Repository: htjb/margarine (language: python)

Issue:
Error estimation for KL and BMD calculations
The master branch of the code is currently missing any form of error estimation on the marginal statistics. However, there is an implementation of a crude estimate on the [error_analysis](https://github.com/htjb/margarine/tree/error_analysis) branch that needs to be merged.

The error estimate uses both samples generated with the density estimate and the original samples to give some indication of the error in the log-probability that can then be propagated through to the marginal stats.
Master branch assumes uniform prior
The code on the master branch currently assumes that the prior is uniform but this is very limiting.

An easy extension is implemented in [error_analysis](https://github.com/htjb/margarine/tree/error_analysis) when prior samples can be provided, prior density estimators trained and marginal Bayesian stats subsequently calculated. Needs testing and merging.
Tidying now unnecessary edge correction code
# Description

This PR:

a) removes the 0.001 and 0.999 edge corrections in margarine/processing.py
b) turns the explicit transformations into quantiles in margarine/processing.py
c) removes the 0.001 and 0.999 edge corrections in margarine/maf.py
d) tidys margarine/maf.py in light of these changes, as well as a more numpythonic return calculation from logprob

This possible now that the correction factors have been implemented, and we no longer need to guard against the then erroneous nans.

Tests pass locally on my machine.



Discussion / hints:

Fix patch:
diff --git a/README.rst b/README.rst
index 31635a5..6c85768 100755
--- a/README.rst
+++ b/README.rst
@@ -7,7 +7,7 @@ Introduction
 
 :margarine: Marginal Bayesian Statistics
 :Authors: Harry T.J. Bevins
-:Version: 0.2.2
+:Version: 0.3.0
 :Homepage:  https://github.com/htjb/margarine
 :Documentation: https://margarine.readthedocs.io/
 
@@ -50,16 +50,6 @@ jupyter notebook `notebook/Tutorial.ipynb`, alternatively
 in the compiled documentation or at
 `here <https://mybinder.org/v2/gh/htjb/margarine/7f55f9a9d3f3adb2356cb94b32c599caac8ea1ef?urlpath=lab%2Ftree%2Fnotebook%2FTutorial.ipynb>`_.
 
-Health Warning
---------------
-
-**The code is still in development.**
-
-Currently the master branch assumes, when calculating marginal statistics, that the
-priors are uniformly distributed. If this is not the case marginal KL divergences
-and marginal Bayesian dimensionalities should be calculated with the
-error_analysis branch.
-
 Documentation
 -------------
 
diff --git a/docs/source/Tutorial_15_0.png b/docs/source/Tutorial_15_0.png
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diff --git a/docs/source/output_9_0.png b/docs/source/output_9_0.png
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diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
index 597c9b8..15b630d 100644
--- a/docs/source/tutorial.rst
+++ b/docs/source/tutorial.rst
@@ -39,22 +39,22 @@ into the uniform parameter space before we train our MAF and KDE.
         the pandas table of samples, a numpy
         array of the parameters in the uniform space and weights.
         """
-
+    
         samples = NestedSamples(root=root)
-
+    
         try:
             names = ['p' + str(i) for i in range(ndims)]
             theta = samples[names].values
         except:
             names = [i for i in range(ndims)]
             theta = samples[names].values
-
+    
         weights = samples.weights
-
+    
         return samples, theta, weights
-
+    
     ndims=5
-
+    
     root = '../tests/test_samples/test'
     samples, theta, weights = load_chains(root)
 
@@ -65,7 +65,7 @@ be useful later in the notebook.
 .. code:: ipython3
 
     from anesthetic.plot import hist_plot_1d, hist_plot_2d
-
+    
     def plotter(theta, names, w=None, ndims=5):
         """ Helper function that uses anesthetic to produce corner plots """
         fig, axes = plt.subplots(ndims, ndims, figsize=(8, 8), sharex='col')
@@ -95,13 +95,13 @@ be useful later in the notebook.
         plt.tight_layout()
         plt.subplots_adjust(hspace=0, wspace=0)
         plt.show()
-
+    
     names = ['log(p' + str(i) + ')' if i in [0, 1, 2] else 'p' + str(i) for i in range(ndims)]
     plotter(theta, names, weights)
 
 
 
-.. image:: output_5_0.png
+.. image:: Tutorial_files/Tutorial_5_0.png
 
 
 Masked Autoregressive Flows
@@ -115,117 +115,13 @@ class with the samples and corresponding weights.
 
     import os
     os.chdir('../')
-
+    
     from margarine.maf import MAF
-
+    from margarine.kde import KDE
+    
     bij = MAF(theta, weights)
     bij.train(100)
 
-
-.. parsed-literal::
-
-    Epoch: 0 Loss: 0.0007185101
-    Epoch: 1 Loss: 0.0007014023
-    Epoch: 2 Loss: 0.0006839341
-    Epoch: 3 Loss: 0.00066634326
-    Epoch: 4 Loss: 0.0006487031
-    Epoch: 5 Loss: 0.00063104363
-    Epoch: 6 Loss: 0.0006133788
-    Epoch: 7 Loss: 0.0005957194
-    Epoch: 8 Loss: 0.00057807605
-    Epoch: 9 Loss: 0.000560463
-    Epoch: 10 Loss: 0.00054290093
-    Epoch: 11 Loss: 0.00052541785
-    Epoch: 12 Loss: 0.0005080515
-    Epoch: 13 Loss: 0.00049085025
-    Epoch: 14 Loss: 0.00047387503
-    Epoch: 15 Loss: 0.00045719987
-    Epoch: 16 Loss: 0.0004409128
-    Epoch: 17 Loss: 0.00042511517
-    Epoch: 18 Loss: 0.00040991933
-    Epoch: 19 Loss: 0.00039544463
-    Epoch: 20 Loss: 0.00038181068
-    Epoch: 21 Loss: 0.00036913107
-    Epoch: 22 Loss: 0.0003575051
-    Epoch: 23 Loss: 0.00034701102
-    Epoch: 24 Loss: 0.00033769925
-    Epoch: 25 Loss: 0.00032958362
-    Epoch: 26 Loss: 0.0003226317
-    Epoch: 27 Loss: 0.00031675794
-    Epoch: 28 Loss: 0.00031182577
-    Epoch: 29 Loss: 0.0003076632
-    Epoch: 30 Loss: 0.00030409006
-    Epoch: 31 Loss: 0.00030094603
-    Epoch: 32 Loss: 0.00029811086
-    Epoch: 33 Loss: 0.00029550795
-    Epoch: 34 Loss: 0.00029309577
-    Epoch: 35 Loss: 0.000290852
-    Epoch: 36 Loss: 0.00028876044
-    Epoch: 37 Loss: 0.00028680352
-    Epoch: 38 Loss: 0.00028495849
-    Epoch: 39 Loss: 0.0002831965
-    Epoch: 40 Loss: 0.0002814832
-    Epoch: 41 Loss: 0.00027978222
-    Epoch: 42 Loss: 0.00027806248
-    Epoch: 43 Loss: 0.00027630394
-    Epoch: 44 Loss: 0.00027450087
-    Epoch: 45 Loss: 0.00027266104
-    Epoch: 46 Loss: 0.0002708026
-    Epoch: 47 Loss: 0.00026895
-    Epoch: 48 Loss: 0.00026712997
-    Epoch: 49 Loss: 0.00026536686
-    Epoch: 50 Loss: 0.00026367788
-    Epoch: 51 Loss: 0.0002620711
-    Epoch: 52 Loss: 0.00026054538
-    Epoch: 53 Loss: 0.00025909403
-    Epoch: 54 Loss: 0.00025770898
-    Epoch: 55 Loss: 0.00025638303
-    Epoch: 56 Loss: 0.00025510968
-    Epoch: 57 Loss: 0.0002538809
-    Epoch: 58 Loss: 0.0002526861
-    Epoch: 59 Loss: 0.00025151268
-    Epoch: 60 Loss: 0.00025035004
-    Epoch: 61 Loss: 0.00024919215
-    Epoch: 62 Loss: 0.00024803917
-    Epoch: 63 Loss: 0.0002468963
-    Epoch: 64 Loss: 0.0002457709
-    Epoch: 65 Loss: 0.00024467031
-    Epoch: 66 Loss: 0.00024360023
-    Epoch: 67 Loss: 0.00024256505
-    Epoch: 68 Loss: 0.00024156799
-    Epoch: 69 Loss: 0.00024061205
-    Epoch: 70 Loss: 0.00023969925
-    Epoch: 71 Loss: 0.00023883079
-    Epoch: 72 Loss: 0.00023800644
-    Epoch: 73 Loss: 0.0002372256
-    Epoch: 74 Loss: 0.00023648709
-    Epoch: 75 Loss: 0.0002357906
-    Epoch: 76 Loss: 0.00023513584
-    Epoch: 77 Loss: 0.00023452267
-    Epoch: 78 Loss: 0.00023394993
-    Epoch: 79 Loss: 0.00023341607
-    Epoch: 80 Loss: 0.00023291916
-    Epoch: 81 Loss: 0.00023245814
-    Epoch: 82 Loss: 0.00023203301
-    Epoch: 83 Loss: 0.00023164476
-    Epoch: 84 Loss: 0.0002312939
-    Epoch: 85 Loss: 0.00023098035
-    Epoch: 86 Loss: 0.00023070279
-    Epoch: 87 Loss: 0.00023045961
-    Epoch: 88 Loss: 0.0002302486
-    Epoch: 89 Loss: 0.00023006761
-    Epoch: 90 Loss: 0.00022991415
-    Epoch: 91 Loss: 0.00022978513
-    Epoch: 92 Loss: 0.00022967775
-    Epoch: 93 Loss: 0.00022958912
-    Epoch: 94 Loss: 0.00022951687
-    Epoch: 95 Loss: 0.00022945873
-    Epoch: 96 Loss: 0.00022941224
-    Epoch: 97 Loss: 0.00022937522
-    Epoch: 98 Loss: 0.00022934566
-    Epoch: 99 Loss: 0.00022932225
-
-
 We can then generate samples from the bijector using the following code
 which technically takes samples on the hypercube and transforms them
 into samples on the target posterior distribution,
@@ -233,12 +129,12 @@ into samples on the target posterior distribution,
 .. code:: ipython3
 
     x = bij(np.random.uniform(0, 1, size=(len(theta), theta.shape[-1])))
-
+    
     plotter(x, names)
 
 
 
-.. image:: output_9_0.png
+.. image:: Tutorial_files/Tutorial_9_0.png
 
 
 Alternatively we can generate samples with the following code which
@@ -260,17 +156,25 @@ results and note that the similarity improves with the number of epochs.
 
 .. code:: ipython3
 
-    from margarine.marginal_stats import maf_calculations
+    from margarine.marginal_stats import calculate
+    
+    stats = calculate(bij).statistics()
+    print(stats.iloc[0, 0], samples.D())
+    print(stats.iloc[1, 0], samples.d())
 
-    stats = maf_calculations(bij, x)
-    print(stats.klDiv(), samples.D())
-    print(stats.bayesian_dimensionality(), samples.d())
+
+.. parsed-literal::
+
+    3.310774486919951 3.3308079438366938
+    4.839712964526534 5.013952162478263
 
 
 .. parsed-literal::
 
-    tf.Tensor(3.2990043, shape=(), dtype=float32) 3.3308079438366938
-    tf.Tensor(4.3727474, shape=(), dtype=float32) 5.013952162478263
+    /Users/harry/Documents/margarine/margarine/marginal_stats.py:72: UserWarning: If prior samples are not provided the prior is assumed to be uniform and the posterior samples are are assumed to be from the same uniform space.
+      warnings.warn('If prior samples are not provided the prior is ' +
+    /Users/harry/Documents/margarine/margarine/marginal_stats.py:194: FutureWarning: The 'inplace' keyword in DataFrame.set_index is deprecated and will be removed in a future version. Use `df = df.set_index(..., copy=False)` instead.
+      results.set_index('Statistic', inplace=True)
 
 
 We could imagine that the above set of parameters is a sub-sample of
@@ -285,134 +189,39 @@ distribution.
 
     theta_reduced = theta[:, 1:-1]
     names_reduced = names[1:-1]
-
+    
     bij = MAF(theta_reduced, weights)
     bij.train(100)
     x = bij.sample(5000)
-
+    
     plotter(x, names_reduced, ndims=3)
+    
+    stats = calculate(bij).statistics()
+    print(stats)
+
 
-    stats = maf_calculations(bij, x)
-    print(stats.klDiv())
-    print(stats.bayesian_dimensionality())
+
+.. image:: Tutorial_files/Tutorial_15_0.png
 
 
 .. parsed-literal::
 
-    Epoch: 0 Loss: 0.00043151045
-    Epoch: 1 Loss: 0.00041672972
-    Epoch: 2 Loss: 0.00040160108
-    Epoch: 3 Loss: 0.0003863657
-    Epoch: 4 Loss: 0.00037109375
-    Epoch: 5 Loss: 0.00035581551
-    Epoch: 6 Loss: 0.00034055865
-    Epoch: 7 Loss: 0.0003253689
-    Epoch: 8 Loss: 0.00031032556
-    Epoch: 9 Loss: 0.000295559
-    Epoch: 10 Loss: 0.00028127024
-    Epoch: 11 Loss: 0.00026773606
-    Epoch: 12 Loss: 0.0002552729
-    Epoch: 13 Loss: 0.0002441286
-    Epoch: 14 Loss: 0.00023431709
-    Epoch: 15 Loss: 0.00022553187
-    Epoch: 16 Loss: 0.00021728151
-    Epoch: 17 Loss: 0.00020923858
-    Epoch: 18 Loss: 0.00020157515
-    Epoch: 19 Loss: 0.0001947428
-    Epoch: 20 Loss: 0.00018895789
-    Epoch: 21 Loss: 0.00018402831
-    Epoch: 22 Loss: 0.00017959857
-    Epoch: 23 Loss: 0.00017541902
-    Epoch: 24 Loss: 0.00017139025
-    Epoch: 25 Loss: 0.0001674916
-    Epoch: 26 Loss: 0.00016375972
-    Epoch: 27 Loss: 0.00016028345
-    Epoch: 28 Loss: 0.0001571421
-    Epoch: 29 Loss: 0.00015434317
-    Epoch: 30 Loss: 0.0001518309
-    Epoch: 31 Loss: 0.00014954529
-    Epoch: 32 Loss: 0.00014746259
-    Epoch: 33 Loss: 0.00014559379
-    Epoch: 34 Loss: 0.00014396895
-    Epoch: 35 Loss: 0.00014262673
-    Epoch: 36 Loss: 0.00014160143
-    Epoch: 37 Loss: 0.00014089704
-    Epoch: 38 Loss: 0.00014046444
-    Epoch: 39 Loss: 0.00014021117
-    Epoch: 40 Loss: 0.00014004944
-    Epoch: 41 Loss: 0.00013994247
-    Epoch: 42 Loss: 0.00013990093
-    Epoch: 43 Loss: 0.00013992687
-    Epoch: 44 Loss: 0.00013996518
-    Epoch: 45 Loss: 0.0001399216
-    Epoch: 46 Loss: 0.0001397332
-    Epoch: 47 Loss: 0.00013941246
-    Epoch: 48 Loss: 0.00013902439
-    Epoch: 49 Loss: 0.00013863404
-    Epoch: 50 Loss: 0.00013828062
-    Epoch: 51 Loss: 0.00013798769
-    Epoch: 52 Loss: 0.0001377799
-    Epoch: 53 Loss: 0.00013768119
-    Epoch: 54 Loss: 0.00013769431
-    Epoch: 55 Loss: 0.00013778618
-    Epoch: 56 Loss: 0.00013789683
-    Epoch: 57 Loss: 0.00013796882
-    Epoch: 58 Loss: 0.00013797365
-    Epoch: 59 Loss: 0.00013791746
-    Epoch: 60 Loss: 0.00013782585
-    Epoch: 61 Loss: 0.00013772433
-    Epoch: 62 Loss: 0.00013762737
-    Epoch: 63 Loss: 0.00013753776
-    Epoch: 64 Loss: 0.00013745317
-    Epoch: 65 Loss: 0.00013737299
-    Epoch: 66 Loss: 0.00013730218
-    Epoch: 67 Loss: 0.00013724956
-    Epoch: 68 Loss: 0.00013722175
-    Epoch: 69 Loss: 0.00013721581
-    Epoch: 70 Loss: 0.00013721827
-    Epoch: 71 Loss: 0.00013721173
-    Epoch: 72 Loss: 0.0001371857
-    Epoch: 73 Loss: 0.00013714364
-    Epoch: 74 Loss: 0.00013709921
-    Epoch: 75 Loss: 0.00013706567
-    Epoch: 76 Loss: 0.00013704767
-    Epoch: 77 Loss: 0.00013704058
-    Epoch: 78 Loss: 0.00013703684
-    Epoch: 79 Loss: 0.00013703172
-    Epoch: 80 Loss: 0.00013702485
-    Epoch: 81 Loss: 0.00013701744
-    Epoch: 82 Loss: 0.00013700967
-    Epoch: 83 Loss: 0.00013700046
-    Epoch: 84 Loss: 0.00013698814
-    Epoch: 85 Loss: 0.00013697242
-    Epoch: 86 Loss: 0.00013695442
-    Epoch: 87 Loss: 0.00013693656
-    Epoch: 88 Loss: 0.0001369206
-    Epoch: 89 Loss: 0.00013690711
-    Epoch: 90 Loss: 0.00013689484
-    Epoch: 91 Loss: 0.00013688198
-    Epoch: 92 Loss: 0.00013686798
-    Epoch: 93 Loss: 0.0001368536
-    Epoch: 94 Loss: 0.00013683976
-    Epoch: 95 Loss: 0.00013682686
-    Epoch: 96 Loss: 0.00013681434
-    Epoch: 97 Loss: 0.00013680183
-    Epoch: 98 Loss: 0.00013678914
-    Epoch: 99 Loss: 0.00013677636
-
-
-
-.. image:: output_15_1.png
+                      Value  Lower Bound  Upper Bound
+    Statistic                                        
+    KL Divergence  1.994334     1.992596     1.997777
+    BMD            2.804455     2.562036     3.311070
 
 
 .. parsed-literal::
 
-    tf.Tensor(1.9784489, shape=(), dtype=float32)
-    tf.Tensor(2.5428524, shape=(), dtype=float32)
+    /Users/harry/Documents/margarine/margarine/marginal_stats.py:72: UserWarning: If prior samples are not provided the prior is assumed to be uniform and the posterior samples are are assumed to be from the same uniform space.
+      warnings.warn('If prior samples are not provided the prior is ' +
+    /Users/harry/Documents/margarine/margarine/marginal_stats.py:194: FutureWarning: The 'inplace' keyword in DataFrame.set_index is deprecated and will be removed in a future version. Use `df = df.set_index(..., copy=False)` instead.
+      results.set_index('Statistic', inplace=True)
 
 
 Kernel Density Estimators
--------------------------
+=========================
 
 We can perform a similar analysis using Kernel Density Estimators rather
 than MAFs which is done with the following code. Note that the
@@ -425,24 +234,36 @@ performed with the MAFs.
     kde = KDE(theta, weights)
     kde.generate_kde()
     x = kde.sample(5000)
-
+    
     plotter(x, names)
+    
+    stats = calculate(kde).statistics()
+    print(stats.iloc[0, 0], '+(-)', stats.iloc[0, 2] - stats.iloc[0, 0], 
+          '(', stats.iloc[0, 0] - stats.iloc[0, 1], ')', samples.D())
+    print(stats.iloc[1, 0], '+(-)', stats.iloc[1, 2] - stats.iloc[1, 0], 
+          '(', stats.iloc[1, 0] - stats.iloc[1, 1], ')', samples.d())
 
-    from margarine.marginal_stats import kde_calculations
 
-    stats = kde_calculations(kde, x)
-    print(stats.klDiv(), samples.D())
-    print(stats.bayesian_dimensionality(), samples.d())
 
+.. image:: Tutorial_files/Tutorial_17_0.png
 
 
-.. image:: output_17_0.png
+.. parsed-literal::
+
+    /Users/harry/Documents/margarine/margarine/marginal_stats.py:72: UserWarning: If prior samples are not provided the prior is assumed to be uniform and the posterior samples are are assumed to be from the same uniform space.
+      warnings.warn('If prior samples are not provided the prior is ' +
+
+
+.. parsed-literal::
+
+    3.609496187306833 +(-) 0.2765981957821131 ( 0.48614620453886204 ) 3.3308079438366938
+    1.913573635193844 +(-) 2.048906422381939 ( 0.5664883865810604 ) 5.013952162478263
 
 
 .. parsed-literal::
 
-    tf.Tensor(3.1492083, shape=(), dtype=float32) 3.3308079438366938
-    tf.Tensor(4.025139, shape=(), dtype=float32) 5.013952162478263
+    /Users/harry/Documents/margarine/margarine/marginal_stats.py:194: FutureWarning: The 'inplace' keyword in DataFrame.set_index is deprecated and will be removed in a future version. Use `df = df.set_index(..., copy=False)` instead.
+      results.set_index('Statistic', inplace=True)
 
 
 Rather than using the ``kde.sample()`` function to generate samples we
@@ -462,13 +283,15 @@ Bayesian statistics.
 
 .. parsed-literal::
 
-    [[-2.30580893  0.2615454   1.0305738   0.95230235 -0.9160518 ]
-     [ 0.449536   -0.21128596  0.37450897 -0.06924562 -0.1435756 ]
-     [-0.23842612  1.89020679  1.01175275 -0.79411737 -1.26997843]
-     [ 0.69827796 -0.85031383  0.12800782  1.87217983 -0.0720315 ]
-     [-1.96867173 -1.70556795 -0.56749216 -1.02797262 -0.87023201]
-     [-0.55155518 -0.60825292  1.81532878 -1.32111645  0.61482667]
-     [-0.8416832   0.18485707  2.00253196  0.49622835 -0.63868005]
-     [-0.52705685 -0.11567092 -1.41513758 -1.26160741 -0.94664933]
-     [ 0.87743055  2.28065551 -0.94326398 -1.12061011 -0.03694226]
-     [ 0.39296398  0.62991565  1.31726872  0.46559934 -0.55856522]]
+    [[ 0.00866746 -1.3674294  -1.04610725  0.74177354  0.59200377]
+     [ 1.25609155 -1.55385202  0.17241134  1.7487886  -0.85378741]
+     [-0.42623049 -1.28543977  0.6308928  -0.95923815 -0.5000081 ]
+     [-0.91927021  1.82074337 -1.4875557   1.13219455  1.80085807]
+     [-0.43319482  0.51338045 -0.58847524 -1.32000678  2.11012111]
+     [-0.48190147  0.53976851 -0.73276747  0.63523436  1.8162574 ]
+     [ 0.3549924  -0.13545207  1.38762986 -1.22339099  1.09403153]
+     [-0.51115334  0.47585545 -0.88141388  2.62513102 -0.1775158 ]
+     [ 0.28904819 -0.26387664  0.80479892  0.02123311 -0.50959412]
+     [ 0.88906095 -1.04406146 -1.67107321 -1.62705777 -0.03433262]]
+
+
diff --git a/margarine/kde.py b/margarine/kde.py
index 7d62db7..019c146 100755
--- a/margarine/kde.py
+++ b/margarine/kde.py
@@ -68,7 +68,7 @@ class KDE(object):
     def __init__(self, theta, weights, **kwargs):
 
         self.theta = theta
-        self.weights = weights
+        self.sample_weights = weights
 
         self.n = (np.sum(weights)**2)/(np.sum(weights**2))
         theta_max = np.max(theta, axis=0)
@@ -90,11 +90,11 @@ def generate_kde(self):
         phi = _forward_transform(self.theta, self.theta_min, self.theta_max)
         mask = np.isfinite(phi).all(axis=-1)
         phi = phi[mask, :]
-        weights_phi = self.weights[mask]
+        weights_phi = self.sample_weights[mask]
         weights_phi /= weights_phi.sum()
 
         self.kde = gaussian_kde(
-            phi.T, weights=self.weights, bw_method=self.bw_method)
+            phi.T, weights=self.sample_weights, bw_method=self.bw_method)
 
         return self.kde
 
@@ -258,7 +258,7 @@ def save(self, filename):
                 | Path in which to save the pickled KDE.
         """
         with open(filename, 'wb') as f:
-            pickle.dump([self.theta, self.weights, self.kde], f)
+            pickle.dump([self.theta, self.sample_weights, self.kde], f)
 
     @classmethod
     def load(cls, filename):
diff --git a/margarine/maf.py b/margarine/maf.py
index d360781..f27367f 100755
--- a/margarine/maf.py
+++ b/margarine/maf.py
@@ -195,6 +195,18 @@ def train(self, epochs=100, early_stop=False):
         self.phi = phi.copy()
         weights_phi = weights_phi.astype('float32')
 
+        """if self.prior is not None:
+            prior_phi = _forward_transform(self.prior, self.prior_min, self.prior_max)
+
+            mask = np.isfinite(prior_phi).all(axis=-1)
+            prior_phi = prior_phi[mask, :]
+            weights_prior_phi = self.prior_weights[mask]
+            weights_prior_phi /= weights_prior_phi.sum()
+
+            prior_phi = prior_phi.astype('float32')
+            self.prior_phi = prior_phi.copy()
+            weights_prior_phi = weights_prior_phi.astype('float32')"""
+
         self.loss_history = []
         for i in range(epochs):
             loss = self._train_step(phi, weights_phi).numpy()
diff --git a/margarine/marginal_stats.py b/margarine/marginal_stats.py
old mode 100644
new mode 100755
index 20241d5..9a48b22
--- a/margarine/marginal_stats.py
+++ b/margarine/marginal_stats.py
@@ -2,28 +2,36 @@
 import numpy as np
 from margarine.processing import _forward_transform
 from tensorflow_probability import distributions as tfd
+import margarine
+import warnings
+import pandas as pd
+from margarine.maf import MAF
 
 
-class maf_calculations(object):
+class calculate(object):
 
     r"""
 
-    This class, once initalised with a trained MAF and samples from
-    that bijector, can be used to calculate marginal KL divergences and
+    This class, once initalised with a trained MAF or KDE and samples,
+    can be used to calculate marginal KL divergences and
     bayesian dimensionalities.
 
     **Paramesters:**
 
-        bij: **instance of MAF class**
-            | This should be a loaded and trained instance of a MAF. Bijectors
-                can be loaded like so
+        de: **instance of MAF class or KDE class**
+            | This should be a loaded and trained instance of a MAF or KDE.
+                Bijectors can be loaded like so
 
                 .. code:: python
 
-                    from ...maf import MAF
+                    from margarine.maf import MAF
+                    from margarine.kde import KDE
 
                     file = '/trained_maf.pkl'
-                    bij = MAF.load(file)
+                    maf = MAF.load(file)
+
+                    file = '/trained_kde.pkl'
+                    kde = KDE.load(file)
 
         samples: **numpy array**
             | This should be the output of the bijector when called to generate
@@ -34,149 +42,155 @@ class maf_calculations(object):
 
                     u = np.random.uniform(0, 1, size=(10000, 5))
                     prior_limits = np.array([[0]*5, [1]*5])
-                    samples = bij(u, prior_limits)
-
-    """
-
-    def __init__(self, bij, samples):
-
-        self.bij = bij
-        self.samples = samples
-
-    def _calc_logL(self):
-
-        r"""
-
-        This is a helper function which is used by klDiv() and
-        bayesian_dimensionality() to calculate the difference between the log
-        probability of the replica samples (replica posterior) and the log
-        probability of the base distribution (prior).
-
-        """
-
-        logprob = self.bij.maf.log_prob(
-            _forward_transform(
-                self.samples, self.bij.theta_min, self.bij.theta_max))
-        base_logprob = self.bij.base.log_prob(
-            _forward_transform(
-                self.samples, self.bij.theta_min, self.bij.theta_max))
-
-        def mask_tensor(tensor):
-            return tf.boolean_mask(tensor, np.isfinite(tensor))
-
-        logprob = mask_tensor(logprob)
-        base_logprob = mask_tensor(base_logprob)
-        logL = logprob - base_logprob
-        return logL
+                    samples = maf(u, prior_limits)
 
-    def klDiv(self):
+    **Kwargs:**
 
-        r"""
+        prior_samples: **numpy array / default=None**
+            | Can be provided if the prior is non-uniform and will be
+                used to generate a prior density estimator to calcualte
+                prior log-probabilities. If not provided the prior is
+                assumed to be uniform.
 
-        Calculates the kl divergence between samples from the MAF
-        (replica posterior) and the base distribution (prior).
-
-        """
-
-        logL = self._calc_logL()
-        return tf.reduce_mean(logL)
-
-    def bayesian_dimensionality(self):
-
-        r"""
-
-        Calculates the bayesian dimensionality of the
-        samples from the MAF.
-        More details on bayesian dimensionality can be found in
-        https://arxiv.org/abs/1903.06682.
-
-        """
-
-        logL = self._calc_logL()
-        return 2*(tf.reduce_mean(logL**2) - tf.reduce_mean(logL)**2)
-
-
-class kde_calculations(object):
-
-    r"""
-
-    This class, once initalised with KDE and samples from
-    that KDE, can be used to calculate marginal KL divergences and
-    bayesian dimensionalities.
-
-    **Parameters:**
-
-        kde: **instance of KDE class**
-            | This should be a loaded instance of a KDE. KDEs
-                can be loaded like so
-
-                .. code:: python
-
-                    from ... import KDE
-
-                    file = '/trained_kde.pkl'
-                    kde = KDE.load(file)
-
-        samples: **np.array**
-            | This should be the output of the KDE when called to generate
-                a set of samples from the replicated probability distribution.
+        prior_weights: **numpy array / default=None**
+            | Weights associated with the above prior samples.
 
