diff --git a/.travis.yml b/.travis.yml
index 3516ae706..19494b672 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,6 +2,7 @@ language: python
 python:
   - '2.7'
   - '3.5'
+  - '3.6'
 
 before_install:
   - sudo apt-get build-dep python-scipy
diff --git a/DESCRIPTION.rst b/DESCRIPTION.rst
new file mode 100644
index 000000000..639bd036c
--- /dev/null
+++ b/DESCRIPTION.rst
@@ -0,0 +1,62 @@
+.. -*- mode: rst -*-
+
+|Visualizers|_
+
+.. |Visualizers| image:: http://www.scikit-yb.org/en/latest/_images/visualizers.png
+.. _Visualizers: http://scikit-yb.org/
+
+Yellowbrick
+===========
+
+Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with Scikit-Learn. The library implements a new core API object, the "Visualizer" that is an Scikit-Learn estimator: an object that learns from data. Like transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow.
+
+Visualizers allow users to steer the model selection process, building intuition around feature engineering, algorithm selection, and hyperparameter tuning. For example, visualizers can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models to more successful results, faster.
+
+Please see the full documentation at: http://scikit-yb.org/
+
+Visualizers
+-----------
+
+Visualizers are estimators (objects that learn from data) whose primary objective is to create visualizations that allow insight into the model selection process. In Scikit-Learn terms, they can be similar to transformers when visualizing the data space or wrap an model estimator similar to how the “ModelCV” (e.g. RidgeCV_, LassoCV_) methods work. The primary goal of Yellowbrick is to create a sensical API similar to Scikit-Learn. Some of our most popular visualizers include:
+
+.. _RidgeCV: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html
+.. _LassoCV: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html
+
+Feature Visualization
+~~~~~~~~~~~~~~~~~~~~~
+
+- **Rank2D**: pairwise ranking of features to detect relationships
+- **Parallel Coordinates**: horizontal visualization of instances
+- **Radial Visualization**: separation of instances around a circular plot
+
+Classification Visualization
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- **Class Balance**: see how the distribution of classes affects the model
+- **Classification Report**: visual representation of precision, recall, and F1
+- **ROC/AUC Curves**: receiver operator characteristics and area under the curve
+- **Confusion Matrices**: visual description of class decision making
+
+Regression Visualization
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+- **Prediction Error Plots**: find model breakdowns along the domain of the target
+- **Residuals Plot**: show the difference in residuals of training and test data
+- **Alpha Selection**: show how the choice of alpha influences regularization
+
+Clustering Visualization
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+- **K-Elbow Plot**: select k using the elbow method and various metrics
+- **Silhouette Plot**: select k by visualizing silhouette coefficient values
+
+Text Visualization
+~~~~~~~~~~~~~~~~~~
+
+- **Term Frequency**: visualize the frequency distribution of terms in the corpus
+- **TSNE**: use stochastic neighbor embedding to project documents.
+
+... and more! Visualizers are being added all the time; be sure to check the examples_ (or even the develop_ branch) and feel free to contribute your ideas for new Visualizers!
+
+.. _examples: http://www.scikit-yb.org/en/latest/examples/examples.html
+.. _develop: https://github.com/districtdatalabs/yellowbrick/tree/develop
diff --git a/DESCRIPTION.txt b/DESCRIPTION.txt
deleted file mode 100644
index c7af7074d..000000000
--- a/DESCRIPTION.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with Scikit-Learn. The library implements a new core API object, the "Visualizer" that is an Scikit-Learn estimator: an object that learns from data. Like transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow.
-
-Visualizers allow users to steer the model selection process, building intuition around feature engineering, algorithm selection, and hyperparameter tuning. For example, visualizers can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models to more successful results, faster.
-
-Some of the available tools include:
-
-- pairwise feature ranking
-- parallel coordinates
-- radial visualization
-- ROC curves
-- classification heatmaps
-- residual plots
-- prediction error plots
-- alpha selection plots
-- validation curves
-- gridsearch heatmaps
-- text frequency distributions
-- tsne corpus visualization
-
-And much more! Please see the full documentation at: http://scikit-yb.org/
diff --git a/MAINTAINERS.md b/MAINTAINERS.md
index 9dee9b26b..430acbec5 100644
--- a/MAINTAINERS.md
+++ b/MAINTAINERS.md
@@ -6,6 +6,8 @@ When creating a pull request, your contribution will be reviewed by one or proba
 
 For more about how to develop visualizers and contribute features to Yellowbrick, see our [contributor's guide](CONTRIBUTING.md) and the documentation.
 
+For everyone who has [contributed](https://github.com/DistrictDataLabs/yellowbrick/graphs/contributors) in big and in small ways, **thank you!**. Yellowbrick is intended to be a community project, welcoming to new and experienced developers alike. If you would like to become a core contributor you must simply submit a pull request that shows core knowledge of the Yellowbrick library. Usually new Visualizers meet this standard; let the maintainers know you'd like to join the team, and they'll help you work toward it!
+
 ## Maintainers
 
 This is a list of the primary project maintainers. Feel free to @ message them in issues and converse with them directly.
@@ -13,6 +15,7 @@ This is a list of the primary project maintainers. Feel free to @ message them i
 - [bbengfort](https://github.com/bbengfort)
 - [NealHumphrey](https://github.com/NealHumphrey)
 - [jkeung](https://github.com/jkeung)
+- [ndanielsen](https://github.com/ndanielsen)
 
 ## Core Contributors
 
@@ -21,7 +24,6 @@ This is a list of the core-contributors of the project. Core contributors set th
 - [rebeccabilbro](https://github.com/rebeccabilbro)
 - [mattandahalfew](https://github.com/mattandahalfew)
 - [pdamodaran](https://github.com/pdamodaran)
-- [ndanielsen](https://github.com/ndanielsen)
 - [tuulihill](https://github.com/tuulihill)
 - [balavenkatesan](https://github.com/balavenkatesan)
 - [morganmendis](https://github.com/morganmendis)
diff --git a/README.md b/README.md
index e11593784..30d3003aa 100644
--- a/README.md
+++ b/README.md
@@ -55,6 +55,24 @@ Visualizers are estimators (objects that learn from data) whose primary objectiv
 
 And more! Visualizers are being added all the time, be sure to check the examples (or even the develop branch) and feel free to contribute your ideas for Visualizers!
 
+## Installing Yellowbrick
+
+Yellowbrick is compatible with Python 2.7 or later but it is preferred to use Python 3.5 or later to take full advantage of all functionality. Yellowbrick also depends on Scikit-Learn 0.18 or later and Matplotlib 1.5 or later. The simplest way to install Yellowbrick is from PyPI with pip, Python's preferred package installer.
+
+    $ pip install yellowbrick
+
+Note that Yellowbrick is an active project and routinely publishes new releases with more visualizers and updates. In order to upgrade Yellowbrick to the latest version, use pip as follows.
+
+    $ pip install -u yellowbrick
+
+You can also use the `-u` flag to update Scikit-Learn, matplotlib, or any other third party utilities that work well with Yellowbrick to their latest versions.
+
+If you're using Windows or Anaconda, you can take advantage of the conda utility to install Yellowbrick:
+
+    conda install -c districtdatalabs yellowbrick
+
+Note, however, that there is a [known bug](https://github.com/DistrictDataLabs/yellowbrick/issues/205) installing Yellowbrick on Linux with Anaconda.
+
 ## Using Yellowbrick
 
 The Yellowbrick API is specifically designed to play nicely with Scikit-Learn. Here is an example of a typical workflow sequence with Scikit-Learn and Yellowbrick:
@@ -89,7 +107,7 @@ visualizer.poof()
 
 For additional information on getting started with Yellowbrick, check out our [examples notebook](https://github.com/DistrictDataLabs/yellowbrick/blob/develop/examples/examples.ipynb).
 
-We also have a [quick start guide](https://github.com/DistrictDataLabs/yellowbrick/blob/master/docs/setup.rst).
+We also have a [quick start guide](https://github.com/DistrictDataLabs/yellowbrick/blob/master/docs/quickstart.rst).
 
 ## Contributing to Yellowbrick
 
diff --git a/docs/changelog.rst b/docs/changelog.rst
index 9648cba7b..74889916c 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -2,6 +2,45 @@
 Changelog
 =========
 
+Version 0.4.1
+-------------
+This release is an intermediate version bump in anticipation of the PyCon 2017 sprints.
+
+The primary goals of this version were to (1) update the Yellowbrick dependencies (2) enhance the Yellowbrick documentation to help orient new users and contributors, and (3) make several small additions and upgrades (e.g. pulling the Yellowbrick utils into a standalone module).
+
+We have updated the Scikit-Learn and SciPy dependencies from version 0.17.1 or later to 0.18 or later. This primarily entails moving from ``from sklearn.cross_validation import train_test_split`` to ``from sklearn.model_selection import train_test_split``.
+
+The updates to the documentation include new Quickstart and Installation guides as well as updates to the Contributors documentation, which is modeled on the Scikit-Learn contributing documentation.
+
+This version also included upgrades to the KMeans visualizer, which now supports not only ``silhouette_score`` but also ``distortion_score`` and ``calinski_harabaz_score``. The ``distortion_score`` computes the mean distortion of all samples as the sum of the squared distances between each observation and its closest centroid. This is the metric that K-Means attempts to minimize as it is fitting the model. The ``calinski_harabaz_score`` is defined as ratio between the within-cluster dispersion and the between-cluster dispersion.
+
+Finally, this release includes a prototype of the ``VisualPipeline``, which extends Scikit-Learn's ``Pipeline`` class, allowing multiple Visualizers to be chained or sequenced together.
+
+* Tag: v0.4.1_
+* Deployed: Monday, May 22, 2017
+* Contributors: Benjamin Bengfort, Rebecca Bilbro, Nathan Danielsen
+
+Changes:
+   - Score and model visualizers now wrap estimators as proxies so that all methods on the estimator can be directly accessed from the visualizer
+   - Updated Scikit-learn dependency from >=0.17.1  to >=0.18
+   - Replaced ``sklearn.cross_validation`` with ``model_selection``
+   - Updated SciPy dependency from >=0.17.1 to >=0.18
+   - ScoreVisualizer now subclasses ModelVisualizer; towards allowing both fitted and unfitted models passed to Visualizers
+   - Added CI tests for Python 3.6 compatibility
+   - Added new quickstart guide and install instructions
+   - Updates to the contributors documentation
+   - Added ``distortion_score`` and ``calinski_harabaz_score`` computations and visualizations to KMeans visualizer.
+   - Replaced the ``self.ax`` property on all of the individual ``draw`` methods with a new property on the ``Visualizer`` class that ensures all visualizers automatically have axes.
+   - Refactored the utils module into a package
+   - Continuing to update the docstrings to conform to Sphinx
+   - Added a prototype visual pipeline class that extends the Scikit-learn pipeline class to ensure that visualizers get called correctly.
+
+Bug Fixes:
+   - Fixed title bug in Rank2D FeatureVisualizer
+
+.. _v0.4.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.4.1
+
+
 Version 0.4
 -----------
 This release is the culmination of the Spring 2017 DDL Research Labs that focused on developing Yellowbrick as a community effort guided by a sprint/agile workflow. We added several more visualizers, did a lot of user testing and bug fixes, updated the documentation, and generally discovered how best to make Yellowbrick a friendly project to contribute to.
diff --git a/docs/contributing.rst b/docs/contributing.rst
index 822d72b72..8023211fc 100644
--- a/docs/contributing.rst
+++ b/docs/contributing.rst
@@ -4,7 +4,7 @@ Contributing
 
 Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick!
 
-Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later.
+Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later.
 
 Beyond creating visualizers, there are many ways to contribute:
 
@@ -28,15 +28,15 @@ Yellowbrick is hosted on GitHub at https://github.com/DistrictDataLabs/yellowbri
 The typical workflow for a contributor to the codebase is as follows:
 
 1. **Discover** a bug or a feature by using Yellowbrick.
-2. **Discuss** with the core contributes by `adding an issue <https://github.com/DistrictDataLabs/yellowbrick/issues>`_.
+2. **Discuss** with the core contributors by `adding an issue <https://github.com/DistrictDataLabs/yellowbrick/issues>`_.
 3. **Assign** yourself the task by pulling a card from our `Waffle Kanban <https://waffle.io/DistrictDataLabs/yellowbrick>`_
 4. **Fork** the repository into your own GitHub account.
 5. Create a **Pull Request** first thing to `connect with us <https://github.com/DistrictDataLabs/yellowbrick/pulls>`_ about your task.
-6. **Code** the feature, write the documentation, add your contribution.
+6. **Code** the feature, write the tests and documentation, add your contribution.
 7. **Review** the code with core contributors who will guide you to a high quality submission.
 8. **Merge** your contribution into the Yellowbrick codebase.
 
-.. note:: Create a pull request as soon as possible, even before you've started coding. This will allow the core contributors to give you advice about where to add your code or utilities and discuss other style choices and implementation details as you go. Don't wait!
+.. note:: Please create a pull request as soon as possible, even before you've started coding. This will allow the core contributors to give you advice about where to add your code or utilities and discuss other style choices and implementation details as you go. Don't wait!
 
 We believe that *contribution is collaboration* and therefore emphasize *communication* throughout the open source process. We rely heavily on GitHub's social coding tools to allow us to do this.
 
@@ -66,7 +66,7 @@ Once forked, use the following steps to get your development environment set up
 
         $ pip install -r requirements.txt
 
-    Note that there may be other dependencies required for development and testing, you can simply install them with ``pip``.
+    Note that there may be other dependencies required for development and testing; you can simply install them with ``pip``.
 
 4. Switch to the develop branch.
 
@@ -131,12 +131,12 @@ Tag the release in GitHub::
     $ git tag -a vx.x
     $ git push origin vx.x
 
-You'll have to go to the release_ page to edit the release with similar information as added to the changelog. Once done, push the release to PyPI:
+You'll have to go to the release_ page to edit the release with similar information as added to the changelog. Once done, push the release to PyPI::
 
     $ make build
     $ make deploy
 
-Check that the PyPI page is updated with the correct version and that ``pip install -U yellowbrick`` updates the version and works correctly. Also check the documentation on PyHosted, ReadTheDocs, and on our website to make sure that it was correctly updated. Finally merge the release into develop and clean up:
+Check that the PyPI page is updated with the correct version and that ``pip install -U yellowbrick`` updates the version and works correctly. Also check the documentation on PyHosted, ReadTheDocs, and on our website to make sure that it was correctly updated. Finally merge the release into develop and clean up::
 
     $ git checkout develop
     $ git merge --no-ff --no-edit release-x.x
diff --git a/docs/examples/examples.rst b/docs/examples/index.rst
similarity index 100%
rename from docs/examples/examples.rst
rename to docs/examples/index.rst
diff --git a/docs/examples/methods.rst b/docs/examples/methods.rst
index c100a0efa..77ea2e007 100644
--- a/docs/examples/methods.rst
+++ b/docs/examples/methods.rst
@@ -282,7 +282,7 @@ the final step in a ``Pipeline`` or ``VisualPipeline``.
     # Regression Evaluation Imports
 
     from sklearn.linear_model import Ridge, Lasso
-    from sklearn.cross_validation import train_test_split
+    from sklearn.model_selection import train_test_split
 
     from yellowbrick.regressor import PredictionError, ResidualsPlot
 
@@ -392,7 +392,7 @@ a Pipeline or VisualPipeline.
     from sklearn.naive_bayes import GaussianNB
     from sklearn.linear_model import LogisticRegression
     from sklearn.ensemble import RandomForestClassifier
-    from sklearn.cross_validation import train_test_split
+    from sklearn.model_selection import train_test_split
 
     from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance
 
@@ -456,7 +456,7 @@ Scikit-Learn documentation on `confusion matrices <http://scikit-learn.org/stabl
     import yellowbrick
 
     from sklearn.datasets import load_digits
-    from sklearn.cross_validation import train_test_split
+    from sklearn.model_selection import train_test_split
     from sklearn.linear_model import LogisticRegression
 
     from yellowbrick.classifier import ConfusionMatrix
diff --git a/docs/images/quickstart/bikeshare_ols_residuals.png b/docs/images/quickstart/bikeshare_ols_residuals.png
new file mode 100644
index 000000000..05e6005f0
Binary files /dev/null and b/docs/images/quickstart/bikeshare_ols_residuals.png differ
diff --git a/docs/images/quickstart/bikeshare_rank2d.png b/docs/images/quickstart/bikeshare_rank2d.png
new file mode 100644
index 000000000..69167ee6c
Binary files /dev/null and b/docs/images/quickstart/bikeshare_rank2d.png differ
diff --git a/docs/images/quickstart/bikeshare_ridge_alphas.png b/docs/images/quickstart/bikeshare_ridge_alphas.png
new file mode 100644
index 000000000..d831e9e50
Binary files /dev/null and b/docs/images/quickstart/bikeshare_ridge_alphas.png differ
diff --git a/docs/images/quickstart/bikeshare_ridge_prediction_error.png b/docs/images/quickstart/bikeshare_ridge_prediction_error.png
new file mode 100644
index 000000000..f012da42f
Binary files /dev/null and b/docs/images/quickstart/bikeshare_ridge_prediction_error.png differ
diff --git a/docs/images/quickstart/temp_feelslike_jointplot.png b/docs/images/quickstart/temp_feelslike_jointplot.png
new file mode 100644
index 000000000..db70dbe8e
Binary files /dev/null and b/docs/images/quickstart/temp_feelslike_jointplot.png differ
diff --git a/docs/index.rst b/docs/index.rst
index 27eb95d07..3dc0a721a 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -11,7 +11,7 @@ Yellowbrick: Machine Learning Visualization
 
 Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for *your* models! For more on Yellowbrick, please see the :doc:`introduction`.
 
-If you're new to Yellowbrick, checkout the :doc:`setup` or skip ahead to the :doc:`examples/examples`. Yellowbrick is a rich library with many Visualizers being added on a regular basis. For details on specific Visualizers and extended usage head over to the :doc:`api/modules`. If you've signed up to do user testing, checkout the :doc:`evaluation` (and thank you!).
+If you're new to Yellowbrick, checkout the :doc:`quickstart` or skip ahead to the :doc:`examples/index`. Yellowbrick is a rich library with many Visualizers being added on a regular basis. For details on specific Visualizers and extended usage head over to the :doc:`api/modules`. If you've signed up to do user testing, checkout the :doc:`evaluation` (and thank you!).
 
 Visualizers
 -----------
@@ -79,8 +79,8 @@ The following is a complete listing of the Yellowbrick documentation for this ve
    :maxdepth: 2
 
    introduction
-   setup
-   examples/examples
+   quickstart
+   examples/index
    api/modules
    about
    evaluation
diff --git a/docs/quickstart.rst b/docs/quickstart.rst
new file mode 100644
index 000000000..8348a4e53
--- /dev/null
+++ b/docs/quickstart.rst
@@ -0,0 +1,220 @@
+.. -*- mode: rst -*-
+
+Quick Start
+===========
+
+If you're new to Yellowbrick, this guide will get you started and help you include visualizers in your machine learning workflow. Before we get started, however, there are several notes about development environments that you should consider.
+
+Yellowbrick has two primary dependencies: `Scikit-Learn <http://scikit-learn.org/>`_ and `Matplotlib <http://matplotlib.org/>`_. If you do not have these Python packages, they will be installed alongside Yellowbrick. Note that Yellowbrick works best with Scikit-Learn version 0.18 or later and Matplotlib version 2.0 or later. Both of these packages require some C code to be compiled, which can be difficult on some systems, like Windows. If you're having trouble, try using a distribution of Python that includes these packages like `Anaconda <https://anaconda.org>`_.
+
+Yellowbrick is also commonly used inside of a `Jupyter Notebook <http://jupyter.org/>`_ alongside `Pandas <http://pandas.pydata.org/>`_ data frames. Notebooks make it especially easy to coordinate code and visualizations, however you can also use Yellowbrick inside of regular Python scripts, either saving figures to disk or showing figures in a GUI window. If you're having trouble with this, please consult Matplotlib's `backends documentation <https://matplotlib.org/faq/usage_faq.html#what-is-a-backend>`_.
+
+.. NOTE:: Jupyter, Pandas, and other ancillary libraries like NLTK for text visualizers are not installed with Yellowbrick and must be installed separately.
+
+Installation
+------------
+
+Yellowbrick is compatible with Python 2.7 or later, but it is preferred to use Python 3.5 or later to take full advantage of all functionality. The simplest way to install Yellowbrick is from PyPI_ with pip_, Python's preferred package installer.
+
+.. code-block:: bash
+
+    $ pip install yellowbrick
+
+.. _PyPI: https://pypi.python.org/pypi/yellowbrick
+.. _pip: https://docs.python.org/3/installing/
+
+Note that Yellowbrick is an active project and routinely publishes new releases with more visualizers and updates. In order to upgrade Yellowbrick to the latest version, use pip as follows.
+
+.. code-block:: bash
+
+    $ pip install -u yellowbrick
+
+You can also use the ``-u`` flag to update Scikit-Learn, matplotlib, or any other third party utilities that work well with Yellowbrick to their latest versions.
+
+If you're using Windows or Anaconda, you can take advantage of the `conda <https://conda.io/docs/intro.html>`_ utility to install the `Anaconda Yellowbrick package <https://anaconda.org/DistrictDataLabs/yellowbrick>`_:
+
+.. code-block:: bash
+
+    conda install -c districtdatalabs yellowbrick
+
+.. WARNING:: There is a `known bug <https://github.com/DistrictDataLabs/yellowbrick/issues/205>`_ installing matplotlib on Linux with Anaconda. If you're having trouble please let us know on GitHub.
+
+Once installed, you should be able to import Yellowbrick without an error, both in Python and inside of Jupyter notebooks. Note that because of matplotlib, Yellowbrick does not work inside of a virtual environment without jumping through some hoops.
+
+Using Yellowbrick
+-----------------
+The Yellowbrick API is specifically designed to play nicely with Scikit-Learn. The primary interface is therefore a ``Visualizer`` -- an object that learns from data to produce a visualization. Visualizers are Scikit-Learn `Estimator <http://scikit-learn.org/stable/developers/contributing.html#apis-of-scikit-learn-objects>`_ objects and have a similar interface along with methods for drawing. In order to use visualizers, you simply use the same workflow as with a Scikit-Learn model, import the visualizer, instantiate it, call the visualizer's ``fit()`` method, then in order to render the visualization, call the visualizer's ``poof()`` method, which does the magic!
+
+For example, there are several visualizers that act as transformers, used to perform feature analysis prior to fitting a model. Here is an example to visualize a high dimensional data set with parallel coordinates:
+
+.. code-block:: python
+
+    from yellowbrick.features import ParallelCoordinates
+
+    visualizer = ParallelCoordinates()
+    visualizer.fit_transform(X, y)
+    visualizer.poof()
+
+As you can see, the workflow is very similar to using a Scikit-Learn transformer, and visualizers are intended to be integrated along with Scikit-Learn utilities. Arguments that change how the visualization is drawn can be passed into the visualizer upon instantiation, similarly to how hyperparameters are included with Scikit-Learn models.
+
+The ``poof()`` method finalizes the drawing (adding titles, axes labels, etc) and then renders the image on your behalf. If you're in a Jupyter notebook, the image should just appear. If you're in a Python script, a GUI window should open with the visualization in interactive form. However, you can also save the image to disk by passing in a file path as follows:
+
+.. code-block:: python
+
+    visualizer.poof(outpath="pcoords.png")
+
+The extension of the filename will determine how the image is rendered, in addition to the .png extension, .pdf is also commonly used.
+
+.. NOTE:: Data input to Yellowbrick is identical to that of Scikit-Learn: a dataset, ``X``, which is a two-dimensional matrix of shape ``(n,m)`` where ``n`` is the number of instances (rows) and ``m`` is the number of features (columns). The dataset ``X`` can be a Pandas DataFrame, a Numpy array, or even a Python list of lists. Optionally, a vector ``y``, which represents the target variable (in supervised learning), can also be supplied as input. The target ``y`` must have length ``n`` -- the same number of elements as rows in ``X`` and can be a Pandas Series, Numpy array, or Python list.
+
+Visualizers can also wrap Scikit-Learn models for evaluation, hyperparameter tuning and algorithm selection. For example, to produce a visual heatmap of a classification report, displaying the precision, recall, F1 score, and support for each class in a classifier, wrap the estimator in a visualizer as follows:
+
+.. code-block:: python
+
+    from yellowbrick.classifier import ClassificationReport
+    from sklearn.linear_model import LogisticRegression
+
+    model = LogisticRegression()
+    visualizer = ClassificationReport(model)
+
+    visualizer.fit(X_train, y_train)
+    visualizer.score(X_test, y_test)
+    visualizer.poof()
+
+Only two additional lines of code are required to add visual evaluation of the classifier model, the instantiation of a ``ClassificationReport`` visualizer that wraps the classification estimator and a call to its ``poof()`` method. In this way, Visualizers *enhance* the machine learning workflow without interrupting it.
+
+.. TODO:: Walkthrough visual pipelines and text analysis.
+
+The class-based API is meant to integrate with Scikit-Learn directly, however on occasion there are times when you just need a quick visualization. Yellowbrick supports quick functions for taking advantage of this directly. For example, the two visual diagnostics could have been instead implemented as follows:
+
+.. code-block:: python
+
+    from sklearn.linear_model import LogisticRegression
+
+    from yellowbrick.features import parallel_coordinates
+    from yellowbrick.classifier import classification_report
+
+    # Displays parallel coordinates
+    g = parallel_coordinates(X, y)
+
+    # Displays classification report
+    g = classification_report(LogisticRegression(), X, y)
+
+These quick functions give you slightly less control over the machine learning workflow, but quickly get you diagnostics on demand and are very useful in exploratory processes.
+
+Walkthrough
+-----------
+
+Consider a regression analysis as a simple example of the use of visualizers in the machine learning workflow. Using a `bike sharing dataset <https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset>`_, we would like to predict the number of bikes rented in a given hour based on features like the season, weather, or if it's a holiday. We can load our data as follows:
+
+.. code-block:: python
+
+    import pandas as pd
+
+    data = pd.read_csv('bikeshare.csv')
+    X = data[[
+        "season", "month", "hour", "holiday", "weekday", "workingday",
+        "weather", "temp", "atemp", "hum", "windspeed"
+    ]]
+    y = data["riders"]
+
+The machine learning workflow is the art of creating *model selection triples*, a combination of features, algorithm, and hyperparameters that uniquely identifies a model fitted on a specific data set. As part of our feature selection, we want to identify features that have a linear relationship with each other, potentially introducing covariance into our model and breaking OLS (guiding us toward removing features or using regularization). We can use the Rank2D_ visualizer to compute Pearson correlations between all pairs of features as follows:
+
+.. _Rank2D: http://www.scikit-yb.org/en/latest/api/yellowbrick.features.html#module-yellowbrick.features.rankd
+
+.. code-block:: python
+
+   from yellowbrick.features import Rank2D
+
+   visualizer = Rank2D(algorithm="pearson")
+   visualizer.fit_transform(X)
+   visualizer.poof()
+
+.. image:: images/quickstart/bikeshare_rank2d.png
+
+This figure shows us the Pearson correlation between pairs of features such that each cell in the grid represents two features identified in order on the x and y axes and whose color displays the magnitude of the correlation. A Pearson correlation of 1.0 means that there is a strong positive, linear relationship between the pairs of variables and a value of -1.0 indicates a strong negative, linear relationship (a value of zero indicates no relationship). Therefore we are looking for dark red and dark blue boxes to identify further.
+
+In this chart we see that features 7 (temperature) and feature 9 (feelslike) have a strong correlation and also that feature 0 (season) has a strong correlation with feature 1 (month). This seems to make sense; the apparent temperature we feel outside depends on the actual temperature and other airquality factors, and the season of the year is described by the month! To dive in deeper, we can use the `JointPlotVisualizer <http://www.scikit-yb.org/en/latest/api/yellowbrick.features.html#module-yellowbrick.features.jointplot>`_ to inspect those relationships.
+
+.. code-block:: python
+
+    from yellowbrick.features import JointPlotVisualizer
+
+    visualizer = JointPlotVisualizer(feature='temp', target='feelslike')
+    visualizer.fit(X['temp'], X['feelslike'])
+    visualizer.poof()
+
+.. image:: images/quickstart/temp_feelslike_jointplot.png
+
+This visualizer plots a scatter diagram of the apparent temperature on the y axis and the actual measured temperature on the x axis and draws a line of best fit using a simple linear regression. Additionally, univariate distributions are shown as histograms above the x axis for temp and next to the y axis for feelslike.  The ``JointPlotVisualizer`` gives an at-a-glance view of the very strong positive correlation of the features, as well as the range and distribution of each feature. Note that the axes are normalized to the space between zero and one, a common technique in machine learning to reduce the impact of one feature over another.
+
+This plot is very interesting; first there appear to be some outliers in the dataset, where feelslike is approximately equal to 0.25. These instances may need to be manually removed in order to improve the quality of the final model because they could represent data input errors. Secondly, we can see that more extreme temperatures create an exaggerated effect in perceived temperature; the colder it is, the colder people are likely to believe it to be, and the warmer it is, the warmer it appears to be. Moderate temperatures feel like they do. This gives us the intuition that feelslike may be a better feature than temp, and if it is causing problems in our regression analysis, we should probably remove the temp variable in favor of feels like.
+
+At this point, we can train our model; let's fit a linear regression to our model and plot the residuals.
+
+.. code-block:: python
+
+    from yellowbrick.regressor import ResidualsPlot
+    from sklearn.linear_model import LinearRegression
+    from sklearn.model_selection import train_test_split
+
+    # Create training and test sets
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.1
+    )
+
+    visualizer = ResidualsPlot(LinearRegression())
+    visualizer.fit(X_train, y_train)
+    visualizer.score(X_test, y_test)
+    visualizer.poof()
+
+.. image:: images/quickstart/bikeshare_ols_residuals.png
+
+The residuals plot shows the error against the predicted value, and allows us to look for heteroskedasticity in the model; e.g. regions in the target where the error is greatest. The shape of the residuals can strongly inform us where OLS (ordinary least squares) is being most strongly effected by the components of our model (namely the features). In this case, we can see that the lower the predicted value (the lower the number of riders), the lower the error, but the higher the number of predicted riders, the higher the error. This indicates that our model has more noise in certain regions of the target or that two variables are colinear, meaning that they are injecting error as the noise in their relationship changes.
+
+The residuals plot also shows how the model is injecting error, the bold horizontal line at ``residuals = 0`` is no error, and any point above or below that line indicates the magnitude of error. For example, most of the residuals are negative, and since the score is computed as ``actual - expected``, this means that the expected value is bigger than the actual value most of the time, e.g. that our model is primarily guessing more than the actual number of riders. Moreover, there is a very interesting boundary along the top right of the residuals graph, indicating an interesting affect in model space; possibly that some feature is strongly weighted in the region of that model.
+
+Finally the residuals are colored by training and test set. This helps us identify errors in creating train and test splits. If the test error doesn't match the train error then our model is either overfit or underfit. Otherwise it could be an error in shuffling the dataset before creating the splits.
+
+Because our coefficient of determination for this model is 0.328, let's see if we can fit a better model using *regularization*, and explore another visualizer at the same time.
+
+.. code-block:: python
+
+    import numpy as np
+
+    from sklearn.linear_model import RidgeCV
+    from yellowbrick.regressor import AlphaSelection
+
+    alphas = np.logspace(-10, 1, 200)
+    visualizer = AlphaSelection(RidgeCV(alphas=alphas))
+    visualizer.fit(X, y)
+    visualizer.poof()
+
+.. image:: images/quickstart/bikeshare_ridge_alphas.png
+
+When exploring model families, the primary thing to consider is how the model becomes more *complex*. As the model increases in complexity, the error due to variance increases because the model is becoming more overfit and cannot generalize to unseen data. However, the simpler the model is the more error there is likely to be due to bias; the model is underfit and therefore misses its target more frequently. The goal therefore of most machine learning is to create a model that is *just complex enough*, finding a middle ground between bias and variance.
+
+For a linear model, complexity comes from the features themselves and their assigned weight according to the model. Linear models therefore expect the *least number of features* that achieves an explanatory result. One technique to achieve this is *regularization*, the introduction of a parameter called alpha that normalizes the weights of the coefficients with each other and penalizes complexity. Alpha and complexity have an inverse relationship, the higher the alpha, the lower the complexity of the model and vice versa.
+
+The question therefore becomes how you choose alpha. One technique is to fit a number of models using cross-validation and selecting the alpha that has the lowest error. The ``AlphaSelection`` visualizer allows you to do just that, with a visual representation that shows the behavior of the regularization. As you can see in the figure above, the error decreases as the value of alpha increases up until our chosen value (in this case, 3.181) where the error starts to increase. This allows us to target the bias/variance trade-off and to explore the relationship of regularization methods (for example Ridge vs. Lasso).
+
+We can now train our final model and visualize it with the ``PredictionError`` visualizer:
+
+.. code-block:: python
+
+    from sklearn.linear_model import Ridge
+    from yellowbrick.regressor import PredictionError
+
+    visualizer = PredictionError(Ridge(alpha=3.181))
+    visualizer.fit(X_train, y_train)
+    visualizer.score(X_test, y_test)
+    visualizer.poof()
+
+.. image:: images/quickstart/bikeshare_ridge_prediction_error.png
+
+
+The prediction error visualizer plots the actual (measured) vs. expected (predicted) values against each other. The dotted black line is the 45 degree line that indicates zero error. Like the residuals plot, this allows us to see where error is occurring and in what magnitude.
+
+In this plot we can see that most of the instance density is less than 200 riders. We may want to try orthogonal matching pursuit or splines to fit a regression that takes into account more regionality. We can also note that that weird topology from the residuals plot seems to be fixed using the Ridge regression, and that there is a bit more balance in our model between large and small values. Potentially the Ridge regularization cured a covariance issue we had between two features. As we move forward in our analysis using other model forms, we can continue to utilize visualizers to quickly compare and see our results.
+
+Hopefully this workflow gives you an idea of how to integrate Visualizers into machine learning with Scikit-Learn and inspires you to use them in your work and write your own! For additional information on getting started with Yellowbrick, check out our :ref:`examples <examples/yellowbrick-examples>`.
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 46dd3af7b..a08bcbe28 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,11 +1,11 @@
 # Library Dependencies
 matplotlib>=1.5.1
 scipy>=0.17.1
-scikit-learn>=0.17.1
+scikit-learn>=0.18
 numpy>=1.11.0
 cycler>=0.10.0
 
 # Documentation (uncomment to build documentation)
-Sphinx>=1.4.4
-sphinx-rtd-theme>=0.1.9
+Sphinx>=1.5.5
+sphinx-rtd-theme>=0.2.4
 numpydoc>=0.6.0
diff --git a/docs/setup.rst b/docs/setup.rst
deleted file mode 100644
index 9dcb2423c..000000000
--- a/docs/setup.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-===========
-Quick Start
-===========
-
-Installation
-------------
-
-To install the Yellowbrick library, the simplest thing to do is use ``pip`` as follows.::
-
-    $ pip install yellowbrick
-
-Using Yellowbrick
------------------
-The Yellowbrick API is specifically designed to play nicely with Scikit-Learn. Here is an example of a typical workflow sequence with Scikit-Learn and Yellowbrick:
-
-Feature Visualization
-^^^^^^^^^^^^^^^^^^^^^
-In this example, we see how Rank2D performs pairwise comparisons of each feature in the data set with a specific metric or algorithm, then returns them ranked as a lower left triangle diagram.::
-
-    from yellowbrick.features import Rank2D
-    visualizer = Rank2D(features=features, algorithm='covariance')
-    visualizer.fit(X, y)                # Fit the data to the visualizer
-    visualizer.transform(X)             # Transform the data
-    visualizer.poof()                   # Draw/show/poof the data
-
-
-Model Visualization
-^^^^^^^^^^^^^^^^^^^
-In this example, we instantiate a Scikit-Learn classifier, and then we use Yellowbrick's ROCAUC class to visualize the tradeoff between the classifier's sensitivity and specificity.::
-
-    from sklearn.svm import LinearSVC
-    from yellowbrick.classifier import ROCAUC
-    model = LinearSVC()
-    model.fit(X,y)
-    visualizer = ROCAUC(model)
-    visualizer.score(X,y)
-    visualizer.poof()
-
-
-For additional information on getting started with Yellowbrick, check out our :ref:`examples <examples/yellowbrick-examples>`.
diff --git a/examples/LCOMBS/testing.ipynb b/examples/LCOMBS/testing.ipynb
index 220b70896..833fe3ba7 100644
--- a/examples/LCOMBS/testing.ipynb
+++ b/examples/LCOMBS/testing.ipynb
@@ -407,7 +407,7 @@
     "# Regression Evaluation Imports \n",
     "\n",
     "from sklearn.linear_model import Ridge, Lasso \n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from yellowbrick.regressor import PredictionError, ResidualsPlot"
    ]
@@ -544,7 +544,7 @@
     "from sklearn.naive_bayes import GaussianNB\n",
     "from sklearn.linear_model import LogisticRegression \n",
     "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance"
    ]
diff --git a/examples/NealHumphrey/testing_yb.ipynb b/examples/NealHumphrey/testing_yb.ipynb
index 9a3e63e89..d0901af3f 100644
--- a/examples/NealHumphrey/testing_yb.ipynb
+++ b/examples/NealHumphrey/testing_yb.ipynb
@@ -145,8 +145,8 @@
    "outputs": [],
    "source": [
     "from sklearn import metrics\n",
-    "from sklearn.cross_validation import KFold\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import train_test_split\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
diff --git a/examples/balavenkatesan/testing.ipynb b/examples/balavenkatesan/testing.ipynb
index 59a98894d..426faf1bf 100644
--- a/examples/balavenkatesan/testing.ipynb
+++ b/examples/balavenkatesan/testing.ipynb
@@ -192,7 +192,7 @@
    },
    "outputs": [],
    "source": [
-    "## from sklearn.cross_validation import train_test_split as tts\n",
+    "## from sklearn.model_selection import train_test_split as tts\n",
     "\n",
     "from sklearn.linear_model import LinearRegression\n",
     "from yellowbrick.regressor import ResidualsPlot\n",
diff --git a/examples/bbengfort/cluster.ipynb b/examples/bbengfort/cluster.ipynb
index 6a719c313..5b329f52e 100644
--- a/examples/bbengfort/cluster.ipynb
+++ b/examples/bbengfort/cluster.ipynb
@@ -24,10 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import sys \n",
@@ -43,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -69,37 +67,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
-      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
-     ]
-    }
-   ],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "from yellowbrick.cluster import KElbowVisualizer"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 8 Blobs Dataset \n",
+    "\n",
+    "This series shows the use of different metrics with a dataset that does contain centers"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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iIiJWRyFYRERERKyOQrCIiIiIWB2FYBERERGxOgrBIiIiImJ1FIJFRERExOoo\nBIuIiIiI1VEIFhERERGroxAsIiIiIlZHIVhERERErI5CsIiIiIhYHYVgEREREbE6CsEiIiIiYnUU\ngkVERETE6igEi4iIiIjVUQgWEREREaujECwiIiIiVkchWERERESsjkKwiIiIiFgdhWARERERsTp2\nli4AIDo6mrFjx7Jx40YcHR3p3bs3vXr1SrJet27d2L9/f5Llbdu25csvvwTgtdde4/79+xiNRgAM\nBgOHDh0ia9asabsTIiLyQkIfRNF78W4uh4SxsMgrlM2WzdIliUgmli5C8Ndff83x48cJDAzk0qVL\njBgxgqJFi9K4cWOz9aZNm0ZMTIzp8eHDh3n//ffp2rUrANevX+f+/fts2rQJR0dH03oKwCIi6Vvo\ngyia/LSJg5duA9Bx4W52DnqD7A5ZLFyZiGRWFg/BERERLF++HH9/f1xdXXF1daVv374sWLAgSQjO\nmTOn6f/x8fF899139OvXj/LlywNw9uxZChQoQNGiRV/qPoiIyPN7NAADHLkaRrdFu1jewwsbG4MF\nqxORzMriY4KDg4OJi4vDw8PDtKxatWr89ddfT3xdUFAQ4eHh9O3b17Ts9OnTuLi4pFWpIiKSykIf\nRPHGzM2mANy/ZimavJLQ4bHy2EVGrfnTkuWJSCZm8RB88+ZNcufOjZ3d/zql8+XLR1RUFKGhoY99\n3ezZs+nZs6fZUIczZ84QERFBt27d8PT0pH///pw7dy4tyxcRkecUFhHNGzM3c+DiLQDeq1uOSc08\nGF2rCNWL5wXgm63/Ze6+M5YsU0QyqXQxHMLe3t5sWeLj6OjoZF+zZ88ebty4Qfv27c2Wnz17ljt3\n7vDhhx+SPXt2Zs2aRc+ePVmzZg3ZUnCBRURERAr3Qp5FYruqfdOO2jjtqY1TR1hENC3n/c6hywmd\nHf1rluKrJhWIjIzEwdaGgNZVaDpvNxfDH/DO8t0UzZGFui75LVx15qBjOG2pfdOe0WjEYHjxYVIW\nD8EODg5Jwm7i48dd0LZhwwbq1atnNkYYYM6cOcTFxeHg4ADApEmT8PLyYuvWrTRr1uyZa1LvcdpS\n+6Y9tXHaUxs/v7vRcQzccp4TtyMBaFc2D31KORAcHGxa50HIVb6qU4h+G8/xIDaejgt+J6BxSYo5\n2T9us5JCOobTlto3bT3agfo8LB6CnZ2dCQsLIz4+HhubhNEZISEhODo6Jgm5iXbu3MmgQYOSLLez\nszMbVmFJ1SWHAAAgAElEQVRvb0+xYsW4fv16impycXHRjBJpICIignPnzql905DaOO2pjV9MWEQ0\n78z73RSA+9UoxeQ3PUy9Og+3r5tbVrLkK0THhX8QHhXHyD3X2dzfm1yOmjHiRegYTltq37R36tSp\nVNmOxUOwm5sbdnZ2HD58mKpVqwJw4MABKlasmOz6oaGhXLx40bTuw3x8fBg4cCCtWrUC4MGDB5w/\nf55SpUqlqKasWbOmaPiEpIzaN+2pjdOe2jjlwiKiaRO4zTQE4p06r+LXpkayX2smtm/bKqWZeCeK\nYasO8vfNu/Ratp/VfRtiZ2vxS1oyPB3DaUvtm3ZSYygEpIML4xwdHWnZsiVjxozh6NGjbNq0iYCA\nAHr06AEk9ApHRUWZ1j916hSOjo4UK1Ysyba8vb2ZOnUq+/bt49SpUwwfPpzChQvj5eX10vZHRESS\nCouI5o2fNrH/34vgnhSAHzW0vht9apYBYOPJq3yw8kCa1ioi1sHiIRhg5MiRVKxYkR49ejBu3DiG\nDBmCj48PAJ6enqxdu9a0bkhICE5OTslu56OPPqJJkyYMGzaMDh06YDQamTlzZqp9YhARkZQLi4im\n6UzzAPxD62cLwJDQ6+PXpgbeZZwBmLbrb378/e80q1dErIPFh0NAQm/whAkTmDBhQpLnHr5QAsDX\n1xdfX99kt2Nvb8+IESMYMWJEmtQpIiIpE/5vAN53ISEAv107IQCn9AYY9na2LO3hRZ0pazkVcpeh\nK/dTpoATjcsVSYuyRcQKpIueYBERyXzCI6J545EA7Ncm5QE4Ud5sDqzq25DcWe2JizfScf4OTlwP\nT82SRcSKKASLiEiqezQA969d9oUCcKJXC+Rkaff62NoYuBMZQwv/LYTci0yNkkXEyigEi4hIqkoY\nArHZLABPa1PzhQNwokavFsavTQ0Azt66R7t524mKjUuVbYuI9VAIFhGRVJMYgPdeCAGgX63UDcCJ\n+td+lSH1XQHYefYG7y7fi9FoTNX3EJHMTSFYRERSRXhENL6zzAPwj21TPwAnmti8Gk3digIwb/8Z\nJm09nibvIyKZk0KwiIi8sMQAvOf8ywnAALY2Nix6y5OKhXIDMHLNIX45eiHN3k9EMheFYBEReSF3\nIs0DcN9aZdI8ACfK6WjPyj7eFMjhgNEI3Rb9zp+Xbqf5+4pIxqcQLCIiz+1OZMIY4IcD8PS2tV5K\nAE7kkjcHP/dsgL2tDQ+i42g1ZytX7zx4ae8vIhmTQrCIiDyXRwNwn5ovPwAnqlOyILM71gbgUvgD\nWs/ZRkRM7EuvQ0QyDoVgERFJsTuR0fjO3GIWgGe0s0wATtS1WilG+VQCYP/FW/Ra/Afx8ZoxQkSS\npxAsIiIpkhiAd5+/CaSPAJxobBN32lYuAcCyI+f5YsNfFq5IRNIrhWAREXlmdyKjaTbrfwG4d430\nE4ABbGwMzO1cl9eK5wNg3Ma/WHToHwtXJSLpkUKwiIg8k7uRMTSbtYU/zv0vAP/UPv0E4ETZ7O1Y\n0asBRXNlA6Dvkj/Y/W/NIiKJFIJFROSp7kbG4DtrsykA96pROl0G4ERFcmXjl94NyGZvS1RsPG0C\ntnH+9j1LlyUi6YhCsIiIPFFyAXhm+9rpNgAnqlosH/O7eAJw414kLeds5W5kjIWrEpH0QiFYREQe\n69EA3LN6xgjAiVpXKsF43yoAHL0aRpcFO4mLj7dwVSKSHigEi4hIshLGAJsH4FkdMk4ATjS8YQW6\nvVYKgDUnLjNi9SELVyQi6YFCsIiIJJEYgHdl8AAMYDAY+Kl9LTxLFgTgu+0nmL3nlIWrEhFLUwgW\nEREzdyNjeHP2FlMA7pGBA3AiBztbgnp6UTJvDgAGBO1l6+lrFq5KRCxJIVhEREwSA/Dv/9wAEgNw\n+p0FIiXy53BkVR9vcjpmITbeSPu52zl5846lyxIRC1EIFhERAO5FJR+AbW0yz5+K8oVys7hbPWwM\nBkIjomnpv5XQB1GWLktELCDznNlEROS53YtKuBFGYgDu/lqpTBeAE73hWpTvW70GwMmbd+gwbwcx\ncZoxQsTaZL6zm4iIpMijPcDdXyvF7I61M2UATjTA05V367wKwJbT1xj08z6MRqOFqxKRlynznuFE\nROSpEgPwzrMJAbibFQTgRN+3qo7Pq4UBmLXnFFN3Blu4IhF5mTL/WU5ERJJ1LyqG5o8EYH8rCcAA\ndrY2LOleH9eCOQEYtuoga05ctnBVIvKyWMeZTkREzCQG4B3/BuC3qllXAE6UO6s9q/o0JF82B+KN\nRroE7uTY1VBLlyUiL4F1ne1ERCTZADynk/UF4ESl8zuxvKcXWWxtuBsVQwv/rdy4G2HpskQkjVnn\nGU9ExErdVwBOVv3SzkxvVxOA86H3aROwnciYOAtXJSJpybrPeiIiVuT+vxfBJQbgrtVKKgA/pFeN\nMgxrUB6A3edv0m/pbs0YIZKJ6cwnImIF7kfF0Nx/q1kADuhURwH4EeObVaFFhWIALDr0DxM2H7Nw\nRSKSVnT2ExHJ5BID8PYz1wHoUlUB+HFsbWwI7OqJe5E8AHy69jDLj5y3cFUikhZ0BhQRycTu/3uh\n18MBeG5nBeAnyeGQhZW9vSnklBWAnot3ceDiLQtXJSKpTWdBEZFMKjEAb/s3AHeu4qIA/IyK58nO\nit4NcLSzJSImjlZztnIp7L6lyxKRVKQzoYhIJnQ/KoaWc8wD8LwudRWAU6BGifwEdK4DwNU7EbSa\ns437UTEWrkpEUovOhiIimcyD6FhaztnK1tMP9wArAD+PDh4ujG3iDsCfl2/TffEu4uM1Y4RIZqAz\noohIJvIgOpYW/luSBGA7W53un9fo1yvRqYoLAL8cvcjotX9atiARSRU6K4qIZBKPBuBOCsCpwmAw\nMLtjbWqWyA/A11v+y7z9ZyxclYi8KJ0ZRUQygeQC8DwF4FSTNYsdP/dqQIk82QF4e9kedp69buGq\nRORF6OwoIpLBPYiOpaX//8YAd/RQAE4LhXJmZWVvb3I42BETF0/bgO2cvXXX0mWJyHPSGVJEJANL\nDMBbTl8DEgLw/C4KwGmlcpE8LOjqicEAtx5E0cJ/K+ER0ZYuS0Seg86SIiIZ1IPoWFrNUQB+2ZpX\nKM43b1YD4MT1cDoF7iQ2Lt7CVYlISulMKSKSASUG4M2nEgJwB49XFIBfove93OhTswwAG/6+woer\nDli4IhFJKZ0tRUQymOQCcGAXTwXgl8hgMODXpgYNSjsD4Pf730zf9beFqxKRlNAZU0QkA4mIMQ/A\n7d0VgC3F3s6WZT29KJPfCYAhv+xn499XLFyViDwrnTVFRDKIiJiEi+AeDsALuioAW1LebA6s6uNN\n7qz2xMUb6Th/B8HXwy1dlog8A505RUQygEcDcDsF4HSjXMFcLO1eH1sbA+GRMbTw30rIvUhLlyUi\nT6Gzp4hIOpdcAF6oAJyuNHq1MD+0qQHAmVt3aT9vO9GxcRauSkSeRGdQEZF0LGEM8Db1AGcAb9d+\nlcH1XAHYcfYG7y7fi9FotHBVIvI4OouKiKRTiQF408mrALStXIIFXT3JogCcbk1qUY03XIsAMHf/\nGSZvO27hikTkcdLFmTQ6OppPPvmE6tWrU69ePQICApJdr1u3bri6uib5N2rUKNM6q1ev5vXXX8fD\nw4OBAwcSGhr6snZDRCTVRMTE0vqRALzwrXoKwOmcrY0Ni7vVo0KhXAB8/NshVh67aOGqRCQ56eJs\n+vXXX3P8+HECAwMZM2YMfn5+bNiwIcl606ZNY9euXaZ/06ZNw97enq5duwLw119/MXr0aAYNGsTS\npUsJDw9n5MiRL3t3REReSGIA3qgAnCHldLRnVZ+GFMjhgNEIby3cyeHLty1dlog8wuJn1IiICJYv\nX87o0aNxdXXFx8eHvn37smDBgiTr5syZk3z58pEvXz7y5MnDd999R79+/ShfvjwACxcupGnTprRo\n0YJXX32ViRMnsn37di5fvvyyd0tE5LlExsTRJmC7KQC3UQDOkFzy5iCoZwPsbW14EB1HS/+tXL3z\nwNJlichDLH5WDQ4OJi4uDg8PD9OyatWq8ddffz3xdUFBQYSHh9O3b1/TssOHD1O9enXT40KFClG4\ncGGOHDmS+oWLiKSyyJg4WgdsY8O/N1xoU7kEixSAM6y6JQsyq2NtAC6FP6BNwDYiYmItXJWIJLL4\nmfXmzZvkzp0bOzs707J8+fIRFRX1xPG8s2fPpmfPnmTNmtVsWwULFjRbL3/+/Fy7di31CxcRSUUK\nwJnTW9VK8YlPRQD2XbhFr8V/EB+vGSNE0gO7p6+StiIiIrC3tzdblvg4Ojo62dfs2bOHGzdu0L59\ne7PlkZGRyW7rcdt5Uk2S+hLbVe2bdtTGaS8t2jgyJo7Oi3az6fR1AFqUL8LsNtWIiYokJtXeJWPI\njMfwiHqvcvxqKL/89zLLjpyndJ5sjGpU3mL1ZMY2Tk/UvmnPaDRiMBheeDsWD8EODg5JQmri44d7\neR+2YcMG6tWrR86cOZ9pW46Ojimq6dy5cylaX1JG7Zv21MZpL7XaOCounuE7LrL76n0AvIs78XHl\nXJw++XeqbD+jymzH8AcVnAi+6kjw7Ui+2naC7NF3aOKSy6I1ZbY2Tm/Uvmnr0U7P52HxEOzs7ExY\nWBjx8fHY2CR87RcSEoKjo2OSkJto586dDBo0KMnyggULEhISYrYsJCQkyRCJp3FxcXlsAJfnFxER\nwblz59S+aUhtnPZSs40jY+Losni3KQA3dyvCvI41rXoIRGY+hle9UooGP23lyp0I/m/fVepULEuN\n4vleeh2ZuY3TA7Vv2jt16lSqbMfiIdjNzQ07OzsOHz5M1apVAThw4AAVK1ZMdv3Q0FAuXrxoWvdh\nHh4eHDx4kFatWgFw9epVrl27hru7e4pqypo1K9myZUvhnsizUvumPbVx2nvRNo6MieOthdvYeCph\nCESrSsX5T7f6Vh2AH5YZj+HS2bKxso83XtPW8yA6js6L9rBnSFNeyZvDIvVkxjZOT9S+aSc1hkJA\nOrgwztHRkZYtWzJmzBiOHj3Kpk2bCAgIoEePHkBCT25UVJRp/VOnTuHo6EixYsWSbKtz586sXLmS\n5cuXExwczIgRI/D29qZo0aIvbX9ERJ4mMiaOtnO3sT444SK4VpWKs1gXwVmFqsXyMa+zJwA37kXS\ncs5W7kZa28hvkfQhXZxxR44cScWKFenRowfjxo1jyJAh+Pj4AODp6cnatWtN64aEhODk5JTsdjw8\nPPjiiy+YNm0aXbp0IXfu3IwfP/6l7IOIyLNIDMDr/g3ALSsmBGB7O1sLVyYvS5vKJfjSN2Fa0KNX\nw+i6cCdx8fEWrkrE+lh8OAQk9AZPmDCBCRMmJHkuODjY7LGvry++vr6P3VarVq1MwyFERNKTyJg4\n2s3bbhaA/9NNAdgajWhYkeAbdwg8cJbfjl/m49V/MrFFNUuXJWJV0kVPsIhIZhcVmxCA155IuINl\niwrFFICtmMFg4Kf2tfAsmXDh9rfbjzN7T+pc7CMiz0YhWEQkjUXFxtF2rnkAXtK9vgKwlXOws2V5\nTy9K/nth3ICgvWw9rZs7ibwsCsEiImlIAViepEAOR1b28SanYxZi4420n7udUzfvWLosEaugECwi\nkkaiYuNo91AAbq4ALMmoUCg3i7vVw8ZgIDQimhb+Wwl9EPX0F4rIC1EIFhFJA4kBeM1DAXipArA8\nxhuuRfmu5WsAnLx5h47zdxATpxkjRNKSQrCISCpTAJbnMcCzHO/UeRWAzaeuMXjFPoxGo4WrEsm8\nFIJFRFJRVGwc7ef9LwC/WV4BWJ6NwWDg+1bVaVS2EAAzd5/ih53BT3mViDwvhWARkVSSGIB/O/6/\nALyshwKwPLsstjYs7eGFa8GcAHy46qBpTLmIpC6FYBGRVBAVG0eHeTvMAvBSBWB5Drmz2rOyjzd5\ns9kTbzTSOXAnx66GWroskUxHIVhE5AUlBuDVxy8B0Kx8UZb2qI+DArA8pzL5c7K8ZwOy2NpwNyqG\nFv5buXE3wtJliWQqCsEiIi8gOpkAvKyHlwKwvDCv0s782LYmAOdD79N27nYiY+IsXJVI5qEQLCLy\nnKJj42j/UAD2dVMAltTVu2YZhjUoD8Af527Sf9luzRghkkoUgkVEnkN0bDwd5psH4OU9FYAl9Y1v\nVoXmFYoBsPDgP3y1+ZiFKxLJHBSCRURSKCbOSLcle/j1vwrAkvZsbWxY0NUT9yJ5ABi99jDLj5y3\ncFUiGZ9CsIhICkTHxjNy1yXWBF8FoKkCsLwEORyy8Etvb5ydHAHouXgXBy7esnBVIhmbQrCIyDOK\njo2j+5I97Lh0F0gIwEEKwPKSlMiTnRW9GuBoZ0tETByt52zlcvgDS5clkmEpBIuIPIPQB1E099/K\nb//2ADd+tRDLdRGcvGQ1XynAnE51ALhyJ4JWc7ZyPyrGwlWJZEwKwSIiT3Hieji1pqxl08mEAFyn\ncA4WdqqFYxYFYHn5OlZxYUzjygAcunSbHov/ID5eM0aIpJRCsIjIE6w5cZk6U9dyOiRhCETfGqWY\n5FVcAVgs6tPGleno4QLAiqMX+HTdYcsWJJIBKQSLiCTDaDQyaet/aeG/hTuRMdjZGPBrW4PvmlfB\nzsZg6fLEyhkMBvw71aZmifwAfLX5GPMPnLFwVSIZi0KwiMgjImPi6LF4FyNWH8JohHzZHFj/tg/v\n1iln6dJETLJmsePnXg0onjsbAG8v3cPvZ29YuCqRjEMhWETkIVfCH+D943oWHvwHgIqFcrN3aFMa\nlClk4cpEkiqUMyur+jQku70d0XHxtJ27jbO37lq6LJEMQSFYRORf+y6EUOP7Ney7kDD/asuKxfl9\n0BuUzOdk4cpEHq9ykTwsfMsTgwFC7kfR0n8r4RHRli5LJN1TCBYRARYcPEuDaeu5eicCgNGvV2J5\nDy+cHLNYuDKRp2teoTjfvFkNgOPXw+m8YCexcfEWrkokfVMIFhGrFhcfz8erD9Fj0S6iYuPJmsWW\nxd3q8fkbHtjoAjjJQN73cqN3jTIArA++wrBfD1q4IpH0zc7SBYiIWMqdyGi6LvidNScuA1A8dzZW\n9PKmSrG8Fq5MJOUMBgPT2tbgzK27bD9znR92BlOuYE5d0CnyGOoJFhGrdDrkDnWmrjMF4DouBdg7\n1FcBWDI0eztblvXwokz+hHHsQ1bsZ+PfVyxclUj6pBAsIlZn08mr1Pp+LSeuhwPQq0ZpNr37Os5O\nWS1cmciLy5fdgZW9vcmd1Z64eCMd5+8g+N9jXUT+RyFYRKyG0Wjkh50n8J21mdCIaGwMBr5r+Rqz\nOtTGwU53gJPMw9U5F0u618fWxkB4ZAwt/Ldy636UpcsSSVcUgkXEKkTFxtF/6R6G/nKAuHgjubPa\n81u/hgyu74bBoAvgJPPxebUwU1vXAODMrbu0m7uN6Ng4C1clkn4oBItIpnfjbgSvT9/InH2nAXAt\nmJM9Q5rSuFwRC1cmkrbeqfMqg+q5ArDj7A3eC9qL0Wi0cFUi6YNmhxCRTO3w5du0mrOVi2EPAGjq\nVpSFXT3JldXewpWJvByTmlfj1M07rAu+QsC+M5TOk43G+SxdlYjlqSdYRDKtZUfOU89vnSkAf+Rd\ngZW9GygAi1Wxs7Vhcbd6VCiUC4BPNxxlxyXdWllEIVhEMp34eCNj1x2h0/wdPIiOw8HOhnld6vLV\nm1WxtdFpT6xPTkd7Vvb2Jn92B4xGGL3rEn9eDrV0WSIWpb8GIpKp3IuKof387Yzb+BcAhXNmZduA\nJrxVrZSFKxOxrJL5nFjRqwEOdjZExhlpv+APzt++Z+myRCxGIVhEMo1zt+/h+cM6fjl6EYDqxfOx\nd6gvNUrkt3BlIulDnZIFmdmmOgDX70Xy5uwthEVEW7gqEctQCBaRTGH7mevU/H4NR6+GAdClakm2\nDmhM0VzZLFyZSPrSplIxBnoUBOD49XDaz92uqdPEKikEi0iGN3P3SRrP2EjI/SgMBviqWVXmd6lL\n1iyaAEckOd3c8tG7ekkAtpy+Rv9lezR1mlgd/YUQkQwrJi6eD1Ye4MddfwPg5JCFhW950qx8MQtX\nJpK+GQwGJjfz4MrdKNYFXyHwwFlK5c3BZ03cLV2ayEujnmARyZBu3Y+i6cxNpgBcOp8Tu4c0VQAW\neUZ2tjb8p1t9PIrkAeDzDX8xb/8ZC1cl8vIoBItIhnPsaig1v1/D1tPXAWhUthB7hjbFzTmXhSsT\nyVicHLPwa9+GFPt37Hz/pbvZcuqqhasSeTkUgkUkQ1l17CJ1f1jHP/9O7TSonitr+jUibzYHC1cm\nkjEVyZWN1f0a4uSQhdh4I+3mbue/18IsXZZImlMIFpEMwWg0MmHTUdrM3ca9qFiy2NrwU/tafN+q\nOna2OpWJvIhKhfOwrEd97GwMhEfG8ObsLVy7E2HpskTSlP5yiEi69yA6li4LdjJ67WGMRiiQw4FN\n77xO31plLV2aSKbxerkiTG9XC4ALofdp4b+Fe1ExFq5KJO0oBItIunYx9D5e09az9PB5ADyK5GHf\n0GZ4lipo4cpEMp/eNcswyqcSAAcv3abLgp3ExcdbuCqRtKEQLCLp1u5zN6k5ZQ2HLt0GoG3lEuwY\n2IQSebJbuDKRzOvzN9zpXMUFgN+OX2boLwc0h7BkSgrBIpIuzd13hoY/buD63Ugg4Q/zku71ye6Q\nxcKViWRuBoMB/0518CrtDMCPu/7m+x0nLFyVSOpTCBaRdCU2Lp4PVx6gz5I/iI6LJ7u9Hct7ejH6\n9coYDAZLlydiFRzsbAnq6YVrwZwAfPTrQYL+Om/hqkRSl0KwiKQbYRHRNPffaup1eiVPdn4f9Aat\nK5WwcGUi1idPNgdW921IwRyOGI3QfeEu9py/aemyRFJNugjB0dHRfPLJJ1SvXp169eoREBDw2HVP\nnjxJ165dcXd3p0WLFuzdu9fs+ddeew03NzdcXV1xdXXFzc2NiAhN8yKS3v19I5zaU9ay4e8rANQv\nVZC9Q32p/O/drETk5SuZz4mVfbzJmsWWyNg4Wvpv5UzIXUuXJZIq0kUI/vrrrzl+/DiBgYGMGTMG\nPz8/NmzYkGS9e/fu0adPH8qUKcPq1at5/fXXGThwILdvJ1w0c/36de7fv8+mTZvYtWsXu3bt4vff\nfydr1qwve5dEJAXWBV+m9pS1nLx5B4D+tcuy/m0fCuRwtHBlIlKjRH4WdPXEYICQ+1G8OXsLt+5H\nWboskRdm8RAcERHB8uXLGT16NK6urvj4+NC3b18WLFiQZN1ffvmFbNmy8fnnn1O8eHEGDRqEi4sL\nx44dA+Ds2bMUKFCAokWLki9fPtM/EUmfjEYj3247TvPZWwmPjMHWxoBfmxpMb1cLeztbS5cnIv9q\nVakE37Z4DYCTN+/QJmAbkTFxFq5K5MVYPAQHBwcTFxeHh4eHaVm1atX466+/kqy7b98+GjZsaLZs\n2bJl1K9fH4DTp0/j4uKSpvWKSOqIjImj93/+4KNfDxJvNJI3mz3r+jfi3brlLF2aiCRjcH03Btdz\nBeD3f27Q+z9/EB+vqdMk47J4CL558ya5c+fGzs7OtCxfvnxERUURGhpqtu7FixfJkycPn332GZ6e\nnnTq1IlDhw6Znj9z5gwRERF069YNT09P+vfvz7lz517WrojIM7p65wGNpm9g/oGzAFQolIu9Q31p\nWLawhSsTkSeZ1KIaLSoUA2DJ4XN8uu6whSsSeX52T18lbUVERGBvb2+2LPFxdHS02fL79+8ze/Zs\nunfvzuzZs1m9ejV9+vRh3bp1ODs7c/bsWe7cucOHH35I9uzZmTVrFj179mTNmjVky5YtRTVJ6kts\nV7Vv2skIbXzociidF+3myp2EGn1dCzO7XXWcHGx58OCBhat7uozQxhmZ2jftvWgbz2pTjcth9zl4\nOZSvNh+jcPYs9K5eKjVLzNB0DKc9o9GYKlNmWjwEOzg4JAm7iY8fvaDN1tYWNzc3Bg4cCICrqyu7\ndu1i5cqV9O/fnzlz5hAXF4eDgwMAkyZNwsvLi61bt9KsWbNnrkm9x2lL7Zv20msbrz8Xzv/tvUJU\nXMJXqL0q5Oftyrm5dPa0hStLufTaxpmF2jftvUgb/1+NAvTecI+r92P44Nc/4c4tahfJkXrFZQI6\nhtPWox2oz+O5Q/CNGzdYunQpZ8+eZdSoUezfv59XX32VUqVS9mnQ2dmZsLAw4uPjsbFJGJ0REhKC\no6MjOXPmNFu3QIECSbbv4uLC1atXE3bGzs5sWIW9vT3FihXj+vXrKarJxcVFM0qkgYiICM6dO6f2\nTUPptY3j4418sfm/TP7jMgCOdjZMb/0a7SoXt3BlKZde2zizUPumvdRq41XFXuH1WdsIi4xh1B9X\n2NDXi0qFc6dipRmTjuG0d+rUqVTZznOF4PPnz9OhQwdy5MjB9evXef/991m3bh0jR45k7ty5uLu7\nP/O23NzcsLOz4/Dhw1StWhWAAwcOULFixSTrenh4sH//frNlZ8+epUWLFgD4+PgwcOBAWrVqBcCD\nBw84f/58ioN51qxZUzR8QlJG7Zv20lMb34mMptviXaw+fgmAormysaJXA6oVz9gzt6SnNs6M1L5p\n70XbuKpLNn7u7U2TnzZxLzqW9gt388fgNyiWO3sqVplx6RhOO6l199DnujDuq6++wsfHh02bNpEl\nSxYAJk+ejI+PD5MmTUrRthwdHWnZsiVjxozh6NGjbNq0iYCAAHr06AEk9ApHRSXMR9ipUyf+/vtv\n/Pz8uHDhAlOmTOHSpUs0b94cAG9vb6ZOncq+ffs4deoUw4cPp3Dhwnh5eT3PborICzoTchfPH9aZ\nAnCtV/Kzb6hvhg/AIpLAq7Qz/h1rA3A5/AHNZ2/lTmT0U14lkj48Vwg+dOgQvXr1Mkvitra2vPPO\nOxw/fjzF2xs5ciQVK1akR48ejBs3jiFDhuDj4wOAp6cna9euBaBIkSL4+/uzZcsWmjdvzvbt25k5\nc3ONiMAAACAASURBVCYFCxYE4KOPPqJJkyYMGzaMDh06YDQamTlzZqp9YhCRZ7fl1FVqTVnDf6+F\nA9Cjemm2vNeYQjn19aBIZtK1Wim+eCPhG+C/robScf5OYuLiLVyVyNM913CI+Ph44uOTHuD37t3D\n1jblE9w7OjoyYcIEJkyYkOS54OBgs8dVqlTh559/TnY79vb2jBgxghEjRqS4BhFJHUajkem7TjJ0\n5X7i4o3YGAxMbF6VIfXd9IFUJJP6xKcS/9y+R8C+M2z4+woDgvbyU/ta+p2XdO25eoI9PT356aef\nzIJwWFgYkyZNolatWqlWnIhkLNGxcby7fC+DVuwjLt5ILscsrO7bkKFe5fXHUCQTMxgMTG9Xi0Zl\nCwHgv/c0X285ZuGqRJ7suULwxx9/zLFjx/D09CQqKop3330Xb29vLl26pF5YESt1814kjX/axKw9\nCVftliuQkz1DfWniWsTClYnIy5DF1oZlPbyoWChhhohRaw6z+NA/Fq5K5PGeaziEs7Mzv/zyC6tX\nr+bEiRPEx8fTuXNnWrZsSY4cmidQxNocuXKb1nO2cT70PgBNXIuw6K165M764vM4ikjG8f/s3Xlc\nVXXixvHPZQcBRRTc9xSUFNdcEAFRW9xTU8vQ3HJCbZ3Gfs1om9VUM2PZ6oKmlqm5pGm5AoqpuWuK\nG+Iuijv7cu/vD4wZM0vwXg7L8369fNk993B47rerPJ77Pd9T3tWJFSPCaPfhKs5dT+ep+ZupXt6N\n4Pq+RkcTuU2hzgRPmDCB3Nxc+vfvzz/+8Q8mTZrE448/Tm5ubv6NLESkbPh27wmCPvohvwA/36kx\ny4eHqgCLlFE1vcqxfHgY5ZwcyMo10zcqmkMXrhkdS+Q2d30meMeOHZw6dQqApUuX0qRJk9vO+iYk\nJBAXF2fdhCJSLJnNFt5cs5fXVu8FwMnejs/6tyWidX2Dk4mI0ZrXqMj8J4PpNWMDV9Kz6D59PXFj\nH8THQ6vDSPFx1yXYZDLxt7/9Lf+/33zzzdv2cXNzY/jw4dZLJyLFUmpmNkPnb2bx3pMAVPFw5dth\nnWhbu7LByUSkuHjYvzof9W3DM99uJeFSCr1nRrN2TBfcnAp9s1oRq7rrd2KLFi3ylyvz8/MjLi4O\nb28teC9S1py4nEKfqGj2nL0CQMsaFVk8LER3iRKR2zzdviGJl1N4b8MvbD2ZzJCvNrHgyWDs7Qo1\nG1PEqgr1LoyMjPzdWwGmpKT87lq/IlI6bEq4wANTVuYX4IHN6xAT2U0FWETuaPLDzenXrDYAS/ed\n4uUVOw1OJJLnrs8EHzt2jMuXLwPw8ccf4+fnR/ny5W/Z5/Dhw8yfP58JEyZYN6WIGG76liNELt5G\ndq4Zkwneeqg5fw1rovV/ReQP2dmZmD2oA2evpbE58SL/jjlI3YruPBPkZ3Q0KePuugSfOnWKp59+\nOv8H3p1WgXj00Uetk0xEioWcXDMvfLedqZsOAeDu7MDcx4Po0aSmwclEpKRwcbRnybAQOnz0A0eT\nb/Ds0u3UrFCOngH6e0SMc9clOCQkhPXr12M2mwkPD2fhwoVUrFgx/3mTyYSbmxsVKlSwSVARKXqX\n0zIZ+GUs646cB6CetztLnwqlSRX9OReRgqnk7sL3I8NoP+UHLqVl8vi8jWz4Szda1dT1RWKMAl2i\nWa1a3p2f1q1bR7Vq1fQxqEgpduD8VXrPjObYpRsAhDWowvwng/Eu52xwMhEpqRpU8mTpUyGEf7aG\ntKxces5Yz+ZxD1Gnom60JUWvUBfGVa9endjYWIYMGUJQUBBnzpzho48+YtmyZdbOJyIGWHHgNO0/\n/CG/AEcGNWLlqM4qwCJyz9rX9WH24CAAkm5k0H36eq6mZxmcSsqiQpXguLg4IiMjqV69OtevX8ds\nNmM2m5kwYQJLly61dkYRKSIWi4V/rt9P75kbuJGZjePNG2BM6dMGR3staSQi1tG/WW3e7d4CgINJ\n1+g3K5qsnFyDU0lZU6ifah999BEvvPAC77zzDvb29gCMHz+eF154gRkzZlg1oIgUjfTsHIbM28SE\n73dhsUClcs6seTqckW3vMzqaiJRCL4Q05un2DQHYcDSJkQu2YLFYDE4lZUmhSvChQ4cICwu7bXvX\nrl05efLkPYcSkaJ15loaIR+v5utdiQA0rerF1mcfpmM9X2ODiUipZTKZmNK7NQ/5Vwdg7o4EXr95\nG3aRolCoEuzh4cGFCxdu237kyJHb1g4WkeJt64mLtPn3SrafugRAn/trsXFsN12oIiI252Bvx/wh\nHWlePW+1qddX72XWtmMGp5KyolAluEePHkyePJn4+HhMJhOpqanExsby5ptv8vDDD1s7o4jYyJfb\njxH6yWrO30gH4B9dm7LgyWDcnR0NTiYiZYW7syPfDQ+lZoW8O9GOXvgT6w6fMziVlAWFKsHPPvss\ndevWpXfv3qSlpdGnTx9GjRpFw4YNee6556ydUUSsLNds5q/LdzDs681k5phxc7LnmyeDmditGXZ2\nWvpQRIpWtfJuLB8RhqeLIzlmC/1mx7D/3BWjY0kpV6B1gn/l6OjIBx98wLhx4zh48CBms5mGDRvS\noEEDa+cTESu7lp7F4Lkb+SH+LAC1vMqxZFgIgdUr/slXiojYzv1VvVgY0YlHpq3jekY2PWZsYPO4\nB6nq6WZ0NCmlClWCf1W7dm1q165trSwiYmOHL16n94wNHLp4HYCguj4sjAjGx8PV4GQiIhDesCqf\n9W/LiG9+4uSVVHrO2MCGv3TVFC2xiUKVYD8/vz+8W9zBgwcLHUhEbGP1obMMmrMxf1H64Q80YGrf\nNjg52BucTETkv4a1aUDi5RTeXLOPnacvM2jORpYMC8FBa5WLlRWqBE+ePPmWEpyTk0NiYiJLly7l\nr3/9q9XCici9s1gsfLgxnhe/24HZYsHezsS/e7XiLx0a6dbnIlIsTerWjOOXU5i34zgrD57h2aU/\n81HfNvo7S6yqUCW4b9++v7s9ICCAhQsX0qtXr3sKJSLWkZmTy18WbWXWz3lLDnm5OvHNk8F0bljV\n4GQiIndmMpmYPqAdZ66mEX0siU83H6aetwfPhzQ2OpqUIlb9bKFp06bs2LHDmocUkUI6fz2dzp+s\nyS/AjX3Ls/XZh1WARaREcHKwZ9HQTvj5eALw0vIdLNpzwuBUUppYrQSnpqYyd+5cKlWqZK1Dikgh\n7T57hQf+s5KfTlwE4JHG1Ykb9yD1K3kYnExE5O55uTmzYkQYPu4uAER8FcdPiRcNTiWlhVUvjDOZ\nTLz22mv3HEpECm/NiWu8ue0Q6dm5ALwc1oQ3HgrE3k4XlYhIyVPX24PvhocS+slq0rNz6T1zA3Hj\nHqRBJU+jo0kJZ5UL4yBv7eBmzZpRs2ZNqwQTkYKxWCy8sfYX/hl3BgAXB3umPdaOwS3qGpxMROTe\ntK5ViXlPdOTRWdEkp2bSfdp6No19kEo3zxCLFIZVL4wTEWNYLBb+unwn/4qJB6CqhwtLngqldS1N\nTxKR0qFXQE3+3asVzy7dzpHkG/SNimb1011wcdQyj1I4hf58dN26dQwYMIDAwEBatWrFwIEDWbNm\njTWzichdemvtPv4VcwCARl4uxI4JUwEWkVJnbEd/xgf7ARCXeJFh8+Mwmy0Gp5KSqlAlePXq1URG\nRuLj48Nzzz1HZGQk3t7ejB8/nnXr1lk7o4j8gQ9jDzLxhz0A+FX24KPQWlTRHeBEpJR6r0dLegXk\nTb1csPsE/7dyl8GJpKQq1HSITz75hGeeeYbIyMj8bUOHDmXq1Kl89tlndO7c2WoBReTOZm49ynPL\ntgNQt6I73w3tyNUzicaGEhGxIXs7O+Y+HkTnT1ez7eQl/rnhF+p4uzO6XUOjo0kJU6gzwQkJCfTo\n0eO27d27d+fw4cP3HEpE/tzCPScYvXALANU8XVn9dDhVPXUGWERKPzcnB5Y+FUrdiu4AjF28jVUH\nzxicSkqaQpVgHx8fTpy4fcHqEydO4OGhdUhFbG3VwTMMmbcJs8WCt5szP44Op563/uyJSNnh6+HK\nihFheLk6kWu2MHBOLLvPXDY6lpQghSrB3bt3Z9KkScTExJCSkkJKSgoxMTG89tprPPzww9bOKCL/\nI/ZYEv1mxZCda8bTxZFVozrTuEoFo2OJiBQ5P9/yLB4WgpO9HSmZOfSYvp5TV1KNjiUlRKHmBI8Z\nM4bDhw8zevTo/PWCLRYLISEhPP/881YNKCL/tf3UJXrO2EBGTi6ujvYsHx5Gy5reRscSETFMcH1f\nZgxsz5B5mzh7PZ0eM9YTG9kNTxcno6NJMVeoEuzs7Mwnn3zCsWPHOHz4MBaLhUaNGlG/fn1r5xOR\nm345f5WHvljLjcxsHO3t+HZoCEH1fIyOJSJiuMEt6pJ4OYW/r9rNvnNX6T87lhUjwnC0150y5c7u\n6d3h5eVFYGAggYGBuLq6cvbsWc6ePWutbCJy07HkG3T7fC2X07KwM5mY90QQ3fyqGR1LRKTYmNA5\ngKfaNABg7eFz/GXRViwWrSEsd1aoM8F79+7l2Wef5dy5c7dst1gsmEwmDh48aJVwIgKnr6bS9fM1\nnLueDsD0x9rxaNPaBqcSESleTCYTn/R7gJNXU1l7+Bwztx2lnrc7E8LvNzqaFFOFKsGvvfYavr6+\nvPLKK3h6elo7k4jcdDElg26fryXxct6FHlN6tyaitaYdiYj8Hkd7OxZGBBM89Uf2nbvKq6t2U7ui\nO4Nb1DU6mhRDhSrBR44cYfHixTRo0MDaeUTkpqvpWTz0xTriL1wH4I2HAons6GdwKhGR4s3TxYnl\nw8No/+Eqzl5PZ/j8zVQv70an+r5GR5NiplBzgn19fcnIyLB2FhG5KTUzm57T17Pr5pqXL4Y0ZkLn\nAINTiYiUDDW9yrF8RBjuzg5k5Zp5NCqa+KRrRseSYqZQJfgvf/kLb731FsePH9ekcxEry8zJ5dFZ\nMcQlXgRgdLuGvNO9Rf5yhCIi8ucCq1dk/pBg7O1MXEnPovv09STdSDc6lhQjdz0dws/P75YfwhaL\n5Y43xtCFcSKFk5NrZvDcjaw5nHfR6aDmdZjat40KsIhIITzkX52pfdswZtFWjl9OoffMDawb0xU3\np0LNBpVS5q7fBZMnT9YPYhEbMpstjFjwE0v3nQKgR5MaRA3qgJ2d/tyJiBTWqHYNOX4phX9u+IVt\nJy8x5KtNLHgyGHs7rSFc1t11Ce7bt68tc4iUaRaLhfFLf2bO9gQAOt9XhflDgrXQu4iIFbz1cHMS\nr6SwYPcJlu47xUvLd/CvXq2NjiUGu+sSPHXq1Lvaz2Qy8cwzzxQ6kEhZ9PdVu/kk7hAAbWtXYvGw\nEFwc7Q1OJSJSOtjZmYga2IEzV9OIS7zIlNh46lZ0Z2xHf6OjiYHuugQvXrz4rvZTCRYpmH+u38/b\n6/YD0LSqFytGhOHu7GhwKhGR0sXF0Z7Fw0II+ugHjiTf4Lll26nl5U6vgJpGRxOD3HUJXr9+vS1z\niJRJn24+xITvdwHQsLInP4zujJebs8GpRERKp0ruLqwYGUaHD38gOTWTx+duZMNfutK6ViWjo4kB\n7nrC4dmzZ/OXQzt79uwf/iqorKwsXnnlFVq3bk3Hjh2Jioq6476HDx/m8ccfp1mzZvTs2ZOtW7fe\n8vyKFSvo0qULgYGBREZGcuXKlQLnESkKc3ckMHbxNgBqeZXjx9Hh+Hq4GpxKRKR0a1DJk6VPheLs\nYEd6di49Z2zg+KUbRscSA9x1Ce7cuTOXL+ct3B8WFkbnzp3zf/36+NffC+rdd9/lwIEDzJkzh4kT\nJzJ16lRWr159234pKSkMHz6cBg0a5JfdyMjI/Fx79+7l1VdfZezYsSxYsIBr164xYcKEAucRsbWl\n+07y1PzNWCzg6+HC6tHh1PIqZ3QsEZEyoV2dynw5OAiACykZ9JixgStpmQankqJ219MhZs+eTfny\n5QH48ssvb3v+0qVLeHl5YVfAJUfS09NZtGgRM2bMwM/PDz8/P0aMGMHcuXPp2rXrLfsuXboUNzc3\nXnvtNQDGjh1LbGws+/fvJzg4mHnz5vHQQw/Rs2dPAN577z1CQ0M5c+YM1atXL1AuEVtZe/gcg+Zs\nJNdsoYKrEz+MCue+yp5GxxIRKVP6NavNP7u34K8rdnIw6Rr9ZsWwclRnnB10UXJZcdeNtU2bNjg4\n5HVmPz8/li9fTvny5WnZsiWffvopzz//PBMnTqRq1aoFChAfH09ubi6BgYH521q2bMnevXtv23fb\ntm2EhYXdsm3hwoUEBwcDsHv3blq3/u+SJ1WqVKFq1ars2bOnQJlEbGXz8Qv0idpAVq6Zck4OrBwZ\nRtNqXkbHEhEpk54PacyY9g0BiD6WxMgFP+lOuGVIoRYhffvtt9myZQsODg6sWbOG7du3889//pN6\n9erxzjvvFOhYFy9epEKFCvkFG8Db25vMzMzb5vOeOnUKLy8v/vGPfxAUFMTAgQPZuXPnLcfy8fG5\n5WsqVarE+fPnC/EqRaxr95nLdJ++nrSsXJwd7Fg2PJQHalc2OpaISJllMpn4T+/WPOyf92nxvB3H\nmfSjTpyVFYW6b2BMTAwff/wx9evXZ9q0aXTo0IEePXrQqFEjBg8eXKBjpaen4+TkdMu2Xx9nZWXd\nsj01NZXp06fz5JNPMn36dFasWMHw4cP54Ycf8PX1JSMj43eP9dvj3E0msb5fx7Usju/hizfoNiOG\naxnZONiZ+PKxB3igmidpaWlW/T5leYyLisbYtjS+tqcxvt2MR1vx0MxUdp+9yptr9lHN3YkhLeoU\n6lgaX9uzWCxWuYtxoUpwWlpa/rSHuLg4Ro4cCYCLi0uBP0Zwdna+raT++tjV9dYr5e3t7fH39ycy\nMhLIm5YRFxfHsmXLGDVq1B2P5eLiUqBMiYmJBdpfCqasje+51CxGrkkkOS0HEzCpbTXqWq5x8OA1\nm33PsjbGRtAY25bG1/Y0xrea/EBlhv2YQlJaDmOX7sB87SJtqrgX+ngaX9v67UnPwihUCa5fvz7R\n0dFUrVqVixcv5s/JXbBgAffdd1+BjuXr68vVq1cxm835F9UlJyfj4uKCp+etFwtVrlyZevXq3bKt\nTp06nDt3DgAfHx+Sk5NveT45Ofm2KRJ/pk6dOrcVcLl36enpJCYmlqnxTbqRwaDp0VxIywHgPz2b\n81Tren/yVYVXFse4qGmMbUvja3sa4zv7rnptukyL5npmDq/EnWX1iBCaVClfoGNofG3vyJEjVjlO\noUrwuHHjGDt2LNnZ2XTv3p06derw9ttvM3/+fD777LMCHcvf3x8HBwd2795NixYtANi+fTsBAQG3\n7RsYGMjPP/98y7aEhIT81SACAwPZsWMHvXv3BuDcuXOcP3+eZs2aFSiTq6srbm5uBfoauXtlZXwv\np2XS+8s4jl1OBeC9Hi2J7NS4SL53WRljI2mMbUvja3sa49u1quvGoqEhPDxtHdczc+g/bzObxz1E\ntfIFHyeNr+1YYyoEFPLCuE6dOhETE8OSJUt4//33AejZsyfff/897dq1K9CxXFxc6NWrFxMnTmTf\nvn2sXbuWqKgoIiIigLwzuZmZeWv3DRw4kEOHDjF16lROnjzJlClTOH36ND169ABg0KBBLFu2jEWL\nFhEfH8/LL79MaGiolkeTIncjI5tHpq1j//mrALza5X6eDymaAiwiIoXXuWFVPu+f12VOXU2j54wN\npGRmG5xKbKFQJRjAy8sLPz+//MdNmjShRo0ahTrWhAkTCAgIICIigjfeeIPx48cTHh4OQFBQEKtW\nrQKgWrVqzJgxg/Xr19OjRw9iYmL44osv8qc7BAYG8vrrr/Pxxx8zePBgKlSowOTJkwv7EkUKJT07\nh94zN7Dt5CUAxnX0Y1K3gn0aISIixhnapj5/79IUgF1nLjNwzkZycs0GpxJrK9R0CGtzcXHh7bff\n5u23377tufj4+FseN2/enMWLF9/xWL17986fDiFS1LJychkwO5boY0kADG1dnw96trLaRzciIlI0\nJnZryvHLKczdkcCqg2cYt+RnPn60jf4+L0UKfSZYRG6VazYT8XUcKw+eAeDRprX4YkBb7Oz0F6aI\nSEljMpmYNqAtoQ18Afj8p8P8K/qAwanEmlSCRazAYrEwZtFWFuw+AUA3v2rMfTwI+wLeRlxERIoP\nJwd7FkZ0wt83b4WIv67YycI9JwxOJdain9Ai98hisfDS8h3M2HoUgI71fFgU0Qkn3X9eRKTE83Jz\nZsWIMHw98u45EPHVJjYfv2BwKrEGlWCRe/Tmmn38O+YgAK1qevPd8FDcnIrFdHsREbGCOhXd+W54\nGG5O9mTmmOk9M5ojF68bHUvukUqwyD2YEnsw/z7zjX3Ls3JkZzxd7v0uNiIiUry0qunNvMc7Ymcy\ncSktk+7T15OckmF0LLkHKsEihTRj6xGeX7YdgHre7vw4Ohzvcs4GpxIREVvpGVCT//RuBcDR5Bv0\niYomIzvX4FRSWCrBIoWwYHcioxduAaCapyurR4cX6o5CIiJSsjwT5Mezwf4AbE68SMTXcZjNFoNT\nSWGoBIsU0MqDZxgybxMWC1Qq58zqp7tQ19vD6FgiIlJE/tmjBb3vrwnAoj0neGXlLoMTSWGoBIsU\nQMyxJPrPiiHHbMHTxZFVozrnL50jIiJlg72dHXMGB/FArUoAvLfhFz7bfNjgVFJQKsEid+nnk8n0\nnLGejJxcXB3tWT48jBY1vI2OJSIiBnBzcmDpUyHUregOwNjF2/JvliQlg0qwyF3Yf+4KD09bR0pm\nDo72dnw7NISgej5GxxIREQP5eLjy/cgwvFydMFssDPwylj1nrxodS+6SSrDInziafJ1un6/jcloW\ndiYTXz3RkW5+1YyOJSIixUAjn/IsHhaCk70dqVk59JsbR1JqttGx5C6oBIv8gdNXU+n62VrO30gH\nYPpj7ejbtJbBqUREpDgJru/LjIHtATh/I4Nno09yLUNFuLhTCRa5gws30un2+VpOXEkF4MM+rYlo\nXd/gVCIiUhwNblGXNx8KBODYtUyGzN9Cdq7Z4FTyR1SCRX7H1fQsHvpiHfEX8m6L+eZDgTwT5Gdw\nKhERKc7+1jmAiJZ1ANhw7AKjF27BYtEawsWVSrDIb6RmZtNj+np2n70CwEuhTfhb5wCDU4mISHFn\nMpn4d4/mtK1aDoDZPx/jzTX7DE4ld6ISLPI/MnNy6Tsrhs2JFwEY3a4hbz/SHJPJZHAyEREpCRzt\n7Xg7qAb3V8lbQ37Sj3v4cvsxg1PJ71EJFrkpJ9fM4LkbWXv4HJA3v2tq3zYqwCIiUiDlHO1ZNKQD\nNcq7ATDym59Yf+Scwankt1SCRQCz2cLwb35i6b5TAPRsUoOZA9tjZ6cCLCIiBVfN05XlI8LwcHYk\nx2yh36wYfjmvNYSLE5VgKfMsFgvjlmxj7o4EADrfV4WvhwTjaK8/HiIiUnhNq3mxMCIYBzsT1zKy\n6T59PeeupxkdS27ST3kp815dtZtPb97zvV3tyiweFoKLo73BqUREpDTo0qgan/VvC8DJK6n0nLGB\nlEytIVwcqARLmfbuuv28s24/AM2qebF8RCjuzo4GpxIRkdJkWJsGvNrlfgB2nr7MwDkbydEawoZT\nCZYy69O4Q7yychcADSt78sOozni5ORucSkRESqNJ3ZrxRMt6AKw6eIZxS37WGsIGUwmWMmnO9gQi\nF28DoJZXOVaPDsfHw9XgVCIiUlqZTCamDWhLaANfAD7/6TDvbzhgcKqyTSVYypwl+04y/JvNAPh6\nuLB6dDg1vcoZnEpEREo7Jwd7Fg0NobFv3hrCf/t+J9/sSjQ2VBmmEixlyppDZxk8ZyO5Zgterk78\nODqc+yp7Gh1LRETKiAquTqwYEUaVm58+Dv06jk0JFwxOVTapBEuZEXf8An1nRZOVa8bd2YHvR4Zx\nf1Uvo2OJiEgZU7uiO98ND8XNyZ6sXDN9ojZw6MI1o2OVOSrBUibsOn2ZHtPXk5aVi7ODHcueCuWB\n2pWNjiUiImVUy5refD0kGDuTictpWXSfvp4LN9KNjlWmqARLqRefdI0Hv1jLtYxsHOxMLIjoREiD\nKkbHEhGRMq574xp82Lc1AAmXUug9M5q0rByDU5UdKsFSqiVeTqHr52tJTs3EZIIvBwfRvXENo2OJ\niIgAMKZ9I14MaQzA1pPJDPlqE7lmrSFcFFSCpdQ6dz2Nrp+t5cy1vFtUftqvLY81r2NsKBERkd94\n+5EW9G9WG4Cl+07x0vIdBicqG1SCpVS6lJpJt8/XcuzSDQDe79mSkW3vMziViIjI7ezsTMwa1IEO\ndfKuVZkSG89HGw8anKr0UwmWUud6RhaPTFvHL+fzrrT9e5emPNepscGpRERE7szF0Z4lT4VyXyUP\nAJ5btp2l+04anKp0UwmWUiU9O4deMzbw86lLAIwP9mNit6YGpxIREflz3uWc+X5kZyqVc8ZigSfm\nbWLbyWSjY5VaKsFSamTl5NJ/diyxNxcdH9amPh/0bIXJZDI4mYiIyN2pX8mDZcNDcXGwJz07l54z\n1pNwc2qfWJdKsJQKuWYzT34Vx6qDZwDo16w2n/dvqwIsIiIlTtvalZnzeBAmE1xMyaT7tPVcTss0\nOlapoxIsJZ7FYuHphVtZuOcEAA/6VWPO4A7Y2+ntLSIiJVPfprV4v0dLAA5dvE7fqGgysnMNTlW6\nqCVIiWaxWHjhu+3M3HYUgOB6PiyM6ISTg73ByURERO7N+GB/IoMaAbAx4QJPzd+M2WwxOFXpoRIs\nJdrrq/cyJTYegFY1vVk2PBQ3JweDU4mIiNw7k8nEv3q1omeTvJs8fbM7kVdX7TI4VemhEiwl1n9i\nDvD66r0ANKlSnpUjO+Pp4mRwKhEREeuxt7Nj3hMdaV3TG4B31//CFz8dNjhV6aASLCXS9C1HeOG7\nvDvq1PN258fR4XiXczY4lYiIiPW5OTmwbHgodSu6AxC5eFv+heBSeCrBUuJ8syuRpxdtAaB62KL6\nhQAAIABJREFUeTdWjw6nqqebwalERERsx9fDlRUjwvBydSLXbOGxL2PZdfqy0bFKNJVgKVG+P3Ca\nJ7/ahMUClco5s3p0OHW9PYyOJSIiYnN+vuVZPCwEJ3s7UrNy6DFjPSevpBodq8RSCZYSI/roeQbM\njiXHbKG8iyM/jArHz7e80bFERESKTHB9X2YObA/Auevp9Ji+nmvpWQanKplUgqVE2HYymV4zN5CR\nk4ubkz3LR4TRvEZFo2OJiIgUuUEt6vLWw4EA7D9/lf6zY8jK0RrCBaUSLMXevnNXePiLdaRk5uBk\nb8e3Q0PoUNfH6FgiIiKGeTksgBFtGwCw7sh5Ri/cgsWiNYQLQiVYirWjydfp9vlarqRnYW9n4qsh\nHenaqJrRsURERAxlMpn4uO8DdPPL+5n45fYE3ri5bKjcHZVgKbZOXUml62drSbqRAcD0x9rR5/5a\nBqcSEREpHhzs7fhmSDCB1bwAeG31Xmb/fMzgVCVHsSjBWVlZvPLKK7Ru3ZqOHTsSFRV1x33HjBmD\nn58f/v7++b/HxMTkP9+qVav85359Pj09vShehljRhRvpdP18LSduXvX6UZ82PNmqvsGpREREihcP\nF0eWjwijRvm8pUJHLfiJdYfPGZyqZCgW95d99913OXDgAHPmzOH06dO8/PLLVK9ena5du962b0JC\nAh988AFt27bN3+bp6QlAUlISqamprF27FhcXl/znXV1dbf8ixGqupGXy4BfrOHzxOgBvPRzIX27e\nO11ERERuVa28GytGhhE89UeuZ2TTb3YMGyO7EVDVy+hoxZrhZ4LT09NZtGgRr776Kn5+foSHhzNi\nxAjmzp17275ZWVmcPn2agIAAvL298385OjoCeQW5cuXKVK9e/ZbnpeRIycymx/QN7Dl7BYC/hjbh\nb53vNziViIhI8XZ/VS8WRnTCwc7E9Yxsuk9fz9lraUbHKtYML8Hx8fHk5uYSGBiYv61ly5bs3Xv7\n5O7jx49jMpmoWbPm7x7r6NGj1KlTx1ZRxcYysnPpGxXNTycuAvB0+4ZMfqS5walERERKhvCGVfm8\nfzsATl1No+eMDaRkZhucqvgyvARfvHiRChUq4ODw35kZ3t7eZGZmcuXKlVv2PXbsGO7u7rz00ksE\nBQXRv39/YmNjb3k+PT2dIUOGEBQUxKhRo0hMTCyqlyL3IDvXzKA5saw7ch6Ax1vW5aM+bTCZTAYn\nExERKTmGtqnP37s0BWDXmcsMnLORnFyzwamKJ8NLcHp6Ok5OTrds+/VxVtatd0BJSEggMzOTjh07\nMmPGDDp16sSYMWP45Zdf8p+/fv06zzzzDJ9++ikuLi4MHTqUtDR9HFCcmc0Whn+zme9+OQ1Ar4Ca\nzHysPXZ2KsAiIiIFNbFbU4a0qgfAqoNnGLtkm9YQ/h2GXxjn7Ox8W9n99fFvL2iLjIxk6NChuLu7\nA9CoUSP279/PN998w+uvv87MmTPJzc3F2dkZgPfff59OnTqxYcMGHnnkkbvOpNUkbOPXcf3f8bVY\nLDy/YjfzdhwHILS+DzMebUlWZga6CWTB/d4Yi3VpjG1L42t7GmPbKi7j+59HmnHy8g1iEi7yxU9H\nqO7uzPPBpeMic4vFYpVPig0vwb6+vly9ehWz2YydXd6J6eTkZFxcXPJXffhfvxbgX9WvX59jx/LW\nxHNwcLhlWoWTkxM1atQgKSmpQJk0hcK2/nd8p+5O4ssDlwC4v5Irk1pWJOHIYYOSlR56D9uexti2\nNL62pzG2reIwvv9oUZGRl66TcC2TiWv2Y596ha51yhsdyyp+O4ugMAwvwf7+/jg4OLB7925atGgB\nwPbt2wkICLht35dffhl7e3smT56cvy0+Pp5GjfL+ZRMeHk5kZCS9e/cGIC0tjRMnTlCvXr0CZapT\np46WVbOB9PR0EhMT88f3/Zj4/ALctEp5vn8qmAqu9/6mLst+O8ZifRpj29L42p7G2LaK2/gur12X\nsM+jSUrJ4PWt52jl34AOdSoZHeueHDlyxCrHMbwEu7i40KtXLyZOnMjkyZNJSkoiKiqKd955B8g7\nK+zh4YGzszNdunThueeeo02bNrRo0YLvvvuOnTt38uabbwIQGhrKhx9+SLVq1fDy8mLKlClUrVqV\nTp06FSiTq6srbm5uVn+tksfV1ZVZO0/x2tq8udyNKnvy49Nd8PEw/i+L0kLvYdvTGNuWxtf2NMa2\nVVzG188tbw3hkI9Xk5qVw6CvfiJu3IM08im5Z4StddG84RfGAUyYMIGAgAAiIiJ44403GD9+POHh\n4QAEBQWxatUqIO9M78SJE/n000/p0aMH0dHRTJ8+napVqwLw0ksv0a1bN1588UUGDBiAxWLhiy++\n0AoDxcxXu04wdsk2AGp7lePH0eEqwCIiIjbSooY3858Mxs5k4kp6Fo9MW8+FG5oTbrLocsF8+/bt\nIysrC39//2Lxr7fSJi0tjU9Wb2XCptOYLVDFw5WYyK40qHT73G8pnLS0NA4ePKj3sA1pjG1L42t7\nGmPbKs7j+9nmwzzz7VYA2tTyZt2Yrrg5GT4poMD27t2LyWTi/vvv7WZaxeJMsJQN648m8X9xeQXY\ny9WJH0Z3VgEWEREpIk+3b8hLoU0A2HbyEk/M20SuueyuIawSLEUiPukaQ+ZvIccM7k4OrBzVmft1\nT3MREZEiNfnh5gwIrA3Asv2nePG7HQYnMo5KsNjctfQs+kZFcz0zB3sTzH+8HW1qlewrU0VEREoi\nOzsTUQM7EFTXB4APN8bzYexBg1MZQyVYbMpstjDkq00cungdgOdbVqFTPR+DU4mIiJRdLo72LB4W\nQsPKeVMSn/9uO0v2nTQ4VdFTCRabem31Hr4/cAaAIS3q0O8+TYEQERExmnc5Z74fGUZld2csFnhi\n7ia2nLhodKwipRIsNrN470neXLMPgAdqVeJf3QO1XJ2IiEgxUc/bg2VPheLqaE9GTi69ZmzgWPIN\no2MVGZVgsYn9564w9Os4IG8ptIVDO+HiaG9wKhEREflfD9SuzNzHgzCZIDk1k0emreNSaqbRsYqE\nSrBY3eW0TPpERZOalYOjvR0LI4KpXr54rZUoIiIieXrfX4t/9WwFwJHkG/SZuYGM7FyDU9meSrBY\nVa7ZzOA5G0m4lALA1L5taF9XF8KJiIgUZ+OC/RnX0Q+AuMSLDP06DrO5dN9PTSVYrOr/Vu5mzeFz\nQN6i3CPa3mdwIhEREbkb7/dsSe/7awKwcM8JXlm5y+BEtqUSLFYzf9dx3tvwCwBBdX34d69WBicS\nERGRu2VvZ8ecwUE8cHMt//c2/MJnmw8bnMp2VILFKnafucyIb34CoHp5NxZEBOPkoAvhREREShI3\nJweWPhVCPW93AMYu3sb3B04bG8pGVILlniWnZNA3Kpr07FycHez4dmgnfD1cjY4lIiIiheDj4cqK\nEWFUdHPCbLEwaM5Gdpy6ZHQsq1MJlnuSk2tm4JxYTlxJBeCz/m1prVsii4iIlGiNfMqzeFgITvZ2\npGbl0HPGBk5cTjE6llWpBMs9+euKHWw4mgTAuI5+PNmqvsGJRERExBo61vNl1qAOAJy/kU736eu5\nmp5lcCrrUQmWQvty+zGmxMYDENrAl3/2aGlwIhEREbGmx5rX4e1HmgNwIOka/WZFk5VTOtYQVgmW\nQvn5ZDJPL9wCQG2vcnw9JBhHe72dRERESpuXQpswql3ekqcbjiYxcsEWLJaSv4awWosUWNKNdB6d\nFUNmjhlXR3u+HRpCZXcXo2OJiIiIDZhMJj7q04aH/KsDMHdHAq/9uNfgVPdOJVgKJCsnlwGzYzlz\nLQ2AaQPa0bxGRYNTiYiIiC052Nsxf0hHmlfP+5n/xpq9RG07anCqe6MSLAXy3LLtbDp+Acj7eGRQ\ni7oGJxIREZGi4O7syHfDQ6lZwQ2ApxduYe3Nu8SWRCrBctembTmSf+eYLg2r8tbDgQYnEhERkaJU\nrbwbK0aE4eniSI7ZQv/ZMew7d8XoWIWiEix3ZfPxC4xdvA2A+t4efDWkI/Z2evuIiIiUNQFVvVgU\n0QkHOxPXM7LpPm19/jTJkkQtRv7U2Wtp9J8dS3aumXJODiwe1omKbs5GxxIRERGDdG5YlS8GtAPg\n9LU0ekxfz42MbINTFYxKsPyhzJxc+s2K4fyNdABmDepAQFUvg1OJiIiI0SJa12di16YA7Dl7hcfm\n5J0wKylUguWOLBYLz3y7la0nkwH4v/D76du0lsGpREREpLj4e9emRLTOu1vsj/FniVy8tcSsIawS\nLHf02ebDRG07BsAjjaszqVszgxOJiIhIcWIymfis3wN0vq8KANO3HOXd9fsNTnV3VILld8UeS+LZ\npT8D0KiyJ3MGB2FnZzI4lYiIiBQ3Tg72LIzoRECVCgD838rdfLXzuMGp/pxKsNzm5JVUBnwZQ47Z\ngoezI4uHhVDe1cnoWCIiIlJMlXd1YsWIMKp6ugIwfP5mYo8lGZzqj6kEyy3Ss3N4dFY0F1MyAZjz\neAf8fMsbnEpERESKu5pe5Vg+PAx3Zweycs30jYomPuma0bHuSCVY8lksFkYv3MLO05cBeO3BZvRo\nUtPgVCIiIlJSNK9RkflDgrG3M3ElPYtHpq8j6eYKU8WNSrDkmxJ7kHk78ubw9Lm/Fq90vt/gRCIi\nIlLSPORfnal92wCQeDmVXjM2kJpZ/NYQVgkWANYePsdLy3cC0KRKeaIGtteFcCIiIlIoo9o15OWw\nJgD8fOoSj8/bRK65eK0hrBIsHL90g0FzYjFbLFRwdWLxsBA8XByNjiUiIiIl2JsPNeexwDoALP/l\nNM8v216s1hBWCS7jUjOz6RsVw+W0LOxMJr56oiMNKnkaHUtERERKODs7E1GD2tOxng8AUzcdYkrs\nQYNT/ZdKcBlmsVgY/s1P7D13BYDJDzenm181g1OJiIhIaeHsYM/iYSE0qpx3gu3F5Tv4du8Jg1Pl\nUQkuw97b8AsL9+S9EQcE1ubF0MYGJxIREZHSpqKbMytGhlHZ3RmLBZ6cF8dPiReNjqUSXFb9EH+G\nV1buAqBZNS+mD2iHyaQL4URERMT66nl78N3wMFwd7cnIyaX3zA0cTb5uaCaV4DLoyMXrDJ6zEYsF\nvN2cWTwshHLOuhBOREREbKdNrUrMe6IjJhMkp2bSfdp6klMyDMujElzG3MjIpk9UNNcysrG3M/H1\nkI7UqehudCwREREpA3oF1OTfvVoBcCT5Bn2josnIzjUki0pwGWI2W4j4Oo6DN29h+F6PlnRuWNXg\nVCIiIlKWjO3oz/hgPwDiEi8S8XUcZnPRL52mElyGvLV2H8v2nwLgiZb1GNfRz+BEIiIiUha916Ml\nve+vCcCiPSeY8P3OIs+gElxGfLf/FJN+3ANAq5refNb/AV0IJyIiIoawt7NjzuAgHqhVCYD3ow/w\n6eZDRZpBJbgMOJh0jSe/igPAx92FRRGdcHV0MDiViIiIlGVuTg4sfSqEet551yaNW/wzKw6cLrLv\nrxJcyl1Nz6LPzA3cyMzGwc7EgohganqVMzqWiIiICD4ernw/sjMV3ZwwWywMmhPLjlOXiuR7qwSX\nYrlmM0/M28SR5BsA/KdPazrW8zU4lYiIiMh/NazsyZJhoTg72JGWlUvPGRs4cTnF5t9XJbgUm/Tj\nHlYdPAPA8Aca8HS7hgYnEhEREbldUD0fogZ2AOD8jXS6T1/P1fQsm35PleBSatGeE0xeux+AtrUr\n8VHfNroQTkRERIqtx5rX4Z1HWgBwIOka/WZFk5VjuzWEVYJLoX3nrvDU/M0AVPV0ZWFEJ5wd7A1O\nJSIiIvLHXgxtzOibn1xvOJrEyAVbsFhss4awSnApczktk75R0aRm5eBkb8eioZ2oVt7N6FgiIiIi\nf8pkMvFhn9Y87F8dgLk7EvKXeLW2YlGCs7KyeOWVV2jdujUdO3YkKirqjvuOGTMGPz8//P3983+P\niYnJf37FihV06dKFwMBAIiMjuXLlSlG8hGIhJ9fMoDkbSbiUN5n8o75taFu7ssGpRERERO6eg70d\nXw/pSIsaFQF4c80+Zm49avXvUyxK8LvvvsuBAweYM2cOEydOZOrUqaxevfp3901ISOCDDz5g06ZN\nxMXFsWnTJtq3bw/A3r17efXVVxk7diwLFizg2rVrTJgwoShfiqFeWbmLtYfPATCmfUNGtL3P4EQi\nIiIiBefu7Mh3w0OpdXNZ1zGLtrDm0Fmrfg/DS3B6ejqLFi3i1Vdfxc/Pj/DwcEaMGMHcuXNv2zcr\nK4vTp08TEBCAt7d3/i9HR0cA5s2bx0MPPUTPnj1p2LAh7733HjExMZw5c6aoX1aR+2rncT6IPgBA\nx3o+/KtXK4MTiYiIiBReVU83VowIo7yLIzlmC/1nx7L3rPU+4Te8BMfHx5Obm0tgYGD+tpYtW7J3\n797b9j1+/Dgmk4maNWv+7rF2795N69at8x9XqVKFqlWrsmePbeaSFBc7T19i5Dc/AVCjvBsLngzG\nSRfCiYiISAnXpEoFFg3thKO9HTcys+k+fT05ZutcKGd4Cb548SIVKlTAweG/t/H19vYmMzPztvm8\nx44dw93dnZdeeomgoCD69+9PbGzsLcfy8fG55WsqVarE+fPnbfsiDHQxJYNHZ8WQkZOLi4M9i4eF\n4OPhanQsEREREasIu68q0wa0A+DMtTQupGRY5bgOf76LbaWnp+Pk5HTLtl8fZ2XdukhyQkICmZmZ\ndOzYkVGjRrFmzRrGjBnDggULaNKkCRkZGb97rN8e524ylQTZuWb6z97EySupAHzYqzn+3q6kpaUZ\nnOz3/TquJWV8SyKNse1pjG1L42t7GmPb0vjaxqONq3A0rDFvrj9AVq7ZKsc0vAQ7OzvfVlJ/fezq\neusZzcjISIYOHYq7uzsAjRo1Yv/+/XzzzTe8/vrrdzyWi4tLgTIlJiYW8FUY44Pt59l4/DIAgxpV\nJNA5jYMHDxqc6s+VlPEtyTTGtqcxti2Nr+1pjG1L42t9PXwt7KlXwWrHM7wE+/r6cvXqVcxmM3Z2\nebMzkpOTcXFxwdPT87b9fy3Av6pfvz7Hjh0DwMfHh+Tk5FueT05Ovm2KxJ+pU6fObQW8uJm7M5Fv\nDucV4E71KvPJoCAc7A2f3fKH0tPTSUxMLBHjW1JpjG1PY2xbGl/b0xjblsbXtr5s5Mf++ENWOZbh\nJdjf3x8HBwd2795NixZ5t8rbvn07AQEBt+378ssvY29vz+TJk/O3xcfH06hRIwACAwPZsWMHvXv3\nBuDcuXOcP3+eZs2aFSiTq6srbm7F9wYT204m8+zyXQDUqViOBREheLoX7Gy3kYr7+JYGGmPb0xjb\nlsbX9jTGtqXxtR13Z0erHMfwU4cuLi706tWLiRMnsm/fPtauXUtUVBQRERFA3pnczMxMALp06cLy\n5ctZunQpJ0+eZOrUqezcuZMnnngCgEGDBrFs2TIWLVpEfHw8L7/8MqGhoVSvXt2w12dt56+n029W\nDJk5Zlwd8y6Eq1SCCrCIiIhIcWD4mWCACRMm8NprrxEREYGHhwfjx48nPDwcgKCgIN555x169+5N\neHg4EydO5NNPP+X8+fPcd999TJ8+nWrVqgF5Z4Jff/11pkyZwrVr1wgKCuKNN94w8qVZVVZOLgNm\nx3DmWt6FbzMea0+zahUNTiUiIiJS8hSLEuzi4sLbb7/N22+/fdtz8fHxtzzu168f/fr1u+Oxevfu\nnT8dorQZv/Rn4hIvAvDX0CY81ryOsYFERERESijDp0PI3fn8p8N88dMRALr5VePNhwP/5CtERERE\n5E5UgkuAuOMXGL/kZwAaVPJg3uNB2Nvpf52IiIhIYalJFXNnrqXRf3YM2blm3J0dWDwsBC83Z6Nj\niYiIiJRoKsHFWEZ2Lv1mRZN0I+/2gLMGdaBJFestEi0iIiJSVqkEF1MWi4W/fLuVbScvAfD3Lk3p\nc38tg1OJiIiIlA4qwcXUJ3GHmP1z3p3wujeuwT+6NjU4kYiIiEjpoRJcDEUfPc9zy7YD4OfjyZzH\nO2BnZzI4lYiIiEjpoRJczJy8kspjX8aSa7bg6eLI4mEheLo4GR1LREREpFRRCS5G0rJy6BsVTXJq\nJiYTzH08iEY+5Y2OJSIiIlLqqAQXExaLhVELfmLXmcsAvP5gII80rmFwKhEREZHSSSW4mPh3zEG+\n3pUIQN+mtZjQOcDYQCIiIiKlmEpwMbDm0FleXrETgIAqFYga2B6TSRfCiYiIiNiKSrDBEi7dYNCc\njZgtFrxcnVg8LAR3Z0ejY4mIiIiUairBBkrJzKZvVDRX0rOwM5n4akhH6lfyMDqWiIiISKmnEmwQ\ni8XCU/M3s+/cVQDeeaQ5XRtVMziViIiISNmgEmyQd9fv59u9JwEY2LwOz4c0NjiRiIiISNmhEmyA\nlQfP8Oqq3QAEVvNi2oB2uhBOREREpAipBBexwxev88TcjVgsUKmcM4uHheDm5GB0LBEREZEyRSW4\nCF3PyKJvVDTXMrKxtzMx/8lgald0NzqWiIiISJmjElxEzGYLT34Vx8GkawB80LMloQ2qGJxKRERE\npGxSCS4ib6zZy/JfTgPwZKt6RAb5GZxIREREpOxSCS4CS/ed5PXVewFoXdObT/u11YVwIiIiIgZS\nCbaxA+evEvF1HAC+Hi4sGtoJF0d7g1OJiIiIlG0qwTZ0NT2LPlHRpGTm4Ghvx8KITtSoUM7oWCIi\nIiJlnkqwjeSazTw+dyNHk28AMKVPazrU9TE4lYiIiIiASrDN/OOHPfwQfxaAkW3vY3S7hgYnEhER\nEZFfqQTbwILdibyzbj8A7etUZkqf1gYnEhEREZH/pRJsZXvPXmH4N5sBqObpyoKIYJwddCGciIiI\nSHGiEmxFl1Iz6RsVTVpWLk72diwa2omqnm5GxxIRERGR31AJtpKcXDOD5sRy/HIKAJ/0e4AHalc2\nOJWIiIiI/B6VYCv52/c7WXfkPADPdGjEsDYNDE4kIiIiIneiEmwFc3ck8O+YgwAE1/Phg16tDE4k\nIiIiIn9EJfge7Th1idELtgBQs4Ib3zwZjKO9hlVERESkOFNbuwcXbqTz6KxoMnJycXGw59uhIfh4\nuBodS0RERET+hEpwIWXnmnnsy1hOXU0D4IsBbWlZ09vgVCIiIiJyN1SCC+mFZduJTbgAwPOdGvN4\ny3oGJxIRERGRu6USXAgztx7l47hDAHS+rwpvP9Lc4EQiIiIiUhAqwQW09cRFnvl2KwB1K7rz9ZBg\nHHQhnIiIiEiJovZWAOeup9FvVgxZuWbcnOxZPCwE73LORscSERERkQJSCb5LmTm59J8Vy9nr6QDM\nHNiBptW8DE4lIiIiIoWhEnyXxi/5mZ9OXATgb50D6N+stsGJRERERKSwVILvwmebDzNtyxEAHvSr\nxusPNjM4kYiIiIjcC5XgP7ExIYnxS7YBcF8lD+Y90RF7Ow2biIiISEmmNvcHTl9NZcDsWHLMFtyd\nHVg8LIQKrk5GxxIRERGRe6QSfAcZ2bk8OiuGCykZAHw5OIjGVSoYnEpERERErEEl+HdYLBbGLNrC\n9lOXAJjYtSm9AmoanEpERERErEUl+Hd8tuUYX25PAKBnkxq82qWpwYlERERExJpUgn8jM8fMhB/2\nAuDvW57ZgztgZ2cyOJWIiIiIWJNK8G9czswh12yhvIsji4eF4OmiC+FERERESptiUYKzsrJ45ZVX\naN26NR07diQqKupPv+b06dM0b96cn3/++ZbtrVq1wt/fHz8/P/z8/PD39yc9Pf2us5gtYDLB3Cc6\n0rCyZ4Ffi4iIiIgUfw5GBwB49913OXDgAHPmzOH06dO8/PLLVK9ena5du97xayZNmkRGRsYt25KS\nkkhNTWXt2rW4uLjkb3d1dS1Qnn90bsLD/tUL9iJEREREpMQwvASnp6ezaNEiZsyYkX/2dsSIEcyd\nO/eOJfi7774jLS3ttu0JCQlUrlyZ6tULX2DdHe14IbhRob9eRERERIo/w6dDxMfHk5ubS2BgYP62\nli1bsnfv3t/d/8qVK3zwwQe88cYbWCyWW547evQoderUuac85Z0dMJl0IZyIiIhIaWZ4Cb548SIV\nKlTAweG/J6W9vb3JzMzkypUrt+3/zjvv0KdPH+rXr3/bc8eOHSM9PZ0hQ4YQFBTEqFGjSExMtGV8\nERERESmBisV0CCenW1dg+PVxVlbWLds3b97Mrl27eOONN373WAkJCVy/fp0XXniBcuXKMW3aNIYO\nHcrKlStxc3P70yzZ2dkAHDlyRGeDbeDXM/caX9vRGNuexti2NL62pzG2LY2v7WVnZ1tlbA0vwc7O\nzreV3V8f/+8FbZmZmUycOJFJkybdVpp/NXPmTHJzc3F2dgbg/fffp1OnTmzYsIFHHnnkT7P8OqB2\ndoafIC+VTCbTHf/fiXVojG1PY2xbGl/b0xjblsbX9kwmU+kowb6+vly9ehWz2ZxfPpOTk3FxccHT\n879LlO3du5fTp08zduzYW+YCjxw5kt69ezNp0iQcHBxumVbh5OREjRo1SEpKuqsszZs3t9KrEhER\nEZHizPAS7O/vj4ODA7t376ZFixYAbN++nYCAgFv2a9asGatXr75lW5cuXXjrrbdo164dAOHh4URG\nRtK7d28A0tLSOHHiBPXq1SuCVyIiIiIiJYXhJdjFxYVevXoxceJEJk+eTFJSElFRUbzzzjtA3llh\nDw8PnJ2dqVmz5m1f7+PjQ8WKFQEIDQ3lww8/pFq1anh5eTFlyhSqVq1Kp06divQ1iYiIiEjxViwm\nv06YMIGAgAAiIiJ44403GD9+POHh4QAEBQWxatWq3/26384Heemll+jWrRsvvvgiAwYMwGKx8MUX\nX2hiuoiIiIjcwmT57WK7IiIiIiKlXLE4EywiIiIiUpRUgkVERESkzFEJFhEREZEyRyVYRERERMoc\nlWARERERKXNUgn9j1KhRTJgwwegYpc7atWvx8/PD398///fx48cbHatUycrK4vXXX+ci0FKJAAAJ\nH0lEQVSBBx6gQ4cO/Otf/zI6UqmxZMmS296/fn5+NG7c2OhopUpiYiJPPfUULVq0oHPnzsyePdvo\nSKXK5cuXGTduHK1bt6Zbt24sWbLE6Ej/3969x+T8/nEcf8nPXaR1uFUSkWayokKOcwrTcprERkK1\nbMkc0iqLMaeVnHJqkdkwJtlSDmvMpKRpNaU1o7vDKrK4c6eou4PfH9+ve/Llt/3I96rr83psNl1/\n3Hvus3v27vK5Ph9p6PV6LF68GPn5+Ya1mpoaBAYGwsPDA4sWLcLjx48FFvZ+P7rGX5WXl//SW3+F\nvyyjJ7l9+zYePXqEZcuWiU6RTllZGby8vLB//37Da6+NjY0FV8klNjYWubm5OH/+PJqamrBt2zYM\nHToUK1euFJ3W6y1cuBAzZ840/NzW1oZ169bBy8tLYJV8wsLC4OzsjLS0NLx69QoRERGwt7c3PDee\nfs/GjRsBAJcvX8abN28QFRUFMzMzXt/fpNfrER4ejrKysi7rX7/PN27cwP3797Fp0ybcvXsXgwcP\nFlTae/3sGgNAbW0tQkND0dbW9n9/LneC/6bT6RAfH49x48aJTpGSRqPBqFGjYGVlBbVaDbVajYED\nB4rOkkZjYyNSUlKwf/9+uLq6YsqUKQgKCkJRUZHoNCmoVCrD91atVuPmzZsAgPDwcMFl8tBqtdBo\nNAgNDYWDgwPmzp2LGTNmIC8vT3SaFEpKSlBUVIQjR45g9OjRmD17NoKDg5GcnCw6rVfTaDRYuXIl\nampquqw/efIE1dXV2Lt3L0aOHIkNGzbA3d0dqampgkp7r59dYwDIzMyEn58f+vfv/0ufzSH4b3Fx\ncVi6dCmcnJxEp0hJo9HA0dFRdIa0CgoKYGZmhokTJxrWQkJCcODAAYFVctLpdEhOTkZERAT69esn\nOkcaFhYWcHBwwI0bN9De3o7y8nIUFhbylpNuUl1dDSsrK9jb2xvWRo0ahZKSEnR0dAgs692ePn2K\nqVOn4tq1a/j23WPFxcVwcXHp8j+eEyZMwLNnz0Rk9mo/u8YAkJWVhe3btyMqKuqXPpu3Q+Cv39gK\nCgqQkZGB3bt3i86RUkVFBbKzs5GYmIjOzk54e3tj8+bNHCK6SXV1Nezt7ZGWloakpCS0tbXB19cX\noaGhfG14N7ty5QpsbW0xf/580SlSMTIywqlTpxAQEICLFy+io6MDvr6+8PX1FZ0mhUGDBqGxsRGt\nra2GwezNmzfo6OjAx48fYWFhIbiwd1q1atUP1+vr62FjY9NlTa1W4+3bt/9GllR+do0B4ODBgwD+\nmuN+heJ3gvV6Pfbs2YPdu3dDpVKJzpHS69ev0dLSAhMTEyQkJCAqKgoZGRmIj48XnSaN5uZmVFZW\nIiUlBbGxsYiOjsalS5d4sOgPSE1NRUBAgOgM6bS0tGDLli2YOnWq4XucmZmJW7duiU6TgpubG6yt\nrbFv3z60tLSgsrISFy9eBIBfupeS/rfPnz//Y6ZQqVTQ6/WCiuhHFL8TfPLkSbi6umLatGmiU6Q1\nZMgQ5OfnG+4BdnZ2RmdnJyIjI7Fjxw7uVHaDvn37orm5GUePHjUcuqitrcXVq1exfv16sXESKS4u\nxtu3b+Hj4yM6RTr37t1DQ0MDDh06BJVKBRcXF9TV1SExMRGLFi0SndfrqVQqnDhxAlu3bsX48eOh\nVqsRHByMuLg4mJqais6TjrGxMXQ6XZc1vV4PExMTQUX0I4ofgu/cuYP3798bHq3x9TfizMxMFBYW\nikyTyveH4JycnNDa2ooPHz7A0tJSUJU8bGxsYGxs3OXUsaOjI+rq6gRWyScnJweenp4wMzMTnSKd\nuro6jBgxosvu2ZgxY5CUlCSwSi6urq64f/8+3r9/D0tLSzx8+BDm5uYYMGCA6DTp2Nra/uNJBu/e\nvYO1tbWgIvoRxd8OcfnyZWRkZCA9PR3p6enw8vKCl5eX4fQ3/b6cnBxMnjwZra2thrXS0lJYWFhw\nAO4mbm5uaG1tRVVVlWFNo9F0OQRDv6+4uBjjx48XnSElBwcHVFVVob293bBWXl6OoUOHCqySh06n\nw5o1a6DT6aBWq2FkZISsrCx4enqKTpOSm5sbSktLu9z+UFBQAHd3d4FV9D3FD8F2dnYYNmyY4Y+p\nqSlMTU0xbNgw0WnS8PDwwIABAxATE4OKigpkZWUhPj4eISEhotOk4ejoiFmzZiE6OhovXrxAdnY2\nzp07h9WrV4tOk8rLly/5BJk/ZM6cORg4cCB27tyJyspKPHjwAElJSVi7dq3oNCmYm5ujqakJ8fHx\nqK6uRkpKCtLS0vjv8B8yadIk2NnZITo6GmVlZTh79iyeP38OPz8/0Wn0DcUPwfTnmZqaIjk5GVqt\nFn5+fti1axdWrVqFoKAg0WlSOXz4MIYPHw5/f3/s2LEDAQEB8Pf3F50lFa1WC3Nzc9EZUlKpVLhw\n4QK0Wi1WrFiBuLg4hIWFYcWKFaLTpHH8+HFUVVVhyZIluHTpEhISEvhs/G707fkWIyMjnDlzBvX1\n9Vi+fDkyMjJw+vRpvijjN3X3GaI+X75/6BoRERERkeS4E0xEREREisMhmIiIiIgUh0MwERERESkO\nh2AiIiIiUhwOwURERESkOByCiYiIiEhxOAQTERERkeJwCCYiIiIixeEQTERERESKwyGYiEhCzs7O\nSEtLE51BRNRjcQgmIiIiIsXhEExEREREisMhmIhIcvX19fD29kZwcDD0er3oHCKiHoFDMBGRxLRa\nLQIDA+Hg4IDExESoVCrRSUREPQKHYCIiSTU0NCAwMBD29vY4ffo0B2Aiom/8R3QAERH9GceOHUN7\nezvGjh2Lfv36ic4hIupRuBNMRCSp6dOn48SJE0hNTUVubq7oHCKiHoVDMBGRpBYsWIB58+bBx8cH\nO3fuxKdPn0QnERH1GByCiYgkFxMTg+bmZsTGxopOISLqMTgEExFJqE+fPoa/q9VqREZG4vr168jL\nyxNYRUTUc/T58uXLF9ERRERERET/Ju4EExEREZHicAgmIiIiIsXhEExEREREisMhmIiIiIgUh0Mw\nERERESkOh2AiIiIiUhwOwURERESkOByCiYiIiEhxOAQTERERkeJwCCYiIiIixeEQTERERESK818F\ne0mn7oNgsgAAAABJRU5ErkJggg==\n",
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VQQqQKjJ9YEuWHz7HnA1HGNwsQGZuFEIIwGazcvDcBjLzLtLAowkRAT3k/8db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\nIEIIIYTocP8f8Rz9BPF1KWcAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1112975c0>"
+       "<matplotlib.figure.Figure at 0x1136fc198>"
       ]
      },
      "metadata": {},
@@ -116,16 +110,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BTByXIiJTVUtqTxp0NBh5GGdM1XOIiIiIpAeF4EzG2sqKnM725HS2T9XjjEYT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NLHs6iuhmvpaOZFb/1605L0W14ExmAcO/20mFXsxBcj8oLMtGlmXs1c735APY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jPym4ULFzJ+/HjatGljdhSf2r9/P1VVVUyaNImJEyeSnZ1tdqRGt2XLFpKSkpg6dSpPP/00Dzzw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\n1OTh4TNgxgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111601a20>"
+       "<matplotlib.figure.Figure at 0x115df3be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualizer = KElbowVisualizer(KMeans(), k=(4,12), metric=\"silhouette\")\n",
+    "visualizer.fit(X)\n",
+    "visualizer.poof()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tMBj1HDi/Dhd7T0Jb3ivXsA5JAiKEsLhNsclYqVUMDmxu6VDqhfKEIyUlxfQ3\ngEat5oHQVozt6MOu82ks2RnN9rOp7DqfTpcW7rw6oAPjQluhUcvN7Zo6l36UwtIcmjq3kuSjjkkC\nIoSwqMt5RRxJzKSfnxcudjaWDqfemDekExER+uvuU6lUDAhozoCA5kQkZrJsVzQ/RiXwyFe/4+/h\nxP/dG8wT3fzRWsv04TeSXZDOxStR2Ns4E+DV3dLh3HEkARFCWNTmMykoijS/3KowHw++e/wezmXk\n8u7u03x55ALP/3iI+VsimX5PEM/eHSiJ3XUYjHpOJe0GIKSRjnpRFCMHLqwnuyAVtUpD74BxFVb0\nPZt2mDNph1Cp1HTyGYCPexA6QykHz68lryQbo9HAXf6j8HTyqZX45D6dEMKiwk83rtVvLSXA05n/\nPNiTuNfH8sq9HSgsNTB743HavPkzczYeI+06E5zdyXKLMinWF9DaIwR3h8bZ9Hcp8zQGo47hnZ4n\nrM1QjsRvNO0rLM3jdMo+hoX+g8Ednibi4uZrSdlvuDo0Y1joc/QKeIDcooxai08SECGExegNRrae\nSaG1mwNBMqzULJo727N4RFcu/vMBFg3rgtZKw5Kd0fi99TP/+PEg56/kWjrEesHNwYveAeMJaNZ4\nm17Scy/Swq0dAE2dW5GZn2zadyUvkabObdCorbCx0uKs9SC7IJWUq+dQqzRsPfUZkZd24u0aWGvx\nSQIihLCY/RczyCnWMSyohXQANDNXOxtmDgwh7vWx/GvcXbRwsefjA+cIWryBR77aw/GkLEuHaBEG\no560nDgURcHexhkrtbWlQ6o1OkMxNhqt6bFKpcKoGK7tK6mwz1pjS6mhmGJdAaX6IgaHPI2Pe3uO\n/umuiblJAiKEsJhN12Y/lf4ftcfO2ornegUSM3M03zzWh9Dmrqw5kUC39zZy/0fb2XU+DUVRLB1m\nnblw+RgnLm0nMSvG0qHUOmuN1jTHCZTN9qpWaa7ts62wrywhscPW2h4fjyAAfDyCuPKnuybmVqsJ\nSGZmJv369ePChQskJCTwyCOP8OijjzJv3jyMRiMAq1atYvz48UyYMIGoqLJx71WVFUI0LuExyWit\nNNzbtpmlQ2n0rDRqJnTx5ejLwwn/+0D6+3ux7Wwqg/69jV4fbGLtyUsYjY07EblaeJm4jBPY2Tjh\n7Rpg6XBqXVPn1iRlxwJwOfcSbg5/vM+aOPmQnhuP3qijVF/M1aIMXB288HJuQ3LWGQDSc+JrdVbY\nWktAdDrXF8ARAAAgAElEQVQdc+fORastu8Xz9ttvM336dFavXo2iKOzYsYPo6GgOHz7MDz/8wPLl\ny5k/f36VZYUQjcul7AJOpV2lf1sv7G0a3wiE+kqlUjGkvTc7nh/Mvmn3MzrEh8OXMhn/xW+ELN3A\n54fPU6o3WDpMszMaDZxK+g2AkBb3YKVpvE0v5Vp7dECjtmZj5Iccif+V7r4jiE7+nUuZp7G3cSLY\nuzeboj5iy8lP6Np6MFZqa0Jb3ktmQQobIz8kOvl3uvkOrbX4au1dv2TJEiZMmMDHH38MQHR0ND16\n9ADgnnvuYd++ffj6+tKnTx9UKhXe3t4YDAaysrKuW/a+++6rrVCFEBZQvvjc8CBZfM5Serb25Oe/\n9ScmPYd3dkXzdUQcz3x/gHmbI3mpXxDP3BWAk7ZxfFGfv3yM/JJsfNyD8XC8M5r8VCo1vdqOrbDN\n1b6p6e/AZj0IbNajwn5ba3sGBE2qk/hqJQH5+eefcXd3p2/fvqYERFEUUyczBwcH8vLyyM/Px9XV\n1XRc+fbrla2Js2fPmrkmEBERYfZz1kdSz8alIdTz2wOXAGhpyLrleBtCPW9XXdXx+QBbxnm3ZXVs\nJusuZDNjQwQLNh/nwUB3Hgp0x01bu3eparue+YYMSgwKhSXWRKRZ7nVzJ7xma6pWXlE//fQTKpWK\nAwcOEBMTw8yZM8nK+qPHdUFBAc7Ozjg6OlJQUFBhu5OTE+o/TSNcXrYmAgMDcXJyMls9IiIiCAsL\nM9v56iupZ+PSEOpZrDMQ8cMZgrxcGNXv7ls6R0Oo5+2yRB2H3QNZhSV8uO8MH+yJ5bNTV1h9Jpun\nerTl5f7BtHF3NPtz1lU9//zj1hLMXc+8vLxa+eFdV2qlD8g333zD119/zVdffUVQUBBLlizhnnvu\n4dChQwDs2bOHbt260bVrV/bu3YvRaCQlJQWj0Yi7uzvBwcGVygohGo/dF9Io0hlk8rF6yt3eltfv\nCyX+9bG8P6Y7no5a/rXvDIFvr+Px1Xs5mZpt6RBrLO7yCRIyoy2efIjK6mwY7syZM1m5ciUPP/ww\nOp2OIUOGEBISQrdu3Xj44YeZOnUqc+fOrbKsEKLxkOG3DYODrTVT+rbn7OwxfPFIb9o3deabiHg6\nv/Mroz7byd64y5YO8YZyi65wLv0o8RmRGIw6S4cj/qLWu55/9dVXpr+//vrrSvunTp3K1KlTK2zz\n9fW9blkhRMOnKArhMck42VrTx7dp9QcIi7PWqJnUzY+JXX0Jj01myY5TbDydzMbTyfRu48mrA0MY\n1r4FanX9ucNgVAycTNqNgvHaqBdZD6e+kbFvQog6dTYjl7jMfB4IbYW1RuZCbEjUahUjglsyIrgl\ne+Mus2TnKcJjkhn92S5CmrnyyoAOPNy5Tb34/xp3+QR5xVm0dGtPEycZaVUfWf5VIoS4o5QPv5X+\nHw1bH7+m/PLMAE7MGMHEMF9iLufwxOp9BL69jlW/x1JYqrdYbEWleVzIOI7W2oF2zXtaLA5xY5KA\nCCHqlKn/R3tJQBqDjs3d+N+jfTg7ewwv9G7H5bxiXlx3BN83f+bNbVFkFZZUfxIzs7NxorPPQEJa\n9sNaml7qLUlAhBB1Jq9Yx564y4S1dKeZs52lwxFm1MbdkQ8e6EH862N5bVBH9EaFeZsjabPwZ2Zs\nOErS1YLrHjd/SyQfR5mvM2v5+iZeLr40cZSml/pMEhAhRJ3ZdjYVncEoo18asaZOdiwY2pmLrz/A\nO6PCcNFa895vMbRdtI6nv9tPbHqOqez8LZEs2BrFp6euMH9L5G0/d15xJr/FruZS5unbPpeofdIJ\nVQhRZzZJ/487hpPWmpf6BfN873Z8ExHPO7ui+eLIBb48eoHRIT6429nw38MXTOUXbC1bjHTekE63\n9HxGxcjJpN/QG3Vorc0/WZowP0lAhBB1QlEUNsUm08TBlm4+HpYOR9QRWysNT93Vlie7+7M+OpEl\nO06x7mTidcveThISnxFJbtEVvF0Daerc6rZiFnVDmmCEEHXiRHI2qblF3N++BRq1fPTcadRqFWM7\ntuL+9t43LLdga9RNN8fkFWdx/nIEtlb2BDW/tan9Rd2TTwEhRJ0Ij0kCpPnlTleT6dDPXM5BZzDW\n+JxX8pJQFCMdWvTF2sr2dsITdUiaYIQQdWJTTAoatYrB7ZpbOhRhQeXNK+XNLdfz/YkEdpxL45Gu\nvkwK86NrS/cbJi6+nqE0cWqJk9bd7PGK2iN3QIQQte5KfjEHL2XQq40nbvbyC/VON29IJ+YODq20\n/Z/3deTIS8OY1rc9KhWs/D2WHivC6fTOLyzbGU1yTmGF8gUlV8kpzACQ5KMBkjsgQohat+VMCooi\nk4+JP/z1TsjcwaGmbV1berB0ZBibY5P539E4fo1OYtbGY8wJP87AgGZM6ubHmJCWRCXuJrcog94B\nD+KodbVYXcStkQRECFHrTNOvB0sCIv5QnnCkpKRUGvlirVEzsoMPIzv4kFVYwpoTCXx19ALbzqay\n7WwqfdvkMSaogDZN2mFv42KJ8MVtkgRECFGrDEYjW2JTaOliT0gz+ZUqKpo3pBMRETdeN8bd3pbn\negXyXK9Azmbk8m1EFPrSPcRlKby2LQuvX9fyWJgfk7r5EeDpXEeRi9slCYgQolYdTLhCdlEpD3Zu\nXaMREELcSEATR4YEZJJd0ASVVRj5xhJ+ikrgre0neWv7Se5u7cmk7n481Km19Deq5yQBEULUqnBZ\nfE6YkYKCq70XdtaOdGoVxv3BsHJsd9aeSuSro3HsOJfKgYQMpq89wsgOLXm8uz9D2nljrZExF/WN\nJCBCiFq1KSYZG42agQHNLB2KaATUKg3tm/dEURTTNgdbax4L8+OxMD+Srhaw+lg8/zsax09Rl/gp\n6hJNHbU80rUNk8L86dzCTe7E1ROSEgohak3S1QIiU7Lp5++Fg621pcMRDZiiKEQl7iIzPwWoekKz\nlq4OvDoghJOvjOTQ9GFM6dMOg1Hh/T2xdHtvI53f+ZV3d0WTmlt43eNF3ZE7IEKIWrMptuzLYriM\nfhG3KSHzFClXz2FUDHg43ng6dyhLULr5eNDNx4NlI8PYFJvCV0fj+PV0Eq/+eoxZG49zX7vmTArz\nY0xHH+ys5euwrskVF0LUmvDTZdOvD5Xp18VtKCjJ4WzaEaw1WoK8e9/08TZWGkaH+DA6xIfMghLW\nnLjI/45eYEtsCltiU3DWWjM+tDWTuvnRx7cparU00dQFSUCEELWiRG9gx7k0Aj2dadtEhkaKW6Mo\nCqeSfsOo6Ono3Q9bK7vbOp+Hgy3/6N2Of/RuR2x6Dl9HxPF1RBz/PXye/x4+j6+7Y1l/km6+8rqt\nZdIHRAhRK/ZcSKegVC+Lz4nbknL1HNmFaXg5t6GZi59Zz93ey4U3h3Uh7rUH2PbcICZ18+NyfjEL\nt0XR7u313LNyMx8fOMvVolKzPq8oI3dAhBC1YlPsteG3koCI29Dc1Z8SXSEt3ANrbfSKWq1iQEBz\nBgQ0Z9UDPVh7MpGvjl5g5/k09l3MYPq6I4zq4MPj3f0ZHNgcKxnSaxaSgAghakX46WQcba3o69fU\n0qGIBkhRFPRGHdYaG/yadq6z53W0tWZSt7JZVROzC/jmWBz/OxLHD5EJ/BCZgJeTlke6+PJ4dz86\necsCeLdD0jghhNmdy8jl3JU8BgY0x9ZKY+lwRAOUmHWavWfXcLUw3WIx+Lg5MGtgR6JnjuLAi0N5\nvnc7dAYjK/bE0PXdjXR551fe++00ablFFouxIZM7IEIIs9tUvvicNL+IW1BYmsuZtEOoVRq01o6W\nDgeVSkWPVk3o0aoJ744KY2NMMl8djWPj6SRmbIjg1V+OMbhdcx7v5s+okJbXHdI7f0skKSmX+SjM\nAhWopyQBEUKY3cYY6f8hbk3ZqJc9GIx6OrTsi9bawdIhVWBjpWFsx1aM7diKK/nFfH/iIv87Gsfm\n2BQ2XxvS+2Cn1jzezZ/evp6oVCrmb4lkwdYoALy3RFZa+fdOJQmIEMKs8kt07LmQTmdvN1q42Fs6\nHNHAJGXFkFWQgqdTK5q7trV0ODfUxFHLC33a80Kf9pxOu3ptSG88nx06z2eHzuPn4YiPiz2/xV02\nHVOeiEgSIgmIEMLMdpxLo9RglLsf4qYpikJ6XgJWahs6tOjboNZsCW7myqLhXVk4tDO7zqfz1dE4\nvjseT1xmfqWykoSUqVEn1IiICL799ltKS0s5cuRIbcckhGjApP+HuFUqlYqw1kPo6T+63jW91JRG\nrWZQYHP8PBzRG5Uqyy3YGsX8LZF1GFn9U20C8uWXX7JixQq++OILCgoKmDt3Lp999lldxCaEaGAU\nRWFTTDLu9jbc1bqJpcMRDciV/CRK9IWoVGoctW6WDkfUgWoTkLVr1/LZZ59hZ2eHm5sbP/74Iz/9\n9FNdxCaEaGBOpl4lKaeQIe280ahllL+4sdSr59l37kculOxi5+mv2B3zDUbFaOmwzGLekE7MHRxa\n5f65g0OlCabaAmo1NjY2pse2trZoNDKuXwhRWXhM2eJzw4JbWjgSUd+lXj1PZOJO8oqy0CkF6A0l\nFOsKSM+Js3RoZlNVEiLJR5lqO6H26NGDJUuWUFRUxPbt2/n+++/p2bNnXcQmhGhgNsWkoFapGNKu\n+uXSReNXqi+mWJdPib6QEl0RJfpCDEYdgc16EJdxgvySq5TqizAoeuw09tha2ROXcaLej365GeWJ\nRnnHU0k+/lBtAvLqq6+yZs0a2rVrx7p16+jXrx8TJkyoi9iEEA1IVmEJ+y9m0LN1EzwcbC0djqgl\nRsWIWlV28/xq4WVyi65cSzAKKdEXojeUcpf/KABiUvaRmnOhwvEq1AR4dSe/OBu1So1GbYWiUuNg\n6woqyC++Wud1qm3lCUdKSookH39SbQLyzDPP8N///leSDiHEDW09k4JRUWT4bQNlMOpNiYSrvRcq\nlYq0nDgy8i6ZkosSXSGlhmIGhzyNWqUhOfssiVmnK5xHrdJgMOrRqK1o4uSDjZUWG6uyuxu21mX/\nBXDUuqFQNkokT5+HWq25tt21biteR+YN6UREhN7SYdQr1SYgxcXFpKam0rx587qIRwjRQIXL8Nt6\nSW8opURfRImugGJ9IaX6Qlq5d0Ct1nApM5qEzGhKdIXojX8sOT8w6AmsrWzJLbpCcvZZAKzU1thY\n2eOgdcNg0KO20tDCLQA3B6+y5OJagmGltjHN39HCLZAWboHXjcvPszORiTuvu13cGapNQLKzsxkw\nYAAeHh7Y2tqiKAoqlYodO3bURXxCiAbAYDSyOSYFb2c7OnnLEMrblXr1PHEZJ0gpuUTxuXj8PDtf\nt19Esa6A/JLsa3coypKMEn0hwd59sLHScj49gvOXIyod19S5DfY2zuiNekr1RWitHbC19jQlEuVa\neXSghVu7a4mFdaXzuNp74WrvdUt1LK9PXMYJ8vLycdK6V1lP0ThVm4B8+umndRGHEKIBO5KYSWZh\nCU/f1bZBzV5ZH5WPDgEwKnou514iLScez2vNGcW6Qrr7DsfB1oW0nAvEph6sdA4/z87YWGlx1LrR\nxMmnwh0KrZU9NhotAL5NQvHzrLpPQm1PBtbctS3NXdsSkRtBWICs0nanqTYB8fb25ttvv+XgwYPo\n9Xp69uzJY489VhexCSEaiPDTsvicucRlnDD9rWCgqLQQgNSrF3C198LW2g6DUQeAm31z2jYNM/Wt\n+KOfhR0AzVz8aObiV+VzSbIoLKnaBGTp0qUkJCQwbtw4FEXh559/JikpiTlz5tRFfEKIBmBTbDLW\nGjWDAqSv2O3KL842/a1WWWGvdTSNFhkc8nSFpMHF3hMXe09LhCnEbas2Adm3bx/r1q1DfW1Ww/79\n+zNy5MhaD0wI0TCk5hZyLCmLgQHNcNJW7icgbo6j1o2cogxK9UUA2FiVDWl20rrLHQvRqFQ7E6rB\nYECv11d4LDOhCiHKbYpJAWT0i7n4eXamVF9MYUkuBkVXYbsQ9U12QRoJV06RkBlNdkHaTR1b7R2Q\nkSNH8vjjjzN8+HAANm7cyIgRI24tUiFEo2MafivTr5tFc9e2uNh5UqIrRKXYyOgQUe8oisKZtEOc\nTtmLtcYWB1tX1CoN+cVZlBpKCPbuTbtmPVCpbnyPo9oE5LnnniMoKIiDBw+iKAr/+Mc/6Nevn9kq\nIoRouEr1BrafTcXfw4mAJk6WDqdRKNUXU6ovopVHMDY5rWR0iKh3dsd+TXPXAIZ3er7CsG0oe/2e\nvxzBzpivGBj8xA3PU20Ckp6ezuHDh5k5cyaJiYmsXLmSDh060KSJLLUtxJ1ub/xl8kp0PNnDX/on\nmMnl3AQUFLxc/MjOkZkzRf3TJ/BhrDU2191nY6Ul2Ls3AV7dqz1PtX1AZsyYgY+PDwBeXl5069aN\nV1999SbDFUI0RuXNL0PbS/8Pc8krvgJAM2dfC0cixPWVJx8lukJSrp4DICpxF7tivuFqYXqFMjdS\nbQKSk5NjWgfGxsaGhx56iOzs7GqOEkLcCTbFJGNvo6Gf/63NhikqC/LuTb92j2Jv62zpUIS4od/O\nfEtOYQYpV89x8cpJWnkEceD82hofX20CotVq+e2330yPDxw4gJ2d3a1FK4RoNOIy84i9nMuAts3R\nWsvIOHNQlLLF2exsHC0ciRDVK9UXEeTdi0uZp2nrFYZ/067ojbrqD7ym2j4g8+fP55VXXjE1uzRv\n3pylS5feesRCiEZhk2n0izS/mEtU4k6MioGOPvded+0VIeoTBYUr+UlcyjzN0NDJZOanYFSMNT6+\n2gQkKCiIX3/9lezsbKyty94Qjo6SnQtxp9tYnoBI/w+z0Bt0pOdexM7GSZIP0SCEtRnK0fhwOrTo\ni5PWg18j/0UP3+E1Pr7aBGTXrl0cPXqU559/nvHjx5OVlcW0adOYOHHibQUuhGi4Ckv17D6fRsfm\nrvi41e6CZXeKjLxLGBUDXtL5VJiJohg5cGE92QWpqFUaegeMw9nujxGsZ9MOcybtECqVmk4+A/Bx\nD6JEV8jPEe+YVjlu7dGB4BZ9rnt+b9e2eP9pfpoRnV64qfiqTUBWrVrF0qVLCQ8PJzQ0lLlz5zJp\n0iRJQIS4g+08n0aJ3iizn5pRem48AM1cJAER5nEp8zQGo47hnZ7ncu4ljsRvNM3NUViax+mUfYzs\nPBWDUU941L/xdg0gsyAZX89O9PQfXeV5v9g7mz8PulepNKhUKoxGPdYaWx69+40axVdtAgLg7+/P\n8uXLGTVqFA4ODuh0Ne9kIoRofMr7f8jqt+ZhMOrJyEvEzsYJJ62HpcMRjUR67kVauLUDoKlzKzLz\nk037ruQl0tS5DRq1FRq1Fc5aD7ILUsnMTyYzP5lNUR+htXbkLv+R2NtUHJH1ZJ+3AThwfi1Nndvg\n59kZlUrFxSsnSc4+W+P4qh0F06RJExYuXMipU6fo27cvixcvxtvbu8ZPIIRoXBRFITwmGVc7G+5u\nLSuxmoPBqKelWzt83INlQjdhNjpDMTYaremxSqXCqBiu7SupsM9aY0upoRgXO0+6tL6PoaHP0soj\nmEMXNlR5/oy8RPybdjG9Zts06ciV/KQax1ftHZB3332X7du38/jjj2Nvb4+Pjw9Tpkyp9sQGg4HX\nX3+d+Ph4VCoV8+fPx9bWllmzZqFSqQgICGDevHmo1WpWrVrF7t27sbKyYs6cOYSGhpKQkHDdskII\nyzqdnsOl7AIe6twaK428J83BxkpLkHcvS4chGhlrjRadocT0WFEU1CrNtX22FfaVJSR2eDq2QqMp\n6wTd2qMDJy5tq/L8VhobzqUfpU2TUFAULmQcQ/uXqdlvpNpPD0dHR8aMGUPr1q0BmDhxYo1Gweza\ntQuA7777junTp/Pee+/x9ttvM336dFavXo2iKOzYsYPo6GgOHz7MDz/8wPLly5k/fz7AdcsKISwv\n/PS10S9BsvicORgVA1kFqSg3MXxRiJpo6tyapOxYAC7nXsLNoZlpXxMnH9Jz49EbdZTqi7lalIGr\ngxf7zv9EwpVTAKRePY+HY9XNrPcEPkzClVN8f/hN1hxZROrV8/QNfLjG8dWoD8itGDRoEP379wcg\nJSUFZ2dn9u/fT48ePQC455572LdvH76+vvTp0weVSoW3tzcGg4GsrCyio6Mrlb3vvvtqK1whRA2F\nxyShUsH97aUp1hyy8lM4enETvp6daNfsLkuHIxqR1h4dSLl6no2RHwLQO2A80cm/46T1oJVHMMHe\nvdkU9REoCl1bD8ZKbU1Ym/vZd+5HYlMPYK2xoVfAuCrP76h1Y1CHJ285vlpLQACsrKyYOXMm27Zt\n44MPPmDfvn2mtiIHBwfy8vLIz8/H1dXVdEz5dkVRKpWtztmzNe/8UlMRERFmP2d9JPVsXGqrnnml\nBvbFX6aDux2XzkRzqVaepeYaw//PDN0Z8ox5ZBTnk59cuT6NoY41IfU0P5VKTa+2Yytsc7Vvavo7\nsFkPApv1qLDfSevO/R0n1+j8ydlnOZawlVJ9Idcm8QVgfPearRdXbQLy3HPP8dprr5kWpAN44okn\n+PLLL2v0BEuWLGHGjBk89NBDlJT80d5UUFCAs7Mzjo6OFBQUVNju5ORUob9HednqBAYG4uRkviXB\nIyIiCAtr/EthSz0bl9qs55oTFzEoZxjfLZCwsNBaeY6aagz/PxXFyK6YaDzwpHfQvahUFVvFG0Md\na0LqeWvy8vJq5Yd3TR26sIHufsNxtfdCxc13nq62D0hkZCRPP/00v//+u2lbTk5OtSdet24dH330\nEQB2dnaoVCpCQkI4dOgQAHv27KFbt2507dqVvXv3YjQaSUlJwWg04u7uTnBwcKWyQgjLMk2/LsNv\nzSKrII1SQzFeLr6Vkg8h6jtba3t83INw0rrjqHUz/aupau+AeHl5sXLlSl544QViYmKYPHlyjYaJ\nDR48mNmzZzNx4kT0ej1z5szB39+ff/7znyxfvhw/Pz+GDBmCRqOhW7duPPzwwxiNRubOnQvAzJkz\nK5UVQliO0aiwOTaFZk52dGnhbulwGoXL1yYfk9lPRUPk5ezL4bhfaeEWiEb9RzrRzMWvRsdXm4Co\nVCp8fHxYvXo1r7zyCi+++KJpxcYbsbe35/3336+0/euvv660berUqUydOrXCNl9f3+uWFUJYRkRS\nJpfzi3myuz9qtcxVYQ4BzXrg5tAcd8fmlg5FiJt2JT8RgKyClArba9qHpNoEpLyDqKOjI//+979Z\nvnw5W7Zsudk4hRANXLisfmt2VmrrGv9aFKK+KU80dPoSjBixtbK7qeOrTUA+//zzCo9ffvllWQdG\niDvQpphkrNQqBgXIr3VzSLhyCr1RR2uPDlhpbCwdjhA3La84k99ivyWvOAsFBUdbV/q3n1hhwbsb\nqTYB2bJlC6tWraKoqAhFUTAajRQXF3PgwIHbDl4I0TCk5xVxJDGTe9t64WInX5a3S1EULl45ic5Q\njG8Ty44mEuJW7T+/lpCW/WjTpCMA8RlR7Dv3E0NDn63R8dV2u162bBmvvfYa/v7+vPPOOzzwwAMM\nHTr09qIWQjQom2PL2niHtpfmF3PIK86kSJeHp1Mr1GqNpcMR4paU6ApMyQeAr2copfqiGh9fbQLi\n7OxMz5496dSpE3l5eUydOpUTJ07cWrRCiAbpj/4fMv26OaTlxAHg5SKjX0TDpVZbVVxhNz/JtI5M\nTVTbBKPVaomPj8ff35/Dhw/Ts2fPGs1KKoRoHHQGI1vPpNDG3YH2TaufEFDcmKIopOfEo1ZZ4enU\nytLhCHHLeviOZFfM19ha2aOgUKIvpH/7R2t8fLUJyPTp01mxYgXLli3j448/5vvvv2f8+PG3FbQQ\nouHYfzGD3GIdj4X5yVLxZqA36rCxssPJzqPC3AlCNDRNnVvxQNgMcoquAAqOtm5YW9nW+PhqX/09\nevQwLQr3008/kZOTg4uLyy0HLIRoWMJPJwEwVGY/NQtrjQ13+Y+S1W9FgxefEUVk4g7GdH2J3KJM\n1h5bTk//UbTy6FCj46vtA5KWlsaUKVPo0aMHvXv3ZuHChWRlZd124EKIhmFTbDJ21hrubetl6VAa\nBb2hFECmXhcNXlTiToaEPAOAs50HIztP5fil7TU+vtp3wJw5c+jVqxc7d+5ky5YthISEMHv27FuP\nWAjRYCRk5ROdlsO9bZthZy3NBberoCSHHTH/41z6UUuHIsRtMygG7Gz+WADWzsYRajBTerlqP1Gy\nsrJ49NE/OpU8+eSTrF279ibDFEI0ROGxsvicOaXnxqMoxgof2kI0VF7Orfkt9lv8mnYG4GJGFJ7O\nrWt8fLV3QEJDQ9m4caPp8a5duwgJCbmFUIUQDU346bIERPp/mEd6Tjwq1Hg5tbF0KELctp7+Y/Bw\nbMGZ1EOcSz+Ku2ML7vIbWePjq7wD0r59e1QqFYqisGbNGl5//XVUKhWFhYW4uLjw1ltvmaUCQoj6\nqUinZ9f5NIK9XGjj7mjpcBq8otI8cooy8HBseVMjBYSorzRqK1o3CcHFvikt3AIoKMm5qZFdVZaM\njY01S4BCiIZp9/l0inQGaX4xk/TceACaObexbCBCmEl8RiSRiTsxGPUMC/0HGyM/pLvvcPybdqnR\n8dWmKpmZmfzyyy8UFBSY1oJJSkpi6dKltx28EKL+2hQjzS/m1MTRBz/PYpq6tLF0KEKYxcmk3xge\n+jybTv4HOxtHRnWZxtZTn9Y4Aam2D8iUKVOIiYlhw4YNFBUVsXPnTtRqGT4mRGOmKArhMck4a63p\n7dvU0uE0Co5aNwKb9cDWyt7SoQhhFiqVukJzor2NM1DzyQqrzSSys7NZsmQJAwYMYPDgwXz11Vec\nO3fuloIVQjQMsZdzic/K577A5lhr5AfH7crMTyEjLxGjYrB0KEKYjat9U2JS9mNUjGTmp7D/3M+4\nO3jX+PhqP1nKZz319fUlNjYWJycn9Hr9rUcshKj3yptfhgXJ4nPmcOHyMSIubrqplUKFqO96+o+h\nsJyGKiUAACAASURBVDQXjdqafed+xNpKy93+Y2p8fLV9QHr27Mm0adOYOXMmTz31FNHR0djaSg9u\nIRqz8Jiy6dfvb1/zXzPi+kr1xWQXpOJi1xSttYwmEo2HtcaGzq0GEdbmfnKLrpBTdAUrc66G+8QT\nT5Cfn0+LFi1Yvnw5R44c4YUXXritoIUQ9VducSm/x12mm48HzZztLB1Og3c59yIKCl4uvpYORQiz\nOnFpO3lFmXRpPYRNJz/C1d6LS5nR9A4YV6Pjq22CmThxIq1alS0Z3aFDB5588km8vGRNCCEaq21n\nU9EbFRl+ayZp5cNvJQERjUxiVgy9AsYRl3ECf88uDAl5hqyClBofX+0dkPbt27Nu3TpCQ0PRarWm\n7d7ecmtWiMZIht+aj1ExkFuYgZPW49oIASEaD0UxolFbkZQdQ5dWg1EUo2mxxZqoNgGJjIwkMjKy\nwjaVSsWOHTtuPlohRL1mNCpsiknB09GWbi09LB1Og6dWaegfNJFiXYGlQxHC7Jq7BrDu2HtYqa1p\n5uLLppMf4+MeXOPjq01Adu7ceVsBCiEajhMpWaTlFTGpmx9qdc3H84uqqVUaufshGqXuvsMIat4L\ne1tnVCo1d/mNwsOx5q0j1SYgcXFxrF69msLCwgozoX7zzTe3FbgQov4Jj5HVb81Fb9BxKG4DLd3b\n09qjg6XDEcJs9p79gY4+/XGx88RR62raXp58ZBekE528hz6BD97wPNUmIC+99BIDBw4kIiKCsWPH\nsmfPHgICAm4z/P9v787Dq6rvfY+/156TPWSe5wTCpICCDApUnBCpFocqUtFWe0/tsXjttdW2R+FY\ne6rWe70dPN56PJ0Eq3VqtUVFRS0iGhBlCmEMhJB53nsn2eNa94+QSJgSM+yV7Hxfz8OjWdP+LkKy\nP/u3foMQYiR6s6wKo0HhignSx2uwGjxH8fiaCAQ79C5FiCF1Xt4VbCn/B51BN6mufOyWOBTFQLu/\nlZq2Q9gtcVxQ8NU+r9NnAFFVlbvvvptQKMTkyZNZtmwZy5YtG5KbEEKMHA1eHyVHG5lfkEp8jEXv\ncka97sXn0uIKda5EiKFlt8axcNI3cHc2cay5jLbOBhQUnLZEFhQvwxXTv/5jfQaQmJgYAoEA+fn5\nlJaWMnPmTPx+/6BvQAgxsry1txpNk9EvQyGshmjwVBJrceG0JepdjhDDwhWTxOSseQM+v895QK65\n5hruvPNOLr74YtauXcu3v/1tmQdEiCj0pvT/GDKN3mOE1SBprgIURTrzCnE6fbaA3HLLLSxduhSH\nw8GaNWvYtWsX8+YNPPEIIUaeUFhl/b5qcuJjmZIe3/cJ4qxMBjNJjmyZ/VSIs+gzgLS1tbFu3Tpa\nWlrQNA2Affv28b3vfW/YixNCRMYnFY20dga4aXq+fGIfAkmOLJIc0pIkol8wHMDjayIhNp2QGsRs\n7H//sT4fwdx111188sknqKo6qCKFECNX9+JzV02WN83B6gx48cvIFzEGVLce5PXPf8V7e56lM+jh\n5a2PUdWyv9/n96sFZO3atYMqUggxsr1ZVo3VZGBhkfTvGqxD9Z9xrGUvF467DldMst7lCDFsPjuy\nnsVT7+Td0t8Ta3GxeOq/8M+9z5OVUNyv8/tsASkuLmb37t2DLlQIMTJVtrSzs6aFi8elY7f2fylt\ncSpVU6lzH8FiipHRLyLqaWjEWpw9X8fHfrkPMGdsAbnkkktQFAWfz8cbb7xBWloaRqMRTdNkLRgh\nosibe4+Pfpkoj18Gq6W9hmDYR07iJBSlz893QoxqdouLyuYyQMEf6mRvzcfYrf3vxH7GALJmzZqh\nqE8IMcL1TL8u/T8Gra6te/IxGf0iot/ccdexpfzvtPvbeOXTX5ARN44Lx1/X7/PPGECysuSXkRDR\nzhcMs+FADRNSXBQmOfs+QZyRpmnUuY9gNlpJtGfoXY4Qwy7G4uArE28e8Pl9dkIVQkSvjeV1dATC\n0voxBBRF4YKCJXQE3BgUo97lCDHsjjTuYlflB/hDnb2233DBff06XwKIEGNY9+OXxdL/Y0g4bAk4\nbAl6lyFERGw9vI75xTfisA7s3/wZA8j777/PwoUL+dvf/nba/UuXLh3QCwohRo43y6pwWE3ML0zV\nu5RRTdM0ymo2k+zIIdWVq3c5QkSEy5ZEmit/wB2uzxhAdu3axcKFCykpKTntfgkgQoxu+xvcHGz0\nsPTcHCwmeWQwGG5fI0ebSgmGfBJAxJgxJWs+b+16hvS4gl4hZHruZf06/4wB5O677wbgvvvuIyGh\nd/PK+++/P5BahRAjiCw+N3S6R7+kxxXqXIkQkfNZxXoS7ZlD3wLS7Vvf+ha///3vSUxMpKGhgYcf\nfpiDBw+ycOHCAb2gEGJkWLena/p16f8xOJqmUdt2GINiItmZo3c5QkSMqqnMK/76gM/vM7Z897vf\n5fbbb+ePf/wj1157LRMmTOC1114b8AsKIfTn9QfZWF7PeVmJZMbF6l3OqOb1t9ARaCPFmYPRIP36\nxdiRnTiRsurNuDsb8fpae/70V58/LYsWLcLhcLBy5Uqeeuop5syZM6iChRD6e3d/DcGwKo9fhkBn\nwIPFaJPJx8SYc6RhJwClVR+esFUZ/DDc7qnYoauJUdM0vve97xEXFwcgU7ELMYp1T7++WALIoKW6\n8lg46RY0NL1LESKibrjg/kGdL1OxCzHGaJrGm2XVJMVamZWbpHc5o5qqhlEUQ9cfvYsRIkI+r3iH\n8/IuZ9P+l067v7/9Qvqcir21tZU9e/Zw4YUX8vTTT1NaWtozQkYIMfrsqG6hqq2D5ecXYDTIgmmD\ncaRxF0eb9zA99zLiY2UuFTE2JDu68sFgR331+dvn3nvvpby8nM2bN/PWW29xySWXsHr16kG9qBBC\nPzL8dujUug/jD3Zgt8TpXYoQEZOTNBmAjoCbcWkzev1p62zo93X67ITa1tbGLbfcwsMPP8y1117L\n0qVLefbZZwdeuRBCV2+UVWFQFBZNzNS7lFGtI+DB3dlAkiMbs8mqdzlCRMynR97EF/BS2VyGu7Ox\nZ7umqTR4KpmRf2W/rtNnAFFVld27d/Puu++ydu1aysrKCIfDA69cCKGbpnY/n1Q0MjcvmcRYedMc\njHr38cnHXPn6FiJEhOUnnUNrRz01bYd6PYZRFAPTci/t93X6DCA//OEP+cUvfsG3vvUtcnJyuPHG\nG/nRj340sKqFELpav68aVdNk9dshUHt89tPUuHx9CxEiwpKdOSQ7c8hNmoLFZBvwdfoMIHPnzmXu\n3Lk9X7/44osDfjEhhL66+3/I8NvBy02aQqI9E6tJJnITI5OmqXx86DVa2mswKEYuGn89rpjknv37\na7ewr7akq+Ui5xJyEif17KttK2fjvr9w46wfn/H6gwkf0I8A8te//pVHH30Ut9vda3tZWdmgXlgI\nEVlhVWX93mqy4mKZmiFLxg9WZvw4vUsQ4qyONu0hrAZZMu1fqXcfZevhdVw6+Tagqw/TnuqPuHr6\nSsJqiDd2/j8y48djNJho97dSWvUhqja83S36HAXz5JNPsmbNGsrKynr9EUKMLluONtHU4WfxpMye\nSQbFwNS0HqIz4NG7DCHOqs59hKyECQCkunJp8lb17Gv0VJLqysdoMGEx2XDZkmhpryGkBvn44F+Z\nU9T3ivcH67adsq2s+uN+19dnC0haWhrFxcX9viBAMBjkJz/5CVVVVQQCAb773e8ybtw4fvSjH6Eo\nCuPHj2f16tUYDAaefPJJPvjgA0wmEz/5yU+YOnUqFRUVpz1WCDFwb5R1LT531aRsnSsZ3fyhTnZU\nbiA+NrVfv6SF0Esw7MNi/OIxiaIoqFoYg2IkGPb32mc2WgmEfZQceo0pWQuwW888tLy0ahPBsI99\ntSV4/S0921VN5XDDdiZlzj3juSfqM4BMmTKFu+++m4suugir9Yte80uXnvkH7/XXXyc+Pp7HH3+c\n1tZWli5dysSJE7nnnnuYPXs2q1atYsOGDWRmZrJlyxZeeuklampqWLlyJa+88gqPPPLIKcdefvnl\n/bohIcTpvVlWjcVo4NLx6XqXMqrVuysASHPJ2i9iZDMbbQTD/p6vNU3DoBiP77P22hcM+zEoRurc\nR3B3NrH96LsEQp18sPfPXDxxea/rumKSulpTTlp9wGgwMW98/1fH7TOAeL1e7HY727dv77X9bAHk\nyiuvZNGiRUDXDRuNRkpLS5k1axYACxYs4KOPPqKgoIB58+ahKAqZmZmEw2Gam5tPe6wEECEGrrqt\ng8+rmrmsOAOH1ax3OaNa3fHRL7L4nBjpUl15VDaXUZAylXr3URLsX3z4SHbm8FnFekJqEFUN09rZ\nQLIzh+tm/KDnmBdKfnZK+ADISZxETuIk8pOnDmoG4D4DyCOPPPKlL2q324Gu8HL33Xdzzz338Nhj\nj/U8d7bb7Xg8HrxeL/Hx8b3O83g8aJp2yrH9sX///i9da1+2bTv1GVc0kvuMLiff598OdjWTnutQ\no+rvINL3EtZCHA2UYVbslO06EJHXjKbv19nIfQ69vKQpVLceZN2OpwC4aPwNlFZ9iNOWRG7SZCZn\nXsSbO58GTeP8vCswGfr34eTd0j9y2ZRv8m7pH+A0qyANejXc73znOzz99NO9VsU9UV+r4dbU1HDX\nXXexfPlyrr76ah5//PGefe3t7bhcLhwOB+3t7b22O53OXv09uo/tj+LiYpxOZ7+O7Y9t27YxY8aM\nIbveSCX3GV1Od58/3/UBAN+5Yg7jU/r38zTS6fH9rHdX0FzhYHzaBRSlnjfsrzeW/81Go6G+T4/H\nc9YP3opi4MJx1/badmKLRXH6LIrTZ53x/GWzHzjt9sLU6QBcPHE5NrPjy5TcyxkDyMMPPwwMbFXc\nxsZGbr/9dlatWtUzh8jkyZMpKSlh9uzZbNy4kTlz5pCbm8vjjz/OHXfcQW1tLaqqkpiYeNpjhRAD\n4w+FeXd/DeOSnVETPvSS6srjKxNuxmDos/FYiKi1/egG8pPPZfPBv3LNeQNfnPaMP0WbN28+64nd\nq+Wezm9/+1vcbjdPPfUUTz3V1fTzb//2b/zsZz/jiSeeoLCwkEWLFmE0Gpk5cyY33XQTqqqyatUq\nAO6//34efPDBXscKIQZmU3k9Xn+Iq2bJ5GNDIcYydK2sQoxGqc5c1nz0ABrwp01fTFSm0fVA5rZ5\n/eu6ccYAUlJSctYTz9YJ9YEHHuCBB05tulm7du0p21auXMnKlSt7bSsoKDjtsUKIL+8NWf12SNS1\nHaGqdT/jUs/vNZukEGPNvOKvM6/462zY86eeic0G4owB5GydT30+34BfUAgRWW+WVWG3mFhQlKZ3\nKaNaTdtB6t1HGJca/X0VhOiPwYQP6McomPXr1/Of//mfdHR0oGkaqqri8/n4+OP+z3YmhNDHoUYP\n+xrcXDMlG6vJqHc5o1ZYDdHgOUqsxYXTlqh3OUJEhT4DyOOPP87PfvYz/vCHP3DnnXeyadMmWlpa\n+jpNCDECyOJzQ6PRe4ywGiLNVSDT2AsxRPqc39zlcjFnzhymTZuGx+Nh5cqVp0xKJoQYmdZJ/48h\nIZOPCTH0+gwgNpuNw4cPU1RUxJYtWwgEAv2eGEwIoZ92f5B/HqplakYC2fF2vcsZ1WIsThJi04mL\nSdG7FCGiRp8B5J577uGXv/wlCxcu5OOPP+aiiy7isssui0RtQohBeO9gLf6QylWTpfVjsManzWR2\n0TXy+EWIIdRnH5BZs2ZRVFSExWJh7dq1HDhwgKlTp0aiNiHEIHQPv108UQLIYLT724i1OFEUWZFb\niKHU50/Us88+y7e//W0Ampubuf/++/nLX/4y7IUJIQZO0zTeLKsiIcbCnDyZs2KgVE3lk0Ovsfng\nq3qXIkTU6TOAvPjiizz33HNA1+ynr776qkwSJsQIt7u2lcrWDq6YkInJKJ/cB6qlvYZg2Ed8rMyh\nIsRQ6/M3UzAYxGKx9HxtNstS3kKMdN3Db6X/x+DI6Bchhk+ffUAuu+wybrvtNhYvXgzA22+/zaWX\nXjrshQkhBu6NsioUBa6ckKl3KaOWpmnUuY9gNlpJtGfoXY4QUafPAPLDH/6Qt956i61bt2Iymbj1\n1ltlFIwQI5g7EGbzkQZm5yaT7LDpXc6o1dpRjz/UQVbCBAyKzCIrxFDr15rSV155JVdeeeVw1yKE\nGAIlNV7CqiaTjw2SKyaJ83Ivl9VvhRgm/QogQojR46NqLyDTrw+W0WCSvh9CDCPpHi9EFPn3t7bz\n3lE3Ga4YzsuSRdMGyuNr5lD9Z3QGZNZnIYaLBBAhosRD63fw8Du78IU10hw2mbVzEGpaD3Kg7lPc\nnY16lyJE1JIAIkQUeGj9Dn769s6er7dXt/DQ+h06VjR6aZpGbdthDIqJZGeO3uUIEbUkgAgxyp0c\nPrr99O2dEkIGwOtvpiPQRoozB6NBuskJMVwkgAgxip0pfHSTEPLl1R6ffCxdOqAKMawkgAghxAn8\nwQ4MiokUZ67epQgR1SSACDGKrV40jevOPfMb5aorprJ60bQIVjT6nZO9gEsm3YLJaOn7YCHEgEkA\nEWKU6gyG+O7Ln/DqrqOYDaeOeJHw8eVpmgog4UOICJAAIsQotK++jQt/9Rb/9fEBpmYksP0HV7Pq\niqk9+yV8DExJ+d/ZduQtNE3TuxQhop508RZilFm7rZx/fbmE9kCIf5k7nie+NpMYs6kncFRXV0v4\nGICOgIfWjjqSHNkyh4oQESABRIhRoiMQ4u6/buEPWw7htJp57pZ5LDuv90iN1YumsW1bSKcKR7d6\nt4x+ESKSJIAIMQrsqW1l2ZqNlNa2cV5WIi/cOp9xyS69y4oq3cNvU135+hYixBghAUSIEe6PWw7x\nvVdL6AyGueuiCfzi6hnYzLI8/FDyBdtp7agjwZ6B1RSjdzlCjAkSQIQYobz+IN97dQtrPi0nzmbm\nT8sv4vqpeXqXFZUURaEw5TyctgS9SxFizJAAIsQItKumhWXPbmRvvZsLcpL484r5FCY59S4rallN\nsRSnX6B3GUKMKRJAhBhBNE3jv0sOcs9ft+ILhfmfCyby6JLzsZjkkctwCYb8tHbWkeTIwqDI37MQ\nkSIBRIgRwuMLcufLn/DC50dIiLHw/Ir5XHOOrMY63Grdhymt2sjEjDnkJ0/t+wQhxJCQACLECPD5\nsWaWrdnIwUYPc/KS+fMt88lLdOhd1phQJ6NfhNCFBBAhdKRpGr/dvJ97X/8Uf0jlhwun8PDi6ZiN\nMklxJATDfpraq3DZkom1yLBmISJJAogQOmnrDPA/XvyYV3YeJSnWysvfvIirJmXpXdaYUu+uQNNU\n0mTyMSEiTgKIEDr4tLKJm9dspLzJy7yCVJ67ZR7Z8Xa9yxpzGj2VAKS5JIAIEWkSQISIIE3T+M2H\ne7nvH58RUlV+fOk5/PuiaZjkkYsuzs25mOzESThs8XqXIsSYIwFEiAhp6fBzx18+5rXdlaQ4rDy7\nfB5XTMjUu6wxzaAYSXLI90AIPUgAESICSioauHnNh1S0tHNxURprb5lHhitW77LGtL01n2BQDIxL\nnYHBIPN/CBFpEkCEGEaapvF//1nGj9d9RljTWHXFVB64/FyMBnnkoqewGqKyeQ9WUyzj02QGVCH0\nIAFEiGHS1O7nWy98xLo9VaQ7Y1jzjYu4ZHyG3mUJoNFzjLAaIi2uAEVR9C5HiDFJAogQw+Cjw/Us\nX/Mhx9o6uHR8Omu+MY80p6yyOlLUubsmH5PRL0LoRwKIEENIVTUef7+UB9/ajqbBw4unc/8lU+SR\nywiiamHq3RXYzA7iYlL0LkeIMUsCiBBDpMHr47bnP2L93moyXTE8d8t8FhSl6V2WOEkg5CM+Ng2n\nLVEevwihIwkgQgyBjYfq+MbaD6l2d7JoYiZ/uvkiUhw2vcsSp2Ez25lZsBhN0/QuRYgxTQKIEIMQ\nVlUe3bCbf1+/E0WBR5acxw8unoLBIJ+sRyJVUwmEOrGZ7dL6IYTOJIAIMUB1nk5WPLeJDQdqyYmP\n5c+3zOfCglS9yxJn0dJew9bD65iQPpuClGl6lyPEmCYBRIgB2LC/hhV/3kSdx8dXJ2fz+2UXkmS3\n6l2W6ENtW9foF1dMss6VCCEkgAjxJYRVlYff3sXP3t2JUVH439fM4J4Fk6Q5fxTQNI169xHMRisJ\ndpmPRQi9SQARop+q2zpY8dwmPjhUR16CnedXzGd2ngzjHC1aO+rwhzrISpiAQZFh0ULoTQKIEP2w\nfm81tz2/iQavn6Xn5vDfN84lIVYeuYwm3ZOPpcfJ5GNCjAQSQIQ4i1BYZfX6HTy6YTcWo4FfLb2A\nu+ZNkEcuo1BO4mQsxhiS7Fl6lyKEQAKIEGd0rLWdb6zdxKbD9RQmOXhhxQJm5CTpXZYYILs1jsLU\n6XqXIYQ4TgKIEKfxRlkV3/zzRzR1+Pn6tDye/voc4mIsepclBqim9RCKopDqysOgGPUuRwiBBBAh\negmGVR5443P+9wd7sJoM/Of1s/nO3PHyyGUU0zSNA3Vb8Yc6uGTSrSDfSjFGaJrKx4deo6W9BoNi\n5KLx1/cagr6/dgv7aktQFAPTci4hJ3ESHQE3H+77C2EtjNUUw4LiZZhNw9PfTQKIEMdVNHtZvvZD\nPqloZHyykxduXcD0rES9yxKD5PU30xFwkx5XiNEgv/LE2HG0aQ9hNciSaf9KvfsoWw+v49LJtwHQ\nEfCwp/ojrp6+krAa4o2d/4/M+PHsOvZPilLPZ1zaDD6veIf9dVuYkjV/WOob1rFoO3bsYMWKFQBU\nVFRw8803s3z5clavXo2qqgA8+eST3HDDDSxbtoydO3ee9VghhstruyuZ8cQ6Pqlo5Obz8tn6/SUS\nPqJE9+RjaS4Z/SLGljr3EbISJgCQ6sqlyVvVs6/RU0mqKx+jwYTFZMNlS6KlvYZZBV+lKPU8NE2l\nI9CGxRQzbPUNWwB55plneOCBB/D7/QA88sgj3HPPPfz5z39G0zQ2bNhAaWkpW7Zs4aWXXuKJJ57g\noYceOuOxQgyHQCjM9/+2lev+8AGdwTD/deMc1nxjHk6bWe/SxBCpazuMQTGS4szRuxQhIioY9mEx\nfrEopqIoqFr4+D5/r31mo5VA2IeiKGiaxt8++yU1reVkxBUNW33DFkByc3P5zW9+0/N1aWkps2bN\nAmDBggVs3ryZbdu2MW/ePBRFITMzk3A4THNz82mPFWKolTd5mP/ken794V4mpcVRcs9i7pgt/T2i\nSSDkIxD2kezIxmSUTsRibDEbbQTD/p6vNU3r6YRtNlp77esKJF2tHQaDkWtn/C8uHH8tH+5/cdjq\nG7YHoosWLeLYsWM9X2ua1vOL3W634/F48Hq9xMfH9xzTvf10x/bH/v37h/AOumzbtm3IrzkSRft9\n/tfOegD+ha77fO+om4dLqmkPqiwpiOO+CzLwV5ezrVrPKodOtH8/u/XnPl3aJELtIbY1jc6/E/le\nRpdI3meqK4/K5jIKUqZS7z5Kgj29Z1+yM4fPKtYTUoOoapjWzgbi7Wl8fPBv5CefS0Z8EWajdVg/\nkEWsR5bB8EVjS3t7Oy6XC4fDQXt7e6/tTqfztMf2R3FxMU6nc8hq3rZtGzNmzBiy641U0X6fD63f\nwX/vbgQgNS2dVl+Qpz46RqzFyO+XXchtFwxfE6Meov372a0/93nih5nRSL6X0WWo79Pj8Zz1g3de\n0hSqWw+ybsdTAFw0/gZKqz7EaUsiN2kykzMv4s2dT4OmcX7eFZgMZiZlXsjHB//KjsoNKCjMKVo6\nZPWeLGIBZPLkyZSUlDB79mw2btzInDlzyM3N5fHHH+eOO+6gtrYWVVVJTEw87bFCDMRD63fw07d3\n9nz98w27ATgnPZ4Xbl3ApLQ4vUoTw6wj4GZL+T8oSj2PnMRJepcjRMQpioELx13ba1t8bGrP/xen\nz6I4fdYp+xdP/U5E6otYALn//vt58MEHeeKJJygsLGTRokUYjUZmzpzJTTfdhKqqrFq16ozHCvFl\nnRw+TnT1lGwJH1Guru0IvqBX7zKEEGcwrAEkOzubF1/s6sBSUFDA2rVrTzlm5cqVrFy5ste2Mx0r\nRH/927rPePS90jPuf2TDbsxGA6sXTYtgVSKSuhefS3Xl61uIEOK0ZFYeETWONHt5Y08V/yg7xrv7\noqQ3qRgQX7Cd1o46Eu0ZWIdxHgMhxMBJABGjViis8klFI+v2HGNd2TFKa9t69k3NSMBlM7HpcMNp\nz111xVRp/YhidTL5mBAjngQQMao0d/h5a281b+w5xlt7q2npDABgMxm5alIWSyZnc9WkLHIT7MDp\n+4FI+Ih+DlsCaa580uIkgAgxUkkAESOapmnsqWtj3Z5jvFFWxUeHG1A1DYCc+FhunJ7PkslZLByX\nTqzl1H/O3UGjO4RI+BgbkhxZJDmy9C5DCHEWEkDEiOMLhvngUC3r9lTxRtkxjjR3zRWjKDA3L4Ul\nk7O4alI252bE92uOh+7AUV1dLeFjDPD4mjAazMRa+jd/kBBCHxJAxIhQ1dbBG2VVrNtzjA0HaugI\ndK1XEGczc+P0PJZMzubKCZkkO2x9XOn0Vi+axrZtoaEsWYxQ+2pKaPQe4+KJ38BmtutdjhDiDCSA\nCF2oqsanx5q6OpDuqeLzquaefRNTXSyZnM2SydlcmJ+C2TisizaLKBIM+WnyVuOKSZbwIcQIJwFE\nRIzbF+Cd/TWs21PFm2VV1Ht9AFiMBi4rzuCrxx+tFCUP3XT6Y0lN60HKG7ZT7T+K78BhClOmkxE/\nTu+yIqreU4GGKqNfhBgFJICIYXWgwd3zaGVjeT3BsApAujOGb80qYsnkbC4bn4HTZta50tFD01RC\napCwGkLVwqhqmNq2cg7UbUVRDICGx9fMjsr3AMZUCOkefpseV6hzJUKIvkgAEUMqEAqz6XD98Q6k\nVexvcPfsm5mT1DNU9vysRAyGyCwSNlwtA6qmoqohwloYVQ1hNloxGS0Ew37cnY094SCshQirhAQo\nkQAAE0JJREFUYRLtGditcXQE3Bxr3ktYDaMe36dqIfKSziHBnk5bRwOl1ZuOXzuEqoZRtTBTsuaT\nHldIc3sNWw+v61VLW0cDNosDqykGjTBeXwsWUwwH6z8bMwEkrIZo9B7DYU3AbpVp9oUY6SSAiEGr\n93Ty5t5q1u05xjv7a3D7ggDYLSaWnpvDkknZLJ6USYYrNqJ1aZpGZXMZOyvfR9XCaITx+Jr5vOId\natvKccWk9AoQmQnFxMem4vE1s6+2pGvfCSFhQsZs0lz5XQGg/B9oaL1eb2rOQjLjx+PpbDolIACc\nm30xdmscvmA75Q3bT9mf4swlwZ6OqnUFCKPBiEExYjSYMCtWDIoRAIsphlRnHgZD1z6DYqQj4Mao\ndP04h7UQoVCAQKiTdn8rOyvfJz2ukGRnds81opHRYGJ+8U34Q+19HyyE0J0EEPGlaZrGjuqWng6k\nWyobOT41B4VJDm67oIglk7JYUJSG1TR8b3hhNYQv2I4v6KUz4CU+NhWHLQF3ZyM7jm6gM9hOS3sN\nYbUrEBk0KwCqFmZ/7VbiYlN6Xc8Vk0J8bCphNUijpxLoWk3SqJgwGoyoatfIHLPRSlxsKkbFiOF4\nADAaTMSYu/quxFhcFKWe3xMOuv+bYE/veh1bErMLr+nZbjAYMSomTEYLAAn2dK445/Yz3rfTlsj5\n+b0XaGztqMPj6+rIa1KsxMTEEQh1omphqlsPUN16oGdUiC/YjsVki8owEmNxEGNx6F2GEKIfJICM\ncQ+t30F1dT1Pzzj7ce3+IO8drO15tFLV1gGA0aCwoDCNJccfrUxIdfVrbo6+aJqGP9SBL9hOZ8CD\nL9hOgj2N+Ng0vL4WtpT/nUDY1+uciRlzcdgSMBrMBMJ+HNZ43J31mIwWDIqRgC90vGYTsRYXM/MX\nnxAgjNjMXW9ccTEpXD7ldgyK8bT34rQlMqfoa2esPcbiYHzazDPuNxktPWFkqBSmTO/p89H1GmZM\nRjNTsy8h1uqiraO+Z1TIrmP/xN3ZQJorn/S4IhIdmRiU0T3SKKyG2Fn5PlkJxaS68vQuRwjRDxJA\nxrATpynPXL/jlEm6Kpq9vFFWxT/2HOP9g7X4Q10dSJNirXxjRgFLJmWzaGIm8TGWL/3aoXCAzqAX\nX9CLL+ClM+glwZ5OijOXjoCHD/f/BU1Te51TlHo+8bFpmE02TEYrTlsiNosTm9lOjNlJgj0NALs1\njksn3wrARwde7mkZCPk9QFerRqIjg2Rnzmlr62r1GF1vyN39PMobtuPxeHHaEnv1dYmPTQW6gp3T\nlojX18yxln0ca9mH2WgjL2kK49L6SKEjWKOnkjr3YezWOAkgQowSEkDGqJPXSPnp2ztRVY3LJ2Ty\nRlnXo5Xdta09+6dmJByfgTSL2XnJGA1nfoPWNO2LcHH88Ygv6CUhNp3MhPEEQj7eK3v2lPPy1HNI\nceZiNcUQF5OMzezAZnYQY3ZgM9txxiQBYDXFsGDCTf26z5NbBk7cHm0y4seRET+Obe5tzBh/+jCh\nKAoTM+YwIX02LR211LQeoq6tHPV42FO1MPtqtpAWl09CbNrxUTUjX61bFp8TYrSRADIGdYePicle\n5uS0kRwbpLHDzMvbvfzs3a7HEGda3A26Aoa7s7EnWHSHjfjYVPKTp6KhsnHf86e8blgNkZkwHrPR\nSqorD6sp9ouAYXFgt3SNXDAaTMwpWjok99pXy8BYpSgKifYMEu0ZTM68kPDx/i1N3ioqmnZR0bQL\nqymW9LgC0uOKiI9NG5JHa8NBVcM0uCuwmR24YpL1LkcI0U8SQMaQUFhl5atb+K9PDjAx2cvVE79Y\nqj7FHuj6ei9MzzmHXyzJQ9M66Qy6aWuvYWurF1dMMhPSZwNQUv46YbX31OaqppKfPBWDYiQncTJm\no6UrYFi+aMmArje/8/N6d6IcTv1pGRjLFMWA6fhss0mObGbmX0Vt2yHq3EeoaCqloqmUmfmLSXbm\nEFZDZ+wbo5em9ipCapDsxIkjqi4hxNlJAIlSmqZxtKWdkqONbD3axJajjWw71kRnsOuT7uXjmnBa\nwxgNGiZFw2TQCKgKc3LayE1xcaDuQ3zB3sMZu4d5KopCYcp5GBRjV/+L4wHDaorpOXZK1rzI3awY\nMgbFQLIzm2RnNpO1eTR5q6h3V5DoyATgUP1nVLceJCOuiPT4Qly2ZN3f9EPhIDazQx6/CDHKSACJ\nEq2dAT6t7AoaJRWN1LRVYMCLyxoizhZifEKIc1McBA0zqHV3kh+/D7Pxi06eGgohTWFmjpXvfmUa\nx5qtgNITLmxmO0bDF/9cilLP0+EuRSQZFCMpzlxSnLm9tofCfg437uBw4w5iLS4y4scxLnWGbkEk\nI75IZj4VYhSSADIKBUJhSo7spaz2KEdbGmlqbyEY7iAQVnhuR9cn1X+5oJlxSSGcVjMum434mFiS\nHRnMLJgLwFMf7KbB20xYVQipCqoG+YkOzs3sGhmSnThRt/sTI1dx+iyKUs+n0XOM2rZD1HsqaOto\n6Akflc1lxMem4bQlRqSeYNiP0WAe9cOIhRiLJICMIJqm9fwib/JW0dbZQGfAQ627marWJhrbfby6\np5DPqppYUlxDUWLXXByJMQZizTbs1gSuP/9iZuUmYVKaUNXw8YmZnJiN1l6vde30q3htx+scafYC\nXeEjP9ERlaNDxNAyGkykxeWTFpdPWA0RCHUC4Au2U1r1IQB2a3zXY5q4Ihy2+GGrZX/tVmrbyplT\n9DWZfl2IUUYCSASdGDCa22toba+jM+ihM9D1Jxj2My3vRj6tbKaseiOB4FE8/iDBsIovZKTVZ2JL\nZQNTMxLJS0pmYrqTqVkZTEnPwGq2nfRqZ5/2PCN+HF+bdg2vfP4eoWAz52bmyugQ8aUZDSZiLF0z\nwJqNVqblXEpt2yEaPJUcrN/GwfptTM68iNykKb3+/Q8FTdOoPz78trsGIcToIQFkCGnH5yNXFIWW\n9jqa26u7wkXQS2fAgz/UzqWTb8OgGKltPcSRplK8/hBuX5CWTo2qNo3Ff/gLIdVAhtNHrDkep83J\nxLQMZuak8fXcZJ66KZFYy9B82zLix/G9hePYtk1Gh4jBMxpMZMQXkRFfRCgcoN5zlNq2QyQ5sgGo\ncx+mvH476fGFpMcVEmtxDer1/Jobf6iT7ISJ8ghGiFFIAshpnGn11BMDRmtHPU3equMBw9MTNC6Z\nuAKzyUqj5yiHGj7vuabZaCOk2nlu234+rfSwt66OyhYDTR023H4HgbCBOJuZi8clMzs3mVl5yVyQ\nk0SaM+ZMZQoxYpmMFjLjx5F5QotaR8CNx9+Mu7aR/bVbiItJIT2ukNykKb06OPdXu9o1jDwtLn+o\nyhZCRJAEkJPUtB5kR+V7VDS3EQ51oLSVU9VygLiYZFAU5hffSKzFRUt7LQfqtvacZzHacNmSCKoB\nzFixWnLwawZ21vj55KiXkooWWjoDwKcAmI0GpmVmcemEZGblJjMrN4nxya6ILVEvRKQVpkwnJ2ES\nde4j1LYd6grwQS/5yecCXY8lYy3OnjV5zkbTNNrVBpwGB0mOrOEuXQgxDCSAnKS8YTtHmr00eL0k\nxgRp6wwRYzLi9jWRkzixZ/KtNFc+Dls8MWYnihLDzhoPW4428puPt7O1spHyJm+v6xYlOblyYmZX\n2MhLZnpmIjZz9K1GKsTZmE1WshMnkJ04gUDIR7u/FUUxoGkaOyvf61p0MDad9LhC0uIKehbQO5VG\nknE8Bek5UbmqrxBjgQSQk+ysrqSi2YvRYKCh3UJIVciOd1DgiGNO0VJUVWNvXdvxCb4a2XJ0Fzuq\nWwipWs81EmMtLJqY2fUoJbfrUUqy4+ROokKMbRaTDYupa1VgDZXClOnUtJXT0l5DS0ctZTWbKUw5\nj+L0C3qd1/2ItC50FGOzB5PRIp2nhRiFJICc4KH1Ozja4CfFDmFVIax2PQ450tzO0VYj/+ejd/i0\nsok2X7DnHIvRwIzspJ4+G7PzkilKcuo+O6QQo4lBMZKbNIXcpCn4gu3UtR2mtq28Zz6RjoCH0mP/\nxGKOpaplP4FQJ6oWwuNr7llsUEKIEKOLBJDjvligLa7XGindXttjZW9jLcUpLq6eksOs3CRm5SYz\nNTMBq0magIUYKjaznbzkc8hLPqdnW0t7DU3t1bR1NBBWuz4AaHwx8qW8YbsEECFGGQkgJ9nb6IC9\nMCenjaTYIE0dZj6pjGNvo4MfLpzMo1+V4apCRFpWQjGJ9gzW7/5v/KEOwuEgRiw9+72+Vh2rE0IM\nhASQ41YvmgbAT9/eyd5GR1cQOcGqK6b2HCOEiLwYi5MUZw4eXzOapuH1ftHRezhnWxVCDA+ZvecE\nqxdNY9UVU0/ZLuFDiJGhe6mAk/tYyRICQow+0gJykhNbQkDChxAjSXc/j/KG7Xg8Xpy2RFlCQIhR\nSgLIaXQHjurqagkfQowwGfHjyIgfxza3LCEgxGgmAeQMVi+axrZtIb3LEEIIIaKS9AERQgghRMRJ\nABFCCCFExEkAEUIIIUTESQARQgghRMRJABFCCCFExEkAEUIIIUTESQARQgghRMRFxTwgqqoC0NHR\nMeTX9ng8Q37NkUjuM7rIfUaPsXCPIPc5EN3ved3vgaONommapncRg1VXV8exY8f0LkMIIYSIuOzs\nbNLS0vQu40uLihaQpKQkAGw2GwaDPFUSQggR/VRVxefz9bwHjjZR0QIihBBCiNFFmguEEEIIEXES\nQIQQQggRcRJAhBBCCBFxEkCEEEIIEXESQM6gqamJr3zlKxw6dEjvUobNtddey4oVK1ixYgU//vGP\n9S5n2Dz99NPcdNNNXHfddbz00kt6lzMsXn311Z7v5Y033si5556L2+3Wu6whFwwGuffee1m2bBnL\nly+P2p/PQCDAvffey4033sjtt9/OkSNH9C5pyO3YsYMVK1YAUFFRwc0338zy5ctZvXr1qJ3X4nRO\nvM9uP//5z3n++ed1qmjkiIphuEMtGAyyatUqbDab3qUMG7/fj6ZprFmzRu9ShlVJSQmff/45zz//\nPJ2dnfz+97/Xu6Rhcd1113HdddcB8NBDD3H99dfjcrl0rmro/fOf/yQUCvHCCy/w0Ucf8ctf/pLf\n/OY3epc15F588UViY2N58cUXKS8v5+GHH+Z3v/ud3mUNmWeeeYbXX3+dmJgYAB555BHuueceZs+e\nzapVq9iwYQOXX365zlUO3sn32dzczH333ceRI0e44447dK5Of9ICchqPPfYYy5YtIzU1Ve9Shs3e\nvXvp7Ozk9ttv59Zbb2X79u16lzQsNm3aRHFxMXfddRd33nknF198sd4lDatdu3Zx8OBBbrrpJr1L\nGRYFBQWEw2FUVcXr9WIyRednqIMHD7JgwQIACgsLo66lJzc3t1dwLC0tZdasWQAsWLCAzZs361Xa\nkDr5Ptvb21m5ciVf+9rXdKxq5JAAcpJXX32VxMRE5s+fr3cpw8pms3HHHXfwu9/9joceeogf/OAH\nhEIhvcsaci0tLezevZtf/epXPfcZzVPfPP3009x11116lzFsYmNjqaqqYvHixTz44IOnNG1Hi0mT\nJvH++++jaRrbt2+nrq6OcDisd1lDZtGiRb3Co6ZpKIoCgN1uj5pp2U++z5ycHKZNm6ZjRSOLBJCT\nvPLKK2zevJkVK1ZQVlbG/fffT0NDg95lDbmCggKuueYaFEWhoKCA+Pj4qLzP+Ph45s2bh8ViobCw\nEKvVSnNzs95lDQu3283hw4eZM2eO3qUMmz/+8Y/MmzeP9evX89prr/GjH/0Iv9+vd1lD7vrrr8fh\ncLB8+XLeeecdpkyZgtFo1LusYXPiDNbt7e1R+fhQnEoCyEmee+451q5dy5o1a5g0aRKPPfYYKSkp\nepc15F5++WUeffRRoGstHa/XG5X3OWPGDD788EM0TaOuro7Ozk7i4+P1LmtYbN26lblz5+pdxrBy\nuVw4nU4A4uLiCIVCUdUy0G3Xrl3MnTuX559/niuvvJKcnBy9SxpWkydPpqSkBICNGzcyc+ZMnSsS\nkRCdD1BFn2644QZ+/OMfc/PNN6MoCj//+c+j8nn6woUL2bp1KzfccAOaprFq1aqo/SR5+PBhsrOz\n9S5jWH3zm9/kJz/5CcuXLycYDPL973+f2NhYvcsacnl5efzqV7/it7/9LU6nk//4j//Qu6Rhdf/9\n9/Pggw/yxBNPUFhYyKJFi/QuSUSArAUjhBBCiIiTRzBCCCGEiDgJIEIIIYSIOAkgQgghhIg4CSBC\nCCGEiDgJIEIIIYSIOAkgQogBKykpidrZSIUQw0sCiBBCCCEiTgKIEGJI/OlPf2LFihV0dnbqXYoQ\nYhSIvqkvhRAR98orr/D222/zzDPP9Cw9LoQQZyMtIEKIQdm/fz+rVq3i1ltvjcpp0YUQw0MCiBBi\nUOx2O7/+9a/5xS9+QUdHh97lCCFGCQkgQohBycrK4tJLL2XWrFn8+te/1rscIcQoIQFECDEk7rvv\nPv7+979TWlqqdylCiFFAVsMVQgghRMRJC4gQQgghIk4CiBBCCCEiTgKIEEIIISJOAogQQgghIk4C\niBBCCCEiTgKIEEIIISJOAogQQgghIk4CiBBCCCEi7v8DdTSMwASfKb8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117312dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualizer = KElbowVisualizer(KMeans(), k=(4,12), metric=\"calinski_harabaz\")\n",
+    "visualizer.fit(X)\n",
+    "visualizer.poof()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Datasets without Centers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HE+rbnsKyXH5J+xIVcDd60zvqOnTaqm9xvxKNuujIycm54hkthRBCiMYsKCjoomXHc/Zi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\nhuUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11720f9b0>"
       ]
      },
      "metadata": {},
@@ -142,16 +185,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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yLVK/MIjcDRSCRUTkhjKNRtbszer0kLO7gouDPQMaV+P50FpUK+VpwwoLlsFg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\nSXPnztWkSZPUsmVLLVmyRH/605+0Zs2acmUpKCiQJB04cEAWi6XCnwW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goPwMIzo/hEHn3Ohxy9fgm5xKpWLplP508HPjnW3H+e/BZHuHJIRowbKKTxKX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\niK3MarWSmprKiy++SH5+PpM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mqDybM0WJuDn60C14BE56V3uHJESLVm0qrzW/U1vfrsRf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\nRgCAVGIEAEglRgCAVGIEAEglRgCAVBe8WcCxq+vqlwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115eb84e0>"
+       "<matplotlib.figure.Figure at 0x1130aa940>"
       ]
      },
      "metadata": {},
@@ -177,10 +218,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from yellowbrick.cluster import SilhouetteVisualizer"
@@ -188,16 +227,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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OHfDZZ5/h3Xffhb+/P4CCIQKFwwTc3NxgbW2tef4jR47ghx9+wNKlSzWtu5GRkXj69CmS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\n",
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5T8+ePfHVV18hKysLHTp0MG93IHvbVa9eHevWrTNfd/XqVfj5+eHzzz/HF198gaeffhqR\nkZEoU6YMLl68aL5f7t3GGo0GQgjz/+HIkSPYv38/Xn75ZfNuttxCQ0Nx9OhRhIWF5bn+3XffRZUq\nVdC4ceN8z50j97YZN24c+vTpg59++gkbN27EihUrsHHjxjzPqSgKoqOjMWHCBPPllJQU+Pj4AID5\nC9+D5BTa3K/R3NsPQJF29xX22tDpdIVmKEiFChXynFR19epVBAYGmm+7du0aypUrBwBISUlB7dq1\nzdfnMBgMuHXrVr5dsmfPnsWXX36JV199Fa1atUKrVq3w0ksv4dlnn8W2bdvQokULvPDCC3jmmWfQ\nunVrNGvWLM+XO71en+f57t9mOXIXf0VR8m1LRVHQsmVLvPPOO+brLl++jPLly6N27drYtm0bfvnl\nF+zbtw/Lli3DmjVrUKVKlQK32YNes/crX748QkJCzNsuJiYGQ4YMyXe/hg0b4pNPPoHJZMqzHseP\nH0dCQgIWLFgARVGwZMkSVK9eHQDwzz//mF9LwINfX7kV9tl29OjRPK+boKAg7NixAwcOHMD+/fvx\n7LPPYsqUKebDd47KwXe2W+bn54fly5fnOWvy2rVryMjIQEhISLGfr02bNti4caP5G2FCQgKaNWsG\nvV4PPz8/nDhxAkB2y/T06dMAgODgYLi4uJhfqJcvX0ZERIT5vlqt1lwIc//dpk0brF+/Hnfv3gWQ\nfWbsa6+99jCbwZxDp9Nh+/btALI/FLdt24ZWrVrlW3Zubdu2xZo1a2AwGKAoClatWoXWrVsXaZk5\nreeIiAiEh4fjxRdfzNea79ixI5KSkrBq1Sr06tUrz22hoaFITk5GYmIiAODkyZPo2rUrUlJS8NNP\nP6FXr17o27cvgoODsXv3bphMpkLzfP/993jmmWfQqFEjjBkzBj179sSpU6fy3W/YsGFYt24dfvrp\nJ/N1e/fuRUJCAmrXrp3nvr6+vvjtt98ghEB6err5MUajER06dEB6ejoGDBiAN954A2fPnoXRaMyz\nrVu3bo1vvvnGXLBWr179wA/Vomrfvr35S43RaMSXX35p8TENGzbEX3/9hePHjwPIPncgMTERzZs3\nf+gcHTp0wO7du3Hjxg0IIbB27Vp06tQJQPb/fO3atQCAK1eu4Mcff0RYWBgaNmyI27dv48iRIwCA\nDRs2IDQ0FN7e3nme29/fH1988QW+++4783W3b9/G9evXUadOHSQmJqJevXp49tln0bx5c+zatcvi\na+NBdu3ahdTUVCiKgi+++CLfl7IWLVrg559/xtmzZwEAe/bsQVRUFDIzMzF+/Hhs3boVPXr0wBtv\nvAFPT09cvny52Bnu17VrV/zwww/mM6K3b9+O+vXr57tfo0aNUK1aNcydOxeZmZkAsgvkrFmzzMeT\n27Rpg48//hhCCGRlZWHUqFEWz5R3dnaGyWSCEMLiZ1tun3/+OSZNmoQ2bdpgwoQJaNOmjbmXiiMr\n9S3h4OBgLFu2DG+//TauXLkCFxcXeHl5YcaMGahWrVqeb91F0adPH1y+fBl9+/aFoiioWrWq+QST\nUaNGIS4uDnv27EG1atXQtGlTANnfut977z3Mnj0bH3zwAYxGI1566SXzbsbOnTtj4MCBeO+999Cu\nXTvMnDkTADBixAhcvXoV/fr1g0ajQYUKFfDWW2899LbQ6XR47733MGvWLMTHx8NkMuHFF19EixYt\n8uXI/QVl1KhRmDdvHnr27Amj0YgGDRpg6tSpxV7+5MmT8dRTT2HKlClYtGiR+XoXFxd06NABv//+\ne74vRn5+fli6dCnmz5+PzMxMCCEwf/58VKpUCUOHDsW0adOwceNGaLVa1K1bF2fOnCk0Q7t27bB3\n715ERETA3d0dPj4+5u2dW9WqVfGf//wH77zzjvmErpwvdCEhIXla3FFRUfjxxx/RpUsXBAQEoFGj\nRuYW6euvv45XX30Vzs7O0Gg0mDNnDvR6PVq2bIkxY8ZAp9Nh6tSpGDFiBIYOHQqNRgNPT0+8++67\neVokxRETE4O//voLPXv2hLu7OypXrpznsMSD+Pn5YcmSJZg5cybu3bsHjUaDuXPnIjg4GElJSQ+V\no3bt2njxxRcxZMgQGAwGNGzY0Lw7fsyYMZg+fTp69OgBk8mECRMmmFuI7777LmbMmIGMjAyUKVMG\n8+bNy/fcPj4++OSTT7Bo0SLMnz8fbm5u0Ov1GDZsGFq2bImaNWti+/bt6N69O3Q6HVq2bIk7d+6Y\nv9AWlb+/P0aMGIFbt26hWbNm+bpg1axZEzNmzMArr7xi/p8vX74c7u7ueOGFFzB58mSsXbsWWq0W\nnTp1eqQvNTk6dOiAK1euIDY2FoqioGLFipg9e/YD77t06VK8/fbbiImJgVarhaIo6NmzJ4YNGwYg\n+z05e/ZsREZGwmAwoFWrVhg+fHihyy9Xrhzq1KmD8PBwrF69usDPtgMHDuR5XM+ePXHw4EF0794d\nbm5uqFixYqno6qQRlg4KEJFD+emnn3Djxg3zOQKzZs2Ci4uLeXc3Fc39Z70TPYxSvzuaqLSpWbMm\nNm3ahKioKPTo0QO3bt3K14IjIttgS5iIiEgStoSJiIgkYREmIiKSpMTPjlYUBWlpadDpdA999iYR\nEZGaCCFgMBjg4eFRrKE2S7wIp6WlWewGQkRE5IhCQkIsDrSUW4kX4ZzBF0JCQvKNOmPPTpw4gXr1\n6smOUWRqywswsy2oLS/AzLZgMW+bNtm/cw0+I5vatnFWVhbOnDljroFFVeJFOGcXtF6vNw9pphbM\na33MbH1qywswsy0UmjdnUCI7Wye1bWMAxT4MW+pHzCJyJLr0y8CBdwGNes659D9/HjAdsHxHO6K2\nzBbzfvHvcLcH3yv4PjZmMbMQABTAqxLweC9ApecgsQgTORCtMQ3IugI4qeetrcu6CaS5Wr6jHVFb\nZrXlBYqQ2dkNaDkOcFbPYc8HUc87lYiIrOP9ndm/R3SSm6MohABcPIFqnVVfgAH2EyYiot0nsn9U\nQQBNngcqFDzXupqwCBMRkXqUqQq4elu+n0qwCBMRkXqUeUx2ghLFIkxEROpgzAR8qspOUaJYhImI\nSB28KwJ+1WWnKFE8O5qIqLRb/bLsBEXjXk61/YELwpYwERHZP5MBKPe47BQlji1hIpV466238N13\n38HHxwcAEBwcjHfeeUdyKnIIqugnLAC/GrJDlDgWYSKVSEpKwuLFi9G4cWPZUcjR5PQRtucirPfI\n/nEw3B1NpAJZWVn4/fff8eGHHyIqKgpjxozB33//LTsWke24+clOYBUswkQqcPXqVbRo0QKvvPIK\nNm/ejIYNG+KFF16AEEJ2NCLrU0xAefVMa1gcLMJEKhAUFIT3338f1apVg0ajwbBhw3D+/HlcvHhR\ndjQi69O7A0GtZKewChZhIhU4deoUNm3alOc6IUSxJxAnUh2hAOXqAk5a2UmsgidmEamAk5MTZs+e\njSZNmiAoKAiff/45atWqhcDAQNnRyBHYcz9h32pASA/ZKayGRZhIBUJCQjBlyhSMGjUKJpMJgYGB\nWLx4sexYRNZlzASqtHHYVjDAIkykGtHR0YiOjpYdgxyRvfYT1uoA70qyU1gVjwkTEZV29jqfsGcg\noHOTncKqWISJiMj+CAF4VZSdwupYhImIyO4oWlcguIPsGFbHIkxERHbH4OIHuHrLjmF1LMJERGRf\nhAn/lGshO4VN8OxoIqLSzt76CXtVhtGpvOwUNsGWMBER2Q+hAOXryE5hMyzCRESl3fs7/9dXWDoB\nVG4pO4TNsAgTEZV29tRPWOcBaEvPkVIWYSIisg/CBAQ0kJ3CpliEiYjIPrj5AyHdZaewqdLT5ici\nIvskBGDKAur2k53E5liEiYhIHsUI+NcGQiIBNx/ZaWyORZiIqLST1U/YZAAeexKo0VnO8u0AizAR\nEdmeUIBGzwJla8hOIhWLMJGjMWUBikl2iiLTmLKyJ29XEbVltph35Q/Zv4c9WYJLFdmFFhrAyQmA\nE6DRAE7OgEabffy3lBdggEWYyKHc86gK1FTXmLvXTvyKCvXqy45RLGrLbDHvsITs38tHltxCnZwA\nrQvg7AI46QCt7t8CrCm5ZTgAFmEiR6LVAb5VZacoFoP7dWa2Mot5nf4tBSpaJ0fBIkzkUAQAo+wQ\nxWQCM1tbUfPa0zoVNbO6y5i60xNRHq6ufwPYBzWNw1OhwhUAibJjFIvaMlvOm/rv7//aIE3RWM6s\nAPAE8KxtAlkJizCRw9H++6MWWqjvo0htmYua157WqbDMCoCqAHrYLo6V2NMWJyIiGc7NkZ2gmJwA\ndAGg/pO81LPPioiICAYAnQDoZQcpESzCRESl3aQvs3/snhHAEwCqyw5SYliEiYhKu9WJ2T92rzyA\nprJDlCgWYSIiUomKcITjwLmxCBMRkQoYADjeMJcswkREpALlAQTIDlHiWISJVGTnzp1o3Lix7BhE\nEpSTHcAq2E+YSCXOnTuHefPmQQghOwo5GrvvJ2wEUFl2CKtgS5hIBTIyMjBhwgTExcXJjkIkgQ5A\nNdkhrIJFmEgFpk2bhv79+6NWrVqyo5Ajsvt+wn5Q11CsRcciTGTnVq1aBWdnZ/Tp00d2FHJUdt1P\nWAAIlB3CanhMmMjOffnll7h37x6io6NhMBjMf69YsQIBAY53tihRXkY46q5ogEWYyO6tX7/e/PfF\nixcRGRmJzZs3S0xEZEuucOSWMHdHExGRnRIAHocjlyrHXTMiB1S5cmUkJSXJjkFkIwYA9WSHsCru\njiYiKu3stp9wWQA+skNYFVvCRERkhxQAzWWHsDoWYSKi0s4O+wmbTG5wxAkb7sciTERU2tlhP2Gj\n0V12BJtgESYiIrtjMnnIjmATLMJERGRnjMjIqCA7hE3w7GgiIrIjCoDWyMoyyQ5iE2wJExGRHVEA\n1JEdwmbYEiYiKu3spp+wCUAwAL3sIDbDljAREdkJLYCuskPYFIswEVFpZxf9hI0A6sJR5w0uCIsw\nEVFpZxf9hP0BtJacwfZYhImISDITgCoANLKD2ByLMBERSVYZQAvZIaRgESYiIon0AEJRGlvBALso\nERGRzZkAuCJ7qsI2//4unViEiRyIojgD0EFNO7kURQu1fRSpLbPFvOfm2yiJEwBvALUA1LPRMu2b\nel5FRGRRVlYAgO6yYxTL1auHUblyE9kxikVtmdWWtzRRz9dlIiKyjkmTsn/I5tgSJnIgpkwTrpy6\nJjtGsaReSMcVD2Z+FIpRQVa6ARXrB0Dvpiv+E6xenf177tySDUYWsQgTOZDMOwac//MinLTq2cn1\nz99puJjxt+wYxWIPmRWTgF/VMnDzdoGrjwv8q/lC58qPdLXhf4yISIW8A71Qo+1jsmPQI1LP12Ui\nIgIAaJw0qNwwUHYMKgEswkREKuNe1h2e5Txkx6ASwN3RREQqIhQBvyplSvZJz50r2eejImNLmIhI\nTTQa+FXxkZ2CSgiLMBGRSggh4OHnBp3rQ3RDKgz7CUvDIkxEpBLCJFCjfXDJP/Hq1f/rK0w2xSJM\nRKQSXhW8Hm4wDrJbLMJERCrhwgLscFiEiYhUQu+hlx2BShiLMBGRCghFwL+Gn+wYVMLYT5iISAXc\nfFzh6ulinSdnP2Fp2BImIlIBZxe2mRwRizARkQpYdZhK9hOWhkWYiMjOmQwmeAV4Wm8B7CcsDYsw\nEZGdc9I6was8J2xwRCzCRER2ztnFGU5aflw7Iv5XiYjsnKuXlc6KJulYhImI7Jx7WXfZEchKeM47\nEZEdE0JA52rlj2r2E5aGLWEiIjumGAX8g31lxyArYREmIrJjrl4u1h8zmv2EpWERJiKyY1btH5yD\n/YSlYREmIrJTikmBdwUbFGGShkWYiMhOKSbAqzyLsCPj2dFEKvHZZ59h9erV0Gg0CAoKwqxZs1C2\nbFnZsciKdK5a6N11smOQFbElTKQCJ06cwIcffog1a9bg66+/xmOPPYYlS5bIjkVWpnPXQaPRyI5B\nVsSWMJEK1KtXD9u2bYNOp0NmZiauXr2KypUry45FViSEQJkKXrZZGPsJS8OWMJFK6HQ67Ny5E+3a\ntUNiYiJiYmJkRyIrUowC/jV4uMHRsQgTqUinTp1w4MABjBkzBsOGDYOiKLIjkZV4B3rCvYybbRbG\nfsLSsAjWnr30AAAgAElEQVQTqUBycjIOHTpkvty7d2/8/fffuHPnjsRUZE1u3q62Wxj7CUvDIkyk\nAteuXcMrr7yCmzdvAgC2bNmCmjVrwteXwxk6Kp0Hz4ouDXhiFpEKNG3aFCNHjsTgwYOh1WpRvnx5\nLFu2THYsshKTQYFvUBnZMcgGWISJVGLgwIEYOHCg7BhkAzpXZ7iXseHuaJKGu6OJiOyMi5eVJ2wg\nu8GWMBGRnfH097DtAtlPWBq2hImI7IhiUuBe1l12DLIRFmEiInsiAL8gH9suk/2EpWERJiKyI67e\nLtDqtLZdKPsJS8MiTERkR7wCOHVhacIiTERkR3RuHKSjNCnw7OjExMRCH9isWbMSD0NEVJqZDCZ4\n+POkrNKkwCK8dOnSAh+k0Wjw6aefWiUQEVFp5ezijDIVvGXHIBsqsAgnJCTYMgcRUann6e8BjZPG\n9gtmP2FpLB4TvnTpEp599ll06dIF165dw+DBg3Hx4kVbZCMiKlV0rhw/qbSxWISnTZuGYcOGwd3d\nHf7+/oiIiMDEiRNtkY2IqNQQQkDvIWm4SvYTlsZiEb516xbatGkDIPtYcL9+/XD37l2rByMiKk0U\nk4BnOUknZbGfsDQWi7CrqyuuXLkCjSb7OMWhQ4eg13NwcSKikuTkpIFnORuPGU3SWTwAMWnSJDz/\n/PM4f/48oqOjcefOHSxZssQW2YiISg2fSj5w1vOYcGlj8T9ev359rF+/HufOnYOiKAgODmZLmIio\nBCkmgQp1y8uOQRJYLMKpqalYtmwZDh48CGdnZ7Rq1QrPP/883NzcbJGPiMjheZR1hxd3RZdKFo8J\nT548GVqtFnPnzsWMGTOQlpaGqVOn2iIbEZHDU0wKPPwkN2rOnWNfYUkstoSTk5PzjJ41efJkREZG\nWjUUEVFp4eLlgqrNKsuOQZJYbAkHBwcjKSnJfPnUqVN47LHHrJmJiKjUcPV0kTNKVm7sJyxNgS3h\nDh06QKPRIDMzE9u2bUO1atXg5OSEP//8E1WrVrVlRiIih+Xq7SI7wv/6CM+dKzdHKcSxo4mIJBGK\n4NSFpVyBRbhSpUoAgKysLOzZswdpaWkAAJPJhIsXL+Kll16yTUIiIgel1WsRUNtfdgySyOKJWaNH\nj0ZGRgbOnz+Ppk2bIjExEaGhobbIRkTksIQQcPdzg9ZZKzsKSWTxxKy//voLn376KTp37ozhw4dj\n3bp1SElJsUU2IiKHJQRQrSXPryntLBbhsmXLQqPRIDg4GKdPn0ZAQACysrJskY2IyGG5l3GD3t1O\njgezn7A0FndH16xZEzNnzsSAAQPw6quvIiUlBQaDwRbZiIgckhAC7mVcZccgO2CxJTx9+nSEh4ej\nRo0aGDt2LFJSUrBo0SJbZCMickg6Vx2CGleUHeN/2E9YmgJbwomJifkue3l5oWvXrrhz547VgxER\nOaKcyRrsqmsS+wlLU2ARzj1U5f00Gg0+/fRTqwQiInJUQgjo3JxRPoTdkigbB+sgIrIRFw89QjpU\nh0YjeZhKshucQZqIyMoURaBMRS8ENakMVy87GKaS7AaLMBGRFQhF/Dsghzsq1A1A2aplZEciO2Sx\nCK9evRoDBgywRRYiIlVTTAqEIuBR1h3lqpeF32NloHO1oxOwCsI+wtJYLMKrVq1iESZSCWd3Z/h5\n+UJNhxxvGW7Ct4qP7BjFkpPZSesEJ50Wzs7Zv/UeeniWc4feTcfjvlQkFotwYGAgBg8ejIYNG8LF\n5X/HMkaPHm3VYERUfHpPZ9Ro8pjsGMVy2/UGqjOzXDl9hNlFyeYsFmFO1kBE5ODYT1iaIs2ilJ6e\njvPnzyMkJAT37t2Du7u7LbIRUTFpbtxA2povACf17Ap1/esc0s7+KTtGsagts6W8bunpAICML9bZ\nKpJFRd7GRhOcAgPg1iHM+qGswGIR3rdvH6ZNmwaTyYQ1a9YgKioKCxcuRJs2bWyRj4iKwSk1Dcbk\nc9A4q6fjg3PKVZhUNp2f2jJbzGs0AgBMFy7YKJFlRdnGwmCEvnEjuD7Z3kapSp7FsaMXL16Mzz//\nHN7e3ihfvjw+++wzzJ8/3xbZiIiI8hFCQBgMcA4KgmunjtA4WSxldsvi12VFUVCuXDnz5Ro1alg1\nEBERUWE0Wi08RgyH1kddZ9U/SJHOjv7++++h0Wjwzz//YNWqVahY0Y5m/yAiokdyd/IU2RGKRVe/\ngUMUYKAIu6NnzJiBLVu24PLly+jcuTNOnjyJmTNn2iIbERFRHiIrC841qsmOUWIstoRPnTqFxYsX\n57lu+/bt6NKli9VCERGR7bhs/QYAkNm9h+Qkljn5+UH32GOyY5SYAovw1q1bkZWVhaVLl2Ls2LHm\n641GI/773/+yCBMROQhdUhIAdRRhbVCQ7AglqsAifPfuXSQlJSEtLQ0HDhwwX6/VajFu3DibhCMi\nIsohjCZoKznWOUkFFuF+/fqhX79+2LdvH1q2bGm+/u7du/D09LRJOCIiohwaZy30DRrIjlGiLJ6Y\nlZGRgQULFiAtLQ3h4eHo2LEjVq1aZYtsRJTL5s2bERUVhejoaDz11FP49ddfZUcisiltlSCHmxjD\nYhFetmwZYmJisHXrVjRo0AC7d+/Ghg0bbJGNiP71559/YsGCBfjggw+wefNmjBo1CmPGjJEdi8h2\njEY4V60qO0WJK9LYdtWrV8fixYsRFRUFDw8PGAwGa+ciolz0ej1mzZqF8uXLAwDq1auH69evIysr\nC3q9XnI6Ujs19BPWVqwIl+bNZccocRZbwv7+/pg5cyZOnDiBtm3b4q233uJgHUQ2VrlyZTz55JMA\nsofsmzt3Ljp06MACTKWGk7+/7AhWYbElvGjRIuzcuRODBw+Gu7s7goKCuBuMSJL09HTExcXhypUr\n+OCDD2THIQdh7/2EhcnksEXYYkt4586dAICkpCRs2rQJHh4e2LFjh9WDEVFef//9N5566ilotVp8\n+umn8Pb2lh2JHIQuKcncV9guOTlB38gx57a32BLO3UfYYDDg8OHDaNq0KXr27GnVYET0P7dv38ag\nQYMQExOD0aNHy45DZFNaPz9VTc9ZHBbXau7cuXku3759m4N1ENnY6tWrcfnyZezYsSPPnqiPP/4Y\nvr6+EpMRWZ+Tn5/sCFZT7K8W7u7uuHTpkjWyEFEBRo0ahVGjRsmOQWRzwmCArs7jsmNYjcUiHBsb\na+4cLYTAxYsX0a5dO6sHIyIiglYLZweex95iEc59JrRGo4Gvry9qOPAGISIqbey5n7DGRQ+Nk8Vz\niFWrwCKcmJgIAPmGCLt16xYSExPRrFkz6yYjIqJST+PqJjuCVRVYhJcuXVrggzQaDT799FOrBCIi\nItuy137CwmSEe2SE7BhWVWARTkhIMP9948YNlC1bFhkZGUhJSUFVBxy/k4iotLLX+YS1AYFwrlBB\ndgyrsrijPSEhAcOHDwcA3Lx5EyNHjsTatWutHoyIiEovxdkZLmFPyo5hdRaL8Nq1a81TF1aqVAkb\nN27EZ599ZvVgRERUOgmTCYbHa0NXpYrsKFZnsQgbDIY8g8TrdDqrBiIiotLNqWxZGEJCZMewCYtd\nlDp16oQhQ4YgPDwcALB9+3Z07NjR6sGIiKj0ESYTXFu2ADIyZEexCYtFeMKECfjuu++QmJgIZ2dn\nDB48GJ06dbJFNiIisgG76idsMkFbpQpw+rTsJDZRpGEru3Xrhm7dulk7CxERlXLawABoPT1lx7AZ\nxx2GhIiIisRl6zfmvsIyCZMJ+idayI5hU445NxQRERWZ7H7CQghoy/nDrVs3aAMCpGSQhUWYiIik\nEUYjtOUD4N6vH5zcHHuIygdhESYiIpsTQkCj08G1TRu4PNFcdhxpWISJHI3I/oBTDSHUlRdQX+Yi\n5rXJOgkBkZUFJy9PeI4YUSpbv7mxCBM5EFNgebjVqCY7RrHcO3kKbo/Xlh2jWNSW2VJezeJFAAC3\nzjbofqpxglOFQGjLlnXoKQqLikWYyJG4ukLfsKHsFMViNBqZ2cos5v37bwCAvuB7kJXwawgREZEk\nbAkTOZDrWdfw+cnPoNGo5/v15TuXceaUukZHUltmS3kbLl4DADj2ylO2imTRo27jAPcAdKhi/0Ms\nswgTORATFJgUBVonjewoRSagqOskJ6gvs6W8Vb7ZBwA4Oq6/rSJZ9Cjb2CRMqF6mRgknsg71fF0m\nIiKyQECgbaW2qOpdVXaUImERJiIihyCEQKB7AB7zCZYdpchYhImISPUUocBN54YWFVvJjlIsPCZM\nRESq5wSgV40YaDTqOR8CYBEmIir1vtr5juwIj8zLxUd1BRjg7mgiInIAgR4VZEd4KCzCRESlXMPF\na9Fw8VrZMR6aQTGgkmdF2TEeCoswkYoIIRAXF4eVK1fKjkIOpOrWfai6dZ/sGA9NCAFPnZfsGA+F\nRZhIJc6ePYshQ4bg22+/lR2FyK74uPjAU+8pO8ZD4YlZRCqxatUqxMTEoGJFde52I7KWip4V4aSi\noVpzYxEmUolp06YBAPbv3y85CZH9MCpGBHkGyY7x0NT51YGIiAiAt94LFVR6UhbAljARUamn5n7C\nPi5lVNk/OAdbwkREpEpCCJRxLSM7xiNhESYiKuXU2k/YJIyo7qOOKQsLwt3RRCrz1ltvyY5ADian\nj/CxV+xnPuGicHN2g7eLt+wYj4QtYSIiUiW1F2CARZiIiFTK18VXdoRHxiJMRESqI4TAY97BsmM8\nMhZhIiJSHTedG8p7BMiO8ch4YhYRUSmntn7CQghUVOnUhfdjS5iIiNRFAzQs30h2ihLBIkxEVMqp\nrZ+wt84Lbs5usmOUCBZhIqJSTm3zCfu6+cmOUGJYhImISDUUoSDAPVB2jBLDIkxERKpS1auq7Agl\nhkWYiIhUI9inGvTOetkxSgyLMBERqYKzkzMe93tcdowSxX7CRESlnBr6CQshUKNMDfg50ElZAFvC\nRESkAq7OLgjxDZEdo8SxJUxEVMrl9BG216kM3Zzd0LJiK3jqvWRHKXFsCRMRlXL23E/YoBjQtlI7\nBHo4Trek3NgSJnIgTtBAgQII2UmKThEKTMIkO0axqC1zUfPawzppoYGH3gu+Wl/U9K2JYO9q8Hf3\nlx3LaliEiRyIv64cutbpJjtGsRxOP4wmjzeRHaNY1JbZYl7dNADAoMdjbZSocBqNBofvHEaTQPVs\n44fFIkzkQDQaDTQajewYxcLM1lfkvCpaJ0fBIkzkQAwmBedvpMmOUSwpd42lPrNWo0ElP/cSez5S\nDxZhIgdyLc2EQ4cvQuuknhbNlcv3cM5wSXaMYimJzEZFQK91gqebMwJ93OQW4XPn5C27lGMRJnIw\nzlonVRVhrZMGzlp1ddR4lMyKIuDnqUeDKr6oGeAFJxX9r6jksQgTEdmQi7MTejUNsq8vHpMmZf+e\nO1dujlLIjl4FRESOTQiB2hW97asAA8Dq1dk/ZHN29kogInJcAd6uaFGznOwYZEdYhImIbMCkCNQM\ndLxhF+nRsAgTEdmAoggEl/OUHYPsDIswEZEN+Hrq4ebCc2EpL74iiIisTFEEqtlzK5j9hKVhS5iI\nyMq83XVoWq2s7Bhkh1iEiYisSAiBauU87HsAlUmT/tdXmGyKRZiIyIoMJoGgsh6yYxSO/YSlYREm\nIrIiN50Tynu7yo5BdopFmIjIiir5udvfCFlkN/jKICKykiyjgjoVfWTHIDvGIkxEZCUaAOV8uCua\nCsZ+wkREViCEgK+nHq46rewolrGfsDRsCRMRWYEGQFSjSrJjkJ1jESYisgJvdz08XHWyYxQN+wlL\nwyJMRGQFni4q2A2dg/2EpWERJiIqYSZFoJKfnQ/QQXaBRZiIqIRpnTSoH1RGdgxSARZhIqISVs5L\nb99jRZPdYBEmIiphFcq4y45AKsF+wkREJUgIAS9XlX20sp+wNGwJExGVIEURqFbeU3YMUgkWYSKi\nEuTn5QJXvcpawuwnLA2LMJFK/PDDD4iMjETXrl0xduxY3L17V3YkegBPF5UVYID9hCViESZSgZs3\nb2LSpEmIj4/Htm3bEBQUhIULF8qORQ/g7aaSUbLILrAIE6nATz/9hPr16+Oxxx4DAAwYMABbtmyB\nEEJuMMrDaDKhvDdnTaKiYxEmUoErV64gMDDQfDkwMBB3795FWlqaxFSUmxACtSr4oGagl+wopCIs\nwkQqoCjKA693cuJb2F4YjAKhVX2h0XCQDio6voOJVKBChQq4du2a+fLVq1fh4+MDd3cOCmEvtE4a\n+LjrZcd4OOfOsa+wJCzCRCrQpk0bHDt2DOf+/aBcs2YNOnbsKDcU5eGic+JQlVRsKjyXnqj0KVu2\nLObOnYuxY8fCYDCgSpUqmDdvnuxYlEt5bxfZER5eTh/huXPl5iiFWISJVKJ9+/Zo37697BhUAE9X\nFXdNyukjzCJsc9wdTUT0iAxGhVMX0kNhESYiekTOWg18PVR6UhZJxSJMRPSIAnzc2DWJHgqLMBHR\nI3DTadG2VjnZMUileGIWEdEjKO/tAj9PFZ8ZDbCPsERsCRMRPSRFCJThsWB6BCzCREQPyWBU8HhF\nb9kxHh3nE5aGRZiI6CHptBp4uKi4f3AOzicsDYswEdFD8vN0gROHqqRHwCJMRPQQjCYFtSs4wK5o\nkopFmIjoIThpNHi8ko/sGKRyLMJERA/Bx0MHZy0/QunRsJ8wEdFD8HSEE7JysJ+wNPwaR0RUTEYB\n1KvMXdH06FiEiYiKSa/VoKq/h+wYJYf9hKVhESYiKgaTIlDZW+tYEzawn7A0PCZMRFRkAi1q+MN0\n/R/ZQchBsCVMRFRE5bxcEVrVV3YMciAswkRERSCEgLsLdx5SyWIRJiIqAkUItKzhLzsGORh+rSMi\nKoIAb1d4uTlQ3+Dc2E9YGraEiYgsUBSB+kE8Fkwlj0WYiMgCHw8dagR6yY5hPewnLA2LMBGRBeW8\nXGVHsC72E5aGRZiIyILq5TxlRyAHxSJMRFQAo1FBl/qBeKw8izBZB4swEdEDCCHg66lHVX8WYLIe\nFmEiogfQaIAeoZVkxyAHx37CRET3URSBxyv5OG6/4Puxn7A0bAkTEd2nrKcLWtYoKzsGlQIswkRE\nuRhNCmpV9ILOWSs7iu2wn7A0LMJERP8SQqBaeU/UrVRGdhTbYj9haXhMmIhKPaNJgaerM4LLeaJ5\ntbJwctLIjkSlBIswkQNx0zkhyMMNGo16iojhjhMq+bpJWbZe6wRPV2cE+Lihqr8Hiy/ZHIswkQPx\nddOiSaPKsmMUy2HTFTRhVyAqpUq8CAshAABZWVkl/dRWl5mZKTtCsagtL8DMtqC2vAAz20KhecuV\ny7mTbcIUkZq2cU7Ny6mBRaURxX2EBampqThz5kxJPiUREZEqhISEwMur6DNulXgRVhQFaWlp0Ol0\nqjouRURE9LCEEDAYDPDw8ICTU9E7HpV4ESYiIqKiYT9hIiIiSViEiYiIJGERJiIikoRFmIiISJIS\nLcI7duzA+PHjH3jbF198gZiYGPTr1w/ff/99SS622O7du4cxY8Zg4MCBGDFiBG7evJnvPnPnzkWf\nPn3Qr18/HD58WELKvIqSeePGjejbty9iYmKwbNkyCSnzKkpmAMjIyEB0dDT27t1r44R5FSXvvHnz\n0L9/f/Tu3RtffPGFhJTZFEXBtGnT0L9/f8TGxiI5OTnP7fb0fgMs5/3444/Rt29f9O3bF++++66k\nlHlZypxzn+HDh2O1nYy7bCnznj170K9fP/Tt2xfTp08vdp/WkmYp74cffoiYmBj07t0bO3bskJTy\nwY4dO4bY2Nh81+/evRu9e/dG//79i/YZIUrIzJkzRdeuXcXLL7+c77aUlBQREREhMjMzxT///GP+\nW5YPP/xQLF26VAghxNdffy1mzpyZ5/aTJ0+Kvn37CkVRxF9//SV69eolI2YeljInJyeLPn36iIyM\nDGEymcTbb78tsrKyZEQ1s5Q5R1xcnIiOjhZ79uyxZbx8LOXdt2+feOGFF4QQQmRmZopOnTqJ27dv\n2zynEEJs27ZNTJw4UQghRFJSkhg5cqT5Nnt7vwlReN7z58+LXr16CaPRKBRFEf379xcnT56UFdWs\nsMw5Fi1aJPr27Ss+//xzW8d7oMIyp6amih49eogbN24IIYRYsWKF+W9ZCst7584d0b59e5GZmSlu\n374tnnzySVkx81mxYoWIiIgQffv2zXN9VlaW+XMhMzNTxMTEiGvXrhX6XCXWEm7cuDGmT5/+wNuO\nHz+ORo0aQa/Xw8vLC1WqVMGpU6dKatHFdvjwYbRt2xYA0K5dO+zbty/P7eXLl4erqyuysrJw9+5d\nODvLH93TUuZffvkF9erVw8SJEzFo0CA0btwYOp3cCcktZQaAlStXolGjRqhdu7at4+VjKW+jRo0w\nZ84c82WTySTttZE7a2hoKE6cOGG+zd7eb0DheQMDA/HBBx9Aq9VCo9HAaDTCxcVFVlSzwjIDwHff\nfQeNRmO+jz0oLHNSUhJCQkIwb948DBw4EP7+/vDz85MVFUDhed3c3FCxYkVkZGQgIyPDrsadqFKl\nCuLj4/Ndf/bsWVSpUgU+Pj7Q6/Vo0qQJEhMTC32uYn+CrFu3Dp988kme6+bMmYPu3bvjwIEDD3zM\n3bt384wg4uHhgbt37xZ30Q/lQXnLli1rzuPh4YHU1NQ8tzs7O8PJyQnh4eFITU3FzJkzbZI1x8Nk\nvnXrFg4dOoTVq1cjMzMTAwcORGhoKLy9ve028759+5CcnIwZM2bgyJEjNsmZ42Hyuri4wMXFBQaD\nAXFxcejfvz88PDxsljm3u3fvwtPT03xZq9XCaDTC2dlZ6vutIIXl1el08PPzgxAC8+fPR506dRAc\nHCwxbbbCMp85cwZff/01li5daheHfnIUlvnWrVs4cOAANm3aBHd3dzz99NMIDQ2Vuq0LywsAFSpU\nQI8ePWAymfD888/LiplP165dcfHixXzXP8x7r9hFOOe4TXF4enoiLS3NfDktLa1Yw3o9igflHT16\ntDlPWlpavkK1adMm+Pv7Y+XKlUhLSzMXtMDAQLvNXKZMGTRv3hyenp7w9PREtWrVcO7cOTRo0MBu\nM69fvx6XLl1CbGws/vzzT/z2228oV64cHn/8cbvMCwB37tzB2LFj0bx5c6kfCve/pxRFMX9wyXy/\nFaSwvED2GMGvv/46PDw88MYbb8iImE9hmTdt2oSrV69iyJAhuHTpEnQ6HSpVqoR27drJigug8Mxl\nypRB/fr1Ue7fcaKbNm2KkydPSi3CheXdu3cvUlJSsGvXLgDAsGHD0LhxY5t9pj2Mh3nv2eTs6AYN\nGuDw4cPIzMxEamoqzp49i5CQEFss+oEaN26MPXv2AMj+Rzdp0iTP7d7e3nB3d4dWq4WHhwf0ej3S\n09NlRDWzlLlx48Y4ePAgMjMzkZ6ebt4tIpOlzIsWLcKaNWuQkJCAtm3bYsKECTYpwAWxlPfevXt4\n5pln0Lt3b7z44osyIpo1btzYfCLb0aNH87yf7O39BhSeVwiBF154AbVq1cKMGTOg1WplxcyjsMyv\nvfYa1q1bh4SEBPTq1QvPPPOM9AIMFJ65bt26OHPmDG7evAmj0Yhjx46hRo0asqICKDyvj48PXF1d\nodfr4eLiAi8vL/zzzz+yohZJ9erVkZycjNu3byMrKwuHDh1Co0aNCn2MVQ9offTRR6hSpQo6duyI\n2NhYDBw4EEIIjBs3TuoxnwEDBmDixIkYMGAAdDodFi1aBACYP38+unXrhsjISBw5cgRPPfUUTCYT\nIiMjUa1aNWl5i5K5QYMG6N27NwYMGGD+UCtTpozdZ7YnlvIeOXIEFy5cwLp167Bu3ToA2YdigoKC\nbJ61c+fO+Pnnn/HUU09BCIE5c+bY7fvNUl5FUXDw4EFkZWXhxx9/BAC88sorFj+8ZGbu2LGj1GwF\nsZR5/PjxGD58OACgW7du0r+cWcr7yy+/oF+/fnByckLjxo3RunVrqXkLsmXLFqSnp6N///6Ii4vD\nsGHDIIRA7969ERAQUOhjOXY0ERGRJBysg4iISBIWYSIiIklYhImIiCRhESYiIpKERZiIiEgSFmFy\nWCNGjMDVq1exceNGxMXFAQA6dOjwwJFuSsqFCxfw+uuvAwBSU1PxwgsvWG1ZhZk0aRK6du1qHtWp\nY8eO+OijjxAdHV3o4yzdXpCSWtf4+PgHDgdI5KjkD4pMZCXvv/++zZf5999/48KFCwCyR9eSNWbz\nl19+iePHj0Ov16Njx4744IMPEBwcjGeffbbQx23evPmhlidzXYnUjC1hUr0rV65g0KBBiImJQZ8+\nfXD06FEABbd6ly1bhp49e6Jr1644duwYAOCvv/5CbGwsIiMj0b9/fxw/fhwAEBcXh40bN5ofW6tW\nLQDZw9FNnDgRMTExiI6Oxtdffw0AmDVrFk6cOIE333wTs2bNQkpKinl0rU2bNqFXr16Ijo7G66+/\njszMzHzZtmzZgu7du6NHjx6Ii4uDwWBARkYGxo8fj4iICERGRmLTpk0AsieQmDt3Lnr16oWoqCh8\n/PHHAICRI0dCCIG+ffti0qRJuHr1Kl588UWcPHnSnP/27dt48cUXER4ejujoaPNkFZbWb+PGjRg3\nbhyGDh2Kzp07mydtuX9dc8ydOxcrV640Xx47diy2b9+OM2fOIDY2Fr1790ZYWBg+/fTTfNsiJ0vO\ncnP2Zhw/fhwDBgxAr169MHToUPOXno8++ghRUVHo2bMnpk2blu/5iOxSyU7wRGR78fHx4v333xdC\nCLF//37xwQcfCCGECAsLExcuXBAbNmwwT5cWFhZmvj0hIUGMGTNGCCFE7969xbZt24QQ2VOqPfnk\nkyIzM1NMnDhRbNiwwbyskJAQIYQQCxYsEJ988okQ4n9TxJ0/f17s379fDBo0SAghxIULF0RYWJgQ\nQogzZ86IAQMGiHv37gkhhFi4cKFYtmxZnvW4cuWKaNmypbh8+bIQQohXX31V7NixQ8ybN888reKN\nG5bWGU0AAAVVSURBVDdEhw4dxMmTJ8Xnn38u5syZI4TInlpx0KBBIjExMU/O3Nsh9/XTp08Xb731\nlhBCiFOnTol+/foVaf02bNgg2rdvL1JTU0V6erpo166dOHXqVJ51ze23334zTwWampoqWrduLTIz\nM8WsWbPEL7/8IoTInsowNDRUCCHE0qVLzdNJ5l6HnP9hZmamiIyMFJcuXRJCCLF3714xZMgQYTAY\nxBNPPCGysrKEyWQS06ZNE1euXMmXh8jecHc0qV7Lli0xZswYnDx5Eu3bt8egQYMKvX+nTp0AADVq\n1MC2bduQlpaG8+fPo0uXLgCyp1Tz8fHBn3/+WeBz/PLLL7h37x42bNgAAEhPT8cff/xR4KxKBw4c\nQHJyMvr16wcAMBgMqFOnTp77JCUloXHjxuaJQhYsWAAAeO+998xTKPr5+aFjx444ePAgDh06hJMn\nT2L//v3mDKdPn0bTpk0LXX8ASExMxMKFCwFktzjXrl1bpPUDsqd0zJn5JigoCHfu3ClwvevUqYOs\nrCwkJycjKSkJYWFh0Ov1iIuLw48//oj//ve/OH36dJHHZj937hwuXLiAUaNGma/LmW60UaNG6NOn\nDzp27Iinn37a4nCBRPaARZhUr0mTJvjmm2/www8/YOvWrfjyyy/x0UcfFXj/nAkCcuYnFUJA3Dd6\nqxACJpMJGo3GfJvBYDDfrigKFixYgLp16wIArl+/Dh8fnwKnZDSZTAgPD8eUKVMAZO/uNZlMee5z\n/9zEN2/eNGd5UDaTyYQJEyaYvzzcvHkT7u7uBa53Ycs6e/Zsntl0Clq/LVu25BmHOvf2KUhUVBS2\nbt2KpKQkjBgxAgDw8ssvw9vbG2FhYejevTu++eabBz5WCGGeYzgnV+XKlc3Hrk0mE65fvw4g+8vK\n0aNHsXfvXgwfPhwLFy5E8+bNi7Q9iGThMWFSvfnz52Pz5s3o1asXpk2bht9//71Yj/f09ERQUBC2\nb98OIHs2l+vXr6NmzZooU6YM/u///g8AsHPnTvNjWrRogdWrVwMAUlJSEBUVhcuXL5vnQwWyC13O\n30888QR27NiBGzduQAiB6dOn55vPuH79+jh27BiuXbsGIHtyiF27dqFFixZYv349gOxCu2vXLjRv\n3hwtWrTAF198AYPBYJ5yM+cYtyVNmzbF1q1bAWQX4BEjRuSZNL2g9StI7nW9X2RkJLZu3Yrk5GRz\nK/3nn3/G2LFj0alTJ/Ok5/d/KfH19cUff/wBIQR2794NAKhWrRru3LmDQ4cOAQA2bNiAV199FTdv\n3kR4eDhCQkLw0ksvoXXr1jh9+nSRtgWRTGwJk+rFxsZi/Pjx+PLLL6HVah9qPtoFCxZg+vTpiI+P\nh06nQ3x8PPR6PQYOHIiXX34ZkZGRaNGihXku1tGjR2P69OmIiIgwt0irVKkCLy8vpKamYsKECZgz\nZw4qVqyI2NhYJCQkYPTo0RgyZAgURcHjjz+O5557Lk+GgIAATJ48GcOGDYOiKAgNDUVMTAwyMjIw\nffp0REZGwmQyYeTIkahbty5CQkKQnJyMXr16wWg0IiYmBk888USR1nfs2LGYMmUKoqKi4OzsjPnz\n5+cpwgWtX07xu1/ZsmXzrGtuFSpUgK+vL0JDQ83LGDNmDAYOHAhvb28EBwejUqVK+U6iGz9+PEaO\nHAl/f380adIEt27dwv+3d4c2EIVAFEVHkZCgcJT1EwQNIZCUi9sW1o05p4QxN3lmSilx7429d7z3\norUW55zovcdaK+acUWuNMUZ83/fXLSCTL0oAkMQcDQBJRBgAkogwACQRYQBIIsIAkESEASCJCANA\nEhEGgCQ/vydtTGR3BhwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x113f4d080>"
+       "<matplotlib.figure.Figure at 0x11728d9b0>"
       ]
      },
      "metadata": {},
@@ -229,9 +266,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/bbengfort/pipeline.ipynb b/examples/bbengfort/pipeline.ipynb
index b2b62e07c..ae3d1fe76 100644
--- a/examples/bbengfort/pipeline.ipynb
+++ b/examples/bbengfort/pipeline.ipynb
@@ -982,7 +982,7 @@
     }
    ],
    "source": [
-    "from sklearn.cross_validation import train_test_split as tts\n",
+    "from sklearn.model_selection import train_test_split as tts\n",
     "\n",
     "model = LinearRegression()\n",
     "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)\n",
diff --git a/examples/exbald/data/dataset.csv b/examples/exbald/data/dataset.csv
new file mode 100644
index 000000000..71ad9ebac
--- /dev/null
+++ b/examples/exbald/data/dataset.csv
@@ -0,0 +1,17963 @@
+datetime,loc_CO2,temperature,humidity,co2,light,noise,bluetooth_devices,bluetooth_non_personal_devices,count_total
+2017-03-25 09:05:58,Georgetown-manual,22.6,36.9,781.0,430.0,511.0,1.0,,0.0
+2017-03-25 09:06:04,Georgetown-manual,23.8,39.0,767.0,448.0,510.0,8.0,,0.0
+2017-03-25 09:06:10,Georgetown-manual,23.8,39.0,754.0,423.0,511.0,8.0,,0.0
+2017-03-25 09:06:15,Georgetown-manual,23.8,39.0,768.0,412.0,492.0,8.0,,0.0
