ) drop_cols = [] @@ -59,29 +64,31 @@ def dq_check_nunique_values(df, interval_cols, dropna=True): for col in interval_cols: if df[col].nunique() < 2: drop_cols.append(col) - warnings.warn('Not enough unique value for variable {0:s} for analysis {1:d}. Dropping this column' - .format(col, df[col].nunique())) + warnings.warn( + 'Not enough unique value for variable {0:s} for analysis {1:d}. Dropping this column' + .format(col, df[col].nunique()) + ) # check non-interval values whether there are at least two different values OR 1 value and NaN if dropna==False for col in sorted(list(set(df.columns) - set(interval_cols))): if df[col].nunique() == 0 or (df[col].nunique() == 1 and dropna): drop_cols.append(col) - warnings.warn('Not enough unique value for variable {0:s} for analysis {1:d}. Dropping this column' - .format(col, df[col].nunique())) + warnings.warn( + 'Not enough unique value for variable {0:s} for analysis {1:d}. Dropping this column' + .format(col, df[col].nunique()) + ) df_clean = df.copy() interval_cols_clean = copy.copy(interval_cols) if len(drop_cols) > 0: - cols = sorted(list(set(df.columns) - set(drop_cols))) - interval_cols_clean = sorted(list(set(interval_cols) - set(drop_cols))) - - df_clean = df_clean[cols] + # preserves column order: https://github.com/KaveIO/PhiK/issues/1 + df_clean.drop(columns=drop_cols, inplace=True) + interval_cols_clean = [col for col in interval_cols if col not in drop_cols] return df_clean, interval_cols_clean -def dq_check_hist2d(hist2d): - +def dq_check_hist2d(hist2d: np.ndarray) -> bool: """Basic data quality checks for a contingency table The Following checks are done: @@ -90,21 +97,26 @@ def dq_check_hist2d(hist2d): 2. If the number of bins in the x and/or y direction is larger than 100 a warning is printed. - :param hist2d: contigency table + :param hist2d: contingency table :return: bool passed_check """ if 0 in hist2d.shape or 1 in hist2d.shape: - warnings.warn('Too few unique values for variable x ({0:d}) or y ({1:d})'.format( - hist2d.shape[0], hist2d.shape[1])) + warnings.warn( + 'Too few unique values for variable x ({0:d}) or y ({1:d})'.format(hist2d.shape[0], hist2d.shape[1]) + ) return False if hist2d.shape[0] > 100: - warnings.warn('The number of unique values of variable x is large: {0:d}. Are you sure this is ' - 'not an interval variable? Analysis might be slow.' - .format(hist2d.shape[0])) + warnings.warn( + 'The number of unique values of variable x is large: {0:d}. ' + 'Are you sure this is not an interval variable? Analysis might be slow.' + .format(hist2d.shape[0]) + ) if hist2d.shape[1] > 100: - warnings.warn('The number of unique values of variable y is large: {0:d}. Are you sure this is ' - 'not an interval variable? Analysis might be slow.' - .format(hist2d.shape[0])) + warnings.warn( + 'The number of unique values of variable y is large: {0:d}. ' + 'Are you sure this is not an interval variable? Analysis might be slow.' + .format(hist2d.shape[0]) + ) return True diff --git a/python/phik/decorators/pandas.py b/python/phik/decorators/pandas.py index c7ef193..6ff0d05 100644 --- a/python/phik/decorators/pandas.py +++ b/python/phik/decorators/pandas.py @@ -15,11 +15,12 @@ LICENSE. """ -from pandas import DataFrame +from pandas import DataFrame, Series # add function to create a 2d histogram -from phik.binning import hist2d +from phik.binning import hist2d, hist2d_from_array DataFrame.hist2d = hist2d +Series.hist2d = hist2d_from_array # add phik correlation matrix function from phik.phik import phik_matrix, global_phik_array @@ -31,6 +32,7 @@ DataFrame.significance_matrix = significance_matrix # outlier matrix -from phik.outliers import outlier_significance_matrices, outlier_significance_matrix +from phik.outliers import outlier_significance_matrices, outlier_significance_matrix, outlier_significance_from_array DataFrame.outlier_significance_matrices = outlier_significance_matrices DataFrame.outlier_significance_matrix = outlier_significance_matrix +Series.outlier_significance_matrix = outlier_significance_from_array diff --git a/python/phik/notebooks/phik_tutorial_advanced.ipynb b/python/phik/notebooks/phik_tutorial_advanced.ipynb index 9b3a95b..025cbe1 100644 --- a/python/phik/notebooks/phik_tutorial_advanced.ipynb +++ b/python/phik/notebooks/phik_tutorial_advanced.ipynb @@ -6,7 +6,7 @@ "source": [ "# Phi_K advanced tutorial\n", "\n", - "This notebook guides you through the more advanced functionality of the phik package. This notebook will not cover all the underlaying theory, but will just attempt to give an overview of all the options that are available. For a theoretical description the user is referred to our paper.\n", + "This notebook guides you through the more advanced functionality of the phik package. This notebook will not cover all the underlying theory, but will just attempt to give an overview of all the options that are available. For a theoretical description the user is referred to our paper.\n", "\n", "The package offers functionality on three related topics:\n", "\n", @@ -18,9 +18,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# import standard packages\n", @@ -42,9 +40,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# if one changes something in the phik-package one can automatically reload the package or module\n", @@ -64,9 +60,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') )" @@ -174,9 +168,9 @@ "source": [ "## Specify bin types\n", "\n", - "The phik-package offers a way to calculate correlations between variables of mixed types. Variable types can be inferred automatically altought we recommend to variable types to be specified by the user. \n", + "The phik-package offers a way to calculate correlations between variables of mixed types. Variable types can be inferred automatically although we recommend to variable types to be specified by the user. \n", "\n", - "Because interval type variables need to be binned in order to calculate phik and the signifiance, a list of interval variables is created." + "Because interval type variables need to be binned in order to calculate phik and the significance, a list of interval variables is created." ] }, { @@ -216,7 +210,7 @@ "source": [ "# Phik correlation matrix\n", "\n", - "Now let's start calculating the corrlation phik between pairs of variables. \n", + "Now let's start calculating the correlation phik between pairs of variables. \n", "\n", "Note that the original dataset is used as input, the binning of interval variables is done automatically." ] @@ -246,61 +240,53 @@ "\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -308,13 +294,12 @@ "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 1.000000 0.590456 0.000000 0.105506 0.000000\n", - "car_color 0.590456 1.000000 0.000000 0.389671 0.000000\n", - "car_size 0.000000 0.000000 1.000000 0.000000 0.768589\n", - "driver_age 0.105506 0.389671 0.000000 1.000000 0.000000\n", - "mileage 0.000000 0.000000 0.768589 0.000000 1.000000" + " car_color driver_age area mileage car_size\n", + "car_color 1.000000 0.389671 0.590456 0.000000 0.000000\n", + "driver_age 0.389671 1.000000 0.105506 0.000000 0.000000\n", + "area 0.590456 0.105506 1.000000 0.000000 0.000000\n", + "mileage 0.000000 0.000000 0.000000 1.000000 0.768589\n", + "car_size 0.000000 0.000000 0.000000 0.768589 1.000000" ] }, "execution_count": 6, @@ -362,61 +347,53 @@ "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
areacar_color1.0000000.3896710.5904560.0000000.000000
driver_age0.3896711.0000000.1055060.0000000.000000
car_colorarea0.5904560.1055061.0000000.0000000.3896710.000000
car_sizemileage0.0000000.0000001.0000000.0000001.0000000.768589
driver_age0.1055060.3896710.0000001.000000car_size0.000000
mileage0.0000000.0000000.7685890.0000001.000000
\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -424,13 +401,12 @@ "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 1.000000 0.590456 0.000000 0.071189 0.000000\n", - "car_color 0.590456 1.000000 0.000000 0.388350 0.000000\n", - "car_size 0.000000 0.000000 1.000000 0.000000 0.665845\n", - "driver_age 0.071189 0.388350 0.000000 1.000000 0.000000\n", - "mileage 0.000000 0.000000 0.665845 0.000000 1.000000" + " car_color driver_age area mileage car_size\n", + "car_color 1.000000 0.388350 0.590456 0.000000 0.000000\n", + "driver_age 0.388350 1.000000 0.071189 0.000000 0.000000\n", + "area 0.590456 0.071189 1.000000 0.000000 0.000000\n", + "mileage 0.000000 0.000000 0.000000 1.000000 0.665845\n", + "car_size 0.000000 0.000000 0.000000 0.665845 1.000000" ] }, "execution_count": 7, @@ -478,61 +454,53 @@ "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
areacar_color1.0000000.3883500.5904560.0000000.000000
driver_age0.3883501.0000000.0711890.0000000.000000
car_colorarea0.5904560.0711891.0000000.0000000.3883500.000000
car_sizemileage0.0000000.0000001.0000000.0000001.0000000.665845
driver_age0.0711890.388350car_size0.0000001.0000000.000000
mileage0.0000000.0000000.6658450.0000001.000000
\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -540,13 +508,12 @@ "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 1.000000 0.594173 0.067452 0.190390 0.149679\n", - "car_color 0.594173 1.000000 0.096630 0.407860 0.136265\n", - "car_size 0.067452 0.096630 1.000000 0.121585 0.770836\n", - "driver_age 0.190390 0.407860 0.121585 1.000000 0.199606\n", - "mileage 0.149679 0.136265 0.770836 0.199606 1.000000" + " car_color driver_age area mileage car_size\n", + "car_color 1.000000 0.407860 0.594172 0.136267 0.096629\n", + "driver_age 0.407860 1.000000 0.190390 0.199606 0.121585\n", + "area 0.594172 0.190390 1.000000 0.149679 0.067452\n", + "mileage 0.136267 0.199606 0.149679 1.000000 0.770836\n", + "car_size 0.096629 0.121585 0.067452 0.770836 1.000000" ] }, "execution_count": 8, @@ -565,11 +532,11 @@ "source": [ "# Statistical significance of the correlation\n", "\n", - "When assessing correlations it is good practise to evaluate both the correlation and the significance of the correlation: a large correlation may be statistically insignificant, and vice versa a small correlation may be very significant. For instance, scipy.stats.pearsonr returns both the pearson correlation and the p-value. Similarly, the phik package offeres functionality the calculate a significance matrix. Significance is defined as:\n", + "When assessing correlations it is good practise to evaluate both the correlation and the significance of the correlation: a large correlation may be statistically insignificant, and vice versa a small correlation may be very significant. For instance, scipy.stats.pearsonr returns both the pearson correlation and the p-value. Similarly, the phik package offers functionality the calculate a significance matrix. Significance is defined as:\n", "\n", "$$Z = \\Phi^{-1}(1-p)\\ ;\\quad \\Phi(z)=\\frac{1}{\\sqrt{2\\pi}} \\int_{-\\infty}^{z} e^{-t^{2}/2}\\,{\\rm d}t $$\n", "\n", - "Several corrections to the 'standard' p-value calculation are taken into account, making the method more rebust for low statistics and sparse data cases. The user is referred to our paper for more details.\n", + "Several corrections to the 'standard' p-value calculation are taken into account, making the method more robust for low statistics and sparse data cases. The user is referred to our paper for more details.\n", "\n", "Due to the corrections, the significance calculation can take a few seconds." ] @@ -599,75 +566,66 @@ "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
area1.0000000.5941730.0674520.1903900.149679
car_color0.5941731.0000000.0966300.4078600.1362650.5941720.1362670.096629
car_size0.0674520.096630driver_age0.4078601.0000000.1903900.1996060.1215850.770836
driver_agearea0.5941720.1903900.4078600.1215851.0000000.1996060.1496790.067452
mileage0.1362670.1996060.1496790.1362651.0000000.770836
car_size0.0966290.1215850.0674520.7708360.1996061.000000
\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
area72.42235437.609814-0.3217211.844649-0.703350
car_color37.60981485.463792-0.61869419.813735-0.61753385.44288719.79218537.585928-0.529541-0.580443
car_size-0.321721-0.61869469.045839-0.53441049.209947driver_age19.79218584.3327051.878689-0.660902-0.547530
driver_age1.84464919.813735-0.53441084.322135-0.721688area37.5859281.87868972.396317-0.683143-0.327484
mileage-0.703350-0.61753349.209947-0.72168891.224339-0.529541-0.660902-0.68314391.22528849.229805
car_size-0.580443-0.547530-0.32748449.22980569.050530
\n", "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 72.422354 37.609814 -0.321721 1.844649 -0.703350\n", - "car_color 37.609814 85.463792 -0.618694 19.813735 -0.617533\n", - "car_size -0.321721 -0.618694 69.045839 -0.534410 49.209947\n", - "driver_age 1.844649 19.813735 -0.534410 84.322135 -0.721688\n", - "mileage -0.703350 -0.617533 49.209947 -0.721688 91.224339" + " car_color driver_age area mileage car_size\n", + "car_color 85.442887 19.792185 37.585928 -0.529541 -0.580443\n", + "driver_age 19.792185 84.332705 1.878689 -0.660902 -0.547530\n", + "area 37.585928 1.878689 72.396317 -0.683143 -0.327484\n", + "mileage -0.529541 -0.660902 -0.683143 91.225288 49.229805\n", + "car_size -0.580443 -0.547530 -0.327484 49.229805 69.050530" ] }, "execution_count": 9, @@ -713,75 +671,66 @@ "\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
area72.38615137.605468-0.3114152.345344-0.320132
car_color37.60546885.449436-0.53945320.518680-0.32742885.47953920.47457537.578383-0.276593-0.615891
car_size-0.311415-0.53945369.053634-0.56290347.013748driver_age20.47457583.3385732.417526-0.615525-0.594356
driver_age2.34534420.518680-0.56290383.331359-0.586750area37.5783832.41752672.402361-0.358918-0.307850
mileage-0.320132-0.32742847.013748-0.58675077.765892-0.276593-0.615525-0.35891877.77329747.012811
car_size-0.615891-0.594356-0.30785047.01281169.057803
\n", "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 72.386151 37.605468 -0.311415 2.345344 -0.320132\n", - "car_color 37.605468 85.449436 -0.539453 20.518680 -0.327428\n", - "car_size -0.311415 -0.539453 69.053634 -0.562903 47.013748\n", - "driver_age 2.345344 20.518680 -0.562903 83.331359 -0.586750\n", - "mileage -0.320132 -0.327428 47.013748 -0.586750 77.765892" + " car_color driver_age area mileage car_size\n", + "car_color 85.479539 20.474575 37.578383 -0.276593 -0.615891\n", + "driver_age 20.474575 83.338573 2.417526 -0.615525 -0.594356\n", + "area 37.578383 2.417526 72.402361 -0.358918 -0.307850\n", + "mileage -0.276593 -0.615525 -0.358918 77.773297 47.012811\n", + "car_size -0.615891 -0.594356 -0.307850 47.012811 69.057803" ] }, "execution_count": 10, @@ -801,11 +750,11 @@ "source": [ "## Specify significance method\n", "\n", - "The recommended method to calculate the significance of the correlation is a hybrid approach, which uses the G-test statistic. The number of degrees of freedom and an analytical, emperical description of the $\\chi^2$ distribution are sed, based on Monte Carlo simulations. This method works well for both high as low statistics samples.\n", + "The recommended method to calculate the significance of the correlation is a hybrid approach, which uses the G-test statistic. The number of degrees of freedom and an analytical, empirical description of the $\\chi^2$ distribution are sed, based on Monte Carlo simulations. This method works well for both high as low statistics samples.\n", "\n", "Other approaches to calculate the significance are implemented:\n", - "- asymptotic: fast, but over-estimates the number of degrees of freedom for low statistics samples, leading to erronous values of the significance\n", - "- MC: Many simulated samples are needed to accurately measure significaces larger than 3, making this method computationaly expensive.\n" + "- asymptotic: fast, but over-estimates the number of degrees of freedom for low statistics samples, leading to erroneous values of the significance\n", + "- MC: Many simulated samples are needed to accurately measure significances larger than 3, making this method computationally expensive.\n" ] }, { @@ -833,75 +782,66 @@ "\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
area72.44020937.661844-0.1236781.742050-0.465002
car_color37.66184485.526574-0.33334019.68156437.661844-0.385023
car_size-0.123678-0.33334069.107448-0.79343449.332305
driver_age1.74205019.681564-0.79343484.0146541.742050-0.947153-0.793434
mileagearea37.6618441.74205072.440209-0.465002-0.123678
mileage-0.38502349.332305-0.947153-0.46500291.30112949.332305
car_size-0.333340-0.793434-0.12367849.33230569.107448
\n", "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 72.440209 37.661844 -0.123678 1.742050 -0.465002\n", - "car_color 37.661844 85.526574 -0.333340 19.681564 -0.385023\n", - "car_size -0.123678 -0.333340 69.107448 -0.793434 49.332305\n", - "driver_age 1.742050 19.681564 -0.793434 84.014654 -0.947153\n", - "mileage -0.465002 -0.385023 49.332305 -0.947153 91.301129" + " car_color driver_age area mileage car_size\n", + "car_color 85.526574 19.681564 37.661844 -0.385023 -0.333340\n", + "driver_age 19.681564 84.014654 1.742050 -0.947153 -0.793434\n", + "area 37.661844 1.742050 72.440209 -0.465002 -0.123678\n", + "mileage -0.385023 -0.947153 -0.465002 91.301129 49.332305\n", + "car_size -0.333340 -0.793434 -0.123678 49.332305 69.107448" ] }, "execution_count": 11, @@ -932,7 +872,6 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": true, "scrolled": true }, "outputs": [], @@ -954,9 +893,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from phik.simulation import sim_2d_data_patefield, sim_2d_product_multinominal, sim_2d_data" @@ -1132,9 +1069,9 @@ "data total: 2000.0\n", "sim total: 2000.0\n", "data row totals: [ 65. 462. 724. 639. 110.]\n", - "sim row totals: [ 71. 492. 728. 607. 102.]\n", + "sim row totals: [ 71. 446. 746. 630. 107.]\n", "data column totals: [388. 379. 388. 339. 281. 144. 56. 21. 2. 2.]\n", - "sim column totals: [423. 380. 382. 332. 278. 126. 58. 16. 2. 3.]\n" + "sim column totals: [374. 361. 380. 341. 319. 143. 59. 20. 2. 1.]\n" ] } ], @@ -1171,12 +1108,12 @@ "data row totals: [ 65 462 724 639 110]\n", "sim row totals: [ 65 462 724 639 110]\n", "data column totals: [388 379 388 339 281 144 56 21 2 2]\n", - "sim column totals: [389 371 393 382 248 143 43 24 2 5]\n" + "sim column totals: [385 357 401 315 300 166 49 26 1 0]\n" ] } ], "source": [ - "simdata = sim_2d_product_multinominal(inputdata.values, 'rows')\n", + "simdata = sim_2d_product_multinominal(inputdata.values, axis=0)\n", "print('data total:', inputdata.sum().sum())\n", "print('sim total:', simdata.sum().sum())\n", "print('data row totals:', inputdata.sum(axis=0).astype(int).values)\n", @@ -1226,7 +1163,7 @@ "source": [ "# Outlier significance\n", "\n", - "The normal pearson correlation between two interval variables is easy to interpret. However, the phik correlation between two variables of mixed type is not always easy to interpret, especially when it concerns categorical veriables. Therefore, functionality is providided to detect \"outliers\": excesses and deficictsover the exptected frequencies in the contigency table of two varaibles. \n" + "The normal pearson correlation between two interval variables is easy to interpret. However, the phik correlation between two variables of mixed type is not always easy to interpret, especially when it concerns categorical variables. Therefore, functionality is provided to detect \"outliers\": excesses and deficits over the expected frequencies in the contingency table of two variables. \n" ] }, { @@ -1244,15 +1181,13 @@ "\n", "$$\\phi_k = 0.77 \\, ,\\quad\\quad \\mathrm{significance} = 46.3$$\n", "\n", - "Let's use the outlier signifiance functionality to gain a better understanding of this sigificance correlation between mileage and car size.\n" + "Let's use the outlier significance functionality to gain a better understanding of this significance correlation between mileage and car size.\n" ] }, { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "c0 = 'mileage'\n", @@ -1434,7 +1369,7 @@ "## Specify binning per interval variable\n", "Binning can be set per interval variable individually. One can set the number of bins, or specify a list of bin edges. \n", "\n", - "Note: in case a bin is created without any records this bin will be automatically dropped in the phik and (outlier) significance calculations. However, in the oulier significance calculation this will currently lead to an error as the number of provided bin edges does not match the number of bins anymore." + "Note: in case a bin is created without any records this bin will be automatically dropped in the phik and (outlier) significance calculations. However, in the outlier significance calculation this will currently lead to an error as the number of provided bin edges does not match the number of bins anymore." ] }, { @@ -1691,9 +1626,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "data.loc[np.random.choice(range(len(data)), size=10), 'car_size'] = np.nan\n", @@ -1745,63 +1678,63 @@ " \n", " \n", " 100.0_1000.0\n", - " -0.736212\n", + " -0.742899\n", " -0.533211\n", " -0.053620\n", - " 2.175087\n", - " -1.461969\n", + " 2.164954\n", + " -1.464646\n", " 5.704340\n", - " -3.262069\n", + " -3.251470\n", " \n", " \n", " 1000.0_10000.0\n", - " -4.077375\n", + " -4.103830\n", " 3.532244\n", - " 0.667050\n", - " 17.885209\n", - " -6.765709\n", + " -0.575525\n", + " 18.121598\n", + " -6.775224\n", " 11.645544\n", - " -12.961110\n", + " -12.925645\n", " \n", " \n", " 10000.0_100000.0\n", - " 24.702076\n", - " 15.781631\n", - " -0.320348\n", - " -0.156340\n", - " 5.229720\n", - " -3.880851\n", + " 25.134858\n", + " 15.764609\n", + " -0.324022\n", + " -0.230920\n", + " 5.041389\n", + " -3.886172\n", " -8.209536\n", " \n", " \n", " NaN\n", - " 1.029392\n", + " 0.132649\n", " 0.488424\n", " -0.073439\n", - " -0.460500\n", - " -0.124655\n", + " -0.465651\n", + " 0.577126\n", " -0.211155\n", - " -0.633265\n", + " -0.619860\n", " \n", " \n", " OF\n", " -8.209536\n", - " -17.158980\n", - " -0.283391\n", - " -13.484761\n", - " -2.208915\n", - " -8.651800\n", - " 43.991714\n", + " -17.148294\n", + " 0.392636\n", + " -13.563245\n", + " -2.147642\n", + " -8.645644\n", + " 44.008459\n", " \n", " \n", " UF\n", - " -0.221302\n", + " -0.223635\n", " -0.153005\n", " -0.013130\n", - " -0.095429\n", - " -0.500819\n", + " -0.096640\n", + " -0.501935\n", " 2.150837\n", - " -1.332095\n", + " -1.326895\n", " \n", " \n", "\n", @@ -1809,20 +1742,20 @@ ], "text/plain": [ "car_size L M NaN S XL \\\n", - "100.0_1000.0 -0.736212 -0.533211 -0.053620 2.175087 -1.461969 \n", - "1000.0_10000.0 -4.077375 3.532244 0.667050 17.885209 -6.765709 \n", - "10000.0_100000.0 24.702076 15.781631 -0.320348 -0.156340 5.229720 \n", - "NaN 1.029392 0.488424 -0.073439 -0.460500 -0.124655 \n", - "OF -8.209536 -17.158980 -0.283391 -13.484761 -2.208915 \n", - "UF -0.221302 -0.153005 -0.013130 -0.095429 -0.500819 \n", + "100.0_1000.0 -0.742899 -0.533211 -0.053620 2.164954 -1.464646 \n", + "1000.0_10000.0 -4.103830 3.532244 -0.575525 18.121598 -6.775224 \n", + "10000.0_100000.0 25.134858 15.764609 -0.324022 -0.230920 5.041389 \n", + "NaN 0.132649 0.488424 -0.073439 -0.465651 0.577126 \n", + "OF -8.209536 -17.148294 0.392636 -13.563245 -2.147642 \n", + "UF -0.223635 -0.153005 -0.013130 -0.096640 -0.501935 \n", "\n", "car_size XS XXL \n", - "100.0_1000.0 5.704340 -3.262069 \n", - "1000.0_10000.0 11.645544 -12.961110 \n", - "10000.0_100000.0 -3.880851 -8.209536 \n", - "NaN -0.211155 -0.633265 \n", - "OF -8.651800 43.991714 \n", - "UF 2.150837 -1.332095 " + "100.0_1000.0 5.704340 -3.251470 \n", + "1000.0_10000.0 11.645544 -12.925645 \n", + "10000.0_100000.0 -3.886172 -8.209536 \n", + "NaN -0.211155 -0.619860 \n", + "OF -8.645644 44.008459 \n", + "UF 2.150837 -1.326895 " ] }, "execution_count": 23, @@ -1886,48 +1819,48 @@ " \n", " \n", " 100.0_1000.0\n", - " -0.735678\n", + " -0.745805\n", " -0.534179\n", - " 2.167263\n", + " 2.157106\n", " -1.468203\n", " 5.695755\n", - " -3.279400\n", + " -3.268662\n", " \n", " \n", " 1000.0_10000.0\n", - " -4.052167\n", - " 3.559705\n", - " 17.912001\n", - " -6.751717\n", - " 11.651568\n", - " -12.952671\n", + " -4.115892\n", + " 3.527045\n", + " 18.088628\n", + " -6.788701\n", + " 11.623068\n", + " -12.983683\n", " \n", " \n", " 10000.0_100000.0\n", - " 25.052541\n", - " 15.868135\n", - " -0.186055\n", - " 5.221619\n", - " -3.896177\n", + " 25.301416\n", + " 15.851054\n", + " -0.260876\n", + " 5.066716\n", + " -3.901544\n", " -8.209536\n", " \n", " \n", " OF\n", " -8.209536\n", - " -17.164792\n", - " -13.548056\n", - " -2.235086\n", - " -8.695547\n", - " 44.737489\n", + " -17.143254\n", + " -13.617567\n", + " -2.069227\n", + " -8.683044\n", + " 44.836608\n", " \n", " \n", " UF\n", - " -0.221109\n", + " -0.224643\n", " -0.153312\n", - " -0.096377\n", + " -0.097599\n", " -0.503408\n", " 2.146765\n", - " -1.340589\n", + " -1.335316\n", " \n", " \n", "\n", @@ -1935,18 +1868,18 @@ ], "text/plain": [ "car_size L M S XL XS \\\n", - "100.0_1000.0 -0.735678 -0.534179 2.167263 -1.468203 5.695755 \n", - "1000.0_10000.0 -4.052167 3.559705 17.912001 -6.751717 11.651568 \n", - "10000.0_100000.0 25.052541 15.868135 -0.186055 5.221619 -3.896177 \n", - "OF -8.209536 -17.164792 -13.548056 -2.235086 -8.695547 \n", - "UF -0.221109 -0.153312 -0.096377 -0.503408 2.146765 \n", + "100.0_1000.0 -0.745805 -0.534179 2.157106 -1.468203 5.695755 \n", + "1000.0_10000.0 -4.115892 3.527045 18.088628 -6.788701 11.623068 \n", + "10000.0_100000.0 25.301416 15.851054 -0.260876 5.066716 -3.901544 \n", + "OF -8.209536 -17.143254 -13.617567 -2.069227 -8.683044 \n", + "UF -0.224643 -0.153312 -0.097599 -0.503408 2.146765 \n", "\n", "car_size XXL \n", - "100.0_1000.0 -3.279400 \n", - "1000.0_10000.0 -12.952671 \n", + "100.0_1000.0 -3.268662 \n", + "1000.0_10000.0 -12.983683 \n", "10000.0_100000.0 -8.209536 \n", - "OF 44.737489 \n", - "UF -1.340589 " + "OF 44.836608 \n", + "UF -1.335316 " ] }, "execution_count": 24, @@ -1988,7 +1921,16 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.7.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } } }, "nbformat": 4, diff --git a/python/phik/notebooks/phik_tutorial_basic.ipynb b/python/phik/notebooks/phik_tutorial_basic.ipynb index 8475d44..b41fc65 100644 --- a/python/phik/notebooks/phik_tutorial_basic.ipynb +++ b/python/phik/notebooks/phik_tutorial_basic.ipynb @@ -12,15 +12,13 @@ "2. Significance matrix\n", "3. Outlier significance matrix\n", "\n", - "For more information on the undertlaying theory, the user is referred to our paper." + "For more information on the underlying theory, the user is referred to our paper." ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# import standard packages\n", @@ -41,9 +39,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# if one changes something in the phik-package one can automatically reload the package or module\n", @@ -63,9 +59,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') )" @@ -189,7 +183,7 @@ "\n", "The phik-package offers a way to calculate correlations between variables of mixed types. Variable types can be inferred automatically although we recommend variable types to be specified by the user. \n", "\n", - "Because interval type variables need to be binned in order to calculate phik and the signifiance, a list of interval variables is created." + "Because interval type variables need to be binned in order to calculate phik and the significance, a list of interval variables is created." ] }, { @@ -241,9 +235,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# bin the interval variables\n", @@ -296,12 +288,14 @@ "outputs": [ { "data": { - "image/png": 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jC4k/YcaYQuJacxX1/iha9/tv1EeLycHNMPaFn9Jx2Wlld6Nu03Y9HIC1//j9\nkntSnwc/9wMARg3dt+Se1Gfs9PPL7oLlYNQdBxQSd+ynzgVg3X3PKST+5PMPBOAzW5xSSHyAm285\nEoCVflDMa3ji++k1HPZQMZ+Dp6+VPqdWu/64QuI/sm2Ku/phxfx8pp6efj4jNjyhkPjj7zsGgOXP\nLuhc56B0LnLFXzcoJD7Aziv8ue7negSGmZmZmZmZmbU8FzDMzMzMzMzMrOW5gGFmZmZmZmZmLc8F\nDDMzMzMzMzNreS5gmJmZmZmZmVnLcwEjJ5I6JE3tZfttkrzOuJlZgZyDzczK4xxsZs3iAoaZmZmZ\nmZmZtTwXMPI1r6TLJT0m6RpJC9bulPR6ze0vS7okuz1U0rWSJmb/Nm5yv83M2oFzsJlZeZyDzaxw\nLmDkayXgvIhYBXgV+NYcPu9c4JyIGA5sD1xUUP/MzNqZc7CZWXmcg82scPOW3YE282xE3J3d/jWw\n/xw+b0tgVUnd9xeVtHBEvF77IEl7A3sDDBs2LIfumpm1labm4MVZNocum5m1jabm4KE5dNjMqscF\njHzFXNwfUnN7ELBhRLzZb/CIC4ALADo7OwP+UW8/zczaUXNz8LP1dtPMrC01+TjYzAYiX0KSr2GS\nNspu7wzc1WP/i5JWkTQI2K5m+3hgv+47ktYutptmZm3JOdjMrDzOwWZWOBcw8vUE8G1JjwGLAz/r\nsf8I4A/APcALNdv3BzolPSzpUWDfZnTWzKzNOAebmZXHOdjMCudLSHISEdOAlXvZ9emax1wDXNPL\nc18Cdiiqb2Zm7c452MysPM7BZtYsHoFhZmZmZmZmZi3PBQwzMzMzMzMza3kuYJiZmZmZmZlZy3MB\nw8zMzMzMzMxaniK8jHIVdXZ2RldXV9ndMLM6SZoUEZ1l98Pq4xxsVm3OwdXmHGxWffXmYY/AMDMz\nMzMzM7OW52VUK+ytF5YrJO78H30KgBn/WLGQ+IM+8iQA1z+1diHxt13uQQA6fn5mIfGn7XMIABuP\nPquQ+HePORiAEeufUEh8gPH3HwPAqBUPLyT+2CdPS/GXPaiY+E+fDcCIwTsWEn/8O1c1Jb5V21aD\nRpfdhbpMmDEGqG7/Ib2GzUecVnY36nbr+JR7i/qcLVr35/hGOxfzOVi0e684uOwuWA4ufnKTQuLu\nueJdAHRcdmoh8aftegQAW252ciHxAW66/SgANtj17ELi//mydHxX9LnCLn/eq5D4l29wIQCrH3ZO\nIfGnnn57fDerAAAgAElEQVQgUNzvuPv3u+pRxfT/0ZNT/6c999FC4gN0LPVC3c/1CAwzMzMzMzMz\na3kuYJiZmZmZmZlZy3MBw8zMzMzMzMxangsYZmZmZmZmZtbyXMAwMzMzMzMzs5bnAoaZmZmZmZmZ\ntTwXMOogqUPS1F623yaps454u0v6ST69MzNrb87BZmblcQ42szK5gGFmZmZmZmZmLc8FjPrNK+ly\nSY9JukbSgrU7Jf1MUpekRyQdX7N9uKR7JD0k6X5Ji/R43uck3StpyWa9EDOzCnIONjMrj3OwmZXC\nBYz6rQScFxGrAK8C3+qx/+iI6ATWBDaTtKak+YCrgQMiYi1gS+C/3U+QtB1wBPDZiHipZ4OS9s4+\nDLqmT59ezKsyM6sG52Azs/I4B5tZKVzAqN+zEXF3dvvXwCY99n9F0mTgAWA1YFVSsn8hIiYCRMSr\nEfFu9vgtgMOBz0XEv3trMCIuiIjOiOgcOnRozi/HzKxSnIPNzMrjHGxmpXABo37R131JywKHAJ+J\niDWBPwJDZhPvb8AiwIp5dtLMrE05B5uZlcc52MxK4QJG/YZJ2ii7vTNwV82+RYE3gFck/T9gVLb9\nCeCjkoYDSFpE0rzZvmeA7YHLJK1WeO/NzKrNOdjMrDzOwWZWChcw6vcE8G1JjwGLAz/r3hERD5GG\nzD0OXAHcnW1/G9gB+LGkh4AJ1FSkI+JxYBdgjKTlm/Q6zMyqyDnYzKw8zsFmVop5Z/8Q6ykipgEr\n97Lr0zWP2b2P504ENuyx+ZLsHxHxAOk6QTMz64VzsJlZeZyDzaxMHoFhZmZmZmZmZi3PBQwzMzMz\nMzMza3kuYJiZmZmZmZlZy3MBw8zMzMzMzMxaniJ6LuNsVdDZ2RldXV1ld8PM6iRpUkR0lt0Pq49z\nsFm1OQdXm3OwWfXVm4ddwKgoSdNJa2bPqSWBlwrqjuOXH78ZbTh+vvGXiYihRXXGilVHDp5bzcgZ\nRap6/6H6r8H9759zcIX5OLiSbTh+e8evp4268rALGAOEpK4iv2lw/HLjN6MNxy83vg0sVX8/Vb3/\nUP3X4P6bzVT1YwAfRzp+q8dvVhvgOTDMzMzMzMzMrAJcwDAzMzMzMzOzlucCxsBxgeO3dfxmtOH4\n5ca3gaXq76eq9x+q/xrcf7OZqn4M4ONIx2/1+M1qw3NgmJmZmZmZmVnr8wgMMzMzMzMzM2t5LmCY\nmZmZmZmZWctzAcPMzMzMzMzMWp4LGGZmZi1C0gKSViq7H1ZNkhaSNCi7vaKkbSQNLrtfZtY4Sf9T\ndh/MWoEn8bS6SNoc2B5YGngPeBK4KCL+6vjvt7E18EXg49mm54HrI2Kc4/fb7pSIWKPINsxakaQv\nAGcC80XEspLWBk6IiG1K7tock/RJoAOYt3tbRFxWWofmkqTRwLiIeE3S94B1gRMjYnLJXZsjkiYB\nmwKLA3cDE4G3I2KXUjs2FyQdAPwSeA24CFgHOCIixpfaMasMSdcB1wG/i4jXC4i/GHAk6Rjpf4AA\n/g+4Hjg1Il7OoY0lem4CJpH+HhQR/2ow/poR8XAjMawYko6JiBNyiLNkRLxUc/+rwPrAVODCaLAI\nkL1HvwP8L/AL4ChgI+Ax4OSI+Hcj8ftt2wWM9lP0G0rSKcBHgJtJyftpUgHgW1n8MQM5ftbGD4EV\ngcuA57LNSwG7An+JiAMGePwv9bULOD8ihjYSfw7a3yoiJlQ1vrWn7ORzC+C2iFgn21aZgp6kXwHL\nAw+SCsMAERH7l9eruSPp4YhYU9ImwInAGcAxEbFByV2bI5ImR8S6kvYDFoiI0yU9GBFrl923OSXp\noYhYKyui7wN8H/hVRKxbctesIiQ9D9xLyqc3AVcCf4yIt3OKfyNwC3BpRPwj2/YRYDfgMxExIoc2\nZgDP9Ni8FOmYLCJiuQbjvwc8BVwFXBkRjzYSby7bzuUEvZe4t0TEFjnGK+UEXdLfI2JYDnEmd+fN\nrCC/KXAF8HnguYg4sMH4fwKmAIsCq2S3fwNsBawVEds2Er/ftl3AaD9Fv6FqD6glzQvcHhEbS1oc\nuDMiVh/I8bO4T0bEir1sF/BkRHxigMd/B7ic9K1FT1+OiEUaiT8H7efy4VBWfGtPku6LiA0lPVBT\nwHg4ItYsu29zQtJjwKqNfqtTpu6ffVbonhIRV9T+PlqdpAdIxfhzgK9HxCNVKoLBLEWkc0nFvN9W\n6Xdg5av5O14U2BbYCRgO/IF0st7QaB5JT0REr5f69bdvLts4mHTcfmhETMm2PR0RyzYaO4v1APA1\n0s9mB+ANUqHnqoiYlkcb/bTd8DGSpJ6jR0T64u0JgDw+N4s8n5L0al+7SMXnefvYPzdt1B5LTAY2\njYg3sssKJzf6udBdHM/ODZ6LiI/33NfQC+hHwz8ca0kfi4jP1ryhPp1tv1PSgznEnyFpiWz42seA\neQAi4t9ZmwM9PsCbkoZHxMQe24cDbzo+DwNnRsTUnjskbZlDfCTd0Ncu4MOtHt8GpEck7QzMI+kT\nwP7APSX3aW5MJY1ue6HsjjTgeUk/Jx2gniZpfqo1X9h3SUPbf5sVL5YDbi25T3NrkqTxwLLAkZIW\nAWaU3CerlgCIiFeBXwG/kvRhYDRwBNDo5UjPSDqMNALjRQBJ/w/YHXi2wdgARMRZkq4GzpH0LHAs\nvX/p00ATMRU4Gjha0vrAjsBdWYHhk40En90JeiOxM9OAV0kj5f6bxb0T+EIOsbsVeT71MjC8+/1T\nK/t952EBSeuQPsMGR8QbABHxTjYCp1GDsi9/FwEWltQREdOyv7X5cojfJxcw2lPRb6iTgQckPQms\nBHwTQNJQ4CHHB2AP4LzswKv7EoylgVdIH3CN2h34WYXjf5f0wdOb7XKID2mo3FeBnte/inQNYKvH\nt4FnP9LB5Fukb8JuBH5Qao/mzpLAo5LuJ70GAKo0hwfwFWAkqcD6sqSPAoeW3Kc5FhG3A7dLWlTS\nIhHxFKkQViVfB9YGnoqI/2THLnuU3Cerlg/MexER/wTOz/41agdSIeR2zZxY80XgBlIOyUVEPAeM\nlrQNMAFYMK/YpGOV2rbuB+7PRn58Kof4hZ6gR8Q2krYDLiDl6xskvRMRPS+7aUSR51OXAcuQ3jc9\nXdFg7G4vAGdnt1+S9NGIeCHr/7s5xD8FeDy7vSdwUfY98CrA8TnE75MvIWlDknYCfpjd/RbpBD2A\nVYHjI+KCHNpYAlgO+GvkMFlRu8Wvaecj1EyC2X2tpOMXT9JY4PSI+MC3j5LuiIiGPqCLjm9WNZI2\n6217dlLd0vTBCfNmEQ1OmNcskjpJE2AuQjpBeRnYMyImldqxOSCp3zkuoiITqZoVQdICwPK9jVyt\nM97OEZHXiXJv8U8EbsgKIz33nRYRh+fUzkKkQv/ywHoRsVQecbPYhZ9PlUHSPMD8EfGfnGIpIt5V\nuix/bdL5QqEjMV3AaFNFv6GULdMWETMkzQesDkwr6iBP0rci4ryCYi9Mum7uqbyKGWrC7M6ShgGv\nZt8SdgCdwGMR8UiObXRSs1JLRDw+m6fMTexSVjkxazWSfk8/Q4OrMoJB0teBOyLiL2X3ZW5Jepr0\nO+jtMsKIBifMa5bsuvBvR8Sd2f1NgPOqMI+KpO5i8BBgPdKlhgLWBLoiYqOy+mbVJ+nkiDiqwPiX\nRcSuOcdcn5R/JkpalTQ67PGI+FOe7bQDSWsBG0VEHiNsauMWdj6VXZqyPrMeB9+f9zxSBR/L93Yu\n8nhehba++BKSNhUR7ylb+z0i3gW6gA8sqVMPSV8Efk6aS2Jf0qy8rwMrSfpmRPy+wfgH9dxEug52\nCEBEnP3BZ81V/PMi4lvZ7U1IQ7X+BqwgaZ+cPhgekFTY7M6SjiDNzv6WpDOBQ0hL5h0v6Rc5/Iw2\nA84ifXu3XhZ7caXJN78WEQ0N/1Pfq5zsL2lUNLjKiVnFnJn9/yXSHBK/zu7vRO/DS1vVMODn2UHM\nJOAO0sTIecy9VKi8JsZrAe91Fy8AIuIuSXkMFS5cRGwOoLQE5no1ExeuDhxXYtesYiT9qOcm4GvZ\nF1ZEgysj6YNzYAnYXNKHsvgNF50lHQuMAuaVNAHYgDSfzRGS1omIkxqMvyhpvpylgLG1ozFqj5Mb\nJWlwRLzTY1vD5yI9RcRDZJeBS1o5x5P0GcD6kmqLDA2PRpY0AjgP+EsWE9LvYoXsS9uGl41uwrF8\noeci/bbtERjtR9LmpEmLhgCTgb0jm1FYNUvqNBD/AVJSXYCULIZHxBOSlgGujYjOBuO/BvwJeISZ\n34Z9l2wYV0Q0dF2VZl1W6Fbg4IiYrDTZ2W8a7X8Wt9DZnSU9QqpyLkiayGi5iJieDaX7czS+UssD\nwIgs5rLA2RGxnaTuGbEbWiJMBa9yMgftNzwrv6SlSUssfhwYC5zR/SEt6XcR8cXGe2oDiaSunvmn\nt22tLhvqvBfpYObjETFPyV2arapfvlDT/11Jn81XkkaU7AC8GRE9vxhoWZIeiYjVZrfNrC9Kcyzc\nTpqss/s4svsEi4i4tMH4k4FHgYuYOXLrStIkmLlcNidpCunb/vlJJ8xLRcSrWX79c6OjqiRdSzp5\nvo80f8E7wM4R8VZO5wqFnovMpu28liHts8gANFRkUFq1a1TPc4LsmPtPEbFKvbFrYhV9LF/ouUh/\nPAKjPZ0ObB1pBvIvAxMkfS0i7qP34bFzLWaue/33iOhesuiZ7ktLGrQaqWK4EOkas/9I2q3RwkUf\nFus+MI2Ip3LqfxauuNmdSd+y/VfS26TZl/+ZNfqG8llIZZ6ImJ7d/jtpoiEiYkI2eqJRRa9ygqQv\n9bWL9C13oy4GriV9+H+dNJnXFyJNFLZMDvFt4FlI0nKRJl7sPpBZqOQ+zTGldeY3BhYGHiCdLNzZ\n75Nax1n97Atgi2Z1pE49+39sze2qfVP1sKSLmDkSaRfS5SRmc2pV0rwII4FDIuJ/JR3baOGiRidw\nAOkY79CIeFDSf/MoXNR4NyLeA/4j6W+RVlQhO/bLY1We5SNi++z27yQdDdyiNGFoHgo9F+lllM37\nu4APNRo/cy6wZV9FBtJklfWal5kjkGs9DwxuIG6too/liz4X6ZMLGO1pvsjmQYiIa7Iq33WSDien\nAxlJgyJiBqlq271tHnJY5SQi/k6adXlbUsI7p9GYPaysdJ2wgA5Ji0daQnUQ+S37U/TszpMlXUE6\nubkZuFTSONJBdh6Xq3RJ+gVwC7ANcBuApAXJlp1t0O4Uu8oJwNXA5fT+nh+SQ/yhNdda7ifpq8Ad\n2Yd/1U4YrDUcCNymdPmZSAcb+5TbpbnyJdLM5n8kfft5b0S81f9TWkP35QtVFRGbZ59hX46I35Td\nnwbtQZosr/tSwjuAn5XXHauaiHgN+K6k9YDLJf2RHJdDzo5/z5E0Jvv/RfI/p3pb0oKRJlpcr3uj\npMXIZ1nh+WuO5YmIkyQ9T/p7WziH+EWfi+wBHEzNilc1dsohPhRbZLgYmCjpKmYuvbs06cvOXzQY\nu1vRx/JFn4v0yZeQtCFJXcDno2bFCElLAX8gVVwXaTD+cGBKRLzZY3sHsElE/Lq359XZ1kKka183\niJxWdcgudan1v5HWRF4S+FREXJdDG0XP7jwvaT3zAK4hTQK0M6nC+tPI1npuIP5g0hDwVUmXCV0c\naV6VBYD/iZyWqVKBq5xImgTsFr1MJCTp2YhYusH4j5Cu036zZtuWpCXaFoqIjzYS3wYmSfMDK2d3\nH69KAaCb0nXVGwObkHLU/0XEJuX2avYkbRERt/Q1ciuPz4VmqOIlR2ZFyi5N/RZpgsevFtTG54CN\nI8dJQiXN31v+z45VPxrZHDENxD8dGB8RN/XYPhL4caOX8jbhXOQW4HsRcU8v+56OHOY1knQkaVnc\n3ooMv4mIUxqMvyqpsFA7v8YNkdO8eUUfy/dyLrIBqXiUy7lIv227gNF+spOo6ZEmtKndvhjwnWhw\n4p+56Me1NcPTHL+ENqoYXzlNviRpU+CZbERPz32dEdHVYPwDgck9h4xKWoe0vOpWjcS3gaONTp5X\nBzYFNiMNsX6WNInnMaV2bA5IOj4ijpX0y2xT98GRSJcE7tnHU1uKpFOBl0gj0N4/eIyKLAMLIOkT\nwCmkg+73R8tFRVaCsdaibInkIv4GJP0/Zv0SJvdJl5vRRhH6ORf5EGmlpEYnIV2CNL9Pw0uBzqad\nQosMVh8XMAawJpzcPhAR6zh+eW1UMb5ymnzJrCp6OXmuVaWT5z+Q5ry4E5gYPWaerwKl1a62BzqY\nOSQ8IuKE0jo1F5SWg+0pqnTyL+ku0hwe5wBfIA0VH1SFQpi1BqWlHU8HPkNagUHAoqSh9Ef0nNOg\njvhrk0ZbLsaskzu+TJrcseFJf5vUxsrAtnzw5PyxRmNb/7IvlY8Evgj8D6lo/n/A9cCpEfFyDm0s\nDBxGurxzaeBt0qqL50fEJTnG35703uyO/7Mc55vplefAGNiKPqApujpW9fjNaKMl4zdp8qX+2j+m\nyBOSouNbe4mIY7P/9yi7L42IiM9Lmo+0RPJKkp6oYBHjd6QThMnMnFC4Mt/05DFsugUsEBE3S1I2\nxPm47JJAFzBsTl1NWrlul0gTYXbP0zaadDnAhg3GvwTYJyL+XLtR0obAL4G1GoxfeBvZXBQ7kX4e\n92eblwKulHRVRJzaYPxCT9CbVAAoso3fkApqn46ZCyN8BNgt29fQCiGZy4Hfkiaz/QpproqrgO9J\nWjGHS56642/dS/yV8rykqiePwBjAVPwyRo5fchutGl9pqdy+Jl86KyKWbLhz/bdf6CgPjyKxekg6\ngHRg+hpwIbAu6dvChteDbwalNecvIy2nJtI3PrtFxB1l9mtuSJoaBS79VjRJu/a2PSIua3Zf6iXp\nHtIcKteQDvCfJ50srFRqx6wyJP2lrzkc+tuXU/y/RsQKjcRvRhuSngRW61lkzorQj+TwM7qR9Pd7\naS8n6J+JxpfwLDR+0W1kBf5ec1p/++ayjYciYq2a+xMjYrjShM+PRsTK/Ty99Pj98QgMK1Kxa+hU\nP34z2mjV+BOBqX1MvnRcQz2aGefVvnYBC7R6fBuQ9oyIcyVtDXwY+BrwK6ASBQzgbNKa808ASFoR\nuJKaGfQr4B5JazQ6QV6JhtfcHkIaQj+ZVFiqigOABYH9SUthbkE6YTCbU5MknQdcyqyTL+5GWuK5\nUWOVVja5rEf8XYFxOcRvRhszgI8BPSdy/Cj5rHLSERGn1W7IigCnScrjssii4xfdxjOSDiMVR16E\n9+c72Z2Zv+9GvSFpk4i4S2mFvH9BWkVHymWd06Lj98kFjIGt6JPbwx2/9DZaNf6XmTk8exY5DoF+\nGRje24RXkvL4cCg6vg083Tn5s8BlEfFI0QcBORvcXbwAiIgnlWZBb3mSppCGB88L7KG0lO1bzJzE\nc80y+zenImK/2vvZhHlXldSdukTERIDsrb9fRLxebo+sgnYFvg4cT4/5HchhicqI2F/SKD44f8RP\nI+JPjcZvUhvfBW6W9BdmnjAPA1YAvpND/KJP0JtRACiyjR2AI4Dbs5gA/yC9R7/SYOxu+wIXZV8m\nTCX9TSBpKPDTCsTvky8hGcAkjahnaLKkTuAMUiI9krSW8frAk8DeEdFQdbufSWGKnnQml/jNaKMd\nri2cw37UPdGspBNJk1Hd38u+0yKioeJO0fFt4FGaxPPjwLKk65vnAW6LiEqMYJB0Membu+6ltHcB\n5qnCJKT64PLas4iclo5utqyANLVKl19IWoP0rfMS2aaX6GNJbDOrXzbUf31mLZBM7J43pMHYi5NO\n0LcFep6gnxYNrgpTdPxmtWH1cQGjDTWhwHA/aYbwD5FmeT4wIq6R9BngxIjYqMH415MmhbmJHpPC\nkJaQamhSmKLjN6ONdri2cA770YyVYFaLiEeqGt/aR3YwuTbwVES8LOnDwMcj4uFsf0u/lyTND3yb\nNH8BpNVIfhoRb5fXq4FF0u+ZOenoPMAqwG8i4ojyejV3sjkwjo6IW7P7nwZOjohPltoxq5TsUrwv\nMuvJ+fUR0fDlF5LmJX3b/IH4wC96zivRqm1k7VRymdZ2oCasAiNpOWauQvIe6Vzwiojo6zLolorf\nZ7suYLSfJhQY3j+pVI/JCvM44WyHSWea8BoKnfynGZMLzWE/PJGqWabV30uSDoiIc2e3zYqjNJFq\nt3eBZyLiubL6U4+en599bTPri6QfklZDugzofv8vRbq05C8RcUCD8a8kXUZ6aY/4uwFLRMQOjcRv\nRhuadZnW50iXy1VqmdYmFQAKaUOzrgJT+/vdEWh4FZisjf1JS1HfTro09QHS73c70u/4tlaO3x/P\ngdGeBkfEWHh/KPs1AJGWJTszh/hvShpBSnoh6YsR8bvswKnhYWe0x6QzRbfRDtcWtopWnejUrKdW\nfy/tBvQsVuzeyzYrSETcno2WW580EuNvJXepHk9J+j5pAluArwJPldgfq57PRsSKPTdKupr0DXFD\nBQxgvV7iPwfcp7S6Rx6KbuMSqr1Ma6Hxm9DG1+l9FZizgUeAhvsP7AWsHRHvZXH/FBGflvRz0kie\nRkc4Fx2/Ty5gtKeiCwz7kkZ2zCCt/ftNSZeQqpJ75RD/m8CFkj5B+iPeE3KdFKY7fpGTzhT9GnpO\n/hPAi+Q3+U/R8edUM07Yih6G5mFulpeWfC9J2gnYGVhW0g01uxYhK95ac0j6BnAM6RJAAT+WdEJE\nXFxuz+bKnqTJF68jvefvzLaZzak3JQ3vnhC2xnD6mEB8Lv1L0mjg2oiYAe9fAjga+HcO8ZvRxkI9\nixcAEXGfpIVyiF/0CXozCgBFtlH0KjDd5iWd+80PLAwQEX9XfhNsFx2/z0at/RRaYIiIhyR9l/SH\n91w2FO8AAEkjc4q/HzAjIiZKWlXSQcDjEfGjPOKTvp16n6TLImJXoOH4vbUhaRNJnydNppZHG18D\nflLgRJFvAI8CEyLiJkm7AJ8E/g68VlCbvfFEmGat7x7gBWBJ4Kya7a8BD5fSo4HrUGCdiPgngNI8\nKveQ5sJqeZLmIc1/sX/ZfbFK2x34maRFmDk8f2nglWxfo3YETgPOk/RvUrHwQ6TC4Y45xO+rjcWA\nW3Nqo+rLtDajAFBkG0WvAgNwETBR0p+BTUnvp+4vU/P4cqHo+H3yHBgDjKQ9IuKXDcbYH/gW8Dhp\nwrkDIuL6bF/D12hLOhYYRSqwTQA2ICXsrYAbI+KkBuPf0MvmLUgfPETENo3Ez9q4PyLWz25/g5SM\nfguMAH6fw9C5V0hFhr8BVwBjIuKlxno9S/zLST//BUgf+AuR+v8ZUt7YrcH4LbHKSdaX+yJiw6rG\nt4HD7yWbHaUJMD/dPXGqpPlIK9lUZgJMv88tL9nlVLUTVP6jgDY+DNBdNCxCUW2o92Vab4gclmnN\nvtD8CdDrCXqjk6kWHb8ZbajAVWBq2liNNJnz1Ih4PK+4zYrfZ7suYAws6jHpZp0xpgAbRcTrkjqA\na4BfRcS5ymcSzymkwsj8pOWKloqIVyUtAPw5ItZsMP4DpKFfF5FOnAVcSVbRjojbG4nf3UbNRKcT\nSddjTs+G5d0XEWs0Gh9YD9iSdLnHNsAk0uu4LiIaGiUh6eGIWFNpFuzngY9l17gJeCiH30Gpq5xI\nWrnIRFt0fGtPkq4DfgGM7R4yXCWSvkT6BuZ/SHlVQETEoqV2bADIRilC+uxcg1QMDtLJycMRsXtJ\nXZtrkn5GOqAfQyrUAxAR15XWKauc7OSwe+6x+YDVgWmRz/Kaw4D/i4g3s+Oi3YF1SSNXL4yIdxtt\nI2tnYWAks67wML4qnw9Fn6A3qQBQeBs1bW0TEb19yZpX/BVIc5s8FhGP5hh3KGlukPdIq6i9nlfs\nvvgSkjYkqa8hu2LmOsaNGNT95oyIaUpLnF0jaRnymbPg3Swx/EfS3yJbiici/ispj6S9HumSl6OB\nQyPiQUn/zaNwUWOQ0vrRg4B5ImI6QES8ISmPD7bIPsDGA+Oza81GkSYbOhMY2mD8QdkH/kLAgqRh\ni/8iFZXyuK6tIyJOq92QFTJOk9SMa53Hk6roVY1v7ek8YA/gR5LGAL+MiCdK7tPcOB34QuQ4A7zN\nsUWy///GrBN3Xl9CXxo1BPgnaWRktyDNiWE2W5K+CPwcmCFpX+Ao4HVgJUnfjIjfN9jEn5h5mfCp\nwPLA70jv2eHkMGeLpK8Ah5Auw9ucdCnYBsDpknaJiCkNxp8H+AbpxHNsRNxTs+97EXFiI/EhFY+A\n+2riLpHzif+giLgvi70wsDIzj1dz0fM1dJO0cCMn6lnBv6fzsi8OcynYSroVGB0RL0n6Gv+fvfuO\nk7Mq+z/++RJ6NUAoUgwqIIj+KKEpTSAQLPTII0oXVBRQQcAOKlWqdFSaCk8EpDxoEmKoKgESCCUU\nEQlNSpCaUJNcvz/OGTIss5tk577nntn9vl+vfWXmvmeuc2aze3bmus+5DvwYuAU4StL5EXFGk/HX\nJC29H0x6z3s3sIykm0mz819p6gX0wAmMvmlZUu2LrkV+RBoAm/WcpLUjYiJAnonxedIa26ZmFmRv\nS1o4Il4nJRuAd5cdNJ3AyIPRqfkDwqmSnqP434UlSDMiRCqkunxEPJMH2CKSPO+JkQsMXQtcK2nh\nAuL/lrREaAAp0XO5pH8DG5GqMTfrcZW8y4mk7mqN1NaqtnV8638i4q/AX/NY96V8+0ng18DvuxYS\na0PPOXlRjYg4uuo+FCUi9unpvKTvR8RxreqPdaSfkq40LwTcA6wfEQ/nC21XAs0mMObJ71EhzYRd\nP7+3/L2ke5qMXfMjYKOIeF3S0sAfImJbSZ8kJWeaXRZ2HukC1R2kYr83R0RtJtfOQFMJjPokSP6g\nezUwX56xsls0KCA6l/H3Bk6W9F/SRcmzgMeA1SQdHhGXNRN/DjxAcxeqRgCjScuna+/pFyFtS1pU\nwnZQ3fLyg0mz5/+bPyeMA5pKYJA+9+2Vf7c2AL4ZERtK2p/0OWLXJuN3ywmMvuk6YNFagqGepJsK\niLZWdpwAACAASURBVL8naX/5d+XpcnsqbZ3TrM0i4q0ctz5hMR9piUEhIuIpYLikzwGvFhU3xx7c\nzamZpP2Rm9Xt/t91f1R7LSJOVdpujIj4j6RLSH+kfx0Rd/T87DlS2+Xkppy4gOJ3OdkHOBR4q8G5\nL3VAfOuH8lrnr5AK9d4N/AHYhDT2bVFdz+bI+DxuXE3d74Wn/rdOvuL2vrXBEbFlg4d3quGAExjW\no7rlqU/UZrJFxOO1pSVNelLSlhFxAzCZtMTj8VqtioIIeCPfnkZamkdE3CupiGV5G9SWA0s6k3T1\n/0+k9y9FXGirT4L8knRFfmT+oHsazSdgDgVWJ80+u4dUvPjR/J5yDGlJdVPqlua97xR5x40mfIo0\ne+fOiDgnt7fF7BK4c+kdSStExNOkGUi1JXlvkS5QNmuhut+tOySdm2//uofvXSGcwOiDImK/Hs7t\nXkD8p3o49/cC4jf6QEjOIhZWqLIu7p+BPxcdt5u2XidliJuNU9Q+4z218Z+62y+Tap0UFfslSeeT\n/j9razsfBi6tLRkqwJ2kokLvm3Uk6agOiG/9jKSrSG/IfkdaivFMPjVC0vjqejbHFgdeJxUrrvHU\n/9Y6rO72gsAudLng0Ae0Yntt63CS5skXwfatOzYAmL+A8F8FLsl/618BJkqaSJp9WdQHt78AoyTd\nQqqDcTmkZRgU8zvw7vchX4Q8QFJtC+ZmP5x3tUJEjMxt3aFU065ZM2qfCyRNjYhHc/zn0iSPQhxL\nSr40GkObSoRF2mVxKHBQTjwfQfFbpX+HtMz8SlLtvxtyDbpNgKY2dMgelfRj0s/MzsBEgLysvYhE\nYbdcxNPMWk5pJ5vPk9bifZZ0pfll0uyUAyPipgLaWBJ4s4gZKVXEt/4lXxX8QRHrjqsiacGIeLPq\nfth7qW5XrL5ABex2Zn2bpPWB+7qOR0qF5zeJiN8X1M4awGqkC8JPka6mF1ZgU9JngTVJxdPH5GPz\nAPN1d7FvLmL/nrQ0cVSX418FzomIpuqdSXqZ9B5PwMbAyrX3S5Luj4i1mox/LelD+WKk79HdpGT5\n1sCnImLbZuLnNv4BHBQRExqcezIiVmq2jRxrBeBUYEhEfLiImHWxlwB2570/p9dEAYXmJX2AVF9m\nTdIsmOMj4rXc5hq1+iRlcALDzFpOeaeZSDubLAz8JSK2UKrsfU00uZONWSdSAbs4VUnSv0hLwW7N\nX38rs4iXvV9OrNbMAwwBTo+I1SvqUuE6/ffE2oekKyNilxLj3xYRG5cVv1Vt9IakzbscmpBr5i0L\n7BoRZzUZf3Hgm6RZC2eSav/tAzwO/KJuBmMzbawOvBi5EH+Xc8vWarhZ63kJiZlVZV7S0pEFyNMV\nI+KJPPWsaTkD/H1gR9La0SAVS7qGlCV+uYh2uml7ZERsV1Z867PGStqFtBVyx11diIiP5iTkpsDn\ngLMkvRwRa1fctf5kArO2B3+HtD6/22WlHeryqjtgfUahV7sbWLDk+KW0IWlobcZHb0U3O/vlD/1N\nJS9ynFd5by2cK/NXYaKHXcCaTV7ovbvAjKpfgq+CdoFRyTvNlB2/J6WuTzEz68ZvgDsl/Rq4jfzH\nTGkv6aK2v/ojaSeeLSJiyYhYirQV2Uv5XFMkrdvN13qAP7BZb3yN9OHsLUmvSnpNUqEFhsskaUXg\n06QExjqk6b0jKu1U/3MEaXbbKqRaKtNIdUnanqQzJP2qu6/a4yLi2Cr7aX1K2YniViSiy2jjtyXE\nfFeugdb28SUNkPQ1ST+X9Oku537UZPjzgM1JW0b/StIpdecabbHabBtnlNBG2fG75SUkZlYJSR8H\n1iAVwmx6LV6D+A93N226p3NzEX8GcDONi2ltFBFFFKky6xiSZpKK2x4bEddU3Z/+SNK9EfFJSZsA\nPwdOAn4SERtW3LXZklTbZezTpDXVteTXcOCBiPh6JR2zPqvseiqtqNfS2zZyDYmGp4AtI2KRJvu1\nZHenSDU9Vmzn+LmN3zBrq9k9gHe3mm32/7Y2Vufb8wJnA0uTdoEZV8QyubLbaMVr6I6XkJhZJSJi\nEukKbVkel3Q4cHFtql9ee7k38GQB8R8EvhYRj3Q9IamI+NbPSBobEVvN7lgbW4dU3Xx3SUcCj5De\n8JV6Nc/eY0b+93Okba//LKkjCsNGxMUAkr5BKrQ4Pd8/l1RTxaxoZe9o04odc3rbxqakLbunNohX\nRNHfKaR6FPX9qy1vW6YD4kO5W822YheYstto5U427+EEhpn1VbsBRwI3S6r9MXsOuJZ0Ra9ZR9H9\nMryDCohv/YSkBUlXeZaWNJBZb4wWB1aorGNzKSLukfQo8Ciz3hxvTsnTke09npZ0HjAUOEHSAnTe\ncuGBpJ/92nLCRfMxs16TtExEPN/l8BElN7tHEUFqsw0iotES2962MQ54vVGtCknd1n6YC/8GtoqI\nJxrEL+IiT9nxodwP6OMlDavfBSYifibpP8A5TcZuVRuteA0NeQmJmfU7kvaJiCL2wK4kvvUtkg4B\nvg18EHiaWQmMV0lX0c+sqm9zQ9J4UlHef5B3IomIx6vtVf+Sd3UaRtpC8hFJywOfiIjrK+7aHJO0\nDylBfCPpd2Ez4KjaDA2z2WmwvECkArfrkD77NFVrS9KLpC07LwNuKKPoci6IfCKwFWmbeZESezcA\nR0bE5KLbLJKkb5J2orqnwbmDIuKMdo6f45S61ewc9qHpgqpVt1FGfCcwzKzfkfRERKzcqfGtbyrq\nTVdVJA1qtN2c2dyStBxQq9txe0Q8W2V/rLPkejxdk6crAk8BERFN7T6SZyicQVpKMBi4ArgsIsY1\nE7dLG7cBpwFXRMSMfGwAaQbptyNio6Laml0/ytymtRM/PLeyjXauo1JlfCcwzKxPknRvd6eA1SJi\ngXaOb/2TpE+R3hC/u8QzIi6prENzQWnr4p+SrphDKnL7s4h4pbpeWaeRJODLwIfzdOSVgeUi4o6K\nu2YdQtKhpGVU34uI+/Kxx/LuPEXEf/cDWf75/J/89QHgfyPiBwW08UhErDq354om6e4yizF24ofn\nVrZR9ve/FW2UEd81MMysr1oW2Ja0bWo9kaa4t3t862ck/Q74CDCRWcUYA+iIBAZwAXA/8MV8fw/g\nQkreTs36nLOBmcCWwM+A14ArgfWr7JR1jog4WdII4NRcD+GnFLvd6LsFHHMNhhOBEyV9jFR/qwgT\nJJ0NXMyswuMrAXsBdxfUxpwo+0p3fy6kOic6dSveUuM7gWFmfdV1wKIRMbHrCUk3dUB863+GAGuW\nsZ66RT4SEbvU3T9a0vt+P8xmY8OIWFfS3QAR8ZKk+Wf3JLN6EfEUMFzS9sAYUqHkotzYTZsPAUcX\n1MaewH45Xq2Y89OkQuR9qTByx314rqgNq+MEhpn1SRGxXw/ndm/3+NYv3Q8sBzxTdUd66Q1Jm0TE\n3wAkfRp4o+I+Wed5J6/1D0i1VUgzMszmWkRcK+klYHNJ2xRR0DYivltA12bXxtuknRxK3c1hDrRi\nBoN1b3IfaKPw+J22tZaZmVlftTTwgKTRkq6tfVXdqbnwdeAsSZMlTQbOBL5WbZesA/0KuApYRtIx\nwN+AY6vtknUSSXfU3d6f9DM1APippCNLavOGMuK2sg1Jjeo4FLIVbA8md3j8XrchaTNJq+fbn5Z0\nmKTP1T8mIkpZgilpaNFtSFpF0s55KdW7yngNLuJpZmbWBiRt3uh4RNzc6r7MDUn1VyMFLJJvTyNV\n/D+l9b2yTpbfAG9F+nkaGxEPVtwl6yD1RQMl3Ql8NiKmSFoEGBcRn2gyftci3gJWAx4GiIhPNhO/\nFW00SFYIuAb4Aunz4V1Nxt8euD4i3mwmTi/bLnNXkGMLKtJ6GrABaTXEaNJ4NxLYHLg7Ir7XbBuz\nab/p3fIkXR0RO+bbO5B2zbkJ+BRwXERc1Gw/u+MlJGZmZm2g3RMVPVgs/7s6qdDiNaQ3w18BvHOE\nzRVJvyLt5HBW1X2xjjWPpIGkmeYDats7R8Q0SdMLiD8ZeBX4BWmZnIBbSR/+i1J2G+OBccBbdceW\nAk4hLd/assn4I4BpkkYClwGja9vBtsBvgaa3ss9j0XsOAXtIWhQgIg5uIvxQYC1gIVJtkxUi4nVJ\nx5OKtDadwOhhBqdI/9fN+lDd7SOALSPiMUlLA2OBiwpooyEnMMyaIOkoYGpEnNTl+NeB1ztl+0Mz\nq56k15hVDGx+YD5gWkQsXl2vZi8ijgaQdAuwbkS8lu8fBfy5wq5ZZ5oA/ChPrb6KlMwYX3GfrLMs\nQfo5EhCSlo+IZ/IHz6ZrOkTE9pJ2As4HTsp1Nt6JiMebjd3CNoYDBwMnRsRIeHer2c8UFP8hUhJk\nV+BQ4EJJVwGXFZGsb8GHc4CdSNuBX8+sn5v/If1sNSsiIiTV6vvU/vbPpLgSD5uSLiRM7XJcpNkf\nzapfxjF/RDwGEBEv1L2uUngJiVkTGiUwJM0bEUVk+AuNZWadQ5KAHYCNIqKUNdtFk/Qw8MmIeCvf\nXwC4NyJWr7Zn1okkLQnsQvrAsHJErFpxl6zDSVoYWLb2QauAeIsAPydtf71eRKxYRNxWtZETOj8H\nViQlGW6KiA8XFPuuiFi37v5ypC22vwSsGBErNRn/Jbr/cD4iIpZtJn5uYzHS92cZ4LCI+I+kfxfx\nPZJ0AmmpxYKkZRcfI82I2Rz4d0R8vYA2RpISVO/bNUfSLRGxWZPxZ5CWigpYAPhQThTOD4wvYilV\nt207gWE2dyT9kLQP9/OkvbknAJ8HJgKbkKbKLUYaVK8DLomIDfJzBwP/FxGfkLQeaareosALwN75\nF/+m+lgRcXKDPnwB+BHpKu1/gS9HxHO5WvulwAeB20hT1NbL2dCvkLLt8wO3Awe2cDqfmfVC/Vru\ndpfHxi+SrpoD7Eh6I3lcdb2yTiVpA2A3UiLvwYgocnq+WWEk/T9g44g4txPbkLQO6f3oWhExqKCY\n3f7tkvShZmeSlP3hvEu89YCTSDMKvxURgwuKuzFpJsY4SR8hzfh4ArgiIjp25yVJHwDWiIjbSmvD\nCQyzOZcHsYuADUlLsO4CziUlMB6IiAPz444iz8yQNBHYKa8LO4I0LfwE0rS0HXJhqd2AbSNi35zA\neDdWN/0YCLycp599lTRQHCrpTODpiDhO0jBSQaBB+etEYOeIeEfS2aRCVl7iYtYmJNVX6p4HGAJs\nHhEbV9SluZYLw22a794SEXdX2R/rPJJOJL2Rf5S0jv6qiHi52l6ZvVeeJbcBsEI+9DRwRxT4waoV\nbXRpa7GIeLWgeFtExE1FxGoH+ftzICmJ9JWCYy8JEBEvFhm3lW3kzyUzivr5mR3XwDCbO5uS3ky9\nDu9bgzeim+f8kXQV6fj8726kYndrAWPSmMgA4Jk5iFWzIjBC0vKkGRW16ZCbkN74ERGj8hQ7SNWN\n1wPuzO0tRJpBYmbto/4K83RSEbcdqulK7+TK9U1Vr7d+71HSh4QXqu6IWSOStgHOBh4hJRUgvS/7\nqKQDI+L6dm8jfyAfTqpjcAWpXsUOkh4Czm12BkBfSl5AmiYBnJW/miZpZdKFxS2BV9IhLQ7cABwZ\nEZMLbGMr4OWi25D0QdJnmx1Is8mfzp8xLgCOiYh3monfEycwzIozrZvjI4DLJf2JNAY+IukTwKQe\nrqx2F6vmDOCUXNRpC+Co2TxewMUR8f3ZPM7MKhIR+1TdB7OqRcR5kgbmJSQL1h2/pcJumdU7Hdi6\n6wdASasAfwHW6IA2ziLVdpif9AF0AeBa4HOki2yHNBNc0krAL0mzR0YCv6x9oK3ffrNd489B+/dF\nc9vxjiBtO/rl2nJuSQNISaX/BTZqvpelt/F74GcRsWeeQbopaXn790k/Xwc0Gb9bRVU5NesvbgF2\nlLRQLu4z2zW5EfEoMAP4MbNmVjwMDMrr35A0n6SPz0U/lmBWRn6vuuN/J61Br2XvB+bjY4FdJS2T\nzy0pqX77IzOrmKQVJV0l6fn8daWkwovCmbWzvCzyFmA0cHT+96gq+2TWxbzAUw2OP01aJtwJbWwa\nEbuSCuVuR/qQ+ztSYcwidiK5gFSc8iBgeeBmSbXdQYp4/1l2fCTt3M3XLsByTYZfOiJG1Neii4gZ\nEfG/FLeLStltLFWbaRMRfwI2i4hpEfEjoLAaJI14BobZXIiIuySNAO4hLcG4cw6fOoKUKV4lx3lb\n0q7AryQtQfpdPA2YNIfxjiLN6niJNBVslXz8aOAySXuQing+C7yWi3j+CLhe0jzAO8A3gcK2/DKz\npl1IKsI7PN//Sj42tLIembXeIcD6pDpNn5H0MeDYivtkVu8C0pLc/yUVcwdYibRjzm87pI3pALku\n2p0R8Xa+P13FbIE5qK7g6EG5kPwtkrbnvdtvtmt8SO/d/9BNvAUbHJsbE3I9uot57//vXkBRtaPK\nbmNK/r7fCOxMWvZaW55U6iQJF/E060OUti2ckf8AbQycExFrV90vM5s9SRO7/r42OmbWl+UPU+vn\nAtgbRsRbkiZFxNzMUjQrlaQ1ge15b4HNayPigU5oI+/iMTwipnY5vlxuY4Mm408i7YL3Zt2xrUmF\n7xeJiOXbOX6ONwHYKyLub3DuyWhiK1ilrUb3Iy3fec//L/DbyNuRN6PsNnKNjZOANUm7J34v76a4\nFLBFRFzZTPwe23YCw6zvkLQqqWjoPMDbpK1S53SWiJlVSNJY0oyLy/KhLwH7RMRW1fXKrLUkXQXs\nA3ybVODuJWC+iPhspR0z6wckLUJKADRV6F3Sd4C7IuLmLsfXIW1/2tTMwrLj51ibAo9HxBMNzg2J\niPHNtmG94wSGWRuT9ENmTSevuTwijqmiP2ZWnlyX5gxgY9KU1X8AB0XEkz0+0ayPkrQ5qebTqNoU\nd7Oq5aW/3wd2JBXCDNKy4muA44vY9rdFbbRsm1Z7P0nbkv5/67//10TEqE5pQ9JnSHVUViLV+/sn\n8JuI+FcR8btt1z+jZmZm1ZN0MfDtiHgp318SOCki9q22Z2atJWld0rbgAfw9b89r1hYkjSbVH7s4\nIp7Nx5Yj1RbYKiK2afc2etqmlTR7t4itYMv+8NyxCQBJpwGrAZcwq1jrisCewCMR0dQuMK1oQ9Jx\npGKmY0nfo8dICYwDgWMj4vJm4vfYthMYZmZm1ZN0d0SsM7tjZn2ZpJ+QZh7+KR/akTTz8BfV9cps\nFkkPR8Tqc3uundqQ9CCwXXfbtEZEU9u0tuDDc0cnACT9MyJWa3BcwD8jYtXexm5VG/VbyUqaF7g5\nIj4taSBwa0Ss1Uz8nngXEjMzs/Ywj6SBXWZg+O+09TdfBv5frTifpONJBeKcwLB28bikw0mzI54D\nkLQssDezdnto9zbK3qb1s918eB5BukrfbIKh7Phlt/GmpPUb1KlbH3iz0RPasI2ZkpaMiBeBDwID\nACLipZwkKY3fGJmZmbWHk4HbJNWmXQ4HXO/G+pv/kLYorL3BXoBZU9zN2sFuwJHAzTmpEMBzpN0d\nvtghbZS9TWvZH547PQGwN3COpMWYlUhaCXglnytC2W0cC9wt6Z/A6sA3ACQNAu4pIH63vITEzMys\nTeRt87bMd28ocks+s04g6WrSB4QxpA9tQ4E7yG/AI+Lg6npnlkjaAIiIuFPSx4FhwIMR8ZeC4m8I\nPBQRr0hamJTMWBeYRKov8EoBbZS5Teu6wDlAow/P34yICe0cv4VtLEfd979W76RIZbaRZ4p+GPhX\nEYVl57hdJzDMzMzMrB1I2qun8xFxcav6YtaIpJ8C25Fmso8h7eRxEynZNrqIneIkTSItpZou6Xxg\nGnAlsFU+vnOzbbRC2R/QOzkB0IpdYFrUxhDqdiGJiIeKit1tm05gmJmZmZmZzZ6k+4C1ScubngVW\njIhXJS0E3B4RnyygjQdrhTQl3RUR69admxgRazcZv+O3ae3kBECLdoEptY28zfXJwMvAesDfgYHA\nO8AeZW4B7xoYZmZmZtYWJH0aOAr4EOl9qkhT9T9cZb/M6kyPiBnA65IejYhXASLiDUkzC2rjfkn7\nRMSFwD2ShkTEeEmrkT4gNuuPpG1at2iwTesfgdK2aZVUxIfnUuO3oI3Tga272wUGaGoXmBa1cRqw\nTURMyTFPybuQDCXVUWl6O+HueAaGmZmZmbUFSQ8B3wEmkKYkAxAR/62sU2Z1JN0OfCYiXpc0T0TM\nzMeXAG6sny3RRBtLkD6Abgq8QKp/8WT+OjgimiqS2Ae2aS01ftltSHoEWCMipnc5Pj/wQER8tLex\nW9WGpHtrs40kDQDurP3sS5oUER9vJn5PPAPDzMzMzNrFKxExsupOmPVgs4h4C6CWvMjmI81gaFou\n0rm3pMWBVcjbnta2VC1Ap2/TWnb8stsoexeY7tpYmbTDTRFtjJf0W9JMnu1JdWDIRWcHFBC/W56B\nYWZmZmZtQdLxpDe/fwLeqh2PiLsq65RZHyNpIGlnk+2BZfPh2jatJ0TEi03G/z5pu9dGH9D/GBHH\ntXP8VrQhaQ1gB0rYBaYVbUiaD9gfWJO0beoFETEj14JZJiIeb7aNbtt2AsPMzMzM2oGkG/PN2hvU\nWg2MLbt5ipn1gqSPADszaweJh4FLazU9Cohf2jatOX4rEgClvoZWk7RMRDxfYvylWrHczwkMMzMz\nM2sLeYvKriIiftbyzpj1UZIOBj4P3AJ8FribtJvETqQdKm6qrnf9m6SREbFdAXGWbHD4LmAdUg6g\n2Vk2xwMnRcQLeSvVPwIzSctr9oyIm5uJ32PbTmCYmZmZWTuQdGjd3QVJH7IejIh9K+qSWZ9T2wo2\nT/lfmFSUcgtJKwPXRMQ6TcZfnLRN64o59mV1586OiAObjD8sIkbl20uQtvPcALgf+E4RtULyh/Jf\nkmZdfJ9UU2J90q4kB0TE3U3E7q7Qq4DrImL53saua2Mm0HUZx4qkuh5N7+wk6b6I+ES+fSNweETc\nmXfKuTQihjQTvycu4mlmZmZmbSEiTq6/L+kkYHRF3THry+YlLR1ZAFgUICKeyLUNmnUh6YP+lcC+\nknYFds/FTzcqIP6xwKh8+2TgWeALpCUx5wE7FtDG2cBPgQ8A/yAlRoZK2iqf27iJ2HcCN5MSFl19\noIm49b4HDAW+FxH3AUh6LCJWKSj+vJLmzbucLBQRdwJExD8lLVBQG40bLjO4mZmZmVkTFiZdNTSz\n4vyGtEPF7aStWk8AkDQIaGppQfaRiNgl375a0g+BGyRtX0DsroZExNr59qmSCtkJBpivtiOSpBMi\n4gqAiBibE6vNeBD4WkQ80vWEpCJ2gSEiTpY0gvQ9eZKUjCly6cXZwF/yUpJRkk4nFV/eEphYYDvv\n4wSGmZmZmbWFPLW99iZ7ADAIcP0LswJFxOmS/gqsAZwcEQ/l41OAzQpoYgFJ89S2mY2IYyQ9Taq5\nsWgB8ZeR9F3SDIYlJClm1UWYp4D4AG9K2gZYAghJO0bE1ZI2J81cacZRdN/Pg5qM/a6IeAoYnhNH\nY0gJ4aJin5HH628Aq5HyCqsCVwO/KKqdRpzAMDMzM7N28fm629OB5/IUZTMrUERMAiaVFP7/SFfi\n/1rX3kWSngXOKCD+r4HF8u2LgKWBKZKWo7ir/18HTiQVptwW+Iaki0g1MfZvJnBEXCHpY3k5yu0R\nMbXu9JvNxK4n6WOkHVRuICUwPpKPv1tDpEnPAufT5TVIGsasJT6FcxFPMzMzMzMzK52kfSLiwk6N\nX0QbeReYb5KWkqwNHBIR1+Rzd0VEd0U+26aNVryGbtt2AsPMzMzMzMzKJumJiFi5U+MX0UZeerFx\nREyVNBi4AvhdXtpzd7O7wLSijVa8hu54CYmZmZmZmZkVQtK93Z0Clm33+C1oY57akouImCxpC+AK\nSR+i8c4k7dhGK15DQ05gmJmZmZmZWVGWJdWNeKnLcZG2JG33+GW38ZyktSNiIkCexfB54ALgE03G\nblUbrXgNDTmBYWZmZpZJGhARzVaYNzPrz64DFq19uK0n6aYOiF92G3uSihS/Kxcr3lPSeU3GblUb\nrXgNDbkGhpmZmfUbkq4GVgIWBE6PiPMlTQXOA7YmFSV7AziFtN3fC8DeEfGMpP2BA4D5gX8Be0TE\n6xW8DDMzs37JCQwzMzPrNyQtGREvSloIuBPYnJSk2C0i/ihpPuBmYIeImCJpN2DbiNhX0lIR8d8c\n5xekLT6L2BLQzMzM5oCXkJiZmVl/crCknfLtlYBVgRnAlfnY6sBawBhJAAOAZ/K5tXLi4gOk2Rmj\nW9VpMzMzcwLDzMzM+olcJX1r0tZvr+d1zAsCb9bVvRAwKSI2bhDiImDHiLhH0t7AFmX32cysL5G0\nPbBmRBwv6ShgakScVHG3rIPMU3UHzMzMzFpkCeClnLz4GLBRg8c8DAyStDGApPkkfTyfWwx4Ji8z\n+XJLemxm1odExLURcXzV/bDO5QSGmZmZ9RejgHklPQgcD4zr+oCIeBvYFThB0j3AROBT+fSPgduB\nvwMPtaTHZmYdQtJgSQ9JukjSPyX9QdLWkv4u6RFJG0jaW9KZDZ77EUmjJE2QdGtOMiPpC5Jul3S3\npL9KWjYfHyRpjKRJkn4j6XFJS+dzX5F0h6SJks6TNKC13wkrk4t4mpmZmZmZWVMkDSbt0LQOMIlU\nKPkeYD9ge2Af4GpgSER8q34JiaSxwNcj4hFJGwLHRcSWkgYCL0dESPoqsEZEHJqTIE9HxHGShgEj\ngUH560Rg54h4R9LZwLiIuKRl3wgrlWtgmJmZmZmZWREei4j7ACRNAsbm5MN9wOBGT5C0KGmm2+W5\neDLAAvnfFYERkpYnbWH9WD6+CbATQESMkvRSPr4VsB5wZ461EPB8Ya/OKucEhpmZmZmZmRXhrbrb\nM+vuz6T7z57zkGZZrN3g3BnAKRFxbS7EfNRs2hdwcUR8f457bB3FNTDMzMzMzMysEhHxKvCYpOEA\nSv5fPr0E8HS+vVfd0/4OfDE/fhtgYD4+FthV0jL53JKSPlTyS7AWcgLDzMzMzMzMqvRlYL9cv/I/\nhAAAIABJREFUPHkSsEM+fhRpackE4IW6xx8NbCPpfmA48CzwWkQ8APwIuF7SvcAYYPnWvARrBRfx\nNDMzMzMzs44haQFgRkRMz9ten9PNEhTrY1wDw8zMzMzMzDrJysAfJc0DvA3sX3F/rEU8A8PMzMzM\nzMzM2p5rYJiZmZmZmZlZ23MCw8zMzMzMzMzanhMYZmZmZmZmZtb2nMAwMzMzMzMzs7bnBIaZmZmZ\nmZmZtT0nMMzMzMzMzMys7TmBYWZmZmZmZmZtzwkMMzMzMzMzM2t7TmCYmZmZmZmZWdtzAsPMzMzM\nzMzM2p4TGGYVkHSRpF9U3Q8zs/7K47CZWXU8BltvOYFh1sYk3STpq1X3w8ysv/I4bGZWHY/B1pUT\nGGZmZmZmZmbW9pzAMGsBSetIukvSa5JGAAvm4wMlXSdpiqSX8u0V87ljgE2BMyVNlXRmPn66pCcl\nvSppgqRNK3thZmYdwuOwmVl1PAZbUZzAMCuZpPmBq4HfAUsClwO75NPzABcCHwJWBt4AzgSIiB8C\ntwLfiohFI+Jb+Tl3AmvnWJcCl0tasDWvxsys83gcNjOrjsdgK5ITGGbl2wiYDzgtIt6JiCtIAy8R\n8d+IuDIiXo+I14BjgM17ChYRv8/Pmx4RJwMLAKuX/BrMzDqZx2Ezs+p4DLbCOIFhVr4PAk9HRNQd\nexxA0sKSzpP0uKRXgVuAD0ga0F0wSYdJelDSK5JeBpYAli7zBZiZdTiPw2Zm1fEYbIVxAsOsfM8A\nK0hS3bGV87+HkjLGG0bE4sBm+XjtsfUDPXmN3+HAF4GBEfEB4JW6x5uZ2ft5HDYzq47HYCuMExhm\n5bsNmA4cLGk+STsDG+Rzi5HW+r0saUngp12e+xzw4br7i+VYU4B5Jf0EWLzMzpuZ9QEeh83MquMx\n2ArjBIZZySLibWBnYG/gRWA34E/59GnAQsALwDhgVJennw7smqsy/woYnR/zT9LUuzeBJ0t+CWZm\nHc3jsJlZdTwGW5H03qVIZmZmZmZmZmbtxzMwzMzMzMzMzKztOYFhZmZmZmZmZm3PCQwzMzMzMzMz\na3tOYJiZmZmZmZlZ25u36g5Y7yy99NIxePDgqrthZr00YcKEFyJiUNX9sN7xGGzW2TwGdzaPwWad\nr7fjsBMYHWrw4MEs9dDHq+5GQ6OnXgzA0AG7VdyTxsbMGAHA0HmGV9yTxsbMvByAYUvsW3FPujfq\nlQuA9v8ebrfytyvuSWMjnzgNSY9X3Q/rvcGDBzPwrlWq7kav1H4/2nWMnhNjZozo+P5D+46hs/Pu\nz1AH999jcGcrcwwu++e7Fb8/tTa2mX/3UuJf//alLYnfqf8Hjj9nbfR2HPYSEjMzMzMzMzNre05g\nmJmZmZmZmVnbcwLDzMzMzMzMzNqeExhmZmZmZmZm1vacwDAzs5aSdIGk5yXdX3dsuKRJkmZKGlJ3\nfClJN0qaKunMOYh9jKQnJU3tcvy7kh6QdK+ksZI+VHdulKSXJV1X1Gs0M2tXHoPNrJM5gWFmZq12\nETCsy7H7gZ2BW7ocfxP4MXDYHMb+P2CDBsfvBoZExCeBK4AT6879EthjDuObmXW6i/AYbGYdygmM\ngnXNONcd/7qkPfPtvSV9sLU9MzNrDxFxC/Bil2MPRsTDDR47LSL+RnoTPSexx0XEMw2O3xgRr+e7\n44AV686NBV6bi5dgZtaxPAabWSdzAqNFIuLciLgk390bcALDzKwa+wEj5/ZJkg6QNF7S+ClTppTQ\nLTOzfsFjsJn1mhMYc0nS9yQdnG+fKumGfHtLSX/It4+RdI+kcZKWzceOknSYpF2BIcAfJE2UtJCk\n9STdLGmCpNGSlq/q9ZmZ9WWSvkIag385t8+NiPMjYkhEDBk0aFDxnTMz6+M8BptZs5zAmHu3Apvm\n20OARSXNl4/dAiwCjIuI/5fv71//5Ii4AhgPfDki1gamA2cAu0bEesAFwDGNGnbm2cys9yRtDfwQ\n2D4i3qq6P2Zm/YnHYDMrghMYc28CsJ6kxYG3gNtIiYxNScmNt4Hr6h47eDbxVgfWAsZImgj8iLp1\ngfWceTYz6x1J6wDnkd44P191f8zM+hOPwWZWFCcw5lJEvAM8Rqpj8Q9S0uIzwEeBB4F3IiLyw2cA\n884mpIBJEbF2/vpERGxTSufNzNqApMtIyd/VJT0laT9JO0l6CtgY+LOk0XWPnwycAuydH79mD7FP\nzHEWzo89Kp/6JbAocHlevndt3XNuBS4HtsrP2bbYV2xm1j48BptZJ5vdh2tr7FbSdlL7AveRBvUJ\nERGS5uT5rwGL5dsPA4MkbRwRt+XlKKtFxKQS+m1mVrmI+FI3p67q5vGD5yL24cDhDY5v3cNzNu3u\nnJlZX+Mx2Mw6mWdg9M6twPLAbRHxHGlrqVvn4vkXAefmJSMDgF2BEyTdA0wEPlVsd83MzMzMzMw6\nm2dg9ELer3q+uvur1d1etO72FcAV+fZRdcevBK6sCzkR2Ky8HpuZ9S2SbgcW6HJ4j4i4r1V9GDPz\n8lY1VYoxM0ZU3YWmdHr/oQ/8DHV4/633+sMY3OnxAa5/+9KOjt/p/weOXw4nMMzMrONExIZV98HM\nrL/yGGxmVXECw8zMrBeGzjO86i70Su2KSqf2H9JrGDpgt6q70Wu12SOd+hre7X+H/gy161VFmztl\n/fyVPUa2Ygxu1WvYZv7dS4lfm9nRqf8HfSV+Wf+/0NzsHdfAMDMzMzMzM7O25xkYHWz01Iur7kKP\n2n19crtfgRn1ygVVd2G22v17OPKJ06rugpmZmZmZFcQzMMzMzMzMzMys7XkGRgcbtuT+VXehoVEv\n/hqAbRfZs+KeNDZ62iVA+649rs1c2W7wdyruSfdGTj4VaN/1z7WZIZvsclLFPWnsb1ceVnUXzMzM\nzMw6jmdgmJlZS0m6QNLzku6vO7akpDGSHsn/DszHl5J0o6Spks6cg9jHSHpS0tQuxzeTdJek6ZJ2\n7XJulKSXJV1X1Gs0M2tXHoPNrJM5gWFmZq12ETCsy7EjgbERsSowNt8HeBP4MTCn01b+D9igwfEn\ngL2BRmWvfwnsMYfxzcw63UV4DDazDuUEhpmZtVRE3AK82OXwDkCtMvHFwI75sdMi4m+kN9FzEntc\nRDzT4PjkiLgXmNng3FjgtTl/BWZmnctjsJl1MicwzMysHSxb96b3WWDZKjvTiKQDJI2XNH7KlClV\nd8fMrEgeg82sIziBYWZmbSUiAoiq+9FVRJwfEUMiYsigQYOq7o6ZWSk8BptZO3MCw8zM2sFzkpYH\nyP8+X3F/zMz6E4/BZtYRnMBoM5K2cBVmM+uHrgX2yrf3Aq6psC9mZv2Nx2Az6whOYLSIEn+/zazf\nk3QZcBuwuqSnJO0HHA8MlfQIsHW+X3v8ZOAUYO/8+DV7iH2ipKeAhfNjj8rH18/HhwPnSZpU95xb\ngcuBrfJzti34JZuZtQ2PwWbWyeatugN9maTBwGjgdmA94ERJXwcWAB4F9omIqZKGAacBrwN/q6a3\nZmatERFf6ubUVt08fvBcxD4cOLzB8TuBFbt5zqZzGt/MrNN5DDazTuYZAeVbFTgb2BzYD9g6ItYF\nxgPflbQg8GvgC6Qkx3LdBXL1ZTMzMzMzM+uvPAOjfI9HxDhJnwfWBP4uCWB+0vS9jwGPRcQjAJJ+\nDxzQKFBEnA+cDzBkyJDwjtlm1l9Jup00m63eHhFxX6v6MGbm5a1qqhQd3/8ZI6ruQtM6/TV0+s+Q\n9V5/GIM7PX4r2rj+7UtLjd/p/wedHr/s/9/ecgKjfNPyvwLGdJ22J2nt1nfJzKyzRcSGVffBzKy/\n8hhsZlVxAqN1xgFnSfpoRPxL0iLACsBDwGBJH4mIR4Hu1iWamVkbGTrP8Kq70Cu1KzbbLrJnxT3p\nvdHTLmHY0g0nK3aEUS+cD8B2g75ecU96Z+SUcwHYZv7dK+5J77TrVUWbO9utcFApcUc+fQZQ3hg5\netolQLl/Q2rjfFm/o7Xfoe1W/nYp8Uc+cRpQfv8dv+f4wwZ+tZT4AKNe+k2vn+saGC0SEVOAvYHL\nJN1LXj4SEW+Sloz8WdJdeN9tMzMzMzMzs/fxDIwSRcRkYK26+zcA6zd43ChSLQwzMzMzMzMza8Az\nMMzMzMzMzMys7TmBYWZmZmZmZmZtzwkMMzMzMzMzM2t7TmCYmVnbkHSIpPslTZL07XxsSUljJD2S\n/x3Yw/OXknSjpKmSzuxybj1J90n6l6RfSVI+vpmkuyRNl7Rrua/QzKx9eQw2s3bnIp4dbNSLv666\nCz2qbRPVrsbMGFF1F3o0cvKpVXdhtmrbdLWrv115WNVdsLkgaS1gf2AD4G1glKTrSDs1jY2I4yUd\nCRwJHNFNmDeBH5MKKK/V5dw5Of7twF+AYcBI4AnSLlH+gTGzfstjsJl1As/AMDOzdrEGcHtEvB4R\n04GbgZ2BHYCL82MuBnbsLkBETIuIv5HeRL9L0vLA4hExLiICuKQWJyImR8S9wMyeOifpAEnjJY2f\nMmVK716hmVn78hhsZm3PMzA62HbLHVh1Fxoa+ezZAGy70B4V96Sx0W/8DoBtF92r4p40Nnpqeo+w\n3Zo/qLgn3Rv5wLEADJ1neMU9aaw2M2Ttg9pzFsvEM75TdRfa1f3AMZKWAt4APguMB5aNiGfyY54F\nlu1F7BWAp+ruP5WPzbGIOB84H2DIkCHBk73ohZlZ++qsMfiZ2TzBzPokJzDMzKwtRMSDkk4Argem\nAROBGV0eE5Kiiv6ZmfVlHoPNrBN4CYmZmbWNiPhtRKwXEZsBLwH/BJ7L049r05Cf70Xop4EV6+6v\nmI+ZmVnmMdjM2p0TGGZm1jYkLZP/XZm09vpS4FqgtuZrL+CauY2bpz+/KmmjXPl+z97EMTPryzwG\nm1m78xISMzNrJ1fm9dfvAN+MiJclHQ/8UdJ+wOPAF3sKIGkysDgwv6QdgW0i4gHgQOAiYCFS5fuR\n+fHrA1cBA4EvSDo6Ij5exoszM2tzHoPNrK05gWFmZm0jIjZtcOy/wFZzEWNwN8fH8/5t/YiIO3nv\n1GYzs37JY7CZtTsnMBqQNDUiFpU0GPhURFyajw8B9oyIg3sRczIwJCJeKLKvZmZWjdpuN51q9LRL\nqu5CU0a9cH7VXWjayCnnVt2Fplz/9qVVd8H6sZFPn1Fq/LLHyFb8DSn7d3TkE6eVGr/s/jt+z0a9\n9JtS4/eWExg9GwzsTlr/V8scjy+70bw2UBHR437YZmb9laRtgRO6HH4sInaqoj9mZv2Jx2Azq0qf\nTGDkmROjgHHAp4A7gQuBo4FlgC+T9raeGhEn5efcD3w+IibXhToeWEPSROBi4G7gsIj4vKRFgTOA\nIUAAR0fElZK+BPwAEPDniDiiQf++C+yb7/4mIk7LfR4N3A6sl/v3eAHfDjOzPiciRpPGzMoMHbBb\nlc332pgZIwDYbqVDKu5J74188nS22ehnVXej164f9xMAtvvo9yruSe+M/NcvARi29AEV96R3+sLs\nnaq1wxi83YcPKyXuyH+flOKvfmQ58R8+HoBtF9mzlPgwa/bIsIFfLSV+7cp82d+jYR//YSnxR006\nBoDtBn29lPi12XVl/R+36v932No/KSU+wKiJvf8b3icTGNlHgeGkRMGdpJkUmwDbkxIME+cgxpHk\nhAWApC3qzv0YeCUiPpHPDZT0QVI2ej3S1lPXS9oxIq6uPUnSesA+wIakJMftkm7Oj18V2CsixvX2\nRZuZmZmZmZn1RX15G9XHIuK+vAxjEjA2IgK4j7Q0pFlbA2fV7kTES8D6wE0RMSUipgN/ADbr8rxN\ngKsiYlpETAX+BNQKJj3eU/JC0gGSxksaP2XKlAJegpmZmZmZmVln6MsJjLfqbs+suz+TNPNkOu99\n/Qu2qF89mdbTyYg4PyKGRMSQQYMGtapPZmZmZmZmZpXrywmM2ZkMrAsgaV1glQaPeQ1YrJvnjwG+\nWbsjaSBwB7C5pKUlDQC+BNzc5Xm3AjtKWljSIsBO+ZiZmZmZmZmZdaM/JzCuBJaUNAn4FvDPBo+5\nF5gh6R5J3+ly7hfAQEn3S7oH+ExEPEOqm3EjcA8wISKuqX9SRNwFXERKdtxOKuJ5d4Gvy8zMzMzM\nzKzP6ZNFPPNOImvV3d+7m3PbdPP8RfO/7wBbdjl9Uz43FdirwXMvAy5rcHxw3e1TgFN66rOZmc2S\nk8hfJe36dB+pGPLCwAhSXaPJwBdzPaJGz58fOI+0c9RM4JCIuCmfW4+UWF4I+Es+F6W9GDOzDuMx\n2MzaRX+egWFmZh1A0grAwcCQiFgLGAD8D2nG29iIWBUYm+93Z3+AvHPUUOBkSbW/gefk86vmr2Fl\nvA4zs07kMdjM2okTGGZm1gnmBRaSNC/pqt9/gB2Ai/P5i4Ede3j+msANABHxPPAyMETS8sDiETEu\nX/G7pLs43gnKzPoxj8Fm1hacwDAzs7YWEU8DJwFPAM8Ar0TE9cCyufYQwLPAsj2EuQfYXtK8klYB\n1gNWAlYAnqp73FP5WKN+eCcoM+t3PAabWTtxAsPMzHpFyVck/STfX1nSBiW0M5B0pW8V4IPAIpK+\nUv+YfOWupzXTF5DeGI8HTgP+Acwouq9mZq3iMdjM+qM+WcSzvxj57NlVd6FHo9/4XdVd6NHoqRfP\n/kEVGvnAsVV3YbbGzLy86i70aOIZXTcPsoKdTSrGtiXwM9LW01cC6xfcztbAYxExBUDSn4BPAc9J\nWj4insnTkJ/vLkBETAfe/YGQ9A/S7lMvASvWPXRF4OmC+29mVgaPwWbW73gGhpmZ9daGEfFN4E2A\nXH1+/hLaeQLYSNLCkgRsBTwIXMus3aD2Aq7p5vnk5y6Sbw8FpkfEA3n686uSNsqx9+wpjplZG/EY\nbGb9TlvPwMjViTeKiH9U3Zd2tN2y36i6Cw2NfO4coP37N2zxfSruSWOjXr0QgK03b98ZGH+9+QcA\nbDPf/1Tck8auf+d/AVj956dW3JPGHv5xn5kZ8o6kAeRpw5IGka4GFioibpd0BXAXMB24GzgfWBT4\no6T9gMeBL/YQZhlgtKSZpKt7e9SdO5BZW/iNzF9mZu3OY7CZ9TttncCIiJmSzgLWqbovZmb2Pr8C\nrgKWkXQMsCvwozIaioifAj/tcvgt0pXAOXn+ZGD1bs6NB9Zqpn9mZhXwGGxm/U5bJzCysZJ2Af6U\nCwSZmVkbiIg/SJpAegMrYMeIeLDibpmZ9Qseg82sP+qEBMbXgO8CMyS9QRqgIyIWr7ZbZmb9m6Ql\nSUXbLqs7Nl9EvFNhn7YFTuhy+LGI2KnotsbMGFF0yJYa+eTpVXehKdeP+0nVXWjayH/9suouNGXU\nC+dX3YV+rb+PwSP/fVLRId8b/+HjS40/etolpcYHGPXSb0qNX/b3aNSkY0qNP3LKuaXGL/v/uOz/\n31ETf1Zq/N5q+wRGRCxWdR/MzKyhu4CVSFXkBXwAeFbSc8D+ETGh1R2KiNHA6Fa3a2ZWAY/BZtbv\ntH0CA0DS9sBm+e5NEXFdlf0xMzMAxgBX5DesSNoG2AW4kLS934YV9q10w5Y+oOou9Ertqvlntul6\nkbRz3Hj9Eaz9rfYs0jsnJp6ZCvmut39nvoYJv0793265AyvuSe+0+zb0c6Ffj8EbffnkUuKO+8Oh\nAGyzUTlXn2uzx7Zb4aBS4gOMfPoMoPzXUNY4XBsj1/1GOfHvOifFH7Z2OTP5ajMXtlv9yFLi12a+\nlN3/LYeWN8PmhjG9/960/Taqko4HDgEeyF+HSDqu2l6ZmRlpl6h3r7RFxPXAxhExDligum6ZmfUL\nHoPNrN9p+wQG8FlgaERcEBEXAMOAz1Xcp/eRtLakz87B47aQdF2+vb2kclJzZmble0bSEZI+lL8O\nB57L2/oVvpWfmZm9h8dgM+t3OiGBAWlNX80SlfWiZ2uTki1zLCKujYhyq9+YmZVnd2BF4Or8tXI+\nNgD4YoX9MjPrDzwGm1m/0wk1MI4D7pZ0I6lA0WZAKbMWJA0GRgHjgE8Bd5LWER4NLAN8GZgEnEHa\nr3o+4ChgJPAzYCFJm+Q+PwacDiwIvAHsExEPd2lvb2BIRHxL0rLAucCH8+lvRMQ/ynidZmZFiIgX\ngO4W8f6rlX0xM+tvPAabWX/U9gmMiLhM0k3A+vnQERHxbIlNfhQYDuxLSmDsDmwCbA/8gFSH44aI\n2FfSB4A7gL8CPyEnIwAkLQ5sGhHTJW0NHEsqrNSdXwE3R8ROeerfol0fIOkA4ACAlVdemUFFvFoz\ns16SNAg4HPg4KVkLQERsWXA7qwP1e5Z+mDTmXpKPDwYmA1+MiJe6iTEf8BtgXdLfvksi4rh8bj3g\nImAh4C/AIRERRb4GM7OieQw2s/6obZeQSFq39gUsDzyVvz6Yj5XlsYi4LyJmkmZbjM2D6H2kAXob\n4EhJE4GbSH8wVm4QZwngckn3A6eS/rj0ZEvgHICImBERr3R9QEScHxFDImLIoEFOX5hZ5f4APASs\nQpqpNpmU+C1URDwcEWtHxNrAesDrwFWk2XhjI2JVYCw9z84bDiwQEZ/IMb6WZ91BGnv3B1bNX8OK\nfg1mZiXwGGxm/U47z8DoaW+kIH3gL8Nbdbdn1t2fSfp+zQB2abAcpOtWVT8HbswzKgaTkh1mZn3J\nUhHxW0mHRMTNwM2SCn/z3MVWwKMR8bikHYAt8vGLSePsEd08L4BFJM1Lusr3NvCqpOWBxXPVfiRd\nAuxIWhr4Hl1nwS1d1CsyM+udfj0GL796US/JzDpJ287AiIjP9PBVVvJiTowGDpIkAEnr5OOvAYvV\nPW4J4Ol8e+85iDsW+EaOOUBSuxYrNTOreSf/+4ykz+XxcMmS2/wf4LJ8e9mIeCbffhZYtofnXQFM\nA54BngBOiogXgRVIs/tqnsrH3sez4MyszXgMNrN+p20TGDWS5pN0sKQr8te38jq6qvycVLzzXkmT\n8n2AG4E1JU2UtBtwInCcpLuZs5kuhwCfkXQfMAFYs/ium5kV6hc52XoocBhpffN3ympM0vykekSX\ndz2Xl/r1tGZ6A9IMug+SplsfKunDPTzezKzdeQw2s36nnZeQ1JxDShicne/vkY99teiGImIyaXeR\n2v29uzn3tQbPfZFZhUZrVqu7/aP8uJvIy0ki4iJS0SIi4jlgh9733systSLiunzzFeAzXc9L+n6t\nSFtBtgPuyuMlwHOSlo+IZ/I05Od7eO7uwKiIeAd4XtLfgSHAraRtCGtWZNbsOTOztuUx2Mz6o7af\ngQGsHxF7RcQN+Wsf3p8oMDOz9jO84HhfYtbUZYBrgb3y7b2Aa3p47hPk2kmSFgE2Ah7K059flbRR\nXhq452zimJl1Co/BZtbndEICY4akj9Tu5OlmMyrsj5mZzRkVFii94R0K/Knu8PHAUEmPAFvn+905\nC1g0L/27E7gwIu7N5w4kTb3+F/AoDYrHmZl1II/BZtbndMISku8BN0r6N2kg/hCwT7VdMjOzOdDT\neui5CxQxDViqy7H/kiriz8nzp9LN1ciIGE/d8kEzsz7CY7CZ9Tltn8CIiLGSVgVqmyU9HBFv9fQc\nM/v/7N17nO1zvcfx1xtJuRRRnNx1KIqNIbdyK5eKkuQgRDiHLlSEKHQq97uQVK6JwnGpbZNb7uzt\nst3JLSS2JFEk3ueP72+1l2lm9t6z1prfWjPv5+Mxj1nr91vr8/2smTW/Wb/v7/v9fCO6Qtuu/kVE\nxAzLMTgiRh2VosHdS9IXgTNtP1/dnxvYwvbxQz9zdOvr6/PEiRPrTiMihknSJNt9decxXJJmBr5i\n+8ghHvNN298fwbSQtD5wcL/Nj9jepJ3t5Bgc0dtyDO6MHIMjYnoN9zjc9SMwgB1t/6Bxx/afJe3I\n1FVJIiJihNl+TdIWwKAfnkf6g3PV5gRgwki3GxExknIMjoixqhc6MGaWpGp96UaP86w159QVNly0\nY0t9t2T8o+V/6YYLfLHmTAY2/qnSH7bBvDvVnMnALnn2JABW2/zwmjMZ3PVnfx2A9WfbquZMBjbh\n5TMBWOLs79WcycAe2nyfulNol+skHQecDbzU2Gj71vpSGjkbLva1ulMYlvGPHAHAfx406HlP13tw\nr6/yvbs/UXcaw7bPMmX1y8WPPKLmTIbn4a+W9/6G8/1PzZkMz/gpJ9adQruM6WPwIicf2pG4j+2w\nBwAf+FpnjpF3HlE+v6+x6WEdiQ9w7bm7A7DOR4eqqzp8V1y2FwBL/W9nfkb3f6v8jDr1Oa7xOWy5\n3TqT/x1HlfzX79u/I/EnTCxxV9q+M/9DbvlJOcYvenzn3qOP7rL7sJ/bCx0YlwBnS/phdf+/q20R\nEVGvcdX37zRtM9VSeRER0VE5BkfEmNMLHRh7AjsBO1f3L6MstRQRETWyvXbdOUREjFU5BkfEWDRT\n3QlMi+3XbZ9o+zPV1w9tv9bYL+ncOvOLiBirJL1L0o8lja/uLy3pC3XnFRExFuQYHBFjUdd3YEyH\nxetOICJijDqFUqztP6r7DwC71ZZNRMTYcgo5BkfEGDMaOjC6ex3YiIjRa17b5wCvA9j+J/Da0E8Z\nHkmPSrpT0u2SJlbblpN0Q7X9IklzDfH8rarnNr5elzSu2rdiFeN3ko6RpE68hoiINssxOCLGnNHQ\ngREREfV4SdI7qDqSJa0C/KWD7a1te1zTmuEnA3vZ/gBwPrDHYE+0fWb13HHA1sAjtm+vdp8A7Aj8\nZ/W1QcdeQURE++QYHBFjzmjowOiaXlpJvVAUNSKiXb4OXAgsIek64DTgyyPY/pLAb6vblwGbTufz\ntgB+DiBpAWAu2zdWy3WfBnxqoCdJ2knSREkTp0yZ0lrmERGtyzE4Isacru7AkDSzpDOn8bA9RyQZ\nQNK3JN0v6VpJZ0naXdJVko6qhtPtKmk+SedKuqX6Wr167uySfiLpZkm3Sfpktf3zks6TdImkByUd\nMlKvJyKiFbYnAWsCq1GWuF7G9uRONQf8RtIkSTtV2+4GPlnd3gxYaDpjbQ6cVd1+N/BsexcNAAAg\nAElEQVRE074nqm3/noB9ku0+233zzTffDCUfEdFuOQZHxFjU1SMGbL8maRFJs9r+xyCPuXQkcpG0\nEqVneTngTcCtwKRq96yN4XSSfgYcaftaSQtTiiu9D9gHuML29pLeDtws6TfV88cBywOvAPdLOtb2\n4wPksBNlSVkWXnhh5uuasScRMRZJmky5ina27Yc63Nwatp+U9E7gMkn3AdsDx0j6FuUq5ID/J5pJ\n+iDwN9t3dTbdiIjOyjE4Isairu7AqDwMXCfpQuClxkbbR4xwHqsDF9h+GXhZ0kVN+85uuv0RYOmm\n+kNzSZoDWA/YWNLu1fbZgIWr25fb/guApHuARYB/68CwfRJwEkBfX595ti2vKyJiuDaiXEk7R9Lr\nlGPhObZ/3+6GbD9ZfX9G0vnAyrYPoxxbkbQk8PHpCPVfTL3yB/AksGDT/QWrbRER3S7H4IgYc7p6\nCknlIeBiSq5zNn11k5eabs8ErNIoVGT73bZfpNTq2LRp+8K2762e80rT81+jNzqWImKMs/2Y7UNs\nrwhsCSwLPNLudqopeHM2blM+MN9VXQlE0kzAvsCJ04gzE/BZqrnX1Wt4CnhB0ipV5fttgAva/Roi\nItotx+CIGIu6/kTZ9gF151C5DvihpAMpP7dPUI2G6OdSSgGlQwEkjauqLE8Avizpy7YtaXnbt41Q\n7hERHSFpEcoVwM0pHbDf6EAz7wLOr0a2zQL8zPYlknaV9MXqMecBP51GnA8Dj9t+uN/2XYBTgLcA\n46uviIiul2NwRIw1Xd+BIWk+ysF4Gcq0CwBsrzOSedi+pZrGMhl4GriTgZeq+grwg2pe4iyU6sz/\nA/wvcBQwueqBfoTSCRIR0ZMk3USpCfQLYLMBPpS2RRV3uQG2Hw0cPQNxrgJWGWD7ROD9LaQYETHi\ncgyOiLGo6zswgDMpc/o+QekI2Baoa+2kw2zvL+mtlI6JSbZ/1PwA289SesHpt/3vlArR/befQul1\nbtxPp0ZE9IptbN9fdxIREWNUjsERMeaoLLncvSRNsr2ipMm2l6223WJ7pRpy+RmwNGUkyKm2Dxzp\nHBr6+vo837Mfqqv5IY1/9EgANlzgi9N4ZD3GP/UDADaYd6dpPLIelzxbZiattvnhNWcyuOvP/joA\n68+2Vc2ZDGzCy2X15SXO/l7NmQzsoc33aRzb+urOZTgkfc72GZK+NtD+Goos/4uk9YGD+21+xPYm\n7Wynr6/PEydObGfIiBhBOQZ3Ro7BETG9hnsc7oURGK9W35+S9HHgD8A8dSRie8s62o2I6DKzV9+7\nraAytidQag5FRIxWOQZHxJjVCx0Y35X0NuDrwLHAXMBu9aYUETF22f6hpJmBF2wfWXc+ddlw6W/W\nncKwjL/n+wAsemr/i6S949Ft9+Sih5etO41h22jxyQAsd/G3as5keO74xP8CsOF79qg5k+EZ/7tD\n606hJTkGFyv8et+OxL31Y98FYKXtOzOQ5ZaflIEza27Uuffh1ReVv81l9u7M2+PuA78KwMqXdOb/\n4M0blP9Tn7z2Sx2Jf8EaxwGwyladGe1845llpHKnfseN3+8KO3fm93vrCeX3u/7VnTvlnrDmUcN+\nbi8so7oZZarLXbbXBj4KtHUYWkREzBjbrwFb1J1HRMRYlGNwRIxVvTACY1nbzzfu2H5O0vJ1JtQt\nGrUmulWj1kS3atSa6FaNOhPdrFFrols9tPk+dacw2l0n6ThKoeWXGhtt31pfShERY0aOwREx5vRC\nB8ZMkua2/WcASfPQG3lHRIx246rvB1TfBRgY0WWuIyLGqByDI2LM6YWOgMOBGyT9orq/GdCdSwuM\nsA0XG7D4dO3GP1LmDG64+O41ZzKw8Q8fBsAG8+xYcyYDu+S5sjLvqlt07yokN5xVRodsMNd2NWcy\nsEte+CkAW9+0Q82ZDOz0D55cdwotaap8fzHlw7Kadnf30lYRET0ux+CIGMu6vgaG7dOATwNPV1+f\ntn16vVlFRIxpc1ZfKwI7AwsA/wH8N7BCpxqVNLOk2yRdXN1fTtINku6UdJGkuYZ47qKS/i7p9urr\nxKZ9m0uaLOluSb1b2TIixoocgyNizOqFERjYvge4p+48IiICbB8AIOm3wAq2/1rd3x/4VQeb3hW4\nl7IaFcDJwO62r5a0PbAHMNSyDg/ZHte8QdI7gEOBFW1PkXSqpHVtX96B/CMiWpZjcESMZV0/AiMi\nIrrWu4B/NN3/R7Wt7SQtCHyc8oG5YUngt9Xty4BNhxF6ceBB21Oq+78ZLI6knSRNlDRxypQpAz0k\nImIk5RgcEWNOOjAiImK4TgNulrR/deXvJuCUDrV1FPAN4PWmbXcDn6xubwYsNI0Yi1VDl6+W9KFq\n2++AparhzbMAnxosju2TbPfZ7ptvvvmG/UIiItokx+CIGHPSgREREcNi+3vAdsCfq6/tbB/Y7nYk\nfQJ4xvakfru2B3aRNIkyH/wf//bkqZ4CFq6GL38N+JmkuaoVrnamLEN4DfAo8FqbX0JERNvlGBwR\nY1FP1MCIiIjuZPtW4NYON7M6sLGkjwGzAXNJOsP254D1ACQtSRnePFierwCvVLcnSXqIMvx5ou2L\ngIuqODuRD88R0SNyDI6IsSYjMCIioqvZ3tv2grYXBf4LuML25yS9E0DSTMC+wImDxZA0n6SZq9uL\nA/8JPFzdb8SZG9iFN87xjogY03IMjohukhEYI0TSLLb/WXceERGjyBaSvljdPg/46RCP/TDwHUmv\nUuZw/4/t56p9R0tarrr9HdsPdCbdiIhRJcfgiBhx6cBoE0nfAj4HTAEeByYBnwBuB9YAzpL0AKWH\nelbgT8BW1ePvB1arlo+aCXgAWLWpInNERAC2rwKuqm4fDRw9nc87Fzh3kH1btCm9iIhRLcfgiKhb\nppC0gaSVKEs+LQdsCPQ17Z61qph8OHAtsIrt5YGfA9+w/TpwBqUzA+AjwB0DdV5k+aiIiIiIiIgY\nq2S77hx6nqTdgLlt71fdPwL4A2UExn62r662fwA4HFiAMgrjEdsbSFoIuMD2CpJ+Dpxh++Kh2uzr\n6/N8f/pw515UC8Y/cgQAGy6+e82ZDGz8w4cBsME8O9acycAuee5HAKy6xeE1ZzK4G876OgAbzLVd\nzZkM7JIXyijWrW/aoeZMBnb6B09G0iTbfdN+dMwISesDB/fb/IjtTdrZTl9fnydOnNjOkBExgnIM\n7owcgyNieg33OJwpJJ33UtPtY4EjbF8oaS1gfwDbj0t6WtI6wMpMHY0REREzwPYEYELdeUREjEU5\nBkdEp6UDoz2uA34o6UDKz/QTwEkDPO5twJPV7W377TuZMpXkdNtZPioiosttMO9OdacwLJc8W/49\nrX3F12vOZPiuXOdwJjyydN1pDNv6i90DwEUPL1tzJsOz0eKTAdjgA/vUnMnwXHLn9+pOIdrgZ7/7\nYEfibvmemwB4/x5HdiT+XYd+FYBVturcSNsbzyzH9xV37MxrmPSj8hrWv3q3jsSfsOZRAHzy2i91\nJP4FaxwHwHsO7szP53d7lp/Pumt9vyPxL7/qmwAst1tn8r/jqJL/BQ+P60h8gE8ufvuwn5saGG1g\n+xbgQmAyMB64E/jLAA/dH/iFpEnAs/32XQjMwdAVnCMiIiIiIiLGpIzAaJ/DbO8v6a3Ab4FJtn/U\n/ADbFwAXDPL85SjFO+/rcJ4RERERERERPScdGO1zkqSlgdmAU23fOr1PlLQXsDOpfREREREREREx\noHRgtIntLVt47kHAQW1MJyIiIiIiImJUSQ2MiIjoapJmk3SzpDsk3S3pgGr7cpJukHSnpIskzTUd\nsRaW9KKk3Zu2bS5pchW7//J/ERFjWo7BEdFN0oERERHd7hVgHdvLAeOADSStQlm9aS/bHwDOB/aY\njlhHUIotAyDpHcChwLq2lwHml7Ruu19AREQPyzE4IrpGppD0sPGPHFF3CkMa//BhdacwpEue+9G0\nH1SjG87q/iUOL3mhuxfNOf2DJ9edQrSBbQMvVnffVH0ZWJJSNBngMmAC8K3B4kj6FPAI8FLT5sWB\nB21Pqe7/BtgUuLxd+UdE9LIcgyOim2QERkREdD1JM0u6HXgGuMz2TcDdwCerh2wGLDTE8+cA9gQO\n6Lfrd8BSkhaVNAvwqcHiSNpJ0kRJE6dMmTLQQyIiRqUcgyOiW2QERg9bf/n96k5hQBNuK/+bNlx8\n92k8sh6NkSEbvmvnmjMZ2PinTwBgxR2PrDmTwU360VcBWP8tW9ecycAm/P10AF7/45I1ZzKwmeZ/\noO4Ueo7t14Bxkt4OnC/p/cD2wDGSvgVcCPxjiBD7A0faflFSc9w/S9oZOBt4HbgeWGKQHE4CTgLo\n6+vzG64hRkSMYl15DI6IMSkdGBER0TNsPy/pSmAD24cB6wFIWhL4+BBP/SDwGUmHAG8HXpf0su3j\nbF8EXFTF2Ql4raMvIiKiR+UYHBF1SwdGRER0NUnzAa9WH5zfAnwUOFjSO20/I2kmYF/gxMFi2P5Q\nU7z9gRdtH1fdb8SZG9gF+GwHX05ERE/JMTgiuklqYERERLdbALhS0mTgFsr864uBLSQ9ANwH/AEY\nblXZoyXdA1wHHGQ7c3wiIqbKMTgiukZGYERERFezPRlYfoDtRwNHDyPe/v3ubzHs5CIiRrkcgyOi\nm2QERkRERERERER0PZWlnaNVkhYFLrb9/n7brwJ2tz2xne319fX5Ha8NVSupPlmFpDVZhaR1vbAK\niaRJtvvqzmW0kbQ+cHC/zY/Y3qSd7fT19XnixLYe1iNiBOUY3Bk5BkfE9BrucThTSCIiYtSwPQGY\nUHceERFjUY7BEdFp6cBor1kknQmsANwNbNO8U9KLtueobn8G+ITtz1fVnU8EFq4eupvt60Yw74iI\nmEEbzr9L3SkMy/g/Hg/AoiccVnMmw/fozrtz5+ML1p3GsH1goScAOPmBD03jkd1phyWvAWDt9ftf\naO8NV07Ys+4Uog3W+M03OhL32o8cAsAiJx/akfiP7bAHAB9d43sdiQ9w2bX7ALDmRp15DVdfVF7D\nTx5YoyPxt1/yWqDzv4P3HNyZ0c6/27OMVF5vle90JP6lN34bgGX27kz+dx9Y8u/kSOaZ5h9+rd7U\nwGivpYDjbb8PeIGyFNT0OBo40vZKwKbAyQM9SNJOkiZKmjhlypS2JBwRERERERHRCzICo70ebxo5\ncQbwlel83keApSU17s8laQ7bLzY/yPZJwElQ5v7xWhsyjoiIiIiIiOgB6cBor/4VUYe6P1vT7ZmA\nVWy/3JGsIiIiIiIiInpcppC018KSVq1ubwlc22//05LeJ2kmoLka86XAlxt3JI3rbJoRERERERER\nvSUdGO11P/BFSfcCcwMn9Nu/F3AxcD3wVNP2rwB9kiZLugf4n5FINiKiF0haSNKVku6RdLekXavt\n4yTdKOn2qj7QykPEWLl63O2S7pC0SdO+zavj792SerMqYUREh+QYHBHdJFNI2sT2o8B7B9i1VtNj\nfgn8coDnPgts3qncIiJ63D+Br9u+VdKcwCRJlwGHAAfYHi/pY9X9tQaJcRfQZ/ufkhYA7pB0EfA2\n4FBgRdtTJJ0qaV3bl3f8VUVE9IYcgyOia2QERkREdDXbT9m+tbr9V+Be4N2UukJzVQ97G/CHIWL8\nzfY/q7uzMbUm0eLAg7YbSzv9hrIaVEREkGNwRHSXjMCIiIieIWlRYHngJmA3YIKkwygd8qtN47kf\nBH4CLAJsXV0J/B2wVBX3CeBTwKyDPH8nYCeAhRdemPlafzkRET2lm47BC7X+ciKiB2UERkRE9ARJ\ncwDnArvZfgHYGfiq7YWArwI/Hur5tm+yvQywErC3pNls/7mKczZwDfAoDLxIte2TbPfZ7ptvvnRf\nRMTYkmNwRHSDdGBERETXk/QmygfnM22fV23eFmjc/gUwaAG5ZrbvBV4E3l/dv8j2B22vSinG/EA7\nc4+I6HU5BkdEt8gUkh424bYD6k5hSOMfPqzuFIY0/un+i8R0l0k/+mrdKUzThL+fXncKQ5pp/nwG\nGg0kiXJl717bRzTt+gOwJnAVsA7w4BAxFgMer4YsL0Ipuvxote+dtp+RNDewC/DZTryOiIhelGNw\nRHSTdGBERES3Wx3YGrhT0u3Vtm8COwJHS5oFeJlqbvQg1gD2kvQq8DqwS7UCFFWM5arb37Gdnq+I\niKlyDI6IrpEOjB629nrduVT2lZfuCcCGS3+z5kwGNv6e7wOw4YJfqTmTgY1/4hgAltnryJozGdzd\nB5XRIevPvk3NmQxswkunAfD6H5esOZOBZWTIjLF9LaBBdq84nTFOBwYcMmR7i2GmFhEx6uUYHBHd\nJDUwIiIiIiIiIqLrZQRGRESMGpLWB/oPT3vE9ibtbmv8H49vd8gR9ejOu9edQks+sNATdafQsh2W\nvKbuFFpy5YQ9604husxIHoOv/cgh7Q75Bo/tsEdH41927T4djQ9w9UWdfQ3bL3ltR+N3+nfwuz07\nW2/u0hu/3dH4dx/Y2fy7dcRwOjAiImLUsD0BmFB3HhERY1GOwRHRaenAiIiIGIYNF9q17hSGZfzj\nRwOw2JkH1pzJ8D2y1d4cd986dacxbF967xVA99bpmZbGVbn1V9yv5kyGZ8Kk7l7FLabPJ6/9Ukfi\nXrDGcQAss3dnapE1rpp/dI3vdSQ+TB3d8f49OvMa7jq0vIYdJn6+I/FP7jsFgO/d/YmOxN9nmYsB\nWOKwI6bxyOF5aPevAbB+3/4diT9hYonb6fw79fOHqb+D4UgNjIiIiIiIiIjoeunAGAZJi0q6a4Dt\nV0nqG0a8z0s6rj3ZRURERERERIw+6cCIiIiIiIiIiK6XDozhm0XSmZLulfRLSW9t3inpBEkTJd0t\n6YCm7StJul7SHZJuljRnv+d9XNINkuYdqRcSERERERER0e1SxHP4lgK+YPs6ST8Bdum3fx/bz0ma\nGbhc0rLAfcDZwOa2b5E0F/D3xhMkbQJ8DfiY7T+PzMuIiIiIiIiI6H4ZgTF8j9u+rrp9BrBGv/2f\nlXQrcBuwDLA0pdPjKdu3ANh+wfY/q8evA+wJfHywzgtJO1WjOiZOmTKlzS8nIqJ7SfqJpGea6w9J\nGifpRkm3V8fGlYd4/kclTZJ0Z/V9naZ9W1TbJ0u6JCPgIiLeKMfgiOgW6cAYPg92X9JiwO7AuraX\nBX4FzDaNeA8BcwKDrqlm+yTbfbb75ptvvuFlHRHRm04BNui37RDgANvjgG9X9wfzLLCR7Q8A2wKn\nA0iaBTgaWLs6Xk8GOrM2X0RE7zqFHIMjogukA2P4Fpa0anV7S+Dapn1zAS8Bf5H0LmDDavv9wAKS\nVgKQNGd14AZ4DNgUOE3SMh3PPiKih9j+LfBc/82U4y3A24A/DPH822w39t8NvEXSmwFVX7NLUhVv\nwDgZBRcRY1WOwRHRLdKBMXz3A1+UdC8wN3BCY4ftOyhTR+4DfgZcV23/B7A5cKykO4DLaBqZYfs+\nYCvgF5KWGKHXERHRq3YDDpX0OHAYsPd0Pm9T4Fbbr9h+FdgZuJPyoXlp4McDPSmj4CIi3iDH4IgY\ncSniOQy2HwXeO8CutZoe8/lBnnsLsEq/zadUX9i+jXLwjoiIoe0MfNX2uZI+S/nQ+5GhnlCNcDsY\nWK+6/6YqzvLAw8CxlA/h3+1g3hERo0GOwREx4jICIyIietW2wHnV7V8AgxaQA5C0IHA+sI3th6rN\n4wBsP2TbwDnAap1JNyJiVMkxOCJGXDowIiKiV/0BWLO6vQ7w4GAPlPR2SkHlvZpWkAJ4ElhaUmM8\n8keBezuQa0TEaJNjcESMuEwhiYiIrifpLMo0vXklPQHsB+wIHF0VQ34Z2GmIEF8C3gN8W9K3q23r\n2f6DpAOA30p6lVJQ+fOdeRUREb0px+CI6BbpwIiIiK5ne4tBdq04nc//LoPMqbZ9InDiMFOLiBj1\ncgyOiG6hMt0sek1fX58nTpxYdxoRMUySJtnuqzuPGJ4cgyN6W47BvS3H4IjeN9zjcDowepSkKZRh\ndu0yL/BsG+O1W/JrXbfnONbyW8R21oFrM0nrUyrcN3vE9iZtbqfdx+D+uv3vYVp6PX/o/deQ/IeW\nY3AHdPExuNPvp16PPxJtJP7ojj+cNoZ1HE4HRgAgaWI3X4lIfq3r9hyTX8RUvf5+6/X8ofdfQ/KP\nmKrT76dejz8SbST+6I4/Um1AViGJiIiIiIiIiB6QDoyIiIiIiIiI6HrpwIiGk+pOYBqSX+u6Pcfk\nFzFVr7/fej1/6P3XkPwjpur0+6nX449EG4k/uuOPVBupgRERERERERER3S8jMCIiIiIiIiKi66UD\nIyIiIiIiIiK6XjowIiIiIiIiIqLrpQMjYgZJWknS/E33t5F0gaRjJM1TZ27RWZI+WHcOERERERFj\nVTow4g0k7VZ3Dj3gh8A/ACR9GDgIOA34C11eNV3Spl2QwyKS3tZ0f21JR0v6mqRZ68xtOvyi7gRi\ndJP0FklL1Z3HcElaTdKWVcfuNpK2qTunGSFpM0lzVrf3lXSepBXqzmt6SZpd0kzV7SUlbSzpTXXn\nFRGtk/TONsZatl2xIkZaOjCiv6/VnYCkC4f6qjs/YGbbz1W3NwdOsn2u7W8B76kxr+lxZN0JAOcA\nswNIGkfpFPg9sBxwfI15TQ/VnUCMXpI2Am4HLqnuj+uSY950kXQ6cBiwBrBS9dVXa1Iz7lu2/ypp\nDeAjwI+BE2rOaUb8FphN0ruBS4GtgVNqzWgGSdpV0lwqfizpVknr1Z1X9I6q4/FzkuboUPy3STpI\n0n2SnpP0J0n3Vtve3qY25un39Q7gZklzt2m0722SHpT0v5KWbkO8aBNJ325TnHn73f9cNVp8J0kt\nf56t3pfflrRDdbzeR9LFkg6VNHer8YeSDozorxtO0FYFFgSuoXwYPrzfV91mljRLdXtd4IqmfbMM\n8Phu0g2/37fY/kN1+3PAT2wfDmwHrFxfWtMl605HJ+1P+Rt4HsD27cBidSY0g/qA1W3vYvvL1ddX\n6k5qBr1Wff84pXP6V0C3jwxrJtt/Az4NHG97M2CZmnOaUdvbfgFYD5ib0glzUL0pRY/5IPAp4PeS\nzpG0SZtHeJ4D/BlYy/Y8tt8BrF1tO6dNbTwLTGr6mgi8G7i1ut2qycAmlHPBCyXdIWkvSYu2IfaQ\n2nWCPkDcK6b9qBmKV9cJ+g5tinNp44akfSnH0knAR4Ej2hD/DMoFyRWBK4H5gYOBv9PhjvNuP9mK\nkdcNJ2jzU/64tgC2BH4FnGX77lqzmuos4GpJz1L+SK8BkPQeyjSSbtYNv9/mTpR1gL0BbL/ehg7h\nlkm6iIF/TgLeMcLpxNjyqu2/9Ps76Ia/2el1F+X4/VTdibTgSUk/pPwPOljSm+mtiz2StCqwFfCF\natvMNeYzHI0/gI8Bp9u+ux1XC2NMecb2ZyTNBXwS2BE4SdLFlM+Tlw799Gla1PbBzRts/5FyzNi+\nxdgNe1COQ3vYvhNA0iO229Wpbdt3AfsA+0haGfgv4FpJv7e9WpvaGcgOwHdaCSBpcv9NwJKN7bbb\nMUXmDOBOygn656rbB1N+L6dQ3lvDIumFwXYBbxlu3AFiNXwa+JDtlyT9jNIR1qr/sP2x6vj8hO21\nqu3XSLq9DfEHlQ6MMUjSXxn8BK1dfzTDZvs1yhDqS6oPj1sAV0k6wPZx9WYHtr8n6XJgAeBS242f\n5UzAl+vLrJB0J4P/ft81wukM5ApJ51BOcuamGsEiaQGq2iI1O2yY+yJadbekLSmjvP4T+Apwfc05\nzYh5gXsk3Qy80thoe+P6UpphnwU2AA6z/Xx1XNqj5pxmxG6UTuHzqxP/xSlXxnrJJEmXUkYf7a1S\nk+T1mnOK3mKAaiTP6cDp1RSMzYC9aLoyPUyPSfoGcKrtpwEkvQv4PPB4i7EBsH24pLOBIyU9DuxH\nezu039hTbt9MmaLydeDDLQfv/An6o8ALwHcpFxNFuaC4URtiN3TyBP15YKXG+6dZ9ftuh7dIWp5y\nfvIm2y8B2H5V0mtDP3W6zFSNRJkTmEPSorYfrf7WOjpyMR0YY5DtOevOYVqqjouPUzovFgWOAc6v\nM6dmtm8cYNsDdeQygE8MsW/hEcticEcCq1A6gNaw/Wq1fX7ggtqymmqS7RcH2iFpiZFOJsaUL1Ou\nhr1CGek1AfjfWjOaMfvXncBw9ZtTflXTtldoz3DtEWH7asoIwbkkzWn7YUpHWC/5AjAOeNj236oP\nw9vVnFP0ln/7H277T8CJ1VerNqd0hFytqYU1nwYupHSCtoXtJ4DNJG0MXAa8tV2xgUMHadPA1W2I\n39ETdNsbS9qEUjz/MNsXSnrV9mOtxm7SyRP004BFKO+b/n7WYuyGp5g6VeRZSQvYfqrK/59tiH8g\ncF91e3vg5Gqw3PuAA9oQf1CaevE4ojtIOg14P/Br4OfVELeYTpIepvyDPrwazdK4MnA48F7btRbV\nq/L7IeUfTjfm9xCwt+1zmrbNBuwL/Jftbi/UGlELSV8Afmv7wbpzmVGSHqFc3RxoqoJtLz7CKQ2L\npD7gp5QP3KKcRGxve1KtiU0HTWO1F9vtGPIc0ZMkvQVYolc+E0v6LnBhNbKj/76Dbe/ZpnZmp3T0\nLwGsaHvBdsStYm8BHFXd3QXYmfJ/YmngANtdvfLgYCTNDLy5qpfUjliy/c+qPuA44EnbHZ1Kmg6M\n6DqSXgdequ42v0FF+SA518hn1Tuq3uKDgNWAXYEPUFaXOQQ4wXatQ3Gr/A4EVqc781sCOI4yb3wX\nSgG8w4D/o/zDGnB0RsRwDVF3BeidKRiSDgA+RBk1N4myIsY1VTHSGAHV/O8v2m7UZlqDUsyz65dM\nlNSY6jIbZc75ZMr//WWBibZXrSu36H2Svm/7mx2Mf5rtti4bXdWlsO1bVFYK2QC4z/av2xB7Lsp0\nswWB8bZ/1rTveNu7tNrGSJK0HLCq7XaMsGmO27ET9GpqysqU4qwATwI3u80n50OgHXwAACAASURB\nVFXH9kKUItUP2L5vGk+ZkdgLAy9UUy4XpRTzvq/THW3pwIgYpSTtSpmu8QdglWooYtfogfz2oHS0\n/BFYv4uKyMYoI2nN6uanKVOpzqjubwE8bfurtSQ2TNWVwh2B3YF32+76IpKj5eq/pNtsL99v2622\nh3x93UTSecB+TYUL3w/sb/sz9WYWvULSMf03UVZgOA2g1dWR9O/LW4uyCskVVfyWO50l7QdsSJnu\nfxllZZUrKQUkJ9j+XovxzwUeBG6kDP9/FdjS9ivtPGZIelPTVOHGtnltP9uO+IO0+d52naR3qpNB\nZWno4ym/gyerzQsC7wF2aUOh2cZni8MpI/FWBK6j1J57FdjadktTeSTtBfw3ZarlYZT/+ddRpon/\n2HY7VjoZuO10YESMLiprkB9M+Wf3DUol93WBXW23dYmp4eiB/GahFO3bgZLnxyjDsXexfX+ducXo\nJmli/ylUA23rVirLtK0OzAHcBlxLGYHR9auSNF39H4htrzNiyQxDUwfMNpQCeWdRRvVsDrxs+2t1\n5TajJN1te5lpbYsYTFVj4WpKsc7GtLDGCRa2T20x/q3APcDJTJ16dhZlFY9GLZqWVAXZxwFvplxI\nWdD2C1UH8U2tjqqSdLvtcU3396F83tkYuKzVDgxJa1MKqM5GWfFiJ9uPVvs62qmqsopKyzXfOtnJ\nIOleYMPGz6Rp+2LAr22/b7ixm2LdBqxne0oV9wjbm0hqrG6zXovx76aMuHgrpajq4lVbs1Peo+9v\n8SUMKkU8I0afWykH3C/a/idwqaRxwPGSHrO9Rb3pdX1+t1OK+K1g+y+Updc+QVkn/dxODkGNMW92\nSYtXhRcbH2RmrzmnGfFpSmGwX1FOHm6w/crQT+kOtteuO4cWHd7v/n5Nt3vtStVkSSczdSTSVpTp\nJBHTa2lKXYQNgN1t/0HSfq12XDTpo0yB3YdyIni7pL+3o+OiyT+rOmF/k/SQy4oq2P57NdW6VW+W\nNFNj2q7LCntPUqb+zdGG+IdQjV6V9BngMklbuxTBb3lZ5AFG2fxrF/D2VuNXjgY+MlgnA6VY5XDN\nAgw08vhJ4E0txG02s+0p1e3fU4qGYvsySUcN/rTp9lr1fvwHZSWYP1XxX1KHV75OB0bE6PPh/tMx\nqjnoq0nasaacmnV7ftv2L3hn+2JJv6GcmEV0ylcpS0Y/TPkQtghleGZPsL1CNa96dcow55MkPWN7\njZpTmyZJ69i+QtKnB9pv+7yRzmlG2F5b0kzAZ9xUgLhHbUcplrdrdf+3wAn1pRO9xvZfgd0krQic\nKelXlKUk2xX/dcrypr+ovj9N+8+p/iHprVWhxRUbGyW9jfYsK3wRsA7wm8YG26dI+iNwbBviz9qY\nemv7l9WIg/Mk7Ul7OlW3A75O05LdTdp1IayTnQw/AW6R9HOmLr27EGUUz49bjN0wUdKPKVObNmbq\nCltvpdR5a9Wtkn5GudByOXCqpEso76t72hB/UJlCEhHRRF2+ikuMbipLSL+3untfr4xggH/VKvgQ\nsCblCuXjlCkk3641sekg6QDb+0n6abWp8eGoUTx6+5pSmyG9NOUoYiRUNQx2oRR4/FyH2vg4sHo7\nR2hKevNAx39J8wILNGrEdCtJE4FP2P5j07YFgYspq6nM2WL8K4B9bV8/wL5HbC/WSvwqzt6UZXEH\n6mQ4x/aBLcZfmtKx0Fxf40LbbTn5l/QmSj2qpYE7gJ/Yfq2ahvROt7jkbDXlejPK/8tfUqaGb0EZ\n7fED2y8N8fSWpAMjIqKJunwVlxh9ev3qf4Oki4Frqq9b+hdu6wUqSyZvSllJpXFF1ba/U1tSM0DS\nQcCzwNlMXc0L28/VltQMkvSflALKS1PmzwPgHlnKNrqLpHmgM38D1cWNf5182n66l9qQ9F7gk/z7\nCfS9bYj9EWCK7Tv6bX87ZQpxq0VI56HU92l5KdBptNPRToYYnnRgREQMQF2+SkqMHgNc/W/WM1f/\nASTNCixZ3b2/1zoxquGvz1Nq9bxWbXYnq6m3k6RHBtjsXjr5l3QtpYbHkcBGlKHiM/XCSJ7oDipL\nOx5CKRD+PGUk1VyUofR79a9pMIz44ygjNd/GG4s7Pk8p7tjyqkWdbqOayrEFZXRB4/PNgpTRBT+3\nfVAr8WNo1VSgvYFPAe+kjGJ4BrgAOMj2821oYw5KsfxPU0aO/AN4CDjR9iltjL8p5b3TiH9CG+vN\nDNx2OjAiIqbq9lVSIrpVtWTbaZRq5KJ8YNrW9m/rzGtGSLqrk5XTY9okTbK9oqQ7bX+geVvduUVv\nkHQDcBTwy6apoDNThrvvZnuVFuPfDvy37Zv6bV8F+KHt5VqJPxJtSHoAWKZ/J3PVCX237f9sMX5H\nT9BHqAOgY21ImkDpUDu1Mc1G0vzAtsC6ra4QUsW7ADifUufks5RaFT8H9qWM5mlpylOn4w/Zdjow\nIiKmqmpgHA8cVa2S0rgScjzQDaukxChVjfr5KfBX4EfACpSrhS2vBz8SJE0CtnS13LCkJYGzeunE\nU9JJwLHdPr98MJK2GWi77dNGOpfhknQ9sAZlTvUVlKvPB9leqtbEomdIenCwE/Ch9rUp/u9sv6eV\n+CPRhqT7KKuEPNZv+yLApa3+vXX6BH2EOgA61oak+wf7GQ+1bwbbuKO5o0vSLbZXqgo+32P7vUM8\nvfb4Q8kqJBERb9Ttq6TE6LW97aMlrQ+8A9gaOB3oiQ4M4E2NzgsA2w9URcS6nqQ7KVfXZgG2qzoy\nX2FqEc9l68xvBqzUdHs2yuixWykjY3rFrsBbga9QlsJch3LCEDG9Jkk6HjiVNxZf3Ba4rQ3xx1cr\nm5zWL/42wCVtiD8SbewGXC7pwab4CwPvAb7UhviL2j64eUPVCXCwpHZMi+x0/E638Zikb1A6R56G\nf9U7+TxTfx+teknSGravlbQx8ByUVXSq4rbdHn9QGYERERHRBSRNtr2spKOBq2yfL+k228vXndv0\nkPQTyvJ+Z1SbtqKsQ9/1NTyqq46DarVae12qKXE/t71B3bnMqGp+NbZfrDuX6C3VNIgvMECBSuDH\nA63uMYw2Nhwovu1ftxp7pNqorpSv3C/+LY1pNy3GvpQytWCgE/SP2v5IN8fvdBtVwfi9KL/fd1Wb\n/0h5jx7cjqKzkpYFTqbUpboL+ILt+yXNB2xh+5hujj9k2+nAiIiIqF9VxPPdwGLAcpR12q/qlSkY\nKkvAfpEy/B/KaiQ/sP2P+rIa26oRMHf10vQLSR+gXHWep9r0LKWWyl31ZRUxOnVqlZNOn6CPUAdA\nx9uI4UkHRkRERBeoroaNAx62/bykdwDvtj252r+M7btrTXIIkna1ffS0tkXnSLqIMhUGSgfY+4Bz\nbO9VX1YzpqqBsY/tK6v7awHft71arYlFT6mm4n2KN44uuMB2y9MvJM1CGeHxb/EpIzxaXn2p0230\nW+XkCcp0ubaupBJDUweXsW1qY3GmrkLyGvAA8DPbL/RC/EHbTQdGRERE95N0q+0V6s5jMAPl10tT\nYEaDaiWYhn9SCg/31BLQ/QvDDbYtYjCSjqIMaz+NNy4Rug3woO1dW4x/FuVE/9R+8bcF5rG9eSvx\nR6KNEVpJpaMn6CPUAdCRNjQCy9hK+gplKeqrKSvq3UZ5T21C6aS6qpvjD9l2OjAieouk63MlKmLs\n6dbOAElbAFtSpo5c07RrTuB12+vWktgYVVXJX5kyEuOWRvX8XiHpfErh0dOrTZ8DVrS9SX1ZRS+R\n9IDtJQfYLuCBNqxCMmD8ae3rpjZGYJWTjp6gj1AHQMfaUIeXsa1i3QmMs/2apLcCv7a9lqSFKaOR\nWvo80en4Q8kqJBE1kzSLq+U6p0c6LyLGrG694nA98BQwL3B40/a/ApNryWiMkrQD8G3K0n8CjpX0\nHds/qTezGbI9cABwHuU9f021LWJ6vSxpJdu39Nu+EvByG+I/J2kz4Fzbr8O/pgBuBvy5DfFHoo1O\nr3LyBQY+QT8CuBtotYOh0/E73cbrwH8A/QtEL1Dta5dZKFM73gw0CiP/vo0rhHU6/qCNRkSbSNoG\n2J3yoWsycA6wLzAr8CdgK9tPS9ofWAJYHPg9pYe3f6xlgJ9Wz50J2NT2g5JetD2HpO8AG1cPn4+y\nbvd2kj5HWX5uVuAmyjCulitKR0QMpFqh4zFg1bpzCfYAlrf9J4Cqjsr1QE90YEiamVL/4it15xI9\n7fPACZLmZOqV84WAv1T7WvVfwMHA8ZL+TOksfDul4/C/2hB/sDbeBlzZjjZsf2WQVU5+0KZVTjp9\ngj4SHQCdbKPTy9hCWSHkFkk3AR+ivJ+oVglpRwHSTscfVKaQRLRJ1eFwPrCa7WclzUPpyHjetqsr\nY++z/fWqA2MjYA3bfx8k3rHAjbbPrIaUzWz7740OjKbHvZ1yherzwN+AQ4BP235VZR30G22f1rEX\nHhEjQtKNtlepO4/BSPo05QPMOykftgXY9ly1JjaGVAUw12qs/FL977iql0budfv7PHpHNZ2qeYWN\ntk+nqjoJaXQadsJItNFukjYAjgMGPEFvtZhqp+OPRBvq4DK2TW0sQynmfJft+9oVd6TiDyYjMCLa\nZx3gF7afBbD9XLUc3NmSFqCMiHik6fEXDtZ5UbkB2EfSgsB5th/s/4BqPucZwBG2J0n6ErAipUcU\n4C3AM214bRHRYZLOA34MjG8MGW7WAyd1hwAbtbOAWkwfSV+rbv4OuEnSBZQO9E/Se9N4bpN0IfAL\n4KXGRtvn1ZdS9Jrq5PAZ23+sOvLeL+kfbVpec+Eq9suUK82fl7QCcA/woxmZFjyNduYANqBa4aGq\nm3DpQP8fhhF7ZmAHSk2H8bavb9q3r+3vthLf9iWSlqRDJ+idjj8SbVS/xxsb9yVt3O4R09XKZXdX\n8d9DWaL9Xtv3tCu+pGeABSUtS1lF7cV2xB5KOjAiOutYSufChSpLwe3ftO+lAZ9Rsf2zaljWx4Ff\nS/pv21f0e9j+wBO2f1rdF3Cq7b3bkXxEjKjjge2AYyT9Avip7ftrzmlGPJ3Oi9rMWX1/qPpquKCG\nXFo1G2XK5TpN20ypiRExTZI+BfwQeF3S/wDfBF4ElpK0s+2LWmzi15STWih1EJYA/o/ynl2JNtRs\nkfRZypTkycDalKlgHwQOkbSV7TtbbOKHwFuBmym1cq623egI/TTQUgcGDHiCPk+bT9Bnsn1jFXsO\n4L2UaTZtm77Q/zU0SJqjlRP1asRif8erLJ/blg5bSVcCm1WjwrcGvgX8Fthf0km2j20x/tLAMcCi\nlJEptwHvlHQ1sKvtv7T0AoZqO1NIItqjaQrJqrb/VE0huRzYoRod8VNgsapC7/7Ai7YPGyLe4sAj\n1fSTwygdFUc11cDYCNgLWLtpuPDSlA+sq9t+psphzmqOekT0AElvo9TF2YcybPVHwBn9C4l1G0lH\nA/NTPsi/0tieK+fRTpL2tn1g3XlE95J0G7AhZRTqHcBKtu+XtAilKGZfi/Hvsb10dXtSFb9RaLMt\nS/5KmgysYvtvkuYFzrS9fnWV+8RWp4VJmmx72er2LJQO9Hkp/3tubMMKFf8axVF9Nv0/4E2UC22b\nu9/yrcOI/3lK0eg/AbsCP6CMcl4S+Ibts1qJPx3t/972wi08/1VgAmWUtKrNnwF+SZl62Y5OsLts\nv7+6fQuwQXV+8lbK73jZFuPfCGxb/W2tDHzR9raSdgTWt/2ZVl/DYDICI6JNqmFU3wOulvQapSdy\nf+AXVQGmK4DFZiDkZ4Gtq4PcH4Hv99v/NcqQtpur6SIX2v62pH2BS6vhk68CX+TfCxBFRBeq5jp/\nDtiacgw5k7I86bbAWvVlNl3motThWa9pW66cj6Dqitu/XZmyvc4AD+9VmwHpwIghNepdVCea91fb\nHqs+G7XqcUnrVKNiH6VM8XisUauiTQQ0phm/RKkthO3JktpRV2jWxo1qystOkhorGM0x6LOmX/Mo\njkMpV+THVye6RwGt1uX5OrAUZfTZHZTixQ9JehdwGdByB0bT1Lx/20XrP6PVKKN3brF9QtXeWra3\nazFus1clvdv2k5QRSI2R368AM7ch/lua/rZulnRidftHQ/zs2iIjMCIiIrqApPMpH8hOB06x/VTT\nvomtXjXsNEmzVXPCoyaSVmy6OxuwKfBP29+oKaW2k3Rbq1eHY3SrRmCsaPt1SSvbvrnaPjNwR+Oq\ndAvxF6IsPzozZWWTNYDbKSuR7G778pZeQGnjYGAcZcj/BpQ6Fd+vRtZeY3uZFuOfQRnZd0m/7TsA\nJ9huaRlMSbfaXqG6fbvtcU37Wv4bbo4p6Q+2/6Np379Gl7TYxsuUzpeBapp81fbbW4w/E/Bl4FPA\nnsDPbS/eSsx+8deijEw5F5gHWIEy6mMNYMJQo8CnM/55lAstV1A6rOa2vb3KEqp32V6qlfhDtp0O\njIiIiHpVH2S+2WrhtDpJ+h3wNGVVpGuAazs5Bzamj6Sbba887Uf2huYTo4iBSFoJuLN/h6qkRSmr\nv53RpnbeR5myMAtludZb2lFgsyn+x4ClKZ0ul1XbZgLeZPuVIZ9cM0nPUzpfRFlie2Hbf6v23dWG\nTqQLKcUp56T8jG6jjPb7CGU1wPVbiV+1cT3wZduTBtj3uO2FWm2jivVu4Eigr50dGFXstwFb8sb3\n6QVuw4ohKqsgfpPqPQocZPuvVZvva9Qn6YR0YETUTNL6VGsnN3nE9iZ15BMR9RgNV5ZVqvN/CFgd\n+BhlGelxQz8r2qW6OtswE9AHHN3JK2EjbTT8nUR3kHSu7U07GP8G26t2Kn6n2pD00UaHSQsx1uy3\naZLtF6spHp+x/YMW489FmSJtylKn61OKYD8GfLd5BGMLbSwFPGd7ygD73mX76VbbiOFJB0ZEREQX\nqIr13kBZNrnn/jmrLPn8IWBNylJtz1FGYaRewQiR9AjlA70oNZAeBb5j+9o682onSd+03b8mVMQM\n63Rn2Eh0tnWijVYLVMa06Y3L2F5i+7qmfS0vYztAG21fKrfT8Ydsuwc/I0VERIw6kv4KzE6Zb/sy\n5STUtttRsK3jJL0O3AJ833YvLt/Z81SWXrzE9guSvkWZ8/y/tm+tObVpknQsAxQgbbD9lRFMJ8aA\nTk9HGonpTsNto5qCMeAuYB3bs7eW2ZBtn2R7p26P38lOBkknM3UZ262Bfy1j2673TafbGInXMJis\nQhIREdEFbM9Zdw4tWp5SHGxLSXsBD1I+0Py43rTGlH1tnyNpDWAd4DDgBOCD9aY1XSZW31enzKk+\nu7q/GXBPLRlFjF4foqx49WK/7QJarpnTbzpb//gf6/b4lR8y9QT9GEn/OkHnjausDMfKnrqM7XHA\n8VVRzC2Yuqxqqzrdxki8hgGlAyMiIqILSLrc9rrT2tatbN8h6SHgIaZ+OF4TSAfGyHmt+v5x4Ee2\nfyWpJwrD2j4VQNLOlEKL/6zun0gpChvRbh09yRqB+K20cSPwN9tX/1tA6f7WUgJgCqUeRXN+jelt\n7+yB+NDZE/ROL2M7Em2MxGsYUDowIiIiaiRpNspVnnklzc3UD0ZzAe+uLbEZJGki8GbgesoJ54dt\nP1ZvVmPOk5J+CHwUOFjSmynFPHvJ3JT3/nPV/TmqbRHDJumdtp/pt3nPDje7dTuCNEYb2H5ugN3D\nasP2hkPs+/BwYvbzMLCu7d/33yHp8R6ID509QZ8oaYPmZWxtf0fSHyij5tqh022MxGsYUGpgRERE\n1EjSrsBuwH8ATzK1A+MFylX04+rKbUZImm+gau0xciS9FdiAsoTkg5IWAD5g+9KaU5tukrYD9geu\npPwtfBjYvzFCI2JaBpheIGASZZqbBukImJH4z1GW7DwLuKITRZerFZ0OAdYFnqe8hrkoJ8972X60\n3W0OksewVjmR9EVKEec7Btj3ZdvHtphXR+NXcc4Azmg+Qa+27wCcYPtNrbYxHTm0vCJM3W10In46\nMCIiIrpAuz501aVa+30/ygknwNWUFTD+Ul9W0Yskzc/Uuh032f5jnflEb6kKCvcf/bUg8ASlMPLi\nLca/HziWMpVgUeCXwFm2b2wlbr82bgCOAn5p+7Vq28yUmjC72V6lXW1NI49Or9TScyfPI9lGNxeC\nrTN+rw0rjIiIGJVsHytpNUlbStqm8VV3XjPgJ8Bfgc9WXy8AP601o+g5kgR8BFiuWs1mVkktFxWM\nMWUP4H5gY9uL2V4MeKK63VLnReUl28fZXh1YlTJy7nhJD0tq1xK/89o+u9F5AWD7Nds/B97Rpjam\nR6evdB/c4/E73UY311GpLX5qYERERHQBSacDSwC3M7UYo4HTaktqxixhe9Om+wdIur22bKJXHQ+8\nTllF5TuUTrFzgZXqTCp6h+3DJZ0NHFnVQ9iP9p6I/+uErKrBcAhwiKT3Apu3qY1Jko4HTgUaNR0W\nArYFbmtTG92g506eR7iNkZgq0ek22h4/HRgRERHdoQ9YuhPzqUfI3yWtYftaAEmrA3+vOafoPR+0\nvYKk2wBs/1nSrNN6UkQz208Am0naGLiMUii5Xa4cpM37gAPa1MY2wBeqeI1izk8CFzKyKzt1ugOg\n506ea2ojmqQDIyIiojvcBcwPPFV3IsP0P8BpVS0MgD9TrhZGzIhXq7n+hlIcljIiI2KG2b5Q0p+B\nNSWt146Ctra/1obUptXGPygrOXR0NYdmklawfWu/zW1ZSSWG7dFR0Ebb46cDIyIiojvMC9wj6Wbg\nlcZG2xvXl9K0SWr+MH8aMHt1+yVKLYPJI55U9LJjgPOBd0r6HvAZYN96U4peIulm2ytXt3cEdgH+\nD9ivOkk/qANtXmF7nXbH7VQbkvoXVRRwgaSNKIs83Apg+652tDeER3s8/rDbkPRh4Gnb91cjFlcF\n7rX9q8ZjbH+6PSn+W9v/KjzarjYkLUZZ6eeeajQS7Yz/hrZ6d6RqRETE6CFpzYG22756pHOZEZL2\nq24uRalTcAHlw/BGwM22P1dXbtGbqloC61LeR5fbvrfmlKKHNK+cIekW4GO2p0iaHbjR9gdajN+/\nU1bAkpTCodhetpX4I9FGtVLLjTR1lgOrVNvcakdJNXXnUtsvtxJnmG13clWQ79v+ZhviHAWsTBlM\nMIFyvBsPrAncZnuPVtuYRvu/t71wizH+z/