     """
 
-    def __init__(self, kde, samples, **kwargs):
-
-        self.kde = kde
-        self.samples = samples
-        self.w = kwargs.pop('weights', np.ones(len(self.samples)))
-
-    def _calc_logL(self):
-
-        r"""
-
-        This is a helper function which is used by klDiv() and
-        bayesian_dimensionality() to calculate the difference between the log
-        probability of the replica samples (replica posterior) and the log
-        probability of the original distribution (prior).
-
-        """
-        logprob = self.kde.kde.logpdf(
-            _forward_transform(
-                self.samples, self.kde.theta_min, self.kde.theta_max).T)
-        self.base = tfd.Blockwise(
-            [tfd.Normal(loc=0, scale=1)
-             for _ in range(self.samples.shape[-1])])
-        base_logprob = self.base.log_prob(
-            _forward_transform(
-                self.samples, self.kde.theta_min, self.kde.theta_max))
-
-        def masking(arr):
-            return arr[np.isfinite(arr)]
-
-        logprob = masking(logprob)
-        base_logprob = masking(base_logprob)
-
-        logL = logprob - base_logprob
-        return logL
-
-    def klDiv(self):
-
-        r"""
-
-        Calculates the kl divergence between samples from the KDE
-        (replica posterior) and the original distribution (prior).
-
-        """
-        logL = self._calc_logL()
-        return tf.reduce_mean(self.w*logL)
-
-    def bayesian_dimensionality(self):
-
-        r"""
-
-        Calculates the bayesian dimensionality of the samples from the KDE.
-
-        """
-
-        logL = self._calc_logL()
-        return 2*(tf.reduce_mean(self.w*logL**2) -
-                  tf.reduce_mean(self.w*logL)**2)
+    def __init__(self, de, **kwargs):
+
+        self.de = de
+        self.theta = self.de.theta
+        self.theta_weights = self.de.sample_weights
+        self.samples = self.de.sample(len(self.theta))
+
+        self.prior_de = kwargs.pop('prior_de', None)
+        self.prior_samples = kwargs.pop('prior_samples', None)
+        self.prior_weights = kwargs.pop('prior_weights', None)
+
+        if self.prior_samples is None:
+            warnings.warn('If prior samples are not provided the prior is ' +
+                'assumed to be uniform and the posterior samples are ' +
+                'are assumed to be from the same uniform space.')
+        else:
+            if self.prior_weights is None:
+                warnings.warn('No prior weights have been provided. ' +
+                    'Assuming there are none.')
+
+    def statistics(self):
+
+        def mask_arr(arr):
+            return arr[np.isfinite(arr)], np.isfinite(arr)
+
+        min = self.de.theta_min
+        max = self.de.theta_max
+
+        if isinstance(self.de, margarine.kde.KDE):
+            logprob_func = self.de.log_prob
+            logprob = logprob_func(self.samples)
+            theta_logprob = logprob_func(self.theta)
+        elif isinstance(self.de, margarine.maf.MAF):
+            logprob_func = self.de.log_prob
+            logprob = logprob_func(self.samples)
+            theta_logprob = logprob_func(self.theta)
+
+        args = np.argsort(theta_logprob)
+        self.theta_weights = self.theta_weights[args]
+        theta_logprob = theta_logprob[args]
+
+        deargs = np.argsort(logprob)
+        logprob = logprob[deargs]
+        wde = [np.sum(self.theta_weights)/len(logprob)]*len(logprob)
+        logprob = np.interp(
+            np.cumsum(self.theta_weights), np.cumsum(wde), logprob)
+
+        mid_point = np.log((np.exp(logprob) + np.exp(theta_logprob))/2)
+
+        if self.prior_samples is None:
+            self.base = tfd.Blockwise(
+                [tfd.Uniform(self.de.theta_min[i], self.de.theta_max[i])
+                for i in range(self.samples.shape[-1])])
+            prior = self.base.sample(len(self.theta)).numpy()
+
+            theta_base_logprob = self.base.log_prob(self.theta).numpy()
+
+            base_logprob = self.base.log_prob(prior).numpy()
+            prior_wde = [np.sum(self.theta_weights)/len(base_logprob)]*len(base_logprob)
+
+            base_logprob = base_logprob[deargs]
+            base_logprob = np.interp(
+                np.cumsum(self.theta_weights), np.cumsum(prior_wde), base_logprob)
+
+            theta_base_logprob = theta_base_logprob[args]
+        elif self.prior_de is not None:
+            if isinstance(self.prior_de, margarine.kde.KDE):
+                self.base = self.prior_de.copy()
+                base_logprob_func = self.base.log_prob
+                de_prior_samples = self.base.sample(len(self.theta))
+                theta_base_logprob = base_logprob_func(self.prior_samples)
+                base_logprob = base_logprob_func(self.theta)
+            elif isinstance(self.prior_de, margarine.maf.MAF):
+                self.base = self.prior_de.copy()
+                base_logprob_func = self.base.log_prob
+                de_prior_samples = self.base.sample(len(self.theta))
+                theta_base_logprob = base_logprob_func(self.prior_samples)
+                base_logprob = base_logprob_func(self.theta)
+        else:
+            if isinstance(self.de, margarine.kde.KDE):
+                self.base = margarine.kde.KDE(
+                    self.prior_samples, self.prior_weights)
+                self.base.generate_kde()
+                base_logprob_func = self.base.log_prob
+                de_prior_samples = self.base.sample(len(self.theta))
+                theta_base_logprob = base_logprob_func(self.prior_samples)
+                base_logprob = base_logprob_func(de_prior_samples)
+            elif isinstance(self.de, margarine.maf.MAF):
+                self.base = MAF(
+                    self.prior_samples, self.prior_weights)
+                self.base.train(epochs=250)
+                base_logprob_func = self.base.log_prob
+                de_prior_samples = self.base.sample(len(self.theta))
+                theta_base_logprob = base_logprob_func(self.prior_samples)
+                base_logprob = base_logprob_func(de_prior_samples)
+
+            base_logprob = base_logprob[deargs]
+            base_logprob = np.interp(
+                np.cumsum(self.theta_weights), np.cumsum(wde), base_logprob)
+
+            base_args = np.argsort(theta_base_logprob)
+            theta_base_logprob = theta_base_logprob[base_args]
+            prior_weights = np.cumsum(self.prior_weights[base_args])
+
+            prior_weights = (np.cumsum(self.theta_weights).max()-
+                np.cumsum(self.theta_weights).min())*(prior_weights -
+                prior_weights.min())/ \
+                (prior_weights.max() - prior_weights.min()) + \
+                np.cumsum(self.theta_weights).min()
+
+            theta_base_logprob = np.interp(np.cumsum(self.theta_weights),
+                prior_weights, theta_base_logprob)
+
+        midbasepoint = np.log((np.exp(base_logprob) + np.exp(theta_base_logprob))/2)
+
+        mid_logL, mask = mask_arr(mid_point - midbasepoint)
+        midkl = np.average(mid_logL, weights=self.theta_weights[mask])
+        midbd = 2*np.cov(mid_logL, aweights=self.theta_weights[mask])
+        de_logL, mask = mask_arr(logprob - base_logprob)
+        dekl = np.average(de_logL, weights=self.theta_weights[mask])
+        debd = 2*np.cov(de_logL, aweights=self.theta_weights[mask])
+        theta_logL, mask = mask_arr(theta_logprob - theta_base_logprob)
+        thetakl = np.average(theta_logL, weights=self.theta_weights[mask])
+        thetabd = 2*np.cov(theta_logL, aweights=self.theta_weights[mask])
+
+        kl_array = np.sort([midkl, dekl, thetakl])
+        bd_array = np.sort([midbd, debd, thetabd])
+
+        results_dict = {'Statistic': ['KL Divergence', 'BMD'],
+            'Value': [kl_array[1], bd_array[1]],
+            'Lower Bound': [kl_array[0], bd_array[0]],
+            'Upper Bound': [kl_array[2], bd_array[2]]}
+
+        results = pd.DataFrame(results_dict)
+        results.set_index('Statistic', inplace=True)
+
+        return results
diff --git a/notebook/Tutorial.ipynb b/notebook/Tutorial.ipynb
index 3f53d72..27452b7 100644
--- a/notebook/Tutorial.ipynb
+++ b/notebook/Tutorial.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "ed7c3806",
    "metadata": {},
    "source": [
     "# Tutorial\n",
@@ -16,6 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "07930f39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,6 +28,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c7050a4c",
    "metadata": {},
    "source": [
     "In order to demonstrate the applications of the code we need to load some example samples from a nested sampling run and we can visualise the posterior distributions with `anesthetic`. We write a helper function to load the chains and transform the parameters in the first three columns, which were generated with a log-uniform prior, into the unifrom parameter space.\n",
@@ -36,6 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "51ad87a4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,6 +71,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7488b9e7",
    "metadata": {},
    "source": [
     "To visualise the posterior we define another helper function that will be useful later in the notebook."