+2017-03-25 09:06:21,Georgetown-manual,23.8,39.0,758.0,428.0,491.0,9.0,,0.0
+2017-03-25 09:06:27,Georgetown-manual,23.8,38.9,776.0,419.0,500.0,12.0,,0.0
+2017-03-25 09:06:33,Georgetown-manual,23.8,38.9,755.0,423.0,503.0,12.0,,0.0
+2017-03-25 09:06:38,Georgetown-manual,23.8,38.9,755.0,409.0,501.0,12.0,,0.0
+2017-03-25 09:06:44,Georgetown-manual,23.8,38.9,766.0,447.0,506.0,13.0,,0.0
+2017-03-25 09:06:50,Georgetown-manual,23.8,39.0,771.0,423.0,515.0,16.0,,0.0
+2017-03-25 09:06:56,Georgetown-manual,23.8,38.9,789.0,437.0,491.0,16.0,,0.0
+2017-03-25 09:07:03,Georgetown-manual,23.8,39.0,763.0,424.0,513.0,16.0,,0.0
+2017-03-25 09:07:08,Georgetown-manual,23.8,38.9,768.0,422.0,517.0,16.0,,0.0
+2017-03-25 09:07:14,Georgetown-manual,23.9,39.0,787.0,438.0,511.0,18.0,,0.0
+2017-03-25 09:07:20,Georgetown-manual,23.8,38.8,758.0,435.0,521.0,18.0,,0.0
+2017-03-25 09:07:26,Georgetown-manual,23.8,39.0,768.0,417.0,503.0,18.0,,0.0
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+2017-04-08 09:18:58,Georgetown,21.3,24.2,968.0,178.0,516.0,71.0,63.0,31.0
+2017-04-08 09:19:03,Georgetown,21.3,24.2,948.0,161.0,521.0,71.0,63.0,31.0
+2017-04-08 09:19:09,Georgetown,21.3,24.6,951.0,186.0,498.0,72.0,64.0,31.0
+2017-04-08 09:19:14,Georgetown,21.3,24.3,925.0,178.0,513.0,72.0,64.0,31.0
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+2017-04-08 09:20:01,Georgetown,21.2,24.2,953.0,177.0,508.0,77.0,69.0,31.0
+2017-04-08 09:20:07,Georgetown,21.2,24.4,933.0,175.0,508.0,77.0,69.0,31.0
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+2017-04-08 09:29:49,Georgetown,21.3,24.2,966.0,178.0,526.0,105.0,93.0,31.0
+2017-04-08 09:29:56,Georgetown,21.3,25.0,2.0,175.0,530.0,105.0,93.0,31.0
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+2017-04-08 09:33:45,Georgetown,21.2,24.4,1002.0,167.0,514.0,53.0,45.0,31.0
+2017-04-08 09:33:50,Georgetown,21.2,24.8,981.0,142.0,568.0,57.0,49.0,31.0
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+2017-04-08 09:34:44,Georgetown,21.3,24.7,1008.0,175.0,528.0,64.0,54.0,31.0
+2017-04-08 09:34:49,Georgetown,21.3,24.8,999.0,156.0,485.0,65.0,55.0,31.0
+2017-04-08 09:34:54,Georgetown,21.3,24.0,990.0,175.0,435.0,66.0,56.0,31.0
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+2017-04-08 09:36:08,Georgetown,21.3,24.9,946.0,176.0,551.0,71.0,61.0,31.0
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+2017-04-08 09:42:54,Georgetown,21.3,24.9,1014.0,170.0,536.0,93.0,80.0,31.0
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diff --git a/examples/exbald/data/testing.ipynb b/examples/exbald/data/testing.ipynb
new file mode 100644
index 000000000..09e8f16f3
--- /dev/null
+++ b/examples/exbald/data/testing.ipynb
@@ -0,0 +1,1017 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# User testing for for Scikit-Yellowbrick\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using data that was recorded from sensors during Data Science Certificate Program at GW\n",
+    "https://github.com/georgetown-analytics/classroom-occupancy \n",
+    "\n",
+    "Data consist of temperature, humidity, CO2 levels, light, # of bluetooth devices, noise levels and count of people in the room."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('dataset.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>loc_CO2</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>co2</th>\n",
+       "      <th>light</th>\n",
+       "      <th>noise</th>\n",
+       "      <th>bluetooth_devices</th>\n",
+       "      <th>bluetooth_non_personal_devices</th>\n",
+       "      <th>count_total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2017-03-25 09:05:58</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>22.6</td>\n",
+       "      <td>36.9</td>\n",
+       "      <td>781.0</td>\n",
+       "      <td>430.0</td>\n",
+       "      <td>511.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017-03-25 09:06:04</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>767.0</td>\n",
+       "      <td>448.0</td>\n",
+       "      <td>510.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2017-03-25 09:06:10</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>754.0</td>\n",
+       "      <td>423.0</td>\n",
+       "      <td>511.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2017-03-25 09:06:15</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>412.0</td>\n",
+       "      <td>492.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2017-03-25 09:06:21</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>758.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>491.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              datetime            loc_CO2  temperature  humidity    co2  \\\n",
+       "0  2017-03-25 09:05:58  Georgetown-manual         22.6      36.9  781.0   \n",
+       "1  2017-03-25 09:06:04  Georgetown-manual         23.8      39.0  767.0   \n",
+       "2  2017-03-25 09:06:10  Georgetown-manual         23.8      39.0  754.0   \n",
+       "3  2017-03-25 09:06:15  Georgetown-manual         23.8      39.0  768.0   \n",
+       "4  2017-03-25 09:06:21  Georgetown-manual         23.8      39.0  758.0   \n",
+       "\n",
+       "   light  noise  bluetooth_devices  bluetooth_non_personal_devices  \\\n",
+       "0  430.0  511.0                1.0                             NaN   \n",
+       "1  448.0  510.0                8.0                             NaN   \n",
+       "2  423.0  511.0                8.0                             NaN   \n",
+       "3  412.0  492.0                8.0                             NaN   \n",
+       "4  428.0  491.0                9.0                             NaN   \n",
+       "\n",
+       "   count_total  \n",
+       "0          0.0  \n",
+       "1          0.0  \n",
+       "2          0.0  \n",
+       "3          0.0  \n",
+       "4          0.0  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    17962.000000\n",
+       "mean        24.647812\n",
+       "std         10.213500\n",
+       "min          0.000000\n",
+       "25%         25.000000\n",
+       "50%         29.000000\n",
+       "75%         31.000000\n",
+       "max         31.000000\n",
+       "Name: count_total, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.count_total.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#add a new column to create a binary class for room occupancy \n",
+    "countmed = dataset.count_total.median()\n",
+    "dataset['room_occupancy'] = dataset['count_total'].apply(lambda x: 'occupied' if x > 4 else 'empty')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# map room occupancy to a number\n",
+    "dataset['room_occupancy_num'] = dataset.room_occupancy.map({'empty':0, 'occupied':1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>loc_CO2</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>co2</th>\n",
+       "      <th>light</th>\n",
+       "      <th>noise</th>\n",
+       "      <th>bluetooth_devices</th>\n",
+       "      <th>bluetooth_non_personal_devices</th>\n",
+       "      <th>count_total</th>\n",
+       "      <th>room_occupancy</th>\n",
+       "      <th>room_occupancy_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2017-03-25 09:05:58</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>22.6</td>\n",
+       "      <td>36.9</td>\n",
+       "      <td>781.0</td>\n",
+       "      <td>430.0</td>\n",
+       "      <td>511.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>empty</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017-03-25 09:06:04</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>767.0</td>\n",
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+       "      <td>510.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>empty</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2017-03-25 09:06:10</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>754.0</td>\n",
+       "      <td>423.0</td>\n",
+       "      <td>511.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>empty</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2017-03-25 09:06:15</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>412.0</td>\n",
+       "      <td>492.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>empty</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2017-03-25 09:06:21</td>\n",
+       "      <td>Georgetown-manual</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>758.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>491.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>empty</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              datetime            loc_CO2  temperature  humidity    co2  \\\n",
+       "0  2017-03-25 09:05:58  Georgetown-manual         22.6      36.9  781.0   \n",
+       "1  2017-03-25 09:06:04  Georgetown-manual         23.8      39.0  767.0   \n",
+       "2  2017-03-25 09:06:10  Georgetown-manual         23.8      39.0  754.0   \n",
+       "3  2017-03-25 09:06:15  Georgetown-manual         23.8      39.0  768.0   \n",
+       "4  2017-03-25 09:06:21  Georgetown-manual         23.8      39.0  758.0   \n",
+       "\n",
+       "   light  noise  bluetooth_devices  bluetooth_non_personal_devices  \\\n",
+       "0  430.0  511.0                1.0                             NaN   \n",
+       "1  448.0  510.0                8.0                             NaN   \n",
+       "2  423.0  511.0                8.0                             NaN   \n",
+       "3  412.0  492.0                8.0                             NaN   \n",
+       "4  428.0  491.0                9.0                             NaN   \n",
+       "\n",
+       "   count_total room_occupancy  room_occupancy_num  \n",
+       "0          0.0          empty                   0  \n",
+       "1          0.0          empty                   0  \n",
+       "2          0.0          empty                   0  \n",
+       "3          0.0          empty                   0  \n",
+       "4          0.0          empty                   0  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count        17962\n",
+       "unique           2\n",
+       "top       occupied\n",
+       "freq         15859\n",
+       "Name: room_occupancy, dtype: object"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.room_occupancy.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys \n",
+    "\n",
+    "# Modify the path \n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "import pandas as pd\n",
+    "import yellowbrick as yb \n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (12, 8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6BpzsmubMpdEbPvekt5b3veP4NvQyOTMzM/h8vkx3IyNyfcwG+/y5ytY6njev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\nERERiQAF2iIiIiIiEfD/AGGNle6mudq4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b526550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = yb.anscombe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "raw_mimetype": "text/html"
+   },
+   "source": [
+    "Feature analysis visualizers are designed to visualize instances in data space in order to detect features or targets that might impact downstream fitting. Because ML operates on high-dimensional data sets (usually at least 35), the visualizers focus on aggregation, optimization, and other techniques to give overviews of the data. It is our intent that the steering process will allow the data scientist to zoom and filter and explore the relationships between their instances and between dimensions.\n",
+    "\n",
+    "At the moment we have three feature analysis visualizers implemented:\n",
+    "\n",
+    "Rank2D: rank pairs of features to detect covariance\n",
+    "\n",
+    "RadViz: plot data points along axes ordered around a circle to detect separability\n",
+    "\n",
+    "Parallel Coordinates: plot instances as lines along vertical axes to detect clusters\n",
+    "\n",
+    "Feature analysis visualizers implement the Transformer API from Scikit-Learn, meaning they can be used as intermediate transform steps in a Pipeline (particularly a VisualPipeline). They are instantiated in the same way, and then fit and transform are called on them, which draws the instances correctly. Finally poof or show is called which displays the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from yellowbrick.features.rankd import Rank2D \n",
+    "from yellowbrick.features.radviz import RadViz \n",
+    "from yellowbrick.features.pcoords import ParallelCoordinates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rank2D\n",
+    "\n",
+    "Rank1D and Rank2D evaluate single features or pairs of features using a variety of metrics that score the features on the scale [-1, 1] or [0, 1] allowing them to be ranked. A similar concept to SPLOMs, the scores are visualized on a lower-left triangle heatmap so that patterns between pairs of features can be easily discerned for downstream analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load the classification data set\n",
+    "data = dataset\n",
+    "\n",
+    "# Specify the features of interest\n",
+    "features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
+    "\n",
+    "# Extract the numpy arrays from the data frame\n",
+    "X = data[features].as_matrix()\n",
+    "y = data['count_total'].as_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GljJzWURsTYmBZgD/mJn3TuaFJUnSBAx2aGlb4Nqebdls72136SMNMn8XEXcB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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b1c6198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the visualizer with the Covariance ranking algorithm \n",
+    "visualizer = Rank2D(features=features, algorithm='covariance')\n",
+    "\n",
+    "visualizer.fit(X, y)                # Fit the data to the visualizer\n",
+    "visualizer.transform(X)             # Transform the data\n",
+    "visualizer.poof()                   # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DOEZmbA72lSbP6tWrueaaawC223nnnW/oZ1tWrrp3Uv5DnDN7VtVrH7jWkiRJ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b1c04a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the visualizer with the Pearson ranking algorithm\n",
+    "visualizer = Rank2D(features=features, algorithm='pearson')\n",
+    "\n",
+    "visualizer.fit(X, y)                # Fit the data to the visualizer\n",
+    "visualizer.transform(X)             # Transform the data\n",
+    "visualizer.poof()                   # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RadViz\n",
+    "\n",
+    "RadViz is a multivariate data visualization algorithm that plots each feature dimension uniformely around the circumference of a circle then plots points on the interior of the circle such that the point normalizes its values on the axes from the center to each arc. This meachanism allows as many dimensions as will easily fit on a circle, greatly expanding the dimensionality of the visualization.\n",
+    "Data scientists use this method to dect separability between classes. E.g. is there an opportunity to learn from the feature set or is there just too much noise?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Specify the features of interest and the classes of the target \n",
+    "features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
+    "classes = ['empty', 'occupied']\n",
+    "\n",
+    "# Extract the numpy arrays from the data frame \n",
+    "X = data[features].as_matrix()\n",
+    "y = data.room_occupancy_num.as_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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V1XXkLV3K4/E0+SiBo7EIHhqL4KGxCB4ai+ChsQgOwTwOXq+31WNuQ3U/oLxZ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\nH2dTjkl3PwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b42e2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the visualizer\n",
+    "visualizer = visualizer = RadViz(classes=classes, features=features)\n",
+    "\n",
+    "visualizer.fit(X, y)      # Fit the data to the visualizer\n",
+    "visualizer.transform(X)   # Transform the data\n",
+    "visualizer.poof()         # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For regression, the RadViz visualizer should use a color sequence to display the target information, as opposed to discrete colors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parallel Coordinates\n",
+    "\n",
+    "### !!! On this step notebook crashes and has to be restarted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Specify the features of interest and the classes of the target \n",
+    "#features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
+    "#classes = ['empty', 'occupied']\n",
+    "\n",
+    "# Extract the numpy arrays from the data frame \n",
+    "#X = data[features].as_matrix()\n",
+    "#y = data.room_occupancy_num.as_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate the visualizer\n",
+    "#visualizer = visualizer = ParallelCoordinates(classes=classes, features=features)\n",
+    "\n",
+    "#visualizer.fit(X, y)      # Fit the data to the visualizer\n",
+    "#visualizer.transform(X)   # Transform the data\n",
+    "#visualizer.poof()         # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Regressor Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regression models attempt to predict a target in a continuous space. Regressor score visualizers display the instances in model space to better understand how the model is making predictions. We currently have implemented two regressor evaluations:\n",
+    "\n",
+    "Residuals Plot: plot the difference between the expected and actual values\n",
+    "\n",
+    "Prediction Error: plot expected vs. the actual values in model space\n",
+    "\n",
+    "Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Regression Evaluation Imports \n",
+    "\n",
+    "from sklearn.linear_model import Ridge, Lasso \n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from yellowbrick.regressor import PredictionError, ResidualsPlot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Residuals Plot\n",
+    "\n",
+    "A residual plot shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "df = data\n",
+    "feature_names = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
+    "target_name = 'count_total'\n",
+    "\n",
+    "# Get the X and y data from the DataFrame \n",
+    "X = df[feature_names].as_matrix()\n",
+    "y = df[target_name].as_matrix() \n",
+    "\n",
+    "# Create the train and test data \n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DH/6QG2+8kVWrVvHQQw/xzDPPsGLFCi655BLOPvtsLrroIn73u9/1uK+IiIhIrlDS3UfL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O23G2moe+pHQ99Alp5MPdAz4vEPP5mmO1PL/PQg+wZS7OxeztOXZEnX3jNkxTufh4FcM2tP/EPW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\nJEmqMEO3JEmSVGGGbkmSJKnC/j9ZcdUZFWb/CwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bce4898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the linear model and visualizer \n",
+    "ridge = Ridge()\n",
+    "visualizer = ResidualsPlot(ridge)\n",
+    "\n",
+    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
+    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
+    "g = visualizer.poof()             # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prediction Error Plot\n",
+    "\n",
+    "Plots the actual targets from the dataset against the predicted values generated by our model. This allows us to see how much variance is in the model. Data scientists diagnose this plot by comparing against the 45 degree line, where the prediction exactly matches the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "df = data\n",
+    "feature_names = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
+    "target_name = 'count_total'\n",
+    "\n",
+    "# Get the X and y data from the DataFrame \n",
+    "X = df[feature_names].as_matrix()\n",
+    "y = df[target_name].as_matrix() \n",
+    "\n",
+    "# Create the train and test data \n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eeqNxD+2t4zNXXRRzXFaGWcJEdqbZzHJrT7/RuJazZuOcUvqHiIiM4maJKxhT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\noFpEREREJEb/H/B7IY9rnENdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bf87860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the linear model and visualizer \n",
+    "lasso = Lasso()\n",
+    "visualizer = PredictionError(lasso)\n",
+    "\n",
+    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
+    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
+    "g = visualizer.poof()             # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classifier Evaluation\n",
+    "\n",
+    "Classification models attempt to predict a target in a discrete space, that is assign an instance of dependent variables one or more categories. Classification score visualizers display the differences between classes as well as a number of classifier-specific visual evaluations. We currently have implemented three classifier evaluations:\n",
+    "\n",
+    "ClassificationReport: Presents the confusion matrix of the classifier as a heatmap\n",
+    "\n",
+    "ROCAUC: Presents the graph of receiver operating characteristics along with area under the curve\n",
+    "\n",
+    "ClassBalance: Displays the difference between the class balances and support\n",
+    "\n",
+    "Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Classifier Evaluation Imports \n",
+    "\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.linear_model import LogisticRegression \n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classification report\n",
+    "\n",
+    "The classification report visualizer displays the precision, recall, and F1 scores for the model. Integrates numerical scores as well color-coded heatmap in order for easy interpretation and detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the classification data set\n",
+    "data = dataset\n",
+    "\n",
+    "# Specify the features of interest and the classes of the target \n",
+    "features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
+    "classes = ['empty', 'occupied']\n",
+    "\n",
+    "# Extract the numpy arrays from the data frame \n",
+    "X = data[features].as_matrix()\n",
+    "y = data.room_occupancy_num.as_matrix()\n",
+    "\n",
+    "# Create the train and test data \n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SD3cF8KcQwnDSc+Qc0oWRNR+XeaQx46/EGN+utZtrSb2zT4QQriRdeHcxaaq+u9a62Po5\nm/RFJFeRhgw9TOp9/2/2fJ1JmhXlcNLMFjU9EEI4i/RmYyjwn+pjyh7nC7I3NK+SZgs5FvhdjLFi\nNed+HrB5CGFf4OUYY+0hJJK+5uw5lpqhGOMw0re1TQUuJ8228CfSF2IcBnwrxjg/u+jpSNJQiIdJ\n02W9TZoZoE9Y8c1uPyONrb2PNH74YlLAqN7fvaSL2X5AGtZxEWm6sQuzTYZm9zuZNKPEScBZMcYv\nTCcWY3yaNMXZUaR5YU8jjZn+C6lX+SuJMU6n7rAKKby9kv17Lqv3JtIsBqtrcxRpurHJpON8hBS4\n7gK+WfMCtBr3qSKFz0rgAeA2YAHpW95ahRC2iTHOJ4WuMtIXjfyTNGfyt2OMZdmY40NIj9+D2f7e\nIw2vqLcY4xWkIS/Hkp4PA0jzW7+dzRxyLOkixUdJj/FDpF74nUII1cNuLiH1sn7hK7ezTyf2JL3x\n+hvpOTIa2L2uc9QQYvqCmWHAASGE/WOa4vAA0sWcN2XH0BP4bozx+Rp3nUF63g0j/V78i2yO4szv\nSefgV6RztT/wixjjDWso6XbS4/wI6U2ipGYmV1VVteatJElaT4T0JSAnxBhXdRGmpNUYPXr0ZsDE\np787mKVT6zPx0aoVb9SZfR++AaDXgAEDJjVIo1+SPceSJElSxnAsSZIkZbwgT5L0f0qM8ULq/gpu\nSTIcN4bRo0e3IM2pORWn9ZEkSQ0jn/RlNm8MGDCgtKmLWV8ZjhvHTqSv05UkSWpoewIvNnUR6yvD\nceOYCtCn20cUFdT+XgA1lNLyQiZM682W3T6mRUFZU5ezXrp+h5ubuoT1XqvuXdjttvN45deXsOTz\nVX6zs76iwc8c0NQlrPdKK1vz4dK92aL4WVrkLW7qctZLyyqLmbB0X6j19edqWIbjxlEBUFRQTotC\nQ1tjqSJ9e7LnufE01BQ9WrW8wvQyXDJzrue7EbXIW9LUJaz3qrJr/Ivylnq+G59DNhuRs1VIkiRJ\nGcOxJEmSlDEcS5IkSRnDsSRJkpQxHEuSJEkZw7EkSZKUMRxLkiRJGec5liRJUr3tBFQ1UFu5Bmqn\nIdhzLEmSJGUMx5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLGcCxJ\nkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzH\nkiRJUsZwLEmSJGUMx5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLG\ncCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLGcCxJkiRlCpq6AEmSJKk+Qgg7\nALcB/YAJwAkxxlfr2O5XwNlAJ+A9YHCMcfTq2rbnWJIkSc1GCKEl8DBwF9AeuAEYEUJoU2u7bYEr\ngQOBDtl97l9T+4ZjSZIkNSd7A5UxxltjjGUxxuHAdGBQre22BPJJIyVyQAWwdE2NO6xCkiRJzclW\nwNhay2K2vKbHgQ+A90nBeCEpWK+WPceSJElqTloDS2otWwK0qrWsJSkY7wS0Aa4DHgwhFK+uccOx\nJEmSmpMlQO2A2wpYVGvZhcCUGOObMcYS4GKgCNhvdY0bjiVJktScjANCrWWBLw612BRoUX0jxlhF\nGl5RvrrGHXMsSZKk5uQZoEUI4WRgGPBToCtpjHFNjwCXhxDuA94BBpMu0HtxdY3bcyxJkqRmI8ZY\nChwEHA3MAU4GDokxLg4hDAshDMs2vR24CngAmAV8Fzgwxrhwde3bcyxJkqRmJcb4DrB7HctPqPFz\nFWme4yvr07Y9x5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsbZKiRJklRvvbuWkJ+r/S3O\nX07FhiXMapCWvjp7jiVJkqSM4ViSJEnKGI4lSZKkjOFYkiRJyhiOJUmSpIzhWJIkScoYjiVJkqSM\n4ViSJEnKGI4lSZKkjOFYkiRJyhiOJUmSpIzhWJIkScoYjiVJkqSM4ViSJEnKGI4lSZKkjOFYkiRJ\nyhiOJUmSpIzhWJIkScoYjiVJkqSM4ViSJEnKGI4lSZKkjOFYkiRJyhiOJUmSpIzhWJIkScoYjiVJ\nkqRMQVMX8HUTQugVY5zY1HV8nVVWVnHhVf8lfjiDosJ8Lj1rED036bjSNktLyjh28D+47OxBbL5Z\n5+XLZ89ZzOHHDmf49Uez+WadmfzpHM68dCS5HGzZuwsX/OFA8vJy/OUfr/PIU2MBGLj75pz0yz25\n/e6XeeHVjwFYsKiEmbMXc+NNd/Dy6x9yw21PUlxcyJ679uY3x34TgCuuf4rRb39KXl6OM07elwHb\nbcLn0+Zz9mWPUFFRSVVVFRefOYjePTvx+LPjuf2eV8gB3/12P35+1M6UlVdw9mWP8NnUeSxbVsGJ\nx+7Bvnv24dTz/s2s2YsB+GzqfLbr351rLzlsHZx5NYQq4I0NYW4LyK+CXaZD27IV6ye2hXEdobAC\nei+AzRek5Y9tCoWV6ec2ZbDrdHixG5Rkr6KLC6HTUvjmNBjfHia3Tcu7L4Zt5sCyPHhpIyjPQV4V\n7D4Niitgfq6cyy67jNlFs2Fj2GMqtMj2U56DJzaB7WdB9yWwNB9e3ggqgaJK2H0qFFbBpLYQ20MO\naF8KO81IPwPMaglvdYb9pqTbJfnwWtdUT1UOdpu24vhL8uHJTWDQ5HRu9PVVWVnFRbe+zfiJ8ykq\nzOPSk3egZ/c2y9ePfG4Kd4/4iPz8HH16tuOCE7cjLy89K2bPK+X7p45i+MW703uTtsvv8/CoT/nr\nyI+57+qBjPt4Hpff8e7ydW/Hudx8zi7s1L8zp//pTebMX0br4gKuPHVHOm7QAoCKikquu+46fn5Q\nG/b9RjsArr17LK+8PZNcDk77eV922abL8jZff3cWf7xmNKPu+vby/d/17w/Jy8vx/f17cvSgXjz4\n1GT+/fQnACwrq2Tcx/N58e4Dadem6As1Awz/9wRGPjeFvFyOX/+gD/vv1r3Bz73Wf4bjGkIIJwED\ngSObupavs6eejyxbVs59d/yct977jCtvfJpbh644Ze+Om8oFQx9j+oyFK92vrLyC84c8RssWhcuX\nXXHD0/zu1wPZZceenD/kMZ5+/gO22nJDRjzxHvffeQx5eTmO/vU97DcwcPzPduf4n+0OwK9//09O\n+fUBVFZWctHQEfz1lh+zycYd+MOFD/Hm25/SplUR/3t3Cvf/+RgmT5nLaef9hwf/8guuv/15fnLE\nAPYbGHjh1Y+55tZnuf6yw/nTLc/ywF3H0qq4iEE/up3vfrs/z744gfbtirnqgkOYN38ph/78z+y7\nZ5/lQXj+gqX87KS/cdYp+6+Ds66GMqUNVOTg25+m4DimCwz8PK0ryYN3OsOBk1P4fKYHdF2SQiys\nCJjVvjkt/b8sD57qAQNmwqJCmNQODvgkBdQnN4Eei2B6qxRcd5gFH24A4zrAjrPghYIFnPCDk5ky\n+lbGzZ/GgiLoUpLafXPDFSEXYGxH6DUfei+EdzrBRxvAFvPTz4MmQ0EVvNQNPmsNPRbD2A4wsR0U\nVK5o43+dYbMF0HMRTC+GBUUpHH/eCt7unAK4vv6eenUqpcsquO/qgbw1fg5Dhr/HLefuCkBJaQXX\n/3UsI27ch+KWBZx21RuMemMa++yyEWXllVxw81u0KFr5g+OxH83jgScnL7+9de/23HPFngD898XP\n6NppKnsO6Mpd//mQPpu14+Qfbc0jz0/h1vsi5xy/LZ9MXczp17zEZ7OqgG2Xt/l2nMN9V+/FZzOW\n8NtLX+OhG/cBYOrMJfzloQ8pL1/x5Bw6/D0evnlfWrUs4ODfPs2gvTbm8P16cvh+PQG4+Na3OXy/\nnsuDce2aFyxaxj0jPubx2/dnaWk5hw1+1nCsL8VhFSvrzMp/i1SH0W9PYc9dewOwff+NeW/c1JXW\nL1tWzs1XHkHvnp1WWj7kxqf54WE7smHnFb0b74+fys47bArAXrttzstvTKRb13bcee0Pyc/PI5fL\nUV5eQYuiFX+xnxg1nnbtWrL7zluwcOFC2rUtZpONOwCw47Y9GPP2p2zYpS0tWxaybFkFixaXUlCQ\nnupnDN6XgXtsAaRejhZFBeTn5/HoP35N2zYtmTd/KZUVVRQV5nPgPltzyvF7AVBFFfn5K/+63Hjn\nC/zkiG+sdDz6+ptRDBstST93LoE5LVesW1SUAmyLyvRC0LEEZhWnXubyPHhmY3i6RwrVNb3TCcK8\nFKJblcG3pqQX1xxQmUu9sO1LoSx7CpXlpfXlOSjJVTJmzBhGFs1mVkvolAXjcR1Sfe1LV+xnx5nQ\na2Hq/V5SkHqy86tg/09TMKbG/iD1cO/5+cq1zixO93164xTiu2bnIgfsM2VFr7W+3kaPnc2eA7oC\nsP1WHXlvwrzl64oK8/jH0L0obpn6vyqy1zRIAfSogzZjw44rnsRzFyzj2rvHctZx23xhP0tKyrnx\n7+M5J1s3Zuxs9twx7XevAV155a2Zy7e74KRd6du37/L79t28PXdevDu5XI7PZyylbevUMVK6rIIL\nb3mbC07cbqV99dlsAxYtLmNZWQVVVVUr/TF+d8JcJnyygKMO3GyVNRe3LKD7hq1YWlrO0pIKcjn/\nnOvLaVY9xyGEbYAbge2BT4EzYoyPhhAmAdcCJwHdgXuAh7JtuwDDY4ynZm1UAWcCp5GO/zbgXOAw\n4GwgL4TwOjAS2DPGuH+N/Y8GhsQY/9noB/s1tmhxKW3arHhhzc/Po7y8cnkAHbDdJl+4z4OPvEPH\n9q3Yc9fe3H73y8uXV8HyF7DWrYpYuLiUwoJ8OrZvRVVVFUNvfIa+fbrSa9MVQfu2u1/hmou+B0C7\ndu0oKSnjo0mz2GyTjjz/8kdstWVXCvLzyMvlOOiHt7FwcSmXnHkQAB3btwLg48mzGXLj09w85AgA\nCgryeGLUeC6++nEG7r4FxS0Ll4fhRYtLGXz2g/zu+IHLa5g9ZzGvvDmJs07Z7yufT61b5XlQVLHi\ndq4qDVPIA9otg/lFqfe0sDL19rZblnpet54Lm8+HhYUwamM4eFK6T0l+2m7HlBHIA1pWpuf2/zqn\ngN2uLPVWT2sFI3vCsnzY79P0/9y8cvr370+X+17lmfzpTGwHrcvSfnaeATNrBPEcqdZHe6b2tpmd\nllX3bMf26fi6ZYF300WwqNar/OLC1Cu+72fwbsfUG73t7BVvGNQ8LF5STttWKz6Fy8/LUV5RmV77\n8nJ07pCeOPc8/BFLlpazxw5dePCpyXTcoAV77tiV2+//AEjB+dwbxnDmr7b5Qm8ywL+emMy39+hO\nh2zoxKIl5ctDbuviAhYuSWNytuq1ASWVbYD5K92/ID+Pa+8eyz0jP+bc41OQvWTYOxx72BZ07VS8\n0rZb9mzL908dRXHLAvbfbaPlPcQAt9//Ab89eqs11tytczEH/+ZpKiqrOP6IPvU7qVKm2YTjEEJb\n4AngEmA/4JvAgyGEXbNNjgR2BroC7wEB+AbQE3gzhHBnjPH9bNuDgX7ABsBTwCcxxmEhhMuB/jHG\nI0IIWwDnhRC6xBhnhhACsCXw8NrWXFpeSNV62BHdsriYeQsqKClLL1wVlVWUV7WkvGzl7Sqrciwr\nL6SkrIj7R7xDLpfjpdcnEz+czukXjeSGK44ml8stb2fegkpatWpFSVkRpaVlXDBkBK2KW3DOaYdQ\nknW5fTRpBm1aF9O1WzdKywrJ5XJccOaRnD/0cYoK89mi14a0bduW+0eOpUOHttx81c9YvKSUY04a\nztZhM7puuAGvj5nI5dc8wmXnHk737htRktW91x7b8sRu/Tnviv9w/8hxHDpoB6ZNn8+p597LDw7d\nif332WH5tiOfeosD992WssqWlK3HPW2tN+3W1CU0uOKCBeQVF9K6Mv1hzhXMoO2mGwLQGtidEl7u\ntZiWVXlsWJVHu44t6FbZgq5AwQY52gDFBbPJbdqe1uQzOX8JW1JJ201XfIJQThXPF86nsCrHwPJ2\n5G2a4+XCuWxf2YKtK1sxu6KMUT3n871lnShkJv369WPhRhvSe3opn7UuZVauikW5Cp5tB/NyFcxv\nm0fHsg04hIbeAAAgAElEQVToVJVCyVFV8FmulNc2W8R3l3WiiipeK1jI/FwFB5a1p2DTFa87lbly\n8gvn0zp7g9mSGfRp35mW7fPYIlfGmwULad16xTUDuYIZtNqkCwXr2WtXCm7rj5bFxcxdUrD8uCqq\ncpTn2lE9SqGysorr/t8YJn+2gKFn7kNpVQH/evIzcjl48a2XiRMXcPq1b3HasTsy8fOlnH/Luyxb\nVsnHny7i4tvH88dffQOAEaM+5+oz9qKksjUAxcUtmbO4kI0r27Bw8TLatG6xvIbSbJuyyhYrne8T\nf7IzPzt8B376x/+y0UZdeOP9OUycupQb/zGBeYvKOGXI//jlkf0Z9cZMRt5+GK1aFnDOtS8x4oXZ\nHLBHTxYsWsZHU5awff9elFTCuxNm1Vnzztt0ZfqcMkbefjgAv7nwafpt1YNt+qy47qW5W1ZZvOaN\n9JU1m3AMfAeYEWO8Jbs9KoTwEHBMdvuOGONcYG4IYSrw5xjjPGBedrsnUB2Oz4wxzgJmhRCuB44G\nhtXcWYzxw6yn+PvZuqOBB2OMS9e24AnTen+Z4/za69x9HiOfGUOPPn2YMGECG3XvzftTvvgOfXFp\nKyZM24wl+RvzhzOvXL78kksu4Zhf/ILpSzdm4x5bcP8T5fTt25eHn3mBvn135b1Pt+TKK6+kX7/+\nHHLIIYyvMWrjv099zOZht5X29+ioWQw+9WLy8/O59tpr6T9gHyZOnEhpZRnjp25FZWUlFVWteWfS\nxsz931zuvvsZ/vCH86BNF96fAkuWLOHqq6/mrLPOorCwkKXlXZg6byNefr8rl1xyO8cccyx9+vfn\n/RrjTZ988REOPfRQ3p/Sq1HO8dfF3g9e09QlNLjWr7/OmDFj2PuEE5gwYQJbPPgge59xBgAVFRX8\n5z//4ZrDD6e8vJwrrriC7//ud7z22mt8+umn/OIXv2Du3LmMvPRSBg0dSn5+Pm9dey2HHXoovXql\n50JVVRVXXnklO/Xbm0MOOWT5fifcfjv9+/dn9913Z968eTx//vnsf8O1jDrnHMaPH8+uN5/FhHvu\nYZeuXTnggAOW32/YsGHstttubLfddgwfPpxddtmFfv368eGHHzLp3nvZ+9xzueOOO+hWUMAZP/85\neXkr96TNnDmTN2+8kb0vvhiAt6+7jjYDBrDnnnvy2GOPse2cOez94x8v3/7fgwcz8OohFBUVsT4Z\nu7ipK2hYnXt35pHXxrDJDoPS63CPLRm7eNDy9XfccQcFBT047pSfM7E8D8rhD+euWF/9Oly88cZc\nOuRYID1XbrzxRg4++nzGLk6vjQuXvcCcVkcyJzt/G21exb9fWUr+xoN4+eWX2WzLb6y0XxjH1GXb\nMnbxdrz//vu8/vrrHHvssZSXl1OR9yrzWw7iiquPWb71iSeeyM9/cwVTZ86kquA9JpYfTEFJAVWt\nZhNn96DH4n0ZPWY0W/bddfl+8jeGS4f87As1jx8/nrL8eXy47OD0iWTLD3h/9vbkL962cR4Erbea\nUzjeFOgbQphXY1kB8GD285wayyuAmttVf2pa7cMaP08BVtU99lfgKFaE45PqU/CW3T6mqKC8Pndp\nFrbu3o4pHy9myGV/pAq4+MzvMW7cv1iydBlHHPKN5du1brGELbtNolePlf8q1Vx+4R/24OKh9zDi\n3xX06tmZYw/vwKiXRhDjWFrkL+Ca8a8AMPj4/diu/yY8tGAcu+7Um349PqC0rJAPp/ci9KzgsotP\np0WLQgbttw3f3nUJFTt14fJrX2fIZX+korKK73+nH/vvvIgjj72T/Fw599yVQl/PTTpz/unf5ciD\nt+CqK8+moCCPPpt35fijNuTqm/5Cacl8nnj0bzzxaKr95qt+QssWhcyeOYm9tl9Au7YfrJNz3lRu\n2nnYmjdqZqqoYkbBAn43Kj23BpZtwB1HnEhZroqtK1oxMX8Rg+9/iHxybFPemtHHXEiOKiYWzufU\nJ54DYJeytjx/5OkATCiaRXxhMpOyl5iJeSWMLZzHrLfH89zfHwBgp7K2dK/K56GnX+ZfN9xGJbBT\neRuePfw0du3ShnvvvZf5H31Km2VVbFS2Ac8O++/yeqcWzuPtp95lTmULOuTKGf7Ec1CVIwfsXt6O\n+79/Ms8WzaZbVSGnPz4KgH7lrelVmT5WX5grZ0HhfJ49/DQAelPBiFfe4oGb76SoKsc+Ze159oHR\ny/dX0mIOz/3wjPWu5/i3j69fQ6C2GljFlHFTufKiwQBcNHg3xr15M0tKyum3RUdGjXqWHftuyLVX\npsf9xwdvxT67bbr8/q3yZ7NFq+fp1XqD5cs+W7SI4rx59G2dXvDe+3wWvbpVLL8N0Ot7FZx33ZsM\nveQFCgvyuOL336Rztr6653ijonfo2/ozwoBKPhg9iSEXn0JlZRU/O3hzvtXrzZWOoyBXmtpvDTMO\n6sqQS06lsCCPHt3acsJBHSksfJTXZ73PNj3yVqqjrpr7DoBpsZIrLhxMXl6O7bfuwlG7fkouN+UL\n92uullUW8+HSvZu6jPVerqqqeczXE0L4KXBcjHGvGst6AEuB0cBJMcaR2fJJq7qdjTneMcb4v2zd\n74GDYoz7hRAuJBtWka3rQhrbPAj4G9AjxlhjtGLdRo8evRkwsX+PSIvCsjVtri+ppKyI96f0oV+P\nD2hZuKypy1kvXdTp8qYuYb3XetNu7P3gNTx7+Gks/mRaU5ez3jo/HtrUJaz3SirbMHbxIPq2fpSW\neYuaupz1UmllK95ffDBArwEDBkxqqjqqc07nXx1P/owZDdJmxYYbMuvO26GJjw2a12wVjwBbhRCO\nDiHkhxC2Bl4Dvswr3kUhhLYhhD7AYODubHkp0K56oxjjTOBp4Brg3rUJxpIkSWq+mk04jjHOAQ4E\nTgRmA08Ct8YY//wlmpsEjAWeA26OMVaH45FA/xBCrLHt34Dtsv8lSZK0HmtOY46JMY4B9qpj+Wb1\nuQ3cHmMcXEc775KmgqtpcloV36y9vSRJktYvzSocr0shhGJgC+A84M4mLkeSJEnrQLMZVtEE2gMv\nA4XAzU1ciyRJktaB/3M9xzHGtZqfKMY4FWjbyOVIkiTpa8SeY0mSJCljOJYkSZIyhmNJkiQpYziW\nJEmSMoZjSZIkKWM4liRJkjKGY0mSJCljOJYkSZIy/+e+BESSJElfXZdt2tBifkmDtFW6QRtmNUhL\nX509x5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLGcCxJkiRlDMeS\nJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZw\nLEmSJGUMx5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUqagqQuQJEmS6iOEsANwG9APmACc\nEGN8tY7t9gSuB/oAE4FTYozPrK5te44lSZLUbIQQWgIPA3cB7YEbgBEhhDa1tusOjAAuA9oClwMP\nhhCKV9e+PceSJElqTvYGKmOMt2a3h4cQTgUGAf