From fe1e5642af15097a133716d682f968d6146c40e9 Mon Sep 17 00:00:00 2001 From: Remington Mallett Date: Sun, 6 Oct 2024 18:18:14 -0400 Subject: [PATCH 1/7] CI95% --> CI95 --- README.rst | 8 +++--- docs/index.rst | 8 +++--- src/pingouin/config.py | 2 +- src/pingouin/correlation.py | 52 ++++++++++++++++++------------------- src/pingouin/pairwise.py | 14 +++++----- src/pingouin/parametric.py | 18 ++++++------- src/pingouin/reliability.py | 6 ++--- src/pingouin/utils.py | 2 +- tests/test_correlation.py | 22 ++++++++-------- tests/test_parametric.py | 34 ++++++++++++------------ tests/test_reliability.py | 8 +++--- 11 files changed, 87 insertions(+), 87 deletions(-) diff --git a/README.rst b/README.rst index ab4c2427..edb72ff1 100644 --- a/README.rst +++ b/README.rst @@ -157,7 +157,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio :widths: auto ====== ===== ============= ======= ============= ========= ====== ======= - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power ====== ===== ============= ======= ============= ========= ====== ======= -3.401 58 two-sided 0.001 [-1.68 -0.43] 0.878 26.155 0.917 ====== ===== ============= ======= ============= ========= ====== ======= @@ -175,7 +175,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio :widths: auto === ===== =========== ======= ====== ======= - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power === ===== =========== ======= ====== ======= 30 0.595 [0.3 0.79] 0.001 69.723 0.950 === ===== =========== ======= ====== ======= @@ -196,7 +196,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio :widths: auto === ===== =========== ======= ======= - n r CI95% p-val power + n r CI95 p-val power === ===== =========== ======= ======= 30 0.576 [0.27 0.78] 0.001 0.933 === ===== =========== ======= ======= @@ -334,7 +334,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto === === ======== ============= === ===== ============= ======= ====== ======= - X Y method alternative n r CI95% p-unc BF10 power + X Y method alternative n r CI95 p-unc BF10 power === === ======== ============= === ===== ============= ======= ====== ======= X Y pearson two-sided 30 0.366 [0.01 0.64] 0.047 1.500 0.525 X Z pearson two-sided 30 0.251 [-0.12 0.56] 0.181 0.534 0.272 diff --git a/docs/index.rst b/docs/index.rst index b6b72815..0339b6d5 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -135,7 +135,7 @@ Quick start :widths: auto ====== ===== ============= ======= ============= ========= ====== ======= - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power ====== ===== ============= ======= ============= ========= ====== ======= -3.401 58 two-sided 0.001 [-1.68 -0.43] 0.878 26.155 0.917 ====== ===== ============= ======= ============= ========= ====== ======= @@ -153,7 +153,7 @@ Quick start :widths: auto === ===== =========== ======= ====== ======= - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power === ===== =========== ======= ====== ======= 30 0.595 [0.3 0.79] 0.001 69.723 0.950 === ===== =========== ======= ====== ======= @@ -174,7 +174,7 @@ Quick start :widths: auto === ===== =========== ======= ======= - n r CI95% p-val power + n r CI95 p-val power === ===== =========== ======= ======= 30 0.576 [0.27 0.78] 0.001 0.933 === ===== =========== ======= ======= @@ -325,7 +325,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto === === ======== ============= === ===== ============= ======= ====== ======= - X Y method alternative n r CI95% p-unc BF10 power + X Y method alternative n r CI95 p-unc BF10 power === === ======== ============= === ===== ============= ======= ====== ======= X Y pearson two-sided 30 0.366 [0.01 0.64] 0.047 1.500 0.525 X Z pearson two-sided 30 0.251 [-0.12 0.56] 0.181 0.534 0.272 diff --git a/src/pingouin/config.py b/src/pingouin/config.py index 6d69c62f..200db5f9 100644 --- a/src/pingouin/config.py +++ b/src/pingouin/config.py @@ -16,6 +16,6 @@ def set_default_options(): # Rounding behavior options["round"] = None - options["round.column.CI95%"] = 2 + options["round.column.CI95"] = 2 # default is to return Bayes factors inside DataFrames as formatted str options["round.column.BF10"] = _format_bf diff --git a/src/pingouin/correlation.py b/src/pingouin/correlation.py index 8edc964f..985242a4 100644 --- a/src/pingouin/correlation.py +++ b/src/pingouin/correlation.py @@ -412,7 +412,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): * ``'n'``: Sample size (after removal of missing values) * ``'outliers'``: number of outliers, only if a robust method was used * ``'r'``: Correlation coefficient - * ``'CI95%'``: 95% parametric confidence intervals around :math:`r` + * ``'CI95'``: 95% parametric confidence intervals around :math:`r` * ``'p-val'``: p-value * ``'BF10'``: Bayes Factor of the alternative hypothesis (only for Pearson correlation) * ``'power'``: achieved power of the test with an alpha of 0.05. @@ -516,60 +516,60 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): >>> x, y = np.random.multivariate_normal(mean, cov, 30).T >>> # Compute Pearson correlation >>> pg.corr(x, y).round(3) - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power pearson 30 0.491 [0.16, 0.72] 0.006 8.55 0.809 2. Pearson correlation with two outliers >>> x[3], y[5] = 12, -8 >>> pg.corr(x, y).round(3) - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power pearson 30 0.147 [-0.23, 0.48] 0.439 0.302 0.121 3. Spearman correlation (robust to outliers) >>> pg.corr(x, y, method="spearman").round(3) - n r CI95% p-val power + n r CI95 p-val power spearman 30 0.401 [0.05, 0.67] 0.028 0.61 4. Biweight midcorrelation (robust) >>> pg.corr(x, y, method="bicor").round(3) - n r CI95% p-val power + n r CI95 p-val power bicor 30 0.393 [0.04, 0.66] 0.031 0.592 5. Percentage bend correlation (robust) >>> pg.corr(x, y, method='percbend').round(3) - n r CI95% p-val power + n r CI95 p-val power percbend 30 0.389 [0.03, 0.66] 0.034 0.581 6. Shepherd's pi correlation (robust) >>> pg.corr(x, y, method='shepherd').round(3) - n outliers r CI95% p-val power + n outliers r CI95 p-val power shepherd 30 2 0.437 [0.08, 0.7] 0.02 0.662 7. Skipped spearman correlation (robust) >>> pg.corr(x, y, method='skipped').round(3) - n outliers r CI95% p-val power + n outliers r CI95 p-val power skipped 30 2 0.437 [0.08, 0.7] 0.02 0.662 8. One-tailed Pearson correlation >>> pg.corr(x, y, alternative="greater", method='pearson').round(3) - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power pearson 30 0.147 [-0.17, 1.0] 0.22 0.467 0.194 >>> pg.corr(x, y, alternative="less", method='pearson').round(3) - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power pearson 30 0.147 [-1.0, 0.43] 0.78 0.137 0.008 9. Perfect correlation >>> pg.corr(x, -x).round(3) - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power pearson 30 -1.0 [-1.0, -1.0] 0.0 inf 1 10. Using columns of a pandas dataframe @@ -577,7 +577,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): >>> import pandas as pd >>> data = pd.DataFrame({'x': x, 'y': y}) >>> pg.corr(data['x'], data['y']).round(3) - n r CI95% p-val BF10 power + n r CI95 p-val BF10 power pearson 30 0.147 [-0.23, 0.48] 0.439 0.302 0.121 """ # Safety check @@ -625,7 +625,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): { "n": n, "r": np.nan, - "CI95%": np.nan, + "CI95": np.nan, "p-val": np.nan, "BF10": np.nan, "power": np.nan, @@ -656,7 +656,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): pval = _correl_pvalue(r, n_clean, k=0, alternative=alternative) # Create dictionnary - stats = {"n": n, "r": r, "CI95%": [ci], "p-val": pval, "power": pr} + stats = {"n": n, "r": r, "CI95": [ci], "p-val": pval, "power": pr} if method in ["shepherd", "skipped"]: stats["outliers"] = n_outliers @@ -669,7 +669,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): stats = pd.DataFrame(stats, index=[method]) # Define order - col_keep = ["n", "outliers", "r", "CI95%", "p-val", "BF10", "power"] + col_keep = ["n", "outliers", "r", "CI95", "p-val", "BF10", "power"] col_order = [k for k in col_keep if k in stats.keys().tolist()] return _postprocess_dataframe(stats)[col_order] @@ -773,7 +773,7 @@ def partial_corr( >>> import pingouin as pg >>> df = pg.read_dataset('partial_corr') >>> pg.partial_corr(data=df, x='x', y='y', covar='cv1').round(3) - n r CI95% p-val + n r CI95 p-val pearson 30 0.568 [0.25, 0.77] 0.001 2. Spearman partial correlation with several covariates @@ -781,25 +781,25 @@ def partial_corr( >>> # Partial correlation of x and y controlling for cv1, cv2 and cv3 >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'], ... method='spearman').round(3) - n r CI95% p-val + n r CI95 p-val spearman 30 0.521 [0.18, 0.75] 0.005 3. Same but one-sided test >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'], ... alternative="greater", method='spearman').round(3) - n r CI95% p-val + n r CI95 p-val spearman 30 0.521 [0.24, 1.0] 0.003 >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'], ... alternative="less", method='spearman').round(3) - n r CI95% p-val + n r CI95 p-val spearman 30 0.521 [-1.0, 0.72] 0.997 4. As a pandas method >>> df.partial_corr(x='x', y='y', covar=['cv1'], method='spearman').round(3) - n r CI95% p-val + n r CI95 p-val spearman 30 0.578 [0.27, 0.78] 0.001 5. Partial correlation matrix (returns only the correlation coefficients) @@ -815,7 +815,7 @@ def partial_corr( 6. Semi-partial correlation on x >>> pg.partial_corr(data=df, x='x', y='y', x_covar=['cv1', 'cv2', 'cv3']).round(3) - n r CI95% p-val + n r CI95 p-val pearson 30 0.463 [0.1, 0.72] 0.015 """ from pingouin.utils import _flatten_list @@ -883,7 +883,7 @@ def partial_corr( if np.isnan(r): # Correlation failed. Return NaN. When would this happen? - return pd.DataFrame({"n": n, "r": np.nan, "CI95%": np.nan, "p-val": np.nan}, index=[method]) + return pd.DataFrame({"n": n, "r": np.nan, "CI95": np.nan, "p-val": np.nan}, index=[method]) # Compute the two-sided p-value and confidence intervals # https://online.stat.psu.edu/stat505/lesson/6/6.3 @@ -896,7 +896,7 @@ def partial_corr( stats = { "n": n, "r": r, - "CI95%": [ci], + "CI95": [ci], "p-val": pval, } @@ -904,7 +904,7 @@ def partial_corr( stats = pd.DataFrame(stats, index=[method]) # Define order - col_keep = ["n", "r", "CI95%", "p-val"] + col_keep = ["n", "r", "CI95", "p-val"] col_order = [k for k in col_keep if k in stats.keys().tolist()] return _postprocess_dataframe(stats)[col_order] @@ -1175,7 +1175,7 @@ def rm_corr(data=None, x=None, y=None, subject=None): >>> import pingouin as pg >>> df = pg.read_dataset('rm_corr') >>> pg.rm_corr(data=df, x='pH', y='PacO2', subject='Subject') - r dof pval CI95% power + r dof pval CI95 power rm_corr -0.50677 38 0.000847 [-0.71, -0.23] 0.929579 Now plot using the :py:func:`pingouin.plot_rm_corr` function: @@ -1219,7 +1219,7 @@ def rm_corr(data=None, x=None, y=None, subject=None): pwr = power_corr(r=rm, n=n, alternative="two-sided") # Convert to Dataframe stats = pd.DataFrame( - {"r": rm, "dof": int(dof), "pval": pval, "CI95%": [ci], "power": pwr}, index=["rm_corr"] + {"r": rm, "dof": int(dof), "pval": pval, "CI95": [ci], "power": pwr}, index=["rm_corr"] ) return _postprocess_dataframe(stats) diff --git a/src/pingouin/pairwise.py b/src/pingouin/pairwise.py index 302efc4a..613b142b 100644 --- a/src/pingouin/pairwise.py +++ b/src/pingouin/pairwise.py @@ -1232,7 +1232,7 @@ def pairwise_corr( >>> pd.set_option('display.max_columns', 20) >>> data = pg.read_dataset('pairwise_corr').iloc[:, 1:] >>> pg.pairwise_corr(data, method='spearman', alternative='greater', padjust='bonf').round(3) - X Y method alternative n r CI95% p-unc p-corr p-adjust power + X Y method alternative n r CI95 p-unc p-corr p-adjust power 0 Neuroticism Extraversion spearman greater 500 -0.325 [-0.39, 1.0] 1.000 1.000 bonf 0.000 1 Neuroticism Openness spearman greater 500 -0.028 [-0.1, 1.0] 0.735 1.000 bonf 0.012 2 Neuroticism Agreeableness spearman greater 500 -0.151 [-0.22, 1.0] 1.000 1.000 bonf 0.000 @@ -1249,7 +1249,7 @@ def pairwise_corr( >>> pcor = pg.pairwise_corr(data, columns=['Openness', 'Extraversion', ... 'Neuroticism'], method='bicor') >>> pcor.round(3) - X Y method alternative n r CI95% p-unc power + X Y method alternative n r CI95 p-unc power 0 Openness Extraversion bicor two-sided 500 0.247 [0.16, 0.33] 0.000 1.000 1 Openness Neuroticism bicor two-sided 500 -0.028 [-0.12, 0.06] 0.535 0.095 2 Extraversion Neuroticism bicor two-sided 500 -0.343 [-0.42, -0.26] 0.000 1.000 @@ -1257,7 +1257,7 @@ def pairwise_corr( 3. One-versus-all pairwise correlations >>> pg.pairwise_corr(data, columns=['Neuroticism']).round(3) - X Y method alternative n r CI95% p-unc BF10 power + X Y method alternative n r CI95 p-unc BF10 power 0 Neuroticism Extraversion pearson two-sided 500 -0.350 [-0.42, -0.27] 0.000 6.765e+12 1.000 1 Neuroticism Openness pearson two-sided 500 -0.010 [-0.1, 0.08] 0.817 0.058 0.056 2 Neuroticism Agreeableness pearson two-sided 500 -0.134 [-0.22, -0.05] 0.003 5.122 0.854 @@ -1267,7 +1267,7 @@ def pairwise_corr( >>> columns = [['Neuroticism', 'Extraversion'], ['Openness']] >>> pg.pairwise_corr(data, columns).round(3) - X Y method alternative n r CI95% p-unc BF10 power + X Y method alternative n r CI95 p-unc BF10 power 0 Neuroticism Openness pearson two-sided 500 -0.010 [-0.1, 0.08] 0.817 0.058 0.056 1 Extraversion Openness pearson two-sided 500 0.267 [0.18, 0.35] 0.000 5.277e+06 1.000 @@ -1278,7 +1278,7 @@ def pairwise_corr( 6. Pairwise partial correlation >>> pg.pairwise_corr(data, covar=['Neuroticism', 'Openness']) - X Y method covar alternative n r CI95% p-unc + X Y method covar alternative n r CI95 p-unc 0 Extraversion Agreeableness pearson ['Neuroticism', 'Openness'] two-sided 500 -0.038737 [-0.13, 0.05] 0.388361 1 Extraversion Conscientiousness pearson ['Neuroticism', 'Openness'] two-sided 500 -0.071427 [-0.16, 0.02] 0.111389 2 Agreeableness Conscientiousness pearson ['Neuroticism', 'Openness'] two-sided 500 0.123108 [0.04, 0.21] 0.005944 @@ -1408,7 +1408,7 @@ def traverse(o, tree_types=(list, tuple)): "n", "outliers", "r", - "CI95%", + "CI95", "p-val", "BF10", "power", @@ -1491,7 +1491,7 @@ def traverse(o, tree_types=(list, tuple)): "n", "outliers", "r", - "CI95%", + "CI95", "p-unc", "p-corr", "p-adjust", diff --git a/src/pingouin/parametric.py b/src/pingouin/parametric.py index f8f87c9f..10d54fd3 100644 --- a/src/pingouin/parametric.py +++ b/src/pingouin/parametric.py @@ -61,7 +61,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 * ``'dof'``: degrees of freedom * ``'alternative'``: alternative of the test * ``'p-val'``: p-value - * ``'CI95%'``: confidence intervals of the difference in means + * ``'CI95'``: confidence intervals of the difference in means * ``'cohen-d'``: Cohen d effect size * ``'BF10'``: Bayes Factor of the alternative hypothesis * ``'power'``: achieved power of the test ( = 1 - type II error) @@ -143,7 +143,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> from pingouin import ttest >>> x = [5.5, 2.4, 6.8, 9.6, 4.2] >>> ttest(x, 4).round(2) - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power T-test 1.4 4 two-sided 0.23 [2.32, 9.08] 0.62 0.766 0.19 2. One sided paired T-test. @@ -151,13 +151,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> pre = [5.5, 2.4, 6.8, 9.6, 4.2] >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True, alternative='less').round(2) - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power T-test -2.31 4 less 0.04 [-inf, -0.05] 0.25 3.122 0.12 Now testing the opposite alternative hypothesis >>> ttest(pre, post, paired=True, alternative='greater').round(2) - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power T-test -2.31 4 greater 0.96 [-1.35, inf] 0.25 0.32 0.02 3. Paired T-test with missing values. @@ -166,7 +166,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> pre = [5.5, 2.4, np.nan, 9.6, 4.2] >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True).round(3) - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power T-test -5.902 3 two-sided 0.01 [-1.5, -0.45] 0.306 7.169 0.073 Compare with SciPy @@ -181,7 +181,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> x = np.random.normal(loc=7, size=20) >>> y = np.random.normal(loc=4, size=20) >>> ttest(x, y) - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power T-test 9.106452 38 two-sided 4.306971e-11 [2.64, 4.15] 2.879713 1.366e+08 1.0 5. Independent two-sample T-test with unequal sample size. A Welch's T-test is used. @@ -189,13 +189,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> np.random.seed(123) >>> y = np.random.normal(loc=6.5, size=15) >>> ttest(x, y) - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power T-test 1.996537 31.567592 two-sided 0.054561 [-0.02, 1.65] 0.673518 1.469 0.481867 6. However, the Welch's correction can be disabled: >>> ttest(x, y, correction=False) - T dof alternative p-val CI95% cohen-d BF10 power + T dof alternative p-val CI95 cohen-d BF10 power T-test 1.971859 33 two-sided 0.057056 [-0.03, 1.66] 0.673518 1.418 0.481867 Compare with SciPy @@ -287,7 +287,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 ci[0] = -np.inf # Rename CI - ci_name = "CI%.0f%%" % (100 * confidence) + ci_name = "CI%.0f" % (100 * confidence) # Achieved power if ny == 1: diff --git a/src/pingouin/reliability.py b/src/pingouin/reliability.py index 33933593..33614e88 100644 --- a/src/pingouin/reliability.py +++ b/src/pingouin/reliability.py @@ -189,7 +189,7 @@ def intraclass_corr(data=None, targets=None, raters=None, ratings=None, nan_poli * ``'df1'``: numerator degree of freedom * ``'df2'``: denominator degree of freedom * ``'pval'``: p-value - * ``'CI95%'``: 95% confidence intervals around the ICC + * ``'CI95'``: 95% confidence intervals around the ICC Notes ----- @@ -247,7 +247,7 @@ def intraclass_corr(data=None, targets=None, raters=None, ratings=None, nan_poli >>> icc = pg.intraclass_corr(data=data, targets='Wine', raters='Judge', ... ratings='Scores').round(3) >>> icc.set_index("Type") - Description ICC F df1 df2 pval CI95% + Description ICC F df1 df2 pval CI95 Type ICC1 Single raters absolute 0.728 11.680 7 24 0.0 [0.43, 0.93] ICC2 Single random raters 0.728 11.787 7 21 0.0 [0.43, 0.93] @@ -367,7 +367,7 @@ def intraclass_corr(data=None, targets=None, raters=None, ratings=None, nan_poli l2 = n * (msb - f2u * mse) / (f2u * (k * msj + (k * n - k - n) * mse) + n * msb) u2 = n * (f2l * msb - mse) / (k * msj + (k * n - k - n) * mse + n * f2l * msb) - stats["CI95%"] = [ + stats["CI95"] = [ np.array([l1, u1]), np.array([l2, u2]), np.array([l3, u3]), diff --git a/src/pingouin/utils.py b/src/pingouin/utils.py index f2e22e2a..df1fd203 100644 --- a/src/pingouin/utils.py +++ b/src/pingouin/utils.py @@ -90,7 +90,7 @@ def _postprocess_dataframe(df): `pingouin.options`. The default rounding (number of decimals) is determined by `pingouin.options['round']`. You can specify rounding for a given column name by the option `'round.column.'`, e.g. - `'round.column.CI95%'`. Analogously, `'round.row.'` also works + `'round.column.CI95'`. Analogously, `'round.row.'` also works (where `rowname`) refers to the pandas index), as well as `'round.cell.[]x[ Date: Mon, 7 Oct 2024 14:33:44 -0400 Subject: [PATCH 2/7] p-* --> p_* (also U-* and W-*) --- README.rst | 16 ++--- docs/index.rst | 16 ++--- src/pingouin/contingency.py | 12 ++-- src/pingouin/correlation.py | 56 +++++++-------- src/pingouin/equivalence.py | 2 +- src/pingouin/multivariate.py | 2 +- src/pingouin/nonparametric.py | 60 ++++++++-------- src/pingouin/pairwise.py | 108 ++++++++++++++-------------- src/pingouin/parametric.py | 130 +++++++++++++++++----------------- src/pingouin/power.py | 2 +- tests/test_contingency.py | 6 +- tests/test_correlation.py | 32 ++++----- tests/test_nonparametric.py | 26 +++---- tests/test_pairwise.py | 52 +++++++------- tests/test_pandas.py | 2 +- tests/test_parametric.py | 90 +++++++++++------------ 16 files changed, 306 insertions(+), 306 deletions(-) diff --git a/README.rst b/README.rst index edb72ff1..e862f335 100644 --- a/README.rst +++ b/README.rst @@ -157,7 +157,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio :widths: auto ====== ===== ============= ======= ============= ========= ====== ======= - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power ====== ===== ============= ======= ============= ========= ====== ======= -3.401 58 two-sided 0.001 [-1.68 -0.43] 0.878 26.155 0.917 ====== ===== ============= ======= ============= ========= ====== ======= @@ -175,7 +175,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio :widths: auto === ===== =========== ======= ====== ======= - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power === ===== =========== ======= ====== ======= 30 0.595 [0.3 0.79] 0.001 69.723 0.950 === ===== =========== ======= ====== ======= @@ -196,7 +196,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio :widths: auto === ===== =========== ======= ======= - n r CI95 p-val power + n r CI95 p_val power === ===== =========== ======= ======= 30 0.576 [0.27 0.78] 0.001 0.933 === ===== =========== ======= ======= @@ -244,7 +244,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto ======== ======= ==== ===== ======= ======= ======= - Source SS DF MS F p-unc np2 + Source SS DF MS F p_unc np2 ======== ======= ==== ===== ======= ======= ======= Group 5.460 1 5.460 5.244 0.023 0.029 Within 185.343 178 1.041 nan nan nan @@ -263,7 +263,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto ======== ======= ==== ===== ======= ======= ======= ======= - Source SS DF MS F p-unc ng2 eps + Source SS DF MS F p_unc ng2 eps ======== ======= ==== ===== ======= ======= ======= ======= Time 7.628 2 3.814 3.913 0.023 0.04 0.999 Error 115.027 118 0.975 nan nan nan nan @@ -287,7 +287,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto ========== ======= ======= ======== ============ ====== ====== ============= ======= ======== ========== ====== ======== - Contrast A B Paired Parametric T dof alternative p-unc p-corr p-adjust BF10 hedges + Contrast A B Paired Parametric T dof alternative p_unc p_corr p_adjust BF10 hedges ========== ======= ======= ======== ============ ====== ====== ============= ======= ======== ========== ====== ======== Time August January True True -1.740 59.000 two-sided 0.087 0.131 fdr_bh 0.582 -0.328 Time August June True True -2.743 59.000 two-sided 0.008 0.024 fdr_bh 4.232 -0.483 @@ -310,7 +310,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto =========== ===== ===== ===== ===== ===== ======= ===== ======= - Source SS DF1 DF2 MS F p-unc np2 eps + Source SS DF1 DF2 MS F p_unc np2 eps =========== ===== ===== ===== ===== ===== ======= ===== ======= Group 5.460 1 58 5.460 5.052 0.028 0.080 nan Time 7.628 2 116 3.814 4.027 0.020 0.065 0.999 @@ -334,7 +334,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto === === ======== ============= === ===== ============= ======= ====== ======= - X Y method alternative n r CI95 p-unc BF10 power + X Y method alternative n r CI95 p_unc BF10 power === === ======== ============= === ===== ============= ======= ====== ======= X Y pearson two-sided 30 0.366 [0.01 0.64] 0.047 1.500 0.525 X Z pearson two-sided 30 0.251 [-0.12 0.56] 0.181 0.534 0.272 diff --git a/docs/index.rst b/docs/index.rst index 0339b6d5..083689b2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -135,7 +135,7 @@ Quick start :widths: auto ====== ===== ============= ======= ============= ========= ====== ======= - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power ====== ===== ============= ======= ============= ========= ====== ======= -3.401 58 two-sided 0.001 [-1.68 -0.43] 0.878 26.155 0.917 ====== ===== ============= ======= ============= ========= ====== ======= @@ -153,7 +153,7 @@ Quick start :widths: auto === ===== =========== ======= ====== ======= - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power === ===== =========== ======= ====== ======= 30 0.595 [0.3 0.79] 0.001 69.723 0.950 === ===== =========== ======= ====== ======= @@ -174,7 +174,7 @@ Quick start :widths: auto === ===== =========== ======= ======= - n r CI95 p-val power + n r CI95 p_val power === ===== =========== ======= ======= 30 0.576 [0.27 0.78] 0.001 0.933 === ===== =========== ======= ======= @@ -235,7 +235,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto ======== ======= ==== ===== ======= ======= ======= - Source SS DF MS F p-unc np2 + Source SS DF MS F p_unc np2 ======== ======= ==== ===== ======= ======= ======= Group 5.460 1 5.460 5.244 0.023 0.029 Within 185.343 178 1.041 nan nan nan @@ -254,7 +254,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto ======== ======= ==== ===== ======= ======= ======= ======= - Source SS DF MS F p-unc ng2 eps + Source SS DF MS F p_unc ng2 eps ======== ======= ==== ===== ======= ======= ======= ======= Time 7.628 2 3.814 3.913 0.023 0.04 0.999 Error 115.027 118 0.975 nan nan nan nan @@ -278,7 +278,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto ========== ======= ======= ======== ============ ====== ====== ============= ======= ======== ========== ====== ======== - Contrast A B Paired Parametric T dof alternative p-unc p-corr p-adjust BF10 hedges + Contrast A B Paired Parametric T dof alternative p_unc p_corr p_adjust BF10 hedges ========== ======= ======= ======== ============ ====== ====== ============= ======= ======== ========== ====== ======== Time August January True True -1.740 59.000 two-sided 0.087 0.131 fdr_bh 0.582 -0.328 Time August June True True -2.743 59.000 two-sided 0.008 0.024 fdr_bh 4.232 -0.483 @@ -301,7 +301,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto =========== ===== ===== ===== ===== ===== ======= ===== ======= - Source SS DF1 DF2 MS F p-unc np2 eps + Source SS DF1 DF2 MS F p_unc np2 eps =========== ===== ===== ===== ===== ===== ======= ===== ======= Group 5.460 1 58 5.460 5.052 0.028 0.080 nan Time 7.628 2 116 3.814 4.027 0.020 0.065 0.999 @@ -325,7 +325,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto === === ======== ============= === ===== ============= ======= ====== ======= - X Y method alternative n r CI95 p-unc BF10 power + X Y method alternative n r CI95 p_unc BF10 power === === ======== ============= === ===== ============= ======= ====== ======= X Y pearson two-sided 30 0.366 [0.01 0.64] 0.047 1.500 0.525 X Z pearson two-sided 30 0.251 [-0.12 0.56] 0.181 0.534 0.272 diff --git a/src/pingouin/contingency.py b/src/pingouin/contingency.py index 9e486848..d9011f3e 100644 --- a/src/pingouin/contingency.py +++ b/src/pingouin/contingency.py @@ -234,8 +234,8 @@ def chi2_mcnemar(data, x, y, correction=True): * ``'chi2'``: The test statistic * ``'dof'``: The degree of freedom - * ``'p-approx'``: The approximated p-value - * ``'p-exact'``: The exact p-value + * ``'p_approx'``: The approximated p-value + * ``'p_exact'``: The exact p-value Notes ----- @@ -304,7 +304,7 @@ def chi2_mcnemar(data, x, y, correction=True): The McNemar test should be sensitive to this. >>> stats - chi2 dof p-approx p-exact + chi2 dof p_approx p_exact mcnemar 20.020833 1 0.000008 0.000003 """ # Python code initially inspired by statsmodel's mcnemar @@ -336,9 +336,9 @@ def chi2_mcnemar(data, x, y, correction=True): stats = { "chi2": chi2, "dof": 1, - "p-approx": sp_chi2.sf(chi2, 1), - "p-exact": pexact, - # 'p-mid': pexact - binom.pmf(b, n_discordants, 0.5) + "p_approx": sp_chi2.sf(chi2, 1), + "p_exact": pexact, + # 'p_mid': pexact - binom.pmf(b, n_discordants, 0.5) } stats = pd.DataFrame(stats, index=["mcnemar"]) diff --git a/src/pingouin/correlation.py b/src/pingouin/correlation.py index 985242a4..4d889d7a 100644 --- a/src/pingouin/correlation.py +++ b/src/pingouin/correlation.py @@ -413,7 +413,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): * ``'outliers'``: number of outliers, only if a robust method was used * ``'r'``: Correlation coefficient * ``'CI95'``: 95% parametric confidence intervals around :math:`r` - * ``'p-val'``: p-value + * ``'p_val'``: p-value * ``'BF10'``: Bayes Factor of the alternative hypothesis (only for Pearson correlation) * ``'power'``: achieved power of the test with an alpha of 0.05. @@ -516,60 +516,60 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): >>> x, y = np.random.multivariate_normal(mean, cov, 30).T >>> # Compute Pearson correlation >>> pg.corr(x, y).round(3) - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power pearson 30 0.491 [0.16, 0.72] 0.006 8.55 0.809 2. Pearson correlation with two outliers >>> x[3], y[5] = 12, -8 >>> pg.corr(x, y).round(3) - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power pearson 30 0.147 [-0.23, 0.48] 0.439 0.302 0.121 3. Spearman correlation (robust to outliers) >>> pg.corr(x, y, method="spearman").round(3) - n r CI95 p-val power + n r CI95 p_val power spearman 30 0.401 [0.05, 0.67] 0.028 0.61 4. Biweight midcorrelation (robust) >>> pg.corr(x, y, method="bicor").round(3) - n r CI95 p-val power + n r CI95 p_val power bicor 30 0.393 [0.04, 0.66] 0.031 0.592 5. Percentage bend correlation (robust) >>> pg.corr(x, y, method='percbend').round(3) - n r CI95 p-val power + n r CI95 p_val power percbend 30 0.389 [0.03, 0.66] 0.034 0.581 6. Shepherd's pi correlation (robust) >>> pg.corr(x, y, method='shepherd').round(3) - n outliers r CI95 p-val power + n outliers r CI95 p_val power shepherd 30 2 0.437 [0.08, 0.7] 0.02 0.662 7. Skipped spearman correlation (robust) >>> pg.corr(x, y, method='skipped').round(3) - n outliers r CI95 p-val power + n outliers r CI95 p_val power skipped 30 2 0.437 [0.08, 0.7] 0.02 0.662 8. One-tailed Pearson correlation >>> pg.corr(x, y, alternative="greater", method='pearson').round(3) - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power pearson 30 0.147 [-0.17, 1.0] 0.22 0.467 0.194 >>> pg.corr(x, y, alternative="less", method='pearson').round(3) - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power pearson 30 0.147 [-1.0, 0.43] 0.78 0.137 0.008 9. Perfect correlation >>> pg.corr(x, -x).round(3) - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power pearson 30 -1.0 [-1.0, -1.0] 0.0 inf 1 10. Using columns of a pandas dataframe @@ -577,7 +577,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): >>> import pandas as pd >>> data = pd.DataFrame({'x': x, 'y': y}) >>> pg.corr(data['x'], data['y']).round(3) - n r CI95 p-val BF10 power + n r CI95 p_val BF10 power pearson 30 0.147 [-0.23, 0.48] 0.439 0.302 0.121 """ # Safety check @@ -626,7 +626,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): "n": n, "r": np.nan, "CI95": np.nan, - "p-val": np.nan, + "p_val": np.nan, "BF10": np.nan, "power": np.nan, }, @@ -656,7 +656,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): pval = _correl_pvalue(r, n_clean, k=0, alternative=alternative) # Create dictionnary - stats = {"n": n, "r": r, "CI95": [ci], "p-val": pval, "power": pr} + stats = {"n": n, "r": r, "CI95": [ci], "p_val": pval, "power": pr} if method in ["shepherd", "skipped"]: stats["outliers"] = n_outliers @@ -669,7 +669,7 @@ def corr(x, y, alternative="two-sided", method="pearson", **kwargs): stats = pd.DataFrame(stats, index=[method]) # Define order - col_keep = ["n", "outliers", "r", "CI95", "p-val", "BF10", "power"] + col_keep = ["n", "outliers", "r", "CI95", "p_val", "BF10", "power"] col_order = [k for k in col_keep if k in stats.keys().tolist()] return _postprocess_dataframe(stats)[col_order] @@ -727,7 +727,7 @@ def partial_corr( * ``'n'``: Sample size (after removal of missing values) * ``'r'``: Partial correlation coefficient * ``'CI95'``: 95% parametric confidence intervals around :math:`r` - * ``'p-val'``: p-value + * ``'p_val'``: p-value See also -------- @@ -773,7 +773,7 @@ def partial_corr( >>> import pingouin as pg >>> df = pg.read_dataset('partial_corr') >>> pg.partial_corr(data=df, x='x', y='y', covar='cv1').round(3) - n r CI95 p-val + n r CI95 p_val pearson 30 0.568 [0.25, 0.77] 0.001 2. Spearman partial correlation with several covariates @@ -781,25 +781,25 @@ def partial_corr( >>> # Partial correlation of x and y controlling for cv1, cv2 and cv3 >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'], ... method='spearman').round(3) - n r CI95 p-val + n r CI95 p_val spearman 30 0.521 [0.18, 0.75] 0.005 3. Same but one-sided test >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'], ... alternative="greater", method='spearman').round(3) - n r CI95 p-val + n r CI95 p_val spearman 30 0.521 [0.24, 1.0] 0.003 >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'], ... alternative="less", method='spearman').round(3) - n r CI95 p-val + n r CI95 p_val spearman 30 0.521 [-1.0, 0.72] 0.997 4. As a pandas method >>> df.partial_corr(x='x', y='y', covar=['cv1'], method='spearman').round(3) - n r CI95 p-val + n r CI95 p_val spearman 30 0.578 [0.27, 0.78] 0.001 5. Partial correlation matrix (returns only the correlation coefficients) @@ -815,7 +815,7 @@ def partial_corr( 6. Semi-partial correlation on x >>> pg.partial_corr(data=df, x='x', y='y', x_covar=['cv1', 'cv2', 'cv3']).round(3) - n r CI95 p-val + n r CI95 p_val pearson 30 0.463 [0.1, 0.72] 0.015 """ from pingouin.utils import _flatten_list @@ -883,7 +883,7 @@ def partial_corr( if np.isnan(r): # Correlation failed. Return NaN. When would this happen? - return pd.DataFrame({"n": n, "r": np.nan, "CI95": np.nan, "p-val": np.nan}, index=[method]) + return pd.DataFrame({"n": n, "r": np.nan, "CI95": np.nan, "p_val": np.nan}, index=[method]) # Compute the two-sided p-value and confidence intervals # https://online.stat.psu.edu/stat505/lesson/6/6.3 @@ -897,14 +897,14 @@ def partial_corr( "n": n, "r": r, "CI95": [ci], - "p-val": pval, + "p_val": pval, } # Convert to DataFrame stats = pd.DataFrame(stats, index=[method]) # Define order - col_keep = ["n", "r", "CI95", "p-val"] + col_keep = ["n", "r", "CI95", "p_val"] col_order = [k for k in col_keep if k in stats.keys().tolist()] return _postprocess_dataframe(stats)[col_order] @@ -1052,8 +1052,8 @@ def rcorr( >>> # Compare with the pg.pairwise_corr function >>> pairwise = df.iloc[:, 0:4].pairwise_corr(method='spearman', ... padjust='holm') - >>> pairwise[['X', 'Y', 'r', 'p-corr']].round(3) # Do not show all columns - X Y r p-corr + >>> pairwise[['X', 'Y', 'r', 'p_corr']].round(3) # Do not show all columns + X Y r p_corr 0 Neuroticism Extraversion -0.325 0.000 1 Neuroticism Openness -0.027 0.543 2 Neuroticism Agreeableness -0.150 0.002 @@ -1214,7 +1214,7 @@ def rm_corr(data=None, x=None, y=None, subject=None): ssfactor = aov.at[1, "SS"] sserror = aov.at[2, "SS"] rm = sign * np.sqrt(ssfactor / (ssfactor + sserror)) - pval = aov.at[1, "p-unc"] + pval = aov.at[1, "p_unc"] ci = compute_esci(stat=rm, nx=n, eftype="pearson").tolist() pwr = power_corr(r=rm, n=n, alternative="two-sided") # Convert to Dataframe diff --git a/src/pingouin/equivalence.py b/src/pingouin/equivalence.py index 4f5bc1cc..91e57946 100644 --- a/src/pingouin/equivalence.py +++ b/src/pingouin/equivalence.py @@ -78,7 +78,7 @@ def tost(x, y, bound=1, paired=False, correction=False): # T-tests df_a = ttest(x + bound, y, paired=paired, correction=correction, alternative="greater") df_b = ttest(x - bound, y, paired=paired, correction=correction, alternative="less") - pval = max(df_a.at["T-test", "p-val"], df_b.at["T-test", "p-val"]) + pval = max(df_a.at["T-test", "p_val"], df_b.at["T-test", "p_val"]) # Create output dataframe stats = pd.DataFrame( diff --git a/src/pingouin/multivariate.py b/src/pingouin/multivariate.py index e306187a..f2ec6da0 100644 --- a/src/pingouin/multivariate.py +++ b/src/pingouin/multivariate.py @@ -144,7 +144,7 @@ def multivariate_ttest(X, Y=None, paired=False): * ``'F'``: F-value * ``'df1'``: first degree of freedom * ``'df2'``: second degree of freedom - * ``'p-val'``: p-value + * ``'p_val'``: p-value See Also -------- diff --git a/src/pingouin/nonparametric.py b/src/pingouin/nonparametric.py index 19c1a7ce..a88922dc 100644 --- a/src/pingouin/nonparametric.py +++ b/src/pingouin/nonparametric.py @@ -172,9 +172,9 @@ def mwu(x, y, alternative="two-sided", **kwargs): ------- stats : :py:class:`pandas.DataFrame` - * ``'U-val'``: U-value corresponding with sample x + * ``'U_val'``: U-value corresponding with sample x * ``'alternative'``: tail of the test - * ``'p-val'``: p-value + * ``'p_val'``: p-value * ``'RBC'`` : rank-biserial correlation * ``'CLES'`` : common language effect size @@ -239,7 +239,7 @@ def mwu(x, y, alternative="two-sided", **kwargs): >>> x = np.random.uniform(low=0, high=1, size=20) >>> y = np.random.uniform(low=0.2, high=1.2, size=20) >>> pg.mwu(x, y, alternative='two-sided') - U-val alternative p-val RBC CLES + U_val alternative p_val RBC CLES MWU 97.0 two-sided 0.00556 -0.515 0.2425 Compare with SciPy @@ -251,23 +251,23 @@ def mwu(x, y, alternative="two-sided", **kwargs): One-sided test >>> pg.mwu(x, y, alternative='greater') - U-val alternative p-val RBC CLES + U_val alternative p_val RBC CLES MWU 97.0 greater 0.997442 -0.515 0.2425 >>> pg.mwu(x, y, alternative='less') - U-val alternative p-val RBC CLES + U_val alternative p_val RBC CLES MWU 97.0 less 0.00278 -0.515 0.7575 Passing keyword arguments to :py:func:`scipy.stats.mannwhitneyu`: >>> pg.mwu(x, y, alternative='two-sided', method='exact') - U-val alternative p-val RBC CLES + U_val alternative p_val RBC CLES MWU 97.0 two-sided 0.004681 -0.515 0.2425 Reversing the order of `x` and `y`. >>> pg.mwu(y, x) - U-val alternative p-val RBC CLES + U_val alternative p_val RBC CLES MWU 303.0 two-sided 0.00556 0.515 0.7575 """ x = np.asarray(x) @@ -304,7 +304,7 @@ def mwu(x, y, alternative="two-sided", **kwargs): # Fill output DataFrame stats = pd.DataFrame( - {"U-val": uval_x, "alternative": alternative, "p-val": pval, "RBC": rbc, "CLES": cles}, + {"U_val": uval_x, "alternative": alternative, "p_val": pval, "RBC": rbc, "CLES": cles}, index=["MWU"], ) return _postprocess_dataframe(stats) @@ -336,9 +336,9 @@ def wilcoxon(x, y=None, alternative="two-sided", **kwargs): ------- stats : :py:class:`pandas.DataFrame` - * ``'W-val'``: W-value + * ``'W_val'``: W-value * ``'alternative'``: tail of the test - * ``'p-val'``: p-value + * ``'p_val'``: p-value * ``'RBC'`` : matched pairs rank-biserial correlation (effect size) * ``'CLES'`` : common language effect size @@ -409,14 +409,14 @@ def wilcoxon(x, y=None, alternative="two-sided", **kwargs): >>> x = np.array([20, 22, 19, 20, 22, 18, 24, 20, 19, 24, 26, 13]) >>> y = np.array([38, 37, 33, 29, 14, 12, 20, 22, 17, 25, 26, 16]) >>> pg.wilcoxon(x, y, alternative='two-sided') - W-val alternative p-val RBC CLES + W_val alternative p_val RBC CLES Wilcoxon 20.5 two-sided 0.285765 -0.378788 0.395833 Same but using pre-computed differences. However, the CLES effect size cannot be computed as it requires the raw data. >>> pg.wilcoxon(x - y) - W-val alternative p-val RBC CLES + W_val alternative p_val RBC CLES Wilcoxon 20.5 two-sided 0.285765 -0.378788 NaN Compare with SciPy @@ -429,17 +429,17 @@ def wilcoxon(x, y=None, alternative="two-sided", **kwargs): a continuity correction. Disabling it gives the same p-value as scipy: >>> pg.wilcoxon(x, y, alternative='two-sided', correction=False) - W-val alternative p-val RBC CLES + W_val alternative p_val RBC CLES Wilcoxon 20.5 two-sided 0.266166 -0.378788 0.395833 One-sided test >>> pg.wilcoxon(x, y, alternative='greater') - W-val alternative p-val RBC CLES + W_val alternative p_val RBC CLES Wilcoxon 20.5 greater 0.876244 -0.378788 0.395833 >>> pg.wilcoxon(x, y, alternative='less') - W-val alternative p-val RBC CLES + W_val alternative p_val RBC CLES Wilcoxon 20.5 less 0.142883 -0.378788 0.604167 """ x = np.asarray(x) @@ -494,7 +494,7 @@ def wilcoxon(x, y=None, alternative="two-sided", **kwargs): # Fill output DataFrame stats = pd.DataFrame( - {"W-val": wval, "alternative": alternative, "p-val": pval, "RBC": rbc, "CLES": cles}, + {"W_val": wval, "alternative": alternative, "p_val": pval, "RBC": rbc, "CLES": cles}, index=["Wilcoxon"], ) return _postprocess_dataframe(stats) @@ -517,7 +517,7 @@ def kruskal(data=None, dv=None, between=None, detailed=False): stats : :py:class:`pandas.DataFrame` * ``'H'``: The Kruskal-Wallis H statistic, corrected for ties - * ``'p-unc'``: Uncorrected p-value + * ``'p_unc'``: Uncorrected p-value * ``'dof'``: degrees of freedom Notes @@ -540,7 +540,7 @@ def kruskal(data=None, dv=None, between=None, detailed=False): >>> from pingouin import kruskal, read_dataset >>> df = read_dataset('anova') >>> kruskal(data=df, dv='Pain threshold', between='Hair color') - Source ddof1 H p-unc + Source ddof1 H p_unc Kruskal Hair color 3 10.58863 0.014172 """ # Check data @@ -580,7 +580,7 @@ def kruskal(data=None, dv=None, between=None, detailed=False): "Source": between, "ddof1": ddof1, "H": H, - "p-unc": p_unc, + "p_unc": p_unc, }, index=["Kruskal"], ) @@ -617,7 +617,7 @@ def friedman(data=None, dv=None, within=None, subject=None, method="chisq"): * ``'Q'``: The Friedman chi-square statistic, corrected for ties * ``'dof'``: degrees of freedom - * ``'p-unc'``: Uncorrected p-value of the chi squared test + * ``'p_unc'``: Uncorrected p-value of the chi squared test If ``method='f'`` @@ -625,7 +625,7 @@ def friedman(data=None, dv=None, within=None, subject=None, method="chisq"): * ``'F'``: The Friedman F statistic, corrected for ties * ``'dof1'``: degrees of freedom of the numerator * ``'dof2'``: degrees of freedom of the denominator - * ``'p-unc'``: Uncorrected p-value of the F test + * ``'p_unc'``: Uncorrected p-value of the F test Notes ----- @@ -661,7 +661,7 @@ def friedman(data=None, dv=None, within=None, subject=None, method="chisq"): ... 