anqtufpKyacxWwGnCg7VNazXPQttOBEREREa2S9Fvg\n47b/Wt2fE/iV7Q8P/cyIqSQdA/zc9vV15xK9SdIdwFqU1RYva17CsR3Lgkq6kLLK0ncpdX4EXAOs\nAWC7/xKuXdeGpE2BrwAH2R5fbXukWrGlZZL+ThmFNx7+n737jpOrrN///7oISO8EBAJEFJEidUVQ\nKYI0GyAiIiJF4YNdEP3iT0REUJoo6gcUkfZRpEqxJCEiTaQllECoSkdKkA7Sr98f514zLLObTXZm\nz0z2ej4e89g59znnfb9nA/fO3HMXfgdMaNxRpZ1a8eG8xPlp3yKqKTWnAtj+yhBiTwVWB+alWttk\nWdvPS5qLqgNj9VmN3VDHBf2dAja1PX8/5wcbv7Gj8O/AzrbvlrQEVcfzmkOJP5BMIYmIiOgAkp5h\n+mJgbwLmotqub6H6spopSwEvNRy/VMoiZsZk4ABJK1NNJTnd9qSac4rusjDVf0cCLGlp2w9JWoAW\nLEpp+6OStgOOB44q62y83IqOi+Gqw/Y5kiYA35e0B/B1WrsY5W1UOwl9vMQ+SdK5wO9aMapwBh/O\nW7XN7HbApcCFTP/v5pNU/20NlW27jISB6b/716g63lphQ+DTwLN9ykU1+mOoGv97eZPtuwFsP9bw\nutoiIzAiIiI6jCQB2wDr296/7nwGQ9K3gU9QfegE2BY4w/YP68squpWkxYDtqT4wLG97pZpTii4n\naT5gqd4PWi2INz/wfartr9e1PaYVcWuoY23gaGB126NbFPO6PiNf3kz192EnYIzt5YYY/wn6/3B+\nhu0hd56XUYTfB5YE9rP9L0l32V6xBbEPp5pqMQ/VtIt3UE3f2Ri4y/beLahjHHCE7TfsmiPpsqGO\njpT0KtUoGwFzAyuUjsI3AZNaMZWq37rTgREREdGZWjHceTiVheE2LIeX2b6+znyie0laD9iRqiPv\nVtsfqTmliKYkrQlsYPsX3VpH6TRf0PbTLYrX798uSSu0YApMWz+c94m3LnAU8CfgS7bHtijuBlQj\nMa6S9FaqER/3AWfb7tqdlyQtAqxi+8q21ZEOjIiIiPpJalypew6gB9jY9gY1pRQx7CQdQfVG/p/A\nGcC5tp+sN6uI1ysf+NcDli1FD1ItWtyyD1btrKPE3oFqGsDZVNM9tqGa+vGLoX6AlrSJ7UuGmmen\nKL+vL1B1IrV0Yeoy2gzbj7cy7nDWIWlR4NVWdYDNsL50YERERNRP0kkNh69Qbc32K9uP1pNRxPCT\n9D/AObYfqzuXiGYkbQEcC9xJ1akAMAZ4G/AF2xd2eh2SjqWaGvEmqsVC5wYuAD5EtbXnV4cSPwYm\naXngCKqOo6eopmEsBPwV2N/2PS2sYzPgyVbXIWkZ4DCqjq8FmP7f6YnAobZfHkr8AetOB0ZERERE\ndIrybd5KVPPDAbB9WX0ZRUwn6VZg674fACW9Bfiz7VU6vQ5JN9l+Z9n14mFgadsvSZoTuK4F27Qu\nBxxJNXpkHHBk7wfaxu03OzX+IOq/aSjb8Uq6kmrb0bN7d2eRNIpqVMzXbK/fghzbWoekvwIH276k\njCDdEDgA+BawpO29hvQCBtCqVU4jIiJiCCSNkXSupEfL4xxJLV+wLaKTSfoccBkwAfhe+XlQnTlF\n9DEn8ECT8gepdo/qhjpeASgf+q+1/VI5foVqJ4yhOpFqccovA0sDl0rq3R1khS6Ij6SP9fPYHnjz\nEMMvYfuMxq1lbb9q+3Rat4tKu+tYvHeakO3fAxvZfs72AUBbt0/PNqoRERGd4STgNKpvR6BaYf0k\nYPPaMooYfl8F3gVcZfv9kt4B/KDmnCIanQhcK+l04P5SthzVjjm/7pI64fhwMgAAIABJREFUHpa0\ngO1nbW/VW1h2C3lpgPsGa3TDgqNflvRp4DJJH6U127W2Oz5Ua/D8tp948zQpmxmTyzSeU3j9v++u\nQKsWv253HdPK7/1i4GNU01571wtp6yCJTCGJiIjoAJJusL3WjMoiZmeSrrX9Lkk3AO+2/aKkqbZX\nqzu3iF6SVgU+yusX2LzA9i3dVEeTOucH5h/q2kuSplJt+/pCQ9kHgF+U+Et3cvwSbzKwq+2bm5y7\nfyhbwZatRj9LtX7E6/59gV/bfnFWYw9XHWWNjaOAVYEbgG+UbVQXBzaxfc5Q4g9YdzowIiIi6ifp\nIqoRF78rRTsBu9verL6sIoaXpHOB3YGvUS1w9wQwl+0P1ppYxGymzbuc7EO1lsalfcrXptr+dEgj\nC9sdv8TaELjX9n1NzvXYnjTUOmLWpAMjIiKiA0haAfgZsAHVkNW/A1+2ff+AN0bMpiRtDCwMjO+d\nox9RN0kLUy1UuC3VTh4GHgXOBw5rxba/7a5jOHZSiYFJ2pLq37exA+l82+O7pQ5J7we2p5qa8ipw\nB3CC7X+0In6/9aYDIyIion6STqFaGfyJcrwYcJTtPerNLGJ4SVoHeB/Vh7YrbF9Xc0oR/yVpAtVW\nlKfYfriUvZlqbYHNbG/R6XUM004q7f7w3LUdAJJ+ArwdOJXpi7WOAT4D3NmKbWzbXYekH1ItZnoR\n1e/obqoOjC8AP7B91lDiD1h3OjAiIiLqJ+l622vPqCxidibpQKqFbH9firYFzrJ9SH1ZRUwn6Xbb\nK8/suU6qQ9KdwCpl15HG8jcBt9h+2xDjt/vDc1d3AEi6w/bbm5QLuMP2SrMae7jqaNxKtmy/e6nt\n95ZtsC+3vfpQ4g8ku5BERER0hjkkLdpnBEb+TsdIszOwZu/ifJIOo1ogLh0Y0SnulfRNqtERjwBI\nWgrYjem7PXR6He3e5eSD/Xx4PoPqW/qhdjC0O36763hB0rtsX9un/F3AC81u6MA6XpO0mO3HgWWA\nUQC2nyidJG2TN0YRERGd4UfAlZJ6h13uABxaYz4RdfgX1RaFvW+w52b6HP2ITrAjsD9waelUMPAI\n1e4On+iGOmz/UNL5VLucbFCKHwR2btEuJ+3+8NztHQC7AcdJWpDpozuWA54q51qh3XX8ALhe0h3A\nysDnASSNBm5sQfx+ZQpJREREhyjb5m1aDv/azu3yIjqRpPOoPiBMpPrQtjlwDeUNuO2v1JddREXS\neoBtXytpNWAr4Fbbf25R/HcDt9l+StJ8VJ0Z6wBTqdYXeKoV9bRLWcfmOKDZh+cv2p7cyfGHsY43\n07C+Ru96J63UzjrKSNEVgX+0YvHaQdebDoyIiIiI6ASSdh3ovO1ThiuXiGYkfRfYmmok+0SqrUgv\noepsm2B7yCPnJE2lmkr1iqTjgeeAc4DNSvnHhhi/7TuplHra+gG9mzsA2rmN7TDX0UPDLiS2b2tV\n7H7rTAdGRERERETEjEm6CViLanrTw8AY209Lmhe42vYaLajj1t6dQCRdZ3udhnM32F5riPGHYyeV\ntn547uYOgOHYxrbddZRtrn8EPAmsC1wBLAq8DOzSzi3gswZGRERERHQESe8FDgJWoHqfKqqh+ivW\nmVdEg1dsvwo8L+mftp8GsP0fSa+1qI6bJe1u+yTgRkk9tidJejvVB8ShGmv78MaC0pFxuKQhb909\n0IdnSa348NzW+MNQxzHAB/rbxhYY8ja2w1DHT4AtbE8rMY8uu5BsTrUQ7JA7wfqTDoyIiIiI6BS/\nBvYBJlMNSY7oNC9Jms/281TfPAP/nZbRqg6MzwHHSDoAeIxqgef7qXYM+VwL4rd7l5N2f3ju9g6A\nOZm+rkajB4G5hhB3OOsYZXtaeX4fVacztieWLWjbJh0YEREREdEpnrI9ru4kIgawke0XAWw3dljM\nRTUFY8jKIp27SVoIeAvlw2hvZ0ML9O5ycknpuIDW7qTS7g/P3d4B0O5tbPurY3mqf/tW1DFJ0q+p\npiJ9lGodGMqis6NaEL9fWQMjIiIiIjqCpMOo3vz+Hnixt9z2dbUlFTEbkvRW4GNMX4DxduC03ikx\nQ4z9LaqOkGYf0M+0/cNOjj8cdUhaBdiG16+vcUErdx9rZx2S5gL2BFal2jb1RNuvlrVglrR971Dr\n6LfudGBERERERCeQdHF52vsGtXcNjE37uSUiZpKkrwAfBi4DPghcT7UY43ZUCzxe0oI6VqX6Zr4t\nH9CHqQOgra9huEla0vajbYy/uO1/tyv+f+tJB0ZEREREdIKyRWVftn3wsCcTMZvq3UmlfGM+H/Bn\n25tIWh443/baNac4YkkaZ3vrFsRZrEnxdcDaVH0Ajw8x/mHAUbYfK1upnkm1BsxcwGdsXzqU+AOZ\no12BIyIiIiJm0rMNj1eArYCxdSYUMZvqXQtxbmABANv30YI1JCQtJOmHkv5P0k59zh3bgvhbNTxf\nWNIJkqZIOq1hTY+h1tEj6WJJv5G0nKSJkp6UdK2kIXXwSFqnn8e6VFv0tsJjVIshNz6WperEmNSC\n+B+y/Vh5fiSwo+23AZtTba/aNlnEMyIiIiI6gu3XvfGVdBQwoaZ0ImZXJ1At8Hg1sCFwOICk0cCQ\nvpkvTqLafvQcYA9JHwc+VRY/Xb8F8X8AjC/PfwQ8DHyEak2PXwLbtqCOY4HvAosAfwf2sb25pM3K\nuQ2GEPta4FKqKXJ9LTKEuI2+QdWZ8A3bNwFIutv2W1oUf05Jc9p+BZjX9rUAtu+QNHeL6mgqU0gi\nIiIioiNJWhS4tnyzFxEtImk1qq1Ab7Z9W4tj32B7rYbjb1OttfFRYKLtdYYY/7reGE3qet3xEOq4\nvncqjaT7bC/f7Nwsxr4Z2M72nU3O3W97uVmN3SfWGODHVIuQfhe40faKLYr9ZapOo8OAjYBFqRZf\n3hRY0fYurainmYzAiIiIiIiOUObm9367NgoYDWT9i4gWsz0VmNqm8HNLmqN3m1nbh0p6kGrR0AVa\nEH9JSftSjWBYWJI8/Vv5Vi2R8IKkLYCFAUva1vZ5kjam2rVlKA6i/zy/PMTY/2X7AWAHSR8FJgLz\ntTD2z0p7/Xng7VT9CisB5wGHtKqeZtKBERERERGd4sMNz18BHilDlCOie/yB6pv4v/QW2D5Z0sPA\nz1oQ/1fAguX5ycASwDRJbwZuaEF8gL2BI6gWptwS+Lykk6l2ItlzKIFtny3pHWU6ytW2n204/cJQ\nYjeS9A6qdS/+StWB8dZSvpXt8QPdO0gPA8fT5zWUNUpaEb+pTCGJiIiIiIiItpO0u+2TujV+K+oo\n29h+EbiVatHOr9o+v5y7bqhTbIajjuF4Df3WnQ6MiIiIiIiIaLe+60l0W/xW1FGmXmxg+1lJY4Gz\ngf+zfcxQ19cYrjqG4zX0J1NIIiIiIiIioiUkTenvFDDkbU7bHX8Y6pijd8qF7XskbQKcLWkFmu9M\n0ol1DMdraCodGBEREREREdEqS1GtG/FEn3JRbUna6fHbXccjktayfQNAGcXwYeBE4J1DjD1cdQzH\na2gqHRgRERERhaRRtoe6wnxExEj2R2CB3g+3jSRd0gXx213HZ6gWKf6vsljxZyT9coixh6uO4XgN\nTWUNjIiIiBgxJJ0HLAfMAxxj+3hJzwK/BD5AtSjZf4Cjqbb7ewzYzfZDkvYE9gLeBPwD2MX28zW8\njIiIiBEpHRgRERExYkhazPbjkuYFrgU2puqk2NH2mZLmAi4FtrE9TdKOwJa295C0uO1/lziHUG3x\n2YotASMiImIQMoUkIiIiRpKvSNquPF8OWAl4FTinlK0MrA5MlAQwCnionFu9dFwsQjU6Y8JwJR0R\nERHpwIiIiIgRoqyS/gGqrd+eL/OY5wFeaFj3QsBU2xs0CXEysK3tGyXtBmzS7pwjImYnkj4KrGr7\nMEkHAc/aPqrmtKKLzFF3AhERERHDZGHgidJ58Q5g/SbX3A6MlrQBgKS5JK1Wzi0IPFSmmew8LBlH\nRMxGbF9g+7C684julQ6MiIiIGCnGA3NKuhU4DLiq7wW2XwI+Dhwu6UbgBuA95fR3gKuBK4DbhiXj\niIguIWmspNsknSzpDkm/lfQBSVdIulPSepJ2k/TzJve+VdJ4SZMlXV46mZH0EUlXS7pe0l8kLVXK\nR0uaKGmqpBMk3StpiXLu05KukXSDpF9KGjW8v4lopyziGREREREREUMiaSzVDk1rA1OpFkq+Efgs\n8FFgd+A8oMf2lxqnkEi6CNjb9p2S3g380PamkhYFnrRtSZ8DVrH99dIJ8qDtH0raChgHjC6PI4CP\n2X5Z0rHAVbZPHbZfRLRV1sCIiIiIiIiIVrjb9k0AkqYCF5XOh5uAsc1ukLQA1Ui3s8riyQBzl59j\ngDMkLU21hfXdpfx9wHYAtsdLeqKUbwasC1xbYs0LPNqyVxe1SwdGREREREREtMKLDc9fazh+jf4/\ne85BNcpirSbnfgYcbfuCshDzQTOoX8Aptr816Iyjq2QNjIiIiIiIiKiF7aeBuyXtAKDKmuX0wsCD\n5fmuDbddAXyiXL8FsGgpvwj4uKQly7nFJK3Q5pcQwygdGBEREREREVGnnYHPlsWTpwLblPKDqKaW\nTAYea7j+e8AWkm4GdgAeBp6xfQtwAHChpCnARGDp4XkJMRyyiGdERERERER0DUlzA6/afqVse31c\nP1NQYjaTNTAiIiIiIiKimywPnClpDuAlYM+a84lhkhEYEREREREREdHxsgZGRERERERERHS8dGBE\nRERERERERMdLB0ZEREREREREdLx0YEREREREREREx0sHRkRERERERER0vHRgRERERERERETHSwdG\nRERERERERHS8dGBERERERERERMdLB0ZEREREREREdLx0YETUQNLJkg6pO4+IiJEq7XBERH3SBses\nSgdGRAeTdImkz9WdR0TESJV2OCKiPmmDo690YEREREREREREx0sHRsQwkLS2pOskPSPpDGCeUr6o\npD9KmibpifJ8TDl3KLAh8HNJz0r6eSk/RtL9kp6WNFnShrW9sIiILpF2OCKiPmmDo1XSgRHRZpLe\nBJwH/B+wGHAWsH05PQdwErACsDzwH+DnALa/DVwOfMn2Ara/VO65FlirxDoNOEvSPMPzaiIiuk/a\n4YiI+qQNjlZKB0ZE+60PzAX8xPbLts+manix/W/b59h+3vYzwKHAxgMFs/2bct8rtn8EzA2s3ObX\nEBHRzdIOR0TUJ21wtEw6MCLabxngQdtuKLsXQNJ8kn4p6V5JTwOXAYtIGtVfMEn7SbpV0lOSngQW\nBpZo5wuIiOhyaYcjIuqTNjhaJh0YEe33ELCsJDWULV9+fp2qx/jdthcCNirlvdc2NvSUOX7fBD4B\nLGp7EeCphusjIuKN0g5HRNQnbXC0TDowItrvSuAV4CuS5pL0MWC9cm5Bqrl+T0paDPhun3sfAVZs\nOF6wxJoGzCnpQGChdiYfETEbSDscEVGftMHRMunAiGgz2y8BHwN2Ax4HdgR+X07/BJgXeAy4Chjf\n5/ZjgI+XVZl/Ckwo19xBNfTuBeD+Nr+EiIiulnY4IqI+aYOjlfT6qUgREREREREREZ0nIzAiIiIi\nIiIiouOlAyMiIiIiIiIiOl46MCIiIiIiIiKi46UDIyIiIiIiIiI6XjowIiIiIiIiIqLjzVl3AjFr\nllhiCY8dO7buNCJiFk2ePPkx26PrziNmTdrgiO6WNri7pQ2O6H6z2g6nA6NLjR07lkWve0vdaTQ1\n8bWzANh8jh1qzqS5bslvi7k+WXMm/bvw5dOBzv8ddnJ+ku6tO4+YdWPHjmX0vzdqS+xxdx8NwNZv\n2bet8becZ+e2xJ/wwm8B2GqxPdsSH2D8479i61X/v7bFH3fLDwDaVsd/44/euz3xp/0CaN+/wfjH\nf1XFX+OA9sSfcggAm252WFvi//Wi/dMGd7mxY8ey+G2r1Z1GUxOePQWAzUftWHMm/Zv46hlA5+bY\nm9/WS32+5kyaG/fIcUBnv88E2GL9g2vOpH8XXnXgLLfDmUISERERERERER0vHRgRERERERER0fHS\ngRERERERERERHS8dGBERERERERHR8UZkB4akEyU9KunmhrIdJE2V9JqknobyxSVdLOlZST8fROxD\nJd0v6dk+5ftKukXSFEkXSVqh4dx4SU9K+mOrXmNERERERETE7GREdmAAJwNb9Sm7GfgYcFmf8heA\n7wD7DTL2H4D1mpRfD/TYXgM4Gzii4dyRwC6DjB8REREREREx4ozIDgzblwGP9ym71fbtTa59zvbf\nqDoyBhP7KtsPNSm/2Pbz5fAqYEzDuYuAZ2YUW9JekiZJmjRt2rTBpBMRERERERExWxiRHRgd4LPA\nuJm9yfbxtnts94wePboNaUVERERERER0pjnrTmCkkfRpoAfYuO5cIiIiIiIiIrpFOjCGkaQPAN8G\nNrb9Yt35RERERERERHSLdGAME0lrA78EtrL9aN35RERERERERHSTEbkGhqTfAVcCK0t6QNJnJW0n\n6QFgA+BPkiY0XH8PcDSwW7l+1QFiH1HizFeuPaicOhJYADhL0g2SLmi453LgLGCzcs+WrX3FERER\nEREREd1tRI7AsL1TP6fO7ef6sTMR+5vAN5uUf2CAezYcbPyIiIiIiIiIkWhEjsCIiIjOI2k3ST8f\nxvoukdQzXPVFRHS6tMMR0elG5AiMVpB0NTB3n+JdbN80XDlMfO2s4apqliS/obnw5dPrTmGGOv13\n2On5xayTNKftV+rOIyJipEo7HBF1yAiMWWT73bbX6vMYts6LiIiZJWmspJsbjveTdFD5BuxwSddI\nukPShuX8bpLOL+fvlPTdhnvPkzRZ0lRJe5WyvSUd2XDNf7/Jk/TpEv8GSb+UNKqU717qvAZ47wzy\nP1nSL0oH8hGSFit5TJF0laQ1ynXrSbpS0vWS/i5p5VI+r6TTJd0q6Vxg3lI+qsS+WdJNkvbpp/69\nJE2SNGnatGmz8C8QESNd2uFZb4fTBkcEZARGV9t8jh3qTqGp3m+9k9+s+W9+o3asOZP+TXz1DCC/\nw1nV+/vrMHPaXk/SB4HvAr3r9qwHrA48D1wr6U+2JwF72H5c0ryl/BzgHKoFkr9R7t0ROFTSKuX5\ne22/LOlYYGdJE4HvAesCTwEXA9fPIM8xwHtsvyrpZ8D1treVtClwKrAWcBuwoe1XVG1f/QNge+Dz\nwPO2Vylvsq8rMdcClrW9OoCkRZpVbPt44HiAnp4e8+8Z/UojImZK2uEB2uG0wREB6cCIiIjK78vP\nycDYhvKJtv8NIOn3wPuAScBXJG1XrlkOWMn2VZLukrQ+cCfwDuAK4ItUb46vlQTVN26PAu8GLrE9\nrcQ/A3j7DPI8y/ar5fn7qN4QY/uvkhaXtBCwMHCKpJUAA3OV6zcCflqunyJpSim/C1ixvBH/E3Dh\njH5ZERFtkHY47XBEzECmkEREjByv8Pp2f56G5y+Wn6/y+s5t94lhSZtQfTO4ge01qb6t6411OvAJ\nqje059o2IOCUhul2K9s+aBZfw3ODuOb7wMXlm7yP8PrX+Qa2nwDWBC4B9gZOmMXcIiJmJO1wE2mH\nI2Kw0oERETFyPAIsWb4hmxv48CDu2bzMcZ4X2Jbqm7yFgSdsPy/pHcD6DdefC2wD7ET1JhrgIuDj\nkpYEKPFWAK4GNi75zAXM7Jyky4GdS8xNgMdsP13ye7Bcs1vD9ZcBnyrXrw70ztVeApjD9jnAAcA6\nM5lHRMRgpR1OOxwRQ5ApJBERI0SZ93wwcA3VG8vbBnHbNVRzqscAv7E9SdJNwN6SbgVuB65qqOOJ\nUr6q7WtK2S2SDgAulDQH8DLwxTLU+SCq+dpPAjfM5Es6CDixDEF+Hti1lB9BNXT5AKqhyL2OA04q\n+d1KNUwbYNlS3tup/62ZzCMiYlDSDqcdjoihGZEdGJJOpOrxfrRhsaDFgDOo5hzeA3yi/AFYHDgb\neBdwsu0vzSD2ocBngEVtL9BQvhHwE6qe5k/aPrvh3HiqnvO/2R5MT3xExCyx/VPK/ON+zj/G6+de\nP2B72z7XvAhsPUCMN7Rjts+gamP7lp8EnDTDxKtrd+tz/DjVt5F9r7uS18/hPqCU/wf4ZD/h821f\nRAyLtMNphyNi1o3UKSQnA1v1KdsfuMj2SlTD7PYv5S8A3wH2G2TsP1CtFt3XfVRD6E5rcu5IYJdB\nxo+IiIiIiIgYcUZkB4bty4DH+xRvA5xSnp9C6U22/Zztv1F1ZAwm9lW2H2pSfo/tKcBrTc5dBDwz\no9jZ/zoihpPtGY46awdJ35Z0Q5/Ht4c7j4iIuqUdjoh4vRE5haQfSzV0PDwMLFVnMs28Yf/r+2tO\nKCKiDWwfChxadx4RESNV2uGI6FQjcgTGjJTtpvpuWRURERERERERNUkHxnSPSFoaoPx8tOZ8IiIi\nIiIiIqJIB8Z0FzB966ddgfNrzCUiIiIiIiIiGozIDgxJv6Pa73plSQ9I+ixwGLC5pDuBD5Tj3uvv\nAY4GdivXrzpA7CMkPQDMV649qJS/q5TvAPxS0tSGey4HzgI2K/ds2eKXHBEREREREdHVRuQinrZ3\n6ufUZv1cP3YmYn8T+GaT8muBMf3cs+Fg40dERERERESMRCNyBEZEREREREREdBdVG27EzJJ0NTB3\nn+JdbN80HPX39PR40qRJw1FVRLSBpMm2e+rOI2ZN2uCI7pY2uLulDY7ofrPaDo/IKSStYPvddecQ\nERERERERMVKkA6OLbTHXJ+tOoakLXz4d6Pz8Np9jh5ozaW7ia2cBsOU8O9ecSf8mvPBboPP/jbda\nYq+aM2lu/GPH151CtMCEu/tdz3lItnzLLQCs+OOj2xL/rn32BWCDT/2oLfGvPO3rAKyxz4/bEh9g\nyo/34e/3rti2+O9Z4S4Atrz0a22JP2HjnwCw8vfb8zu6/Tv7ALDZ+3/YlvgXXfwtoP35t+vfuPff\nN7rbFusfXHcKTV141YEAbL3y/jVn0r9xt1d7FWy91OdrzqS5cY8cB8C6e7Xv78hQTD6+aqO2XGDX\nGVxZjwnPngLAsbe/v+ZM+veFlS+e5XuzBkZEREREREREdLx0YEREREREREREx0sHRkRERERERER0\nvHRgRERERERERETHSwdGA0lflXSzpKmSvlbKFpM0UdKd5eeiA9y/uKSLJT0r6ed9zq0r6SZJ/5D0\nU0kq5RtJuk7SK5I+3t5XGBEREREREdGd0oFRSFod2BNYD1gT+LCktwH7AxfZXgm4qBz35wXgO8B+\nTc4dV+KvVB5blfL7gN2A04b+KiIiIiIiIiJmT+nAmG4V4Grbz9t+BbgU+BiwDXBKueYUYNv+Ath+\nzvbfqDoy/kvS0sBCtq+ybeDU3ji277E9BXit1S8oIiIiIiIiYnaRDozpbgY2LNNA5gM+CCwHLGX7\noXLNw8BSsxB7WeCBhuMHStlMkbSXpEmSJk2bNm0W0oiIiIiIiIjoTunAKGzfChwOXAiMB24AXu1z\njQEPf3b/rf942z22e0aPHl1XGhERERERERHDLh0YDWz/2va6tjcCngDuAB4pU0B6p4I8OguhHwTG\nNByPKWURERERERERMQjpwGggacnyc3mq9S9OAy4Adi2X7AqcP7NxyxSUpyWtX3Yf+cysxImIiIiI\niIgYqeasO4EOc46kxYGXgS/aflLSYcCZkj4L3At8YqAAku4BFgLeJGlbYAvbtwBfAE4G5gXGlQeS\n3gWcCywKfETS92yv1o4XFxEREREREdGt0oHRwPaGTcr+DWw2EzHG9lM+CVi9Sfm1vH56SURENCHp\nIOBZ20cNIcYlwH6lTY6IiEFKGxwRnSBTSCIiYkSQNKruHCIiRqq0wRHRChmBMQskbUm1Y0mju21v\nN5x5XPjy6cNZ3Uzr9PwmvnZW3SkMaMILv607hRnq9H/j8Y8dX3cKMQOS5gfOpBqJNgr4PlX72mP7\nMUk9wFG2Nym3rCnpSmAJ4Ajbv5K0CdU3eh8uMX8OTLJ9cpnWdwawOXBEibGLpBOo/gbuYfsaSRsD\nx5TzBjay/Uw7X3tERN3SBkdEt0kHxiywPQGYUHceERGzga2Af9n+EICkhXljB3GjNYD1gfmB6yX9\naRB1/Nv2OiX+3sB8tteStBFwItX0vv2o1j66QtICwAt9g0jaC9gLYPnlly8pRER0ta5tgxdberAv\nMSJmJ+nA6GJbzv+ZulNoasJzpwKw+agda86kuYmvngHAFnN9suZMmusd1bD1sl+uOZP+jXvwZ0Dn\n/xtvtcYBNWfS3Pgph9SdQie5CfiRpMOBP9q+vNqsqV/n2/4P8B9JFwPrAU/OoI4z+hz/DsD2ZZIW\nkrQIcAVwtKTfAr+3/UDfILaPB44H6OnpMTw/iJcXEdHRurgNjoiRKGtgREREbWzfAaxD9Sb6EEkH\nAq8w/e/TPH1vaXLceH2ze56bUQzbhwGfo9op6gpJ7xj0i4iI6FJpgyOi26QDIyIiaiNpGeB5278B\njqR6I30PsG65ZPs+t2wjaZ6y5fUmwLVUW1yvKmnu8k3ejHaO2rHU/T7gKdtPSXqr7ZtsH15i5s1z\nRMz20gZHRLfJFJKIiKjTO4EjJb0GvAx8nuobuF9L+j5wSZ/rpwAXUy0g933b/wKQdCZwM3A3cP0M\n6nxB0vXAXMAepexrkt4PvAZMBcYN8XVFRHSDtMER0VXSgREREbUZYFHktze59qAB4nwT+GaT8rF9\njjfp5/7OXXQmIqJN0gZHRLfJFJJBkrSPpKmSbpb0uzJ8bjFJEyXdWX4uOsD9b5J0kqSbJN1Ytpzq\nPbduKf+HpJ9qBqsnRURERERERIw06cAYBEnLAl+h2hN7dap9sj8J7A9cZHsl4KJy3J89AWy/k2ov\n7B9J6v39H1fOr1QeW7XjdURERERERER0q3RgDN6cwLyS5gTmA/4FbAOcUs6fAmw7wP2rAn8FsP0o\n1ZZTPZKWBhayfZVtA6fOIE5ERERERETEiJMOjEGw/SBwFHAf8BAQKQjpAAAgAElEQVTViskXAkvZ\nfqhc9jCw1ABhbgQ+KmlOSW+hWt15OWBZoHGv6wdK2RtI2kvSJEmTpk2bNqTXFBEREREREdFN0oEx\nCGVti22AtwDLAPNL+nTjNWX0RN99rRudSNU5MQn4CfB34NWZycP28bZ7bPeMHj16Zm6NiIiIiIiI\n6GpdvQtJWexyZ2BF2wdLWh54s+1rWlzVB4C7bU8r9f4eeA/wiKSlbT9UpoI82l8A268A+zTk/nfg\nDuAJYEzDpWOAB1ucf0RERERERERX6/YRGMcCGwA7leNngP9tQz33AetLmq90mmwG3ApcAOxartkV\nOL+/AOXe+cvzzYFXbN9SpqA8LWn9EvszA8WJiIiIiIiIGIm6egQG8G7b60i6HsD2E5Le1OpKbF8t\n6WzgOuAV4HrgeGAB4ExJnwXuBT4xQJglgQmSXqMaYbFLw7kvACcD8wLjyiMiIiIiIiIiim7vwHhZ\n0ijK2hOSRgOvtaMi298Fvtun+EWq0RiDuf8eYOV+zk0CVh9KfhERERERERGzs26fQvJT4FxgSUmH\nAn8DflBvShERERERERHRaqo2z+hekt5BNQpCwEW2b605ny2Bw/sU3217u1bW09PT40mTJrUyZEQM\nI0mTbffUnUfMmrTBEd0tbXB3Sxsc0f1mtR3u6ikkkhaj2vnjdw1lc9l+ua6cbE8AJtRVf0RERERE\nRMTsqKs7MKgW1VyOaitSAYsAD0t6BNjT9uQ6k2u3rUfvXXcKTY2b9gsAtlp4j5ozaW78UycCsOU8\nO9ecSXMTXvgtAFutcUDNmfRv/JRDANhirk/WnElzF758OgDv3PfHNWfS3E1H7zPji6Lj/eGuNdoS\n9yMrTgFglXO/15b4t25XLef0vu2Pakv8v52zHwCrHNi+//9uPXgfHn5wmbbFf/Oy/wLgtYff3pb4\nc7z5DgBWOOHItsS/93PfAGDDbdsT//LzqvhvO/LotsT/xzf2BeDJfy3XlviLLHN/W+LG8Npi/YPr\nTqGpC686EICt3/aNmjPp37h/VG3D1kt/seZMmhv3ULWp5Bod+j5uSnkft+UCu87gynpMePYUoH1/\nw1qh9+/gLN3bwjzqMBH4oO0lbC8ObA38kWpXj2NrzSwiIiIiIiIiWqbbOzDWL1M2ALB9IbCB7auA\nuetLKyIiIiIiIiJaqdunkDwk6f8Bp5fjHYFHytaqbdlONSIiIiIiIiKGX7ePwPgUMAY4rzyWL2Wj\ngE/UmFdEREREREREtFBXj8Cw/Rjw5X5O/6NV9UhaGTijoWhF4EDg1FI+FrgH+ITtJ/qJMRdwArAO\n1e/9VNs/LOfWBU4G5gX+DHzV3b6/bUREREREREQLdfUIDEmjJR0p6c+S/tr7aHU9tm+3vZbttYB1\ngeeBc4H9gYtsrwRcVI77swMwt+13lhj/I2lsOXccsCewUnls1erXEBEREREREdHNuroDA/gtcBvw\nFuB7VKMgrm1znZsB/7R9L7ANcEopPwXYdoD7DMwvaU6qkRYvAU9LWhpYyPZVZdTFqTOIExERERER\nETHidPUUEmBx27+W9FXblwKXSmp3B8Yngd+V50vZfqg8fxhYaoD7zqbq8HgImA/Yx/bjknqABxqu\newBYtlkASXsBewEsv/zyjJ7llxAR3U7SPMBngdWAeXrLbe9RW1IRESNE2uCIiHp0+wiMl8vPhyR9\nSNLawGLtqkzSm4CPAmf1PVdGTwy0bsV6wKvAMlQjRr4uacWZqd/28bZ7bPeMHp3ui4gR7v+ANwNb\nApdSLWj8TK0ZRUSMHGmDIyJq0O0dGIdIWhj4OrAf1SKZ+7Sxvq2B62w/Uo4fKVNAKD8fHeDeTwHj\nbb9s+1HgCqAHeJDqj16vMaUsImIgb7P9HeA526cAHwLeXXNOEREjRdrgiIgadHUHhu0/2n7K9s22\n3297XdsX9J6X9K0WV7kT06ePAFwA7Fqe7wqcP8C99wGblrzmB9YHbitTUJ6WtL4kAZ+ZQZyICJg+\nAu1JSasDCwNL1phPRMRIkjY4IqIGXd2BMQg7tCpQ6XTYHPh9Q/FhwOaS7gQ+UI7787/AApKmUi00\nepLtKeXcF6hGj/wD+CcwrlV5R8Rs63hJiwLfoepMvQU4ot6UIiJGjLTBERE16PZFPGdErQpk+zlg\n8T5l/6balWQw9z9LPx0qticBqw81x4gYOWyfUJ5eCszUejqzs7I99R9tr96n/GDgMtt/kXQJsJ/t\nSZLuAXpsPzbcuUZE90ob3Fza4Ihot9l9BMZAi2pGRHQtSUtJ+rWkceV4VUmfrTuvTmX7QNt/qTuP\niJg9pA2eOWmDI6JVMgKj1RVKWwKH9ym+2/Z2ra5r3LRftDpkS41/6sS6UxjQhBd+W3cKAxo/5ZC6\nU5ihC18+ve4UBnTT0e1c07d2JwMnAd8ux3cAZwC/riuhDjJK0q+A91AtirwNcBzVt4JnN7uhTBM8\nk2oh5VHA922fMUz5RkT3OZm0wf1JGxwRbdO1IzAkjZI0o08nb9jutN1sT7C9Vp9HyzsvImLEW8L2\nmcBrALZfodqqOWAl4H9trwY8CWw/iHu2Av5le80y9Hl83wsk7SVpkqRJ06ZNa23GEdFt0gb3L21w\nRLRN147AsP2qpJ2AHw9wzQ+GMaVht/Uqrd5kpTXG3fpDALZecb+aM2lu3F1HAbDVop+rOZPmxj9R\nTatd/9NH15xJ/676zb4AbDnvLjVn0tyE//wfAOuO+/YMrqzH5K0PbUWY5yQtTpkqJ2l94KlWBJ4N\n3G37hvJ8MjB2EPfcBPxI0uFU3xJe3vcC28cDxwP09PQYXmpRuhHRhdIG92+Y2uCIGIm6tgOjuELS\nz6mG7D3XW2j7uvpSiogYFvtSrXz/VklXAKOBj9ebUsd4seH5q8C8M7rB9h2S1gE+CBwi6SLbB7cr\nwYjoemmD+5c2OCLapts7MNYqPxsbOAOb1pBLRMSwkDQHMA+wMbAy1Xo/t9t+udbEupikZYDHbf9G\n0pNAZw7RiojapQ1uvbTBETFYXd2BYfv9decQETHcbL8m6X9trw1MrTuf2cQ7gSMlvQa8DHy+5nwi\nokOlDW6LtMERMShd3YEhaSngB8AytreWtCqwge2sAB0Rs7uLJG0P/N525gIXtu8BVm84PqrJNZs0\nPB9bnk4oj4iIwUgb3ETa4Ihot67dhaQ4maqxW6Yc3wF8rbZsIiKGz/9Q7bT0oqSnJT0j6em6k4qI\nGCHSBkdE1KCrR2BQtrCS9C2otrCS1JYtrCTdAzxDtRjRK7Z7JK0J/AJYALgH2Nl20z9eknYGvtFQ\ntAawju0bJK1L1RkzL/Bn4KvpzY+IgdheUNJiVNvVzVN3PhERI0na4IiIenT7CIzh3sLq/bbXst1T\njk8A9rf9TuBcXt9B8Tq2f1vuXQvYhddvMXUcsCfVH8GVqPbCjojol6TPAZcC44GDys8D68wpImKk\nSBscEVGPbu/A+Dqv38LqVODLw1j/24HLyvOJwPaDvG8n4HQASUsDC9m+qoy6OBXYttlNkvaSNEnS\npGnTpg0t84jodl8F3gXcWxY0Xpv2duBGRMR0aYMjImrQ1R0YtidTbWH1Hqq5iKvZntKu6oC/SJos\naa9SNhXYpjzfAVhukLF2BH5Xni8LPNBw7oFS9sYE7ONt99juGT169EwlHxGznRdsvwAgaW7bt1Ft\n5xcREe2XNjgiogZdvQaGpClUIxnOsP3PNlf3PtsPSloSmCjpNmAP4KeSvkM1EuSlGQWR9G7geds3\ntzfdiJjNPSBpEeA8qjbpCeDemnOKiBgp0gZHRNSgqzswgI9QjWY4s+wbfQZwpu37Wl2R7QfLz0cl\nnQusV7aG2gJA0tuBDw0i1CeZPvoC4EFgTMPxmFIWEdEv29uVpwdJuhhYmGoOdkREtFna4IiIenT7\nFJJ7bR9he13gU1Q7e9zd6nokzS9pwd7nVJ0WN5fRGEiaAziAakeSgeLMAXyCsv5FeQ0PAU9LWl+S\ngM8A57f6NUTE7Mv2pbYvsD3DUWAREdFaaYMjIoZPt4/AQNIKVKMwdqTa4vSbbahmKeDcqn+BOYHT\nbI+X9FVJXyzX/B44aQZxNgLut31Xn/IvMH0b1XHlERERERERERFFV3dgSLoamAs4C9ihScdAS5S4\nazYpPwY4ZibiXAKs36R8ErD6EFKMiIiIiIiImK2p2rmzO0la2fbtdedRh56eHk+aNKnuNCJiFkma\nbLun7jxi1qQNjuhuaYO7W9rgiO43q+1wV47AkPRp278BPiTpDQtn2j66hrQAkLQlcHif4rsbFnuK\niIiIiIiIiJnUlR0YwPzl54K1ZtGE7QnAhOGoa4v1Dh6OambahdccCMDWK7VjOZKhG3fnEQBstdDu\nNWfS3Pinq6VU1v7Cj2vOpH/XH7sPAFu86VM1Z9LchS+dBsChUz9ccybNfXu1P9adQrTAaucf1Ja4\nU7ep4r5v+6PaEv9v5+wHwKabHdaW+H+9aH8A3rV7+75LuPakfVnhxCPaFv/ePaq/X6uc+722xL91\nu+8CsPo32tPO33xk1UZvtcYBbYk/fsohALxnxx+1Jf7fz/g6AJtdvG9b4l/0/tq+54oW2nq5r9ad\nQlPj7q9ml2895is1Z9K/cQ/8FICtR+9dcybNjZtW7YvQrr+DQ9X7d7TT3wevet5B9SYygFu2PWiW\n7+3KDgzbv5Q0Cnjadud+youIiIiIiIiIlujabVRtvwrsVHceEREREREREdF+XTkCo8EVkn4OnAE8\n11to+7r6UoqIiIiIiIiIVuv2Doy1ys/eSaoCDGxaTzoRERERERER0Q5d2YEhqXdVpz9SdVio4XT3\n7gsbEREREREREU116xoYC5bHusDngaWBZYD/AdZpV6WSRkm6XtIfy/Gakq6UdJOkP0haaIB7x0r6\nj6QbyuMXDed2lDRF0lRJfbdgjYiIiIiIiBjxunIEhu3vAUi6DFjH9jPl+CDgT22s+qvArUBvR8UJ\nwH62L5W0B/AN4DsD3P9P22s1FkhaHDgSWNf2NEmnSNrM9kVtyD8iIiIiIiKiK3XrCIxeSwEvNRy/\nVMpaTtIY4ENUnRa93g5cVp5PBLafhdArAnfanlaO/9JfHEl7SZokadK0adOaXRIRERERERExW+r2\nDoxTgWskHVRGX1wNnNymun4CfBN4raFsKrBNeb4DsNwMYrylTB+5VNKGpewfwMplismcwLb9xbF9\nvO0e2z2jR4+e5RcSERERERER0W26ugPD9qHA7sAT5bG77R+2uh5JHwYetT25z6k9gC9Imky1JsdL\nb7h5uoeA5csUkn2B0yQtZPsJqnU8zgAuB+4BXm3xS4iIiIiIiIjoal25BkYj29cB17W5mvcCH5X0\nQWAeYCFJv7H9aWALAElvp5pi0l+eLwIvlueTJf2TagrKJNt/AP5Q4uxFOjAiIiIiIiIiXqerR2AM\nF9vfsj3G9ljgk8BfbX9a0pIAkuYADgB+0V8MSaMljSrPVwRWAu4qx71xFgW+wOvX2YiIiIiIiIgY\n8dKBMTQ7SboDuA34F3DSANduBEyRdANwNrC37cfLuWMk3QJcARxm+452Jh0RMVzKGkX7tSHuWEmf\nanXciIjZSdrgiJjddP0UkuFm+xLgkvL8GOCYQd53DnBOP+d2alF6EREjxVjgU8BpNecRETESjSVt\ncETUICMwIiKipSR9W9Idkv4GrFzK1pJ0laQpks6VtKikJcsiyEhaU5IlLV+O/ylpPkknS/qppL9L\nukvSx0s1hwEblp2d9pE0j6STJN0k6XpJ7y9x/iRpjfL8ekkHlucHS9pT0iaSLpF0tqTbJP1Wkob5\nVxYR0TJpgyNidpYRGC0maUvg8D7Fd9vertV1XXjNga0O2VLj7jyi7hQGNP7pgWb81O/6Y/epO4UZ\nuvClzv7i5dur/bHuFEYcSetSrRW0FtXfmOuAyVTbXn/Z9qWSDga+a/tr5U3vQsCGwCSqN8R/o9r5\n6fnyPnZp4H3AO4ALqKbh7Q/sZ/vDpd6vA7b9TknvAC4siytfXmLeC7xCtSgzpb69S+y1gdWopgJe\nUa75W9t+SRERbZI2OCJmdxmB0WK2J9heq8+j5Z0XEREdakPgXNvP236a6s3u/MAiti8t15xCtS4Q\nwN+p3qxuBPyg/NyQ6k1vr/Nsv2b7FmCpfup9H/AbANu3AfdS7fR0eYn5XuBPwAKS5gPeYvv2cu81\nth+w/RpwA9XQ6DeQtJekSZImTZs2bdC/kIiIYZQ2OCJmaxmB0cW2fNf36k6hqQnXfheArd/2jZoz\naW7cP44EOj+/d+7745oz6d9NR1ejQ7ac/zM1Z9LchOdOBeDG+5arOZPm1lz+/rpT6CSXUb1ZXgE4\nH/h/gKne6PZ6seH5zA4tvhboodr1aSKwBLAn1TeSzeK/Sj9/G20fDxwP0NPT4//MZCIRER2oa9tg\nHpnJTCJitpARGBER0UqXAdtKmlfSgsBHgOeAJyRtWK7ZBej9JvBy4NPAneXbt8eBDzLj4cPPAAs2\nHF8O7AxQhi0vD9xu+yXgfmAH4Mpy3X4lz4iI2U3a4IiYrWUERkREtIzt6ySdAdwIPEr17RvArsAv\nytDhu4Ddy/X3lAXbet/M/g0YY/uJGVQ1BXhV0o3AycCxwHGSbqKaZ72b7d5v9S4HNrP9H0mXA2N4\n/fDoiIjZQtrgiJjdpQMjIiJayvahwKFNTq3fz/XLNTz/AdU87N7j3fpcu0D5+TKwaZ9Qu/cT/zvA\nd8rzf9EwBLpxa+xy/KVmMSIiukXa4IiYnWUKSURERERERER0vHRgDELZYuoaSTdKmirpe6V8TUlX\nlj2v/1C2oZpRrOUlPStpv4ayHcu+3FMl9d2CNSIiIiIiImLESwfG4LwIbGp7Tap9tbeStD5wArC/\n7XcC5wKD2dbiaGBc74Gkxfn/2bvvODmruv3jnysk1FClSgihiFQJEqqFJs1HkSIqCogoqNjwURSU\nn4KVIChNVJSqqFQFlSpNHpWShEBoEpUiPYiANCHJ9fvj3CuTZTdt7tmZ2b3er9e+Mnvuub/nzO7m\nnvucOed84TuUtYHrAStK2q7uFxARERERERHRzTKAMRdcPFt9O6L6MiW/dc+mR1cCe8wujqRdgXuB\nOxqKV6fs/NyT0Pr3/cVJ/uuIiIiIiIgYqjKAMZckLSBpMmVH5ytt30gZiHhX9ZQ9gVVmc/5ISm7t\nI3sd+ivwekljJA0Hdu0vju1TbI+zPW655ZZr7gVFREREREREdJEMYMwl2zNsj6WkftpU0vrA/sBB\nkiZScmG/NJsQRwDfa5jJ0RP3X8DHgXMoKaXuA2bU/gIiIiIiIiIiuljSqM4j209JugbYyfYxwA4A\nktYC/mc2p24GvFvS0cBSwExJL9o+yfZvgN9UcQ4kAxgRERERERERs8gAxlyQtBzwcjV4sQiwPTBe\n0vK2H5c0DDgc+GF/MWy/pSHeEcCztk+qvu+JszRwEPCeFr6ciIiIiIiIiK6TJSRzZyXgGkm3ATdT\n9sD4LbCXpHuAu4GHgdPnM/7xku4E/ggcZfueOhodERERERERMVhkBsZcsH0bsFEf5ccDx89HvCN6\nfb/XfDcuIiIiIiIiYgjIDIyIiIiIiIiI6Hiy3e42DCqSdgTG9yq+1/ZuddYzbtw4T5gwoc6QETGA\nJE20Pa7d7Yj5k2twRHfLNbi75Roc0f3m9zqcJSQ1s305cHm72xERERERERExmGQAo4ttv+U32t2E\nPl35p8MB2Pl1X2hzS/p26dSjAdh5uY+1uSV9u3RaSWaz+QeObXNL+nfD2Z8DYMeRH2xzS/p2+bNn\nAjDz0bXa3JK+DVsx+/QOBjuP+nRL4l764AkA7LTMAS2Jf9mTPwZgx4U/0JL4l794NgDbD9uzJfEB\nrpx5HjuPPrhl8S994Digde9jPe9Drf4d77Tk/q2J//RpAxJ/55U+0ZL4lz7y/ZbEjYHVqmtYs3qu\ngTss+P42t6R/V7z0c6C11+lmXDnzPAB2XGzfNrekb5c/dxbQ+T+/ndc8pM0t6d+lf/3OfJ+bPTAi\nIiIiIiIiouNlACMiIiIiIiIiOl4GMCIiIiIiIiKi42UAIyIiIiIiIiI6XgYw5oKkVSRdI+lOSXdI\n+kxVPlbSDZImS5ogadPZxNi0et5kSbdK2q3h2Hsl3VbF7p2CNSIiIiIiImLIywDG3JkOfM72usDm\nwCckrQscDRxpeyzwler7/twOjKueuxPwI0nDJb0G+A6wne31gBUlbdfKFxMRERERERHRbTKAMRds\nP2J7UvX438BdwMqAgSWqpy0JPDybGM/bnl59u3B1LsDqwFTb06rvfw/s0VcMSQdWMz0mTJs2ra+n\nRERERERERAxKw9vdgG4jaQywEXAjcDBwuaRjKINBW87h3M2A04BVgX1sT5f0V+D1VdwHgV2BBfs6\n3/YpwCkA48aNc1/PiYiIiIiIiBiMMgNjHkgaCVwAHGz7GeDjwGdtrwJ8Fjh1dufbvrFaJrIJcJik\nhW3/q4pzDnA9cB8wo3WvIiIiIiIiIqL7ZABjLkkaQRm8ONv2hVXxB4Gex+cB/W7i2cj2XcCzwPrV\n97+xvZntLYC/APfU2faIiIiIiIiIbpcBjLkgSZTZFXfZ/m7DoYeBrarH2wJTZxNjNUnDq8erAmtT\nZlsgafnq36WBg4Cf1PwSIiIiIiIiIrpa9sCYO28C9gGmSJpclX0JOAA4vhqYeBE4cDYx3gwcKull\nYCZwkO0nqmPHS9qwevw125mBEREREREREdEgAxhzwfb/Aern8MZzGeOnwE/7ObbXfDYtIiIiIiIi\nYkjIEpKIiGgbSQdLWrSF8b8m6W19lG8t6betqjciohvkGhwR3SYzMGomaUdgfK/ie23vVnddV/7p\n8LpD1urSqUe3uwmzdem0H7a7CbN1w9mfa3cT5ujyZ89sdxNma9iKWY3VBQ4GfgY83/uApAVsN5WV\nyfZXmjk/ImKQyzU4IrpKZmDUzPbltsf2+qp98CIiYqBI2lfSbZJulfRTSWMkXV2VXSVpdPW8MyS9\nu+G8Z6t/t5Z0raTzJd0t6WwVnwZeC1wj6ZqecyQdK+lW4MuSft0Qb3tJv+qnjQtU9d8uaYqkz/Zu\nk6SdqvonAbs3nLuYpNMk3STpFknv6qeOAyVNkDRh2rRpzf1QIyLmUq7B/31ersERkRkY3WynDb7c\n7ib06bIp3wRg57W+2OaW9O3Se8oEmZ3HfLbNLenbpfd9D4ANP/29Nrekf7eeUH52Oy62b5tb0rfL\nnzsLgKceXqXNLenbUq/9R7ubMNckrQccDmxp+wlJywBnAmfaPlPS/sAJwK5zCLURsB4le9MfgTfZ\nPkHS/wLbNGxqvBhwo+3PVRmg7pK0nO1pwIeA0/qJPxZY2fb6VbuX6vU6FgZ+TMkY9VfgnIbDXwau\ntr1/dd5Nkn5v+7nGGLZPAU4BGDdunHl0Dq84IqJJuQa/4lXX4Mfm8IojYlDKDIyIiJidbYHzem5u\nbT8JbAH8vDr+U0qWpTm5yfaDtmcCk4Ex/TxvBnBBVZer+HtXN7VbAJf2c97fgdUlnShpJ+CZXsfX\npiznm1rF/VnDsR0oWaImA9cCCwOj5+I1RUS0Wq7BERENMgMjIiLqMp1qYFzSMGDBhmP/aXg8g/7f\nf17steb6dOA3lFTV59me3tdJtv+lko56R+BjwHuA/eey3QL2sP2XuXx+REQnyjU4Iga9zMCIiIjZ\nuRrYU9JrAKrpy38C3lcd/wBwffX4Pl5JLb0LMGIu4v8bWLy/g7Yfpkx5PpxyI90nScsCw2xfUD33\njb2ecjcwRtIa1feN6asvBz5VTZdG0kZz0e6IiIGQa3BERIPMwIiIiH7ZvkPSN4HrJM0AbgE+BZwu\n6RCgZ100lPXNF1Wbv10GPNdXzF5OAS6T9LDtbfp5ztnAcrbvmk2clas29QzMH9brdbwo6UDgd5Ke\np9zw99y0fx04DritOv9e4B1z0faIiJbKNTgiYlYZwJgLkk6jXEgfb9icaCzwQ8o6venAQbZv6uf8\n7YGjKFP5XgIOsX11dWwv4EuAKSPcezdspBQR0Xa2z6RsGtdo2z6e9xiweUPRF6vyaynrmnue98mG\nxycCJzZ8P7KPJryZcmM+uzbeyqs/8cP2fg2PL6Osw+79nBeAj84ufkREu+QaHBHxiiwhmTtnADv1\nKjsaONL2WOAr1ff9eQJ4p+0NgA9SNkRC0nDgeMruz28AbgM+2W+UiIghRtJE4A3MuuFbREQMgFyD\nI6LTZAbGXLD9B0ljehcDS1SPl6TMnujv/Fsavr0DWETSQsBMysZFi0n6ZxXvr/3FqabeHQgwevRo\nll2iv2dGRAwOtjfuXSbpRmChXsX72J4yMK2KiBgacg2OiE6TAYz5dzBwuaRjKDNZtpzL8/YAJtn+\nD4CkjwNTKOsUpwKf6O/EV+W//k9/z4yIGLxsb9buNkREDFW5BkdEO2UJyfz7OPBZ26sAnwVOndMJ\nktYDxlOt85M0ooqzEfBayhKSw/oNEBERERERETFEZQBj/n0QuLB6fB6w6eyeLGkU8CtgX9t/q4rH\nAtj+m20D5zL3MzkiIiIiIiIihowMYMy/h4GtqsfbUpZ/9EnSUsDvgENt/7Hh0EPAupKWq77fHphd\niqqIiIiIiIiIISl7YMwFSb8AtgaWlfQg8FXgAOD4KpPIi1Sba/bjk8CawFckfaUq28H2w5KOBP4g\n6WXgfmC/1ryKiIiIiIiIiO6VAYy5YHuvfg69amfmfs7/BvCNfo79EPjhfDYtIiIiIiIiYkjIEpKI\niIiIiIiI6Hgqe0dGHSTtSMky0uhe27vVXde4ceM8YcKEusNGxACRNNH2uHa3I+ZPrsER3S3X4O6W\na3BE95vf63AGMLqUpGmUPTPqsizwRI3x6pb2Na/T2zjU2req7eXm/LToRPNxDW7133fit7+OxO+u\n+LkGd7HcB3ekTm9j2tecVrRvvq7DGcAIACRN6ORPItK+5j3qWCgAACAASURBVHV6G9O+GMxa/feT\n+O2vI/EHd/wY3Dr976fT2wed38a0rzmd1L7sgRERERERERERHS8DGBERERERERHR8TKAET1OaXcD\n5iDta16ntzHti8Gs1X8/id/+OhJ/cMePwa3T/346vX3Q+W1M+5rTMe3LHhgRERERERER0fEyAyMi\nIiIiIiIiOl4GMCIiIiIiIiKi42UAIyIiIiIiIiI6XgYwYhaSDm53GzqdpE0krdjw/b6SLpJ0gqRl\n2tm2OZG0R7vb0M0kbdbuNkRERERr5D54znIfPLR1wr1wNvGMWUh6wPboNrfh4tkdt73LQLWlL5Im\nAW+z/aSktwK/BD4FjAXWsf3udrZvdjrk97sq8JTtp6vvtwF2Be4HTrL9UjvbNzud8POLzifpQuBC\n4Ne2n213e+ogaXnbj9cY7w22b6srXtRH0ldsf63JGMvafqLh+72BTYHbgR+7hpvPqqP0SeBh4FTg\nS8AWwF3At2z/q9k6YujphPf53Ae3Tif8fqt25F64CZmBEb2p3Q2g3ICMAq4HjgGO7fXVbgvYfrJ6\n/F7gFNsX2P5/wJptbNfc6ITf77nAYgCSxgLnAQ8AGwInt7Fdc6MTfn7R+Taj3Ig8IOlcSbtJWrCu\n4JKWlHSUpLslPSnpn5LuqsqWqiH+Mr2+XgPcJGnpGj9du0XSVElfl7RuTTHniqSvtCju1TXGWkbS\nVyR9RMWXJf1W0nckLV1XPf34SA0xruh5IOlwYB9gIrA98N0a4gP8jPJesjFwDbAiMB54ATijpjpi\n6OmE9/ncB7dOJ/x+IffCTRne7gZEx+mEKTkrUm5y9gLeD/wO+IXtO9raqlcsIGm47enAdsCBDcc6\n/f9UJ/x+F7H9cPV4b+A028dKGgZMbmO75kYn/Pyi8z1u+92SlgDeBRwAnCLpt5Rr2RWzP32OzgWu\nBra2/ShANZ33g9WxHZqM/wTlU6BGKwOTKP8HVm8yPsBtlE7tXsDFkp4DfgH80vZ9NcSfnY8Azc4w\n6D17RMBaPeW239BMfErnfAqlc7539Xg85b3xDMrf1XyT9Ex/h4BFmondEKfH7sBbbD8n6eeUv6M6\nvNb22yUJeND21lX59ZI6/b0kOlcnvM/nPrh1OuH3C7kXbkqn/5FFC0j6N33/8dV149IU2zOAy4DL\nJC1EuYBfK+lI2ye1t3VAucm+TtITlE96rgeQtCbwdDsbVrVjCv3/flcY4Ob0pfHGdlvgMADbM8t9\naHtJ+g39//xeM8DNie5kANvPAD8FflrNYtgTOJSGT6fn0xjb42epsAxkjJe0f5OxAQ6h3DwfYnsK\ngKR7ba9WQ+wetn078GXgy5I2Bd4H/F81PXXLZoIPQAf9PuAZ4BuU9wFR3gveWUNsaH3n/ClgE9uP\n9T4g6R81xF9E0kaUmb4jbD8HYPtlSTNqiA8wrJqNsjgwUtIY2/dV/9dqm/EUg0/ug5uW++Dm5V64\nCRnAGIJsL97uNsxJdcH+H8pFewxwAvCrdraph+1vSroKWAm4omEt7zDKGsB2e8dsjrV93R9wtaRz\ngUeApSmfJCNpJaAT1vwdM5/HInq8at8L2/8Eflh9Net+SV8AzuzpgEpaAdgPaLrzWX0KdA7wvaoz\n+1Xq/8Rlljs02zdRlql8DnhrDfFb2kG3vYuk3YBTgGNsXyzpZdu9Z67Mr1Z3zs8CVgVe9fMBfl5D\n/Ed4ZanIE5JWsv1I1f7pNcQH+DZwd/V4f+An1Y3/OsCRNdURg1Dug5uT++Ba5F64CdnEMzqOpLOA\n9YFLKNOJb29zk7qKpL9TOknHVqP4PZ2bY4G1bY9rc/vGAJtT3vjOtf1QVb4R8A7bX29f60DSyP42\nXpS0hu2/DXSbIhpVHdtDKcsIlq+KHwMuBsY3rE2uo65dKJsjjrG94pyePw9x32+7jo5yf/G/AVxc\nDYz0Pjbe9hdrqmcx4OvAGsDGtkfVFHcv4Ljq24OAj1MGkdYFjrR9Sh31DDRJCwAL2X6+xniyPV3S\ncMomgg/ZfqSO+BHtkPvg5nT6fXDVnjHkXni+ZQAjOo6kmcBz1beNf6CiTDteYuBb1T2qzs1RwJbA\nZ4ANgP8FjgZ+YHtmG5vX88byI8qnlh33xiLpb8Bhts9tKFsYOBx4n+1O36AqOpCkb9n+UrvbMT8k\nLQKskZvo/knaENjCdh0zbHpitrRzXi1P2ZSyvwnAQ8BNdWQIaahjHLAKMAO4x/bdczhlXuOPBp6x\n/VTVIRgH3J2/1ehmuQ9uTqffB0PuhZuVAYyIQUrSZ4DvUVLMbW77wTY3CfjvG8u3gTfRgW8sktYA\nTgIWoHzyuR5lutyvKZ98Doq0mNE6kk7oXUTZsPIsANufbkGdZ9net8Z4m1JulG9WyRKyE6VjeElN\n8ZegrPkdBVzaOBtD0sm2D6qpnhG2X+5VNkuKz7pJWruOjnorBxgk7UDZ6X5qFRfK72JN4KBmN5qV\ntBXlRvwpykakf6RMk34Z2Md208t4JB0KfBT4D+Ua/fmqns2BU23Xle0kIrpQp94HQ+6Fm5UBjIhB\nRiWN4nhKKscvAG+n7BL9Gdu1pflrVie/sQBIOoTy5vIosGMH7f4dHa7aY+E6ymadPXs99HSwsH1m\nk/Ev7l0EbEO1htb2Lk3G/yqwM2WfrCsp15JrKBt7Xm77m83Er+q4gNJ5voGyf8HLwPtt/0fSJNtv\nbDL+NpQNVBemZL040FV2kzriz6HuB2w3tc56AAYY7gJ2dq+ML5JWAy6xvU6T8W8BdrA9rYr5Xdu7\nSerZHLbZTDlIuoMy42JRyqaqq1f1LQbcaHv9ZuuIiO7TLffBkHvh+ZUBjIhBppqWdjJwnEuKq54c\n0ycD99veq83t6+g3lmqq9iGUVIvjKe1bnNJp+Es72xbdQdLilH0Rlgc+b/thSX+3XUf6USRNAu4E\nfkKZXizKrvDvA7B9XZPxp1CWKyxEuWkZZfuZainJjW4+RSiSJtse2/D9lyn/13YBrqxhAONmYD/b\nd0h6N+UGbB/bN0i6xfZGTcbvPcvmv4eADzY7xXsABhimAuv0vEc0lC8I3Nns9GBJt/X8nVRLYW7u\n+Z1KusP2es3Eb6yjiv8IsGLPp5aSbs8ARsTQ1On3wVV7ci/chGQhiRh83tp7BNf2ZGBLSQe0qU2N\nJlHeRD5RvbFc0fPGIqkT3lgmA9cCb7T9NHCKpHcAF0u6oFv3MYiBY/vfwMGSNgbOlvQ7yu7sdRlH\nmXL6Zcqn2ZMlvdDswEWD6dWa3Ocl/c0lHSy2X6jWZtdhIUnDejqcLrvaPwT8ARhZQ/wFez4psn1+\nNSBwoaQvUk9GlQ8Bn6MsX+itjmvYcKCvT+IeAkbUEP804GZJv+SVzDWrUAbBTq0h/gRJp1JmBe1C\nuaYiaVHKlOQ6TJL0c2Ax4CrgTEmXUVIS3llTHRHRfTr9PhhyL9yUzMCIiAElaVR/U+QkHWD7xwPd\npl5t2Nj2xD7KFwZ+Z3u7NjQrulS1j8FBlA0e96459ijK1NPHgF2aXbbQEPdGYBvbzzcOMkhaErim\njuUXko6mpN/7fa/ynYATbb+uyfgTKDu5P9pQNgr4LWVD0qbSKEq6Gjjc9p/6OHav7dWajH8Y8B6g\nrwGGc21/u5n4VR3rUgYXGvfYuNh2051/SSOAAyhZU24FTrM9o5rFs7xrSDdbfUK4J2VA6nzKJ5l7\nAQ8A37f93GxOj4hom9wLNycDGBERDdQF6beie0haBsA1pjbto47/Ad5U1ycikhay/aqZBZKWBVay\nPaWOelpJ0tuAabZv7VW+FOUTr6b28ah+ry+6pnSg/dTRsgGGiIiI/nT6vXAGMCIiGqgL0m9FZ1NJ\n7Xg0ZT3rU5R9EZagTKc/tPe+Bk3UswINnVvbj9URdwDjrw28i1d30O+qs554tWo2zWHArpS9Wgw8\nDlwEHGX7qSbjj6Ss696dMnPkJeBvwA9tn9FM7D7q2IOywWlPHT9odqPciIihrNPvhTOAERHRh07f\nGTo6l6Q/A8cB5zd8crEAZbr7wbY3bzL+WMonI0sya4aKpygbbE3q5PhVHV+kTPf/Ja/s9TCKskTi\nl7aPajJ+qzvo3R7/csqA2pk9y2wkrQh8ENiu2Swhki4CfgX8nrIUZjHK7/pwymBY07OFBqKOiIih\nrFPvhTOAERHRoNN3ho7OJ2lqf3s4zO7YPMSfDHzU9o29yjcHfmR7w06OX8W6B1jP9su9yhcE7qjh\nZ9TqDnq3x/+L7dfP67F5iH9r49+JpJttbyJpGCXLydrNxB+oOiIihqJOvxeuc1f0iIjBYBIwFRhn\n+wrbBwP7AN+Q9Iv2Ni26xERJJ0vaTNJrq6/NJJ0M3FJD/MV6Dy4A2L6B8il0p8cHmAm8to/ylapj\nzRpje3zjJp62H7U9Hlg18blf0heqZUJAWTJUzYz5x2zOm1vPSXpzFXcX4EmAatqxaog/UHVERAxF\nHX0vnDSqERGz6ob0W9HZ9gU+DBxJr/0dqCdF5aVVatazmDVDxb7AZV0QH+Bg4CpJUxvqGA2sCXyy\nhvj3S/oCZQbDY/DfPT32o54OerfHfy9wKHBdwyDGo5S/0ffUEP9jwE8krQXcTvn/gKTlgO/XEH+g\n6oiIGIo6+l44S0giIiK6jKSd6XsDzEu6IX5VxzBg01513Nyzb0iTsZemdNDfBfTuoI9vNitMt8eP\niIjoVhnAiIiIqJmkHSkbMDZ2zi+yXdcMhkGh1ZlOon+tzgIjaXVeyUIyA7gH+LntZ+qIP1B1RERE\nZ8kARkRERI0kHQesRVmC0ZhhY19gqu3PNBl/OGW6/KsGSIBTe2+M2WnxqzoaM508SNmzoO5MJ63u\noHdt/AHIAvNp4J3AdZTN326h/G53o/x+r20m/kDVERERnScDGBERETWSdI/ttfooF3BPDRk2fkHp\nqJ3JrJ3PDwLL2H5vJ8ev6mh1JpVWd9C7PX6rs8BMAcbaniFpUeAS21tLGk2ZibRRM/EHqo6IiOg8\n2cQzostI+pPtLdvdjojo14uSNrF9c6/yTYAXa4i/cR8DJA8CN1Qd006PD7PJdCKpjkwnH6bvDvp3\ngTuApgYABkH8niww9/cqrysLDJR7zBnAQsBIANsPSBpRU/yBqiMiOkjugyMDGBFtJmm47elz+/xc\ntCM63n7ADyQtziufnq8CPF0da9aTkvYELqhSRvZsiLkn8K8uiA+tz3TS6g56t8dvdRaYnwA3S7oR\neAswHv6bIaSuDUgHoo6IaLHcB8e8yhKSiBpJ2hf4PGDgNuBc4HBgQeCfwAdsPybpCGANYHXgAdt7\n9RFrPeD06txhwB62p0p61vZISV8Ddqmevhxwhe0PSdob+HR13o2UtcBN7+ofEfNG0orMukHlozXF\nHUPprG1LGVAQsBRwNXCo7XtbEH9J4Jo64jfU07JMJ5J2Ak6i5LF/VQe92c1Uuz1+VUfLssBU8dcD\n1gFut313HTHbUUdEzL3cB8dAyABGRE2qC+2vgC1tPyFpGcoF/CnblvQRYB3bn6su3O8E3mz7hX7i\nnQjcYPvsal3yArZf6LlwNzxvKeB6yie7zwNHA7vbflnSyVWMs1r2wiPiVarOIbZnVv9/1wfuqzv9\npaTXVPX8s864AxW/lQagg97V8fuobxfbF7co9prAhsBdtu+sOfZylP1BZgB/t/1snfEjYu7kPjgG\nSpaQRNRnW+A8208A2H5S0gbAOZJWoowEN35yeXF/F+3Kn4EvSxoFXGh7au8nVJsC/gz4ru2Jkj4J\nbEyZVguwCPB4Da8tIuaSpF2BHwEzJX0M+BLwLPB6SR+3/Zsm448GHrf9ImWq/H6S3gjcCfx4Xqbi\nzqaOkcBOVOkpq70vruhZUlJD/AWAj1A6npfa/lPDscNtf6PZOqq23tAQd5maO//DbN9QxR4JrE2Z\nqVLLIFXv9veQNLLZTrqk3fsoPrnKQIPtC5uMfw2wZ9WJ2Qf4f8AfgCMknWL7xGbiV3WsC5wAjKHM\nTrkFWF7SdcBnbD/dbB0RMU9yHxwDYli7GxAxyJ0InGR7A+CjwMINx56b3Ym2f06ZGvcCcImkbft4\n2hHAg7ZPr74XcKbtsdXX620f0eRriIh581XKp81bAj8F9rW9HfCm6lizLuGV9++jgP+hTJPdBDil\n2eCS3kNZjrITZT+ETYB9gMnVzWgdfgRsRZlSfGK1OWWPvjrX80TS4Q2P160GYCZKuk/SZjXE3w94\nTNI91VKY2yjLbm6V9Kqp0DWrYwbDOcD+wDson4K+E1is+vcdNcRfrqcTQ5nKvYXtjwCbAQfUEB/g\nNOATttcE3gzcbXs14I/AqTXVERHNyX1w1C4zMCLqczXwK0nftf3PaurckpRpv1BSEM41SatTpsOe\nUH3i+oaqjp7j7wTeBmzTcNpVwEWSvmf78aoNi9vuvRFcRLRQz34Xkh6w/Zeq7P6epSVNGmb7+erx\n24BNqk/rfybp1hriHw5sbvt5ScsCZ9veUdIbKAMPdWygtqntNwBIOony6f+FlNShqiH+7kDPLI7v\nUD6Rv1TSpsBxNP8aPge8HlgcuBXYyPbfJK0AXAn8opngkv63v0NU2TaatCVl8Otm2z+o6tza9odq\niA3wsqSVbT9EmX3U01H5D7BATXUs0vB/6yZJP6we/3g2P7+IaJ3cB8eAyAyMiJrYvgP4JnBd1Yn4\nLmVk+DxJE4EnZnN6X94D3C5pMmX9fO/1e/9LWRt9k6TJkr5WrS0+HLhC0m2UG+mV5vc1RcT8aRio\n2L+hbAHKFNpm/aPhk6j7KMs8/rtfRQ1E+cQLSsdzeQDbtwFL1FTHf38OtqfbPhCYTLk5raOD3mhl\n25dWdd1EmVLcrBm2n6g2NH3W9t+q+I/VEBvgW8DSlAGSxq+R1HDv5pLid3tgQUnXVAM7dW6K9lnK\n+9DXKGlfr5b0VUqGmdNne+bc+5uk/yfpTZKOpfz9oJJCNfe3EQMs98ExULKJZ0RERI0kbQJMqfao\naCwfQ9mw7GdNxl+FciO3ACU165spnbelgM/bvqrJ+OOBsZQ9C3ai7FHxreqTrOttr9dM/KqOnwE/\nc69sGtUmbz+wPaLJ+E9R2i9gC2B0z6wVSbfbXr/J+BdTOuaLA+tS9l+4kPJp4Ja2d2wy/p+AT9me\n2Mexf9hepZn4veKtDHwPGGd79RrjLgm8H1iLMuP3QeAi15QtpNq470uUn/+twFG2/13Vu07P/iQR\nETG4ZAAjIiKiDSRdYHuPJs5fh1k7hzfXuMnm26k6hravrMqGASNs/6eOOlpJ0la9iibafrZa4vFu\n299vMv4SwCcosxZOAnYEPgTcD3zD9iNNxn898KTtaX0cW6HGmR4RERFdJQMYEW0maUfK5m+N7rW9\nWzvaExEDQ9IttjdqYfw/296i2+JL2r5n0CRao1cWmMts/7HhWNNZYAYiy8xA1BERrZf74JhXWSMY\n0Wa2L2/YLbnnKxftiMGv1Z8gLDznp3Rk/JZmkJDUdKaWVseXtICkj0r6uqQ39Tp2eH/nzYPGLDAn\n1J0FhhZnmRnAOiKixXIfHPMqWUgiIiIGp1YPkMx3/GoPiT4PAU1vRlrt19Ff/Ld3enxK53xR4CbK\nAMN1tnsyazRmWJlfrc4C0+r4A1VHRER0mAxgREREtMdQ7mS9BdibkmKzkYBNa4g/jbIfRePP2NX3\ny3dB/FZ3zmfJAgMcKOkr1JcFptXxB6qOiIjoMBnAiIiIaDFJy9t+vFfxF1tdbQfHvwF43vZ1rwoq\n/aWJuD3+Dmxn+4E+4v+jC+K3unM+QdJOjVlgbH9N0sPAD7og/kDVERERHSabeEZERNSoj+UFAiYC\nG1Hed58coHasb/v2JmMsA9BXm+uI3yqSPgH8n+1b+zj2Kdsndnj8lqaZnYd2tHRD1YHYsDWbwkZE\nDC4ZwIiIiKiRpJmU5QWNRlFSndr26k3GfxK4EPgFcLVrfiOXNBo4GtgOeIoyALME5dP/Q23fV2d9\nc2hLqzOpdHUHfQDiT7L9xm6NP1B1RETEwEkWkoiIiHodAvwF2MX2arZXAx6sHjc1eFGZBkwGvgY8\nKOl4SZvXELfHOcCvgBVtv872msBKwK+BX9ZYz9xodSaV3qn7En9WnbwMqZPqiIiIAZIBjIiIiBrZ\nPhb4CPAVSd+VtDj1ZgR5zvZJtt8EbAE8RNnk8e+SvlVD/GVtn2N7Rk+B7Rm2f0kNGULmUauniXZ7\nB73V8Ts2k02H1REREQMkAxgRERE1s/2g7T2Ba4ErKSkx6/LfTqvtB2wfXU2RfzvwnxriT5R0sqTN\nJL22+tpM0snALTXE7yTd3kFP5zwiIoaUZCGJiIhoEdsXS/oXsJWkHWxfUUPYa/qp627gyBri7wt8\nuIq1clX2EHAxcGoN8edFpv+3131dHn+g6oiIiAGSTTwjIiJqJOkm25tWjw8ADqLsH7ED8BvbR7Wz\nfZ1K0httT+pV1tJMJ5IutL37UIwv6a3AY7b/IqlnOdJdtn9XayNfXW9LNh6VtBol08+d1WBeREQM\nQhnAiIiIqJGkW2xvVD2+GXi77WmSFgNusL1BC+q82va2dcdtVXxJvbNCCLgIeCfl3mTSq8+ap/i7\nAFfYfrGZOPNZd6s66N+y/aWaYh0HbEqZiXs5JePMpcBWwC22D6mjnn7qfsD26Bri/Nr2rtXjdwHH\nUZZsbQl82/YZzdYRERGdJwMYERERNZJ0K7A1ZZ+pKxtTODYObjQR/7beRcBalMwn2H5DJ8ev6pgJ\n3MCse3ZsXpW52cESSS8Az1E65b8ALm/clLSV6uigSzqhdxGwD3AWgO1PNxn/DmB9YBHK8qCVbT8v\naQRlAGP9JuNf3N8hYFvbizUTv6qjcaDwT8AHbN8raVngKtsbNltHRER0nuyBERERUa8lgYmUzpol\nrWT7EUkjqWdPh/uAZ4BvAC9UMa+nzF6oQ6vjA+wJfBo42valAJLutb1NTfHvBrYF3g18Djhd0q+A\nX9i+rtngc+ig15GpZTfgOuAKXvmbeR/l76oOtu1qIAle2Qx0JvVs8P4WYG/g2V7losz8qEPjJ3AL\n2r4XwPYTDa8rIiIGmczAiIiIGACSFgVW6OloNRlrN+CzwDHVRqF/t716040coPhVHSOBrwOjKIMM\n19ZVh6RJvWa+rAi8B9gLGGV7lSbj/4v+O+jn2F6hyfiLU342ywOft/1wnb8DSeMpSy0Wpiy7WJsy\n+2Ur4O+2P9Zk/Espg1Ov2nBW0h9sv7WZ+FWcGZRZNgIWAlatBgoXBCbUMVMoIiI6TwYwIiIiulC1\np8bXgTWAjW2P6qb4DfVsBHwXWN/2cjXF7HepjqRVbd/fZPyWd9CrWBsDxwC/Az5pe0wdcavYW1Bm\nYtwgaQ3KrI8HgPNtd+0MBklLAevY/nO72xIREfXLAEZEREQXk7QhsIXtH3Zj/KoOAYvbfqameFvb\nvraOWO1W/WwOovwO9m5B/GUAbD9Zd+yBiF/VsTQwo66/n4iI6FwZwIiIiOgyVad2U2Dlqugh4CbX\n9KY+QPH3pOxjcD5lv4p3Ufau+GE3zwDoBpJGA0dTfu5PU5ZhLAFcDRxq+76a4m8HPFV3/KqO1wJH\nUf5uRlL+RgFOA75p++Vm64iIiM5Tx0ZNERERMUAk7QBMBY4A3l59HQlMrY51dPzK9yl7UuwD/BT4\nGHAz8Fbge80Gl7SKpF9Kul7Sl6rsGj3Hft3p8edQ95QawpwD/ApYyfbrbK8JrAT8GvhljfFXbFF8\ngJ8Bp9lekjIYdgGwDmWD+u/XVEdERHSYzMCIiIjoIpLuAnbu/Sm2pNWAS2yv08nxq1hTbG9Qdfwf\npXSkX5I0HJhUQyrYKykd2huADwMbA++0/c+aUtm2Ov7u/R2izFBpaq8QSVNtv25ej3VK/CrOrY2p\nUiVNtL1x9fhu22s3W0dERHSepFGNiIjoLsOBB/sofwgY0Ud5p8UHmA5g+2VJN9t+qfp+ek0pMJdr\n2LPjU5L2Bv4gaRdmTb/ZqfHPAc7uJ9bCNcSfKOlk4EzgH1XZKsAHgVu6ID7AtOrnfg2wOyX9b8/y\npMwwjogYpDKAERER0V1OA26W9Etm7Ry+Dzi1C+IDPCpppO1nbe/UU1ilO32phvgjJC1s+0UA2z+T\n9ChwObBYF8S/jZLC9vbeByS9rYb4+1JmjhzJrPucXEw9v+NWxwfYn5Kh5VBgMvDJqnwZ4LCa6oiI\niA6TJSQRERFdRtK6wC706hzavrMb4s+m3sWAxWw/3mScz1KWolzXq3wjSvrT7Ts8/luA+20/0Mex\ncbYnNBM/IiKiW2UAIyIiIgZcqzOdxOxJ2hHYlVl//hfZvqwb4ld1bAPsQZkhNAO4B/iJ7b/WVUdE\nRHSWDGBERER0EUlLUqbI7wosT9kn4XHgIuAo2091cvyqjh2AkynZTnrSX44C1gQOsn1FDXV0dQe9\nlfElHQesBZzFK/udjKIs/Zhq+zOdHL+q49vAisBVlJ/TvZQBjIOAb9k+r9k6IiKi82QAIyIiootI\nuhy4GjjT9qNV2YqUDRK3s91UqtNWx6/itTqTSld30Acg/j221+qjXMA9NWQhaWn8KtYU2xtUj4cD\n19l+k6Slgettr99sHRER0XkygBEREdFFJP3F9uvn9VinxK/iTAXWsT29V/mCwJ2212wyfld30Acg\n/m3Ah23f3Kt8U+DUnoGBTo1fxboV2Mb2k5JGA+fa3rw6doft9ZqtIyIiOk+ykERERHSX+yV9gTJD\n4jEASSsA+/FK1pBOjg+tz3TyoqRNeneggU2AFxOf/YAfSFqcV2Z4rAI8XR3r9PgA3wJukXQP8Hrg\n4wCSlgNuramOiIjoMJmBERER0UWqKfKHAu8CVqDsUfEYJUXleNtPdnL8hnpalulE0huBHwB9daA/\nYXviUI7fUM+KNPz8e5YM1WUA4i8DrA78tY69WSIiovNlACMiIqLLVFPxbftmSesBOwF32b6khtib\nAXfbflrSopTBjDcCd1A2R3y62ToGyiDooLcsfquzlEujSgAAIABJREFUwAxUlhlJ42jIQmL77jrj\nR0REZ8kSkoiIiC4i6avAzsBwSVdSOonXAodK2sj2N5us4jRgw+rxccBzwFHAdsDpwO5Nxh+oTCcC\nVuWVDvRwSY/V3EHvyvizywIjqeksMK2OX9WxFXAs8BSwMfBHYGlJLwP72K5ruVNERHSQzMCIiIjo\nIpKmAGOBhYBHgVG2n5G0CHCj7Tc0Gf+uniwgkibZfmPDscm2xzYTv4rT6kwqLU3TOgjitzoLTEvj\nV7FuAXawPa2K+13bu0naHjikjmw5ERHReTIDIyIiortMtz0DeF7S32w/A2D7BUkza4h/u6QP2T4d\nuFXSONsTJK0FvFxDfIAxtsc3FlQDGeMl7V9D/OOBt/XXgQaa7UB3e/zhvLK3RqOHgBFNxh6I+AAL\n2J5WPX6AMlsF21dWaWgjImIQygBGREREd3lJ0qK2n6dMnQf+uyyjjgGMjwDHSzoceAL4s6R/ULKF\nfKSG+ND6TCfd3kFvdfxWZ4HpK/5o4L01xQeYIOlUykyeXSjLqKj2bVmgpjoiIqLDZAlJREREF5G0\nkO3/9FG+LLCS7Sk11bMEsBpVZ7pnoKGm2D2ZTnahZDqBejOpHAa8B+irg36u7W8P5fhVHetQMs3U\nngVmgOKPAA4A1qWkTT3N9oxqKdXytu+vo56IiOgsGcCIiIiIASdpDcqGoD0ZJP4C/LxnSUwN8VuW\nprWK3+oOekvbP9AkLW/78RbX8Rrb/2xlHRER0V4ZwIiIiIgBJenTwDuAPwBvB26hZJPYjbJJ5bXt\na93QJulS2zs3GWOZPoonARtR7j2bmmFT1XEUcIztJ6pUqudSllCNAPa1fV2zdUREROfJAEZEREQM\nqJ5MKtWU/0UpmSm2ljQauMj2Rk3GX4KSpnVUFfsXDcdOtn1Qk/F3sn1Z9XhJSjrPTYHbgc82u9ym\n6pB/hzLr4jDKnhKbULKSHGj7libjv7G/Q8Bvba/UZPyZQO8lHKMo+3rY9urNxK/qmGJ7g+rxNcAX\nbN9cbTb7c9vjmq0jIiI6TzbxjIiIiHYYTlk6shAwEsD2A9XeBs06ndLZvwDYX9K7gfdXe4dsXkP8\nbwGXVY+PpaSzfSdlScyPgF2bjH8y8FVgKeBPlEGR7SVtVx3bosn4NwPXUQYseluqydgAhwA96Uyn\nAEi61/ZqNcTuMVzScNvTgUVs3wxg+x5JC9VYT0REdJAMYERERMRA+wklS8WNwFuA8QCSlgOaXl4A\nrGF7j+rxryV9Gbha0i41xO5tnO2x1ePvSfpgDTFH2L4UQNJ42+cD2L5K0jE1xL8L+Kjtqb0PVBln\nmmL7WEnnUH4e/6AMxtQ95fdk4JJqKcllko4HLgS2BSbXXFdERHSIDGBERETEgLJ9vKTfA+sAx9q+\nuyqfBry1hioWkjTM9swq7jclPUTZc2NkDfGXl/S/lBkMS0qSX1mTO6yG+C9K2gFYErCkXW3/WtJW\nlFkrzTqC/tv5qRriY/tBYM9q0OhKYNE64jbEP7FaivRxYC3KPe3rgF8D36izroiI6BwZwIiIiIgB\nZ/sO4I4Whf8N5ZP43zfUd4akR4ETa4j/Y2Dx6vEZwLLANEkrUs+n/x8DjqZsSrkj8HFJZ1D2xDig\n2eC2z5e0drUk5UbbzzYcfrHZ+ACS1qZkULmaMoCxRlX+3/1DavAocAq9XoOknXhliU9ERAwi2cQz\nIiIihgxJH7J9+lCOX2WB+QRlKclY4DO2L6qOTbLd3yafHRF/oOqIiIjOkwGMiIiIGDIkPWB79FCO\nXy292ML2s5LGAOcDP62W9txSQxaYlsYfqDoiIqLzZAlJREREDCqSbuvvELDCUI8PDOtZcmH7Pklb\nA+dLWpW+M5N0WvyBqiMiIjpMBjAiIiJisFmBsnfEv3qVi5KWdKjHf0zSWNuTAapZDO8ATgM26IL4\nA1VHRER0mAxgRERExGDzW2BkT+e2kaRrE599gemNBbanA/tK+lEXxB+oOiIiosNkD4yIiIiIiIiI\n6Hh15CqPiIiIiIiIiGipDGBERERERERERMfLAEZEREQMCZJ2kXRo9fgISZ9vd5siIiJi7mUTz4iI\niBgSbF8MXNzudkRERMT8yQyMiIiI6HqSxki6W9IZku6RdLakt0n6o6SpkjaVtJ+kk/o4dw1Jl0ma\nKOl6SWtX5e+UdKOkWyT9XtIKVflykq6UdIekn0i6X9Ky1bG9Jd0kabKkH0laYGB/EhEREYNXBjAi\nIiJisFgTOBZYu/p6P/Bm4PPAl2Zz3inAp2xvXD335Kr8/4DNbW8E/BL4QlX+VeBq2+sB5wOjASSt\nA7wXeJPtscAM4AO1vbqIiIghLktIIiIiYrC41/YUAEl3AFfZtqQpwJi+TpA0EtgSOE9ST/FC1b+j\ngHMkrQQsCNxblb8Z2A3A9mWS/lWVbwdsDNxcxVoEeLy2VxcRETHEZQAjIiIiBov/NDye2fD9TPq/\n5xkGPFXNmOjtROC7ti+WtDVwxBzqF3Cm7cPmusUREREx17KEJCIiIoYs288A90raE0DFhtXhJYGH\nqscfbDjtj8B7qufvACxdlV8FvFvS8tWxZSSt2uKXEBERMWRkACMiIiKGug8AH5Z0K3AH8K6q/AjK\n0pKJwBMNzz8S2EHS7cCewKPAv23fCRwOXCHpNuBKYKWBeQkRERGDn2y3uw0RERERXUPSQsAM29Ml\nbQH8oJ8lKBEREVGj7IERERERMW9GA+dKGga8BBzQ5vZEREQMCZmBEREREREREREdL3tgRERERERE\nRETHywBGRERERERERHS8DGBERERERERERMfLAEZEREREREREdLwMYEREREREREREx8sARkRERERE\nRER0vAxgRERERERERETHywBGRERERERERHS8DGBERERERERERMfLAEZEREREREREdLwMYES0gaQz\nJH2j3e2IiIiIiIjoFhnAiOhgkq6V9JF2tyMiIiIiIqLdMoARERERERERER0vAxgRA0DSRpImSfq3\npHOAhavypSX9VtI0Sf+qHo+qjn0TeAtwkqRnJZ1UlR8v6R+SnpE0UdJb2vbCIiIiIiIiBkgGMCJa\nTNKCwK+BnwLLAOcBe1SHhwGnA6sCo4EXgJMAbH8ZuB74pO2Rtj9ZnXMzMLaK9XPgPEkLD8yriYiI\niIiIaI8MYES03ubACOA42y/bPp8yCIHtf9q+wPbztv8NfBPYanbBbP+sOm+67WOBhYDXt/g1RERE\nREREtFUGMCJa77XAQ7bdUHY/gKRFJf1I0v2SngH+ACwlaYH+gkn6vKS7JD0t6SlgSWDZVr6AiIiI\niIiIdssARkTrPQKsLEkNZaOrfz9HmT2xme0lgLdW5T3PbRz0oNrv4gvAe4ClbS8FPN3w/IiIiIiI\niEEpAxgRrfdnYDrwaUkjJO0ObFodW5yy78VTkpYBvtrr3MeA1Ru+X7yKNQ0YLukrwBKtbHxERERE\nREQnyABGRIvZfgnYHdgPeBJ4L3Bhdfg4YBHgCeAG4LJepx8PvLvKUHICcHn1nHsoy1BeBP7R4pcQ\nERERERHRdpp1WX5EREREREREROfJDIyIiIiIiIiI6HgZwIiIiEFL0mmSHpd0ez/HJekESX+VdJuk\nNw50GyMiBqtcgyOibhnAiIiIwewMYKfZHN8ZeF31dSDwgwFoU0TEUHEGuQZHRI0ygBEREYOW7T9Q\nNs/tz7uAs1zcACwlaaWBaV1ExOCWa3BE1G14uxsQ82fZZZf1mDFj2t2MiJhPEydOfML2cu1uR7Ay\ns2byebAqe6T3EyUdSPmEkMUWW2zjtddee0AaGBH1yzW4Y+QaHDFEze91OAMYXWrMmDEsPWm1djej\nT1fOPA+A7Yft2eaW9C3ta16nt7GnfTMfXavNLenbsBXvQdL97W5HzBvbpwCnAIwbN84TJkxoc4si\nYn7lGtx9cg2OGFzm9zqcJSQRETGUPQSs0vD9qKosIiJaL9fgiJgnGcCIiIih7GJg32on/M2Bp22/\naupyRES0RK7BETFPsoQkIiIGLUm/ALYGlpX0IPBVYASA7R8ClwBvB/4KPA98qD0tjYgYfHINjoi6\nZQAjIiIGLdt7zeG4gU8MUHMiIoaUXIMjom5ZQhIRERERERERHa9lAxiSVpF0jaQ7Jd0h6TMNxz4l\n6e6q/OiqbEFJp0uaIulWSVs3PP+9km6rnj++oXxVSVdVx66VNKrh2AxJk6uvi+fQ1rMl/UXS7ZJO\nkzSiKl9a0q+q+DdJWr/hnM9Uz79D0sEN5RtK+nP1On4jaYmqfISkM6vyuyQd1nDOXlX5bZIuk7Ts\nfP7YIyIiIiIiIgalVs7AmA58zva6wObAJyStK2kb4F3AhrbXA46pnn8AgO0NgO2BYyUNk/Qa4DvA\ndtXzV5S0XXXOMcBZtt8AfA34dkP9L9geW33tMoe2ng2sDWwALAJ8pCr/EjC5ir8vcDxANZBxALAp\nsCHwDklrVuf8BDi0eh2/Ag6pyvcEFqrKNwY+KmmMpOFV3G2qem4DPjmH9kZEREREREQMKS0bwLD9\niO1J1eN/A3cBKwMfB46y/Z/q2OPVKesCVzeUPQWMA1YHptqeVj3v98Aevc8BrqEMjMxPWy9xBbiJ\nksKpd5vuBsZIWgFYB7jR9vO2pwPXAbtX56wF/KF6fGVDWw0sVg1YLAK8BDwDqPpaTJKAJYCH+2qn\npAMlTZA0Ydq0aX09JSIiIiIiImJQGpA9MCSNATYCbqR08N8i6UZJ10napHrarcAukoZLWo0yS2EV\nyq7Er2+YrbArr+SLvpVXBg52AxavZmwALCxpkqQbJO06l+0cAewDXNY7vqRNgVUpgxu3V6/hNZIW\npeye3NOmO3hlIGXPhvLzgeeAR4AHgGNsP2n7ZcqgzhTKwMW6wKl9tc/2KbbH2R633HLLzc1LioiI\niIiIiBgUWj6AIWkkcAFwsO1nKJlPlqEsKzkEOLeaeXAa8CAwATgO+BMww/a/KB38c4Dr/z97dx5m\nR1mg/f97ExZBQBAiYiAmo1EMKAhtwHEBQSSgI+CgBheQATMKOCoiy8y8Oi6MIKMIo8AgoOCLRFYJ\nGgj5BRAcCRAwLEkEwiIkoERkEXkBQ+7fH/U0FIfTp0+S7pyT7vtzXefqOk89VXVXScrOk2cB7gOe\nK6c/HNhR0m+BHYFFtX2vtb0t8DHge5Je10bck4FrbF9bvh8LbCBpDvA54Lcl03zgOOAKqsaOObXr\n/hNwsKSbgPWoelpANdzkOeA1wFjgS5L+rjSafJaqgec1VENInp8fIyIiIiIiIiIGeRnV8pfzC4Fz\nbF9UihcCF/UO15C0FNi4DBH5Yu3Y3wB3Ati+FLi0lE+mNBbYfpAXekisC/yj7cfKvkXl5z2SrqZq\nILi7RdavAiOBf+4tKw0uB5T9Au4F7in7zqD0lJD0n+W+eoeavK+UvwF4fzndx4DLS4+LhyX9L9UQ\nmY3KcXeXY84Djurv2UZEREREREQMJ4O5Como/oI/3/Z3a7t+Dryn1HkDsCbwJ0nrSHp5Kd8VWGJ7\nXvn+qvJzQ+BgqokykbSxpN57OJqqF0fv6iFr9dYB3gHMa5H1IGA3YF/bS2vlG0has3w9iKp3xhMN\nmUZTNaL8tKF8NeDfgVPL8fcDO5d9L6fqgfI7ql4j4yX1jgnZlWq+kIiIiIiIiIgoBrMHxjuo5pO4\nrQzBgGpVjzOBMyXdTjW8Yn/bLn/xn156ZCwqx/Y6UdLWZfvrtu8s2zsB35JkqokzDynlbwL+p5xr\nNapJQ/tswKBqZPg9cF3V7sJFtr9eznNWOf9c4MDaMReW+Tb+BhzS2/MD2FdSb46LgB+V7R8AP5I0\nl2rSzh/ZvhVA0teAayT9reT4VIusEREREREREcPOoDVg2P411V/Um/lEk/r3AW/s41z79lF+AdXk\nmI3lv6FaErXdrE2fg+3rqCYdbbbvXX2Un0hZbrWh/EmqST2bHXMqL/TUiIiIiIiIiIgGgzoHRgyu\nGUvP73SElpJvxXR7Puj+jKu9+s7+K0VERERExCphWDVgSLqYagWQuiNtT+9EnoiIiIiIiIhoz7Bq\nwLC9d6czDKRdV2s6IqXjev9VPvmWT7fng+7P2Jvvdf/13X5qdsbdhx/W6QgREREREaucQVuFJCIi\nIiIiIiJioKQBIyIiIiIiIiK6XhowIiIiIiIiIqLrpQEjIiIiIiIiIrpeGjAiIiIiIiIiousNWgOG\npM0lXSVpnqS5kj7fsP9Lkixp41rZ0ZIWSLpD0m618u0k3Vb2nSRJpXwtST8r5ddLGlM75nJJj0n6\nRRtZzynXvF3SmZLWqO3bSdKccg+/qpV/vtSfK+kLtfKtJV1X8l4qaf1SvqakH5XyWyTtVDvm6nL9\nOeXzqrYfdERERERERMQwMJg9MJYAX7I9HtgBOETSeKgaN4D3Aff3Vi77JgFbAhOBkyWNKLtPAT4N\njCufiaX8QOBR268HTgCOq13/eOCTbWY9B9gCeDOwNnBQybQBcDLwQdtbAh8u5VuVPBOArYEPSHp9\nOdfpwFG23wxcDHy5lH8aoJTvCnxHUv35f9z2NuXzcJu5IyIiIiIiIoaFQWvAsP2Q7ZvL9l+A+cCo\nsvsE4AjAtUP2BKbYfsb2vcACYIKkTYH1bc+ybeBsYK/aMWeV7QuAXXp7Z9ieCfylzazTXAA3AJuV\nXR8DLrJ9f6nX27DwJuB620/ZXgL8CvhQ2fcG4JqyPQP4x7I9Hriydp7HgJ528vWSNFnSbEmzFy9e\nvCyHRkRERERERKzSVsocGGVox1uB6yXtCSyyfUtDtVHAA7XvC0vZqLLdWP6iY0pDwuPARiuQcw2q\nXhuXl6I3ABuWIR43SdqvlN8OvEvSRpLWAfYANi/75lI1rEDVY6O3/Bbgg5JWlzQW2K62D+CsMnzk\n//Q2wjSyfZrtHts9I0eOXN7bjIiIiIiIiFjlrD7YF5C0LnAh8AWqYSX/SjV8pBudDFxj+9ryfXWq\nhoZdqIaWXCdplu35ko4DrgD+CswBnivH/BNwkqT/A0wFni3lZ1L13JgN/B74Te2Yj9teJGk9qmf1\nSaqeJhERERERERHBIPfAKD0aLgTOsX0R8DpgLHCLpPuohmrcLOnVwCJe3CNhs1K2iBeGdNTLqR8j\naXXgFcAjy5n1q8BI4LBa8UJguu2/2v4T1dCQrQFsn2F7O9vvBh4F7izlv7P9PtvbAecCd5fyJba/\nWOa42BPYoHbMovLzL8BPqebWiIiIiIiIiIhiMFchEXAGMN/2dwFs32b7VbbH2B5D1UCwre0/UPVW\nmFRWFhlLNVnnDbYfAp6QtEM5537AJeUyU4H9y/Y+wJVlHotlzXoQsBuwr+2ltV2XAO8swz7WAban\nmsuD3pVCJI2mmv/ipw3lqwH/Dpxavq8j6eVle1dgie155dwbl/I1gA9QDVGJiIiIiIiIiGIwe2C8\ng2ooxM615UH36Kuy7bnAecA8qjkoDrHdO8TiYKrVPRZQ9Wi4rJSfAWwkaQFVz4mjes8n6VrgfKqJ\nPRfWl2Vt4lRgE6ohInMkfaVkml+y3Eo1uefptnsbFy6UNA+4tGR9rJTvK+lO4HfAg8CPSvmrqHqb\nzAeO5IUVUtYCpku6lWooyiLghy2yRkTEMpA0sSxVvUDSUU32v6Ise31LWRr7gE7kjIgYivIOjoiB\nNGhzYNj+NdB0MspanTEN348BjmlSbzawVZPypylLmzbZ965lyNrnc7B9PNWSrG2d3/aJwIlNyu8D\n3tik/K9U82xERMQAK8tx/4Bq+eqFwI2SptqeV6t2CDDP9j9IGgncIekc2882OWVERLQp7+CIGGgr\nZRWSiIiIDpkALLB9T/lleAovrBTVy8B6ZZjiusCfqSadjoiIFZN3cEQMqEFfhaSbSLqYahLRuiNt\nT+9EnhU1Y+n5nY7QUvKtmG7PB92f8e7DD+u/Ugx1zZbo3r6hzvep5lR6EFgP+GjDfEgREbF88g6O\niAE1rBowbO/d6QwREdF1dqOag2hnqtWyZki61vYT9UqSJgOTAUaPHr3SQ0ZEDFF5B0dE24ZVA8ZQ\ns+tqTaf/6Ljef5VPvuXT7fmg+zP25nv3ni+ZvqYrXHPJlzsdYTjpa4nuugOAY8sqVgsk3QtsQTV5\n8/NsnwacBtDT07PMK15FRAxDeQdHxIDKHBgRETGU3QiMkzRW0prAJKquynX3A7sASNqEasLle1Zq\nyoiIoSnv4IgYUOmBERERQ5btJZIOBaYDI4Azbc+V9Jmy/1TgG8CPJd1GtXrWkbb/1LHQERFDRN7B\nETHQ0oARERFDmu1pwLSGslNr2w8C71vZuSIihoO8gyNiIGUISURERERERER0vUFrwJC0uaSrJM2T\nNFfS50v5h8v3pZJ6Go45WtICSXdI2q1Wvp2k28q+k8o60UhaS9LPSvn1ksbUjvl2uc78+jF9ZD2n\nXPN2SWdKWqNh/9skLZG0T61sYjlmgaSjauWvlDRD0l3l54Zt3N8xkh6Q9OSyPeWIiIiIiIiI4WEw\ne2AsAb5kezywA3CIpPHA7cCHgGvqlcu+ScCWwETgZEkjyu5TgE8D48pnYik/EHjU9uuBE4Djyrn+\nHngH8BZgK+BtwI4tsp5DNdvxm4G1gYNquUaU817RUPYDYHdgPLBvyQ9wFDDT9jhgZvne3/1dCkxo\nkS8iIiIiIiJiWBu0BgzbD9m+uWz/BZgPjLI93/YdTQ7ZE5hi+xnb9wILgAmSNgXWtz2rLK90NrBX\n7ZizyvYFwC6lp4WBlwFrAmsBawB/bJF1mguqJZs2q+3+HHAh8HCtbAKwwPY9tp8FppQsjZnOasj6\nkvsr159l+6G+8vWSNFnSbEmzFy9e3F/1iIiIiIiIiCFjpcyBUYZ2vBW4vkW1UcADte8LS9most1Y\n/qJjbC8BHgc2sn0dcBXwUPlMtz2/jZxrAJ8ELi/fRwF7U/UAaScrwCa1xog/AJu0cUxbbJ9mu8d2\nz8iRI5fl0IiIiIiIiIhV2qA3YEhal6oHwxdsPzHY1yvXfD3wJqqeFKOAnSW9q41DTwausX1t+f49\nqqWcli5PjtKjw8tzbERERERERES8YFCXUS09Gi4EzrF9UT/VFwGb175vVsoW8eIhHb3l9WMWSlod\neAXwCHAAMMv2kyXHZcDbgWvpg6SvAiOBf64V9wBTyvyfGwN7SFrSIivAHyVtavuhMvyld+hJq2Mi\nIiIiIiIiooXBXIVEwBnAfNvfbeOQqcCksrLIWKrJOm8owzGekLRDOed+wCW1Y/Yv2/sAV5ZeD/cD\nO0pavTSi7Eg1B0dfWQ8CdgP2rfe2sD3W9hjbY6jm2DjY9s+BG4FxksZKWpNqcs6pTTLt35D1JffX\nxnOJiIiIiIiIGPYGcwjJO6jmk9hZ0pzy2UPS3pIWUvWI+KWk6QC25wLnAfOo5qA4xPZz5VwHA6dT\nTXx5N3BZKT8D2EjSAuAwyoofVI0NdwO3AbcAt9i+tEXWU6nmqriu5PxKqxsr820cCkynahg5r+QH\nOBbYVdJdwHvL95b3V5Z8XQisI2mhpP9odf2IiIiIiIiI4WbQhpDY/jWgPnZf3McxxwDHNCmfTbUc\namP508CHm5Q/x4uHgvSXtd/nYPtTDd+nAdOa1HsE2KWPc/R1f0cAR7QZNyIiIiIiImLYWSmrkERE\nRERERERErIhBncSz20i6GBjbUHyk7emdyLOiZiw9v9MRWkq+FdPt+aD7M15zyZc7HSEiIiIiIgbI\nsGrAsL13pzNERERERERExLIbVg0YQ82uq71k+o+u0Puv8sm3fHrzTXzlpzucpG+X//mHQPc/w9f9\nVzsLIK18dx9+WKcjRERERESscjIHRkRERERERER0vTRgRERERERERETXSwNGRERERERERHS9NGBE\nRERERERERNcbtAYMSWdKeljS7bWybSTNkjRH0mxJE2r7jpa0QNIdknarlW8n6bay7yRJKuXvlnSz\npCWS9mly/fUlLZT0/X5ynlOueXvJvEYp30LSdZKekXR4wzETyzELJB1VK3+lpBmS7io/N2zj/vYt\n93erpMslbdzuM46IiIiIiIgYLgazB8aPgYkNZd8GvmZ7G+Ar5TuSxgOTgC3LMSdLGlGOOQX4NDCu\nfHrPeT/wKeCnfVz/G8A1beQ8B9gCeDOwNnBQKf8z8C/Af9Url1w/AHYHxgP7lvwARwEzbY8DZpbv\nfd6fpNWBE4H32H4LcCtwaBuZIyIiIiIiIoaVQWvAsH0NVSPAi4qB9cv2K4AHy/aewBTbz9i+F1gA\nTJC0KbC+7Vm2DZwN7FXOf5/tW4GljdeWtB2wCXBFGzmnuQBuADYr5Q/bvhH4W8MhE4AFtu+x/Sww\npeTvvY+zyvZZvVn7uj9A5fPy0rNk/dozeQlJk0vPldmLFy/u79YiIiIiIiIihoyVPQfGF4DjJT1A\n1bPh6FI+CnigVm9hKRtVthvL+yRpNeA7wOGt6jU5bg3gk8Dl/VTtKyvAJrYfKtt/oGpE6fMY238D\nPgvcRtVwMR44o68L2z7Ndo/tnpEjR/Z/UxERERERERFDxMpuwPgs8EXbmwNfpMVf1lfAwcA02wv7\nrfliJwPX2L52IEKUHh1uVac0mnwWeCvwGqohJEe3OiYiIiIiIiJiOFrZDRj7AxeV7fOphlEALAI2\nr9XbrJQtKtuN5a28HThU0n1UvTz2k3RsqwMkfRUYCRzW/y30mRXgj2XYC+Xnw/0csw2A7btLg8d5\nwN+3kSEiIiIiIiJiWFnZDRgPAjuW7Z2Bu8r2VGCSpLUkjaWarPOGMhzjCUk7lDki9gMuaXUB2x+3\nPdr2GKphJGfbPqqv+pIOAnYD9rX9kvk0mrgRGCdprKQ1qSbnnFq7j/3L9v61rE3vj6oRY7yk3vEg\nuwLz28gQERERERERMaysPlgnlnQusBOwsaSFwFepVhM5say+8TQwGcD2XEnnAfOAJcAhtp8rpzqY\nakWTtYHLygdJbwMuBjYE/kHS12xvuRxRTwW9J5QKAAAgAElEQVR+D1xXVmi9yPbXJb0amE01seZS\nSV8Axtt+QtKhwHRgBHCm7bnlXMcC50k6sJzzI/3c34OSvgZcI+lv5ZhPLcc9RERERERERAxpg9aA\nYXvfPnZt10f9Y4BjmpTPBrZqUn4jLx5e0uycP6Zq/GhVp+kzsP2Hvs5vexowrUn5I8AufRzT1/2d\nStWIEhERg0DSRKolq0cAp9t+ybBCSTsB3wPWAP5ke8fGOhERsezyDo6IgTRoDRgRERGdJmkE8AOq\nIXoLgRslTbU9r1ZnA6qJnCfavl/SqzqTNiJiaMk7OCIG2rBpwJB0MTC2ofhI29M7kWcgzFh6fqcj\ntJR8K+byP/+w0xH61e3P8O7D25mXN4a4CcAC2/cASJoC7Ek1pK/Xx6iGD94PYPvhl5wlIiKWR97B\nETGg2mrAKBNofhz4uzI/xGjg1bZvGNR0A8j23p3OEBERK90o4IHa94XA9g113gCsIelqYD3gRNtn\nN55I0mTK3E2jR48elLAREUNM3sERMaDa7YFxMrCUauWQrwN/AS4E3jZIuaINu6724U5HaKr3X+WT\nb/n05tvx/d/ucJK+/eqXRwDd/wx3mH50h5M0N2u3b3U6QrzY6lTzM+1CNWH0dZJm2b6zXsn2acBp\nAD09PV7pKSMihqa8gyOibe02YGxve1tJvwWw/WhZQjQiIqKbLQI2r33frJTVLQQesf1X4K+SrgG2\nBu4kIiJWRN7BETGgVmuz3t/KJDwGkDSSqkdGREREN7sRGCdpbGl4nwRMbahzCfBOSatLWoeqe/P8\nlZwzImIoyjs4IgZUuz0wTgIuBl4l6RhgH+DfBy1VRETEALC9RNKhwHSqJfzOtD1X0mfK/lNtz5d0\nOXArVeP86bZv71zqiIihIe/giBhobTVg2D5H0k1UY9ME7GU7LaMREdH1bE8DpjWUndrw/Xjg+JWZ\nKyJiOMg7OCIGUltDSCS9EngYOBf4KfBHSWv0c8yZkh6WdHut7D8kLZI0p3z2qO07WtICSXdI2q1W\nvp2k28q+k8qKKEg6TNI8SbdKminptbVjLpf0mKRftHFvh5ZzW9LGtfJXSLpU0i2S5ko6oLZvYsm5\nQNJR9eckaYaku8rPDUv5RpKukvSkpO/3kWNq/VlFRERERERExAvanQPjZmAx1WQ6d5Xt+yTdLGm7\nPo75MTCxSfkJtrcpn2kAksZTjYnbshxzcplzA+AU4NPAuPLpPedvgR7bbwEuAOpLNhwPfLLNe/tf\n4L3A7xvKDwHm2d4a2An4jqQ1S64fALsD44F9S36Ao4CZtscBM8t3gKeB/wMc3iyApA8BT7aZNyIi\nIiIiImLYabcBYwawh+2NbW9E9Zf3XwAHUy2x+hK2rwH+3Ob59wSm2H7G9r3AAmCCpE2B9W3Psm3g\nbGCvcv6rbD9Vjp9FNatx77VnUi312i/bv7V9X7NdwHqlx8e65V6WABOABbbvsf0sMKXk772Ps8r2\nWbWsf7X9a6qGjBeRtC5wGPDN/rJKmixptqTZixcvbuf2IiIiIiIiIoaEdhswdrA9vfeL7SuAt9ue\nBay1jNf8XBn2cWbvEAtgFPBArc7CUjaqbDeWNzoQuGwZc/Tn+8CbgAeB24DP217aIivAJrYfKtt/\nADZp4zrfAL4DPNVfRdun2e6x3TNy5Mj27iIiIiIiIiJiCGi3AeMhSUdKem35HEE1D8YIlm051VOA\nvwO2AR6i+ov7CpH0CaCHgZ/4ZzdgDvAaqrzfl7R+uweXHiNuVUfSNsDrbF+8IkEjIiIiIiIihrp2\nGzA+RjVE4+flM7qUjQA+0u7FbP/R9nOlJ8MPqYZjACwCNq9V3ayULaI2NKRWDoCk9wL/BnzQ9jPt\n5mjTAcBFriwA7gW2aJEVqkadTUu2TakmPm3l7UCPpPuAXwNvkHT1gN1BRERERERExBDRVgOG7T/Z\n/pztt5bPobYX2362/OW+Lb1/uS/2BnpX3ZgKTJK0lqSxVJN13lCGYzwhaYcyF8V+wCXlXG8F/oeq\n8aK/hoLlcT/VsrFI2gR4I3APcCMwTtJYSWtSTT46tXYf+5ft/Xuz9sX2KbZfY3sM8E7gTts7DfB9\nRERERERERKzyVm+nkqSRwBFUq4S8rLfc9s4tjjmXavWOjSUtBL4K7FSGTRi4D/jncp65ks4D5lFN\nlHmI7efKqQ6mWtFkbap5LnrnujieanLN88vKqvfb/mC59rVUvSXWLdc+sD6HR0POfyn39mrgVknT\nbB9ENTfFjyXdBgg40vafyjGHAtOpeqCcaXtuOd2xwHmSDqRa1eQjtevcB6wPrClpL+B9tuf19fwi\nIiIiIiIi4gVtNWAA5wA/Az4AfIaqd0HLZTBs79uk+IwW9Y8BjmlSPhvYqkn5e1uc612tsjXUPQk4\nqUn5g8D7+jhmGjCtSfkjlF4bTfaN6SfHfTS5z4iIiIiIiIhofw6MjWyfAfzN9q9s/xPQZ++LiIiI\niIiIiIiB1G4PjL+Vnw9Jej/V0qKvHJxIg0PSxcDYhuIj+xpasiqYsfT8TkdoKflWzK9+eUSnI/Sr\n25/hrN2+1ekIERERERExQNptwPimpFcAXwL+m2ouhy8OWqpBYHvvTmeIiIiIiIiIiOXTVgOG7V+U\nzceB9wxenFgWu6724U5HaKr3X+WTb/n05hv/ryd0OEnf5v1n1X7Z7c9wzMn/1eEkzd138OGdjhAR\nERERscppaw4MSW+QNFPS7eX7WyT9++BGi4iIiIiIiIiotDuJ5w+BoylzYdi+FZg0WKEiIiIiIiIi\nIurabcBYx/YNDWVLBjpMREREREREREQz7TZg/EnS6wADSNoHeGjQUkVERERERERE1LTbgHEI8D/A\nFpIWAV8APtvqAElnSnq4d96MUna8pN9JulXSxZI2qO07WtICSXdI2q1Wvp2k28q+kySplB8maV45\n10xJry3l75E0p/Z5WtJeLXIeWs5tSRvXyneS9HjtPF+p7ZtYci6QdFSt/JWSZki6q/zcsJTvKumm\nch83Sdq5dswxkh6Q9GTr/wkiIiIiIiIihq+2GjBs32P7vcBIYAvb77R9Xz+H/RiY2FA2A9jK9luA\nO6nm1UDSeKo5NbYsx5wsaUQ55hTg08C48uk952+BnnKuC4Bvl6xX2d7G9jbAzsBTwBUtcv4v8F7g\n9032Xdt7LttfL1lHAD8AdgfGA/uW/ABHATNtjwNmlu8AfwL+wfabgf2Bn9SucSkwoUW+iIiIiIiI\niGGv5TKqkg7roxwA29/t61jb10ga01BWb0iYBexTtvcEpth+BrhX0gJggqT7gPVtzyrXPRvYC7jM\n9lUN5/pEkxj7lLpPtcj52/o9tWECsMD2PeW4KSX/vPJzp1LvLOBq4MjeaxRzgbUlrWX7mdq9tXv9\niIiIiIiIiGGnvx4Y6/XzWRH/BFxWtkcBD9T2LSxlo8p2Y3mjA2vnqpsEnLsCGf++DFG5TNKW/WQF\n2MR279wgfwA2aXLOfwRuLo01y0TSZEmzJc1evHjxsh4eERERERERscpq2QPD9tcG46KS/o1qFZNz\nBuBcnwB6gB0byjcF3gxMX85T3wyMtv2kpD2An1MNYWmLbUtyQ6YtgeOA9y1PINunAacB9PT0+EXN\nKBERERERERFDWH9DSI6w/W1J/01ZgaTO9r8s6wUlfQr4ALCL7d5zLgI2r1XbrJQtKtuN5b3nei/w\nb8COTXo0fAS42PbfljUjgO0natvTJJ1cJvnsKyvAHyVtavuh0oDycC3rZsDFwH62716eTBERERER\nERHDVX9DSOaXn7P7+CwTSROBI4APNsxLMRWYJGktSWOpejrcUIZjPCFph7L6yH7AJeVcb6VaGeWD\nth/mpfZlBYaPSHp1bcWTCVTP6hHgRmCcpLGS1qQapjK1dh/7l+39a1k3AH4JHGX7f5c3U0RERERE\nRMRw1d8QkkvL5jzgX4ExtWMMnN3XsZLOpZrQcmNJC4GvUq06shYwo7QNzLL9GdtzJZ1XrrMEOMT2\nc+VUB1OtaLI21TwXvXNdHA+sC5xfznW/7Q+Wa4+h6iXxq9a3D5L+hapR5dXArZKm2T6IagLQz0pa\nAvw/YFLpMbJE0qFUQ1NGAGfanltOdyxwnqQDqVY1+UgpPxR4PfCV2nKs77P9sKRvAx8D1inP6XTb\n/9Ff7oiIiIiIiIjhpGUDRs3/Bb4M3AYsbecA2/s2KT6jRf1jgGOalM8GtmpS/t4W57qP5pN9Nqt7\nEnBSk/LvA9/v45hpwLQm5Y8AuzQp/ybwzT7OdQRVA0pERAyC0vvvRKpG59NtH9tHvbcB11E1WF+w\nEiNGRAxZeQdHxEBqtwFjse2p/VeLiIjoHpJGAD8AdqVaNepGSVNtz2tS7zjgipeeJSIilkfewREx\n0NptwPiqpNOBmcDzk2XavmhQUg0CSRcDYxuKj7S9vKuUdNyMped3OkJLybdi5v3nFzsdoV/d/gzv\nO/jwTkeIzpsALLB9D4CkKcCeVEMW6z4HXAi8beXGi4gY0vIOjogB1W4DxgHAFsAavDCExMAq04Bh\ne+9OZ4iIiJVuFLxo0emFwPb1CpJGAXsD76HFL8+SJgOTAUaPHj3gQSMihqC8gyNiQLXbgPE2228c\n1CSxzHYd8dFOR2hqxnM/A2DX1T7c4STN9fYa6PZ82+/33Q4n6dv1Zx8GdP8z3P1NR3c4SXOXzf9W\npyPEi32Pqkfe0jIpdFO2TwNOA+jp6XnJ0uIREbFc8g6OiLa124DxG0njG8erRUREdLlFVKtS9dqs\nlNX1AFPKL84bA3tIWmL75ysnYkTEkJV3cEQMqHYbMHYA5ki6l2oODAG2/ZZBSxYREbHibgTGSRpL\n9UvzJKqlq59n+/n5kST9GPhFfnGOiBgQeQdHxIBqtwFj4qCmiIiIGAS2l0g6FJhOtYTfmbbnSvpM\n2X9qRwNGRAxheQdHxEBrqwHD9u8HO0hERMRgsD0NmNZQ1vSXZtufWhmZIiKGi7yDI2IgrdaJi0r6\nvKTbJc2V9IVS9kpJMyTdVX5uWKt/tKQFku6QtFut/HJJt5TznFrWkK5f5x8lWVJPP3kul/SYpF80\nlJ9Trnm7pDMlrVHKJemkkulWSdvWjplYjlkg6aha+fGSflfqXyxpg1L+cUlzap+lkrZZvicbERER\nERERMTSt9AYMSVsBn6ZaF3pr4AOSXg8cBcy0PQ6YWb4jaTzVeLktqYaynFxrqPiI7a2BrYCRwIdr\n11kP+DxwfRuxjgc+2aT8HKrlY98MrA0cVMp3B8aVz2TglHLNEcAPyv7xwL4lP8AMYKsyb8idwNEA\nts+xvY3tbUqGe23PaSNzRERERERExLDRiR4YbwKut/2U7SXAr4APAXsCZ5U6ZwF7le09gSm2n7F9\nL7CAqvED20+UOqsDawL1JZW+ARwHPN1fINszgb80KZ/mAriBaubk3kxnl12zgA0kbVpyLbB9j+1n\ngSmlLravKPcLMKt2rrp9yzERERERERERUdOJBozbgXdJ2kjSOsAeVMsrbWL7oVLnD8AmZXsU8EDt\n+IWlDABJ04GHqRogLihl2wKb2/7lQAQuQ0c+CVzeT6aWWWv+CbisSflHgXNb5Jgsabak2YsXL27/\nBiIiIiIiIiJWcSu9AcP2fKqeEVdQNQjMAZ5rqGNe3Jui1fl2AzYF1gJ2lrQa8F3gSwMY+2TgGtvX\nruiJJP0bsIRqeEq9fHvgKdu393Ws7dNs99juGTly5IpGiYiIiIiIiFhldGQST9tn2N7O9ruBR6nm\nhPhjGYZB+flwqb6IqodGr81KWf18TwOXUA3XWI9qToyrJd0H7ABM7W8iz75I+irV/BqH1Yr7ytQy\nq6RPAR8APl4aaeom0aL3RURERERERMRw1qlVSF5Vfo6mmv/ip8BUYP9SZX+qBglK+SRJa0kaSzVx\n5g2S1q01eKwOvB/4ne3HbW9se4ztMVTzTXzQ9uzlyHkQsBuwr+2ltV1Tgf3KaiQ7AI+X4S83AuMk\njZW0JlWjxNRyronAESXLUw3XWQ34CJn/IiIiIiIiIqKp1Tt03QslbQT8DTjE9mOSjgXOk3Qg8Huq\nv9Bje66k84B5VEMvDrH9nKSXU/WsWIuqIeYqoOma0v2RdC3VaiPrSloIHGh7ejnf74HrJAFcZPvr\nVGtZ70E1oehTwAEl6xJJhwLTgRHAmbbnlst8n2qYy4xyrlm2P1P2vRt4wPY9y5M/IiIiIiIiYqjr\nSAOG7Xc1KXsE2KWP+scAxzSU/RF4WxvX2ml58pTyps+nDP84pI9906gaOBrLX9/i+ldTDXWJiIiI\niIiIiCY6MoQkIiIiIiIiImJZdGoIyUon6c3ATxqKn7G9fSfyDIQZz/2s0xFamrH0/E5HaKnb811/\n9mH9V+qwbn+Gl83/VqcjRERERETEABk2DRi2bwO26XSOiIiIiIiIiFh2w6YBYyiauOFBnY7Q1OWP\nng7Arqt9uMNJmuvtNdDt+XbZuXt7D8y88mig+5/hxFd+usNJmrv8zz/sdISIiIiIiFVO5sCIiIiI\niIiIiK6XBoyIiIiIiIiI6HppwIiIiIiIiIiIrpcGjIiIiIiIiIjoemnAiIiIiIiIiIiu13UNGJI2\nkHSBpN9Jmi/p7ZJeKWmGpLvKzw1r9Y+WtEDSHZJ2a3K+qZJur31fS9LPyjHXSxrTT57jJN1ePh+t\nlY8txy8o51uzlEvSSaX8VknblvKXSbpB0i2S5kr6WpNrfUmSJW28PM8uIiIiIiIiYqjqugYM4ETg\ncttbAFsD84GjgJm2xwEzy3ckjQcmAVsCE4GTJY3oPZGkDwFPNpz/QOBR268HTgCO6yuIpPcD2wLb\nANsDh0tav+w+DjihnOfRcl6A3YFx5TMZOKWUPwPsbHvrcr6JknaoXWtz4H3A/W08o4iIiIiIiIhh\npasaMCS9Ang3cAaA7WdtPwbsCZxVqp0F7FW29wSm2H7G9r3AAmBCOde6wGHANxsuUz/XBcAuktRH\npPHANbaX2P4rcCtVw4OAncvxzTKd7cosYANJm5bvvY0pa5SPa9c6ATiioazx+UyWNFvS7MWLF/dV\nLSIiIiIiImLI6aoGDGAssBj4kaTfSjpd0suBTWw/VOr8AdikbI8CHqgdv7CUAXwD+A7wVMM1nj/G\n9hLgcWCjPvLcQtVgsU4Z1vEeYPNS/7FyfON1+8wkaYSkOcDDwAzb15fyPYFFtm/p88lUeU+z3WO7\nZ+TIka2qRkRERERERAwp3daAsTrVkI1TbL8V+CtluEgv26ZFLwUASdsAr7N98YqEsX0FMA34DXAu\ncB3w3Aqc7znb2wCbARMkbSVpHeBfga+sSNaIiIiIiIiIoazbGjAWAgt7eyZQDdHYFvijpE0Bys+H\ny/5FVD0iem1Wyt4O9Ei6D/g18AZJVzceI2l14BXAI30Fsn2M7W1s7woIuLPU36AcX79uq0z1cz4G\nXEU1b8frqHqe3FLybgbcLOnVfWWKiIiIiIiIGG66qgHD9h+AByS9sRTtAswDpgL7l7L9gUvK9lRg\nUllZZCzVxJk32D7F9mtsjwHeCdxpe6faMb3n2ge4svTqeIky5GOjsv0W4C3AFaX+VeX4Zpn2K6uR\n7AA8bvshSSMlbVDOtTawK/A727fZfpXtMSXvQmDb8iwiIiIiIiIigmrIRrf5HHBOWZb0HuAAqoaW\n8yQdCPwe+AiA7bmSzqNq5FgCHGK7vyEeZwA/kbQA+DPVKiZ9WQO4tszx+QTwidq8F0cCUyR9E/ht\nOS9UQ072oJpQ9KmSH2BT4KyySspqwHm2f9Hfw4iIiBUjaSLVClcjgNNtH9uw/+NU73QBfwE+29+c\nRBER0Z68gyNiIHVdA4btOUBPk1279FH/GOCYFue7D9iq9v1p4MNtZnmaaiWSZvvuoax40lBu4JAm\n5bcCb23jmmPayRYREf0rjcY/oOr1thC4UdJU2/Nq1e4FdrT9qKTdgdOols6OiIgVkHdwRAy0rmvA\niPZd/ujpnY7Q0oyl53c6Qkvdnm/mlUd3OkK/uv0ZXv7nH3Y6QnTeBGBBaXRG0hSq5a6f/+XZ9m9q\n9WdRzUUUERErLu/giBhQacAAJL0Z+ElD8TO20/obEbFqa7a0dat3+4HAZc12SJoMTAYYPXr0QOWL\niBjK8g6OiAGVBgzA9m3ANp3Osay2/ewJnY7Q1M2nfBGA3dbdv5+anTH9ybMA2HW1tkYSrXS9vRre\nt8PXO5ykb1fMqlb97fpnuObHOpykuSue/WmnI0QTkt5D9cvzO5vtt30aVddmenp6Wi7nHRERyybv\n4IhoRxowIiJiKOt3aWt4fqWp04Hdbfe5tHZERCyTvIMjYkB11TKqERERA+xGYJyksWV1q0lUy10/\nT9Jo4CLgk7bv7EDGiIihKu/giBhQ6YERERFDlu0lkg4FplMt4XdmWYL7M2X/qcBXgI2Ak8uy2Uts\nN1sNKyIilkHewREx0NKAERERQ5rtacC0hrJTa9sHAQet7FwREcNB3sERMZAyhCQiIiIiIiIiul5X\nNWBIepmkGyTdImmupK+V8ldKmiHprvJzw9oxR0taIOkOSbs1OedUSbfXvp8gaU753CnpsX4yjZZ0\nhaT5kuZJGlPKx0q6vlz7Z2VcH6qcVMpvlbRtw/lGSPqtpF/Uyvq8v4iIiIiIiIjosgYM4BlgZ9tb\nUy1rOlHSDsBRwEzb44CZ5TuSxlNNBrQlMJFq7NyI3pNJ+hDwZP0Ctr9oexvb2wD/TTVpUCtnA8fb\nfhMwAXi4lB8HnGD79cCjVMs+AewOjCufycApDef7PDC/oazp/UVEREREREREpasaMFzpbXBYo3wM\n7AmcVcrPAvYq23sCU2w/Y/teYAFVIwOS1gUOA77Z4pL7Auf2tbM0kKxue0bJ96Ttp1TNMLQzcEEf\nmc4u9zIL2EDSpuV8mwHvp1omqq6v+2vMM1nSbEmzFy9e3OK2IiIiIiIiIoaWrmrAgOeHWMyh6ukw\nw/b1wCa2HypV/gBsUrZHAQ/UDl9YygC+AXwHeKqP67wWGAtc2SLOG4DHJF1Uhn0cX3p4bAQ8ZntJ\nk+u2yvQ94AhgacN1+rq/F7F9mu0e2z0jR45sETsiIiIiIiJiaOm6Bgzbz5XhHZsBEyRt1bDfVL0y\n+iRpG+B1ti9uUW0ScIHt51rUWR14F3A48Dbg74BP9XsTzTN9AHjY9k2t6rVzfxERERERERHDTdc1\nYPSy/RhwFdXcFn+sDcPYlBfmoVgEbF47bLNS9nagR9J9wK+BN0i6uuESk2gxfKRYCMyxfU/pbfFz\nYFvgEaqhIb3L0PZet1WmdwAfLJmmADtL+r+lTl/3FxERERERERF0WQOGpJGSNijbawO7Ar8DpgL7\nl2r7A5eU7anAJElrSRpLNXHmDbZPsf0a22OAdwJ32t6pdp0tgA2B6/qJdCNVQ0XveI2dgXmll8RV\nwD59ZNqvrEayA/C47YdsH217s5JpEnCl7U/Ujml2fxERERERERFBNUSim2wKnFXmmVgNOM/2LyRd\nB5wn6UDg98BHAGzPlXQeMA9YAhzSz5CQXpOoJv9sOVTD9nOSDgdmlok7bwJ+WHYfCUyR9E3gt8AZ\npXwasAfVhKJPAQe0kefYZvcXEREREREREZWuasCwfSvw1ibljwC79HHMMcAxLc55H9A4j8Z/LEOm\nGcBbmpTfQ1nxpKHcwCH9nPNq4Ora9z7vLyIiIiIiIiK6bAhJREREREREREQzXdUDo1MkvRn4SUPx\nM7a370Sedt18yhc7HaGl6U+e1ekILc1Yen6nI7R0xayvdDpCv7r+GT77005HiIiIiIiIAZIGDMD2\nbcA2nc4REREREREREc2lAWMVtsvO3+p0hKZmXnk0AFt//oQOJ2nulhOrnivv2uv4Didp7tqffxmA\n3V728Q4n6dv0p88BYPdxR3Q4SXOX3fVtAHZd7cMdTtJct/dciYiIiIjoRpkDIyIiIiIiIiK6Xhow\nIiIiIiIiIqLrpQEjIiIiIiIiIrpeGjAiIiIiIiIiousN6QYMSfdJuk3SHEmzG/Z9SZIlbdzi+Anl\n2DmSbpG0d23fduXcCySdJEmlfC1JPyvl10sa03DO9SUtlPT9Wtm1tes8KOnnA/UMIiIiIiIiIoaC\n4bAKyXts/6leIGlz4H3A/f0cezvQY3uJpE2BWyRdansJcArwaeB6YBowEbgMOBB41PbrJU0CjgM+\nWjvnN4Br6hex/a5atguBS5b9NiMiIiIiIiKGriHdA6OFE4AjALeqZPup0lgB8LLe+qUxY33bs2wb\nOBvYq9TbEzirbF8A7FLrnbEdsAlwRbPrSVof2Blo2gND0mRJsyXNXrx4cVs3GhERERERETEUDPUG\nDAP/n6SbJE0GkLQnsMj2Le2cQNL2kuYCtwGfKQ0ao4CFtWoLSxnl5wMApe7jwEaSVgO+Axze4nJ7\nATNtP9H0ZuzTbPfY7hk5cmQ78SMiIiIiIiKGhKE+hOSdthdJehUwQ9LvgH+lGj7SFtvXA1tKehNw\nlqTLljPLwcA02wtLh4xm9gVOX87zR0RERERERAxZQ7oBw/ai8vNhSRcDOwJjqeayANgMuFnSBNt/\n6Odc8yU9CWwFLCrH9tqslFF+bg4slLQ68ArgEeDtwLskHQysC6wp6UnbRwGUyUQnAHsTERERERER\nES8yZIeQSHq5pPV6t6l6Xdxo+1W2x9geQzX0Y9u+Gi8kjS2NEEh6LbAFcJ/th4AnJO1Q5rfYjxcm\n3pwK7F+29wGudOXjtkeX6x4OnN3beFGr+wvbTw/YQ4iIiIiIiIgYIoZsAwbVZJm/lnQLcAPwS9uX\nL+M53knVW2MOcDFwcG1Fk4OphnssAO6mWoEE4AyqOS8WAIcBR9GeScC5y5gvIiL6IWmipDvK8tYv\neSerclLZf6ukbTuRMyJiKMo7OCIG0pAdQmL7HmDrfuqM6Wf/T4Cf9LFvNtVwksbyp4EP93PeHwM/\nbijbqdUxERGx7CSNAH4A7ErV6+5GScK+Cm8AAAvjSURBVFNtz6tV2x0YVz7bUy2Tvf3KzhoRMdTk\nHRwRA20o98CIiIiYACywfY/tZ4EpVMtd1+1JNazPtmcBG5TlsiMiYsXkHRwRA2rI9sBYFpJ2A45r\nKL7XdldPqDnzyqM7HaGlW078YqcjtHTtz7/c6QgtTX/6nE5H6Ndld3270xFamrH0/E5HiM57fmnr\nYiEv/Ze9ZnVGAQ8NbrSIiCEv7+CIGFBpwABsTwemdzrHsrjpppv+JOn3A3jKjYE/9Vurc5JvxXV7\nxuGW77UDeK5YCSRNBiaXr89Iur2TeVZQt/9568+qnh9W/XtY1fO/sdMBYtnkHdx1VvV7SP7OW673\ncBowVlG2Rw7k+STNtt0zkOccSMm34ro9Y/LFIOld2rpXfdnrZamD7dOA02DV/+8h+TtvVb+HoZC/\n0xmGibyDm1jV88Oqfw/J33nL+x7OHBgRETGU3QiMK8tir0m14tPUhjpTgf3KTPg7AI+X5bIjImLF\n5B0cEQMqPTAiImLIsr1E0qFUwwRHAGfanivpM2X/qcA0YA+qZbGfAg7oVN6IiKEk7+CIGGhpwIhe\np3U6QD+Sb8V1e8bki0FhexrVL8j1slNr2wYOWcbTrur/PSR/563q95D80Za8g5ta1fPDqn8Pyd95\ny3UPqt4ZERERERERERHdK3NgRERERERERETXSwNGREREHyRNlHSHpAWSjmqyX5JOKvtvlbRtJ3L2\npY38Hy+5b5P0G0lbdyJnX/rLX6v3NklLJO2zMvP1p538knaSNEfSXEm/WtkZ+9PGf0OvkHSppFvK\nPXTV/AWSzpT0cF9Lbnb7n+HhLu/gzlrV38Gw6r+H8w5uwnY++eSTTz755NPwoZpw7m7g74A1gVuA\n8Q119gAuAwTsAFzf6dzLmP/vgQ3L9u6rWv5avSupxtjv0+ncy/j8NwDmAaPL91d1Ovdy3MO/AseV\n7ZHAn4E1O529lu/dwLbA7X3s79o/w8P9k3dw9+ev1eu6d/Ay/G/Qte/hvIObf9IDI15E0hc6naHb\nlVbmV9e+7yfpktJ6+MpOZuuPpH/sdIZVmaTtO50hVqoJwALb99h+FpgC7NlQZ0/gbFdmARtI2nRl\nB+1Dv/lt/8b2o+XrLGCzlZyxlXaeP8DngAuBh1dmuDa0k/9jwEW27wewvSreg4H1JAlYl+qX5yUr\nN2bfbF9Dlakv3fxneLjLO7izVvV3MKz67+G8g5tIA0Y0OqzTASRNbfXpdD7gf4BnASS9GzgWOBt4\nnO6fEfiETgeQ9FpJr6h9f4+kEyUdpmqN+G52fqcDxEo1Cnig9n1hKVvWOp2yrNkOpPpXkG7Rb35J\no4C9gVNWYq52tfP83wBsKOlqSTdJ2m+lpWtPO/fwfeBNwIPAbcDnbS9dOfEGRDf/GR7u8g7urFX9\nHQyr/ns47+AmsoxqNFKnAwBvp/oP+VzgerojU90I270tiR8FTrN9IXChpDkdzNWObniW51H9n93j\nkrahahT4FrA1cDJwUAez9acbnl/EgJP0Hqpfnt/Z6SzL6HvAkbaXVv/4tMpZHdgO2AVYG7hO0izb\nd3Y21jLZDZgD7Ay8Dpgh6VrbT3Q2VsSqI+/gjlrV38PD7h2cBoxo1A3r6r4a2BXYl6pb1y+Bc23P\n7WiqF4yQtLrtJVQvu8m1fd3+Z6ob/vdd2/aDZfsTwJm2vyNpNaoXcDfrhucXK88iYPPa981K2bLW\n6ZS2skl6C3A6sLvtR1ZStna0k78HmFJ+cd4Y2EPSEts/XzkRW2on/0LgEdt/Bf4q6Rqqxtxu+cW5\nnXs4ADjW1WDmBZLuBbYAblg5EVdYN/8ZHu7yDu6sVf0dDKv+ezjv4CYyhGQYkvQXSU80+fwFeE2n\n89l+zvbltvenmsxlAXC1pEM7HK3XucCvJF0C/D/gWgBJr6caRtJRZSbrW5t8bgM26XQ+XtyLYWdg\nJkC3dHcrMzk3G750KbBRp/PFSnUjME7S2DK8aRLQOIxtKrBfmUV7B+Bx2w+t7KB96De/pNHARcAn\nu/Bfm/rNb3us7TG2xwAXAAd30S/O7fz3cwnwTkmrS1oH2B6Yv5JzttLOPdxP1ZiPpE2ANwL3rNSU\nK6ab/wwPd3kHd9aq/g6GVf89nHdwE93+r8UxCGyv1+kM/ZG0FvB+ql4YY4CTgIs7mamX7WMkzQQ2\nBa4oLZ5QNQh+rnPJnveBFvtGr7QUfbtS0nnAQ8CGVDNXo2rCnmc7Gaz4r+XcF0OM7SWl4XQ61Uzg\nZ9qeK+kzZf+pVLOu70HV0PoU1b+EdIU283+FqmHu5PIvaEts93Qqc12b+btWO/ltz5d0OXArsBQ4\n3XbTpeY6oc3/Db4B/Lg0kov/v727DfVzjuM4/v6YmyljIUuU+5CbpnkgPNAmPDAilDI3RYQQUW5i\nuStqaG4iSjb3C1GoNYvE3BvxSCa1lbTc1NiD0deD61r+TueocXau65z/+1VXp/P7Xf9f36vOuU7n\n2+/7+zbbydd3FvQISZ4DTgB2T7IWuA3YDvr/OzzsfAd3a7K/g2Hyv4d9B4+x5t//e0n9kGQJcDjN\nD/TzfXmJTBZJ1gCPAouq6s92bBawCDik6z+MSfal2VmzJ/BiVa1rx48CTq2qO7qLDpLsVFUbxpg7\noKq+neiYJEmSJFlCon46DzgIuBp4f7DEJcmUPZBmHM2hOcRndZK5Sa6mqYNbRdOOqWsrgX2AxQPJ\ni1nAdYzenmuifZHknMGBJNOT3EmTAZckSZLUARMY6p2q2qaqZrTXzgPXjKrauev4+q6qfq6qS2kO\nhFoBXA8cV1UP9+SciTnAfvQ3wXIScFGS5UkOTHI6TVuqHYDZ3YYmSZIkDS9LSKQpJslM4B6aQ4hu\noKkrm0fTF3pll7ENahMX99P0rT6mqtZ2HNI/JLmepr3rD8DJPeqCI0mSJA0ld2BIU89nwDfA0VW1\nvKquARYAd7YH6XQqycwkj9Ec0nMKzanVbyaZ221kjfYU6huBy4DLgU+AxUkO7jYySZIkabi5A0Oa\nYpLsPdZuhiSXVNXjEx3TiBjWAI8AD1TVH+3Y7Hbs+6o6t+P4vgLeBm6uql/bsVNpDkF9qapu6jA8\nSZIkaWiZwJA0oSZBgmVOVX06yvh04PWqmtdBWJIkSdLQs4RE0oT6t7Muuk5etJYluSHJtM0DbZeU\nJ4BdugtLkiRJGm4mMCTpn/rehlaSJEkaSpaQSNIo+t4lRZIkSRo27sCQpAF975IiSZIkDSt3YEjS\ngL53SZEkSZKGlQkMSRrQ9y4pkiRJ0rAygSFJkiRJknrPMzAkSZIkSVLvmcCQJEmSJEm9ZwJDkiRJ\nkiT1ngkMaZJJ8n7XMUiSJEnSRPMQT6ljSbbd3K5TkiRJkjQ6d2BI4yjJ+Um+TPJFkqVJ5if5MMnn\nSVYkmdXet7Cdfw9YOsZahyX5KMnqds2D2vEN7dfb27nVSdYlebIdP2/gc48lmTZBjy9JkiRJW407\nMKRxkuQw4BXg2Kpan2RXoIBfqqqSXAwcWlXXJVkIzAeOr6qNY6z3IPBBVT2TZHtgWlVtTLKhqnYa\nuG8m8C5wIfA7cC9wZlVtSvJIu8aSrfbgkiRJkjQBtu06AGkKmQssq6r1AFX1U5IjgBeS7AlsD3w3\ncP9rYyUvWquAm5PsDbxcVd+MvCFJgKeB+6rq0yRXAnOAj5spdgR+HIdnkyRJkqROWUIibV0PAg9V\n1RHApcD0gbnf/u2DVfUscBqwEXgjydxRblsIrK2qJ9vvAzxVVbPb6+CqWvg/n0GSJEmSOmcCQxo/\nK4Gzk+wG0JaQ7AKsa+cv2JLFkuwPrKmqxcCrwJEj5ucDJwJXDQy/BZyVZI/NMSTZ5z88iyRJkiT1\niiUk0jipqq+T3AW8k+RP4HOaHRLLkvxMk+DYbwuWPAdYkGQT8ANw94j5a4G9gI/acpHXqurWJLcA\ny5NsA2wCrgC+/+9PJkmSJEnd8xBPSZIkSZLUe5aQSJIkSZKk3rOEROpYkpOBe0YMf1dVZ3QRjyRJ\nkiT1kSUkkiRJkiSp9ywhkSRJkiRJvWcCQ5IkSZIk9Z4JDEmSJEmS1HsmMCRJkiRJUu/9BYZCNUYe\niRMlAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -392,7 +386,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Indeed there is a correlation between these variables and the correlation is also significant. To better understand the correlation, we can have a look at the signficance of excesses and deficits in the 2-dimensional contigency table, so-called \"outlier significances\"." + "Indeed there is a correlation between these variables and the correlation is also significant. To better understand the correlation, we can have a look at the significance of excesses and deficits in the 2-dimensional contingency table, so-called \"outlier significances\"." ] }, { @@ -563,7 +557,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The values displayed in the matrix are the signficiances of the outlier frequencies, i.e. a large value means that the measured frequency for that bin is significantly different from the expected frequency in that bin.\n", + "The values displayed in the matrix are the significances of the outlier frequencies, i.e. a large value means that the measured frequency for that bin is significantly different from the expected frequency in that bin.\n", "\n", "Let's visualise for easier interpretation." ] @@ -575,12 +569,14 @@ "outputs": [ { "data": { - "image/png": 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fT1ZV87zhh6quwvLW85I/Qz4LpFR1Ila+2ZksGmtFdF5tmzu8H4dl0A0Gg+HS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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -605,7 +601,7 @@ "source": [ "# $\\phi_k$ functions for dataframes\n", "\n", - "In our data we have 5 different columns, meaning we have to evaluate 4+3+2+1=10 pairs of variables for possible correlations. In a large dataset, with many different variables, this can easily become a cumbersome taks. Can we do this more efficient? yes! We have provided functions that work on dataframes, to allow you to calculate the phik correlation, significance and outlier significance for all different variable combinations at once.\n" + "In our data we have 5 different columns, meaning we have to evaluate 4+3+2+1=10 pairs of variables for possible correlations. In a large dataset, with many different variables, this can easily become a cumbersome task. Can we do this more efficient? yes! We have provided functions that work on dataframes, to allow you to calculate the phik correlation, significance and outlier significance for all different variable combinations at once.\n" ] }, { @@ -621,7 +617,7 @@ "source": [ "# $\\phi_k$ correlation matrix\n", "\n", - "Now let's start calculating the corrlation phik between pairs of variables. \n", + "Now let's start calculating the phik correlation coefficient between pairs of variables. \n", "\n", "Note that the original dataset is used as input, the binning of interval variables is done automatically." ] @@ -651,61 +647,53 @@ "\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -713,13 +701,12 @@ "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 1.000000 0.590456 0.000000 0.105506 0.000000\n", - "car_color 0.590456 1.000000 0.000000 0.389671 0.000000\n", - "car_size 0.000000 0.000000 1.000000 0.000000 0.768589\n", - "driver_age 0.105506 0.389671 0.000000 1.000000 0.000000\n", - "mileage 0.000000 0.000000 0.768589 0.000000 1.000000" + " car_color driver_age area mileage car_size\n", + "car_color 1.000000 0.389671 0.590456 0.000000 0.000000\n", + "driver_age 0.389671 1.000000 0.105506 0.000000 0.000000\n", + "area 0.590456 0.105506 1.000000 0.000000 0.000000\n", + "mileage 0.000000 0.000000 0.000000 1.000000 0.768589\n", + "car_size 0.000000 0.000000 0.000000 0.768589 1.000000" ] }, "execution_count": 12, @@ -736,7 +723,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When no interval colums are provided, the code makes an educated guess" + "When no interval columns are provided, the code makes an educated guess" ] }, { @@ -748,7 +735,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['driver_age', 'mileage']\n" + "interval columns not set, guessing: ['driver_age', 'mileage']\n" ] }, { @@ -771,61 +758,53 @@ "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
areacar_color1.0000000.3896710.5904560.0000000.000000
driver_age0.3896711.0000000.1055060.0000000.000000
car_colorarea0.5904560.1055061.0000000.0000000.3896710.000000
car_sizemileage0.0000000.0000001.0000000.0000001.0000000.768589
driver_age0.1055060.3896710.0000001.000000car_size0.000000
mileage0.0000000.0000000.7685890.0000001.000000
\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -833,13 +812,12 @@ "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 1.000000 0.590456 0.000000 0.105506 0.000000\n", - "car_color 0.590456 1.000000 0.000000 0.389671 0.000000\n", - "car_size 0.000000 0.000000 1.000000 0.000000 0.768589\n", - "driver_age 0.105506 0.389671 0.000000 1.000000 0.000000\n", - "mileage 0.000000 0.000000 0.768589 0.000000 1.000000" + " car_color driver_age area mileage car_size\n", + "car_color 1.000000 0.389671 0.590456 0.000000 0.000000\n", + "driver_age 0.389671 1.000000 0.105506 0.000000 0.000000\n", + "area 0.590456 0.105506 1.000000 0.000000 0.000000\n", + "mileage 0.000000 0.000000 0.000000 1.000000 0.768589\n", + "car_size 0.000000 0.000000 0.000000 0.768589 1.000000" ] }, "execution_count": 13, @@ -858,12 +836,14 @@ "outputs": [ { "data": { - "image/png": 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dp7ZDOSMiA3Op+1a7rrP2Y5aI3Oiw/ToReU9EYuz9HhCR90UkxKWecBH5ym7HcRF5UUT+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\nVhE5HXgYeENEWgIx3asaQFXfE5HNuN/n/+C+TGbi5nHurqpT/YwvzP6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\n", 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fWXEze5X695DPSBC25ddpZgti71Oo80rC8Lu++AmEPfOa+0brMLMlJG6vk4U0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\nyHMIYq7hdjO7LSbtAcBhwGg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3M+7AXrTKy2lU2OMO6sULi4spr6j6Uv1NG+FQPNEjXaTLYvcFbgB+CUwCDgOmAtPD41SCacJ0JeipkDQIeB0oAM4BzgP2AZ6KaKMNQfiZ6wgCtP+KIPLj41HNPQAcB1wR9u944Iyo63UBXiOIP3URcDrQFnhJUmEifW5JdGyTz/ayinrp20or6NgmP+F2xg/vxburt7Jy4864ZUYO7EKvzoU8tTCzhuFQs46d2p1AmoJ0zbG7AIea2XKA0DJfA5xrZg+FaQJmEkRj/CCBNn8NFANjzGx32MZi4ENgLDDTzDYCF9dUkJRHMG9/TVJfM1staV9gAnC6mT0elptFEPs50qRdRSDkA81sS1judWAVcD7B7g4ZhVl997cEMZJj0q1Da0YNKuLmfy1psNxJw/uwdeduXolyumUGmbGOnS6LvapG1CHLws+XY6T1TrDNY4EngWpJeRGiXQXUxnWWdI6kRZJKgAoCqwswJPysKftUTR0zKwNeinG9F4HtEdfbASyIvF4kkiaFU4z5GzduTPC2modtpRV0iGGZ2xfGtuSxOPGgXggatMS5OWLMgT159p31VFQl+I3RwvCheHy2Rp3vjpFek1aQYJtFwM8JxBp57EUYt1nSKcBDwFzgNGAUcErUdXoAO8ysrqsXopVYRDA8j77et6gbJ7oWM5tqZiPMbETXrl0TvK3mYflnJfXm0j07FdC2dV69uXc8xg/vzbwVW1i/NfpH9wWHDymiqH1r/pVh3vBIfCjevGwhsNj3x8irCdV6GvCmmV1SkyHp6KiyxQT7JBVEiTtaiVsI4j//Nsb1diTT8ZbAnCWf8aNvD6Jt61x27gocWuOH9aZsdyVvLNvcaP0+XQoZPqALv3hscYPlThrRmw3bypn7cYPRc1suvo7d7MwC9gUWWKzJYkAhsCsq7eyo85p9liYAfwcInWHHUVewswgcZu+HQ/WM5uHXPuEHR+/F1AtGcs9LH9N3j7ZcNXYo9728os4S2KvXf5s3lm3iZ4+8U6f+hOG9qaiq5pm3ozex+IJWeTkcv19P/vHm6oTn7S2NGudZSyebhD0ZeAuYKWkagZXuTSDIB8xsDsGceIqk64A3CZxq345sxMzek/QUcI+k9gQW/CdAKRC5EdofgO8BL0v6I7AW6A4cDbxmZo820X02CdvKKph493+44bT9mDbpELaXVXD/7OXc/szSOuVyc0RujPXZ8cN68/rSTWwp2V0vr4bRe3ejY5t8ZiyML/5MIAN0nT3CNrOPJI0CbiRYOiskENssvnDE/R/BnPsKgjn1i8BZwBtRzZ0H3APcReAJn0KwpcrBEdfbFF7vJuB2oBOwnsAZ1/B4tIXycXEJE/84t8Eyh0+O9iEGjLnllUbbf2FxcVZsROgWOwZmdl6MtAcI1o4j01YREYTGzBSVPzpGOx8SrIHHu3YVcHV4RBLd9hYi1q1Dj/d7BFY+stw6gg3TnK8RGaDr7LHYqUTSaUAv4F2gA3AhMBj4fjr75bQAPIJKRrOTwBIPInhS7V1gvJm9ldZeOWnHI6hkMLE2SXOcgMx48syF7ThJkgG6dmE7TlIoM97HdmE7ThL4AyqOk6W4sB0nC8kAXbuwHSdZ3GI7Trbhb3c5TvYhX8d2nOwkA3TtwnacZMnJAGVnTFxxx2kppDrmmaQTwpDYyyRdG6fMaElvS3pfUqPvyLrFdpwkUIrf7pKUS/C+/3HAGmCepBlmtiSiTCfgT8AJYSTdbo216xbbcZIkR4kfCTASWGZmK8Kw2dOBk6LKnAU8YWarAcys0bjNbrHTSLwdKJ2WTYq94r0JYtbXsAY4JKrMECA/3IiyPXBnTfz9eLiwHSdJktR1kaT5EedTzWxqZHMx6kSHeswDhhPE5ysE5kp6w8w+indRF3YaGXPPm40XcpqdZy+ONphfIIK17CTYZGYxN5AIWUPdOPR9gOhoj2vCdnYCOyW9ChwAxBW2z7EdJxmk2kitiRwJMA8YLGmApFbAmQTx6iP5F3BkuONMG4KheoPbXrnFdpwkSeUU28wqw11knycIwzXNzN6XdFGYf6+ZfSDpOYLot9XA/Wb2XkPturAdJwlE6h9QiRWKy8zujTr/PfD7RNt0YTtOkmTAg2cubMdJFn8JxHGyjHRvj5soLmzHSZJMeAnEhe04SdLyZe3Cdpyk8Tm242QZwXJXunvROC5sx0kGeWgkx8lKMkDXLmzHSRa32I6TZfgc23GyFLfYTkZRsn4lS/5+G1tXvEt+m/b0OWwCg8b9EOXkNlq3eNFsVjz/ECXrV5DbqjUd++3NgRfeTF7rQgA2ffAma+Y+zdYV71K+pZiBY3/I4BMvbOpbahJavqxd2E5IRel25t11Ge169GfYRb+jdONalj5xF2bVDJlwUYN1P339X3zw2G0MOO57DP3upVSW7mDz0vlYdVVtmU1L3qBk7TL2GHowxQtebOrbaTIkEn3POq24sB0AVr/6JFW7d3HQpFvIK2wL34TK8p0sm3k/ex13TpAWg90lW/nwH3fyzdN/wp5HnFyb3v3A0XXKDT3lMr7xX1cAsGHxq012H81BJgzFPYJKDCQVpLsPzc2mJXMp2vuQOgLuOeI4qit2seXjhXHrFS94CYDeo8Y12L5ysudPLdVxxZuC7Plpx0HSoZJmSFonaWcYdP3siPzzJJmkkZLmSCoDrgnz9pU0U9KO8HhcUo+Ium0l3R0Gey+VtFLSFEkd0nCrX4mdxZ/Qtnu/OmmFXXqQ26qAnZ99Erfe1lVLaNu9L2v+M4PZvxjP85ceztzfnc/nyxc3dZfTghA5SvxIF1kvbKAf8DpwATAe+H/AXyRNjCr3KPA0MBZ4WtKgsF4BcA5wHrAP8JS+GIu1IQhncx0wBvgVcAzweBPeT5NQUbqd/Dbt66XntWlPRemOuPV2bd/Mzs9Ws/zZBxh68o8ZdvGt5LYqZP6UK9m1fXNTdjk9JGGt02mxs36ObWbTa/4fCvJVgkiQFxKIuYa7zOzOiLJ/BYqBMWEgdyQtBj4kEP9MM9sIXBxRJw9YCbwmqW9NgPfMIcZfYnQg3Hr51VTtKuXAC26i6z6HAtB5r/2Z88uTWf3KPxg8/kep72aa8Tl2C0BSZ0l3SfoEqAiPSQRB2COZGXV+LPAkUB1Gh6wR7SqgNpyspHMkLZJUErb9WpgV3T6SJkmaL2n+xo0bU3B3qSO/TQcqy+pb5srykpiWPLIeQJchw2rT8grb0rHvUErWr0x9R1sAOUkc6SLrhQ08AJxBEAjueOBgYBrBEDuSz6LOi4Cf88WXQc2xF2EcaEmnAA8Bc4HTgFHAKWH9eg44M5tqZiPMbETXrl2/6n2llLY9+lESNZcu2/IZVbvK6s2969brH4w5ra5pNwBl35+XCCx2oke6yOqheOjdHgdcGhn1UYr5Fxc96NxCYLHvj1F2U/h5GvCmmV0S0fbRX6nTaaJo70NZ9dLfqCzfSV5B4BkvXvASOfmt6TJ4WNx63fY7guXP/JktHy2k676HAVBRVsL21R8y4NizmqXvzU0GLGNnt7CB1gTOrV01CZLaAxNofPY4C9gXWGBm8coWRrYdcnasgi2dvkedwuo5f2fR1GsZcPw5lG1ax7Jn7qf/tyfWWQJ79den0nnQQex3znUAdOz3TbrtfxTvPXwTQ06+hPy2nVj54sMoN4++R59aW69s83q2fRLEuK+uqmBn8SqKF75MbusCuu5zWPPe7FfEhZ1mzGybpHnA9ZK2EwRbvxbYBjS2JDUZeAuYKWkagZXuTbDd6QNmNgd4EZgi6TrgTQKn2reb4FaanPw2HTj4irtZ8titLLznGvIL29H/mDMZNO6COuWqqyoxq6qTtv95k1n65B/58P/dSdXuXXTeaz9GXnF37fwbYPNHC3jvrzfWnhcvnEXxwlkUdOnB6Bv/2bQ3l0JSvY1uU5HVwg45C5hKMBfeDNxNsEx1aUOVzOwjSaOAG8P6hcBaAku+LCz2fwRz7isI5tQvhtd7I+V30Qy06zmAkVdOabBMLBHmFbRhn4k/Z5+JP49br8+hJ9Ln0BO/ch9bAm6xWwBmtoxgbTmayWH+AwQOtlh1PwROjZUX5lcBV4dHJBnwq3e+LBlgsLNf2I6TSppii5+mwIXtOEmSCYt4LmzHSQIp4e1x04oL23GSJANG4i5sx0mWDDDYLmzHSQZ3njlOlpIBunZhO05SyIfijpOVKAOeP3JhO04S+IYBjpOluLAdJwvxt7scJ8vIlKF4Jjz26jgthyaIUirphDCE9TJJ1zZQ7mBJVZLivnFYg1tsx0mSVD6gIikXmEIQwGMNME/SDDNbEqPcLcDzCfUxZT10nK8BNUPxRI8EGAksM7MVYZjr6cBJMcpdRhATf0MijbrFTiPPXnxIurvgfAlS7DvrDXwacb4GqPOHIak3QfTbYwii7DaKC9txkkLkJPeASpGk+RHnU81sap0G6xMdPPMO4OdmVpWoR96FnUZue2VFurvgxOCnR+8VNy/YRjep5jaZ2YgG8tcQxqkP6QOsiyozApgeiroIGCup0sziRoF0YTtOkqT47a55wGBJAwiCZZ5JEBCzFjMbUPN/SQ8ATzckanBhO05SBDuBpK49M6uUdCmBtzsXmGZm70u6KMy/t8EG4uDCdpwkSfX72Gb2DPBMVFpMQZvZeYm06cJ2nCTJgCdKXdiOkwwiMx7+cGE7TjL4Fj+Ok520fFm7sB0nKTyYoeNkKS1f1i5sx0maDDDYLmzHSQ6588xxsg1f7nKcLMUttuNkIS1f1i5sx0kKCXLdYjuZRPGqj/nnXb/hk/cXUdiuAyPHnc7x515OTm5uQvWrq6u586KTWfvR+5z/P/ex92HHBOlVVcx57H4+mPsyn32yDIA+Q/blhAt+St9v7N9k99NUZMJQPBP8AE4zULpjG1N/+n2E+MFN/8ex37+MV/7+Z57/yx0Jt/HWzMfYvvGzeukVu8qZ/ci97PmN/Zn4i1uZeN1t5OTlMeWyM1iz9N1U3kazoCSOdPGlhS1pX0kmaXQj5eZI+seXvY7TPMyd8QgVu8o597d/YsiIIzjspLM4/tzLePXxaZTv3NFo/dId23j2/j9wwgU/rZeX37qA/350DhN+fB3fHPUtvnnIaM777T106NKV1578a1PcTpOS6vDDTUFzWOxLgP9uhus4X4EP33yFoQcfSUHb9rVpBx4znopd5Sx/561G6z/359vpv+8wBg87rF5eTm4ubdp3rJOWl9+K7gMGU7J181fvfDMSLHcp4SNdNJmwJRUCmNkSM/u4qa4Tdc2C5rhONrJh9XK69h1YJ61z917kFxSyYfXyBuuuW/4h8577B+MvTvz7u3L3LtYufY/u/QZ9qf6mk6yy2JIukfSppJ2SngJ6RuWbpJ9IukPSRuDdML12KC7pW2G5faLqdpa0W9IPI9KOkPSKpFJJmyXdJ6l9RP55YVsjw2uUAdckcB+HSpohaV14L29LOjtGudGSFksqlzQvvM4mSZOjyp0kaX5YrljS7yTlJ/IzbUmU7dhOYbsO9dLbtOtI2Y7tDdb9512/4fCTv0dRn/4JX++lh/9Eacl2Dhl3RrJdTTNK6l+6SEjYkk4i2K3gaeC7BKKdFqPoNQSCPwe4PEb+K8B64PSo9FPCzyfD6x0OzAKKgVOBK4GxwF9itPlo2K+x4Wdj9ANeBy4AxhMEYf+LpIk1BcI4zs8QBGc/Ffg/4G9AYWRDkk4HngDeAiYAvwEmATcn0I8WRywLY1iDlmfRrKfYuHoFx57z44Svs2TubGY9/CfGTfoZ3frGjwjaUskEi53octd1wHNmdnF4/rykrgTiiKTYzOJ+BZtZtaTHgTOAX0dknQG8YGZbwvP/Bf4T2ZaktcAsSfua2XsRde8yszsTvA/MbHpEmwJeJQj5eiHBlwQEXySlwHgzKwvLbgcei6r7e+AhM7skIn0XMEXSzWZWZwIpaRKB8Onbt2+iXW4WCtt3oKykvpOsvGQHBTEsOUBVZQVP3/u/fOusH2HVRtmO7ZSXlgCwu7yU8tISCtq0q1Nn9YeLefg3lzNq/ESOOu0Hqb+RJqZmjt3SaVTY4Z5BBxFsMRLJE9QX9swErvkYcLmkA8zsHUlFBDscnB9erw1wKHCZpMj+vVQ14f8AAA8rSURBVAZUAMOBSGEncs1aJHUmsKwnEezCULNIuzai2MHAizWiDpkR1dQQoC/w96h+vgwUAPsSjFBqCQPFTwUYMWJEdFD4tNKt78B6c+mtG9axu7yUblFz7xp2l5WxbWMxM6bcxIwpN9XJe/iGK9ijV1/++5HZtWkbP13Jn6/9IYOHH8Ypl/86urnMIM2WOFESsdhdw3LRewbF2kOo/iJmfeYCqwms9DvAfwGVQE2c5M4EYvtTeESzZ9R5IteM5AFgFPBbYAmwHbiYuvsl9QAWR1Yys3JJJRFJReFnneiSDfSzRfONQ45mzvT76ljZt1+eSX7rAgYeMDJmnVaFbbjo9r/VSduxZSN/++2VjLnwagYddGht+vbNG5h6zXkU9erH2b+6I+GHXloi2SLsjQTC6xaVHn0O9bcmqV/AzCT9nUDYvwg/nzWzmnHg1rCdycQWTfQuCQlbvtBrPg64NDK8q6RoX0MxwRdadN3IcWXNtGESsCjG5VYm2q+WwKETzuK1//cgD/7qEr418UdsWb+aFx64i6NOO7/OEtjNZ32LgQcewuk/+19y8/IYdNCoOu1sWb8GgJ4DhtJv7wOB4AGV+392PmU7tnHKFZNZv/zD2vJ5rVrRe3AdX2qLJ51OsURpVNjhfkFvE1i0yFjH3/0K150OXC3pROBooNZxZWY7Jb0BDDWzG77CNWLRmmA0sKsmIfS0T6DuF8Q84AeSCiOG4xOi2lpKMHzvb2b3pbifzU6b9h350R/+ypN3TmbaLy6ksF0HjjrtBxx/3hV1ylVXVVFdVZVU2zu2bGLd8g8AmPbfdWdvnbv35rrHXv1qnW9GMmXj+0SdZ/8DPCHpHgLP9dHACV/2oma2QNIygvlmGfW92T8jcJRVA/8AdhDMZ8cB15nZR1/yutskzQOuD51h1cC1wDYg0kN0B/Bj4ClJtxMMza8lcKhVh21VS/op8FdJHYBngd3AXsDJwKlmVvpl+pkuevQfzMVRQ+toGhNhl559uHXO8kbTMplMsNgJLXeZ2ZMEzrPxBHPhg4AfNlipcR4jWBp7KloAZvYacBTBcPivwFMEYv+U5OfU0ZxFMEx+CLiTYLnroajrryX4EulG4CS8jMC5l0swJ68p9xjBSOZA4PGw7CXAQgKRO1lINi13YWZ3A3dHJSsiP+ZtmNnoOOm/BH7ZwPXepIFRgZk9QOAISwozW0bghY9mclS52UDtq0eSjiAYyr8TVe5ZAmvtfE3IBIvtr23GQdItBE6xYmAo8CsCT/krDdVzshshfx+7uQm923GnF2ZWmURzrQkeQOlOMMd/AfiJmVV/pU46mU2GrGNn2/vY0wgeYol5SOqfaENmdqWZ7WlmrcxsDzObaGbrm6DPToaRCe9jZ5XFJpgnR/sBIoleA3ecpPCdQNKAma0CVqW5G06W0/JlnWXCdpxmIQOU7cJ2nCTx5S7HyUIyYIrtwnacZMkAXbuwHSdpMkDZLmzHSYJgfbrlK9uF7TjJkCFPnrmwHSdJMkDXWfdIqeM0PSl+plTSCZKWSlom6doY+WeHobAXS/qPpAMaa9MttuMkRWrjhYfBQqcAxwFrgHmSZpjZkohiK4GjzexzSWMIApQc0lC7Luw08tOjMy+m9tedJgiNNBJYZmYrACRNJwjeUStsM/tPRPk3CMJlN4gPxR0nWZIbiheFO8XUHJOiWutNEBmohjVhWjx+SAKBPdxip5GDb5qT7i44MZh33egG85Mcim8ysxENNlefmJF3JX2LQNhHNHZRF7bjJEmKl7vWUDcGfR9ivF4saX/gfmBM9A4zsfChuOMkSYqd4vOAwZIGSGoFnEnUrjOS+hIEyjwn0Qi9brEdJxlSHBrFzColXQo8TxAFd5qZvS/pojD/XuB6YA/gT8GWcVQ2Mrx3YTtOsqT6kVIze4aoXW8id6oxswuov09eg7iwHScJhD9S6jhZSQbo2oXtOEmTAcp2YTtOkvhrm46Thfgc23GykAzQtQvbcZImA5TtwnacJPDQSI6TjSjlr202CS5sx0kWF7bjZBupjaDSVLiwnVrKNqxi9dN3sfPTJeQWtKNo+Fh6HXMuyslttO7n77/K+lcfoeyzleTkF9C2z1AGTvwNua0KAaiurKD41UfY/PYL7N6+iVYdiuhywLH0PPpscvJaNfWtpRRf7nIyhsqyHXz0l6sp6NqPgWffyK4t61jz7D1gRu/jfthg3Y3zZ7L66TvpccSZ9PnORVSV72DHikVYdVVtmbUvTGXjvKfodez5tOk5mNJ1H7P2pT9TVV5C33GXNfXtpYx073udKC7sGEhaBfzDzK5Od1+ai41vzaC6YheDzrqB3IK2AFTt2sn6lx+kx5Fn1qZFU7FzG58+M4W+4y6n68En1qZ33vvIOuU2L55F15ET6HH46QB02OsgKrZvZPM7szJK2EBGKNsDLTgAbPvoLToMPriOgLvsdwzVFbvYseqduPU+f282AHsc9J0G27eqKnJb1/1yyC1oR5woQC0aJfEvXWSMxZZUaGZl6e5HMkgqMLPydPcjEco3rab9XgfVSWvdqTs5+QWUb1wN3zgsZr2dn35AQdGebFrwDOtfeZjKks9p02swe479Me367ltbruuIsWyc9zTtBw6jTY9BlK7/mI1vzaDbISc36X01BZkwx24yiy3pKEmzJZVI2iZpjqSDJPWUNE3SCkllkj6SdGMYFqambn9JFgZKf0jSVuCpBK/bT9KjkjZJKg2DrJ8VkV8k6UFJm8P8OZIajEYR1jtd0ruSdkn6VNJNkvIi8s8L+zwybLMMuCa5n1r6qCrbQV5hu3rpuYXtqCzbEbdeRckWyjd9yvo5D9Pn+EkM+t7/kJNfyEcP/pyKki215XofP4nO+xzJ0vsuZ9Fvx7L0/ivotM+R9Drm3Ca5n6YkxaGRmoQmsdiSRgMvArOBc4GdwOEEYVUrgS3AT4DPgSHAZKAr8KOopm4liPV0GlBFI0jqBswFSoGrCcK67kvdYHH/BAaF+ZsIxDdb0kFmtixOu8cDjwEPheX3B35LEK7moqjijwL3AL8BtjbW5xaP0aCJMjOqd5cx8MzJdBwyEoB2ffdh8a1nsuGNf9L72PMBKH5tOpvfeYm+J15OYfe9KC1ezrpZ08gr7FBbJiP4mu/ddTPwDvAdM6uZRD0XkV/rlJL0OoHwp0m6zMx2R5R7w8x+nMR1rwI6AsPNbH2YNiviWicQfMGMNrNXwrSXgVUEgo3+YqnhBmCOmdWYl+fC2FM3S7rRzNZElL3LzO6M1UgYU3oSQN++femWxI01NbmF7akq31kvvWpXCXkF9S15DXmF7QFoP+DAL9oqaEubXkMo27AKCBxs616aRt8Tr6h1sLUfcAA5uXmsfvouuo06hfx2nVN4N01Ny1d2yofiktoSbD/yYISoI/Ml6UpJS8LhagXwN6A10Deq+MwkL38M8FyEqKMZCWysETWAme0EniZOrOZwC5ZhwONRWY8R/PwOTbTPZjbVzEaY2YiuXbs2eCPNTUFR32AuHcHurRuo3l1OQdfoX0tEva79QhMW/as2pODPa9fn67CqStr0HFSnRGGvwVh1Fbu3fpaKW2gWakIjJXqki6aYY3cmuP944roSuA14kmArk5FAjVUuiCqb7G98jwauC9AzTpufAV3i1CkC8mPUqzmPrpc5f6URdBwykm3L5lG1q7Q2bct7s8nJb037/vH3gOv0jUPBjO0rFtWmVZaXULruIwp7DAQCJxxA6bq6kXNL1wbnrTr3SNl9NAdf1zn250A1gYhicRrwuJldV5Mgae84ZZNdC9ncwHUhEH2sEXB3gnl/LDYRjCqi63UPP6PrZd76DdB15AQ2zH2CZY9cT88jJ7Lr83Wse/kBuh92Wp0lsHf/cDbt+x9A/+/+DIC2vYfS6ZuH88mTv6fy+AvJa9uR4n9PRzl5dBsVeLzz23Wh0zePYM0LU6mu3E1hj4GUrl/G+pcfpPO+R5PftlNa7vnLkglz7JRb7HBo+ybwfSnmj6AQ2BWVdnaKLj8L+I6k7nHy3wS6STqqJkFSG2Ac8FqsCmZWBSwg+EKK5HSCL7C5X7XTLYG8wvYMOf82qK7m44d/wbpZgah7ffu8OuWsugqz6jppA069jk57H8Gnz97D8kcno5w8hpz/h9r5N8CA/7qWouHj2DD3ST5+6Fo2vvlPig4+kf6n/KwZ7i61fJ3Xsa8FXgKelTSVwDl2KDCfwFt+uaQ3geUEoh4Ur6EkuR34PvBvSTcReMW/CbQ1s9+Z2fOhs+6xcB/izQSOvELg9w20+2vgeUl/AaYD+xF4xe+LcpxlNIXd+jP0h39osMz+V0+vl5bbupB+E66i34Sr4tbLLWjLnmMuZs8xF3/lfqadr6PFBjCzVwn2+20DPEzgaDqaYJ+iGwiWhG4MP3cDl6fouhsJvN6LgDsInGKTgEiv0CkEXy53EDjEBBwTb6krbPcFgq1XRhCsp9f4CS5NRb+dzEHh+9iJHumiyZ48Cz3PR8XJ/kGMtNofg5mt4kt+L5rZJ8AZDeRvJLDqDbXRP0baYwRfUPHqPAA8kGA3nQzGX9t0nGyk5es6c4QdOuIaejG4Kta6ueOkmgzQdUa93XUuwbJTvCPzHjp2MpJMeEAlYyw2gdPq4AbyVzZXR5yvMx4aKaWY2WaC5SnHSRuZsttmJg3FHcdJkIyx2I7TUsgEi+3Cdpwk8Tm242QbX/NAC46TlaT7dcxEcWE7TrJkgLJd2I6TJD7HdpwsJBPm2L6O7ThJkupHSiWdIGmppGVhnIDofEm6K8xfLGlYY226sB0nSVIZQSUMljkFGAPsDUyMESpsDDA4PCYRhLduEB+Kp5F5141OdxecJGmCR0pHAsvMbAWApOkEQT6XRJQ5CXgofHvxDUmdJPVsIBqvCztdLFiwYJOkT9LdDycu/WIlLly44PnCfBUl0U6BpPkR51PNbGrEeW+CEF41rCEI300jZXrTQEReF3aaMLOWFVjcSQgzOyHFTcay/9FxBRIpUwefYztOellD3S2o+gDrvkSZOriwHSe9zAMGSxoQbkx5JjAjqswMwnDekkYB2xqaX4MPxR0nrZhZpaRLgecJQn9NM7P3JV0U5t8LPAOMBZYRbDgZKxhoHeRhwhwn+/ChuONkIS5sx8lCXNiOk4W4sB0nC3FhO04W4sJ2nCzEhe04Wcj/B35pSlAe8vLjAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -936,11 +918,11 @@ "source": [ "# Statistical significance of the correlation: $Z$-score\n", "\n", - "When assessing correlations it is good practise to evaluate both the correlation and the significance of the correlation: a large correlation may be statistically insignificant, and vice versa a small correlation may be very significant. For instance, scipy.stats.pearsonr returns both the pearson correlation and the p-value. Similarly, the phik package offeres functionality the calculate a significance matrix. Significance is defined as:\n", + "When assessing correlations it is good practise to evaluate both the correlation and the significance of the correlation: a large correlation may be statistically insignificant, and vice versa a small correlation may be very significant. For instance, scipy.stats.pearsonr returns both the pearson correlation and the p-value. Similarly, the phik package offers functionality the calculate a significance matrix. Significance is defined as:\n", "\n", "$$Z = \\Phi^{-1}(1-p)\\ ;\\quad \\Phi(z)=\\frac{1}{\\sqrt{2\\pi}} \\int_{-\\infty}^{z} e^{-t^{2}/2}\\,{\\rm d}t $$\n", "\n", - "Several corrections to the 'standard' p-value calculation are taken into account, making the method more rebust for low statistics and sparse data cases. The user is referred to our paper for more details.\n", + "Several corrections to the 'standard' p-value calculation are taken into account, making the method more robust for low statistics and sparse data cases. The user is referred to our paper for more details.\n", "\n", "As a result, the calculation may take a few seconds." ] @@ -970,75 +952,66 @@ "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
areacar_color1.0000000.3896710.5904560.0000000.000000
driver_age0.3896711.0000000.1055060.0000000.000000
car_colorarea0.5904560.1055061.0000000.0000000.3896710.000000
car_sizemileage0.0000000.0000001.0000000.0000001.0000000.768589
driver_age0.1055060.3896710.0000001.000000car_size0.000000
mileage0.0000000.0000000.7685890.0000001.000000
\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
area72.41414337.597152-0.2927771.792994-0.644337
car_color37.59715285.456758-0.62725919.823435-0.63630985.45407319.83944137.584186-0.622065-0.620222
car_size-0.292777-0.62725969.041964-0.53976649.224630driver_age19.83944184.3318221.814443-0.679303-0.509666
driver_age1.79299419.823435-0.53976684.350588-0.706470area37.5841861.81444372.420956-0.666399-0.361338
mileage-0.644337-0.63630949.224630-0.70647091.208501-0.622065-0.679303-0.66639991.22964849.233473
car_size-0.620222-0.509666-0.36133849.23347369.041094