@@ -75,18 +80,17 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "2693b2e4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Pze9Dp9u5MxvLAwCYsVqavKSUkpltlXRCo5X9flyfuAAAAEyrlmJezOw3JP1Yo8tFvyHpR2b2lnZ2DAAAoJFWl41uknTlWE4XM+uV9C1Jn2tXxwAAABpp9dtGlZCMbt8EjgUAAJgyrT55+Ucz+4akT9bbb1MZ0vrH4NNZc/zmJSv89qM+gFfyx9u8ha6d5vqkdFq42DUrv/Ue11699sthfx98q7XPUEYIsLVVVb89FJdM+3xhbjv/Ir9/TDoXA3gtJPIDAKDDtBqw+0dm9mZJL63/aGNK6Qvt6xYAAEBjrT55UUrpZkk3t7EvAAAAuZpOXszsoEa/Gp3ZpNFvUC9pS6+AZjohKR0AoDBNJy8ppcXNtpdOjO+IqWpCUUJbfLY//NCTfvsqn7QuzfYxNWnfbr//C1/ht//sh377ivMyXbaFYX64cKlvP7nHt5etDCcIMS7xNcdilWVAUjoA6Gp8YwgAAJQKkxcAAFAqhUxezOxDZvZzM/uZmX3BzJYW0Q8AAFA+LX/baIrdKukDKaURM/ugpA9I+uMpv0pefEfyhRczTh73h68MMS6H9vvtvWtcuza8y5+v5q9Xefkb/fYQYzO6U+jzyAnXzORxCdszrzHGwAAAUDKF/E2WUvpmSmmk3vyhpNVF9AMAAJRPJ/wzfIOkrxfdCWBK9fWNZnge+1OtFt0jAJgx2rZsZGbfknRug003pZS+VN/nJkkjkj7R5DwDkgYkae2aNafbDegsQ0O+HUtVAADOWNsmLymlVzbbbmbrJf2apOtTOn3SjpTSRkkbJan/issnltwj7y+MGP9h4fQ9MQdKqHUUc66El1E5xydPS0cP+uOXhpwsjYRjFOorxTgaVcKQ5uV5OcO/VDdu2qyNmzZLkob37W++MwAAU6iobxvdIOl9kl6fUjpSRB8wOQMb1mtw6xYNbt2i3t7eorsDAOgiRcW8fFjSYkm3mtkdZvaRgvoBAABKppCvSqeULiziugAAoPyKyvPSGfLywMR4kdqIb/fMa3p8irWQYoxMPJ8axJ+EeknqyRuycI4pinEBAKBTdMJXpQEAAFrG5AUAAJQKkxcAAFAqTF7Q+apVn622ry/3kI5Dxl0AmDLdHbAbg1fzglvzAnpDEURbsKT5/rGI4qzZ2T7GoOBuDMDduTP7usuGjLsAMGV48gIAAEqFyQsAACgVJi8AAKBUujvmJcqLQ8jEXcT4kzgXzNl/9tz8PnVjjAsAAE3w5AUAAJQKkxcAAFAqTF4AAECpMHmZiEqP/zM+6VijWJS43Sr+TyvyrjETzYSkdACAtiFgF51nJiSlAwC0DU9eAABAqTB5QfG6cZmIWkcAcMZYNkLxunGZiFpHAHDGePICdAKexABAy5i8YPp14zJRnqGh0adPY38kJjMAcBpMXtB+cbIi+b+o4xIK8icz8Q+TGwBdxFKJYg1WrFieqmvXFt0NBEMPPyzGpfMMPbxLVSY1HWXbtm17U0q9RfcDKLtSBexW167V4NYtRXcDQf/V1zEuHaj/mus1ODhYdDcwjpntLLoPwEzAshEAACgVJi8AAKBUSrVsVHqp5tut1jdCe+XFfZGDBQA6Cn97AugM8VtpBBsDOA2evADoDDHTMk+8AJwGT14AAECpMHkBAAClwrJRO+UFgraUIDAvmLQL55/xvk10eeHUSd+uxI9BvOc5559sf6bQxk2btXHTZknS8L79hfUDANqpC//mA2augQ3rNbh1iwa3blFvL4lcAcxMTF4AAECpMHkBAAClQsxLO2ViH0K7dipnf2VjWkpUSLNtJh1TknN8XhxRB8W4zGh9ff7e9vVRgRyAJCYvADpVnKgwSQRQx7IRAAAoFSYvAMphbBmJEgJA1+uuZaPJxirEGJU8IZ9IOnHMX37ewnBAg7lkbcS3e8KQnQrb42vKtGfAfDWOQyx4eSpsnzU7nCC8D2Lel3jPYh6YMI6qhHs6a44yMuMY+4RcjeJdWEoCutIM+JsMAAB0EyYvAACgVJi8AACAUumumJc8MXYixofE9fUQb5KOHW56vM2Z5/c/6GvP2Pwl2T7lxUrE+I+5C0IfQp/zXmMRYixSXv6bSo9vnwqvKR5/7ES4XLjeyaP+cguW+v2PHfHb58z122Ms02y/vSFyxQDAGSvsby4zW2Nm3zWze83sHjP7g6L6AgAAyqPIJy8jkv4wpXS7mS2WtM3Mbk0p3VtgnwAAQIcr7MlLSml3Sun2+v8flLRd0vlF9QcAAJRDR8S8mFlV0uWSfjSlJ47xHbVa8+0xh8rRg74d8nfUhu5ybVv1DH/6h7f745ec7bcffNIfv/ZZyogxKTGeIsbERJnYipxaSUXEXsQYlTgOsY/Hj4btfhzTMT9u6dEHXdv6LvXHj/g8L7VH7/P7L/SxSOl4GIOeEIMTY58kKeb0oUYVAJyxwicvZrZI0s2S3ptSOtBg+4CkAUlau2bNNPcOp7Nx02Zt3LRZkjS8d1+xnQEAdJVCv2piZrM1OnH5RErp8432SSltTCn1p5T6e1csn94O4rQGNqzX4NYtGty6RYwLChNLBlAuAOgKhT15MTOT9FFJ21NKf1VUPwAUpFqVdu78Vbuvb+LnoPI00JWKXDZ6qaR3SrrLzO6o/+xPUkq3FNclANNm505ifwCckcImLymlrZKm9p9J8RdhbMdA0BGfvCwGjqbDT/ntIVjWFi71+z94p9//rBX+9N/4tN++uuqPP2etMmLw55KwRBMCdm3hMr89BiU3Kho4nRr9ZRULG8YA3piULmw/ddc/++2zcgJ+d+3wm3cP+e3z5vv2Sj8u1rvaH79vt99/hd8uNXijzw3XiLeFJwgAcFodkF4VAACgdUxeAABAqRT+VWkAU8d9hX3f/uY7A0BJzazJS4wTsJ7G+/1ye3jwFJKVZWIvTvl27fYtfnuMtbjzB759ro+FsPOq/vQb/59sF198jW+vOM+311ycOcbvEItJhtcY40mmOAyppSR4J0PsUehTOtg8j0xl3WV+/0NP+fbWr/t2HOeze313Pu1jk2a/4Lmuba+90R8fCjfq6eFsJ8+70LdP5SQXjLFJcf8Yv1U3sGG9BjaslyT1X3N982sAQEmxbAQAAEqFyQsAACgVJi8AAKBUcmNezGy1pBslvUzSeZKOSrpb0tckfT2lmEikg8UYlpDnJR16MrSf9u37fuqPX7jYt28PdSUXLfLH3/Ydf/kDX3XtWdVsfhCdCPEgi30el7Rnp2vbWn9NzYmFHMNwxbifqRZjXE4ez+4T4jdSLIhZCW/T/Y/5/UOhxtqn/6fvwqpVrj3yYz+Oj//0Edc+78Xr/PVCLFPtx34c7dIX+P0X+EKOUjZnkC0K+XjyYmAysUkA0L2aTl7M7GOSzpf0VUkflPSEpHmSLpZ0g6SbzOz9KaXb2t1RAAAAKf/Jy39OKd3d4Od3S/q8mc2R1CAtLAAAQHs0XTM4zcRl/PYTKaUHprZLAGakatVXgD6TQowAoPxloyWS/kSjS0dfTyn9w7ht/z2l9Htt7t/UCvk90gkfK6F5IV7kyT2+vfcJ3z7q83vUHvM1bnZuud+111zpH1LVTvo4h5P3DSma9aSPw9EyX9uocv5Frp2GH/b7LzrLNW3puX57K3lYJiOGRJ0mP4nrwux5/hThNdV+frs/YG6oOfWyV/jjv/tN1z6++ynX/tljB/z2LX4+3nfCj9OsdWv8+U8c8+01odaRpMqzrvL7POXfS7Z0pT8g5uPpmZ05Z+lQiBHAFMmL1vxY/b83S7rRzG42s7G/KV7Uvm4BAAA0ljd5eUZK6f0ppS+mlF4v6XZJ3zGz5TnHAQAAtEXeM/y5ZlYZ+zp0SukvzOxRSbdJWtT8UACYZn19fumzr08aGiqsOwDaI2/y8hVJr5D0rbEfpJQ2m9njkv66nR07I3E9vRbrwfhcGTZngT/8kC9kZ0vP8duXnu3b9+1w7fu/+XPX3n3A5zRZ/qCPc/jRDl+z5xVvfI6imKNEt4d6SSt90GM6csi1K6ue4fcP8RmZGJQWYlImJJNHpkFaoBiLdCDUBpq30LcP+zwwadsP/fbwPqg94cd1x93+/A8d87l0Lqr598VPvu7H9cXvDfc0vMbKpQ1WVI/7+Chbcb7fHnMQxRiXGDvU7vw8ZRUnKlMdwwWgIzT9myql9L7T/PwfJV3UaBsAAEA7tfTPNzNbbmZ/bWa3m9k2M/uvxL0AAIAitPrs+VMaza77ZklvkTQs6dOTvbiZ3WBmO8zsATN7/2TPBwAAZr5WJy+rUkp/nlL6Rf3Pf5C0MveoJsysR9LfSHqtpEsl/aaZXTqZcwIAgJmv1ejMb5rZjZI+U2+/RdI3JnntqyQ9kFJ6SJLM7FOS3iDp3jM+YwzQjcXsMgG9IUgyBEGmgz6gNiapO3rXkGtXn++Da1fs8gnmYoBuTb4/d35tu6Ir/uLF/gerfPHG2mMPuXbPc17m988EgoYhjwGNUx0YmrnHDQIoZ/ngVDsrBEoP7/L7L/fb7YW+yOGhD37YtU+N+Nf00cef8qeb7d8np2p+XJ7fH4Jr+y7w7Sf3umbt3hBALKnyzCv9D2KuttxxIkAXAMa0+hvxdyT9g6Tj9T+fkvRuMztoZgeaHnl650sa/7fSI/WfOWY2YGaDZjY4vHdf3IyCbNy0Wf1XX6f+q68T4wIAmE4tTV5SSotTSpWU0uz6n0r9Z4tTSkva2cGU0saUUn9Kqb93BTHCnWJgw3oNbt2iwa1bxLgAAKZT08mLmVVztpuZrW62TxOPShpfJGZ1/WcAAACnlRfz8iEzq0j6kqRtGv2W0TxJF0p6uaTrJf2pRpd8Juonki4ys3UanbTcKOntEzpDjGGJib1iHMFJnzROc3wBQB31yc90PBRuPO4TvM1a6pOZHfyFj334/gM+Odo/PukTlf3bC1a4dqVBPEi6+2eubXt80b/Km9/l948F/1ZW/QlDQjjN8UUNpzy2IsYdNRIT51VC7NFw87dX7atfdO1ZZ/vkz7VhP64vWuJf885jPlZqx1N+nM7df9if72Ofce0FVz3TtStXXpvt5Nz5vh2HOr6XR3ziPM2akz0nAHSpvCR1b61/A+gdkjZIWiXpqKTtkr4m6S9SSseanKLZuUfM7D0aDfztkbQppXTPmZwLAAB0j9xvG6WU7pV0UzsunlK6RdIt7Tg30I02btqsjZs2S5KG9+1vvjMAlFRLX5U2szc1+PHTku5KKT3RYBuAAgxsWK+BDeslSf3XXF9sZwCgTVrN8/IuSS+W9N16+zqNxsCsM7M/Syn9rzb0LV9e0bUYbzE7xHeEYnmZPDExHuRZz/Wbw/WXP9vHVrz6Tl+4cf4/+ZwsF7/uMte2l4QcLZKO/sPnXXvBjRt8l7dtce1K/8v9CWLelviaYgxMyLmSCc6YbKG7GIckZccl9nmxz+OiA/6JQuU1r3Ptud/8uj/dCT+u16z251tzzYWu/eS2IdeOL3nh1f59oBde7fc/d50yDoSvk8dikzH+KsbAAAB+qdXJyyxJz0op7ZEkM1sp6eOSXijpNknFTF4AAEDXafWrJWvGJi51T9R/tl/SydMcAwDF6usbfXQ29qdaLbpHAKZAq09etpjZVyV9tt5+S/1nCyU91Y6OAcCkDQ359mSXPQF0hFYnL78v6U2Sxhb3/07SzSmlpNF8L50p5sqIsRQxvuOUj42ws3weFh095E838lO/fXnI2zL/F659/W/2+/0XL3bN2m1bFM3/N78b+uhjRipXXOO3z/W5Z+JryuQLaXcNnXjPG8VynPT5dNIJ37aVfX77k3tcO8bt2Ate4Nrz5t7l2nMe8xUtKs/zMSzLV/raSSd2+HFUb6itdM4avz3mrZGy45JXdytT/Ii/dAFgTEuTl5RSMrOtkk5o9Lfqj+sTFwAAgGnV0j+zzew3JP1Yo8tFvyHpR2b2lnZ2DEDJVas+3qSvL/cQAGhFq8tGN0m6ciyni5n1SvqWpM+1q2MASm7nTr7yDaAtWp28VEIyun1q/ZtKxYnxHTm1juzsVa6dDj7p2yG/iL32bb4dYmRmX/gsf72Hfu7bs33/7MXnK7KlIe4mvqb5Pm7GFi712+eEmjrxL5NWag9NRoyhafiu8fEcFl5THKfKqgv89jX+bVw77PPtVN76r1x71etDzaoYFxTu+ZwrX+K3x/dRT0/ztpTN6xLvy0iouzUr5r7JmQQQiAqgi7Q6eflHM/uGpE/W228Taf0BAEABWg3Y/SMze7Okl9Z/tDGl9IX2dQsAAKCxVp+8KKV0s6Sb29gXAACAXE0nL2Z2UNmEE9JokEJKKS1pS6/OVF6ujLxaRzHPy8Kz/OGrL/L7hziDtO8xvznGq/Rf6/ffcYc///NDzhYpE+NiS8I5Q06RdPgpv/+sMMSV0I55XtqtUR6ZSk74VIwXWXy2P2Ucp6te6dq1h+72xx/2eV4y3XnJDf74ndubX+9Cn1cmPbk7e9JM7E9P83bMjzPV+XcAoMSa/s2VUlrcbDsAAMB0459zAACgVJi8AACAUilk8mJmHzKzn5vZz8zsC2a2tIh+AOgyVJkGZoRpjtb8pVslfSClNGJmH5T0AUl/POmzZhJ15STuyknQlkIwrK2+xG8/MOy3x0KP8fhzQoHBZSv9/vF4KRtQe+ywb4eCf5mA3hgM24kZT2PgdHT8iGtavCfHQyHHxctdu/LMUBCzJxbkPBnaofjlJVf67Qd9ssI4JvH6Uva9lN2BAN1pQZVpYEYo5DdkSumbKaWxr/b8UNLqIvoBAADKpxP+ebdB0tdPt9HMBsxs0MwGh/fum8ZuoZmNmzar/+rr1H/1dWJcIIlCjACmTduWjczsW5LObbDpppTSl+r73CRpRNInTneelNJGSRslqf+KyztwzaM7DWxYr4EN6yVJ/VdfV2hf0CEoxAhgmrRt8pJSemWz7Wa2XtKvSbo+pWn6jZcXVxC6YTE5WkxiN3ue377IJ0/TyRNNrx8LQTb8xR/jYOIafWxnXmMY4jIs8cfXEGNi5oT7Hl9ULLi5pNeffr9PImch6V32nvv3SebtOn+RbzeIb7EFOfkciXEBgJYVErBrZjdIep+ka1NKR/L2B9CajZs2a+OmzZKk4X37m+8MACVV1D/3PixpsaRbzewOM/tIQf0AZpSBDes1uHWLBrduUW9vb/4BAFBChTx5SSldWMR1AQBA+RWV56UgOQEfeUUKYz6QTOxFPF/II9MTYjdqPp9Iw7wzcZ8YGxG3xxwmpRTGKZOrJsTEjITYojgu4R5lYo3ifT96qOn1c+NXGuWtIZ8IAEwZogQBAECpMHkBAAClwuQFAACUSnfFvOTFHcT8HXH/vHiSkF+kYa2iZteL8StSbv2lTHzIjIitiPlu4msMc+4YYxJjk/LuUYyhmbcgbI/vi5x6UTEGp1Ef0RnGCjWOb8f6RwA6TndNXgBgPAo1AqXEshEAACgVJi8AzgyFGAEUhGWj8Sb7yHjWnImdL8bQNNq/lVwwM81E6/zEmJOJ5rrJi3WKw5KpQRXa3RLfQiFGAAXhyQsAACgVJi8AAKBUmLwAAIBSYfICAGPG8r6M/alWi+4RgAYI2C1SKwHC3RCgO1mTTT440UDtzP7kBpkxyPsClAJPXgC0hq9GA+gQPHkB0Bq+Gg2gQ/DkBQAAlAqTl6k0/pE6a+Wdg3HBmSKAF+hILBsBGFWtji4NnU43xrgQwAt0JCYvAEYR0wKgJCyV6JfVihXLU3Xt2qK7gWDo4YfFuHQexqXzDD28S1WWnjrOtm3b9qaUeovuB1pXqicv1bVrNbh1S9HdQNB/9XWMSwdiXDpP/zXXa3BwsOhuIDCzJuul6EQE7AIAgFJh8gIAAEqFyQsAACgVJi8TkZL/M1OvOdOlmv+DztDu9zqfpfaIZSMISMY0KFXALgCgw8Sv2JMLB9OAJy8AAKBUePICAG20cdNmbdy0WZI0vG9/sZ0BZggmLxORiY/IeXA1FY9P4zniWn03PqKd6D3I7B/GLTOu4XzdeI87Qd44T3b7NBnYsF4DG9ZLGs3zAmDyWDYCAAClwuQFAHB6fJsIHYhlIwDA6fFtInQgJi/NnBrx7Z6c21U7FX4QPuQjJ327Eh581RrkHKn0NO9Dh6zrFyre94nGsMTj4z2O4xLP3zM75/rKxtnMRPG9GO9D3nszfj5mhft68oRvz547sevHB83d+FmZCn19/t719RXXF3QtJi8AgNYNDRXdA4CYFwAAUC5MXgAAQKkweQEAAKXS3TEvecGusR0DePOSYh0/6ttzQoBhDMZtRV4wamb/Egb05vU5BtjGdl4g58njYXu4pzFwdO58366EQFI0lvf5iO0YKB0/bzHAPRyfjhzwl194Vtg/jPOpnEDtRn0sw+cH6AI8eQEAAKXC5AUAAJQKkxcAAFAq3R3zorgGHzbHmJRM0i0/90sH9jbdbrP87U57H/Hbzzon28WYqCvFOIKYMC0MaRnX6GOfTx7z7VlzfDu+5hhrFGJW0iFf2dfmLfL7z5nn9z8Y9o8xNTEB3byFmvFiLIjUIEljjGkJ7+UQoxLvWzr0ZNPjLYxTbh/jOMV/ujVKLphRws8TMAMV9uTFzNaY2XfN7F4zu8fM/qCovgAAgPIo8snLiKQ/TCndbmaLJW0zs1tTSvcW2CcAANDhCnvyklLanVK6vf7/ByVtl3R+Uf0BAADl0BExL2ZWlXS5pB9N64VzC7mFPBIH9/nNcQ3/2CHfjmv4+3e7ti1Z4bfH46VMzhKrhCGLOUgyOUzCa5wd4kXyihZOtUaxEnmxBrWc/Dk9PjYpHTvot5844rc/Nezbxx927coFz23enxBzk074mBxr+Hrifc2Jx8iJr5p2rfQnxoiF+5Ke9jFhMQ9LOvy0Pz62Y4xZzJsUP4+ZmJhwz+NnqdF7PxP31gV5X6rV0UrSYyZaeLFR4UbqIWGKFT55MbNFkm6W9N6U0oEG2wckDUjS2jVrprl3OJ2NmzZr46bNkqThvfua74xpw7hg0nbubPyPjFbFicpMnOChcIX+c87MZmt04vKJlNLnG+2TUtqYUupPKfX3rlg+vR3EaQ1sWK/BrVs0uHWLGJfOwbgA6AZFftvIJH1U0vaU0l8V1Q8AAFAuRS4bvVTSOyXdZWZ31H/2JymlW6bsCnnr9HH7CR8vko6HGJRYCyW0a48+6Lcf8PlBtHipa1ZmhzX5Bo9XLay5p8NPhUNizEp4jTFPTF7sRSfksYjjUvM1btLxw2G7fw22eJnfvPshvz3EQtQeH/LtX9zlrxdiL+zsc337rF7fnwWLlZXzGH6iNaumWqwjFHPnxGWEMCaSlHaH9/9ZPqYrM45H/SpxGo55j8Lx4fOpo/7zmWJNqljr6OxVocfhNcwP+X4AdKzCJi8ppa3qiL8pAQBAmVAeAAAAlErh3zYCgJnMfQNs3/7mOwNoycyavOTVMok5UOLhI82365RfU6/d/UO//WiIxZgfatzs9vlEancPhv6FNXtJ9qq3+B8cesq3j4VrhjgB64nxFyFvRSeI4xLidFKmZk483ucTOfWl/+G3n+/zVJz6zD+4duXSZ/rrXXCxP/7hEDPzL3/H77/vMb+9UexEfC/G/DzxvVuZ7oei4foxBmbkhN875HCRJM1b4NsHfW2iFN6r6d6f+P1j7a94j/b4+6zV1WwfxrF1l/nzhVpiWupriVkK/ZekWk7tsBbyvgxsWK+BDeslSf3XXH/a/gJoHctGAACgVJi8AACAUmHyAgAASoXJCwAAKJWZFbCbJxTU0/Ejoe0L/qU9O337aCj419O8aFv68fdc+8S9PvBzziU+kNSWN0jn/uQT/pwheDXt2uHPEYIk7cLL/fliQGEMSMwUDJxkKp5MjZRGydpin3ICdEOwaC3cA+3xBTCPfeHrrj17qQ/MHNn2M9fOfCh6fVK62hc/6tqV1/+2a6f4vpIyAbA2OxYVzPkotrtQYzxfTtK6TPCrJAvJ+Wp3/8DvEBMmhgSMJz/3Bb/72pAMcG34vKz0tc7Sd77s2+dd4K8XPt+xMGRMeidJmtsgiBdA4XjyAgAASoXJCwAAKBUmLwAAoFRmVsxLJp7jVNN2OhFiXJ7wSeRsbUhedtsX/flCobjDH/usa99997Brzwn9O3jbL1z7ZTe+QBnHvuaalTdvcO1aLGa33McJZGIXYvKzGJMyq81J7DIxNg2MhD6HWKPa9//Rb18WYoXO8fdg7gW+/Yuv3enau/b798HlD/o4o3mrfaHHOde+2Pfns3/r2pU3+hgYSdJJn+RNS0Kf43s1xsBMdYxLphBkED9LIT6lct6FmUNqD97R9JRH/7+PufbJ/T5p3cGD/h6dU/F9ePTL21y7Gt/bT/sCmukun0TSLn+Zb89f4o+f1eDXYUjOlx0XyrMBReDJCwAAKBUmLwAAoFSYvAAAgFIpd8xLXg6RsE6fybUR8770rnbNWoxxCYUT9/zVx137rDU+NiLODP/n40+59ouW+Fwfa27drqj3XB+Hsyiuy7/Ax19oQchdEe9BlBdL0ULhuebnj/s3yPMS43BqIZYh5tpYEApePuDv2+Evb3HtY0/7mJad+3x7aYjz+cD3fX6fP7zE77/ughAb9VyfS6d2q499kiQ987mu2bPqGX57o9ww48X36kTlFS3NfHbC5vDZScdDQVBJmh36OHeea9aO+c/Pn//Q38e3n+Pfu7tufcC1V63whRp3fuQrrr321b4Qo+30eZVSyNeT4j1tUFDTloUYssl+HjpRtSrtHPee7+s77a5npK8ve5/6+qShoam9DrpKuScvAIDJ2bmzwT8Ep1CjScpMmPShUCwbAQCAUmk6eTGzBWb2PjP7IzObZ2brzezLZvafzCz7jHWCzOwGM9thZg+Y2fsnez4AADDz5S0bbZa0S9J8SV+TtF3ShyS9XtLfSnrnmV7YzHok/Y2kV0l6RNJPzOzLKaV7J3CS+IPm+8f4j7DmbYt8zIqOhHX9Yz72Yc5sP/d7cmi/a58Ij2L/0ysvcu1btz3q2k8/HXJKSDpndchFcenzfJ/PD/k2TvrcM7k1c+b4uIRs/o/J1jZqoSZPiDlJh5/y22OujaUrfHuJH7eF+/a59pM3+xo7Kxf6cX/ssL9nrz3bx9icf9lK1z75kB+32bN9zR57fjZfT+XC57t2OuTfK7bo7HBETmzFRGMvprxGVQMxhuRxf58W9F/s2v/phc9y7ff8ha9N9C/O9rFNtb0+LujCNb6Wkl1xpb/+iI+x0aEDob/+s5H5/EvSSV9HS7PnZfcZr901qABIyp+8XJxS+g0zM0m7Jb0ypZTMbKukO3OOzXOVpAdSSg9Jkpl9StIbJLU+eQEAAF2npYDd+oTllpRG//lVb082wut8jT7VGfOIpBfGncxsQNKAJK1dsyZuRkE2btqsjZs2S5KG9+5rvjOmDeMCoBvkPdMcHIttSSn9Mi+9mT1D0sHTHjWFUkobU0r9KaX+3hXL8w/AtBjYsF6DW7docOsWMS6dg3EB0A2aPnlJKf1rSTKzeZJ+T9LVGl2M3yrplZO89qOSxj9KWV3/2ZmL6/IxViLW1ZnlYxVi7orK6/6Va5/6j//Otffv97ES6673a/of+5ivrXLqzsdd+6weH+uxZInvjySlkyE3zYrz/A4hxqU2dI9r91z2Ur9/jIGJNXXiGv1kYyVi/ZlG+UqO+1iiWHMmPb3HtSurqq5d+/qnfHu33//gIf8+2DLs592vPs/nF9n9tL+nj97jz9d3tc/RYuec49pa1quMU/4+25Klfnsch/DezGzPy9+TJ8ZmxPiQmNcl1AHL9EdSeniH/8GFl7rmyW9+x5/isB+Xd5zjxz3WAnvWc/x93rPL1zJasfWfXbvyTP951LpLfDvW0GqUgyjv+XJ8f+fFmAGYEq1Gk31c0mWS/lrShyVdWv/ZZPxE0kVmts7M5ki6UdKXc44BAABdrtV/Jjw7pTT+n1HfNbNJBdamlEbM7D2SviGpR9KmlNI9OYcBQKm4OKR9+5vvDKAlrU5ebjezF6WUfihJZvZCSYOTvXhK6RZJt0z2PADQqQY2rNfAhvWSpP5rri+2M8AM0erk5QWSvm9mY8VI1kraYWZ3afTLR889/aEAgI7R7lpGwDRodfJyQ1t7MVXyEnnFooYxoDcEv6Zd97l25cYNrn3BrBD2EwoM/u+v9Um4hu7c7doXXr3OtQ/e5wN6JWnueT5xVrrTJ1xT1Se+qzz7JX57DMyMgZex6OFU1xxppaDgHF+gMh16ym8PQcS1J3b57Yt9oKet8Anfnvk2/w38Nd/3K5779/tEZKvO8v1ZdZFPitdzcSiq2BfaRw4pY14oJnnyRPPtmcDp7CknJ5wwBgiHwFMLn6UUi2dKqlwUClR+YZNrz7nWFxE98sVvu/ZzL/X3ed8TPindguf5z8vqs5/w13/rb/o+3vYt1+4Jgd6xEGN6eliRLQ4JEePvlMkGTheh3bWMgGnQap6Xnfl7AQAAtB+5qwEAQKkweQEATK++vtEl6rE/1WrRPULJzKyMSnEdd3aIt4gxLnHdP8QZ2Bqf5Crte8xvv/61/vgf3uaacy9e7dorHnnSHz/H3/5lf/guZSxakv3Z+HOcH+ItQuK7TOHFmFQrU4ixzWv4Ddfa/ThYiMOJyQNtiY9pSSFew3pDkrh1fhwXrfSFFmd998euPXLQxwXN/5ch5Gu+75/1+nG2dc9WZDGGJca4THfsRG5R0+YJHXUwm2DblvokcpW3/Z4/433bXHt+/yOuPbLTf776XukLLT75le+79rJfD/Fdj/rV7Z53/hu/fdFS3+4J75vFsTimsuMSPy9ljHnpBENDvj3VsXaY8XjyAgAASoXJCwAAKBUmLwAAoFRmVsxLrrCuWgmxFkt8Tod02MeopMcfdm0dCzlTrvJFEO2Ej7FZumefv/zikGPlVIw/UTbfRohxseW+UKPN87krMgX0YuG4GIvRbpkYGzWIVfJ5VmKuGFu41Ldf+jrXru3wsRW65w7fPurHbd5rrvbne8E1vns7wvEh5iUTZ9Sgml86/JS/xoIQyzTVhRcnLX5WQjzYQl/MUpJqjz3g94kxJg8/5E/5tn/t2rM+9T/8/pc9zzWXXXKZa6dBX/jUVq1x7ZjHRUd8nI6dHYucxpi4BueI4nuX2A1gWvDkBQAAlAqTFwAAUCpMXgAAQKnMrJiXvNwVMZYi1DKK8Rg21+fisHPCmnrMGxOunx79hWvPete7/f4hL4VVL1Fkq33tophvw+YvDkfE2isxxqXgNflGMTaV5n2KMS61PT72KOZ9sfMu8O1Lr3LtdPiAbz+9118w1O2xi0LeliXL/fa58/32+L6SZMvO9T/Iq5FTdCxF3vUaxORUzvW1h1LIKVS54e1++35fy8te8/rQh/Be+ZnPx2Ov9LFOlWf6vDCZul1z/DilET9OtiAbx5OJ0crUnCLGBSgCT14AAECpMHkBAAClwuQFAACUSiExL2b2IUm/LumEpAcl/XZK6am2Xzgvn0is+xPW9W1V1Z9u326//4mwhn7J5b4d815c8Nzm/ZNki5f5H9TiPjmxE3lr8tMdW9Ho/DGuIGccKn2XNr9EiC9JTz/hty8KsQ1xnJeGfD9P+ZiYOI4xP1DMiSIpU0dHId5Cs0MNqumOpciL7Yj9P+7jhiRlYkwsjGMKr6ly3oV+exiXtH+PP9+bf9dv3/uo3x5qE6WjIa9LjA8bCe+zUyeVEXIMFR6LdCaqVWnnuPi6vr7CugJMlaKevNwq6dkppedKuk/SBwrqBwDMbDt3jk66xv7EoohACRUyeUkpfTOlNPbPnh9KWt1sfwAAgDGd8FXpDZI+fbqNZjYgaUCS1q5Zc7rdMM02btqsjZs2S5KG9+5rvjOmDeMCoBtYahBnMSUnNvuWpHMbbLoppfSl+j43SeqX9KbUQkf6r7g8DW7dMnWdzKsnE7sU4xTC/rF+jU4cc82YryRzvVp+baNMn2JdnbgGH2MV2qD/6us0qXGJ4xBfQ3zNOW+VdND/pW0x30c4PsX8PuGepZC3xeb5/D+Z/jaqSxRfY4ylaIMJjctEYzka1aiKcTLHfQ2pFPIixfuY9gz57SvCA9mYVymMa6xFZgtjvFiIcYlxRtOg/5rrNTg4OL0XNcv9zBSu4D6a2baUUn9hHcCEte3JS0rplc22m9l6Sb8m6fpWJi4AUEbuadi+/cV2plP19fkJc18fsTloqqhvG90g6X2Srk0pHSmiDwAwHQY2rNfAhvWSRp+8oIE4USnDt7hQqKK+bfRhSYsl3Wpmd5jZRwrqBwAAKJlCnryklC7M3wsAACCrE75tVJxGgZVN9w+3KyQjyxR2i0mxYoK5mMwsBudK2cDOk8eaby+jiRYltLA9BDrbYl84MXP8iE9GZvG+x6R1Mdg2JjeMwautBF5HRSc/m+j1GhXYjEIwuVXCfYsBvMvPb3q85izx7RhoHT9/8Z6zFAHMGJQHAAAApcLkBQAAlAqTFwAAUCrdFfOSl04mE1sRE77FhHE5sQ5zQhKsTMHBMHdsFEeQKSY5wcRaRcdSnIncPsZxCfctxqjkHJ4Z15xkaNnzxdilFsYxc44SjEue+HnIxDKF+xJvUxyHkzEpZHwvtxAz1qw/MxWFGNEFumvyAgAz3VghRmAGY9kIANBZxjLujv2pVovuEToMT14AAJ2FjLvI0V2Tl3Z/ADL5P/LylYQHX6086s07ZxljXCYr9zWHdl5unLg9nj/GLuXFWjTs0wwU38/xJeeNU4xVmnBunAl+nrphTIAZimUjAABQKkxeAABAqTB5AQAApdJdMS9TrZX6Lm7/nDwTU7EG343r+HkxLtkDJnf+VmJckJX33pzqPCx58WAzBXld0IX4LQwAZdYNeV3Gvjo9vh2/kYSuwuQFANDZ+Oo0AmJeAABAqfDkBUB34F/rwIzBkxcAKJNq1afO78YAXcoHdD0mLwDQyeJkRRoN0B37042Bq0ND/h5ITGa6DMtGANBJGn31eaZ/m2iyCOjtOpZK9KFYsWJ5qq5dW3Q3EAw9/LAYl87DuHQexqQzDT28S1We1nScbdu27U0p9TbaVqonL9W1azW4dUvR3UDQf/V1jEsHYlw6D2PSmfqvuV6Dg4NFdwOBme083TZiXgAAQKkweQEAAKXC5AUAAJQKk5dOMv6rf6f7g4mb6nvImJyZyd437ntnSDX/B8WIX6HvsoDjUgXsAgAwFTZu2qyNmzZLkob37S+2M2ciFuTssq+H8+QFANB1Bjas1+DWLRrcukW9vQ2/jYsOxuQFAACUCstGk1E75dsW5oITfYzXZY/9zliMd5jofYvr9HHc8jBO7ZE3rnn3PX4eKz2T71PZnclnJffzMdHP2yQ/r0ADPHkBAAClwuQFAACUCpMXAABQKsS8NDPhNfSwtnv8WNgc1pLnzvftRrEXE+1DN6wvx9cU72stJ/dEJdzn40d8e+4C3x454ds9eR+bBvd8Jo5DdGrEtzP3Kbw3R0769slwn+fMyzlfXn/C+eO4TPR8ZZT3WZGyv3fi+z3zOycnFikvFjCeL6+PE41Jw6hG1clj9e0S64JPLwAAM1xfn58I9vXN6DwwTF4AACi7GfRUpRU8jwMAAKXCk5dm4lprZg09ODnSfPu8hb597LBvN1qPjjEs8xf7dl4ujMw5J5g7owxijEvNj0M6cdy1LcYaxeNj7EZeTE0ra/QxvmImjkvs80l/3zPv5Ux9It9OR572p1+83O+eF6t0/Hjz7TMxtiLGm+S9zyTJmteJSk/v9bsvPjvu4Zuz5vh2XsxYfB/Ez1fPDBgXTDneFQAAoFSYvAAAgFJh8gIAwEw39m2ksT/VatE9mhRiXgAA6HSN8rZMRPw2Uhnj6sbprslLXoDeiZBULgaW5SVbGmke6JkOhwDE2XP9/o1yq80LQYYhGDW3GORMCELMBPzNbr59lt9uMdnZKR/UmEIgto2Ee5YZp5wkXDGAsZFOG5dM8GwQX3OjBG/x85D5fPhxygTkzvL32eb493567AG//bwL/fnja4gBulEmIL9BwH1MaNjpgdV54xg/K5JS+L1nIeDWlp0bj/DNYyFwOv4ejF9UyAu0ztxzSBqduOSNbxcp7F1iZmvM7Ltmdq+Z3WNmf1BUXwAAQHkU+eRlRNIfppRuN7PFkraZ2a0ppXsL7BMAAOhwhT15SSntTindXv//g5K2Szq/qP4AAIBy6IiYFzOrSrpc0o8abBuQNCBJa9esmeSFcuZqIVYirvOnJ/f47XGtNqwd1x64w19+8TK//8oQcBWvrwbxGiHhWjoR1o9DPIgtCtecIhs3bdbGTZslScN7903uZDEWKcYehBiVWNAvxk5kYi3CPandscVv7n+1P9/hp/zxMWlWZo0+r2CnZHNC3EyM24nnOMMCm2c8LvH8MSFj7G8j4f2fjh7w2/OS0h075NuHnvJdDPFftR0/8dt7V/vt9/7QtXuuvMFfvqf5+6RRH88kxqWtn5X4Oy3G2T097Lc3+B0T33untv/Ab173nHAOHxOTfnG379KyXr//0nP89hDLlElmGGNeauG9GGPQ0JUKj4wys0WSbpb03pTSgbg9pbQxpdSfUurvXbE8ewIUYmDDeg1u3aLBrVvEuHQOxqXzMCbA1Ct08mJmszU6cflESunzRfYFAACUQ5HfNjJJH5W0PaX0V0X1AwAAlEuRMS8vlfROSXeZ2R31n/1JSumWtl0x5qqIYv6BuLb7+JBr26oL/PbdD4Xj/fpyOuJXxdI9fm1ZT+zOdKly3Rsb93VMWP/NFB2MsQt58RlF5B/JyVUT43oycTzHDvp2yEGSHvqZ3773cdes3X+733/X/X7/BYv89ddc5NtLQqG6mNdCkmoh1mBWTuxE0flD8t4HRw9lfxbjLXb/wp9y1Tq/f8gHUvvOzX77Ql+ENM0PsRIHw+dp32P+eiEGJp046rfHHEphu6TiizlmCi2GGJwY8nIgxNTEfDwj2eKytft/7Np2nv+9VtseQhHnh89DiOWr3fdTv73vmf74Fef5diX0McbtxUKPmRiZGLvUEaGcaLPCRjmltFUNS5wCANBeLpB63/5iO4MJKzxgFwCA6eYCqXt78w9AR2HyAgAASmVmLQ7G9eBM/pCw3hvWq1PM6/Koj32wvkv99gfv9O37fb4DrbvYt++/x7eXLPXH/9jnpZAkveptvn0kxHfEPDBxTXtOGOKYsyQTW9HmNf1GtTliH8I+FmJIavd8z7UrFzzPb38ojMteHwuR7vLbdXb4V9cpfw9s3WX++F33+e0Lz/Lb94d8QJK0xH9F1ubMz+7TSWIcwcmQwyXW2JKkGG9x3MeQ1H4Qwtn27/Xt3lW+vefR0IdQl+e4j32wS1/gt+fUWkpH/WfJ5vsYm4bi5yHGpLSQ82dC4mcjEwPjt1t4n6UYx3cwuzxiZ63wx9z2Vb/D0hDTNeJj805t+a5rV17l8yZl3gc/vtW1e657a+hQeM15eV1iXqhWfseg9HjyAgAASoXJCwAAKBUmLwAAoFRmVsxLnrgUOuLX0GM+gZiXpbbtn/zxsZbLvT6m5fDHPufas5b5/AjzXubX6O3yK7Jd3uvX/dPTPk6gEuJwFOuGxLXeGBMTl4JnhxiaM6yxMyExNinENmTyvJzja1yd+uJGf/zRsM6/aIk//nxf/zN95QuuXTvh19B79sWaVv4epbNX+vOfW1VGqKMT31sxbuaMags1k5efJBNL0Xx/axDbUTscakzNDvk5DoV4rSf8fX3srz/r2nPm+GsuudDHJs1eFVLt3+FjobQm5GEKn52eS1/s928QKhE/45kYs6mOcYk5TGK8R4xZi/Ee4QOdYg6jJ0OtI0m6/17fDp/5Q5/8mmsvfImPAbMl/vfayS99xbXnvPdZ/vwhxqx27/f9+S4Kvxfz8rjE9y55XroCT14AAECpMHkBAAClwuQFAIBOU62OLtOP/enrK7pHHWWGLQ7GPC+hHWoNxfXrdOhJ146xCzFfyMm//3vXfuB7Q669YIG/3sbv7XTtV231+1/77pcrSvf43DF2eb/f4Zy1rlnb469RuSjmvgjz1dxaR5OMcYlj0Cg/SFzHj3065de0a3eHfDiHfZ2d2j3b/fbwGnZ88+euveeQj7FZ2OPv0ZVrfC2W9HSI7Tge4iKu+ReKrNfH6VioD5Ndtw/v1cnGHuXl64mxHbE+07HDueeLnw896t+L6Se+Rk4KsUVfftDHpLzqvKWu/d9u9jWqBi45x7VXPu5zmMx+6ZW+Pyf8ONeW+7wylTBGkvJjjaY6Jizz3o95m0LMTRiXdNx/FjK5pxp93sNrOHW3//zc93N/X4fu+JZr94TX/Pq3+99Rh2/6P117/lU+BqbyW+/1/TkaaladCrGJi0OsU/z9MVPs3Nk4Zw0k8eQFAACUDJMXAABQKkxeAABAqTB5AQCg2/T1+YDgarXoHk3IDAvYDWJwaiy8eOhA8+2PPODbW3xBsSd//rhrr3vOua593zYfwPiO85e59t/s9IXsrhrcoagy3yf6mvcmX+yxtn3Q7//cl4QTTDDBU14ys4mKAYyNAgZjH0LbzgqFE2Pys8sud82RH/rEXCNP+qR1y5b6xF9DB3yw6lMhQLjnI9927cvf4gtBRumuH2V/+MwQdBgDqXOKU7a9sNzckNwwJksL75va/duy54jjEhIijjzpg0n//DN3uPZlIcD95/t8MOqzwvYn9vqCf5V7/OfxvNf75IGa54NdLSaAi0HKUoNirzGAMgak5wSf55lggrV0wCedS48/7Nr2nBe6du3zH8+cwxb54PG9d+xy7VkV/9779lP+vr/jHJ8E8vYQWH3JRUv99Vb5APja9h+7ds/Vb/QdjL/HY4HOuS0UOZ3uz1MZDA35dsnuCU9eAABAqRQ6eTGzG8xsh5k9YGbvL7IvAACgHCY8eTGz+6biwmbWI+lvJL1W0qWSftPMLm1+FAAA6HZNF1jN7KB+tag7tiC2YOznKaUljY9syVWSHkgpPVS/1qckvUHSvU2Paiqu2YV1zrAGb2F9OZ3wa7lavNTv3/8i1166c7drn3jcJy97+qSPnbjrKb+G/+7zfAzM8K6Q/EzS6hdV/Q/u98UfY5GzmMApPf2E3x7jR+Ka/GRjXKIYz9JAOhHiK474+5AOPeW3x9iKO3yMyalD/nwH9viCgI897mNglszy9+CKq1a79tYf+DgCLV3qmvbCV/h2SBwoZQsxZmKLYihF6FP71+zD+cP7IoVkaHbuuuwZdoaPbogpe2TbI679jjX+/f/3D/tkaNec5eNw4jsz3oFVL32G78+dPi7HXvcmNXXsSPZnMQYlJomLvZjosEx0XEPSOlt0tj/dLB9nlwZDMdljIRmhJK3x79fFvT4G5vhx/xm+6qCPFdpzwvfpwkU+BmXhlRf568Wkjgv89WqP3u/alfheizEu8XdMo99hJYvnQL686LCPSVoq6Y9SSnskycx+kVLK/uaauPMljY8Me0TSC+NOZjYgaUCS1q5pkAEThdi4abM2btosSRreu6/5zpg2jEvnYUyAqdf0n9kppX8r6b9K+qSZ/Vszq6hx4fi2SSltTCn1p5T6e1cszz8A02Jgw3oNbt2iwa1bxLh0Dsal8zAmwNTLXSNIKW2T9Mp6c4uk+Nz0TD0qafyjlNX1nwEAAJxWq0kF5kiaLemopHlm9l5JH0kpNVhAbdlPJF1kZus0Omm5UdLbJ3G+7LrmqVjsLsS4xAJgIbYi/fPX/PEhDqAy1+edOHXcF5r73gG/hv6chX6teOmysHYc8lZIUu+jvljkgpjjYJn/l9ypwW+6ds+Vr/H7xzwUeYXnJiuOSYP1aJsXcozk5IZJe0IMymEfj9GzwMfELF7uz//Jn/lYpSM1/zBxz9ZfuPYlS8IaeyjMmG77umvbjb+vyBb72IRMzEvog3qmON9OdCoUyIz3/Lh/71ooalobbrD8scfnB4n5OW7d5d/LL+td7Nr7R/xr3jvi+3jJfP/vpnNX+nG9+ys+HuzZv32N788On39E657tmumoj42SGhTQjP92i/EWE83rksnvE98XLcRzjN+86gL/g2eHX9Hx94ekJ/7HF1z7kUf95+mL+/x9edFifw9uDXmUFoTCpqu/5wuhLvuPb3btynm+z7Un/PsoLfGfHYv3OObraRTe0iC2yC3n7dufPQYdrdXfiB+X9CxJfynpzZIuk/S/JnPhlNKIpPdI+oak7ZI+k1K6p/lRAABMnlvO6+3NPwAdpdUnL89OKY3/GvN3zWwS3woalVK6RdItkz0PAADoHq0+ebndzH75PWEze6GkwSb7AwAAtEWrT15eIOn7ZjYWbLBW0g4zu0uj+V6e25beTVbM0RDWPW1+SFMTYmTSM5/vt/+zr21kYb16/hq/NvveUPNj74M+TuDe3X4t+eWvfaYyToU18LBmbbN8fIdd/nK/f1wjj+vDMfZhgrVVcsXrx+uN7uSboc5O2hVqPu0OMS+rfZ6KOWurrj3yyS+79v/xQv+V+4d3+Zo7K5b7Nf1aiEexF1/trx/GpPaL7OqnLQ35eGItodjO1NTJiX2YaB6LOM4hf0imzk+DWImMGGN20p/zXTf6ek53fG27a//7fp9f5wcP+jiEObP9ax4e9jFil77K5xOpvP1/c+30iM8fUrv/p37/S30eJ0nZ+zDdnxeFz/9x/5rTMf/ezY25Wb4y86Pea31u0Np3/Pv32hB7tHy2j3/63XU+Jqx6ua9dNPvsEDf00x/66z3tx7nn6jf4/cPvuEyep3jPYtxQo31Qeq1+8m5oay8AAABa1NLkJaW0s90dAQAAaAXP0gAAQKlM8YJth4vr+jnb7Wy/Ppyu8NULepb52iw9YY1//6e+5drnv+FK1172I7/m37Mom/+vcs11vk8XP8+3Qw6EbOxCWG+Oa/Z5a+STrakT158rDebLJ0IuipDbws7xMSop5IHI2H6na87vv8S15+729Z6efW3IehpiN+zyfn/9e+/y21/0MteurPBr/pKkpef49nyf40QjIaYkxibF+zjZWi15+UMyMTf++rZoaeaUdq2vHZQe9/lyKnv3uvYVb/Wv8bZP+VpE113m44TiLVj8ssv89V9wld9/q/8iY+U1v+lP0Cg2IorvzRibNNVy7rvmxJi1kIMo1KCqXOzfu6ceD/Fikmyl/z236KwHXPu65/v7XDvuf8cc2+Vj+WbFmlTXXuuvd9FzfAeWhM9fJm9LeK+H15iJSyK+pSswygAAFK1aHZ2ojf3p6yu6Rx2tu568AADQiXbuzD55m059ff4pV1+fNDRUWHfyMHkBAKDbxYnKZJem24xlIwAAUCrlfvKSF0yaF5wag/EW+gDcyjlVf7lQpK32hC/wZ/0+MO3sFSEh1K4h11xw0bmu3fPHf6modvcP/A9mhSELBf9sdgj6jQGymUKJObPryc6+Y/BcDBSVsuMU7nMsxFZ5zkv99pBsMF7BBr/v2j3XXed3eDwUM39WyLl40Bdi7Pmd9/vtoaBfavDot7I4BCXmBTKfDO/NOK6TlZfYK/QnhTGy+Hok1fb4AF2FQqf28lAk9JC/b1cfOe7aPW//V37/A76wo3rC53nRWf56IbhdR3wh1pj8zMLnX1J+8sBo0p+XcHxM0Ba/VBADv2OCx1B8tuElX/lG1150wcX+kv/oC4/O3rDBt3/wT/6EoU5Q5WWv99vD710LweyxQKYt9OOaDZru7CcEaA+evAAAgFJh8gIAAEqFyQsAACiVcse85K0vZxKiNS8AaCf9mnsmGdJivyZuoXBjTJ5ml4XkZqurvntxrfdpn8RLkuwcX6zOlp/v2wtCccmRmIgvJw5osgX+JqrR+eNrCHEx6XgYhxgHUPHnrKzzSbVSr79ncVzTBb4gZrynKcRKpCcf9+cL8SjW68dMUjYJXbwPMR6rxxe/m3KZcWg+7pYp1Hg8s0+l1xfITCGRnYVr1Ibudu2ey0Jh0j0+FsmqPtlgJsYl9jHGpyzzMWYxlkpzQ8I3qf2FGCcqvi9CbFQ6dsS1bbZ/TT2vuDFzytpDPqljvM89rwvvjRDrp+oz/P6vfac//+4H/fnD79FM7FEcl/g7Ki+xJroCT14AAECpMHkBAADeWNK6sT/VatE9cgp5BmpmH5L065JOSHpQ0m+nlJ4qoi8AACDo8KR1RS3g3irpAymlETP7oKQPSPrjKb9KXoGuuH4d22E9Oea2sEt8ocV04qjfPsevN5/66bf99uV+Db5hDpSK71MmHuPoIb895kSI68OxnSn41+b15FaKpoU+xjwuqoU+j+TkiTkrFPjb+4i/3AofoxJjWirn+TX9zD2MhSQXhDFoJFMkNPximGxBzInKG5daiNlpFPuRuS8hN0y4RmWlr92SXrMqnC/uv87vvy/ExCwLhVSP+/iPjFjksNHnLy/2aLrHKV4v/n6IOVBCf9LTw5lTWigkGmPveq59i98ecsekkM8nnfD3vbIqfH5izNqscI9Tzu+saLrHAB2hkGWjlNI3U0pjv9l+KKlBhCMAADMUhRgnpRO+bbRB0qeL7gQAANOm6EKMJde2yYuZfUvSuQ023ZRS+lJ9n5skjUj6RJPzDEgakKS1a9a0oac4Exs3bdbGTZslScN79xXbGfwS49J5GBNg6lmjOizTcmGz9ZLeLen6lFLO4vSo/isuT4Nbt5z5RWN8R4wzGAm5K2I9mRinEGMvQk6IGFthvWHyFe59rOkhKbMebAuXNu9TXr6QNqwP9199nSY1Lnlin2NsQhzXU2F7jG0IcUKZOIAYNxTOn4nBaUVc14+vIcaQtBIblGNKx6WV902mllh4DSEHSYq5bxQ+D48+4C95rn+sbnNz8rrEvC3x8zo7vC9i/6Xs52mSn5dJj8lE887EGlmNYniOHw37+HOm4Yf99phPJ4yjLVkR9vcxaAqxgZkYmPia2hTT4iaV+/Zr586dU3LelpmV68lLAf01s20ppf5G2wqJeTGzGyS9T9LrW524AAAwVQY2rNfg1i0a3LpFvaGYJDpfUXlePixpsaRbzewOM/tIQf0AAAAlU0jAbkrpwiKuCwAAyq8Tvm00ffLiCGaFNfC89b3MGn/ITxLqEOXWWopr8GoQ46LmeR5ylSEHQibGJcYihNcQ1/HjOOfFlywKNavmhFinvONbiYuIcTlxnb/TTcX7Jtw3m9U8zscueJ5rp0P7c84f3gcTjf8qQ82cvBiX+Dsp/k5r5Zwxjm6pz59jIY9SzDWVuY+xDlbIf5V5b2V+78Z2CX6Hoe0oDwAAAEqFyQsAACgVJi8AAKC5WKix4GKN3RXzAgAAJi4WapQKjaFk8jLeRAciJmuKYpBmpvCbv16mqGKjPuUFGWYS8ZVQfM25xfHCa84EIIYgxhgYmlfMMpMU7wwCCmMQcdmLyTUKZs8LXI6B0HHc4jlD2xaf7bdnAnBjkdGcB8tlu+etyCR4y0nMKUmzmxdKtPj5Ce0YwJtfSDGnT5lxmYHjhElj2QgAgHajEOOU4skLAADtRiHGKcWTFwAAUCo8eZmIvIRwebPqqSiSmBc7UYZEW1MtL7Yhk6wsjGMmFinn+KmIVyl7vMWZ9D9z38K4xVNONJ7L+HWWkff7opVjGiTPbCrvGlNQdBTgXQQAAEqFyQsAAFONAN224jkrAABTjQDdtmLyMhF56/x5+Qnakduj7LET02GiuXImej5MD2IlJq/hezfn/TzR31t8PjAN+G0AAABKhckLAACYuFjvaBprHTF5AQBgMmJwbrcE6A4NjS4rjv2Rpm0yQ8zLdGItuDMwDsXgvpcT45aP4NxRsXhjG987PHkBAABTr43LSjx5AQBgIqrV0actY7phiehMtPFJDE9eAABoJsa0SD7WI/4ljcam8EmMpRKt05nZsKSduTu2ZoWkvVN0rm6//hWSbp+ic82k+1L0tRmXzrs+Y9I5118hqbf+//PFuHTi9ftSSr2NNpRq8jKVzGwwpdTP9TtL0f0q8vpFv/Zmiu5bt1+/kaL71O3XP52i+9Ut12fZCAAAlAqTFwAAUCrdPHnZyPU7UtH9KvL6Rb/2ZoruW7dfv5Gi+9Tt1z+dovvVFdfv2pgXAABQTt385AUAAJQQkxcAAFAqXTN5MbMPmdnPzexnZvYFM1t6mv2GzOwuM7vDzAan4Lo3mNkOM3vAzN7fYPtcM/t0ffuPzKw62WvWz7vGzL5rZvea2T1m9gcN9rnOzJ6uv9Y7zOz/moprT7Cf0z4uRY1J/dyMy+mvybg072NX/Q6rn5txOf11u3tcUkpd8UfSqyXNqv//ByV98DT7DUlaMUXX7JH0oKQLJM2RdKekS8M+vyfpI/X/v1HSp6fo2qskXVH//8WS7mtw7eskfbWbxqXIMWFcGJcyjQnjwrh08rh0zZOXlNI3U0oj9eYPJa2ehsteJemBlNJDKaUTkj4l6Q1hnzdI+rv6/39O0vVmky8AkVLanVK6vf7/ByVtl3T+ZM871QoYl8LGRGJcmmBccnTb7zCJcWmi68elayYvwQZJXz/NtiTpm2a2zcwGJnmd8yXtGtd+RNkB/uU+9Q/A05KWT/K6Tv1x4eWSftRg84vN7E4z+7qZXTaV1z0D0zEuHTEmEuMSMC4T01W/wyTGJej6cZlRVaXN7FuSzm2w6aaU0pfq+9wkaUTSJ05zmqtTSo+a2TmSbjWzn6eUbmtPj9vPzBZJulnSe1NKB8Lm2zVaO+KQmb1O0hclXdSGPjAuAePSmYoeF8akMcalMxU5LjNq8pJSemWz7Wa2XtKvSbo+1RflGpzj0fp/nzCzL2j08dyZvsEelbRmXHt1/WeN9nnEzGZJOkvSvjO8nmNmszX6xvpESunzcfv4N1tK6RYz++9mtiKlNKVFvTpsXAodE4lxOQ3GRR03JhLjMnZexiUofFzOJFCmjH8k3SDpXkm9TfZZKGnxuP//vqQbJnHNWZIekrROvwqquizs8/vyQVWfmaLXa5I+Lum/NNnnXP0qUeFVkh4ea8/UcSlyTBgXxqVMY8K4MC6dPC7TMrid8EfSAxpd/7uj/mdsUM+TdEv9/y+ovwnulHSPRh8JTva6r9NoJPaDY+eT9GeSXl///3mSPlvv348lXTBFr/dqja6x/mzca36dpN+V9Lv1fd5Tf513ajTQ7CXdMC5FjQnjwriUbUwYF8alU8eF8gAAAKBUuvXbRgAAoKSYvAAAgFJh8gIAAEqFyQsAACgVJi8AAKBUmLycATM7NMnjP2dmF+Tsc7aZ3Wpm99f/u6z+818zsz+bzPVnqmkal7fWq6jWzKx/3M+fY2abJ3P9mWqaxqVhZV/Gpb3M7KP19O8/q4/ToqL7hF8xs/822c9fp2LyMs3q9R16UkoP5ez6fknfTildJOnb9bYkfU3Sr5vZgjZ2s+tMYFzulvQmhcyYKaW7JK02s7Vt6mJXmsC43Crp2Sml52o098UHJMZlGvy7lNLz6vf9YY3m9kAHqP/jalnR/WgXJi+TYKM+ZGZ3m9ldZva2+s8r9VTIP68/NbnFzN5SP+wdkr407hyHzOz/rf9r/ttm1lvfNL4i6N9JeqMkpdHEPFs0mooaDbRzXFJK21NKO05z6a9oNJMlGmjzuDSr7Mu4TJKZVevj8wkz215/yrIg1VPAm5lJmq/RxGWYJqcbFzPrkfQhSe8ruo/twuRlct4k6fmSnifplZI+ZGar6j+vSrpU0jslvXjcMS+VtG1ce6GkwZTSZZL+SdKf1n++MqW0u/7/j0taOe6YQUkvm8oXMsO0c1yaYVyam65xiZV9GZepcYmk/55SepakA5J+T5LM7GMa/R31TEl/XVz3ulajcXmPpC+P+ztkxmHyMjlXS/pkSulUSmmPRn+ZXln/+WdTSrWU0uOSvjvumFWShse1a5I+Xf//v68f69Sftoz/F80TGk09jcamZVwaYFyaa/u4WOPKvozL1NiVUvpe/f9/ee9TSr+t0fu7XdLbCupbN4vj8mpJb9UMn0gyeZl+RzVac+J0xiYpe+r/KlX9v0+M22de/TyYOq2OSzOMy9RreVzsV5V935F83RPGZWrEz8Av2ymlU5I+JenN09ojSNlxuVLShZIeMLMhSQvM7IFp71WbMXmZnH+W9DYz66mvvV+j0QJY35P05vpa/kpJ1407ZrtG31hjKpLG1vffLmlr/f+/LOm36v//Wxq37i/pYo0GjqKxdo5LM4xLc20bFzO7QaPr+69PKR0J12VcpsZaMxtb0nu7pK1mdqH0y5iX10v6eVGd62JxXP5DSunclFI1pVSVdCSldOHpDy8nJi+T8wWNVtW8U9J3JL2v/tj7ZkmPaLRM+t9Lul3S0/Vjvib/y/mwpKvM7G5Jr9BoVVBJ+ktJrzKz+zUaH/CX4455ef08aKxt42Jm/9LMHtFoXMbXzOwb445hXJpr5+flw5IWS7rVzO4ws4+MO4ZxmRo7JP2+mW3X6LdY/lbS35nZXZLu0ugSH2kcpl+jcZnxqCrdJma2KKV0yMyWa/Rfly9NKT1uZvM1uqb/0pTSKTM7lFJqOTdC/V+m/5BSur5NXZ/R2jguczUaw3H1uG+9oEWMS2czs6qkr6aUnl10X/Ar3Twus4ruwAz2VRtNlDVH0p/X/4WplNJRM/tTSedrNC/CRK2V9IdT1svu085xeT9/QZ4xxgVAy3jyAgAASoWYFwAAUCpMXgAAQKkweQEAAKXC5AUAAJQKkxcAAFAq/z/7hWsHYadO8wAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<Figure size 576x576 with 25 Axes>"
+       "<Figure size 800x800 with 25 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -129,6 +133,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4769ccfd",
    "metadata": {},
    "source": [
     "## Masked Autoregressive Flows\n",
@@ -139,120 +144,15 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "f713dc99",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 0 Loss: 0.0007185101\n",
-      "Epoch: 1 Loss: 0.0007014023\n",
-      "Epoch: 2 Loss: 0.0006839341\n",
-      "Epoch: 3 Loss: 0.00066634326\n",
-      "Epoch: 4 Loss: 0.0006487031\n",
-      "Epoch: 5 Loss: 0.00063104363\n",
-      "Epoch: 6 Loss: 0.0006133788\n",
-      "Epoch: 7 Loss: 0.0005957194\n",
-      "Epoch: 8 Loss: 0.00057807605\n",
-      "Epoch: 9 Loss: 0.000560463\n",
-      "Epoch: 10 Loss: 0.00054290093\n",
-      "Epoch: 11 Loss: 0.00052541785\n",
-      "Epoch: 12 Loss: 0.0005080515\n",
-      "Epoch: 13 Loss: 0.00049085025\n",
-      "Epoch: 14 Loss: 0.00047387503\n",
-      "Epoch: 15 Loss: 0.00045719987\n",
-      "Epoch: 16 Loss: 0.0004409128\n",
-      "Epoch: 17 Loss: 0.00042511517\n",
-      "Epoch: 18 Loss: 0.00040991933\n",
-      "Epoch: 19 Loss: 0.00039544463\n",
-      "Epoch: 20 Loss: 0.00038181068\n",
-      "Epoch: 21 Loss: 0.00036913107\n",
-      "Epoch: 22 Loss: 0.0003575051\n",
-      "Epoch: 23 Loss: 0.00034701102\n",
-      "Epoch: 24 Loss: 0.00033769925\n",
-      "Epoch: 25 Loss: 0.00032958362\n",
-      "Epoch: 26 Loss: 0.0003226317\n",
-      "Epoch: 27 Loss: 0.00031675794\n",
-      "Epoch: 28 Loss: 0.00031182577\n",
-      "Epoch: 29 Loss: 0.0003076632\n",
-      "Epoch: 30 Loss: 0.00030409006\n",
-      "Epoch: 31 Loss: 0.00030094603\n",
-      "Epoch: 32 Loss: 0.00029811086\n",
-      "Epoch: 33 Loss: 0.00029550795\n",
-      "Epoch: 34 Loss: 0.00029309577\n",
-      "Epoch: 35 Loss: 0.000290852\n",
-      "Epoch: 36 Loss: 0.00028876044\n",
-      "Epoch: 37 Loss: 0.00028680352\n",
-      "Epoch: 38 Loss: 0.00028495849\n",
-      "Epoch: 39 Loss: 0.0002831965\n",
-      "Epoch: 40 Loss: 0.0002814832\n",
-      "Epoch: 41 Loss: 0.00027978222\n",
-      "Epoch: 42 Loss: 0.00027806248\n",
-      "Epoch: 43 Loss: 0.00027630394\n",
-      "Epoch: 44 Loss: 0.00027450087\n",
-      "Epoch: 45 Loss: 0.00027266104\n",
-      "Epoch: 46 Loss: 0.0002708026\n",
-      "Epoch: 47 Loss: 0.00026895\n",
-      "Epoch: 48 Loss: 0.00026712997\n",
-      "Epoch: 49 Loss: 0.00026536686\n",
-      "Epoch: 50 Loss: 0.00026367788\n",
-      "Epoch: 51 Loss: 0.0002620711\n",
-      "Epoch: 52 Loss: 0.00026054538\n",
-      "Epoch: 53 Loss: 0.00025909403\n",
-      "Epoch: 54 Loss: 0.00025770898\n",
-      "Epoch: 55 Loss: 0.00025638303\n",
-      "Epoch: 56 Loss: 0.00025510968\n",
-      "Epoch: 57 Loss: 0.0002538809\n",
-      "Epoch: 58 Loss: 0.0002526861\n",
-      "Epoch: 59 Loss: 0.00025151268\n",
-      "Epoch: 60 Loss: 0.00025035004\n",
-      "Epoch: 61 Loss: 0.00024919215\n",
-      "Epoch: 62 Loss: 0.00024803917\n",
-      "Epoch: 63 Loss: 0.0002468963\n",
-      "Epoch: 64 Loss: 0.0002457709\n",
-      "Epoch: 65 Loss: 0.00024467031\n",
-      "Epoch: 66 Loss: 0.00024360023\n",
-      "Epoch: 67 Loss: 0.00024256505\n",
-      "Epoch: 68 Loss: 0.00024156799\n",
-      "Epoch: 69 Loss: 0.00024061205\n",
-      "Epoch: 70 Loss: 0.00023969925\n",
-      "Epoch: 71 Loss: 0.00023883079\n",
-      "Epoch: 72 Loss: 0.00023800644\n",
-      "Epoch: 73 Loss: 0.0002372256\n",
-      "Epoch: 74 Loss: 0.00023648709\n",
-      "Epoch: 75 Loss: 0.0002357906\n",
-      "Epoch: 76 Loss: 0.00023513584\n",
-      "Epoch: 77 Loss: 0.00023452267\n",
-      "Epoch: 78 Loss: 0.00023394993\n",
-      "Epoch: 79 Loss: 0.00023341607\n",
-      "Epoch: 80 Loss: 0.00023291916\n",
-      "Epoch: 81 Loss: 0.00023245814\n",
-      "Epoch: 82 Loss: 0.00023203301\n",
-      "Epoch: 83 Loss: 0.00023164476\n",
-      "Epoch: 84 Loss: 0.0002312939\n",
-      "Epoch: 85 Loss: 0.00023098035\n",
-      "Epoch: 86 Loss: 0.00023070279\n",
-      "Epoch: 87 Loss: 0.00023045961\n",
-      "Epoch: 88 Loss: 0.0002302486\n",
-      "Epoch: 89 Loss: 0.00023006761\n",
-      "Epoch: 90 Loss: 0.00022991415\n",
-      "Epoch: 91 Loss: 0.00022978513\n",
-      "Epoch: 92 Loss: 0.00022967775\n",
-      "Epoch: 93 Loss: 0.00022958912\n",
-      "Epoch: 94 Loss: 0.00022951687\n",
-      "Epoch: 95 Loss: 0.00022945873\n",
-      "Epoch: 96 Loss: 0.00022941224\n",
-      "Epoch: 97 Loss: 0.00022937522\n",
-      "Epoch: 98 Loss: 0.00022934566\n",
-      "Epoch: 99 Loss: 0.00022932225\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "os.chdir('../')\n",
     "\n",
     "from margarine.maf import MAF\n",
+    "from margarine.kde import KDE\n",
     "\n",
     "bij = MAF(theta, weights)\n",
     "bij.train(100)"
@@ -260,6 +160,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4c908353",
    "metadata": {},
    "source": [
     "We can then generate samples from the bijector using the following code which technically takes samples on the hypercube and transforms them into samples on the target posterior distribution,"
@@ -268,18 +169,17 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "ca97fca8",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 576x576 with 25 Axes>"
+       "<Figure size 800x800 with 25 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -291,6 +191,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "384493f8",
    "metadata": {},
    "source": [
     "Alternatively we can generate samples with the following code which takes in an integer and returns an array of shape (int, 5). The `.sample()` function is a proxy for `__call__`."
@@ -299,6 +200,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "8da77e3b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,6 +209,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0bcd4ae9",
    "metadata": {},
    "source": [
     "We can then go ahead an calculate the corresponding kl divergence and Bayesian dimensionality. \n",
@@ -317,27 +220,39 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "7c268549",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tf.Tensor(3.2990043, shape=(), dtype=float32) 3.3308079438366938\n",
-      "tf.Tensor(4.3727474, shape=(), dtype=float32) 5.013952162478263\n"
+      "3.310774486919951 3.3308079438366938\n",
+      "4.839712964526534 5.013952162478263\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harry/Documents/margarine/margarine/marginal_stats.py:72: UserWarning: If prior samples are not provided the prior is assumed to be uniform and the posterior samples are are assumed to be from the same uniform space.\n",
+      "  warnings.warn('If prior samples are not provided the prior is ' +\n",
+      "/Users/harry/Documents/margarine/margarine/marginal_stats.py:194: FutureWarning: The 'inplace' keyword in DataFrame.set_index is deprecated and will be removed in a future version. Use `df = df.set_index(..., copy=False)` instead.\n",
+      "  results.set_index('Statistic', inplace=True)\n"
      ]
     }
    ],
    "source": [
-    "from margarine.marginal_stats import maf_calculations\n",
+    "from margarine.marginal_stats import calculate\n",
     "\n",
-    "stats = maf_calculations(bij, x)\n",
-    "print(stats.klDiv(), samples.D())\n",
-    "print(stats.bayesian_dimensionality(), samples.d())"
+    "stats = calculate(bij).statistics()\n",
+    "print(stats.iloc[0, 0], samples.D())\n",
+    "print(stats.iloc[1, 0], samples.d())"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "fda1ed6b",
    "metadata": {},
    "source": [