'red': {0: 7, 1: 5, 2: 8, 3: 6, 4: 5, 5: 7, 6: 9, 7: 6, 8: 4, 9: 6, 10: 7, 11: 3}, ... 'rose': {0: 8, 1: 5, 2: 6, 3: 4, 4: 7, 5: 5, 6: 3, 7: 7, 8: 6, 9: 4, 10: 4, 11: 3}}) >>> pg.friedman(df) - Source W ddof1 Q p-unc + Source W ddof1 Q p_unc Friedman Within 0.083333 2 2.0 0.367879 Compare with SciPy @@ -674,13 +674,13 @@ def friedman(data=None, dv=None, within=None, subject=None, method="chisq"): >>> df_long = df.melt(ignore_index=False).reset_index() >>> pg.friedman(data=df_long, dv="value", within="variable", subject="index") - Source W ddof1 Q p-unc + Source W ddof1 Q p_unc Friedman variable 0.083333 2 2.0 0.367879 Using the F-test method >>> pg.friedman(df, method="f") - Source W ddof1 ddof2 F p-unc + Source W ddof1 ddof2 F p_unc Friedman Within 0.083333 1.833333 20.166667 1.0 0.378959 """ # Convert from wide to long-format, if needed @@ -730,7 +730,7 @@ def friedman(data=None, dv=None, within=None, subject=None, method="chisq"): p_unc = scipy.stats.chi2.sf(Q, ddof1) # Create output dataframe stats = pd.DataFrame( - {"Source": within, "W": W, "ddof1": ddof1, "Q": Q, "p-unc": p_unc}, index=["Friedman"] + {"Source": within, "W": W, "ddof1": ddof1, "Q": Q, "p_unc": p_unc}, index=["Friedman"] ) elif method == "f": # Compute the F statistic @@ -741,7 +741,7 @@ def friedman(data=None, dv=None, within=None, subject=None, method="chisq"): p_unc = scipy.stats.f.sf(F, ddof1, ddof2) # Create output dataframe stats = pd.DataFrame( - {"Source": within, "W": W, "ddof1": ddof1, "ddof2": ddof2, "F": F, "p-unc": p_unc}, + {"Source": within, "W": W, "ddof1": ddof1, "ddof2": ddof2, "F": F, "p_unc": p_unc}, index=["Friedman"], ) return _postprocess_dataframe(stats) @@ -770,7 +770,7 @@ def cochran(data=None, dv=None, within=None, subject=None): stats : :py:class:`pandas.DataFrame` * ``'Q'``: The Cochran Q statistic - * ``'p-unc'``: Uncorrected p-value + * ``'p_unc'``: Uncorrected p-value * ``'dof'``: degrees of freedom Notes @@ -809,14 +809,14 @@ def cochran(data=None, dv=None, within=None, subject=None): >>> from pingouin import cochran, read_dataset >>> df = read_dataset('cochran') >>> cochran(data=df, dv='Energetic', within='Time', subject='Subject') - Source dof Q p-unc + Source dof Q p_unc cochran Time 2 6.705882 0.034981 Same but using a wide-format dataframe >>> df_wide = df.pivot_table(index="Subject", columns="Time", values="Energetic") >>> cochran(df_wide) - Source dof Q p-unc + Source dof Q p_unc cochran Within 2 6.705882 0.034981 """ # Convert from wide to long-format, if needed @@ -857,7 +857,7 @@ def cochran(data=None, dv=None, within=None, subject=None): p_unc = scipy.stats.chi2.sf(q, dof) # Create output dataframe - stats = pd.DataFrame({"Source": within, "dof": dof, "Q": q, "p-unc": p_unc}, index=["cochran"]) + stats = pd.DataFrame({"Source": within, "dof": dof, "Q": q, "p_unc": p_unc}, index=["cochran"]) return _postprocess_dataframe(stats) diff --git a/src/pingouin/pairwise.py b/src/pingouin/pairwise.py index 613b142b..b92ef14e 100644 --- a/src/pingouin/pairwise.py +++ b/src/pingouin/pairwise.py @@ -145,14 +145,14 @@ def pairwise_tests( independent * ``'Parametric'``: indicates if (non)-parametric tests were used * ``'T'``: T statistic (only if parametric=True) - * ``'U-val'``: Mann-Whitney U stat (if parametric=False and unpaired + * ``'U_val'``: Mann-Whitney U stat (if parametric=False and unpaired data) - * ``'W-val'``: Wilcoxon W stat (if parametric=False and paired data) + * ``'W_val'``: Wilcoxon W stat (if parametric=False and paired data) * ``'dof'``: degrees of freedom (only if parametric=True) * ``'alternative'``: tail of the test - * ``'p-unc'``: Uncorrected p-values - * ``'p-corr'``: Corrected p-values - * ``'p-adjust'``: p-values correction method + * ``'p_unc'``: Uncorrected p-values + * ``'p_corr'``: Corrected p-values + * ``'p_adjust'``: p-values correction method * ``'BF10'``: Bayes Factor * ``'hedges'``: effect size (or any effect size defined in ``effsize``) @@ -205,14 +205,14 @@ def pairwise_tests( >>> pd.set_option('display.max_columns', 20) >>> df = pg.read_dataset('mixed_anova.csv') >>> pg.pairwise_tests(dv='Scores', between='Group', data=df).round(3) - Contrast A B Paired Parametric T dof alternative p-unc BF10 hedges + Contrast A B Paired Parametric T dof alternative p_unc BF10 hedges 0 Group Control Meditation False True -2.29 178.0 two-sided 0.023 1.813 -0.34 2. One within-subject factor >>> post_hocs = pg.pairwise_tests(dv='Scores', within='Time', subject='Subject', data=df) >>> post_hocs.round(3) - Contrast A B Paired Parametric T dof alternative p-unc BF10 hedges + Contrast A B Paired Parametric T dof alternative p_unc BF10 hedges 0 Time August January True True -1.740 59.0 two-sided 0.087 0.582 -0.328 1 Time August June True True -2.743 59.0 two-sided 0.008 4.232 -0.483 2 Time January June True True -1.024 59.0 two-sided 0.310 0.232 -0.170 @@ -221,7 +221,7 @@ def pairwise_tests( >>> pg.pairwise_tests(dv='Scores', within='Time', subject='Subject', ... data=df, parametric=False).round(3) - Contrast A B Paired Parametric W-val alternative p-unc hedges + Contrast A B Paired Parametric W_val alternative p_unc hedges 0 Time August January True False 716.0 two-sided 0.144 -0.328 1 Time August June True False 564.0 two-sided 0.010 -0.483 2 Time January June True False 887.0 two-sided 0.840 -0.170 @@ -231,7 +231,7 @@ def pairwise_tests( >>> posthocs = pg.pairwise_tests(dv='Scores', within='Time', subject='Subject', ... between='Group', padjust='bonf', data=df) >>> posthocs.round(3) - Contrast Time A B Paired Parametric T dof alternative p-unc p-corr p-adjust BF10 hedges + Contrast Time A B Paired Parametric T dof alternative p_unc p_corr p_adjust BF10 hedges 0 Time - August January True True -1.740 59.0 two-sided 0.087 0.261 bonf 0.582 -0.328 1 Time - August June True True -2.743 59.0 two-sided 0.008 0.024 bonf 4.232 -0.483 2 Time - January June True True -1.024 59.0 two-sided 0.310 0.931 bonf 0.232 -0.170 @@ -243,7 +243,7 @@ def pairwise_tests( 5. Two between-subject factors. The order of the ``between`` factors matters! >>> pg.pairwise_tests(dv='Scores', between=['Group', 'Time'], data=df).round(3) - Contrast Group A B Paired Parametric T dof alternative p-unc BF10 hedges + Contrast Group A B Paired Parametric T dof alternative p_unc BF10 hedges 0 Group - Control Meditation False True -2.290 178.0 two-sided 0.023 1.813 -0.340 1 Time - August January False True -1.806 118.0 two-sided 0.074 0.839 -0.328 2 Time - August June False True -2.660 118.0 two-sided 0.009 4.499 -0.483 @@ -259,7 +259,7 @@ def pairwise_tests( >>> df.pairwise_tests(dv='Scores', between=['Group', 'Time'], alternative="less", ... interaction=False).round(3) - Contrast A B Paired Parametric T dof alternative p-unc BF10 hedges + Contrast A B Paired Parametric T dof alternative p_unc BF10 hedges 0 Group Control Meditation False True -2.290 178.0 less 0.012 3.626 -0.340 1 Time August January False True -1.806 118.0 less 0.037 1.679 -0.328 2 Time August June False True -2.660 118.0 less 0.004 8.998 -0.483 @@ -327,13 +327,13 @@ def pairwise_tests( "Paired", "Parametric", "T", - "U-val", - "W-val", + "U_val", + "W_val", "dof", "alternative", - "p-unc", - "p-corr", - "p-adjust", + "p_unc", + "p_corr", + "p_adjust", "BF10", effsize, ] @@ -373,7 +373,7 @@ def pairwise_tests( stats = pd.DataFrame(dtype=np.float64, index=range(len(combs)), columns=col_order) # Force dtype conversion - cols_str = ["Contrast", "Time", "A", "B", "alternative", "p-adjust", "BF10"] + cols_str = ["Contrast", "Time", "A", "B", "alternative", "p_adjust", "BF10"] cols_bool = ["Parametric", "Paired"] stats[cols_str] = stats[cols_str].astype(object) stats[cols_bool] = stats[cols_bool].astype(bool) @@ -402,10 +402,10 @@ def pairwise_tests( stats.at[i, "dof"] = df_ttest.at["T-test", "dof"] else: if paired: - stat_name = "W-val" + stat_name = "W_val" df_ttest = wilcoxon(x, y, alternative=alternative) else: - stat_name = "U-val" + stat_name = "U_val" df_ttest = mwu(x, y, alternative=alternative) options.update(old_options) # restore options @@ -419,20 +419,20 @@ def pairwise_tests( stats.at[i, "std(A)"] = np.nanstd(x, ddof=1) stats.at[i, "std(B)"] = np.nanstd(y, ddof=1) stats.at[i, stat_name] = df_ttest[stat_name].iat[0] - stats.at[i, "p-unc"] = df_ttest["p-val"].iat[0] + stats.at[i, "p_unc"] = df_ttest["p_val"].iat[0] stats.at[i, effsize] = ef # Multiple comparisons - padjust = None if stats["p-unc"].size <= 1 else padjust + padjust = None if stats["p_unc"].size <= 1 else padjust if padjust is not None: if padjust.lower() != "none": - _, stats["p-corr"] = multicomp( - stats["p-unc"].to_numpy(), alpha=alpha, method=padjust + _, stats["p_corr"] = multicomp( + stats["p_unc"].to_numpy(), alpha=alpha, method=padjust ) - stats["p-adjust"] = padjust + stats["p_adjust"] = padjust else: - stats["p-corr"] = None - stats["p-adjust"] = None + stats["p_corr"] = None + stats["p_adjust"] = None else: # Multiple factors if contrast == "multiple_between": @@ -554,10 +554,10 @@ def pairwise_tests( stats.at[ic, "dof"] = df_ttest.at["T-test", "dof"] else: if paired: - stat_name = "W-val" + stat_name = "W_val" df_ttest = wilcoxon(x, y, alternative=alternative) else: - stat_name = "U-val" + stat_name = "U_val" df_ttest = mwu(x, y, alternative=alternative) options.update(old_options) # restore options @@ -569,16 +569,16 @@ def pairwise_tests( stats.at[ic, "std(A)"] = np.nanstd(x, ddof=1) stats.at[ic, "std(B)"] = np.nanstd(y, ddof=1) stats.at[ic, stat_name] = df_ttest[stat_name].iat[0] - stats.at[ic, "p-unc"] = df_ttest["p-val"].iat[0] + stats.at[ic, "p_unc"] = df_ttest["p_val"].iat[0] stats.at[ic, effsize] = ef # Multi-comparison columns if padjust is not None and padjust.lower() != "none": _, pcor = multicomp( - stats.loc[idxiter, "p-unc"].to_numpy(), alpha=alpha, method=padjust + stats.loc[idxiter, "p_unc"].to_numpy(), alpha=alpha, method=padjust ) - stats.loc[idxiter, "p-corr"] = pcor - stats.loc[idxiter, "p-adjust"] = padjust + stats.loc[idxiter, "p_corr"] = pcor + stats.loc[idxiter, "p_adjust"] = padjust # --------------------------------------------------------------------- # Append parametric columns @@ -803,7 +803,7 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize="hedges"): * ``'diff'``: Mean difference (= mean(A) - mean(B)) * ``'se'``: Standard error * ``'T'``: T-values - * ``'p-tukey'``: Tukey-HSD corrected p-values + * ``'p_tukey'``: Tukey-HSD corrected p-values * ``'hedges'``: Hedges effect size (or any effect size defined in ``effsize``) @@ -861,7 +861,7 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize="hedges"): >>> import pingouin as pg >>> df = pg.read_dataset('penguins') >>> df.pairwise_tukey(dv='body_mass_g', between='species').round(3) - A B mean(A) mean(B) diff se T p-tukey hedges + A B mean(A) mean(B) diff se T p_tukey hedges 0 Adelie Chinstrap 3700.662 3733.088 -32.426 67.512 -0.480 0.881 -0.074 1 Adelie Gentoo 3700.662 5076.016 -1375.354 56.148 -24.495 0.000 -2.860 2 Chinstrap Gentoo 3733.088 5076.016 -1342.928 69.857 -19.224 0.000 -2.875 @@ -926,7 +926,7 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize="hedges"): "diff": mn, "se": se, "T": tval, - "p-tukey": pval, + "p_tukey": pval, effsize: ef, } ) @@ -1186,9 +1186,9 @@ def pairwise_corr( * ``'n'``: Sample size (after removal of missing values). * ``'r'``: Correlation coefficients. * ``'CI95'``: 95% parametric confidence intervals. - * ``'p-unc'``: Uncorrected p-values. - * ``'p-corr'``: Corrected p-values. - * ``'p-adjust'``: P-values correction method. + * ``'p_unc'``: Uncorrected p-values. + * ``'p_corr'``: Corrected p-values. + * ``'p_adjust'``: P-values correction method. * ``'BF10'``: Bayes Factor of the alternative hypothesis (only for Pearson correlation) * ``'power'``: achieved power of the test (= 1 - type II error). @@ -1232,7 +1232,7 @@ def pairwise_corr( >>> pd.set_option('display.max_columns', 20) >>> data = pg.read_dataset('pairwise_corr').iloc[:, 1:] >>> pg.pairwise_corr(data, method='spearman', alternative='greater', padjust='bonf').round(3) - X Y method alternative n r CI95 p-unc p-corr p-adjust power + X Y method alternative n r CI95 p_unc p_corr p_adjust power 0 Neuroticism Extraversion spearman greater 500 -0.325 [-0.39, 1.0] 1.000 1.000 bonf 0.000 1 Neuroticism Openness spearman greater 500 -0.028 [-0.1, 1.0] 0.735 1.000 bonf 0.012 2 Neuroticism Agreeableness spearman greater 500 -0.151 [-0.22, 1.0] 1.000 1.000 bonf 0.000 @@ -1249,7 +1249,7 @@ def pairwise_corr( >>> pcor = pg.pairwise_corr(data, columns=['Openness', 'Extraversion', ... 'Neuroticism'], method='bicor') >>> pcor.round(3) - X Y method alternative n r CI95 p-unc power + X Y method alternative n r CI95 p_unc power 0 Openness Extraversion bicor two-sided 500 0.247 [0.16, 0.33] 0.000 1.000 1 Openness Neuroticism bicor two-sided 500 -0.028 [-0.12, 0.06] 0.535 0.095 2 Extraversion Neuroticism bicor two-sided 500 -0.343 [-0.42, -0.26] 0.000 1.000 @@ -1257,7 +1257,7 @@ def pairwise_corr( 3. One-versus-all pairwise correlations >>> pg.pairwise_corr(data, columns=['Neuroticism']).round(3) - X Y method alternative n r CI95 p-unc BF10 power + X Y method alternative n r CI95 p_unc BF10 power 0 Neuroticism Extraversion pearson two-sided 500 -0.350 [-0.42, -0.27] 0.000 6.765e+12 1.000 1 Neuroticism Openness pearson two-sided 500 -0.010 [-0.1, 0.08] 0.817 0.058 0.056 2 Neuroticism Agreeableness pearson two-sided 500 -0.134 [-0.22, -0.05] 0.003 5.122 0.854 @@ -1267,7 +1267,7 @@ def pairwise_corr( >>> columns = [['Neuroticism', 'Extraversion'], ['Openness']] >>> pg.pairwise_corr(data, columns).round(3) - X Y method alternative n r CI95 p-unc BF10 power + X Y method alternative n r CI95 p_unc BF10 power 0 Neuroticism Openness pearson two-sided 500 -0.010 [-0.1, 0.08] 0.817 0.058 0.056 1 Extraversion Openness pearson two-sided 500 0.267 [0.18, 0.35] 0.000 5.277e+06 1.000 @@ -1278,7 +1278,7 @@ def pairwise_corr( 6. Pairwise partial correlation >>> pg.pairwise_corr(data, covar=['Neuroticism', 'Openness']) - X Y method covar alternative n r CI95 p-unc + X Y method covar alternative n r CI95 p_unc 0 Extraversion Agreeableness pearson ['Neuroticism', 'Openness'] two-sided 500 -0.038737 [-0.13, 0.05] 0.388361 1 Extraversion Conscientiousness pearson ['Neuroticism', 'Openness'] two-sided 500 -0.071427 [-0.16, 0.02] 0.111389 2 Agreeableness Conscientiousness pearson ['Neuroticism', 'Openness'] two-sided 500 0.123108 [0.04, 0.21] 0.005944 @@ -1409,7 +1409,7 @@ def traverse(o, tree_types=(list, tuple)): "outliers", "r", "CI95", - "p-val", + "p_val", "BF10", "power", ], @@ -1467,18 +1467,18 @@ def traverse(o, tree_types=(list, tuple)): options.update(old_options) # restore options # Force conversion to numeric - stats = stats.astype({"r": float, "n": int, "p-val": float, "outliers": float, "power": float}) + stats = stats.astype({"r": float, "n": int, "p_val": float, "outliers": float, "power": float}) # Multiple comparisons - stats = stats.rename(columns={"p-val": "p-unc"}) - padjust = None if stats["p-unc"].size <= 1 else padjust + stats = stats.rename(columns={"p_val": "p_unc"}) + padjust = None if stats["p_unc"].size <= 1 else padjust if padjust is not None: if padjust.lower() != "none": - reject, stats["p-corr"] = multicomp(stats["p-unc"].to_numpy(), method=padjust) - stats["p-adjust"] = padjust + reject, stats["p_corr"] = multicomp(stats["p_unc"].to_numpy(), method=padjust) + stats["p_adjust"] = padjust else: - stats["p-corr"] = None - stats["p-adjust"] = None + stats["p_corr"] = None + stats["p_adjust"] = None # Standardize correlation coefficients (Fisher z-transformation) # stats['z'] = np.arctanh(stats['r'].to_numpy()) @@ -1492,9 +1492,9 @@ def traverse(o, tree_types=(list, tuple)): "outliers", "r", "CI95", - "p-unc", - "p-corr", - "p-adjust", + "p_unc", + "p_corr", + "p_adjust", "BF10", "power", ] diff --git a/src/pingouin/parametric.py b/src/pingouin/parametric.py index 10d54fd3..c131e1e3 100644 --- a/src/pingouin/parametric.py +++ b/src/pingouin/parametric.py @@ -60,7 +60,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 * ``'T'``: T-value * ``'dof'``: degrees of freedom * ``'alternative'``: alternative of the test - * ``'p-val'``: p-value + * ``'p_val'``: p-value * ``'CI95'``: confidence intervals of the difference in means * ``'cohen-d'``: Cohen d effect size * ``'BF10'``: Bayes Factor of the alternative hypothesis @@ -143,7 +143,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> from pingouin import ttest >>> x = [5.5, 2.4, 6.8, 9.6, 4.2] >>> ttest(x, 4).round(2) - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power T-test 1.4 4 two-sided 0.23 [2.32, 9.08] 0.62 0.766 0.19 2. One sided paired T-test. @@ -151,13 +151,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> pre = [5.5, 2.4, 6.8, 9.6, 4.2] >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True, alternative='less').round(2) - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power T-test -2.31 4 less 0.04 [-inf, -0.05] 0.25 3.122 0.12 Now testing the opposite alternative hypothesis >>> ttest(pre, post, paired=True, alternative='greater').round(2) - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power T-test -2.31 4 greater 0.96 [-1.35, inf] 0.25 0.32 0.02 3. Paired T-test with missing values. @@ -166,7 +166,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> pre = [5.5, 2.4, np.nan, 9.6, 4.2] >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True).round(3) - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power T-test -5.902 3 two-sided 0.01 [-1.5, -0.45] 0.306 7.169 0.073 Compare with SciPy @@ -181,7 +181,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> x = np.random.normal(loc=7, size=20) >>> y = np.random.normal(loc=4, size=20) >>> ttest(x, y) - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power T-test 9.106452 38 two-sided 4.306971e-11 [2.64, 4.15] 2.879713 1.366e+08 1.0 5. Independent two-sample T-test with unequal sample size. A Welch's T-test is used. @@ -189,13 +189,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> np.random.seed(123) >>> y = np.random.normal(loc=6.5, size=15) >>> ttest(x, y) - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power T-test 1.996537 31.567592 two-sided 0.054561 [-0.02, 1.65] 0.673518 1.469 0.481867 6. However, the Welch's correction can be disabled: >>> ttest(x, y, correction=False) - T dof alternative p-val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen-d BF10 power T-test 1.971859 33 two-sided 0.057056 [-0.03, 1.66] 0.673518 1.418 0.481867 Compare with SciPy @@ -318,7 +318,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 stats = { "dof": dof, "T": tval, - "p-val": pval, + "p_val": pval, "alternative": alternative, "cohen-d": abs(d), ci_name: [ci], @@ -327,7 +327,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 } # Convert to dataframe - col_order = ["T", "dof", "alternative", "p-val", ci_name, "cohen-d", "BF10", "power"] + col_order = ["T", "dof", "alternative", "p_val", ci_name, "cohen-d", "BF10", "power"] stats = pd.DataFrame(stats, columns=col_order, index=["T-test"]) return _postprocess_dataframe(stats) @@ -385,12 +385,12 @@ def rm_anova( * ``'ddof1'``: Degrees of freedom (numerator) * ``'ddof2'``: Degrees of freedom (denominator) * ``'F'``: F-value - * ``'p-unc'``: Uncorrected p-value + * ``'p_unc'``: Uncorrected p-value * ``'ng2'``: Generalized eta-square effect size * ``'eps'``: Greenhouse-Geisser epsilon factor (= index of sphericity) - * ``'p-GG-corr'``: Greenhouse-Geisser corrected p-value - * ``'W-spher'``: Sphericity test statistic - * ``'p-spher'``: p-value of the sphericity test + * ``'p_GG_corr'``: Greenhouse-Geisser corrected p-value + * ``'W_spher'``: Sphericity test statistic + * ``'p_spher'``: p-value of the sphericity test * ``'sphericity'``: sphericity of the data (boolean) See Also @@ -478,7 +478,7 @@ def rm_anova( >>> import pingouin as pg >>> data = pg.read_dataset('rm_anova_wide') >>> pg.rm_anova(data) - Source ddof1 ddof2 F p-unc ng2 eps + Source ddof1 ddof2 F p_unc ng2 eps 0 Within 3 24 5.200652 0.006557 0.346392 0.694329 2. One-way repeated-measures ANOVA using a long-format dataset. @@ -492,7 +492,7 @@ def rm_anova( >>> aov = pg.rm_anova(dv='DesireToKill', within='Disgustingness', ... subject='Subject', data=df, detailed=True, effsize="np2") >>> aov.round(3) - Source SS DF MS F p-unc np2 eps + Source SS DF MS F p_unc np2 eps 0 Disgustingness 27.485 1 27.485 12.044 0.001 0.116 1.0 1 Error 209.952 92 2.282 NaN NaN NaN NaN @@ -504,7 +504,7 @@ def rm_anova( 4. As a :py:class:`pandas.DataFrame` method >>> df.rm_anova(dv='DesireToKill', within='Disgustingness', subject='Subject', detailed=False) - Source ddof1 ddof2 F p-unc ng2 eps + Source ddof1 ddof2 F p_unc ng2 eps 0 Disgustingness 1 92 12.043878 0.000793 0.025784 1.0 """ assert effsize in ["n2", "np2", "ng2"], "effsize must be n2, np2 or ng2." @@ -605,16 +605,16 @@ def rm_anova( "ddof1": ddof1, "ddof2": ddof2, "F": fval, - "p-unc": p_unc, + "p_unc": p_unc, effsize: ef, "eps": eps, }, index=[0], ) if correction: - aov["p-GG-corr"] = p_corr - aov["W-spher"] = W_spher - aov["p-spher"] = p_spher + aov["p_GG_corr"] = p_corr + aov["W_spher"] = W_spher + aov["p_spher"] = p_spher aov["sphericity"] = spher col_order = [ @@ -622,13 +622,13 @@ def rm_anova( "ddof1", "ddof2", "F", - "p-unc", - "p-GG-corr", + "p_unc", + "p_GG_corr", effsize, "eps", "sphericity", - "W-spher", - "p-spher", + "W_spher", + "p_spher", ] else: aov = pd.DataFrame( @@ -638,15 +638,15 @@ def rm_anova( "DF": [ddof1, ddof2], "MS": [ms_with, ms_reswith], "F": [fval, np.nan], - "p-unc": [p_unc, np.nan], + "p_unc": [p_unc, np.nan], effsize: [ef, np.nan], "eps": [eps, np.nan], } ) if correction: - aov["p-GG-corr"] = [p_corr, np.nan] - aov["W-spher"] = [W_spher, np.nan] - aov["p-spher"] = [p_spher, np.nan] + aov["p_GG_corr"] = [p_corr, np.nan] + aov["W_spher"] = [W_spher, np.nan] + aov["p_spher"] = [p_spher, np.nan] aov["sphericity"] = [spher, np.nan] col_order = [ @@ -655,13 +655,13 @@ def rm_anova( "DF", "MS", "F", - "p-unc", - "p-GG-corr", + "p_unc", + "p_GG_corr", effsize, "eps", "sphericity", - "W-spher", - "p-spher", + "W_spher", + "p_spher", ] aov = aov.reindex(columns=col_order) @@ -797,8 +797,8 @@ def rm_anova2(data=None, dv=None, within=None, subject=None, effsize="ng2"): "ddof2": [df_as, df_bs, df_abs], "MS": [ms_a, ms_b, ms_ab], "F": [f_a, f_b, f_ab], - "p-unc": [p_a, p_b, p_ab], - "p-GG-corr": [p_a_corr, p_b_corr, p_ab_corr], + "p_unc": [p_a, p_b, p_ab], + "p_GG_corr": [p_a_corr, p_b_corr, p_ab_corr], effsize: [ef_a, ef_b, ef_ab], "eps": [eps_a, eps_b, eps_ab], } @@ -846,7 +846,7 @@ def anova(data=None, dv=None, between=None, ss_type=2, detailed=False, effsize=" * ``'DF'``: Degrees of freedom * ``'MS'``: Mean squares * ``'F'``: F-values - * ``'p-unc'``: uncorrected p-values + * ``'p_unc'``: uncorrected p-values * ``'np2'``: Partial eta-square effect sizes See Also @@ -914,7 +914,7 @@ def anova(data=None, dv=None, between=None, ss_type=2, detailed=False, effsize=" >>> aov = pg.anova(dv='Pain threshold', between='Hair color', data=df, ... detailed=True) >>> aov.round(3) - Source SS DF MS F p-unc np2 + Source SS DF MS F p_unc np2 0 Hair color 1360.726 3 453.575 6.791 0.004 0.576 1 Within 1001.800 15 66.787 NaN NaN NaN @@ -925,14 +925,14 @@ def anova(data=None, dv=None, between=None, ss_type=2, detailed=False, effsize=" >>> df.anova(dv='Pain threshold', between='Hair color', detailed=False, ... effsize='n2') - Source ddof1 ddof2 F p-unc n2 + Source ddof1 ddof2 F p_unc n2 0 Hair color 3 15 6.791407 0.004114 0.575962 Two-way ANOVA with balanced design >>> data = pg.read_dataset('anova2') >>> data.anova(dv="Yield", between=["Blend", "Crop"]).round(3) - Source SS DF MS F p-unc np2 + Source SS DF MS F p_unc np2 0 Blend 2.042 1 2.042 0.004 0.952 0.000 1 Crop 2736.583 2 1368.292 2.525 0.108 0.219 2 Blend * Crop 2360.083 2 1180.042 2.178 0.142 0.195 @@ -943,7 +943,7 @@ def anova(data=None, dv=None, between=None, ss_type=2, detailed=False, effsize=" >>> data = pg.read_dataset('anova2_unbalanced') >>> data.anova(dv="Scores", between=["Diet", "Exercise"], ... effsize="n2").round(3) - Source SS DF MS F p-unc n2 + Source SS DF MS F p_unc n2 0 Diet 390.625 1.0 390.625 7.423 0.034 0.433 1 Exercise 180.625 1.0 180.625 3.432 0.113 0.200 2 Diet * Exercise 15.625 1.0 15.625 0.297 0.605 0.017 @@ -954,7 +954,7 @@ def anova(data=None, dv=None, between=None, ss_type=2, detailed=False, effsize=" >>> data = pg.read_dataset('anova3') >>> data.anova(dv='Cholesterol', between=['Sex', 'Risk', 'Drug'], ... ss_type=3).round(3) - Source SS DF MS F p-unc np2 + Source SS DF MS F p_unc np2 0 Sex 2.075 1.0 2.075 2.462 0.123 0.049 1 Risk 11.332 1.0 11.332 13.449 0.001 0.219 2 Drug 0.816 2.0 0.408 0.484 0.619 0.020 @@ -1023,7 +1023,7 @@ def anova(data=None, dv=None, between=None, ss_type=2, detailed=False, effsize=" "ddof1": ddof1, "ddof2": ddof2, "F": fval, - "p-unc": p_unc, + "p_unc": p_unc, effsize: np2, }, index=[0], @@ -1037,7 +1037,7 @@ def anova(data=None, dv=None, between=None, ss_type=2, detailed=False, effsize=" "DF": [ddof1, ddof2], "MS": [msbetween, mserror], "F": [fval, np.nan], - "p-unc": [p_unc, np.nan], + "p_unc": [p_unc, np.nan], effsize: [np2, np.nan], } ) @@ -1124,7 +1124,7 @@ def anova2(data=None, dv=None, between=None, ss_type=2, effsize="np2"): "DF": [df_fac1, df_fac2, df_inter, df_resid], "MS": [ms_fac1, ms_fac2, ms_inter, ms_resid], "F": [fval_fac1, fval_fac2, fval_inter, np.nan], - "p-unc": [pval_fac1, pval_fac2, pval_inter, np.nan], + "p_unc": [pval_fac1, pval_fac2, pval_inter, np.nan], effsize: all_effsize, } ) @@ -1186,7 +1186,7 @@ def anovan(data=None, dv=None, between=None, ss_type=2, effsize="np2"): aov = aov.iloc[1:, :] aov = aov.reset_index() - aov = aov.rename(columns={"index": "Source", "sum_sq": "SS", "df": "DF", "PR(>F)": "p-unc"}) + aov = aov.rename(columns={"index": "Source", "sum_sq": "SS", "df": "DF", "PR(>F)": "p_unc"}) aov["MS"] = aov["SS"] / aov["DF"] # Effect size @@ -1206,7 +1206,7 @@ def format_source(x): aov["Source"] = aov["Source"].apply(format_source) # Re-index and round - col_order = ["Source", "SS", "DF", "MS", "F", "p-unc", effsize] + col_order = ["Source", "SS", "DF", "MS", "F", "p_unc", effsize] aov = aov.reindex(columns=col_order) aov.dropna(how="all", axis=1, inplace=True) @@ -1239,7 +1239,7 @@ def welch_anova(data=None, dv=None, between=None): * ``'ddof1'``: Numerator degrees of freedom * ``'ddof2'``: Denominator degrees of freedom * ``'F'``: F-values - * ``'p-unc'``: uncorrected p-values + * ``'p_unc'``: uncorrected p-values * ``'np2'``: Partial eta-squared See Also @@ -1327,7 +1327,7 @@ def welch_anova(data=None, dv=None, between=None): >>> df = read_dataset('anova') >>> aov = welch_anova(dv='Pain threshold', between='Hair color', data=df) >>> aov - Source ddof1 ddof2 F p-unc np2 + Source ddof1 ddof2 F p_unc np2 0 Hair color 3 8.329841 5.890115 0.018813 0.575962 """ # Check data @@ -1367,7 +1367,7 @@ def welch_anova(data=None, dv=None, between=None): "ddof1": ddof1, "ddof2": ddof2, "F": fval, - "p-unc": pval, + "p_unc": pval, "np2": np2, }, index=[0], @@ -1412,12 +1412,12 @@ def mixed_anova( * ``'ddof1'``: Degrees of freedom (numerator) * ``'ddof2'``: Degrees of freedom (denominator) * ``'F'``: F-values - * ``'p-unc'``: Uncorrected p-values + * ``'p_unc'``: Uncorrected p-values * ``'np2'``: Partial eta-squared effect sizes * ``'eps'``: Greenhouse-Geisser epsilon factor (= index of sphericity) - * ``'p-GG-corr'``: Greenhouse-Geisser corrected p-values - * ``'W-spher'``: Sphericity test statistic - * ``'p-spher'``: p-value of the sphericity test + * ``'p_GG_corr'``: Greenhouse-Geisser corrected p-values + * ``'W_spher'``: Sphericity test statistic + * ``'p_spher'``: p-value of the sphericity test * ``'sphericity'``: sphericity of the data (boolean) See Also @@ -1452,7 +1452,7 @@ def mixed_anova( >>> aov = mixed_anova(dv='Scores', between='Group', ... within='Time', subject='Subject', data=df) >>> aov.round(3) - Source SS DF1 DF2 MS F p-unc np2 eps + Source SS DF1 DF2 MS F p_unc np2 eps 0 Group 5.460 1 58 5.460 5.052 0.028 0.080 NaN 1 Time 7.628 2 116 3.814 4.027 0.020 0.065 0.999 2 Interaction 5.167 2 116 2.584 2.728 0.070 0.045 NaN @@ -1463,7 +1463,7 @@ def mixed_anova( >>> df.mixed_anova(dv='Scores', between='Group', within='Time', ... subject='Subject', effsize="ng2").round(3) - Source SS DF1 DF2 MS F p-unc ng2 eps + Source SS DF1 DF2 MS F p_unc ng2 eps 0 Group 5.460 1 58 5.460 5.052 0.028 0.031 NaN 1 Time 7.628 2 116 3.814 4.027 0.020 0.042 0.999 2 Interaction 5.167 2 116 2.584 2.728 0.070 0.029 NaN @@ -1573,7 +1573,7 @@ def mixed_anova( # Update values aov.rename(columns={"DF": "DF1"}, inplace=True) aov.at[0, "F"], aov.at[1, "F"] = f_betw, f_with - aov.at[0, "p-unc"], aov.at[1, "p-unc"] = p_betw, p_with + aov.at[0, "p_unc"], aov.at[1, "p_unc"] = p_betw, p_with aov.at[0, effsize], aov.at[1, effsize] = ef_betw, ef_with aov_inter = pd.DataFrame( { @@ -1582,7 +1582,7 @@ def mixed_anova( "DF1": df_inter, "MS": ms_inter, "F": f_inter, - "p-unc": p_inter, + "p_unc": p_inter, effsize: ef_inter, }, index=[2], @@ -1597,13 +1597,13 @@ def mixed_anova( "DF2", "MS", "F", - "p-unc", - "p-GG-corr", + "p_unc", + "p_GG_corr", effsize, "eps", "sphericity", - "W-spher", - "p-spher", + "W_spher", + "p_spher", ] aov = aov.reindex(columns=col_order) aov.dropna(how="all", axis=1, inplace=True) @@ -1638,7 +1638,7 @@ def ancova(data=None, dv=None, between=None, covar=None, effsize="np2"): * ``'SS'``: Sums of squares * ``'DF'``: Degrees of freedom * ``'F'``: F-values - * ``'p-unc'``: Uncorrected p-values + * ``'p_unc'``: Uncorrected p-values * ``'np2'``: Partial eta-squared Notes @@ -1668,7 +1668,7 @@ def ancova(data=None, dv=None, between=None, covar=None, effsize="np2"): >>> from pingouin import ancova, read_dataset >>> df = read_dataset('ancova') >>> ancova(data=df, dv='Scores', covar='Income', between='Method') - Source SS DF F p-unc np2 + Source SS DF F p_unc np2 0 Method 571.029883 3 3.336482 0.031940 0.244077 1 Income 1678.352687 1 29.419438 0.000006 0.486920 2 Residual 1768.522313 31 NaN NaN NaN @@ -1678,7 +1678,7 @@ def ancova(data=None, dv=None, between=None, covar=None, effsize="np2"): >>> ancova(data=df, dv='Scores', covar=['Income', 'BMI'], between='Method', ... effsize="n2") - Source SS DF F p-unc n2 + Source SS DF F p_unc n2 0 Method 552.284043 3 3.232550 0.036113 0.141802 1 Income 1573.952434 1 27.637304 0.000011 0.404121 2 BMI 60.013656 1 1.053790 0.312842 0.015409 @@ -1724,7 +1724,7 @@ def ancova(data=None, dv=None, between=None, covar=None, effsize="np2"): # Create output dataframe aov = stats.anova_lm(model, typ=2).reset_index() aov.rename( - columns={"index": "Source", "sum_sq": "SS", "df": "DF", "PR(>F)": "p-unc"}, inplace=True + columns={"index": "Source", "sum_sq": "SS", "df": "DF", "PR(>F)": "p_unc"}, inplace=True ) aov.at[0, "Source"] = between for i in range(len(covar)): diff --git a/src/pingouin/power.py b/src/pingouin/power.py index acf0d1db..ca2c73a4 100644 --- a/src/pingouin/power.py +++ b/src/pingouin/power.py @@ -655,7 +655,7 @@ def power_rm_anova(eta_squared=None, m=None, n=None, power=None, alpha=0.05, cor >>> data = data.dropna() >>> pg.rm_anova(data, effsize="n2").round(3) - Source ddof1 ddof2 F p-unc n2 eps + Source ddof1 ddof2 F p_unc n2 eps 0 Within 3 24 5.201 0.007 0.346 0.694 The repeated measures ANOVA is significant at the 0.05 level. Now, we can diff --git a/tests/test_contingency.py b/tests/test_contingency.py index ea76fe93..09fde40e 100644 --- a/tests/test_contingency.py +++ b/tests/test_contingency.py @@ -145,12 +145,12 @@ def expect_assertion_error(*params): _, stats = pg.chi2_mcnemar(df_mcnemar, "treatment_X", "treatment_Y") assert round(stats.at["mcnemar", "chi2"], 3) == 20.021 assert stats.at["mcnemar", "dof"] == 1 - assert np.isclose(stats.at["mcnemar", "p-approx"], 7.66e-06) + assert np.isclose(stats.at["mcnemar", "p_approx"], 7.66e-06) # Results are compared to the exact2x2 R package # >>> exact2x2(tbl, paired = TRUE, midp = FALSE) - assert np.isclose(stats.at["mcnemar", "p-exact"], 3.305e-06) + assert np.isclose(stats.at["mcnemar", "p_exact"], 3.305e-06) # midp gives slightly different results - # assert np.allclose(stats.at['mcnemar', 'p-mid'], 3.305e-06) + # assert np.allclose(stats.at['mcnemar', 'p_mid'], 3.305e-06) def test_dichotomize_series(self): """Test function _dichotomize_series.""" diff --git a/tests/test_correlation.py b/tests/test_correlation.py index db9cedb0..afc0104a 100644 --- a/tests/test_correlation.py +++ b/tests/test_correlation.py @@ -26,26 +26,26 @@ def test_corr(self): # Pearson correlation stats = corr(x, y, method="pearson") assert np.isclose(stats.loc["pearson", "r"], 0.1761221) - assert np.isclose(stats.loc["pearson", "p-val"], 0.3518659) + assert np.isclose(stats.loc["pearson", "p_val"], 0.3518659) assert stats.loc["pearson", "CI95"][0] == round(-0.1966232, 2) assert stats.loc["pearson", "CI95"][1] == round(0.5043872, 2) # - One-sided: greater stats = corr(x, y, method="pearson", alternative="greater") assert np.isclose(stats.loc["pearson", "r"], 0.1761221) - assert np.isclose(stats.loc["pearson", "p-val"], 0.175933) + assert np.isclose(stats.loc["pearson", "p_val"], 0.175933) assert stats.loc["pearson", "CI95"][0] == round(-0.1376942, 2) assert stats.loc["pearson", "CI95"][1] == 1 # - One-sided: less stats = corr(x, y, method="pearson", alternative="less") assert np.isclose(stats.loc["pearson", "r"], 0.1761221) - assert np.isclose(stats.loc["pearson", "p-val"], 0.824067) + assert np.isclose(stats.loc["pearson", "p_val"], 0.824067) assert stats.loc["pearson", "CI95"][0] == -1 assert stats.loc["pearson", "CI95"][1] == round(0.4578044, 2) # Spearman correlation stats = corr(x, y, method="spearman") assert np.isclose(stats.loc["spearman", "r"], 0.4740823) - assert np.isclose(stats.loc["spearman", "p-val"], 0.008129768) + assert np.isclose(stats.loc["spearman", "p_val"], 0.008129768) # CI are calculated using a different formula for Spearman in R # assert stats.loc['spearman', 'CI95'][0] == round(0.1262988, 2) # assert stats.loc['spearman', 'CI95'][1] == round(0.7180799, 2) @@ -70,7 +70,7 @@ def test_corr(self): # Shepherd stats = corr(x, y, method="shepherd") assert np.isclose(stats.loc["shepherd", "r"], 0.5123153) - assert np.isclose(stats.loc["shepherd", "p-val"], 0.005316) + assert np.isclose(stats.loc["shepherd", "p_val"], 0.005316) assert stats.loc["shepherd", "outliers"] == 2 _, _, outliers = skipped(x, y, corr_type="pearson") assert outliers.size == x.size @@ -79,7 +79,7 @@ def test_corr(self): stats = corr(x, y, method="percbend") assert round(stats.loc["percbend", "r"], 4) == 0.4843 assert np.isclose(stats.loc["percbend", "r"], 0.4842686) - assert np.isclose(stats.loc["percbend", "p-val"], 0.006693313) + assert np.isclose(stats.loc["percbend", "p_val"], 0.006693313) stats = corr(x2, y2, method="percbend") assert round(stats.loc["percbend", "r"], 4) == 0.4843 stats = corr(x, y, method="percbend", beta=0.5) @@ -87,7 +87,7 @@ def test_corr(self): # Compare biweight correlation to astropy stats = corr(x, y, method="bicor") assert np.isclose(stats.loc["bicor", "r"], 0.4951418) - assert np.isclose(stats.loc["bicor", "p-val"], 0.005403701) + assert np.isclose(stats.loc["bicor", "p_val"], 0.005403701) assert stats.loc["bicor", "CI95"][0] == round(0.1641553, 2) assert stats.loc["bicor", "CI95"][1] == round(0.7259185, 2) stats = corr(x, y, method="bicor", c=5) @@ -136,20 +136,20 @@ def test_partial_corr(self): # With one covariate pc = partial_corr(data=df, x="x", y="y", covar="cv1") assert round(pc.at["pearson", "r"], 7) == 0.5681692 - assert round(pc.at["pearson", "p-val"], 9) == 0.001303059 + assert round(pc.at["pearson", "p_val"], 9) == 0.001303059 # With two covariates pc = partial_corr(data=df, x="x", y="y", covar=["cv1", "cv2"]) assert round(pc.at["pearson", "r"], 7) == 0.5344372 - assert round(pc.at["pearson", "p-val"], 9) == 0.003392904 + assert round(pc.at["pearson", "p_val"], 9) == 0.003392904 # With three covariates # in R: pcor.test(x=df$x, y=df$y, z=df[, c("cv1", "cv2", "cv3")]) pc = partial_corr(data=df, x="x", y="y", covar=["cv1", "cv2", "cv3"]) assert round(pc.at["pearson", "r"], 7) == 0.4926007 - assert round(pc.at["pearson", "p-val"], 9) == 0.009044164 + assert round(pc.at["pearson", "p_val"], 9) == 0.009044164 # Method == "spearman" pc = partial_corr(data=df, x="x", y="y", covar=["cv1", "cv2", "cv3"], method="spearman") assert round(pc.at["spearman", "r"], 7) == 0.5209208 - assert round(pc.at["spearman", "p-val"], 9) == 0.005336187 + assert round(pc.at["spearman", "p_val"], 9) == 0.005336187 ####################################################################### # SEMI-PARTIAL CORRELATION @@ -157,25 +157,25 @@ def test_partial_corr(self): # With one covariate pc = partial_corr(data=df, x="x", y="y", y_covar="cv1") assert round(pc.at["pearson", "r"], 7) == 0.5670793 - assert round(pc.at["pearson", "p-val"], 9) == 0.001337718 + assert round(pc.at["pearson", "p_val"], 9) == 0.001337718 # With two covariates pc = partial_corr(data=df, x="x", y="y", y_covar=["cv1", "cv2"]) assert round(pc.at["pearson", "r"], 7) == 0.5097489 - assert round(pc.at["pearson", "p-val"], 9) == 0.005589687 + assert round(pc.at["pearson", "p_val"], 9) == 0.005589687 # With three covariates # in R: spcor.test(x=df$x, y=df$y, z=df[, c("cv1", "cv2", "cv3")]) pc = partial_corr(data=df, x="x", y="y", y_covar=["cv1", "cv2", "cv3"]) assert round(pc.at["pearson", "r"], 7) == 0.4212351 - assert round(pc.at["pearson", "p-val"], 8) == 0.02865483 + assert round(pc.at["pearson", "p_val"], 8) == 