\n", "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 72.414143 37.597152 -0.292777 1.792994 -0.644337\n", - "car_color 37.597152 85.456758 -0.627259 19.823435 -0.636309\n", - "car_size -0.292777 -0.627259 69.041964 -0.539766 49.224630\n", - "driver_age 1.792994 19.823435 -0.539766 84.350588 -0.706470\n", - "mileage -0.644337 -0.636309 49.224630 -0.706470 91.208501" + " car_color driver_age area mileage car_size\n", + "car_color 85.454073 19.839441 37.584186 -0.622065 -0.620222\n", + "driver_age 19.839441 84.331822 1.814443 -0.679303 -0.509666\n", + "area 37.584186 1.814443 72.420956 -0.666399 -0.361338\n", + "mileage -0.622065 -0.679303 -0.666399 91.229648 49.233473\n", + "car_size -0.620222 -0.509666 -0.361338 49.233473 69.041094" ] }, "execution_count": 17, @@ -1058,12 +1031,14 @@ "outputs": [ { "data": { - "image/png": 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PTMez5CCKVhvBqPELUwyT7tt/ik+//oV3JvRySRlu1Z1cdnsikuVXJtN1F5E/RGRVdvOY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\n64ELrJmZWQ9cYM3MzHrgAmtmZtaDvwF9QOu+Oy8e3wAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1080,7 +1055,7 @@ "source": [ "# Outlier significance\n", "\n", - "The normal pearson correlation between two interval variables is easy to interpret. However, the phik correlation between two variables of mixed type is not always easy to interpret, especially when it concerns categorical veriables. Therefore, functionality is providided to detect \"outliers\": excesses and deficicts over the exptected frequencies in the contigency table of two varaibles. \n" + "The normal pearson correlation between two interval variables is easy to interpret. However, the phik correlation between two variables of mixed type is not always easy to interpret, especially when it concerns categorical variables. Therefore, functionality is provided to detect \"outliers\": excesses and deficits over the expected frequencies in the contingency table of two variables. \n" ] }, { @@ -1093,15 +1068,13 @@ "\n", "$$\\phi_k = 0.59 \\, ,\\quad\\quad \\mathrm{significance} = 37.6$$\n", "\n", - "Let's use the outlier signifiance functionality to gain a better understanding of the sigificance correlation between car color and area.\n" + "Let's use the outlier significance functionality to gain a better understanding of the significance correlation between car color and area.\n" ] }, { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "c1 = 'car_color'\n", @@ -1250,12 +1223,14 @@ "outputs": [ { "data": { - "image/png": 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Bjen22Bes2jWGUmUK06JtNYa+0oqhT80iJjqeJ7rW5uV3nkhV1srF+2n3ZG0K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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1296,15 +1271,13 @@ "\n", "$$\\phi_k = 0.77 \\, ,\\quad\\quad \\mathrm{significance} = 46.3$$\n", "\n", - "Let's use the outlier signifiance functionality to gain a better understanding of this sigificance correlation between mileage and car size.\n" + "Let's use the outlier significance functionality to gain a better understanding of this significance correlation between mileage and car size.\n" ] }, { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "c0 = 'mileage'\n", @@ -1493,12 +1466,14 @@ "outputs": [ { "data": { - "image/png": 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eWVXN84UfqroKy1vPS/4M+UyQUtWJWPFmZ7JorBnReZVt7vB7HJZBNxgMhksf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XfAyEpYRSn1HcauWwQFBalse31kIW71cuJWL89Vjr0DjuWs9/llqpT2x6VREBFTxuepZytiVy0j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\nAfL/MJIrCJeqh3gAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1526,7 +1501,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A full correlation report can be created automatically for a dataset by pairwise evalation of all correlations, significances and outlier significances. \n", + "A full correlation report can be created automatically for a dataset by pairwise evaluation of all correlations, significances and outlier significances. \n", "\n", "Note that for a dataset with many different columns the number of outlier significances plots can grow large very rapidly. Therefore, the feature is implemented to only evaluate outlier significances for those variable pairs with a significance and correlation larger than the given thresholds." ] @@ -1542,57 +1517,67 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['driver_age', 'mileage']\n" + "interval columns not set, guessing: ['driver_age', 'mileage']\n" ] }, { "data": { - "image/png": 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dp7ZDOSMiA3Op+1a7rrP2Y5aI3Oiw/ToReU9EYuz9HhCR90UkxKWecBH5ym7HcRF5UUT+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\nVhE5HXgYeENEWgIx3asaQFXfE5HNuN/n/+C+TGbi5nHurqpT/YwvzP6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\n", 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gLDNbGb4QbgZ+D3QhWLuYTuDOyMlCMmA3L31n9c1sGsn30M9OELblr9PMFsTe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\nSnfrmp7Qd16qV7po1UN9M3u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\n", 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5i+ZNbz9W1N3djb92H+OLb5dz82YUlSoU5ecZrzmsNM5Mgwe0Ji7OMHLcr1y+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\nG7cc4b2ezRLsExMTQ2TkY/zPXGXoiF+pXq0UL/iWtkkakvL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BRlvlAaAe8AFGR6fzGJlr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\ngg==\n", 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\n", 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pLH7vH+p2q4ZXRY+byh5ccYI6XaulGY7EmEQciztkkHEs7kBCTGKB6VuYXLsS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qznAHcAXwMWA8u7p51OuX5zDqWbyc7A56b5JUQ0Lad/IyIipklmqhEREdMkSTUiImKaJKlGRERM\nkyTViIiIaZKkGhERMU2SVCMiIqZJkmpERMQ0SVKNiIiYJv8PYDgekVD6tBQAAAAASUVORK5CYII=\n", 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\n", 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vFKY+h3L5XhFVzfMlHKq6Essrzyv/GfKZ5KSqE7Dix87yorFmMudVtqnD7zFY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YvE9nR8Bnbn0m/5997NLC0xs7ZCRDCztMDM2irjyaFUBTdKlXcDoEzTetQZ+xr/jptRpHab5P/D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\n9bLpE5BYKQAAAABJRU5ErkJggg==\n", + "image/png": 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WlJwzP8U+r+zCvjrDriQl6hSRK9aVtLlFwrtaFfwahxK7fZ9LeXG+c/VGDeHgFz+WprkArD4aQ7PAii5lSs6XNaha5zui43OcO+sfjU8hNuUc3j9cCNV8A2kcXJvtxw7QokZ9Nh8928aJqcnsP3mUFjXr58kn5HVUIkJLW1dEcuhkfW7poqz/KkWJeN2Bw0BPwB8YA3wnIqG2fAoQCDSz/z7qlPc9wAcIxdqj8xYRuR0shwnMA94CKgNvAvNFJMDOeytwC9ANqIm11+dHLux7A9hZUAVEpDMwAWuvUH/gc6x9RAvuk4MEYJCdZxTwgYhcapfZCPgKuNe2fz4wT0SyJqw9A3QEWgKNgfbAC3Zed+AnYAFWm40GZopIY1seAswEHgP8gCeBWSJSzS77YaAr0Npum9P5tA1AANY9CgXqAnHA/wqp9zmRHBFF1MoNDNqzhBFJm6kzvB8bH33dpa5HpYqkxeZ8F3RabDwevq5/sMoCn1rB1L1hALs//JIfa3bn2MLlObq/nRGHg44Tx7Hh/162fi3Po6ySILBDS6p2a8+ej2YWqnv81z+p1rMT1XpegsPDgxbP3YvD0wM3H+88umXlrFwh7u5c+tXbHJgxlzO7D7jUKep3zqd2Dar1tIY0SpMvdxxjY2QcD7Wv41Lep3Ygq47GsOJIDKkZmby1PozUjEyS0q2u5ivqVmHS5sNEJaVyIiGFSZsPA5CUnlGidro73PjqjvHMWLOI3SfCqeRVgdik+Bw6sUkJ+Hr55Mm763gYkfExPHnFzbg73Lii2SX0bNQOH0/r+7No259c36EvrUIa4u3hxYtX30FmZma2vFS5WMZ4VTVBVcepapiqZqrqAuAg0EFEmgDXAKNVNUpVM1R1g1P2QcCbqpqoqmFYDu8OW3YpcEJVZ9v5ZgJRwFCnvJ+r6mFVjcdysCNEJPubICJdsRxbYY4kFNiuqhvU2gfxCyAIqFZQJlUdq6q77Hr/DazEcngAVwErVXWVqqbb9oVgPaBk2f+hqp5S1SjgQ6e6N8VymO/ZdV8KrMZ60ACoBZxW1Z/VYiHWQ0DWDJh6wBJVPWFvNv4N4LIvzi5jtqqeUdVE4GOsh5k8iMhouzdgfVRUVEFNA1jjiVmTVnotmkrLsQ8Q2Kklc2v14Fvv1mwd/zF9l87ArULe/3Bp8Ql4+FXKkebhV5G0uIRCr1tS5LY/IymFqFUbiVi8gsy0NHa+/TmeVSrj1yzvk3qj+0dyestuTq7Z5LLs4pRVErZ3mjiWDQ+/imYU/gN9ZvcB/hr1DB0/HsOQiJV4BQUQu2MfiUdO5NDzqVXdclbn6Xhz21okRLj0yzfJTE1j/YMv56tW1O9cvVsHW13SYUfOpyoAfLv7OMGTlxE8eRlDfjp7/+fvj+LFP/fxwzVtCarg6TJvk8CKfHpFcx5fvpuG01YSnZxK08CKhFTyAuCpTqG0DvLl0q/X0vf79QysXxUPh1A1n/LOBRHhy9vHkZqexoPfvA1AfEoSft45H3r9vH2IS0nMkz89M4PBk5/m6paXcvyNRTx++Ui+2/A7R2IiAVi6ez1jF0xlzujXCX91LmHREcSlJHLkdGSJ1aEgyoPjLfZyIhEJxorgtgOdgXBgvIjcAkQA41R1jnOWXJ9bOn3OXeOC5AJ4AY2AzXa0+glwN9CqELN/Bp6yI9/1WA5wE3C8kHxnLy5SAegETCzAviz7f89HXktE/Mlbb8hZ9/XAThG5BliI5cRTgC22/HOs6Dsr2r3JrmNR6IF17/KgqlOwomM6duyoHCp4d6KwWfMJmzU/+7zn/Mkc+vZnko5aP+AHZ8ylw/vP4d+8Iac2bMuRN3b7Ppo9fkeOtMqtm7Dnk1lFrMb5k9v+1i89TFC39kXKG9y3K8E9O1FzgDXj1jPQn4B2zQlo24z1//cyp7fsLnJZ54Kz7R7+vlx3ai3dvn0PIHuS1OAjy1k1/GGiVm3Ik//wnCUcnrMkO//gO4Zxat3WHDr1bh3MyT//IeHg+Tmr3O1cFDp//irewUEsG3A3mp5/N2tAm6ZF+s7Vu/VadkwootMvhBFNqjOiSc4Zv7+GR/Pg0p3MGdSGlkGV8slpMaRhMEMaBgNwOiWNL3espn01axStgrsb7/Zqwru9rN2dpm07SrtqvriVoHP4/ObnCfYNZMAnj5GeaT2obY84wKguV2fr+Hh606BqLbYfc93TsPXoPnq9d3/2+eonpjBjzaLs84nL5zBxueUGGlWrzQv9b2dbPmWVNOXhBRrFmlxlj1F+BcxQ1V1YkVlLIBYrgnsQmCEizewsi4FnRMRXRBpiObysiPVPoKaI3CgiHiIyCiuiy5L/DNwlIqG2s3raTs+SPwT8nSvCzo84YA6wCsuBjcWK0ovzbuzJwGZgiX3+K9BTRHqJiCfwHOCZy/6HRaSqiFS37c2yfxcQCTxp1/1KrEjZB0BVM7Ci8lm2vbOAe1Q1KxzcAxwCjgJnsLr5C502KCKtgRexuq5LnOh1W6k9vB/e1aqACKE3X4vDw524fXl3+4pcthbNyKDJQ7fi8PSg8QM3AXBi6ZpsHYeXJw5PjzyfS4uDM+cR1KUNwX27Ig4HTR4ZRcrJGM7szPuDsea2Z1jQbAA/tx3Mz20Hc2r9NraN/5jNz79X7LLOl7TYOObW7J5ty7IBowFY3GEo0X9vcZknoH0LxOHAKyiASz59iaPzl+bpzq1362AOTC+5SVXOFHRvO00aj3+zBiwfdC8ZySkFllOU71xQ13b4hASX+GzmLJYdPsWdv2zjqwGt6Fjdv1D9fyLPkJGpRCWl8tDSXfSvF0QTe0z4WHwyEfEpqCprj8fyxrqDPN+55MZGJ934FM1qhDJo0hMkp51t27mbltOyZn2GtuuNl7snLw64ky1H97H7hOud+lqFNMTL3ZMKHl48fvlIavgHMX3NQgC83D2zx4ZrBwQz5aZn+eCPbzmdWAbbjJaTruYiR7wi4gC+BFKxHCxAEpAGvGJ3ty4XkT+AK7HGXR/CGnvcC0QDXwM3AqhqtIhcC7yNFbkuAX4Dsh6vpwG1gWW2ne9gRX5H7EjvIaBDEc2/C8vptwD22fYtEJF2qlrolEEReQvrAaN3lrNW1V32w8LHQA2sMdkdTva/ijX2uwnLeU4F2gGRqpphT5b6COuBYj3wna2HiFyONebdC9ho13OeiPRX1U3AJMAbqILVBf0UlqPvXEAdGto6D6uq69kn58mON6biXa0K/Tf9iHtFH+L2hbNy2EPZY7m9Fk0lcuV6drz+KZlpaawY/ACdP3uFNhMe58zO/awY/ACZaWkAVKwbwrVhS7PLviF5K/FhR5hXr29pmA5Ys23/vPlJLpk8Hu9qVTi1cTsrrrkv2yZn+9Ni43KMUWemppF2Jp60M/FFKqukST5xdkKVm7eXnRad3fXsbDtAhw+eJ6BNUzLT0jg0ezEbH5uQo7ygLm3xqVU6zqqge+tTpyaN7r2BjOQUhhw/O19y3T1jCZs1H5/aNbh6x0IWNr+axMMRhX7nAOqNGszhH34lPb50hjHeWHeQ2JQMhs3fnJ12aY3KzL22LQBDftrEpTUr82SnUACeWrGHrSfj8XAIgxtWY0L3s5PaDsQmMfrXHUQlpVKrkjcvXdqQvnWqlIiddQKrc2+PoSSnpXB8wsLs9HtmvcGsdUsYNuVZPh7xODNvG8vfYTu44bMx2TrP9htF94ZtGfCxNYXnls79uKvbNXg43Fm5fzNXfPgQqenWd9vbw5NZt79Eg6ohxCUn8r+/FjBm3pQSqUNhZM1q/rcjRQn6xIrdp2GNlQ5Q1SQ7vS/Wj7mP7XgRkfnAb6r6gYtyXgPqqeqNLmTuwH6sSHSJC/mVtg11sMaVv8HqZgVr4lUF4BQQYkeMznk/BtJU9VGntE1YDwzfF1L38cAwoKeqRhegVxlrElonuzcgt3w0cLuqds2T2ZL/idWT8KmIPAF0U9UhTvIfgVWq+raIbAOeV9WfnK4dA1RV1TxTWkWkLrAcmKCqkwuqbxYdO3bUxzaUwRNqKXAxbMZeHm2H8m1/lu0JD11+gS0pPhU//A0Aua/LBbbk3NBJaxCRDara8XzKca/lr74PuPyJLZDTzy0572sXh6J2NU/C6s4clOV0bVZgdXk+KyLuItINK0pbAiAiDUSkioi4ibUEaTSQvSpbRNrZXa1+WJHvkSynK9YyoQZi0Rx4F3hJVTOxnH0o0NY+XgT+Adrmdro264CrRaS+Xd4VWOPU21zoZiMizwIjgStcOV0R6WDXrSrwKTA/y+mKSIiI1LSv1wVrNvhYp7ytRcRbrOVYT2BFzdOd7O0uIm2z2gnoztkx3nXArSLib3f/3w8cy8fphgBLgU+K6nQNBoOhvFIeupqLso63LnAPloM7Ltaa1XgRuUlV04BrgQFY47xTgVudIr4OwFasMdbXgZtU1Xliz1PASaxIsQbgvJY1CFiE1ZX6MzDNnvyDqqao6vGsw752mv05y+54Eelun36BFSEvwxoT/RBrzDRPZJqL17Ai7L1O9X7OSf4BVtS92/57t5OsAdY4dgIwA3hGVX9xkmdNRosE+mI59xS7fsuBccD3IpI1Pv2aU/4ngGSsLvworPZ3jo63i8hN9uldQH1grFMdcq4bMBgMhouE8uB4Cx3jVdVwXM/CzZJv5+wSm9yy77DGLvPLm6fL2Um2ByhSf5WqTudstJiVVsnps2JFxS8WpTynfAXeEVXN9yUcqroCKyrPT/4kBUxyUtWPscaPXcmisWYy55e3hdPn8cD4/HQNBoPhosHsTmQwGAwGQ9lRXiZX/ecdbwHdrv1La/avwWAwGEoBE/GWD5y7pA0Gg8FQvimtF2jYL21aDxxV1YHnU9Z/3vEaDAaD4eKhFCPeh7HeT5Fns57iYhyvIV/KcgP60qA821+ebYfybX/WmtjyiE5aU7jSxUwpdTWLSC3gaqwXIz12vuUZx2swGAyGi4LzmFwVJCLOGzVPyVq+avM+1vJX3/OxLwvjeA35Uh7fPgRno60NTZpeYEuKT4fd1tLymDt6FqL57yRg2nKgfH53sr43mYvvu8CWFB9Hv0lA+X5zVUnhKNYOBNmczO/NVSIyEOtVvxtEpNd5mJaNcbwGg8FguCgQAbeSn1zVDbhGRAZgvSPfT0RmqurN51rguT0bGAwGg8HwL8TNIcU+CkJVn1XVWqoaCtwALD0fpwsm4jUYDAbDRYJQKhFviWMcr8FgMBgMRUBVl2G98/+8MI7XYDAYDBcHAm7lYADVOF6DwWAwXBQIYrqaDQaDwWAoK8wYr+E/R71RQ+j8+atkJCVnpy0feC+Ry9fm0fVtFEq7t54i6NJ2iJuDU+u2sv6hV4nbczBbp/XLj1D/9qG4V/Ih5p8drH/gJWJ37CuTujSaMR2/Ll3Y0LwFZGTkkXuFhlLrqSep2K4d4nCQsHUbh199lZSDlv3ejRpR6+mnqdiyBe4BAaW+pnjOgUje+CecE0mpeLk5uDwkgAldGuLn6fq/+COr9/Dn8Vj2n0nio8saM7JR9WxZSkYm49cf5MeDUSRlZDKsflVe79wAj3NcIFkUinOvxeGg1fiHqH/HMDx8KxK3L5zfe99KWmwc9W4dTJOHbsG3UShpZ+IJm7WAzc+9i7q4hyXFtrBTPDH1TzbuO0n0mWQyfr63QP2lm47y1Gd/se9YLEH+3jw1vB2jBzQH4Jtl+xg/cx3HY5Lw8nDQr2MdPrzvMvwqep63nQ/0vI7bul5Nq5oN+Hr9r9z+xcsANKseyhe3jaVB1RAANhzazUPfvsPO42Euy/nytnH0bdqRip4VOH4mmjd/ncnnq+dly/s06cgnNzxBncDq/H1wO7d98TKHTh13WVaJIxQ6S/nfQDnoDTeUJ07+tYnZvu2zD1dOF8Czsi9H5i1lQZN+/BDcjei1W+nx08RseZ3h/al/xzB+7T6SOYGXcPKvTXT98s0yqUPgoIGIm1uBOm6+vpxeupTt/fqzudtlJG7dQsOJn2TLNT2dmMU/E/b886VtLgCdq/nx89VtCb+5Gxuvu4R0VV7bGJavfsvASrzVtSFtquTdI+T9LYfZFB3H6iEdWDesI5uj43l786FSs72497rV+IcIurQdv3QdwWy/9vx1y1NkJLMuUPkAACAASURBVKcA4OZTgQ2PvMacoC4s6Tyc6n270OyJO0rNdgAPdwfDezRg6iOFv/QkLT2DYS8v4e4BzYmZcwdfP3MFT0z9k80HTgLQrUV1Vr4zmJg5d7DvfzeRnpHJmC9c/x8qLsdiT/LKz/9j2l8L8qRfN+U5Ah+/kqAn+jFvy0q+ufOVfMt5fckMQl8Ygv9jfblm0pO8Muge2texXphSpaI/P9wzgTHzpxD4+JWsP7STb+/Kv6ySxop4i3+UNcbxGi4I0eu2cmDa96TGxKLp6ex6bzr+TevjGVgZgIr1ahG1agMJB4+gmZmEzZyHf/OGpW6Xo1IlajzwIEffertAvcStW4n+fg4ZsbGQns6J6TPwrl8ft8qW/SkHDxL9/RyS95ZNhF6rkjdVvD2yz91EOHAmKV/9u5rVpGfNALxczERZcjia0c1CCPDyIMjbk3uahfDVntKLWIpzrz0q+9HkkVtZe/cLJB46BkDs9r1kpqQCsG/y10St2kBmWhpJxyIJ+2o+Vbu1LzXbAZrUqsydVzWjRd3AQnVPxaVwJjGVW/o0QkTo1KQazWoHsONQDAC1q1YiyL9Ctr6bm4N9x2JLxM65m5bx0+YVRCfkLC82KZ7wUxGAtbNPRmYGDavVyrecHREHSU1PA0Dtfw2CLP2h7Xqx/dgBvt+4lJT0VMYt+Iw2IQ1pEly3ROpQFEp6HW9pUKjjFREvEflcRMJFJE5E/hGR/i70xoqIisjlTmkiIm+ISLR9vClOezaJSKiI/CEiiSKyyzmvLR9pXzdBRH4UkUAn2fUi8qedd1khdegtIltF5LRtx1wRCSlC3d8Wkb12vXeJyK255G4i8oqIHHNqm8pO7faeLYsRkYki4uGUt5mILBWRWBHZJyJDcpV9l50eLyKLRaSmC/s8bbuOFFKP60Vkp23jDhEZXFjdz5XAds0YGrWGgbsX0/KF+wuNHLOo1qMjSRGRpJ46DUD4NwvxbVgH30ahiLs79UYN4dji0t8eOeSxR4n6+mvSTp4sVr5KHTuSFhlJxunTpWRZ4aw5EUvdmaupM3M188NPcm+LQr/iLlH7OHuuHEtM5UxqeonYmZvi3OvKrRqj6RnUvq4fQyJWMXD3YhrdPzLfsqv26MTp7WXz8FMUggN8uKFXQ6b/upuMjEz+2nmc8Mg4LmtRI1tn1bYIAoZNw3/o5/yw6gAPD25dJrbFvPMryR8u56PrH+e1xTMK1P3khidJ+GAZu8d9R0RsNIu2/wlAixr12Xz0bHsnpiaz/+RRWtSsX6q2Z5E1xlvco6wpyhivO3AY6AkcAgYA34lIK1UNAxCRBsB1QESuvKOBwUAbrP/LvwIHgMm2/GvgL7vMAcD3ItJIVaNEpAXwKdaOEBuBKcBErDeHAJzCenF1U6BPIXXYAVylqsdExAt4GZgEXFNIvgRgELAH6AQsFpF9qvqnLR8PXAp0tdumBZA1wPkM0BFoCbgB84EXgLEi4g78ZLfDFVhtO19E2qnqHhHpCbwG9Ab2Ah/YbZW7L+tJIBLId09h+wFjJnAtsBirnWeLSKiqRhZS/2IRuWIdC1sOIiH8KP4tGnHZt++RmZ7OjglTCsxXISSYjp+MZeNjE7LTkiOiiFq5gUF7lpCZnk7i4eP83mdUSZqbB5+WLanUvj2HX30Nz+rVC89g4xEcTJ2xL3J4whulaF3hdAn2J/zmbhxLSOGLPRHUqeR9TuVcHhLApzuO0r2GPxmZ8OkOK7JMTM/Id8z4fCjOvfapVR3Pyn74NQ5lXr2++DYKpc/v04nbE8bx3/7MoVv/tqFU6diStXe9UOI2nw839GzI6A+W88jk1QB88mB3alc9+1/4spY1iJlzB0dPxvPZ4p2EBpfIe/kLJeDxK/Dx9GZUl6uzI+D8eOCbt/i/b9+ha/1W9GrcnpQ0q8ehklcFouJzPnzGJiXg6+VTanY7I3JhItjiUmjEq6oJqjpOVcNUNVNVFwAHgQ5Oah8DTwOpubKPAt5R1SOqehR4B7gNQEQaA+2BsaqapKpzgK3AMDvvTcB8VV2hqvHAGGCoiPjadv2mqt8Bx4pQhxOq6qyXARTab6mqY1V1l13vv4GVWE4WEQkAHgHuVtVwtdimqlmOdxDwoaqeUtUo4EMga7CpKVATeE9VM1R1KbAauMUp72xV3a6qqVgPCj3sBxzs69cDbgZeL6QatYDTqvqzbeNCrAeKBrkVRWS0iKwXkfVRUVGFNQ+hIwcxPG4jw+M20mvRVBIOHiEh7AioErttD9te+oQ6111VYBleQQH0+WUaeyfOIvybhdnpLcc+QGCnlsyt1YNvvVuzdfzH9F06A7cK5+ZMXBE4aCBtN26g7cYNNJw6xXKer77mcjJVfrgHBNBo2udEzZpFzMKFhWcoIWbvP0HtL1dR+8tVDP9law5ZzYpe9A0J5K5lu86p7Mfa1KF1YCV6/rSRfgs3cXXdKng4hKre5z/BB/J+b4pzr7Mm7m196RMyklM4vXU34d8spOaAnM+kta7tS5sJj/NH/7tJiY4pEbuz+GrpHvyGfIbfkM8YMKZ493zX4RhunPAb05/oQ/L80Wz9dARvf7+JhWvD8+iGBFXiqg51GDmh7LYpTExNZvLKH/hi1Fiq+gYUqJupmazev5lalatyX0/rZzs+JQk/74o59Py8fYhLSSw1m3NTHsZ4i/34KiLBQGNgu30+HEhV1UWSN2RvAWx2Ot9sp2XJDqhqXAHy7EdYVd0vIqn2tTecg911gC1YmxhnAHcXM38FrKg3awZQKyAduE5EHgXOAB+oatYMG7EPnM5riYh/rnRnecsC8mLL99ufPwKeA/IfyLNYD+wUkWuAhVhOPQWrLXJgb4M1BaBjx47KobjcKjkImzWfsFnz85WrqvXW8nzwqOxH71+mcWTeUra/NjmHLKBNUw59+zNJR08AcHDGXDq8/xz+zRtyasO2Au0qKqfmL+DUfGuiiZuvL23W/k399961hHYXeevlyzjw8CPEb8j7lXPz86PRtM+JXbqU45M/LRGbisrwBsEMbxCcrzxDlYNxhX01XFPB3Y03uzbkza7Ws+n03RG0qVKpxCKJ3N+bnvMnF/len95i7/OrSn7UuKo7l0x9heVXjyZ2254SsdmZm/o05qY+jc8p77awUzQJ8eeqDrUBa3x4QKe6LF5/iKsvyTsOmp6Ryf6IM+dlb3FxiAMfTy9C/KsSFVf4Q4u7mzsNgqxhje0RBxjV5epsmY+nNw2q1mL7sQOlZq8zwkU4q9keo/wKmKGqu0SkElaX6CP5ZKkEOI/kxwKV7HHe3LIsuW8+eXPLi4WqHlLVykAQVpdvccOByVgPBkvs81qAP9aDQD2srvZxInKFLf8ZeFhEqopIdeAhO93HvnYk8KSIeIjIlVjdyFn9MYuA60Wkte3wX8TqqvcBsMeD3VV1bhHqnQF8AczCcrizgHtUNaGY9S+UGv164F2tCgB+TerTcsz9HPnpd5e67r4V6bPkc06u3sjmZ9/JI49et5Xaw/tZ5YkQevO1ODzciduXNzIoCTLi4tjSvQc7Bg9hx+Ah7Bs9GoCdQ4eRsCXPMwqOihVp9PlnxG/8h6PvvOuyTPH0RDw88nwuDWbvP8GR+GRUlcPxybyyIYyeNSrnq5+akUlyeiYKpGUqyemZZNrO7FhCChGJKagq6yLP8PamcJ5pV3qTY4pzr+MPHCZyxTpaPH8vDk8P/JrWp+6IARxd8AcAwb27cOlXb7Fy2P8RvW5rnvylgaqSnJpOaprVU5Kcmk5Kqutek3YNgth7LJalm46iquw/FsvCteG0rmf9v/lq6R4ORcahqoSfiGPMjLX0aXtuY/W5cXO44eXuiZs4cHM4rM8ONy5vegltazXGIQ58vX1497qHiUmMc7mcqKpvACM6Xk5Frwo4xMGVzTpzY8crWLrb2sp27qbltKxZn6HteuPl7smLA+5ky9F97D5ROv9v8yAXzxgvACLiAL7E6k5+0E4eD3ypqgfzyRaPFWFm4QfEq6qKSG5Zljwun7y55eeEqp4SkRnAZhEJUdVCZ4yIyFtY0WZv1exH7axw4iVVTQK2iMg3WGOovwKvApWBTVgObyrQDmtfxwx7gtNHWF3064HvbD1U9XcRGQvMwXLu79n1PiIiFYE37esUij1h7U2gF9ZYeQdgnoj0V9VNRSmjqFTv24Uu01/Ho5IPySeiOThzHttfOxsJ9lo0lciV69nx+qfUHnIFVS5pjX+LhtS77ey8soXNrybxcAQ73piKd7Uq9N/0I+4VfYjbF87KYQ+RFntet79A0p0mVDm8vABIi47O7npuOHUK8es3cPzTT6l8xRVUbN0a74YNqTLk7Fy17VcPJC0iAs+QEFotPfvQ0X7rFlKOHGVb376lYvvu04mMW3+Q2NR0/D3duaJWIC92qJctH/7LVroG+/NYmzoADPtlK6uPW8+1ayPP8Oife5nXrzWX1ahMWFwS963czcmkNEIqejG2Yz36hBQ+Y/dcKexeO39vAFbf+BidP3+NYdF/kxJ5ii1jPuDEUms/15Zj7sfD35dei87OK4hauYFlA4rVwVUswiPjaHDbrOzzitd+Rt1qlTgww9rAZsCYhXRvUYNnb2hPg5r+fPZoLx6ZvIrwyHj8fTwZ2bsRd17VDICdh2J4dtrfxMSnEFDJi/6d6vDa7Z1LxM4X+t/OuIF3ZZ/f0rk/4xZ8xvaIA3w04jFqVa5GUloK68J30u/jR0lJt0YOn+03iu4N2zLg40dRVe7rPpTJNz6NQxyEn4rgkdnvM2+LNRnuZPxphk15lo9HPM7M28byd9gObvhsTInYXxTKyws0RAvosslWsiLUaUAoMMB2NIjIJqzIL8t5VcWKSt9Q1TdE5E/gf6o61da/Axitql3sMd4tQNWs7mYRWQHMUtXJIvIaUFdVb7Jl9bEixSrO3dMichdws6r2KnKlRWphTRiroqqnCtEdjzXu3FNVo53SGwD7bBsP2WkfAemq+qiLckYDt6tq13yu8ydWT0KePku7rf7Bauu6wDogyxZPLOccBXTJmvDmlPcJoJuqDnFK+xFYpar5rpnp2LGjPrah9JxcaZK1oXlpv7SiNOiw2+qIibmj8DWh/0YCpi0HYJY0ucCWFJ+s703m4vsusCXFx9FvEgByX5cLbMm5oZPWICIb8tuMvqhUblhFL3unSDFJDhYOnnne1y4ORe1qngQ0AwZlOV2bvliRYFv7OAbcA2SNc34BPCYiIfZymMeB6QCqugcrGhwrIt5292lrrCgPrC7tQSLS3Y7yXgJ+cHLSbiLijRW1O+wyXPblichQEWkiIg4RqQq8C/xTBKf7LDASuMLZ6dr278eabPW8vXSoGTACWGDnDRGRmmLRBWty2FinslvbNvvYzrFGVtvY6S3tvHWwxl0/UNUYYBtQ26nN7wJO2J8Pu6jGOqC7iLS1y24HdMfFGK/BYDCUd8pDV3NR1vHWxXKmbYHj9rrSeBG5SVWjVfV41oE1aSnGnoUM1nKg+VizlbdhTe5xjuhuwFpyEwNMAK6zZwCjqtuBe7EccCTW2O79TnlvwerunYTlSJKwunOz7I4Xke72aQjWUpo425ZMIMe62Xx4DagD7HWq93NO8huxItBou25jVDWrf7EB1uSwBGAG8Iyq/pLL/gi7bn2xnHuKLfPGGouNB9ZiLbkaY7dLeq42PwVk2ucZdt23i8hNtv5yYBzWUq04rAeb13LZYjAYDOWerOVE//YXaBQ6xquq4biehetKNzTXuQJP2Ycr/TCsscf8ypuF5YBcyaZjR4j5yCs5ff4Iazy1WKhqgfW2l0j1y0e2AqtrPr+8T2Ktw3UlO40V/RfFxmVYXdDOaS1ynX+MteTLYDAYLlrKyxiv2STBYDAYDBcNZj/ecoA9u9oV/VW19N9RaDAYDIYSQcREvOUC5y5pg8FgMBhKm/+84zUYDAbDxUN5eHOVcbyGfMla11heyVoTWx7JWg9bXinP352sNbHlEZ205kKbcEExk6sMBoPBYChDRMzkKkM559i1l1xoE86Jmj+tBSBpzNWFaP77qPCytdtN5g+3X2BLzg3H0P8B5fvNVbq3sA2//n1Io2etv+X4zVUlw4V5IUZxMY7XYDAYDBcFVlfzhbaicIzjNRgMBsNFg8NEvAaDwWAwlA0m4jUYDAaDoSwRKAeriYzjNRgMBsPFgYl4DQaDwWAoYxzlIOQ1jtdgMBgMFwUm4jX85/jpaDRv7zpGVEoang6hdzV/XmlVF18PN5f6IfPWUcHNkb3n5LUhgbzdtl62fMr+40zcF0FyhjKgRgCvt66LVymujp+5+QgT14Wx/1Qivl7ujGhZk/G9G+PuyHvN1YdOMfjrdTnSEtIymDWsHYOb1eDLzUe4b8EWKrifrfucER3pEVqlVGyf8cc+Pl60k70RZ/Cr4MGN3evz6k3tcc+nvTYdjObuiX+y88hpmtWqzNT7L6VtPcu2b1YdYPy3mzh+Ogkvdzf6tQ/hwzs74+fjWSq21xs1hM6fv0pGUnJ22vKB9xK5fK1L/eDeXWj39lP4NqxLyskYtk+Ywv6p351TWSXBtj3HeWLCz2zYdpTo04lk7nmtQP3RL8xlxbqD7A2L5vPXh3Lb0A455O/9bxVvTl1BUnIaQ69qyaTx1+LlWfI/1XUDazDxxifpWr8lKWlpfP/PUh6Z/T4ZmRk59Cbd+BQ3X3J291MPN3dSM9Lwe7Qvnu4eTLzhSS5v2onAin7sizrCcz9NZvH2v0rc3iJhxngN/zU6Blbip8uaEujlQUJ6Bk9vDuPNXUd4uVXdfPP82rMF9Sp550lfFhnLJ3sj+O7SpgR7e3DXun28s/sozzWvXWr2J6Zn8NaVzekUUpmohFSGf7eeAG8PnujWII9utzqBRD19Vfb5irBorvtuPVc0qJqd1jkkgN9v61pq9uawPSWdd2+/hM6Ngog6k8zgCUt556dtPD0077bOqWkZDJmwlIcHNue+fk2Z8stuhkxYyu6Ph+Lp4Ua3psGsfHUAQX7exCelce+nfzHm63/44M7OpWb/yb828Vv3kYXqibs73ed+zKan3mLflG8J7NiKvn/MIPrvzZzesrtYZZUUHu5uDO/fivtGdmbI/TML1W/TtDojBrTimbeX5JEtWbmHN6Ys5/cv7qJmNT+GPjCTsR/8xoQnXW77fV5MvPFJIuNiqPH0QCr7VOLXhz7k/p7D+OiP73Lo3ff1m9z39ZvZ5/+7dQyZmgmAu8ONwzGR9Hz3fg7FHGdAi0v57q5XaPXyzYSfiihxmwujvES85eDlWobyQkgFLwK9PLLPHSKEJaScU1mzD5/khrpVaeJXgcqe7jzcuCbfHT5ZUqa6ZHSHunSrE4inm4MQP29uaFmTv47EFCnvzC1HGNy0OhVLITIpCvf1a0r35sF4ergRUqUiI7vXZ/WuSJe6y7YfJz1TeXhgc7w83Pi/q5ujwNJt1g9l7aCKBPmdfRhycwj7Is6URTUKxSvQH09/Xw5++RMAp9Zv5czOA/g3b3jBbGpSvyp3Du9Ii0bBRdJ/4Oau9L20Id4uvitfzN3IHddZZQX4V+CF+3szY+7GkjYZgHpVavLdht9ISU/lxJlTLN6xhhY16hWYx8fTm2HtejFjzSIAElOTGb/wM8JPRaCqLNy2moMnI+hQt2mp2FwUHCLFPsrcxsIURMRLRD4XkXARiRORf0Skvy3zFJHvRSRMRFREeuXKKyLyhohE28ebImdrKSKhIvKHiCSKyC4RuTxX/pH2dRNE5EcRCXSSbReReKcjXUTmF1CP/xORgyJyRkTWi8hlRaj72yKy1673LhG5NR+9UXb978qV/qiIHBeRWBGZJiJeTrJAEZlr1y1cREbmytvXvmai3UZ1nWS97bRYEQkrpA7N7frG2MdvItK8sLqfK2uj42i6aCONF21kUUQMd9Uv+Mdo2OpdtF3yD3et3cvhxLNOendcEs39KmSft/CrQFRKOqdS00vL9DysOhRD86qF7xqZmJbBj7uOc3PrWjnSN584Q+13fqX1xGW8vnIv6ZmZpWVqHlbsOE6L2pVdynYcPk2rugE4/VekVd0Adhw+nX2+aucJAm75Cv+bv+KHNeE8PLDUvjIABLZrxtCoNQzcvZiWL9yPuLkenkiOjCZs1nzq3z4UcTgI6tKWinVrErVqQ7HL+jeyfW8kbZrWyD5v07QGJ07GEx2TWOLX+uCPb7mh4xVU8PCipn9V+rfoyuLtBb+6cVi73kTFn2bF3n9cyqv5BtI4uDbbjx0ocXuLQlbEW9yjrCnK47k7cBjoCRwCBgDfiUgr4BiwCngfmO0i72hgMNAGUOBX4AAw2ZZ/DfxllzkA+F5EGqlqlIi0AD4FrgY2AlOAicANAKraIusitjPfn48NiEhnYALQwy7rXmCuiFRX1QxXeWwSgEHAHqATsFhE9qnqn05lBwDPAttzXfMq4Bmgj91Oc4HxdhrAJ0AqEAy0BRaKyGZV3S4iQcAPwF3AfOBl4Fsg60WsCcA0u/2eK8B+7GtfB4RjPWg9AHwD5O2DLAEuqeLLrgHtiUhKZVZ4FLV8vPLVndOtKe0DKpKUkcmbO48y6u+9/NKzBe4OITE9Ez+Ps1/PrHHihPQMAssgqvxi02E2RsQycWCrQnV/3HmcKhU86V43+7mQy+oEsn50d+pUrsCOqDhu/WET7g7hyW6lH5n9b+leNuyPZur93VzK45PT8ffxyJHm7+NJXFJa9vllzYKJ+fImjkYn8NlvewitVnrbVkeuWMfCloNICD+Kf4tGXPbte2Smp7NjwhSX+uFfL+SSz16hwwfPA7DuvnEkHjl+TmX924hPTMXf92xvQ9bnuIQUqgT4lOi1lu/9h7svu5Yz7/2Ou5s70/9ayI+bC94Va1SXAXyx5meXMneHG1/dMZ4Zaxax+0R4idpaHMrDGG+hEa+qJqjqOFUNU9VMVV0AHAQ6qGqqqr6vqqsAVw5sFPCOqh5R1aPAO8BtACLSGGgPjFXVJFWdA2wFhtl5bwLmq+oKVY0HxgBDRcTXxXV6ANWAOflUIxTYrqobVFWBL4AgO09BdR+rqrvsev8NrARyD9q9DnwI5O4HHQV8rqrbVTUGy3lm1b2iXc8xqhpvt9884BY771Db3tmqmgyMA9qISFPbrrWq+iXWQ0yBqOpp+94p1gNhBuDy119ERtvR8fqoqKjCiuaHI9E0WriBRgs3cPOaPTlkNSp40quaP/dv2J9v/i5VfPF0OPD3cOelVnU4lJjC3vgkAHzcHcSlnf1KxaVb0WJF95KLXr7ZepSqbyyh6htLuNZpotS83cd58Y/d/HhjR4KKMKHoqy1HGNk6JEcEWS/Ah9AAHxwitKzmx7PdGzJ35/ESs/2rFfvxu2kmfjfNZMArv2an//h3OM/N3MDCF67I0V3sTCVvd84kpuVIO5OYim8Fjzy6IVUqclW7EEa+W3LbFIaOHMTwuI0Mj9tIr0VTSTh4hISwI6BK7LY9bHvpE+pcd5XLvH5N6tPt2/dYc+vTfOPZkoUtBtL8qbuoOaAnQLHKOle+mrcJ37bj8G07jgF3Ti/Rsiv5eHIm/uzEsDPxVi+Qb8X8H2DPBRFhyf+9zw//LKPiI72p8sSVBPj48saQB/PNUyugGj0bteOLvxe5LO/L28eRmp7Gg9+8XaK2FgcRa1vA4h5lTbFDBxEJBhqTK8LLhxbAZqfzzXZaluyAqsYVIM+OLFV1v4ik2tfeQE5GAd+rakI+dvwMPGVHvuuBO4BNQJF/CUWkAlbUO9Ep7RKgI3A/cH2uLC2An3LVLVhEqgB1gAxV3ZNL3tMpb3a7qWqCiOy3089pk1kROQ1UwnrYetGVjqpOwepZoGPHjlpYmUNrVWForfxn6WaoEl6MMV4B1L5qE98K7DiTyDUhVhS5IzaRql7uJRrt3tAqhBtaheRI+2V/FA8u3MacER1pWc2v0DKOxCaxIvwUHw1oWaCeYHX5lBQ39WjATT1yTvpa/M8R7pn8J/Ofu5xWdQPyzdu8dmXenbcdVc1+WNgaHsP9/V2Py6VnKPtPxLmUnQths+YTNivfUSFU1foFdYF/y0ac2X2QiF9WARC35yBHFy6nZv8eHFuU9+GgoLLOlZuuactN17Qt0TKzaNGoGpt3Hef6AVaH1OZdEQQHVSrxaDfQx486gdX5eNlsUtPTOJWexv/+WsAr19zD03M/dpnn1s4D+PPAVg6ePJZH9vnNzxPsG8iATx4jPbOgTsTS56KIeJ0REQ/gK2CGqhbFAVQCYp3OY4FKdtdwblmW3DefvLnlWTb5YHWlTi/AjjisaHgVkAKMBUbbUWBRmYzlDJfY13XDcsL/p6quBu9c1R3b/hKpe3FQ1cqAP/Ag4HqA5jz54Ug0RxNTUFWOJKbwxq6jXBbk2uTdZ5LYFptIhioJ6RmM336Y6t6eNLK71q6rHcQ3h6LYE5fE6dR0Pth7jOtrB5WG2dksO3iSO37cxKxh7ekU4np8NDezth6lS63K1A+smCN9yb5ITtjRyu6T8UxYtY+BjYs2+eZcWLo1glveX8nsJ3pzSaOqBer2alEdN4fw0cKdpKRl8MminQD0aWmNLX61Yj+HouJRVcIj4xkzayN9WtUoqMjzoka/HnhXsx7g/JrUp+WY+zny0+8udWP+2YFvo7oE97ZGXSrVr03IwF7EbN5V7LJKClUlOSWN1DRr/kFyShopBcxFSE1NJzklDUVJS8skOSWNTHv8/5bB7Zn2/Xp27DtBTGwSr076g1FD2pe4zdEJsRw4eZT7egzFzeGGf4VKjOoygM1H9uWb59Yu/Zn+18I86ZNufIpmNUIZNOkJktPObTLlf40ihw8i4gC+xBqXzL8/Iif/z955h1dRfA34PWlAOiUEktBrCJ0AwdAEUUFQqjQpKj/9sCFgBwVsYMWOSBURFBAQFLHRQYTQpfcSSkIIIb3O98fuDTfJTeUGF2HC5gAAIABJREFUDM7rs49355ydObM33LNz5sxsHGA9bPAE4pRSSkSyyyzy2FyuzS630Bu4AuQVCxuBMcoNAo4BdwM/iUgzpVTOx7dsiMh7QEPgTitn/QSwVymV24I1W33HtN9efS8U5sj5SyBSRAKVUrbTXovIkdhE3jpwlpjUdLycHens681LgdcTjh7aeoRW5dx5pq4fkcmpvLz3NBeSUnB1dCC4nDtft66Ds7lm9s6KXoysXZl+mw+RlJFBt8rlGFvPP7em7cKUTceISUqj13fXw853VC3HjwNbAvDAwu2EVinLC22vR+oX7Avn2ZCaOepadyqKx1fuJS4lnYpuLgxo6M8LNpYl2Yu3Fu8hJiGF7m//kVnWNtCXVeO7ANDtzd9pF+jLy30a4+LsyNIXO/HYtC28/O0OAv29WPpiJ1zMefSDZ2N4+ZsdRMenUNbNha7NA3j7Ifv/+Fuo1DmEkLmTcXZ3JelSFCfnr2D/29Mz5R1XzSBiYxgHJk8n7sRZ/n5kHC0+GYdbNX9SY2I59e1Kjs9aUqC6ioPT4Vep2em9zHPXRhOo5u/NybUvANDt0bm0Da7OKyM7AnDPI3NYv+0kAFt2nuHxV5ex5psRdGxdk3vb1+X5Ee3pNGQmiUlp9LkniEmj7srRpj3oPf0lPuo3mhfvGUJ6RgZrD+9g9JKPqFLWlwOvLaTB6wM5G30JgJAaDQnwrsjinWuy1FG1XCX+r31vklKTuTjlulN+fME7LNiec7lUcVNSlhNJQQZ95gh1NsZcaTelVKINnXPAQ0qpdVZlW4A5SqkZ5vkjGCPNEHOOdy/gYwk3i8gGYIFS6ksReRuoppQabMpqYoRZy1uHp0Xkd+AvpZTN8Kmp8xmQqpQabVW2G3hTKbUkn75PwpiP7aCUirIqX44RGrY84pUDEoFvlFJPicgC4KRSapyp38nsWyVzjjcaCFJKHTXl84DzSqmXROQxYJhSKtSUuQGRQHPrSIOZBT5TKVU9rz5k648ThgO/QymV68g3ODhYrfAvmavN/H40NktIfPW+W2xJ4SnzhvHjlbH04VtsSdFw6D0HgAVS7xZbUngGKWMdsDo6+RZbUnikzsvG/0eG5KP570RN24qI7FBKBd9IPVUa+KgxC/rkr5iNMc2m33DbhaGgv6zTgECgR3anK8ZyI0sWh4uIlJbrWSbzgDEi4i8ifsBYzJCwOb+5G5hgXtMLI9PWkiD1LdBDRNqZjud1YGk2pxsA3Al8nY/924H7RKSmGHTBmCv+J6+LRORlYBDQxdrpmgw370lT8wjDyFoeZ9X3R83lPGWB8VZ9j8fIWn5dRNxEJBR4ACOiAEYGdEMR6WPe29cwRteHTLsczHJn41RKi4jNLCAR6SIizUTEUUQ8gQ8xnP7BfO6ZRqPRlCikCIlVtyK5qiDreKsBj2M4l4tyfd3sYFPlMMZIzx9j/jMRsKw5nY6xHGYfhpP72SyzMAAjOSkaY7lPX6VUJIBSaj/Gsp9vgQiM+c0nspk3BGO0myN11rSxnXk6D2MJzTrgGkYW8uMFmKd+GyMR6qhVv18x7buqlLpoOTBC8NeUUjGmfDXwLrAWYynPaYy5ZQtPAGXMvi0ERpp9xrwHfYC3zHvT2rxXFtpj3OdVpn2JwG9Wfd9v9f14m/XHYCy5qg3ca2ZLazQazW2FgxT+uNnkO8erlDoN5GpaXmFOcz70BfOwJT8FdMzj+gXAgjzkkzGW89iSuVt9VhijxlzD0bnUUeCvRCnV0UbZhxgjTFv6VzDWOOdW3x+AzTRTM5yf13cSZPV5Mbmsb9ZoNJrbiZIyx6v3atZoNBrNbUNJWE70n3e8Zna1LboqpTbeVGM0Go1GU2SMEe+/3/P+5x2vdUhao9FoNCWYYpqzFZEqGLlClYAM4Cul1MdFre8/73g1Go1Gc3tQjHO8acBYpdROc9viHSLyu1LqQFEq045XkyuW9bAlFcua2JKIZT1sScWyJrYkYlkTWxJR0/J+u9B/geJ4zZ9S6gJwwfwcKyIHMVbyaMer0Wg0mv8uNzDirSAiYVbnX5l71+dsQ6Q60Az4u0gtoR2vJg9K4u5DcH20td7/1r2Mu6h0CDeWlkc8mP0lWCWDiouMHVSXu5a8v52eCcbfTcaKEflo/vtwuH8mULJ3rrIXRRzxXi7IzlUi4o6xydOzSqlrRWkItOPVaDQazW2CSPGEmo26xRnD6X6rlFp6I3Vpx6vRaDSa2wQpFsdrboM8Czhobox0Q2jHq9FoNJrbAgEcpFhe7hKKsUXxPvMFOwCvKKVWFaUy7Xg1Go1Gc9tQTFnNm8hjm97Coh2vRqPRaG4bimuO155ox6vRaDSa2wKR4pnjtTcl803nGo1Go9GUULTj1diNGsN6MSDtAP1id2YeFTu0ylV/kDrMg3G7MnVbzXjTpl6nP79mkDqMODoWl+k5aLxorrGmtgBt+vbrSYfwQ1Qa2DezzOf+brTc8AuhB7fTZs9m6n00BUd3t2Kzd9mZy9zxy25qL9tOgxVhPL3tGLGpaTZ1o5JT6b7mH+r/GEad5dvp9uc/bLscm0XnVFwSgzcdouaybQT+GMbre08Xm+3WhK76mp4JeX/XFTqE0HHLUu67uIMu+/+g2iMPZpHXemoY957cxH0Xwmj25ds4uDgXq83/nL7CvRNWU/Gh+Tg+MCtf/ZXbztD46R/w7P81bV9YyYEz0Tb17hq/CscHZpGWnmEXO5/s0JftL80h6ZMNzBn6ahZZp3rBHJzwHfEfr2PNs59TtVylXOtZO/oLEj9ZT+zUNcROXcOhid9nylrXCOK3Zz4h6v1fiXj3FxaNeItKnuXtYn9BcSjCfzcb7Xg1duXyX7tZ7NE884hYn/e2k6uaPJCpu+1/43PIqw/qgYPTzXO4ABV7dS+wk3fy8qTqU48Rf+hIlvJrYTvZ9cBANge25O82XRBHR6q/8GxxmAtAqwoerOwUxLFeLdnerRlpSjH5n3M2dd2cHPmoZS0O3N+CIw8E81R9P4ZsOkRahgIgJSODBzccpF1FL/b1aMGu7s3pU7VCsdluIaB/DySf71qcnGj93WecmvU9P1dqwfaho2k05SU8GxkbdlS8qy11xj7G5m7D+S2wE27VA6g//plitdvZ0YF+bWsw4+l2+eoePR/DkA/X8cXIUK4sGEL3VlXp+dbvOZzrt+uO2c3hWjgfc5k3f5nD7L9+ylJe3s2LpY9P4dWVX1Fu7N2EnTnI9yNsPwRbeOr7D/AY3QmP0Z2oP7F/ZnlZV0++2rSc6uN7UW1cT2KTE5gzNOe/6+LCyGqWQh83G+14Nf9anD3daTjhSXa98N5Na9PRw51qY57ixFvvF0i/xstjCJ/9DalXrmYpTz5/kbTo62UqI50y1ava1VZr/F1LUb7U9ZGdowin4pJs6pZ2dKC2RxkcRFCm7tXUdKJTjBHyd6ciqVTGhf+rWxk3J0dKOzoQ5F18o3UAJ0936r/yJPvH5f1du5TzwtnLg7MLfgTg6o59xB4+gUf92gBUGdyT0/OWEHvwGKlXr3FoyhdUHdKrWG2vF+DNo13qEVS1bL66v+0Kp20DX9o2qISTowMv9G5M+JUE1v9zMVMnJj6FN77fxZThuUeLisKy3ev4cc8GouJjspT3btaR/edPsGTnGpLTUpj400ya+Nemnm+1Qrexev9fLNm5htikBBJTk/ls3RJCazW2VxcKxG3jeEXkKREJE5FkEZmbTfagiBwUkVgROSAiPa1kIiLviEiUebxrLkS2yKuLyFoRSRCRQyJyl5WssoisEJHzIqLM/TFt2VZORCJFZFMB+zLHrK92AXTfF5GjZt8OicjQbPKvROSwiGSIyHAb148WkYsiEiMis0WkVDa7l4lIvIicFpFB2a7tbLaZYN6jalayX0QkzupIEZF9efQj1+/I3pRrFkjvyK10P7yahuOfyHfkeNeGb+l1YRPtfvgUt2r+WWRN3h7D0WkLSbp4ubjMzUGNl0Zzft5CUiLyb9OjaSM8Gjfk/LzvbMo9WzYn9OB22h3diU+3uwmfOc/e5mbh78vXqL1sOzWXbeenc1d4rE7u4UKAjr/tpeoP2xi6+TCDa1TEp7ThuHdExVHFtRQDNx4k8Mcweq3bz4GYhGK1vcGkMZycsZDkS3nf9+SIKM5+v5KqQ3uDgwNlWzXFtYofV7bsAMAzsA7X9h7K1L+27zClfX1wLuddrPYXFKUUKvu5gv1nrmSWjfsmjP+7N5BK3mVuik1BlWuyJ/xY5nlCShLHL4cT5Fcz12smPzCSyPdWs+m5r+hQp3mueu3rNGX/hZN2tTdvBAdxKPRxsyloi+eBN4HZ1oUi4g/MB8YAnsDzwAIRqWiqPAb0BJoAjYHuwONWVSwEdgHlgXHAEhHxMWUZwGqgTz62vQMcLEgnRKQtUKsguibxQA/ACxgGfCwid1jJ9wBPADtttHUP8BLQGagO1AQmWal8DqQAvsBgYJqIBJnXVgCWAq8C5YAwIHMiRSnVVSnlbjmALcDiXPqc33dkNyI2bOfnhj1YWrENG/s8Q7WB9xH4/KO56v/efjArqnfip/pdSTwfQYefvsx01OVaNMQntDlHPp1vbzNzxb1xQ7xaNid8dgHadHCgztsTOPbqm6CUTZVr23eyObAlf7Voz9lps0k6F25ni7PSuoInx3q1ZHf35jxZrzJV3Erlqb/u7sYc69WSaa1r07qCR2b5hcQUlp+NYkTtSuzp0Zy7Kpdl2ObDpGTYN/Rpwbt5Q8q3ac6JaQX7rsMX/0y9l57k/qv7aPfHtxyYNJXEcGPE6OTuSuq1uEzd1Bhj7tq5GOfXC8NdTf3Z8M9F1u27QEpqOpOX7CElLZ2E5HQAwo5GsuXQJZ7q3uCm2eReqgwxiXFZymIS4/Eo5WpT/8Vln1Pz1T74v9yDrzYtZ+UT71Gzgn8OvUb+tXmt2yM8v/TTYrHbFrdVqFkptVQptRyIyiYKAK4qpX5RBj9jOCuLcxsGfKCUOqeUCgc+AIYDiEhdoDkwQSmVqJT6AdiH6WiVUpeUUl8A23OzS0TaAA2BfN+hJiJOwKfAUwXps2nDBKXUIaVUhlLqb2Aj0MZK/rlS6k/AVkxvGDBLKbVfKRUNvMH1vrth9PNVpVScuTh7BcbOKAC9gf1KqcVKqSRgItBERHLs+m9GAtoB3+TSjfy+I+u6HjMjG2GRkZF53hsw5l8tiVEdV80g/uQ54k+dA6WI+ecI/7z+OVX73pPr9ZEbw8hITSU1JpYdo97CvUYAnoG1QISWX0xgx6i3UOnp+dpRVCr26k7bIztoe2QHjb75ijpvv8ax196GArTpN2wQcQcPc23H7nx1Uy5GcGXdRgK/+MAeZgOw5PRlaizdRo2l2xi4MetzZ+UyLtxZyZvHtx7Nt57Sjg70rlqBTw+Fs/9qfGZZqwoedK5cFhcHB56oW5no5DSOXku0i+0B/XvQPWIn3SN20mb5DJp8NIG9zxXsu3avW5PgeVPZ+b8XWeHVkDUtulNn9Ah87+0AQFpcAk4e7pn6Tp7G59S4eLvYDsb8q2f/r/Hs/zXdJv1aqGvrB3gzZ1R7nvlqC/4PL+TytSQaVPHGv7wrGRmKp6ZvYeqIEJwcb94oLC45Ec/SWR9MPEu7EptsO8qx7dR+4pITSElLZd7WVWw+vpduDe/IolPLJ4BfnvqQUYumsunYnmKzPQdSMhzvja7jDQMOisj9wM8Yo8NkYK8pD8IYFVrYY5ZZZCeUUrG5yPNERBwxRo3/AxoV4JLRwAal1F4pwo0WkTJAS+CLAl4SBPxodb4H8BWR8kBVIF0pdSSbvIPVtZn3TSkVLyLHzfJDZGUosFEplVs8J7/vKBPzNVhfAQQHByvOxGZXycKpBSs5tWBlrnKllLFreQFRSiEiOHu6Uy64IaHfTwXIHAX3PLeeTf1GEblpR4HrzIuIZT8RscxINHH09CB0/980mGZuw2q22SZsHQcef5aYbVnbLNs2BK+QlpTv1B4AJ28v3BsG4h4UyLHxb+RoSxwdKVPNfnO8fatVoG+13BOe0pXiVFxygetLzVCcjk8myNuNBl6uObKc7cm571dy7nvj78bZy4Nu4dto+Y35XTsY9/2eY+vZPngUUVuy3nfPBnWIO3qSiD+MmaW4oye5tHo9vne359Lq9Vw7eBSvxvU4v/QXALwa1SfpUmSOOfgbYXDH2gzumO9MVa70Da1B39AaAFyNS2bOH0dpWceHawkphB27zMD31gKQbia7VX3kO75/oRPtgvKeOigq+y+cYFjIfZnnri6lqeUTwP7zJwp0vcJYP2uharlK/DHqU95YNYf521bb29w8sYx4/+3ckONVSqWLyDxgAVAaI3TaTyllebx0B6xn8mMAd3OeN7vMIs8Zs7DNM8DfSqkdIpKn4xWRKhgh7hYFrNsWX2I4w4I+4trqO4CHDZlFbon3uQPZh5zWcmuGYkwD2KQA35HdqHxve6J37icpIgrPejVp+OoTnFls+x+eV4PaiLMTMfuO4FimNI3ffJbE8AhiDh5HpaWxzO96hqhrlcrcu30Jq1v0JjnS9tKLGyX9Wix/NW+feV7arxLNVy1hR9c+pEblbPPQ6JdxKHU9lBs081Mu//wrFxYuAYzRdMzfO0g+f4FS/n7UeHE00Zv+KhbbwRgBh/h44F/GhXMJKUzed5Z2vp42dcOiYklXimbl3ElXMPPoBSKTU2lezhgd9q1WgS+PXGD9pRjaVvRkxtGLlCvlRB1P+885psbEsrrW9e+6TEBlOm5cwrpQ29/11T0HcK9VjQodQri8fiuuNarg27UjRz+cAcDZBT/S/KvJnPtuJUkXI6n34kjOfLPM7nZbo5QiOTWdlFRjxJ6UkoaIUMrZdn7DjmOXaVqjHFfiknl6+l90b1WF+gHeKKU4N2dgpt7Zy/GEPLeC7R8+gI9n6Ru209HBEScHRxzFAUcHB0o5uZCWkc6y3et5r/fT9G52Jz/v28xr3R5lb/gxDl/KuYTMq4w7rasHsf7oLtIy0unf4i7a127Ks4uNByc/Lx/WPPsZn69fwvSNxXvfbSO3ZM62sNyQ4zWTod4FOmLMc7YAVohIV6XUbiAOY17RgicQp5RSIpJdZpHn+6gtIn4YjregjvQj4HWlVHZnVyBE5D2MkPadSuUyoZcTW30Ho3/59b1A98acs64ELMnD9vy+I7tRqXMIIXMn4+zuStKlKE7OX8H+t6dnyjuumkHExjAOTJ5Oad8KtJw2EdcAX9LiE4ncsov13R9HpRmZtUlWSTaOpUuZZVHFGnpOjbzepsWppkRGZYaeG33zFTHbdnDm0+mkX4sl3errUCmppMXGkR5rzJW51q1NzXHP4eTlSVrMNaL+3MDJKTf8UpNcOXItgTf3neFqShreLk50ruTNuEZVMuUDNx6kdQVPng30JyVDMW7XKU7HJ+EsQqCXK9+2rU+lMi4A1PYow+etavPCjhNcTk6jcVlX5oXWw8WheH7QrBOqHM37nmz1XbdZPoOozWEceW86CSfPsvP/xtH4/XGUqepP2rVYzn63ktNzjX8CEb9v5OiHMwn9ZR6OZUpz/sdfOfTmJ8Vit4XTEXHUemxR5rlbv6+pVtGdEzOMZTbdJv1Kuwa+vNyvKQCjZ25lz8krODsJfUNr8MEjrQFj1Fip7PV51aQUo/++3mXsEnoe3/VhJna//q7hIa27MvGnmUz6eSZ9vnqZz/qPZf7wCfx96gADZl5f5/vyvcNoV7sp3T4bjbOjE2/e/zj1K1UjPSODQ5dO0/PLFzly6QwAI9reTy2fACZ0e5QJ3a7nd3iM7nTD9hcUB/ttqVxsSMH9CIjIm0CAUmq4ef4cEKqU6mWlsxzYpJR6X0S2AHOUUjNM2SPAY0qpEHOOdy/gYwk3i8gGYIFS6kur+pyAVKCGUuqUWdYT+A6wxI/KmMcVwF8pleXXWUSuYoRXLZ31BS4Do5RSC/Lp8ySM+dgOSqnsc9wWnU3ATKXUXKuyBcBJpdQ487yT2bdK5hxvNBCklDpqyucB55VSL4nIY8AwpVSoKXPDGAE3V0odsmpjBlBKKZUl2zqbbXl+R7ldFxwcrMbsKL5wY3EySBkvNF/vn2NK/F9Ph3Dj6414sE0+mv9OKi4yRvXLXevdYksKT88E4+8mY8WIfDT/fTjcPxMAGRlyiy0pGmraVkRkR0FeRp8XgU381JxfHyv0dW0qT7rhtgtDQZcTOYlIacARcBSR0qZD3A60E5Gmpl4zjEQfy/zhPGCMiPibo9SxwFwAc35zNzDBrK8XRubzD1btlgYs8bxS5jnALxiZwk3N4zWM7Oim2Z2uSV2MzGqLPhhznXnGQkTkZWAQ0MWW0xURF9MmAZzNflju6TzgURFpICJlgfFWfY/HyFp+XUTcRCQUeIDrCVLLgIYi0ses/zVgbzanWwboZ6kzD/L7jjQajeY24fZaTjQeSMRYHvOQ+Xm8Umo9RsbtEhGJxXCabyulfjOvmw6sxMhW/gcjuWe6Vb0DgGCM0d8UoK9SynpuMxEj7ApGUlEigFIqWSl10XJgzH+mmp8BMNe3tjP1I7LpA1xWSuWXpvk2RiLUUas1s69YyX8zbboDIykpEWhvtrkaI8S7FjhtHhOsrn0CY5QegbGsaqRSar95bSTGKPst8960Nu+VNT3Nfq/NbrSI7BeRwWZd+X1HGo1Gc1sgt1NWs1JqIsaPty3ZZ8BnucgU8IJ52JKfwph7zK3dAt0RM8Q7N1uZu03lwtWbp55SqmM+8g8BmxN7SqkrGM4zt2v/AHKNlSqlFmI4bFuyoGznuX5HGo1Gcztx22c1azQajUbzb+K2z2q+HTCzq23RVSm18aYao9FoNJoiI5SM9/H+5x1vXiFpjUaj0ZQsSsJyov+849VoNBrN7cF/Yucqze2NZT1sScWyJrYkYlkPW1KxrIktiVjWxJZE1LStt9qEW4uUjDnef7+FGo1Go9HcRugRryZXFkjJ230Iro/US6L9FttXeZc82wG6XTXsX3PulXw0/310CngbgMRx3W6xJYWnzFurgJK9c5V90MlVGo1Go9HcNASQEhBq1o5Xo9FoNLcNDiVgBlU7Xo1Go9HcJoge8Wo0Go1Gc7OQEpLVrB2vRqPRaG4TBNGhZo1Go9Fobh56xKvRaDQazU1Ej3g1/ylaTptE9Yd6ZJ47ODuTkZLKYs/mNvUHqcOkxSdgvD0STn+3im3/Gw9Atf7daDTpGUpXqkBGcgrnf9lA2NNvkBYbX6x9cKsRQPAn46nYoRXpySmcmP0Du198z6aud5P6hMx6C8/AWlw7eJytj47j6p5DRarLnrRa8TUV2ofwS/kGqPR020oODtR9+RkCHuqDk7sb8SdP83ePoaTFxAJQploAQe+Mp1xoKzJSUjg7/wcOT7C/7cvnhvHron2cPBzJnfc34MWp3W3qrV60lw+eX4VL6es/WW/N7UfTNtUyz9f8eIBvPtpERPg1yvq48cKH3Wncuordbc7O/L3n+CLsNMevxONRyon+QX5M6lgXJ4ecDmDzmSv0/D4sS1l8ajoLejejZ/1KJKel8+raIyw5eIGktHT6NfDj/S6BODsWjzPpH3wXE7qNoGo5Xy5ei2L4vDfYdGxPFp1pA1/goVb3Zp47OzqRkp6K5+jOAMROXZNFv4xLKb5Yv5RnFn1QLDbnhfGSBO14Nf8hto+cwPaREzLPQ+ZMRmWoPK9Z1eQB4o6fyVEeuXknv4cOJDkqGic3V1pNf50mbz7LjlFv2d1uCw7OznT6fQ5HP/+WTf1Ho9LT8axbI1fdDj9+waGPvuboFwuo/fgAOvz4BSvr3ENGamqh6rInfv164ODkmK9e3Zefwbt1M7bc3Z+ks+dxD6xDRlIyAOLsTKvlczgz81t2PWLY7la7eGwv7+vB4GdCCVt/guSktDx1G7Tw5+OlQ2zKwjacZMbktbz6RU/qN/Uj6lJuLx2zPwmp6bx3VyAt/b2JTEih3+IdlN16kufuqJVDN7RqOSKfvzvzfMPpKPou3kGXmhUAeP+vE+y8EEPY/9qSnqHou3gHUzYf59X2dexu9131W/FOzyfpP2s8204doLJnBZt6Ixe+y8iF72aezxn6KhkqI/PcY3SnzM+uLqW59M4qFu/80+72FpSSkNX877dQUyJxdC1DlT73cPLrZUW6PuHcRZKjojPPVXo6HrWr5XHFjVNjeC8Sz0dwaOpc0hMSyUhO4eo+23sOV+zYCnFy4vBHX5ORksqRT78BEXw7hRS6Lnvh5OlOnRef5NBreY9Mnbw8qT5yKP88M56ks+cBiDt4lIzkFAACBvUi+UIEJz+/bnvs/uKxvV3XerS9ty6eZcvcUD1ff7iRIc+2pUFzfxwcBJ/KHvhU9rCTlXnzWItqhFYth4ujA/4epRkQ5Mdf56LzvxCYvy+cnvUr4eZijIFWHY3giZbVKFfGBR+3UjzRsjrz9pwrFrsndR/B66tm8/fJ/SilOB8TyfmYyDyvcXUpTZ9mHfl66yqb8r7NOxERF83GY7uLw+QCIDgU4b+bTYFaFJGnRCRMRJJFZK5VeXURUSISZ3W8aiUXEXlHRKLM412R6/t5mdevFZEEETkkIndZye4TkU0iclVELorIDBHxsJKXEpHZInLNlI/Jw34RkXEicsbU/05EPAvQ7wdFZItp3zob8k4istOs84SIPJZNPtq0Lca0tZSVrJyILBOReBE5LSKDrGQuIrJERE6Z97djLva5mPetQP8yRWSCWd9d+WvfGFX73E1y5BUiNmzPU++uDd/S68Im2v3wKW7V/LPIfEJb0PdqGA/G7aJKn7s59NHXxWkyFUKaEn8qnI6rZtA7ciud187Dq2Fdm7peQbW5ujerM7q69zBeQbULXZe9qPfqGE7PXkhyxOU89TyC6qLS06n0wL10PryJDmGrqTYi888P75ZNSTwbTvDiGdx1fCutf5qHR4Pitb0gHPt+vRcQAAAgAElEQVTnEr0af8TQ9l/yzUebSE8zRl3p6Rkc2XuBmKgEhrSdRv+Wn/HJ+F9JTky9JXZuOnuFBj75O/2E1HSWH7rIQ42u/90rQFkFiZRShMcmEZNk3744iAPB1QLxcffm6KTFnH17BZ/2H0tp51J5Xten2Z1Exl1lw9FdNuXDQroxb+svdrW1MFh2rirscbMpaIvngTeB2bnIvZVS7ubxhlX5Y0BPoAnQGOgOPG4lXwjsAsoD44AlIuJjyrzMNv2AQCAAsH6UnwjUAaoBdwIviMi92GYoMAQINesrA3yad5cBuAJ8BEzJLhARZ2AZMN20tT/woYg0MeX3AC8BnYHqQE1gklUVnwMpgC8wGJgmIkFW8k3AQ8DFPOx7HogoQD8QkVpAX+BCQfRvlBrDenFy3vI8dX5vP5gV1TvxU/2uJJ6PoMNPXyKO18OkkZt3sMQ7mGX+7Tj43iziT4UXq82uAb5UG9CNw598w3K/dpz/eT0dfvwCB2fnHLrO7m6kmvOhFlJj4nD2cCt0XfbAq2lDyoY05/T0+fnqlvGrhLOXJ261q7O2SWd2DhtF7ZeepkLHOwAo7edL5d7dOD39G/6s346IX9fTYsEXSDHZXhAat67CzD9G8MPuUUyc3ps1Kw7w/ZfG/r7RkfGkpWaw4edDfPTDEL769RGO7b/E/E+23HQ75+05x84LMYxqnX9ofvmhi5Qv40K7quUyy+6u6cPnYaeIjE/mYlwyX4SdBiAhLZe5+iLi61kOFydn+jbvRLsP/o+mbw2hWZV6jO/6cJ7X5eVYq5T1pUOdZny99We72looxJjjLexxsylQi0qppUqp5UBUIesfBnyglDqnlAoHPgCGA4hIXaA5MEEplaiU+gHYB/Qx21yglFqtlEpQSkUDMzAcp4WhwBtKqWil1EFTPjwXO3oAs5RSZ5VSccA7QH8Rcc2n338opRZhPHhkpxzgCXyjDLYDB4EGVn2fpZTab9r/hlXf3cx+vqqUilNKbQJWYDwcoJRKUUp9ZJbb/BcnIjUwHPPkvPpgxWfAixjO3iYi8pgZ2QiLjMw75ARQfVAP+sXupF/sTjqumpFZ7hpQiYodWnIiH8cbuTGMjNRUUmNi2THqLdxrBOAZmHNeLPF8BOdXbyT0uw/ztakwZLc/PTGZyE07ubB6AxmpqRx8fxYu5b3xDKyZ49rUuHicPd2zlDl7upFqJn8Vpq6i4NevB3ef28nd53YSvHgGQR9M4MBLb+WeTGVFelISAEff/ZyMpGRi9x/mwtKf8bm7AwAZSclEb91J5B8bUKmpnPx0Fs5lvXGvZx/bi4JftbJUruqNg4NQM7AiQ0a1ZcMqI+JQyky46vlwC8r7uuNVzpW+/2vFtrXHi8WW7/4Jx+e93/B57zce+O56RGfF4Uu8tvYwy/u3pIKrS771fLsvnEGN/LEKAvJiaC2a+HoSMmszneb9RY+6vjg7CBVd8x6JFpbEVGM+/9N1i7l4LYqo+Bg+/HMh3Rq2yfWagLIV6VCnGfP+th1mHhrSjU3H9nAq6qY82+eK4Fjo42Zjr+Sq0yKigN+B55VSllhXEGCdIrfHLLPITiilYnORZ6c9sB9ARMpijFyz190zl2vFPKzPS2GMmPfYvCIflFKXRGQh8LCIfAm0whh9bzJVgoAfs9nnKyLlgapAulLqSDZ5h0KY8CnwCpCYn6KI9ANSlFKrJI83dyilvgK+AggODlacic1VF+DUgpWcWrAyR3mNoT25vGUX8ScLNzellCI3+xycnHCvVbVQ9eVHdvsbvz6KCqG2M7CzE7P/GIFjH8lS5t24Hkc+XwAYYeeC1lUUzi9eyfnFhu1OXh50ObmNZrOnGkIzatDpwHp2Dh9F9F87slwb+48ZIle2E9+u7T9M2dbFZ7s9ECEzG97Duww+lT1y/duxNwMa+jOgYdZpkd+OR/LUqn380D+YhhXzDzOfu5bIhtNX+LRr1p+7Ms6OTL0niKn3GOWzdp2hWWUvHB3s27erCbGcvXIp8x4WhKGtu7HlxD5OXrY1DoGhrbsy5dd59jKxSJSUrOYbtfAy0BLD4bQAPIBvreTuQIzVeQzgbs7zZpdZ5Dn+akWkC8YI8jWrei36eV5r8gswwpxT9sIY+QHkOeItAAtNm5KBjcA4pdRZKxuz24dpY4H7bgsR6QU4KaXyzVwSEXfgbeDZgtRtD2oM7cmJuXmb5tWgNt5N6iMODji5udLsg5dIDI8g5qAxSqk+qAeuVSoD4FrVj8ZvPculP4v35fAn56+gQkgTfDu3QRwcqPfsMJIvR3Pt4IkcuhHrtqHS06n3zFAcXJyp++RgAC6t2Vroum6UtJhY/qzfjo3terKxXU/C+hmpBps69uZq2N4c+gmnznJly3Zqj/0/HFyccatbk8q9uhGxei0A579fQdngJpTv0AYcHKj+xDBSrkQTd9j+tqenZZCSlEZGhiIjw/hsmbu15u+1x7kSaUQTzhyLYv7Hmwm9+3qm7z0PNmb5nDCiL8cTezWRH2ZuJ6RzzuhJcbDuVBSPrNjDgj7NaennXaBrFuw7T0iANzXLumUpD49N4nxsEkoptoVHM2XTcca3q10cZjPnr594umM/fDzK4u3qwbOd+vPTvs256g8N6crcv2yHkdvUbIS/tw+Ld66xKb+ZCA6FPm42NzTiNcO2lkVpl0TkKeCCiHgqpa4BcRjhWAueQJxSSolIdplFnmWYJSIhwAKgr9UIMc5KPym3a62YDVQB1mH0+QOM8HOR0wVFpD7wPdALY6RfB/hJRM4rpX7Gdt8xbSxQ33Np1w14FyjoS0MnYYTDTxZQ/4aoENIU1wBfzixenUPWcdUMIjaGcWDydEr7VqDltIm4BviSFp9I5JZdrO/+OCrNWFLi2aAWTd95DpeynqREX+P8qvXsftm+oebsxB45yZaHnqfVl5MoXbE8V3buZ8P9I8lITc1hf0ZqKht6PknrmW/SZMpYrh08zoaeT2bq5leXvUmxSqhyLF3KLIvKDD0HL55B9F9hHP9wOgC7Hh1D40/f5q4Tf5Ny+QpH3vqYqA3GQ0P8sZPsfvx5Gk6dhEuF8lzbu58dA0eiisH2+Z9sZt7UTZnnfyzdz9DRbbm3f2Me6TSD2Wv+h6+/F7s2neLdMT+RFJ9KWR83OvcKYtBTd2ReN2RUKNeuJDCsw3RcSjnRsXt9Bj8daqtJuzNl0zFiktLoZbU+944qZflxQEsAHvhuO6FVyvFC6PUHgQX7wnk2JOc88MnoBEas3EtkfDIBnmV448663FXTJ4eePXhj1WwquHtzZOIiklJTWLTzT976ZS5Vyvpy4LWFNHh9IGejLwEQUqMhAd4Vc3Wsw0K6sXT3OuKSE4rF1sJQEka8UphQg4i8CQQopYbnIvfFSAbyVkrFiMgWYI5SaoYpfwR4TCkVYs7x7gV8LOFmEdkALFBKfWmeNwN+BR5VSq3M1tZ5YJhS6nfz/HWgrlJqQAH6cTeGM66qlMr5eJ1TfwTwkFKqo1VZX4wRbjOrso8wRqJPicgC4KRSapwp62T2rZLpPKOBIKXUUVM+DzivlHopW9vnzLbXmedNge1cn293wUjuigRClFKnsl2/GyMxzbJI0gdjdP2OUuqd3PocHBysxuzI9zngX4nlZfILpOS9TN5i+yrvkmc7QLerhv1rzr1yiy0pPJ0C3gYgcVxBn2n/PZR5y5h3lZEht9iSoqGmbUVEdiilgm+knuYtaqv1Wwu/0YunS+8bbrswFHQ5kZOIlAYcAUcRKW2WtRaReiLiYM5dfgKsU0pZwqjzgDEi4i8ifsBYYC6AOXrdDUww6+uFkfn8g9lmQ2A18HR2p2tV93gRKWuOPv9nqduG/eVEpJa5rKgB8CHwen5OV0QczX47AQ6mnZbUzl1AHXNJkZhZw925Pmc8D3hURBqYc9LjrfoeDywFXhcRNxEJBR4AvrFqu5TZNoCL2bYA/2CM3puaxwjgkvnZEua2pjPQ0Er/PEZm+ed59V2j0Wg0xUNBx+TjMZJ4XsLIpE00y2piOMdYDIeQDAy0um46sBIjW/kf4GezzMIAIBhj9DcFI5xsSacdizE6myXX1wjvt7p2AnAcOA2sB95TSmXGN039duZpBWAVEI8x3zvbTCTKjyFmX6cB7czPMwCUUseBRzAeNq6ZNvwAzDLlqzFCwmtNG0+bNlt4AmNZUwTGXPFIpZR1/w6b7fljjPoTgWpKqTSl1EXLgbHkKcM8Tzf7vl9EBpt2RGXTTweizWkCjUajua0oCRtoFGiOVyk1EWPdrC0W5nGdAl4wD1vyU0DHXGQPA7kuKlNKJWM4vkdykbtbfT4CFDp2p5SaSy6jaFO+CFiUh/xDjNG1LdkVcs/CRilVvYA2rsMIJVuX5ZYZXuB6NRqNpuQhJWLLSL1Xs0aj0WhuC0RKRnLVf97xmtnVtuiqlNp4U43RaDQazQ2hXwtYArAOSWs0Go2mJFM8G2iY2xF/jJFgPFMplWMb4cLwn3e8Go1Go7l9sPeIV0QcMVaBdMHY+2G7iKxQSh0oap3a8WpyxbKmtKRSku23rIctqVjWxJZELGtiSyJq2tZbbcItpZi2jGwFHFNKnQAQke8wln9qx6vRaDQaTRGzmiuISJjV+VdWS079ybpHwjmgdRHNA7Tj1eRBSdz5CW6PnatWuJc82wHujzPsL4k7KFlGiwkvdb3FlhQe1ynmq/picl3d+e/Ga2D+OgVECr4ZozWX89i5ytYbKorWiol2vBqNRqO5fch/F+DCcg5jt0ALAdh+VWyB0Y5Xo9FoNLcJqjgc73aM7YFrAOEYOy4OupEKtePVaDQaze2Bwu6OVymVZr5571eM5USzs23vW2i049VoNBrNbUKxjHhRSq3C2O/fLmjHq9FoNJrbhwz7O157ox2vRqPRaG4fimHEa2+049VoNBrN7YEqnlCzvdGOV2M3Wk6bRPWHemSeOzg7k5GSymLP5jb1xcGBRpOeoeYjfXD2cCP22Gn+vHMoqTGxha7LXrjVCCD4k/FU7NCK9OQUTsz+gd0vvmdTt9X016nYoRUedaqx9ZFXOPn1skzZrbIfoM3PX+PTIYSVXg1Q6ek55OXuaEHI0hlZypzc3dg++Gku/PgbVQb3oukXb5GemJQp/7vf/xG1cVux2Fu/UnU+H/AcLarWJzI2mueXfsbyPett6tao4McnD46hQ51mJKelMnvLT7y47DMAvhk+kc71g3FzKcPFa1G8+/t8Zm1eUSw2WzN/XzjTdpzmeHQ8Hi5OPNjAj0kd6uDkYHsjh3Wno3hl7WFORMdTvowLY0Nq8kjTKjn0ui7cxoYzV4h5/u5c67IHUz9fxTsfryQxKYU+PVox7cNHKFXK2aaueA/C1bUUYq5sHdC7DTM/faxIdRUb2vFq/ktsHzmB7SMnZJ6HzJmMysh9nXmjSc9Q4Y5m/NamPwlnzuMVVIf0pOQi1WUPHJyd6fT7HI5+/i2b+o9GpafjWbdGrvrRew5x+vtVNH3n+RyyW2E/gP+DPXBwcsxT58qWHayqdP0BoHy7VrRe9CURv19/GdeVv3ez+e4bWjFRIBwdHPnx/97ly43L6PLxM3So24yVI9+n2dtDORpxNouus6MTvz/zCZ+v/4H+M8eTnpFBXd+qmfLJv37No/PfIiUtlXq+1Vg3+gt2nT3MzjPFu/1mYlo673auT0s/by4npNDvh518tM2Z50Jq5tBNTc9g4NJdvHlnXR5pUoWdF6/RdeE2gv28aFzRM1Pvu/3nSb8Jfy+//rmHKR+tYM2K8fhV9qbX4KlMmLyEKRNz39Biz6bJ1K5ZyS512R9VIuZ4//3vT9KUSBxdy1Clzz1ZRoHWOHt7Uu/ZoWz733gSzhhr0WP2HyUjOaXQddmLGsN7kXg+gkNT55KekEhGcgpX9+X+o330iwVcWrM182EhN26W/U6e7tR7+UkOjLc9Qs+NKoN6cn75atITEovJstypX6kafl4VmPrnQjJUBmsP72Dz8b0MaZ1z96jhbe7jfMxlpv65kISUJJLTUtgXfixTfuDCSVLSUgFQ5n+1KgQUex/+16wqoVXK4eLogJ9Hafo3qMzWc9E2da8kpXItJY2BQf6ICC0qe1GvvBuHLl9/O2lMciqTNx/jzY7Fv3vZ1ws38uiQjgQFBlDW251XX+jF3AUbbnldtzsFcrwi8pSIhIlIsojMtSoPEZHfReSKiESKyGIRqWwlFxF5R0SizONdEREreXURWSsiCSJySETuspLdJyKbROSqiFwUkRki4mEl3y8icVZHmoisLEBf5oiIEpHaBdB9UES2mPatsyFXIhJvZcPMbPLRpu0xIjJbREpZycqJyDLz+tMiMshKdkP31YadI0TkmGnjahHxy6/vN0rVPneTHHmFiA3bbcq9G9VFpaVTpe+99Lqwie6HV1PnCdsjrPzqshcVQpoSfyqcjqtm0DtyK53XzsOrYd0brvdm2R84cQynZi4k6dLlAl/jWKY0fj3v5eyC5VnKvZoEcs/prXTatZq6Lz6BOOY9ii4qYmM3PhGhoV/O0WJIjYacirrAqqemEvneataO/oKGfrWy6Hw+4HniP17H4YmLuBATxar9W4rF7rzYfC6awAq23zbq61aKfoGV+WbfOdIzFH+HR3PmWhJ3BJTN1Jm4/igjmlXF192l2G3df/AcTRpWyzxv0rAqlyJiiLoSm+s17bu9TqW6I+n90FROnY68obqKBZVR+OMmU9AR73ngTWB2tvKywFdAdaAaEAvMsZI/BvQEmgCNge7A41byhcAuoDwwDlgiIj6mzMts0w8IxNimK/NRXikVpJRyN9+n6wGcARbn1QkRaQvUyksnG1eAj4C83r3YxGKHUmqEVVv3AC8BnTHuT01gktV1nwMpgC8wGJgmIkGm7EbvayYi0gF4G+NtGuWAkxj3vVipMawXJ+ctz1XuGlAJF29PPOtWZ0WNzmzqO4pGE5+m0l13FLoue+Ea4Eu1Ad04/Mk3LPdrx/mf19Phxy9wcL6xOaqbYb9Xs4aUC2nOyS/nF+q6yg/cQ3JUdJb526jN21nXqge/Vm/D9oeewb/ffdR69lF7mwzAoYuniIiL5vkuD+Hk4EiXwFZ0qNMMV5fSOXQDvCsyILgLn6xdhN9L3fn5n838OPJdnB2vz5g9+d17eDzbibbvP87S3etITs0ZQSlO5u09x84LMYxqlfsUxYMNKjN583HKvv8bXb7dxsR2dQjwLAPAzgsx/BUezcgWVXO93p7ExSfh5emaeW75HBubZFN//c+vcmrvJxza9j5+lb3pPuA90tLSi1RXsWDZQON2cLxKqaVKqeVAVLbyX5RSi5VS15RSCcBnQKiVyjDgA6XUOaVUOPABMBxAROoCzYEJSqlEpdQPwD6gj1n3AqXUaqVUglIqGpiRrW5r2gMVgR9y64OIOAGfAk8VpM+mDX8opRZRtH05hwGzlFL7Tfvf4Hrf3TD6+apSKk4ptQlYAQwx2y3yfbVBD2CxaUeKaUd7EcnxACIij5mRjbDIyMgcFWWn+qAe9IvdSb/YnXRcdT1ZxzWgEhU7tOREHs7Gkriz7/XPSU9K5uq+w5z+7mf8unXIoleQuopKdvvTE5OJ3LSTC6s3kJGaysH3Z+FS3hvPwJyjr4JSXPb7P9iDbhd30u3iTlovnUHjqRP454W3bCZT5UWVwT05tzCrbQmnzpFw+hwoRez+Ixye8jl+Pe+xp/mZpGWk0/PLF7mv4R1cfGcVY+8axKIdf3IuOiKHbmJqMpuO7WH1/r9ITU/j/d+/pbybF4GVqmfRy1AZbD6+hwBvH0Z26GN3m7/bf56KH/5OxQ9/p+ei6y+0WXnkEq+tP8KyB4Op4Gp7tHo4Ko6hP+5mxn2NuPr83YQ9GsrUbSdZfTyCDKV49vcDvNc5sNiSqb5dtAl3/4dx93+Yrn3fwd2tNNdir08xWD57eOR88AFoHxqIi4sT3t5ufDxlGCdPR3LwcDhAoesqHlSJcLz2Tq5qD1hvpRUE7LE632OWWWQnlFKxucjzq9uaYcASpVR8HraNBjYopfbmEZUtChvEeA/VFmCMUuqUWR4E/GiltwfwFZHyQFUgXSl1JJs8q9e5TmHua3aErG/XsHxuCBy3VjRfg/UVQHBwsOJM3iGiUwtWcmpBzuh+jaE9ubxlF/Enz+V67dW9hy2N5tlGQeoqKtntb/z6KCqE2jfruLjsD1+0kvBFhu1OXh50PbuNFl9PBcgMC3c5sp6wIaO4smWHzTpK+1eifLtW7HnmtbwbUwrs+28mC/vCj9Fx6hOZ55uf+4qvt+bcJGhv+DFCazUucL1Ojk7UquBvFxutGRDkx4CgrLM1v52I5KnV//BD3xY09PHI5Uo4EBlHnXJudKlpBPbqlnfnnpo+/HbiMiH+Zdl5IYahK3YDkGEmV9X5Yh3zH2hKaJVyN2z74AfbMvjBtpnng0Z8xp5/TvNgL+NtUnv2ncG3ohfly+XeB2tErv8TDgoMuKG67IVShXv4vBXY7bFKRBoDrwHWKZ7uQIzVeQzgbs5HZpdZ5Dm+JRHpguFcc/xCiIgr0BeYm4dtVTBCsfn8whSaDhjh4PoYo+KfzJE12O47GP0rTN8Le1+zswp4UEQai0gZsy4FuNrQtQs1hvbkxNy8E4niTpwlYsN2gsb9Hw4uznjWr0m1/t0I/2ltoeuyFyfnr6BCSBN8O7dBHByo9+wwki9Hc+3gCZv6Ds7OOJRyQURwcHbCoZRLDgd1M+xPi4nlt9rtWN+mJ+vb9OTv3sbyjg1texO9fW+u11UZ+ADRf+8i4WTW7OGKXdpTqmJ5ANzr1qTui09w8ec/i83+Rv61KeXkQhnnUoy9axCVvSowd+vPOfTmb1tNSI2GdK7fEgdx4NlOA7gcd5WDF0/h41GW/sF34VaqDA7iwN2BrRkY3IU1h8NstGhf1p2O4tGVe/m2VzOC/bzz1G3i68nx6ATWnY5CKcWJ6ARWH4+kkY8HXqWcOPbknfw1PJS/hoeytJ/xlrrNw+6gZT71FpWhA9ox65t1HDh0juircbz5/jKGD2pvU3f/wXPs3nuK9PQM4uKSGDtuPv6VyxFYz6/QdRUbysxqLuxxk7HLiNdMVPoFGKWU2mgligM8rc49gTillBKR7DKLPMswS0RCgAVA32wjRAu9MeZibS/8M/gIeF0pld3Z3RBKKUvKXoqIjAKuYcxH78N238HoX0H7Xuj7asPGP0VkAkYY3guYarZj/yEkRoKSa4AvZxavziHruGoGERvDODB5OgCbB46h9ay36RP1N8kRV9j76sdcWrO1QHUVB7FHTrLloedp9eUkSlcsz5Wd+9lw/0gyUlNt2n/nb7Pw7Wi8D9sntDmtZ7zJHx2HELF+2023PzniekKVQ+lSZllUZui59dIZXNkSxtH3p2fqBQzqyfGPZuWoq0LHEJpNn4yjmyvJEVGc+34FR9+bnkPPXgxpfS8jQu/H2cGJjcf30OWTZ0hJS6VKWV8OvLaQBq8P5Gz0JY5cOsNDcyby5cAXqOhRjp1nD3P/tOdJTU9DKcXIdr35cuCLOIgDp69c4NnFH7Fi78b8DbhB3tlynJjkNHovvh5ZuCOgLMsfNBxnz0VhhFYpy/NtalGzrCvTujbkuT8OcvZaIp4uTvQP8mNYkwBEhErumfmXJJvfXUU3l2ILPd97VxNeeKY7d/Z4k8SkVPr0aMmkl/tmyrv2fYd2berxytieXIqIYeTY2Zw7fwU311Lc0aoOP33/HM7OTgWq66ZRAtbxio3f6tyVRd4EApRSw63KqmE4vSlKqS+z6W8B5iilZpjnjwCPKaVCzDnevYCPJdwsIhuABZZ6RKQZxhshHlVK2cxYFpHfgb+UUrmOZkXkKpDM9ZcX+wKXMRzaggL0ewTwkFKqYx46jhgjzzvMcPYC4KRSapwp72T2rZI5xxsNBCmljpryecB5pdRL5nmR7msB+lIXI6EtwJx7tklwcLAas+MmZyPaCcvL5BdIyXuZvMX2Fe4lz3aA++MM+2Vkvn+K/zrUNOOhL+GlnEuZ/u24TvnF+BBT7HmTxYPXQERkRx4voy8QwU2rq+1/FD6w6eDz6A23Xaj2CqIkIk4iUhrjlUiOIlLaLPMH1gCfZ3cOJvOAMSLiby5hGYsZEjZHr7uBCWZ9vTAydH8w22wIrAaezsPpBgB3Al/n04W6GBnATc0DjKSjPGOAIuJo9tsJcDDtdDZlQSLS1NRxx0hwCgcOWvX9URFpICJlgfFWfY8HlgKvi4ibiIRiZB1/Y9Zd5Ptqow+lRaShuQSpKsYc7sd5OV2NRqMpsZSA5KqCxi/GA4kYy2MeMj+PB0ZgLJOZYLWWNc7quunASozQ6z/Az2aZhQFAMMbobwpGONmSTjsW8AFmWdWdPblqCMZo93i2ckz9dgBKqQil1EXLYapcVkrlt2PAELOv04B25mdL+q4v8D1GePkExlxvd6VUqtnmauBdYC1w2jwmWNX9BFAGiMBY3jPS6h2PN3RfxVjjPNg8LY0Rqo8DtgF/Aa/m02+NRqMpgdxGWc1KqYnAxFzEk3Ipx5xzfME8bMlPAR1zkT0MPJyPXZOBybnIbK9gN2QFStFUSs0ll5GkUmoNkGc8UCn1IfBhLrIrGGtxbckmcWP3Ncjq81WMSIJGo9Hc3ihKxJaReq9mjUaj0dw+lIDkqv+8480WwrWma7ZMYo1Go9H8q9GvBSwR5BWS1mg0Gk0JQztejUaj0WhuEpYNNP7laMeryRXLmtKSSkm237IetqRiWRNbEslcE1sS8bqZ7779l6JHvBqNRqPR3ES049WUZE7e2eRWm1Akaqw13h9xvG2jW2xJ4am1aR8ACS/ce4stKRqu7xrbY5bknasyNoy5xZYUHof2xqrFkrhbG9gxOqVDzRqNRqPR3GQyCr4N8q2ieHbe1mg0Go1GYxM94tVoNBrN7YMONWs0Go1Gc5PQc7ZkxvYAACAASURBVLwajUaj0dxkSsAcr3a8Go1Go7k90C9J0Gg0Go3mZqJDzZr/GG533ov38JE4lSuPSk0lYdsmoj6ZgkqIz6FbqlEzKr3zRZYyhzKuXJowhoQNf+JcvTblnhhLqbqBOHqVvSlrit0730vZR5/AsVwFSE0hYesmIqdOtmk/QJnmrSj/5FicA6qSHnOV6PmziF2xpEh13Sjz/wln2s4zHI+Ox8PFiQcDKzOpfR2cHGwvXFh3OopX1h3mxNUEypdxYWzrGjzSpAoAyWkZvLrhCD8cukhSWjr9AivzXqf6ODve+CKIJzv0ZXib+2jkV4uFYb/z8Lw3AAisVJ15wydQy8cfgB1nDvPM9x9w8OIpm/WUdfVk1pBX/p+9846v6XwD+PfJlilBYkQiMYuaQVGzKFqtUrVqlVZ1DzqoUrSlLVUtWpQaVVqjZu09a4/YJCGEJERkz/f3xzmJm+QmEZJo/M738zmf3PM+z/ue59zce5/zPO+i3WONCI++zacrpvPHgQ3p8tZV/ZjaYyhebqXZH+BP/3ljuXzrutm2HpSTl8IZOm0Hh8/d4GZkPCnb389RPyUlldFz9jJnrT9RsYlUKleczZNfpLiTXQa9Nu8tYeuRKyRsfhcrq4KZgNJg+hdUeLlT+rmFtTWpiUn85VzPrH7DX8bg3qIhTpW92ffKcALmLr9b18aaOuOH4tW9I1bFbAn8Yw2H3v0SlZxcILZnSxFINRvTiQzyjfiTRwh5ux9BnZ7kSq+OiKUlrgPfMqubcOIIQR0bpx83Pn2b1NgY4v7doymkJBGzbQPh344uNPvjThzh6pC+BLZvQtBLHcDSCrdX3zavbGmFx1eTubNyCQFPN+bG50Mp+dYwbCpVyXtb+WF7UgrftK7G5bdas/3lJ9h2+RaTDwSa1U1KSaXn30cZWLs8Ie88xbxOtflk61mOh94BYOL+Sxy5HsmBAU04OqgZR2/cYcLeS/li57XIcMb9M4fZe1dnKX9xxnDcPmxHyaHtWXl8J4sGjsu2nak9hpKYnIzHxx3pPWc003t+RPUyPgCUcHBh2eDxjFw1A7cP23Hw8mkWD8q+rQfF2sqCbq2qMPOjdvekP3rOXvaeDGH3tB7c/udN5o5oj51Nxhjo942nSU4p+MjtwJBR/OVUL/0I+mM1l/9al61+xLEzHHhjNLcOn8oiq/7Ja7j51WRtzWdZVeVp3OpVp+ZnQwrS/KykpZrzehQyhuM1yDdSwm6Qeud2+rlKTcW6bPl7quv49HPEbN+Eio8DIOlKENFrl5MYcLFAbDVHSugNUiPv2k9qCtaeXmZ1LZ1dsHR0ImrdKgASzviTGHQJmwoV89xWfvBqXS+aerpiY2lBWSc7uj9Whn1Xb5vVvRWfxJ3EZHrWKIuIUL+MC1VLOHDmphaNr70YxpD63rgVs6GUvQ1D6nkx7+TVfLFz+dFtrDi2g5sxkRnKI+OiCboVAoCIkJKaQiV3T7Nt2NvY0bVuK0au+oWYhDh2XzzGyuM76dOoAwBd6rbE/9ollhzeQkJyIqNXz6J2uUpU9fDOl3vITFUvNwY+U5MaFUrkqhsRFc8PS47wy7A2eJd2RkSo6VsSO9u7jjcyOoGxv+1j/OvNCsTe7LC0L0b5rk9niGIzc37aQm5s2UdKfEIWWblOrTk7ZT6JEZEkhEdwdsp8fF/pWpAmm0FpEW9ej0ImXxyviDwmIltEJFJELojICyayp0TkjIjEishWEfE2kYmITBCRm/rxjYiIiXyriISJyB0ROSYiz5vIWonICRG5rdddLiLlcrCxiYj8KyJRInJcRJ68h/sqIyIrReSaiCgRqZBJ/p2InNfbPCMifTPJ64jIIf3eD4lInUzy90Xkuv6+zRYRW73cVkR+FZEgve0jItLBpF5vEYk2OWJ1++rncj+VRSReRBbkdu/3i23Nuniv2kWFf/bh0KwNd5b+nmsdsbXDoUUbotevLCiz7hm7WnWpsG4Pvhv/xaFFGyL/nG9WLyXiJlEb1+L0TGewsMC2Rm2sS5ch7vjhPLdVEOwOjuCxEuZ3vPRwsKXbY6WZf+IqKamK/Vdvc/lOPE3KFQe0oEGZ/BYp4GpUPJEJSQVud8TEjcRP2c6PL33IV+vmmtWp4u5FSmoK50OvpJcdCz5PjTK+ANQo48uxqxfSZbGJ8VwMv0qNsr4Fa/w9cOJSOFaWFizdfp6yL/xCtd5zmLb8aAadETN38/rztSntZl+otnl1bUdC2C1Cdxy4r/oi2kPT3XPBoXwZrJ0LcefVIhLxPnAfr4hYASuAn4G2QAtglYjUBW4By4BBwCpgLLAYSFvI9TWgM1Ab7S3bCFzS2wJ4FzillEoWkUbAJhGpopQKAU4BTyulrukOaywwHXjOjI1uwEpgiG5PT91GX6VURA63lwqsA74G9piRxwCdgHNAA2CdiFxQSu0RERv9fZkMTAMGAytEpLJSKlFEngY+AVoD14DlwBd6mRVwRX8vLwMdgT9F5HGlVKBS6ncg3aOJSH9gJHD3V988U4H7+1bdIwknjxDU6UksS7rj9EwXkq5fy7WOQ/M2pETeJv7YwYI07Z6IP36EwPZNsCzpjvNzXUnOwf7oTWsp9fEXlHznYwDCJo4jJfTGfbWVn8w7cZXD1yOZ+nSNbHVeqlaGN9b7M2zLGQB+aPsYns7FAGjnU5Jph4Jo7uVGSqpi+uHLAMQmpeJiW7C2u37YFnsbO/o98Ux6BJwZR7tiRMZl7CuPjIvByU5zVI62xQiLvp1Vblu4jswcwaHRREYncO5KBBcXDeR8cARt319KZU9X2jbw5uCZ6+w5eY3Jb7ckOCyqUG3z6fcCAfP+vu/61/7ZSdV3+3Jj6z7E0pIq7/QBtEg66U50fpmZO0VgcFV+RLzVgLLA90qpFKXUFmA30AfoAvgrpf5SSsUDo4HaIlJNr9sPmKiUClZKXQUmAv3TGlZKHVdKpfXMK8AaKK/LbiilTH/JUoBK2djYBLih25GilFoAhOn2ZYt+jWlk46yUUqOUUmeUUqlKqf3ATqCxLm6J5kAnK6USlFJTAEFztGn3/qtSyl93/mPT7l0pFaOUGq072VSl1GogAMguou0HzFNKZZszEZEewG1gcw46r4nIQRE5GBYWlp1aOg5tOuK9di/ea/fiMX5qBllKeChx/+7GfeSEXNtxfLoT0RtW5aqX3zi2fQafDfvx2bCfMt9NzyBLCQ8ldv9uPEZ/Y7autZcPHl98S+i44VxqVY8rfV6geK8B2DfOmh7Mra37YdGpa7hP3oT75E10XnIovXzV+Rt8vuMcy1+sT0l7G7N1z96Mpu+qY8zs+Di3P2zLwVea8v2/gay7qP3PP3rCl9oeTjSeu4enFu7n2UruWFsI7tm0l9/EJsbz885lzOs3ilJOrlnk0fFxOBdzyFDmbOdAVHysJk+Iw9kus9yeqITYfLHv942ncW7/E87tf6LjsOzTsuYopqeUR/Z7gmK2VtSqWIruravyz/4AUlMVb32/he/fbllgg6kq9OpEt6jDdIs6TMu1M9PL7T1L496iAZcewPH6fzmdiCOn6HB0Be32LCL4702kJCaSEHozP0y/RxRK5f0obPJjVLNkU1YTcAaOpRUqpWJE5CJQAzij/z1mUu+YXna3IZHVQBvAFlgPHDSReQHH9eukAK/mYGNmO9NszBdEpBha1Js2VLcGcDyTMzyul6/T/64wkR0DPESkhFIqwydVRDyAKoC/met6A82BV3KwzRkYAzwFDMxOTyk1A5gB4OfnpyDn1GLMprXEbFqbvYKlFdZlzffTpauU8sCujh/hk8bmqFcQRG9cQ/TGNdnKxdISq3Lm+6htfCuRdDkwfTBY0pVAYvfuwP6JZsTu3Zmntu6HHtXL0qN62QxlGwLCeGu9P0u71qNmKads654Kj6aymwNtfUoCUMXNgacrlmRDQBjtK5aimLUlk9pUZ1Kb6gDMPnaFuh7OWFqY+6oXDBZigb2NLeVcShEWlTEpdS70MlYWllQqVZ4LYVq6ubZnJfxDtAFg/iGX6PfEM+n69jZ2VCzlif+1/Bkg1rvtY/Ru+9h91a1VUXvPxcxbeScmgYNnb9DzC+0zmZKi/XR4dZvJ4tHP0Kx2zt+leyFw4SoCF2Z9yPXp25nwPUeICQi+77ZT4hM4+PZYDr6tfZcrvvoSEYf8UYUZgRaRebz58Vh1BggFhomItYi0Q0uR2gOOQGQm/Ugg7VchszwScDTt51VKPavrdwTWK3V3s0Wl1GWlVHGgJPCZbos59gBlRaSnbmM/oKJuY37xM5rzXK+f38+9YyIHQESs0dLKc5VS5u6vL7BTKRWQg21j0aLrKznoPDAObTpi6V4aACuPMrgOeou4w//mWMex3bMknDxG8rWsX3ixtkGsrdNfo78uKBzbPoOVx1373V59h7hD+83qJpw7jbWnN8XqNdT0y3ri0KQFCRfO5rmt/GBb0E0Grj7B78/Xwa9M8Rx1a7s7czEilm1BN1FKcSkilnUXw3hcd9bXouIJiY5HKcW/124zfu8lRjTNLpmUNywtLLG1ssFSLLC0sNBeW1jSplpD6nhWwUIscLKzZ9KL7xIRG2V2OlFsYjzLjm5jTKdXsbexo4lvLZ6v3Zz5+7UN7Jcf3U7Nsr50qdsKWysbPu84kONXL3D2RlC+3ENmlFLEJySTmJwCQHxCMgmJ5qfQVCxXnGa1yvHV/H9JSEzmdOBN/tx6lmca++LiaEvw0tc4POtlDs96mdUTOgNwYEZvGlUvUyC2p+HTtzOXfss9erewtsbC1gYRwcLaCgtbm/SniGJl3SlWxh2AEo1qU3PkGxwf9WOB2m2W/4c+XqVUkoh0Bn4EPkaLSP8EEoBotGjUFGcgrfMis9wZiM6cMlVKJQH/iMi7InJRKbUyk/yWiMwFjolIOZP0dJr8pj4w6zu0fs71wCbg/h/vTBCRb9Gi51Ymtt/PvWMiR0QsgPlAImB+Xo7meL/KwbY6aBmDurneyANi4+2L22vvYeHoTGr0HWL37yRi5pR0ucf4qcSfOEzk77+mlzm260Tk4qyDaKw8ylJ+0T/p5xU2HCDp+lWCe3YsOPt9fCkx5H0snJxIjYoidt9Obv48OV1e5rvpxB07xO35s0i+Fkzo+M8p+d4nWJUuS2p0NFEb1xC1etk9tZXfTNh7kciEZLosvdvN38TTlb9f1HonOi85RFNPV4Y94Yuvqz3T29dk6JYzXImMw9nWiu7Vy9CvlhZRXbody6trTxAWm4inkx1jmlemjR4dPyifdRjA6GcHpZ/3adSB0atn4R9yiR+7f4BncXfikhI4EHSa9j+9T0JyIgCftu9Hs0p16PiTNkf2jT++ZXafEYR+8w83YyIZ8sc3nArRnj3Do2/Tdcan/NT9Qxb0H8X+wFP0mDUyX+w3R9D1O1TsMTv93KHdj3iXdubSYi251HHYcprVKsenfbSHtN8/78igbzZQ6rmfcS9ejC8GNuGp+tqI99Il7qbI43Xn7eFqX2CpZ4CST9TB3tPD7DSilmtnErrzIKe+/gWAVht+xaNlIwBKNa1Ho5nj2NSyD6Hb/8WxoheN503Azr0EsVeuc+yTiVzfuLvA7DbPwxmlnFekIPLbIrIHmIsW+PdTSjXVyx3Q+lbrKaXO6HpzlFIzdfkrwGtKKbO7aIvIJmCNUup7MzJPtAFJJZRSt3Kxzwq4qF9rfU66JvpJgI9SKjCT7AugK9DCNEWsR/6zgfJpzlhEgoDBSql1IrIQCFBKjdBlrYGFSqnS+rno9SsAHZVScWbsagpsAEorpcyOxBCR94AvuevQHQFL4LRSyvwsebRU819OBT+KtSDw2ar1Xlx88vGHbEneqbjrBACxH7V/yJbcH/bfaD/eMsTsV/g/jZq+D4DUHR88ZEvyjkXzSQAslKoP2ZL7o5c6i4gcUkr5PUg7flU91L+/9MxzPctWPzzwtfNCfk0nqiUidiJiLyJDgTLAb2gjdWuKSFcRsQM+R+v3TEuZzgM+EJFyIlIW+FCvh4hUE5EOIlJMTw+/jNaXuV2XdxGRqiJiISKlgEnAkeycrojU1dtxRot8g+/R6dqh9S8D2OrnabJPgV5A28z9ssA2tH7nd/TpQWkR6xaTex8oItVFxBUtVf6bSf3pwGNAJ3NOV6cfsDQ7p6szAy2tXkc/fgbWAE/nUMfAwMCgaFIEUs35lb/oA4Sg9fU+heaIEpRSYWjR4JdABNAI6GFS7xe0aUYngJNoDuEXXSZoo6BD0aLkd4HuSqm0XFo5tEFKUXr9VMB0/vDPIpI2LQngIyAcLSouY6qbC3FoaWHQ+pBNneBXgBdw3mRO7XAApVQi2lSpvmijiV8BOuvlKKXWAd8AW4Eg/Ril2+6NNv2oDnDdpO3eJvdnB7yEllnIgIgMF5F/9OvEKqWupx36vcTr/xsDAwODRwdVNBbQyJe1mpVSw4Bh2cg2oU05MidTaA7xIzOy02iOOrtr/ojWr5yd/PVM53nPP2j1sh3KmZNMlx8h+ylAKKUmoUXqmcuDMD9a3FQnHjA7ikYplW2fr1JqdE7tGhgYGBRp/k9GNRsYGBgYGBjcI//3jldPSUebOX7OvbaBgYGBwX+KItDH+3+/LaCekn49V0UDAwMDg/82qvD7bPXppJ3Qpn1eBAYopczvUKLzfx/xGhgYGBg8QhR+xLsRqKmUqoW2bv+nuVX4v494DbInbT5sUSVtTmxRJG0+bFElbU5sUSRtTmxRpJc6+7BNeLg8hCUjlVIbTE73AS/mVsdwvAYGBgYGjwj3nWouKSKm26PN0NeuzyuvoO3AlyOG4zXIlg2XP37YJtwX7by0HZGi33rqIVuSdxx/0jaPSpp1X7PfHjrWg/4AivbKVcp/zEO2JO9Ijc+Bor1yVb5xfxFveE4rV+mrJpY2IxqhlFqh64wAkjHZsjU7DMdrYGBgYPBooECl5P/gKqVUm5zk+sY7zwJP5bQ9axqG4zUwMDAweHQo/FHN7dE2CGqhlLqnTZ8Nx2tgYGBg8GigFBRAxJsLP6Gt579R39F2X+aVEzNjOF4DAwMDg0cCBahCjniVUnnerNpwvAYGBgYGjwaKhxHx5hnD8RoYGBgYPBooIOW/v0mC4XgNDAwMDB4RVKGnmu8Hw/Ea5AsrfjvMxiUnCDwbTsvnHmPoxI7Z6oZcvs20UZs4sf8K1jZWPP3S4wwa3hKACe+u5ujuIOLjknAt5UC3wQ3p0LN2odzDkvM3+PLfAEJjE7CxtKCtVwm+a14FZxvzX5PjYVG8ufUMZyNiqOrqwNRW1ahVyimL3jN/H2bH1dtEDGmJlUXBrNI6b08AUzef40JoFM521nRv5M24F2phZZn1eueu3+GTJcfYdzGclFSFXwU3JvWsR9XSzgC8Of8AC/cHpesnpaRiY2nBrZ9yXZAnV95s8SL9Gz/D42Ur8sfBjQyYNzZd1rqqH1N7DMXLrTT7A/zpP28sl29dz9KGjZU103oMo021Brg5OHMhLJjhK35mnf9eABr51GBsp8HU96pKSmoq284d5p0/J3H9zs0Htt8cJ8/fYOi36zl0KoSbt2NJPflFtrrnAsP5aOIG9hy9QkqKokHNsvzwaUeq+pQE4Le/jzDo8xUUs7VOr7Nqai9aNvQpENsBao19D98BXbBytCfiyCkOvjmGyFMXsug5Va5A3W8/omSTuoilBbcOnODgO18SdS4AAJcalak38WNc69fErqTrw5lTXERSzcZazQb5QgkPR3q93YR2Lz2eo15SYgqf9v6TOk28WXTwTX7fN4TWL1RPl3d/8wnm7h7Mcv/3GD2rC3Mn7uL8iaw/vgXBE6Vd2NilHldfbcGJlxuTkqoYu++SWd3ElFR6rD1O9yoeXBnUnF5VS9Nj7XESM6W5Fp+9TnIhPIHHJSYzsXtdQr5/gV3D27L19A0mbThjVjcyLolOtctyclxHgid2xs/Hja5Td6bLp/ZpQMRPL6Yf3Rt40dWvfL7YeS0ynHH/zGH23tUZyks4uLBs8HhGrpqB24ftOHj5NIsHjTPbhpWFJVciQmkx6Q1cPmjDyJUz+HPQOLzdygDgau/MjF1/U+GzF/Ae0ZmohFjm9P0sX+w3h7WVJd2ersGsMc/nqns7Kp5OLatyZvXbXN8+jAaPl6PzO39k0GlcuzxRB0akHwXpdL26dcD3la5sbNaLpW4NCd97lMbzvzGra1PcieCVW1hdtT3LPJpy898TNF8xLV2empRM0J/r2D9wRIHZe0/cy8b3mY9CxnC8BvnCkx2q0OTpyjgXL5aj3sYlJ3DzcKTrqw2ws7fBxs4K38fc0+UVqpTExlaLMEVAgGtBOW70kW94OtlRsphN+rmlhXApMs6s7s6rESQrxZu1y2NracGQ2uVRwPbgiHSdyIRkvj4QwNgmeR70mGcGt6zMk1XcsbGypJyrPT0bebPnQrhZ3QY+JRjQrCJuDrZYW1nwbtuqnLsexc3ohCy6MQnJLD8cTJ/G+fPjv/zoNlYc28HNmMgM5V3qtsT/2iWWHN5CQnIio1fPona5SlT18M7SRmxiPF+smUXQrRCUUqw5uZuA8BDqe1cDYJ3/XpYc3kJUfCxxSQn8tG0JTSvWyhf7zVHVpyQDu9anRqVSueo2fNyTgV3r4+Zij7W1Je/3bczZgHBu3r6n6Z/5joOPJ2G7DhETEIxKTSVwwUpcqpv/vN48cIJLs5eQGBGJSk7mzPe/4VLNFxu34gBEnQvg0uwlRPqfL8xbyIi+gEZej8KmUByviPQQkdMiEiMiF0WkmV7+lIicEZFYEdkqIt4mdUREJojITf34RvRJUpnabiEiSkTGZSrvJSJB+jX/FhG3HOwLFJE4k714N2Snm8m+ESJyWUTuiMgiEXE2kduKyGxddl1EPshUv46IHNLv/ZCI1Mn0fp0VkUgRCRWRuZnaXiAiIXrb50RkUA522orI9yJyTUQiRGSaiFhnp1/QnD4cQmlPZ0b0/YtudX5kWPc/CDgTlkHnxxEbeK7qJAa1/hU3dwcatvItNPv2XLtNuZnbKTNzBysuhvJGbU+zeqdvxVCzhCOmH8kaJRw5fSsm/fyLfRcZVLMcHvY25pooUHaeD6N6WZd70z0XRmkXO0o42maRLTt0hVJOtjSrkrtTeRBqlPHl2NW76c3YxHguhl+lRtnc//fuTm5U8SiP/zXz2YnmlevgHxKQb7bmJzsOBlG6pCMlitunlx05E0KpJydQ9ZkpjP15G8nJKQV2/aBFa3Cq5IVT5QqIlRU+/V7g2rqduVcE3Jv7ERcSSuKtwnkwvjdUkdiPt8Adr4i0BSYAAwAnoDlwSURKAsuAkYAbcJCMi0u/BnQGagO10JbjGpypbWvgB2B/pvIawC9AH8ADiAWmkTOdlFKO+tHuHm6tr95+U6AsUAz40UQ+GqgMeAOtgI/0FU4QERtgBbAAcAXmAiv0coDdQFOllAvgi9YXb/pg8TVQQSnlDDwHjBOR+tnY+QngB9QEqgD1gILLu+VC+PUotq06Q+cB9Vn47xs0bOXL6EHLSEq8++Py9pftWH7qPSYu6UXT9lWwtrEsNPualC3O1VdbcLZfU96t64WXs/kIPiYpJUvfr4uNFdFJyQAcDr3DvpBIXq9l3nEXJHN3X+Jw4C0+aFctV93gW7G8u/AQ33Sra1a+YG8AvRtXyPCAURA42hYjMi46Q1lkXAxOtvbZ1NCwsrDk91e+YO6+tZy9EZRF/ni5Snze8RWGLfvRTO2HS/D1SN76cg0TP2qfXta8vjcnlr/JjR3DWPJ9dxatPcm3c3YXmA3xIWGE7TxEp3Pr6R53DK9u7Tn8/te51itWzgO/qaM4/MH4ArPtvkjr483rUcgURsT7BTBGKbVPKZWqlLqqlLoKdAH8lVJ/KaXi0RxVbRFJ+7XoB0xUSgXr+hOB/pna/hDYAGTuzOoNrFJK7VBKRaM59y4iknXky/3TCfhVKXVFv8YEoLuIpP1S9AXGKqUilFKngZkm9rdEc6aTlVIJSqkpaFnV1gB6m6Z5whQgPf+jlPJXSqXlBZV+VMzBzilKqVtKqTBgCtoOGlkQkddE5KCIHAwLCzOn8sDY2FlRw68cDVr5Ym1jyYuDG3LndhyXL2Qc+GJpaUHNBp6EXY9i9YKjBWLL4rPXKf3Ldkr/sp0uqzJeo6yjLW28SjBg/UmzdR2sLYlKzBiJ3ElKxtHailSl+GD7Wb5pVrnABlMt3BeI61tLcH1rCZ1+2J5evuJIMCOWHmPluy0o6ZQ1gjUlLCqejpO3MbhlJXo0yprSvXIrhh3nwng5n9LMORGdEIeznUOGMmc7e6ISsk/BigjzB4wmMTmJtxZ9l0VesZQn/7w1iXf//J5dF/Jvi8vfVx/HqcGXODX4ko6vz7+vNsJuxfD0a/MZ0r0BPTveHRfhW94NH09XLCwseLyKByNfb8HSjafyy3Qq9OpEt6jDdIs6TMu1M6k56k3cGtRkuWdzFtvV4sQXP/HUlrlYFrPLtg3bkq603jCb89MWErRoTb7Zll+oVJXno7Ap0FHNImKJFm2tFJELgB3wNzAMqAGkfxuUUjEiclEvP5NZrr+uYdK2N5oDqYe2ZJcpNYA9Jm1fFJFEtIjvUDbm/i4iFsARYJhSKrdvquiH6bktUFlELqNFwZnt72xi3/FMi2kf18vX6ff3JLAGcEaL2F/IcHGRaWiOvJhu89o82OkpIi5KqQwdbfo2WDMA/Pz8CuTT6FutFP4Hr96zfmpyKiEF1MfbvWppulc1t+GIRnKqIuCO+T7ex9wc+PHoFZRS6dGgf3g0r9X05E5iModDo+i33h+AFP3fXPW3PcxrX5OmZYs/sO29nqhArycqZChbfzKEIfMOsOKd5jzumfM1ImIS6fj9Np6tXY5Pn6lhVmfB3kAaVyyJbynHB7Y3N/xDLtHviWfSz+1t7KhYyjPb9DHAry+PwMPJjY5TPyA5XIwxoAAAIABJREFUNeNDkJdbaTa9+yNj185hwb/5u7dx72dr0fvZ++8zjoiM4+nX5tGpVVVGDG6Ro66IkPuS+/dO4MJVBC5clX7eYtXPXF78D3FXbwAQMHc59ScPx6V6JW4dyvrQaV3cmVYbZhO8cgv+X/2cf4b9n1HQEa8HYI22MXAzoA5QFy3V6QhEZtKPREtHY0YeCTia9PNOAUbq0WZmcms7M72BCmhp4a3AehHJ7dfxH2CQiFQQERe0RbIB7PXrp13T3PVztU8ptUtPNXsC3wKBpspKqTd0/WZoKfusI2Pu2vmuiJQSkdLAOyZ25hspyakkxieTmppKaor2OiU5a99J6xeqc+bINQ7vCiQlJZXlvx7ExdUer0oluB0ew7aVp4mLSSQlJZWD2wPYuvIMtZt45aep2bL47HWuRMWjlOLynTjG7L9EC0/zQwOalXPFUmD68WASUlL55XgwAC08XXGxseJ8/6bs6d6APd0bsPRZbTrUzpca0MDD2Wx7D8rW0zfoN2svi4c0pYFPiRx178Ql8czkbTSpVIqvumY/VWvB3kD6NsnfaNfSwhJbKxssxQJLCwvttYUly49up2ZZX7rUbYWtlQ2fdxzI8asXzKaPAab3/IjHylSg0/ShxCdl/OiXdSnFlvd+Yur2Jfyyc3m+2m8OpRTxCUkkJmnOPz4hiYTEZLO6d6LjaT94Pk3qejH+/bZZ5P/sPM+NcO0n7cylMMb9sp3nWhXctJybB05Qvlt77NxLgAgVXn4eC2sroi5kfd+tnBxovf5Xwncf5tinE822Z2Frg4WNdZbXhUYRSTUX9DzetHDhR6VUCICITEJzvDvQojlTnIEo/XV0JrkzEK2UUiLSCXBSSmW34XDmupnbzoBSyrQT5Wt9i6dmwCpz+jqzgfLANrT3cSJaWjdYv37aNePv4d6ytU8pdVVE1gGL0KJ7U1kKsEtEXgaGoD2MZOZLoDhwFM05z0R7+AnN4d7yzMIf97BgcnqSgc3LT/Hye014+qVavNrmV2ZuGoh7OWfKVyzBR5OfZcrwDUTejKVSTQ9G/9pF68cVYfWCI0wZsQGVqnAv58zro1rTpF3l/DQ1W85ExPD53ovcTkiiuK017bxLMLrx3Qx+l1VHaVymOMP8KmBjacEfHWvx1tYzjNp7kaqu9vzRsRY2+rxZD4e7ad54fYqRu711gaWev1rjT2RcEs9N2ZFe9mTlUqx6V4uoOv2wnaaVSvLJMzX4+0gwBwNvcepaJPP23B10dOyLDniV0NK9+y6GczUiNt+mEaXxWYcBjH727ljAPo06MHr1LL5YM4uuMz7lp+4fsqD/KPYHnqLHrJHpep+270ezSnXo+NP7eLmV5vXmXYhPSuD6+LupzsELJ7DwwHoGPfkcFUt5MqrjQEZ1HJgud3q/db7eSxpB127j+/Tk9HP7+uPwLlucgA3vA9Dx9fk8Wc+b4a81Z/nmMxw4eRX/i6HM/ftu94b/yjfxKlOczfsuMWDEcqLjEvEo4UjvZ2sx/NXmBWI3wKkJM7FzL0GHo39j5WBP1IUgdnZ9h6RI7aeo5dqZhO48yKmvf6H8C20p0bAWLjUq4dP/bgJuTfVniL0SgoN3OZ4P3JJe3iP+BNGBwaz0Kcx9sR+OI80rcg9bBz7YBUSuoG0WPE8/74rmeKcD/ZRSTfVyByAMqKeUOiMie4A5SqmZuvwV4DWl1BMiMhktzZzWAeSC1g+6WSn1vIh8BXgrpXrrdX3R0tcllFJmnW8mm08DHyulVubhPtuhOWMvpVSqiFzT72+jLh8DVFFK9TDRLZ+WbhaRIGCwUipLXiwt7axHwOauPQuIUUq9ew92vgYMUEo1zknPz89PfbWs6G0kD9DOawIA0W8VPfsdf9oMQNKsng/ZkvvDepA2J1WGPPGQLck7avo+7a//mIdsSd6RGp8DPJxFK/KBXuosInIop83o74X65Yqr3W/knL43R7HPVj7wtfNCYQyumgO8LSLuIuIKvAesBpYDNUWkq4jYAZ+j9XumDZSaB3wgIuVEpCzaQKrfdNlItP7aOvqxEi2SG6DLfwc6iUgz3aGPAZaZc7oi4iUiTUXERkTsRGQYUBJtZHG2iIibiFTUpxVVByahDSJLy6/OAz4TEVd9wNirJvZvQ3tQeEef7vOWXr5Fb7u3bpfofdlfApt1mbs+3chRRCxF5GmgZ1pdM3aWE5GyeltP6O/dqJzuzcDAwKDIkpKa96OQKQzHOxY4AJwDTqMNBPpSH2HbFc2pRACNgB4m9X5BS/WeAE6iDTT6BUApFaWUup52oKW0Y5RSt3S5P/A6mgMOResLfSOtYRH5WUTSRgY4oUXfEcBVoD3QQSmV2/pyJdEGNMWg9aPO1gcnpTEKuAgEAduBb9OiWaVUItpAq77AbbTovbNeDlAdbXBYNNoDwFk0xw1aL8YQtJR2BPAd8J5SaoV+b16izUVO6xitqLcVgzZt6ROlVK7zlA0MDAyKGkoZo5oBUEoloTm9N8zINgFmJxvqKdiP9CO3a/Q3U7YQWJiN/usmr/3R5gnnCaXUOSDbvI4+3ecVspm6o5Q6Apide6uUGgGYXXdNf2DJNpeilLrM3cFdKKV2oA0cMzAwMHjEKRp9vMYmCQYGBgYGjwaKh7L2cl4x1mrOAT0lHW3mMCawGRgYGPwHKQprNRsRbw7oKenXc1U0MDAwMHj4FJGI13C8BgYGBgaPCOqhjFLOK4bjNTAwMDB4NNBHNf/XKfAFNAyKJn5+furgwYMP2wwDA4P/E/JjAY16Hs5qZ/eGea7n+OPmQl1Aw4h4DQwMDAweDYpIxGs4XoNsKYrL/sHdpf+Iz2mp7f8odp0AuP5i0XzvSy/R3vsvDwzORfO/x4gGvwCQ+vfAXDT/e1h0/hUo2ktG5hcPY5RyXjEcr4GBgYHBI4FSD2clqrxiOF4DAwMDg0eGVCPiNTAwMDAwKCSMPl4DAwMDA4PCQwEq9b8/j9dYMtLAwMDAwKAQMSJeAwMDA4NHA/Vw1l7OK4bjNbgvbKysmdZjGG2qNcDNwZkLYcEMX/Ez6/z3AtC6qh9TewzFy600+wP86T9vLJdvXTfblqu9M7/2GU67xxoRHn2bT1dM548D2pbBj5WuwLz+o6hYqhwAhy6f5Z3FEzl9PbBA7uv7KSuYMGkpcXGJdO3cmOlT3sDW1jqL3rnzVxk2fA579p0hJSWVBvUrM2Xiq1St4gnAb/M3M/D1HylWzCa9zuplI2nZ/PECsfvvKzf57lQwofFJ2FgKrT2K82Udb5ysc/6K/xkUxrsHL/FdPR96+7gDcCYyltHHL3P8dgwRicmEdG1UIDYDJCemsHLCLi4euErcnQRKeDrT9o0GVGnilUX38OqzLB+3A2tby/Sylye1x7d+WQD++nwLFw9cJSk+GUc3e5r1qY1fZ7O7juYrJ4MiGPrbvxy+GM7NqARSlpvdCTSdVQcuM2L+QQLDoqnl7caMN5tSvbwrAEOm7+b3HRfTdZOSU7GxsiDyj74FZn+tse/hO6ALVo72RBw5xcE3xxB56kIWPafKFaj77UeUbFIXsbTg1oETHHznS6LOBQDgUqMy9SZ+jGv9mtiVdH1oU5uKQh+vkWo2uC+sLCy5EhFKi0lv4PJBG0aunMGfg8bh7VaGEg4uLBs8npGrZuD2YTsOXj7N4kHjsm1rao+hJCYn4/FxR3rPGc30nh9RvYwPANciw3lxxnDcPmxHyaHtWXl8J4sGZt/Wg7B+42HGT1zC5rXjCDwzi0sBNxg11uyWzty+HcNzzzTk7LHp3AiaR0O/yjzf7csMOo0bVSU6/M/0o6CcLkCDEo6saFmdc8/7sb99HZKVYoJ/cI51bicmM+XMNao6F8tQbmUhPOfpxqT6PgVmbxqpKam4eDgw6OdOfLalP08N9mPR8M1EXIsyq1/+cXc+3/5K+pHmdAGa96vD0BW9GLl1AC9PfJpNPx/g6umwAr8HaysLujX1YeabT+aqe/5aJH2+386015twa8HLPNugPJ2/2kSyvr7w9CFNufNH3/SjRzNfXmxScP8Hr24d8H2lKxub9WKpW0PC9x6l8fxvzOraFHcieOUWVldtzzKPptz89wTNV0xLl6cmJRP05zr2DzS7lXjhoIrG7kSG4zW4L2IT4/lizSyCboWglGLNyd0EhIdQ37saXeq2xP/aJZYc3kJCciKjV8+idrlKVPXwztKOvY0dXeu2YuSqX4hJiGP3xWOsPL6TPo06ABAZF03QrRAARISU1BQquXsWyD3NXbCFgf3aUqO6F66ujoz8tDu/LdhsVrdhgyoM7N8ONzcnrK2teP/t5zl77io3b94pENtyo5y9LSVMInNLEQKi43Os89XJKwyqVBo3m4xRcSWnYvTycaeqs32B2GqKTTFrnnrND9eyTlhYCNWaeeNa1olrZ/LuMD0qumFlo0fDoh23ggv+/1G1nAsD21Shhpdrrrobjl7lyeoePFm9NFaWFnz0Qi2u3oplu3/WbFBMfBLL9gbSt1WlArBaw8HHk7Bdh4gJCEalphK4YCUu1c1f7+aBE1yavYTEiEhUcjJnvv8Nl2q+2LgVByDqXACXZi8h0v98gdl7L6hUleejsClwxysiC0QkRETuiMg5ERlkIntKRM6ISKyIbBURbxOZiMgEEbmpH9+IiJhpv4WIKBEZZ1I2PNP+uXEikioiJbOxsYJ+/Vjdnjb3eG+lRGShiNwWkQgR+d1EZisis/X7vi4iH2SqW0dEDunXPCQidbK5xhb9/qxMytxEZLmIxIhIkIj0ysHG/iKSkun9aHkv95cX3J3cqOJRHv9rl6hRxpdjV++mqmIT47kYfpUaZX2z1Kvi7kVKagrnQ6+klx0LPk+NMhl1IyZuJH7Kdn586UO+Wjc3v80HwP/0ZWo/fje6qP24Dzdu3L4nZ7pjlz+lS7tSooRzetmRY5co6dmbKo+/ztivF5GcnFIgdqexPzyKKisOUmnFQdZcvcWrlUtnq3vkVjTHImLo6+teoDblleibsdy8HIm7r5tZecjZm3zVdi7fd13M1l8Pk5KccQTrygm7+KLZr/zQ7U+cStpTpWnWlPXDRCntuHuuUAr8gyKy6C7dG0gpFzua18j+//igBC1ag1MlL5wqV0CsrPDp9wLX1u28p7ruzf2ICwkl8dbtArMvrygFqakqz0dhUxh9vF8DA5VSCSJSDdgmIkeAIGAZMAhYBYwFFgNpa+W9BnQGaqONEt8IXALSN6EXEWvgB2C/6QWVUl8BX5nojQaaK6XCs7HxD2Av0FE/lohIZaVUbo/dy4ADgDcQC9Q0kY0GKuuy0sBWETmllFonIjbACmAyMA0YDKzQr5loYndvzP+PpgKJgAdQB1gjIseUUv7Z2LlXKZV7Huw+sbKw5PdXvmDuvrWcvRGEo20xwqIzfhkj42Jwss0aQTnaFSMyLiarrl1GXdcP22JvY0e/J55Jj4Dzm+joeFxc7l437XVUdFwGh5qZ4OBw3nzvZyaNv9u31/zJGpw89CPeXu74n7pM9z7fYmVlyafDuhWI7QCNSjpx7nk/QuIS+T0glPL2tmb1UpTikyOBfFnHG4usz7IPjZTkVP78fCt1nqlMqQrFs8gr1C3D23+8SPEyToReimDxiE1YWAot+tdN13nu4yd5dmgTLp8IJeDQtbsR8H+ENrXL8un8g2w7GUKTqu58s/wEickpxCYmZ9Gdv/UCfVpWwky8kW/Eh4QRtvMQnc6tJzU5mdgr19ncul+u9YqV88Bv6igOfzC+wGy7P4rG4KoCj3iVUv5KqYS0U/2oCHQB/JVSfyml4tEcVW3dOQP0AyYqpYKVUleBiUD/TM1/CGwAzmR3fT1K7gOYDZNEpApQDxillIpTSi0FTgBdc7ovEWkHlAeGKaUilVJJSqkjJip9gbFKqQil1Glgpon9LdEc6mSlVIJSagpacqy1SfsuwCjgo0zXddBtG6mUilZK7QJW6vf4QIjIayJyUEQOhoXdW6pPRJg/YDSJyUm8teg7AKIT4nC2c8ig52xnT1RCbJb60fFxOBfLrOtAVHxW3djEeH7euYx5/UZRyin3tF5u/P7HNhxLvoRjyZfo8PxoHB3tuHMnLl1+545mg5NjseyaICwsknadRvHG4I707N4ivdzXpzQ+FUpjYWHB4zUr8Pmn3VmyfM8D25zG0svhVPz7ABX/PkCvXRk//mWK2dDKw4XX/806QAbgt4s3qO5ij18Jp3yz50FJTVUsGbUFK2sLOg0z/4zoVs4Zt3LOWFgIpSu50WpgPfy3BGTRs7C0oEKd0twJjeHfpafy3dbft1/Euec8nHvOo+OY9XmqW82zOHPeacY7M/ZS7pVFhN+Jp7pnccqVyPgduBIezXb/6/Rpmb9p5gq9OtEt6jDdog7Tcu1Mao56E7cGNVnu2ZzFdrU48cVPPLVlLpbF7LJtw7akK603zOb8tIUELVqTr/Y9MKpopJoLZVSziExDczrFgCPAWuBL4FiajlIqRkQuAjXQHGkNU7n+uoZJm97AK2hO86ccLt8MLTJcmo28BnBJKWU6miPDtbLhCeAsMFdEOqBF40OVUttFxBUoa8b+zibXPK4y7sl4XC9fp59/BUwHMnf+VAFSlFLnMrXdguypKyLhwC1gPvC1UirLI7ZSagYwA7RtAS/n0GAav748Ag8nNzpO/YDkVC2V6h9yiX5PPJOuY29jR8VSnvhfu5Sl/rnQy1hZWFKpVHkuhGnp5tqelfAPyaoLYCEW2NvYUs6lFGFRWdNzeaF3z5b07tky/bxXv+84diKAl17UfviPnQjAw6N4ttFuREQ07Tp9znPPNGTExy/leC0RIT+34OzqVZKuXmZ7TgBIVhAUnWBWtjP0DvvC77B5tZaVuJ2YzMnbsfjfjuWruhXyzcZ7RSnF8nHbib4VR9/vO2BpdW/xQG7vaWpKaoH08fZuUZHeLSred/0Xm/ikD5i6HZPAnM3naFAp4/9y/tYLNKnmjm/p7DMt90PgwlUELry7eUiLVT9zefE/xF29AUDA3OXUnzwcl+qVuHXoZJb61sWdabVhNsErt+D/1c9Z5P8FjFHNOkqpNwAnNCe4DEgAHIHITKqRuh5m5JGAo0k/7xT0qC+Xy/cDluSgl5sd2eEJtAO2oqWSJ6Kli0vqbaa1Y67NHK8pIn5AU+DHfLB3B1oK3B0tUu4JDMvhvu6Z6T0/4rEyFeg0fSjxSXd/5Jcf3U7Nsr50qdsKWysbPu84kONXL3D2RlCWNmIT41l2dBtjOr2KvY0dTXxr8Xzt5szf/w8Abao1pI5nFSzEAic7eya9+C4RsVEFMp2ob+9W/Dp3I6dOXyYiIppx4/+k/8tPmdW9cyeWpzuNoukTjzF+XNbU3D/rD3HjhvZgcOZsMGPHL+b5ZwtuWs7Sy+EExyaglOJKTALj/a/wpLv5H+0f/HzZ0a4Wm9rUZFObmtR2deCDx8rxSU1t0JpSiviUVBL1FYDiU1JJSCm41YBWjt9FWOBtXp7YHmu77GOBc3suE31Ty0KEBd5m66+Heax5BQCib8VxfMMFEmKTSE1J5fzeKxzfcBFfv7LZtpdfKKWIT0wmUe/Dj09MJiEp+/78QxfDSUlJJSwyjten7+HZBl5U88yYWp+/7QJ9W1UuULtBGzBVvlt77NxLgAgVXn4eC2sroi5k/a5aOTnQev2vhO8+zLFPJ5ptz8LWBgsb6yyvCwtVREY1F9o8XqVUCrBLRF4GhgDRQOZfBmcgLfLMLHcGopVSSkQ6AU5KqcU5XVNEigHdgOdzUMvNjuyIAwKVUr/q54tEZASaw9xh0k68yevs7i1dLiIWaP2+7yqlks307+TJXqWUaeh4QkTGoDner3O+vZzxcivN6827EJ+UwPXxd9NNgxdOYOGB9XSd8Sk/df+QBf1HsT/wFD1mjUzX+bR9P5pVqkPHn94H4I0/vmV2nxGEfvMPN2MiGfLHN5wK0VKIxe0d+bH7B3gWdycuKYEDQadp/9P7JCQnkt+0b1efj97vQqv2I/R5vE34YuTdcWsdnh9Ns6bVGf7RSyxfuZcDh87jf/oyvy3Ykq5z6vBUvLxKsXnrMfq/Npno6Hg83Ivzcs+WDP+o4Pp3z92J48uTl7mdmEJxG0taexRneM3y6fJeu87QqKQT71Yrh0umUczWFoKTtSXO+pzf4NhEGq47mi73+fsAnvY2HOhQl/wmIiSKA8tPY2VjyYQO89PLn/u0GRXqlGFK9z95Z/FLFC/tyMUD11g6ZjuJsUk4uhWjdofKtBig2SQC/y49zcrxu1BKUby0Ix0/aMxjLSrku82ZCQqLpuLgv9LPHbrPw7uUI5dmaFmQjmPW06x6aT59sTYA78/ax7HAW1hbWfBiEx8mDsi4cfveM6EE34ylW9OCn851asJM7NxL0OHo31g52BN1IYidXd8hKVL7OWm5diahOw9y6utfKP9CW0o0rIVLjUr49H8hvY011Z8h9koIDt7leD7w7nehR/wJogODWelj/uG1YFBFYslIyc/01z1dUGQWEAP4A/2UUk31cgcgDKinlDojInuAOUqpmbr8FeA1pdQTIjIZLc2c1hHoAqQAm5VSz5tcqzdaSttHZXOjeh/vcaBUWrpZRHYAC5VS2eZSRGQgMEIp5WtSdgL4TCm1QkSu6fe3UZeNAaoopXro/cOzgfJpdolIENogq31oKeFQvVlLoCRwA+0h4jAQAdRQSp3X684DrimlPsnOXhMbuwMfK6Xq5aTn5+enDjUomuurGPvxPjyM/XgfDo/Cfrwickgp5fcg7dRytlerG+X9PfDedPSBr50XCjTVLCLuItJDRBxFxFJEnkZLdW4BlgM1RaSriNgBn6P1e6aNFJkHfCAi5USkLNpAqt902Ui0vs46+rESbfDSgEwm9APmZed0AfS+0qPAKBGxE5EXgFpk3yecxnLAVUT66ff2IlAO2G1i/2ci4qoPGHvVxP5taA8K7+jTjt7Sy7egpY3LmtxbR11WH9ivlIpBS9ePEREHEWmKFtHfDRdMEJEOIuKhv66G9t6tyOXeDAwMDIokxuAqbQTzELQpQBZoU4jeU0qtABCRrmgDoxagTQnqYVL3F8AXbYQxwCy9DD0yTU+tikgcEKOUumVSVg5tlPAbmY0SkZ/1dl7Xi3qgOcUI4DLwYm5TiZRSt0TkObS08FS0AWHPm0xZGoU2OCoILS09QSm1Tq+bKCKd9XsaD5wGOptMJUofUKU/lADcMBkQ9QZaxBwK3ASGpE0lEhEv4BRQXSl1GXgK+E1EHNGi5gWYTLUyMDAweFRIm8f7X6dAHa/uvLIdbauU2gSYXUxVj1I/ItN0mmx0+5spu0o292ficNPOA9Gm+OQJpdROwOw6gPoUqlf0w5z8CFoUm9s1AtGmGpmW3eLuCOnM+pe5O7gLpdRQYGhu1zEwMDB4FCgK83iLZieegYGBgYFBZtTDSR3nFWOt5hwQkZ8zLbWYdvw3J7AZGBgY/J9TFKYTGY43B5RSryulHM0cr+de28DAwMCgUHmIK1eJyFB9Xf3sV7bRMVLNBgYGBgaPBIqHs3KViJQH2qINzs0VI+I1MDAwMDB4ML5HGwh8T17fiHgNsiV9IYqiir4YRVEkbSGKokraYhRFkbTFKIoivdTZh23Cw0UV/qhmfVrpVaXUsXvdScpwvAYGBgYGjwj3vb9uSRE5aHI+Q980BgAR2YS2Jn9mRgDD0dbtv2cMx2uQLTKkaC5bmBapF0X7i7LtcNf+orh0YVq0WBTf+6L8vkP+ReoKuM+lmsNzWjJSKdXGXLmIPA74AGnRridwWEQaKqUy7yyXjuF4DQwMDAweDdR9O977u5xSJ9B2fgNARAIBP5MVDM1iOF4DAwMDg0eGIrA5keF4DQwMDAweDRTwMBeuUkpVuBc9w/EaGBgYGDwaFHKq+X4xHK+BgYGBwSPBAwyuKlQMx2tgYGBg8GhQRCJeY+Uqg3ylu18bTn2+iOjJW7kwZglPVqptVm/sc4MJ/noltydtYuv706hexidd5u1WhjVvTuLWxA2EjF/Dj90/xNLCMt9stLGyZtbLwwkct5w732/m8PC5tK/RGABrSyv+evUrAsYtR03fR4vK9e6pzUqlyhM3ZTvz+4/OUN6t3lOc+nwRd77fjP/nf/B87eYPbP+bLV7kwCdziJ+ygzl9R9739VztnVk2eDzRk7cSOG45PRtknIpYELZnR62x79E5eAcv3j7IU1vn4VK9klk9p8oVaP73NLqE7qXrzf20WjcLpyp3PzsuNSrTat0suoTtK5DFJHJ671tX9eP0qEXE/LCNLe9NxcvN3LRPjZw+4418arDhnSnc/G49od/8w5+DvqS0c4l8sd+nb2faH1xKt8hDdL6ynToThiGW2nUtbKxpNOtLng/cQrc7h2l/eDll2t/b/7z15rn0Umfzpa0HJTU170dhYzheg3yjTbWGTOj8JgPmj8Xp/dY0nziES2HXsuh1q/cUrzR+lmYTX8ftw3bsvXQig8Oa1nMYoVERlPn4Wep81YcWlevyRouu+WanlYUlVyJCaTHpDVw+aMPIlTP4c9A4vN3KALDr4jFenjOakMgcZwRkYGqPoRwIOp2hrKxLKRYMGM0HS3/A+f2nGLbsRxa+MoZSTq4PZP+1yHDG/TOH2XtXP9D1pvYYSmJyMh4fd6T3nNFM7/lR+gNQQdluDq9uHfB9pSsbm/ViqVtDwvcepfH8b8zq2hR3InjlFlZXbc8yj6bc/PcEzVdMS5enJiUT9Oc69g8cke92QvbvfQkHF5YNHs/IVTNw+7AdBy+fZvGgcdm2k9Nn3NXemRm7/qbCZy/gPaIzUQmxzOn7Wb7Yb2lfjEPvfcXSkk+wvlE3Sj/1BI8N1bYMFysrYq+EsKlFH/5yqc/xkT/w5J+TcfAul2ObFXp1wsIq44Px/bb1oKSlmg3Ha/B/wxfPDmLM2tnsD/BR4j76AAAWdElEQVRHKcW1yDCuRYZl0fMpWZZdF48REH6NVJXKgn/XUb1MhbvyEmX589AmEpITuXHnFutO7aOGSUT8oMQmxvPFmlkE3QpBKcWak7sJCA+hvnc1klKS+WHLYnZfPEbKPX4ju/u14XZcFJvPHMxQ7unqzu24KNb57wVg7ck9xCTEUbHkg/34LD+6jRXHdnAzJvK+r2dvY0fXuq0YueoXYhLi2H3xGCuP76RPow4Fars5HHw8Cdt1iJiAYFRqKoELVmYb8d48cIJLs5eQGBGJSk7mzPe/4VLNFxu34gBEnQv4X3t3Hl5Fdf9x/P0hIYQlEYKBiiCISFWooIbqT4UiahUsRVAerUtR61YRl9qnVq0CLrgvVau4iwsuoKgIuIugtcqiSAOyFEEQlEWMLAmE8P39MZMYkpt7c0m4Nzd+X89znyd3zsw53wxDvnPOnJlhyePjKchfVOtxQtX7ftBBvclfuYTxs99jy7atjHj9Ubrt2Ylftm4fsZ5ox/gb+R8zfvZ7bCjaTGHxFu6fOp4j9jmwVuJfPPo51nw4i+3FxRSuXM3SZyeSe0QwqlOyuZC5I+9n07JvwIyVk6ay8asV5BzSpcr6GmY3o+vwoXz2t9t3WL4zddUK88S7y0maKqmo3HtyF4TLD5A0U9L68POOpAPirSdG21HbUOBWSevCz20q9yBPSR0kvS9ps6QvJVX1ZJQnwldNdSq3LL/C+4G3SZoYJdbTJC2TtEnSK5JyYv1+8WqgBuS135/cZs1ZNHIcy0e9xn2nXEFmw0aV1n1+5tt0ym3Hvq3akd4gjSGHncAb+T89m/if77/AqXnH0rhhI9rslkvfLv+3Q3lta5WVQ+fW7chfuSTubbMym3D9787nipfurVQ2c9l85q9aSv8De9JADRjQrRdbthXzxTeLayHqyuJpr3OrvSjZXsKi1cvLls1ZsYgue3RMeOzLnp9EVqe9yNq3A0pPZ+8hA1n5xvRqbduqVx6Fq1az9fsfaj2ueHTZoyNzyu2bzVuL+N/ab+jSpmPE9eM5xnvt2538VV/tkrhze/Xgh/zI/6aZrVqS3bkDBVWUA3Qb9RcWPfgcRd9GHx2qTl21IVV6vPVhctXFZvZohWUrgZOBZQQnF0OB54Fop42R6okmVhvnAycC3QiOh7eBJcDosPw54GOgX/gZL2lfMyvrIko6EtinYsNm1qXcOgL+B4yLFKSkLsBDwAnAbOBh4AHg1Dh+15haZ+eQkd6Qkw/uQ887L6S4ZBuv/vl2/tH3bP7x2ugd1l1VsJbpiz9n4chxbCvZxvL1q+lzz9Cy8g8WfcZ5Rw7gx7vfJT0tnSc/nsQrcz6ozXDLpDdI49lzRjLmP5NZ8N2yuLe/of8FPPbviaxYv7pS2XbbzlOfTGHs2SPJbJjB1pJtDH7kajZvLaqN0GvUXrPMxhQUbtphWUHhJrIymyQ89qJVa1gzfRb9F77J9m3b2Lz8W97tMyTmdo33bE3ev4Yz+y+31HpM8WrWqDFrNu6Y/AsKN5HVqEnE9at7jP9qz05c1+8cBoz+W63H3PGsQbTM68qn51YexlZ6Ooc/ewdLxkzgxwWRT0hzDulK7hEHM+vSm2jSturr2dWpq9b45KrkMbMfzGypmRkgoASIPHa169oYAtxpZivM7BvgTuAsAEmdgYOB4WZWaGYvAXOBsguZktKB+4CLY4TSi+CRZS9VUX46MNHMppnZRuBaYJCkrIorSjo/7MXPXLOm8hBxNIXFWwC4b+o4vv1xHes2FXDXu8/Rr+v/VVp3+Ann0qP9/rS9qj+Zl/yGkZMe473L/kXjho2QxJvD7uHlz6bS9LKjaPnX39KiSRa3Doy1G+IniafPHsHWbcVc/PwdcW/fre2+HLNfD+5+97mI5Ufv14PbBl5M77svImNYT35z15959Iyr6dZ235qGXuP2NhYVkt246Q7LsjObsqFo8y6PvcNp/Rm8YTaDN8ym9+RH6Dp8KDk9ujKhbS9eyDyQuSPv5+j3xpDWOLPKOhrt3oI+bz3OogfGsuz5STWOqaY2bikkO7Pi/mzChi2bK61b3WN8n9y2TLn4Li598W4+XDxnp+KquK9LtR1wNN1uuYL3+57HlnXrKwbI4U/fxvatxcy8+IbIFUv0eGA4sy69CSspqTqA6tRVy8ws7k+i1YfEe7OktZI+ktS7fIGkH4AiggQ2amfriSZKG12A8v9b5oTLSsuWmNmGKsoBLgemmdkXMUIYAow3s01VlO8Qh5n9D9gKdK64opk9bGZ5ZpaXm5sbo9kd/bB5A8u//65aB3G3tp14YdY7fPPDGkq2lzDmP5No0SSLA/bYm5wm2eyV8wvunzqOrduK+X7Tjzzx8esRE3hNPXbGNbTOyuGkh69i2/Yofzyq0LvzwXRouQdf3/Qqq26ZxF+POY2TDurNrKvGANC97b5MW/wZs77+EjNj5rL5fPJVPsfs16O2f5W421u4+mvSG6TRKbdd2bJubTuRv2rJLo996diJjMs6mHFZBzO133m06LYfX78whcJvvsNKSvhqzAQyWmRXeZ23YfNsjnrrcVa89h75o0ZHXCfR8lct2eGkpElGJvvkto14+aI6x/heOb/gnUvv44bJT/DMp2/sdFwV9zXAHsf15NeP3Mi0/hdS8N+FlbY59LGbyGy9O9NPGoZt2xax3obZzcjJ68oRL9zNwFUfctyM8QCcuOIDco88JK66alOqDDWneuK9EugI7EkwhDpRUtnQrJk1B3Yj6DV+trP1RBOljWZA+RkYBUCzcGi4YllpeRaApHbABcB10dqW1IRguPvJKKtFbas2PfHx6wzrPZjcrBY0b5LFZX1O4fW5H1Vab8ay+Qw++GhaZeUgiTN+fTwN09JZvGYF6zYVsGTtN/y51yDSGqSxW+NmDDmsH3NW1O61oQf/8Df236MD/R/8K0Vhb71URnpDGqVnhD+nl/1c0cPTX2Gf606i+6gz6T7qTEZPn8Ck//6b4+67tOz37Nmpe9kf5O5tO9OzU/caXydNa5BGo/QM0tSAtAYNgp8bpMXV3uatRbz8+VSu738eTTIyObzjgQzo1ounP5myS2OPZN2MubQbfDyZrVqCRIczBtCgYTobFlce+k/PakqfNx9j7UezmXPVnRHra9AogwYZDSv9XBuq2vcTPv+Arm06Muigo2iUnsF1/f7EF98sjnj5ItYx3ma3XN677H7+9cF4Hpo+odZiB2h91GEc/uztTD9pGOtmzK1U3uPBkey2/z580P9CSoq2RKghUFywgQltejKl+4lM6X4iU/udD8Abhwxi3SdfxFVXrUqRyVUpfY3XzD4p93WMpD8QXC+9r9w6mySNBtZI2t/MKl2Mq049MeKI1MZGILvcatnARjMzSRXLSstLe8D3ANebWcWEWdEg4Hsg2gXQWG3VmhsmP87uzZqzcMSLFBVv5cXZ73LTlCdp16I18657jgOu/wPL13/HrW8+TausFnx+zVM0zWjM4jUrOOnhqygo3Bj8Ug/9nXsGX86Vx51JyfbtvL9gFpePv6fW4twr5xdc2GsQRcVb+PaWn4YpLxh7K2NnvMmCES/SoWVwa9FblwSTpjpcM5Bl36/iquOH0LNTd/rdfzmFxVvKhtghGG4sKt7K2vBa37RFnzHi9UcZf94oWmfnsGbjD4x6Ywxvz/+0RvH/o+/ZjPjduWXfzzy0LyNef5SRkx6N2l752AEueu52Hj/zGlbfNoV1mwr483O3MS+cxLOrYo9k3q2PkNmqJX0/f4X0pk3YsHgZ00+6hOKC4BDtPfkRVk+fybybH6LdwGNp+esD2a1LJ/Y+a2BZHZMOOIHNy1fRtP2eDFj6XtnyU4vmsnHpCl7b++haiTXavj/p4au4/5QreOas4XyydB6nPvrTfb4V9320Y/zcI3/PPrltGd7vTwzv96eyOrIu71Pj+LteexENd8ui9+SyV82yZvospvY7jyZ7tWHfC0+lpGgLA7/9sKx8xgXDWTp2Ik3a7cEJ8yaV7eui736aUJWWGUyiLPpuHVZSErOuXSkVrvEqGePbu4qkKcAUM7u3wvJ0gkRzuJlF6/lGrSfGNju0IenfwBNm9khYfg5wvpkdFl7j/QLILR1uljQNGGtmo8Ph6y0EIycArYG1wKVmNrZcm28DH5tZlT1jSaOA9mZ2evi9I/Al0LLCUPcO8vLybFaP1DwvS+V32qZy7JDa74X19/Emz2m2AEmzor0Ttzo6p2favdmRb+GKpu/6hTVuOx4pO9Qsqbmk4yRlSkqXdDrBRKM3JR0r6SBJaZKygbuA9cD8eOqJ0X6sNp4C/iJpT0ltgCsIh4TNbCHwOTA8bHcgwWzo0glSnQlmQ3cPPwD9gbJxJ0ltgaOAMTF21bNAf0k9JTUFrgdejpZ0nXMuFaXKNd7U7NIEGgI3AvsRzCj+EjjRzBZIOpBgmLgtUAjMAI43syIASVcDPc2sb7R6YrTfPFobBLfwdCSYrQzwaLis1KkEiXg98DVwcumtRBWHw8Pbf9eaWWG5xWcS9Hb/VzGwcCi7r5lNN7N8SRcSJOCWwDvA2TF+N+ecSz0pcjtRyibeMElFnGJpZuOo4r7WsHxUuZ+rrCdG+7HaMOBv4SdS+VKgdzXbUoRlNwM3V7F+swrfxwJjI63rnHP1hb+dyDnnnEukFOnxpuw13kSQdHWFRzOWfqYkOzbnnHOVbbf4P4nmPd4owiHpWA/ecM45Vwf4ULNzzjmXSCky1OyJ1znnXL2QKj3eevUADVd78vLybObMmbFXdM65WlAbD9DoqEy7kfgfoHE6iX2AhideF5GkNQSvPNxVdid4GlcqSuXYIbXjT+XYIbXj39Wxtzez+N7OUoGkNwjijNdaMzu+Jm3HwxOvSwpJMxN5hlmbUjl2SO34Uzl2SO34Uzn2usZvJ3LOOecSyBOvc845l0CeeF2yPBx7lTorlWOH1I4/lWOH1I4/lWOvU/war3POOZdA3uN1zjnnEsgTr3POOZdAnnidc85VmyTPGzXkO9AllaQ0SdcnO476SNIhkrqW+54r6VlJcySNltQs2vZ1laSGkqYlO454SWqR7BhqSlIjoDjZcaQ6n1zlkir8j7zZzNKSHUskkvaKtY6ZfZ2IWOIlaTow0szeCb+/CrQBngT+AHxhZhclL8KdkwLHzB+B78zszfB7HjCBYN8vBn5vZguSGOJOC/d9oZl5p60GPPG6pKrr/5ElbSd49jqAIqxidTgBrAX2NLMtkpoDq4GuZrZQUjvg32bWLrlRxi8FEu8c4I9mNif8PhuYA9wBXAS0M7PfJzHEnVbX932q8LcTubqgLp/9fQFkAmOAZ4CVyQ0nLunA1vDnw4BvzWwhgJktD5Oxq317AXMBwhOcXwHHmNn3kv5O0Ot1P2OeeN0uJ6lPlOKMhAWyE8yse3iddAjwIfAl8BTwspkVJjW42PKBwcCLwKnAO6UFkvYECpIUV0wxrvvX9b9b2wiO6yLgcOBLM/s+LNsMNE5WYNURXqKo6mS4To5MpRofana7nKSvYq1jZnsnIpaaCGdzHgucBfQF+pjZ7KQGFYWkI4GJBH9ES4AjS68tSvoLcKiZnZLEEKsk6YkYq5iZnZOQYOIkaTzBm73GAA8A08zs6rCsCzDBzDonMcSoJA2JtY6ZjUlELPWVJ17nqknSLwl6vqcBXwHnmFnMk4pkkpQFdAYWmtmGcst/CWwwszo5dC5pkJm9XEVZBnCtmV2b4LCqJRxNeAbIAz4GBptZQVh2C9DYzC5NYoguyXzYwLkoJOVIGirpU+AVYCPQy8yOqutJF8DMNpjZrPJJN1y+oK4m3dDdksZJalV+YdiLn0NwzbquOjQ8PrLM7LelSTd0HfBjsgKrDkn3Soo4HC6ps6QPEh1TfeOJ17noVgIXEyTdocB/gE6S+pR+khpd/dWF4KXr+ZL+KClL0oPAq8AdZnZscsOLKtpJwxfU7ZMGCCaHzZNUto/D++2vAWZQbq6A2zk+1OxcFJKWEn3WtZlZxwSF87Mj6TfAeIIJSe8AF5rZt8mNKrrwwSS3AycDVxDcw3sbwUS3K83ssSSGVy2STgH+CUwGngXuIhjtOdfM5icztvrAE69zrk6S1BK4D+hNMEO7PcEf/pR4alUqnjSUF94KNRvIAR4ws2FJDqne8KFm51ydI+lUYD7BLTkHhEPL1wPjw8ddZic1wBjCk4YLCB6v+DFwAMEkt5QQnjS8C8wiuC59hqRrJdX1W7lSgide51xddDNwupmdY2Y/AJjZM0BXgh7YvGQGF009OGl4iKCnfpOZHW9mNwG/Bo4GPpPUI6kB1gM+1Oycq3MkNTWzTVHKB5jZq4mMqbrC+9bPN7O3KyxvBdwPHG5mbZMSXDWE9yEPNbPvIpRdBNxoZjmJj6z+8MTrnHO1KJVPGqBa91A/YGbnJjisesWHmp1zrhZFS7pheZ1NuqFYt0O1T05Y9YcnXuecc+VVdQ/1K8Dtdfwe6pTgQ83OOecqSfXboeoy7/E655zbQarfDlXXeeJ1zjlXJtVvh0oFPtTsnHOuTKrfDpUKPPE655wrk+q3Q6UCT7zOOedcAvk1Xueccy6BPPE655xzCeSJ1znnnEsgT7zOuYSS1FuSSfKZse5nySdXOecSKnzQfg6w2sy2Jzse5xLNE69zLi6SMsxsa7LjcC5V+VCzcz8DkoZKmidpi6TV4TtXkXSapE8kFUhaK2mSpM7ltusQDgufLmmypE3AqGq0d66k+ZKKJK2TNK10aLniULOkqeH3ip+zytU3TNKXYX2LJF0jKb2295NzieAHrnP1nKSRwBXA34G3gGZAv7C4EXADwSMCs4GRwCRJXSr0am8Nt78YiDpMJukQYDRwDvBBWO+hUTYZBGSU+z4UuByYEdY3AjgbuAz4HNg/rD8TuDZaLM7VRT7U7Fw9JqkpwSverjWzO6qxfg6wDjjSzD6S1AH4CrjOzG6oZpsDgSeBdmb2Y4Ty3sD7YfmKCmXHAa8BJ5vZRElNwvgHmdkb5db7I3CvmTWvTkzO1SXe43WufutC0DN8K1KhpO7AcKA7sDugsKg98FG5VT+No823gSXAV5LeBt4DXjaztdE2ktQFeBG40swmlou/MfCSpPK9hDQgU1Kuma2JIzbnks4Tr3M/D5WGtsLe5FvAhwTDwqXvWs1nx6FfgCqf3VupIbONkvKAI4BjgAuB2yQdbWazIm0TPoD/deAZM7unXFHpPJTBwMIIm35f3bicqys88TpXv80jeL3bccDcCmX7A7nANWY2H0DS4fzU691pZlYCTAOmSRoexnEaUCnxSmoETAC+BC6pUJwfxt/RzCbXNC7n6gJPvM7VY2Hv805ghKRCgmHgxgSTqx4BtgDDwnU6ALcQY/JULJIGAB0JEu8a4BCgHUHyjeQhoA3BBKpcqSzvF4TxjwJGhcvfJvi79SvgIDO7siaxOpcMnnidq/+uJUiAlwB3A+uBaWa2VtIZwM0EQ83zCWYOv1vD9tYD/YGrgSxgOXAj8HgV6/cmuKa8oMLys4EnzewGSSuBYcAdQCHBsPOTNYzTuaTwWc3OOedcAvkDNJxzzrkE8sTrnIuLpNGSNlbxyU92fM7VdT7U7JyLS3jrT3YVxcVmtiyR8TiXajzxOueccwnkQ83OOedcAnnidc455xLIE69zzjmXQJ54nXPOuQT6fw+gsNGj1i+rAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1614,12 +1599,12 @@ "source": [ "To summarize, the main functions in the phik correlation package working on a dataframe are:\n", "\n", - "- df[twocols].hist2d()\n", - "- df.phik_matrix()\n", - "- df.global_phik()\n", - "- df.significance_matrix()\n", - "- df.outlier_significance_matrix() -- note: on a dataframe with exactly 2 columns\n", - "- df.outlier_significance_matrices()" + "- `df[twocols].hist2d()` or `series.hist2d(other_series)`\n", + "- `df.phik_matrix()`\n", + "- `df.global_phik()`\n", + "- `df.significance_matrix()`\n", + "- `df[twocols].outlier_significance_matrix()` or `series.hist2d(other_series)`\n", + "- `df.outlier_significance_matrices()`" ] }, { @@ -1631,7 +1616,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['driver_age', 'mileage']\n" + "interval columns not set, guessing: ['driver_age', 'mileage']\n" ] }, { @@ -1836,7 +1821,8 @@ } ], "source": [ - "data[['driver_age', 'mileage']].hist2d()" + "data[['driver_age', 'mileage']].hist2d()\n", + "# Alternatively: data['driver_age'].hist2d(data['mileage'])" ] }, { @@ -1848,7 +1834,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['driver_age', 'mileage']\n" + "interval columns not set, guessing: ['driver_age', 'mileage']\n" ] }, { @@ -1871,61 +1857,53 @@ "\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -1933,13 +1911,12 @@ "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 1.000000 0.590456 0.000000 0.105506 0.000000\n", - "car_color 0.590456 1.000000 0.000000 0.389671 0.000000\n", - "car_size 0.000000 0.000000 1.000000 0.000000 0.768589\n", - "driver_age 0.105506 0.389671 0.000000 1.000000 0.000000\n", - "mileage 0.000000 0.000000 0.768589 0.000000 1.000000" + " car_color driver_age area mileage car_size\n", + "car_color 1.000000 0.389671 0.590456 0.000000 0.000000\n", + "driver_age 0.389671 1.000000 0.105506 0.000000 0.000000\n", + "area 0.590456 0.105506 1.000000 0.000000 0.000000\n", + "mileage 0.000000 0.000000 0.000000 1.000000 0.768589\n", + "car_size 0.000000 0.000000 0.000000 0.768589 1.000000" ] }, "execution_count": 27, @@ -1960,18 +1937,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['driver_age', 'mileage']\n" + "interval columns not set, guessing: ['driver_age', 'mileage']\n" ] }, { "data": { "text/plain": [ - "(array([[0.6057528 ],\n", - " [0.67603174],\n", - " [0.76858899],\n", - " [0.41913005],\n", - " [0.76858899]]),\n", - " array(['area', 'car_color', 'car_size', 'driver_age', 'mileage'],\n", + "(array([[0.67603168],\n", + " [0.41913015],\n", + " [0.60575269],\n", + " [0.76858883],\n", + " [0.76858883]]),\n", + " array(['car_color', 'driver_age', 'area', 'mileage', 'car_size'],\n", " dtype=object))" ] }, @@ -1993,7 +1970,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['driver_age', 'mileage']\n" + "interval columns not set, guessing: ['driver_age', 'mileage']\n" ] }, { @@ -2016,75 +1993,66 @@ "
var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
areacar_color1.0000000.3896710.5904560.0000000.000000
driver_age0.3896711.0000000.1055060.0000000.000000
car_colorarea0.5904560.1055061.0000000.0000000.3896710.000000
car_sizemileage0.0000000.0000001.0000000.0000001.0000000.768589
driver_age0.1055060.3896710.0000001.000000car_size0.000000
mileage0.0000000.0000000.7685890.0000001.000000
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var2areacar_colorcar_sizedriver_ageareamileage
var1car_size
area72.41414937.598215-0.2850721.833756-0.632824
car_color37.59821585.465972-0.63605119.817000-0.57739285.46830219.78760737.609394-0.634132-0.586560
car_size-0.285072-0.63605169.049134-0.49639249.232915driver_age19.78760784.3143151.857272-0.586955-0.532817
driver_age1.83375619.817000-0.49639284.343356-0.684187area37.6093941.85727272.400210-0.593527-0.334542
mileage-0.632824-0.57739249.232915-0.68418791.236359-0.634132-0.586955-0.59352791.22158449.249503
car_size-0.586560-0.532817-0.33454249.24950369.047125