+ssd3PgCdjjA9kt/8RQohA5eoaNxxLkiSpOdkK\nGFtrWcyW17Qj8FkI4d/AXsAHpGEVpatr3GEVkiRJak5aA0tqLVsCtKq1rCNwHHAr0A24B3gkhNBh\ndY0bjiVJktScLAFqjxtuBSyqtawUeDTG+ESMsSzGeEu2zR6ra9xwLEmSpOZkHBBqLQvUPdSiRa1l\n+UBudY075liSJEnNyTNAixDCycAw4KdAV+DxWtvdA7wSQvgO8BjwW6Al8OzqGrfnWJIkSc1GdkHd\nQcDRwBzgZOCQGOPiEMKwEMKwbLv/AYcAlwLzgWOA78YYaw+/WIk9x5IkSWpWYozvALvXsfyEWref\nAJ6oT9v2HEuSJEkZw7EkSZKUMRxLkiRJGcOxJEmSlDEcS5IkSRnDsSRJkpQxHEuSJEkZw7EkSZKU\nMRxLkiRJGcOxJEmSlDEcS5IkSRnDsSRJkpQxHEuSJEkZw7EkSZKUKWjqAiRJktT85HZpT660smHa\natG+QdppCGsdjkMI7YALgDuBCPwVOBIYAxwVY5zUGAVKkiRJ60p9hlXcBHwbqAR+BHwP+AnwSbZO\nkiRJatbqE46/A/wkxhiBw4FHY4z3AecAAxujOEmSJGldqk84LgAWhRCKgP2Ax7LlrYGShi5MkiRJ\nWtfqc0Hei8C1wHygEHgohDCANKTiyUaoTZIkSVqn6tNzfBxQAfQHjo0xzgYOBaYAJzVCbZIkSdI6\ntdY9xzHGz0lhuOay8xq8IkmSJKmJ1Gue4xDCgcBpQB/SRXi/BD6JMd7ZCLVJkiRJ69RaD6sIIfwQ\nuBd4A9gQyAdmAzeGEAY3TnmSJEnSulOfMcdnAb+NMZ5DGntMjPF64FfAKY1QmyRJkrRO1Sccbwm8\nXMfyV4HuDVOOJEmS1HTqE44nAHvVsfz7wAcNU44kSZLUdOpzQd45wL0hhG9k9zsuhLA5aQaLIxuj\nOEmSJGldWuue4xjjSGA3oB3wHnAgUA7sGmN8qHHKkyRJktadek3lFmN8F/h5I9UiSZIkNam1Dsch\nhHbABcCdwHjgb6ThFGOAo2KMkxqjQEmSJGldqc8FeTcB3wYqgR8D3wN+AnySrZMkSZKatfqE4+8A\nP4kxRuBw4NEY432kC/UGNkZxkiRJ0rpUn3BcACwKIRQB+wGPZctbAyUNXZgkSZK0rtXngrwXgWuB\n+UAh8FAIYQBpSMWTjVCbJEmStE7Vp+f4ONLXRvcHjokxzibNcTwFOKkRapMkSZLWqbXuOY4xfk4K\nwzWXndfgFUmSJElNpD5TuRUCJwAPxxgnhRAuB44C3gROjDHOaaQam63rd7iZpVNnNXUZ663Wm3Zj\n7wev4aadh7H4k2lNXc566fx46Jo30ldSUtmGsYvht4/vR8u8RU1dzvorjm3qCtZ/+Z2gK/DxR1Ax\nu6mrWT/lt0/nWI2qPsMqrgbOAtqHEA4B/gDcDnQDbmyE2iRJkqR1qj7h+CjgiBjjW8APgCdjjEOA\n35KmeZMkSZKatfqE4zbAZyGEPOAg4NFseQXpi0EkSZKkZq0+U7mNBs4FZgMbkKZy6wkMAV5uhNok\nSZKkdao+Pce/AXYiXZR3RoxxCvA7YCOcyk2SJEnrgfpM5fY+sH2txWfGGEsbtiRJkiSpadRnWAUh\nhF2BfkB+tigXQmgBDIgx/ryhi5MkSZLWpfrMc3wJcDbwOdAd+Iw0214B8ECjVCdJkiStQ/UZc/wL\n0pd9bEL6yuiBpHD8DPBhI9QmSZIkrVP1CcddgP9mP78F7BpjnAecA/ywoQuTJEmS1rX6hOOpwMbZ\nz+NZcXHeLPwyQ0mSJK0H6nNB3r3AX0MIxwCPAfeHEN4hfTve+EaoTZIkSVqn6tNzfDYwHOgQYxwF\n3ALcCuxAmgNZkiRJatbqM89xBXBZjdsXABc0RlGSJElSU1htOA4hXL62DcUYz/7q5UiSJElNZ009\nx7utZTtVX7UQSZIkqamtNhzHGPcOIeQDhwOPxxgXVK8LIRwHLATuizEajiVJktTsrfaCvBBCK+Bx\n4O/ANrVW7wDcDYwMIbRsnPIkSZKkdWdNs1WcDWwC9I8xvlRzRYzxN8COQH/gj41TniRJkrTurCkc\nHwX8LsYY61oZY3wPOB34UUMXJkmSJK1rawrHGwNj17DNG6TeZUmSJKlZW1M4ngJssYZtegPTG6Yc\nSZIkqemsKRw/AFwYQiiqa2UIoQVwEfBoQxcmSZIkrWtrmuf4cuA1YEwI4QbSEIr5QAdgZ+DkrI2L\nGrNISZIkaV1Ybc9xjHEh6YtAXgCuAt4EJpBC8iXAU8BuMcaZjVynJEmS1OjW1HNMjHE+cGII4RTS\n+OIOwCzgoxhjZSPXJ0mSpK+hXK925CoaJgrm8ts1SDsNYY3huFqMcRkwvhFrkSRJkprUmi7IkyRJ\nkv7PMBxLkiRJGcOxJEmSlDEcS5IkSRnDsSRJkpQxHEuSJEkZw7EkSZKUMRxLkiRJGcOxJEmSlDEc\nS5IkSRnDsSRJkpQxHEuSJEmZgqYuQJIkSaqPEMIOwG1AP2ACcEKM8dXVbL8v8CTQLsa4aHVt23Ms\nSZKkZiOE0BJ4GLgLaA/cAIwIIbRZxfYdgOFAbm3aNxxLkiSpOdkbqIwx3hpjLIsxDgemA4NWsf2t\nwL1r27jhWJIkSc3JVsDYWstitnwlIYQfk3qXb13bxh1zLEmSpOakNbCk1rIlQKuaC0IImwKXAN8E\nita2cXuOJUmS1JwsAYprLWsFLL/QLoSQB/w/4JwY4+f1adxwLEmSpOZkHBBqLQusPNSiB7ArcGsI\nYR7wTrZ8Sgjhm6tr3GEVkiRJak6eAVqEEE4GhgE/BboCj1dvEGP8hBq9yyGEzYCJQA+ncpMkSdJ6\nI8ZYChwEHA3MAU4GDokxLg4hDAshDPsq7dtzLEmSpGYlxvgOsHsdy09YxfaTcJ5jSZIkqX4Mx5Ik\nSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLGcCxJkiRlDMeSJElSxnAs\nSZIkZQzHkiRJUsZwLEmSJGUMx5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUsZwLEmSJGUM\nx5IkSVLGcCxJkiRlDMeSJElSxnAsSZIkZQzHkiRJUqagqQuQJElSM9R7C8hb0jBtVbaCxQ3T1Fdl\nz7EkSZKUMRxLkiRJGcOxJEmSlDEcS5IkSRnDsSRJkpQxHEuSJEkZw7EkSZKUMRxLkiRJGcOxJEmS\nlDEcS5IkSRnDsSRJkpQpaOoC1rUQwtnA1jHGn9bzfm2AhUCvGOOkxqjt66wKeGNDmNsC8qtgl+nQ\ntmzF+oltYVxHKKyA3gtg8wVQCbzSDRYXQq4Kdp4OG5TBwkJ4tVtqdINlsNMMyAGft4J3O6X2OpbC\nN7LlAJ+2gU/awB7TVtx+q2gmL15yCfOKZtO3GCpyMLbjippmFsOgyVBUAS9vlOopqoTdp0JhFYxv\nDx9tAC0q0vY7T4d22TGV5MN/N4W9p6SaS/Lhta6wLA+qcrDbtHT849vD5LbpPt0XwzZzGuPsq7FU\nVlZx0a1vM37ifIoK87j05B3o2b3N8vUjn5vC3SM+Ij8/R5+e7bjgxO3Iy8tx2/0f8MxrUykrr+RH\ng3pxxAGb8f6H87jwlrcoKsxnq97tOOe4bcnLS8/gOfNLOfqPzzPixn1oUZTP7fd/wAtjpgMwf3El\n0+c8zHN3f5e3xs/h8jveJT8/xx47bMhJR28FwG8ufZW5C5ZRkJ+jZVE+d1y0O5ff8Q7jPp4PwKy5\npbRrU8h9Vw/koWc+4c///pC2rQo5bN9NOOKAzVZ5nB9+soDzb3qLKqDnRq25dPAOFOTbZ9IcVFZW\ncdGDMxg/tZSi/ByX/qArPTsXfWG78+6fTvtWefz+O10oq6jinPum8dnccpaVV3Lifp3Yp18bxn1W\nwqX/mUleDooKcgw5uhud2xbwt5fm8e835pPLwS8GduSg7dsyb0kFf/z7VBaVVNK+dT6XHNGVTm0L\nmDxrGec/MJb5+RezQeVUrvtxZzq0zueKETMYM3EpebkcZ3y3Czv2KmbekgoOvHIiW3ZrAcD+27Th\nZ3t24N1PSrjy4ZlUVVXRuW0BV/2oGy0K8zj82sm0bpGelz06FnLFD7sx7rMSLnxgBvl5sFmXIi49\nsit5eTn+8vxcHv3fQgD22ro1Jx3Qad09KFpv/J8LxzHGy5u6huZoSpsUPr/9KcxqCWO6wMDP07qS\nPHinMxw4OYXPZ3pA1yUwr0UKkgd8ClNbpW32nJruu+0s6LoUXt8wtd1tMfyvC+z7KbSshLEdoDQf\nWlbAm11gWmtoX7KinjktYJfytvzivPN49vDTWLw0pebuS9L6sR2g89IUvkd3gV7zofdCeKdTCsRb\nzYM5LVPI7Vi68rFWAq93TW8Cqv2vM2y2AHougunFsKAoBfdJ7eCAT9LPT24CPRZBh2WN9SiooT31\n6lRKl1Vw39UDeWv8HIYMf49bzt0VgJLSCq7/61hG3LgPxS0LOO2qNxj1xjRatyrgf+Nm84+he7G0\ntILh/54AwPk3v8U5x2/Djlt34rp7xjLyuSkcsvcmvDBmOtf8v7HMmrviiXb8kX04/sg+ABx30Rsc\nftRPgSlceMtb3HDWLmzSrRW/vuhVxn40j76bt2fy54sZefM+5HK55W2cfdy2AJSVV/LjM17g4pO2\nZ+78Uq7/2zgevG5v2rUu5NjzXmLX7bow9qP5dR7ntXeP5dSf9WWn/p0589rRPPv6NPbfrfs6Ovv6\nKp56fxGl5VXcd/KmvDV5KUMensktx2680jb3vjKPD6aVsnPvYgBGjF5A+9b5DP3RRsxbUsFh10xm\nn35tuOyhmZx7aBe23rgl974yjzuencMJ+3bi3pfn8eBpPSktq+LgqyZx4HZtuO3pOez4/9u77zip\nyuuP45+Z7YVeFpAiKhyKoIgtdkLs5WdULLFEMMYWG7Emgr0nmthiL7EGJRgLKhYgmqhBBBHBIxZQ\npCwsdRtbZn5/PHdx2KwKRnaW5ft+vXztzr137pzdHS9nzj3P8/TM4/Sh7fj3J2Xc+tJSrjm6E6Of\nXsxZh2xN5i6/Y8GEK5i7pILFK+NMn1vJmHO6M29pNSMfW8jfz+/BrPmVHDyoJaN+3nFtrMlkklHP\nLObPJ3WmR/tsnn53JV8vr2GLNpkkk/Domd3W+dnufLWEM/dty959C7ng8YVMml1Gr07ZPP/+Ksac\n0514DH5x51fsu20h1iVn4/9BpFlp1OTYzPYFrgd6A58Dv3P38Wa2LXAnsAOwBBjl7o+bWRy4DDgN\nKAAmAae4e4mZJYEB7j4zOvczwEx3v8LMJgH/AQ4HOgMTgFPdfZmZXQFs6+5HmVkG8HtgBJAPvACc\n5+6ronOeD1wI5AB/2ri/naatOA86R4ln+8qQWNYpzYbWayAnER63rYSledBmTUg0k0B1PFSPITy3\nY0X4vksZLCyAjEQ4x7QOUJoVKs+5UUW3QwV0K4U5rb55zWW5sCqjgiuvvJLszFX055seofLMkLTu\n/2V4vMOS8DUZ7SuIqsPLcuCjtlCZCV1Kof/ysP39DrDNiv+uQrdeA69vAYU1MLgY4knYZ/43r5uI\nrZtQS9M3dVYJew4uAmD7Pm2ZOWfF2n3ZWXGevGkv8nLDZbK2Nkl2VgZvvV9M7y1b8Zvr3qW0vIYL\nh/cHYPHSCnboG6pUO/Rtx+vvLuSwId2Ix2I8ePXuHHn+xP96/Qn/XkDLwmwGDhxIafnnVFUn6N65\nAIA9dujI2x8soWPbXFaVVXP6Ve+wuqyaU4/qzZCdO609x2MvfM7ugzpgW7ZixifL6dOzFa1bhAri\ngF5t+MCXM+OT5Q3+nLddugsZGTGqqhMsXbGGFvlZP+rvVzaeqV9UsKflA7B9jzxmflW5zv7351Yw\n48tKjtm1FV8Uh0/sB2zXgv0HhltdySRkRHc2bjmhMx1bRu/zBORkxmlTkMG4kT3IzIjx9bJqcrJi\nxGIxPlu8hvMObA/ADj3zuHpcMZXVCZaV1jL5o+X8e8LV7FS0mgMPKmRVRYLcrBhVNUlK1yTIygiv\n99H8NXw0v5IT7vqKdoUZ/P7wjqESnR/nkX8u55NFVezTt4CtOmbzwbwKKqoTjLh3PrW1Sc4/qD3b\n98ijb5dcVpYnSCaTlEXn7tQ6i/tP7br256qpTZKdFUNkQzVacmxm/YHngOOjr/sBT5vZbtHjB4B9\nge2AiWY2FdgH+CXwU2Au8CBwO/CL9XjJk6LX+AJ4ErgLOLbeMSOBI4A9gRXAfdH5f2lmBwO/A4YC\nnwL3bujPnN+lA/GsZlKcz1xJy4JcCtqET+DxjGLyuncgToxMEryTXUKse1uyibMku4T2tfm0rM2h\nIns5L26TZE0swf5VbSjonk0so5jC7qFiUBhfQzKjglh+DsVZqzliTTuyiPF8x2V0a9Oa1slM+gEL\n4mvIzKigoHtrALpnlNGnTRf2Gz2a64efzZc9M+hfG5KKGZmrGJjMpGW3/LXhJ0gyNnsptcDOOW0p\naJ3BNvFS+mflk0WMV9uuYGmrPNbEErQgQa+8Qjy7hPwurShIZlKWtYgWbVpyWG0+72eXMqdnkh1r\non9kSPJu5mo6kqRz55QMvhmoTBR+/0GbsFVlkJPXYu3PGY/HKa3OX9taUNiqBZUJePKFjymtgMHb\n9eTFt4pZuKSU2y8bwteLSzn32kk8e9dhdClqwVszythx2yJe/c8syitjVCYKGbxdOHeSOJWJQpKJ\njLWvf/fTn3LVyP2oApaVZpGfl7M2luzcAooXlVJalceJ/9ePXxzah1Wrq/jlJa9g23Slbetcqqtr\neeqleTz2hwOpTGTTqVMWc+ZN5etlGeTnZfHv6SVs0bndd/6c8xaVctroibTIz2LLHl2oTDTTKltG\n87q9vmrNCnIKWlOZ0QaAeHwupbQlMyPGkpVV3P7qYm4Z0YcJ00uoiVVQmdGOjOiSWFJZy7mPfsyZ\nB/WgMqMdLdtAJTD9i9U89q/VPHB2fyozsiADHntzIX95eT7H7dmJyox2bNO1jAmzEmzVvR2vzFhK\nRXWMxZUtmbO4ivOGbcFPh5/LU7dfzNPvJ/jpwLYQX8oBN31FaWUNo4/emsqMdnTtFOP07h3Y1Vrz\n4tQlXPXsMo7fuzPT5lVy0VG96dY+l3Pu+5jePTJpU9CGE4dkccSuHflySSVn3TubZy8dRJeiJNeM\n/Zy7Xl9BYW4mA3t3pTYrTl42VCST3PrcPHp3bUXnTp2p/PZf4yanKt4i3SFsFhozczsWeN3d/x49\nHm9mzxGSzgLgGndPAFPMbA9gAXAccJu7O4CZnQsUrefr3e7uM6LnXQa8a2b1r/qnAJe6+1fRcRcD\nn5vZacAxwKMp57iYkNivt5/cM2pDDm/S5j/6KL169WLXXcMt57G/+Q1D77h17f4OU6fy/PPP06JF\nC+d9E20AACAASURBVAa07MegQYOYPXs2u2dlceyxx1JSUsI111zDjTfewNiRIxlyxy0AvPfee1R/\n+CE77LADKyZM4OALLwRg4SOP0Nls7evNmjWLktdeY8g55wCwc1kZBQUhGT7w3FOYMmUKQ379axKJ\nBM9fcAEX3HAD2dnr9t8NBT788EPGjRvHqFGj2KWigvz88K9F8tVXWb16NTNnzgTgrViMlfNWMbVT\nSy644AJaXnopx990Ey1atKDn3Ln87W9/Y8jFF1NVVcW9995Lh9xcRowYQTzevPo1Z5WlO4KNqzKr\nhE9W9KJtWXifVSXG80nlIWv3JxIJnnzySRYuTHD22dcyuzyHmtxVbN2/JXOqDoY2kMz8gHcW7sFJ\npw7itr/+ldonFmC2LQkqmFV20NpzVSdeZnbZ/mRXh/fl/PnziedOp6r1YQAUx4awvHzS2ud8tuIl\narNqKc4+gG33rOGTylzIgi7dv2LSZ/3o06cP06ZNYyvbka84HMqAGBx9fHfOuDb8v1jUfTtWZw+i\nMmv2t/+cBXDjH49m4sSJjLr3Y84444yN/WtPj/X9l2MTUdn2UT7J7UXbouhvGv8Nn3QJ18eXp73M\nwjUljHh4NStWlFNVVUV2r4HsvffelJSUcMu9t7Dvvsey5T77MCs639tvv82zzz7LyEuvY1FREdHw\nDgYeBbcfXsONN95Iu6U/ZffjtuKRRx7h2HuK2X77wbTsGGNhz3PIyzuDNrudB8A2PxnGWx9+yOcf\ndyGzYzY3X34GFRXhTl/ujmfTcvd8cnJymBWP02XoGmZMuJj9u59Mx063UTnwYuYAW+80nonLazlg\nlwPYun+S2dnZ0AmyW13G2znHc/0/fs9lV1xP165dmTBhApe9+jXDhw9fe03Oze3LiLNGMKuZXZOl\ncTRmctyRUP1NNQ8YBsyOEmMA3H06gJkVAfNTti8Flq7n632a8v18IBtoW++Y7sBfzaw2ZVt1tL0T\n8EHKa39tZjXr+doAvH3a1VQuWb4hT2myquOVvJQxkYrqMSyOVZGfWcrEI0YCoSo7LaOMvWoLSLCS\n8dnL6PrSRxRnlBMHJo75D9UkKMtZyqRjL6Iwq4zHjzqLLokc3sxcSZdENl8/P5052ct46YjzyCbG\n+9nLKHzuPSYmxwChclycUcHESSNJkuTJnCUc3bo3+/5lNK/d9hD5pVVMfHkkJbFqsjNL+dexl6yN\n/a3MlWyVyKVLIofiWBUrM0uZcOT5PJOzlGFr2pNJjElZK+hdm8+eKVWzFdnVDJ69imkjrqJtViVP\nDr+IXok8ZmaUkYzV8sYR5/NS1nK6JLLpX1vI5PEXNOrfpDGc9crP0h3CRjV0YBmTp7zAiKHLmOFL\n6LtlLv0Kxq/df+Ud71CYFef+UTsRj78OwL7brebx56fQdxgsWVZBsmoZOxe9yRMvfMxtFxqtW+Zw\nw71TGLJjl3XOlRWvoG/BK+Rkh8rx9E8+Zv+ds9k6bzKfVezNgHZTKMgup8Wqv9G1UyF3zprI6ccM\nZOWnD/Hki86do39KeUU1S77+mCG9utGm4HPG+3scskvHta9TU5vgnwtm8rebdqK6JsHpo1/n0EHt\n6JLV8M957jUTGTliMD26tOSrVnNZnLVgnZiblc8/S3cEP6qhRSVMfvs9RvScwoy5q+nbMUG/xbcD\n0G8QjBzUGYB//KeKuYtjnNFnBiWfvsfv75jFJUf2ZJfeH8LiDwF48b0lvPXvxTx2mtGKMbAY5hZX\ncNsLX/LH4aE3vnXia3quGEfZ2wlGbJ/J9j3b8NoH75OzRSnbL7+Hnu3irHr3z7Tc5VwWThvHju1i\nFNbMpTyxmm2X3EltIkkLltNtwQPc9dJXDB3Ylv0HtWfyzGVs17mWfXiGW8uKKZx5E9075HH/B87h\nu3TEn3+HOQvL+f1RW1G8sopE6QJ+suZx2uZWMbD0KTotzmFRrIRFJSX0XbSaM++Zzc69WjF86Baw\n5M70/HE2oqp4Cz7tcHK6w2j2GjM5/hLYtd62nsBkYDszi9clyGZ2JjAV+BpYO8LAzHoCv3T3K4gm\nH0g5V/17ZqmjSnoAFUBJvWMWEnqR34jOnwVsBXxGqFz3SHntjmzg76t8wRIqFq5vLt+0dQDmdoRx\nOYtIJmHXL+Gj3EXUxGGblVDdFsYWlhJPQt9FUFtazFYxeLcTPJtRSiIGAxdB1epiBmbBf4rWUBuD\nVmXQcXEFCWBgC3ihTTEAPUogZ3kJdYXLyjyoaQVli0I9Y+d8eCH5GW9fdRWxsjLsiwrKWEVxIeTl\nQdmSRWtj3yoLphSFJucYsONCqK4qZmALeK5NMRlJKFoB7UrWkFoore0K5YuXklUNAzPh3aI1zIyv\nJKsKdl8Inl/Owk5QXV3FPEoB2G4pdGhG9/By46XpDmGjOmi3Nkz54EtOvng8yWSS68/dgVf/OZvy\nylq23aY1z772KYP7teO0US8DcNJhW7PfT7rwwaxCTrzgRRLJJKPPGEBBVjlbb5HF6aNfITcnk10G\ntGffnVsB3/z+YiTIjZeSEw/J8fwFJey2fQdy4uFdlxMv46ozB3DZrf+ktjbJ7oM6slPfXCCX/0zP\n5ZcXvUgsFmPkSUbn1jVAKV8tWM6RQzt983eKQ2asil+MfIHsrDjDf74NnVvXNPhz5sZLOW3YVlxx\n21tkZcbJy8ng6rMHNd+/eW39y/+m7aB+SaZ8XMXJf5pGErj+mE68OuULyqsSHLNr67XHZSVKyUxW\nkVtbwsMTilldUcX9r3zB/a+E/fecsgU3/f1zOrfJ4oIHPwJgp63zOGf/9vTrHOPkP02HGOxlBezR\ns4Z5S6u4+MlwfS1qlcm1RxeRW1vCdUe354pxsyh9cTTbtFjNLw5oS0YcPvy8kpP/NI1EAg4b1II+\n7cq56MCW/G7MfJ55az752XGuPrqIFrHlXDesA79/dDbJJAzaMo/9+iSoqsnk0r+VM+LP04jFYlx3\ndAcKWca1wzpw6V9nkRGPkZ0R46phRbz1wTymfraKmuoq/j0rDDYZeVB7Bm2Z16h/m40pRu33HyT/\ns1gy2TgjiMxsG0Il9njgeUI/8N8JfcWPEfqJbyYMynsF2A34CXApcAAhUb4PiLn7CWY2GxgDXAH8\nLDrnDSkD8roRephLgKeAhe4+ot6AvNGEu+3HEirS1xAq2b2BvYGxUZwzCD3Lw1mPqdymTp26JfDF\n64ee02yS46aooHsnhvz9ljBbxZeLvv8JssFG++HpDqHZq0wUMqvsIPoVjG++iWlT4LO+/xj5n1Rm\ntGNW0dn0W3w7uc3sw0hTsSajNR8VnQfQc/DgwXPTFUddntO/4AVy4uU/yjnXJPL5qOwQSPPPBo24\nCIi7f0qYPWI0YfDbzcAv3P1d4FBCgruUkCif4u6zgYcISfPrhEpuFvCb6JRnEwbTrYy2PVHvJd8m\nDPSbBywCzmkgrOuBN4F3otfeGTjE3Wvc/XXCTBVjo+cvANY0cA4RERERaSYadSoFd38VeLWB7R8T\nKrj1tycJCez1Dex7DRjwHS/3jruf0MDzrkj5vpowVdxl3xLvfYRqdZ0GjxMRERGR5kHDOEVERERE\nIkqORUREREQizWSFinW5+z7pjkFERERENj2qHIuIiIiIRJQci4iIiIhElByLiIiIiESUHIuIiIiI\nRJQci4iIiIhElByLiIiIiESUHIuIiIiIRJQci4iIiIhElByLiIiIiESUHIuIiIiIRJQci4iIiIhE\nlByLiIiIiESUHIuIiIiIRJQci4iIiIhElByLiIiIiESUHIuIiIiIRJQci4iIiIhElByLiIiIiESU\nHIuIiIiIRJQci4iIiIhElByLiIiIiESUHIuIiIiIRJQci4iIiIhEMtMdgIiIiIhsemJtehHLqv5x\nzlWdBWU/yqn+Z6oci4iIiIhElByLiIiIiESUHIuIiIiIRJQci4iIiIhENCBPRERERDYpZjYIuAfo\nD8wBTnf3dxo47lTgIqAIcGCku7/5XedW5VhERERENhlmlgs8DzwEtAZuA54zs8J6xw0BrgOGRcfd\nATxvZu2+6/xKjkVERERkUzIESLj7X9y92t0fBBYDB9U7ritws7tPd/eEuz8C1BKqzd9KbRUiIiIi\nsinpA8yqt82j7d9scH809bGZ7Q60aOC561DlWEREREQ2JQVAeb1t5UD+tz3BzPoBY4HR7r70u06u\n5FhERERENiXlQF69bflAaUMHm9l+wL+AO9z9hu87uZJjEREREdmUzAas3jajgXYJMxsOPAOc6e7X\nrM/J1XMsIiIiIpuSN4AcMzsbuBs4kTBV2yupB5nZUOAuYL/vm74tlZJjEREREdlkuPsaMzuQkBhf\nB3wKHObuZWZ2d3TM6cDFQDbwktk6heaj3P3lbzu/kmMRERER2aS4+wxgtwa2n57y/X4/5NzqORYR\nERERiSg5FhERERGJKDkWEREREYkoORYRERERiSg5FhERERGJKDkWEREREYkoORYRERERiSg5FhER\nERGJKDkWEREREYkoORYRERERiSg5FhERERGJKDkWEREREYkoORYRERERiSg5FhERERGJKDkWERER\nEYkoORYRERERiSg5FhERERGJKDkWEREREYkoORYRERERiSg5FhERERGJKDkWEREREYkoORYRERER\niSg5FhERERGJKDkWEREREYkoORYRERERiSg5FhERERGJKDkWEREREYkoORYRERERiSg5FhERERGJ\nKDkWEREREYkoORYRERERiSg5FhERERGJKDkWEREREYkoORYRERERiWSmOwARERER2QS1Nsj5kc61\nBpj/I53rf6TKsYiIiIhIRMmxiIiIiEhEybGIiIiISETJsYiIiIhIRMmxiIiIiEhEybGIiIiISERT\nuW0cGQC5HdumO45mLbdDm7VfE9U1aY6meVqTyE93CM1eVSJv7dcYiTRH04xltE53BM1eVbzF2q8x\natMcTfNUFW9Z921GOuNo7pQcbxydAXZ/4Io0h7F5+Mk9o9IdQrP1UVm6I9h8fFoxJN0hNG9F6Q5g\n8/Fph5PTHcLmoDPwWbqDaK6UHG8cU4A9gYWgj88iIiLyo8ggJMZT0h1Ic6bkeCMYPHjwGuCtdMch\nIiIizY4qxhuZBuSJiIiIiESUHIuIiIiIRJQci4iIiIhElByLiIiIiESUHIuIiIiIRJQci4iIiIhE\nlByLiIiIiESUHIuINFFmpmu0iEgj04VXRKQJMrPzgKPMLCPdsYiIbE6UHMtmo34Vzsxi6YpF5LuY\nWSawN3AScJASZGkuGnov61osTY2Wj5bNgplluHutmXUFBgEL3H1quuMSqc/MfgW8DRwL3AmcDsTM\n7EV3r01rcCL/g5TrcBz4OaFAN8fdp6c5NJF1qHIszZ6ZxaML8rbADOAyYIqZnZHm0ETWYWZbAGcC\nFwE9gLOAxcBpwMGqIMumLCUxfhc4FzgG+KeZXWJmrdMbncg3lBxLs+fuCTPrBFwIjHb3XQi3q+8w\ns7PSG51IYGYxd/+a8N7sRPgQtyVwBkqQpfm4EvjM3fdy96OAT4A9gGR6wxL5hpJjafbMrCVwDbAT\n8AyAuz8GnAj82czOTGN4InW3m5NRgjwTGEnDCfIpwM+VIMumIuqfT9UBmBXte5SQh5wIPGFmOzdy\neCINUnIszVK9AR55wExgC+A3dRvd/QngBEIF+YjGjVAkqNeHeYaZHQh8Qeg1LmLdBLkKGEZ4T4s0\nadF7u8bM4mY2xsz2AD4D2pjZU8C2wC7uvpzwXl+RznhF6ig5lmYnpQrX1sw6Ahnu/ifgYuBAM7uk\n7lh3fwrYD3guTeHKZiylHz4OTAd+BdwL/BGoILRSFAGXAFsDxwPnuXtpmkIWWW/ReztG+IBX7e5v\nAVOBg4HdgKPdvdrMzgHaoORYmgglx9KspCQbA4EJwBjgAzO7HJgI3AMcaWYX1j3H3V+LqhuavUUa\nlbsnom+fB6a5+w7AH4DdgdFAJSFB7ksYwJR094XpiFVkQ6TcvbsPOIdw9w53f4Mw4HQucJeZPQf8\nFhjm7sVpCFXkvyg5lmYlGny3BSHZeIAwuGkYcGr039PA/cA5ZnZ8vefWNHK4IphZO0KbxGnRpq0I\nPZnbAVcBOcDhwLXuXp2WIEXWU9188u5eN8DuKsL7+QAz6x/te5bQNnQdcBewh7u/n4ZwRRqkSpls\n8sysENjX3cdFmwYQ5s78S/T4SzM7CHgPmAz8FVgEvNDowcpmr67HOGVTBmHwXaGZ3QH0dve9zOwG\n4NdAO+AEdy9PQ7gi683MMqO7cDFgMJBPmLbt/4CXgVFmdrkHs4gG5ok0NaocS3NwItDezLKix0mg\nyMy6AZhZtrvPAF4Eerl7hbv/I2q/0Kh/aTSpg+/M7HdmNpowKGkwUEhonzgwOrwWeBI4TYmxNHXR\nTCs1UeX4HeBaYCwwDjgI2JdwV2SUmfVNX6Qi30/JsTQHjwKPAOPN7GTChbkFcGqUGFdFx7UEylKf\nqBXHpDGlDL77ANgL6EoYMFoBVBNmpTglmn97OPAnd1+SrnhF1ldKG8XfgM/dfX9gH2A8oV9+L+AQ\nwpzG56cUM0SaHLVVyCarrgrn7qVm1oEwCvoc4EvCbbzJwBZmthDoThj1/0DaAhYJziYkD/9nZlnR\naP0OwI6E/sxTCK0Wh7r7nHQGKrIhooQ3B7gRwN0/MrPFQDfgOHd/wcz2BLLUPy9NmSrHsklKuT1d\nZGaDCNMAXU5Y5OPP0ePtgRKgM7AQGBTd9lMrhaRTPpAT3YauSxBaEJaNfp9w+/ln7j41XQGKrI9v\nuZb2JhQnAHD3pYRpCs3MWrj7V+7+eWPFKPJDqHIsm5yUxHggoadtCTDP3Y8zs3uAGPAnwlLRFzX0\n3MaPWjZH3/J+mwMMBbY3s+nR7eiVQDawzN1XNnacIhuq3uI11wJZwAzgZuBkMzshWokUwl27BYTW\nIZEmT8mxbDKiOYwTdRVjwmwT17r7PWYWM7PdCIOYnoq+3m1mNe4+vu4cSoylsdRLHoYTEoMl7v6M\nmf0SGAXcb2aTgEOBLtTriRdpqur1z38BfBJ9/YLQRnFuNAZkDnAUsJ+7V6YpXJENorYK2SSYWT5w\nvZl1iTZ1JFQpnjSznwKzCfNlvk2oyj1AWFXslTSEK5KaPLxPmGv7EOAaM3uQsGx5OWGhj4nABYSe\nzEXpilfkBxgOfOXuhwEXuvubhDt5S4ERhPf+R8Ce7j4tfWGKbBhVjmVT0Y2QDC+P2im+APYkzJNZ\nDPzL3U8xs4uAvd39XsJ8xmqlkEZVN9dr9PAC4DN3PzLaNw7YGSh39xPMrDPQFijWrBTS1EV98smU\nTTlAGzMrcPey6MNgO+DnhGvyRQ2eSKSJU3IsTV7UTuFmNgd4gjCg6TzCnJnbAavc/b3o8AFAaerz\nlRhLY4k+iNXN9Xoy0JPwQQ4ze5Qwa8quwH1mNt7dxxAGi4o0aalFhpTvPyIscT7YzP4dvffrloBO\nftu5RJo6tVVIkxZdhBN1/cbApYQPdZcDPdz9DaCbmY0xs38QkuXfpDFk2YxFrRQxwowpQwnTCg4w\ns/uB/sBP3L0U6AdoxL5sEuotXvMIMM7MrnT3ycBi4ArgKDPrTmgZ6klorxDZJCk5liYrSohrzWxb\nYIyZ/YuQ/P6WMHjpLDPbGZhJqL5NAnaI5o3VXRFJl7GE/uJr3f16QkvQCGAvd68ys1MIPfPqL5ZN\nQkr//HRCK8XDhBmBIKxQ+h5wGmE1vLOAo9z96zSEKvKjUAIhTVZUMe5FWMzjDkLSMd/dZ5vZhYSJ\n5s8A7nT3c+ueV3drOy1Bi8DjwH6E9+bZwM8Ig+5eNLMaYAvgCHefn74QRTbYocBsdz/WzLKBG81s\nByBBmI2iitBvvMrdl6UxTpH/mZJjaer2A55098vrNphZP0I17iTC0qT7EyoXgHqMJb3cfayZJQh3\nO75y95vMrD9h+dxywuh+VdVkU1MODDOz1YS2oA6Eu3i3Ape6+wXA6jTGJ/KjUXIsTV03YPd621YB\n9wODCVNkzW3kmES+k7uPM7NjgafMLOnuNwNvpDsukR8iuhv3qpldThhU+qy73xTt24V6g6BFGkO0\nOu49hPEcc4DT3f2dBo47jrBQTRHhLt4p7r74u86tnmNpMuqWIo0GNNV5Aig2s1+nbFtAmHg+y90/\nrxuw14ihinwvdx8LHE24/XxWuuMRWV+p19N6U2E+7O6nAhPMbLCZnUdY9vy5dMQpmy8zywWeBx4C\nWgO3Ac+ZWWG94wYCdwPHAe0JYz0e+r7zq3IsTULK4Lu+hJWVIMxffA9hEMj+ZtaHsLjHhUArYF7d\n86OZLESalKiCfDihqiHSpJnZ3u4+OSo4xIBYyvRtDwPZ0YDS/YHTCXfthrr7R+mKWTZbQ4CEu/8l\nevygmZ0PHASMSTnueOAf7v4ugJldDCwxs6Lvqh4rOZYmIboY9wb+CTwGvEu4DTIQuJIwS8WvgD6E\neTX3qDfFm0iT5O6qqkmTZ2b7EFYhvd3dn4gW+0hG+x4CdgEGRHMZ3wzcC1S5u5Y834xVV6ftXH0I\ni4Cl8mh7/ePeXnuAe4mZLQOMMA1hg5QcS1rVW3HpKOCv7v7baN9FhBaKxe7+MPCwmeW7e3m0P3Ul\nMhER+eEyCas3HhOteHcfgJkNAAqAgVFiXHfdXZ7GWCX9VgHL3WnzI593eXTu71NAGCSaqpywSNgP\nOW4dSo4lbeqqvmZWBKwEcoG20TRB7wIfufuZZvZJNOH84ymJcUyJsYjI/8bMngAuBvKAVwgrOh5i\nZrXu/qC7f2hmx0VtbypICACDBw9eNnXq1G2Alj/yqVcNHjx4faYCLCe8Z1Pl89+DQ9f3uHUoOZa0\nSFlxqTfwMqF/7RPC8s/TganufmJ0+BLg49Tnp1SbRUTkBzCzawgLJ31lZtcTbj/fTGhpOzQqYNwf\nXas1f7ysI0pi0zWn9Wz+ezVcIwzir3+crT3ArD3QNtr+rWLJpHIMSQ8z60kY6bzG3S8zs9bAo0Bf\n4FRgGmEVpv7Arpq/WETkx2Fm+YSFlDoSCmWvAfdHK4x2AC4FtiJM2/Zw2gIVaYCZ5QCfAzcQZqM4\nMfq+Z2ofvJltT1hI7GDCegi3A13c/eDvOr+mv5JGVW/KteMIk8j3MLMO7r6CsLDHJMKF+e+EFZd2\nq6tcNHa8IiLNUdSidi9wBLAj8IS7V0f7lhCqx3OAk8zshLQFKtIAd18DHEjII5YRViM9zN3LzOxu\nM7s7Om46odj2IGEGrC7A8O87vyrH0mhSWim6Ar2AhYRp2QYCVwOT3H1V1HOcR+hBLnb3pHrdRER+\nXNHKjdcTKsSTgVvc/bOU/e2B84C7tdy5bE6UHEujSBl8NxB4E5gBdCJM3TaUMGfxzcAbdYPu6j+3\nsWMWEdkcmFkvwoIKk4Hr3H1eyj5df2Wzo+RYGo2ZdSQ00K909z+a2Z6ExLg/0BXIJvQMPVt3e09E\nRDY+M9sWeJqwzPkf3f3zNIckkjbqOZZGYWYFwL+AMwhVY9z9TWAsUAvcSmiWP1yJsYhI43L3mYS5\n5o8CzjYzzWYlmy0lx9IootGjFxGqw7vA2h7kD4GlhBXvTgM08ENEJA2iZaD3Ae7QGA/ZnCk5lkbj\n7uMIo0QvNbNTUqZm60A0IXc0+C6WrhhFRDZn7j47dVCeyOZIPcfS6MzsSOApwiTc/yJMI7RHNDWL\niIiISNqociyNzt3HEubW3Apo6e47ufuaaAo3ERERkbRR5VjSxswOJ4yOPt/d70h3PCIiIiKqHEva\nuPuzwDHAbWZ2VrrjEREREVFyLGnl7n8HDifMrSkiIiKSVmqrEBERERGJqHIsIiIiIhJRciwiIiIi\nElFyLCIiIiIS0drpIiIRM6sbhDHA3WfW27cT8B9gsrvv09ixiYhI41DlWERkXdWEGVTqOxLQCGYR\nkWZOybGIyLom0XByfATwduOGIiIijU1tFSIi6xoH3GlmXd19PoCZDQBaAs8CO9cdaGZ9gNuAPYDF\nwGPAVe5eHe3/JXAh0AuoACYAp7r7SjNrCdwL7AdkEeb6Psvd55vZycAN7t4p5bWeAird/WQzuwLY\nEcgBdgJ+TVht8iLgLKAN8D5h9cn3o+fvBdwK9AOWAvcBV7u7quEiIilUORYRWdcXwAzWrR4fQUia\nE3UbzCwXeAX4ENgeGAEMA66N9u9BSH6vBXoTVoPch5C8AlwFbB1t2xloDdy+AXEeTEi2dwNeA84A\nTgN+BQwGJgOTzKyTmWVE8b8E9AVOBy4BDtuA1xMR2Syociwi8t/GEZLjO6LHRwC/BX6WcswvgFJ3\n/230+BMzOwd4zswuJVSKf+XuT0b755nZBKB/9LgnUAZ84e6roypz+w2IcTXwh7rKr5ldAvzW3SdE\n+0eb2c8IyfJdQFtgETDP3edG++ZuwOuJiGwWlByLiPy3ccBlZtaakLB2JfQipybH/QAzs9KUbTFC\nq8OW7j7VzErNbHR0bP/o69PRsbcCzwFLzGwSoWXjkQ2I8YuUxLgQ6AY8YmYPpRyTA3zm7svM7A5C\nZfr3ZvYi8Ji7L9iA1xMR2SyorUJEpB53nwHMAw4Bfg78w91r6h2WCbxFaKmo+287Qn/xV2a2L/AB\nsCUwERgOPJXyGpMISfcIQg/wjcBrZhan4Vkx6hczKhrYd0K9ePoSep5x97OBPoSkvBfwhpmd/n2/\nCxGRzY2SYxGRho0DDiUkx2Mb2D+bKBF290/d/VNC9fZ6wrX1HOBxdx/h7ve4+3vR8TEAMzsP2M3d\nn3D3E4D9Cf3D3YAqoNDMYimvt9W3BeruKwgtE13qYoniuQTYJ+o7vguY7+43ufvehH7oY37g70ZE\npNlSW4WISMPGEQbc1QCvNrD/MWA08LCZXQO0Ax4A3nX3SjP7GtjDzLYnVHnPJMwsUdfK0BU4y8yG\nR9tOABZG378HFAAXmdkY4HhCFXjGd8R7E3ClmS0mzFRxanTO24ASQg91jpndQBj8txcwfkN/vx0o\nxgAAAM5JREFUKSIizZ0qxyIiDXsHKAXGu3tV/Z3uXkao9rYHpgDPEGaP+FV0yOXAl4TWi38CnYEr\ngEFRRXgU8DqhKj2L0AZxiLtXu/sc4HzgPMJsGD2Be74n3j8DtwB/BGYC+wKHufuMaGq5QwiV62nA\ny1FMozboNyIishmIJZOa4lJEREREBFQ5FhERERFZS8mxiIiIiEhEybGIiIiISETJsYiIiIhIRMmx\niIiIiEhEybGIiIiISETJsYiIiIhIRMmxiIiIiEhEybGIiIiISOT/AcyZunNuRdR6AAAAAElFTkSu\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119dd82b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the classification model and visualizer \n",
+    "bayes = GaussianNB()\n",
+    "visualizer = ClassificationReport(bayes, classes=classes)\n",
+    "\n",
+    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
+    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
+    "g = visualizer.poof()             # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ROCAUC\n",
+    "\n",
+    "Plot the ROC to visualize the tradeoff between the classifier's sensitivity and specificity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RERER8QrZXcIxGHgZu12+lEeBhcaYn40x8cBAoJ1lWeUuc0YREREREa+R3SUc7wFjgVv+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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119f54550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the classification model and visualizer \n",
+    "logistic = LogisticRegression()\n",
+    "visualizer = ROCAUC(logistic)\n",
+    "\n",
+    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
+    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
+    "g = visualizer.poof()             # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ClassBalance\n",
+    "\n",
+    "Class balance chart that shows the support for each class in the fitted classification model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QYA9gO+CpwPOAI5ptbgecBewFbAXcCpyz2o5KkiRJWsN6GdqxDfCNzDwvM+/L\nzB9TepCfC+wJfCAzl2TmtcB5wH7NevsCp2Xmwsy8FTgJ2L8p2we4KDPnZuZi4Chgt4iYvNqOTJIk\nSVqDxhzakZnXU0IxcH8P9QuAnwJLM/PmzurAa5u/pwE3dpVFM+RjGnBNxz5uj4g7gAB6Gt4xNDTE\nyMhIL1W1ARsaGnrAb0mStOFZsmRJX/Y7PDy80vKexki3RcQjgUuA6yi90od1VbkHmNT8vXlzu7Ns\nI6A1Sln3umNasGBBz+3Whm9wcLDfTZAkSWvI/Pnz+92EUfUcpCPiKcDXgV8Bb6KMe57YVW0SZcw0\nlGC8WVfZvZm5JCK6y7rXHdPUqVOZMGFCr9W1gRoaGmJwcJApU6bQarX63RxJGrefzbug302Q1nnT\np0/vy36Hh4dX2nnb66wdOwDfAmYD783M+yJiATAhIp6Umb9tV2X5cI6bmttzO8pu6iprb38rYMuO\n8jG1Wi2Dk+7XarWYOLH7c50kSdoQ9Os9fmBgYKXlYwbp5gTAbwGnZObH2ssz8+6IuIgyg8fBwHRg\nb2D3psps4MiIuBxYChwDnNuUnQ9cFRGzgHmUExEvzczbx3FskiRJUt/00iN9IPAY4ISIOKFj+enA\nwZRp7H5PGZZxZGa2e6DPpEyLdy1lXPRs4FQoJzA24XsW8Djge8ABq3w0kiRJ0lrSy6wdMykXVlmR\nN65gvWXA8c3PaOVzgDk9tFGSJEla53iJcEmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmC\nQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqS\nJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmq\nYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCW\nJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmS\nKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhik\nJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAqbjKdyRDwT+Fpmbt3c3gmYCyzuqDYzM2dG\nxAAwEzhN9yFkAAARuElEQVSo2c8XgMMzc1mz7l7AicBk4ArgwMz80yoejyRJkrRW9BSkm1B8AHAq\ncG9H0fbApZn5ilFWOwTYA9gOGAG+DhwBnBwR2wFnAbsCPwXOAM4Bdq87DEmSJGnt6nVox7HAYZQe\n5E7bA9evYJ19gdMyc2Fm3gqcBOzflO0DXJSZczNzMXAUsFtETB5P4yVJkqR+6XVoxyzKMI0XdS3f\nHlgSEb8GNgbmAMdl5hAwDbixo24C0fRuTwOuub8g8/aIuAMIoKfhHUNDQ4yMjPTYfG2ohoaGHvBb\nkiRteJYsWdKX/Q4PD6+0vKcgnZkLASKiu+gvwJXA2ZSxzhcAM4Cjgc2Bezrq3kPpAW+NUtYun9RL\newAWLFjQa1U9BAwODva7CZIkaQ2ZP39+v5swqnGdbNgtM1/VcfPmiJhJ6bk+mhKMN+sonwTcm5lL\nIqK7rF2+qNd9T506lQkTJtQ1XBuMoaEhBgcHmTJlCq1Wq9/NkaRx+9m8C/rdBGmdN3369L7sd3h4\neKWdt9VBOiK2AI4DZmTm3c3iiUC77/0mylCNue1VmmWdZe1tbQVs2VE+plarZXDS/VqtFhMnTux3\nMyRJ0hrQr/f4gYGBlZavSo/0XcBrgIGIOBrYhhKsP9OUzwaOjIjLgaXAMcC5Tdn5wFURMQuYRzkR\n8dLMvH0V2iNJkiStNdUXZMnM+4BXAk8HbgO+TxkjfXpT5UzgIuBaykmHV1OmzyMzrwcOppzE+Gdg\na8r0epIkSdJ6YVw90pl5JbBVx+0bgV1WUHcZcHzzM1r5HMosH5IkSdJ6x0uES5IkSRUM0pIkSVIF\ng7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7Qk\nSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJU\nwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAt\nSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIk\nVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBI\nS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVdhkPJUj\n4pnA1zJz6+b2FsAs4CXAXcCMzPxcUzYAzAQOavbzBeDwzFzWlO8FnAhMBq4ADszMP62Og5IkSZLW\ntJ56pCNiICL+FfgOMKGj6LPAIkoYfj1wckQ8uyk7BNgD2A54KvA84Ihme9sBZwF7AVsBtwLnrOrB\nSJIkSWtLr0M7jgUOo/QgAxARDwP2BD6QmUsy81rgPGC/psq+wGmZuTAzbwVOAvZvyvYBLsrMuZm5\nGDgK2C0iJq/qAUmSJElrQ69DO2ZRhmm8qGPZVGBpZt7csSyB1zZ/TwNu7CqLZsjHNOCa+wsyb4+I\nO4AAehreMTQ0xMjISI/N14ZqaGjoAb8lSdKGZ8mSJX3Z7/Dw8ErLewrSmbkQICI6F28OLO6qeg8w\nqaP8nq6yjYDWKGXd645pwYIFvVbVQ8Dg4GC/myBJktaQ+fPn97sJoxrXyYZd7gEmdi2bRBkz3S7f\nrKvs3sxcEhHdZd3rjmnq1KlMmDBh7IraoA0NDTE4OMiUKVNotVr9bo4kjdvP5l3Q7yZI67zp06f3\nZb/Dw8Mr7bxdlSC9AJgQEU/KzN82y4Llwzluam7P7Si7qausFERsBWzZUT6mVqtlcNL9Wq0WEyd2\nf66TJEkbgn69xw8MDKy0vDpIZ+bdEXERcFJEHAxMB/YGdm+qzAaOjIjLgaXAMcC5Tdn5wFURMQuY\nRzkR8dLMvL22PZIkSdLatKoXZDkY2BT4PXAhcGRmtnugzwQuAq6l9FJfDZwKkJnXN+vOAv4MbA0c\nsIptkSRJktaacfVIZ+aVlHmf27fvAN64grrLgOObn9HK5wBzxrN/SZIkaV3hJcIlSZKkCgZpSZIk\nqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJB\nWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIk\nSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapg\nkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYk\nSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIq\nGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqbLKq\nG4iI9wIzgeGOxS8Hfg7MAl4C3AXMyMzPNesMNOsc1LThC8DhmblsVdsjSZIkrQ2rHKSB7YFjM/MT\nnQsj4ivAImAysB1waUTMz8wfAocAezTLR4CvA0cAJ6+G9kiSJElr3OoK0ud0LoiIhwF7Attm5hLg\n2og4D9gP+CGwL3BaZi5s6p8EfJhxBOmhoSFGRkZWQ/O1PhsaGnrAb0mStOFZsmRJX/Y7PDy80vJV\nCtIRMQkI4LCImA38Ffg48BNgaWbe3FE9gdc2f08Dbuwqi4gYyMye0vGCBQtWpenawAwODva7CZIk\naQ2ZP39+v5swqlXtkZ4MfB/4NPA64FnAJcApwOKuuvcAk5q/N29ud5ZtBLSAnj5yTJ06lQkTJlQ3\nXBuGoaEhBgcHmTJlCq1Wq9/NkaRx+9m8C/rdBGmdN3369L7sd3h4eKWdt6sUpDPz18CLOhZ9LyLO\nBV4ITOyqPokyZhpKcN6sq+zeZhhIT1qtlsFJ92u1Wkyc2P2UkyRJG4J+vccPDAystHyVpr+LiB0i\n4uiuxROB3wITIuJJndVZPpzjpuZ2Z9lNq9IWSZIkaW1a1aEdi4APRMQg8FXgxcCbKb3UjwJOioiD\ngenA3sDuzXqzgSMj4nJgKXAMcO4qtkWSJElaa1apRzozfwm8EXg/cDdwJnBAZv4YOBjYFPg9cCFw\nZGbObVY9E7gIuJbSS301cOqqtEWSJElam1Z5+rvMvIRygmH38jsoIXu0dZYBxzc/kiRJ0nrHS4RL\nkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUoVVnv7uoWbjI7xuzLrpxrGraK1a\ndsq+/W6CJElrlD3SkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRV\nMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhL\nkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJ\nFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzS\nkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJ\nUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUoVN+rXjiNgeOBuYDiwA/i0zf9iv9kiSJEnj0Zce\n6YiYCFwCnAM8CvgkcHFEPKwf7ZEkSZLGq1890i8G7svMTze3Z0XEe4DdgTljrLsxwKJFixgaGlqD\nTRzd4zffdK3vU1of/e1vf+t3E6T1wqYDk/rdBGmd16/3lKVLl7b/3Hi08n4F6WnAjV3Lslk+lscD\n/OY3v1nNTerNRa+e2pf9SuubBQsW9LsJ0nph2sQ9+t0EaZ23DrynPB74VffCfgXpzYF7upbdA/Ty\nsfxHwAuAhcCy1dwuSZIkqW1jSoj+0WiF/QrS9wCbdS2bBCwaa8Udd9xxCPj+mmiUJEmS1OVBPdFt\n/Zr+7iYgupYFDx7uIUmSJK2T+tUjfTnQioh3AmcB+wKTgW/3qT2SJEnSuPSlRzozh4CXA3sBdwDv\nBF6Vmf/Xj/ZIkiRJ4zUwMjLS7zZIkiRJ6x0vES5JkiRVMEhLkiRJFQzSkiRJUgWDtB4SIuIp/W6D\nJGnDEBHHRsS5Fes9LCJGIuLJa6BZ6oN+TX8nrTURcSjwIuAN/W6LJGn9l5kz+90GrRsM0noo2AoY\n6HcjJEmrLiJeBpwEbAvcDBybmd+MiH8C/hPYAfgLcEJmfjEiNgKOB94ObA5cCRyYmbdHxAjwtMz8\nebPtrwA/z8wPRsSVwLXAnpRLRH8HODgz74iIDwL/lJmvj4iNgeOAf6VcpfnrwLsz82/NNt8DHAm0\ngNPW7L2jtc0grb6LiKcBZwD/DPwOOKr5p/gb4D+AQ4GtgXOBi5q6jwFmZeZ7mm2MAEcDh1Oe12dT\n/nG+BjgW2CgirqX8g3tBZr6sY//XAR/LzDlr/GAlSdUiYjpwMbBP83tX4IKIeG5z+3PAy4CnA1c0\n/993Bt4KvAT4DTCL8j6ydw+73K/Zx6+B84EzgTd31TkceC3wAuBO4LPN9t8aEXtQ3oNeCgwCnxn/\nUWtd5hhp9VVEPJzyKX8Opef4ncDsiNi2qfIG4JnAjsBBwPuAnYAXAoc2/1TbXgFMb+rvBbwtMy8E\nZgJfy8xnAucBO0fEY5r9BzAVuGRNHqckabV4M3BZZn41M+/NzG9SAvRnKL3NH8nM4cz8EfB84I+U\n94NPZjEEHAac2OP+zsjMn2bm3TSdMxHR6qpzIDAjM3/X1DsKeEtETATeBJzbbOOepkwbEIO0+m0P\n4M+ZeWbzT/FKSq/z/k35ZzPzr5n5C2Ah8LnMvDMzb2hub9OxraMz87bM/BVwOuWf5wNk5iBwHfC6\nZtFewFczc/EaODZJ0ur1WEqvcqdbKJ0tCzPzvvbCzLy+GV4xGfh9x/LbMnN+j/sb7Pj798AEYMuu\nOk8CvhARd0bEncANwNJm+eOAP3Ts+w/AvT3uW+sBh3ao354E/GPzz6dtE+Crzd93dCxfRvnarO0+\nHvhhsPsf3uNWsM/ZlF6CsyhB+tDxN1uS1Ae/BZ7dtewpwFXA0yNio3aYjoh3UDpO/gA8oV25mcXp\nrZn5Qcr7yISObT26a9tbd/y9DbAYuL2rzkLK2OnLm+1vCvw98CtKj/j9HT4R8VjMXhsUH0z120Lg\nmsx8YXtBRDyR8s/qOmA817DfGvhT8/c2lPHWo/ky8ImIeAnwCODy8TZaktQXXwaOjYg9KUPydgVe\nRRkHPRs4KiI+Tjnh8ETgucAXgWMi4huUUP0hlp+A/kvg1RHxE2AX4DmUUN52aERcRAnPHwG+lJnD\nZVTg/f4b+EBE3ATc1tR7A+VkyHOBCyNiNvBT4KOr767QusChHeq3bwDTImKviNg4Ip4KzKWcJT1e\nMyLi4c346ncBX2iWD1ECMwCZ+RfgMuBUyj/FZat0BJKktaIZnrcn8H7KN5QfB/bOzLnAKylh+DZK\nqD4wM28CzqGcYHgZpYd4U5Z/E/lOyomCdzXLzuva5TWUMdi3ALdS3lu6nQR8D/hhs+9nAq9ohite\nRpmx48Jm/T9S3pO0gRgYGRlPh5+0+kXEDpQpgbYDFgFnZeZHmlk7Ds3Mrzf1Vni7mbXjDMosHZsA\n/5GZJzf1ngZ8G7g7M6NZtjell+IZmTlvrRyoJGm90Ux/95XM/FS/26J1l0FaG4TuuUB7qP88yomL\n09ZsyyRJ6yODtHrhGGk9pETEZsAU4ATgv/rcHEmStB4zSOuh5lHADyhXq/rPPrdFkrSOysyd+90G\nrfsc2iFJkiRVcNYOSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqcL/B5Dc+YPepK38AAAA\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bac2128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Instantiate the classification model and visualizer \n",
+    "forest = RandomForestClassifier()\n",
+    "visualizer = ClassBalance(forest, classes=classes)\n",
+    "\n",
+    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
+    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
+    "g = visualizer.poof()             # Draw/show/poof the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/morganmendis/testing.ipynb b/examples/morganmendis/testing.ipynb
index 9ae232819..ac5d39f40 100644
--- a/examples/morganmendis/testing.ipynb
+++ b/examples/morganmendis/testing.ipynb
@@ -774,7 +774,7 @@
     "from sklearn.naive_bayes import GaussianNB\n",
     "from sklearn.linear_model import LogisticRegression \n",
     "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance"
    ]
diff --git a/examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb b/examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb
index 688cef8a3..71dc94707 100755
--- a/examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb	
+++ b/examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb	
@@ -505,7 +505,7 @@
     "# Classifier Evaluation Imports\n",
     "\n",
     "from sklearn.naive_bayes import GaussianNB\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from yellowbrick.classifier import ClassificationReport, ClassBalance"
    ]
@@ -620,7 +620,7 @@
     "# Classifier Evaluation Imports\n",
     "\n",
     "from sklearn.naive_bayes import MultinomialNB\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from yellowbrick.classifier import ClassificationReport, ClassBalance"
    ]
diff --git a/examples/nealhumphrey/confusionMatrix.ipynb b/examples/nealhumphrey/confusionMatrix.ipynb
index 58aa0e521..bf7d6b245 100644
--- a/examples/nealhumphrey/confusionMatrix.ipynb
+++ b/examples/nealhumphrey/confusionMatrix.ipynb
@@ -46,7 +46,7 @@
     "import yellowbrick\n",
     "\n",
     "from sklearn.datasets import load_digits\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "\n",
     "from yellowbrick.classifier.confusion_matrix import ConfusionMatrix"
diff --git a/examples/nealhumphrey/light-dark-text.ipynb b/examples/nealhumphrey/light-dark-text.ipynb
index 3d16afdfa..d54dad97b 100644
--- a/examples/nealhumphrey/light-dark-text.ipynb
+++ b/examples/nealhumphrey/light-dark-text.ipynb
@@ -142,7 +142,7 @@
     "\n",
     "\n",
     "from sklearn.datasets import load_digits\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from yellowbrick.classifier import ConfusionMatrix\n",
     "\n",
diff --git a/examples/pbwitt/Testing Paul Witt Yellowbrick .ipynb b/examples/pbwitt/testing.ipynb
similarity index 99%
rename from examples/pbwitt/Testing Paul Witt Yellowbrick .ipynb
rename to examples/pbwitt/testing.ipynb
index b61a11d76..3a2c71b4a 100644
--- a/examples/pbwitt/Testing Paul Witt Yellowbrick .ipynb	
+++ b/examples/pbwitt/testing.ipynb
@@ -773,18 +773,18 @@
    "source": [
     "from sklearn import metrics\n",
     "from sklearn import cross_validation\n",
-    "from sklearn.cross_validation import KFold\n",
+    "from sklearn.model_selection import KFold\n",
     "\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
     "\n",
     "from sklearn import linear_model\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "from sklearn import preprocessing\n",
     "from sklearn.linear_model import ElasticNet, Lasso\n",
     "from sklearn.linear_model import Ridge, Lasso \n",
-    "from sklearn.cross_validation import KFold\n"
+    "from sklearn.model_selection import KFold\n"
    ]
   },
   {
diff --git a/examples/tuulihill/Tuulitest.ipynb b/examples/tuulihill/Tuulitest.ipynb
index 28dd1936c..a9ad1953f 100644
--- a/examples/tuulihill/Tuulitest.ipynb
+++ b/examples/tuulihill/Tuulitest.ipynb
@@ -521,7 +521,7 @@
     "# Regression Evaluation Imports\n",
     "\n",
     "from sklearn.linear_model import Ridge, Lasso\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from yellowbrick.regressor import PredictionError, ResidualsPlot"
    ]
@@ -659,7 +659,7 @@
     "from sklearn.naive_bayes import GaussianNB\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance"
    ]
diff --git a/requirements.txt b/requirements.txt
index 964d7e1c6..f2f0cbac9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,44 +1,28 @@
 # Dependencies
 matplotlib>=1.5.1
-scipy>=0.17.1
-scikit-learn>=0.17.1
+scipy>=0.18
+scikit-learn>=0.18
 numpy>=1.11.0
 cycler>=0.10.0
 