0.02865483 # With three covariates (x_covar) pc = partial_corr(data=df, x="x", y="y", x_covar=["cv1", "cv2", "cv3"]) assert round(pc.at["pearson", "r"], 7) == 0.4631883 - assert round(pc.at["pearson", "p-val"], 8) == 0.01496857 + assert round(pc.at["pearson", "p_val"], 8) == 0.01496857 # Method == "spearman" pc = partial_corr(data=df, x="x", y="y", y_covar=["cv1", "cv2", "cv3"], method="spearman") assert round(pc.at["spearman", "r"], 7) == 0.4597143 - assert round(pc.at["spearman", "p-val"], 8) == 0.01584262 + assert round(pc.at["spearman", "p_val"], 8) == 0.01584262 ####################################################################### # ERROR diff --git a/tests/test_nonparametric.py b/tests/test_nonparametric.py index a6b605d6..bbd184aa 100644 --- a/tests/test_nonparametric.py +++ b/tests/test_nonparametric.py @@ -68,11 +68,11 @@ def test_mwu(self): mwu_pg_greater = mwu(x, y, alternative="greater") # Similar to R: wilcox.test(df$x, df$y, paired = FALSE, exact = FALSE) # Note that the RBC value are compared to JASP in test_pairwise.py - assert mwu_scp[0] == mwu_pg.at["MWU", "U-val"] - assert mwu_scp[1] == mwu_pg.at["MWU", "p-val"] + assert mwu_scp[0] == mwu_pg.at["MWU", "U_val"] + assert mwu_scp[1] == mwu_pg.at["MWU", "p_val"] # One-sided assert ( - mwu_pg_less.at["MWU", "p-val"] + mwu_pg_less.at["MWU", "p_val"] == scipy.stats.mannwhitneyu(x, y, use_continuity=True, alternative="less")[1] ) # CLES is compared to: @@ -90,13 +90,13 @@ def test_wilcoxon(self): # The p-value, however, is almost identical wc_scp = scipy.stats.wilcoxon(x2, y2, correction=True) wc_pg = wilcoxon(x2, y2, alternative="two-sided") - assert wc_scp[0] == wc_pg.at["Wilcoxon", "W-val"] == 20.5 # JASP - assert wc_scp[1] == wc_pg.at["Wilcoxon", "p-val"] + assert wc_scp[0] == wc_pg.at["Wilcoxon", "W_val"] == 20.5 # JASP + assert wc_scp[1] == wc_pg.at["Wilcoxon", "p_val"] # Same but using the pre-computed difference # The W and p-values should be similar wc_pg2 = wilcoxon(np.array(x2) - np.array(y2)) - assert wc_pg.at["Wilcoxon", "W-val"] == wc_pg2.at["Wilcoxon", "W-val"] - assert wc_pg.at["Wilcoxon", "p-val"] == wc_pg2.at["Wilcoxon", "p-val"] + assert wc_pg.at["Wilcoxon", "W_val"] == wc_pg2.at["Wilcoxon", "W_val"] + assert wc_pg.at["Wilcoxon", "p_val"] == wc_pg2.at["Wilcoxon", "p_val"] assert wc_pg.at["Wilcoxon", "RBC"] == wc_pg2.at["Wilcoxon", "RBC"] assert np.isnan(wc_pg2.at["Wilcoxon", "CLES"]) wc_pg_less = wilcoxon(x2, y2, alternative="less") @@ -124,7 +124,7 @@ def test_kruskal(self): # Compare with SciPy built-in function H, p = scipy.stats.kruskal(x_nan, y, z, nan_policy="omit") assert np.isclose(H, summary.at["Kruskal", "H"]) - assert np.allclose(p, summary.at["Kruskal", "p-unc"]) + assert np.allclose(p, summary.at["Kruskal", "p_unc"]) def test_friedman(self): """Test function friedman""" @@ -158,14 +158,14 @@ def test_friedman(self): # Wide-format stats = friedman(df) assert np.isclose(stats.at["Friedman", "Q"], Q) - assert np.isclose(stats.at["Friedman", "p-unc"], p) + assert np.isclose(stats.at["Friedman", "p_unc"], p) assert np.isclose(stats.at["Friedman", "ddof1"], 2) # Long format df_long = df.melt(ignore_index=False).reset_index() stats = friedman(data=df_long, dv="value", within="variable", subject="index") assert np.isclose(stats.at["Friedman", "Q"], Q) - assert np.isclose(stats.at["Friedman", "p-unc"], p) + assert np.isclose(stats.at["Friedman", "p_unc"], p) assert np.isclose(stats.at["Friedman", "ddof1"], 2) # Compare Kendall's W @@ -181,7 +181,7 @@ def test_friedman(self): # Using the F-test method, which is more conservative stats_f = friedman(df, method="f") - assert stats_f.at["Friedman", "p-unc"] > stats.at["Friedman", "p-unc"] + assert stats_f.at["Friedman", "p_unc"] > stats.at["Friedman", "p_unc"] def test_cochran(self): """Test function cochran @@ -192,14 +192,14 @@ def test_cochran(self): df = read_dataset("cochran") st = cochran(dv="Energetic", within="Time", subject="Subject", data=df) assert round(st.at["cochran", "Q"], 3) == 6.706 - assert np.isclose(st.at["cochran", "p-unc"], 0.034981) + assert np.isclose(st.at["cochran", "p_unc"], 0.034981) # With Categorical df["Time"] = df["Time"].astype("category") df["Subject"] = df["Subject"].astype("category") df["Time"] = df["Time"].cat.add_categories("Unused") st = cochran(dv="Energetic", within="Time", subject="Subject", data=df) assert round(st.at["cochran", "Q"], 3) == 6.706 - assert np.isclose(st.at["cochran", "p-unc"], 0.034981) + assert np.isclose(st.at["cochran", "p_unc"], 0.034981) # With a NaN value df.loc[2, "Energetic"] = np.nan cochran(dv="Energetic", within="Time", subject="Subject", data=df) diff --git a/tests/test_pairwise.py b/tests/test_pairwise.py index 1b039699..4e1350a1 100644 --- a/tests/test_pairwise.py +++ b/tests/test_pairwise.py @@ -56,8 +56,8 @@ def test_pairwise_tests(self): return_desc=True, padjust="holm", ) - np.testing.assert_array_equal(pt.loc[:, "p-corr"].round(3), [0.174, 0.024, 0.310]) - np.testing.assert_array_equal(pt.loc[:, "p-unc"].round(3), [0.087, 0.008, 0.310]) + np.testing.assert_array_equal(pt.loc[:, "p_corr"].round(3), [0.174, 0.024, 0.310]) + np.testing.assert_array_equal(pt.loc[:, "p_unc"].round(3), [0.087, 0.008, 0.310]) # ------------------------------------------------------------------- # Simple within: EASY! @@ -68,8 +68,8 @@ def test_pairwise_tests(self): pt = pairwise_tests( dv="Scores", within="Time", subject="Subject", data=df, return_desc=True, padjust="holm" ) - np.testing.assert_array_equal(pt.loc[:, "p-corr"].round(3), [0.174, 0.024, 0.310]) - np.testing.assert_array_equal(pt.loc[:, "p-unc"].round(3), [0.087, 0.008, 0.310]) + np.testing.assert_array_equal(pt.loc[:, "p_corr"].round(3), [0.174, 0.024, 0.310]) + np.testing.assert_array_equal(pt.loc[:, "p_unc"].round(3), [0.087, 0.008, 0.310]) pairwise_tests( dv="Scores", within="Time", @@ -95,7 +95,7 @@ def test_pairwise_tests(self): # ------------------------------------------------------------------- # In R: >>> pairwise.t.test(df$Scores, df$Group, pool.sd = FALSE) pt = pairwise_tests(dv="Scores", between="Group", data=df).round(3) - assert pt.loc[0, "p-unc"] == 0.023 + assert pt.loc[0, "p_unc"] == 0.023 pairwise_tests( dv="Scores", between="Group", @@ -126,13 +126,13 @@ def test_pairwise_tests(self): ) # ...Within main effect: OK with JASP assert np.array_equal(pt["Paired"], [True, True, True, False]) - assert np.array_equal(pt.loc[:2, "p-corr"].round(3), [0.174, 0.024, 0.310]) + assert np.array_equal(pt.loc[:2, "p_corr"].round(3), [0.174, 0.024, 0.310]) assert np.array_equal(pt.loc[:2, "BF10"].astype(float), [0.582, 4.232, 0.232]) # ..Between main effect: T and p-values OK with JASP # but BF10 is only similar when marginal=False (see note in the # 2-way RM test below). assert pt.loc[3, "T"].round(3) == -2.248 - assert pt.loc[3, "p-unc"].round(3) == 0.028 + assert pt.loc[3, "p_unc"].round(3) == 0.028 # ..Interaction: slightly different because JASP pool the error term # across the between-subject groups. JASP does not compute the BF10 # for the interaction. @@ -190,10 +190,10 @@ def test_pairwise_tests(self): # T, dof and p-values should be equal assert np.array_equal(pt_merged["T"], pt["T"].iloc[4:]) assert np.array_equal(pt_merged["dof"], pt["dof"].iloc[4:]) - assert np.array_equal(pt_merged["p-unc"], pt["p-unc"].iloc[4:]) + assert np.array_equal(pt_merged["p_unc"], pt["p_unc"].iloc[4:]) # However adjusted p-values are not equal because they are calculated # separately on each dataframe. - assert not np.array_equal(pt_merged["p-corr"], pt["p-corr"].iloc[4:]) + assert not np.array_equal(pt_merged["p_corr"], pt["p_corr"].iloc[4:]) # Other options pairwise_tests( @@ -222,19 +222,19 @@ def test_pairwise_tests(self): assert np.array_equal( pt1.loc[:5, "T"].round(3), [-0.777, -1.344, -2.039, -0.814, -1.492, -0.627] ) - assert np.array_equal(pt1.loc[:5, "p-corr"].round(3), [1.0, 1.0, 0.313, 1.0, 0.889, 1.0]) + assert np.array_equal(pt1.loc[:5, "p_corr"].round(3), [1.0, 1.0, 0.313, 1.0, 0.889, 1.0]) assert np.array_equal( pt1.loc[:5, "BF10"].astype(float), [0.273, 0.463, 1.221, 0.280, 0.554, 0.248] ) # ...Between main effect: slightly different from JASP (why?) # True with or without the Welch correction... - assert (pt1.loc[6:8, "p-corr"] > 0.20).all() + assert (pt1.loc[6:8, "p_corr"] > 0.20).all() # ...Interaction: slightly different because JASP pool the error term # across the between-subject groups. # Below the interaction JASP bonferroni-correct p-values, which are # more conservative because JASP perform all possible pairwise tests # jasp_pbonf = [1., 1., 1., 1., 1., 1., 1., 0.886, 1., 1., 1., 1.] - assert (pt1.loc[9:, "p-corr"] > 0.05).all() + assert (pt1.loc[9:, "p_corr"] > 0.05).all() # Check that the Welch corection is applied by default assert not pt1["dof"].apply(lambda x: x.is_integer()).all() @@ -287,7 +287,7 @@ def test_pairwise_tests(self): # JAMOVI, because they both pool the error term. # The dof are not available in JASP, but in JAMOVI they are 18 # everywhere, which I'm not sure to understand why... - assert np.array_equal(pt.loc[:3, "p-unc"] < 0.05, [False, False, False, True]) + assert np.array_equal(pt.loc[:3, "p_unc"] < 0.05, [False, False, False, True]) # However, the Bayes Factor of the simple main effects are the same...! np.array_equal(pt.loc[:3, "BF10"].astype(float), [0.374, 0.533, 0.711, 2.287]) @@ -381,7 +381,7 @@ def test_pairwise_tests(self): parametric=False, nan_policy="listwise", ) - np.testing.assert_array_equal(st["W-val"].to_numpy(), [9, 3, 12]) + np.testing.assert_array_equal(st["W_val"].to_numpy(), [9, 3, 12]) st2 = pairwise_tests( dv="Value", within="Condition", @@ -391,7 +391,7 @@ def test_pairwise_tests(self): parametric=False, ) # Tested against a simple for loop on combinations - np.testing.assert_array_equal(st2["W-val"].to_numpy(), [9, 3, 21]) + np.testing.assert_array_equal(st2["W_val"].to_numpy(), [9, 3, 21]) # Two within factors from other datasets and with NaN values df2 = read_dataset("rm_anova") @@ -413,13 +413,13 @@ def test_pairwise_tests(self): pt = pairwise_tests( dv="Scores", within="Drug", subject="Subject", data=df, alternative="greater" ) - np.testing.assert_array_equal(pt.loc[:, "p-unc"].round(3), [0.907, 0.941, 0.405]) + np.testing.assert_array_equal(pt.loc[:, "p_unc"].round(3), [0.907, 0.941, 0.405]) assert all(pt.loc[:, "BF10"].astype(float) < 1) # 1.1.2 Tail is less pt = pairwise_tests( dv="Scores", within="Drug", subject="Subject", data=df, alternative="less" ) - np.testing.assert_array_equal(pt.loc[:, "p-unc"].round(3), [0.093, 0.059, 0.595]) + np.testing.assert_array_equal(pt.loc[:, "p_unc"].round(3), [0.093, 0.059, 0.595]) assert sum(pt.loc[:, "BF10"].astype(float) > 1) == 2 # 1.2 Non-parametric @@ -432,7 +432,7 @@ def test_pairwise_tests(self): data=df, alternative="greater", ) - np.testing.assert_array_equal(pt.loc[:, "p-unc"].round(3), [0.910, 0.951, 0.483]) + np.testing.assert_array_equal(pt.loc[:, "p_unc"].round(3), [0.910, 0.951, 0.483]) # 1.2.2 Tail is less pt = pairwise_tests( dv="Scores", @@ -442,7 +442,7 @@ def test_pairwise_tests(self): data=df, alternative="less", ) - np.testing.assert_array_equal(pt.loc[:, "p-unc"].round(3), [0.108, 0.060, 0.551]) + np.testing.assert_array_equal(pt.loc[:, "p_unc"].round(3), [0.108, 0.060, 0.551]) # Compare the RBC value for wilcoxon from pingouin.nonparametric import wilcoxon @@ -458,11 +458,11 @@ def test_pairwise_tests(self): # 2.1 Parametric # 2.1.1 Tail is greater pt = pairwise_tests(dv="Scores", between="Gender", data=df, alternative="greater") - assert pt.loc[0, "p-unc"].round(3) == 0.932 + assert pt.loc[0, "p_unc"].round(3) == 0.932 assert float(pt.loc[0, "BF10"]) < 1 # 2.1.2 Tail is less pt = pairwise_tests(dv="Scores", between="Gender", data=df, alternative="less") - assert pt.loc[0, "p-unc"].round(3) == 0.068 + assert pt.loc[0, "p_unc"].round(3) == 0.068 assert float(pt.loc[0, "BF10"]) > 1 # 2.2 Non-parametric @@ -470,12 +470,12 @@ def test_pairwise_tests(self): pt = pairwise_tests( dv="Scores", between="Gender", parametric=False, data=df, alternative="greater" ) - assert pt.loc[0, "p-unc"].round(3) == 0.901 + assert pt.loc[0, "p_unc"].round(3) == 0.901 # 2.2.2 Tail is less pt = pairwise_tests( dv="Scores", between="Gender", parametric=False, data=df, alternative="less" ) - assert pt.loc[0, "p-unc"].round(3) == 0.105 + assert pt.loc[0, "p_unc"].round(3) == 0.105 # Compare the RBC value for MWU from pingouin.nonparametric import mwu @@ -543,7 +543,7 @@ def test_pairwise_tukey(self): # Pingouin: [0.0742, 0.4369, 0.4160, 0.0037, 0.7697, 0.0367] assert np.allclose( [0.074, 0.435, 0.415, 0.004, 0.789, 0.037], - stats.loc[:, "p-tukey"].to_numpy().round(3), + stats.loc[:, "p_tukey"].to_numpy().round(3), atol=0.05, ) # Compare with JASP in the Palmer Penguins dataset @@ -558,7 +558,7 @@ def test_pairwise_tukey(self): assert np.array_equal(stats["T"], [-0.4803, -24.4952, -19.2240]) # P-values JASP: [0.8807, 0.0000, 0.0000] # P-values Pingouin: [0.8694, 0.0010, 0.0010] - sig = stats["p-tukey"].apply(lambda x: "Yes" if x < 0.05 else "No").to_numpy() + sig = stats["p_tukey"].apply(lambda x: "Yes" if x < 0.05 else "No").to_numpy() assert np.array_equal(sig, ["No", "Yes", "Yes"]) # Effect size should be the same as pairwise_tests stats_tests = df.pairwise_tests(dv="body_mass_g", between="species").round(4) @@ -577,7 +577,7 @@ def test_pairwise_tukey(self): assert np.array_equal(stats["T"], [-0.9969, -10.1961, -9.1992]) # P-values JASP: [0.5818, 0.0000, 0.0000] # P-values Pingouin: [0.5766, 0.0010, 0.0010] - sig = stats["p-tukey"].apply(lambda x: "Yes" if x < 0.05 else "No").to_numpy() + sig = stats["p_tukey"].apply(lambda x: "Yes" if x < 0.05 else "No").to_numpy() assert np.array_equal(sig, ["No", "Yes", "Yes"]) def test_pairwise_gameshowell(self): diff --git a/tests/test_pandas.py b/tests/test_pandas.py index 38d9eb68..61c507da 100644 --- a/tests/test_pandas.py +++ b/tests/test_pandas.py @@ -105,7 +105,7 @@ def test_pandas(self): assert corrs.at["Neuroticism", "Agreeableness"] == "*" assert corrs.at["Agreeableness", "Neuroticism"] == str(corrs2.at[2, "r"]) corrs = df_corr.rcorr(padjust="holm", stars=False, decimals=4) - assert corrs.at["Neuroticism", "Agreeableness"] == str(corrs2.at[2, "p-corr"].round(4)) + assert corrs.at["Neuroticism", "Agreeableness"] == str(corrs2.at[2, "p_corr"].round(4)) corrs = df_corr.rcorr(upper="n", decimals=5) corrs2 = df_corr.pairwise_corr().round(5) assert corrs.at["Extraversion", "Openness"] == corrs2.at[4, "n"] diff --git a/tests/test_parametric.py b/tests/test_parametric.py index de0be6d0..82a62934 100644 --- a/tests/test_parametric.py +++ b/tests/test_parametric.py @@ -51,7 +51,7 @@ def test_ttest(self): tt = ttest(a, y=0, alternative="two-sided") assert round(tt.loc["T-test", "T"], 5) == 5.17549 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.00354 + assert round(tt.loc["T-test", "p_val"], 5) == 0.00354 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [2.52, 7.48]) # Using a different confidence level tt = ttest(a, y=0, alternative="two-sided", confidence=0.90) @@ -61,13 +61,13 @@ def test_ttest(self): tt = ttest(a, y=0, alternative="greater") assert round(tt.loc["T-test", "T"], 5) == 5.17549 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.00177 + assert round(tt.loc["T-test", "p_val"], 5) == 0.00177 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [3.05, np.inf]) # One-sided (less) tt = ttest(a, y=0, alternative="less") assert round(tt.loc["T-test", "T"], 5) == 5.17549 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.99823 + assert round(tt.loc["T-test", "p_val"], 5) == 0.99823 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, 6.95]) # 2) One sample with y=4 @@ -76,19 +76,19 @@ def test_ttest(self): tt = ttest(a, y=4, alternative="two-sided") assert round(tt.loc["T-test", "T"], 5) == 1.0351 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.34807 + assert round(tt.loc["T-test", "p_val"], 5) == 0.34807 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [2.52, 7.48]) # One-sided (greater) tt = ttest(a, y=4, alternative="greater") assert round(tt.loc["T-test", "T"], 5) == 1.0351 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.17403 + assert round(tt.loc["T-test", "p_val"], 5) == 0.17403 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [3.05, np.inf]) # One-sided (less) tt = ttest(a, y=4, alternative="less") assert round(tt.loc["T-test", "T"], 5) == 1.0351 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.82597 + assert round(tt.loc["T-test", "p_val"], 5) == 0.82597 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, 6.95]) # 3) Paired two-sample @@ -97,13 +97,13 @@ def test_ttest(self): tt = ttest(a, b, paired=True, alternative="two-sided") assert round(tt.loc["T-test", "T"], 5) == -2.44451 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.05833 + assert round(tt.loc["T-test", "p_val"], 5) == 0.05833 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-7.18, 0.18]) # One-sided (greater) tt = ttest(a, b, paired=True, alternative="greater") assert round(tt.loc["T-test", "T"], 5) == -2.44451 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.97084 + assert round(tt.loc["T-test", "p_val"], 5) == 0.97084 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-6.39, np.inf]) # With a different confidence level tt = ttest(a, b, paired=True, alternative="greater", confidence=0.99) @@ -113,13 +113,13 @@ def test_ttest(self): tt = ttest(a, b, paired=True, alternative="less") assert round(tt.loc["T-test", "T"], 5) == -2.44451 assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p-val"], 5) == 0.02916 + assert round(tt.loc["T-test", "p_val"], 5) == 0.02916 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, -0.61]) # When the two arrays are identical tt = ttest(a, a, paired=True) assert str(tt.loc["T-test", "T"]) == str(np.nan) - assert str(tt.loc["T-test", "p-val"]) == str(np.nan) + assert str(tt.loc["T-test", "p_val"]) == str(np.nan) assert tt.loc["T-test", "cohen-d"] == 0.0 assert tt.loc["T-test", "BF10"] == str(np.nan) @@ -129,19 +129,19 @@ def test_ttest(self): tt = ttest(a, b, correction=False, alternative="two-sided") assert round(tt.loc["T-test", "T"], 5) == -2.84199 assert tt.loc["T-test", "dof"] == 10 - assert round(tt.loc["T-test", "p-val"], 5) == 0.01749 + assert round(tt.loc["T-test", "p_val"], 5) == 0.01749 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-6.24, -0.76]) # One-sided (greater) tt = ttest(a, b, correction=False, alternative="greater") assert round(tt.loc["T-test", "T"], 5) == -2.84199 assert tt.loc["T-test", "dof"] == 10 - assert round(tt.loc["T-test", "p-val"], 5) == 0.99126 + assert round(tt.loc["T-test", "p_val"], 5) == 0.99126 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-5.73, np.inf]) # One-sided (less) tt = ttest(a, b, correction=False, alternative="less") assert round(tt.loc["T-test", "T"], 5) == -2.84199 assert tt.loc["T-test", "dof"] == 10 - assert round(tt.loc["T-test", "p-val"], 5) == 0.00874 + assert round(tt.loc["T-test", "p_val"], 5) == 0.00874 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, -1.27]) # 5) Independent two-samples, Welch correction @@ -150,19 +150,19 @@ def test_ttest(self): tt = ttest(a, b, correction=True, alternative="two-sided") assert round(tt.loc["T-test", "T"], 5) == -2.84199 assert round(tt.loc["T-test", "dof"], 5) == 9.49438 - assert round(tt.loc["T-test", "p-val"], 5) == 0.01837 + assert round(tt.loc["T-test", "p_val"], 5) == 0.01837 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-6.26, -0.74]) # One-sided (greater) tt = ttest(a, b, correction=True, alternative="greater") assert round(tt.loc["T-test", "T"], 5) == -2.84199 assert round(tt.loc["T-test", "dof"], 5) == 9.49438 - assert round(tt.loc["T-test", "p-val"], 5) == 0.99082 + assert round(tt.loc["T-test", "p_val"], 5) == 0.99082 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-5.74, np.inf]) # One-sided (less) tt = ttest(a, b, correction=True, alternative="less") assert round(tt.loc["T-test", "T"], 5) == -2.84199 assert round(tt.loc["T-test", "dof"], 5) == 9.49438 - assert round(tt.loc["T-test", "p-val"], 5) == 0.00918 + assert round(tt.loc["T-test", "p_val"], 5) == 0.00918 array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, -1.26]) def test_anova(self): @@ -175,7 +175,7 @@ def test_anova(self): # Compare with JASP aov = anova(dv="Pain threshold", between="Hair color", data=df_pain, detailed=True).round(3) assert aov.at[0, "F"] == 6.791 - assert aov.at[0, "p-unc"] == 0.004 + assert aov.at[0, "p_unc"] == 0.004 assert aov.at[0, "np2"] == 0.576 aov = anova( dv="Pain threshold", between="Hair color", data=df_pain, effsize="n2", detailed=True @@ -188,7 +188,7 @@ def test_anova(self): assert aov.at[0, "ddof1"] == 3 assert aov.at[0, "ddof2"] == 13 assert aov.at[0, "F"] == 4.359 - assert aov.at[0, "p-unc"] == 0.025 + assert aov.at[0, "p_unc"] == 0.025 assert aov.at[0, "np2"] == 0.501 # Error: between is an empty list with pytest.raises(ValueError): @@ -202,7 +202,7 @@ def test_anova(self): assert aov.at[0, "ddof1"] == 3 assert aov.at[0, "ddof2"] == 13 assert aov.at[0, "F"] == 4.359 - assert aov.at[0, "p-unc"] == 0.025 + assert aov.at[0, "p_unc"] == 0.025 assert aov.at[0, "np2"] == 0.501 # Two-way ANOVA with balanced design @@ -210,7 +210,7 @@ def test_anova(self): aov2 = anova(dv="Yield", between=["Blend", "Crop"], data=df_aov2).round(4) array_equal(aov2.loc[:, "MS"], [2.0417, 1368.2917, 1180.0417, 541.8472]) array_equal(aov2.loc[[0, 1, 2], "F"], [0.0038, 2.5252, 2.1778]) - array_equal(aov2.loc[[0, 1, 2], "p-unc"], [0.9517, 0.1080, 0.1422]) + array_equal(aov2.loc[[0, 1, 2], "p_unc"], [0.9517, 0.1080, 0.1422]) array_equal(aov2.loc[[0, 1, 2], "np2"], [0.0002, 0.2191, 0.1948]) # Same but with standard eta-square aov2 = anova(dv="Yield", between=["Blend", "Crop"], data=df_aov2, effsize="n2").round(4) @@ -221,7 +221,7 @@ def test_anova(self): aov2 = df_aov2.anova(dv="Scores", between=["Diet", "Exercise"]).round(3) array_equal(aov2.loc[:, "MS"], [390.625, 180.625, 15.625, 52.625]) array_equal(aov2.loc[[0, 1, 2], "F"], [7.423, 3.432, 0.297]) - array_equal(aov2.loc[[0, 1, 2], "p-unc"], [0.034, 0.113, 0.605]) + array_equal(aov2.loc[[0, 1, 2], "p_unc"], [0.034, 0.113, 0.605]) array_equal(aov2.loc[[0, 1, 2], "np2"], [0.553, 0.364, 0.047]) # Two-way ANOVA with unbalanced design and missing values @@ -229,7 +229,7 @@ def test_anova(self): # Type 2 aov2 = anova(dv="Scores", between=["Diet", "Exercise"], data=df_aov2).round(3) array_equal(aov2.loc[[0, 1, 2], "F"], [10.403, 5.167, 0.761]) - array_equal(aov2.loc[[0, 1, 2], "p-unc"], [0.023, 0.072, 0.423]) + array_equal(aov2.loc[[0, 1, 2], "p_unc"], [0.023, 0.072, 0.423]) array_equal(aov2.loc[[0, 1, 2], "np2"], [0.675, 0.508, 0.132]) # Type 1 aov2_ss1 = anova(dv="Scores", between=["Diet", "Exercise"], ss_type=1, data=df_aov2).round( @@ -260,7 +260,7 @@ def test_anova(self): aov3_ss1.loc[:, "np2"], [0.049, 0.219, 0.020, 0.003, 0.060, 0.057, 0.044, np.nan] ) array_equal( - aov3_ss1.loc[:, "p-unc"], [0.123, 0.001, 0.619, 0.711, 0.229, 0.245, 0.343, np.nan] + aov3_ss1.loc[:, "p_unc"], [0.123, 0.001, 0.619, 0.711, 0.229, 0.245, 0.343, np.nan] ) # Unbalanced df_aov3 = read_dataset("anova3_unbalanced") @@ -282,7 +282,7 @@ def test_anova(self): aov3_ss1.loc[:, "np2"], [0.068, 0.210, 0.015, 0.001, 0.029, 0.039, 0.017, np.nan] ) array_equal( - aov3_ss1.loc[:, "p-unc"], [0.046, 0.0, 0.658, 0.772, 0.429, 0.318, 0.606, np.nan] + aov3_ss1.loc[:, "p_unc"], [0.046, 0.0, 0.658, 0.772, 0.429, 0.318, 0.606, np.nan] ) array_equal( aov3_ss1.loc[:, "Source"], @@ -305,7 +305,7 @@ def test_anova(self): aov3_ss2.loc[:, "np2"], [0.062, 0.210, 0.015, 0.002, 0.025, 0.039, 0.017, np.nan] ) array_equal( - aov3_ss2.loc[:, "p-unc"], [0.057, 0.0, 0.653, 0.754, 0.482, 0.318, 0.606, np.nan] + aov3_ss2.loc[:, "p_unc"], [0.057, 0.0, 0.653, 0.754, 0.482, 0.318, 0.606, np.nan] ) # Type 3 @@ -316,7 +316,7 @@ def test_anova(self): aov3_ss3.loc[:, "np2"], [0.064, 0.214, 0.017, 0.001, 0.026, 0.036, 0.017, np.nan] ) array_equal( - aov3_ss3.loc[:, "p-unc"], [0.053, 0.0, 0.619, 0.779, 0.477, 0.353, 0.606, np.nan] + aov3_ss3.loc[:, "p_unc"], [0.053, 0.0, 0.619, 0.779, 0.477, 0.353, 0.606, np.nan] ) aov3_ss3 = anova( @@ -340,7 +340,7 @@ def test_welch_anova(self): assert aov.at[0, "ddof1"] == 3 assert aov.at[0, "ddof2"] == 8.3298 assert aov.at[0, "F"] == 5.8901 - assert aov.at[0, "p-unc"] == 0.0188 + assert aov.at[0, "p_unc"] == 0.0188 assert aov.at[0, "np2"] == 0.5760 def test_rm_anova(self): @@ -361,7 +361,7 @@ def test_rm_anova(self): dv="Scores", within="Time", subject="Subject", data=df, correction="auto", detailed=True ).round(5) assert aov.at[0, "F"] == 3.91280 - assert aov.at[0, "p-unc"] == 0.02263 + assert aov.at[0, "p_unc"] == 0.02263 assert aov.at[0, "ng2"] == 0.03998 # Same but with categorical columns @@ -374,7 +374,7 @@ def test_rm_anova(self): detailed=True, ).round(5) assert aov.at[0, "F"] == 3.91280 - assert aov.at[0, "p-unc"] == 0.02263 + assert aov.at[0, "p_unc"] == 0.02263 assert aov.at[0, "ng2"] == 0.03998 # With different effect sizes @@ -402,11 +402,11 @@ def test_rm_anova(self): data = read_dataset("rm_anova_wide") aov = data.rm_anova(detailed=True, correction=True).round(5) assert aov.at[0, "F"] == 5.20065 - assert aov.at[0, "p-unc"] == 0.00656 + assert aov.at[0, "p_unc"] == 0.00656 assert aov.at[0, "ng2"] == 0.34639 assert aov.at[0, "eps"] == 0.69433 - assert aov.at[0, "W-spher"] == 0.30678 - assert aov.at[0, "p-GG-corr"] == 0.01670 + assert aov.at[0, "W_spher"] == 0.30678 + assert aov.at[0, "p_GG_corr"] == 0.01670 # With different effect sizes aov = data.rm_anova(detailed=True, correction=True, effsize="n2").round(5) assert aov.at[0, "n2"] == 0.34639 # n2 == ng2 @@ -487,8 +487,8 @@ def test_mixed_anova(self): array_equal(aov.loc[:, "F"], [5.05171, 4.02739, 2.72800]) array_equal(aov.loc[:, "np2"], [0.08012, 0.06493, 0.04492]) assert round(aov.at[1, "eps"], 3) == 0.999 # Pingouin = 0.99875, JAMOVI = 0.99812 - assert round(aov.at[1, "W-spher"], 3) == 0.999 # Pingouin = 0.99875, JAMOVI = 0.99812 - assert round(aov.at[1, "p-GG-corr"], 2) == 0.02 + assert round(aov.at[1, "W_spher"], 3) == 0.999 # Pingouin = 0.99875, JAMOVI = 0.99812 + assert round(aov.at[1, "p_GG_corr"], 2) == 0.02 # With categorical: should be the same aov = mixed_anova( dv="Scores", @@ -504,8 +504,8 @@ def test_mixed_anova(self): array_equal(aov.loc[:, "F"], [5.05171, 4.02739, 2.72800]) array_equal(aov.loc[:, "np2"], [0.08012, 0.06493, 0.04492]) assert round(aov.at[1, "eps"], 3) == 0.999 # Pingouin = 0.99875, JAMOVI = 0.99812 - assert round(aov.at[1, "W-spher"], 3) == 0.999 # Pingouin = 0.99875, JAMOVI = 0.99812 - assert round(aov.at[1, "p-GG-corr"], 2) == 0.02 + assert round(aov.at[1, "W_spher"], 3) == 0.999 # Pingouin = 0.99875, JAMOVI = 0.99812 + assert round(aov.at[1, "p_GG_corr"], 2) == 0.02 # Same with different effect sizes (compare with JAMOVI) aov = mixed_anova( @@ -531,7 +531,7 @@ def test_mixed_anova(self): array_equal(aov.loc[:, "F"], [5.692, 3.054, 3.502]) array_equal(aov.loc[:, "np2"], [0.094, 0.053, 0.060]) assert aov.at[1, "eps"] == 0.997 - assert aov.at[1, "W-spher"] == 0.996 + assert aov.at[1, "W_spher"] == 0.996 # Unbalanced group df_unbalanced = df[df["Subject"] <= 54] @@ -546,7 +546,7 @@ def test_mixed_anova(self): array_equal(aov.loc[:, "F"], [3.561, 2.421, 1.828]) array_equal(aov.loc[:, "np2"], [0.063, 0.044, 0.033]) assert aov.at[1, "eps"] == 1.0 # JASP = 0.998 - assert aov.at[1, "W-spher"] == 1.0 # JASP = 0.998 + assert aov.at[1, "W_spher"] == 1.0 # JASP = 0.998 # With three groups and four time points, unbalanced (JASP -- type II) df_unbalanced = read_dataset("mixed_anova_unbalanced.csv") @@ -561,13 +561,13 @@ def test_mixed_anova(self): array_equal(aov.loc[:, "DF1"], [2, 3, 6]) array_equal(aov.loc[:, "DF2"], [23, 69, 69]) array_equal(aov.loc[:, "F"], [2.3026, 1.7071, 0.8877]) - array_equal(aov.loc[:, "p-unc"], [0.1226, 0.1736, 0.5088]) + array_equal(aov.loc[:, "p_unc"], [0.1226, 0.1736, 0.5088]) array_equal(aov.loc[:, "np2"], [0.1668, 0.0691, 0.0717]) # Check correction: values are very slightly different than ezANOVA assert np.isclose(aov.at[1, "eps"], 0.9254, atol=0.01) - assert np.isclose(aov.at[1, "p-GG-corr"], 0.1779, atol=0.01) - assert np.isclose(aov.at[1, "W-spher"], 0.8850, atol=0.01) - assert np.isclose(aov.at[1, "p-spher"], 0.7535, atol=0.1) + assert np.isclose(aov.at[1, "p_GG_corr"], 0.1779, atol=0.01) + assert np.isclose(aov.at[1, "W_spher"], 0.8850, atol=0.01) + assert np.isclose(aov.at[1, "p_spher"], 0.7535, atol=0.1) # Same but with different effect sizes aov = mixed_anova( @@ -615,7 +615,7 @@ def test_ancova(self): aov = ancova(data=df, dv="Scores", covar="Income", between="Method").round(4) array_equal(aov["DF"], [3, 1, 31]) array_equal(aov["F"], [3.3365, 29.4194, np.nan]) - array_equal(aov["p-unc"], [0.0319, 0.000, np.nan]) + array_equal(aov["p_unc"], [0.0319, 0.000, np.nan]) array_equal(aov["np2"], [0.2441, 0.4869, np.nan]) aov = ancova(data=df, dv="Scores", covar="Income", between="Method", effsize="n2").round(4) array_equal(aov["n2"], [0.1421, 0.4177, np.nan]) @@ -624,13 +624,13 @@ def test_ancova(self): aov = ancova(data=df, dv="Scores", covar=["Income"], between="Method").round(4) array_equal(aov["DF"], [3, 1, 29]) array_equal(aov["F"], [3.1471, 19.7811, np.nan]) - array_equal(aov["p-unc"], [0.0400, 0.0001, np.nan]) + array_equal(aov["p_unc"], [0.0400, 0.0001, np.nan]) array_equal(aov["np2"], [0.2456, 0.4055, np.nan]) # With two covariates, missing values and unbalanced design aov = ancova(data=df, dv="Scores", covar=["Income", "BMI"], between="Method").round(4) array_equal(aov["DF"], [3, 1, 1, 28]) array_equal(aov["F"], [3.0186, 19.6045, 1.2279, np.nan]) - array_equal(aov["p-unc"], [0.0464, 0.0001, 0.2772, np.nan]) + array_equal(aov["p_unc"], [0.0464, 0.0001, 0.2772, np.nan]) array_equal(aov["np2"], [0.2444, 0.4118, 0.0420, np.nan]) # Same but using standard eta-squared aov = ancova( From 1c8694ed21d1e3e26b5302491464815b952d4dba Mon Sep 17 00:00:00 2001 From: Remington Mallett Date: Mon, 7 Oct 2024 15:02:35 -0400 Subject: [PATCH 3/7] CI[97.5%] --> CI97.5 --- README.rst | 4 +-- docs/index.rst | 4 +-- src/pingouin/regression.py | 52 +++++++++++++++++------------------ tests/test_regression.py | 56 +++++++++++++++++++------------------- 4 files changed, 58 insertions(+), 58 deletions(-) diff --git a/README.rst b/README.rst index e862f335..eef01b6c 100644 --- a/README.rst +++ b/README.rst @@ -374,7 +374,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto ========= ====== ===== ====== ====== ===== ======== ========== =========== - names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] + names coef se T pval r2 adj_r2 CI2.5 CI97.5 ========= ====== ===== ====== ====== ===== ======== ========== =========== Intercept 4.650 0.841 5.530 0.000 0.139 0.076 2.925 6.376 X 0.143 0.068 2.089 0.046 0.139 0.076 0.003 0.283 @@ -394,7 +394,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame :widths: auto ======== ====== ===== ====== ========== =========== ===== - path coef se pval CI[2.5%] CI[97.5%] sig + path coef se pval CI2.5 CI97.5 sig ======== ====== ===== ====== ========== =========== ===== Z ~ X 0.103 0.075 0.181 -0.051 0.256 No Y ~ Z 0.018 0.171 0.916 -0.332 0.369 No diff --git a/docs/index.rst b/docs/index.rst index 083689b2..8bdf212e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -365,7 +365,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto ========= ====== ===== ====== ====== ===== ======== ========== =========== - names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] + names coef se T pval r2 adj_r2 CI2.5 CI97.5 ========= ====== ===== ====== ====== ===== ======== ========== =========== Intercept 4.650 0.841 5.530 0.000 0.139 0.076 2.925 6.376 X 0.143 0.068 2.089 0.046 0.139 0.076 0.003 0.283 @@ -385,7 +385,7 @@ The :py:func:`pingouin.normality` function works with lists, arrays, or pandas D :widths: auto ======== ====== ===== ====== ========== =========== ===== - path coef se pval CI[2.5%] CI[97.5%] sig + path coef se pval CI2.5 CI97.5 sig ======== ====== ===== ====== ========== =========== ===== Z ~ X 0.103 0.075 0.181 -0.051 0.256 No Y ~ Z 0.018 0.171 0.916 -0.332 0.369 No diff --git a/src/pingouin/regression.py b/src/pingouin/regression.py index b1826701..4d4ffeb9 100644 --- a/src/pingouin/regression.py +++ b/src/pingouin/regression.py @@ -89,8 +89,8 @@ def linear_regression( * ``'pval'``: p-values * ``'r2'``: coefficient of determination (:math:`R^2`) * ``'adj_r2'``: adjusted :math:`R^2` - * ``'CI[2.5%]'``: lower confidence intervals - * ``'CI[97.5%]'``: upper confidence intervals + * ``'CI2.5'``: lower confidence intervals + * ``'CI97.5'``: upper confidence intervals * ``'relimp'``: relative contribution of each predictor to the final\ :math:`R^2` (only if ``relimp=True``). * ``'relimp_perc'``: percent relative contribution @@ -198,7 +198,7 @@ def linear_regression( >>> # Let's predict the tip ($) based on the total bill (also in $) >>> lm = pg.linear_regression(df['total_bill'], df['tip']) >>> lm.round(2) - names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] + names coef se T pval r2 adj_r2 CI2.5 CI97.5 0 Intercept 0.92 0.16 5.76 0.0 0.46 0.45 0.61 1.23 1 total_bill 0.11 0.01 14.26 0.0 0.46 0.45 0.09 0.12 @@ -215,7 +215,7 @@ def linear_regression( >>> # We'll add a second predictor: the party size >>> lm = pg.linear_regression(df[['total_bill', 'size']], df['tip']) >>> lm.round(2) - names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] + names coef se T pval r2 adj_r2 CI2.5 CI97.5 0 Intercept 0.67 0.19 3.46 0.00 0.47 0.46 0.29 1.05 1 total_bill 0.09 0.01 10.17 0.00 0.47 0.46 0.07 0.11 2 size 0.19 0.09 2.26 0.02 0.47 0.46 0.02 0.36 @@ -229,7 +229,7 @@ def linear_regression( >>> X = df[['total_bill', 'size']].to_numpy() >>> y = df['tip'].to_numpy() >>> pg.linear_regression(X, y).round(2) - names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] + names coef se T pval r2 adj_r2 CI2.5 CI97.5 0 Intercept 0.67 0.19 3.46 0.00 0.47 0.46 0.29 1.05 1 x1 0.09 0.01 10.17 0.00 0.47 0.46 0.07 0.11 2 x2 0.19 0.09 2.26 0.02 0.47 0.46 0.02 0.36 @@ -269,8 +269,8 @@ def linear_regression( >>> lm_dict = pg.linear_regression(X, y, as_dataframe=False) >>> lm_dict.keys() - dict_keys(['names', 'coef', 'se', 'T', 'pval', 'r2', 'adj_r2', 'CI[2.5%]', - 'CI[97.5%]', 'df_model', 'df_resid', 'residuals', 'X', 'y', + dict_keys(['names', 'coef', 'se', 'T', 'pval', 'r2', 'adj_r2', 'CI2.5', + 'CI97.5', 'df_model', 'df_resid', 'residuals', 'X', 'y', 'pred']) 7. Remove missing values @@ -307,7 +307,7 @@ def linear_regression( >>> w = [1, 0.1, 1, 1, 0.5, 1] # Array of weights. Must be >= 0. >>> lm = pg.linear_regression(X, y, weights=w) >>> lm.round(2) - names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] + names coef se T pval r2 adj_r2 CI2.5 CI97.5 0 Intercept 9.00 2.03 4.42 0.01 0.51 0.39 3.35 14.64 1 x1 1.04 0.50 2.06 0.11 0.51 0.39 -0.36 2.44 """ @@ -462,8 +462,8 @@ def linear_regression( ul = coef + marg_error # Rename CI - ll_name = "CI[%.1f%%]" % (100 * alpha / 2) - ul_name = "CI[%.1f%%]" % (100 * (1 - alpha / 2)) + ll_name = "CI%.1f" % (100 * alpha / 2) + ul_name = "CI%.1f" % (100 * (1 - alpha / 2)) # Create dict stats = { @@ -638,8 +638,8 @@ def logistic_regression( * ``'se'``: standard error * ``'z'``: z-scores * ``'pval'``: two-tailed p-values - * ``'CI[2.5%]'``: lower confidence interval - * ``'CI[97.5%]'``: upper confidence interval + * ``'CI2.5'``: lower confidence interval + * ``'CI97.5'``: upper confidence interval See also -------- @@ -703,7 +703,7 @@ def logistic_regression( >>> lom = pg.logistic_regression(df['body_mass_g'], df['male'], ... remove_na=True) >>> lom.round(2) - names coef se z pval CI[2.5%] CI[97.5%] + names coef se z pval CI2.5 CI97.5 0 Intercept -5.16 0.71 -7.24 0.0 -6.56 -3.77 1 body_mass_g 0.00 0.00 7.24 0.0 0.00 0.00 @@ -716,7 +716,7 @@ def logistic_regression( >>> lom = pg.logistic_regression(df['body_mass_kg'], df['male'], ... remove_na=True) >>> lom.round(2) - names coef se z pval CI[2.5%] CI[97.5%] + names coef se z pval CI2.5 CI97.5 0 Intercept -5.16 0.71 -7.24 0.0 -6.56 -3.77 1 body_mass_kg 1.23 0.17 7.24 0.0 0.89 1.56 @@ -732,7 +732,7 @@ def logistic_regression( >>> y = df['male'] >>> lom = pg.logistic_regression(X, y, remove_na=True) >>> lom.round(2) - names coef se z pval CI[2.5%] CI[97.5%] + names coef se z pval CI2.5 CI97.5 0 Intercept -26.24 2.84 -9.24 0.00 -31.81 -20.67 1 body_mass_kg 7.10 0.77 9.23 0.00 5.59 8.61 2 species_Chinstrap -0.13 0.42 -0.31 0.75 -0.96 0.69 @@ -770,7 +770,7 @@ def logistic_regression( >>> # And then run the logistic regression >>> lr = pg.logistic_regression(df['HoursStudy'], df['PassExam']).round(3) >>> lr - names coef se z pval CI[2.5%] CI[97.5%] + names coef se z pval CI2.5 CI97.5 0 Intercept -4.078 1.761 -2.316 0.021 -7.529 -0.626 1 HoursStudy 1.505 0.629 2.393 0.017 0.272 2.737 @@ -930,8 +930,8 @@ def logistic_regression( ul = coef + crit * se # Rename CI - ll_name = "CI[%.1f%%]" % (100 * alpha / 2) - ul_name = "CI[%.1f%%]" % (100 * (1 - alpha / 2)) + ll_name = "CI%.1f" % (100 * alpha / 2) + ul_name = "CI%.1f" % (100 * (1 - alpha / 2)) # Create dict stats = { @@ -1076,8 +1076,8 @@ def mediation_analysis( * ``'path'``: regression model * ``'coef'``: regression estimates * ``'se'``: standard error - * ``'CI[2.5%]'``: lower confidence interval - * ``'CI[97.5%]'``: upper confidence interval + * ``'CI2.5'``: lower confidence interval + * ``'CI97.5'``: upper confidence interval * ``'pval'``: two-sided p-values * ``'sig'``: statistical significance @@ -1150,7 +1150,7 @@ def mediation_analysis( >>> df = read_dataset('mediation') >>> mediation_analysis(data=df, x='X', m='M', y='Y', alpha=0.05, ... seed=42) - path coef se pval CI[2.5%] CI[97.5%] sig + path coef se pval CI2.5 CI97.5 sig 0 M ~ X 0.561015 0.094480 4.391362e-08 0.373522 0.748509 Yes 1 Y ~ M 0.654173 0.085831 1.612674e-11 0.483844 0.824501 Yes 2 Total 0.396126 0.111160 5.671128e-04 0.175533 0.616719 Yes @@ -1167,7 +1167,7 @@ def mediation_analysis( 3. Mediation analysis with a binary mediator variable >>> mediation_analysis(data=df, x='X', m='Mbin', y='Y', seed=42).round(3) - path coef se pval CI[2.5%] CI[97.5%] sig + path coef se pval CI2.5 CI97.5 sig 0 Mbin ~ X -0.021 0.116 0.857 -0.248 0.206 No 1 Y ~ Mbin -0.135 0.412 0.743 -0.952 0.682 No 2 Total 0.396 0.111 0.001 0.176 0.617 Yes @@ -1178,7 +1178,7 @@ def mediation_analysis( >>> mediation_analysis(data=df, x='X', m='M', y='Y', ... covar=['Mbin', 'Ybin'], seed=42).round(3) - path coef se pval CI[2.5%] CI[97.5%] sig + path coef se pval CI2.5 CI97.5 sig 0 M ~ X 0.559 0.097 0.000 0.367 0.752 Yes 1 Y ~ M 0.666 0.086 0.000 0.495 0.837 Yes 2 Total 0.420 0.113 0.000 0.196 0.645 Yes @@ -1189,7 +1189,7 @@ def mediation_analysis( >>> mediation_analysis(data=df, x='X', m=['M', 'Mbin'], y='Y', ... seed=42).round(3) - path coef se pval CI[2.5%] CI[97.5%] sig + path coef se pval CI2.5 CI97.5 sig 0 M ~ X 0.561 0.094 0.000 0.374 0.749 Yes 1 Mbin ~ X -0.005 0.029 0.859 -0.063 0.052 No 2 Y ~ M 0.654 0.086 0.000 0.482 0.825 Yes @@ -1235,8 +1235,8 @@ def mediation_analysis( logreg_kwargs = {} if logreg_kwargs is None else logreg_kwargs # Name of CI - ll_name = "CI[%.1f%%]" % (100 * alpha / 2) - ul_name = "CI[%.1f%%]" % (100 * (1 - alpha / 2)) + ll_name = "CI%.1f" % (100 * alpha / 2) + ul_name = "CI%.1f" % (100 * (1 - alpha / 2)) # Compute regressions cols = ["names", "coef", "se", "pval", ll_name, ul_name] diff --git a/tests/test_regression.py b/tests/test_regression.py index 992abbb4..400e2e06 