     "We could imagine that the above set of parameters is a sub-sample of perhaps signal parameters that we are interested in and having marginalised out the nuisance parameters we can use `margarine` to determine how well constrained the sub-space is.\n",
@@ -348,132 +263,37 @@
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "4ec6def6",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 0 Loss: 0.00043151045\n",
-      "Epoch: 1 Loss: 0.00041672972\n",
-      "Epoch: 2 Loss: 0.00040160108\n",
-      "Epoch: 3 Loss: 0.0003863657\n",
-      "Epoch: 4 Loss: 0.00037109375\n",
-      "Epoch: 5 Loss: 0.00035581551\n",
-      "Epoch: 6 Loss: 0.00034055865\n",
-      "Epoch: 7 Loss: 0.0003253689\n",
-      "Epoch: 8 Loss: 0.00031032556\n",
-      "Epoch: 9 Loss: 0.000295559\n",
-      "Epoch: 10 Loss: 0.00028127024\n",
-      "Epoch: 11 Loss: 0.00026773606\n",
-      "Epoch: 12 Loss: 0.0002552729\n",
-      "Epoch: 13 Loss: 0.0002441286\n",
-      "Epoch: 14 Loss: 0.00023431709\n",
-      "Epoch: 15 Loss: 0.00022553187\n",
-      "Epoch: 16 Loss: 0.00021728151\n",
-      "Epoch: 17 Loss: 0.00020923858\n",
-      "Epoch: 18 Loss: 0.00020157515\n",
-      "Epoch: 19 Loss: 0.0001947428\n",
-      "Epoch: 20 Loss: 0.00018895789\n",
-      "Epoch: 21 Loss: 0.00018402831\n",
-      "Epoch: 22 Loss: 0.00017959857\n",
-      "Epoch: 23 Loss: 0.00017541902\n",
-      "Epoch: 24 Loss: 0.00017139025\n",
-      "Epoch: 25 Loss: 0.0001674916\n",
-      "Epoch: 26 Loss: 0.00016375972\n",
-      "Epoch: 27 Loss: 0.00016028345\n",
-      "Epoch: 28 Loss: 0.0001571421\n",
-      "Epoch: 29 Loss: 0.00015434317\n",
-      "Epoch: 30 Loss: 0.0001518309\n",
-      "Epoch: 31 Loss: 0.00014954529\n",
-      "Epoch: 32 Loss: 0.00014746259\n",
-      "Epoch: 33 Loss: 0.00014559379\n",
-      "Epoch: 34 Loss: 0.00014396895\n",
-      "Epoch: 35 Loss: 0.00014262673\n",
-      "Epoch: 36 Loss: 0.00014160143\n",
-      "Epoch: 37 Loss: 0.00014089704\n",
-      "Epoch: 38 Loss: 0.00014046444\n",
-      "Epoch: 39 Loss: 0.00014021117\n",
-      "Epoch: 40 Loss: 0.00014004944\n",
-      "Epoch: 41 Loss: 0.00013994247\n",
-      "Epoch: 42 Loss: 0.00013990093\n",
-      "Epoch: 43 Loss: 0.00013992687\n",
-      "Epoch: 44 Loss: 0.00013996518\n",
-      "Epoch: 45 Loss: 0.0001399216\n",
-      "Epoch: 46 Loss: 0.0001397332\n",
-      "Epoch: 47 Loss: 0.00013941246\n",
-      "Epoch: 48 Loss: 0.00013902439\n",
-      "Epoch: 49 Loss: 0.00013863404\n",
-      "Epoch: 50 Loss: 0.00013828062\n",
-      "Epoch: 51 Loss: 0.00013798769\n",
-      "Epoch: 52 Loss: 0.0001377799\n",
-      "Epoch: 53 Loss: 0.00013768119\n",
-      "Epoch: 54 Loss: 0.00013769431\n",
-      "Epoch: 55 Loss: 0.00013778618\n",
-      "Epoch: 56 Loss: 0.00013789683\n",
-      "Epoch: 57 Loss: 0.00013796882\n",
-      "Epoch: 58 Loss: 0.00013797365\n",
-      "Epoch: 59 Loss: 0.00013791746\n",
-      "Epoch: 60 Loss: 0.00013782585\n",
-      "Epoch: 61 Loss: 0.00013772433\n",
-      "Epoch: 62 Loss: 0.00013762737\n",
-      "Epoch: 63 Loss: 0.00013753776\n",
-      "Epoch: 64 Loss: 0.00013745317\n",
-      "Epoch: 65 Loss: 0.00013737299\n",
-      "Epoch: 66 Loss: 0.00013730218\n",
-      "Epoch: 67 Loss: 0.00013724956\n",
-      "Epoch: 68 Loss: 0.00013722175\n",
-      "Epoch: 69 Loss: 0.00013721581\n",
-      "Epoch: 70 Loss: 0.00013721827\n",
-      "Epoch: 71 Loss: 0.00013721173\n",
-      "Epoch: 72 Loss: 0.0001371857\n",
-      "Epoch: 73 Loss: 0.00013714364\n",
-      "Epoch: 74 Loss: 0.00013709921\n",
-      "Epoch: 75 Loss: 0.00013706567\n",
-      "Epoch: 76 Loss: 0.00013704767\n",
-      "Epoch: 77 Loss: 0.00013704058\n",
-      "Epoch: 78 Loss: 0.00013703684\n",
-      "Epoch: 79 Loss: 0.00013703172\n",
-      "Epoch: 80 Loss: 0.00013702485\n",
-      "Epoch: 81 Loss: 0.00013701744\n",
-      "Epoch: 82 Loss: 0.00013700967\n",
-      "Epoch: 83 Loss: 0.00013700046\n",
-      "Epoch: 84 Loss: 0.00013698814\n",
-      "Epoch: 85 Loss: 0.00013697242\n",
-      "Epoch: 86 Loss: 0.00013695442\n",
-      "Epoch: 87 Loss: 0.00013693656\n",
-      "Epoch: 88 Loss: 0.0001369206\n",
-      "Epoch: 89 Loss: 0.00013690711\n",
-      "Epoch: 90 Loss: 0.00013689484\n",
-      "Epoch: 91 Loss: 0.00013688198\n",
-      "Epoch: 92 Loss: 0.00013686798\n",
-      "Epoch: 93 Loss: 0.0001368536\n",
-      "Epoch: 94 Loss: 0.00013683976\n",
-      "Epoch: 95 Loss: 0.00013682686\n",
-      "Epoch: 96 Loss: 0.00013681434\n",
-      "Epoch: 97 Loss: 0.00013680183\n",
-      "Epoch: 98 Loss: 0.00013678914\n",
-      "Epoch: 99 Loss: 0.00013677636\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 576x576 with 9 Axes>"
+       "<Figure size 800x800 with 9 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tf.Tensor(1.9784489, shape=(), dtype=float32)\n",
-      "tf.Tensor(2.5428524, shape=(), dtype=float32)\n"
+      "                  Value  Lower Bound  Upper Bound\n",
+      "Statistic                                        \n",
+      "KL Divergence  1.994334     1.992596     1.997777\n",
+      "BMD            2.804455     2.562036     3.311070\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harry/Documents/margarine/margarine/marginal_stats.py:72: UserWarning: If prior samples are not provided the prior is assumed to be uniform and the posterior samples are are assumed to be from the same uniform space.\n",
+      "  warnings.warn('If prior samples are not provided the prior is ' +\n",
+      "/Users/harry/Documents/margarine/margarine/marginal_stats.py:194: FutureWarning: The 'inplace' keyword in DataFrame.set_index is deprecated and will be removed in a future version. Use `df = df.set_index(..., copy=False)` instead.\n",
+      "  results.set_index('Statistic', inplace=True)\n"
      ]
     }
    ],
@@ -487,13 +307,13 @@
     "\n",
     "plotter(x, names_reduced, ndims=3)\n",
     "\n",
-    "stats = maf_calculations(bij, x)\n",
-    "print(stats.klDiv())\n",
-    "print(stats.bayesian_dimensionality())"
+    "stats = calculate(bij).statistics()\n",
+    "print(stats)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a3de6120",
    "metadata": {},
    "source": [
     "# Kernel Density Estimators\n",
@@ -504,26 +324,41 @@
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "c307af09",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 576x576 with 25 Axes>"
+       "<Figure size 800x800 with 25 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harry/Documents/margarine/margarine/marginal_stats.py:72: UserWarning: If prior samples are not provided the prior is assumed to be uniform and the posterior samples are are assumed to be from the same uniform space.\n",
+      "  warnings.warn('If prior samples are not provided the prior is ' +\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tf.Tensor(3.1492083, shape=(), dtype=float32) 3.3308079438366938\n",
-      "tf.Tensor(4.025139, shape=(), dtype=float32) 5.013952162478263\n"
+      "3.609496187306833 +(-) 0.2765981957821131 ( 0.48614620453886204 ) 3.3308079438366938\n",
+      "1.913573635193844 +(-) 2.048906422381939 ( 0.5664883865810604 ) 5.013952162478263\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harry/Documents/margarine/margarine/marginal_stats.py:194: FutureWarning: The 'inplace' keyword in DataFrame.set_index is deprecated and will be removed in a future version. Use `df = df.set_index(..., copy=False)` instead.\n",
+      "  results.set_index('Statistic', inplace=True)\n"
      ]
     }
    ],
@@ -535,15 +370,16 @@
     "\n",
     "plotter(x, names)\n",
     "\n",
-    "from margarine.marginal_stats import kde_calculations\n",
-    "\n",
-    "stats = kde_calculations(kde, x)\n",
-    "print(stats.klDiv(), samples.D())\n",
-    "print(stats.bayesian_dimensionality(), samples.d())"
+    "stats = calculate(kde).statistics()\n",
+    "print(stats.iloc[0, 0], '+(-)', stats.iloc[0, 2] - stats.iloc[0, 0], \n",
+    "      '(', stats.iloc[0, 0] - stats.iloc[0, 1], ')', samples.D())\n",
+    "print(stats.iloc[1, 0], '+(-)', stats.iloc[1, 2] - stats.iloc[1, 0], \n",
+    "      '(', stats.iloc[1, 0] - stats.iloc[1, 1], ')', samples.d())"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6037d4d5",
    "metadata": {},
    "source": [
     "Rather than using the `kde.sample()` function to generate samples we could transform samples from the hypercube with the following code and the `__call__()` function. However, we note that this is a much slower method of generating samples as it is designed to be bijective. Transformation from the hypercube is useful if we would like to use a trained KDE or MAF as the prior in a subseqeunt nested sampling run however is not necessary if we simply want to calcualte marginal Bayesian statistics."
@@ -552,22 +388,23 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "a30d6f05",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-2.30580893  0.2615454   1.0305738   0.95230235 -0.9160518 ]\n",
-      " [ 0.449536   -0.21128596  0.37450897 -0.06924562 -0.1435756 ]\n",
-      " [-0.23842612  1.89020679  1.01175275 -0.79411737 -1.26997843]\n",
-      " [ 0.69827796 -0.85031383  0.12800782  1.87217983 -0.0720315 ]\n",
-      " [-1.96867173 -1.70556795 -0.56749216 -1.02797262 -0.87023201]\n",
-      " [-0.55155518 -0.60825292  1.81532878 -1.32111645  0.61482667]\n",
-      " [-0.8416832   0.18485707  2.00253196  0.49622835 -0.63868005]\n",
-      " [-0.52705685 -0.11567092 -1.41513758 -1.26160741 -0.94664933]\n",
-      " [ 0.87743055  2.28065551 -0.94326398 -1.12061011 -0.03694226]\n",
-      " [ 0.39296398  0.62991565  1.31726872  0.46559934 -0.55856522]]\n"
+      "[[ 0.00866746 -1.3674294  -1.04610725  0.74177354  0.59200377]\n",
+      " [ 1.25609155 -1.55385202  0.17241134  1.7487886  -0.85378741]\n",
+      " [-0.42623049 -1.28543977  0.6308928  -0.95923815 -0.5000081 ]\n",
+      " [-0.91927021  1.82074337 -1.4875557   1.13219455  1.80085807]\n",
+      " [-0.43319482  0.51338045 -0.58847524 -1.32000678  2.11012111]\n",
+      " [-0.48190147  0.53976851 -0.73276747  0.63523436  1.8162574 ]\n",
+      " [ 0.3549924  -0.13545207  1.38762986 -1.22339099  1.09403153]\n",
+      " [-0.51115334  0.47585545 -0.88141388  2.62513102 -0.1775158 ]\n",
+      " [ 0.28904819 -0.26387664  0.80479892  0.02123311 -0.50959412]\n",
+      " [ 0.88906095 -1.04406146 -1.67107321 -1.62705777 -0.03433262]]\n"
      ]
     }
    ],
@@ -579,6 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "7403fd3b",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -586,7 +424,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -600,7 +438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/requirements.txt b/requirements.txt
index de7be1c..f7b7172 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@ numpy
 tensorflow
 tensorflow_probability
 scipy
+pandas
diff --git a/setup.py b/setup.py
old mode 100644
new mode 100755
index 713702b..7f496c7
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@ def readme(short=False):
 