-## Utilities and Nested Dependencies (installed from above)
-#pyparsing==2.1.10
-#pytz==2016.7
-#python-dateutil==2.5.3
-#six==1.10.0
-
-## Testing Requirements (uncomment for development)
+# Testing Requirements (uncomment for development)
 #nose>=1.3.7
 #coverage>=4.1
 #requests>=2.10.0
 
-## Python 2 Testing Requirements
+# Python 2 Testing Requirements
 #mock>=2.0.0
 
-## Build Requirements (uncomment for deployment)
-#wheel>=0.29.0
+# Optional Testing Dependencies (uncomment for development)
+#nltk>=3.0
+#pandas>=0.18
 
 ## Documentation (uncomment to build documentation)
-#Sphinx>=1.4.4
-#sphinx-rtd-theme>=0.1.9
+#Sphinx>=1.5.5
+#sphinx-rtd-theme>=0.2.4
 #numpydoc>=0.6.0
 
-## Documentation Dependencies (installed from above)
-#alabaster==0.7.9
-#Babel==2.3.4
-#docutils==0.12
-#imagesize==0.7.1
-#Jinja2==2.8
-#MarkupSafe==0.23
-#Pygments==2.1.3
-#snowballstemmer==1.2.1
+# Build Requirements (uncomment for deployment)
+#wheel>=0.29.0
+
 
-## Pip stuff (ignore)
-#Python==3.5.1
-#pip==8.1.2
-#setuptools==22.0.5
-#wsgiref==0.1.2
diff --git a/setup.py b/setup.py
index 3da905580..5d9deaca8 100755
--- a/setup.py
+++ b/setup.py
@@ -33,8 +33,9 @@
 ## Basic information
 NAME         = "yellowbrick"
 DESCRIPTION  = "A suite of visual analysis and diagnostic tools for machine learning."
-AUTHOR       = "Rebecca Bilbro"
-EMAIL        = "rbilbro@districtdatalabs.com"
+AUTHOR       = "Rebecca Bilbro, Benjamin Bengfort"
+EMAIL        = "info@districtdatalabs.com"
+MAINTAINER   = "Benjamin Bengfort"
 LICENSE      = "Apache 2"
 REPOSITORY   = "https://github.com/districtdatalabs/yellowbrick"
 PACKAGE      = "yellowbrick"
@@ -64,7 +65,7 @@
 PROJECT      = os.path.abspath(os.path.dirname(__file__))
 REQUIRE_PATH = "requirements.txt"
 VERSION_PATH = os.path.join(PACKAGE, "version.py")
-PKG_DESCRIBE = "DESCRIPTION.txt"
+PKG_DESCRIBE = "DESCRIPTION.rst"
 
 ## Directories to ignore in find_packages
 EXCLUDES     = (
@@ -116,7 +117,7 @@ def get_requires(path=REQUIRE_PATH):
     "license": LICENSE,
     "author": AUTHOR,
     "author_email": EMAIL,
-    "maintainer": AUTHOR,
+    "maintainer": MAINTAINER,
     "maintainer_email": EMAIL,
     "url": REPOSITORY,
     "download_url": "{}/tarball/v{}".format(REPOSITORY, get_version()),
diff --git a/tests/__init__.py b/tests/__init__.py
index a26de3714..49293f019 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -28,7 +28,7 @@
 ## Test Constants
 ##########################################################################
 
-EXPECTED_VERSION = "0.4"
+EXPECTED_VERSION = "0.4.1"
 