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -53,8 +53,8 @@ def test_linear_regression(self): assert_almost_equal(lm["pval"][1], sc.pvalue) assert_almost_equal(np.sqrt(lm["r2"][0]), sc.rvalue) assert lm.residuals_.size == df["Y"].size - assert_equal(lm["CI[2.5%]"].round(5).to_numpy(), [1.48155, 0.17553]) - assert_equal(lm["CI[97.5%]"].round(5).to_numpy(), [4.23286, 0.61672]) + assert_equal(lm["CI2.5"].round(5).to_numpy(), [1.48155, 0.17553]) + assert_equal(lm["CI97.5"].round(5).to_numpy(), [4.23286, 0.61672]) assert round(lm["r2"].iloc[0], 4) == 0.1147 assert round(lm["adj_r2"].iloc[0], 4) == 0.1057 assert lm.df_model_ == 1 @@ -74,8 +74,8 @@ def test_linear_regression(self): assert_equal([0.605, 0.110, 0.101], np.round(lm["se"], 3)) assert_equal([3.145, 0.361, 6.321], np.round(lm["T"], 3)) assert_equal([0.002, 0.719, 0.000], np.round(lm["pval"], 3)) - assert_equal([0.703, -0.178, 0.436], np.round(lm["CI[2.5%]"], 3)) - assert_equal([3.106, 0.257, 0.835], np.round(lm["CI[97.5%]"], 3)) + assert_equal([0.703, -0.178, 0.436], np.round(lm["CI2.5"], 3)) + assert_equal([3.106, 0.257, 0.835], np.round(lm["CI97.5"], 3)) # No intercept lm = linear_regression(X, y, add_intercept=False, as_dataframe=False) @@ -146,8 +146,8 @@ def test_linear_regression(self): np.testing.assert_allclose(res_pingouin["T"], res_sm.tvalues) np.testing.assert_allclose(res_pingouin["se"], res_sm.bse) np.testing.assert_allclose(res_pingouin["pval"], res_sm.pvalues) - np.testing.assert_allclose(res_pingouin["CI[2.5%]"], res_sm.conf_int()[:, 0]) - np.testing.assert_allclose(res_pingouin["CI[97.5%]"], res_sm.conf_int()[:, 1]) + np.testing.assert_allclose(res_pingouin["CI2.5"], res_sm.conf_int()[:, 0]) + np.testing.assert_allclose(res_pingouin["CI97.5"], res_sm.conf_int()[:, 1]) # Relative importance # Compare to R package relaimpo @@ -190,8 +190,8 @@ def test_linear_regression(self): assert_equal(lm["se"].round(5).to_numpy(), [0.60498, 0.10984, 0.10096]) assert_equal(lm["T"].round(3).to_numpy(), [3.133, 0.356, 6.331]) # R round to 3 assert_equal(lm["pval"].round(5).to_numpy(), [0.00229, 0.72296, 0.00000]) - assert_equal(lm["CI[2.5%]"].round(5).to_numpy(), [0.69459, -0.17896, 0.43874]) - assert_equal(lm["CI[97.5%]"].round(5).to_numpy(), [3.09602, 0.25706, 0.83949]) + assert_equal(lm["CI2.5"].round(5).to_numpy(), [0.69459, -0.17896, 0.43874]) + assert_equal(lm["CI97.5"].round(5).to_numpy(), [3.09602, 0.25706, 0.83949]) assert round(lm["r2"].iloc[0], 4) == 0.3742 assert round(lm["adj_r2"].iloc[0], 4) == 0.3613 assert lm.df_model_ == 2 @@ -203,8 +203,8 @@ def test_linear_regression(self): assert_equal(lm["se"].round(5).to_numpy(), [0.08525, 0.10213]) assert_equal(lm["T"].round(3).to_numpy(), [3.158, 7.024]) assert_equal(lm["pval"].round(5).to_numpy(), [0.00211, 0.00000]) - assert_equal(lm["CI[2.5%]"].round(5).to_numpy(), [0.10007, 0.51466]) - assert_equal(lm["CI[97.5%]"].round(4).to_numpy(), [0.4384, 0.9200]) + assert_equal(lm["CI2.5"].round(5).to_numpy(), [0.10007, 0.51466]) + assert_equal(lm["CI97.5"].round(4).to_numpy(), [0.4384, 0.9200]) assert round(lm["r2"].iloc[0], 4) == 0.9090 assert round(lm["adj_r2"].iloc[0], 4) == 0.9072 assert lm.df_model_ == 2 @@ -218,8 +218,8 @@ def test_linear_regression(self): assert_equal(lm["coef"].round(4).to_numpy(), [3.5597, 0.2820]) assert_equal(lm["se"].round(4).to_numpy(), [0.7355, 0.1222]) assert_equal(lm["pval"].round(4).to_numpy(), [0.0000, 0.0232]) - assert_equal(lm["CI[2.5%]"].round(5).to_numpy(), [2.09935, 0.03943]) - assert_equal(lm["CI[97.5%]"].round(5).to_numpy(), [5.02015, 0.52453]) + assert_equal(lm["CI2.5"].round(5).to_numpy(), [2.09935, 0.03943]) + assert_equal(lm["CI97.5"].round(5).to_numpy(), [5.02015, 0.52453]) assert round(lm["r2"].iloc[0], 5) == 0.05364 assert round(lm["adj_r2"].iloc[0], 5) == 0.04358 assert lm.df_model_ == 1 @@ -230,8 +230,8 @@ def test_linear_regression(self): assert_equal(lm["coef"].round(5).to_numpy(), [0.85060]) assert_equal(lm["se"].round(5).to_numpy(), [0.03719]) assert_equal(lm["pval"].round(5).to_numpy(), [0.0000]) - assert_equal(lm["CI[2.5%]"].round(5).to_numpy(), [0.77678]) - assert_equal(lm["CI[97.5%]"].round(5).to_numpy(), [0.92443]) + assert_equal(lm["CI2.5"].round(5).to_numpy(), [0.77678]) + assert_equal(lm["CI97.5"].round(5).to_numpy(), [0.92443]) assert round(lm["r2"].iloc[0], 4) == 0.8463 assert round(lm["adj_r2"].iloc[0], 4) == 0.8447 assert lm.df_model_ == 1 @@ -265,8 +265,8 @@ def test_logistic_regression(self): assert_equal(np.round(lom["se"], 3), [0.758, 0.121]) assert_equal(np.round(lom["z"], 3), [1.74, -1.647]) assert_equal(np.round(lom["pval"], 3), [0.082, 0.099]) - assert_equal(np.round(lom["CI[2.5%]"], 3), [-0.167, -0.437]) - assert_equal(np.round(lom["CI[97.5%]"], 3), [2.805, 0.038]) + assert_equal(np.round(lom["CI2.5"], 3), [-0.167, -0.437]) + assert_equal(np.round(lom["CI97.5"], 3), [2.805, 0.038]) # Multiple predictors X = df[["X", "M"]].to_numpy() @@ -278,8 +278,8 @@ def test_logistic_regression(self): assert_equal(lom["se"].to_numpy(), [0.778, 0.141, 0.125]) assert_equal(lom["z"].to_numpy(), [1.705, -1.392, -0.048]) assert_equal(lom["pval"].to_numpy(), [0.088, 0.164, 0.962]) - assert_equal(lom["CI[2.5%]"].to_numpy(), [-0.198, -0.472, -0.252]) - assert_equal(lom["CI[97.5%]"].to_numpy(), [2.853, 0.08, 0.24]) + assert_equal(lom["CI2.5"].to_numpy(), [-0.198, -0.472, -0.252]) + assert_equal(lom["CI97.5"].to_numpy(), [2.853, 0.08, 0.24]) # Test other arguments c = logistic_regression(df[["X", "M"]], df["Ybin"], coef_only=True) @@ -322,8 +322,8 @@ def test_logistic_regression(self): assert_equal(np.round(lom["se"], 5), [0.72439, 0.00017]) assert_equal(np.round(lom["z"], 3), [-7.127, 7.177]) assert np.allclose(lom["pval"], [1.03e-12, 7.10e-13]) - assert_equal(np.round(lom["CI[2.5%]"], 3), [-6.582, 0.001]) - assert_equal(np.round(lom["CI[97.5%]"], 3), [-3.743, 0.002]) + assert_equal(np.round(lom["CI2.5"], 3), [-6.582, 0.001]) + assert_equal(np.round(lom["CI97.5"], 3), [-3.743, 0.002]) # With a different scaling: z / p-values should be similar lom = logistic_regression(data["body_mass_kg"], data["male"], as_dataframe=False) @@ -331,8 +331,8 @@ def test_logistic_regression(self): assert_equal(np.round(lom["se"], 4), [0.7244, 0.1727]) assert_equal(np.round(lom["z"], 3), [-7.127, 7.177]) assert np.allclose(lom["pval"], [1.03e-12, 7.10e-13]) - assert_equal(np.round(lom["CI[2.5%]"], 3), [-6.582, 0.901]) - assert_equal(np.round(lom["CI[97.5%]"], 3), [-3.743, 1.578]) + assert_equal(np.round(lom["CI2.5"], 3), [-6.582, 0.901]) + assert_equal(np.round(lom["CI97.5"], 3), [-3.743, 1.578]) # With no intercept lom = logistic_regression( @@ -342,8 +342,8 @@ def test_logistic_regression(self): assert np.round(lom["se"], 5) == 0.02570 assert np.round(lom["z"], 3) == 1.615 assert np.round(lom["pval"], 3) == 0.106 - assert np.round(lom["CI[2.5%]"], 3) == -0.009 - assert np.round(lom["CI[97.5%]"], 3) == 0.092 + assert np.round(lom["CI2.5"], 3) == -0.009 + assert np.round(lom["CI97.5"], 3) == 0.092 # With categorical predictors # R: >>> glm("male ~ body_mass_kg + species", family=binomial, ...) @@ -357,8 +357,8 @@ def test_logistic_regression(self): assert_equal(np.round(lom["coef"], 2), [-27.13, 7.37, -0.26, -10.18]) assert_equal(np.round(lom["se"], 3), [2.998, 0.814, 0.429, 1.195]) assert_equal(np.round(lom["z"], 3), [-9.049, 9.056, -0.596, -8.520]) - assert_equal(np.round(lom["CI[2.5%]"], 1), [-33.0, 5.8, -1.1, -12.5]) - assert_equal(np.round(lom["CI[97.5%]"], 1), [-21.3, 9.0, 0.6, -7.8]) + assert_equal(np.round(lom["CI2.5"], 1), [-33.0, 5.8, -1.1, -12.5]) + assert_equal(np.round(lom["CI97.5"], 1), [-21.3, 9.0, 0.6, -7.8]) def test_mediation_analysis(self): """Test function mediation_analysis.""" @@ -383,8 +383,8 @@ def test_mediation_analysis(self): # Direct effect assert_almost_equal(ma["coef"][3], 0.3956, decimal=2) - assert_almost_equal(ma["CI[2.5%]"][3], 0.1714, decimal=2) - assert_almost_equal(ma["CI[97.5%]"][3], 0.617, decimal=1) + assert_almost_equal(ma["CI2.5"][3], 0.1714, decimal=2) + assert_almost_equal(ma["CI97.5"][3], 0.617, decimal=1) assert ma["sig"][3] == "Yes" # Check if `logreg_kwargs` is being passed on to `LogisticRegression` From 88cafad5e522ad174d105f0f01c96fcc74b8bbf9 Mon Sep 17 00:00:00 2001 From: Remington Mallett Date: Mon, 7 Oct 2024 15:06:26 -0400 Subject: [PATCH 4/7] mean(A) --> mean_A (also std) --- src/pingouin/pairwise.py | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/src/pingouin/pairwise.py b/src/pingouin/pairwise.py index b92ef14e..21cfb476 100644 --- a/src/pingouin/pairwise.py +++ b/src/pingouin/pairwise.py @@ -320,10 +320,10 @@ def pairwise_tests( "Time", "A", "B", - "mean(A)", - "std(A)", - "mean(B)", - "std(B)", + "mean_A", + "std_A", + "mean_B", + "std_B", "Paired", "Parametric", "T", @@ -414,10 +414,10 @@ def pairwise_tests( ef = compute_effsize(x=x, y=y, eftype=effsize, paired=paired) if return_desc: - stats.at[i, "mean(A)"] = np.nanmean(x) - stats.at[i, "mean(B)"] = np.nanmean(y) - stats.at[i, "std(A)"] = np.nanstd(x, ddof=1) - stats.at[i, "std(B)"] = np.nanstd(y, ddof=1) + stats.at[i, "mean_A"] = np.nanmean(x) + stats.at[i, "mean_B"] = np.nanmean(y) + stats.at[i, "std_A"] = np.nanstd(x, ddof=1) + stats.at[i, "std_B"] = np.nanstd(y, ddof=1) stats.at[i, stat_name] = df_ttest[stat_name].iat[0] stats.at[i, "p_unc"] = df_ttest["p_val"].iat[0] stats.at[i, effsize] = ef @@ -564,10 +564,10 @@ def pairwise_tests( # Append to stats if return_desc: - stats.at[ic, "mean(A)"] = np.nanmean(x) - stats.at[ic, "mean(B)"] = np.nanmean(y) - stats.at[ic, "std(A)"] = np.nanstd(x, ddof=1) - stats.at[ic, "std(B)"] = np.nanstd(y, ddof=1) + stats.at[ic, "mean_A"] = np.nanmean(x) + stats.at[ic, "mean_B"] = np.nanmean(y) + stats.at[ic, "std_A"] = np.nanstd(x, ddof=1) + stats.at[ic, "std_B"] = np.nanstd(y, ddof=1) stats.at[ic, stat_name] = df_ttest[stat_name].iat[0] stats.at[ic, "p_unc"] = df_ttest["p_val"].iat[0] stats.at[ic, effsize] = ef @@ -798,8 +798,8 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize="hedges"): * ``'A'``: Name of first measurement * ``'B'``: Name of second measurement - * ``'mean(A)'``: Mean of first measurement - * ``'mean(B)'``: Mean of second measurement + * ``'mean_A'``: Mean of first measurement + * ``'mean_B'``: Mean of second measurement * ``'diff'``: Mean difference (= mean(A) - mean(B)) * ``'se'``: Standard error * ``'T'``: T-values @@ -921,8 +921,8 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize="hedges"): { "A": labels[g1], "B": labels[g2], - "mean(A)": gmeans[g1], - "mean(B)": gmeans[g2], + "mean_A": gmeans[g1], + "mean_B": gmeans[g2], "diff": mn, "se": se, "T": tval, @@ -963,8 +963,8 @@ def pairwise_gameshowell(data=None, dv=None, between=None, effsize="hedges"): * ``'A'``: Name of first measurement * ``'B'``: Name of second measurement - * ``'mean(A)'``: Mean of first measurement - * ``'mean(B)'``: Mean of second measurement + * ``'mean_A'``: Mean of first measurement + * ``'mean_B'``: Mean of second measurement * ``'diff'``: Mean difference (= mean(A) - mean(B)) * ``'se'``: Standard error * ``'T'``: T-values @@ -1089,8 +1089,8 @@ def pairwise_gameshowell(data=None, dv=None, between=None, effsize="hedges"): { "A": labels[g1], "B": labels[g2], - "mean(A)": gmeans[g1], - "mean(B)": gmeans[g2], + "mean_A": gmeans[g1], + "mean_B": gmeans[g2], "diff": mn, "se": se, "T": tval, From c6f0b2636572760b2a711382d54c562ebcbb8763 Mon Sep 17 00:00:00 2001 From: Remington Mallett Date: Mon, 7 Oct 2024 15:12:56 -0400 Subject: [PATCH 5/7] T-test --> T_test (index) --- src/pingouin/equivalence.py | 4 +- src/pingouin/pairwise.py | 8 +-- src/pingouin/parametric.py | 16 ++--- tests/test_bayesian.py | 8 +-- tests/test_parametric.py | 132 ++++++++++++++++++------------------ 5 files changed, 84 insertions(+), 84 deletions(-) diff --git a/src/pingouin/equivalence.py b/src/pingouin/equivalence.py index 91e57946..70f5ee58 100644 --- a/src/pingouin/equivalence.py +++ b/src/pingouin/equivalence.py @@ -78,10 +78,10 @@ def tost(x, y, bound=1, paired=False, correction=False): # T-tests df_a = ttest(x + bound, y, paired=paired, correction=correction, alternative="greater") df_b = ttest(x - bound, y, paired=paired, correction=correction, alternative="less") - pval = max(df_a.at["T-test", "p_val"], df_b.at["T-test", "p_val"]) + pval = max(df_a.at["T_test", "p_val"], df_b.at["T_test", "p_val"]) # Create output dataframe stats = pd.DataFrame( - {"bound": bound, "dof": df_a.at["T-test", "dof"], "pval": pval}, index=["TOST"] + {"bound": bound, "dof": df_a.at["T_test", "dof"], "pval": pval}, index=["TOST"] ) return _postprocess_dataframe(stats) diff --git a/src/pingouin/pairwise.py b/src/pingouin/pairwise.py index 21cfb476..552c238c 100644 --- a/src/pingouin/pairwise.py +++ b/src/pingouin/pairwise.py @@ -398,8 +398,8 @@ def pairwise_tests( df_ttest = ttest( x, y, paired=paired, alternative=alternative, correction=correction ) - stats.at[i, "BF10"] = df_ttest.at["T-test", "BF10"] - stats.at[i, "dof"] = df_ttest.at["T-test", "dof"] + stats.at[i, "BF10"] = df_ttest.at["T_test", "BF10"] + stats.at[i, "dof"] = df_ttest.at["T_test", "dof"] else: if paired: stat_name = "W_val" @@ -550,8 +550,8 @@ def pairwise_tests( df_ttest = ttest( x, y, paired=paired, alternative=alternative, correction=correction ) - stats.at[ic, "BF10"] = df_ttest.at["T-test", "BF10"] - stats.at[ic, "dof"] = df_ttest.at["T-test", "dof"] + stats.at[ic, "BF10"] = df_ttest.at["T_test", "BF10"] + stats.at[ic, "dof"] = df_ttest.at["T_test", "dof"] else: if paired: stat_name = "W_val" diff --git a/src/pingouin/parametric.py b/src/pingouin/parametric.py index c131e1e3..2f7f5df3 100644 --- a/src/pingouin/parametric.py +++ b/src/pingouin/parametric.py @@ -144,7 +144,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> x = [5.5, 2.4, 6.8, 9.6, 4.2] >>> ttest(x, 4).round(2) T dof alternative p_val CI95 cohen-d BF10 power - T-test 1.4 4 two-sided 0.23 [2.32, 9.08] 0.62 0.766 0.19 + T_test 1.4 4 two-sided 0.23 [2.32, 9.08] 0.62 0.766 0.19 2. One sided paired T-test. @@ -152,13 +152,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True, alternative='less').round(2) T dof alternative p_val CI95 cohen-d BF10 power - T-test -2.31 4 less 0.04 [-inf, -0.05] 0.25 3.122 0.12 + T_test -2.31 4 less 0.04 [-inf, -0.05] 0.25 3.122 0.12 Now testing the opposite alternative hypothesis >>> ttest(pre, post, paired=True, alternative='greater').round(2) T dof alternative p_val CI95 cohen-d BF10 power - T-test -2.31 4 greater 0.96 [-1.35, inf] 0.25 0.32 0.02 + T_test -2.31 4 greater 0.96 [-1.35, inf] 0.25 0.32 0.02 3. Paired T-test with missing values. @@ -167,7 +167,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True).round(3) T dof alternative p_val CI95 cohen-d BF10 power - T-test -5.902 3 two-sided 0.01 [-1.5, -0.45] 0.306 7.169 0.073 + T_test -5.902 3 two-sided 0.01 [-1.5, -0.45] 0.306 7.169 0.073 Compare with SciPy @@ -182,7 +182,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> y = np.random.normal(loc=4, size=20) >>> ttest(x, y) T dof alternative p_val CI95 cohen-d BF10 power - T-test 9.106452 38 two-sided 4.306971e-11 [2.64, 4.15] 2.879713 1.366e+08 1.0 + T_test 9.106452 38 two-sided 4.306971e-11 [2.64, 4.15] 2.879713 1.366e+08 1.0 5. Independent two-sample T-test with unequal sample size. A Welch's T-test is used. @@ -190,13 +190,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> y = np.random.normal(loc=6.5, size=15) >>> ttest(x, y) T dof alternative p_val CI95 cohen-d BF10 power - T-test 1.996537 31.567592 two-sided 0.054561 [-0.02, 1.65] 0.673518 1.469 0.481867 + T_test 1.996537 31.567592 two-sided 0.054561 [-0.02, 1.65] 0.673518 1.469 0.481867 6. However, the Welch's correction can be disabled: >>> ttest(x, y, correction=False) T dof alternative p_val CI95 cohen-d BF10 power - T-test 1.971859 33 two-sided 0.057056 [-0.03, 1.66] 0.673518 1.418 0.481867 + T_test 1.971859 33 two-sided 0.057056 [-0.03, 1.66] 0.673518 1.418 0.481867 Compare with SciPy @@ -328,7 +328,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 # Convert to dataframe col_order = ["T", "dof", "alternative", "p_val", ci_name, "cohen-d", "BF10", "power"] - stats = pd.DataFrame(stats, columns=col_order, index=["T-test"]) + stats = pd.DataFrame(stats, columns=col_order, index=["T_test"]) return _postprocess_dataframe(stats) diff --git a/tests/test_bayesian.py b/tests/test_bayesian.py index 8c11f053..f3c9fd48 100644 --- a/tests/test_bayesian.py +++ b/tests/test_bayesian.py @@ -37,10 +37,10 @@ def test_bayesfactor_ttest(self): assert bayesfactor_ttest(3.5, 20, 1) == appr(17.185) # Compare against BayesFactor::testBF # >>> ttestBF(df$x, df$y, paired = FALSE, rscale = "medium") - assert ttest(x, y).at["T-test", "BF10"] == "0.183" - assert ttest(x, y, paired=True).at["T-test", "BF10"] == "0.135" - assert int(float(ttest(x, z).at["T-test", "BF10"])) == 1290 - assert int(float(ttest(x, z, paired=True).at["T-test", "BF10"])) == 420 + assert ttest(x, y).at["T_test", "BF10"] == "0.183" + assert ttest(x, y, paired=True).at["T_test", "BF10"] == "0.135" + assert int(float(ttest(x, z).at["T_test", "BF10"])) == 1290 + assert int(float(ttest(x, z, paired=True).at["T_test", "BF10"])) == 420 # Now check the alternative tails assert bayesfactor_ttest(3.5, 20, 20, alternative="greater") > 1 assert bayesfactor_ttest(3.5, 20, 20, alternative="less") < 1 diff --git a/tests/test_parametric.py b/tests/test_parametric.py index 82a62934..ad05da43 100644 --- a/tests/test_parametric.py +++ b/tests/test_parametric.py @@ -49,121 +49,121 @@ def test_ttest(self): # R: t.test(a, mu=0) # Two-sided tt = ttest(a, y=0, alternative="two-sided") - assert round(tt.loc["T-test", "T"], 5) == 5.17549 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.00354 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [2.52, 7.48]) + assert round(tt.loc["T_test", "T"], 5) == 5.17549 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.00354 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [2.52, 7.48]) # Using a different confidence level tt = ttest(a, y=0, alternative="two-sided", confidence=0.90) - array_equal(np.round(tt.loc["T-test", "CI90"], 3), [3.053, 6.947]) + array_equal(np.round(tt.loc["T_test", "CI90"], 3), [3.053, 6.947]) # One-sided (greater) tt = ttest(a, y=0, alternative="greater") - assert round(tt.loc["T-test", "T"], 5) == 5.17549 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.00177 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [3.05, np.inf]) + assert round(tt.loc["T_test", "T"], 5) == 5.17549 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.00177 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [3.05, np.inf]) # One-sided (less) tt = ttest(a, y=0, alternative="less") - assert round(tt.loc["T-test", "T"], 5) == 5.17549 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.99823 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, 6.95]) + assert round(tt.loc["T_test", "T"], 5) == 5.17549 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.99823 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-np.inf, 6.95]) # 2) One sample with y=4 # R: t.test(a, mu=4) # Two-sided tt = ttest(a, y=4, alternative="two-sided") - assert round(tt.loc["T-test", "T"], 5) == 1.0351 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.34807 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [2.52, 7.48]) + assert round(tt.loc["T_test", "T"], 5) == 1.0351 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.34807 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [2.52, 7.48]) # One-sided (greater) tt = ttest(a, y=4, alternative="greater") - assert round(tt.loc["T-test", "T"], 5) == 1.0351 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.17403 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [3.05, np.inf]) + assert round(tt.loc["T_test", "T"], 5) == 1.0351 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.17403 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [3.05, np.inf]) # One-sided (less) tt = ttest(a, y=4, alternative="less") - assert round(tt.loc["T-test", "T"], 5) == 1.0351 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.82597 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, 6.95]) + assert round(tt.loc["T_test", "T"], 5) == 1.0351 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.82597 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-np.inf, 6.95]) # 3) Paired two-sample # R: t.test(a, b, paired=TRUE) # Two-sided tt = ttest(a, b, paired=True, alternative="two-sided") - assert round(tt.loc["T-test", "T"], 5) == -2.44451 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.05833 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-7.18, 0.18]) + assert round(tt.loc["T_test", "T"], 5) == -2.44451 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.05833 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-7.18, 0.18]) # One-sided (greater) tt = ttest(a, b, paired=True, alternative="greater") - assert round(tt.loc["T-test", "T"], 5) == -2.44451 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.97084 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-6.39, np.inf]) + assert round(tt.loc["T_test", "T"], 5) == -2.44451 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.97084 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-6.39, np.inf]) # With a different confidence level tt = ttest(a, b, paired=True, alternative="greater", confidence=0.99) - array_equal(np.round(tt.loc["T-test", "CI99"], 3), [-8.318, np.inf]) + array_equal(np.round(tt.loc["T_test", "CI99"], 3), [-8.318, np.inf]) # One-sided (less) tt = ttest(a, b, paired=True, alternative="less") - assert round(tt.loc["T-test", "T"], 5) == -2.44451 - assert tt.loc["T-test", "dof"] == 5 - assert round(tt.loc["T-test", "p_val"], 5) == 0.02916 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, -0.61]) + assert round(tt.loc["T_test", "T"], 5) == -2.44451 + assert tt.loc["T_test", "dof"] == 5 + assert round(tt.loc["T_test", "p_val"], 5) == 0.02916 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-np.inf, -0.61]) # When the two arrays are identical tt = ttest(a, a, paired=True) - assert str(tt.loc["T-test", "T"]) == str(np.nan) - assert str(tt.loc["T-test", "p_val"]) == str(np.nan) - assert tt.loc["T-test", "cohen-d"] == 0.0 - assert tt.loc["T-test", "BF10"] == str(np.nan) + assert str(tt.loc["T_test", "T"]) == str(np.nan) + assert str(tt.loc["T_test", "p_val"]) == str(np.nan) + assert tt.loc["T_test", "cohen-d"] == 0.0 + assert tt.loc["T_test", "BF10"] == str(np.nan) # 4) Independent two-samples, equal variance (no correction) # R: t.test(a, b, paired=FALSE, var.equal=TRUE) # Two-sided tt = ttest(a, b, correction=False, alternative="two-sided") - assert round(tt.loc["T-test", "T"], 5) == -2.84199 - assert tt.loc["T-test", "dof"] == 10 - assert round(tt.loc["T-test", "p_val"], 5) == 0.01749 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-6.24, -0.76]) + assert round(tt.loc["T_test", "T"], 5) == -2.84199 + assert tt.loc["T_test", "dof"] == 10 + assert round(tt.loc["T_test", "p_val"], 5) == 0.01749 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-6.24, -0.76]) # One-sided (greater) tt = ttest(a, b, correction=False, alternative="greater") - assert round(tt.loc["T-test", "T"], 5) == -2.84199 - assert tt.loc["T-test", "dof"] == 10 - assert round(tt.loc["T-test", "p_val"], 5) == 0.99126 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-5.73, np.inf]) + assert round(tt.loc["T_test", "T"], 5) == -2.84199 + assert tt.loc["T_test", "dof"] == 10 + assert round(tt.loc["T_test", "p_val"], 5) == 0.99126 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-5.73, np.inf]) # One-sided (less) tt = ttest(a, b, correction=False, alternative="less") - assert round(tt.loc["T-test", "T"], 5) == -2.84199 - assert tt.loc["T-test", "dof"] == 10 - assert round(tt.loc["T-test", "p_val"], 5) == 0.00874 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, -1.27]) + assert round(tt.loc["T_test", "T"], 5) == -2.84199 + assert tt.loc["T_test", "dof"] == 10 + assert round(tt.loc["T_test", "p_val"], 5) == 0.00874 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-np.inf, -1.27]) # 5) Independent two-samples, Welch correction # R: t.test(a, b, paired=FALSE, var.equal=FALSE) # Two-sided tt = ttest(a, b, correction=True, alternative="two-sided") - assert round(tt.loc["T-test", "T"], 5) == -2.84199 - assert round(tt.loc["T-test", "dof"], 5) == 9.49438 - assert round(tt.loc["T-test", "p_val"], 5) == 0.01837 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-6.26, -0.74]) + assert round(tt.loc["T_test", "T"], 5) == -2.84199 + assert round(tt.loc["T_test", "dof"], 5) == 9.49438 + assert round(tt.loc["T_test", "p_val"], 5) == 0.01837 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-6.26, -0.74]) # One-sided (greater) tt = ttest(a, b, correction=True, alternative="greater") - assert round(tt.loc["T-test", "T"], 5) == -2.84199 - assert round(tt.loc["T-test", "dof"], 5) == 9.49438 - assert round(tt.loc["T-test", "p_val"], 5) == 0.99082 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-5.74, np.inf]) + assert round(tt.loc["T_test", "T"], 5) == -2.84199 + assert round(tt.loc["T_test", "dof"], 5) == 9.49438 + assert round(tt.loc["T_test", "p_val"], 5) == 0.99082 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-5.74, np.inf]) # One-sided (less) tt = ttest(a, b, correction=True, alternative="less") - assert round(tt.loc["T-test", "T"], 5) == -2.84199 - assert round(tt.loc["T-test", "dof"], 5) == 9.49438 - assert round(tt.loc["T-test", "p_val"], 5) == 0.00918 - array_equal(np.round(tt.loc["T-test", "CI95"], 2), [-np.inf, -1.26]) + assert round(tt.loc["T_test", "T"], 5) == -2.84199 + assert round(tt.loc["T_test", "dof"], 5) == 9.49438 + assert round(tt.loc["T_test", "p_val"], 5) == 0.00918 + array_equal(np.round(tt.loc["T_test", "CI95"], 2), [-np.inf, -1.26]) def test_anova(self): """Test function anova. From ef8b121f16b56c14a5ffa169dc87cfed15b806b9 Mon Sep 17 00:00:00 2001 From: Remington Mallett Date: Mon, 7 Oct 2024 15:50:01 -0400 Subject: [PATCH 6/7] effect sizes --- README.rst | 2 +- docs/index.rst | 2 +- notebooks/03_EffectSizes.ipynb | 6 +++--- src/pingouin/effsize.py | 14 +++++++------- src/pingouin/pairwise.py | 12 ++++++------ src/pingouin/parametric.py | 20 ++++++++++---------- src/pingouin/utils.py | 4 ++-- tests/test_effsize.py | 14 +++++++------- tests/test_parametric.py | 2 +- 9 files changed, 38 insertions(+), 38 deletions(-) diff --git a/README.rst b/README.rst index eef01b6c..5a246f4f 100644 --- a/README.rst +++ b/README.rst @@ -157,7 +157,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio :widths: auto ====== ===== ============= ======= ============= ========= ====== ======= - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power ====== ===== ============= ======= ============= ========= ====== ======= -3.401 58 two-sided 0.001 [-1.68 -0.43] 0.878 26.155 0.917 ====== ===== ============= ======= ============= ========= ====== ======= diff --git a/docs/index.rst b/docs/index.rst index 8bdf212e..63df0995 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -135,7 +135,7 @@ Quick start :widths: auto ====== ===== ============= ======= ============= ========= ====== ======= - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power ====== ===== ============= ======= ============= ========= ====== ======= -3.401 58 two-sided 0.001 [-1.68 -0.43] 0.878 26.155 0.917 ====== ===== ============= ======= ============= ========= ====== ======= diff --git a/notebooks/03_EffectSizes.ipynb b/notebooks/03_EffectSizes.ipynb index d474eea0..8555cd10 100644 --- a/notebooks/03_EffectSizes.ipynb +++ b/notebooks/03_EffectSizes.ipynb @@ -54,7 +54,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -90,7 +90,7 @@ ], "source": [ "from pingouin import compute_effsize\n", - "eftype = 'hedges' # 'cohen', 'hedges', 'eta-square', 'odds-ratio', 'AUC'\n", + "eftype = 'hedges' # 'cohen', 'hedges', 'eta_square', 'odds_ratio', 'AUC'\n", "ef = compute_effsize(x=x, y=y, eftype=eftype, paired=False)\n", "print(eftype, ': %.3f' % ef)" ] @@ -192,7 +192,7 @@ "source": [ "from pingouin import convert_effsize\n", "# Convert from Cohen's d to eta-square:\n", - "eta = convert_effsize(ef=d, input_type='cohen', output_type='eta-square')\n", + "eta = convert_effsize(ef=d, input_type='cohen', output_type='eta_square')\n", "print('Eta:\\t%.3f' % eta)\n", "\n", "# Convert from Cohen's d to hedges (requires sample size):\n", diff --git a/src/pingouin/effsize.py b/src/pingouin/effsize.py index 359c0252..d46855a9 100644 --- a/src/pingouin/effsize.py +++ b/src/pingouin/effsize.py @@ -501,8 +501,8 @@ def convert_effsize(ef, input_type, output_type, nx=None, ny=None): * ``'cohen'``: Unbiased Cohen d * ``'hedges'``: Hedges g * ``'pointbiserialr'``: Point-biserial correlation - * ``'eta-square'``: Eta-square - * ``'odds-ratio'``: Odds ratio + * ``'eta_square'``: Eta-square + * ``'odds_ratio'``: Odds ratio * ``'AUC'``: Area Under the Curve * ``'none'``: pass-through (return ``ef``) @@ -570,7 +570,7 @@ def convert_effsize(ef, input_type, output_type, nx=None, ny=None): >>> import pingouin as pg >>> d = .45 - >>> eta = pg.convert_effsize(d, 'cohen', 'eta-square') + >>> eta = pg.convert_effsize(d, 'cohen', 'eta_square') >>> print(eta) 0.048185603807257595 @@ -630,10 +630,10 @@ def convert_effsize(ef, input_type, output_type, nx=None, ny=None): else: a = 4 return d / np.sqrt(d**2 + a) - elif ot == "eta-square": + elif ot == "eta_square": # Cohen 1988 return (d / 2) ** 2 / (1 + (d / 2) ** 2) - elif ot == "odds-ratio": + elif ot == "odds_ratio": # Borenstein et al. 2009 return np.exp(d * np.pi / np.sqrt(3)) elif ot == "r": @@ -670,8 +670,8 @@ def compute_effsize(x, y, paired=False, eftype="cohen"): * ``'hedges'``: Hedges g * ``'r'``: Pearson correlation coefficient * ``'pointbiserialr'``: Point-biserial correlation - * ``'eta-square'``: Eta-square - * ``'odds-ratio'``: Odds ratio + * ``'eta_square'``: Eta-square + * ``'odds_ratio'``: Odds ratio * ``'AUC'``: Area Under the Curve * ``'CLES'``: Common Language Effect Size diff --git a/src/pingouin/pairwise.py b/src/pingouin/pairwise.py index 552c238c..97ec677a 100644 --- a/src/pingouin/pairwise.py +++ b/src/pingouin/pairwise.py @@ -100,8 +100,8 @@ def pairwise_tests( * ``'cohen'``: Unbiased Cohen d * ``'hedges'``: Hedges g * ``'r'``: Pearson correlation coefficient - * ``'eta-square'``: Eta-square - * ``'odds-ratio'``: Odds ratio + * ``'eta_square'``: Eta-square + * ``'odds_ratio'``: Odds ratio * ``'AUC'``: Area Under the Curve * ``'CLES'``: Common Language Effect Size correction : string or boolean @@ -787,8 +787,8 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize="hedges"): * ``'cohen'``: Unbiased Cohen d * ``'hedges'``: Hedges g * ``'r'``: Pearson correlation coefficient - * ``'eta-square'``: Eta-square - * ``'odds-ratio'``: Odds ratio + * ``'eta_square'``: Eta-square + * ``'odds_ratio'``: Odds ratio * ``'AUC'``: Area Under the Curve * ``'CLES'``: Common Language Effect Size @@ -951,8 +951,8 @@ def pairwise_gameshowell(data=None, dv=None, between=None, effsize="hedges"): * ``'cohen'``: Unbiased Cohen d * ``'hedges'``: Hedges g * ``'r'``: Pearson correlation coefficient - * ``'eta-square'``: Eta-square - * ``'odds-ratio'``: Odds ratio + * ``'eta_square'``: Eta-square + * ``'odds_ratio'``: Odds ratio * ``'AUC'``: Area Under the Curve * ``'CLES'``: Common Language Effect Size diff --git a/src/pingouin/parametric.py b/src/pingouin/parametric.py index 2f7f5df3..483939ad 100644 --- a/src/pingouin/parametric.py +++ b/src/pingouin/parametric.py @@ -62,7 +62,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 * ``'alternative'``: alternative of the test * ``'p_val'``: p-value * ``'CI95'``: confidence intervals of the difference in means - * ``'cohen-d'``: Cohen d effect size + * ``'cohen_d'``: Cohen d effect size * ``'BF10'``: Bayes Factor of the alternative hypothesis * ``'power'``: achieved power of the test ( = 1 - type II error) @@ -143,7 +143,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> from pingouin import ttest >>> x = [5.5, 2.4, 6.8, 9.6, 4.2] >>> ttest(x, 4).round(2) - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power T_test 1.4 4 two-sided 0.23 [2.32, 9.08] 0.62 0.766 0.19 2. One sided paired T-test. @@ -151,13 +151,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> pre = [5.5, 2.4, 6.8, 9.6, 4.2] >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True, alternative='less').round(2) - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power T_test -2.31 4 less 0.04 [-inf, -0.05] 0.25 3.122 0.12 Now testing the opposite alternative hypothesis >>> ttest(pre, post, paired=True, alternative='greater').round(2) - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power T_test -2.31 4 greater 0.96 [-1.35, inf] 0.25 0.32 0.02 3. Paired T-test with missing values. @@ -166,7 +166,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> pre = [5.5, 2.4, np.nan, 9.6, 4.2] >>> post = [6.4, 3.4, 6.4, 11., 4.8] >>> ttest(pre, post, paired=True).round(3) - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power T_test -5.902 3 two-sided 0.01 [-1.5, -0.45] 0.306 7.169 0.073 Compare with SciPy @@ -181,7 +181,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> x = np.random.normal(loc=7, size=20) >>> y = np.random.normal(loc=4, size=20) >>> ttest(x, y) - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power T_test 9.106452 38 two-sided 4.306971e-11 [2.64, 4.15] 2.879713 1.366e+08 1.0 5. Independent two-sample T-test with unequal sample size. A Welch's T-test is used. @@ -189,13 +189,13 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 >>> np.random.seed(123) >>> y = np.random.normal(loc=6.5, size=15) >>> ttest(x, y) - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power T_test 1.996537 31.567592 two-sided 0.054561 [-0.02, 1.65] 0.673518 1.469 0.481867 6. However, the Welch's correction can be disabled: >>> ttest(x, y, correction=False) - T dof alternative p_val CI95 cohen-d BF10 power + T dof alternative p_val CI95 cohen_d BF10 power T_test 1.971859 33 two-sided 0.057056 [-0.03, 1.66] 0.673518 1.418 0.481867 Compare with SciPy @@ -320,14 +320,14 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70 "T": tval, "p_val": pval, "alternative": alternative, - "cohen-d": abs(d), + "cohen_d": abs(d), ci_name: [ci], "power": power, "BF10": bf, } # Convert to dataframe - col_order = ["T", "dof", "alternative", "p_val", ci_name, "cohen-d", "BF10", "power"] + col_order = ["T", "dof", "alternative", "p_val", ci_name, "cohen_d", "BF10", "power"] stats = pd.DataFrame(stats, columns=col_order, index=["T_test"]) return _postprocess_dataframe(stats) diff --git a/src/pingouin/utils.py b/src/pingouin/utils.py index df1fd203..b7319873 100644 --- a/src/pingouin/utils.py +++ b/src/pingouin/utils.py @@ -327,8 +327,8 @@ def _check_eftype(eftype): "cohen", "r", "pointbiserialr", - "eta-square", - "odds-ratio", + "eta_square", + "odds_ratio", "auc", "cles", ]: diff --git a/tests/test_effsize.py b/tests/test_effsize.py index 0b656c32..38742b30 100644 --- a/tests/test_effsize.py +++ b/tests/test_effsize.py @@ -234,8 +234,8 @@ def test_convert_effsize(self): assert round(cef(d, "cohen", "pointbiserialr"), 4) == 0.1961 cef(d, "cohen", "pointbiserialr", nx=10, ny=12) # When nx and ny are specified assert np.allclose(cef(1.002549, "cohen", "pointbiserialr"), 0.4481248) # R - assert round(cef(d, "cohen", "eta-square"), 4) == 0.0385 - assert round(cef(d, "cohen", "odds-ratio"), 4) == 2.0658 + assert round(cef(d, "cohen", "eta_square"), 4) == 0.0385 + assert round(cef(d, "cohen", "odds_ratio"), 4) == 2.0658 cef(d, "cohen", "hedges", nx=10, ny=10) cef(d, "cohen", "pointbiserialr") cef(d, "cohen", "hedges") @@ -245,8 +245,8 @@ def test_convert_effsize(self): assert cef(rpb, "pointbiserialr", "none") == rpb assert round(cef(rpb, "pointbiserialr", "cohen"), 4) == 1.7107 assert np.allclose(cef(0.4481248, "pointbiserialr", "cohen"), 1.002549) - assert round(cef(rpb, "pointbiserialr", "eta-square"), 4) == 0.4225 - assert round(cef(rpb, "pointbiserialr", "odds-ratio"), 4) == 22.2606 + assert round(cef(rpb, "pointbiserialr", "eta_square"), 4) == 0.4225 + assert round(cef(rpb, "pointbiserialr", "odds_ratio"), 4) == 22.2606 # Using actual values np.random.seed(42) x1, y1 = np.random.multivariate_normal(mean=[1, 2], cov=[[1, 0.5], [0.5, 1]], size=100).T @@ -270,15 +270,15 @@ def test_convert_effsize(self): with pytest.raises(ValueError): cef(d, "coucou", "hibou") with pytest.raises(ValueError): - cef(d, "AUC", "eta-square") + cef(d, "AUC", "eta_square") def test_compute_effsize(self): """Test function compute_effsize""" compute_effsize(x=x, y=y, eftype="cohen", paired=False) compute_effsize(x=x, y=y, eftype="AUC", paired=True) compute_effsize(x=x, y=y, eftype="r", paired=False) - compute_effsize(x=x, y=y, eftype="odds-ratio", paired=False) - compute_effsize(x=x, y=y, eftype="eta-square", paired=False) + compute_effsize(x=x, y=y, eftype="odds_ratio", paired=False) + compute_effsize(x=x, y=y, eftype="eta_square", paired=False) compute_effsize(x=x, y=y, eftype="cles", paired=False) compute_effsize(x=x, y=y, eftype="pointbiserialr", paired=False) compute_effsize(x=x, y=y, eftype="none", paired=False) diff --git a/tests/test_parametric.py b/tests/test_parametric.py index ad05da43..41baa884 100644 --- a/tests/test_parametric.py +++ b/tests/test_parametric.py @@ -120,7 +120,7 @@ def test_ttest(self): tt = ttest(a, a, paired=True) assert str(tt.loc["T_test", "T"]) == str(np.nan) assert str(tt.loc["T_test", "p_val"]) == str(np.nan) - assert tt.loc["T_test", "cohen-d"] == 0.0 + assert tt.loc["T_test", "cohen_d"] == 0.0 assert tt.loc["T_test", "BF10"] == str(np.nan) # 4) Independent two-samples, equal variance (no correction) From 56212534ca4fd0b0caff2b5ea14bcb7be3fd0d1c Mon Sep 17 00:00:00 2001 From: Remington Mallett Date: Tue, 8 Oct 2024 09:37:05 -0400 Subject: [PATCH 7/7] updated notebooks --- notebooks/00_QuickStart.ipynb | 129 +++++++++++------------ notebooks/01_ANOVA.ipynb | 156 +++++++++++++-------------- notebooks/02_BayesianTTests.ipynb | 32 +++--- notebooks/03_EffectSizes.ipynb | 6 +- notebooks/04_Correlations.ipynb | 168 +++++++++++++----------------- notebooks/06_Rounding.ipynb | 46 ++++---- 6 files changed, 242 insertions(+), 295 deletions(-) diff --git a/notebooks/00_QuickStart.ipynb b/notebooks/00_QuickStart.ipynb index fc7b7bfc..227c06f5 100644 --- a/notebooks/00_QuickStart.ipynb +++ b/notebooks/00_QuickStart.ipynb @@ -38,16 +38,16 @@ " T\n", " dof\n", " alternative\n", - " p-val\n", - " CI95%\n", - " cohen-d\n", + " p_val\n", + " CI95\n", + " cohen_d\n", " BF10\n", " power\n", " \n", " \n", " \n", " \n", - " T-test\n", + " T_test\n", " -3.401\n", " 58\n", " two-sided\n", @@ -62,8 +62,8 @@ "" ], "text/plain": [ - " T dof alternative p-val CI95% cohen-d BF10 power\n", - "T-test -3.401 58 two-sided 0.001 [-1.68, -0.43] 0.878 26.155 0.917" + " T dof alternative p_val CI95 cohen_d BF10 power\n", + "T_test -3.401 58 two-sided 0.001 [-1.68, -0.43] 0.878 26.155 0.917" ] }, "execution_count": 1, @@ -122,8 +122,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " BF10\n", " power\n", " \n", @@ -143,7 +143,7 @@ "" ], "text/plain": [ - " n r CI95% p-val BF10 power\n", + " n r CI95 p_val BF10 power\n", "pearson 30 0.595 [0.3, 0.79] 5.274e-04 69.723 0.95" ] }, @@ -191,8 +191,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " power\n", " \n", " \n", @@ -210,7 +210,7 @@ "" ], "text/plain": [ - " n r CI95% p-val power\n", + " n r CI95 p_val power\n", "bicor 30 0.576 [0.27, 0.78] 8.694e-04 0.933" ] }, @@ -286,7 +286,7 @@ { "data": { "text/plain": [ - "HZResults(hz=1.6967733646126668, pval=0.00018201726664169367, normal=False)" + "HZResults(hz=np.float64(1.6967733646126668), pval=np.float64(0.00018201726664169367), normal=False)" ] }, "execution_count": 4, @@ -337,7 +337,7 @@ " DF\n", " MS\n", " F\n", - " p-unc\n", + " p_unc\n", " np2\n", " \n", " \n", @@ -367,7 +367,7 @@ "" ], "text/plain": [ - " Source SS DF MS F p-unc np2\n", + " Source SS DF MS F p_unc np2\n", "0 Group 5.460 1 5.460 5.244 0.023 0.029\n", "1 Within 185.343 178 1.041 NaN NaN NaN" ] @@ -424,7 +424,7 @@ " DF\n", " MS\n", " F\n", - " p-unc\n", + " p_unc\n", " ng2\n", " eps\n", " \n", @@ -457,7 +457,7 @@ "" ], "text/plain": [ - " Source SS DF MS F p-unc ng2 eps\n", + " Source SS DF MS F p_unc ng2 eps\n", "0 Time 7.628 2 3.814 3.913 0.023 0.04 0.999\n", "1 Error 115.027 118 0.975 NaN NaN NaN NaN" ] @@ -512,9 +512,9 @@ " T\n", " dof\n", " alternative\n", - " p-unc\n", - " p-corr\n", - " p-adjust\n", + " p_unc\n", + " p_corr\n", + " p_adjust\n", " BF10\n", " hedges\n", " \n", @@ -578,7 +578,7 @@ "1 Time August June True True -2.743 59.0 two-sided \n", "2 Time January June True True -1.024 59.0 two-sided \n", "\n", - " p-unc p-corr p-adjust BF10 hedges \n", + " p_unc p_corr p_adjust BF10 hedges \n", "0 0.087 0.131 fdr_bh 0.582 -0.328 \n", "1 0.008 0.024 fdr_bh 4.232 -0.483 \n", "2 0.310 0.310 fdr_bh 0.232 -0.170 " @@ -635,11 +635,11 @@ " B\n", " Paired\n", " Parametric\n", - " W-val\n", + " W_val\n", " alternative\n", - " p-unc\n", - " p-corr\n", - " p-adjust\n", + " p_unc\n", + " p_corr\n", + " p_adjust\n", " hedges\n", " \n", " \n", @@ -691,12 +691,12 @@ "" ], "text/plain": [ - " Contrast A B Paired Parametric W-val alternative p-unc \\\n", + " Contrast A B Paired Parametric W_val alternative p_unc \\\n", "0 Time August January True False 716.0 two-sided 0.144 \n", "1 Time August June True False 564.0 two-sided 0.010 \n", "2 Time January June True False 887.0 two-sided 0.840 \n", "\n", - " p-corr p-adjust hedges \n", + " p_corr p_adjust hedges \n", "0 0.216 fdr_bh -0.328 \n", "1 0.030 fdr_bh -0.483 \n", "2 0.840 fdr_bh -0.170 " @@ -752,7 +752,7 @@ " DF2\n", " MS\n", " F\n", - " p-unc\n", + " p_unc\n", " np2\n", " eps\n", " \n", @@ -799,7 +799,7 @@ "" ], "text/plain": [ - " Source SS DF1 DF2 MS F p-unc np2 eps\n", + " Source SS DF1 DF2 MS F p_unc np2 eps\n", "0 Group 5.460 1 58 5.460 5.052 0.028 0.080 NaN\n", "1 Time 7.628 2 116 3.814 4.027 0.020 0.065 0.999\n", "2 Interaction 5.167 2 116 2.584 2.728 0.070 0.045 NaN" @@ -857,8 +857,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -908,7 +908,7 @@ "" ], "text/plain": [ - " X Y method alternative n r CI95% p-unc BF10 power\n", + " X Y method alternative n r CI95 p_unc BF10 power\n", "0 X Y pearson two-sided 30 0.366 [0.01, 0.64] 0.047 1.5 0.525\n", "1 X Z pearson two-sided 30 0.251 [-0.12, 0.56] 0.181 0.534 0.272\n", "2 Y Z pearson two-sided 30 0.020 [-0.34, 0.38] 0.916 0.228 0.051" @@ -940,14 +940,6 @@ "execution_count": 11, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/raphael/GitHub/pingouin/pingouin/correlation.py:1116: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " mat_upper = mat_upper.applymap(replace_pval)\n" - ] - }, { "data": { "text/html": [ @@ -1025,14 +1017,6 @@ "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/raphael/GitHub/pingouin/pingouin/pairwise.py:763: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " mat_upper = mat_upper.applymap(lambda x: ffp(x, precision=decimals))\n" - ] - }, { "data": { "text/html": [ @@ -1138,8 +1122,8 @@ " pval\n", " r2\n", " adj_r2\n", - " CI[2.5%]\n", - " CI[97.5%]\n", + " CI2.5\n", + " CI97.5\n", " \n", " \n", " \n", @@ -1184,15 +1168,10 @@ "" ], "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", - "0 Intercept 4.650 0.841 5.530 7.362e-06 0.139 0.076 2.925 \n", - "1 X 0.143 0.068 2.089 4.630e-02 0.139 0.076 0.003 \n", - "2 Z -0.069 0.167 -0.416 6.809e-01 0.139 0.076 -0.412 \n", - "\n", - " CI[97.5%] \n", - "0 6.376 \n", - "1 0.283 \n", - "2 0.273 " + " names coef se T pval r2 adj_r2 CI2.5 CI97.5\n", + "0 Intercept 4.650 0.841 5.530 7.362e-06 0.139 0.076 2.925 6.376\n", + "1 X 0.143 0.068 2.089 4.630e-02 0.139 0.076 0.003 0.283\n", + "2 Z -0.069 0.167 -0.416 6.809e-01 0.139 0.076 -0.412 0.273" ] }, "execution_count": 13, @@ -1241,8 +1220,8 @@ " coef\n", " se\n", " pval\n", - " CI[2.5%]\n", - " CI[97.5%]\n", + " CI2.5\n", + " CI97.5\n", " sig\n", " \n", " \n", @@ -1302,12 +1281,12 @@ "" ], "text/plain": [ - " path coef se pval CI[2.5%] CI[97.5%] sig\n", - "0 Z ~ X 0.103 0.075 0.181 -0.051 0.256 No\n", - "1 Y ~ Z 0.018 0.171 0.916 -0.332 0.369 No\n", - "2 Total 0.136 0.065 0.047 0.002 0.269 Yes\n", - "3 Direct 0.143 0.068 0.046 0.003 0.283 Yes\n", - "4 Indirect -0.007 0.025 0.898 -0.069 0.029 No" + " path coef se pval CI2.5 CI97.5 sig\n", + "0 Z ~ X 0.103 0.075 0.181 -0.051 0.256 No\n", + "1 Y ~ Z 0.018 0.171 0.916 -0.332 0.369 No\n", + "2 Total 0.136 0.065 0.047 0.002 0.269 Yes\n", + "3 Direct 0.143 0.068 0.046 0.003 0.283 Yes\n", + "4 Indirect -0.007 0.025 0.898 -0.069 0.029 No" ] }, "execution_count": 14, @@ -1335,7 +1314,7 @@ "outputs": [ { "data": { - "image/png": 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GDMixvuRz9N9//+G///7L9brx8fEwMjLCsmXLYGlpiSNHjuDWrVu4desWVqxYAUtLSyxatAiNGjVS2LOQ8jEZolLp9OnTSE1NhZWVFRo3bpzt+MuXL3Hr1i0cPHgQLVu2RLly5ZCWlobMzEyZpn7gU9dU2bJlvzqmcuXKAfjUypDTX9E56dOnDzZt2oRTp06hQYMGuHLlCtq3by8zdbtcuXLQ09PDpUuX8r1exYoVZRat+1xuLVq5SU1Nzfa6SK5duXJl6OnpAYDMIN7PSX75SFrUjIyMYGFhgZs3byImJgYvXrzAsGHDAHxqhbpy5QqSk5MREBAAJycnaSuP5HVdvHgxBg0aVKhnyE9hn6EwPD09sWfPHvTo0QOurq6wsLCQXsfR0bHAyVBaWlqesVWuXBmCIGD8+PF49uwZxo8fj65du6JBgwYoU6YMYmJicpxQUFDK+vzl59y5c9JuLQlra+tckyHJ52jSpEmYPn16vtfX1tbGt99+i2+//RYRERG4fv06Tp8+jWvXrmH8+PE4f/58gf+4opKPY4aoVJJ0kc2dOxcLFizI9rVs2TJoaGjg9OnTSEpKgrm5OcRicY5/IU+aNAmtW7fONm2+sCpUqIDq1avj2bNnObYk+Pj4YN26dTLjT+rXr4/GjRvj4sWLuHDhAtLT02W6yIBP0/ffvn2b4y/PS5cuYfXq1dIZXk2aNEFkZGS2addisTjbX9H5efjwYbaye/fuQUtLC02aNEHdunWhq6uLhw8fSsetfE4ynfrzbgV7e3s8ffoUZ8+eBfC/VYRtbGzw8eNHbN++HampqTJjOiTTpB89epTtHhkZGfD09MTOnTsL9WwSkr/u7969m+PxgIAAiEQiubpGTpw4gSpVqmDNmjWwsbGRJkKpqanS9ye3VsbPJScn4/nz59nK79+/j8qVK6NmzZoIDg5GSEgIunXrhhkzZsDKykraOiY5tyD3yomyPn/58fT0RHBwsMxXXp+DvD5HALB27Vp4eXkhPT0doaGhWLVqlXQGXvXq1aWzJtu2bYuoqCjpv2NJ0k6lG5MhKnXCw8MREBAAU1NTtGrVKsc61atXR9u2bfHx40ecPHlSmmCsXr1aJlG5d+8ebt26hRYtWiikdah///6Ij4/HihUrZFalffbsGRYsWABvb+9srQx9+vTBmzdvsGXLFujp6aFLly7ZrikIAhYuXCiTdERHR2P+/Pnw8vKStnBIpn57enrKrL+zZcsW6Zilglq3bp3M2iunT5+Gv78/HBwcoK+vDx0dHfTq1QvR0dFYu3atzLlXrlzBqVOnUKtWLbRs2VJabm9vD0EQsHnzZujr60t/QUkG+W7duhXa2tpo166d9Jw2bdqgRo0aOHToEO7duydzHy8vL3h7e0vHFRVW9erVYWNjg0ePHmHPnj0yxw4ePIi7d+/CxsZGru1ddHV1kZaWJm3BAT4lBYsXL5Z+Br9cIyk3K1askHnvt27ditDQUPTv3x+amprSbrQvB2XHx8fj999/B4ACrWuUE2V9/r6Wtra2TAw1a9aUtkB+uaSFj48P/vzzT1y9ehU6OjooU6YMNm/ejDVr1sg8c3p6Ot69ewcdHR3p6taSluaCvpdUMrGbjEqdY8eOQRAEODs75/lX2YABA3Djxg0cPHgQhw4dwsCBA3H48GH07dsXHTp0QHJyMk6ePAk9PT24u7srJLZx48bh2rVr2LlzJ+7cuQNra2t8+PABp0+fRkpKClasWCEzmw34NAB8+fLlCAoKQv/+/bONdxkwYAAuXLiAM2fOIDg4GB06dEBmZiZOnTqF+Ph4/Pjjj6hZsyaAT+ssnTlzBqdPn8bLly9ha2uLZ8+ewc/PD6ampnnOhPvSy5cv0a9fP3Tq1AlRUVE4d+4cjIyMZBYanDVrFu7evYvNmzcjICAALVq0QGhoKC5cuAA9PT0sX75c5j1q0aIFKlasiPDwcHTt2lV6rF69eqhatSpiYmJga2sr8xppampi2bJlGDt2LEaMGAEHBwfUrFkTjx49gp+fH2rUqIEffvih4G/SFxYsWABXV1d4eHjg7NmzsLCwQEhICK5fvw5DQ0MsXLhQrus6Oztj69atGDhwILp27YrMzExcu3YNL1++hIGBAeLi4hAfHy8dl5SXgIAA6XpPz58/x7Vr19CgQQNMnjwZAFC7dm00bdoUAQEBGD58OFq2bIn379/j3LlzSE9PR9myZfH+/Xu5nkNZn7+vZWhoiBcvXmD+/Pmwt7dHly5dpO/19OnT0bFjRzRo0AAvX77EpUuXoK+vj/nz5wMAqlWrhlGjRsHb2xu9e/eGvb09NDQ0cPXqVTx//hyTJk2SfkYlY+v27t2LhIQEjBw5skCD96lkYcsQlTqSLrIvu5O+1K1bN1SoUAEPHz5EcHAwFi9ejPnz56NMmTLYv38/zp49i44dO2Lv3r3SH+Zfq0yZMtixYwemTp2KtLQ07NmzB5cvX0bLli2xY8cO9O7dO9s5hoaGaNu2LYBPv0C/JBKJsHbtWvz8888oW7YsDh48iFOnTqF+/fr4888/s82GW7VqFWbOnIn09HTs3btXOqD7yynT+Vm5ciUaN26Mw4cP486dO+jXrx8OHjyI6tWrS+sYGBjgwIED+Pbbb/Hu3Tvs2rULDx8+RL9+/XDkyBE0a9ZM5pqamprSVp8vN9qUfP/ltGfg0zisgwcPShf727FjByIiIjBy5Ejs37+/QAlFbmrXro3Dhw9jyJAhePbsGXbt2oVXr15h5MiR8PHxgZmZmVzXnTFjBqZOnQoNDQ3s2bMH586dg6mpKbZs2YIJEyYAAC5fvlyga/39998wNDTE/v378fTpU7i5uWHPnj3SX8gaGhr466+/MGDAAISFhWHnzp24ffs2OnbsiMOHD6Ndu3Z49eoV3rx5U+jnUNbn72u5u7ujRo0aOHz4MM6fPw8AqFu3Lo4cOYIhQ4YgODgYO3bsQHBwMPr27YtDhw7JdIfOmjULv/32G8qXL4+jR4/iwIED0NPTg6enp8yYozZt2sDV1RUJCQnYvXt3jl2aVPKJBHk7kolIJc2dOxdHjx6Fj48PZ8wo2ciRI3Hr1i0EBARwZWOiIsSWISIiIlJrTIaIiIhIrTEZIiIiIrXGMUNERESk1tgyRERERGqNyRARERGpNSZD+RgxYgRGjBih7DCIiIioiHAF6nxERkYqOwQiIiIqQmwZIiIiIrXGZIiIiIjUGpMhIiIiUmtMhoiIiEitMRkiIiIitVbikyEvLy+0a9euwPXFYjE2b96M7t27o2nTpujTpw98fX2LMEIiIiIqzUp0MnT58mWsXbu2UOcsW7YMK1asQMuWLfHTTz/BwMAAM2bMwIkTJ4ooSiIiIirNSmQyJAgCdu3ahcmTJyMjI6PA57169Qo7d+7EyJEj4enpiaFDh2LLli1o0aIFPD09kZ6eXoRRExERUWlUIpMhFxcXLFy4EDY2NmjSpEmBzzt58iSysrLg6uoqLdPU1ISrqyvevXuHgICAogiXiIiISrESmQxFRERgwYIF+Pvvv6Gnp1fg8x49eoTy5cujTp06MuWShOrRo0cKjZOIiIhKvxK5HceFCxego6NT6POioqJgZGSUrdzQ0BDApyQrJw4ODrleMzIyEiYmJoWOhYiIiPImFovh7++P6OhoGBoawsbGBpqamsUeR4lMhuRJhAAgOTk5x5akMmXKAABSUlK+Ki4iIiJSDF9fX7i7u8vsAWpiYoIFCxbAycmpWGMpkcnQ1xCJRIU+dv78+VzPyavViIiIqLRSZquMr68vxo0bB0EQZMrfvn2LcePGwcvLq1gTIpVKhsqVK4fU1NRs5ZKy8uXLF3dIREREJY4yW2XEYjHc3d2zJULAp9nkIpEI8+fPR48ePYotOSuRA6jlVb16dbx79y5beXR0NADkOJ6IiIhInUhaZT5PhID/tcooeqHitLQ0hISEICAgABcuXMCqVauy3ftzgiAgIiIC/v7+Co0jLyrVMtSkSROcO3cOoaGhqFmzprQ8MDAQAGBlZaWs0IiIiGQoo5uqMK0yGRkZSExMREJCAhITE2X+/8OHD/jw4QMSExMxa9YsVKxYEQCwfv167N69G66urpgyZQoAIDQ0FJ07dy50rJKGjOKgUslQjx49sHbtWuzYsQM///wzgE9v/O7du2FkZITWrVsrOUIiIqLi7abKyspCQkIC9PX14e/vX6BWmbp16yIzM7NA1//mm2+kyVBiYiLevHmDqKgo6fFKlSqhcuXKqFixIipUqABBEKSNFHmRzAQvDqU2Gfr48SPOnj2LqlWrSvcuq1evHlxcXLBjxw4kJyejefPm8PX1xb1797B69Wpoa2srOWoiIlJ3ihg8nJKSgpiYGOlXbGxsrv8fGxuLzMxMPHr0qMCtLZJESCQSoUKFCqhQoYI0malYsaL0/ytUqCAzHtfV1RXdunWDqamptKxatWoy6/yJxWLY2Njg7du3ObZQiUQimJiYwMbGpkCxKkKpTYbi4uIwe/ZsWFtby2zk+uuvv6Jq1ao4fPgwTp48iTp16mDt2rXo0aOHEqMlIiLKv5sKgMzgYR8fH1y7dg09evRAt27dAAC3b99G3759C33v2NjYAre2bNiwAZ06dUL58uWhoVHw4cVmZmYwMzPLs46mpiYWLFiAcePGQSQSybwWklnfHh4exbreUIlPhnbu3JljeY0aNRAcHJytXEtLC1OnTsXUqVOLOjQiIirFimvMjiAIeP/+PcLDw3Hu3Lk8u6kASAcP29nZISAgAHv37oWhoaE0GapSpQoAQFdXF1WrVkXVqlVRpUqVHP9f8n2VKlWgo6ODOnXqwMTEJN9WmV69ehVpMuLk5AQvL68cuwo9PDy4zhAREVFRU/SYnbi4OLx69QpVqlRBrVq1AABBQUGYNGkSwsLCkJycXKjrSbqzunbtimrVqqFt27bSY2ZmZggKCkL58uXzXFsvJyWpVcbJyQk9evQoEStQi4ScUkOSkiy6mNfCjEREVHrkNmZHkgx8OWZHLBYjKioK4eHhCA0NRVhYGMLCwvDLL79IBw7//PPP2LZtG6ZMmYJ58+YBAMLCwmTGvRgaGqJixYp49uxZvjEePHgQdnZ2X/2suckpGaxevbpSWmVKArYMERGR2ijImJ0ffvgB//77L8LDwxEeHo6IiAhkZGRkq+/m5gZLS0sAn1prTExMoKurKz1ubGyMPXv2oEaNGjA1NUWZMmVKzODhktQqUxKwZSgfbBkiIlIdx48fx8SJEwt9npaWFqpXrw5TU1PUrFkTNWrUwNChQ2VmTRWUpGUKQI7dVMW9FQWxZYiIiFSMIAgICwtDUFAQgoKCMGjQIJiYmAAATp06VaBr9O7dG927d0fNmjVhamoKY2NjhbWalLTBw8RkiIiIlEQRs7liY2Px5MkTBAcHIzg4GE+ePEFISAiSkpKkderXry9Nhpo2bYrjx4/ne91Ro0YV6ZgddlOVLEyGiIio2Mkzm0sQBOzfvx+PHz9GcHAwgoKCEBMTk2NdbW1t1K9fHxYWFtKp6AAwbtw4bNmyReljdoBPM7uKMuGiguOYoXxwzBARkWIVZDaXpaUl9uzZAwCYO3eutI6trS3evHkjc06tWrVgYWGBhg0bSv9bt27dXHcd4Jgd+hKToXwwGSIiUhzJbKrcFh6UtMz8/fffcHJyQuXKlfHw4UNporJixQokJSWhUaNGsLCwgLm5OcqVK1foODi1nD7HbjIiIioWsbGx2LFjR4E2Co2Li4OrqysaNWoEsVgMLa1Pv65mzpypkFg4Zoc+x2SIiIiKhGRbCT8/P/j7++Pp06cFPjchIQG///57EUbHMTv0P0yGiIjoq0lGXEi6sxYuXIiNGzdmq2dmZiYz5ic3Bd1QlEgRCr4VLRERUQ7mzp2L1q1b48GDB9IyCwsLaGhooFmzZhg3bhy2bt2Khw8f4tq1azAxMcl1Ty2RSITq1asXy2wuIgm2DBERqanCrPMjFovx+PFj+Pn54enTpzJdWJGRkXj79i1u3ryJZs2aAfi0aKGjoyMqVKiQ7VolZaNQIgnOJssHZ5MRkSrKb52f9PR0PHjwQDreJyAgAImJidK6t27dkm5FERAQgIyMDLRo0QJly5aV+/6czUXKwmQoH0yGiEjV5LbOj0TDhg3x6tUrpKamypRXqFABbdq0ga2tLYYMGYKqVat+VRyKWIGaSBGYDOWDyRARqZL81vn5nIGBAdq2bQsbGxu0bdsWjRo1YrJCKoljhoiI1IQgCNi7d2+BEqHVq1dj8ODBuQ50JlIlnE1GRKQmbt++jTlz5hSoro6ODhMhUhtsGSIiUjGCIODx48c4ceIE9PT0MGXKFABAy5YtYWRkhKioqHyvwXV+SJ0wGSIiUgGCICAoKAj//PMP/vnnH7x48QLAp6Rm4sSJ0NTUhKamJvz8/GBnZ1cidm0nKimYDBERlWLBwcHSBOjZs2fScl1dXXTp0gXOzs7IysqSDnzW0dHhOj9EX2AyRERUyoSEhEgToM/3+9LR0UHnzp3h7OyMbt26oXz58jme7+TkBC8vrxzXGeI6P6SOOLU+H5xaT0RFQZ41dhITE9G3b18EBwdLy3R0dNCpUydpApTTis+KjIFIFbFliIiomOW3+rPEs2fP8OTJEzg7OwP4tOihpqYmtLW1YW9vD2dnZ3Tv3h0VK1aUKw7u2k70CVuG8sGWISJSpNxWf5aM1/Hy8oKTkxOCg4PRpUsXlClTBg8ePICenh4A4MmTJ6hevToqVapU7LETqSquM0REVEzEYjHc3d1znMUlCAIEQcD8+fMhFothbm6OBg0awM7ODrGxsdJ6jRo1YiJEpGDsJiMiKib+/v75rv4cEREBf39/2NnZ4ezZs9DW1i6m6IjUF1uGiIiKSXR0dKHqMREiKh5MhoiIisGbN29w4sSJAtXl6s9ExYvdZERERejBgwfYsGEDTpw4gaysrDzrcvVnIuVgMkREVER+/vlnbNu2Tfq9vb09WrZsiT/++AMAuPozUQnBbjIiIgVJT09Hamqq9PvWrVtDS0sLAwYMwL///os9e/Zg5syZ8PLygrGxscy5JiYm0mn1RFS8uM5QPrjOEBEVxMGDB+Hp6Ynx48dj3LhxAICMjAxER0fD1NQ0W32u/kxUcrCbjIhIAdLT0/H27VscP35cmgxpa2vnmAgBXP2ZqCRhMkREVEhPnjzBxo0bYWtri6FDhwIABg4cCG1tbfTt21fJ0RFRYbGbLB/sJiMi4NNg5+vXr2Pjxo24ePEiAKBOnTq4cuUKNDQ4/JKoNGPLEBFRHjIzM3Hy5Els2LABDx8+BABoaGjAyckJEydOZCJEpAKYDBGRWspvAHNycjL27duHzZs3IzQ0FABQpkwZDB06FGPHjkXt2rWVFDkRKRqTISJSO76+vnB3d5fZJ8zExAQLFixAmzZtsHXrVuzYsQPx8fEAAAMDA3z77bcYNWoUDAwMlBQ1ERUVJkNEpFZ8fX0xbty4bDvHv337FmPHjoWWlhYyMzMBALVr18b48eMxePBglC1bVhnhElExYDJERGpDLBbD3d09WyIE/G816MzMTLRo0QKTJk1Cjx49uPYPkRpgMkREasPf31+mayw38+bNQ7t27YohIiIqCTgNgojURnR0dIHqvXv3rogjIaKShMkQEakNfX39AtUzNDQs2kCIqERhNxkRqbyIiAjMnz8faWlpMDExwdu3b3McNyQSiWBiYgIbGxslRElEysKWISJSWRkZGdi4cSPs7e3h6+uLS5cuYfz48QA+JT6fk3zv4eHBQdNEaobJEBGppICAADg6OmLhwoX4+PEjWrVqhdOnT2Ps2LHw8vKCsbGxTH0TExN4eXnByclJSRETkbJwb7J8cG8yotIlLi4OS5Yswd69ewF8Gif0yy+/wMXFRWbrjPxWoCYi9cExQ0SkErKysrB//34sXrwY79+/BwAMHToUP//8c46rRmtqasLOzq64wySiEojJEBGVeo8fP8a8efNw+/ZtAECjRo2wdOlStGnTRsmREVFpwGSIiEqtrKwsLFq0CH///TfEYjHKlSuHH3/8EWPGjIG2traywyOiUoLJEBGVWhoaGnj79i3EYjGcnJzw22+/wdTUVNlhEVEpwwHU+eAAaqKS5fXr19DV1ZXOBouKisKjR4+k/1aJiAqLU+uJqNQ4fPgwunTpgvnz50vLjIyMmAgR0VdhMkREpUbDhg2RkZGB9+/fIyUlRdnhEJGKYDJERCVWdHQ0jh07Jv2+SZMmOHnyJPbv34+yZcsqMTIiUiUcQE1EJY5YLMbOnTuxbNkyfPz4ERYWFmjYsCEAwMrKSsnREZGqYTJEREqR2wrQ9+/fx7x583D//n0AQLNmzXLcVJWISFGYDBFRsfP19YW7uzsiIyOlZUZGRmjcuDEuXboEQRBQsWJFzJkzByNHjuQ2GURUpJgMEVGx8vX1xbhx47K19kRFRSEqKgoAMGDAAPz6668wNDRURohEpGaYDBFRsRGLxXB3d8+z26tKlSr4448/2BpERMXmq5KhJ0+e4NGjR3j//j0SEhJQpkwZGBsbo2HDhrC0tIRIJFJUnESkAvz9/WW6xnISGxsLf39/bqJKRMWm0MnQq1ev4O3tjdOnT+PDhw8AIPNXniQBqlSpEpycnDBq1CjUqlVLQeESUWkWHR2t0HpERIpQ4GQoKioKS5YswdmzZ6GhoYHmzZvD0tIS9evXR+XKlVGuXDl8+PAB79+/x9OnT3H37l3s378f+/fvR8+ePfHjjz+ievXqBbpXREQEli9fjps3byIjIwNt27bF3LlzUbNmzTzPi4uLw4oVK3Dp0iV8+PABderUwfjx49G7d++CPiYRFaGCjgHiWCEiKk4F2pts7969WLlyJWrWrInhw4fD0dER5cuXz/fiMTExOHr0KA4fPozo6GjMnDkTw4cPz/Oc+Ph4DBo0CElJSRg1ahR0dHSwdetWaGpqwsfHBwYGBjmel56ejoEDB+LFixcYNmwY6tSpg3/++Qf37t3DokWLMHjw4HzjzQn3JiNSjJSUFOjo6MDGxgZv377NcdyQSCSCiYkJ/Pz8OGaIiIqPUABdu3YVfH19C1I1R1lZWcLRo0eFLl265Ft39erVgoWFhfDw4UNpWXBwsNCoUSPB09Mz1/NOnjwpmJubCxs3bpSWpaWlCd27dxfs7OwEsVgsV+xdunQpUNxElLtTp04JTZs2FR48eCCcPHlSMDU1FUxNTYXq1atLvyRlJ0+eVHa4RKRmCrQdh6+vLxwdHeVOuEQiEfr164dTp07lW/fEiRPSLjgJc3NztG3bFidOnMj1vNDQUABAu3btpGU6Ojqws7NDTEwMYmNj5Y6fiOQnCAL27NmDmJgYbN68GU5OTvDy8pLuOi9hYmICLy8vODk5KSlSIlJXBRozpK2tLfP9jBkz0Lp1a7i6uhbqZjo6OnkeT0hIQGhoKDp16pTtWJMmTXD9+nXparVfql27NgDgxYsXMonUmzdvoKuri0qVKhUqVhmCAHFqKjR0daUDxLMyMiCIxRBpakLjs9dHnJoKANDQ0YFI41OumZWZCSEzEyINDWh89hoUqm5aGiAI0NDWhuj/uw8EsRhZGRmASARNXd0SVTcrPR1CVhZEWlrQ0NIqfN2sLGSlpwMANMuU+V/dHF73QtUVBGSlpX163fN5PwtTN9f3UxGfkxxe99L0ORGJRFi7Zg12bN2KcePHAwCcnJzQo0cP+F2/jndRUahmbIy2dnbQ1NTM/f382s/JZ++nIj8nMu8nf0bwZ0Rh33v+jFDce59D3YKSa6PWixcv4vnz5/KcmifJgmtGRkbZjkkSoNym5To4OKBDhw5Yvnw5Ll++jNDQUGzYsAHXrl3Dt99+m2ci5uDgkOtXZGQkUqOj4efiisz/nz0HAOFHj8HPxRUvNv0tc61bbt/Cz8UVae9ipGVvfU/Dz8UVT9f9JVP39tiJ8HNxRUpYmLQs+sJF+Lm4InjFKpm696ZMh5+LK5JevJSWvbt6HX4urniy2FOm7v0fZ8PPxRUfHj+RlsUF3IafiysC3T1k6j766Vf4ubji/b3/pGUJDx/Bz8UVD+f8JFP38YLF8HNxRayfv7QsMeQp/Fxc8d/0H2TqBnkuh5+LK95dviItS379Bn4urrg7YYpM3ZDVa+Hn4oqoM2elZalv38LPxRUB346Vqfv8r43wc3FFxD8npWXp79/Dz8UV/sPdZOq+3LINfi6uCDt0RFomTv4IPxdX+Lm4QhCLpeWvd+2Bn4srXu/aIy0TxGJpXXHyR2l52KEj8HNxxcst22Tu5z/cDX4urkh//15aFvHPSfi5uOL5Xxtl6gZ8OxZ+Lq5IfftWWhZ15iz8XFwRsnqtTN27E6bAz8UVya/fSMveXb4CPxdXBHkul6n73/Qf4OfiisSQp9KyWD9/+Lm44vGCxTJ1H875CX4urkh4+Eha9v7ef/BzccWjn36VqRvo7gE/F1fEBdyWln14/AR+Lq64/+NsmbpPFnvCz8UV2+f9LB0XpBkTi1ZXbuDRjJnSepqamjC4dgPVdu1DvZRU6RihlLAw+Lm44vbYiTLXfbruL/i5uOKt72lpWdq7GPi5uOKW27cydV9s+ht+Lq4IP/q/TV4zP3yQvp+fe7V9J/xcXBG674C0LCstTVpX8ssOAEL3HYCfiytebd8pcw1JXf6M4M8IgD8jJPL7GfHu6nVpWdKLl/BzccW9KdNl6gavWAU/F1dEX7goLSvMz4iCkmudIQMDAyQlJclzap6Sk5MBIMfdqMv8f/b38ePHbMcAQEtLC1OmTMG0adMwbtw4aXnv3r0xffr0HM8hIsWLi4uDJoBTp3yh07xpvpMmiIiUrUCzyb50+fJl/PDDD3BxcUH37t1Ro0YNabLypYLMOpO4e/cuhg0bBnd392xdcAcPHsQvv/yC7du3o23bttnOvXr1KiZMmAADAwOMGzcOxsbGuHHjBvbt2wdHR0esWLECGhqFbwhzcHAABAH/+vqyCZxN4IV/79WoCVwQBKxfvx6rfv8dIgDNWrbCps1eMDQ0LLImcHaTlYz3Xp66/Bmhfj8j5KlbXN1kcrUMeXh4QBAEeHt7w9vbO9d6IpEIjx8/LvB1y5UrB+DTFNwvpf7/ByO35GrdunXQ0tLC7t27YWZmBgDo1q0bTExMsHLlSnTr1k3+QeAikcwLDuDTh/aLsVQAstUD8OlNyeGNKVTdzz4c0rA0NXOcflwS6mrk0C1ZqLoaGjm/Pjm87oWqm8N7qYi6QC7vpyI+Jzm87iXtc5KcnIwZM2bg5MlPXRMjR47EggULpN3TCnnvv/ZzUhrfe/6MyL0uf0b8r24p+Bkhb92v/RlRUHIlQ6ampjA1NZX7pnldFwDevXuX7ZhkRdqcxhMBQEhICFq2bClNhCQGDhyIlStXws/P76tmxBFRzl6/fo0xY8bgyZMn0NbWxqJFizBixAhlh0VEVGByJUM7d+7Mv5IcKlSoADMzMwQGBmY7FhgYCGNjY1SrVi3Hc3V1dSH+bLCbRFZWFgDZLUOISDGuXLmCiRMnIj4+HtWqVcPmzZvRpk0bZYdFRFQocs0m+1xGRgaCg4Nx//59vH79GhkZGV91vZ49e+LOnTsyCVFISAj8/Pzy3FajXbt2uHPnDoKCgmTK9+/fDwA5jjMiIvkIgoBNmzbB1dUV8fHxaNGiBU6dOsVEiIhKJbkGUAPAhw8f8Pvvv+Off/5B+v8PZAI+jftxcnLCrFmzULFixUJfNz4+Hs7OzsjIyMCYMWOgoaEBb29vaGtr4/DhwzAwMEBMTAyuX78OMzMztGjRAgAQFhaGwYMHIzMzE8OHD4eJiQkCAgJw4sQJ2NnZYcuWLfIPoAa34yCSSElJwezZs3HkyKfpyEOGDMHSpUtznURBRFTSyZUMJSUlwcXFBc+fP4eRkRGsrKxgaGiIhIQE3L17F5GRkWjQoAEOHDiQ4zT5/ISGhmLp0qW4efMmdHR0YG1tjdmzZ0s3avX394ebmxv69+8PT8//rZ8RFhaGP/74A9euXUNSUhJMTEzg7OyMCRMm5LvgY26YDBHJGjZsGK5cuQJNTU14eHhg9OjR0lk0RESlkVzJ0PLly7FlyxaMHTsWU6dOlUk0BEHAmjVrsHHjRkyePBlTp05VaMDFjckQkazLly9j+vTp+Ouvv2BnZ6fscIiIvppcyVC3bt1QpUoV7Nu3L9c6Q4cORUJCQoH2IyvJmAyRuhMEARERETIzSD9+/ChdCoOIqLSTawB1ZGSkdKxOblq0aIGIiAi5giKikiEtLQ0zZ85E165d8eLFC2k5EyEiUiVyJUOVKlWS7hKfmzdv3hRq9WkiKpmePn2KpKQkBAQEKDsUIqIiIVcyZGtri4sXL+L69es5Hr98+TIuXrwIW1vbrwqOiJRLV1cXmzdvxu7du+Hi4qLscIiIioRcY4ZevXqF/v37IyMjA87OzmjVqhUqVKiAqKgo3LlzB2fPnkXZsmVx6NAh1KlTpyjiLjYcM0TqZs+ePQgNDcWcOXOUHQoRUbGQe52h+/fvY/bs2Xj9+rV0Wq3kUrVq1YKnp2e+44pKAyZDpKrEYjH8/f0RHR0NQ0NDtGjRAgsXLsT27dsBAIcOHWLrLhGpBbm240hJSUGzZs1w6tQp3L17F0FBQUhKSoKenh4aNWqEVq1acd0RohLM19cX7u7uiIyMlJbp6OggPT0dIpEIs2bN4qrtRKQ25EqGBgwYAGtra3h4eKB169Zo3bq1ouMioiLi6+uLcePGZduvT7KS/OTJkzF9+nRlhEZEpBRyDaAOCwuDnp6eomMhoiImFovh7u6e58bFR44cyXHTYyIiVSVXMtSwYUM8evRI0bEQURHz9/eX6RrLSUREBPz9/YspIiIi5ZOrm+yHH37ArFmzMGTIEHTt2hU1atSArq5ujnUlA5CJSPmio6MVWo+ISBXIlQx98803AICYmBg8fPgwxzqCIEAkEuHJkyfyR0dECmVoaKjQekREqkCuZGjKlCmKjoOIisG7d+/yPC4SiWBiYgIbG5tiioiISPnkSoYaNGiA5s2bw8jISNHxEFEREovF0NDQQFZWFkQikcxAaslyGB4eHtDU1FRWiERExU6uAdTz58/H/PnzFR0LERWxAQMGwMfHB5s2bYKxsbHMMRMTE3h5ecHJyUlJ0RERKYdcLUNpaWmoW7euomMhoiIQEBCAmjVrSpOfVq1aAQAcHR1lVqC2sbFhixARqSW5kqGBAwfi+PHj6N+/Pxo0aKDomIhIQW7fvo3hw4fD0NAQhw8flmkN0tTUhJ2dnRKjIyIqGeRKhipWrAgA6Nu3L8zMzFCjRg2UKVMmWz2RSIR169Z9XYREJDdDQ0NUrVoVZmZm0NfXV3Y4REQlklwbtTZs2LBgF1eBqfXcqJVKu8jISOjr66Ns2bLKDoWIqESSq2WIiQFRyRUSEoJXr16he/fuAD4NjCYiotzJlQyZmpoqOg4iUoAXL17AxcUFsbGx2LZtG7p06aLskIiISjy5kiGJZ8+e4ejRowgKCkJCQgIOHTqEixcvIiEhAX369IGGhlwz94lIDqGhoXBxcUF0dDQaNWqE5s2bKzskIqJSQe5kyMvLC2vWrJHubi1ZsO3WrVvYtm0b/v33X6xZswba2tqKiZSIchUREYEhQ4YgIiIC9evXx759+2BgYKDssIiISgW5mm7OnDmDVatWoWnTpvD29pbuVQYAQ4cOhZ2dHS5evIg9e/YoLFAiyll0dDRcXFzw5s0b1K5dG/v370fVqlWVHRYRUakhVzLk7e0NMzMzbN++Hba2ttDT05Meq1WrFry8vFC3bl0cPXpUYYESUXZxcXEYOnQoXrx4gRo1auDAgQPZVpYmIqK8yZUMBQcHw8HBATo6Ojke19TURMeOHfHmzZuvCo6IchcfH4+hQ4ciODgYxsbG2L9/Pyc3EBHJQa5kSFNTE8nJyXnWSUhI4NL+REUkMTERI0aMQGBgIKpVq4b9+/ejdu3ayg6LiKhUkisZsrKywoULF/Dhw4ccj8fExOD8+fOwtLT8quCIKLuPHz/Czc0N9+7dQ+XKlbFv3z7Ur19f2WEREZVaciVD48aNQ2xsLFxdXfHvv/8iJiYGABAeHo7Tp0/D1dUVHz58kBlYTURfLy0tDaNHj8atW7dQsWJF7N27t8ArwhMRUc7kmlpva2uLBQsWYOHChZg+fToAQBAEdO3aFQCgoaGBOXPmoGPHjoqLlIigo6ODRo0a4b///sPu3bthZWWl7JCIiEo9ufYmk4iKisKxY8cQGBiIxMRElCtXDhYWFujTpw9q1aqlyDiVhnuTUUkjCAJCQ0NhZmam7FCIiFTCVyVD6oDJECmbWCzGli1bMGrUKOjq6io7HCIilcP9MohKuHnz5sHDwwMTJkwA/3YhIlI8JkNEJZyzszMqVKiAIUOGSLe9ISIixfmqjVqJqOh16NABN2/eROXKlZUdChGRSmLLEFEJIwgC1q1bh5CQEGkZEyEioqLDZIiohFm1ahU8PT0xePBgxMfHKzscIiKVx2SIqAT5888/sWrVKgDAlClToK+vr9yAiIjUQIHGDAUEBMh9gzZt2sh9LpEqE4vF8Pf3R3R0NAwNDfHo0SMsWbIEwKcZZGPHjlVyhERE6qFAydDIkSPlnsXy5MkTuc4jUmW+vr5wd3dHZGRktmMzZszAlClTlBAVEZF6KlAy1K9fP07pJVIQX19fjBs3Ltc1gxo1alTMERERqTeuQJ0PrkBNiiQWi2FjY5NjixAAiEQimJiYwM/PD5qamsUcHRGReuIAaqJi5O/vn2siBHyaVh8REQF/f/9ijIqISL0xGSIqRtHR0QqtR0REX69AY4bc3NzkurhIJML27dvlOpdIFRkaGiq0HhERfb0CJUO3bt2S6+IcdE0ky8bGBkZGRoiKisrxuGTMkI2NTTFHRkSkvgqUDHHwMJFiaGpqYtGiRTnOJpP88eDh4cHB00RExahAyZCpqWlRx0Gk8j58+ICKFSvCyckJXl5e2dYZMjExgYeHB5ycnJQYJRGR+uGu9UTF4MOHD3B0dETHjh3x22+/wcnJCT169JBZgdrGxoYtQkRESsBkiKgYXLp0Ca9fv8b58+cxa9Ys6OrqQlNTE3Z2dsoOjYhI7TEZIioGffr0Qfny5WFgYAADAwNlh0NERJ9hMkRUTLp06aLsEIiIKAdcdJGoiLx9+xYjR47EmzdvlB0KERHlgckQURHIyMjAxIkTceHCBcyYMUPZ4RARUR6+qpvs/fv3OHXqFIKCgpCQkIA1a9bgzp07yMrKQps2bRQVI1Gps2TJEty6dQsVKlTA8uXLlR0OERHlQe5k6J9//oG7uztSU1MhCIJ0wbhLly7h77//xrBhw+Du7q6wQIlKixMnTsDLywsAsHr1atStW1fJERERUV7k6ibz9/fHnDlzYGhoiAULFmDQoEHSY127doWFhQX27t0LHx8fRcVJVCo8e/YMP/zwAwBg4sSJcHR0VHJERESUH7mSoQ0bNsDAwAAHDhzA4MGDYWxsLD3WrFkz7Nq1C8bGxtizZ4/CAiUq6ZKTkzF27FgkJyfD1tYWc+fOVXZIRERUAHIlQw8fPkTPnj1RqVKlHI+XL18eXbt2xcuXL78qOKLSQhAEzJ49GyEhITAyMsJff/0FLS2uXEFEVBrIlQxlZWXlWyc9PR2ZmZnyXJ6o1Nm2bRt8fHygqamJjRs3wtDQUNkhERFRAcmVDFlYWODSpUtIT0/P8XhSUhIuX76Mhg0bflVwRKXB7du34eHhAQD45ZdfYG1treSIiIioMORKhkaNGoWwsDCMGzcOgYGB0qQoKysLDx8+xLhx4xAVFYXhw4crNFiikiY2Nhbjx49HRkYGevXqhbFjxyo7JCIiKiS5BjU4OjoiJCQEGzdulJlJ1rRpU4jFYgiCgJEjR8LZ2VlhgRKVRKtXr8bbt29Rr149rFy5UrrEBBERlR5yj/CcPn06OnfujEOHDuHx48dITExEuXLlYGFhgf79+8PGxkaRcRKVSD///DPEYjFGjx6NChUqKDscIiKSg0gQBEHZQXwpIiICy5cvx82bN5GRkYG2bdti7ty5qFmzZr7nHj58GDt27MDLly9RrVo19OrVC5MmTUKZMmXkisXBwQEAcP78ebnOJyIiopLtq+b+JiUl4dKlSwgKCkJiYiIqV66M5s2bw87ODjo6OnJdMz4+Hm5ubkhKSsKoUaOgo6ODrVu3wtXVFT4+PjAwMMj13L/++gtr1qxB586dMWzYMDx48ACbNm1CWFgYVq1aJe9jEsl4/fo1jh07hilTpkBDg9v7ERGVdnInQ0ePHsXSpUuRmJiIzxuXRCIRjI2NsXDhQrRv377Q1922bRvCwsJw6NAhWFpaAgA6dOiAfv36YfPmzZgzZ06O57169Qp//fUXHB0dsXr1aohEIgwdOhR6enrYsWMHJk+ejHr16sn3sET/LyMjA+PGjcOjR4/w4cMH/PLLL8oOiYiIvpJcf9ZeunQJP/30EwBgwoQJ2LBhAw4ePIitW7di8uTJSElJwaRJk/DgwYNCX/vEiRNo3ry5NBECAHNzc7Rt2xYnTpzI9bxjx44hIyMDs2bNkhnEOnz4cEycOBElsDeQSiFtbW2MHTsWNWrUwDfffKPscIiISAHkahnauHEjKlWqhIMHD2Ybx2NnZ4devXphyJAhWL9+vXTDyoJISEhAaGgoOnXqlO1YkyZNcP36dURHR+e4oN3t27dRp04dmJqaAgBSU1OhpaWFOnXq4Pvvvy/U8xHlZdCgQXB2doaurq6yQyEiIgWQq2UoODgYPXr0yHVAc506ddC9e3fcu3evUNeNiooCABgZGWU7JkmAIiMjczz35cuXMDU1xfXr19G3b180a9YMLVq0wOzZs5GYmFioOIi+FBISgtjYWOn3TISIiFSHXC1DFSpUyHdLDpFIVOhfGMnJyQCAsmXLZjsmmQ328ePHHM9NTEzEq1evMGnSJIwYMQJTpkzB7du3sWPHDoSFhWHnzp3Q1NTM8VzJjLGcREZGwsTEpFDPQaolPj4eo0aNQkZGBnbu3IlGjRopOyQiIlIguVqGhgwZgn/++SfXMUEvXrzA6dOn0b9//0JdVzKuJ6+F63I7lp6ejrCwMMyePRuzZs1Ct27dMG/ePEyePBl37tzh1HiSS1ZWFqZPn443b95AW1ubiTERkQqSq2WoefPmqFu3LoYPH45evXqhdevWMDIyQlpaGh4+fIj9+/dDQ0MD+vr62LFjh8y5bm5uuV63XLlyAICUlJRsx1JTUwEA5cuXz/HcsmXLIiUlRWZFbADo378/1q1bB39/f3Tv3j3Hc/NKlPJqNSLVt379epw7dw66urrYvHkz9PX1lR0SEREpmFzJ0HfffSf9/2PHjuHYsWPSFpvPZ22tWLEi27T7vJIhyeDnd+/eZTsWHR0NIOfxRABgbGyM6OjobF1zVapUAfC/Ljiigrpy5QqWL18OAFiyZInMDEciIlIdciVDS5YsKZI9mCpUqAAzMzMEBgZmOxYYGAhjY2NUq1Ytx3ObNGmC58+fIyoqSiZhCg0NBQB2b1ChREREYPLkycjKysKwYcMwdOhQZYdERERFRK5kaMCAAYqOQ6pnz574+++/ERgYiCZNmgD4NJPHz88vz3VdnJ2dcfz4cWzevFlmITxvb28AQLdu3YosZlIt6enpGD9+POLi4mBlZYVFixYpOyQiIipCciVDXbp0gbOzM/r06aPwVZ3HjBkDHx8fjBkzBmPGjIGGhga8vb1hZGSEMWPGAABiYmJw/fp1mJmZoUWLFgCAjh07onfv3ti5cydiY2NhY2ODmzdv4vTp0xg2bBgaN26s0DhJdS1cuBB3795FpUqV4OXlJfe+dkREVDrItVFr165dERYWBpFIhMaNG6Nfv37o1atXnvuGFUZoaCiWLl2KmzdvQkdHB9bW1pg9e7Z0XSN/f3+4ubmhf//+8PT0lJ6XmZkJb29vHDp0COHh4ahevTqGDh2Kb775Ru5uPW7Uql58fHwwefJkAMD27dvRtWtXJUdERERFTe5d6+/evYt//vkHp0+fxvv376GlpYX27dujb9++cHBwkHuj1pKGyZD6CA4ORq9evZCSkoLp06dj9uzZyg6JiIiKgdzJkERmZiauXr2K48eP49KlS0hJSUH58uXRs2dP9O3bF23atFFUrErBZEg9JCUlwcnJCc+fP0eHDh2we/fuXBfpJCIi1fLVydDn0tPTce7cOaxYsUK6bYaJiQkGDx4MV1dXVKxYUVG3KjZMhlSTWCyGv7+/dK+7hg0bYsaMGQgMDMSZM2ekSzIQEZHqk2sA9ZcSExNx5swZnDp1CgEBAUhPT0fVqlXRrVs3PHnyBGvWrMGuXbuwYcMGNG3aVBG3JJKbr68v3N3dZfa5MzExgYeHB5o3b85EiIhIzcjdMpSWloYLFy7gxIkTuHr1KtLT06Grq4suXbqgX79+aN++vbSb4dq1a5gwYQLq1q2L48ePK/QBihpbhlSLr68vxo0bhy8/9pIB9l5eXnByclJGaEREpCRytQzNnj0b58+fx8ePHyEIAlq2bIl+/frB0dERFSpUyFa/ffv2sLCwwMuXL786YCJ5icViuLu7Z0uEgE8rp4tEIsyfPx89evTgeCEiIjUiVzJ0/Phx1KhRA6NHj0a/fv2kU97z0qpVKzg6OspzOyKF8Pf3l+ka+5IgCIiIiIC/vz/s7OyKMTIiIlImuZKhXbt2oXXr1vnWS0lJQdmyZQEAP/30kzy3IlIYyf52iqpHRESqQUOek+bMmYOdO3fmWWf9+vXo0qWLXEERFQVDQ0OF1iMiItVQoJahsLAwJCUlSb8PDw/H8+fPERQUlGP9jIwM3Lx5EykpKYqJkkgBbGxsYGJikmtXmUgkgomJCWxsbIo5MiIiUqYCJUP379/Hjz/+KJ1xIxKJsH//fuzfvz/XcwRBQLt27RQTJZECaGpqYsiQIVizZk22Y5LPtoeHBwdPExGpmQIlQ7169cLjx48RFxcHQRDg4+ODhg0bolGjRjnW19bWhqGhIVxdXRUaLNHXSE5OxqFDhwAAenp6SE5Olh6TrDPEafVEROqnwAOoZ82aJf3/W7duYcCAAXBzcyuSoIiKwrJlyxAeHo6aNWvi7NmzePjwoXQFahsbG7YIERGpKblmk124cEHRcRAVqdu3b2Pr1q0APiVFFSpU4PR5IiICIOdsMqLSJD09HbNmzYIgCBg0aBDs7e2VHRIREZUgTIZI5a1fvx4hISGoUqUK5s+fr+xwiIiohGEyRCotJCQEa9euBQAsXLgQBgYGSo6IiIhKGiZDpLLEYjFmzpyJjIwMdO3aFX369FF2SEREVAIxGSKVtX37dty5cwfly5fH0qVLpWsJERERfU5hydC5c+cwb948RV2O6KuEh4dj6dKlAIB58+ahevXqSo6IiIhKKoUlQ0FBQfDx8VHU5Yi+yqVLl5CSkgJra2uuh0VERHmSa50hopLO1dUVFhYW0NfXh4YGe4OJiCh3TIZIZbVu3VrZIRARUSnAP5lJpWzYsAEvXrxQdhhERFSKKCwZMjU15V/ipFQXLlzAokWL0KNHD8TGxio7HCIiKiUU1k3Wv39/9O/fX1GXIyq0+vXro2PHjrCwsECVKlWUHQ4REZUSHDNEKsPMzAx79uxBRkaGskMhIqJShGOGqNRLSUmR/r9IJIKOjo4SoyEiotKGyRCVaunp6ejduzdmz56NDx8+KDscIiIqhZgMUam2fv16BAUF4cyZMxCLxcoOh4iISiEmQ1RqBQcHy+xIX7lyZSVHREREpRGTISqVPt+Rvlu3bnB2dlZ2SEREVEoxGaJSafv27bh79y7Kly+PJUuWcEd6IiKSG5MhKnXCwsKkO9L//PPP3JGeiIi+SoHWGdqxY4fcN+CO4aRIgiBg7ty5+PjxI2xsbDBixAhlh0RERKVcgZKhtWvXIjk5Wfq9IAgFurhIJGIyRAp19OhRXLx4ETo6Ovj999+5Iz0REX21AiVDJ0+exNSpU/HgwQPY2tqiT58+RR0XUTaxsbFwd3cHAHz//feoX7++kiMiIiJVUKBkyMjICNu2bcOIESMQEBCA77//Hs2aNSvq2IhkzJ8/H+/fv0ejRo0wadIkZYdDREQqosB9DOXKlcO6deugq6sLd3f3AneVESnC+fPncfToUWhoaGDFihXQ1tZWdkhERKQiCjXgwtTUFFOnTkViYiJu3LhRVDERZVOnTh3Y2triu+++Q/PmzZUdDhERqRCRwCaePDk4OAD41DJBypWVlYXMzExuxEpERArFqThUoqWmpkr/X0NDg4kQEREpXIGSoZSUFIXcTFHXIfWQlpaGXr164ddff5VZ2oGIiEiRCpQMdevWDYcPH0ZWVpZcNxGLxdi9eze6desm1/mkni5cuICgoCAcP34c6enpyg6HiIhUVIGm1s+ePRtLly7Fpk2bMHz4cDg7O6NKlSr5nhcaGorDhw/Dx8cHKSkp+Omnn746YFIfjo6O2Lt3L9LT07kjPRERFZkCD6COi4vD8uXL8c8//yArKwsWFhawtLREvXr1ULlyZZQtWxaJiYl4//49nj17hjt37iAsLAyampro168fpk+fjmrVqhX18ygcB1ATERGptkLPJouKisL27dtx6tQpREZGfrrIZzuGSy5Xp04dODg4YMSIETA2NlZgyMWLyVDxO3v2LBo3bgxTU1Nlh0JERGrgq6bWh4aGIjAwELGxsUhKSkKlSpVQrVo1mJubo2bNmoqMU2mYDBWvsLAwdO7cGRoaGjh+/DgsLCyUHRIREam4Ao0Zyk3NmjVVJukh5RMEAXPmzJHuSN+gQQNlh0RERGqA6wxRiXHkyBFcunQJurq63JGeiIiKDX/bUIkQGxuL+fPnA+CO9EREVLyYDFGJ8PmO9BMnTlR2OEREpEa+aswQkbzEYjH8/f0RHR2N0NBQ6Y70K1eu5I70RERUrJgMUbHz9fWFu7u7dGkGCQcHBzRr1kxJURERkbqSq5ts2LBhWLNmjaJjITXg6+uLcePGZUuEAODcuXPw9fVVQlRERKTO5EqGAgMD8fHjR0XHQipOLBbD3d0deS1tNX/+fIjF4mKMioiI1J1cyVCNGjUQGhqq6FhIxfn7++fYIiQhCAIiIiLg7+9fjFEREZG6k2vM0LJlyzBx4kRMnz4d3bt3R40aNaCrq5tj3YYNG35VgKQ6oqOjFVqPiIhIEeRKhgYPHgyRSIQzZ87g33//zbPukydP5AqMVI+hoaFC6xERESmCXMlQv379ZDZnJSoIGxsbmJiY4O3btzmOGxKJRDAxMYGNjY0SoiMiInUlVzLk6emp6DhIDWhqauLHH3/ErFmzIBKJZBIiSXLt4eEBTU1NZYVIRERq6KtXoE5OTsa9e/dw6dIlAEBCQsLXXpJU2LVr16Crq4tKlSrJlJuYmMDLywtOTk5KioyIiNSV3IsuxsTEYPHixTh79izEYjFEIhEeP36MPXv24MiRI1i6dClat26tyFiplEtJScGLFy+QlpaGI0eOIDk5GdHR0TA0NISNjQ1bhIiISCnkSobi4uLg4uKC8PBwtGzZEmlpaXj8+DEAoGzZsoiIiMDYsWOxb98+WFhYKDRgKr3Kli2LkydP4vbt21xpmoiISgy5usnWrl2LyMhIbNiwAXv27EHnzp2lx0aPHo2tW7ciMzMTGzZsUFigpBo0NDRgbW2t7DCIiIik5EqGLly4gG7duskkQZ+zsbFB9+7d8d9//8kVVEREBGbMmIG2bduiVatWmDx5cqEXeczMzMSAAQPQpUsXuWIgxUlNTcXGjRu5ajkREZVIciVD79+/R82aNfOsY2RkhLi4uEJfOz4+Hm5ubrh58yZGjRqFSZMm4b///oOrq2uhrrdx40YEBgYW+v6keFu3bsXChQsxZMiQPLfiICIiUga5xgwZGxtLxwjl5sGDBzA2Ni70tbdt24awsDAcOnQIlpaWAIAOHTqgX79+2Lx5M+bMmZPvNR4/foyNGzdCW1u70PcnxYqJicHatWsBAKNGjeL6VEREVOLI1TLUo0cP3Lx5E/v27cvxuLe3N+7cuYOuXbsW+tonTpxA8+bNpYkQAJibm6Nt27Y4ceJEvuenp6dj7ty5aN++PRo3blzo+5NirVy5EomJibCyssLAgQOVHQ4REVE2ciVDEyZMQP369eHh4QFnZ2ecOnUKADB37lw4Ozvj999/h5mZGSZMmFCo6yYkJCA0NFQmEZJo0qQJoqOj89236s8//8Tbt2+xYMGCQt2bFC84OBi7du0C8Gk3eg2Nr17WioiISOHk+u1Uvnx57N27F0OHDkV4eDieP38OQRDg4+OD169fo2/fvti7dy8qVqxYqOtGRUUB+DTe6EuS/ary2vX8wYMH2Lx5M3766Sfub1UCLFq0CFlZWXB0dIStra2ywyEiIsqR3Isuli9fHvPnz8cvv/yCly9f4sOHDyhXrhzq1q0LHR0dua6ZnJwM4NN6NF8qU6YMAOQ6IyktLQ1z585Fx44d0a9fv0Ld18HBIddjkZGRMDExKdT1CLh06RIuXLgAbW1t/PTTT8oOh4iIKFdyJUNz585F586d0a5dO5QvXx7169dXSDCSmUZ5DbLN7dgff/yBd+/ewdvbWyGxkPwyMzOl3ZSjRo1C3bp1lRwRERFR7uRKho4dO4Zjx45BU1MTLVu2RJcuXdCxY8ev/qVXrlw5AJ+2bfhSamoqgE8tUl+6d+8etm3bhtmzZ0NbW1s6BT8zMxNZWVmIi4uDrq4u9PT0crzv+fPnc40pr1Yjytm+ffsQHBwMfX19fP/998oOh4iIKE9yJUN+fn64du0arl69iuvXr8PT0xPLli1DjRo1YG9vj86dO8Pa2rrQU9tNTU0BAO/evct2TDJwOqfxRNeuXUNWVhY8PT3h6emZ7bitrS369++f4zFSrMTERCxfvhwAMGPGDFSuXFnJEREREeVNrmSoUqVK6NWrF3r16gUACAoKwpUrV3D9+nXs378fu3fvRtmyZWFnZ4f169cX+LoVKlSAmZlZjoslBgYGwtjYGNWqVct2rF+/fmjVqlW28kWLFiEhIQHLly/ngOpisn79esTExKBOnTpwc3NTdjhERET5knsA9ecaNmwoXQvo0qVL2LlzJxITE/PsfspNz5498ffffyMwMBBNmjQBAISEhMDPzw/ffPNNjufUrFkzxxWxy5cvj9TUVNjZ2RU6Diq88PBwbN68GQDw66+/yj2QnoiIqDjJnQylp6fjwYMHCAgIwO3bt3Hv3j2kpKRAEARUrVoV7du3h42NTaGvO2bMGPj4+GDMmDEYM2YMNDQ04O3tDSMjI4wZMwbAp1WNr1+/DjMzM7Ro0ULeRyAFq1atGn7++Wf4+fmhe/fuyg6HiIioQORKhkaMGIGHDx8iPT1dmvx07NgR1tbWsLGxQb169eQOSF9fH3v27MHSpUvx119/QUdHB9bW1pg9ezYMDAwAAM+fP8fs2bPRv39/JkMliI6OjjSJJSIiKi1Eghw7ZzZs2BAAoKenhyFDhqB///4wNzdXeHAlgWQ2mTxdfupCEARkZGSwW4yIiEoluVag3rBhA0aNGoUaNWpg27Zt6Nu3L2xtbTFt2jTs3LkTwcHBio6TSrATJ06gS5cuOHfunLJDISIiKjS5usk6d+6Mzp07AwDev38PPz8/+Pn5ISAgAP/++y9EIhEqVaoEa2tr6Y7lpLo2b96Mly9f4sGDB3JtzktERKRMcnWT5SY+Ph6nT5+Gl5cXIiIiIBKJ8OTJE0VdXinYTZa/pKQkbNmyBWPHjpUunElERFRafNXU+tTUVAQEBODmzZvw8/NDUFAQBEFAmTJl0KlTJ9jb2ysqTirBypcvj+nTpys7DCIiIrnIlQytX78efn5+uH//PjIzMyEIAmrVqgVXV1fY29vD2tqag2nVwIMHD2BlZZXnXnJEREQlndzJkK6uLmxsbGBvbw97e3uYmZkpOjYqwUJCQtC7d280a9YM+/fvZ/cYERGVWnIlQ5s2bYKNjQ3KlCmj6HiolFi4cCHEYjEMDQ2ZCBERUakmVzIkGQsUFhYGHx8fBAcHIyUlBfr6+mjQoAGcnJxy3B6DVMPly5dx4cIFaGlp4eeff1Z2OERERF9F7gHUe/bswZIlS5CZmZnt2Pr16/Hzzz9j6NChXxUclTxisRgLFiwAAIwePRp169ZVckRERERfR65k6MaNG1i4cCGqVq2KCRMmoFWrVjA0NMSHDx8QEBCAP//8EwsXLkS9evXQpk0bRcdMSrRv3z4EBQVBX18f33//vbLDISIi+mpyJUN///03KlSogL1796JGjRrScgMDA9SuXRtt27bFwIEDsWXLFiZDKiQpKQm///47AGDGjBmoXLmykiMiIiL6enJtx/HgwQM4ODjIJEKfq1mzJhwcHPDff/99TWxUwqxfvx4xMTGoU6cO3NzclB0OERGRQsiVDGVkZOQ7g6hcuXJITU2VKygqecLCwuDl5QUA+PXXX7mOFBERqQy5kqG6devi6tWruSY7KSkpuHLlCurUqfNVwVHJ4enpibS0NNja2qJ79+7KDoeIiEhh5EqGBg8ejDdv3mDatGkIDw+XOfbs2TNMmjQJYWFhGDRokEKCJOW6e/cujh49CpFIhPnz53PFaSIiUilyDaAeNmwY/P39cebMGXTt2hVGRkaoUKECoqKikJiYCEEQ0L17d7i6uio6XipmgiDAw8MDwKck2MrKSskRERERKZZcyZBIJMIff/yBY8eO4ejRowgKCkJMTAz09PRgbW2N/v37o1+/fgoOlZQhLS0NlpaWCA4OxuzZs5UdDhERkcKJBEEQlB1ESebg4AAAOH/+vJIjUa6EhARUqlRJ2WEQEREpXKFahiIjI3Ht2jW8f/8eRkZG6NixI9eaURNMhIiISFUVOBlas2YNNm/eDLFYLC0rU6YM5syZw203VFBsbCzmzp2LH374AY0aNVJ2OEREREWmQMnQ8ePHsWHDBpQtWxaOjo4wMjLCmzdvcOHCBXh4eKBWrVqwtbUt6lipGK1evRq+vr4IDw/HyZMnOYOMiIhUVoGSoYMHD6JixYo4dOgQzMzMpOUPHz7EiBEjsHv3biZDKua7775DdHQ0Ro0axUSIiIhUWoGSoZCQEPTs2VMmEQIAKysrdOrUCffu3SuS4Eh5ateuLV1xmoiISJUVaNHF5ORkVKlSJcdjtWvXxvv37xUaFClPSkqKskMgIiIqVgVKhjIzM6GpqZnjMW1tbWRmZio0KFIOsViMPn36YNq0aYiJiVF2OERERMVCru04SDXt378fjx8/xvnz53NNfomIiFQNkyECACQlJeH3338HAHz//fdcP4qIiNRGgdcZCgoKgo+PT7byJ0+eAECOxwBwW45S4s8//8S7d+9Qu3ZtjBo1StnhEBERFZsCbcfRsGHDXKdXS07/8rggCBCJRNJkqbRSh+04wsPD0bFjR6SmpmLLli3o2bOnskMiIiIqNgVqGZoyZUpRx0HFTCwWw9/fH9HR0di3bx9SU1Nha2uLHj16KDs0IiKiYsVkSA35+vrC3d0dkZGRMuUODg5cYJGIiNQOB1CrGV9fX4wbNy5bIgQAixcvhq+vrxKiIiIiUh4mQ2pELBbD3d0deQ0Tmz9/vsxmvERERKqOyZAa8ff3z7FFSEIQBERERMDf378YoyIiIlIuJkNqJDo6WqH1iIiIVAGTITViaGio0HpERESqgMmQGrGxsYGJiUmuM8ZEIhGqV68OGxubYo6MiIhIeZgMqRFNTU0sWLAAQPZFMiXfe3h4cF8yIiJSK0yG1IyWlhZWrVoFY2NjmXITExN4eXnByclJSZEREREpR4H3JqPSLywsDOPHj4euri7OnDmD8PBwREdHw9DQEDY2NmwRIiIitcRkSI3Ex8ejfv360NfXh5mZGWrVqqXskIiIiJSOyZAasbS0xJkzZxAfH89tN4iIiP4fxwypGQ0NDRgYGCg7DCIiohKDyZAauHPnDtauXYuPHz8qOxQiIqISh91kKk4QBCxevBj+/v6IjY2Fh4eHskMiIiIqUdgypOIuXLgAf39/lClTBuPHj1d2OERERCUOkyEVlpWVhaVLlwIAvvnmG1SvXl3JEREREZU8TIZUmI+PD548eYKKFSti8uTJyg6HiIioRGIypKLS09OxfPlyAMDEiRNRuXJlJUdERERUMjEZUlG7d+/GmzdvYGhoiO+++07Z4RAREZVYTIZUUHJyMv744w8AwPfff49y5copNyAiIqISjMmQCvLy8kJMTAxq166N4cOHKzscIiKiEo3JkIqJi4vDxo0bAQCzZ8+Gtra2kiMiIiIq2ZgMqZi1a9ciKSkJlpaWcHZ2VnY4REREJR6TIRUSHh6O7du3AwDmzZsHDQ2+vURERPnhdhwqpEqVKvjpp59w+/Zt2NvbKzscIiKiUkEkCIKg7CBKMgcHBwDA+fPnlRwJERERFQX2o6iIrKwsZYdARERUKjEZUgG3b99Gly5dcPr0aWWHQkREVOowGVIBf/31F54+fYpz584pOxQiIqJShwOoVcCaNWuwadMmDBs2TNmhEBERlTpMhlRAhQoVMHPmTGWHQUREVCqxm6wUe/v2LTgZkIiI6OswGSql0tPTMWDAADg7O+P169fKDoeIiKjUYjdZKbVnzx68fv0aKSkpqFatmrLDISIiKrVKZMtQREQEZsyYgbZt26JVq1aYPHkyQkND8z3v3bt3mDdvHtq3bw9LS0s4ODhg9erVSE9PL4aoi09ycjJWr14NAPj+++9Rrlw5JUdERERUepW4lqH4+Hi4ubkhKSkJo0aNgo6ODrZu3QpXV1f4+PjAwMAgx/NSU1MxatQohIWFYfjw4ahVqxZu376NjRs3IiQkBBs2bCjmJyk6mzdvRkxMDGrXro3hw4crOxwiIqJSrcQlQ9u2bUNYWBgOHToES0tLAECHDh3Qr18/bN68GXPmzMnxvF27duH58+fYsGEDunTpAgAYNmwYTExMsHnzZvj5+aFt27bF9hxFJS4uTprYzZ49G9ra2kqOiIiIqHQrcd1kJ06cQPPmzaWJEACYm5ujbdu2OHHiRK7n+fn5oXLlytJESKJ3794AgDt37hRNwMVs3bp1SEpKgqWlJZydnZUdDhERUalXopKhhIQEhIaGyiRCEk2aNEF0dDSio6NzPNfT0xM7d+7MVh4XFwcA0NIqcY1ghRYeHo5t27YBAObNmwcNjRL19hEREZVKJSpDiIqKAgAYGRllO2ZoaAgAiIyMlP7/56pWrYqqVatmK9+xYwcAoFWrVrneV7IzfU4iIyNhYmKSd+DFZOXKlUhPT4etrS3s7e2VHQ4REZFKKFFNC8nJyQCAsmXLZjtWpkwZAMDHjx8LfL29e/fi4sWLaNOmDVq3bq2YIJUkJCQEBw8eBAD89NNPEIlESo6IiIhINZSoliHJasp5/aIvaBJw7NgxLFiwANWqVcPvv/+eZ93z58/neiyvVqPitGzZMmRlZcHR0REtW7ZUdjhEREQqo0S1DEnWy0lJScl2LDU1FQBQvnz5fK+zc+dOzJ07F/r6+tiyZQuqV6+u2ECL2e3bt3H69GloaGjkOpuOiIiI5FOiWoZMTU0BfFo88UuSgdM5jSf63Nq1a/Hnn3/CyMgI3t7eqFevnuIDLWb//fcfNDU1MXjwYDRo0EDZ4RAREamUEpUMVahQAWZmZggMDMx2LDAwEMbGxnluPbF+/Xr8+eefqFWrFrZu3YoaNWoUZbjF5rvvvkPnzp2hp6en7FCIiIhUTonqJgOAnj174s6dOzIJUUhICPz8/KRrBuXk6tWrWLduHWrWrIldu3apTCIkUa9ePRgbGys7DCIiIpUjEiSjlkuI+Ph4ODs7IyMjA2PGjIGGhga8vb2hra2Nw4cPw8DAADExMbh+/TrMzMzQokULAICzszNCQkLg5uaW4zpF5ubmaNSoUaHjkQygzmuQdVG5efMmKleujIYNGxb7vYmIiNRFieomAwB9fX3s2bMHS5cuxV9//QUdHR1YW1tj9uzZ0n3Jnj9/jtmzZ6N///5o0aIF4uLiEBISAuB/6wp9aezYsXIlQ8qSnp6OH3/8EW/evMHWrVvRvXt3ZYdERESkkkpcy1BJo6yWobi4OMyZMwd37tzBtWvXuDM9ERFRESlxLUP0iYGBATZv3oz4+HgmQkREREWoxA2gJln6+vrKDoGIiEilMRkqYSTdY+Hh4coOhYiISC0wGSph1q1bh127dmHChAnKDoWIiEgtMBkqQcLDw7F9+3YAwA8//KDkaIiIiNQDk6ESZOXKlUhLS4OtrS06deqk7HCIiIjUApOhEiIkJAQHDx4EAMybNw8ikUjJEREREakHJkMlxLJly5CVlQVHR0e0atVK2eEQERGpDSZDJcCdO3dw+vRpaGhoYM6cOcoOh4iISK1w0UUlEYvF8Pf3R1RUFP766y8AwJAhQ9CgQQMlR0ZERKRemAwpga+vL9zd3REZGSlTLtl0loiIiIoPk6Fi5uvri3HjxiGnLeHmzp0LAwMDODk5KSEyIiIi9cQxQ8VILBbD3d09x0RIYv78+RCLxcUYFRERkXpjMlSM/P39s3WNfU4QBERERMDf378YoyIiIlJvTIaKUXR0tELrERER0ddjMlSMDA0NFVqPiIiIvh6ToWJkY2MDExOTXFeXFolEqF69OmxsbIo5MiIiIvXFZKgYaWpqYsGCBQCQLSGSfO/h4QFNTc1ij42IiEhdMRkqZk5OTvDy8oKxsbFMuYmJCby8vDitnoiIqJiJhLzmeRMcHBwAAOfPn1fodSUrUEdHR8PQ0BA2NjZsESIiIlICLrqoJJqamrCzs1N2GERERGqP3WRERESk1pgMERERkVpjMkRERERqjckQERERqTUmQ0RERKTWmAwRERGRWmMyRERERGqNyRARERGpNSZDREREpNa4HUc+rKysIBaLYWJiouxQiIiIqBBMTEywa9eufOuxZSgfurq60NJSzV1LIiMjERkZqewwlEbdnx/ga6Duzw/wNeDzq/fzS7BlSI0V1Sa0pYW6Pz/A10Ddnx/ga8DnV+/nl2DLEBEREak1JkNERESk1pgMERERkVpjMkRERERqjckQERERqTUmQ0RERKTWOLWeiIiI1BpbhoiIiEitMRkiIiIitcZkiIiIiNQakyEiIiJSa0yGiIiISK0xGVIjXl5eaNeuXY7HUlNTsWLFCnTu3BnNmjWDi4sLbt68WcwRFo0HDx5g7NixaN26NaysrNCvXz/4+PjI1FHl5w8ODsa4ceNgY2ODNm3aYNq0aXj9+rVMHVV+/s+Fh4ejZcuWmDt3rky5qj//0KFDYWFhke2rb9++0jrv37+Hu7s72rdvjxYtWmD06NF4/PixEqNWrNTUVKxevRpdunRBs2bN4OzsjKNHj2aro2qfg7CwsBzf+8+/jhw5AkA1n7+gOLVeTVy+fBmTJ09GpUqVcP369WzHJ0+ejIsXL2L48OGoW7cuDh06hODgYGzfvh2tW7dWQsSK8fz5cwwYMACVKlXC8OHDoaenB19fX9y9exdz587FN998A0B1n//ly5cYOHAgKlWqhJEjR0IsFmP79u3IyMiAj48PTExMAKju839OEASMHj0afn5+6N+/Pzw9PaXHVP35W7ZsiTZt2sDJyUmmXF9fH/b29khPT8eIESMQHByM0aNHo2rVqti5cydiYmJw+PBh1KlTR0mRK0ZWVha+++47+Pn5Sd/j06dPw9/fHx4eHhg6dCgA1fwcfPz4EWfPns1WnpWVhSVLlkAQBBw9ehQ1a9ZUyecvMIFUWlZWlrBz506hSZMmgrm5uWBnZ5etzo0bNwRzc3PB29tbWpacnCw4ODgI/fv3L8ZoFW/s2LFC8+bNhbdv30rLxGKx4OLiIjRv3lxISkpS6eefNm2a0LRpUyE0NFRaFhQUJJibmwuLFi0SBEG13//Pff7vYM6cOdJyVX/+sLAwwdzcXNizZ0+udQ4cOCCYm5sL//77r7QsOjpaaNWqlTB16tTiCLNIHT58WDA3Nxd27dolLROLxUK/fv2Edu3aCVlZWSr/OfjS+vXrBXNzc8HX11cQBNX/d5AfdpOpOBcXFyxcuBA2NjZo0qRJjnX++ecfaGtrY8iQIdKycuXKYdCgQQgMDMSrV6+KKVrFEovFCAgIQIcOHWBkZCQt19DQgKOjIz5+/IgnT56o7PMDgJaWFnr16oUaNWpIyywsLKCvr4+goCAAqvv+f+7NmzdYuXIlpkyZku2Yqj9/SEgIAKBevXq51jlx4gQMDQ3RrVs3aVm1atXg6OiICxcuIDk5ucjjLEqHDx+GmZkZhg0bJi3T0NDA999/j6FDh+Ljx48q/zn43Js3b7BhwwbY29vD0dERgOr/O8gPkyEVFxERgQULFuDvv/+Gnp5ejnUePXqEOnXqoFy5cjLlkuTp0aNHRR5nUdDQ0MDx48cxe/bsbMfi4uIAAJqamir7/ACwcuVKLFmyRKYsMjIS8fHxqF69OgDVff8lsrKyMHfuXFhYWGDUqFHZjqv68z99+hQAUL9+fQDIMbEJDAzM8Y+lJk2aICMjQ5pQlUYZGRm4f/8+bG1toaHx6VdecnIyBEGAvb09pkyZAj09PZX/HHxu9erVEAQBc+bMkZap0/PnhMmQirtw4QJcXFwgEolyrRMVFQVjY+Ns5YaGhgA+JVSlkUgkQs2aNWVaRYBPfeiHDx9GuXLl0LhxY5V9/i/Fxsbi8uXLGDduHMqVK4dvv/0WgOq+/xLbt2/Ho0ePsGTJEukvw8+p+vMHBwdDV1cXa9asQatWrdCyZUt06NABO3bsAPApMUhMTMzzNYiMjCzWmBUpLCwMGRkZMDU1xbZt29CxY0e0bNkSbdu2xYYNGyD8/7BZVf8cSLx48QKnTp1Cnz59ZFoL1eX5c6Ol7ACoaOno6ORbJzk5GWXLls1WXqZMGQBASkqKwuNSFkEQ8Msvv+Ddu3eYPHkydHV11eb5Bw4cKP2lNnPmTJibmwNQ7ff/xYsX+OOPPzB9+nTUrVsXaWlp2eqo8vMDn1qG0tLSEBUVhSVLliAlJQUHDx7E4sWLER8fLx08nNdr8PHjx2KNWZESExMBAEeOHEFCQgImTpwIQ0NDHDt2DH/88QdSUlLwww8/qPznQGLPnj3SyQSfU5fnzw2TIcpXXq1KpYkgCPjtt99w8uRJWFtbY+LEiQU6T1Wef8aMGdDR0cGpU6ewYsUKhIWFwcPDI9/zSuvzi8VizJs3D40aNZLOGpRHaX1+CRcXF4jFYri5uUnL+vTpg2HDhsHLywsuLi75XqM0vwbp6ekAgNDQUBw5cgQNGzYEADg6OmLkyJHYunWrzGuTm9L8Gkikp6fDx8cHNjY2sLCwKNS5qvD8eWE3GaFcuXJITU3NVi4pK1++fHGHpHAZGRmYOXMm9u3bh6ZNm2LDhg3Q1tYGoB7PDwB9+/aFo6Mj1q5dC0dHR+zbtw9Pnz5V2effunUrHj16hFmzZiE+Ph5xcXGIj48H8OmXQlxcHNLT01X2+SVcXV2z/bLX0NCAi4sLMjIycOPGDQBQ2ddA0trRtGlTaSIkMWDAAGRkZODOnTsq/zkAgFu3biExMTHbEguA+vwczA1bhgjVq1fHu3fvspVHR0cDgMxMrNIoJSUFU6dOxdWrV2FtbY0NGzbI/MNW9efPSa9evXDq1Ck8fvxYZZ//ypUryMzMxPDhw7MdO3nyJE6ePImlS5eq7PPnp0qVKgA+DTCvWLGiyr4GknEwBgYG2Y5JypKTk9Xic3D58mVoaGjIzBqUUIfnzwtbhghNmjTBs2fPsv1VEBgYCACwsrJSRlgKkZGRgSlTpuDq1avo3Lkz/v7772x/4ajq8yckJKBHjx5YtGhRtmOSGUVlypRR2eefM2cOvL29Zb68vLwAAO3bt4e3tzfat2+vss8PfBr02qtXL6xZsybbsRcvXgAAatasiSZNmkif93OBgYHQ0tJCo0aNijzWolKlShUYGxvj+fPn2Y6FhYUBAExMTFT6cyBx584dmJubSxPhz6nD8+eFyRChZ8+eSE9Px759+6RlHz9+xKFDh9C0aVOYmZkpMbqvs3btWly7dg1dunTBunXroKurm62Oqj5/pUqVoK2tjX/++UfmL7709HTs2LED5cqVg42Njco+v6WlJezs7GS+2rZtC+DTGjp2dnYwNDRU2ecHPv2ST0hIwMGDB5GQkCAtT0hIwLZt22BqaoqWLVuiZ8+eiIiIwLlz56R13r17h1OnTqFbt245/rspTZydnfHq1Sv8+++/0rL09HTs2bMHBgYGaN26tUp/DgAgMzMTT58+zXW9OVV//vywm4zQoUMHdOjQAcuXL0dkZCTq1KmDAwcO4O3btzJbFpQ20dHR8Pb2hpaWFtq3bw9fX99sdWxtbVX2+QHAw8MDbm5uGDZsGIYNGwYNDQ0cOXIET58+xaJFi6Cvr6/Sz18Qqvz8IpEI8+fPx5QpUzBkyBAMGzYM6enp2L9/P2JjY7F582ZoaWlh4MCB2LNnD2bOnIlvv/0WBgYG2LFjB0QiEaZNm6bsx/hqEyZMwPnz5zFz5ky4urrC1NQUPj4+eP78OVauXAltbW2V/hwAn5ZHSE9Pl64v9iVVf/78cG8yNTJy5Ei8ePEix73JkpOTsXr1avj6+iIlJQUWFhaYMWMGbGxslBCpYpw+fRrTp0/Ps87mzZvRsWNHlXx+iYCAAKxbtw4PHjwA8KnFZPz48ejQoYO0jio//+fS0tLQtGnTbHuTqfrzX7hwAV5eXnj8+DG0tLTQokULTJs2Dc2aNZPWiY2Nxe+//46LFy9CLBajWbNmmDVrVqnuIvvc+/fvsWbNGpw9exaJiYkwNzfH5MmT0blzZ2kdVf4cPHjwAIMHD8ZPP/2U4+KjgGo/f36YDBEREZFa45ghIiIiUmtMhoiIiEitMRkiIiIitcZkiIiIiNQakyEiIiJSa0yGiIiISK0xGSIiIiK1xmSIiIiI1BqTISIiIlJrTIaIqNDOnz+P8ePHw9bWFpaWlmjfvj0mTpyI8+fPKzu0Qlm3bh0sLCxkNigtLv7+/rCwsMDixYuL/d5EJIsbtRJRoSxcuBC7du2CqakpHBwcULlyZURFReHy5cu4cOEChgwZgoULFyo7zBLP1NQUU6ZMkdkfjIiUg8kQERWYv78/du3ahR49emDVqlXQ0vrfj5DExES4ubnhwIEDsLe3R9euXZUYaclXo0YNTJ06VdlhEBHYTUZEhXDp0iUAgKurq0wiBAAVKlTAjz/+CAA4e/ZscYdGRCQ3JkNEVGAZGRkAgJCQkByPt27dGn/88QdGjx4tUx4XF4dly5bB0dERzZo1Q7NmzdCrVy9s3LgRmZmZ0nqScTTHjh3DgQMH4OjoCCsrK/Ts2RPHjh0D8Gm80oABA9CsWTP06NEDu3fvlrmXZBxQcHAwFi1ahLZt26JVq1YYPXo07ty5U6DnfP36NWbOnAk7OztYWlrC0dERmzZtkj5/fq5du4ZRo0bB1tYWTZs2hbOzMzZt2oT09PRszyoZM3TkyBFYWFjk+RUWFqawGInof9hNRkQF1q5dO+zcuRPLli3Dq1ev0Lt3bzRt2hSampoAgDJlysDR0VHmnMTERAwZMgSRkZHo0qULunbtiri4OJw9exarV69GQkIC5syZI3OOt7c3Xr9+jV69eqFt27Y4evQoZs+ejaCgIOzcuRM9e/ZE69atcfz4cSxYsABGRkbZuuXmzZuH0NBQODs7Izk5GadPn8aoUaOwceNGtG/fPtdnDAwMxKhRo5Camoru3bujevXquH37NlatWoWAgABs2rRJ+rw5uX37NiZMmIDKlSvDyckJurq6uHHjBlatWoXXr19jyZIlOZ7XqFEjTJkyJVv506dPcebMGZiZmaFKlSoKiZGIviAQERXC/PnzBXNzc+lXy5YthbFjxwre3t5CZGRktvqbNm0SzM3NhQMHDsiUR0RECJaWlkK7du2kZX5+foK5ubnQqFEj4eHDh9Lyffv2Se938eJFabm/v79gbm4uTJ8+XVq2du1aaVyvX7+Wlv/3339C48aNBQcHB0EsFsvUPXv2rCAIgpCVlSX07t1bsLKykrm/IAjCkiVLBHNzc2HXrl15vj5Tp04VzM3NhTdv3kjL0tPThb59+wqNGjUSEhMTZZ510aJFuV4rNjZW6Ny5s9C8eXMhODhYYTESkSx2kxFRofz222/YtGkTOnToAG1tbSQlJeHy5ctYunQpHBwcsHLlSmRlZUnrt2/fHh4eHujXr5/MdUxMTFCzZk3ExcVlu0erVq1gaWkp/b5ly5YAgDp16qBTp07ScslMrPDw8GzXGDFiBMzMzGTqOjk5ITQ0FPfu3cvx2e7fv4+QkBAMGjRI5v4AMH36dGhra+PIkSO5vDKfSJ794cOH0jJtbW1s3rwZ/v7+KF++fJ7nS2RkZGDatGkIDw/HkiVLYG5urrAYiUgWu8mIqNA6deqETp06ITk5Gbdv38bNmzdx4cIFvH79Gl5eXsjKysKsWbMAAI0bN0bjxo2RnJyM+/fv4/Xr13j16hUePnyI169fQywWZ7t+rVq1ZL4vW7YsgE8zsD6nq6sLADJjcSSsra2zlTVt2hTHjx9HUFAQWrVqle14YGAgAODNmzdYt25dtuN6enoIDg6GIAgQiUQ5vjaDBw/GuXPnMGPGDKxZswYdOnRAx44d0bZtW+jo6OR4Tk4WLVqEgIAAfPfddzJdj4qIkYhkMRkiIrnp6enB3t4e9vb2mDNnDg4dOoRff/0Vu3btwpQpU1C2bFmkpaVh1apV2L9/P1JSUgAARkZGaNOmDSpXrox3795lu64k+flSYZIJIyOjbGVVq1YFACQlJeV4zocPHwAAV69exdWrV3O9dnJycq4tPPb29tixYwe2bNmCGzduYOfOndi5cyf09fUxZcoUjBw5Mt/Y9+7di3379qFdu3b44YcfFB4jEcliMkREBZKUlIQBAwagTp062LRpU7bjIpEIgwcPxunTp3Ht2jW8ffsWderUgaenJ/bs2YMePXrA1dUVFhYW0NfXBwA4OjrmmAwpQmpqarayxMREAEDlypVzPKdcuXIAgMWLF2PQoEFy39va2hrW1tb4+PEjbt++jUuXLuHo0aNYtGgRzMzMYG9vn+u5AQEBWLx4MWrUqIFVq1ZlGwitqBiJ6H84ZoiICqR8+fJITEzEjRs3EBMTk2ddDQ0NVKtWDQBw4sQJVKlSBWvWrIGNjY00EUpNTUVERAQAQBAEhcf7+ZgdCclYoaZNm+Z4joWFBQDg0aNH2Y5lZGTA09MTO3fuzPO+27dvxx9//AHgU+LSsWNHuLu7Y/78+QCQ5/T+8PBwTJs2DVpaWvjzzz+lr5WiYyQiWUyGiKjAXF1dkZ6ejmnTpiE6Ojrb8fPnz+PGjRvo1q2btItGV1cXaWlp0u4dABCLxVi8eLG09aYo1sbZsmWLTIx3797FP//8gyZNmqBhw4Y5ntOmTRvUqFEDhw4dyjbI2svLC97e3tIxO7m5du0aNm7ciP/++0+mXDLIu3r16jme9/HjR0yaNAnv37/HkiVLijRGIpLFbjIiKrAJEyYgJCQEZ86cQffu3dG+fXvUrl0bmZmZuH//Pu7evYu6devit99+k57j7OyMrVu3YuDAgejatSsyMzNx7do1vHz5EgYGBoiLi0N8fDwMDQ0VGmt8fDz69++Pbt26ISkpCWfOnEGZMmXy3DdNU1MTy5Ytw9ixYzFixAg4ODigZs2aePToEfz8/FCjRo1sY3i+NHXqVPj7+8PNzQ09e/aEkZERnj17hosXL6JevXro06dPjuctWbIEQUFBaNKkCd6+fYs///xTZlYeAHTt2hWNGjX66hiJSBaTISIqMC0tLaxduxZnz57F8ePH8eDBA1y5cgXa2tqoVasWfvzxR7i5uaFMmTLSc2bMmAE9PT0cP34ce/bsgYGBAerVq4dffvkFz58/x5IlS3D58mUMHjxYobH+/PPPuHv3Lk6ePAkNDQ107twZ06dPR7169fI8r3Xr1jh48CA2bNiAmzdv4uLFizA2NsbIkSMxYcIE6SDs3DRt2hS7du3Chg0b4Ofnh7i4OBgaGsLNzQ0TJ06Ujvn50uvXrwF8mi2WW8uOqakpGjVq9NUxEpEskVAUnfVEREqybt06rF+/Hn/++Sc3iyWiAuGYISIiIlJrTIaIiIhIrTEZIiIiIrXGMUNERESk1tgyRERERGqNyRARERGpNSZDREREpNaYDBEREZFaYzJEREREao3JEBEREak1JkNERESk1pgMERERkVr7PwUAxJsX3zLLAAAAAElFTkSuQmCC\n", 