 setup(
     name='margarine',
-    version='0.2.2',
+    version='0.3.0',
     description='margarine: Posterior Sampling and Marginal Bayesian Statistics',
     long_description=readme(),
     author='Harry T. J. Bevins',


Test patch:
diff --git a/tests/test_stats.py b/tests/test_stats.py
index c914830..b45bde1 100755
--- a/tests/test_stats.py
+++ b/tests/test_stats.py
@@ -1,7 +1,7 @@
 import numpy as np
 from anesthetic.samples import NestedSamples
 from margarine.maf import MAF
-from margarine.marginal_stats import maf_calculations, kde_calculations
+from margarine.marginal_stats import calculate
 import pytest
 from margarine.kde import KDE
 import pytest
@@ -20,31 +20,43 @@ def load_chains(root):
 
     names = ['p' + str(i) for i in range(ndims)]
     theta = samples[names].values
+    weights = samples.weights
 
-    try:
-        weights = samples.weights
-    except:
-        weights = samples.weight
-
-    return samples, theta, weights
+    return samples, theta, weights, names
 
 ndims=5
 
 root = 'tests/test_samples/test'
-samples, theta, weights = load_chains(root)
+samples, theta, weights, names = load_chains(root)
 
 def test_maf():
 
+    def check(i):
+        if i ==0:
+            anesthetic_value = samples.D()
+        else:
+            anesthetic_value = samples.d()
+        if stats['Value'][i] < anesthetic_value:
+            assert(
+                np.abs(stats['Value'][i]-anesthetic_value)/
+                (stats['Upper Bound'][i] - stats['Value'][i]) <=5)
+        else:
+            assert(
+                np.abs(stats['Value'][i]-anesthetic_value)/
+                (stats['Value'][i] - stats['Lower Bound'][i]) <=5)
+
     bij = MAF(theta, weights)
-    bij.train(100)
+    bij.train(250)
 
     x = bij.sample(5000)
 
-    stats = maf_calculations(bij, x)
-    klerr = samples.ns_output()['D'].std()
-    bderr = samples.ns_output()['d'].std()
-    assert((stats.klDiv()-samples.D())/klerr <= 3)
-    assert((stats.bayesian_dimensionality()-samples.d())/bderr <=3)
+    stats = calculate(bij).statistics()
+    [check(i) for i in range(2)]
+
+    prior = np.random.uniform(-4, 4, (len(theta), 5))
+    stats = calculate(bij, prior_samples=prior,
+        prior_weights=np.ones(len(prior))).statistics()
+    [check(i) for i in range(2)]
 
     L = samples.logL.values
     estL = bij.log_like(theta, samples.ns_output()['logZ'].mean())
@@ -86,15 +98,31 @@ def test_maf_save_load():
 
 def test_kde():
 
+    def check(i):
+        if i ==0:
+            anesthetic_value = samples.D()
+        else:
+            anesthetic_value = samples.d()
+        if stats['Value'][i] < anesthetic_value:
+            assert(
+                np.abs(stats['Value'][i]-anesthetic_value)/
+                (stats['Upper Bound'][i] - stats['Value'][i]) <=5)
+        else:
+            assert(
+                np.abs(stats['Value'][i]-anesthetic_value)/
+                (stats['Value'][i] - stats['Lower Bound'][i]) <=5)
+
     kde = KDE(theta, weights)
     kde.generate_kde()
     x = kde.sample(5000)
 
-    stats = kde_calculations(kde, x)
-    klerr = samples.ns_output()['D'].std()
-    bderr = samples.ns_output()['d'].std()
-    assert((stats.klDiv()-samples.D())/klerr <= 3)
-    assert((stats.bayesian_dimensionality()-samples.d())/bderr <=3)
+    stats = calculate(kde).statistics()
+    [check(i) for i in range(2)]
+
+    prior = np.random.uniform(-4, 4, (len(theta), 5))
+    stats = calculate(kde, prior_samples=prior,
+        prior_weights=np.ones(len(prior))).statistics()
+    [check(i) for i in range(2)]
 
     L = samples.logL.values
     estL = kde.log_like(theta, samples.ns_output()['logZ'].mean())