\n", "" ], "text/plain": [ - "var2 area car_color car_size driver_age mileage\n", - "var1 \n", - "area 72.414149 37.598215 -0.285072 1.833756 -0.632824\n", - "car_color 37.598215 85.465972 -0.636051 19.817000 -0.577392\n", - "car_size -0.285072 -0.636051 69.049134 -0.496392 49.232915\n", - "driver_age 1.833756 19.817000 -0.496392 84.343356 -0.684187\n", - "mileage -0.632824 -0.577392 49.232915 -0.684187 91.236359" + " car_color driver_age area mileage car_size\n", + "car_color 85.468302 19.787607 37.609394 -0.634132 -0.586560\n", + "driver_age 19.787607 84.314315 1.857272 -0.586955 -0.532817\n", + "area 37.609394 1.857272 72.400210 -0.593527 -0.334542\n", + "mileage -0.634132 -0.586955 -0.593527 91.221584 49.249503\n", + "car_size -0.586560 -0.532817 -0.334542 49.249503 69.047125" ] }, "execution_count": 29, @@ -2105,7 +2073,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['mileage']\n" + "interval columns not set, guessing: ['mileage']\n" ] }, { @@ -2276,7 +2244,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval_cols not set, guessing: ['driver_age', 'mileage']\n" + "interval columns not set, guessing: ['driver_age', 'mileage']\n" ] } ], @@ -2525,7 +2493,16 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.7.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } } }, "nbformat": 4, diff --git a/python/phik/notebooks/phik_tutorial_spark.ipynb b/python/phik/notebooks/phik_tutorial_spark.ipynb index 5d67cc5..f8e3ca6 100644 --- a/python/phik/notebooks/phik_tutorial_spark.ipynb +++ b/python/phik/notebooks/phik_tutorial_spark.ipynb @@ -9,15 +9,17 @@ "This notebook shows you how to obtain the Phi_K correlation matrix for a spark dataframe.\n", "Calculating the Phi_K matrix consists of two steps:\n", "\n", - "- Obtain the 2d contingency tables for all variable pairs. To make these we use the `popmon` package, which relies on the `spark histogrammar` package.\n", + "- Obtain the 2d contingency tables for all variable pairs. To make these we use the [`popmon` package](https://github.com/ing-bank/popmon), which relies on the [`spark histogrammar` package](https://github.com/histogrammar).\n", "- Calculate the Phi_K value for each variable pair from its contingency table.\n", "\n", "Make sure you install the popmon package to make the 2d histograms, that are then used to calculate phik." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "!pip install popmon" ] @@ -29,12 +31,14 @@ "outputs": [], "source": [ "import itertools\n", + "\n", "import pandas as pd\n", + "import popmon\n", + "from popmon.analysis.hist_numpy import get_2dgrid\n", + "\n", "import phik\n", "from phik import resources\n", - "from phik.phik import spark_phik_matrix_from_hist2d_dict\n", - "import popmon\n", - "from popmon.analysis.hist_numpy import get_2dgrid\n" + "from phik.phik import spark_phik_matrix_from_hist2d_dict" ] }, { @@ -51,6 +55,7 @@ "outputs": [], "source": [ "from pyspark.sql import SparkSession\n", + "\n", "spark = SparkSession.builder.config('spark.jars.packages','org.diana-hep:histogrammar-sparksql_2.11:1.0.4').getOrCreate()\n", "sc = spark.sparkContext" ] @@ -91,7 +96,7 @@ "metadata": {}, "outputs": [], "source": [ - "print (combis)" + "print(combis)" ] }, { @@ -118,7 +123,7 @@ "outputs": [], "source": [ "grids = {k:get_2dgrid(h) for k,h in hists.items()}\n", - "print (grids)" + "print(grids)" ] }, { @@ -162,13 +167,6 @@ "source": [ "phik_matrix" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -187,7 +185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/python/phik/outliers.py b/python/phik/outliers.py index 615b7bb..e8451f1 100644 --- a/python/phik/outliers.py +++ b/python/phik/outliers.py @@ -13,7 +13,7 @@ LICENSE. """ -from typing import Tuple, Union +from typing import Tuple, Union, Optional import itertools import numpy as np import pandas as pd @@ -23,10 +23,12 @@ from scipy.special import betainc from phik import definitions as defs -from .binning import bin_data +from .binning import bin_data, hist2d_from_rebinned_df from .betainc import log_incompbeta from .statistics import z_from_logp from .data_quality import dq_check_nunique_values +from .utils import array_like_to_dataframe, guess_interval_cols + def poisson_obs_p(nobs:int, nexp:float, nexperr:float) -> float: """ @@ -101,7 +103,7 @@ def log_poisson_obs_p(nobs:int, nexp:float, nexperr:float) -> Tuple[float,float] """ if nobs == 0: # p=1, 1-p=0 --> logp=0,log(1-p)=-inf - return (0, -np.inf) + return 0, -np.inf if nexperr > 0: nexpalt = nexp if nexp>0 else nexperr @@ -122,8 +124,8 @@ def poisson_obs_z(nobs:int, nexp:float, nexperr:float) -> float: Calculate the Z-value for measuring nobs observations given the expected value. The Z-value express the number - of sigmas the observed value diviates from the expected value, and is based on the p-value calculation. If the uncertainty - on the expected value is known the Linnemann method is used. Otherwise the Poisson distribution is used to estimate the p-value. + of sigmas the observed value deviates from the expected value, and is based on the p-value calculation. + If the uncertainty on the expected value is known the Linnemann method is used. Otherwise the Poisson distribution is used to estimate the p-value. :param int nobs: observed count :param float nexp: expected number @@ -131,32 +133,32 @@ def poisson_obs_z(nobs:int, nexp:float, nexperr:float) -> float: :returns: Z-value :rtype: float """ - pvalue = poisson_obs_p(nobs, nexp, nexperr) + p_value = poisson_obs_p(nobs, nexp, nexperr) # special cases: numerically too close to zero or one. # try to evaluate log(p) or log(1-p) - if pvalue==0 or pvalue==1: + if p_value==0 or p_value==1: tlogp = log_poisson_obs_p(nobs, nexp, nexperr) - if pvalue==0: + if p_value==0: logp = tlogp[0] - Z = z_from_logp(logp) - if pvalue==1: + z_value = z_from_logp(logp) + else: log1mp = tlogp[1] - Z = z_from_logp(log1mp, flip_sign = True) + z_value = z_from_logp(log1mp, flip_sign=True) # default: else: - Z = -stats.norm.ppf(pvalue) + z_value = -stats.norm.ppf(p_value) - return Z + return z_value def poisson_obs_mid_p(nobs:int, nexp:float, nexperr:float) -> float: """ Calculate the p-value for measuring nobs observations given the expected value. - The Lancaster mid-P correction is - applied to take into account the effects of discrete statistics. If the uncertainty on the expected value is known the - Linnemann method is used for the p-value calcuation. Otherwise the Poisson distribution is used to estimate the p-value. + The Lancaster mid-P correction is applied to take into account the effects of discrete statistics. + If the uncertainty on the expected value is known the Linnemann method is used for the p-value calculation. + Otherwise the Poisson distribution is used to estimate the p-value. :param int nobs: observed count :param float nexp: expected number @@ -178,7 +180,7 @@ def log_poisson_obs_mid_p(nobs:int, nexp:float, nexperr:float) -> Tuple[float,fl The Lancaster mid-P correction is applied to take into account the effects of discrete statistics. If the uncertainty on the expected value is known the - Linnemann method is used for the p-value calcuation. Otherwise the Poisson distribution is used to estimate the p-value. + Linnemann method is used for the p-value calculation. Otherwise the Poisson distribution is used to estimate the p-value. :param int nobs: observed count :param float nexp: expected number @@ -202,16 +204,16 @@ def log_poisson_obs_mid_p(nobs:int, nexp:float, nexperr:float) -> Tuple[float,fl lq1 = tlogpp1[1] logmidq = np.log(0.5) + lq1 + np.log( 1 + np.exp(lq-lq1) ) - return (logmidp, logmidq) + return logmidp, logmidq def poisson_obs_mid_z(nobs:int, nexp:float, nexperr:float) -> float: """Calculate the Z-value for measuring nobs observations given the expected value. The Z-value express the number - of sigmas the observed value diviates from the expected value, and is based on the p-value calculation. + of sigmas the observed value deviates from the expected value, and is based on the p-value calculation. The Lancaster midP correction is applied to take into account the effects of low statistics. If the uncertainty on the - expected value is known the Linnemann method is used for the p-value calcuation. Otherwise the Poisson distribution is + expected value is known the Linnemann method is used for the p-value calculation. Otherwise the Poisson distribution is used to estimate the p-value. :param int nobs: observed count @@ -220,26 +222,26 @@ def poisson_obs_mid_z(nobs:int, nexp:float, nexperr:float) -> float: :returns: Z-value :rtype: tuple """ - pvalue = poisson_obs_mid_p(nobs, nexp, nexperr) + p_value = poisson_obs_mid_p(nobs, nexp, nexperr) # special cases: numerically too close to zero or one. # try to evaluate log(p) or log(1-p) - if pvalue==0 or pvalue==1: + if p_value==0 or p_value==1: tlogp = log_poisson_obs_mid_p(nobs, nexp, nexperr) - if pvalue==0: + if p_value==0: logp = tlogp[0] - Z = z_from_logp(logp) - if pvalue==1: + z_value = z_from_logp(logp) + else: log1mp = tlogp[1] - Z = z_from_logp(log1mp, flip_sign = True) + z_value = z_from_logp(log1mp, flip_sign=True) # default: else: - Z = -stats.norm.ppf(pvalue) + z_value = -stats.norm.ppf(p_value) - return Z + return z_value -def get_independent_frequency_estimates(values:np.ndarray, CI_method:str='poisson') -> Union[np.ndarray, np.ndarray]: +def get_independent_frequency_estimates(values:np.ndarray, CI_method:str='poisson') -> Tuple[np.ndarray, np.ndarray]: """ Calculation of expected frequencies, based on the ABCD-method, i.e. independent frequency estimates. @@ -248,8 +250,6 @@ def get_independent_frequency_estimates(values:np.ndarray, CI_method:str='poisso exact_poisson: error calculated from the asymmetric exact poisson interval :returns exp, experr: expected frequencies, error on the expected frequencies """ - if not isinstance(values, np.ndarray): - raise TypeError('values is not a numpy array.') # Initialize exp = np.zeros(values.shape) @@ -285,39 +285,36 @@ def get_uncertainty(x:float, CI_method:str='poisson') -> float: exact poisson interval (exact_poisson). https://www.ncbi.nlm.nih.gov/pubmed/2296988 #FIXME: check ref - :param float x: value + :param float x: value, must be equal or greater than zero :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error.\ exact_poisson: error calculated from the asymmetric exact poisson interval - :return xerr: the uncertainty on x (1 sigma) - """ - assert CI_method in ['exact_poisson', 'poisson'], 'CI method %s not valid' % CI_method - assert x>=0, 'x must be equal or greater than zero' + :return x_err: the uncertainty on x (1 sigma) + """ - if CI_method=='exact_poisson': + if CI_method == 'exact_poisson': xerr = get_exact_poisson_uncertainty(x) - if CI_method=='poisson': + elif CI_method == 'poisson': xerr = get_poisson_uncertainty(x) + else: + raise NotImplementedError('CI method {} not valid'.format(CI_method)) return xerr -def get_poisson_uncertainty(x:float) -> float: +def get_poisson_uncertainty(x: float) -> float: """ - Calculate the uncerainty on x using standard poisson error. In case x=0 the error=1 is assigned. + Calculate the uncertainty on x using standard poisson error. In case x=0 the error=1 is assigned. :param float x: value - :return xerr: the uncertainty on x (1 sigma) + :return x_err: the uncertainty on x (1 sigma) :rtype: float """ - err = np.sqrt(x) if x>=1 else 1.0 - return err + return np.sqrt(x) if x >= 1 else 1.0 -def get_exact_poisson_uncertainty(x:float, nsigmas:float=1) -> float: +def get_exact_poisson_uncertainty(x:float, nsigmas: float=1) -> float: """ - Calculate the uncerainty on x using an exact poisson confidence interval. - - Calculate the uncerainty on x using an exact poisson confidence interval. The width of the confidence interval can + Calculate the uncertainty on x using an exact poisson confidence interval. The width of the confidence interval can be specified using the number of sigmas. The default number of sigmas is set to 1, resulting in an error that is approximated by the standard poisson error sqrt(x). @@ -327,7 +324,7 @@ def get_exact_poisson_uncertainty(x:float, nsigmas:float=1) -> float: https://www.ncbi.nlm.nih.gov/pubmed/2296988 :param float x: value - :return xerr: the uncertainty on x (1 sigma) + :return x_err: the uncertainty on x (1 sigma) :rtype: float """ # see formula at: @@ -335,34 +332,26 @@ def get_exact_poisson_uncertainty(x:float, nsigmas:float=1) -> float: pl = stats.norm.cdf(-1*nsigmas, loc=0, scale=1) pu = stats.norm.cdf(1*nsigmas, loc=0, scale=1) - lb = stats.chi2.ppf(pl, 2*x)/2 if x!= 0 else 0 - ub = stats.chi2.ppf(pu, 2*(x+1))/2 + lb = stats.chi2.ppf(pl, 2*x)/2 if x != 0 else 0 + ub = stats.chi2.ppf(pu, 2*(x + 1)) / 2 # average err is almost equal to sqrt(x)+0.5 - err = (ub-lb)/2 - - return err + return (ub - lb) / 2 -def get_outlier_significances(obs:np.ndarray, exp:np.ndarray, experr:np.ndarray) -> Union[np.ndarray, np.ndarray]: +def get_outlier_significances(obs:np.ndarray, exp:np.ndarray, experr:np.ndarray) -> Tuple[np.ndarray, np.ndarray]: """ Evaluation of significance of observation - Evaluation of the significance of the difference between the observed number of occurences and the expected number of - occurences, taking into account the uncertainty on the expectednumber of occurences. When the uncertainty is - not zero, the Linnemann method is used to calculate the pvalues. + Evaluation of the significance of the difference between the observed number of occurrences and the expected number of + occurrences, taking into account the uncertainty on the expected number of occurrences. When the uncertainty is + not zero, the Linnemann method is used to calculate the p-values. :param obs: observed numbers :param exp: expected numbers :param experr: uncertainty on the expected numbers :returns: pvalues, zvalues """ - if not isinstance(obs, np.ndarray): - raise TypeError('obs is not a numpy array.') - if not isinstance(exp, np.ndarray): - raise TypeError('exp is not a numpy array.') - if not isinstance(experr, np.ndarray): - raise TypeError('experr is not a numpy array.') pvalues = np.zeros(obs.shape) zvalues = np.zeros(obs.shape) @@ -374,17 +363,15 @@ def get_outlier_significances(obs:np.ndarray, exp:np.ndarray, experr:np.ndarray) return pvalues, zvalues -def outlier_significance_matrix_from_hist2d(data:np.ndarray, CI_method:str='poisson') -> Union[np.ndarray, np.ndarray]: +def outlier_significance_matrix_from_hist2d(data:np.ndarray, CI_method:str='poisson') -> Tuple[np.ndarray, np.ndarray]: """ Calculate the significance matrix of excesses or deficits in a contingency table :param data: numpy array contingency table - :param string CI_method: method to be used for undertainty calculation. poisson: normal poisson error.\ + :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error.\ exact_poisson: error calculated from the asymmetric exact poisson interval - :return: pvalue matrix, outlier significance matrix + :return: p-value matrix, outlier significance matrix """ - if not isinstance(data, np.ndarray): - raise TypeError('data is not a numpy array.') # get expected values exp, experr = get_independent_frequency_estimates(data, CI_method=CI_method) @@ -398,10 +385,10 @@ def outlier_significance_matrix_from_rebinned_df(data_binned:pd.DataFrame, binni """ Calculate the significance matrix of excesses or deficits - :param data_binned: input data. Dataframe must contain exactly two columns + :param data_binned: input data. DataFrame must contain exactly two columns :param dict binning_dict: dictionary with bin edges for each binned interval variable. When no bin_edges are\ provided values are used as bin label. Otherwise, bin labels are constructed based on provided bin edge information. - :param string CI_method: method to be used for undertainty calculation. poisson: normal poisson error. \ + :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error. \ exact_poisson: error calculated from the asymmetric exact poisson interval :param ndecimals: number of decimals to use in labels of binned interval variables to specify bin edges (default=1) :param bool dropna: remove NaN values with True @@ -411,21 +398,9 @@ def outlier_significance_matrix_from_rebinned_df(data_binned:pd.DataFrame, binni a numeric variable) :return: outlier significance matrix (pd.DataFrame) """ - if not isinstance(data_binned, pd.DataFrame): - raise TypeError('data_binned is not a pandas DataFrame.') - assert len(data_binned.columns) == 2, 'data DataFrame should contain only two columns' - if not dropna: - data_binned = data_binned.fillna(defs.NaN).copy() - if drop_underflow: - data_binned = data_binned.replace(defs.UF, np.nan).copy() - if drop_overflow: - data_binned = data_binned.replace(defs.OF, np.nan).copy() - - # create a contingency table c0, c1 = data_binned.columns - df_datahist = data_binned.groupby([c0, c1])[c0].count().to_frame().unstack().fillna(0) - df_datahist.columns = df_datahist.columns.droplevel() + df_datahist = hist2d_from_rebinned_df(data_binned, dropna, drop_underflow, drop_overflow) if 1 in df_datahist.shape or 0 in df_datahist.shape: warnings.warn('Too few unique values for variable {0:s} ({1:d}) or {2:s} ({3:d}) to calculate outlier ' @@ -433,34 +408,19 @@ def outlier_significance_matrix_from_rebinned_df(data_binned:pd.DataFrame, binni .format(c0, df_datahist.shape[0], c1, df_datahist.shape[1])) return np.nan - if c0 in binning_dict.keys(): - - # check for missing bins. This can occur due to NaN values for variable c1 in which case rows are dropped - orig_vals = data_binned[~data_binned[c0].isin([defs.UF, defs.OF, defs.NaN])][c0].value_counts().sort_index().index - missing = list(set(orig_vals) - set(df_datahist.index)) - imissing = [] - for v in missing: - imissing.append(np.where(orig_vals == v)[0][0]) - - index_vals = ['{1:.{0}f}_{2:.{0}f}'.format(ndecimals, binning_dict[c0][i][0], binning_dict[c0][i][1]) - for i in range(len(binning_dict[c0])) if not i in imissing] - index_vals = index_vals + list(df_datahist.index[len(index_vals):]) # to deal with UF and OF - df_datahist.index = index_vals + for c, a in [(c0, 'index'), (c1, 'columns')]: + if c in binning_dict.keys(): + # check for missing bins. This can occur due to NaN values for variable c1 in which case rows are dropped + orig_vals = data_binned[~data_binned[c].isin([defs.UF, defs.OF, defs.NaN])][c].value_counts().sort_index().index + missing = list(set(orig_vals) - set(getattr(df_datahist, a))) + imissing = [] + for v in missing: + imissing.append(np.where(orig_vals == v)[0][0]) - - if c1 in binning_dict.keys(): - - # check for missing bins. This can occur due to NaN values for variable c0 in which case rows are dropped - orig_vals = data_binned[~data_binned[c1].isin([defs.UF, defs.OF, defs.NaN])][c1].value_counts().sort_index().index - missing = list(set(orig_vals) - set(df_datahist.columns)) - imissing = [] - for v in missing: - imissing.append(np.where(orig_vals == v)[0][0]) - - col_vals = ['{1:.{0}f}_{2:.{0}f}'.format(ndecimals, binning_dict[c1][i][0], binning_dict[c1][i][1]) - for i in range(len(binning_dict[c1])) if not i in imissing] - col_vals = col_vals + list(df_datahist.columns[len(col_vals):]) # to deal with UF and OF - df_datahist.columns = col_vals + vals = ['{1:.{0}f}_{2:.{0}f}'.format(ndecimals, binning_dict[c][i][0], binning_dict[c][i][1]) + for i in range(len(binning_dict[c])) if not i in imissing] + vals += list(getattr(df_datahist, a)[len(vals):]) # to deal with UF and OF + setattr(df_datahist, a, vals) pvalues, zvalues = outlier_significance_matrix_from_hist2d(df_datahist.values, CI_method=CI_method) outlier_overview = pd.DataFrame(zvalues, index=df_datahist.index, columns=df_datahist.columns) @@ -468,15 +428,15 @@ def outlier_significance_matrix_from_rebinned_df(data_binned:pd.DataFrame, binni return outlier_overview -def outlier_significance_matrix(df:pd.DataFrame, interval_cols:list=None, CI_method:str='poisson', ndecimals:int=1, - bins=10, quantile:bool=False, - dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True, retbins:bool=False): +def outlier_significance_matrix(df: pd.DataFrame, interval_cols:Optional[list]=None, CI_method:str='poisson', + ndecimals:int=1, bins=10, quantile:bool=False, dropna:bool=True, + drop_underflow:bool=True, drop_overflow:bool=True, retbins:bool=False): """ Calculate the significance matrix of excesses or deficits - :param df: input data. Dataframe must contain exactly two columns + :param df: input data. DataFrame must contain exactly two columns :param interval_cols: columns with interval variables which need to be binned - :param string CI_method: method to be used for undertainty calculation. poisson: normal poisson error.\ + :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error.\ exact_poisson: error calculated from the asymmetric exact poisson interval :param bins: number of bins, or a list of bin edges (same for all columns), or a dictionary where per column the bins are specified. (default=10)\ E.g.: bins = {'mileage':5, 'driver_age':[18,25,35,45,55,65,125]} @@ -490,76 +450,71 @@ def outlier_significance_matrix(df:pd.DataFrame, interval_cols:list=None, CI_met :param bool retbins: if true, function also returns dict with bin_edges of rebinned variables. :return: outlier significance matrix (pd.DataFrame) """ - if not isinstance(df, pd.DataFrame): - raise TypeError('df is not a pandas DataFrame.') - assert len(df.columns) == 2, 'df should contain only two columns' - if isinstance( interval_cols, type(None) ): - interval_cols = df.select_dtypes(include=[np.number]).columns.tolist() - if interval_cols: - print('interval_cols not set, guessing: {0:s}'.format(str(interval_cols))) - assert isinstance( interval_cols, list ), 'interval_cols is not a list.' + if len(df.columns) != 2: + raise ValueError('df should contain only two columns') + + if interval_cols is None: + interval_cols = guess_interval_cols(df) df_clean, interval_cols_clean = dq_check_nunique_values(df, interval_cols, dropna=dropna) data_binned, binning_dict = bin_data(df_clean, interval_cols_clean, retbins=True, bins=bins, quantile=quantile) - os_matrix = outlier_significance_matrix_from_rebinned_df(data_binned, binning_dict, CI_method=CI_method, - ndecimals=ndecimals, dropna=dropna, - drop_underflow=drop_underflow, drop_overflow=drop_overflow) + os_matrix = outlier_significance_matrix_from_rebinned_df( + data_binned, binning_dict, CI_method=CI_method, ndecimals=ndecimals, + dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) + if retbins: return os_matrix, binning_dict return os_matrix -def outlier_significance_matrices_from_rebinned_df(data_binned, binning_dict={}, CI_method='poisson', ndecimals=1, - combinations=[], dropna=True, drop_underflow=True, +def outlier_significance_matrices_from_rebinned_df(data_binned: pd.DataFrame, binning_dict=None, CI_method='poisson', + ndecimals=1, combinations:Union[list, tuple]=(), dropna=True, drop_underflow=True, drop_overflow=True): """ Calculate the significance matrix of excesses or deficits for all possible combinations of variables, or for those combinations specified using combinations. This functions could also be used instead of outlier_significance_matrices in case all variables are either categorical or ordinal, so no binning is required. - :param data_binned: input data. Interval variables need to be binned. Dataframe must contain exactly two columns + :param data_binned: input data. Interval variables need to be binned. DataFrame must contain exactly two columns :param dict binning_dict: dictionary with bin edges for each binned interval variable. When no bin_edges are\ provided values are used as bin label. Otherwise, bin labels are constructed based on provided bin edge information. :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error.\ exact_poisson: error calculated from the asymmetric exact poisson interval - :param bins: specify the binning, either by proving the number of bins, a list of bin edges, or a dictionary with\ - bin specifications per variable. (default=10) :param ndecimals: number of decimals to use in labels of binned interval variables to specify bin edges (default=1) - :param bool quantile: when the number of bins is specified, use uniform binning (False) or quantile binning (True) :param combinations: in case you do not want to calculate an outlier significance matrix for all permutations of\ the available variables, you can specify a list of the required permutations here, in the format\ [(var1, var2), (var2, var4), etc] :param bool dropna: remove NaN values with True :param bool drop_underflow: do not take into account records in underflow bin when True (relevant when binning\ - a numeric variable) +a numeric variable) :param bool drop_overflow: do not take into account records in overflow bin when True (relevant when binning\ a numeric variable) :return: dictionary with outlier significance matrices (pd.DataFrame) """ - if not isinstance(data_binned, pd.DataFrame): - raise TypeError('data_binned is not a pandas DataFrame.') + if binning_dict is None: + binning_dict = {} # create a list of all possible combinations of variables, in case no selection of combinations is specified if not combinations: combinations = itertools.combinations(data_binned.columns, 2) - outliers_overview = {} - for i, comb in enumerate(combinations): - c0, c1 = comb - zvalues_overview = outlier_significance_matrix_from_rebinned_df(data_binned[[c0, c1]], binning_dict, - CI_method=CI_method, ndecimals=ndecimals, - dropna=dropna, drop_underflow=drop_underflow, - drop_overflow=drop_overflow) - outliers_overview[':'.join(comb)] = zvalues_overview + outliers_overview = [] + for i, (c0, c1) in enumerate(combinations): + zvalues_overview = outlier_significance_matrix_from_rebinned_df( + data_binned[[c0, c1]].copy(), binning_dict, CI_method=CI_method, ndecimals=ndecimals, + dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) + outliers_overview.append((c0, c1, zvalues_overview)) return outliers_overview -def outlier_significance_matrices(df:pd.DataFrame, interval_cols:list=None, CI_method:str='poisson', ndecimals:int=1, bins=10, - quantile:bool=False, combinations:list=[], +def outlier_significance_matrices(df: pd.DataFrame, interval_cols:Optional[list]=None, CI_method:str='poisson', ndecimals:int=1, + bins=10, quantile:bool=False, combinations:Union[list, tuple]=(), dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True, retbins:bool=False): """ Calculate the significance matrix of excesses or deficits for all possible combinations of variables, or for @@ -567,7 +522,7 @@ def outlier_significance_matrices(df:pd.DataFrame, interval_cols:list=None, CI_m :param df: input data :param interval_cols: columns with interval variables which need to be binned - :param string CI_method: method to be used for undertainty calculation. poisson: normal poisson error. \ + :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error. \ exact_poisson: error calculated from the asymmetric exact poisson interval :param ndecimals: number of decimals to use in labels of binned interval variables to specify bin edges (default=1) :param bins: number of bins, or a list of bin edges (same for all columns), or a dictionary where per column the bins are specified. (default=10)\ @@ -584,30 +539,31 @@ def outlier_significance_matrices(df:pd.DataFrame, interval_cols:list=None, CI_m :param bool retbins: if true, function also returns dict with bin_edges of rebinned variables. :return: dictionary with outlier significance matrices (pd.DataFrame) """ - if not isinstance(df, pd.DataFrame): - raise TypeError('df is not a pandas DataFrame.') - if isinstance(interval_cols, type(None)): - interval_cols = df.select_dtypes(include=[np.number]).columns.tolist() - if interval_cols: - print('interval_cols not set, guessing: {0:s}'.format(str(interval_cols))) - assert isinstance(interval_cols, list), 'interval_cols is not a list.' + if interval_cols is None: + interval_cols = guess_interval_cols(df) df_clean, interval_cols_clean = dq_check_nunique_values(df, interval_cols, dropna=dropna) data_binned, binning_dict = bin_data(df_clean, interval_cols_clean, retbins=True, bins=bins, quantile=quantile) - os_matrices = outlier_significance_matrices_from_rebinned_df(data_binned, binning_dict, CI_method, ndecimals, - combinations=combinations, dropna=dropna, - drop_underflow=drop_underflow, - drop_overflow=drop_overflow) + os_matrices = outlier_significance_matrices_from_rebinned_df( + data_binned, binning_dict, CI_method, ndecimals, combinations=combinations, dropna=dropna, + drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) + + # Convert to dict + os_matrices = {":".join([c0, c1]): v for c0, c1, v in os_matrices} + if retbins: return os_matrices, binning_dict return os_matrices -def outlier_significance_from_array(x, y, num_vars:list=None, bins=10, quantile:bool=False, ndecimals:int=1, CI_method:str='poisson', - dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: +def outlier_significance_from_array(x: Union[np.ndarray, list, pd.Series], y: Union[np.ndarray, list, pd.Series], + num_vars:list=None, bins:Union[int,list,np.ndarray,dict]=10, quantile:bool=False, + ndecimals:int=1, CI_method:str='poisson', dropna:bool=True, + drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: """ Calculate the significance matrix of excesses or deficits of input x and input y. x and y can contain interval, \ ordinal or categorical data. Use the num_vars variable to indicate whether x and/or y contain interval data. @@ -619,7 +575,7 @@ def outlier_significance_from_array(x, y, num_vars:list=None, bins=10, quantile: E.g.: bins = {'mileage':5, 'driver_age':[18,25,35,45,55,65,125]} :param bool quantile: when the number of bins is specified, use uniform binning (False) or quantile binning (True) :param ndecimals: number of decimals to use in labels of binned interval variables to specify bin edges (default=1) - :param string CI_method: method to be used for undertainty calculation. poisson: normal poisson error. \ + :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error. \ exact_poisson: error calculated from the asymmetric exact poisson interval :param bool dropna: remove NaN values with True :param bool drop_underflow: do not take into account records in underflow bin when True (relevant when binning a \ @@ -628,27 +584,20 @@ def outlier_significance_from_array(x, y, num_vars:list=None, bins=10, quantile: (relevant when binning a numeric variable) :return: outlier significance matrix (pd.DataFrame) """ - if not isinstance(x, (np.ndarray, list, pd.Series)): - raise TypeError('x is not array like.') - if not isinstance(y, (np.ndarray, list, pd.Series)): - raise TypeError('y is not array like.') - if not isinstance(bins, (int,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') - df = pd.DataFrame(np.array([x, y]).T, columns=['x', 'y']) + df = array_like_to_dataframe(x, y) - if isinstance( num_vars, type(None) ): - num_vars = df.select_dtypes(include=[np.number]).columns.tolist() - if num_vars: - print('num_vars not set, guessing: {0:s}'.format(str(num_vars))) - assert isinstance( num_vars, list ), 'num_vars is not a list.' + if num_vars is None: + num_vars = guess_interval_cols(df) - return outlier_significance_matrix(df, interval_cols=num_vars, bins=bins, quantile=quantile, ndecimals=ndecimals, - CI_method=CI_method, - dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow) + return outlier_significance_matrix( + df, interval_cols=num_vars, bins=bins, quantile=quantile, ndecimals=ndecimals, + CI_method=CI_method, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) -def outlier_significance_from_binned_array(x, y, CI_method:str='poisson', dropna:bool=True, drop_underflow:bool=True, +def outlier_significance_from_binned_array(x: Union[np.ndarray, list, pd.Series], y: Union[np.ndarray, list, pd.Series], + CI_method: str='poisson', dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: """ @@ -657,7 +606,7 @@ def outlier_significance_from_binned_array(x, y, CI_method:str='poisson', dropna :param list x: array-like input :param list y: array-like input - :param string CI_method: method to be used for undertainty calculation. poisson: normal poisson error. \ + :param string CI_method: method to be used for uncertainty calculation. poisson: normal poisson error. \ exact_poisson: error calculated from the asymmetric exact poisson interval :param bool dropna: remove NaN values with True :param bool drop_underflow: do not take into account records in underflow bin when True (relevant when binning \ @@ -666,11 +615,9 @@ def outlier_significance_from_binned_array(x, y, CI_method:str='poisson', dropna a numeric variable) :return: outlier significance matrix (pd.DataFrame) """ - if not isinstance(x, (np.ndarray, list, pd.Series)): - raise TypeError('x is not array like.') - if not isinstance(y, (np.ndarray, list, pd.Series)): - raise TypeError('y is not array like.') - df = pd.DataFrame(np.array([x, y]).T, columns=['x', 'y']) - return outlier_significance_matrix(df, CI_method=CI_method, dropna=dropna, drop_underflow=drop_underflow, - drop_overflow=drop_overflow) + df = array_like_to_dataframe(x, y) + + return outlier_significance_matrix( + df, CI_method=CI_method, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) diff --git a/python/phik/phik.py b/python/phik/phik.py index 501f409..8c8f87f 100644 --- a/python/phik/phik.py +++ b/python/phik/phik.py @@ -12,6 +12,7 @@ modification, are permitted according to the terms listed in the file LICENSE. """ +from typing import Tuple, Union, Optional import numpy as np import itertools @@ -24,10 +25,11 @@ from .statistics import get_chi2_using_dependent_frequency_estimates, estimate_simple_ndof from .binning import create_correlation_overview_table, bin_data from .data_quality import dq_check_nunique_values, dq_check_hist2d +from .utils import array_like_to_dataframe, guess_interval_cols -def spark_phik_matrix_from_hist2d_dict(spark_context, hist_dict): - """Correlation matrix of bivariate gaussian using spark parallellization over variable-pair 2d histograms +def spark_phik_matrix_from_hist2d_dict(spark_context, hist_dict: dict): + """Correlation matrix of bivariate gaussian using spark parallelization over variable-pair 2d histograms See spark notebook phik_tutorial_spark.ipynb as example. @@ -39,41 +41,35 @@ def spark_phik_matrix_from_hist2d_dict(spark_context, hist_dict): :param hist_dict: dict of 2d numpy grids with value-counts. keys are histogram names. :return: phik correlation matrix """ - if not isinstance(hist_dict, dict): - raise TypeError('hist_dict should be a dictionary') + for k, v in hist_dict.items(): if not isinstance(v, np.ndarray): raise TypeError('hist_dict should be a dictionary of 2d numpy arrays.') + hist_list = list(hist_dict.items()) hist_rdd = spark_context.parallelize(hist_list) phik_rdd = hist_rdd.map(_phik_from_row) phik_list = phik_rdd.collect() - phik_overview = create_correlation_overview_table(dict(phik_list)) + phik_overview = create_correlation_overview_table(phik_list) return phik_overview -def _phik_from_row(row): +def _phik_from_row(row: Tuple[str, np.ndarray]) -> Tuple[str, str, float]: """Helper function for spark parallel processing :param row: rdd row, where row[0] is key and rdd[1] :return: union of key, phik-value """ - if not len(row) >= 2: - raise RuntimeError('row should have at least two elements.') - key = row[0] - grid = row[1] - if not isinstance(key, str): - raise TypeError('key is not a string.') - if not isinstance(grid, np.ndarray): - raise TypeError('grid is not a numpy array.') + + key, grid = row c = key.split(':') if len(c) == 2 and c[0] == c[1]: - return key, 1.0 + return c[0], c[1], 1.0 try: phik_value = phik_from_hist2d(grid) - except: - phik_value = None - return key, phik_value + except TypeError: + phik_value = np.nan + return c[0], c[1], phik_value def phik_from_hist2d(observed:np.ndarray, noise_correction:bool=True) -> float: @@ -89,9 +85,7 @@ def phik_from_hist2d(observed:np.ndarray, noise_correction:bool=True) -> float: :param observed: 2d-array observed values :param bool noise_correction: apply noise correction in phik calculation :returns float: correlation coefficient phik - """ - if not isinstance(observed, np.ndarray): - raise TypeError('observed is not a numpy array.') + """ # chi2 contingency test chi2 = get_chi2_using_dependent_frequency_estimates(observed, lambda_='pearson') @@ -103,12 +97,10 @@ def phik_from_hist2d(observed:np.ndarray, noise_correction:bool=True) -> float: pedestal = 0 # phik calculation adds noise pedestal to theoretical chi2 - phik = phik_from_chi2(chi2, observed.sum(), *observed.shape, None, None, pedestal) - - return phik + return phik_from_chi2(chi2, observed.sum(), *observed.shape, pedestal=pedestal) -def phik_from_rebinned_df(data_binned:pd.DataFrame, noise_correction:bool=True, dropna:bool=True, +def phik_from_rebinned_df(data_binned: pd.DataFrame, noise_correction:bool=True, dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: """ Correlation matrix of bivariate gaussian derived from chi2-value @@ -128,8 +120,6 @@ def phik_from_rebinned_df(data_binned:pd.DataFrame, noise_correction:bool=True, a numeric variable) :return: phik correlation matrix """ - if not isinstance(data_binned, pd.DataFrame): - raise TypeError('data_binned is not a pandas DataFrame.') if not dropna: # if not dropna replace the NaN values with the string NaN. Otherwise the rows with NaN are dropped @@ -140,17 +130,33 @@ def phik_from_rebinned_df(data_binned:pd.DataFrame, noise_correction:bool=True, if drop_overflow: data_binned.replace(defs.OF, np.nan, inplace=True) - # phik_list = [_calc_phik(co, data_binned[list(co)], noise_correction) - # for co in itertools.combinations_with_replacement(data_binned.columns.values, 2)] + # cache column order (https://github.com/KaveIO/PhiK/issues/1) + column_order = data_binned.columns + if NCORES == 1: + # Useful when for instance using cProfiler: https://docs.python.org/3/library/profile.html + phik_list = [ + _calc_phik(co, data_binned[list(co)], noise_correction) + for co in itertools.combinations_with_replacement(data_binned.columns.values, 2) + ] + else: + phik_list = Parallel(n_jobs=NCORES)( + delayed(_calc_phik)(co, data_binned[list(co)], noise_correction) + for co in itertools.combinations_with_replacement(data_binned.columns.values, 2) + ) + + if len(phik_list) == 0: + return pd.DataFrame(np.nan, index=column_order, columns=column_order) + + phik_overview = create_correlation_overview_table(phik_list) + + # restore column order + phik_overview = phik_overview.reindex(columns=column_order) + phik_overview = phik_overview.reindex(index=column_order) - phik_list = Parallel(n_jobs=NCORES)(delayed(_calc_phik)(co, data_binned[list(co)], noise_correction) - for co in itertools.combinations_with_replacement(data_binned.columns.values, 2)) - - phik_overview = create_correlation_overview_table(dict(phik_list)) return phik_overview -def _calc_phik(comb, data_binned, noise_correction): +def _calc_phik(comb: tuple, data_binned: pd.DataFrame, noise_correction: bool) -> Tuple[str, str, float]: """Split off calculation of phik for parallel processing :param tuple comb: union of two string columns @@ -159,24 +165,23 @@ def _calc_phik(comb, data_binned, noise_correction): :return: """ c0, c1 = comb - combi = ':'.join(comb) if c0 == c1: - return (combi, 1.0) + return c0, c1, 1.0 datahist = data_binned.groupby([c0, c1])[c0].count().to_frame().unstack().fillna(0) # If 0 or only 1 values for one of the two variables, it is not possible to calculate phik. # This check needs to be done after creation of OF, UF and NaN bins. if any([v in datahist.shape for v in [0, 1]]): - return (combi, np.nan) + return c0, c1, np.nan datahist.columns = datahist.columns.droplevel() phikvalue = phik_from_hist2d(datahist.values, noise_correction=noise_correction) - return (combi, phikvalue) + return c0, c1, phikvalue -def phik_matrix(df:pd.DataFrame, interval_cols:list=None, bins=10, quantile:bool=False, noise_correction:bool=True, - dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: +def phik_matrix(df:pd.DataFrame, interval_cols:Optional[list]=None, bins:Union[int,list,np.ndarray,dict]=10, quantile:bool=False, + noise_correction:bool=True, dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: """ Correlation matrix of bivariate gaussian derived from chi2-value @@ -199,26 +204,21 @@ def phik_matrix(df:pd.DataFrame, interval_cols:list=None, bins=10, quantile:bool a numeric variable) :return: phik correlation matrix """ - if not isinstance(df, pd.DataFrame): - raise TypeError('df is not a pandas DataFrame.') - if not isinstance(bins, (int,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') - if isinstance( interval_cols, type(None) ): - interval_cols = df.select_dtypes(include=[np.number]).columns.tolist() - if interval_cols: - print('interval_cols not set, guessing: {0:s}'.format(str(interval_cols))) - assert isinstance( interval_cols, list ), 'interval_cols is not a list.' + if interval_cols is None: + interval_cols = guess_interval_cols(df) df_clean, interval_cols_clean = dq_check_nunique_values(df, interval_cols, dropna=dropna) data_binned, binning_dict = bin_data(df_clean, cols=interval_cols_clean, bins=bins, quantile=quantile, retbins=True) - return phik_from_rebinned_df(data_binned, noise_correction, dropna=dropna, drop_underflow=drop_underflow, - drop_overflow=drop_overflow) + + return phik_from_rebinned_df( + data_binned, noise_correction, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) -def global_phik_from_rebinned_df(data_binned:pd.DataFrame, noise_correction:bool=True, - dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: +def global_phik_from_rebinned_df(data_binned:pd.DataFrame, noise_correction:bool=True, dropna:bool=True, + drop_underflow:bool=True, drop_overflow:bool=True) -> Tuple[np.ndarray, np.ndarray]: """ Global correlation values of bivariate gaussian derived from chi2-value from rebinned df @@ -237,20 +237,19 @@ def global_phik_from_rebinned_df(data_binned:pd.DataFrame, noise_correction:bool a numeric variable) :return: global correlations array """ - if not isinstance(data_binned, pd.DataFrame): - raise TypeError('data_binned is not a pandas DataFrame.') - phik_overview = phik_from_rebinned_df(data_binned, noise_correction, dropna=dropna, drop_underflow=drop_underflow, \ - drop_overflow=drop_overflow) + phik_overview = phik_from_rebinned_df( + data_binned, noise_correction, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) from numpy.linalg import inv V = phik_overview.values Vinv = inv(V) - global_correlations = np.array([[np.sqrt(1 - 1/(V[i][i] * Vinv[i][i]))] for i in range(V.shape[0]) ]) + global_correlations = np.array([[np.sqrt(1 - 1/(V[i][i] * Vinv[i][i]))] for i in range(V.shape[0])]) return global_correlations, phik_overview.index.values -def global_phik_array(df:pd.DataFrame, interval_cols:list=None, bins=10, quantile:bool=False, noise_correction:bool=True, - dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: +def global_phik_array(df:pd.DataFrame, interval_cols:list=None, bins:Union[int,list,np.ndarray,dict]=10, quantile:bool=False, + noise_correction:bool=True, dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> Tuple[np.ndarray, np.ndarray]: """ Global correlation values of bivariate gaussian derived from chi2-value @@ -273,25 +272,21 @@ def global_phik_array(df:pd.DataFrame, interval_cols:list=None, bins=10, quantil a numeric variable) :return: global correlations array """ - if not isinstance(df, pd.DataFrame): - raise TypeError('df is not a pandas DataFrame.') - if not isinstance(bins, (int,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') - if isinstance( interval_cols, type(None) ): - interval_cols = df.select_dtypes(include=[np.number]).columns.tolist() - if interval_cols: - print('interval_cols not set, guessing: {0:s}'.format(str(interval_cols))) - assert isinstance( interval_cols, list ), 'interval_cols is not a list.' + if interval_cols is None: + interval_cols = guess_interval_cols(df) df_clean, interval_cols_clean = dq_check_nunique_values(df, interval_cols, dropna=dropna) data_binned, binning_dict = bin_data(df_clean, cols=interval_cols_clean, bins=bins, quantile=quantile, retbins=True) - return global_phik_from_rebinned_df(data_binned, noise_correction=noise_correction, dropna=dropna, \ - drop_underflow=drop_underflow, drop_overflow=drop_overflow) + return global_phik_from_rebinned_df( + data_binned, noise_correction=noise_correction, dropna=dropna, drop_underflow=drop_underflow, + drop_overflow=drop_overflow + ) -def phik_from_array(x, y, num_vars:list=[], bins=10, quantile:bool=False, noise_correction:bool=True, dropna:bool=True, +def phik_from_array(x: Union[np.ndarray, pd.Series], y: Union[np.ndarray, pd.Series], num_vars: Union[str, list]=None, + bins:Union[int, dict, list, np.ndarray]=10, quantile:bool=False, noise_correction:bool=True, dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> float: """ Correlation matrix of bivariate gaussian derived from chi2-value @@ -316,25 +311,21 @@ def phik_from_array(x, y, num_vars:list=[], bins=10, quantile:bool=False, noise_ a numeric variable) :return: phik correlation coefficient """ - if not isinstance(x, (np.ndarray, pd.Series)): - raise TypeError('x is not array like.') - if not isinstance(y, (np.ndarray, pd.Series)): - raise TypeError('y is not array like.') - if not isinstance(bins, (int,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') - - if isinstance(num_vars, str): + if num_vars is None: + num_vars = [] + elif isinstance(num_vars, str): num_vars = [num_vars] if len(num_vars) > 0: - df = pd.DataFrame(np.array([x, y]).T, columns=['x', 'y']) + df = array_like_to_dataframe(x, y) x, y = bin_data(df, num_vars, bins=bins, quantile=quantile).T.values - return phik_from_binned_array(x, y, noise_correction=noise_correction, dropna=dropna, - drop_underflow=drop_underflow, drop_overflow=drop_overflow) + return phik_from_binned_array( + x, y, noise_correction=noise_correction, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) -def phik_from_binned_array(x, y, noise_correction:bool=True, dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> float: +def phik_from_binned_array(x: Union[np.ndarray, pd.Series], y: Union[np.ndarray, pd.Series], noise_correction:bool=True, dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> float: """ Correlation matrix of bivariate gaussian derived from chi2-value @@ -354,10 +345,6 @@ def phik_from_binned_array(x, y, noise_correction:bool=True, dropna:bool=True, d a numeric variable) :return: phik correlation coefficient """ - if not isinstance(x, (np.ndarray, pd.Series)): - raise TypeError('x is not array like.') - if not isinstance(y, (np.ndarray, pd.Series)): - raise TypeError('y is not array like.') if not dropna: x = pd.Series(x).fillna(defs.NaN).astype(str).values @@ -366,12 +353,12 @@ def phik_from_binned_array(x, y, noise_correction:bool=True, dropna:bool=True, d if drop_underflow or drop_overflow: x = x.copy() y = y.copy() - if drop_underflow: - x[np.where(x == defs.UF)] = np.nan - x[np.where(x == defs.OF)] = np.nan - if drop_overflow: - y[np.where(y == defs.UF)] = np.nan - y[np.where(y == defs.OF)] = np.nan + if drop_underflow: + x[np.where(x == defs.UF)] = np.nan + y[np.where(y == defs.UF)] = np.nan + if drop_overflow: + y[np.where(y == defs.OF)] = np.nan + x[np.where(x == defs.OF)] = np.nan hist2d = pd.crosstab(x, y).values diff --git a/python/phik/report.py b/python/phik/report.py index 6b7c525..1b77727 100644 --- a/python/phik/report.py +++ b/python/phik/report.py @@ -12,7 +12,7 @@ modification, are permitted according to the terms listed in the file LICENSE. """ -from typing import Union +from typing import Tuple, Union, Callable, Dict import os import itertools @@ -28,10 +28,11 @@ from .significance import significance_from_rebinned_df from .outliers import outlier_significance_matrix_from_rebinned_df from .data_quality import dq_check_nunique_values +from .utils import guess_interval_cols -def plot_hist_and_func(data, func, funcparams, xbins=False, labels=['',''], xlabel='', ylabel='', title='', - xlimit=None, alpha=1): +def plot_hist_and_func(data: Union[list, np.ndarray, pd.Series], func: Callable, funcparams, xbins=False, labels=None, + xlabel='', ylabel='', title='', xlimit=None, alpha=1): """ Create a histogram of the provided data and overlay with a function. @@ -47,8 +48,8 @@ def plot_hist_and_func(data, func, funcparams, xbins=False, labels=['',''], xlab :param alpha: alpha histogram :return: """ - if not isinstance(data, (list, np.ndarray, pd.Series)): - raise TypeError('data is not array like.') + if labels is None: + labels = ['', ''] # If binning is not specified, create binning here if not np.any(xbins) and not xlimit: @@ -147,7 +148,7 @@ def tick(lab): return lab # reduce default fontsizes in case too many labels? - nlabs = max(len(y_labels), len(x_labels)) + # nlabs = max(len(y_labels), len(x_labels)) # axis ticks and tick labels if len(x_labels) == matrix_colors.shape[1] + 1: @@ -221,7 +222,7 @@ def correlation_report(data:pd.DataFrame, interval_cols:list=None, bins=10, quan noise_correction:bool=True, store_each_plot:bool=False, lambda_significance:str="log-likelihood", simulation_method:str='multinominal', nsim_chi2:int=1000, - significance_method:str='asymptotic', CI_method:str='poisson') -> Union[pd.DataFrame, pd.DataFrame, dict, dict]: + significance_method:str='asymptotic', CI_method:str='poisson') -> Tuple[pd.DataFrame, pd.DataFrame, Dict[str, pd.DataFrame], Dict[str, str]]: """ Create a correlation report for the given dataset. @@ -254,16 +255,11 @@ def correlation_report(data:pd.DataFrame, interval_cols:list=None, bins=10, quan :returns: phik_matrix (pd.DataFrame), global_phik (np.array), significance_matrix (pd.DataFrame), \ outliers_overview (dictionary), output_files (dictionary) """ - if not isinstance(data, pd.DataFrame): - raise TypeError('df is not a pandas DataFrame.') - if isinstance( interval_cols, type(None) ): - interval_cols = data.select_dtypes(include=[np.number]).columns.tolist() - if interval_cols: - print('interval_cols not set, guessing: {0:s}'.format(str(interval_cols))) - assert isinstance( interval_cols, list ), 'interval_cols is not a list.' + if interval_cols is None: + interval_cols = guess_interval_cols(data) - data_clean, interval_cols_clean = dq_check_nunique_values(data, interval_cols, dropna=True) + data_clean, interval_cols_clean = dq_check_nunique_values(data, interval_cols) # create pdf(s) to save plots output_files = dict() @@ -326,7 +322,7 @@ def correlation_report(data:pd.DataFrame, interval_cols:list=None, bins=10, quan phik_matrix.loc[c0, c1] < correlation_threshold: continue - zvalues_df = outlier_significance_matrix_from_rebinned_df(data_binned[[c0, c1]], binning_dict, CI_method=CI_method) + zvalues_df = outlier_significance_matrix_from_rebinned_df(data_binned[[c0, c1]].copy(), binning_dict, CI_method=CI_method) combi = ':'.join(comb).replace(' ','_') xlabels = zvalues_df.columns diff --git a/python/phik/significance.py b/python/phik/significance.py index 4055f6e..02de01f 100644 --- a/python/phik/significance.py +++ b/python/phik/significance.py @@ -13,7 +13,7 @@ modification, are permitted according to the terms listed in the file LICENSE. """ -from typing import Union +from typing import Tuple, Union import numpy as np import pandas as pd @@ -30,8 +30,10 @@ from .statistics import estimate_ndof, theoretical_ndof from .simulation import sim_chi2_distribution from .data_quality import dq_check_nunique_values, dq_check_hist2d +from .utils import array_like_to_dataframe, guess_interval_cols -def fit_test_statistic_distribution(chi2s:list, nbins:int=50) -> Union[float,float,float,float]: + +def fit_test_statistic_distribution(chi2s: Union[list, np.ndarray], nbins:int=50) -> Tuple[float, float, float, float]: """ Fit the hybrid chi2-distribution to the data to find f. @@ -45,8 +47,6 @@ def fit_test_statistic_distribution(chi2s:list, nbins:int=50) -> Union[float,flo :param int nbins: in order to fit the data a histogram is created with nbins number of bins :returns: f, ndof, sigma (width of gauss), bw (bin width) """ - if not isinstance(chi2s, (list, np.ndarray)): - raise TypeError('chi2s is not array like.') def myfunc(x, N, f, k, sigma): return N * (f * stats.chi2.pdf(x, k) + (1 - f) * stats.norm.pdf(x, k, sigma)) @@ -87,50 +87,49 @@ def hfunc(x:float, N:float, f:float, k:float, sigma:float) -> float: return N * (f * stats.chi2.pdf(x, k) + (1 - f) * stats.norm.pdf(x, k, sigma)) -def significance_from_chi2_ndof(chi2:float, ndof:float) -> Union[float,float]: +def significance_from_chi2_ndof(chi2: float, ndof: float) -> Tuple[float, float]: """ Convert a chi2 into significance using knowledge about the number of degrees of freedom - Convertions is done using asymptotic approximation. + Conversion is done using asymptotic approximation. :param float chi2: chi2 value :param float ndof: number of degrees of freedom - :returns: pvalue, significance + :returns: p_value, significance """ - pvalue = stats.chi2.sf(chi2, ndof) - Zvalue = -stats.norm.ppf(pvalue) + p_value = stats.chi2.sf(chi2, ndof) + z_value = -stats.norm.ppf(p_value) - # scenario where pvalue is too small to evaluate Z + # scenario where p_value is too small to evaluate Z # use Chernoff approximation for p-value upper bound # see: https://en.wikipedia.org/wiki/Chi-squared_distribution - if pvalue == 0: + if p_value == 0: z = chi2 / ndof u = -math.log(2 * math.pi) - ndof * math.log(z) + ndof * (z - 1) - Zvalue = math.sqrt(u - math.log(u)) + z_value = math.sqrt(u - math.log(u)) + + return p_value, z_value - return pvalue, Zvalue -def significance_from_chi2_asymptotic(values:np.ndarray, chi2:float) -> Union[float,float]: +def significance_from_chi2_asymptotic(values:np.ndarray, chi2:float) -> Tuple[float,float]: """ Convert a chi2 into significance using knowledge about the number of degrees of freedom - Convertions is done using asymptotic approximation. + Convention is done using asymptotic approximation. :param float chi2: chi2 value :param float ndof: number of degrees of freedom - :returns: pvalue, significance + :returns: p_value, significance """ - if not isinstance(values, np.ndarray): - raise TypeError('values is not a numpy array.') ndof = theoretical_ndof(values) - pvalue, zvalue = significance_from_chi2_ndof(chi2, ndof) + p_value, z_value = significance_from_chi2_ndof(chi2, ndof) - return pvalue, zvalue + return p_value, z_value def significance_from_chi2_MC(chi2:float, values:np.ndarray, nsim:int=1000, lambda_:str='log-likelihood', - simulation_method:str='multinominal') -> Union[float,float]: + simulation_method:str='multinominal') -> Tuple[float, float]: """ Convert a chi2 into significance using knowledge about the shape of the chi2 distribution of simulated data @@ -138,23 +137,21 @@ def significance_from_chi2_MC(chi2:float, values:np.ndarray, nsim:int=1000, lamb :param float chi2: chi2 value :param list chi2s: chi2s values - :returns: pvalue, significance + :returns: p_value, significance """ - if not isinstance(values, np.ndarray): - raise TypeError('values is not a numpy array.') # determine effective number of degrees of freedom using simulation chi2s = sim_chi2_distribution(values, nsim=nsim, lambda_=lambda_, simulation_method=simulation_method) - # calculate pvalue based on simulation (MC method) - pexp = 1. - stats.percentileofscore(chi2s, chi2) / 100. - zexp = -stats.norm.ppf(pexp) + # calculate p_value based on simulation (MC method) + empirical_p_value = 1. - stats.percentileofscore(chi2s, chi2) / 100. + empirical_z_value = -stats.norm.ppf(empirical_p_value) - return pexp, zexp + return empirical_p_value, empirical_z_value def significance_from_chi2_hybrid(chi2:float, values:np.ndarray, nsim:int=1000, lambda_:str='log-likelihood', - simulation_method:str='multinominal') -> Union[float,float]: + simulation_method:str='multinominal') -> Tuple[float,float]: """ Convert a chi2 into significance using a hybrid method @@ -171,10 +168,8 @@ def significance_from_chi2_hybrid(chi2:float, values:np.ndarray, nsim:int=1000, :param float chi2: chi2 value :param list chi2s: chi2s values :param float avg_per_bin: average number of data points per bin - :returns: pvalue, significance + :returns: p_value, significance """ - if not isinstance(values, np.ndarray): - raise TypeError('values is not a numpy array.') # determine effective number of degrees of freedom using simulation chi2s = sim_chi2_distribution(values, nsim=nsim, lambda_=lambda_, simulation_method=simulation_method) @@ -204,7 +199,7 @@ def significance_from_chi2_hybrid(chi2:float, values:np.ndarray, nsim:int=1000, def significance_from_hist2d(values:np.ndarray, nsim:int=1000, lambda_:str='log-likelihood', simulation_method:str='multinominal', - significance_method:str='hybrid') -> Union[float,float]: + significance_method:str='hybrid') -> Tuple[float,float]: """ Calculate the significance of correlation of two variables based on the contingency table @@ -216,26 +211,26 @@ def significance_from_hist2d(values:np.ndarray, nsim:int=1000, lambda_:str='log- :param str significance_method: significance_method. Options: [asymptotic, MC, hybrid] :return: pvalue, significance """ - if not isinstance(values, np.ndarray): - raise TypeError('values is not a numpy array.') # chi2 of the data chi2 = get_chi2_using_dependent_frequency_estimates(values, lambda_=lambda_) if significance_method == 'asymptotic': # calculate pvalue and zvalue based on chi2 and ndof (asymptotic method) - pvalue, zvalue = significance_from_chi2_asymptotic(values, chi2) - + pvalue, zvalue = significance_from_chi2_asymptotic( + values, chi2 + ) elif significance_method == 'MC': # calculate pvalue based on simulation (MC method) - pvalue, zvalue = significance_from_chi2_MC(chi2, values, nsim=nsim, lambda_=lambda_, - simulation_method=simulation_method) - + pvalue, zvalue = significance_from_chi2_MC( + chi2, values, nsim=nsim, lambda_=lambda_, simulation_method=simulation_method + ) elif significance_method == 'hybrid': # low statistics : calculate pvalue and zvalue using h(x|f) and endof # high statistics: calculate pvalue and zvalue using chi2-distribution and endof - pvalue, zvalue = significance_from_chi2_hybrid(chi2, values, nsim=nsim, lambda_=lambda_, - simulation_method=simulation_method) + pvalue, zvalue = significance_from_chi2_hybrid( + chi2, values, nsim=nsim, lambda_=lambda_, simulation_method=simulation_method + ) else: raise NotImplementedError('simulation_method {0:s} is unknown'.format(simulation_method)) @@ -246,9 +241,9 @@ def significance_from_rebinned_df(data_binned:pd.DataFrame, lambda_:str="log-lik nsim:int=1000, significance_method:str='hybrid', dropna:bool=True, drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: """ - Calculate significance of correlation of all variable combinations in the dataframe + Calculate significance of correlation of all variable combinations in the DataFrame - :param data_binned: input binned dataframe + :param data_binned: input binned DataFrame :param int nsim: number of simulations :param str lambda_: test statistic. Available options are [pearson, log-likelihood] :param str simulation_method: simulation method. Options: [mutlinominal, row_product_multinominal, \ @@ -261,43 +256,50 @@ def significance_from_rebinned_df(data_binned:pd.DataFrame, lambda_:str="log-lik a numeric variable) :return: significance matrix """ - if not isinstance(data_binned, pd.DataFrame): - raise TypeError('data_binned is not a pandas DataFrame.') if not dropna: # if not dropna replace the NaN values with the string NaN. Otherwise the rows with NaN are dropped # by the groupby. - data_binned = data_binned.replace(np.nan, defs.NaN).copy() - + data_binned.replace(np.nan, defs.NaN, inplace=True) if drop_underflow: - data_binned = data_binned.replace(defs.UF, np.nan).copy() + data_binned.replace(defs.UF, np.nan, inplace=True) if drop_overflow: - data_binned = data_binned.replace(defs.OF, np.nan).copy() + data_binned.replace(defs.OF, np.nan, inplace=True) - signifs = {} - for i, comb in enumerate(itertools.combinations_with_replacement(data_binned.columns.values, 2)): - c0, c1 = comb + # cache column order (https://github.com/KaveIO/PhiK/issues/1) + column_order = data_binned.columns + signifs = [] + for i, (c0, c1) in enumerate(itertools.combinations_with_replacement(data_binned.columns.values, 2)): datahist = data_binned.groupby([c0, c1])[c0].count().to_frame().unstack().fillna(0) if 1 in datahist.shape or 0 in datahist.shape: - signifs[':'.join(comb)] = np.nan + signifs.append((c0, c1, np.nan)) warnings.warn('Too few unique values for variable {0:s} ({1:d}) or {2:s} ({3:d}) to calculate significance' .format(c0, datahist.shape[0], c1, datahist.shape[1])) continue datahist.columns = datahist.columns.droplevel() datahist = datahist.values - pvalue, zvalue = significance_from_hist2d(datahist, nsim=nsim, lambda_=lambda_, - simulation_method=simulation_method, - significance_method=significance_method) - signifs[':'.join(comb)] = zvalue + pvalue, zvalue = significance_from_hist2d( + datahist, nsim=nsim, lambda_=lambda_, simulation_method=simulation_method, + significance_method=significance_method + ) + signifs.append((c0, c1, zvalue)) + + if len(signifs) == 0: + return pd.DataFrame(np.nan, index=column_order, columns=column_order) significance_overview = create_correlation_overview_table(signifs) + + # restore column order + significance_overview = significance_overview.reindex(columns=column_order) + significance_overview = significance_overview.reindex(index=column_order) + return significance_overview def significance_matrix(df:pd.DataFrame, interval_cols:list=None, lambda_:str="log-likelihood", simulation_method:str='multinominal', - nsim:int=1000, significance_method:str='hybrid', bins=10, dropna:bool=True, drop_underflow:bool=True, - drop_overflow:bool=True) -> pd.DataFrame: + nsim:int=1000, significance_method:str='hybrid', bins:Union[int, list, np.ndarray, dict]=10, dropna:bool=True, + drop_underflow:bool=True, drop_overflow:bool=True) -> pd.DataFrame: """ Calculate significance of correlation of all variable combinations in the dataframe @@ -319,28 +321,24 @@ def significance_matrix(df:pd.DataFrame, interval_cols:list=None, lambda_:str="l a numeric variable) :return: significance matrix """ - if not isinstance(df, pd.DataFrame): - raise TypeError('df is not a pandas DataFrame.') - if not isinstance(bins, (int,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') - if isinstance( interval_cols, type(None) ): - interval_cols = df.select_dtypes(include=[np.number]).columns.tolist() - if interval_cols: - print('interval_cols not set, guessing: {0:s}'.format(str(interval_cols))) - assert isinstance(interval_cols, list), 'interval_cols is not a list.' + if interval_cols is None: + interval_cols = guess_interval_cols(df) df_clean, interval_cols_clean = dq_check_nunique_values(df, interval_cols, dropna=dropna) data_binned = bin_data(df_clean, interval_cols_clean, bins=bins) - return significance_from_rebinned_df(data_binned, lambda_=lambda_, simulation_method=simulation_method, nsim=nsim, - significance_method=significance_method, dropna=dropna, - drop_underflow=drop_underflow, drop_overflow=drop_overflow) + return significance_from_rebinned_df( + data_binned, lambda_=lambda_, simulation_method=simulation_method, nsim=nsim, + significance_method=significance_method, dropna=dropna, + drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) -def significance_from_array(x, y, num_vars:list=[], bins=10, quantile:bool=False, lambda_:str="log-likelihood", nsim:int=1000, +def significance_from_array(x: Union[np.ndarray, pd.Series], y: Union[np.ndarray, pd.Series], num_vars=None, bins:Union[int,list,np.ndarray,dict]=10, + quantile:bool=False, lambda_:str= "log-likelihood", nsim:int=1000, significance_method:str='hybrid', simulation_method:str='multinominal', dropna:bool=True, - drop_underflow:bool=True, drop_overflow:bool=True) -> Union[float,float]: + drop_underflow:bool=True, drop_overflow:bool=True) -> Tuple[float,float]: """ Calculate the significance of correlation @@ -365,28 +363,24 @@ def significance_from_array(x, y, num_vars:list=[], bins=10, quantile:bool=False a numeric variable) :return: p-value, significance """ - if not isinstance(x, (np.ndarray, pd.Series)): - raise TypeError('x is not array like.') - if not isinstance(y, (np.ndarray, pd.Series)): - raise TypeError('y is not array like.') - if not isinstance(bins, (int,list,np.ndarray,dict)): - raise TypeError('bins is of incorrect type.') - - if isinstance(num_vars, str): + if num_vars is None: + num_vars = [] + elif isinstance(num_vars, str): num_vars = [num_vars] if len(num_vars) > 0: - df = pd.DataFrame(np.array([x, y]).T, columns=['x', 'y']) + df = array_like_to_dataframe(x, y) x, y = bin_data(df, num_vars, bins=bins, quantile=quantile).T.values - return significance_from_binned_array(x, y, lambda_=lambda_, significance_method=significance_method, nsim=nsim, - simulation_method=simulation_method, dropna=dropna, - drop_underflow=drop_underflow, drop_overflow=drop_overflow) + return significance_from_binned_array( + x, y, lambda_=lambda_, significance_method=significance_method, nsim=nsim, + simulation_method=simulation_method, dropna=dropna, drop_underflow=drop_underflow, drop_overflow=drop_overflow + ) -def significance_from_binned_array(x, y, lambda_:str="log-likelihood", significance_method:str='hybrid', nsim:int=1000, +def significance_from_binned_array(x: Union[np.ndarray, pd.Series], y: Union[np.ndarray, pd.Series], lambda_:str="log-likelihood", significance_method:str='hybrid', nsim:int=1000, simulation_method:str='multinominal', dropna:bool=True, drop_underflow:bool=True, - drop_overflow:bool=True) -> Union[float,float]: + drop_overflow:bool=True) -> Tuple[float, float]: """ Calculate the significance of correlation @@ -407,10 +401,6 @@ def significance_from_binned_array(x, y, lambda_:str="log-likelihood", significa a numeric variable) :return: p-value, significance """ - if not isinstance(x, (np.ndarray, pd.Series)): - raise TypeError('x is not array like.') - if not isinstance(y, (np.ndarray, pd.Series)): - raise TypeError('y is not array like.') if not dropna: x = pd.Series(x).fillna(defs.NaN).astype(str).values @@ -419,17 +409,18 @@ def significance_from_binned_array(x, y, lambda_:str="log-likelihood", significa if drop_underflow or drop_overflow: x = x.copy() y = y.copy() - if drop_underflow: - x[np.where(x == defs.UF)] = np.nan - x[np.where(x == defs.OF)] = np.nan - if drop_overflow: - y[np.where(y == defs.UF)] = np.nan - y[np.where(y == defs.OF)] = np.nan + if drop_underflow: + x[np.where(x == defs.UF)] = np.nan + y[np.where(y == defs.UF)] = np.nan + if drop_overflow: + y[np.where(y == defs.OF)] = np.nan + x[np.where(x == defs.OF)] = np.nan hist2d = pd.crosstab(x, y).values if not dq_check_hist2d(hist2d): return np.nan, np.nan - return significance_from_hist2d(hist2d, lambda_=lambda_, significance_method=significance_method, - simulation_method=simulation_method, nsim=nsim) + return significance_from_hist2d( + hist2d, lambda_=lambda_, significance_method=significance_method, simulation_method=simulation_method, nsim=nsim + ) diff --git a/python/phik/simulation.py b/python/phik/simulation.py index aa51b62..523ca3f 100644 --- a/python/phik/simulation.py +++ b/python/phik/simulation.py @@ -13,7 +13,8 @@ LICENSE. """ -import copy +from typing import Union + import numpy as np import pandas as pd from numba import jit @@ -23,6 +24,7 @@ from .statistics import get_dependent_frequency_estimates from .statistics import get_chi2_using_dependent_frequency_estimates + @jit def sim_2d_data(hist:np.ndarray, ndata:int=0) -> np.ndarray: """ @@ -33,16 +35,15 @@ def sim_2d_data(hist:np.ndarray, ndata:int=0) -> np.ndarray: :param int ndata: number of simulations :return: simulated data """ - if not isinstance(hist, np.ndarray): - raise TypeError('hist is not a numpy array.') if ndata <= 0: ndata = hist.sum() - assert ndata>0, 'ndata has to be positive.' + if ndata <= 0: + raise ValueError('ndata (or hist.sum()) has to be positive') # scale and ravel - hc = copy.copy(hist) * ((1.0 * ndata) / hist.sum()) - hcr = hc.ravel() + hc = hist[:] * ((1.0 * ndata) / hist.sum()) + hcr = hc.ravel() # first estimate, unconstrained hout = np.zeros(hcr.shape) @@ -63,7 +64,7 @@ def sim_2d_data(hist:np.ndarray, ndata:int=0) -> np.ndarray: hran = np.random.uniform(0,hmax) if hran 0: @@ -79,7 +80,7 @@ def sim_2d_data(hist:np.ndarray, ndata:int=0) -> np.ndarray: # --- jit turned off for now, somehow not working for patefield; computer dependent! #@jit -def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: +def sim_2d_data_patefield(data: np.ndarray) -> np.ndarray: """ Simulate a two dimensional dataset with fixed row and column totals. @@ -92,12 +93,9 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: This table is used as probability density function. :return: simulated data """ - if not isinstance(data, np.ndarray): - raise TypeError('data is not a numpy array.') # number of rows and columns - nrows = data.shape[0] - ncols = data.shape[1] + nrows, ncols = data.shape # totals per row and column nrowt = data.sum(axis=1) @@ -111,17 +109,15 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: fact = [x] for i in range(1, ntotal + 1): - x = x + np.log(i) + x += np.log(i) fact.append(x) # initialize matrix for end result - matrix = np.zeros(data.shape[0] * data.shape[1]) + matrix = np.empty(data.shape[0] * data.shape[1]) matrix[:] = np.nan # Construct a random matrix. - jwork = [] - for i in range(0, ncols - 1): jwork.append(int(np.round(ncolt[i]))) @@ -134,7 +130,6 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: jc = int(jc - nrowtl) for m in range(0, ncols - 1): - id = jwork[m] ie = int(np.round(ic)) ic = int(np.round(ic - id)) @@ -151,11 +146,11 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: r = np.random.uniform() # Compute the conditional expected value of MATRIX(L,M). + done1 = False + done2 = False - done1 = 0 - - while (True): # infinit loop!!! - nlm = int(np.round((ia * id) / (ie) + 0.5)) + while True: # infinite loop!!! + nlm = int(np.round((ia * id) / ie + 0.5)) iap = int(np.round(ia + 1)) idp = int(np.round(id + 1)) igp = int(np.round(idp - nlm)) @@ -163,7 +158,7 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: nlmp = int(np.round(nlm + 1)) iip = int(np.round(ii + nlmp)) - x = np.exp(fact[iap - 1] + fact[ib] + fact[ic] + fact[idp - 1] - \ + x = np.exp(fact[iap - 1] + fact[ib] + fact[ic] + fact[idp - 1] - fact[ie] - fact[nlmp - 1] - fact[igp - 1] - fact[ihp - 1] - fact[iip - 1]) if (r < x) or np.isclose(r, x): @@ -173,27 +168,26 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: sumprb = x y = x nll = nlm - lsp = 0 - lsm = 0 + lsp = False + lsm = False # Increment entry in row L, column M. - while not lsp: j = int(np.round((id - nlm) * (ia - nlm))) if np.isclose(j, 0): # if j == 0: - lsp = 1 + lsp = True else: - nlm = nlm + 1 + nlm += 1 x = x * j / (nlm * (ii + nlm)) - sumprb = sumprb + x + sumprb += x if (r < sumprb) or np.isclose(r, sumprb): - done1 = 1 + done1 = True break - done2 = 0 + done2 = False while not lsm: @@ -202,39 +196,32 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: j = nll * (ii + nll) if np.isclose(j, 0): - lsm = 1 + lsm = True break - nll = nll - 1 + nll -= 1 if np.isclose((id - nll) * (ia - nll), 0): # make sure not to divide by zero y = np.inf else: y = y * j / ((id - nll) * (ia - nll)) - sumprb = sumprb + y + sumprb += y if (r < sumprb) or np.isclose(r, sumprb): nlm = nll - done2 = 1 + done2 = True break - - if not lsp: - break - if done2: break - if done1: - break - - if done2: + if done1 or done2: break r = np.random.uniform() r = sumprb * r matrix[l + m * nrows] = nlm - ia = ia - nlm - jwork[m] = jwork[m] - nlm + ia -= nlm + jwork[m] -= nlm matrix[l + (ncols - 1) * nrows] = ia @@ -254,30 +241,28 @@ def sim_2d_data_patefield(data:np.ndarray) -> np.ndarray: return matrix -def sim_2d_product_multinominal(data:np.ndarray, axis:str) -> np.ndarray: +def sim_2d_product_multinominal(data:np.ndarray, axis: int) -> np.ndarray: """ Simulate 2 dimensional data with either row or column totals fixed. :param data: contingency table, which contains the observed number of occurrences in each category.\ This table is used as probability density function. - :param axis: fix row totals (rows) or column totals (cols). + :param axis: fix row totals (0) or column totals (1). :return: simulated data """ - if not isinstance(data, np.ndarray): - raise TypeError('data is not a numpy array.') - if axis == 'cols': + if axis == 1: return np.array([list(sim_2d_data(data[i])) for i in range(data.shape[0])]) - if axis == 'rows': + elif axis == 0: return np.array([list(sim_2d_data(data.T[i])) for i in range(data.shape[1])]).T else: - raise ValueError + raise NotImplementedError("Axis should be 0 (row) or 1 (column).") -@jit +@jit(forceobj=True) def sim_data(data:np.ndarray, method:str='multinominal') -> np.ndarray: """ - Simulate a 2 dimenstional dataset given a 2 dimensional pdf + Simulate a 2 dimensional dataset given a 2 dimensional pdf Several simulation methods are provided: @@ -292,24 +277,21 @@ def sim_data(data:np.ndarray, method:str='multinominal') -> np.ndarray: col_product_multinominal] :return: simulated data """ - assert method in ['multinominal', 'hypergeometric', 'row_product_multinominal', 'col_product_multinominal'], 'selected method not recognized.' - if not isinstance(data, np.ndarray): - raise TypeError('data is not a numpy array.') if method == 'multinominal': return sim_2d_data(data) elif method == 'hypergeometric': return sim_2d_data_patefield(data) elif method == 'row_product_multinominal': - return sim_2d_product_multinominal(data, 'rows') + return sim_2d_product_multinominal(data, 0) elif method == 'col_product_multinominal': - return sim_2d_product_multinominal(data, 'cols') + return sim_2d_product_multinominal(data, 1) else: - raise ValueError + raise NotImplementedError('selected method not recognized.') # @jit -def sim_chi2_distribution(values, nsim:int=1000, lambda_:str='log-likelihood', simulation_method:str='multinominal') -> list: +def sim_chi2_distribution(values: Union[pd.DataFrame, np.ndarray], nsim:int=1000, lambda_:str='log-likelihood', simulation_method:str='multinominal') -> list: """ Simulate 2D data and calculate the chi-square statistic for each simulated dataset. @@ -320,20 +302,18 @@ def sim_chi2_distribution(values, nsim:int=1000, lambda_:str='log-likelihood', s :param str lambda_: test statistic. Available options are [pearson, log-likelihood]. :returns chi2s: list of chi2 values for each simulated dataset """ - vals = values.values if isinstance(values, pd.DataFrame) else values - if not isinstance(vals, np.ndarray): - raise TypeError('values is not a numpy array.') + values = values.values if isinstance(values, pd.DataFrame) else values exp_dep = get_dependent_frequency_estimates(values) - chi2s = Parallel(n_jobs=NCORES)(delayed(simulate)(exp_dep, simulation_method, lambda_) for i in range(nsim)) + chi2s = Parallel(n_jobs=NCORES)(delayed(simulate)(exp_dep, simulation_method, lambda_) for _ in range(nsim)) return chi2s -@jit -def simulate(exp_dep, simulation_method, lambda_): - """split off simulate function to allow for parallellization""" +@jit(forceobj=True) +def simulate(exp_dep: np.ndarray, simulation_method: str, lambda_: str) -> float: + """split off simulate function to allow for parallelization""" simdata = sim_data(exp_dep, method=simulation_method) simchi2 = get_chi2_using_dependent_frequency_estimates(simdata, lambda_) return simchi2 diff --git a/python/phik/statistics.py b/python/phik/statistics.py index 299105d..7e295d3 100644 --- a/python/phik/statistics.py +++ b/python/phik/statistics.py @@ -13,46 +13,38 @@ modification, are permitted according to the terms listed in the file LICENSE. """ +from typing import Union -import copy import numpy as np from scipy import stats -def get_dependent_frequency_estimates(vals:np.ndarray) -> np.ndarray: +def get_dependent_frequency_estimates(vals: np.ndarray) -> np.ndarray: """ Calculation of dependent expected frequencies. Calculation is based on the marginal sums of the table, i.e. dependent frequency estimates. - :param values: The contingency table. The table contains the observed number of occurrences in each category + :param vals: The contingency table. The table contains the observed number of occurrences in each category :returns exp: expected frequencies """ - if not isinstance(vals, np.ndarray): - raise TypeError('vals is not a numpy array.') # use existing scipy functionality return stats.contingency.expected_freq(vals) -def get_chi2_using_dependent_frequency_estimates(vals:np.ndarray, lambda_:str = 'log-likelihood') -> float: +def get_chi2_using_dependent_frequency_estimates(vals: np.ndarray, lambda_:str = 'log-likelihood') -> float: """ Chi-square test of independence of variables in a contingency table. The expected frequencies are based on the marginal sums of the table, i.e. dependent frequency estimates. - :param values: The contingency table. The table contains the observed number of occurrences in each category + :param vals: The contingency table. The table contains the observed number of occurrences in each category :returns chi2: """ - if not isinstance(vals, np.ndarray): - raise TypeError('vals is not a numpy array.') - values = copy.copy(vals) - - # create np array - if type(values) == list: - values=np.array(values) + values = vals[:] # remove rows with only zeros, scipy doesn't like them. values = values[~np.all(values == 0, axis=1)] @@ -60,12 +52,12 @@ def get_chi2_using_dependent_frequency_estimates(vals:np.ndarray, lambda_:str = values = values.T[~np.all(values.T == 0, axis=1)].T # use existing scipy functionality - exp = stats.chi2_contingency(values, lambda_=lambda_) + exp, _, _, _ = stats.chi2_contingency(values, lambda_=lambda_) - return exp[0] + return exp -def estimate_ndof(chi2values:list) -> float: +def estimate_ndof(chi2values: Union[list, np.ndarray]) -> float: """ Estimation of the effective number of degrees of freedom. @@ -75,14 +67,11 @@ def estimate_ndof(chi2values:list) -> float: :param list chi2values: list of chi2 values :returns: endof0, endof """ - if not isinstance(chi2values, (np.ndarray, list)): - raise TypeError('chi2values is not array like.') - endof0 = np.mean(chi2values) - return endof0 + return np.mean(chi2values) -def estimate_simple_ndof(observed:np.ndarray) -> int: +def estimate_simple_ndof(observed: np.ndarray) -> int: """ Simple estimation of the effective number of degrees of freedom. @@ -92,8 +81,6 @@ def estimate_simple_ndof(observed:np.ndarray) -> int: :param observed: numpy array of observed cell counts :returns: endof """ - if not isinstance(observed, np.ndarray): - raise TypeError('observed is not a numpy array.') # use existing scipy functionality expected = stats.contingency.expected_freq(observed) @@ -104,7 +91,7 @@ def estimate_simple_ndof(observed:np.ndarray) -> int: return endof -def theoretical_ndof(observed:np.ndarray) -> int: +def theoretical_ndof(observed: np.ndarray) -> int: """ Simple estimation of the effective number of degrees of freedom. @@ -114,41 +101,36 @@ def theoretical_ndof(observed:np.ndarray) -> int: :param observed: numpy array of observed cell counts :returns: theoretical ndof """ - if not isinstance(observed, np.ndarray): - raise TypeError('observed is not a numpy array.') - ndof = observed.size - np.sum(observed.shape) + observed.ndim - 1 - return ndof + return observed.size - np.sum(observed.shape) + observed.ndim - 1 -def z_from_logp(logp:float, flip_sign:bool = False) -> float: +def z_from_logp(logp: float, flip_sign: bool = False) -> float: """ Convert logarithm of p-value into one-sided Z-value - :param float logp: logarithm of p-value + :param float logp: logarithm of p-value, should not be greater than 0 :param bool flip_sign: flip sign of Z-value, e.g. use for input log(1-p). Default is false. :returns: statistical significance Z-value :rtype: float """ - if logp > 0: - raise ValueError('logp={:f} cannot be greater than zero'.format(logp)) # pvalue == 0, Z = infinity if logp == -np.inf: return np.inf if not flip_sign else -np.inf - pvalue = np.exp(logp) + p_value = np.exp(logp) - # scenario where pvalue is numerically too small to evaluate Z - if pvalue == 0: + # scenario where p-value is numerically too small to evaluate Z + if p_value == 0: # kicks in here when Z > 37 # approach valid when ~ Z > 1.5. u = -2.*np.log(2 * np.pi) - 2.*logp - Zvalue = np.sqrt(u - np.log(u)) + z_value = np.sqrt(u - np.log(u)) else: - Zvalue = -stats.norm.ppf(pvalue) + z_value = -stats.norm.ppf(p_value) if flip_sign: - Zvalue *= -1. + z_value *= -1. - return Zvalue + return z_value diff --git a/python/phik/utils.py b/python/phik/utils.py new file mode 100644 index 0000000..3b3d357 --- /dev/null +++ b/python/phik/utils.py @@ -0,0 +1,32 @@ +from typing import Union + +import pandas as pd +import numpy as np + + +def array_like_to_dataframe(x: Union[pd.Series, list, np.ndarray], y: [pd.Series, list, np.ndarray]): + """Concat two array-like data structures into a DataFrame + + :param x: pd.Series, list or np.ndarray + :param y: pd.Series, list or np.ndarray + :return: pd.DataFrame + """ + x_name = x.name if isinstance(x, pd.Series) else 'x' + y_name = y.name if isinstance(y, pd.Series) else 'y' + + return pd.DataFrame(np.array([x, y]).T, columns=[x_name, y_name]) + + +def guess_interval_cols(df: pd.DataFrame) -> list: + """Select columns that have a dtype part of np.number + + :param df: DataFrame + :return: list of interval columns + """ + interval_cols = df.select_dtypes(include=[np.number]).columns.tolist() + if interval_cols: + print('interval columns not set, guessing: {}'.format(str(interval_cols))) + + if not isinstance(interval_cols, list): + raise ValueError('Could not guess interval columns') + return interval_cols diff --git a/python/phik/version.py b/python/phik/version.py index 48aa262..a6182b2 100644 --- a/python/phik/version.py +++ b/python/phik/version.py @@ -1,6 +1,6 @@ """THIS FILE IS AUTO-GENERATED BY PHIK SETUP.PY.""" name = 'phik' -version = '0.9.11' -full_version = '0.9.11' +version = '0.10.0' +full_version = '0.10.0' release = True diff --git a/setup.py b/setup.py index 1583c28..2310ac1 100644 --- a/setup.py +++ b/setup.py @@ -19,8 +19,8 @@ NAME = 'phik' MAJOR = 0 -REVISION = 9 -PATCH = 11 +REVISION = 10 +PATCH = 0 DEV = False # note: also update README.rst @@ -30,11 +30,12 @@ if DEV: FULL_VERSION += '.dev' -TEST_REQUIREMENTS = ['pytest>=4.0.2', - 'pytest-pylint>=0.13.0', - 'nbconvert>=5.3.1', - 'jupyter_client>=5.2.3', - ] +TEST_REQUIREMENTS = [ + 'pytest>=4.0.2', + 'pytest-pylint>=0.13.0', + 'nbconvert>=5.3.1', + 'jupyter_client>=5.2.3', +] REQUIREMENTS = [ 'numpy>=1.15.4', @@ -43,7 +44,7 @@ 'matplotlib>=2.2.3', 'numba>=0.38.1', 'joblib>=0.14.1' - ] +] if DEV: REQUIREMENTS += TEST_REQUIREMENTS @@ -54,7 +55,7 @@ EXCLUDE_PACKAGES = [] EXTERNAL_MODULES = [] -with open("README.rst", "r") as fh: +with open("README.rst") as fh: long_description = fh.read() @@ -77,14 +78,10 @@ def write_version_py(filename: str = 'python/phik/version.py') -> None: release = {is_release!s} """ - version_file = open(filename, 'w') - try: - version_file.write(version_str.format(name=NAME.lower(), - version=VERSION, - full_version=FULL_VERSION, - is_release=not DEV)) - finally: - version_file.close() + with open(filename, 'w') as version_file: + version_file.write( + version_str.format(name=NAME.lower(), version=VERSION, full_version=FULL_VERSION, is_release=not DEV) + ) def setup_package() -> None: @@ -121,6 +118,7 @@ def setup_package() -> None: classifiers=( "Programming Language :: Python :: 3", "Operating System :: OS Independent", + "License :: OSI Approved :: Apache Software License", ), # The following 'creates' executable scripts for *nix and Windows. # As an added bonus the Windows scripts will auto-magically diff --git a/tests/phik_python/bases.py b/tests/phik_python/bases.py index 15ca317..5557cd0 100644 --- a/tests/phik_python/bases.py +++ b/tests/phik_python/bases.py @@ -23,7 +23,7 @@ def run_notebook(self, notebook): except CellExecutionError: # store if failed status = False - executed_notebook = os.getcwd() + '/' + notebook.split('/')[-1] + executed_notebook = os.getcwd() + os.sep + notebook.split(os.sep)[-1] with open(executed_notebook, mode='wt') as f: nbformat.write(nb, f) diff --git a/tests/phik_python/test_phik.py b/tests/phik_python/test_phik.py index 59cbf4b..f801ee1 100644 --- a/tests/phik_python/test_phik.py +++ b/tests/phik_python/test_phik.py @@ -43,13 +43,16 @@ def test_phik_matrix(self): # open fake car insurance data df = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') ) + cols = list(df.columns) # get the phi_k correlation matrix between all variables interval_cols = ['driver_age', 'mileage'] phik_corr = df.phik_matrix(interval_cols=interval_cols) - self.assertTrue(np.isclose(phik_corr.values[1,0], 0.5904561614620166)) - self.assertTrue(np.isclose(phik_corr.values[2,4], 0.768588987856336)) + self.assertTrue(np.isclose(phik_corr.values[cols.index('car_color'), cols.index('area')], 0.5904561614620166)) + self.assertTrue(np.isclose(phik_corr.values[cols.index('area'), cols.index('car_color')], 0.5904561614620166)) + self.assertTrue(np.isclose(phik_corr.values[cols.index('mileage'), cols.index('car_size')], 0.768588987856336)) + self.assertTrue(np.isclose(phik_corr.values[cols.index('car_size'), cols.index('mileage')], 0.768588987856336)) def test_global_phik(self): """Test the calculation of global Phi_K values""" @@ -65,8 +68,13 @@ def test_global_phik(self): interval_cols = ['driver_age', 'mileage'] gk = df.global_phik(interval_cols=interval_cols) - self.assertTrue(np.isclose(gk[0][0][0], 0.6057528003711345)) - self.assertTrue(np.isclose(gk[0][4][0], 0.768588987856336)) + area = (np.where(gk[1] == 'area'))[0][0] + car_size = (np.where(gk[1] == 'car_size'))[0][0] + mileage = (np.where(gk[1] == 'mileage'))[0][0] + + self.assertTrue(np.isclose(gk[0][area][0], 0.6057528003711345)) + self.assertTrue(np.isclose(gk[0][car_size][0], 0.76858883)) + self.assertTrue(np.isclose(gk[0][mileage][0], 0.768588987856336)) def test_significance_matrix(self): """Test significance calculation""" @@ -77,13 +85,15 @@ def test_significance_matrix(self): # open fake car insurance data df = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') ) - + cols = list(df.columns) # get significances interval_cols = ['driver_age', 'mileage'] sm = df.significance_matrix(interval_cols=interval_cols, significance_method='asymptotic') - self.assertTrue(np.isclose(sm.values[1,0], 37.66184429195198)) - self.assertTrue(np.isclose(sm.values[2,4], 49.3323049685695)) + self.assertTrue(np.isclose(sm.values[cols.index('car_color'), cols.index('area')], 37.66184429195198)) + self.assertTrue(np.isclose(sm.values[cols.index('area'), cols.index('car_color')], 37.66184429195198)) + self.assertTrue(np.isclose(sm.values[cols.index('mileage'), cols.index('car_size')], 49.3323049685695)) + self.assertTrue(np.isclose(sm.values[cols.index('car_size'), cols.index('mileage')], 49.3323049685695)) def test_hist2d(self): """Test the calculation of global Phi_K values""" @@ -102,6 +112,21 @@ def test_hist2d(self): self.assertEqual(h2d.values[1,1], 10) self.assertEqual(h2d.values[5,5], 217) + def test_hist2d_array(self): + """Test the calculation of global Phi_K values""" + + import pandas as pd + from phik import resources + + # open fake car insurance data + df = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') ) + + # create contingency matrix + interval_cols = ['mileage'] + h2d = df['mileage'].hist2d(df['car_size'], interval_cols=interval_cols) + self.assertEqual(h2d.values[1, 1], 10) + self.assertEqual(h2d.values[5, 5], 217) + def test_outlier_significance_matrix(self): """Test the calculation of outlier significances""" @@ -119,3 +144,22 @@ def test_outlier_significance_matrix(self): self.assertTrue(np.isclose(om.values[0,1], 21.483476494343552)) self.assertTrue(np.isclose(om.values[2,4], -1.246784034214704)) + + def test_outlier_significance_matrices(self): + """Test the calculation of outlier significances""" + + import pandas as pd + from phik import resources + + # open fake car insurance data + df = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') ) + + # calculate outlier significances + interval_cols = ['mileage', 'driver_age'] + om = df.outlier_significance_matrices(interval_cols=interval_cols) + + assert isinstance(om, dict) + print(om) + + # self.assertTrue(np.isclose(om.values[0,1], 21.483476494343552)) + # self.assertTrue(np.isclose(om.values[2,4], -1.246784034214704)) \ No newline at end of file