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", 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" ] @@ -1376,9 +1355,17 @@ "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\remra\\STUDIES\\pingouin\\src\\pingouin\\plotting.py:581: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " data[\"wthn\"] = data[within].replace({_ordr: i for i, _ordr in enumerate(order)})\n" + ] + }, { "data": { - "image/png": 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L719FihShXbt2evvVrFkTd3d39uzZk2yITxBDTt+0n3766aNWOX2oq1evYmpqyqpVq5LddvfuXQACAwOpVq0a169fR6VSUaJEiWT7fugQ0NuuX7+Ovb293pt6Eg8PD3mftMqZM2eyIQoLCwtiY2OTrcIyNTUFdG9WZmZmXL16FYDdu3dz8OBBvX2fPn0KII/BJz02Ka0ocnNz486dOx/ddltbWxo3boxWq+XGjRvcvn2bBw8eEBQUJAeZHzvXIumxTAqI3pQlSxYKFixIYGAgkZGRWFtby7cVKFBAb9+kD5G359ykdD6lUik/l29K7fnNlStXsucsqS3vO1+rVq0YOXIkrVu3xsnJiSpVqlC1alU8PDxQKl9/57t+/Tq5cuXSG1YCXTDw888/Jzvu29cPyJPuY2JisLW1/ahrfdfzUKRIETJlypTm13327NmTLZ22tramYMGCXL16ldjYWK5du4YkSbx69SrFOW7GxsY8efKEZ8+e6T1GefLkee/5o6KiePDgAe7u7nrBRhIPDw+WLl2a7Predex8+fLp/Z4UnLx9nzf/hgHMzc1p0KABoHs/u337NsHBwdy8eZMTJ04AKf8Nve/1HhwcTExMTIrpEHLnzi2/hm7evElMTAwajSbFxzk+Ph6NRkNQUFCKr5vvmQhohI8WGRmJWq3WW979tqRvoBEREZiamup9c/lUUVFRevlY3pQ0rpzUY5IWqX3j+5BriIyMBGDhwoWp7vP2Y/Pmt9EkmTNn/pCmpmj37t388ccf3Lt3D9C9kbu4uFC0aFGOHz+ebKLr+yR9E0zpgwZ0j3lgYCCxsbF6Ac3bAUZST8f7zh8VFYWpqWmK816sra0xNzdPNhE7pX0/9HytW7cma9asrFixgoCAAIKCgli0aBH29vYMHjxY/nCLiIhI9XWXknfN20lq08dca9Lz8OZj/CY7Ozvu37//we1709tBWpKk642MjCQiIgKACxcuvLN399WrV3rHMzMze+/5kybEvuvaIPnfdVIwkpK3e1eSfMhqxzNnzjBx4kT5C4qpqSlFixalePHiPHnyJMXX1Pte70l/96n9HSVJepzv3LnzQe+xwmsioBE+moWFBZaWlsl6IFLbNz4+HrVaneyDOzY2NtXg4V0sLS159uxZircl/ZG/vfz8S7GwsEClUnHx4sX3BkCZMmUiODiYxMTEZPvGxMSk6fwXL16kb9++5MiRg2nTplGiRAny5s2LQqFg4cKFci/Nx0iaEPr8+fMUb096A06vx9zS0pLY2FgiIiLkSehJ4uPjiYuLS/flvrVr16Z27dpERERw6tQp9u/fz5YtWxgwYACFCxfG0dERCwuLZCtRksTExKT6AfouH3OtSc/Ds2fP9IankoSHh6f5OYiPj09x+5vPbdL19ezZk759+6bpPKl589re147P7dGjR3Tt2hUzMzPGjh2Lh4cHBQoUQKVSsX37dvbu3Zum4yZd4/teQ0n7NW3alClTpqTtIr5TYg6N8NGcnJx4+vQpL168SHbbwYMHmT59utw17OjoiEajSXE5d8+ePSldunSKy57fpWjRokRGRnLjxo1kt509exaAwoULf9Qx04uTkxMajSbZ0k7QfbP9/fff5TYWL14crVbLxYsXk+175cqV954rpdUs27ZtQ6vVMnLkSBo2bEi+fPnk/ZKGsN78dvkhK2KShsQCAgKS3RYVFUVgYCD58+dPtzw/RYsWTfV8AQEBSJKUbs9vQkIC8+fPl5eoZ8qUidq1azNx4kR69OiBVqvl/PnzgO61/Pjx4xRf982aNaNu3bofff6PudZ37Xv//n1evHjx3tVjqbl7967cu5gkNjaWoKAgihUrhomJiZzyILXX5qxZs1i4cOF7h/hSYmVlRZ48ebh37x4vX75MdvuZM2eAL/N3vXfvXuLi4ujTpw//+9//KFSokDy36Pbt28D7e/1SUrBgQYyNjbl06VKy2549e4abmxvDhw+nYMGCmJiYcPXq1RTP8+effzJv3jzCwsI+ug3fOhHQCB+tefPmSJLE2LFj9d68nj9/zsiRI1m4cKH8LaNJkyYATJ8+Xa+7+Pz585w+fRo3Nze5l8bY2PiDMt96enoCupIGb/ZkPHjwgLlz52JsbEzDhg0//ULToHnz5gBMmDBBb9JeVFQUo0aNYtGiRWg0GnlfhULB77//rrfvtm3bPiigSerxevMxS+qCDwkJ0dv3xIkTbN26FUAvD0pKx3hbrVq1sLa2ZvXq1XIXfNJxxo8fT1xcXLrWDEp6fqdNm6b34fby5Uv5G2t6nc/ExIStW7cyc+bMZLlkkiZz5sqVC9C9liVJ4vfff5efQ9DlHLp//75eqoIP9THX2rRpU4yMjFiwYIFeW2NiYhgzZozevh8rMTGRadOmyR+gkiTxxx9/EBMTQ4sWLQDdpPsyZcpw+PBhdu7cqXf/jRs3MnfuXI4cOZLmwLZ58+bExcUxYcIEvdfo1atXWblyJZkyZZJzJX1Oqf0NXb9+nRUrVgCkmEvoQ45bt25dbt++zT///KN324IFCwCoUKECpqamNGjQgFu3brFs2TK9/U6dOsWUKVNYv379Jw1Lf6vEkNM37PTp0+9NUNegQQMKFSr0Ucf19PRk//797Nq1i6CgICpXroxarWbHjh28evWKAQMGyBN2K1WqRIsWLVi/fj1NmzalcuXKREdHs23bNiwtLRkxYoR8XDs7O+7cucPIkSOpWrVqqm9eTZs2lc/fpEkTqlSpIuehiYqKYtiwYckmBH4p5cuXp0OHDvz11180bNiQqlWrYmJiwt69e3ny5AmtW7emXLlyAJQsWRJvb2+WLFlCs2bNqFatGk+fPmXv3r3ky5eP4ODgd54raZ7Cjh07sLCwoHnz5jRo0IBly5YxevRozpw5Q/bs2QkKCuLo0aPY2toSGhqqlywt6RiTJk2iYsWK/PTTT8nOY2VlxYQJE/j5559p3bo1tWvXJmvWrJw8eZIbN25QunRpfHx80ukR1E167dy5s5zALmkl3IEDB3jx4gU+Pj4pToxNq/79+9OrVy+aN29OvXr1yJw5M1euXOHkyZOULVuWH374AYCWLVuye/duNm7cSFBQEOXKlePZs2fs3r2bPHnypDgxOD2vNW/evAwaNIjx48fTvHlzatWqhYWFBYcPH+bBgwc0bNiQZs2apekxMDY2ZsOGDQQGBlKyZEkuXrzI+fPnKVeuHG3atJH3GzNmDO3ataNv375UqVKFIkWKcPfuXQ4ePIiNjc0nFX718fHh6NGjbNmyhaCgIMqXL09oaCh79+5FkiSmT5/+3vkn6aF69er88ccf+Pn5cefOHfLly8f9+/c5cOCAPMcnpYSDH2LgwIEEBAQwfPhwdu/eTZEiRbh8+TJnzpyhVq1a8nytQYMGcf78eSZPnsy+fftwdXWVX2tGRkZMmDBBb8K6oCMCmm/Y6dOn5YycqXF2dv7ogEahUDBr1ixWrVqFv78///77L2ZmZhQuXJjOnTsnS341fvx4XFxcWLt2LWvXrsXU1JQqVarw888/661UGjFiBOPGjWP9+vWo1epUAxqFQsGMGTNYtWoV69atY926dZibm1OqVCm6dOlC+fLlP+p60tuwYcMoUaIEa9asYfPmzahUKgoWLEjv3r3lHpwkAwcOpGDBgqxYsYJ//vmHHDlyMG7cOIKCguRvg6nJnTs3/fr1Y/ny5axatYpChQrRokULFi5cyKxZs9i7dy8qlYrcuXPTp08fWrZsSZUqVTh06JBc3bht27acO3eOs2fPcvv2bTp37pziuerUqcPq1auZP38+R44cISEhgXz58jFw4EC8vLzSddI3IGd7XbVqFVu2bMHIyAhnZ2dGjBjxwcuAP1TNmjVZsmQJixYt4sCBA0RERJArVy569eqFj4+P/MGhUqmYP38+S5YsYdOmTaxatQorKysaN25M//790/yN+WOu1cvLiwIFCrBkyRJ2796NJEkUKlQIX19fWrZsmebHwMzMjMWLFzNp0iTWrFlD9uzZ+emnn/D19dVbyu3g4IC/vz/z5s3j0KFDnDhxAjs7O5o2bZpsOfnHMjU15c8//2TJkiVs2bKFNWvWkClTJqpXr46vry/FihVL87E/hr29PcuWLWPatGmcPHmSo0ePkitXLjp06ICvry916tThyJEjSJL00UkM7e3t+ffff5k9ezYHDhzgxIkT2Nvb06NHD3r27CnvlyVLFv755x/8/PzYs2cPf/31F1myZKFGjRr07NlTHn4U9CmktAwGCoIgCN+EGjVqEBERIc/tEoSvleizEgRBEAThqycCGkEQBEEQvnoioBEEQRAE4asn5tAIgiAIgvDVEz00giAIgiB89URAIwiCIAjCV08ENIIgCIIgfPVEQCMIgiAIwldPBDSCIAiCIHz1REAjCIIgCMJXTwQ0giAIgiB89URAIwiCIAjCV08ENIIgCIIgfPVEQCMIgiAIwldPBDSCIAiCIHz1REAjCIIgCMJXTwQ0giAIgiB89URAIwiCIAjCV08ENIIgCIIgfPVEQCMIgiAIwlfPyNANEARBEIQP8SD8AZuDNgPQxKkJeTPnNXCLhIxEIUmSZOhGCIIgCMK7BDwOoMaKGkTERwCQyTQT+73245HLw8AtEzIKEdAIgiAIBvX06VNiYmLeuU/nTZ259PSS3raSOUqytOnS9x7fwsKCHDlyfFIbhYxPBDSCIAiCwURERNClSxe0Wu1nO4dSqWTJkiVkypTps51DMDwR0AiCIAgG9a4emqj4KKafnM7G6xsBsIqzouzDspzOc5pihYsxv+H89x5f9NB8H8SkYEEQBMGgUgs2tt7YSvcd3XkU+QgswERlIt9mntWc2W1n45DV4Us1U8jgxLJtQRAEIUMJjQmlvX97Gq9pzKPIR1gYWzCz3kwe93/M1DpTAfj3x39xzOpo4JYKGYnooREEQRAyjHXX1tFrey+eRz8HoHqB6ixushgHW11PTI2CNdjBDqxNrQ3ZTCEDEgGNIAiCYHBPo57y0/afWB+4HgBrE2t+r/M7Pu4+KBQKA7dO+BqIgEYQBEEwGEmSWHV5FX139uVl7EsA6heuj18jP5E4T/goIqARBEEQDOJhxEN8t/qy/eZ2AGzNbJlZbybtXduLXhnho4mARhAEQfiiJEli8bnF/LLnFznzb/OizZnXcB45rMTyaiFtREAjCIIgfDF3w+7is8WHfXf3AZDdIjtzG8ylZbGWoldG+CQioBEEQRA+O62kZe7puQzeN5iYRF0SvbYl2jKz3kyyWWQzcOuEb4EIaARBEITPKigkiC6bu3DswTEAclnnYkHDBTR2amzglgnfEhHQCIIgCJ+FWqtm2olpjDgwgnhNPABd3Lrwe53fsTGzMWzjhG+OCGgEQRCEdHf52WW8N3tz9vFZAPJnzs+ixouoXai2gVsmfKtEQCMIgiCkmwRNAhOPTGT8kfEkahMB6FWmFxNrThTZfYXPSgQ0giAIQroIeBxA502dufz8MgCFsxRmSZMlVMlfxcAtE74HIqARBEEQPkmcOo7RB0cz9fhUNJIGpUJJ//L9GV19NBbGFoZunvCdEAGNIAiCkGbHHxzHe5M3QaFBABTLXoylTZZSLk85A7dM+N6IgEYQBEH4aNEJ0QzbP4yZp2YiIWGkNGLwD4MZVmUYpkamhm6e8B0SAY0gCILwUQ7cPUDXLV25E3YHALccbixtupRSOUoZtmHCd00ENIIgCMIHiYiPYOCegfgF+AFgojJhZNWR/FrxV4xVxgZunfC9EwGNIAiC8F47bu6g29ZuPIx4CEC53OVY2nQpxbIXM3DLBEFHBDSCIAhCql7GvqT/rv4sv7gcAHMjc8bVGEffcn1RKVUGbp0gvCYCGkEQBCFFGwI30GNbD55FPwOgav6qLG6ymMJZChu4ZYKQnAhoBEEQBD3Po5/Te0dv/rn6DwBWJlZMqTUF39K+KBVKA7dOEFImAhpBEAQBAEmS+PvK3/Te0ZvQ2FAA6hSqw8JGC8lvk9/ArROEdxMBjSAIgsDjyMf02NaDzUGbAbAxs2FanWl0KtUJhUJh4NYJwvuJgEYQBOE7JkkSyy4so/+u/oTHhwPQxKkJ8xvOJ5d1LgO3ThA+nAhoBEEQvlP3X93HZ4sPe+7sASCbRTZm159Nq+KtRK+M8NURAY0gCMJ3RitpWXB2AYP2DiIqIQqAVsVbMbv+bLJbZjdw6wQhbTJkQBMYGMgff/xBQEAAkiRRqlQpBgwYQIkSJQzdNEEQhK/azdCbdN3SlcP3DwOQwyoH8xvOp1nRZoZtmCB8ogwX0Ny7d4927dphbGxMt27dMDU1Zfny5XTo0IG1a9fi5ORk6CYKgiB8dTRaDTNOzmDYgWHEqeMA6FSqE9PqTMPW3NbArROET5fhApo///yT6Oho/v77b9zc3ACoWbMmDRs2ZN68ecycOdPALRQEQfi6XH1+lS6bu3Dq0SkA8mbKy8LGC6lXuJ6BWyYI6SfDBTQPHjzAwsJCDmYA8ufPT/78+QkKCjJgywRBEL4uiZpEphybwpjDY0jQJADQo3QPJtWaRCbTTAZunSCkrwyX8rFAgQLExMTw9OlTeVtcXBzPnz/Hzs7OgC0TBEH4epx/cp6yi8sy7MAwEjQJONg6cKDjAeY1nCeCGeGblOECGh8fH3Lnzs3PP//M1atXuX37NgMHDiQ6OhofHx9DN08QBCFDi1fHM2z/MMosKsOFpxdQoODn8j9zqfslqhWoZujmCcJnk+GGnHLkyEGPHj0YPXo0np6e8vbRo0dTuXLlVO9Xs2bNVG97/vw5JUuWZOXKlenaVkEQhIzk1MNTeG/25tqLawAUzVaUpU2WUiFvBQO3TBA+vwwX0MycOZN58+ZRsmRJ2rZti5GRERs2bGDUqFFoNBratWv30cfUaDQ8efLkM7RWEATB8GISYxhxYATTT05HK2lRKVQM+mEQw6sOx8zIzNDNE4QvIkMFNBERESxevBhHR0dWrlyJiYkJAI0aNaJr165MnDiRmjVrkiNHjmT33bdvX6rHfVfvjSAIwtfs0L1DdN3SlVsvbwHgau/KsqbLcM/pbuCWCcKXlaHm0Ny7d4+EhAQaNGggBzNJPD09SUxM5Ny5cwZqnSAIQsYRGR9Jr229qLa8Grde3sJYacyYamM443NGBDPCdylD9dCYmpoCuiGit0mSBIBWq/2ibRIEQchodt/ejc8WH4LDgwEok6sMS5suxcXOxcAtEwTDyVA9NEWKFMHe3p6NGzcSHR0tb9dqtfzzzz8YGRlRunRpA7ZQEATBcF7FvaLLpi7UXVmX4PBgzIzMmFp7Kse7HBfBjPDdy1A9NEqlkpEjR9K7d29atGjB//73P4yMjNi+fTvnz5+nd+/eKc6fEQRB+NZtDtpM963deRKlW+BQKV8lljRZgmNWRwO3TBAyhgwV0IBuAu/y5cuZO3cus2fPRq1WU6RIEaZMmULTpk0N3TxBEIQvKiQmhD47+rDmyhoALI0tmVRrEj3L9ESpyFCd7IJgUBkuoAEoU6YMf/75p6GbIQiCYDCSJPHvtX/5aftPvIh5AUDNgjVZ1HgRBW0LGrh1gpDxZMiARhAE4Xv2JPIJvbb3YsP1DQBkMs3EtDrT8HbzRqFQGLh1gpAxif5KQRCEDEKSJJZfWE6xecXkYKaRYyOu9rxKF/cu33UwI0kSc07Pob1/ewDWXlkrr34VBBA9NIIgCBlCcHgwvlt92XlrJwBZzLMwq94s2pZo+10HMkmmnZjGL3t+wSbGhpzkZMqxKZhkM2FAxQGGbpqQQYgeGkEQBAPSSlr8zvrhMs9FDmZaFmvJtZ7XaOfaTgQz/5l/dn6ybQsCFhigJUJGJXpoBEEQDOT2y9v4bPHhwL0DANhZ2jGvwTxaFGth4JZlHJIkseH6Bu6H3092W4ImwQAtEjIqEdAIgiB8YRqthtmnZzN031Bi1bEAdHDtwPS608lqkdXArcs4Dt8/zMA9Azn16FSKt/+v2P++cIuEjEwENIIgCF/Q9ZDreG/y5sTDEwDkts6NXyM/Gjo2NHDLMo7Lzy4zZN8Qtt3cJm+rXqA6Zx6fgRjd75Ymlvh4+BiohUJGJObQCIIgfAFqrZqJRyZSakEpOZjxcffhas+rIpj5T3B4MJ02dqLkgpJyMFM+T3kOdTqEqZEpUQlR8r7RCdH03dnXUE0VMiDRQyMIgvCZXXx6Ee/N3px7cg6AAjYFWNx4MTUdahq4ZRlDaEwoE49OZM7pOcRr4gFwyurExJoTaerUlDOPz3Do3qFk97vw9MIXbqmQkYmARhAE4TNJ0CQw/vB4JhydgFqrRoGC3mV7M77meKxMrAzdPIOLTYxl1qlZTDw6kfD4cAByWuVkdLXRtCzWkrVX1+KxyCPVwCV/5vxfsLVCRicCGkEQhM/gzKMzeG/25srzKwA4ZnVkSZMlVMpXycAtMzy1Vs3yC8sZeXAkjyIfAbpsyIN/GEyVAlVYcWEF/Xf31xtiKmRbiAcRD+TfzYzNmFZ32hdvu5BxiYBGEAQhHcUmxjLy4Ej+OPEHWkmLUqHklwq/MKraKMyNzQ3dPIOSJInNQZsZsm8IgSGBAJioTPD18MXB1oFVl1cxdP9QeX8zIzNaFW9F99LdKZe7HLfDbrPiwAqu3rrK3y3+pmLeioa6FCEDEgGNIAhCOjkafBTvTd7cfHkTABc7F5Y2WUqZ3GUM3DLDOxZ8jIF7B3L8wXEAFCho6NgQW1Nbll9cTkR8hLyvczZnupfuTgfXDtia28rbC2cpTCe3Tvy6+lfyZs77xa9ByNhEQCMIgvCJohKiGLpvKHNOz0FCwkhpxG+Vf2No5aGYqEwM3TyDuvbiGkP2DWFz0GZ5Wwm7EgBsvbFV3maqMqVlsZb4evhSKV8lkSFZ+GgioBEEQfgE++7so+uWrtx7dQ8A95zuLGu6DFd7V8M2zMAeRjxk1MFRLLuwDK2kBcDe0p6ohCguP78s7+eY1RFfD186luwokgoKn0QENIIgCGkQHhfOL7t/YfH5xYCuh2FUtVH8UvEXjJTf71trWGwYk49NZuapmcSp4wAwNzInVh3Ls+hnABgrjfF09sTXw5dqBaqJ3hghXXy/f3WCIAhptO3GNny3+sordCrkqcDSpkspmq2ogVtmOHHqOOacnsOEIxMIiwsDdPNkJCS5vIODrQO+Hr50KtUJO0s7QzZX+AaJgEYQBOEDhcaE0m9XP1ZeWgmAhbEFE2pM4KeyP6FSqgzcOsPQaDX8dekvRhwYobesGkBCQqVQ0axoM3w9fKnpUBOlQiSoFz4PEdAIgiB8gPXX1tNze0+eRz8HdLWFFjdZjIOtg4FbZhiSJLH95nb67+7PjdAbyW7Pnzk/Pu4+eLt5k9M6pwFaKHxvREAjCILwDs+invHTjp9Yd20dANYm1vxe53e6unf9bnsbjt4/iu9WX66FXNPbrkBBE6cm+Hr4UqdQne+210owDBHQCIIgpECSJFZdXkXfnX15GfsSgPqF6+PXyO+7zYGy/+5+em7rSVBokN52O0s7epbuSRf3LuTJlMdArRO+dyKgEQRBeMvDiId039pdrvhsa2bLjHoz6ODa4btbkaPWqvnr4l+MPDgy2RyZsrnLMqzyMOoXqf9dr+wSMgbxChQEQfiPJEksOb+EAbsHyJlrmxdtzryG88hhlcPArfuyHkY8ZM7pOcw9M1evppJKoaKJYxP+qPsHBW0LGrCFgqBPBDSCIAjA3bC7+GzxYd/dfQBkt8jO3AZzaVms5XfTK6PRath5ayfzzsxjx60dSEjybcZKYzqW7MiMejOwNLE0YCsFIWUioBEE4bumlbTMPT2XIfuGEJ0YDUDbEm2ZWW8m2SyyGbh1X8bjyMcsPb+UhQELkw0rGSuN6VG6B+NqjMPa1NpALRSE9xMBjSAI360boTfosrkLR4OPApDTKicLGi2giVMTA7fs89NKWvbc3oNfgB+brm9Ci1bvdpVChY+HD6OqjsLeyt5ArRSEDycCGkEQvjtqrZrpJ6Yz4uAIOT1/F7cu/F7nd2zMbAzbuM/sWdQzll1YxqJzi7gTdifFff5X/H+MrzGewlkKf+HWCULaiYBGEITvypXnV/De5M2Zx2cAXQK4hY0XUqdQHQO37PORJIkD9w7gF+DHhsANJGoTU9yveoHqTK41mTK5y3zhFgrCpxMBjSAI34UETQKTjk5i3OFx8gd6rzK9mFhz4jc7NyQkJoQ/L/zJwoCF3Hx5U95uojJBrVXLVbBL2pdkcq3J1ClU57uZAC18e0RAIwjCNy/gcQDem7259OwSAIWzFGZJkyVUyV/FwC1Lf5IkcST4CH4Bfqy7to4ETYJ8W55MeXgR/YJ4TTwABWwKMK76ONqUaPPdZj0Wvh0ioBEE4ZsVp45jzKExTDk2BY2kQalQ0r98f0ZXH42FsYWhm5euXsa+ZMXFFSwMWEhgSKC83drEmlI5SnHl+RUeRjwEIKt5VoZXGU730t0xNTI1VJMFIV2JgEYQhG/SiQcn8N7szfWQ6wAUy16MpU2WUi5POQO3LP1IksSJhyfwC/Djn6v/yBOcAcrkKoNbDjf23NnDkeAjAJgbmdO/Qn9+rfgrmc0yG6rZabb/7n7+PPgnAGcencHB4fssDCqkTAQ0giB8U6ITohm2fxgzT81EQkKlUDGk0hCGVRn2zfRGhMeFs/LSShYELODK8yvydisTK9qVaEdJ+5IsOreIhecWAv8twXb3YUTVEV9t5ev119bz478/kjkmMzWoQfet3THKakSLYi0M3TQhgxABjSAI34wDdw/QdUtXeTlyqRylWNpkKW453Qzcsk8nSRJnHp/B76wff1/9m5jEGPk2txxudC/dHedszow9PBa/AD/5thbOLRhfYzxO2ZwM0ex0M/rQaL3MxUnbREAjJBEBjSAIX72I+AgG7RnEgoAFgG4Vz8iqI/m14q8Yq4wN3LpPExkfyerLq1kQsIALTy/I2y2MLWjj0gZfD1+ymGdh+IHh+G71lW+vkr8Kk2tNpnye8gZodfp7EvUk2bYXMS8M0BIhoxIBjSAIX7Wdt3bSbUs3OWV/udzlWNp0KcWyFzNwyz7NuSfn8Dvrx+orq/WKQ5awK0H30t1pV6Id8Zp4xh0ex4KzC+Sl6C52LkyuNZn6het/M0uwzz4+y8vYl8m2m6q+jSFEIX2IgEYQhK/Sy9iX9N/Vn+UXlwNgZmTG+Brj6VuuLyqlysCtS5vohGj+vvI3fgF+cuI/0F1bq+Kt8PXwpXye8kQnRjPtxDSmHp8qBzv5MudjbPWxtCvR7qu9ftANrT2KfMTlZ5e58vwKO2/t5OD9g3LOnDe9OQlaEERAIwjCV2fj9Y302NaDp1FPAaiavyqLmyz+alP1X3p2Cb+zfqy8vJKI+Ah5u3M2Z7qX7k4H1w7YmtuSqElk/tn5jDk0hmfRzwCwNbPlt8q/0atsL8yMzAx1CWkSHhfOledXuPz8MpefXebyc10QExYX9kH3z2GV4zO3UPiaiIBGEISvxovoF/Te0Zu1V9cCulU9U2pNwbe071eXGC42MZZ/rv6DX4AfJx6ekLebqEz4sdiP+Hr4UilfJRQKBZIk8e/Vfxm6fyi3Xt4CdL02/cr1Y1ClQRm+/lSCJoGgkCC9wOXy88sEhweneh+lQin3yuS0ysn/iv2P2Wdmy7crFArGVB/z2dsufD1EQCMIQoYnSRJrr66l947ehMSEAFCnUB0WNlpIfpv8Bm7dxwl8EYhfgB/LLy7nVdwrebtjVke6uXejY6mOZLPIJm8/cPcAg/YOkoeglAol3qW8GVVtFLkz5f7SzX8nSZIIDg9OFrgEhQSlWj8KIG+mvJSwL0EJuxIUy1aMTUGb8L/uD+ie53U/rsPa1Jofi//Isv3LCLsVxuLGi7+LqujChxMBjSAIGdrjyMf02NaDzUGbAchsmpnpdafTqVSnr2bSa5w6jvXX1uMX4CcnuQMwVhrj6eyJr4cv1QpU07uei08vMnjfYHbe2ilva1a0GRNqTMA5u/MXbX9KwmLDkgUuV55f0Rsye1tm08xy4FLCrgQl7EvgYuci9zBFJUTRal0rtt/cDkDHkh1Z1HiRvFLth3w/kLNKTn7d8iulcpb63JcofGVEQCMIQoYkSRJ/XviTn3f9THh8OABNnJowv+F8clnnMnDrPsyN0BssDFjInxf+JDQ2VN7uYOtAN/dudHbrjJ2lnd597r26x/ADw1l1aZWcd+WHvD8wpfYUKuat+EXbDxCvjicwJFAvcLn87DKPIh+leh9jpTHO2Z31ApcSdiXIkylPqkHo8+jnNFzdkLOPzwIwrPIwxlQf89UErYLhiYBGEIQM5/6r+3Tb2o3dt3cDutpDcxrMoVXxVhn+Ay5Bk8CGwA34Bfhx4N4BebtKoaJZ0Wb4evhS06Fmsjk/ITEhjD88nnln58kFJYtlL8bEmhNp7Nj4s1+3VtJy79W9ZIHLjdAbaCRNqvcraFNQr9fFxc4Fx6yOH5X/52boTeqtqsedsDsoFUrmNZiHb2nf999REN4gAhpBEDIMraRlwdkFDNo7SF6O3Kp4K2bVn5WsJyOjuf3yNovOLWLp+aV6Cd/yZ86Pj7sP3m7eKZYdiE6IZsbJGUw5PkUersltnZsx1cfgVdILI2X6v02HxIS8DlzeWF0UnRid6n2ymmdNNlxUPHtxrE2tP6ktpx6eotGaRoTEhGBuZM7almtp7NT4k44pfJ9EQCMIQoZw6+UtumzuwuH7hwHdktx5DebR3Lm5gVuWukRNIltubGHB2QXsubNH3q5UKGns2BhfD1/qFKqTYl4YtVbN0vNLGXVwlJwF18bMhiGVhtC7bG/Mjc0/uX0xiTFce3EtWa9L0pLvlJgZmVEse7Fkw0U5rHKkey/R5qDNtF7Xmlh1LNkssrG1zdZ3Fg8Niw1jz23d4xwRl/pcHeH7JAIaQRAMSqPVMPPUTIbtH0asOhaATqU6Ma3ONGzNbQ3cupTdf3WfRecWseT8EjkXDkCeTHno6taVLu5dyJMpT4r3lSSJDdc3MGTfEG6E3gB0GW/7lOvD4EqDyWKe5aPbo9FquB12O1ngcjvsdooJ6QAUKCiUpVCywKVwlsJfJDGf31k/em7viVbS4mDrwM52OymStUiq+18PuU6N5TWIfR5LDWrw478/srXX1q++RpWQfkRAIwiCwVx7cQ3vTd6cenQK0C3fXdh4IfUK1zNwy5JTa9Vsv7mdBWcXsPPWTnnCrgIF9YvUx9fDlwZFGrxziOjw/cMM3DNQvl4FCjqW6sjoaqPJlznfe9sgSRLPop8lC1yuvbgmB4MpsbO0Sxa4FMteDEsTy498FD6dJEkMPzCc8UfGA1A6V2m2ttmKvZX9O+/Xa3svnkQ9wQYbQDds1nN7T/Z57fvcTRa+EiKgEQThi0vUJDLl2BTGHB4jT4Dt7tGdybUnk8k0k4Fbp+9hxEMWn1vM4nOL9Vb25LDKQVe3rnR17/reXDiXn11myL4hbLu5Td7WyLERE2tOxMXOJcX7RCVEcfX51WRLo5Py8KTEwtiC4tmL6wUuJexLZJj5R4maRHy2+MjlKhoUacA/Lf95b2AlSRLnn5xPtv3ai2ufpZ3C10kENIIgfFEXnl6g86bOcuVoB1sHFjdeTPWC1Q3bsDdotBp23d6FX4AfW29s1Ru2qVOoDr4evjR2bPzelTzB4cGMODCCFRdXyD065fOUZ3KtyVTJXwXQ9fzcDL2ZLHC5E3Yn1eMqFUqKZCmSbJKug61Dhs2YHBkfSct/W8or17q6dWV+o/nvnfR8PeQ6/Xb2S7EcQiHbQp+lrcLXSQQ0giB8EfFqXWXoSccmodaqUaCgX/l+jK0+1iBDHyl5EvmEJeeXsOjcIr20/HaWdniX8sbHwwcHW4f3Hudl7EsmHpnI7NOzidfEA+CU1YlfKv5CbuvcnHp4isXnFnP5+WUCXwTK+6Qkl3UuXOxc9AIX52zO6TJp+Et5EvmEhqsbcv6prpdlVNVRjKg64p2TjCPiIxhzaAwzT81ErVUD6AU/VqZWzK4/O7W7C98hEdAIgvDZnXp4Cu/N3vIQQdFsRVnaZCkV8lYwcMt0S8X33tmLX4Afm65v0su5UqNgDXw9fGlWtBkmKpP3His2MZZZp2Yx8ehEORmghbEFua1z8yzqGT5bfFK9r7WJ9evAxf51TpesFlk//SIN6HrIdeqtrMf98PuoFCoWNl6It5t3qvtrJS1/XfyLQXsHyauxHGwdmF53Ou453Fl9ZDWnbp3i35b/4pbT7UtdhvAVEAGNIAifTUxiDCMOjGD6yeloJS0qhYqBPwxkRNURBq8M/Tz6OcvOL2PhuYV6wztZzbPSuVRnfDx8cMzq+M5jJBVdvPD0AmuurOHAvQPEqeP09olJjOHmy5vy70ZKI5yyOiUbLsqfOX+GTxr4sY4FH6PJ3014GfsSC2ML1v24jvpF6qe6/5lHZ+i9o7c8adrC2IKhlYYyoOIA+fXSslhLTnEKO6uMMS9IyDhEQCMIwmdx+P5humzuIleHdrV3ZVnTZbjndDdYmyRJ4sC9A/gF+LEhcINewcQq+avg6+GLp7NnsmArtaKL119cRy2pUz3fm0UXkwIXp6xOmBqZfrZrzCg2BG6grX9b4tRx2Fnasa3tNkrnKp3ivs+injF031CWXlgqb2vt0poptaaQN3PeL9Vk4SsnAhpBENJVZHwkQ/YNYe6ZuYCurs/wKsMZVGnQBw3bfA4hMSEsv7AcvwA/vd4SGzMbOpbsSDePbhTLXgzQJW87/ej0RxVdTOKY1ZEOJTpQrWA1vaKL35s5p+fQZ0cfJCSKZCnCzvY7U5x7lKhJZO6ZuYw8OFJ+fF3tXZldf7Y8aVoQPpQIaARBSDd7bu/BZ4sP98PvA1AmVxmWNl2a6tLkz0mSJI4EH8EvwI9119bJy8MBKuatiHcpb4rbFedm6E3+vPDnBxddLGhbkJjEGB5GPJS31y9cn4k1J1IyR8nPek0ZnVbSMmTvEKYcnwJAudzl2Np2K9kssiXbd++dvfTZ0YfAkEAAbM1sGVdjHN08un2Wcg/Cty9DvWoePnxIzZo137nPxIkT8fT0/EItEgThQ7yKe8WAXQPkIQMzIzPGVBvDzxV+/uIfTmGxYay4uAK/AD/5wxLA3MgcV3tXbMxsCA4Pxner7zuLLhawKaA3VJTdIjurLq9i+cXl8jLuMrnKMLnW5Ay15NxQEjQJeG/yZtXlVYCuMvqaFmuwMLbQ2+9u2F0G7B7AhusbAN0SdF8PX8ZWH/vVT4AWDCtDBTRZsmRhypQpybZrtVomTJiAJEmUKVPGAC0TBCE1m4M202NbDx5HPgagUr5KLGmy5L0TatOTJEmcfHiSGadmJJsbo1Qo0UpaYtWx8mTTN2Uxz5Isi66LnYtcdDEsNozJxyYz89RMecJvkSxFmFBzAi2cW3xzE3nTIjwuHM9/PNl/dz+gS5I4u8FsvWA2JjGGyUcnM+X4FPlxrJyvMrPqz6JUjlIfdJ4NgRtYfnA5KlTsv7sfB4f3L6EXvh8ZKqCxsLCgadOmybbPnTuXiIgIZsyYQd68YoKYIGQEITEh9N3Zl9WXVwNgaWzJpFqT6Fmm52dP7habGMu1F9c49egU66+t5/Sj00QlRqW4b1JvyscWXYxTxzHn9BwmHJkgJ3Wzt7RnZNWRdHXv+t6ket+LRxGPaLC6AZeeXQJgQo0JDK40WH5MJUli3bV1DNg9gAcRDwBdNfHf6/xOq+KtPjggXH5hOZ02dcImxoYa1ODX3b9inNWYjqU6fp4LE746GSqgSUlwcDDz58+natWq1K+f+nI/QRC+DEmS+Pfav/y0/SdexLwAoGbBmixqvIiCtgXT9VxvFl288vyKPEn31stb7yy6WNC2ICXtS6ap6KJGq2HlpZUMPzBc/gC2MrFiYMWB/FzhZ6xMrNL1Gr9mV59fpf6q+jyIeICR0oglTZbgVdJLvv3ys8v02dmHg/cOAmCiMuGXCr8wpPKQj34cJx+bnGzblONTREAjyDJ8QDN9+nQkSWLQoEGGboogfPeeRj2l57ae8vyHTKaZ+KPOH3Rx6/JJQy9pLboIuiEl52zONC/anMaOjSluVzxNmYclSWL7ze0M3jeYK8+vALpJwD1K9+C3Kr9lmHpIGcWhe4dotrYZr+JeYWVixfr/radOoTqALlPyyAMjmXd2nhx4NnFqwrQ60yiU5ePLFcQkxsjB5ZvC48I/7SKEb0qGDmju3LnDjh07aN68OYUKvfuP4F2TiZ88eULOnDnTu3mC8N2QJIm/Lv2lV1OnYZGGLGi0gDyZ8nzUsdJSdNFIaYRW0ur1yjhnc6ZXmV60d21PZrPMabuw/5x8eJJBewdx+P5heVvbEm0ZW33sB5U6+N78c/UfOmzoQIImgRxWOdjRbgelcpRCo9Ww5PwShu4bSmhsKKBbyj6z3sw0V1A/cPcAPlt8iEpIPqRYv7DotRdey9ABzerVq5EkiU6dOhm6KYLw3XoQ/gDfrb7suLUD0E2inVVvFm1LtH1nr8ynFF10zu6MJElcfX6VW2G35Fo+ZkZmtCreCl8PX8rnKf/JE3KDQoIYun8o/oH+8rY6heowseZEgyYAzMimn5hO/939AV0Jix3tdlDApgDHgo/Re0dvuV6TtYk1I6qOoE+5PmnKP/Qq7hW/7v6VxecXA2CkMKJI1iI8CX4CQPWC1Zleb3o6XZXwLciwAU1CQgIbN26kXLlyODk5vXf/ffv2pXrb+5aCC4KQnCRJLDq3iF92/0JkQiSgSzs/p/4c7K3s9fZ7HPk4WeDyvqKLOa1yJsuiq9FqWH5xOX9d+ksvkZ1zNmd8PXzxKumFrbntJ1/b48jHjD44miXnl8hLt91zujO51mRqOdT65ON/i7SSll92/8L0k7og4oe8P7C5zWZiE2Np799eXq4N0LFkRybWnEhO67T1jG+8vpGe23ryJEoXvFTIU4HFTRaTJ1MeVh1axd5bexlVbZSYzyToybABzenTp4mMjKRBgwaGboogfHfuhN2h6+auHLh3ANBVm57XYB61C9XmyvMrbLy+UW+uS9IwVEqsTKySVYsuYVdCzjkSmxgrTzI+8fCEfD8TlQk/FvsRXw9fKuWrlC7Lo8PjwplybArTT06X5+Y42DowvsZ4/lf8f599ddbXKk4dR8eNHfnn6j8AeDp7sqTxEvwC/Bh7eCzRidEAlM5Vmtn1Z1M+T/k0nedp1FN67+jNumvrAN3KuQk1J9CrTC/uvrqLyzwXIp9GUoMa/O/f/7G111YKZymcPhcpfPUybEBz6NAhlEoltWvXNnRTBOG7odFqmHN6DkP3DyUmMQYAl+wu5MqUiwG7B8gZgFOSWtHFfJnzpRgoBL4IxC/AjxUXV+gFRI5ZHenm3o2OpTqmmGE2LeLV8cw/O59xh8fJczuyW2RnRNURdPPoZrCSDF+DsNgwmq1tJs8v6l22N7UK1qLM4jJyna7sFtmZWHMind06pykolCSJ5ReX039Xf/m1UKdQHfwa+VHApgAAPbf15EHEA2ywAXT1n3pu68nuDrs//SKFb0KGDWgCAgJwdHQka1aROVIQPpekootXnl9h3919/HXpr2STc6+8uMKVF1f0tqW16GK8Op71getZcHYBR4KPyNuNlcZ4Onvi6+FLtQLV0i1ZnVbSsvryaobtHyYHY5bGlvxS8RcGVBggJ88TUvYg/AH1V9Xn6ourAPxa8VeuPL/C7NOzAVApVPQu25uR1UbKdatevHhBZGTkB5/jceRjxh0ex6mHuqSHeU3z8kvFX2hYpCGaUA27bu5i/939nLt2DhutDVZxumEmqzgr7ty5w507qc/J+hTW1tZkz579sxxb+DwUkiRJhm7E29RqNW5ubjRu3JgJEyZ88vGS5tC8a56NIHzrwmLDks1zeV/RxcymmZMFLmkpungj9AYLAxby54U/5R4S0A33dHPvRme3zum6LFqSJHbd3sXgvYO5+OwioOtB6ubejRFVR+jNARJSdunZJeqvqs/jyMcYK42pV7geO2/tlLMw1yxYk1n1Z8lFPUEXzPTu04fEhITUDvvVMDYxYfasWSKo+YpkyB6aJ0+ekJCQQK5cuQzdFOEro1Wrib13DwDzAgVQGmXIl/hnFa+OJzAkMFlOl3cVXUxiaWxJG5c2NHduTgm7EuTJlCfNvSUJmgQ2Xt/IgrML5Lk4oPtW37RoU3w9fKnlUCvd562ceXSGQXsH6Z3zf8X/x7jq4yiStUi6nutbtf/ufpqvbU5EfATmRuaYG5mz5cYWQFfjalqdaTQr2izZayMyMpLEhARyORTHxNwipUN/FRJiY3h85yqRkZEioPmKZMh3+7Aw3RiqtbXoDhY+XEJYGLcmTybuke6D2yx3bgoPGoSJ7aevismItJKWe6/uJQtcboTeeG/RxeLZixMeF86JhyfQSBoUKOhdtjfja47/5JUjd8LusDBgIcsuLON59HN5e/7M+fFx98HbzTvNq1/e5WboTX7b/xv/XvtX3la9QHUm15pMmdyiBtyHWn15NZ02diJRm4iJyoRYdSyx6ljMjMwYUmkIv1b8FXNj83cew8TcAnPLTF+oxYKgkyEDGldXV4KCggzdDOEr8+DPP+VgBiDu0SPuzJhB7tatUVlaYmRpicrSEqWp6VdXUDAkJuR14PLf/1dfXE0x2ViSlIouFrcrzo3QG3hv8uby88uAbhLukiZLqJSvUprbl6hJZMuNLfgF+LH79utJmkqFkkaOjfD18KVuobofVHrgYz2NesrYQ2NZeG6hnK+mpH1JJteaTJ1Cdb6659pQJEli6vGpDNr7Oit7gkY3dPRjsR+ZWnsq+W3yG6p5gvBeGTKgEYS3PX36lJiYmHfuc+v6ddTat+r73LrF7XHj9DYplEpUZmYozc1RmptjZG6OpbU1dlmzykHPmwGQysLidTBkZvZZPyCTii6+PdfladTTVO9jqjLVFV18a65LTqucem2NTYxl9KHRTD0+Fa2kRalQ8kuFXxhVbdR7v3Gn5v6r+yw6t4il55fKOUMA8mTKQ1e3rnRx7/LRmYQ/VGR8JL8f/50/TvwhLxsuYFOAcdXH0aZEG7EE+yNotBr67OzDvDPz9La72Lkwq94sqhesbqCWCcKHEwGNkOFFRETQu3dvtG8HK5/ireBIAfQzN8fifcGKSoWRhUWyQEcvAPqAYEij1XAn7E6ywOV9RRcLZSkkBy0udi6UsNcVXTRSvvtP+VjwMbw3e3Mj9Aag+6Ba2mRpmoZi1Fo1229uxy/Ajx03dyAhye2rX6Q+vh6+NCjS4L1tSqsETQJ+Z3X5T5KKY2Y1z8qwKsPoUbrHe1daCfpiE2Ops7IOR4OPytsym2ZmXI1xdC/d/bM9j4KQ3sQrVcjwMmXKxOzZs9/bQxN1+zYPV6zghVrN5oQEmpqZ4dapExYFCqCJi0MTG4s2NjbFn03VamwkCXV0NJqYGDTR0aijo9HGvlUYUaNBHRmJ+iOWpYaqYrll8opbZuHctojilkkYt5WhxCnUqd4nu2lWimcpSgk7F1xzlsI1t3uaii5GJ0QzdN9QZp+ejYSEkdKI3yr/xtDKQz8698rDiIcsObeExecX8zDiobw9h1UOurp1pat71886JKGVtKy9spZhB4bJ5RPMjczpX6E/v1b89ZPrOX2PLjy5QK2/aumtPPNx82F8zfFkt/y4ybB3w+6y8tLK9G6iIHwwEdAIX4UcOXK8fycHBwo7OXFpxw7YvZvSPXtS7IcfPum8kkaDJiYG9X9BTlKgo/fzf7dFRL3keux9ghIeEqR9yk3lC26bvCLMKPX0/2ZaFYUSbCicYEPh+P/+T7Ali8bsvz2igWOgPMGtt3uEknqK3u4d+m/7obAAeh36hbvh9wBdav+lTZZSMkfJD75+jVbD7tu7WRCwgK03tur1HtUpVAdfD18aOzbGWGWchkf3w+29s5dBewdx7sk5QLdSqqt7V0ZUHUEua7Ea8mPFJsYydN9QZp6aKfewFbApwPr/rf+oGlaBLwLxD/RnfeB6zj89j02MDTWo8bmaLQjvJAIa4ZtikS8fdrVrw+7dmKVDhXWFSoWRtTVGb6y40y+6+N98l+jL3E28i2QkpfhXpVQoKWSZD2dLB4qa5sXJKBeOkh25EyyRYmJ1QdMbgZImNhbeTBGl1aKJikITlfok4CSRygRmZT2Hf2ZdFlcTrZLu0WXwjqiA6bz13LTc+ToI+i/4eXu4LFSKZvWt9Sy4spR7Ea+zA2e3yI63mzc+7j4UylIo7Q/sBzr35ByD9w5mz5098rYWzi0YX2M8TtneX+NN0CdJEhuub+Cn7T/pzXnqXbY3M+vNfO/8MEmSOPfkHP6B/vhf9+d6yHW92z93YCsI7yICGkFIRXoWXXTO5vxRE28lrRZNbGzKvUIp9BYl/X9AHci4zId5bqQbnnONzcaI5xUomJgZDSHEEPKeM79WG6jBD0Qpy6I2VWGZOStZTXNjHGiFUfAhHlmefWdPkcrcHIUybRNz74TdYdj+Yay5skbeViV/FSbXmpzmOkHfu6vPr9J3Z1/23X2dYNRUZcrGVhupV6ReqvfTaDUcf3BcDmKCw4P1bs+bKS9OWZ04+fAkiTGJn639gvA+IqARBCAiPoIrz68ky+nyKUUXP4VCqcTovwDhQ7yMfUm/nf3469JOQDe3ZGy5oXQr2BopNu6dwVBcZDjhr56hjY7BQmOEktff0lUoyaw1hVggNpzop+EfcREKVObm+sFOKj1CSdtfKmOYfGkuCy8ukTPSuti5MLnWZOoXri+WYKfBq7hXjD44mtmnZ+vlJ8pplZM9HfZQ3K54svskaBI4cPcA/oH+bAzaqJdPCMApqxMtnFtQNndZZp2axd67ewHIYfwBQ8OC8JmIgEb4ppx4cII/j/4JwKWnl3BwcNC7PVGTSFBoULLA5V1FF1UKFU7ZnJIFLvlt8meIpcHrr62n1/ZePIt+BuiSyS1qvOidQ0KSJHHw3kEWBCxgQ+AGEu10wYNCgtq5q9K1aHtq5ayMKl6dvJfo7eGxNwIlvWEySdLNL4qJgRcv3nkNMYpEVtoE8pftNWKUusnSOdSW/BT/A00feWC67iJ3LW+9f3XZf4FRWnuGviVaScuy88sYsm+IvBosiau9K9vbbid3ptzytpjEGHbd2oX/dX+2BG0hPF4/eHXL4YansyctnFvgmNWRGSdn0GZ9G7lquQIFcYlxn//CBCEVIqARvhlbb2yl2d/NsI62pgY18N7szfn485gbm8uBy/WQ6/I3/5QkFV10ye4iBy5FsxXNkEuBn0U946cdP7Hu2joArE2smVp7Kj4ePqkGWiExISy/sJyF5xbKS7gBbMxs6FiyI908uunV5vkYklaLNi7uvcNjb26Pi47gX20AfpZnCDXSfRhm0pjgHebC/8KdMJVUJPCAj60MpDQ3T3Hp/If0FClU6Z/870s78eAEfXb24ezjs4CujlVS0sGaBWuy/n/ryWyWmfC4cLbe2Ir/dX923NwhByegC1Aq5q2Ip7MnzYs2p6BtQUD3RaHE/BIEhgTqnVNC+uhVeIKQnkRAI3wzhu0fptelLkkSYw6PSXHf9Cq6aAiSJLH68mr67OzDy9iXANQvXB+/Rn7kzZw3xf2PBh/FL8CPf6/9K2d/BaiYtyK+Hr78WOzHNCfXS6JQKnWBgoUFvKf+jSRJrLu2jqH7h3LrpW7yspmRGT+5duNnpy5Yq41SDYzeXl2mjo6Gt3IUaWNjSYiNhZAPnzOURGlmpj8XKJV5QhkxGHoS+YTB+waz4uIKeVtu69xyHa92JdoxqdYk/r32L/6B/uy9s1cvwDdSGlG9QHU8nT1p6tRUr0TFjZAb+G715eD9g3rnVKCgXuF6eJX0wtXEleFDhn/eixSEVIiARvhmvD3On8TV3jXZcNGnFF00pEcRj+i+rTtbb2wFdD0rM+vNpINrh2TXExYbxoqLK1h4biHXXlyTt2cyzUQH1w74evhSwr7EF20/wIG7Bxi0dxBnHp8BdCvAvEt5M7LayDRlFZYkCW1cXIqBTmoB0JuBEhr9ulfauDgS4uIgNDSVM6buQ4OhZNstLFB8QiHVBE0Cs07NYsyhMUQm6HIkudi5oJW08nNfq2AtHoQ/IP+M/HrL782MzKhbqC6ezp40cmxEFvMs8m2R8ZGsD1zP7FOzOff0nN45HWwc6FmmJ21LtJUDnzt37qT5GgThU4mARvhmVMpXSa8wIeiW+K773zoDtSj9SJLEkvNLGLB7ABHxEQA0L9qcuQ3m6n2LliSJkw9P4hfgx9qra4lTv57TUCZXGXw9fGnt0togQwMXn15k8L7B7Ly1U97W1KkpE2tOxDm7c5qPq0iafGxujkm2bB91X0mS0MbHo4mKyhDBUKoBUGrzhiws2Pf0GH33/CwPIWazyEa/cv1YeG6h3oqkpIm7oBuebOTYCE9nT+oVrqdXkFSj1bD/7n6WX1zO+mvridPoz4spm6sss+vPpmyesh99jYLwOYmARvhmzG0wl+DwYIJu6gqbuti5ML/hfAO36tPde3UPny0+7L2j+0DKbpGdOQ3m8GOxH+VemfC4cFZeWolfgJ9cdBJ0K7HalWiHr4cvbjndDNb+4QeGs+rSKjmJ2w95f2Byrcn8kO/TEh9+KoVCgcrMDJWZWdqDobcCnbcnTac2mVpKIRjSxsWR+JHBkB2wROFGpKo4SgsLrluEM3zfGCKVCagkBdWj8pJFY4ZkZopLAQ8qFKlOuSJVMM9kqyvJ8V/P0NXnV1lxcQUrL6/kceTjZOcpZFuINS3WiMrlQoYlAhrhm5HdMjvHuxznwLkDzJswj2XNln10+vaMRCtpmXdmHoP3DpaLL7Yt0ZaZ9WaSzSIbkiRx5tEZ/AL8WHNlDTGJr0tDuOVww9fDl7Yl2mJtap3aKT6rkJgQxh8ez7yz8+R5O87ZnJlUaxKNHRt/lUN+b9ILhrJ+3FJ9ORj6yB4hORhS65fNsJCMsVAbc1T9iMEWh4lTasisMWH6k+qUjHvjb+ChGo7u4RavExVqjJREKOMJJYoiygS6GxVgXeYELpnr5h+ZKkwYVMibPs5dMI3OROyjR6/rkxmLRHpCxiECGuGbolQo5dUYGWFJdVrdCL1Bl81d5IKBOa1ysqDRApo4NSEyPpKFAQtZcHYB55+el+9jYWxBG5c2+Hr4UjpXaYMFDNEJ0cw8NZPJxybLw2O5rXMzpvoYvEp6iWKH6AdDZMny/jugC4L+vvI3v+z6hZeRz7DWmmCjNcdSoyKT1pRXyjgum4UgKSA71syz7kypTHn1g6OYGKRE/VV+KrUWW4yxwYY9VveZni1ALtfhHmvHsOflyX8zivs7Zya/DhMTvVVjT99cti8IX5h4ZxGEDEStVTP9xHRGHBwhz3/xLuXNH3X/4G7YXbpv7c6qy6uISnhdAqGEXQl8PXxp79reoAUa1Vo1S88vZdTBUXJa/cymmRlaeSi9y/b+5FVU37NTD0/ReVPn10ullRCnjOUFumXWdpZ2PI/W9ai45XBje7vt5LB6neROkiROPTrF8gvL2XB5M5qYGDJpTMikNcFeZUPp7G4c1lznSNQlAKwUZgw0aUhLRQkku1g5IHo7GJISEkhMSCAxTJeAMkqbcqV4QfgSREAjCBnEledX8N7kLa/+yZc5H7PrzSYkNoS6K+ty+tFpeV8zIzNaFW+Fr4cv5fOUN+jwTVJ9oKH7hhIUqpu/ZKoypXfZ3gypPERv1Yzw4SLjI1l7dS2Tjk7idtjtZLeXz1Oepo5NOff0nDwZvm6huvz747/yMOP9V/f569JfrLi4gpsvb8r3VRorKVO0Gh1cOxAaG8pv+3+Te9MaOTZifsP5Ka440yYkpD5HKDqauMeP4cCBz/FwCMJ7iYBGEAwsUZPIpKOTGHt4rJwTpHXx1libWtNhYwf5gwZ0c1B8PXzxKumFrbmtoZosO3z/MAP3DOTUo1OALidJx1IdGV1tNPky5zNw674+oTGhbA7azPrA9ey6vUtOhpekVI5SdHXrSrOizchslpn//fs/dtzaAUDHkh1Z1HgRceo4lp1fxopLKzh476De/V3tXelYsiNtS7QlMj4Sny0+HLp/CNBNNp9VfxatirdKNUBWmpigNDHB2MYmxdtj79wRAY1gMCKgEQQDOvfkHN6bvLn47CIA9pb2ZLPIxt9X/5b3MVGZ0LJYS3w9fKmcr3KGmEx75fkVhuwbIufDAd03+wk1Jhgkt83X7FHEIzZe34j/dX8O3TuklxwSdMnuWhZrybQ60+Ql+s+inlF9eXU5E/DQSkOpkr8KnTd1xj/QXy/jr72lPe1KtMOrpBclc5RErVUz7cQ0Rh4cKQ9rtndtz/S608lm8XErvQQhIxEBjSAYQJw6jjGHxjDl2BQ0kgYFCkxUJjyLfibXZCqSpQi+Hr50LNUxw3zQBIcHM/LgSJZfWC4vwS6fpzyTa02mSv4qBm7d1+P2y9ty9eqTD0+mul8H1w78Xud37Czt5G03Qm9Qf1V97oTdQalQUqtgLf68+CcTjk6Q9zFVmdKsaDO8SnpRp1AdeSL2hacX6LK5C+ee6JLk5cucjwUNF1C/SP3PdKWC8OWIgEYQvrATD07QeVNneb4J6OrgxGviMVYa09y5Ob4evlQvUD1D9MaArpr3xCMTmX16NvEa3QoYp6xOTKw5kWZFm2WYdmZUkiRx5fkVOYi59OyS3u3mRuYkaBLk3plyucsxu/7sZDlfTj48ScPVDXkZ+xIFCrSSlt13dsu3V8pXCS9XL34s/qNeGY+UAuheZXoxoeaEdF3WfzP05vt3EoTPRAQ0gvCFxCTG0GtbL/68+Gey2xxsHejm3o3Obp31vo0bWmxiLLNOzWLi0Yly9eWcVjkZVW0U3m7eYgn2O2glLWcfn9UFMYH+epNyAXJY5aCUfSkuPL3A0+ingG54aErtKbR3ba+XdiBeHc+IgyP4/fjvctmCpB4yB1sHvFy9aO/aPsUK60fuH6Hrlq5yJuGi2YqypMkSKuatmG7XGvA4gHFHxnHw3EFqUCPdjisIH0O8GwnCZ5agSWD84fFMPT5Vb26DEiXNnJvh6+FLLYdaGSpvjlqrZvmF5Yw8OFIubJjJNBODfhhE33J9RVXlVKi1ao4GH8U/0J8N1zfwMOKh3u0FbArQwrkFHjk9WHZhGTtv68pAGCmN6FeuH8OrDieTaSbgdRmLFRdXsOLiCmLUrxMnWhpb0rZEW7xKevFD3h9S7CGLiI9gyN4hzDs7Tz7H4B8G81uV3zAzMkuX6z358CRjD49l+83tANhgky7HFYS0EAGNIHwmd8LuMOfUHBYELNALZDKbZqZ/+f74ePjo1WHKCCRJYsuNLQzZN0QuamiiMqFXmV4MrTw0w8zlyUji1fHsu7sP/0B/NgVtIiRGv8J38ezF8XT2xNPZkwKZCzD28Fi8NnrJK5jqFqrLjHozKJqtKKArFbHy0spkS61B99qZUmsKHUp2eGden203ttF9W3c5oCqdqzRLmizB1d41Xa758P3DjD08Vi7HAboVVL0K92LXrV3pcg5B+FgioBGEdJSoSWTLjS34Bfix+/ZuvdtszWyZUHMCPu4+qJQqA7UwdceCjzFo7yCOPTgG6JZgt3dtz5jqYyhgU8CwjctgohKi2HlrJ/6B/my9sVWucJ2kdK7SeBbVBTFO2ZzQSlpWXFxBvZX15EnfDrYOzKg7g0aOjYhMiEx1qXWSmgVrsqn1pnf2jr2IfkG/Xf1YfXk1oJubM7b6WPqW7/vJw4OSJLHv7j7GHh7L4fuH5e0eOT0YXmU4jZ0ac+/uPXYhAhrBMNI9oHn16hXXrl0jV65cFChQIL0PLwgZ0v1X91l8bjFLzi+Rs+QmMVIaMaTSEEZWHZkhA5lrL64xdN9QNgVtkrfVL1yfiTUnUjJHSQO2LGMJiw1jy40t+Af6s+v2Lr1K5goUVM5fGc+injR3bq6Xg+f0o9P03tFbToxoYWzBb5V/o2+5vhwNPkr7De3ZELhBrxcvu0V2LIwtuB9+H4Cubl2Z32h+qkGJJEmsubKGvjv7yj1E1QtUZ1HjRSnOq/kYkiSx49YOxh4eq7ciq0KeCgyvMpx6heuJSeFChvBJAc3mzZv566+/WLVqFSYmJpw8eZIePXoQF6f7Q2/WrBnjx49Hqcw4cwMEIb2otWp23NzBgoAF7Li5Q56k+abK+SqzpMkSimQtYoAWvtvDiIeMOjiKZReWyRNNy+Qqw+Rak6lesLqBW5cxPI16qssRE+jPgXsH9BLdGSuNqelQE8+injRxaoK9lb3efZ9FPWPIviEsu7BM3tbGpQ3ebt7svr2bIrOL6AW/ZkZmNCvajEZFGjH1+FQ5N9HoaqMZXmV4qkHDg/AH9NjWg203twG6Yanf6/xOF7cunxRoaCUtm4M2M+7wOAKeBMjbq+avyvAqw6lRsIYIZIQMJc0Bzc6dOxk4cCCmpqaEhISQK1cuRo8eTVxcHJ6enjx69IiNGzfi7OyMl5dXerZZEAzqUcQjFp9bzOLzi/UmfZoZmcnf2q1MrJhcazLdS3fPUJN9AV7FvWLS0UnMPDVTbm/hLIWZUGMCLYu1/O4/pO69useGwA2sD1zP8QfH9QJVcyNz6hepj2dRTxo6NtRbGp0kUZPInNNzGHVolJzluXj24tQoWINjD45R+6/aevtXzlcZr5Je/FjsR55EPaHeynrcD7+PSqFiYeOFeLt5p9hOraTF76wfg/YOkoe8mhVtxtwGc8llnSvN16+VtKy/tp5xR8bpLS+v7VCb4VWGUzl/5TQfWxA+pzQHNH/99RfZs2dn3bp12Nvbc+XKFe7evUu9evUYP348AD/++CP+/v4ioBG+ehqtht23d+MX4MfWG1v1srmWsCvB/fD78odXnUJ1WNhoIflt8huquSmKU8cx9/Rcxh8ZT1icrpigvaU9I6uOpKt7V4xVxgZuoeEEvgiUc8QkJZ1Lktk0M42dGuNZ1JO6hetiYWyR6nH23N5D35195SKSViZWFLApwLXn17j64qq8X9JS6w4lO+Bg6wDo5jA1+bsJL2NfYmlsyb8//ptqwrugkCB8tvhwJPgIoCtOObfBXFo4t0hzQKrWqll7ZS3jj4x/XQQTaFikIcOqDKN8nvJpOq4gfClpDmiuX7+Op6cn9va6btaDBw+iUCioXfv1t48yZcqwevXqT2+lIBjIk8gnLD2/lEXnFsnzGUA3x+HHYj9yI/QGe+/qVnpkNs3M9LrT6VSqU4bq5dBoNay8tJLhB4bzIOIBoPugHVhxID9X+BkrEysDt/DLkySJc0/OyUHM9ZDrerfbWdrRzKkZns6eVC9YHROVyTuPdyfsDgN2D2Dj9Y2Abk6NscqYqIQorjy/AuheH/8r/j86luxIxbwV9V4j/oH+tF3flnhNPHaWdmxru43SuUonO0+iJpHfj//O6EOj5QSHnUp14o86f6S5CGiiJpGVl1Yy4egEbr28JW9vXrQ5w6oMwz2n+wcdR5IkTj5IPeuxIHxuaQ5oJEnC2Pj1N7ojR46gUCioUKGCvC0uLg5z89SXFgpCRqSVtOy7sw+/AD82BW3SmzdRvUB1fD18iUyI5Nc9v/Iq7hUAjR0bs6DRgk/q6k9vkiSx/eZ2Bu8bLH+oGiuN6VG6B79V+S1DJfD7EjRaDccfHJeDmODwYL3b82bKi6ezJy2cW1Axb8UPmsAdkxjDpKOTmHxsMgmaBHm7hESCJgGVQkXdwnXpWLIjjR0bp7jUes7pOfTZ0QcJiSJZirCz/U651+ZNAY8D6LqlKxeeXgB0OW38GvlRp1Cdj3wkdOLV8fx54U8mHZvEvVf3AF0g9mPxHxlWedgH1+SKSYxh5aWVzDw5k8fBj0ViPcFg0hzQFCxYkFOnTiFJEvfv3+fy5cu4uLiQJYvuW0J4eDh79uyhYMGC6dZYQXgfdWQk4Rcu6H6Ojv6o+z6Pfs6y88tYdG4Rt8Nuy9uzmmelU6lOdPPohqnKFN+tvuy6vUu+bXb92bR2aZ2hemVOPjzJoL2D9JbXtnFpw9jqYz951cvXJEGTwIG7B/AP9Gdj0EaeRz/Xu90xqyMtnFvg6eyJR06PD34OJUli+cXlDNg9gJexL5PdXipHKbxcvWhTog05rHKkeAytpGXI3iFMOT4F0NXE2tJmS7JcP7GJsYw6OIo/Tvwhly3oW64vY2uMTVPvWmxiLIvPLWbK8SnyHDClQknbEm0ZWmkoztmdP+g4DyMeMvf0XOafnS9nkRaJ9QRDSnNAk7SCqV69erx8+RJJkmjdujUAGzduZMaMGYSEhDB06NB0a6wgvEvso0fcnDCBx69eAXB39mzyjhyJee7cqd5HkiQO3juIX4Af/oH+JGoT5dsq56uMr4cvLYq1wERlgt9ZPwbuHUhUQhQArYq3Ylb9WRmqpyMoJIjf9v/G+sD18rbaDrWZVGvSBw8dfO1iEmPYdWsX/tf92RK0Rf6wTeKWw01OdOeczfmjAlGNVsPCgIWMOjQqWXBkZ2lHB9cOeJX0em8Cu3h1PN6bveV8MU2dmrK6xepk83MO3juIzxYfeSioWPZiLGmyJE3zWaITollwdgG/n/idp1G6UgtGSiO8XL0YUnkIhbMUfu8xkrIXjz8ynu03t6e4sk8QDCXNAU2HDh2IjY1l6dKlKJVKfHx88PT0BODhw4fExMQwbNgw6tcXVVyFL+PB8uWoIyLk39XR0dyaPBmbMmVQqFSgUKBQqVAolcRq4rjw/BKnHp/maewLjJBooXDA1NiMMnnLU7lAFXJnzgMRSq4c3Ey/a5M4FnYeAHvTbPxeaihNctdCce8FkaqXKJRKFEolKJXyOfhvm0KphP+2vb3Pm/uhVKa5l+dJ5BNGHxrN4nOL5QnL7jndmVRzErUL1X7Pvb9+4XHhbL2xFf/r/uy4uUMvp4sCBRXzVsTT2ZPmRZtT0Pbje42vPL+C31k/ll5YSkzi6xIESoWS+oXr06tML2oXqv1ByevC48JpvrY5B+4dAKBH6R7Mrj9bb4grPC6cgXsGsvDcQkA3VDi08lCGVBqCqZHpR7U9Ij6CuafnMu3kNDlHjYnKBO9S3gyqNOiDkiYmaBJYcm4Jk45NSjZUB5AnUx7alWhHtUzVWDR50Ue1TxDSyyfloenWrRvdunVLtr19+/b4+vrqzbERhM8t7vHjZNsSw8J4sXt3CntDQaAgeYG8+jc8iUZ9egd30PJ35iDmZr1AvFIXJDSKcKB/iAeZr57jFueSHfOT/Rd0vRkMvf07b2yLUiawzPg0y5WniVPo5vrkxZafVXVoEOqKav15bqkuJQue3g7wPiT4SrbPm2397/dk902h/W/+/s59FIqUr/+/cz2Pfs7moM34B/qz985evd41lUJFjYI18HT2pKlT0zSVmHge/ZzVl1ez/MJyLjy7oHebuZE5vh6+jKo2isxmmT/4mI8iHlF/VX0uP78MwIQaExhcabBeILs5aDM9tvXgcaTu9Vw2d1mWNFmCi53LR7U/LDaMWadmMfPUTHlVm5mRGd3cu/HrD7+SJ1Oe9x7j9svb/LrnV7bd3KY3RwjA2sSadiXa0aFkB8rnKY9SoeTOnTsf1UZBSE/pkik4OjqaGzduEB4eTrVq1VAoFCKYEb44sxw5iArXH15QWVtj6lCAh+HBPHgVTGx8NCqUKCUFpgpj7CyyY2+eHTOVKZJWCxoNklbLLV4wwmwvl4x0XfM5NFYMC6/MD7F5wEiDJElIGg1otel7EZKEpNYFJu/qzE9Aw7rMN1ic5QrhKt1qF1u1KT5hrniGF8YYFdEEvuMIXzctEhq0FFNIFCUTgxTN0SKhMjLG1NgMMxMLVGFGKC7e4qVyOmHvCtCSAiaFAq1SwdPoZ9yPDOZR1BPUaGmuMKcx5dEqJFAocc1ZknJ5y6N6aUzUxh1EpRTEpdAT9zj6Kb+fmkaeuCjyKhzoVqY7VcyqEX7+PAqlkvCECOaenc+++wewU2jJY5SHbmW68z+X1qhijYh98CDV4PbNc4fGhzH7zFxmn51DeIKux9LC2IKepXsyoOKAVOf0JIlNjGXumbnMOT1Hb2Uf6Iao6jjUoU+5PtR0qCmqrQsZikKSpDQPgoaEhDB+/Hj27NmDRqNBoVBw7do15s+fj7+/PxMnTqR06eRLD7+0mjVrArBv3z4Dt0T4nG5dPcmjqTOISlSxNC6O1hZG7K+q5c9nW/XS1JfJVQZfD19au7ROVhcnUZPI1ONTGX1otPyNtLtHdybXnixXQX6TJEm6IESrBa0W6b+ACK0W6b/f5Z/ftc9/AZLe9hTur1Unsu7pHibeXcKDeF1NIAulGd3tm9PDrimWCjNdUJbUpv+Omeq5U9tHkj7ueiTpveci7W81wifQKkCpMtJlbE+lBy1RKXHK+AGbVFc4ZHSHWOUbGZElJeXV+WhuXIbapq6YqUxT7Vl7GBXF9BMnyOlQDFPzr7cie3xsNE/uXGPq1Kk4OCRfcSZkTGkOr1++fEmrVq149OgR7u7uxMfHc+2arjqvubk5jx8/xsfHh7///hsnJ6d0a7AgpKb3hdGcyXuQH8IKY/TEkV/tD3L/kW7ippWJFe1KtMPXwxe3nG4p3v/C0wt4b/Lm/FPdXBkHWwcWN178zjIACoVCHhr5nCRJYtftXQzeO1hOiW+kNKKbezeGVx3+3m/dGUGyQOrNoOeNgE7SaAh8fo29t3az//Ze7r28ixIFShQYSQpsTGyokucHKuX9gVJ2JTFWqJIHaG/0oKUWcIXFhHLx8QWuPLtERMwrlChRSQpUKORePBUK7M2zUy5nWbKa2eq3/R0B45v7RMS+4mnEE5QSmCiMsDPPjpFChaTRoNWoiU2IAa32v/MpUZG+q+WUEqBW83ZfohaJC2bP2Wl9j72WwXJPH4BKUlA+Jif1IgtQNTovlpKuxz2BIBJIXfh/PZZP7lxL12sQhA+R5oBm1qxZPHnyhPnz51O9enXmzJkjBzSdOnXC2dmZrl27Mn/+fGbMmJFe7RWEVF14eoFQozgOWz6kBo6EqxIwUhoxp/4c2pZoi7WpdYr3i1fHM/7IeCYenYhaq5aXxY6rMe6dlY2/lDOPzjBo7yB5EinA/4r/j3HVx2XIGlGpkXsFUrhNK2k5+fCkLkdMoD93X919faMZ5LbOTfOizfF09qRy/sppHuqIiI9g3bV1LL+4/PWSdgvdv4I2BYmMjyQkVjdxNrtFdibVmkSnUp3SXL5i+onp9N89CWyhaLai7Gy3k/w2+dFKWuaensuQfUOITtSlF2hZrCWz68/G3tI+ee9dCr1jj8KCWXh2IRuurUerUaNEQRYTG9qXaEdLJ08sjMxfB1z/BXRajYYLr66x/tEu1j/exdOE0NeNlSCbxpzyJk745PWkbDa35AHoWz2Kb/cmxrx6BadOfTM9NMLXJc0Bzf79+6lduzbVq6f87bVcuXLUqVOHgICAFG8XhPSWL3M+eTlqEo+cHviW9k31PqcfncZ7k7eclr5otqIsabKEinkrfta2foiboTcZdmAY/1z9R95WvUB1JteaTJncZQzYsvSRqEnk8P3D+Af6s+H6hmRVygvZFpJzxJTJXSbNQYVaq2bvnb2suLiCDdc36A0/5rDKQYPCDbgReoOjD44Cup6v3mV7M6LqiBRrNX0IraRlwK4BzDg1A4BK+SqxqfUmsphn4dqLa3Td3JUTD0/IbZjXYB7NnZu/PoBKpRvOSWEu4u2Xt5l4fCLLLy7XJX00AvvM9vxS8Re6l+6eYm6a6yHXWXN5DWuurOHmy5vJbjdWGtPGpQ1DKg+haLaiabpmAO2dO3DqFKbmlphbJh+iFYTPKc0BTVhYGHnz5n3nPvb29rx8mTzplCB8DtPqTKO/X2t+CMvPfaC42o4pdaeluG9sYiwjDoxg2slpaCUtKoWKgT8MZETVEZgZmX3Zhr/lWdQzxhwaw8JzC+UsxSXtSzK51mTqFKqToRL4faw4dRx7bu/B/7o/m4M2J0tK52rvimdRXY4YFzuXT7rWy88us+LiClZdXpWsqnXzos1pWawlJx6cYOapmfIKqVoOtZhZbybFshdL83nj1HF03NhRDkRbOLdgpedKlAolYw+NZdyRcfL8rC5uXZhaeyq25rbvPW5QSBDjj4xn9eXV8tL83Na5GfjDQHzcfZJlIQ4OD2btlbWsvrJazi78tlxWufi5ws90cevyQW0QhIwszQFNjhw55CGm1Fy6dIkcOTL+2L7wbSgeYs78B9V5otGwlDiGPC1NsRdmyVZlH7l/hC6bu8jfVF3tXVnaZCkeuTwM0OrXIuMj+f347/xx4g95GCJ/5vyMqzGOtiXaZriq3R8qMj6S7Te343/dn203tsnXlqR8nvJ4FvWkuXPzD0ru9i7Pop6x5soall9cnuxDvEr+Kni5euHp7MnWG1vptb2X3KNXwKYA0+tOp6lT008KosJiw2i2tpk8nNWnbB+m1Z3GuSfn6LK5i7xc28HWgYWNFlLToeZ7j3nl+RXGHR7HP1f/kRPZ5c+cn8GVBtO5VGe9vDQvol/w77V/WXNlDUeDj6Z6zEp5K/FzhZ9p4tRErFQSvhlpfiXXrVuXJUuW8Pfff8sZgt+0bNkyAgIC6Ny58yc1UBA+1ON165KtpHn099/YuOsy5EYlRDFk7xDmnJkD6LrZh1UZxuBKg99bfPBzStAk4HfWj7GHx/Ii5gWgK6kwrMowepTu8dGJ1DKC0JhQXY6Y6/7sub1HLqQIumR0VfNXxdPZk2ZFm31QPpR3iVPHsSVoC8svLmfnrZ16ldAL2RbCq6QXHVw7UNC2IAGPA2i4uqE83GNuZM6QSkP4peIvKdZZ+hjB4cHUX1Wfay90X/Sm1p5Kj9I9GLR3ENNPTkcraVEqlPxc/mfGVB/zzqrdAOefnGfckXH4B/rrXc/QykPp4NpBro4eER/BhsANrLmyhr139upd/5uMlca0LdGWvuX6pjox/lO9OZwnCF9amgOa7t27c+jQIUaPHs2qVavQ/je7ffDgwVy9epVbt26RL18+unfvnm6NFYR3UUdGJtsW/+QJ1wYP5pKjEYNe/klw9CNAt3R7adOlH52sLD1pJS3/XP2H3/b/xp0wXUIycyNz+lfoz68Vf/2ohG0ZwaOIR2y8vhH/6/4cundI74PVRGVCbYfaeDp70sSpSbJ6RR9LkiROPDzB8gvLWXt1rV55g8ymmWlVvBUdS3WkQp4KchI+n80+LDm/RO7l+F/x/zG19lTyZc73SW0BuPTsEvVX1edx5GOMlcYsb7Yceyt7XBe4ys9tCbsSLGmy5L3zn04/Os3Yw2PZemOrvK1otqL8Vvk3Wru0xkhpRGxiLJuCNrHmyhq23dimFzCqFCq9x97e0p4epXvQvXR37K3sP/laU3Pv1T28N3mTm9RLjQjC55TmgMbKyoo1a9bwxx9/sGnTJmJidOnAN27ciImJCU2bNmXgwIFkyiQmhglfhpWTE69OndLbloiGMQnr2PRAV2zSRFIxIHMz+pf6FdtMnza88Sn23tnLoL2DOPdEl21YpVDR1b0rI6qOyFAVu9/n9svbcvXqkw9P6t1maWxJgyIN8HT2pEGRBinm8flYd8Pu8telv1hxcYVeAVGVQkX9IvXxcvWisVNjeR5UoiaR+WfnM+LACDnoKWFXgln1Z1GtQLVPbg/A/rv7ab62ORHxEWQyzcTK5ivZFLSJJeeXALpgbljlYQyqNOidPYFHg48y9vBYdt9+ndnaxc6F4VWG08K5BVpJy57be1hzZQ0br28kMuF1AG+kNMJIaUScOk4OZtxyuNGvfD9aFW/12Xv59t7ZS+t1rdGEakRAIxhMmgOahw8fkidPHkaOHMmwYcO4e/cuERERWFhY4ODggImJ4brwhe9THq8OzA/dxPaIQIreK8emHI84lOUWN9S6XpmSsdkZ8bw8BRLNCT43hwfGxlgXL46NmxuZ3Nwwsf38kyLPPTnH4L2D2XNnj7ythXMLxtcYj1O2jJ+vSZIkrr64Ki+vTsqJk8TWzJYmTk3wdPaktkPtTx7GAV1do3XX1rHi0gq96uGg+9D2KulFG5c2yXof9t/dT58dfeQVbDZmNoytPpbupbun27yR1ZdX02ljJxK1ieS2zs0vFX7Bd6uvPAm5Qp4KLG6yONVJxpIkceDeAcYeHsvBewf1rmt4leE0dmrMiQcn+Gn7T6wLXCfXYgJdEJfTOidPI5+i1qpRa9UoFUqaF21O33J9qZSv0mefQC5JEr8f/53B+wajlbTkMfm04UNB+BRp/qv28vKiRIkSzJw5E5VKReHChvu2KwgAv1+cy+/sxcbIhqLAv0YXeaV+haWxJZNqTaJL/pZEXrhI+LlzRF6/jpSYSMSFC0RcuADLlmHh4EBmNzcyu7tjnjdvun4Y3Am7w7D9w1hzZY28rUr+KkyuNTlNlZO/JK2k5ezjs3IQ8/ay3xxWOeQcMVXzV5XndnyK9y21bl+iPV4lvShhXyLZfe+9uscvu3+RK44rUNDNoxvjaoz75KGuJJIkMeXYFAbvGwyAU1YnCmUpxM+7fwZ0vVMTa06kZ5meekUn37z/rtu7GHt4LMcfHJe3l8tdjmGVh5HTOid/X/mbPjv78DDiod59i2cvjlqrJig0SL7NxsyGrm5d+ansT+S3yZ8u1/g+UQlRdNncRV7NZaIyISom6oucWxBSkuaAJiQk5L3LtgXhS1pybkmybeZG5lzucVmusGxeNwd2deuijo4m4tIlws+dI+LSJTQxMcTcuUPMnTs8Wb8ek2zZ5ODGqmhRlEZp+1N5Ef2CcYfHMf/sfHlpsIudC5NqTqJBkQYZdgm2WqvmaPBROUfM2x+qBWwKyDlikgoTpofLzy6z/OJyVl1epZdTKGmpdceSHVOtIRSbGMuUY1OYdGySHAD9kPcHZtefna6TYDVaDf129pMnlztldeJp1FOCQoMAqFuoLn6N/FIMLCRJYsuNLYw7PI4zj8/I2yvnq0ynkp24H36fAXsGcCP0ht79StqXpIBNAc4/PS/3OCWdu2+5vnQo2SHF/DOfy43QG9T9qy73wu/J2xI0CVjw7onOgvA5pTmgKVOmDMePHychIUEMLwkZQkqrO+ws7eRg5k1GlpZkqVCBLBUqoFWriQoKIvzcOcLPnSMhJISEkBBe7NnDiz17UJqbk9nVlczu7mQqWRIjy/dnQI1KiGL6ielMOT6FqATdt9a8mfIytvpY2ru2T/Fbu6HFq+PZd3cf/oH+bArapDe8AVAsezE5R0ypHKXSLRh7FvWM1ZdXs+LSihSXWncs2ZGWxVqmOgdHkiT8A/0ZsHuAXEwxp1VOptaeStsSbdM1aIxNjKWdfzs2XN8A6CbcJgUyWcyzMKPuDNq7tk92Tq2kxT/Qn3GHx+kN0/2Q9wdK2JXg1KNTdNnSRe8+TlmdqFuoLmGxYWwM2qh3v7qF6tKvfD/qFKrzRZfzP458zOC9g1l1aRXaN4opZDHLQme3zjTI1oC54+d+sfYIwpvSHND8+OOPjBs3jrp161K5cmXy5MmDmVnKCcm8vLzS3EBB+FDtSrRjwtEJyba9j9LIiEzFi5OpeHHytG9P3MOHvDp3jvDz54m5fRttbCxhp04RduoUKJVYOTlh4+5OZjc3TO31520kahJZfG4xow+N5lm0rnikrZktv1X+jV5lexk8ad/bohKi2HlrJ/6B/my9sVVvoilA6Vyl5Rwxn5JB9m1x6jg2B21mxcUVyZZaF85SGC9XL9q7tk8xGH3T1edX6bOzD/vv7gd0wx79y/dnaOWhqZa6SKvQmFCa/N1EHiIyUhrJz3Gr4q2YVX8WdpZ2evfRaDX8c/Ufxh8Zr9ezUix7MYyVxhx7cIxjD47J2/Nmykur4q0olKUQ225sY/bp2fKqLAtjCzqW7Ejvsr1xzu6crtf2Lhqtht23d+MX4MfmoM1yewBK5SjFoB8G0bxoc0yNTLlz584Xa5cgvC3NAU2/fv3kn//5559U91MoFCKgEb6I0dVH65ZCH9W9HjuV6sTo6qM/6hgKhQLzvHkxz5uXnE2bkvjqFeEXLuiGpq5cQUpMJCowkKjAQB6uWoVZ7ty6nptSpdiRcJ7fDgzj1stbgG6YpF+5fgyqNCjNKfQ/h7DYMLbc2IJ/oD+7bu/Sm5+iQEHl/JXxLKrLEZOe8zEkSeL4g+OsuLgi2VJrGzMbWhVvhVdJL3mp9bu8invFyAMjmXtmrhwMNXJsxLQ60z5Lfat7r+5Rb2U9uTcGdMNyuaxzMb/hfJo4NdHbP1GTyOrLq5lwdILe8JGdpR2hMaFyrhrQ1Yz6sdiPeDp7cjvsNrNPz+bKiSvy7fky5+OnMj/R1b3rF83m+zjyMUvPL2XxucVyz1eSnFY5WdtyLZXzV/5i7RGE90lzQDNx4sT0bIcgfDIjpRETa03Ex8GHX3/9ld7len/yahZjGxuyVatGtmrV0MbHE3H1qm5o6vx51BERxD16xJGXAcw+f56rZrpCf0qUdHL1YnTNsZ+cNC69PI16qssRE+jPgXsH5JIKoEu4VtOhJp5FdTli0jtXyZ2wO6y8tDLVpdYdS3akkWOjD+q90mg1LLuwjCH7hshDYkWyFGFGvRk0KNIgXdud5PyT8zRY1YCn0fp1wnw9fJlca7JevqAETQLLLyxn4tGJegU2lQolWknL82hd9XdrE2s8nT1p49KGotmKsjBgIa3WtSI09nWxyEr5KtGvXD+aFm36xbL5JvXGLDy3kC1BW1Icxu3q1pU5DeZ8lQkfhW9bmv9Kmjdv/v6dBOEbojQ1xcbdHRt3dyStllMB2xhyaAQHoy/I+1SNysNPoaUodF9B3J21hPw3NGVsY/PF23vv1T02BG7A/7o/x4KP6Q0VmBuZU79IfTyLetLQsWG69yCFx4Xz77V/WXFxBUeCj+jd5pbDjY4lO9KmRJtkQzTvcuLBCXrv6E3AE13BWysTK4ZXGU6/8v0+W6bnXbd24fmPJzGJMfK2wlkKs7jxYqoWqCpvi1PHseTcEiYdncTDyIfJjqOVtJiqTGnk2Ii2JdrSoEgDLj69yIxTM1h3bZ0cYBorjWnt0pq+5fp+0VIcqfXGWJtYE6+JJ0GTgInKhLkN5tLVvesXa5cgfIxPDvujo6PZs2cP169fJzY2FhsbG4oUKUL16tWx/IDJk4KQXiSNhkf//MONffsAeLZzJwV9fXVVi9PRvVf3GHFgBCsvrZSDhPL2Zfgtayscb8YT9TwISfvGknDQLQn/Lxgyy5Pns61uCnwRKCe6S0ralySTaSYaOzbG09mTuoXqYmmSvn+faq2aPbf3sOLSCjZe36g3lJXTKiftXdvTwbVDikut3+VJ5BMG7R3EX5f+krd1cO3ApFqTPmsSQr+zfvTc3hOtpJv8qkTJLxV/YVS1UXJ+nZjEGOafmc/EoxP1eleSqBQqaheqTRuXNjQr2gxzI3PWXVtHtT+rcerR6ySQdpZ2cjbfHFZfpv6dRqth1+1dLAxYyNYbW/V6Y6oXqI6tmS3+13VlF3Jb52b9/9ZTLk+5ZMeJTojm5MOTHAk+wrGLx8iESKYqGMYnBTT79+9nyJAhREREIL1RQ0ehUJApUyYmTZpE9erVP/q4cXFxzJ8/ny1bthAaGkq+fPnw9vYWvULCOz1et47n27ej+a8Mx8tjx3icNSu5W7VKl+OHxIQw4cgE5p6ZK1dLds7mzKRak2js2FgOUtTR0URc1OW7Cb90CW1s7Osl4evW6ZaEu7uT2d0daycnFGlcEg66eSnnnpyTg5jrIdf1bs9ukZ1mRZvh6exJjYI1PktPxqVnl+Sq1m8utTY3Mqe5c3O8XL2o5VDro1d2JWgSmHlyJmMOj5FXirnndGd2/dlUzFsxXa/hTZIk4b3Jmz8v/ilvK5atGCuar5B7TSLiIhh2YBiLzy0mVh2b7BiV81WmjUsbWhZrSXbL7ITEhDDn9BzmnZnHo8hH8n5uOdzoW64vrV1af9YhHEmSUGvVJGgSuP/qPssvLmfl5ZU8jnws72NjZkOjIo2okr8KS84v4cC9A4DuNd63XF9uhN7g6ourvIp7RVBIEDde3uD2y9s8inwkB302MTbUoMZnuw5BeBeFJL1Vze8DXblyhdatW2NkZETbtm3x8PDAzs6OiIgITp8+zapVq0hMTGTt2rUULfrhqyO0Wi1du3bl5MmTtG3bFgcHB3bu3MmpU6cYPXp0ioUw36dmTV1F233/fXMXvk1Xfv6ZhJAQnmi1LI2Lw9vMlJxGxlgULIhJ1qzyP+MsWeSfjaytUSjfvew1OiGamadmMvnYZCLiIwDdN9Yx1cfgVdLrnfMbUloS/iaVhQWZkpaEu7p+0JJwjVbD8QfH5SAmODxY7/a8mfLi6axbXv1D3h8+yxLxpKXWyy8uT5YtuGr+qniV9HrnUuv32XFzB/129ZMn1GazyMaEGhPwdvNO1+uRJAmNpEGj1aCVtDyJekLdv+pyK0w3sVuBgn7l+9G7bG8UKDj75CxTjk0h4HGA3rJl0D3uFfNWxC2HmzxUExwezOHgw1x6dkkeVlKgIL9NfpyzOWNrZotG0pCoSSRRq/un1qpJ1CTK2X/f/qfRalBLuv+TftZqtbr/Ja18LRpJ93/Svy/BJsaGGrdqUKB4Gcwtv96emtjoCO5dPcPUqVNxcHAwdHOED5TmgKZnz54cO3aMv//+G2fn5EsIr169Stu2balZsybTpk374OP6+/szZMgQRowYQbt2uiW3Wq2WFi1a8OLFC44cOfLR3fUioPk+XPi5L5qQUJ5qJTmgya5UYETqAYvCyOh1gPPf/8b//ay0zcyqx9sYc3yCnMo+s2lmhlQaQu9yvd9bLfltkiS9XhJ+7hwxby9xVamwdHTEqpQrFiVdMMqWVf6wjVXHciT4CNtubGPX7V3JcsQUyFyAmg41qV6gOo5ZHeUPsTc/rJN+TvqgS/r5Q2+P08Rx6dklzj4+S1BIkN6cHFszW1zsXCiWvRjWJtapHkutVet9gGu0up/Vku5DPCohilsvbxEWFyYfO4t5FrJZZEOSpFTbmbQ9aZ+kfxK63yVJSvazkP5EQCMYUpr7ugMCAqhVq1aKwQxA8eLFqVWrFqfeKhb4PuvXrydfvny0adNG3qZUKunXrx+XL18mJiZGzM0RUrQjywPyRcax3eQpxBXkrnE4h7K+omgOF8yj1VjEaLCM1mIVI2Hy3yIfSa0m4flzEp4/l48jIXHA8gFzsl7gvomuR8ZYUlI7vhCVYwoRt+sQgw/uIdQkgVCTBF6YxBGP+uMCBVMNNoWMqRiZg0rRuSgTY4+ZBqIDA4kODIQ1a7llEsZhy0ccsnzIVdMQpHfE8ffC77Hk/BK5IOKXFhYXxpHgI8kmAKeHl7EveRn7Mt2PawgKdE+iQqFAgSLZ/0qFEoVC979SoUSJEqVSKf+uUqhe/6xUoVL890+p+2ekNEKJkqjEKF5Ev9ALDAGymmelkG0h8mXOh6mRKcZKYyLiIzj96HSKk5mTGCuNyW+Tn0K2hSiSpQgOtg5YGFvot0OpIvxJOPtv7f+sj6EgpCbNAU1MTAzZsr27LkrWrFmJiIj44GMmJiZy8eJFPD09Uf43DBAdHY2FhQVVq1alatWq7zmC8DV48eIFkZGR79/xI81POEtMlkiKROcgF/B75kuEG0VByH85PxSAle6fudaIrBozbDVm2KpNsdGakllrSrQikZMWT3hsHA1qJTZqG0rFZaNSVB6sJGPUCjUKtNggYauCIkZK1AozwpXxhKk0vFIlEq6KJ1yZQKQygVhlItGqBGIVGngrIIlRwLpM4azLFISZVkW5mJxUic5DlZg8ZNGYUTjBlsIJtniHuRCiiuXof8HNafMnxCmTL6f9ViV9kL/5wZn0v5HSCCOFrtK0SqnCWGks/2+kMtL9rzTCWGUs/2yiMsFYqfv9bvhdzjw6I5elAF3W27C4sGS9OCqFikr5KtHFvQv2lvaYqEwwNzLnefRz/r32LxsCNxCV+LqWUW2H2vxc/mfqFq77RbL5Pox4KK9UehDxQN6e3SI7nUt1pqt7V7l8wuH7hzkSfIRD9w7p5QNKYmNmQ6V8laicrzJV8lfBPaf7B82/umN1h/2IgEYwjDQHNPny5ePEiRNotVo5+HiTRqPh5MmT5Mnz4Xk4Hj58SGJiIrlz5+bPP/9k6dKlPHv2DBsbGzp16kT37t0zbO0b4cO8ePGC3n36kJiQkO7HLkMZvd89Hpb94PtG/fcPoCj5eHvW1zWS0yi17HbcRaxJ8kmhHytOqeGQ1UMOWT1EKSkoHpeVqv8FN4USbMimMadZRGGaRRQmTqHmlPlTDls+5IjlQ0KN4t57fJVCpfsg/++DPennpA93E5UJSoWSiPgIQmJCkn3I2ZrZUiRLERyzOZLZNHOqx3jz93fdZqIy4XbYbaYem8qFZxcAMFWZ8lPZn/i5/M9kNsuMicpE1+PwGYKBWy9v0W1LN73CkElexun3BlmZWPFLhV/oV76fnHMmqUr2hKMT2BK0RS+br5erF33K9fki2Xw1Wg07b+3EL8CPbTe36c2VqVmwJp1LdcbO0o4TD0/Qc3tPjj84rrcE/U2OWR3pWbon1QtWx8XO5YuWVBCE9JDmgKZx48bMmDGD4cOH89tvv2Fh8Xo+QVhYGBMnTuTmzZv06dPng4+Z9K3d39+f8PBwevTogZ2dHZs2bWLGjBnExsbSv3//FO+bNE8mJU+ePCFnzpwf3A7h84mMjCQxIYFcDsUxMf96C9klxMbw+M5V8pnkJcEqDmNJiZEGjLSgUksYqSWUiRqM1BJGkhIjlLp9pDf+R/93gFilmlilGhMLK5QFcvEghyPxRpnJ9lJLpofhmD4IxUwyompMHqrG5IEXoMiXEyMXJ8xci2GaO7duKOGtwCK1D6ekpdbLLy5nU9CmFJdae5X0wsXOJd0eu5CYEIbtH8bCgIVyINDCuQW/1/mdAjYF0u08KVFr1fxx/A9GHBhBgvbdQXV2i+z8UvEXepTuIZdRiE2MZfXl1cw8NZPLzy/L++bNlJefyuqy+WYxz/JZrwF0vTFLzumGGN/sjclmkY1q+auR1SIrl59fpvOmznq9T29e28vYl2gkDWZGZixtspQ2Jdok208QviZpDmi8vb05fPgw69evZ/v27RQrVgxra2uePXvGvXv3iI2Nxc3NjS5durz/YP9J+O9b+4MHD/D395dXR9WvX58OHTqwdOlSvLy83jvUJWR8JuYWX/WkwSTbO+x456RBTVwcCaGhJIaG8iA4kGs3T/Hs/+ydZ3RU5dqGr6kpk95DSCFACB0SqhQRpEOkF1EQRUQ9igW7HtuHWLCAHo2gKCjSQXqTIk3pvSSBkALpPZPJ9P39mGQnQwKEGsq+1mIRdnnn3QEy9zzlfi6eRVGkI8Cswd/sjKNwmf+GyQBZZb9syDUaFE5OYDZjLi5GsFgQUtIxpaRjWrcdg68v7lFROLdujeYKLeHHMo8x94htqnX5PCKoaLUe13IcPer1uKkdRWarmdgDsby37T0K9AWAbabRzD4z6RF++Q8kNwOrYGXe0Xm8vvl1snXZV7w20CWQ1zu9zsToiWLh98Wii3y//3t+PPijnd9Mp+BOTG4/mcGNB99yN1+L1cL6s+uZdXBWlWhMsFswCrmC5IJklp5eWuXeZn7N6BrSlY7BHdmetF2stQr3DGfFyBW08G9xS/cuIXE7uO7/gWq1ml9//ZXZs2ezYsUKDh48KJ6rW7cugwcP5umnn76mSdxOTjazqhYtWlRp9R4yZAj79u3j4MGD9O7du8q9V+pgulL0RkLiViEIAscLzrA8fjnLTy+vGE7oavvl7eTNIxExDA0bQEeXpsgKtRhzc20CKC/P9nVeHqb8fCjz1rGWlGAtKbnsaxqzs8neuJHsjRtBocDBzw9NeDiuzZtT4qFmZcZWfo5fwNGsqq3W41qOY2iTodfdan0ltidt58X1L4pRDXcHdz7s9iHPtX0OlUJ1018PbN//IxlH+P3Y7/x0+Cex5f5yBLsF82bnN3my9ZPiGIZ9F/fxzb/fsOTUEjs335HNRjK5/WTa1GlzS/ZemdTCVOYcnsPsQ7PtPGwUMoVohlc5SqOQKYiuEy3Wv3QK7oS3szdZJVmMWDKCv5P/BmwTu/8Y+sdtiShJSNwObugjhVqt5vnnn+f555+npKQErVaLRqPBxcXlutYLCLA5ZHp5Vf0PVn6s5Ao/zCUkbjf69HTM/v6if4xVsPLvhX9tHjGnl9vN8wGo41qHIZE2j5guoV1q9KlesFgwFRRUFTvlgic3F3N1RdYWC4b0dAzp6eTttk107gS0oQlZqjAKHa14BATTILw1fnXCUcm9UWUXYfFW2aJAN4GUwhRe2/wai0/aBobKkPFU66eY2mPqNY09uBbic+NZcHwBC08urGI0KEdexT8m3DOctzq/xdiWY1Er1JgsJhaeWMiMvTP498K/4nW+zr6im2+g661NYZutZtbFr+Orf79iR/KOatvMy8WMo9KRDnU7iAKmQ90OuKjtfwbvv7ifIYuHcKHI1sn0due3+eihj26JR5GERG1xQ4ImKyuL2bNnEx0dTZ8+fcR26j59+tCpUydeeuklXF1da7yet7c3AQEBnDt3rsq5Cxds/xGlWhiJO4nz33+PXi7H6qgi39FCvDWT87Ic0lUlhCnNqFWeOPj40rtZDEObDKNtUNtrLraUKRSiEeDlsBqNGCsJnbOn95J7+jhOBXrcLGrklVqsHFAQbHIl2AQUF6FP+JsU/rZbT+7kVOHN4+NjZ0ao9vJC5eWFXHX5yIrerOeL3V8wbdc00Um3Q90OfNv321sS1bhQdIFFJxax4MQCcdbTpTgqHe1qhBp6NeTdru/yaPNHUcqV5OpymXVwFv/b/z+7SEirgFaim29NBmheL2armQ1nN/Dtvm/ZkbzDbq+VcXNws+tAig6MvqLL8JzDc3hu7XMYLAZc1C7MHTSXIY2H3KrHkJCoNa5b0Fy4cIFHH32U7OxsNBoNffr0AaC0tBSr1cr8+fPZuXMnv//+O35+Nf8kNnDgQGbPns2mTZvo1asXYKut+eOPP/Dy8qJNm1sf4pWQuFbkehPeeuiILx3xtT+ZCvIzhah9VnLeZzdqHx/Uvr6ofXxw8PFB7eODwsXlhjr45Go1aQ46fsteyrxj80jMTwRPwBNccOBZt34M0DfCLTkPa4l9l4tMoUCmUmE1GitSW6Wl6C9cQH/h8t4kSnd3ezPCMhfmf7UneO/QFxwpiccqEwhwCeCzhz/jsRaP3dTOmRxdDktPLWXBiQXsTN5pF8WonI7RqDSUmEpEgeDr7MuMPjMY0XQECrmCE1knmLl3Jr8d+028Ri6T80ijR5jcfjJdQ7veku5KvVnPvov7+Dvpb1acWcGxzGPVTrd2d3Cne73udAvrRpeQLrTwb1GjyIrRYuSlDS/xw4EfAFsX04qRK2ji2+SmP4uExJ3AdQuamTNnkpeXx/Tp0+nfv7943MnJiU2bNrF+/XqmTJnC119/zbRp02q87qRJk9iyZQtTpkxhzJgxBAUF8eeff3Lu3Dm+/PJLVFf4VCghcbt5NnAzbiorgWYN7Z0b09apEWGCF6rCUozZ2TaRQJlASE1Fn5pa7TpyR0eb0KkkckTR4+t7WcFToC9gycklzDs2j10pu+zORQdGM7blWEY1GyWmdwSrlZJz52yjGA4fRn/xoq2w2GJ7I5WpVGjCw3EMCkLp7o5Vp7OL/JgLK9q5zYWFmAsL0Z23T6sFALNpi5loDBoV3nXCcNqtJf3MkiqjJxQazTWJhWJDMX+e+ZMFJxawOXGzWNcCZY7CTj7E58VjESzIZXKsgpUSU0Wa+pnoZ/i+//cArEtYxzf/fsOW8xX1d24ObkxoPYH/tPsP9Tzr1XhfNd37ntQ9ogfM3ot7xZlglZHL5DT2acyjzR9laOOhRHhHXLOgSi9OZ9iSYWJb+sCIgfw2+Dex7VxC4l7kugXNvn376Nu3r52YqUzfvn3ZuHEjf//9d7XnL4eLiwt//PEHM2bMYNWqVRQXFxMREcEPP/xwXYMuJSRuJY2CW/JIx0cYFDmIum72nkuCIGDRajFkZ2PMycGYnY0xN7fizzk5WPW2iIBVr79iRESuVosCR+XjzXlZHtsKDrAqazvJ8nzyFQaQ2Wp0Hmv+GI+3fLzaVmuZXI5Lw4a4NGxI0MiR6DMzRXGjjYtDMJnQxsWhjYsDmQxN/fq4t25N4JAhOAYFIZjNmPLz7ep3tFnpnE7Yhy4nC3+zE25WW/pDiRxliQV9wjn0CVXTyAByBwdx1ES1KS4vL4wKgXUJ61hwYgFr4tfYpWJc1C4MjhxMHdc6/HzoZ+Lz4sVzlbuA1Ao1S4Yt4aF6D/Hdvu/4dt+3nM07K55v6NWQF9u/yLiW48QW7RsluySbXSm7RAFzOOPwFWcqNfNrxn/a/ofxrcff0BDRPal7GLZ4mDiu48NuH/Ju13clXxmJe57rFjSFhYV4enpe8ZqAgAC0Wu0Vr6kOT09PPvjgAz744IPr3J2ExO3hx4E/XrZtWyaToXR1Renqiqaaa8oFj53IqSR2DNnZFYLHaER/8SL6i7baDjfgEZQ8wsMAmBQCMk8PPJ1CcLjgi9qQRJ6PVoz2KN3dq/2U7+jvj2Pfvvj37YtZq6Xo6FEKDh+m6OhRrHo9JWfPUnL2LGlLlqAuawn3aN0al0aNEBRy5h6Zy5vx/yXLOQtCbAW2M7p9QXevdpjKBI8xLw9jTo5d55ZgsnmjWA0GDGlpGNLS7PZlxso+5ww2uiSx3eUCWnlFJMNBpqKnbydGNhxMVFgH3tj3Mb8d+63Ks5VHaHycfZg1YBbbkrbx+J+P23U79arfi8ntJ9OnQZ8bfsNPKUxhZ/JOUcCczjl91Xt8nX2ZEDWBCVETCPe8sZlBgiDw48EfeXH9i5isJtwc3Jg/ZD4DIgbc0LoSEncLN+wUbDabUVbjdWG1Wtm7d+81OQVLSNxPVBY8zmFhVc4LgkB61nnW7P2Df45vxJCTQx2ThjpmFwJNGkKsHjiZbW/CKosMcgopzjlOdUMlZCpVRTrL19cW/aiU0lK6uaF0ccGrUye8OnWyTQk/fZrCw4dtU8Jzc+1awgVHNftdc1ghP4rOOR9nR2fe7fIuL3d8uaJw9jL/9wVBwFxcbBM7lbq1DDnZ7Ms7yir9ATYp48mr5ICsEGS00wXQWxtGt5JgNAkqlp+YzySfKZTIbeKoVakvfYrDSPA2sEJxHKtgJdDJn8bekQxdPFSssXFSOjG2pc3N93rrSQRBID43nh3JO9iRsoOdyTtJLkyucp1KrsJV7VrFfbhX/V5MjJpITKOYm9K2rjfreX7t88w5Mgew+fusGLmCCO+IG15bQuJu4boFzaBBg/jss894/fXXeeutt/D1rSiEzM3NZfr06Zw5c4aXXnrpZuxTQuK+odRUysq4lcw7Oo+N5zZWpCk8bKmRcS0H80CLxwj1CMVcUiJGdMojPIYy8WHMycFSZnMgmExiC3d1yFQqm8gpr+MpEzueHTrg178/5uJiio4cIefAXkzJF5DpjbTTu9GOLlhkAo4RDfDXRSHLKwK/K3cCyWQyVG5uqNzccAoL42jmURYc38rCzIWkkAKVbu/oG81g7270VbfCvciCMS+P+Nx4Xrau4LCDzRDQ0+zA6znteFgbzA9ex1imOAGAg1VBemkm6am26/ytLjyu6sQYr1746UNQn8ik0Nssprau1KpusVo4lnlMjL7sTNlJVklWletc1C5EB0Yjk8k4nnmc3NJcUcwEuATwZKsneSrqqRuOxlQmtTCVoYuHsj9tP2BzXf7lkV9uWupMQuJu4boFzbhx49i9ezfr1q1j/fr1BAYG4uLiQklJCenp6VitVjp16nRNTsESEvcrgiCwK2UX847OY/GpxXZpEU9HT0Y1G8XYlmNpH9TeLnWk1GhQajQ4h4ZWu65Fp8NQWfCUi56yry1lKWHBZMKQkYEhI6PadWRKJSUaOafMF8hxKcHRqiRc5kOwXoPCYsUUd44Lcee48PvvOAYH49G6tc2xuF49ZNXMekvITWDBiQUsOLGgildMq4BWjG42mlHNRhHiHiIe15v1jFsxjiVFS8Roi8aiYrhPD7pGDebDpDmsKTohXm8oG+DZstSXUYWRPKQNRoUcK4fJ4HCVPSmcncV6HsHLjdOO+ey3JrK35DR7845QZKwa+/Jx9qFLSBceCH4AGTL+Ov8XG89utOu46lW/F89EP8PAiIE33UTw76S/Gb5kONm6bGTI+KTHJ7zR6Q1p5p3Efcl1Cxq5XM7s2bNZtmwZa9euJS4ujqysLJydnYmKiiImJoZhw4ZVO7hSQkLCxrm8c/x27DfmHZ1nZ8KnlCvp17AfY1uMZUDEgCv6jFwJhbMzziEhOIeEVHveUlpaUbNTSfCUHys37BPMZpwLoQ2XWjCURY/K30AFAX1qKhmpqWSsWoVCo8GteXM8O3akONSTJfEr+OP4H1W8Yhp6NWR0s9GMbj6aSB97l3CjxcjUHVP5dPendl1BnYI78V2/78grzWPo8jFkaCvEmFKmZGSzkTzf4mlaqsKqGBKKX+fng8WCTmbimHCOQyV7OGLN4kRxriiIKhNgcaGtvB7tNI3p5B1FgHtd/i48xKItP3PCmEyuohRBVhGNmRA14aZ3S4FNAM/cO5NXN72KRbDg6ejJgqEL6N2gqou6hMT9wg0Z68lkMoYNG8awYcMA0Ov1ZGZm4uPjI5rsSUhI2FPeaj336Fx2p+62O1feaj262Wh8Nb6XWeHmoXBywik4GKfg4CrnEvMTeWPtKxw8vZ06Jg1BZlf6eneig6YJ5BfZBE9RWSRJqOpkC5BbmsfiU/PYmPoRhxyzECoFDgIdfBkW2p/RLcbQvlE35JfU4hnMBmIPxPLf7f+1i1g19GrIL4/8woWiC0xYNcFOHDmrnHmlwys81/Y5OzffSyNYubpcsQNpx/ntHM46Wq0HTJjZg1YlPkTp/Ygq9SPQXNmB9wSlnKAd0I42QBvMMgHcNLgr6qKO90Gdu59sr3N2Xj0KZ+cbiqDoTDomrp7I/OPzAWjh34IVI1fc1DSWhMTdyDULmq1bt7J582bGjRtnN2/pyy+/5Pfff0ev1yOXy+nZsyfvv//+VTuhJCTuB8xWM5vObbJNtT6zEoPFIJ4rb7Ue23IsTf2a1uIubZQYS5i2axrT90y37VMNIQ1b8U7fmVWGGFoNhioprbysVNbn7mG14SD/OKRikVWIHXeLAw9rQ+hTHEYrvR/yk2ZYN5cjsnliDY/c25PjhiRis1fyt+KcGClxd3Dn84c/J0+fx6hlo0Qb/3LGtxrP9/2/r9bN92LRRbH+ZUfyjoq5WpWQIaNVQCvRgbdzSGf8XfyxmkyY8vJITTnF1sOrOH12L04lFgLMGgLMzgSaXdFYbT9KlYIMCnWUFMZTQnyV14AyzyEvL1Hg2JkTln0tv8wMvKSCJAYvGsyRjCMAjG42mtkDZ6NR3xkfIFMKUmp7CxL3MdckaP773/+yZMkSALp16yYKmq+++orZs2cjk8l44IEHkMlkbNq0ibNnz7J8+fJrGlApIXEvcTTjKHOPzuWP439UmWo9pPEQxrUcR/d63e+ImTqCILD45GKmbJ4iioW6bnWZ3nM6I5qOqDaqIHdwwCkoCJm/N1sTElmQt4w1RWsolZWKxb0uKg39fLvQR9+Q1qkqrHm5YLkkGiIIoiDSYuQP3yNscrcJAoUgY1BRA6yOKiav/Q96THa3OiocWTpsCf0jB4jPcTbvrChedqbstDknX4JKrqJdUDtRwDwQ/EAV4zmz1cyac2uZdXAWG85usNXGaECmkdG7QW8mRj1Fh4gByI3minRWpc4t8c95eQhmmwmgVa9Hn5aG/pJW9cooXV3tx014e7NLOMvTxz8g31iAQqbgi55f8FKHl2q9XsZkMbEqbhWxB2M5cOIA3eleq/uRuH+psaDZunUrixcvpkmTJrz66qviCILMzEzmzJmDTCbj448/FtNPW7Zs4fnnn2fevHlMmDDh1uxeQqKWyfvnHzwyM1G5udlan93cyBaKWXBqEfOOzeNY5jG76x8Ke4ixLccytPHQO6oL5WjGUV7c8CI7kncA4KBw4LUHXuPNzm9e9tO/2Wpm6/mtLDixgOWnl9ulhRwUDvRr2I/RzUbTP6I/zipn8ZzVZEJ75gyFhw5RcOgQpjz7lmZBbmvBLpQbSFEVoRYULHNPqFjbosAkt2KVCXiZHfkmtRvF02N522s6hzU5HJSnkm2tOllbo9LQMbgjXUO60jW0K+2C2uGkqr6zKakgiZ8P/czPh38WDerAVhvzVOuneKr1U/a1MU4qnOrWxelyrepWK+bi4qrDRSv92VRQIKbuzMXFmIuLKU1ORkBgnscpvvM+glUm4GFx4NOMrjywKJn4zR9XjfaUGRMqXV1vqdhJKUxh9sHZ/HT4J7F+yQOPW/Z6EhJXo8aCZunSpXh4eDBv3jy7adobNmzAbDYTGhoqihmAHj16EBUVxYYNGyRBI3HPkrluXZXCdysCreUGQhUh5Cv8MTmrCPQPJzKsNT5+ISgNrpB4gdKy1mWFRlNtJ9DtIK80j/e2vkfswVixPXxQ5CC+7PVltTUZgiDwz4V/WHB8AYtPLbZrXVbIFPQI78HoZqMZHDn4sjb7cpUKt+bNsTSoy88hiaz5+0+i8t3pUlKXpgZvXK1q+mrr0VdbDzNWDjhlskNzgfPeZnydfVlbbJuA7WF1JNzozvN1tlKsqDRCoKxO2d2iplWpH631frTW+9PMqR4asz/qYm/UmZmU+PyDqaw9Xe3tjUUhY23CWn48+KNdp5IMWzTmmehn6N+w/3V1KsnkclTu7qjc3eEyRoyC2YyxoMAuwpOfdYEp6bGsMx8FoLHeiy8yHiTQrMFUaov+lFS7WqVW/MqC55I/KxyvbdimxWphw9kNxB6MZV3COvHfjFwmp3/D/gwLHMbKb1de05oSEjeLGguaY8eO0a1bNzsxA7Bnzx5kMhndu1cNM7Zs2ZKlS5fe+C4lJO5QBE9XSktLcDAK4kRrOTI8rI54WB3BBOiBvAJKT2+j2klO5QZ7bm62VEOlaE91x260qBRsb0yzD83mna3vkFdqi5BE+kQyo88MetXvZf+MgsCxzGMsOLGAhScWVjGQ6xTcidHNRjO86XBxZtSVyC7J5ut/v+a7fd9RbCwGGRz0gtlex/E1O/GgLpgu2iDalgbgIChoqfdFjgx3aya7nRNQOsgwywQK5HoOOFeY7wWofWjv1Ig2QgittN6E5Msx5+VXFCzriyjJL6IkIaHafeUpDRQoiuiiKqGBsiUlLkpaN+5Kv3ajqBfe8rJ1LTcLmVKJQ5n5IcDZvLOMWPQWJ8y2VvQnWj3Bt92/QllcWjWlVWkURfn8sKu14oOtC+7S+p3K0R6VlxdypZIMbQZzDs9h1sFZdn//AS4BTGg9gaejnybEPYTExERWIgkaidqhxoKmsLAQf39/u2NWq5WDB20dBh07dqy6uFKJyWSqclxC4l7hcc0iCnwLkAsyvAVnHqnbi8F1e9PGvSmUlGIuKqr4VVyMqezrcsM7AARBvKYmyBQKOwGkLIv0VBZAlY/JHR3tBNDO5J28sP4FjmbaPvW7ql35oNsHvNDuBbvow9m8syw4bvOKudTGv6V/S9ErJtSjeg+cS8nQZjB9z3R+OPADOpNt4nflqdgA2cpSlrrFs9LjPE29GmM16DlVfBYzVWcghVrcae/WnG4RPenZfgT1/RpVEXqC2VwxXLPcfyc7G0N2NoUZqciKSlCUtV55mR3wMvvSwlDWXVYAXMiicPNMjlA2XfxyA0R9fJA7XF9rfXWsS1jHmOVjKNAXoJQrmdFnBs+2edb2fK6eONapU+194jiNS+p37Op78vPFqeoWnY5SnY7SywxNFWSgVQsky/JQKrQMUfqQqXLCL6g+3VsNokfrR3Dy9K61CKOERGVqLGhcXV3Jz8+3O3bs2DG0Wi0qlYq2bdtWuScpKUnqcpK452lTpw1jW9imWte01VowmzFrtaLAuVTwXHrMWlpaca/FgqmgwFZzUQNkKhVKV1cEjSMJhoucLD3PQwoFrRRNaFqvLcPbjcPXLwRrXgEpFLH07EoWnFjAgbQDdus08Gpg84ppNprGvo1r/P25UHSBz3d/zuxDs8XBko4KR0xWU5VWaZVchdlqxmQ1cSSnUv2RAI6Cgk6KCPqWNqBZugpfS1ldztEEtCu/ILFZM9yjonBv1cqW2qEs6uHnh4OfHzRuTFJBEj8d+ok52jmk+6aj8JHha3amuSqE4QG96ezWAmetuWK2Vl6eWMAsThc/V/2gTaWrqyhw7MZMlP25Jukdq2Dlk52f8N9t/0VAwF/jz9IRS+kc0rlG32u7cRqXMVsUrFZMBQUV9Ttlz1ke4dHnZGMtthkuygRwNchohjfN8K5YJAc4+jdn5v6NTKEQC5izVDfXOFBC4lqosaBp3rw5e/bswWq1ijUDa9asAWzRGadLbMOzs7PZtWsXXbp0uYnblZC4s1g8fDE92/S85vtkSiUqDw9UHh41ut5qMtkKRcuEjqlM7Nj9udIxq6GiLVwoazsmD+ohox6VajhyLSQe+oHZLslsdE2q4hXjL3NngHNbhvh0I8qnJSqVB8p0EzrteTEadKUW4093fcovR34RDfFc1bZC6OJqXHcBTFZbRFcpV9LCvwUphSnk6HJABu8//H+iC64+PV2cM6WNj8dqNNqmhh86VDElPCoK96goFP6+Ym3MpnOb7GpjejXsY6uNieiPUl71x6Fgtdqmi5cNC608Nb38d6Fc8JQV8uoSq3ZUQZngKY/slIkdh0p/LpGbGPfnOP488ycAHep2YOnwpQS5BVW73vUik8tt6SUvr4rnFAT+vfAvsQdjWXRiEYLJhJ/ZmQCzhi5urejl05EIRSDW/EIx2lM+OFWwWGzfi+xsCq2XnyYuIXGrqbGgGTFiBM8//zyvvPIKY8aMIT4+nkWLFiGTyRgzZozdtXl5ebz00kvo9XpiYmJu+qYlJO4U6nvVvy2vI1epqrwJXQmrwYCpqIhtJ9bx885vMRTm42FxpK7ci+6+HfBRuLO55CBrzEfYo0q2GcKV4W5R87A2lN7FYbTW+5XVBp0hjTPVvpbc0dEu5aVTC/yTf5SdeQfIkZfQSOlKjpOZAoXhskLGSelEx+COYgu1i9qFoYuHkqPLQSlXMidmDo+3fFy83jEwEMfAQPz79cNcXEzh0aMUHjpE0fHj9lPCFy8mTa1jp1MyOZoLyJ3A362O2Kl0tXSZTC4Xi2ldGjWqcl6MdpSJnSpuy7m5Yru2KHjOn6+yznlVIa8F7eS8sgCAx9y680mdl9CczUTnY8HBxweFs3OV+26UIkMR84/NJ/ZgrF1HnqujK4+0HMsz0c/Q3L95tfdadDq74aLG3Fzbs+3de9P3KSFRE2osaHr06MGYMWOYP38+GzduBGyq/tFHH+XBBx8Ur5s0aRL//PMPBoOBPn368PDDD9/8XUtISFyRhOIkXtr0EhvObgBA6aHkuTbP0TSoHbPjV7E6bjWl8lIoC65oVBpi6vVlWJ3edNE0R6YttU+DVYoGmYuKxKgE2HxVDHo9hqwsTFiJc8jjlFMWyY6FHHDOpFRue0OnUnZJKchpKwuji2NTOrq3oJVXU5w9vFE6uLI3OY4h/0ym0FiEq9qVZSOW0bP+5aNgSldXvDt3xrtzZwx6HVu2zCVp9xbC0q0EmDXUMTrzmLExjxU2xuKowtsvGk/nNripb9yJ2S7acTnBU1hoN05CFD5lEZ5t6kT+67+HErkJlSDnjey2DD5bh+xDi8mutJbC2dmuZufSqenKa3BnP5x+mNgDscw/Pp8SU0U9V1RgFM+2eZZRzUbhona5wgq2/Tg5O9u5TBsSEyVBI1FrXJOx3nvvvUfv3r3Ztm0bZrOZTp060a1bN7trEhMT0Wg0TJw4kUmTJt3MvUpISFyFIkMRH//9Md/s/Qaz1SYkogKiCPUIZe7RuczcN1O8Vq1Qi14xAyIG2HnFXAlBELDodJiLiynOy2Ll6RUsjFvKaV0S2UodJtll0g4ChJpceSerPa31/mJXGCSTRzJ5wCaXJP7rvweTzIqP2YmZqV3xm76cU25/VRRCl/1SVSqAzqSY3xOX8eOpuWToMsEBCIXOyggmOfemWY4TltQ0FHoTBf/8S8E//yJTKHBp3NiWmmrdWuwuupnI5HLUnp6oPT0hIsLunMVq4f1t7zN11y+AbRTET2Gv0yzU225qulDWWGHR6ShNTqY0ObnK60CZ4Kmc0qo0NV3t44NBLWPxycXEHoxl38V94n1OSidGNxvNpDaTaBtUtRaypuTocpi2c9p13y8hcaNc8+iDdu3a0a5du8ueX758eZXWbgkJiVuLVbDy+7HfeeOvN0STM1e1K3KZnEMZhziUcQiw+YX0qFfmFdN4MB6OHtf0OoX6Qnan7mZn8k7WnV3H8czjFZOlr1APGuoazK/dviPaOeKynV8/lW7hC9UuAOoZ3fk27SECzS5YDFpxKviViAH60YNChQGzkwp3nzr4+4ShdnNHGe4GHRWYsrPRpaRQmpSEYLFQfOIExSdOcGHePJxCQnAvnxIeFnZLO3fyS/MZs3wM68+uB6BLSBeWDF+Cv4t9J6lQ1gFXeUJ65ZSWIScHoaxN26LTUZqSQmlK9eMHSuQmnJVaRimdeVDVBquHC1GNu9GjzWC869RDcZ0/t00WE9/v/54P/v4A8pCcgiVqjRsaTlkdkpiRkLi9HEg7wH/W/Ye9F22hfhkyBAS7epUHgh+wecU0GV7lTfNKZGoz2Zmyk53JO9mRsoOjGUcrBEwlyl/zUpQyJe92fZe3uryFWlF98bBVsPLqxlf5Zu82ADoHd2Zp/99wMyqqTXkV5qaTnn6O0sJ83ExK3K0V7dJK5HhbnEALaLMoTMqq9jUrNl4xJbxcDGSsXInc0RHn0FBcIiNxadIEB2/valvgr4cTWScYtHAQ5/Jt3VIvtHuBL3t9Wa1hn0wmEw35NA0aVDkvCILNgbiyyMnORp+TTe7FRMgvwsFiE2Yaq4qGRk8aGss6TwuB5LNc3PAFFymbMVWWwrq0LV3t41Ot8/CGsxt4eePLnMmx1VfVVVfvlCwhcTu46YJGQkLi9pBVksV/1v2HJaeW2B0vFxblXjEjm40kzCPsqusJgkByYbJt/lGZgInPrX7AYjlKuRKz1Sy+ZmVfmQ51O/DTwJ+uOHBTb9YzdsVY8RmGNh7K70N+rzJgsnxe0I8HF7C5ZDO4AW5lDrXhfXkmcixdfdogaHXX1AJ/uSnhVr0ebVwc2rg4WFnJKE6pROXuXmF2WCkNprrEB0jp6lqlVXvxycWMXzkenUmHo9KRHwf8yNiWY6/4Pb4SMpkMVdlra8LDScxPZNbBnczJnkO2Rza4g4fVgTZOEYypO5Aurq1QF5VWdGzl5IjdSla9Hv3Fi+gvXqz2teRqtShwUt1NfFKynL8Kba39cpmcCVETmFhvIp/89xOMpbrrfqY7gbt9//crkqCRkLjLSC5I5qUNL7E6fnUVH5dyr5hRzUbRxLfJFdcRBIHTOadF8bIzeSepRVUN1tQKNU5KJwoNheKxcuFSXqfjp/EjqyQLi2DBWeXMJ90/4T/t/nPFoZv5pfkMWjRInB/1YrsX+ar3V3b3JOYnMvvgbH458ovdcM8g1yBbp1LUU4S4h1zxOS+l2hb4SlEgfVYWxqwsW/FzWYeSiNmMqcygrrT65e2Qq9Uo3dzAzYWZDruYbdpu27/al7ktp9FG1grd+au3wF8Js9XM2vi1xB6MtRvZoJApiImMYVKbSTwc/jByWdUUmiAIWEpK7NvSK6W1DDk5ogC0Go1kp59ntn4VC7VnxCnq0Tp/puS0ISLJSIHLzyjlMtISq04zv9tQqdW4ut4589Ykro4kaCQk7gJydbksO72M7/d/Lzr8luPu4M4TrZ5gTPMxtKnT5rIpEbPVzNGMo+xI3sGOlB3sStll83i5BDcHNzoFd8JP48eBtAOczD4p+siUYxEsOCodaR/UnsPph8WZTj3De/LjgB/tBzdWQ0phCn3n9+VU9ikApveczisdX0Emk2GymFgZt5JZB2exOXGzeI9cJqdfw35MjJpI34Z9q/WNqQnX0gJvLi6m4MABCg4coPjMGbFeRVzL0RGliwsypRKL0YiluFgs4gWbCMjKu8jb6p3sldtqm9rqApiW2BnPU9s5x/Yq61129MUlRdFZFPPz0V+YfWg2F4sroipBrkFMjJ7IU62fquJhY7aaKTYUU2wsRmvUil8XG4rRqrUU+xRT7FqMNkhLsVFHscGCSWdEnl9CUsF59hkTKJXZns/X7MTzua0YUByODBlgxiO/mElqB6qLbxgxk6ssJUehJ1tZSo5CR7aylGxFKTnKUgoUhmrusuGgdMBsMeNqVhJVFAy5IZyucwT3UD/mD5l/1b/H68HV1RVf3xvvhJO4fUiCRkLiDkVr1LLyjM21d+O5jWI0pJzGPo2Z1mMaAyIGVBsJ0Zv17L+435ZCStnJ7tTdaI1Vi2v9NH6i/0un4E4kFSQxbdc0sWD1UoJcg3i0+aPsT9vP9qTtAHg6evJ1768Z23LsVWtMjmYcpd8f/UgrTkMlVzF30FxGNx/Nubxz/HTop2qjMROiJvBk6yevORpzoyhdXfF56CF8HnrINiX89GkKDh2i8PBhTHl5WPV6jGUpG4VGg0fbtrg1a4ZTaCiC0cjB1H2M3/cqKXqbmHna+WFedngIVLqrtsDXhAflBporoslXNMXgKEfQOGGRqcnfdYhP9+wiS6YlgyLSrPmkWfLQWfVXX/RKVPqrzVaW8oH/P0z3OUCgWUOg2YU6Jg2BJhdCrO7UMbvgb3TC1VJeG6Qm1KoGq7ttxtklCAo5Vg8X5F7uKL29cfT1Q+MXiGtAEE5+/uw6uQnFTyvQmpXMQc+H+dH4jhpG+GWGfUrcf0iCRkLiDsJgNrDh7AYWnFjAqrhVlJqrJjaa+Dbhl5hfaFfXvtuw2FDMntQ9ooDZe3FvlcgKQKh7KF1Du9I1tCtdQroQ4R2BgMCyU8t4ctWTdgZrlelQtwMvtH2BzJJM3tv2nuhfMrzJcGb2nUmAS8BVn29L4hYGLxpMsbEYdwd3Fg9fTJGhiJ6/9eSvxL/E68qjMc9EP0OfBn2uOxpzM5GrVLi1aIFbixYI48ZRmpxM4aFDFBw+TGlSEpaSEvL37CF/zx5QKFgXXsDHrMMomHBQODCu1Tia+jZluaGYYqMZrdFKscGKsaQEQatDoTOgLDGi0ptxMgg4G2S4m9V4WRzwtDjiaXHEw+KAkorUkZvVATerA2EmN9sQ1ILyMyrAFagoALciUCQ3kKfQk6cwUKDQk6fUUyA3oHOEUgcZJicVFo0awdkRg1pGXF68XRqyTWAbhjcdTrBbMK4OrrioXXBVu9p9rVFr7NJbltLS6lNaZcfKO9hkFiuK3CLILcKSkEoJUAKUSzsPQECFtiyl5iyoCNwaB51u6l+zxF1M7f+UkJC4z7FYLWxL2saC4wtYfmY5BfqCaq8LcAngq15fMarZKGQyGdkl2exK2SUKmMMZh7EKVT1gGvs0FsVLl9AudlEOs9XMH8f/YOrOqVUGUIKt6Hd4k+FMbj8ZVwdXnlr1FP9e+BeAQJdAvu//PYMiB9XoOecfm8/4leMxWU34a/zp37A/j694XExXAdR1q8uE1rZoTLB78BVWu3EEQUBn0lWffjFqq//apK2asvEqxkltpnW+O51L6tBa58f3ngdYINg6f4JMLryQ3oqkrH+J1SzljEOe3XiJKqjLflVTviETwM2qxtfqQl2FFyEKX/xwwdvijKfFAXezGlejAmejDEeDgFpvEYMqlafAh19hZrBOZuJXz5P85nEKo9z276mVLJj3XUcQ7dgCZbYbSkMlHyAHN5SOl58Cr3Bywik42M6ArzIWvb5K3U7l8RLmYlu3nhELahRQqZuuOK36oZoS9yeSoJGQqAXKZ+csOLGAxScX26VY5DI57g7u5Ottw2DVCjWvdnyVsS3HcjDtIM+ufZYdyTuqFSBymZzWAa1FAdM5pHO1AzNNFhO/H/udqTuniu3DlfF28uaZ6Gd4ru1z+Gp8+XTXp0zdOVWM+ExoPYEven1RIx8bQRD4fPfnvLnlTcDmSpxZksmcI3PEPfdv2J+J0ROvGI2xClZKjCVXFx2VvhaFx2Wuq67V/HpJcM9gsXsccgGsZe/rbXX+fJbR1dZaXgJP57egQG3mjJ+RpDpycuo44ezkhqu6LMLh4IpCpuBA+gG2JG6xi464ObjxeIvHea7NczTxu3LBdzmC1VpRAF2586uauWDGokLWyE/wrfdhspW2yKCv2YkXc6Loow1DTibZbL7sa8kUiipdXpetB6rUAq9wdMSpbl2c6lbf8m3R6zHm5rL0/56lqdbd7lycIoc2NfpOSNwPSIJGQuIGsJqu8FG3Go5nHueP43+w8ORCkgqS7M61rdMWJ6UTu1J2iWKmhV8Lwj3D+eP4H0zbVdWF1UHhQLugdqKA6RjcETcHt8u+vsFs4NcjvzJ159RqO5qa+TXjpfYv8WjzR3FSObHv4j76zO/DiawTAIR7hjN74Gy617u6eZrFaqFQX8iE1RNYcWaFeLw8VeXh6EHbOm1p4tsEuUzOyjMr+f3Y79ULEkOxnUX/rcRR6WgnMKr9+pI0i6uDKxeLLvJ/O/9PjDi93OFl/u/Bj7AmX7TV3Rw6hCEjAw+jkg4XlHS4AHIHNa7NG+LeujXn/QViz8xjwYkFdqnG9kHtmdRmEiOajqixm3M5Mrlc9LG5Ensv7GXyhsmil5GjwoEXGo7jP4HDcdSZMV0y+sJcVISpuLjqFPj8fEz5+TXbm0pVVfiUiSGVmxsKV1dSLDlsydrDigsb0Tin83/aTlREaATWuJ9nzJVeROK+QiYIlzFiuIfo0aMHAFu2bKnlnUgkJiby2muvERjeBAenms+eudMwlJaQnniKJx0dCQsIwKFOHXFgomOdOjgEBopGZOfyzrHwxEIWnFjAyWz7dtYW/i0Y2XQkgiAw/Z/pYrqpsp9LZVzULnQK7iQW8bYNalvFs6U6Sk2l/HToJ6bunGoXDQKbKd7ARgN5vs3zRNeJpsRUQqY2k6///ZpFJxdhFazIkNG9XnceDH0Qg8VgH+24JGVT/nV19T+3AmeVc7UCQ/z6auKk7GtXB1c0Kk21BndXY87hOTy39jkMFgMuahfmDprLkMZDqlynT0+3TQU/fBhtfLydD44VgWOO2fytucB+91y6tnmEZ9pMonVg6xv6/lyJi0UXeWvLW/x27Dfx2PAmw/m85+c18i6yGo2YtVr79vdKEaBLj1WeAn+tnFUVsMQjjr+VuXQ51w1ZQAKPyhsz5NtF172mxL2FFKGRqBXSE0/V9hZuGoasLAxZWRQdOWJ33OSgIMVBy3HhIknqIrzUhYSoXHHw82No0+E09WvK7pTdfLb7M4oMRXb3losZbydvMfrSNbQrLQNaiikZg9k2vTq9OP2yaZW80jz+TvqbPRf2YLDYv5nIkaNRa1DIFGw8u5FVcasu+4wCAlvOb2HL+Rv/UHC5CMeVoh+X+1qj0lzR6+ZWY7QYeWnDS/xw4AcAIrwjWDFyxWU9gBwDA3Hs35+sNiH8vvt7UvbuoF2hNw/o6uAsqGil96OV3g9ywUHnhnvxaYqjnHBp2BCZ4uY9Z6mplK/++YpPdn2CzmRrsm4V0IoZfWbQNbRrjdeRq9XXNAXeoteLU8fLhU5RbiZnU4+RnpFISX42riYlXmVF0A5CxTP/6Z7AUvcEPHQeAGxxScURGVVlo8T9iiRoJGqFeyVCE/zEEwRi++RdfCGFogtJqIttwkFlsFDf4ER97C3rzakCqcdOcl61B4W6iC4qb5LUKpJVRcicHAl2C8ZX44ubgxsKmYJiYzGLTi7i58M/24kWk/Xa0l2XYsVqNx6hOmTIKgRHdVENtStOKicuFF3gSMYREvISqqwxoskIvuz1JR5OHjirnKs1eLsbSStOY/iS4exJ3QPAwIiB/Db4N9wdq0/v6M16lp5aSuyBWHan7rYddIY1rg482ngEEz36UydFZ2sJz8/HkJFB1vr1ZK1fj8LFBfeWLXFv3Rq3Fi1QODld154FQWDZ6WVM2TSF5ELbkEtfZ1+mdp/Kk62fvOXiUOHoiMLRkRR5Aaszt7IqcRW7UnbZBLwDUNYoF+4ZTrhHPTzVHhSXFpBadIG0nCQuLXnapql+bpXE/YkkaCRqBQcnDU6ay9d63C3Mzd1AnCWOE3knSDWnYvW34uSrJNToRpjJnXCTOyFGV0KNboSa3HAQFCgFGfWM7tQzutv6UiuRoyglSV1IkqqIJHUGSaoizquLyFSWXLkzpgyFTIFCrqi2XdvX2VesWXF3dLeLeBzLPMYPB34grzQPsNXzxA6IpXVA68v6yiTkJjD70Gx+PfIr2bps8bhcJscqWHFQOPD7kN8Z1mRYzb+hdwm7U3YzbMkwcRDoh90+5N2u71Yr1hJyE/jx4I/8cuQX8fsLtmjOpOhJjG05Fm9nb/G48MQTlCYni3U3pcnJWLRa8nbvJm/3btuU8CZN8GjdGvfWrVHXcEr4kYwjTN4wWXRmVslVTG4/mXe7vntZEXazMFvMrDu7jqWnlrL1/FY7I8DqSMxPJDE/0e6YQlH136GzW80iQxL3B5KgkZC4AeYcnkOBc4HdsVK5mTOOeZxxzLM7LhdkBJidCTO6E2ZyI8zoTj2T7Zen2TZg0cfihE+pE21K7T1dLAoZpZ5OmL1dEHw9Ufj74hAYiKZOXVxdPDGYDXy842MWn1psJ2aUMiUjmo3gzU5v0ty/eZX9pxen85/1/2H56eWALR00rcc0nmv7XLVvzkaLkRWnVzDr0Cy2nt9a8WwyOe2C2nE04yil5lI8HD1YNWoVXUK7XNs39A5HEARiD8QyecNkTFYTbg5uzB8ynwERA+yuK3c7jj0Qa5emU8qVDI4czKQ2k3go7KFqxaJMJsM5LAznsDDqDBmCMTeXwsOHKTx0iOJTp2xTwo8fp/j4cVLnzcMpNBT31q3xiIrCKSysyppZJVm8t/U9Zh+aLXZ1DYwYyJe9vqShd8Ob8n0pNhSTWpRKSmEKqYW238/ln+N45nGSC5OvGgm8FH+NP8HuwYS4hxDsZvv9eOZxfj36q911z3d44absX+LeQBI0EhK3CIVMQT3PetTzqEdKYQpxuXGkqUrIUJXSPHogkx/6CD+NHwDmkhIM6eno09LQV/rdkJUFFgsKi4BLjg5ydBCXBcTZ7gMSHS2ckWVRR13IIHU9klRFZGnMPNZpIi93fAUf56qf4AVB4Jcjv/DqplfFQuQ+DfoQ2z+WUI/QKtdfLhoT4h7ChNYTcFG78Ppfr2O2mglxD2H9mPVXnSV1t6E363l+7fNiu3kT3yasGLmCCO8I8ZqUwhRmH5zNT4d/EqM3YDMznBg9kSdbP1kjA8LKqL298X34YXwffhhLaSlFx4/bBM6RI1i0WkqTkylNTibjzz9ReXri3ro17lFROEQ04PsjP/Lh3x+KNVqNfRrzde+v6d2gd41f32QxcbH4op1YSSlMqRAwRamX9U6q9nkUaoJcg4jwjiDUPbSKcAlyC6q20F0QBNrXbc/8v+fDWXiry1tMbj+5xq8rce8jCRoJiZuERqWhY3BHuoZ0pUtoF5r6NuXLf77kq3++Eutdutfrzsw+M6tMoFZqNCgbNEDTwL7eRjCbMWRn2wkdQ3o6JRdTodRWq+OpV9CRQDqWBtrdK7+QQ86Ob9FW6rxyrFOHNHUJkzY8Lzrzejl5MaPPDMY0H2P36d5gNvDnmT/58eCPbEvaJh5XyBQMiBjAxOiJ9Arvxae7P+WVTa8Atgnf68aso45rnZv0Xb0zSC1MZejioexP2w/YpoL/8sgvuDq4YrFa2HB2A7EHY1mXsE40Nyz315nUZhK96/e+KfUpCicnPNu1w7NdOwSLhZKzZ+1awk35+eRs3UrO1q3o5RYsThfpqvHhuJcjrz78LpPaTLLr4hIEgWxd9hXFSnpx+nX79ciR08inEQ+GPsigyEG0r9sedwf3q47HqA6ZTMakNpPo5dWL1/a8xrAmw65rHYl7F0nQSEjcAF3DutK1pU3AtA5ojUqhQhAE/jj+B2OWjyGtOA2wfUL/qvdXDI4cfE0/hGVKpdgODrAmbg2vbv6SeCEeL4sjoSY3woxutFXWp7tLKzQFRow5OSAIWEtL0SUmoku0r0UwY+U5lTP9VN1wqlOH3q1G4evYCItWi9LVlYTcBGYdnMWvR3+1G15ZHo15svWTBLkFYbaaeX7t88w6NAuAHvV6sHzk8iv64NyN/J30N8OXDCdbl41cJmdq96m80ekNMksymbl3JrMOzSKlsKI4NcAlgAmtJ/B09NO3dPaUTKHApVEjXBo1ou7o0ejT04nfsYGzuzcSnK/A0aqgR0kIPUpCELJBW3SGBf++yiGvQo5ZU0ktSiW1MLVK99uV8NP4UdetLg4KB4oNxdWmk7ydvOnXsB8xjWLoVb/XPffvQeLORRI0EhI3wNe9v7Ybjnco/RAvrn9R7GJxVDryZqc3ea3Ta9dsilaO2Wom9kAs/7fj/yo8ZGSQrzTQqkEzpvX/nkY+jcTrrUYj+owMWwqrLKpTkJqIPi0dB6scJXJCTbYiZc5C/tnFlFuhaVUWEhS5OKoL6aPyJVXtRHhkG4Z3eopeDfuIUYYSYwmjl41mdfxqAB5r8Rg/x/yMWqG+rme8ExEEgZl7Z/LqplexCBY8HT1ZMHQBKoWKkUtHsuLMCruBoQ+HP8yk6EnENIq5Li+ba6U8FZRamMrp7NPMPTqXfy78g+At4OHhQFddMJ21deioC8RZUOGaVkSzNGgGJKlc2aFx42+NG8cdc7DIBFzULnapn8pfOyodOZpxlPXn1rP53OYqHkONvBsR0yiGgRED6Rjc8Y6YvSVx/yH9q5OQuAnk6HJ4Z8s7doWXw5oMY3rP6dXWpNSE/NJ8pu2cxvcHvrdzyVXKlQxrPIz/9fsfXs5VuzzkajXOISE4h4RgMBv4ZOcnTMudibmeCT+zM8+HPcq4wAHIcgrISzlH8YVknHW2NImLSUFrkx+t9X4VC6aDbOdS4vx34linDlYfd2ac/41z2iNo1Cpe6PIqn/T45J4K/+tMOiaunsj84/MBaObbjJhGMby44UXic+PF67ydvBnfajwToyfetAJbqEgFXS4NlFKYcsVUUIHCwCrXs6xyPYvaKqe9IYi+xgjaF/riYVTaitILmjC2oAk4O+HSohk+bdrjXtYSLggCx7OOszpuNT8c+EF0EC5HIVPQOaQzAyMGMrDRQLs6IgmJ2kISNBISN4DZaua7fd/x3rb3xMLIpr5Nmdl3Zo3GA1THmZwzfLD9A5aeWmrnFuykdGJi9EQ+7fEpjqqruwP/e+Ffnlr1FKeybSaGDbwb8NPAn+hQtwMrzqzgx5xf2W7dDnXA2aok3OTJYO8H6ekaTR29E4b0DAwZGQhmM4LZjP7iRfQXbe224wlkPLY0mCq/hLP7PxVrdMpTZCovL2Tyu89zJqkgicGLBnMk4wgAYR5hxOfG88muT8RrOgV3YlKbSQxrMqxGTs2XojVqRbFSLlAqf32h6AJ6s/6a1pTL5DT2acxDYQ8R5hFmi7CUFdz6a/xRyBUIgkBpUpKt7ubwYUqTk0FXivbf/Wj/3Y+gkJPmJ2et8gx/yo+RqdKJ67s5uNGnQR9iImLo27AvXk5Sy7TEnYUkaCQkboBHlz3KQf1BANwd3PnooY94ts2z15xysApWNp3bxEd/f8Q/F/6xO+fu4M4bnd5gygNTarSu1qjl3a3vMnPvTAQEFDIFUx6YwqPNH2Xe0XkMXTyU3NJc8fpQ91Cejnqa8a3HVynmFaxWjNnZ6NPTSTi9l43/LiRApybM5IanxfZGXj6/p/iUvfuzXK22FznlhckBAcjVd2ZqavO5zYxcOlKcpQWIM7dc1a6MbTmWZ6KfqbYFvhyTxURacdplIyuphal2618NjUojpoBC3EPQqDTsSNnBofRD4jVPtnqSqT2mXrWDSiaT4VyvHs716lFn6FAyL5zlwF+LKT12kuBsUFkgKN3KRBowkQYkOmspCvehXqeHeaDDIByUDjXet4TE7UYSNBISN8C5vHPInGVMiJrA1O5Tq51sfSW0Ri3zjs7j892fi86t5fhp/Pj4oY95svWTNa5J2HRuExNXTxTXaunfkhFNRrD5/GY+2/2ZeJ1CpiCmUQwToyfSM7znZTtwZHI5Dv7+/FV4gBHnX0HnpcMryIvVo1fTwqO5rUanUveV2GputWI1GsWWYvtFZai9vUWh41BJ8Cjd3GoldSUIAi9teIlv931bJY0TFRjFs22eZVSzUWhUGnJ0ORxMO3hZsZJWnFbjriClXEmQa1CVmpXKrcwejh7IZDKKDcVM3TmVr//9WvQa6hTciRl9ZhBdJ7rGzxqfG8+quFWsjl/NrpRdtq4sd9C4quioC2SI0IrWBZ6oDRbCdS5wQg8n1hC/eDfuUVG4R0Xh2rgxctWtrxOSkLgWJEEjIXEDNPdvztejvr6mNxSA5IJkvtv3HbEHY9EatXbnQtxD+OzhzxjeZHiNW33zSvN4ZeMrzD06F7B5fbQLaseprFO8s+0d8bryaMyTrZ8k0DXwcsvZ8dOhn5i0ZhIWwUI9j3qsH7NeLEJ2cXXFJcK+fsJqNmPIzKzWV8daWgqCgDEnB2NODkXHjtndq3B2tkVxLo3s+PkhU978H1c6k465R+by7rZ37Vx8VTIV7eq2I9InEotgYcGJBXy++3NSi1KvKRXk6+x7RbES4BJw1b9jq2Bl7pG5vLXlLdHbpq5bXb7o+QUjm468qgA0W83sSd3D6rjVrIpfZVcDBLZUZq/6vYhpFEP/hv3xd/FHsFjQJiTYBmkeOoQhM9PWEr5lCzlbtiB3dMSteXObwGnZEqWra42/JxIStwpJ0EhI3AC/PPIL9evUr9G1giCwK2UXM/bOYMWZFaJfSTmR3pF80uMTHol8pMbzjgRBYOmppfxn/X/IKskCbCmqQkMhu1J2ARXRmGein6Fn/Z7XtPYH2z/gox0fAbZIxdpH1141rSFXKnEKCsIpKKjKeubCQnvjwLLfjTm29nCLTofu3Dl0587ZL6pQ4ODnV2WiuWOdOig1NZsJVjkV9O+Ff/kz7k/2X9xf7Uwsk2Bid+ruiplL1VA5FVSdWKnrVhcn1fXNXCpnT+oeJm+YzIG0A4BNfLzR6Y2rds0VGYpsA0fjV7EuYZ2dWAOo41qHAQ0HENMohu71ulfZp0yhwDUyEtfISIJGj8aQni7W3ZQkJGDV6ynYv5+C/ftBJsMlIkI09Cu3GJCQuN1IgkZC4gaoSXrEYDaw6OQivvn3Gw5nHK5yPiogio8e+oh+DftdU7olrTiN59Y+x8q4lba9IENAoNBQCNiKWZ+OeprxrcbXOBpTjsliYtKaSaIrbu/6vVkyfAmuDtf/SVwmk6Hy8EDl4YFr48Z256wGA/qMDDuRU/5LMBrBYsGQno4hPZ3CS9ZVurnhGBiI4OdJibuaLI2ZZHUx56xZJGtTxeLbdG16FRF5ORQyBXXd6laIFLeQKo625amgW0FqYSpv/PUGC04sEI+Nbjaazx7+jGD34GrvSSpIYnXcalbHr2Z70vYqQq1VQCtiImIY2GggUYFRNRa2MpkMxzp1CKhTh4ABAzAVFlJ09CgFhw9TfOwYVqMRbVwc2rg4Li5ciENAAO5RUXhERaFp0OCmTgmXkLgSkqCRkLhFZGoziT0Qyw8Hfqjwj6lEp+BOvP/g+zwc/vA1vTEKgsAPB37gtc2voTNVdKGUFwA/EvkIE6MmXlM0pjJao5bhS4az4ewGAMa3Gs+PA368pd4qcgcHnENDcQ61b3EXrFaMeXkUpiSSmRRHUWoS5swsVDnFOJbaOsDMRUVoi4rKp0HgV/arhcxCiqqEJLWSJJU3yWoV51VFJKuLKJVX+Mc4KZ0Y32o8D4Y9KIqVmqSCbgU6k47pe6bz6a5PRa+X6MBovunzDZ1DOttdaxWs7L+4n9Xxq1kVt4rjWcftzqsVarrX687AiIEMiBhw00z+VO7ueHftinfXrliNRopPnbKNYqg8JXzdOrLWrbNNCW/VCveoKNyaNbvuKeESEjVBEjQSEjeZQ+mHmLF3BgtPLKx26nX3sO7898H/8mDYg9e89sazG3l69dOkFqXaHQ9zD2Ni9ESeaPXENUdjKpOpzaT/H/05mG7r3Hqv63t82O3DW1qoW54KunS4YeWCW7uUiQMQBC4WleiUHFo27DPM5EaI0Q0VchwEBQ2NnjQ0elZ5zQxlCUmqIvReTgzsNA7/0EgcA+ug8vSstaLkxScX89rm18S/W3+NP9N6TGNcq3GiMNWZdPyV+Ber4laxJn5NFaHs4+xD/4b9iWkUQ8/wnjcUUasJcrXaJlhatUIYNw5dUpIobsQp4bt2kbdrFzKlEtcmTcTUlNpLavuWuLlIgkZC4iZgtppZeWYlM/bOYGfKzmqv6dugL+91fY+OwR2vaW29Wc+Sk0t4f/v7nC84Lx6XIaNfw3682P5FHg5/+LqiMZWJz42nz+99OF9wHrlMzg/9f2Bi9MQbWlMQBHJLcytamKsRK2nFadecCro0/VP+u0KuYNnxJazZ+wdOBaU2kWN0I8LqQ7jJE0ejrfsowKwhwKyBi1C8eCXF2NJ2ckdHW+fVJYXJDv7+t6zV/GDaQV7a+JJY86RWqHm5w8u83eVt3BzcSC9OZ038GlbFr+KvxL+qFCU39mksuvR2qNuhViJLYOuI04SHowkPp87QoRhycig6fJiCw4fRnjqFYDZTdOwYRceOkTp3Lk5hYXiUiRun0NB7yphRonaQBI2ExA1QpC9i+p7pfLfvuypt1+U80ugR3u36Lm3qtLmmtU9nn2b2odn8fPhncVoy2N7UH2v+GNMennZD0ZjK/JP6DwMXDCS3NBdnlTOLhi1iQMSAq95XYiy5YmQlpTDlmruCLidWgt2DCXQJrPKGbRWsbD63mQ/+/oDVcatFM0KZRoamZXOGRk/CbDXTZ+V4FKVGGpi9ebvRJNqq6ttqdspbzQUBq16P7vx5dOfP229MJkPt61u1+yog4LpbzTO0Gbyz5R1+OfKL2OY9KHIQXzz8BSWmEmb8O4PV8avFgZjlKGQKuoR2EethGng1qG75WsfBxwffnj3x7dnTNiX82LGKKeElJZQmJVGalET6ihWovLxwb90aj6goXKSWcInrRBI0EhI3QN8/+pKhyqj23PAmw3mnyzu0DGhZ4/X0Zj3LTi3jx4M/VhvpGRgxkN8G/4a7o/t17/lSVp5Zyahlo9Cb9fg4+7D20bW0C2qH2WoWu4IuJ1Yu7Z65Es4q56pdQZXESrBb8DV1BWWXZPPLkV/48eCPJOZXDOD0dfblqdZP8XT004S6h/LB9g/4v53/B0CQRxDfjVhG+7rt7daymkwYMjPtPXXKW831elureVYWxqwsio4etbtXodFU6bxyDAzEwde32lZzg9nAjL0z+L8d/ycOdmzq25THWjzGhaILdJ/XvUpK0d3Bnb4N+zIwYiB9G/TF06lqGu1ORuHkhGf79ni2b29rCY+Pt4mbgwcxZGVhysurcUu4uaTkMq8icb8jEwTh+ubC30X06NEDgC1bttTyTiQSExN57bXXCGvaFifN3TuFt7SkiKST+9naYCuFzoXiJ2y5TM6oZqN4p8s7NPFtUuP1TmefZtbBWcw7Nq9akVDfsz6/Dvq1SmHo9VKeCvpyz5d8tvszBAQ8HDzoFNKJfH0+KYUp15wKCnILuqxYCXEPwdPxxutTBEFgZ8pOYg/EsvTUUrtOnm5h3ZgUPYlBkTZH2/zSfMYsH8P6s+sB6BLShSXDl+Dv4n9Nr2cqKKjovKrUgWXKzb3yzQoFDv7+Yqu5Q2Ag+01neePoZxwvtnnBaFQaGno1JCEvwW5eF0A9j3rENIohplEMXUK63JaBl7cbQRDQp6XZxM2hQ5ScPQuV35IqtYS7NGlC+vLlxB06xBy9nhdbtKDz669LhcYSIlKERkLiBinvLnq85eO83fntGg8p1Jv1LD21lFkHZ9lFY8rbr8HmJPtGpzd4t+u71zQzqDwVdLnISmphapWJyQWGAtYmrK12PR9nnyuKlepSQTeTAn0B847OI/ZALKdzTovHPRw9GNdyHM9EP0Nj34pW8BNZJxi0cBDn8m1+Ni+2e5HpvaZfsyiQyWSoPT1Re3ri2rSp3TmLXo+hrNXczlcnIwPBZLK1mqelYUhLE1vN/YFfaUeOojlJqiKS1IUkZRfhpHIjWS0jLKw5AyNt9TBNfJvc83UlMplM9Cyyawk/dIji48ftWsIvRRsfT+pvvxE28cbqvCTuHSRBIyFxAyjlSiZGTeTNzm9Sz7Neje45lX2K2QdnM/foXLuZPgEuAZSaSkUfmejAaH6O+blKyqo8FXQlsVJ5VtPVUMgU1PeqT6h7aLVipa5b3SuauN0qBEHgQNoBYg/EsuDEAjsB1j6oPZPaTGJE0xFV9rb45GLGrxyPzqTDUenIrAGzeLzl4zd9fwpHR5zDwnAOC7Pft9WKMTcXfXo6Oefj2LpvGWTlEWpyw8diiyb4WJzwsTjRRm8fLZKlqXA8X4zDkW2kB56pSF8FBKBwvPYhmHcb1baElxn6mQoKqlxffOLE7d+kxB2LJGgkJG6AlaNX8kCLB656XamplGWnbbUx5d0sYBNEfRv0pcRYwtakrQA4KByYFD2JrmFd2Zmyk/nH59uJlYvFF685FVQuUvw0fmw4u4G4XNsn3sdbPM7PMT/fUekMrVHLguMLiD0YazeAUaPSMKb5GCa1mUTrwNZV7jNbzbyz5R0+3/M5YBshsWLkCqICo27b3gGKjMWsSd/EN/9+w8H0gwhqAerazrlYVLRR1megZyfaquoTUKrGmJGFITMTLBYEk4nSlBRKU1KqrKv29rbNvQoIsJuDpfK4dQZ/tYldS7jVyskpUzBmZ9tdo3C+/UJb4s5FEjQSEjfA1cYAnMo+ZauNOTqvSjSmU91O6M16Np3bhMFiAGw1OAaLgRn7ZjBj34yrvr6Ps89l00DlBnHlgy3TitPo/0d/Ucx81O0j3u367h3zZng88zixB2L57dhvYrEsQHO/5jzb5lnGtBiDm0P1dVe5ulxGLRvFX4l/AdCjXg8WDluIj7PPbdn7+fzzosHdtqRtVQRnkGsQ41uNZ3DjwbQOaF3ley6YzRiysytSV+VprLQ0LDqbeaIxNxdjbi7Fx+0N9OSOjlUnmpe3mt8j3UIyuZw6w4aR9MMPdscDYmJqaUcSdyKSoJGQuEmYrWbSi9NJyE1g2ellrDu7jqSCpGqvzdBmsOzMsirHK78ROqucLytWgt2CCXYPrnEq6HT2afrM70NKYQoKmYLZA2czvvX463rOm0l5HVHsgVi7uUkOCgdGNhvJpOhJdKjb4Yqi63D6YYYsHiJ+r6d0nMK0h6fVeEL59WAVrOy7uE+cWn0iq/rUR+fgznzb71taBbS64noypVIsHq6MIAiYi4rs6nPKRY8xO7ui1TwxEV1i4iWLynDw87PrvBKnmt+FwyS9HngAhbMz2vXr4cABgh97DK8Hrh4dlbh/kASNhMQN8NaWt7i4/aLYFVTugXItBLkGEdMohia+TezEi5eT102JnuxK2UXMghjy9floVBqWjlhKnwZ9bnjdGyEhN4EfD/7IL0d+sevqaujVkEltJjGu5Ti8nb2vus7vx37n6dVPozfrcVY583PMz4xqNuqW7LnEWMLmxM2sjlvNmoQ14jDQ6ugU3Ilv+35bbWrsWpDJZKjc3VG5u+MaGWl3zmo02lrNK3VelUd2rAYDCIJt6nlmJkVHjtjdq3BxqSJyHMpbze/g2UvurVpR180NDhzApVGj2t6OxB2GJGgkJG6ATWc3UeBcUO05D0cPGvs0JiowihD3EFRyFXOPzuVops3HxE/jx7d9v2V4k+G3LO2z7NQyxiwfg8FiwF/jz9pH1xJdJ/qWvNbVMFlMrIxbSeyBWLacr7BQUMqVDI4czKQ2k3go7KEafS9MFhOvbX6NGXttablwz3BWjFxBC/8WN3XPF4suii69WxK3iKnBcuq41KHAUCDO1Ap1D2V6r+kMbTz0lqfy5Go1TsHBOAXbD6sUBAFTfn6Vieb69HRMeTbxaNFqKYmPpyQ+3u5emUKBQ0CAWJ9T2UhQao+WuNO5IwXNqFGjOHy46lTiyMhIVq5cWQs7kpConlCzOyqzgVxFKVYZBLsFMzF6Ik9HPS36nZgsJr7850ve2vKW+IY4tuVYvur1VY2iENfLzL0zeWnDSwgIRHhHsGHMhhp3Yt1MUgpTmH1wNj8d/okMbYUJYah7KBOjJ/Jk6yevWotUmUxtJiOWjmBH8g4A+jTow/wh8/FyuvHZQIIgcCTjiJhKKp9pVY5SrqRraFea+jZl6/mtnMw+CdjSg291fotXO756TeaAtwKZTIbaywu1lxduzZrZnbOUltpazS/x1TFkZiKYTAgWC/qLF9FfvFhlXZWHh336qux3lZcXMvmNjd2QkLgZ3JGCJj4+nm7dutGvXz+74x4eHrWzIQmJyzA1oxOBcjlGhYAsLIiQ8AdwDWiMs6NNqBxOP8xTq57icIZNoIe6h/LjgB/p3aD3LduTVbDy5l9v8sWeLwDoWLcjq0avum0FsgAWq4UNZzcQezCWdQnrxNogGTL6R/Tn2TbP0rt+72v2rtl3cR9DFg3hYrHtDfftzm/z0UMf3ZAHjt6sZ9v5bayOX83q+NVcKLpgd97D0YN+DfuJ3jBTd07l233fiucfa/EYn/b4lCC3oOvew+1C4eSEc716ONezF7aC1YoxJ8dO6JSnr8zFtgJtU0EBpoICtKdP290rV6srZl9VjuwEBCB3cLhtzyYhcccJmosXL1JSUkK3bt145JFHans7EhJXRK9RQqkVtUUG59JIP7eUdMDgIOeX0CTmWHdhwYoMGS+0e4GpPabiona5ZfsxmA2MXzmeBScWALbZQPOHzL9tPjIZ2gx+PvQzsw7NIqWwovU4wCWACa0nMCFqAqEeode19s+Hfua5dc9htBhxUbswd9BchjQecl1rZZdkszZhLaviVrHp3KYqLr31PeuLAx87h3TGaDHy2e7PGL9yvDibql1QO2b0mUGHuh2uaw93EjK53FZA7OeHe6tWdufMWq1951W54MnKAqsVq9FIaXIypcmXzDKTyVB7e1ebvlK6u98x3XUS9w53nKCJL8vp1q9fv5Z3IiFxdVq/+T513d3RnjlD8ZkzaM+c4Z+Co3zs9y8pVtsn23pGd/6b25kHDvpSpNuINTISlwYNbvqn1wJ9AUMWDWFb0jYAnmvzHDP7zrzl05cFQWDr+a3EHozlzzN/YraaxXMPhz/MpOhJxDSKuW6vG6PFyOT1k4k9GAtAhHcEK0auuKbREoIgcDrnNKvjVrMqfhX/pP4jujGDLXL0QPADooiJ9IlEJpMhCAJ/HP+DN/56Q4wKBboE8unDn/JYi8dueML53YDSxQWXhg1xaWjvgG01m21Fx2X1OZVbzi06nW3+VU4OxpwcuKTVXOHsbBfVEYuS/f2RVzP/SnxNo5GSsm4uq8l02esk7k/uOEGTkJAAQIMGtgmyJSUlaDSa2tyShMQVUXt749WpE8o2zfl08xvEHtwMgBIFT5vaMzYlFDUKOwt3mUKBc3g4LpGRuEZGomnY8IaKLi8UXaDv/L5i+/C0HtN4o9Mbt/RTcK4ul7lH5/LjwR+Jz60oLvVy8mJ8q/E8E/1MjcdAXI604jSGLxnOntQ9wLUN5zRZTOxM2cnqOFsqqXwMQjkalYbeDXoTExFDv4b98NX42p3ff3E/kzdM5p8L/wC2VvIpD0zhzc5v3tIo292CXKkUxxZURhAEzIWFFZ1XlaI6xtxcEAQsOh26c+fQnTt3yaK2SJFoHFhJ8Fj1ehI+/ZSU9HQAzn/3HSHvv4/a5/alUiXubO44QRMXF4eDgwMzZsxgzZo1aLVa/Pz8ePrppxk7duxl7ysfQFkd6enpBF7i7yBRuxhLdbW9hRvi0v2vjlvNs2ufFT/Ft63Tlp9jfqa5f3NMRUU2MXP6NNozZyi9cAHBYqEkIYGShAQyV68GuRznsLAKgRMRgbKGQv5E1gn6zu/LhaILKOVK5sTMuSVW/2B7s/rnwj/EHohl8cnFdl0/nYI7ManNJIY1GXZNc6cux+6U3QxbMowMbQYyZHzQ7QPe7fruFaMiBfoC1iesZ3X8atYlrBPHSJRT160uMRExDGw0kG5h3ardZ3pxOm9teYu5R+eKx4Y2HsoXPb+olaLquw2ZTIbKwwOVhweujRvbnbMaDDYvnfIUVqV6HavRCFYrhowMDBkZFF66rkKBYKmwRTDm5ZHy6680mDLlNjyVxN3AHSdoEhISMBgMZGZm8sknn1BaWsqSJUuYOnUqBQUFvPjii7W9RYkbwNXVFZVaTVriydreyg2jUqsxKoyMXjaahScWAuCkdOL/uv8fk9tPFlM9Kjc3PNu2xbNtW8BWk6CNixPTVKXJyWC1iuZoWevWgUyGU0gIrpGRuERG4tKoUbVmaNuTtjNo4SAKDYW4ql1ZPnI5D4c/fNOftchQxO/Hfif2QCzHsyrSB65qV8a2HMsz0c/Q3L/5TXktQRCIPRDL5A2TMVlNuDu4M3/IfPpH9K/2+nN550SX3p0pO+1SXgBt6rRhYMRAYhrF0NK/5WWjVnqznq//+ZpPdn2C1qgFoIV/C2b0mUG3sG435dnud+QODjiHhuIcal9HJVitmPLyqqSu9OnpmPJtDtuVxUw5uqSk27FtibsEmSBUntVe+8yfPx+LxWIXjbFarYwePZqTJ0+ybds2fH19r7BCVcqjN1u2bLnKlRK3g+zsbIqLi69+4TUyfPFwEvMTcdG70O5CO/bV3YdfoB9LRiwRrykfNbD89HKOZ1a8MSvlSrqHd2dI5BDa1Glz1VSNIAhsSdvC2/+8LQ6C7F6vO7MHzibcM/ya9m3R6dDGx4sCR3f+PFirzmpyDA62EzjLUtcz7s9xGC1GAl0CWTdm3VUdaa+Vw+mHiT0Qy/zj8+0KZ1sHtObZNs8yuvnom5p+0Zv1PL/2eeYcmQNAE98m/DnyT7vUlcVqYe/FvWJr9ansU3ZrOCod6VGvBzGNYhgQMYA6rnWu+JqCILDizAqmbJrC+YLzgG2kxNTuU3mq9VO3vAZJ4spYSkvRp6dz/rvvMGZnk261Mkev50lHR+o3aEDkhx/W9hYl7hDuuAjNmDFjqhyTy+WMHDmSt956iwMHDtC3b99a2JnEzcLX1/eaRWlNmNJ/Co8uf1T8s9ZRy+f9Pyc8PJzjmceZdXAWvx37rSIN4Wxzpp0YPZFxLcdVqaG4HCmFKUxaM4n1Z9cD4O7gzle9v2J8q/HXVbOicHYWh/ABWPR6ShISxCJj3blzNn+Q1FT0qalkb97M7x6n+NrHNrgxQhPG2uEraRBwc0zldCYdi04sIvZgLPsu7hOPOymdGN1sNJPaTKqR6LtWUgtTGbp4KPvT9gMwrMkwfnnkF1zULmiNWjad28Tq+NWsiV9Dji7H7l4/jR8DIwYyMGIgD4c/jEZds3TdscxjvLThJbGQWilX8kK7F/jvg//Fw9Hjpj6fxPWhcHJCEx5O2DPPkPDZZ2CwpTllSiV1H330KndL3E/ccYLmcnh723w9dLq7u/ZC4tYR0yiGriFdOXbmGAAtA1pSZCii488d+ffCv+J1KrmKoU2GMjFqIt3CutX4jdkqWPlh/w+8ueVNMSUxpPEQvuv7HYGuN69GS+HoiFvz5rg1t6VwrAYDJWfPUnzmDEWnT/Fx/kL+cLNFJVqX+vFlYluKjn7ByYAAW/QmMhLXRo2uuVjydPZpYg/EMvfoXLvakya+TZgUPYnHWz5+y97k/076m+FLhpOty0Yuk/NJ9094tPmj/Hb0N1bFr2Lr+a0YLUa7e5r5NSMmIoaYRjG0DWp7TR1H2SXZ/Hfbf5l1aJbokdOvYT++6vUVjXwkS/07EZdGjWg8dSqm9eth7VrqPf+8NP5Awo47StCkpaXx9NNP06tXLyZPnmx3LrGsVS/4EptvCYlyXtn4CjtSduCBBwBHM47y99q/xfMR3hFMjJrI2JZjaxyNKedMzhkmrJogDlD01/jzv37/Y2iToTdt/5dD7uCAa9OmqBrVZ7J1IUtP2cTMAPeOTFP3xZSdhGA0isWUudu3A6D28bGJm8aNcYmMRO3rW0W8GcwGVpxZQeyBWP5OrvheqeQqhjUZxrNtnqVzSOdb1i0lCAIz987k1U2vYhEsuDm4MbDhQBadXMSbW960u1YpV9ItrJsYibmeAl2TxcT/9v+PD7Z/IIq2Rt6N+Lr31/RtKEV+73QcAwPx6dYN1q7FQepukriEO0rQBAYGUlhYyJIlS3jiiSdwd7e1ZhYWFvLrr78SFBREVFRULe9S4k5lbcLaao+PbjaaidETeTD0wWt+YzZZTHy++3M+2vGRGCF4stWTTO81HU8nzxvec03JL83nkYWPsDNlJwCT20/mq95fIZfJsZrN6BITxUJjbXw8Vr0eY04Oebt2kbdrFwAqLy+xiyq/joafU5Yx58gvZOuyxdcJ9wznmehneKLVE/hp/G7pM+lMOp5a9ZRYUK2SqygyFDH/xHzxGk9HT/o17EdMoxh61+9do3bty7E+YT2vbHqFMzlnAJsD8AcPfsBzbZ+7bo8cCQmJO4c7StDIZDLef/99/vOf/zBixAhGjx6N0Whk0aJF5ObmMnv2bJRXMF2SuL9xVVftAmro1ZA/hv5xXesdSDvAU6ue4limLYVVz6MeswbOuiVdRFcipTCFPr/34XSOzXJ+es/pvNLxFVGcyZVKXCIicImIgIEDESwWdMnJaE+fpvjMGUri47HodJjy8sjfs4f8PTZPl4cUOtydGnHEyQv3Jk0Z1m0iD9fvecvN4jK1mcw9OpdPdn5il9oyWW1GaQ29GooGd51COqGU39j/+bicOF7Z9ArrEtYBIJfJeSb6GT566KPbOg5CQkLi1nLHqYOePXvyww8/MGvWLL766iuUSiWtW7fmq6++omXLlrW9PYk7mCkPTGHC6gl2x97o9MY1r6Mz6Xh/2/t89e9XWAUrcpmcl9q/xEcPfVTjYtObxdGMo/Sd35d0bTpqhZq5g+YyqtmoK94jUyjQhIejCQ/Hv39/LhSksuSvH0g4sJX6BQ60LvXHw+qAr8WZ3towemvD4G9QHlpJUmScWIfjVLfuTRk6KAgCJ7NPii69leuZwObS2ym4EzGNbPUwN6uGpUBfwEd/f8S3+74VW7kfCnuIb/p8c9OncktISNQ+d5ygAejevTvdu3ev7W1I3GU8FfUUrg6uzNs+D87Cpw9/ylNRT13TGtvOb+Pp1U+LrrLN/Jrxc8zPtAtqdyu2fEW2JG5h8KLBFBuLcXdw589Rf9bYD8UqWNl8bjOxB2NZHbcai2ABJ5A5yehTvzf/CRlAVKkfujhbu7i5uBhzcTEF+/dTsN/WZaRwcbFFfsrqcJxCQmoscIwWIzuTd4qt1eXt0JVRK9S80uEVXn3g1ZsaKbFYLfx06Cfe3fau2A1Vz6MeX/b6kkGRg6QZQhIS9yh3pKCRkLheRjQdQRunNry29TV61u9Z4/sK9AW8tuk1fjr8E2Cr53i367u82flN1Ar1rdruZZl/bD7jV47HZDUR5BrE+jHra2Rcl12SzZzDc5h1aBaJ+YnicV9nX55q/RRPRz9t75PTuw+CIKBPS7PV35T9MhUUYNFqKTx0iMJDtvZwhbMzmogIXBo1wjUyEuewMGSVUsB5pXmiS+/6s+spMhTZ7c1Z5YzOZOtSjAqIYsWoFYS4h9zIt6kK285v46WNL4lpQo1Kw7td3+WlDi/dFPdiCQmJOxdJ0Ejc9/x55k+eW/sc6VrbjJgOdTvw08CfaOrX9LbvRRAEPtv9GW9teQuwRYjWj1lPXbe6V7xnZ8pOYg/EsvTUUrEWBeDB0AeZ1GYSgyMH46CsfhimTCYTZ/L49uiBIAgYMjNFcVN85gym3FwsOh1FR45QdOQIYOu+ym7gxW6fXLaYTvBP9kFbJKgSbeu0pVNwJ7tZSk+0eoLv+32Pk+r6Z1ddyvn880zZPIXlp5eLx55o9QSfdP/kprbUS0hI3LlIgkbiviVTm8kL619gySmbk7CzyplpPabxfNvna8Ud1mK18OL6F/n+wPcAdAvrxoqRKy7r/VKgL2De0XnEHogVC4bB1r0zruU4nol+hsa+jau990rIZDIcAwJwDAiwtcgChuxstGfOUHj6FLvObmOL8Tg7NBdIMhTBxYp7HVDS1bUVAyMGMuSBcRzJOcGY5WMoNBSilCuZ0WcGz7Z59qalfbRGLdN2TuPLf74U50p1rNuRGX1m0Dao7U15DQkJibsDSdBI3HcIgsC8o/N4eePL5Ottc2J61e/FjwN+JMwjrFb2VGoq5dHlj/LnmT8BGNVsFL8+8muVqIogCOxP20/sgVgWnlhIqblUPNc+qD2T2kxiRNMROKucb9reig3FbMrewarcVawtWEuuJhcq1Ub7WDV0Lg7kwZK6tC0NwElQYj0cz9Qtg4n1OIKAgL+jD4uHLqJrg5tTG2cVrPx+7Hfe/OtNMbIW5BrE5z0/Z3Sz0VKdjITEfYgkaCTuK5IKknhmzTNsOrcJsPmcfN37a8a2HFtrb4K5ulwGLhjIPxf+AWBKxyl81vMzu/ZprVHLguMLiD0Yy6H0Q+JxjUrDmOZjmNRmEq0DW9+0PaUUprA6bjWr41ezLWlbFZfeFv4txKnVbeq0wVJYJI5qyDhzlNctS/nb5YLt2lIfPj/fFdf/m0dc+K6KieING6Jwuva0078X/mXyhsniWAZHpSOvP/A6r3d6/bZ3oUlISNw5SIJG4r7AYrXw3b7veGfrO+KQxeFNhvNt32/xd/GvtX2dzz9Pn/l9iM+NR4aMb/p8w4vtKybKH888TuyBWH479hvFxoqBns39mvNsm2cZ02IMbg5uN7wPq2DlYNpBcWr10cyjdudVchUP1XtIdOkN9bCfliz38MCrQweyGngwVvspcbk2MTPG9SFeM7TBYs1AECyUJCRQkpBA5urVIJfjHBYmdlFpGjZEqbm8ILlYdJE3t7zJ78d+F4+NbDqSzx7+rMp+JCQk7j8kQSNxz3My6yQTVk8Q/U/quNbh+37f80jkI7W6r0Pph+g3vx+ZJZk4KByYP2Q+Q5sMRW/Ws+TkEmIPxrIndY94vYPCgZHNRjIpehId6na44YhSqamULee3sCpuFWvi14ipm3K8nLzo37A/MY1i6FW/11WF059n/mTsirEUG4tRK9T8r9//mBBl8wUya7Wik3HxmTOUJieD1YouMRFdYiJZ69aBTIZTSEjFRPHISJQuLpSaSvnyny+Ztmua2CXVOqA1M/rMoEtolxv6HkhISNw7SIJG4p7iYtFFlp1aJn792/bfmLpzqtj583TU03ze8/Nan6S88exGhi0ZhtaoxdPRk5WjVuLv4s+UTVP45cgv5JXmidc29GrIpDaTGNdyHN7O3jf0uhnaDNbEr2F1/Go2n9tsV4MDtrlG5S69HYM71sil12K18MH2D/i/nf8H2GpZlo1YRvu67cVrlC4ueERH4xEdbbtHp0MbHy8KHN3582C1UpqcTGlyMlkbNyIgsCO0hK+cdnHBbPOT8dP48Un3T3ii1RO1UrgtISFx5yIJGol7hsPph+k+rzvkQXe688jCR8h3shX91vesz+yBs3mo3kO1vEv49civTFg1AYtgIcQthJc7vsyHf3/IlvNbxGuUciWDIwczqc0kHgp76LqjMYIgcDzruOjSW153Uo5cJqdzSGexHibCO+Ka1s8vzWfM8jGsP7segC4hXVgyfMlV03gKZ2fcW7XCvVUrACx6PSUJCWIdzqHUfUz32schVRaYQSnIebQgkud1PQk4KqfQsA/Xxo1ReXhc034lJCTuXSRBI3HP8OKGFynQF4jTtgVBAOC1B17jg24f3NTOn+tBEASm7pzKe9veA2zRhlJzKS9vfFm8JtQ9lInRE3my9ZMEuARc1+sYLUb+TvpbdOlNLky2O++qdqVvw74MjBhI3wZ9rzvqcyLrBIMWDhL9ZV5s9yLTe02/rkGPCkdH3Jo3Rx/uz3SHTfxsXYuA7e+vuyySFy82IbjUGcglJ20rOVu3AuAQECCmp1wbNUItTWCWkLhvkQSNxD3D2byzVY55O3nzec/Pa2E39pitZp5b+xyzD80Wj2WVZAG2WUb9I/rzbJtn6V2/93WlUnJ1uaxLWMfq+NVsOLvBroAYIMwjjIERA4lpFEPX0K437H68+ORixq8cj86kw1HpyKwBs3i85ePXvZ7RYuTbvd/y0Y6PRIfhJr5N+Kb3N/Ss3xOr0UhJYmKFm3FCAoLRiCEjA0NGBrnbtwOg9vERi4xdIiNR+/pKLdwSEvcJkqCRuGdo4tuEDG2G3bGWAbU/0DQxP5F+8/sRlxtndzzAJYAJrScwIWrCdXXpxOfGi1GYXSm7sApWu/Ptg9qL9TDN/JrdlDd2s9XM21ve5os9XwAQ4h7CipEriAqMuq71BEFgbcJaXtn4Cgl5CYCtlf6jhz5iUptJYg2PXK3GtazdG8BqNqO7ROBY9XqMOTnk7dpF3q5dAKi8vMQ2cZfISBwCAiSBIyFxjyIJGol7hv/1+x/d53anVGcrdPVx9uH7ft/Xyl4EQWDr+a3M2DuD1fGr7c71qNeDZ9s8S0yjmGtKz5itZvak7hHrYeJz4+3OOymd6FW/FwMjBtI/ov91p6wuR44uh1FLR4m1Pj3q9WDhsIXXPVjyVPYpXt74sugJpJApeLbNs3zQ7YOrpsHkSqVtcGZEBMTEIFgs6JKSxCLjkvh4LDodprw88vfsIX+PrVtM5eGBS6NGtjRV48Y41qkjCRwJiXsESdBI3DNE+kRy8rmTLN61mE1nN7Fk+BIa+TS6rXvI1eUy9+hcfjz4YxXB0aNeD37o/wMNvRvWeL0iQxEbz25kVfwq1iWss+t+Agh0CRRTSd3rdb+p85Eqczj9MIMXDRbrcaZ0nMK0h6fVqAvqUvJK8/hw+4f8b///xNlPPcN78nXvr697fpZMoUBTvz6a+vXx798fwWqlNCVFFDjauDgsWi2mggLy9+4lf+9eAJSurmINjktkJE5169Z4oriEhMSdhSRoJO4pPJ086Vm/J5vYhJvjjRvO1QRBEPjnwj/EHohl8cnF4kyhclRyFfOHzGd40+E1Wi+pIEl06d2etN1u2CRAq4BWooiJCoyycxS+Ffx29DcmrpmI3qzHWeXMzzE/M6rZqGtex2w1M+vgLN7b9p4ozBp4NeDLXl8yMGLgTY2UyMpM+5zDwvDr0wfBakV/8aJN3Jw+jfbMGczFxZiLiynYv5+C/fsBULi42CI/ZXU4TiEhksCRkLhLkASNhMR1UmQo4vdjvxN7IJbjWcfF405KJ0xWE2arGW8nb1aPXk3H4I6XXccqWNl/cb/o0lt5LQC1Qs1DYQ8R0yiGAREDCHEPuWXPVBmTxcSUTVOYuW8mAOGe4awYuYIW/i2uea0tiVt4aeNLnMg6Adg6rd7r+h4vtn/xslPAbyYyuRyn4GCcgoPx69kTQRDQp6VV1OCcOYOpoACLVkvhoUMUHrKNl1A4O6OJiMClUSNcIyNxDgtDppR+bEpI3IlI/zMlJK6Rw+mHiT0Qy/zj88UxCmBzr23m24z5J+ZjFazU86jHhsc2VOvtojPp+CvxL9GlN7Mk0+68t5M3AyIGMDBiIL3q98LVwfWWP1dlMrWZjFg6gh3JOwDo06AP84fMx8vJ65rWOZt3limbprAybiVg6+h6svWTTO0+tVZHTshkMpyCgnAKCsK3Rw8EQcCQmSmKm+IzZzDl5mLR6Sg6coSiI0cAkDs4oImIEIuMncPDkUsCR0LijkD6nyghUQN0Jh2LTiwi9mCsnTmdk9KJ0c1G80z0M6xJWMPHOz4GIDowmjWPrrErzE0rThNdev9K/Au9WW/3Go19GouppA51O9SaE+6+i/sYsmgIF4svAvBOl3f4sNuH17SfIkMRU3dM5Zu934iDLTuHdGZGnxnX3RF1K5HJZDgGBOAYEIBPt24AGLKz7QSOMSsLq8FA8fHjFB+3RdFkajUuDRqINTia+vWRq2+sJV5CQuL6kASNhMQVOJ19mtgDscw9OpdCQ6F4vIlvEyZFT+Lxlo+jUWl4Zs0z/HLkF8AWzVgyfAkalYajGUfF1ur9afvt1lbIFHQJ7SK69DbwanBbn606fj70M8+tew6jxYiL2oV5g+YxuPHgGt9vFaz8euRX3t7ythh1CnYL5oueXzCi6Yi7qqPIwdcXB19fvLvY5kUZc3MriozPnMGQkYFgNFJ86hTFp04BIFOp0ISH41Lmg+PSoAFyh1ufUpOQkJAEjYREFQxmAyvOrCD2QCx/J/8tHlfJVQxrMoxn2zxL55DOyGQytEYtMQtj2HB2AwBjW4xlRNMRvPnXm6yKW0VqUard2m4ObvRt0JeYRjH0bdAXTyfP2/psl8NoMTJ5/WRiD8YCtplOK0auoLFv4xqvsTtlN5M3TOZg+kHAFr16s/ObTHlgSq27NN8M1N7eeHXqhFenTgCYCgpEcaM9cwb9xYsIJpNtCGeczXNIplDgHB4ueuFoGjZE4XRrOtEkJO53JEEjIVFGYn4isw7OYs7hOWTrssXj4Z7hPBP9DE+0egI/jZ94PFObSf8/+otv4I19GrPs9DLmHZtnt249j3qiwV2X0C437NJ7s0krTmP4kuHiZO+YRjHMGzQPd0f3Gt2fUpjCG3+9wcITC8VjjzZ/lE97fEqwe/At2fOdgMrDA68OHfDq0AEAU1GRTcyUdVGVXriAYLFQkpBASUICmatXQ1n3VXkXlaZhQ5QaTS0/iYTEvYEkaCTua8xWM2vi1xB7IJaN5zaKxxUyBTGNYpjUZhIPhz9s1xotCAIbzm7gsRWP2fnCnM45DdgKXzvU7SDWwzTxbXLHplp2p+xm2JJhZGgzkCHjw24f8k7Xd2rUCq4z6fh89+d8vvtzcWp3mzptmNFnBg8EP3Crt37HoXJzw7NtWzzbtgXArNXaBE5Zmqo0ORmsVnSJiegSE8latw5kMpxCQsQiY5fISJQuLrX8JBISdyeSoJG4L7lYdJGfDv3E7EOzxeJXgCDXIJ6OepoJURMIcgsSj5utZnal7GJ13GoWnVxkdw+As8qZXvV7ERMRQ/+I/naRnDsRQRCIPRDL5A2TMVlNuDu4M3/IfPpH9K/RvYtOLuL1za+LKbUAlwA+7fEpj7d8/Jb74twtKF1c8IiOxiM6GgCLToc2Pl4UOLrz58FqpTQ5mdLkZLI22gS1Y3BwhcBp1AiVe80iZRIS9zuSoJG4b7AKVjaf20zswVhWx60WXWplyOjdoDeToifRP6K/6H5bqC9kw9kNrIpfxfqE9eTr8+3Wk8vkPNLoESZETaB7ve44Kh1v+zNdD3qznufXPs+cI3MAW4HznyP/rJGD8cG0g0zeMJndqbsBm0fOKx1e4e0ub9/21vK7DYWzM+6tWuHeqhUAFr2ekoQEsQ5Hd+4cgsWCPjUVfWoq2Zs3A+BYp07FRPHGjVF5eNTeQ0hI3MFIgkbinkJr1Iq1IDqTDoDskmzmHJ7DrEOzSMxPFK/1dfblqdZP8XT004R7hgO2Oppyl96/k//GbDVX+zrBbsFsGbvlmsYY3AmkFqYydPFQseNqWJNh/PLIL7ior5zmyNBm8PaWt/n1yK8ICAAMjhzM9F7Txe+dxLWhcHTErXlz3Jo3B8BqMFBy9qwocErOnUMwmdCnpaFPSyNn61YAHAICKgROZCRq7yvPvZKQuF+QBI3EPcP5/PN0n9edgrQCutOdQQsH0SSiCRvObrAbH/Bg6INMajOJwZGDUSlU7Lu4j58O/cSquFWczD5pt6ZaoaZ7WHesgpVNibYhiu2C2rFm9Bp8Nb639flulO1J2xmxZATZumzkMjmfdP+E1zu9fsX6HoPZwDf/fsPUnVMpNhYD0NyvOd/0+Ybu9brfrq3fF8gdHHBt2hTXprZ5VlajkZJLJooLRiOGjAwMGRnkbt8OgNrX126iuNrX946t2ZKQuJVIgkbinuHZtc+SVJCEBx6AbVBk+aRrD0cPxrUcxzPRzxDiHsLmxM08u/ZZ1iasJasky24dX2df+kf0JyYihgfDHuSlDS/x27HfABgQMYCFQxeiUd89nSmCIDBj7wymbJqCRbDg6ejJwmEL6VW/1xXvWRm3kimbpnAu/xxgcy/++KGPeTr66esaSilxbcjValzLhAqA1WxGd4nAser1GLOzycvOJm/nTgBU3t7iqAaXyEgcAgIkgSNxXyD9VJK4ZziaebTKMZVcxayBs+gc0pktiVuYsnkKWxK3VBkg2cS3iWhw1z6oPQq5giJDEUMXD+WvxL8AmBg1kf/1/99d9WauM+l4evXT/HH8DwBa+rdk+cjlV0wTncg6wUsbXmLL+S0AKOVKnm/7PO8/+P4d45tzPyJXKm2DMyMiICYGwWJBl5QkFhmXxMdj0ekw5eaSv2cP+XtsqVeVhwcujRrZ0lSNG+NYp44kcCTuSe6en8wSElfB28mbDG2G3TFPR0++2/cd41eOtzuulCvpGtqVgREDGRgxkPpe9e3OpxWn0W9+P1EkffzQx7zT5Z276o3gfP55hiwewpGMI4DNG2b2wNmXNbnL1eXy323/JfZgLFbBCthcj7/u/TWRPpG3a9sSNUSmUKCpXx9N/fr49++PYLVSmpJS4WYcF4dFq8VUUED+3r3k790LgNLVVazBcYmMxKluXWmiuMQ9gSRoJO4ZqmsXztJlkaWzpZQ8HD1El94+Dfrg4ehR7Tqns0/TZ34fUgpTUMgU/BTzE0+0euIW7vzms/ncZkYtG0VeaR4KmYLpvaYzuf3kagWZyWLihwM/8MH2D8ROrgjvCL7u/TX9Gva73VuXuE5kZaZ9zmFh+PXpg2C1or940SZuysz+zMXFmIuLKdi/n4L9tsJwhYuLLfJT1kXlFBIiCRyJuxJJ0EjcM1R29y1HIVPwYvsXGRgxkM4hnVEpVFdcY1fKLmIWxJCvz0ej0rBsxDJ6N+h9q7Z80xEEgS/2fMFbW97CKljxdfZl8fDFdAvrVu31G89u5OWNL4umgO4O7rz/4Ps83+75O87RWOLakMnlOAUH4xQcjF/PngiCgD4traIG58wZTAUFWLRaCg8dovDQIcDWXq6JiBDrcJzDwpBJE8Ul7gKkf6US9wzN/JpVSTl1r9edr3p/VaP7l51axpjlYzBYDPhr/Fk3Zt0dORn6cmiNWp5c+SRLTi0BbK69y0csr3b8QHxuPK9uepU18WsAmxfPxOiJfPzQx3dd95ZEzZDJZDgFBeEUFIRvjx4IgoAhM9NuorgpNxeLTkfRkSMUHTkC2LqvNBERYpGxc3g4ckngSNyBSP8qJe4Zvu/3Pd3ndUer0wLg5+LH//r9r0b3ztw7k5c2vISAQIR3BBvGbKCeZ71bud2bytm8swxaOEhsOx/fajzf9/++itlfob6Qj3d8zMy9M8VW9gdDH+SbPt/QKqDV7d62RC0ik8lwDAjAMSAAn27dEAQBY3a23URxY3Y2VoOB4uPHKT5+3HafWo1LgwZiDY6mfn3kaimaJ1H7SIJG4p6hoXdDTjx7gmW7l7H27FqWDF9yVeM7q2Dljc1vMP2f6QB0rNuRVaNX4ePsczu2fFNYG7+WMcvHUGgoRClXMrPPTCa1mWRXL2OxWphzeA7vbH1HTM2FeYQxved0hjQeclcVO0vcGmQyGQ5+fjj4+eHdtSsAxtxcO4FjyMhAMBopPnWK4lOnbPepVGjCw3Fp3Ngmcho0QO7gUJuPInGfIgkaiXsKd0d3utXrxlrWXtX91mA28MTKJ8Qp0YMiB/HHkD9wUjndhp3eOFbBytQdU3l/+/sICAS4BLBk+BI6h3S2u25H8g4mb5gsdjtpVBre6vwWr3R85a55VonaQe3tjVenTnh16gSAqaBAFDfaM2fQX7yIYDLZhnDGxQG27ivn8HDR7E/TsCEKJ+nfmcStRxI0EvclBfoCBi8azPak7QA81+Y5ZvadiUKuqN2N1ZAiQxFjV4xlZdxKwBZZWjpiKXVc64jXJBUk8frm18WaGoDHWzzOtB7T7AZvSkjUFJWHB14dOuDVoQMApqIim5gp66IqvXABwWKhJCGBkoQEMlevhrLuq/IuKk3Dhig1d48xpcTdgyRoJO47LhRdoO/8vpzIOgHApz0+veoIgDuJMzlnGLRwEHG5tk/Ez0Q/w4w+M3BQ2sL8JcYSPt31KV/s+UI0EGwf1J4ZfWbQvm77Wtu3xL2Hys0Nz7Zt8WzbFgCzVmsTOGVpqtLkZLBa0SUmoktMJGvdOpDJcAoJqZgoHhmJ0uXK0VQJiZogCRqJ+4oTWSfoO78vF4ouoJKrmPPIHB5r8Vhtb6vG/HnmT8auGEuxsRi1Qs3/+v2PCVETt9Ny2wAAGhZJREFUAFsK6o/jf/DmX29ysfgiAHVc6/DZw5/xaPNHq/XpkZC4mShdXPCIjsYjOhoAi06HNj5eFDi68+fBaqU0OZnS5GSyNm4EwDE4uELgNGqEyt29Nh9D4i5FEjQS9w3bzm9j8KLBFBoKcVW7smLkCnqE96jtbdUIi9XC+9vfZ+rOqQAEuQaxbMQyMeKy7+I+Jm+YzL8X/gXAQeHAaw+8xhud37hqLZGExK1C4eyMe6tWuLdqBYBFr6ckIUGsw9GdO4dgsaBPTUWfmkr25s0AONapUzFRvHFjVB4eAOTv20fqhg0AFJ08CeHSpHeJCiRBI3FfsPDEQsb9OQ6jxUigSyDrx6ynZUDL2t5WjcgvzWfM8jGsP7segC4hXVgyfAn+Lv6kFafx1pa3mHd0nnj98CbD+bzn54R5hNXSjiUkqkfh6Ihb8+a4NW8OgNVgoOTsWVHglJw7h2AyoU9LQ5+WRs7WrQA4BASgdHenJC4OrdU2luPiwoUEu7uLHVkSEpKgkbinEQSBr/75iimbpwDQ2KcxGx7bQIh7SC3vrGYczzzO4EWDxYnXL7Z7kem9pmMRLHyy8xM+2fkJJaYSwDZ4ckafGTwY9mBtbllCosbIHRxwbdqUEm9vlK1b42oyUXrhAqVJSeiSktClpCCYzZCWZvsF5JQJmhyrlcPLlhFet+5VX8fZ2ZmAgIBb+iwStY8kaCTuWSxWCy9vfJkZe2cAtsjGylEr75qJ0YtPLmb8yvHoTDoclY7MGjCLx1o8xvLTy5myeQpJBUkA+Dr7MrX7VJ5s/eRd06UlIVFOUVERL7zwAtYyoVJTVhmNcOECvPbaVa+Vy+X8/PPPuLm5Xe82Je4CJEEjcU+x+ORi5m2fhwMOPLnySXZpdwEwrMkwfhv8WxXn3DsRs9XM21ve5os9XwAQ6h7K8pHLUcgUdJ/XXWw1V8qVTG4/mfe6voe7o1REKXF34ubmxrfffotOp7videl//knBwYN2xzyiowkcNOiqr+Hs7CyJmfsASdBI3DP8fOhnJqyegIfOg+50t7VlO8Pk9pP5qvdXd0WXT44uh1FLR7Hl/BYAetTrwXd9v+Obvd8w+9BsrILtU+yAiAF82etLIrwjanO7EhI3hZqkg0Kfe47kWbPEKeEebdsSOnEiCsc7/0OKxO1BEjQS9wzT90yvcsxP48c3fb65/Zu5Dg6nH2bwosEkFyYD8ErHVwjUBNLh5w4UGgoBWw3Q172/vqsmgEtI3AwUjo6Ev/giFr1e/LOERGUkQSNxz1BsLK5yTK24O4bm/Xb0NyaumYjerMdZ5cwL7V5gxZkVxOfGA+Dh6MGH3T7k2TbPolKoanm3EhK1hyRkJC7HnR+Dl5CoIQMiBlQ51r9h/1rYSc0xWUxMXj+ZsX+ORW/WE+wWTFRAFJ/t/oz43HjkMjnPtXmOhBcSeLH9i5KYkZCQkLgMUoRG4p7hy15fUqAvYON+m/tor/q9+LLXl7W8q8uTqc1kxNIR7EjeAUCYexipRamkFqUC0L1ed77p/Q3N/ZvX5jYlJCQk7gqkCI3EPYNGrWHhsIXsfmo3ANMenoZGfWcOwdt3cR/Rs6JFMeOkdCKpMAmLYCHcM5wVI1fw1+N/SWJGQkJCooZIERqJe447vTX750M/89y65zBajMhlcqyClVJzKS5qF97t8i4vdXhJHDQpISEhIVEzJEEjIXGbMJgNTN4wmR8P/igeswpWZMh4otUTTO0+lUDXwFrcoYSEhMTdiyRoJCRuA2nFaQxeNJh9F/fZHX8g+AFm9JlBmzptamlnEhISEvcGkqCRkLjF7EzeycAFA0UvGbBNy57eazojm45EJpPV4u4kJCQk7g0kQSMhcYsQBIHXNr/GV/98hYAAgFqu5q0ub/F6p9dxVjnX8g4lJCQk7h3ueEFz8eJFBg4cSK9evfj0009rezsSEjXibN5Zev/em8T8RPFY/4b9+b7/93fNpG8JCQmJu4k7WtAIgsDbb79NSUlJbW9FQqJGlJpKeW/re3y992tx7pKHoweLhi2iV/1etbw7CQkJiXuXO1rQzJ8/n4OXTFeVkLgTEQSBJaeW8ML6F8gqyRKPD2s8jIXDFqKQK2pxdxISEhL3PnessV5KSgpffvkl//nPf2p7KxISV+Rw+mG6/tKVkUtHimLGUenIipErWDJiiSRmJCQkJG4Dd6SgsVqtvPnmmzRq1Ihx48bV9nYkJKolU5vJ06ueJmpWFLtSd4nHI30iOfXcKQZFDqq9zUlISEjcZ9yRKae5c+dy4sQJ/vzzT+TymmmuHj16XPZceno6gYGSYZnEzcFoMTJz70w++vujKhO+xzQfw6yBs6QOJgkJCYnbzB0naBITE/nmm2+YPHky4eHhGAyGG15ToVBIgkbihhEEgTXxa3hl0yuczTsLgAwZAgIKmYLpvaYzuf1kyVdGQkJCoha4owSNxWLhrbfeonHjxowfP/6a7t2yZcst2pWEBJzMOsnLG19mc+JmwCZkAAQEfJ19WTx8Md3CutXiDiUkJCTub+4oQTNnzhxOnDjBvHnzKCgoAMBkMgFgNBrJy8vDxcUFtVpdi7uUuJ/IK83j/W3v88OBH7AIFgD8Nf5klmQC0KZOG5aPWE6we3BtblNCQkLivueOEjQ7duzAbDbz6KOPVjm3du1a1q5dy7Rp0xgyZEgt7E7ifsJsNfPjgR/57/b/kleaByAa4qUUpgAwvtV4vu///R0/3VtCQkLifuCOEjRvvPEGRUVFdsdMJhMTJ06kc+fOPPXUUzRo0KCWdidxv/BX4l+8tOElTmafBMDNwY1hjYex7PQyCg2FKOVKZvaZyaQ2k6R6GQkJCYk7hDtK0DRr1qzKsfKiYF9fXx544IHbvSWJ+4izeWd5ddOrrIpbBdjqZJ5s9SRezl5M3zMdAYEAlwCWDF9C55DOtbxbCQkJCYnK3JE+NBISt5MiQxFvbH6DJv9rIoqZLiFd2P7EdrJLs/lizxcICHSs25GDEw9KYkZCQkLiDuSOitBISNxOLFYLvx75lXe2viMW+Ya4hzC953Sa+jZlyOIhxOXGAfBM9DPM6DMDB6VDbW5ZQkJCQuIy3PGCxsHBgbi4uNrehsQ9xq6UXUzeMJlD6YcAcFY581bnt3i146tsOLuB9j+3R2vUolao+V+//zEhakIt71hCQkJC4krc8YJGQuJmklKYwuubX2fRyUXiscdaPMa0HtMIdAnk/e3vM3XnVACCXINYNmIZ7eu2r63tSkhISEjUEEnQSNwXlBhL+Hz353y+53P0Zj0Abeu0ZUafGXQM7kh+aT4DFwxk/dn1AHQN7criYYvxd/GvzW1LSEhISNQQSdBI3NMIgsDCEwt5/a/XuVB0AYBAl0A+ffhTHmvxGHKZnOOZxxm8aDDn8s8BMLn9ZL7o+QUqhao2ty4hISEhcQ3IBEEQansTEhI3A7PVzH+3/ZfFuxbT8lhLNP01nBXO8s+FfwBwUDjwasdXeavLW7ioXQBYfHIx41eOR2fS4ah0ZPbA2TzW4rHafAwJCQkJietAitBI3DO8v+19pu2ahofOA4DVcaspcC4AYGjjoXzR8wvqedYDbOLn7S1v88WeLwAIdQ9l+cjlRAVG1cbWJSQkJCRuEEnQSNwzzD8+v8oxlVzFpsc32Q2OzNHlMGrpKLactw007VGvBwuHLcTH2ed2bVVCQkJC4iYjCRqJewa5rKpPZF23unZi5nD6YQYvGkxyYTIArz3wGp/0+ASlXPqvICEhIXE3IzkFS9wzPNX6qSrHKvvH/Hb0Nx6Y8wDJhck4q5xZOHQhn/f8XBIzEhISEvcA0k9yiXuGt7q8hVKuZOGuhQC80O4F3uz8JiaLiSmbpjBz30wAwj3D+XPknzT3b16b25WQkJCQuIlIXU4S9xyJiYm89tprfPHFF2j8NIxYOoIdyTsA6NugL/OHzMfTybOWdykhISEhcTORUk4S9xSphaksPrEY+P/27jyoqvr/4/jTK4teAQGVEdE00e64oaOJTKKkIqWFIi75FTVbHB1HUzNnIMYmc/tStuqYVuooMJohIq7VCAwyhmuKWfq1NBVCTYhFUiDg94c/bnOlRQnv9djrMcPo+fA5n/M+zNzLi/c5ByDzx0z6fNTHGmZiB8Sy4z87FGZERB5A6tDIA+PoT0cZvHEwpl9MDP5+MGmd0igyF+Hm4sbGiI2M6jLK0SWKiMg9ontoxBAuX77Mr7/++pdzXt7+MqZfTLjdvPVL89xuuuHq5Eri04k87Pow586d+9N9zWYzrVu3btCaRUTEftShkfteSUkJL7zwAtXV1ffsGCaTibVr1+Lh4XHPjiEiIveOAo0Ywp10aKbvnM7hvMM2Y4FtA/nwqQ//dn11aEREjE2BRh4Y/yv4H4M3DCavNA8AP3c/0p5N45EWjzi4MhERudcUaOSBUnSziLTzaQAMfngwnk08HVuQiIjYhQKNiIiIGJ5+D42IiIgYngKNiIiIGJ4CjYiIiBieAo2IiIgYngKNiIiIGJ4CjYiIiBieAo2IiIgYngKNiIiIGJ4CjYiIiBieAo2IiIgYngKNiIiIGJ6TowsQaWgTJ04kPz/f0WWIyD3i6+tLQkKCo8uQ+4wCjTxwTpw4QVVVFb6+vo4uRUQaWH5+PlevXnV0GXIfUqCRB46Pjw8A+/btc3AlItLQhgwZ4ugS5D6le2hERETE8BRoRERExPAUaERERMTwFGhERETE8BRoRERExPAUaERERMTwGtXU1NQ4uggRERGRf0IdGhERETE8BRoRERExPAUaERERMTwFGhERETE8BRoRERExPAUacYjz589jsVjo0qULV65ccXQ59VJZWUl+fr6jyxAxNIvFwqRJkxxdhjwAFGjEIbZv347ZbKa6uprk5GRHl3PX8vLyCA8PJyMjw9GliIgICjTiADU1NezYsYOgoCAsFgvbtm1zdEl3LTc3l/Pnzzu6DBER+X8KNGJ3R48eJTc3l759+zJo0CAuXLjAoUOHHF2WiIgYmAKN2F1qaioAQUFBhIaGApCUlGQzJzo6GovFQnl5uc348uXLsVgs5ObmWsfKyspYunQpAwYMoGfPnjz77LOcOXOGrl27smLFCuBWR8VisbB8+XKb9crLy7FYLERHR1vHrl+/TmxsLIMGDaJ79+4MGjSIN954g19++QWA5ORkJk+eDMDrr7+OxWJpiC+LyL/epEmT6N+/f53xuXPn2rzOkpOTsVgsnDx5kpiYGPr160fPnj157rnnOH36tM2+NTU1bNiwgaeeeooePXrQv39/YmNjuXbt2j0/H7EvJ0cXIP8uFRUV7N27l7Zt29K1a1cA/Pz8+OKLL3jttddwc3O7q/Wqq6uZOnUqX3/9NWPHjuWRRx4hLS2NSZMmUV1dXa8a58yZw5EjR5g8eTLt2rXj7NmzJCYmcubMGRITE+nbty/Tp09n9erVREZGEhQUVK/jiMg/M3v2bNq1a8dLL73E1atXWbduHVOnTiU9PR0np1vf3hYsWEBSUhLh4eFMnDiRvLw8EhMTyc7OJikpCS8vLwefhTQUBRqxq4yMDIqLixk9erR1LCwsjPXr17Nr1y6eeeaZu1pvx44dHD16lJiYGKZMmQJAVFQUM2bMIC0t7a7rKywsZP/+/URFRfHyyy9bx81mM5mZmZSWltKuXTsee+wxVq9eTUBAACNHjrzr44jIP+fv78/HH39s3XZycmLlypUcPHiQ/v37c/jwYT777DOb9weAYcOGMXbsWNasWWPTnRVj0yUnsavay01PPvmkdaz2/7dfdroTX375JWazmQkTJljHGjVqxLRp0+pVn5ubG25ubuzevZvk5GRKSkqAW12b5ORk3N3d67WuiDS8YcOG2Wx36dIFgJ9//hmAzz//HIDBgwdTWFho/fD19aVz586kp6fbt2C5p9ShEbspKioiIyMDb29vvL29rffBtGjRAm9vb3Jycjh79iydO3e+4zUvXLhAmzZtcHFxsRn39/evV40uLi4sWrSI2NhYYmJiWLBgAT179iQ0NJTIyEg8PT3rta6INLwWLVrYbNe+D9Rebr5w4QIAQ4cO/cP9nZ2d72F1Ym8KNGI3e/bsobKyksLCQuvNwLfbunXrX7aAq6qqbLYrKytp2rRpnXmurq53VNPt6wEMHz6cAQMGsG/fPjIzMzlw4ABxcXGsW7eO5ORkfHx87mhtEbFVXFyM2WyuEyQaN278l/v90esUbnVj/0p1dTWurq6sXr367goVQ1KgEbupvdy0cOFCWrZsafO5kpISYmJiSE1NZd68eZhMt66GVlRU2IST259MaN++PUeOHKGqqsrmTfHHH3+0mVf7uYqKCpvx29e7ceMGp0+fpk2bNkRERBAREUF1dTXr16/nzTffJDU1lRdffLEeZy/y7xYfH8/ixYvZtm2b9YGAGzduAODh4QGAyWSq8xqFuq/TO+Xn50dWVhadOnWq84NIWlqaOq4PGN1DI3Zx6dIljh07Rrdu3Rg/fjyhoaE2H5GRkfTr14+CggLS09Np1aoVAN9++611jeLiYrKysmzWDQsL4/r169awVCs+Pt5m29PTEycnJ7777jub8Z07d9psX7lyhfHjx/PJJ59Yx0wmEz169AB+D0a1/9b3SSqRfxs/Pz8ATp06ZR375ptvAKyPZLds2ZKSkhIuXbpknXPx4kVOnjxZr2MOGTIEgFWrVtmMHz9+nBkzZrBhw4Z6rSv3J3VoxC5qA8eYMWP+dM6ECRM4ePAgW7duZd68eaxZs4b58+fz/PPPU1NTw+bNm2nevDmFhYXWfSIiItiyZQuxsbHk5OTQqVMnsrKyOHDgAPB7S7pp06aEhoayd+9e5s+fT2BgIMePHyc9Pd3mp7QOHTrwxBNPEB8fT1lZGb169aKoqIiEhAS8vLx4+umnAayPeu7atQsXFxdGjRplfUxUROoaMGAA7du3Jy4ujoKCApo0acL69esxm81ERkYCMGLECHbu3Mm0adOIioqitLSUhIQE/Pz86vWbuUNCQggLC2PTpk3k5+czcOBACgoKSEhIwMPDg9mzZzf0aYoDqUMjdpGamkqTJk0IDw//0zmhoaH4+Piwf/9+mjdvzooVK/Dy8mL58uXEx8czevRopk+fbrNP48aN+eijjxgzZgx79uwhLi6Omzdv8vbbbwPY3Cy8cOFCIiMjyczMZMmSJVy+fJn4+Hhru7tWXFwc06dP5+jRoyxevJi1a9fSu3dvNm3aZO0c+fv7M2nSJE6fPs3SpUv56aefGupLJfJAcnZ2Zt26dQQFBbFx40bef/99OnbsSGJiIr6+vsCtALJw4UKqqqpYtmwZKSkpzJkzhxEjRtT7uO+++y7z5s3j0qVLLFu2jC1bthAUFMSmTZvo2LFjQ52e3Aca1dTU1Di6CJH6Kioqwmw213nK6cSJE4wbN44lS5b8ZVdIREQeDOrQiKElJibSq1cv6+OZtXbv3g1AQECAI8oSERE7U4dGDO3cuXOMHDkSX19fxo0bh4eHB8eOHSMlJYWIiAj++9//OrpEERGxAwUaMbycnBxWrVpFTk4O169f56GHHiIyMpIpU6ZYH/8WEZEHmwKNiIiIGJ5+fBURERHDU6ARERERw1OgEREREcNToBERERHDU6ARERERw1OgERHDKC8vZ926dTZj0dHRWCyWOn94VET+XRRoRMQwJk6cWOcvJ4uIgAKNiBhIQUGBo0sQkfuUAo2IiIgYngKNiNjV6NGj6dGjB+Xl5TbjkZGRWCwWvvrqK5vxJUuWYLFYsFgs5OXlUVpaisViITo62mZeSUkJixYtIjg4mICAAEaNGsWePXts5iQnJ1uPsXbtWsLCwujevTuhoaF8+OGHVFVV3ZuTFpF7ToFGROxq4MCBVFRUcOzYMetYcXGx9abew4cP28zfv38//v7+zJw5E3d3d1xcXJg5cyahoaE28+bOnUtaWhrDhw8nPDycH374gTlz5rBv3746Nbz11lusXLmSPn36EBUVxc2bN3nvvff44IMP7sEZi4g9KNCIiF2FhIQA2HRiDh06RHV1NWaz2SbQ5Obmcv78eUJCQpg1axYeHh64uroya9asOoGmdevW7Ny5k1dffZUlS5bwzjvvAJCUlFSnhosXL5KSksKyZcuIiYlh8+bNODs7/+FcETEGBRoRsauAgAC8vLxsAk12djaenp4MHTqUnJwcKioqAMjKygLg8ccf/9t1J0+eTLNmzazbISEhmEwmcnNz68wNCwujffv21u22bdvi7+/PtWvX6lwKExFjUKAREbsymUwEBwdz6tQpSktLgVuBpm/fvvTq1YubN29y8uRJ4NblJnd3d/r06fO363bo0MFm29nZmWbNmlFWVva3cwHc3d0BrGFKRIxFgUZE7C4kJISqqioOHjzItWvX+P777wkMDCQwMBCAI0eOUFlZSXZ2NsHBwTg5Of3tmq6urnd8fBcXlzpjjRo1AqCmpuaO1xGR+8ffv0uIiDSw4OBgTCYT2dnZ1o5IYGAgnTp1okWLFhw+fJjevXtz/fr1O7rcJCKiQCMidufl5UVAQADZ2dnU1NTg6emJxWIBbgWbzMxMMjIyMJlMDBw40MHViogR6JKTiDjEwIEDOXv2LOnp6Tz66KPWSz6BgYGUlZXx6aefEhAQgLe3t3UfZ2dnfvvtN0eVLCL3MQUaEXGI2se38/LyrPfOAPTr1w+A0tJS65xaPj4+3Lhxg1deeYWUlBS71Soi9z8FGhFxiG7dutGqVSvg9xAD4O/vbx2//f6Z+fPn07lzZ/bu3cv27dvtVquI3P8a1eiWfhERETE4dWhERETE8BRoRERExPAUaERERMTwFGhERETE8BRoRERExPAUaERERMTwFGhERETE8BRoRERExPAUaERERMTwFGhERETE8BRoRERExPAUaERERMTw/g9W7bD8GIlIVgAAAABJRU5ErkJggg==\n", 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", 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" ] @@ -1525,7 +1512,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1539,7 +1526,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.10" }, "toc": { "base_numbering": 1, diff --git a/notebooks/01_ANOVA.ipynb b/notebooks/01_ANOVA.ipynb index 1eb7a91d..28030333 100644 --- a/notebooks/01_ANOVA.ipynb +++ b/notebooks/01_ANOVA.ipynb @@ -166,7 +166,7 @@ " DF\n", " MS\n", " F\n", - " p-unc\n", + " p_unc\n", " np2\n", " \n", " \n", @@ -196,7 +196,7 @@ "" ], "text/plain": [ - " Source SS DF MS F p-unc np2\n", + " Source SS DF MS F p_unc np2\n", "0 Hair color 1360.726 3 453.575 6.791 0.004 0.576\n", "1 Within 1001.800 15 66.787 NaN NaN NaN" ] @@ -225,7 +225,7 @@ "- DF : degrees of freedom\n", "- MS : mean squares (= SS / DF)\n", "- F : F-value (test statistic)\n", - "- p-unc : uncorrected p-values\n", + "- p_unc : uncorrected p-values\n", "- np2 : partial eta-square effect size \\*\n", "\n", "\\* *In one-way ANOVA, partial eta-square is the same as eta-square and generalized eta-square.*\n", @@ -272,12 +272,12 @@ " \n", " A\n", " B\n", - " mean(A)\n", - " mean(B)\n", + " mean_A\n", + " mean_B\n", " diff\n", " se\n", " T\n", - " p-tukey\n", + " p_tukey\n", " hedges\n", " \n", " \n", @@ -359,21 +359,21 @@ "" ], "text/plain": [ - " A B mean(A) mean(B) diff se T \\\n", - "0 Dark Blond Dark Brunette 51.2 37.4 13.8 5.169 2.670 \n", - "1 Dark Blond Light Blond 51.2 59.2 -8.0 5.169 -1.548 \n", - "2 Dark Blond Light Brunette 51.2 42.5 8.7 5.482 1.587 \n", - "3 Dark Brunette Light Blond 37.4 59.2 -21.8 5.169 -4.218 \n", - "4 Dark Brunette Light Brunette 37.4 42.5 -5.1 5.482 -0.930 \n", - "5 Light Blond Light Brunette 59.2 42.5 16.7 5.482 3.046 \n", + " A B mean_A mean_B diff se T p_tukey \\\n", + "0 Dark Blond Dark Brunette 51.2 37.4 13.8 5.169 2.670 0.074 \n", + "1 Dark Blond Light Blond 51.2 59.2 -8.0 5.169 -1.548 0.436 \n", + "2 Dark Blond Light Brunette 51.2 42.5 8.7 5.482 1.587 0.415 \n", + "3 Dark Brunette Light Blond 37.4 59.2 -21.8 5.169 -4.218 0.004 \n", + "4 Dark Brunette Light Brunette 37.4 42.5 -5.1 5.482 -0.930 0.789 \n", + "5 Light Blond Light Brunette 59.2 42.5 16.7 5.482 3.046 0.037 \n", "\n", - " p-tukey hedges \n", - "0 0.074 1.414 \n", - "1 0.436 -0.811 \n", - "2 0.415 0.982 \n", - "3 0.004 -2.337 \n", - "4 0.789 -0.627 \n", - "5 0.037 2.015 " + " hedges \n", + "0 1.414 \n", + "1 -0.811 \n", + "2 0.982 \n", + "3 -2.337 \n", + "4 -0.627 \n", + "5 2.015 " ] }, "execution_count": 3, @@ -601,7 +601,7 @@ " ddof1\n", " ddof2\n", " F\n", - " p-unc\n", + " p_unc\n", " np2\n", " \n", " \n", @@ -620,7 +620,7 @@ "" ], "text/plain": [ - " Source ddof1 ddof2 F p-unc np2\n", + " Source ddof1 ddof2 F p_unc np2\n", "0 Hair color 3 8.33 5.89 0.019 0.576" ] }, @@ -661,8 +661,8 @@ " \n", " A\n", " B\n", - " mean(A)\n", - " mean(B)\n", + " mean_A\n", + " mean_B\n", " diff\n", " se\n", " T\n", @@ -755,13 +755,13 @@ "" ], "text/plain": [ - " A B mean(A) mean(B) diff se T df \\\n", - "0 Dark Blond Dark Brunette 51.2 37.4 13.8 5.577 2.475 7.907 \n", - "1 Dark Blond Light Blond 51.2 59.2 -8.0 5.637 -1.419 7.943 \n", - "2 Dark Blond Light Brunette 51.2 42.5 8.7 4.966 1.752 6.563 \n", - "3 Dark Brunette Light Blond 37.4 59.2 -21.8 5.329 -4.091 7.995 \n", - "4 Dark Brunette Light Brunette 37.4 42.5 -5.1 4.613 -1.106 6.822 \n", - "5 Light Blond Light Brunette 59.2 42.5 16.7 4.686 3.564 6.772 \n", + " A B mean_A mean_B diff se T df \\\n", + "0 Dark Blond Dark Brunette 51.2 37.4 13.8 5.577 2.475 7.907 \n", + "1 Dark Blond Light Blond 51.2 59.2 -8.0 5.637 -1.419 7.943 \n", + "2 Dark Blond Light Brunette 51.2 42.5 8.7 4.966 1.752 6.563 \n", + "3 Dark Brunette Light Blond 37.4 59.2 -21.8 5.329 -4.091 7.995 \n", + "4 Dark Brunette Light Brunette 37.4 42.5 -5.1 4.613 -1.106 6.822 \n", + "5 Light Blond Light Brunette 59.2 42.5 16.7 4.686 3.564 6.772 \n", "\n", " pval hedges \n", "0 0.140 1.414 \n", @@ -913,7 +913,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -975,7 +975,7 @@ " ddof1\n", " ddof2\n", " F\n", - " p-unc\n", + " p_unc\n", " ng2\n", " eps\n", " \n", @@ -996,7 +996,7 @@ "" ], "text/plain": [ - " Source ddof1 ddof2 F p-unc ng2 eps\n", + " Source ddof1 ddof2 F p_unc ng2 eps\n", "0 Year 2 38 17.365 4.398e-06 0.383 0.988" ] }, @@ -1045,18 +1045,18 @@ " Contrast\n", " A\n", " B\n", - " mean(A)\n", - " std(A)\n", - " mean(B)\n", - " std(B)\n", + " mean_A\n", + " std_A\n", + " mean_B\n", + " std_B\n", " Paired\n", " Parametric\n", " T\n", " dof\n", " alternative\n", - " p-unc\n", - " p-corr\n", - " p-adjust\n", + " p_unc\n", + " p_corr\n", + " p_adjust\n", " BF10\n", " hedges\n", " \n", @@ -1127,12 +1127,12 @@ "" ], "text/plain": [ - " Contrast A B mean(A) std(A) mean(B) std(B) Paired Parametric \\\n", - "0 Year 2010 2014 5.092 1.006 4.575 0.876 True True \n", - "1 Year 2010 2018 5.092 1.006 3.357 0.953 True True \n", - "2 Year 2014 2018 4.575 0.876 3.357 0.953 True True \n", + " Contrast A B mean_A std_A mean_B std_B Paired Parametric \\\n", + "0 Year 2010 2014 5.092 1.006 4.575 0.876 True True \n", + "1 Year 2010 2018 5.092 1.006 3.357 0.953 True True \n", + "2 Year 2014 2018 4.575 0.876 3.357 0.953 True True \n", "\n", - " T dof alternative p-unc p-corr p-adjust BF10 hedges \n", + " T dof alternative p_unc p_corr p_adjust BF10 hedges \n", "0 1.629 19.0 two-sided 1.197e-01 1.197e-01 holm 0.717 0.537 \n", "1 5.762 19.0 two-sided 1.494e-05 4.481e-05 holm 1532.361 1.736 \n", "2 4.228 19.0 two-sided 4.549e-04 9.099e-04 holm 72.806 1.305 " @@ -1261,7 +1261,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1320,13 +1320,13 @@ " ddof1\n", " ddof2\n", " F\n", - " p-unc\n", - " p-GG-corr\n", + " p_unc\n", + " p_GG_corr\n", " ng2\n", " eps\n", " sphericity\n", - " W-spher\n", - " p-spher\n", + " W_spher\n", + " p_spher\n", " \n", " \n", " \n", @@ -1349,10 +1349,10 @@ "" ], "text/plain": [ - " Source ddof1 ddof2 F p-unc p-GG-corr ng2 eps \\\n", + " Source ddof1 ddof2 F p_unc p_GG_corr ng2 eps \\\n", "0 Year 2 38 17.365 4.398e-06 4.932e-06 0.383 0.988 \n", "\n", - " sphericity W-spher p-spher \n", + " sphericity W_spher p_spher \n", "0 True 0.988 0.895 " ] }, @@ -1717,13 +1717,13 @@ " ddof1\n", " ddof2\n", " F\n", - " p-unc\n", - " p-GG-corr\n", + " p_unc\n", + " p_GG_corr\n", " ng2\n", " eps\n", " sphericity\n", - " W-spher\n", - " p-spher\n", + " W_spher\n", + " p_spher\n", " \n", " \n", " \n", @@ -1746,10 +1746,10 @@ "" ], "text/plain": [ - " Source ddof1 ddof2 F p-unc p-GG-corr ng2 eps sphericity \\\n", + " Source ddof1 ddof2 F p_unc p_GG_corr ng2 eps sphericity \\\n", "0 Within 3 24 5.201 0.007 0.017 0.346 0.694 True \n", "\n", - " W-spher p-spher \n", + " W_spher p_spher \n", "0 0.307 0.163 " ] }, @@ -1774,14 +1774,6 @@ "execution_count": 20, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/raphael/GitHub/pingouin/pingouin/pairwise.py:761: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " mat_upper = mat_upper.applymap(replace_pval)\n" - ] - }, { "data": { "text/html": [ @@ -1871,14 +1863,6 @@ "execution_count": 21, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/raphael/GitHub/pingouin/pingouin/pairwise.py:763: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " mat_upper = mat_upper.applymap(lambda x: ffp(x, precision=decimals))\n" - ] - }, { "data": { "text/html": [ @@ -2067,7 +2051,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -2114,7 +2098,7 @@ " DF2\n", " MS\n", " F\n", - " p-unc\n", + " p_unc\n", " np2\n", " eps\n", " \n", @@ -2161,7 +2145,7 @@ "" ], "text/plain": [ - " Source SS DF1 DF2 MS F p-unc np2 eps\n", + " Source SS DF1 DF2 MS F p_unc np2 eps\n", "0 Sex 0.253 1 18 0.253 0.283 6.010e-01 0.016 NaN\n", "1 Year 31.733 2 36 15.866 17.514 4.873e-06 0.493 0.988\n", "2 Interaction 2.106 2 36 1.053 1.162 3.242e-01 0.061 NaN" @@ -2208,7 +2192,7 @@ " DF2\n", " MS\n", " F\n", - " p-unc\n", + " p_unc\n", " ng2\n", " eps\n", " \n", @@ -2255,7 +2239,7 @@ "" ], "text/plain": [ - " Source SS DF1 DF2 MS F p-unc ng2 eps\n", + " Source SS DF1 DF2 MS F p_unc ng2 eps\n", "0 Sex 0.253 1 18 0.253 0.283 6.010e-01 0.005 NaN\n", "1 Year 31.733 2 36 15.866 17.514 4.873e-06 0.395 0.988\n", "2 Interaction 2.106 2 36 1.053 1.162 3.242e-01 0.041 NaN" @@ -2313,7 +2297,7 @@ " T\n", " dof\n", " alternative\n", - " p-unc\n", + " p_unc\n", " BF10\n", " hedges\n", " \n", @@ -2438,7 +2422,7 @@ "5 Year * Sex 2014 Men Women False True 0.753 18.0 two-sided \n", "6 Year * Sex 2018 Men Women False True -0.169 18.0 two-sided \n", "\n", - " p-unc BF10 hedges \n", + " p_unc BF10 hedges \n", "0 1.197e-01 0.717 0.537 \n", "1 1.494e-05 1532.361 1.736 \n", "2 4.549e-04 72.806 1.305 \n", @@ -2499,7 +2483,7 @@ " T\n", " dof\n", " alternative\n", - " p-unc\n", + " p_unc\n", " BF10\n", " hedges\n", " \n", @@ -2672,7 +2656,7 @@ "8 Sex * Year Women 2010 2018 True True 9.348 9.0 two-sided \n", "9 Sex * Year Women 2014 2018 True True 2.896 9.0 two-sided \n", "\n", - " p-unc BF10 hedges \n", + " p_unc BF10 hedges \n", "0 6.010e-01 0.439 -0.228 \n", "1 1.197e-01 0.717 0.537 \n", "2 1.494e-05 1532.361 1.736 \n", @@ -2698,7 +2682,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2712,7 +2696,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.10" }, "toc": { "base_numbering": 1, diff --git a/notebooks/02_BayesianTTests.ipynb b/notebooks/02_BayesianTTests.ipynb index 46269d0f..747156f2 100644 --- a/notebooks/02_BayesianTTests.ipynb +++ b/notebooks/02_BayesianTTests.ipynb @@ -65,16 +65,16 @@ " T\n", " dof\n", " alternative\n", - " p-val\n", - " CI95%\n", - " cohen-d\n", + " p_val\n", + " CI95\n", + " cohen_d\n", " BF10\n", " power\n", " \n", " \n", " \n", " \n", - " T-test\n", + " T_test\n", " 2.890732\n", " 58\n", " two-sided\n", @@ -89,11 +89,11 @@ "" ], "text/plain": [ - " T dof alternative p-val CI95% cohen-d BF10 \\\n", - "T-test 2.890732 58 two-sided 0.0054 [0.28, 1.53] 0.746384 7.71 \n", + " T dof alternative p_val CI95 cohen_d BF10 \\\n", + "T_test 2.890732 58 two-sided 0.0054 [0.28, 1.53] 0.746384 7.71 \n", "\n", " power \n", - "T-test 0.811273 " + "T_test 0.811273 " ] }, "execution_count": 2, @@ -172,16 +172,16 @@ " T\n", " dof\n", " alternative\n", - " p-val\n", - " CI95%\n", - " cohen-d\n", + " p_val\n", + " CI95\n", + " cohen_d\n", " BF10\n", " power\n", " \n", " \n", " \n", " \n", - " T-test\n", + " T_test\n", " 2.716719\n", " 29\n", " greater\n", @@ -196,11 +196,11 @@ "" ], "text/plain": [ - " T dof alternative p-val CI95% cohen-d BF10 \\\n", - "T-test 2.716719 29 greater 0.0055 [0.34, inf] 0.746384 8.309 \n", + " T dof alternative p_val CI95 cohen_d BF10 \\\n", + "T_test 2.716719 29 greater 0.0055 [0.34, inf] 0.746384 8.309 \n", "\n", " power \n", - "T-test 0.990495 " + "T_test 0.990495 " ] }, "execution_count": 4, @@ -215,7 +215,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -229,7 +229,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/notebooks/03_EffectSizes.ipynb b/notebooks/03_EffectSizes.ipynb index 8555cd10..ca23c3fb 100644 --- a/notebooks/03_EffectSizes.ipynb +++ b/notebooks/03_EffectSizes.ipynb @@ -54,7 +54,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -203,7 +203,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -217,7 +217,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/notebooks/04_Correlations.ipynb b/notebooks/04_Correlations.ipynb index 422c71cd..a54ae57a 100644 --- a/notebooks/04_Correlations.ipynb +++ b/notebooks/04_Correlations.ipynb @@ -43,8 +43,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " BF10\n", " power\n", " \n", @@ -64,7 +64,7 @@ "" ], "text/plain": [ - " n r CI95% p-val BF10 power\n", + " n r CI95 p_val BF10 power\n", "pearson 8 0.185 [-0.6, 0.79] 0.661 0.468 0.072" ] }, @@ -94,8 +94,8 @@ "\n", "1. The sample size `n` (after removal of NaN)\n", "2. the correlation coefficient (`r`)\n", - "3. the parametric 95% confidence intervals of the coefficient (`CI95%`)\n", - "4. the p-value (`p-unc`)\n", + "3. the parametric 95% confidence intervals of the coefficient (`CI95`)\n", + "4. the p-value (`p_unc`)\n", "5. the Bayes Factor for the alternative hypothesis (`BF10`)\n", "6. the achieved power of the test (`power`, = 1 - type 2 error)\n", "\n", @@ -130,8 +130,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " power\n", " \n", " \n", @@ -149,7 +149,7 @@ "" ], "text/plain": [ - " n r CI95% p-val power\n", + " n r CI95 p_val power\n", "spearman 8 0.319 [-0.38, 1.0] 0.221 0.201" ] }, @@ -321,8 +321,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -475,7 +475,7 @@ "8 Openness Conscientiousness pearson two-sided 500 -0.013 \n", "9 Agreeableness Conscientiousness pearson two-sided 500 0.159 \n", "\n", - " CI95% p-unc BF10 power \n", + " CI95 p_unc BF10 power \n", "0 [-0.42, -0.27] 7.323e-16 6.765e+12 1.000 \n", "1 [-0.1, 0.08] 8.169e-01 0.058 0.056 \n", "2 [-0.22, -0.05] 2.615e-03 5.122 0.854 \n", @@ -546,8 +546,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " power\n", " \n", " \n", @@ -569,10 +569,10 @@ "" ], "text/plain": [ - " X Y method alternative n r CI95% \\\n", + " X Y method alternative n r CI95 \\\n", "0 Neuroticism Extraversion spearman two-sided 500 -0.325 [-0.4, -0.24] \n", "\n", - " p-unc power \n", + " p_unc power \n", "0 8.385e-14 1.0 " ] }, @@ -625,8 +625,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " power\n", " \n", " \n", @@ -648,10 +648,10 @@ "" ], "text/plain": [ - " X Y method alternative n r CI95% \\\n", + " X Y method alternative n r CI95 \\\n", "0 Neuroticism Extraversion bicor two-sided 500 -0.343 [-0.42, -0.26] \n", "\n", - " p-unc power \n", + " p_unc power \n", "0 2.908e-15 1.0 " ] }, @@ -700,8 +700,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " power\n", " \n", " \n", @@ -723,10 +723,10 @@ "" ], "text/plain": [ - " X Y method alternative n r CI95% \\\n", + " X Y method alternative n r CI95 \\\n", "0 Neuroticism Extraversion percbend two-sided 500 -0.327 [-0.4, -0.25] \n", "\n", - " p-unc power \n", + " p_unc power \n", "0 5.985e-14 1.0 " ] }, @@ -773,8 +773,8 @@ " n\n", " outliers\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " power\n", " \n", " \n", @@ -800,7 +800,7 @@ " X Y method alternative n outliers r \\\n", "0 Neuroticism Extraversion shepherd two-sided 500 16.0 -0.319 \n", "\n", - " CI95% p-unc power \n", + " CI95 p_unc power \n", "0 [-0.4, -0.24] 6.791e-13 1.0 " ] }, @@ -854,10 +854,10 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", - " p-corr\n", - " p-adjust\n", + " CI95\n", + " p_unc\n", + " p_corr\n", + " p_adjust\n", " power\n", " \n", " \n", @@ -1019,7 +1019,7 @@ "8 Openness Conscientiousness spearman two-sided 500 -0.007 \n", "9 Agreeableness Conscientiousness spearman two-sided 500 0.161 \n", "\n", - " CI95% p-unc p-corr p-adjust power \n", + " CI95 p_unc p_corr p_adjust power \n", "0 [-0.41, -0.25] 0.000 0.000 holm 1.000 \n", "1 [-0.11, 0.07] 0.662 1.000 holm 0.072 \n", "2 [-0.22, -0.04] 0.003 0.015 holm 0.843 \n", @@ -1223,8 +1223,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -1326,7 +1326,7 @@ "" ], "text/plain": [ - " X Y method alternative n r CI95% \\\n", + " X Y method alternative n r CI95 \\\n", "0 Age Neuroticism pearson two-sided 500 -0.036 [-0.12, 0.05] \n", "1 Age Extraversion pearson two-sided 500 -0.004 [-0.09, 0.08] \n", "2 Age Openness pearson two-sided 500 0.035 [-0.05, 0.12] \n", @@ -1335,7 +1335,7 @@ "5 Age BMI pearson two-sided 500 -0.053 [-0.14, 0.03] \n", "6 Age Gender pearson two-sided 500 -0.023 [-0.11, 0.06] \n", "\n", - " p-unc BF10 power \n", + " p_unc BF10 power \n", "0 0.418 0.078 0.128 \n", "1 0.921 0.056 0.051 \n", "2 0.438 0.076 0.121 \n", @@ -1393,8 +1393,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -1535,7 +1535,7 @@ "" ], "text/plain": [ - " X Y method alternative n r CI95% \\\n", + " X Y method alternative n r CI95 \\\n", "0 Age Neuroticism pearson two-sided 500 -0.036 [-0.12, 0.05] \n", "1 Age Extraversion pearson two-sided 500 -0.004 [-0.09, 0.08] \n", "2 Age Openness pearson two-sided 500 0.035 [-0.05, 0.12] \n", @@ -1547,7 +1547,7 @@ "8 Gender Agreeableness pearson two-sided 500 0.020 [-0.07, 0.11] \n", "9 Gender Conscientiousness pearson two-sided 500 0.029 [-0.06, 0.12] \n", "\n", - " p-unc BF10 power \n", + " p_unc BF10 power \n", "0 0.418 0.078 0.128 \n", "1 0.921 0.056 0.051 \n", "2 0.438 0.076 0.121 \n", @@ -1610,8 +1610,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -1778,7 +1778,7 @@ "" ], "text/plain": [ - " X Y method alternative n r CI95% \\\n", + " X Y method alternative n r CI95 \\\n", "0 Age Neuroticism pearson two-sided 500 -0.036 [-0.12, 0.05] \n", "1 Age Extraversion pearson two-sided 500 -0.004 [-0.09, 0.08] \n", "2 Age Openness pearson two-sided 500 0.035 [-0.05, 0.12] \n", @@ -1792,7 +1792,7 @@ "10 Gender Conscientiousness pearson two-sided 500 0.029 [-0.06, 0.12] \n", "11 Gender BMI pearson two-sided 500 0.071 [-0.02, 0.16] \n", "\n", - " p-unc BF10 power \n", + " p_unc BF10 power \n", "0 0.418 0.078 0.128 \n", "1 0.921 0.056 0.051 \n", "2 0.438 0.076 0.121 \n", @@ -2029,8 +2029,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -2183,7 +2183,7 @@ "8 (Physio, PupilDilation) (Psycho, Sleepiness) pearson two-sided \n", "9 (Physio, BPM) (Psycho, Sleepiness) pearson two-sided \n", "\n", - " n r CI95% p-unc BF10 power \n", + " n r CI95 p_unc BF10 power \n", "0 10 0.135 [-0.54, 0.7] 0.711 0.411 0.065 \n", "1 10 0.542 [-0.13, 0.87] 0.106 1.234 0.388 \n", "2 10 0.160 [-0.52, 0.72] 0.660 0.422 0.072 \n", @@ -2244,8 +2244,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -2314,7 +2314,7 @@ "2 (Behavior, Rating) (Physio, BPM) pearson two-sided 10 \n", "3 (Behavior, Rating) (Psycho, Sleepiness) pearson two-sided 10 \n", "\n", - " r CI95% p-unc BF10 power \n", + " r CI95 p_unc BF10 power \n", "0 0.135 [-0.54, 0.7] 0.711 0.411 0.065 \n", "1 0.542 [-0.13, 0.87] 0.106 1.234 0.388 \n", "2 0.160 [-0.52, 0.72] 0.660 0.422 0.072 \n", @@ -2369,8 +2369,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -2397,7 +2397,7 @@ " X Y method alternative n \\\n", "0 (Behavior, ReactionTime) (Psycho, Sleepiness) pearson two-sided 10 \n", "\n", - " r CI95% p-unc BF10 power \n", + " r CI95 p_unc BF10 power \n", "0 -0.294 [-0.78, 0.41] 0.41 0.524 0.132 " ] }, @@ -2442,8 +2442,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -2540,7 +2540,7 @@ "4 (Behavior, ReactionTime) (Physio, BPM) pearson two-sided 10 \n", "5 (Behavior, ReactionTime) (Psycho, Sleepiness) pearson two-sided 10 \n", "\n", - " r CI95% p-unc BF10 power \n", + " r CI95 p_unc BF10 power \n", "0 0.542 [-0.13, 0.87] 0.106 1.234 0.388 \n", "1 0.160 [-0.52, 0.72] 0.660 0.422 0.072 \n", "2 0.076 [-0.58, 0.67] 0.836 0.394 0.054 \n", @@ -2605,8 +2605,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " BF10\n", " power\n", " \n", @@ -2675,7 +2675,7 @@ "2 (Behavior, ReactionTime) (Physio, PupilDilation) pearson two-sided 10 \n", "3 (Behavior, ReactionTime) (Physio, BPM) pearson two-sided 10 \n", "\n", - " r CI95% p-unc BF10 power \n", + " r CI95 p_unc BF10 power \n", "0 0.542 [-0.13, 0.87] 0.106 1.234 0.388 \n", "1 0.160 [-0.52, 0.72] 0.660 0.422 0.072 \n", "2 0.327 [-0.38, 0.79] 0.356 0.566 0.155 \n", @@ -2736,8 +2736,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " \n", " \n", " \n", @@ -2753,7 +2753,7 @@ "" ], "text/plain": [ - " n r CI95% p-val\n", + " n r CI95 p_val\n", "pearson 500 0.267 [0.18, 0.35] 1.277e-09" ] }, @@ -2796,8 +2796,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " \n", " \n", " \n", @@ -2813,7 +2813,7 @@ "" ], "text/plain": [ - " n r CI95% p-val\n", + " n r CI95 p_val\n", "pearson 500 0.266 [0.18, 0.35] 1.652e-09" ] }, @@ -2867,8 +2867,8 @@ " alternative\n", " n\n", " r\n", - " CI95%\n", - " p-unc\n", + " CI95\n", + " p_unc\n", " \n", " \n", " \n", @@ -3009,7 +3009,7 @@ "8 Openness Conscientiousness spearman ['Age', 'Gender', 'BMI'] \n", "9 Agreeableness Conscientiousness spearman ['Age', 'Gender', 'BMI'] \n", "\n", - " alternative n r CI95% p-unc \n", + " alternative n r CI95 p_unc \n", "0 two-sided 500 -0.329 [-0.41, -0.25] 5.247e-14 \n", "1 two-sided 500 -0.016 [-0.1, 0.07] 7.287e-01 \n", "2 two-sided 500 -0.135 [-0.22, -0.05] 2.495e-03 \n", @@ -3450,8 +3450,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " \n", " \n", " \n", @@ -3467,7 +3467,7 @@ "" ], "text/plain": [ - " n r CI95% p-val\n", + " n r CI95 p_val\n", "pearson 500 0.267 [0.18, 0.35] 1.307e-09" ] }, @@ -3497,14 +3497,6 @@ "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/raphael/GitHub/pingouin/pingouin/correlation.py:1116: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " mat_upper = mat_upper.applymap(replace_pval)\n" - ] - }, { "data": { "text/html": [ @@ -3665,14 +3657,6 @@ "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/raphael/GitHub/pingouin/pingouin/correlation.py:1116: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " mat_upper = mat_upper.applymap(replace_pval)\n" - ] - }, { "data": { "text/html": [ @@ -3834,14 +3818,6 @@ "execution_count": 28, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/raphael/GitHub/pingouin/pingouin/correlation.py:1118: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " mat_upper = mat_upper.applymap(lambda x: ffp(x, precision=decimals))\n" - ] - }, { "data": { "text/html": [ @@ -4002,7 +3978,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -4016,7 +3992,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.10" }, "toc": { "base_numbering": 1, diff --git a/notebooks/06_Rounding.ipynb b/notebooks/06_Rounding.ipynb index 918685e8..fe7d964b 100644 --- a/notebooks/06_Rounding.ipynb +++ b/notebooks/06_Rounding.ipynb @@ -79,8 +79,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " BF10\n", " power\n", " \n", @@ -100,7 +100,7 @@ "" ], "text/plain": [ - " n r CI95% p-val BF10 power\n", + " n r CI95 p_val BF10 power\n", "pearson 8 0.1849 [-0.6, 0.79] 0.661133 0.468 0.071911" ] }, @@ -111,7 +111,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The full r value is 0.1849000654084097\n" + "The full r value is 0.18490006540840967\n" ] } ], @@ -158,8 +158,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " BF10\n", " power\n", " \n", @@ -179,7 +179,7 @@ "" ], "text/plain": [ - " n r CI95% p-val BF10 power\n", + " n r CI95 p_val BF10 power\n", "pearson 8 0.185 [-0.6, 0.79] 0.661 0.468 0.072" ] }, @@ -218,7 +218,7 @@ "data": { "text/plain": [ "{'round': None,\n", - " 'round.column.CI95%': 2,\n", + " 'round.column.CI95': 2,\n", " 'round.column.BF10': }" ] }, @@ -230,7 +230,7 @@ "source": [ "# The default Pingouin options are:\n", "# 'round': None -> by default, no rounding is applied\n", - "# 'round.column.CI95%': 2 -> except for the CI95% column, which is always rounded to 2 decimals\n", + "# 'round.column.CI95': 2 -> except for the CI95 column, which is always rounded to 2 decimals\n", "# 'round.column.BF10': custom string formatting for the Bayes Factor column\n", "pg.options" ] @@ -244,9 +244,9 @@ "data": { "text/plain": [ "{'round': 4,\n", - " 'round.column.CI95%': 3,\n", + " 'round.column.CI95': 3,\n", " 'round.column.BF10': None,\n", - " 'round.column.p-val': None}" + " 'round.column.p_val': None}" ] }, "execution_count": 6, @@ -256,8 +256,8 @@ ], "source": [ "pg.options['round'] = 4\n", - "pg.options['round.column.CI95%'] = 3\n", - "pg.options['round.column.p-val'] = None\n", + "pg.options['round.column.CI95'] = 3\n", + "pg.options['round.column.p_val'] = None\n", "pg.options['round.column.BF10'] = None\n", "pg.options" ] @@ -290,8 +290,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " BF10\n", " power\n", " \n", @@ -311,7 +311,7 @@ "" ], "text/plain": [ - " n r CI95% p-val BF10 power\n", + " n r CI95 p_val BF10 power\n", "pearson 8 0.1849 [-0.598, 0.787] 0.661133 0.467674 0.0719" ] }, @@ -341,7 +341,7 @@ "data": { "text/plain": [ "{'round': None,\n", - " 'round.column.CI95%': 2,\n", + " 'round.column.CI95': 2,\n", " 'round.column.BF10': }" ] }, @@ -393,8 +393,8 @@ " \n", " n\n", " r\n", - " CI95%\n", - " p-val\n", + " CI95\n", + " p_val\n", " BF10\n", " power\n", " \n", @@ -414,7 +414,7 @@ "" ], "text/plain": [ - " n r CI95% p-val BF10 power\n", + " n r CI95 p_val BF10 power\n", "pearson 8 0.185 [-0.6, 0.79] 0.661 0.468 0.072" ] }, @@ -425,7 +425,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The full r value is 0.1849000654084097\n" + "The full r value is 0.18490006540840967\n" ] } ], @@ -440,7 +440,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -454,7 +454,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.10" } }, "nbformat": 4,