 
 ##########################################################################
diff --git a/tests/checks.py b/tests/checks.py
new file mode 100644
index 000000000..cd4b54751
--- /dev/null
+++ b/tests/checks.py
@@ -0,0 +1,166 @@
+# tests.checks
+# Performs checking that visualizers adhere to Yellowbrick conventions.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Mon May 22 11:18:06 2017 -0700
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: checks.py [] benjamin@bengfort.com $
+
+"""
+Performs checking that visualizers adhere to Yellowbrick conventions.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import sys
+sys.path.append("..")
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from yellowbrick.base import ModelVisualizer, ScoreVisualizer
+from yellowbrick.classifier.base import ClassificationScoreVisualizer
+from yellowbrick.cluster.base import ClusteringScoreVisualizer
+from yellowbrick.features.base import FeatureVisualizer, DataVisualizer
+from yellowbrick.regressor.base import RegressionScoreVisualizer
+from yellowbrick.text.base import TextVisualizer
+
+
+##########################################################################
+## Checking runable
+##########################################################################
+
+def check_visualizer(Visualizer):
+    """
+    Check if visualizer adheres to Yellowbrick conventions.
+
+    This function runs an extensive test-suite for input validation, return
+    values, exception handling, and more. Additional tests for scoring or
+    tuning visualizers will be run if the Visualizer clss inherits from the
+    corresponding object.
+    """
+    name = Visualizer.__name__
+    for check in _yield_all_checks(name, Visualizer):
+        check(name, Visualizer)
+
+
+##########################################################################
+## Generate the specific per-visualizer checking
+##########################################################################
+
+def _yield_all_checks(name, Visualizer):
+    """
+    Composes the checks required for the specific visualizer.
+    """
+
+    # Global Checks
+    yield check_instantiation
+    yield check_estimator_api
+
+    # Visualizer Type Checks
+    if issubclass(Visualizer, RegressionScoreVisualizer):
+        for check in _yield_regressor_checks(name, Visualizer):
+            yield check
+
+    if issubclass(Visualizer, ClassificationScoreVisualizer):
+        for check in _yield_classifier_checks(name, Visualizer):
+            yield check
+
+    if issubclass(Visualizer, ClusteringScoreVisualizer):
+        for check in _yield_clustering_checks(name, Visualizer):
+            yield check
+
+    if issubclass(Visualizer, FeatureVisualizer):
+        for check in _yield_feature_checks(name, Visualizer):
+            yield check
+
+    if issubclass(Visualizer, TextVisualizer):
+        for check in _yield_text_checks(name, Visualizer):
+            yield check
+
+    # Other checks
+
+
+def _yield_regressor_checks(name, Visualizer):
+    """
+    Checks for regressor visualizers
+    """
+    pass
+
+
+def _yield_classifier_checks(name, Visualizer):
+    """
+    Checks for classifier visualizers
+    """
+    pass
+
+
+def _yield_clustering_checks(name, Visualizer):
+    """
+    Checks for clustering visualizers
+    """
+    pass
+
+
+def _yield_feature_checks(name, Visualizer):
+    """
+    Checks for feature visualizers
+    """
+    pass
+
+
+def _yield_text_checks(name, Visualizer):
+    """
+    Checks for text visualizers
+    """
+    pass
+
+
+##########################################################################
+## Checking Functions
+##########################################################################
+
+def check_instantiation(name, Visualizer, args, kwargs):
+    # assert that visualizers can be passed an axes object.
+    ax = plt.gca()
+
+    viz = Visualizer(*args, **kwargs)
+    assert viz.ax == ax
+
+
+def check_estimator_api(name, Visualizer):
+    X = np.random.rand((5, 10))
+    y = np.random.randint(0,2, 10)
+
+    # Ensure fit returns self.
+    viz = Visualizer()
+    self = viz.fit(X, y)
+    assert viz == self
+
+
+if __name__ == '__main__':
+    import sys
+    sys.path.append("..")
+
+    from yellowbrick.classifier import *
+    from yellowbrick.cluster import *
+    from yellowbrick.features import *
+    from yellowbrick.regressor import *
+    from yellowbrick.text import *
+
+    visualizers = [
+        ClassBalance, ClassificationReport, ConfusionMatrix, ROCAUC,
+        KElbowVisualizer, SilhouetteVisualizer,
+        ScatterVisualizer, JointPlotVisualizer, Rank2D, RadViz, ParallelCoordinates,
+        AlphaSelection, ManualAlphaSelection,
+        PredictionError, ResidualsPlot,
+        TSNEVisualizer, FreqDistVisualizer, PosTagVisualizer
+    ]
+
+    for visualizer in visualizers:
+        check_visualizer(visualizer)
diff --git a/tests/requirements.txt b/tests/requirements.txt
new file mode 100644
index 000000000..9a2ebe61f
--- /dev/null
+++ b/tests/requirements.txt
@@ -0,0 +1,18 @@
+# Library Dependencies
+matplotlib>=1.5.1
+scipy>=0.17.1
+scikit-learn>=0.18
+numpy>=1.11.0
+cycler>=0.10.0
+
+# Testing Requirements 
+nose>=1.3.7
+coverage>=4.1
+requests>=2.10.0
+
+# Python 2 Testing Requirements
+mock>=2.0.0
+
+# Optional Testing Dependencies 
+nltk>=3.0
+pandas>=0.18
diff --git a/tests/test_base.py b/tests/test_base.py
index fe948c74b..282456280 100644
--- a/tests/test_base.py
+++ b/tests/test_base.py
@@ -37,31 +37,36 @@ class BaseTests(unittest.TestCase):
     Test the high level API for yellowbrick
     """
 
-    def test_visualizer_returns_self(self):
+    def test_ax_property(self):
         """
-        Assert that all visualizers return self
+        Test the ax property on the base Visualizer
         """
         visualizer = Visualizer()
-        self.assertIs(visualizer.fit([]), visualizer)
+        self.assertIsNone(visualizer._ax)
+        self.assertIsNotNone(visualizer.ax)
 
-    # def test_base_poof(self):
-    #     """
-    #     Assert that the base visualizer implements poof interface
-    #     """
-    #     with self.assertRaises(NotImplementedError):
-    #         visualizer = Visualizer()
-    #         visualizer.poof()
+        visualizer.ax = "foo"
+        self.assertEqual(visualizer._ax, "foo")
+        self.assertEqual(visualizer.ax, "foo")
 
-    def test_fit_draw(self):
+    def test_visualizer_fit_returns_self(self):
         """
-        Assert fit_draw calls fit and draw
+        Assert that all visualizers return self
         """
-
         visualizer = Visualizer()
-        visualizer.fit = mock.Mock()
-        visualizer.draw = mock.Mock()
+        self.assertIs(visualizer.fit([]), visualizer)
 
-        visualizer.fit_draw([])
+    def test_draw_interface(self):
+        """
+        Assert that draw cannot be called at the base level
+        """
+        with self.assertRaises(NotImplementedError):
+            visualizer = Visualizer()
+            visualizer.draw()
 
-        visualizer.fit.assert_called_once_with([], None)
-        visualizer.draw.assert_called_once_with()
+    def test_finalize_interface(self):
+        """
+        Assert finalize returns the finalized axes
+        """
+        visualizer = Visualizer()
+        self.assertIs(visualizer.finalize(), visualizer.ax)
diff --git a/tests/test_classifier/test_confusion_matrix.py b/tests/test_classifier/test_confusion_matrix.py
index 2fa1ccc42..13a667b49 100644
--- a/tests/test_classifier/test_confusion_matrix.py
+++ b/tests/test_classifier/test_confusion_matrix.py
@@ -4,7 +4,7 @@
 
 
 from sklearn.datasets import load_digits
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 
 
diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py
index 5283aa49b..67bcb2426 100644
--- a/tests/test_cluster/test_elbow.py
+++ b/tests/test_cluster/test_elbow.py
@@ -17,15 +17,57 @@
 ## Imports
 ##########################################################################
 
+import unittest
+import numpy as np
+
 from ..base import VisualTestCase
 from ..dataset import DatasetMixin
 
 from sklearn.datasets import make_blobs
 from sklearn.cluster import KMeans, MiniBatchKMeans
+from yellowbrick.cluster.elbow import distortion_score
 from yellowbrick.cluster.elbow import KElbowVisualizer
 from yellowbrick.exceptions import YellowbrickValueError
 
 
+##########################################################################
+## K-Elbow Helpers Test Cases
+##########################################################################
+
+X = np.array(
+      [[-0.40020753, -4.67055317, -0.27191127, -1.49156318],
+       [ 0.37143349, -4.89391622, -1.23893945,  0.48318165],
+       [ 8.625142  , -1.2372284 ,  1.39301471,  4.3394457 ],
+       [ 7.65803596, -2.21017215,  1.99175714,  3.71004654],
+       [ 0.89319875, -5.37152317,  1.50313598,  1.95284886],
+       [ 2.68362166, -5.78810913, -0.41233406,  1.94638989],
+       [ 7.63541182, -1.99606076,  0.9241231 ,  4.53478238],
+       [ 9.04699415, -0.74540679,  0.98042851,  5.99569071],
+       [ 1.02552122, -5.73874278, -1.74804915, -0.07831216],
+       [ 7.18135665, -3.49473178,  1.14300963,  4.46065816],
+       [ 0.58812902, -4.66559815, -0.72831685,  1.40171779],
+       [ 1.48620862, -5.9963108 ,  0.19145963, -1.11369256],
+       [ 7.6625556 , -1.21328083,  2.06361094,  6.2643551 ],
+       [ 9.45050727, -1.36536078,  1.31154384,  3.89103468],
+       [ 6.88203724, -1.62040255,  3.89961049,  2.12865388],
+       [ 5.60842705, -2.10693356,  1.93328514,  3.90825432],
+       [ 2.35150936, -6.62836131, -1.84278374,  0.51540886],
+       [ 1.17446451, -5.62506058, -2.18420699,  1.21385128]]
+)
+
+y = np.array([0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0])
+
+
+class KElbowHelperTests(unittest.TestCase):
+
+    def test_distortion_score(self):
+        """
+        Test the distortion score metric function
+        """
+        score = distortion_score(X, y)
+        self.assertEqual(score, 7.6777850157143783)
+
+
 ##########################################################################
 ## KElbowVisualizer Test Cases
 ##########################################################################
@@ -80,3 +122,70 @@ def test_invalid_k(self):
 
         with self.assertRaises(YellowbrickValueError):
             model = KElbowVisualizer(KMeans(), k="foo")
+
+    def test_distortion_metric(self):
+        """
+        Test the distortion metric of the k-elbow visualizer
+        """
+        visualizer = KElbowVisualizer(KMeans(), k=5, metric="distortion")
+        visualizer.fit(X)
+
+        expected = [
+            7.6777850157143783, 8.3643185158057669,
+            9.5203330222217666, 8.9777589843618912
+        ]
+        self.assertEqual(len(visualizer.k_scores_), 4)
+        # kMeans is stochastic so numbers will change
+        # self.assertEqual(visualizer.k_scores_, expected)
+
+    def test_silhouette_metric(self):
+        """
+        Test the silhouette metric of the k-elbow visualizer
+        """
+        visualizer = KElbowVisualizer(KMeans(), k=5, metric="silhouette")
+        visualizer.fit(X)
+
+        expected = [
+            0.69163638040000031, 0.4534779796676191,
+            0.24802958481973392, 0.21792458448172247
+        ]
+        self.assertEqual(len(visualizer.k_scores_), 4)
+        # kMeans is stochastic so numbers will change
+        # self.assertEqual(visualizer.k_scores_, expected)
+
+    def test_calinski_harabaz_metric(self):
+        """
+        Test the calinski-harabaz metric of the k-elbow visualizer
+        """
+        visualizer = KElbowVisualizer(KMeans(), k=5, metric="calinski_harabaz")
+        visualizer.fit(X)
+
+        expected = [
+            81.662726256035683, 50.992378259195554,
+            40.952179227847012, 37.068658049555459
+        ]
+        self.assertEqual(len(visualizer.k_scores_), 4)
+        # kMeans is stochastic so numbers will change
+        # self.assertEqual(visualizer.k_scores_, expected)
+
+    def test_bad_metric(self):
+        """
+        Assert KElbow raises an exception when a bad metric is supplied
+        """
+        with self.assertRaises(YellowbrickValueError):
+            visualizer = KElbowVisualizer(KMeans(), k=5, metric="foo")
+
+    def test_timings(self):
+        """
+        Test the twinx double axes with k-elbow timings
+        """
+        visualizer = KElbowVisualizer(KMeans(), k=5, timings=True)
+        visualizer.fit(X)
+
+        # Check that we kept track of time
+        self.assertEqual(len(visualizer.k_timers_), 4)
+        self.assertTrue(all([t > 0 for t in visualizer.k_timers_]))
+
+        # Check that we plotted time on a twinx
+        self.assertTrue(hasattr(visualizer, "axes"))
+        self.assertEqual(len(visualizer.axes), 2)
diff --git a/tests/test_features/test_pcoords.py b/tests/test_features/test_pcoords.py
index 0661fc753..ae3cd30e0 100644
--- a/tests/test_features/test_pcoords.py
+++ b/tests/test_features/test_pcoords.py
@@ -61,7 +61,7 @@ def test_integrated_pcoords(self):
         y = occupancy['occupancy'].astype(int)
 
         # Convert X to an ndarray
-        X = X.view((float, len(X.dtype.names)))
+        X = np.array(X.tolist())
 
         # Test the visualizer
         visualizer = ParallelCoordinates()
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index 977912511..cf5981695 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -38,15 +38,22 @@ class Thing(object):
     pass
 
 
-class EstimatorSpec(BaseEstimator):
+class MockEstimator(BaseEstimator):
 
     def fit(self, X, y=None, **kwargs):
         return self
 
-MockEstimator = mock.Mock(spec = EstimatorSpec)
+class MockVisualEstimator(Visualizer):
 
+    def fit(self, X, y=None, **kwargs):
+        self.draw(**kwargs)
+        return self
+
+    def draw(self, **kwargs):
+        pass
 
-class TransformerSpec(BaseEstimator, TransformerMixin):
+
+class MockTransformer(BaseEstimator, TransformerMixin):
 
     def fit(self, X, y=None, **kwargs):
         return self
@@ -54,12 +61,11 @@ def fit(self, X, y=None, **kwargs):
     def transform(self, X, **kwargs):
         return X
 
-MockTransformer = mock.Mock(spec = TransformerSpec)
-
 
-class VisualTransformerSpec(Visualizer, TransformerMixin):
+class MockVisualTransformer(Visualizer, TransformerMixin):
 
     def fit(self, X, y=None, **kwargs):
+        self.draw(**kwargs)
         return self
 
     def transform(self, X, **kwargs):
@@ -68,11 +74,6 @@ def transform(self, X, **kwargs):
     def draw(self, **kwargs):
         pass
 
-    def poof(self, **kwargs):
-        pass
-
-MockVisualTransformer = mock.Mock(spec = VisualTransformerSpec)
-
 
 ##########################################################################
 ## VisualPipeline Tests
@@ -145,27 +146,64 @@ def test_visual_steps_property(self):
 
         pipeline = VisualPipeline([
             ('a', MockTransformer()),
-            ('b', VisualTransformerSpec()),
+            ('b', MockVisualTransformer()),
             ('c', MockTransformer()),
-            ('d', VisualTransformerSpec()),
+            ('d', MockVisualTransformer()),
             ('e', MockEstimator()),
         ])
 
+        self.assertNotIn('a', pipeline.visual_steps)
         self.assertIn('b', pipeline.visual_steps)
+        self.assertNotIn('c', pipeline.visual_steps)
         self.assertIn('d', pipeline.visual_steps)
+        self.assertNotIn('e', pipeline.visual_steps)
+
+    def test_pipeline_poof(self):
+        """
+        Test the poof call against the VisualPipeline
+        """
+
+        pipeline = VisualPipeline([
+            ('a', mock.MagicMock(MockTransformer())),
+            ('b', mock.MagicMock(MockVisualTransformer())),
+            ('c', mock.MagicMock(MockTransformer())),
+            ('d', mock.MagicMock(MockVisualTransformer())),
+            ('e', mock.MagicMock(MockEstimator()),)
+        ])
+
+        pipeline.poof()
+        pipeline.steps[1][1].poof.assert_called_once_with(outpath=None)
+        pipeline.steps[3][1].poof.assert_called_once_with(outpath=None)
+
+    def test_pipeline_savefig_poof(self):
+        """
+        Test the poof call with an outdir to save all the figures
+        """
+        pipeline = VisualPipeline([
+            ('a', mock.MagicMock(MockTransformer())),
+            ('b', mock.MagicMock(MockVisualTransformer())),
+            ('c', mock.MagicMock(MockTransformer())),
+            ('d', mock.MagicMock(MockVisualTransformer())),
+            ('e', mock.MagicMock(MockVisualEstimator()),)
+        ])
+
+        pipeline.poof(outdir="/tmp/figures")
+        pipeline.steps[1][1].poof.assert_called_once_with(outpath="/tmp/figures/b.pdf")
+        pipeline.steps[3][1].poof.assert_called_once_with(outpath="/tmp/figures/d.pdf")
+        pipeline.steps[4][1].poof.assert_called_once_with(outpath="/tmp/figures/e.pdf")
 
-    @unittest.skip("mocks don't work for some reason")
+    @unittest.skip("need to find a way for fit to return self in mocks")
     def test_fit_transform_poof_and_draw_calls(self):
         """
-        Test calling fit, transform, draw and poof on the pipeline
+        Test calling fit, transform, and poof on the pipeline
         """
 
         pipeline = VisualPipeline([
-            ('a', MockTransformer()),
-            ('b', MockVisualTransformer()),
-            ('c', MockTransformer()),
-            ('d', MockVisualTransformer()),
-            ('e', MockEstimator()),
+            ('a', mock.MagicMock(MockTransformer())),
+            ('b', mock.MagicMock(MockVisualTransformer())),
+            ('c', mock.MagicMock(MockTransformer())),
+            ('d', mock.MagicMock(MockVisualTransformer())),
+            ('e', mock.MagicMock(MockEstimator()),)
         ])
 
         X = [[1, 1, 1, 1, 1],
@@ -180,14 +218,10 @@ def test_fit_transform_poof_and_draw_calls(self):
 
         pipeline.transform(X)
         for name, step in pipeline.named_steps.items():
+            if name == 'e': continue
             step.transform.assert_called_once_with(X)
 
-        pipeline.draw()
-        for name, step in pipeline.named_steps.items():
-            if name in {'a', 'c', 'e'}: continue
-            step.draw.assert_called_once_with()
-
         pipeline.poof()
         for name, step in pipeline.named_steps.items():
             if name in {'a', 'c', 'e'}: continue
-            step.poof.assert_called_once_with()
+            step.poof.assert_called_once_with(outpath=None)
diff --git a/tests/test_regressor/test_residuals.py b/tests/test_regressor/test_residuals.py
index 51a9dc3d7..87a71b92e 100644
--- a/tests/test_regressor/test_residuals.py
+++ b/tests/test_regressor/test_residuals.py
@@ -25,7 +25,7 @@
 
 from sklearn.svm import SVR
 from sklearn import cross_validation as cv
-from sklearn.cross_validation import train_test_split as tts
+from sklearn.model_selection import train_test_split as tts
 
 ##########################################################################
 ## Data
diff --git a/tests/test_utils/__init__.py b/tests/test_utils/__init__.py
new file mode 100644
index 000000000..adf429c8c
--- /dev/null
+++ b/tests/test_utils/__init__.py
@@ -0,0 +1,18 @@
+# tests.test_utils
+# Tests for Yellowbrick utilities
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Thu May 18 15:15:05 2017 -0400
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: __init__.py [] benjamin@bengfort.com $
+
+"""
+Tests for Yellowbrick utilities
+"""
+
+##########################################################################
+## Imports
+##########################################################################
diff --git a/tests/test_utils/test_decorators.py b/tests/test_utils/test_decorators.py
new file mode 100644
index 000000000..b2c0dfa9a
--- /dev/null
+++ b/tests/test_utils/test_decorators.py
@@ -0,0 +1,102 @@
+# tests.test_utils.test_decorators
+# Tests for the decorators module in Yellowbrick utils.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Thu May 18 15:14:34 2017 -0400
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: test_decorators.py [] benjamin@bengfort.com $
+
+"""
+Tests for the decorators module in Yellowbrick utils.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import unittest
+
+from yellowbrick.utils.decorators import *
+
+
+##########################################################################
+## Decorator Tests
+##########################################################################
+
+class DecoratorTests(unittest.TestCase):
+    """
+    Tests for the decorator utilities.
+    """
+
+    def test_memoization(self):
+        """
+        Test the memoized property decorator on a class.
+        """
+
+        class Visualizer(object):
+
+            @memoized
+            def foo(self):
+                return "bar"
+
+        viz = Visualizer()
+        self.assertFalse(hasattr(viz, "_foo"))
+        self.assertEqual(viz.foo, "bar")
+        self.assertEqual(viz._foo, "bar")
+
+
+    def test_docutil(self):
+        """
+        Test the docutil docstring copying methodology.
+        """
+
+        class Visualizer(object):
+
+            def __init__(self):
+                """
+                This is the correct docstring.
+                """
+                pass
+
+
+        def undecorated(*args, **kwargs):
+            """
+            This is an undecorated function string.
+            """
+            pass
+
+        # Test the undecorated string to protect from magic
+        self.assertEqual(
+            undecorated.__doc__.strip(), "This is an undecorated function string."
+        )
+
+        # Decorate manually and test the newly decorated return function.
+        decorated = docutil(Visualizer.__init__)(undecorated)
+        self.assertEqual(
+            decorated.__doc__.strip(), "This is the correct docstring."
+        )
+
+        # Assert that decoration modifies the original function.
+        self.assertEqual(
+            undecorated.__doc__.strip(), "This is the correct docstring."
+        )
+
+        @docutil(Visualizer.__init__)
+        def sugar(*args, **kwargs):
+            pass
+
+        # Assert that syntactic sugar works as expected.
+        self.assertEqual(
+            sugar.__doc__.strip(), "This is the correct docstring."
+        )
+
+
+##########################################################################
+## Execute Tests
+##########################################################################
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_utils/test_helpers.py b/tests/test_utils/test_helpers.py
new file mode 100644
index 000000000..24a32dff0
--- /dev/null
+++ b/tests/test_utils/test_helpers.py
@@ -0,0 +1,164 @@
+# -*- coding: utf-8 -*-
+# tests.test_utils.test_helpers
+# Tests for the stand alone helper functions in Yellowbrick utils.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Fri May 19 10:43:43 2017 -0700
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: test_helpers.py [] benjamin@bengfort.com $
+
+"""
+Tests for the stand alone helper functions in Yellowbrick utils.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import unittest
+
+from yellowbrick.utils.helpers import *
+
+from sklearn.pipeline import Pipeline
+from sklearn.decomposition import PCA
+from sklearn.neighbors import LSHForest
+from sklearn.linear_model import RidgeCV, LassoCV
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.cluster import KMeans, MiniBatchKMeans
+from sklearn.cluster import AffinityPropagation, Birch
+
+
+
+##########################################################################
+## Helper Function Tests
+##########################################################################
+
+class HelpersTests(unittest.TestCase):
+
+    ##////////////////////////////////////////////////////////////////////
+    ## get_model_name testing
+    ##////////////////////////////////////////////////////////////////////
+
+    def test_real_model(self):
+        """
+        Test that model name works for sklearn estimators
+        """
+        model1 = LassoCV()
+        model2 = LSHForest()
+        model3 = KMeans()
+        model4 = RandomForestClassifier()
+        self.assertEqual(get_model_name(model1), 'LassoCV')
+        self.assertEqual(get_model_name(model2), 'LSHForest')
+        self.assertEqual(get_model_name(model3), 'KMeans')
+        self.assertEqual(get_model_name(model4), 'RandomForestClassifier')
+
+    def test_pipeline(self):
+        """
+        Test that model name works for sklearn pipelines
+        """
+        pipeline = Pipeline([('reduce_dim', PCA()),
+                             ('linreg', LinearRegression())])
+        self.assertEqual(get_model_name(pipeline), 'LinearRegression')
+
+    def test_int_input(self):
+        """
+        Assert a type error is raised when an int is passed to model name.
+        """
+        self.assertRaises(TypeError, get_model_name, 1)
+
+    def test_str_input(self):
+        """
+        Assert a type error is raised when a str is passed to model name.
+        """
+        self.assertRaises(TypeError, get_model_name, 'helloworld')
+
+
+##########################################################################
+## Numeric Function Tests
+##########################################################################
+
+
+class DivSafeTests(unittest.TestCase):
+
+    def test_div_1d_by_scalar(self):
+        result = div_safe( [-1, 0, 1], 0 )
+        self.assertTrue(result.all() == 0)
+
+    def test_div_1d_by_1d(self):
+        result =div_safe( [-1, 0 , 1], [0,0,0])
+        self.assertTrue(result.all() == 0)
+
+    def test_div_2d_by_1d(self):
+        numerator = np.array([[-1,0,1,2],[1,-1,0,3]])
+        denominator = [0,0,0,0]
+        result = div_safe(numerator, denominator)
+
+    def test_invalid_dimensions(self):
+            numerator = np.array([[-1,0,1,2],[1,-1,0,3]])
+            denominator = [0,0]
+            with self.assertRaises(ValueError):
+                result = div_safe(numerator, denominator)
+
+    def test_div_scalar_by_scalar(self):
+        with self.assertRaises(ValueError):
+            result = div_safe(5, 0)
+
+
+##########################################################################
+## Features/Array Tests
+##########################################################################
+
+class NarrayIntColumnsTests(unittest.TestCase):
+
+    def test_has_ndarray_int_columns_true_int_features(self):
+        x = np.random.rand(3,5)
+        features = [0, 1]
+        self.assertTrue(has_ndarray_int_columns(features, x))
+
+    def test_has_ndarray_int_columns_true_int_strings(self):
+        x = np.random.rand(3,5)
+        features = ['0', '1']
+        self.assertTrue(has_ndarray_int_columns(features, x))
+
+    def test_has_ndarray_int_columns_false_not_numeric(self):
+        x = np.random.rand(3,5)
+        features = ['a', '1']
+        self.assertFalse(has_ndarray_int_columns(features, x))
+
+    def test_has_ndarray_int_columns_false_outside_column_range(self):
+        x = np.random.rand(3,5)
+        features = ['0', '10']
+        self.assertFalse(has_ndarray_int_columns(features, x))
+
+
+##########################################################################
+## String Helpers Tests
+##########################################################################
+
+class StringHelpersTests(unittest.TestCase):
+
+    def test_slugifiy(self):
+        """
+        Test the slugify helper utility
+        """
+
+        cases = (
+            ("This is a test ---", "this-is-a-test"),
+            ("This -- is a ## test ---" , "this-is-a-test"),
+        )
+
+        for case, expected in cases:
+            self.assertEqual(expected, slugify(case))
+
+
+##########################################################################
+## Execute Tests
+##########################################################################
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_utils.py b/tests/test_utils/test_types.py
similarity index 70%
rename from tests/test_utils.py
rename to tests/test_utils/test_types.py
index 40e7b4cb0..81a21215b 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils/test_types.py
@@ -1,18 +1,16 @@
-# tests.test_utils
-# Test the yellowbrick utilities module.
+# tests.test_utils.test_types
+# Very difficult test library for type detection and flexibility.
 #
-# Author:   Jason Keung <jason.s.keung@gmail.com>
-# Author:   Patrick O'Melveny <pvomelveny@gmail.com>
 # Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
-# Created:  Thu Jun 02 15:33:18 2016 -0500
+# Created:  Fri May 19 10:58:32 2017 -0700
 #
-# Copyright (C) 2016 District Data LAbs
+# Copyright (C) 2017 District Data Labs
 # For license information, see LICENSE.txt
 #
-# ID: test_utils.py [] jason.s.keung@gmail.com $
+# ID: test_types.py [] benjamin@bengfort.com $
 
 """
-Test the yellowbrick utilities module.
+Very difficult test library for type detection and flexibility.
 """
 
 ##########################################################################
@@ -32,7 +30,7 @@
 from sklearn.cluster import KMeans, MiniBatchKMeans
 from sklearn.cluster import AffinityPropagation, Birch
 
-from yellowbrick.utils import *
+from yellowbrick.utils.types import *
 from yellowbrick.base import Visualizer, ScoreVisualizer, ModelVisualizer
 
 
@@ -42,43 +40,6 @@
 
 class ModelUtilityTests(unittest.TestCase):
 
-    ##////////////////////////////////////////////////////////////////////
-    ## get_model_name testing
-    ##////////////////////////////////////////////////////////////////////
-
-    def test_real_model(self):
-        """
-        Test that model name works for sklearn estimators
-        """
-        model1 = LassoCV()
-        model2 = LSHForest()
-        model3 = KMeans()
-        model4 = RandomForestClassifier()
-        self.assertEqual(get_model_name(model1), 'LassoCV')
-        self.assertEqual(get_model_name(model2), 'LSHForest')
-        self.assertEqual(get_model_name(model3), 'KMeans')
-        self.assertEqual(get_model_name(model4), 'RandomForestClassifier')
-
-    def test_pipeline(self):
-        """
-        Test that model name works for sklearn pipelines
-        """
-        pipeline = Pipeline([('reduce_dim', PCA()),
-                             ('linreg', LinearRegression())])
-        self.assertEqual(get_model_name(pipeline), 'LinearRegression')
-
-    def test_int_input(self):
-        """
-        Assert a type error is raised when an int is passed to model name.
-        """
-        self.assertRaises(TypeError, get_model_name, 1)
-
-    def test_str_input(self):
-        """
-        Assert a type error is raised when a str is passed to model name.
-        """
-        self.assertRaises(TypeError, get_model_name, 'helloworld')
-
     ##////////////////////////////////////////////////////////////////////
     ## isestimator testing
     ##////////////////////////////////////////////////////////////////////
@@ -428,30 +389,6 @@ def test_clusterer_visualizer(self):
         model = ScoreVisualizer(KMeans())
         self.assertTrue(is_clusterer(model))
 
-class DivSafeTests(unittest.TestCase):
-
-    def test_div_1d_by_scalar(self):
-        result = div_safe( [-1, 0, 1], 0 )
-        self.assertTrue(result.all() == 0)
-
-    def test_div_1d_by_1d(self):
-        result =div_safe( [-1, 0 , 1], [0,0,0])
-        self.assertTrue(result.all() == 0)
-
-    def test_div_2d_by_1d(self):
-        numerator = np.array([[-1,0,1,2],[1,-1,0,3]])
-        denominator = [0,0,0,0]
-        result = div_safe(numerator, denominator)
-
-    def test_invalid_dimensions(self):
-            numerator = np.array([[-1,0,1,2],[1,-1,0,3]])
-            denominator = [0,0]
-            with self.assertRaises(ValueError):
-                result = div_safe(numerator, denominator)
-
-    def test_div_scalar_by_scalar(self):
-        with self.assertRaises(ValueError):
-            result = div_safe(5, 0)
 
 class StructuredArrayTests(unittest.TestCase):
 
@@ -467,84 +404,6 @@ def test_isstructuredarray_list(self):
         x = [[1,2,3], [1,2,3]]
         self.assertFalse(isstructuredarray(x))
 
-class NarrayIntColumnsTests(unittest.TestCase):
-
-    def test_has_ndarray_int_columns_true_int_features(self):
-        x = np.random.rand(3,5)
-        features = [0, 1]
-        self.assertTrue(has_ndarray_int_columns(features, x))
-
-    def test_has_ndarray_int_columns_true_int_strings(self):
-        x = np.random.rand(3,5)
-        features = ['0', '1']
-        self.assertTrue(has_ndarray_int_columns(features, x))
-
-    def test_has_ndarray_int_columns_false_not_numeric(self):
-        x = np.random.rand(3,5)
-        features = ['a', '1']
-        self.assertFalse(has_ndarray_int_columns(features, x))
-
-    def test_has_ndarray_int_columns_false_outside_column_range(self):
-        x = np.random.rand(3,5)
-        features = ['0', '10']
-        self.assertFalse(has_ndarray_int_columns(features, x))
-
-
-##########################################################################
-## Decorator Tests
-##########################################################################
-
-class DecoratorTests(unittest.TestCase):
-    """
-    Tests for the decorator utilities.
-    """
-
-    def test_docutil(self):
-        """
-        Test the docutil docstring copying methodology.
-        """
-
-        class Visualizer(object):
-
-            def __init__(self):
-                """
-                This is the correct docstring.
-                """
-                pass
-
-
-        def undecorated(*args, **kwargs):
-            """
-            This is an undecorated function string.
-            """
-            pass
-
-        # Test the undecorated string to protect from magic
-        self.assertEqual(
-            undecorated.__doc__.strip(), "This is an undecorated function string."
-        )
-
-        # Decorate manually and test the newly decorated return function.
-        decorated = docutil(Visualizer.__init__)(undecorated)
-        self.assertEqual(
-            decorated.__doc__.strip(), "This is the correct docstring."
-        )
-
-        # Assert that decoration modifies the original function.
-        self.assertEqual(
-            undecorated.__doc__.strip(), "This is the correct docstring."
-        )
-
-        @docutil(Visualizer.__init__)
-        def sugar(*args, **kwargs):
-            pass
-
-        # Assert that syntactic sugar works as expected.
-        self.assertEqual(
-            sugar.__doc__.strip(), "This is the correct docstring."
-        )
-
-
 ##########################################################################
 ## Execute Tests
 ##########################################################################
diff --git a/tests/test_utils/test_wrapper.py b/tests/test_utils/test_wrapper.py
new file mode 100644
index 000000000..4dd77f2d9
--- /dev/null
+++ b/tests/test_utils/test_wrapper.py
@@ -0,0 +1,119 @@
+# tests.test_utils.test_wrapper
+# Testing for the wrapping utility.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Mon May 22 09:25:52 2017 -0700
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: test_wrapper.py [] benjamin@bengfort.com $
+
+"""
+Testing for the wrapping utility.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import unittest
+import numpy as np
+
+from yellowbrick.base import Visualizer
+from yellowbrick.utils.wrapper import *
+from sklearn.naive_bayes import MultinomialNB
+
+try:
+    from unittest import mock
+except ImportError:
+    import mock
+
+
+##########################################################################
+## Fixture
+##########################################################################
+
+class MockVisualizer(Visualizer):
+
+    def __init__(self, ax=None, **kwargs):
+        self.ax = ax
+        self.fit = mock.MagicMock()
+        self.finalize = mock.MagicMock()
+        self.poof = mock.MagicMock()
+        self.set_title = mock.MagicMock()
+
+    @property
+    def ax(self):
+        return self._ax
+
+    @ax.setter
+    def ax(self, val):
+        self._ax = val
+
+
+class WrappedEstimator(MockVisualizer, Wrapper):
+
+    def __init__(self, **kwargs):
+        self.estimator = mock.MagicMock(spec=MultinomialNB())
+
+        Wrapper.__init__(self, self.estimator)
+        MockVisualizer.__init__(self, **kwargs)
+
+    def draw(self):
+        return True
+
+    def foo(self, a, b):
+        return a+b
+
+
+##########################################################################
+## Wrapper Test Case
+##########################################################################
+
+class WrapperTests(unittest.TestCase):
+
+    def test_wrapper_methods(self):
+        """
+        Asssert that local wrapper methods are called
+        """
+        obj = WrappedEstimator()
+
+        # Assert that all the wrapper methods are called
+        self.assertTrue(obj.draw())
+        self.assertEqual(obj.foo(2,2), 4)
+        self.assertIsNotNone(obj.estimator)
+
+    def test_super_methods(self):
+        """
+        Assert that Visualizer super methods are called
+        """
+
+        obj = WrappedEstimator()
+
+        # Assert that visualizer methods are called
+        obj.fit()
+        obj.finalize()
+        obj.poof()
+        obj.set_title()
+
+        self.assertIsNone(obj.ax)
+        obj.fit.assert_called_once_with()
+        obj.finalize.assert_called_once_with()
+        obj.poof.assert_called_once_with()
+        obj.set_title.assert_called_once_with()
+
+    def test_wrapped_methods(self):
+        """
+        Assert that wrapped estimator methods are calle d
+        """
+        obj = WrappedEstimator()
+
+        # Assert that estimator methods are called
+        obj.predict()
+        obj.predict_proba()
+        obj.score()
+
+        obj._wrapped.predict.assert_called_once_with()
+        obj._wrapped.predict_proba.assert_called_once_with()
+        obj._wrapped.score.assert_called_once_with()
diff --git a/yellowbrick/base.py b/yellowbrick/base.py
index 38b288265..d0fd7b0c9 100644
--- a/yellowbrick/base.py
+++ b/yellowbrick/base.py
@@ -16,10 +16,11 @@
 
 import matplotlib.pyplot as plt
 
+from .utils.wrapper import Wrapper
 from sklearn.base import BaseEstimator
 from .exceptions import YellowbrickTypeError
 from .utils import get_model_name, isestimator
-from sklearn.cross_validation import cross_val_predict as cvp
+from sklearn.model_selection import cross_val_predict as cvp
 
 
 ##########################################################################
@@ -44,13 +45,23 @@ class Visualizer(BaseEstimator):
 
     kwargs : dict
         Keyword arguments that are passed to the base class and may influence
-        the visualization as defined in other Visualizers.
+        the visualization as defined in other Visualizers. Optional keyword
+        arguments include:
+
+        =============   =======================================================
+        Property        Description
+        -------------   -------------------------------------------------------
+        size            specify a size for the figure (currently unimplemented)
+        color           specify a color, colormap, or palette for the figure
+        title           specify the title of the figure
+        =============   =======================================================
 
     Notes
     -----
-        These parameters can be influenced later on in the visualization
-        process, but can and should be set as early as possible.
-
+    Visualizers are objects that learn from data (e.g. estimators), therefore
+    they must be ``fit()`` before they can be drawn or used. Visualizers also
+    maintain a reference to an ``ax`` object, a matplotlib Axes where the
+    figures are drawn and rendered.
     """
 
     def __init__(self, ax=None, **kwargs):
@@ -59,13 +70,39 @@ def __init__(self, ax=None, **kwargs):
         self.color = kwargs.pop('color', None)
         self.title = kwargs.pop('title', None)
 
+    ##////////////////////////////////////////////////////////////////////
+    ## Primary Visualizer Properties
+    ##////////////////////////////////////////////////////////////////////
+
+    @property
+    def ax(self):
+        """
+        The matplotlib axes that the visualizer draws upon (can also be a grid
+        of multiple axes objects). The visualizer automatically creates an
+        axes for the user if one has not been specified.
+        """
+        if not hasattr(self, "_ax") or self._ax is None:
+            self._ax = plt.gca()
+        return self._ax
+
+    @ax.setter
+    def ax(self, ax):
+        self._ax = ax
+
+    ##////////////////////////////////////////////////////////////////////
+    ## Estimator interface
+    ##////////////////////////////////////////////////////////////////////
+
     def fit(self, X, y=None, **kwargs):
         """
-        Fits a transformer to X and y
+        Fits a visualizer to data and is the primary entry point for producing
+        a visualization. Visualizers are Scikit-Learn Estimator objects, which
+        learn from data in order to produce a visual analysis or diagnostic.
+        They can do this either by fitting features related data or by fitting
+        an underlying model (or models) and visualizing their results.
 
         Parameters
         ----------
-
         X : ndarray or DataFrame of shape n x m
             A matrix of n instances with m features
 
@@ -73,21 +110,27 @@ def fit(self, X, y=None, **kwargs):
             An array or series of target or class values
 
         kwargs: dict
-            keyword arguments passed to Scikit-Learn API.
+            Keyword arguments passed to the drawing functionality or to the
+            Scikit-Learn API. See visualizer specific details for how to use
+            the kwargs to modify the visualization or fitting process.
+
+        Returns
+        -------
+        self : visualizer
+            The fit method must always return self to support pipelines.
         """
         return self
 
-    def gca(self):
-        """
-        Creates axes if they don't already exist
-        """
-        if self.ax is None:
-            self.ax = plt.gca()
-        return self.ax
+    ##////////////////////////////////////////////////////////////////////
+    ## Visualizer interface
+    ##////////////////////////////////////////////////////////////////////
 
     def draw(self, **kwargs):
         """
-        Rendering function
+        The fitting or transformation process usually calls draw (not the
+        user). This function is implemented for developers to hook into the
+        matplotlib interface and to create an internal representation of the
+        data the visualizer was trained on in the form of a figure or axes.
 
         Parameters
         ----------
@@ -96,14 +139,33 @@ def draw(self, **kwargs):
             generic keyword arguments.
 
         """
-        ax = self.gca()
+        raise NotImplementedError(
+            "Visualizers must implement a drawing interface."
+        )
 
-    def poof(self, outpath=None, **kwargs):
+    def finalize(self, **kwargs):
         """
-        The user calls poof, which is the primary entry point
-        for producing a visualization.
+        Finalize executes any subclass-specific axes finalization steps.
 
-        Visualizes either data features or fitted model scores
+        Parameters
+        ----------
+        kwargs: dict
+            generic keyword arguments.
+
+        Notes
+        -----
+        The user calls poof and poof calls finalize. Developers should
+        implement visualizer-specific finalization methods like setting titles
+        or axes labels, etc.
+        """
+        return self.ax
+
+    def poof(self, outpath=None, **kwargs):
+        """
+        Poof makes the magic happen and a visualizer appear! You can pass in
+        a path to save the figure to disk with various backends, or you can
+        call it with no arguments to show the figure either in a notebook or
+        in a GUI window that pops up on screen.
 
         Parameters
         ----------
@@ -112,9 +174,16 @@ def poof(self, outpath=None, **kwargs):
 
         kwargs: dict
             generic keyword arguments.
+
+        Notes
+        -----
+        Developers of visualizers don't usually override poof, as it is
+        primarily called by the user to render the visualization.
         """
-        if self.ax is None: return
+        # Ensure that draw has been called
+        if self._ax is None: return
 
+        # Finalize the figure
         self.finalize()
 
         if outpath is not None:
@@ -122,6 +191,10 @@ def poof(self, outpath=None, **kwargs):
         else:
             plt.show()
 
+    ##////////////////////////////////////////////////////////////////////
+    ## Helper Functions
+    ##////////////////////////////////////////////////////////////////////
+
     def set_title(self, title=None):
         """
         Sets the title on the current axes.
@@ -135,96 +208,62 @@ def set_title(self, title=None):
         if title is not None:
             self.ax.set_title(title)
 
-    def finalize(self, **kwargs):
-        """
-        Finalize executes any subclass-specific axes finalization steps.
-        The user calls poof and poof calls finalize.
-
-        Parameters
-        ----------
-        kwargs: dict
-            generic keyword arguments.
-        """
-        pass
-
-    def fit_draw(self, X, y=None, **kwargs):
-        """
-        Fits a transformer to X and y then returns
-        visualization of features or fitted model.
-
-        Parameters
-        ----------
-
-        X : ndarray or DataFrame of shape n x m
-            A matrix of n instances with m features
-
-        y : ndarray or Series of length n
-            An array or series of target or class values
-
-        kwargs: dict
-            Generic keyword arguments.
-        """
-        self.fit(X, y, **kwargs)
-        self.draw(**kwargs)
-
-    def fit_draw_poof(self, X, y=None, **kwargs):
-        """
-        Fits a transformer to X and y then shows
-        the visualization of features or fitted model.
-
-        Parameters
-        ----------
-
-        X : ndarray or DataFrame of shape n x m
-            A matrix of n instances with m features
-
-        y : ndarray or Series of length n
-            An array or series of target or class values
-
-        kwargs: dict
-            Generic keyword arguments.
-        """
-        self.fit_draw(X, y, **kwargs)
-        self.poof(**kwargs)
-
 
 ##########################################################################
-## Score Visualizers
+## Model Visualizers
 ##########################################################################
 
-class ScoreVisualizer(Visualizer):
+class ModelVisualizer(Visualizer, Wrapper):
     """
-    Base class to follow an estimator in a visual pipeline.
+    The ModelVisualizer class wraps a Scikit-Learn estimator (usually a
+    predictive model like a regressor, classifier, or clusterer) so that all
+    functionality that belongs to the estimator can be accessed from the
+    visualizer, thereby allowing visualzers to be proxies for model objects,
+    simply drawing on behalf of the wrapped model.
+
+    Parameters
+    ----------
+    model : Estimator
+        A Scikit-Learn estimator to wrap functionality for, usually regressor,
+        classifier, or clusterer predictive model.
+
+    ax : matplotlib Axes, default: None
+        The axis to plot the figure on. If None is passed in the current axes
+        will be used (or generated if required).
+
+    kwargs : dict
+        Keyword arguments that are passed to the base class and may influence
+        the visualization as defined in other Visualizersself.
 
-    Draws the score for the fitted model.
+    Notes
+    -----
+    Model visualizers can wrap either fitted or unfitted models.
     """
 
     def __init__(self, model, ax=None, **kwargs):
         """
-        These parameters can be influenced later on in the visualization
-        process, but can and should be set as early as possible.
-
         Parameters
         ----------
-        models: object
-            the Scikit-Learn models being compared with each other.
-
-        ax : matplotlib Axes, default: None
-            The axes to plot the figure on.
+        ax: matplotlib axes
+            the axis to plot the figure on.
 
         kwargs: dict
-            keyword arguments.
+            keyword arguments for Scikit-Learn model
         """
-        super(ScoreVisualizer, self).__init__(ax=ax, **kwargs)
-
         self.estimator = model
         self.name = get_model_name(self.estimator)
 
+        Wrapper.__init__(self, self.estimator)
+        Visualizer.__init__(self, ax=ax, **kwargs)
+
     def fit(self, X, y=None, **kwargs):
         """
+        Fits the wrapped estimator so that subclasses that override fit can
+        ensure that the estimator is fit using super rather than a direct call
+        down to the estimator. Score estimators tend to expect a fitted model.
+
         Parameters
         ----------
-
         X : ndarray or DataFrame of shape n x m
             A matrix of n instances with m features
 
@@ -232,89 +271,60 @@ def fit(self, X, y=None, **kwargs):
             An array or series of target or class values
 
         kwargs: dict
-            keyword arguments passed to Scikit-Learn API.
-        """
-        self.estimator.fit(X, y, **kwargs)
-        return self
-
-    def predict(self, X):
-        """
-        Parameters
-        ----------
-
-        X : ndarray or DataFrame of shape n x m
-            A matrix of n instances with m features
-
-        """
-        return self.estimator.predict(X)
-
-    def draw(self, X, y):
-        """
-        Parameters
-        ----------
+            Keyword arguments passed to the drawing functionality or to the
+            Scikit-Learn API. See visualizer specific details for how to use
+            the kwargs to modify the visualization or fitting process.
 
-        X : ndarray or DataFrame of shape n x m
-            A matrix of n instances with m features
-
-        y : ndarray or Series of length n
-            An array or series of target or class values
+        Returns
+        -------
+        self : visualizer
+            The fit method must always return self to support pipelines.
         """
-        pass
+        self.estimator.fit(X, y)
+        return self
 
 
 ##########################################################################
-## Model Visualizers
+## Score Visualizers
 ##########################################################################
 
-class ModelVisualizer(Visualizer):
-    """
-    A model visualization accepts as input an unfitted Scikit-Learn estimator(s)
-    and enables the user to visualize the performance of models across a range
-    of hyperparameter values (e.g. using VisualGridsearch and ValidationCurve).
+class ScoreVisualizer(ModelVisualizer):
     """
-    def __init__(self, model, ax=None, **kwargs):
-        """
-        Parameters
-        ----------
-        ax: matplotlib axes
-            the axis to plot the figure on.
-
-        kwargs: dict
-            keyword arguments for Scikit-Learn model
-
-        These parameters can be influenced later on in the visualization
-        process, but can and should be set as early as possible.
-        """
-        super(ModelVisualizer, self).__init__(ax=ax, **kwargs)
-        self.estimator = model
+    The ScoreVisualizer reports the performance of a Scikit-Learn estimator
+    (usually a predictive model like a regressor, classifier, or clusterer) in
+    a visual manner. They hook into the Scikit-Learn pipeline through the
+    ``score(X_test, y_test)`` method, reporting not just a single numeric
+    score, but also a visual report of the score in model space.
 
+    Parameters
+    ----------
+    model : Estimator
+        A Scikit-Learn estimator to wrap functionality for, usually regressor,
+        classifier, or clusterer predictive model.
 
-    def fit(self, X, y=None, **kwargs):
-        """
-        Parameters
-        ----------
+    ax : matplotlib Axes, default: None
+        The axis to plot the figure on. If None is passed in the current axes
+        will be used (or generated if required).
 
-        X : ndarray or DataFrame of shape n x m
-            A matrix of n instances with m features
+    kwargs : dict
+        Keyword arguments that are passed to the base class and may influence
+        the visualization as defined in other Visualizersself.
 
-        y : ndarray or Series of length n
-            An array or series of target or class values
+    Notes
+    -----
+    Score visualizers can wrap either fitted or unfitted models.
+    """
 
-        kwargs: dict
-            keyword arguments passed to Scikit-Learn API.
+    def score(self, X, y, **kwargs):
         """
-        pass
-
-    def predict(self, X):
+        The primary entry point for score visualizers is the score method,
+        which makes predictions based on X and scores them relative to y.
         """
-        Parameters
-        ----------
+        raise NotImplementedError(
+            "ScoreVisualizer subclasses should implement score"
+        )
 
-        X : ndarray or DataFrame of shape n x m
-            A matrix of n instances with m features
 
-        """
-        pass
 
 ##########################################################################
 ## Multiple Models and Mixins
diff --git a/yellowbrick/classifier/class_balance.py b/yellowbrick/classifier/class_balance.py
index 9ff088997..64f4e5583 100644
--- a/yellowbrick/classifier/class_balance.py
+++ b/yellowbrick/classifier/class_balance.py
@@ -5,7 +5,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import precision_recall_fscore_support
 
 
@@ -100,19 +100,17 @@ def draw(self):
 
         Returns
         -------
-
         ax : the axis with the plotted figure
 
         """
-        # Create the axis if it doesn't exist
-        if self.ax is None:
-            self.ax = plt.gca()
-
         #TODO: Would rather not have to set the colors with this method.
         # Refactor to make better use of yb_palettes module?
 
         colors = self.colors[0:len(self.classes_)]
-        plt.bar(np.arange(len(self.support)), self.support.values(), color=colors, align='center', width=0.5)
+        self.ax.bar(
+            np.arange(len(self.support)), self.support.values(),
+            color=colors, align='center', width=0.5
+        )
 
         return self.ax
 
diff --git a/yellowbrick/classifier/classification_report.py b/yellowbrick/classifier/classification_report.py
index 93907221c..1fb7772e0 100644
--- a/yellowbrick/classifier/classification_report.py
+++ b/yellowbrick/classifier/classification_report.py
@@ -4,7 +4,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import precision_recall_fscore_support
 
 from ..utils import get_model_name
@@ -19,7 +19,7 @@ class ClassificationReport(ClassificationScoreVisualizer):
     """
     Classification report that shows the precision, recall, and F1 scores
     for the model. Integrates numerical scores as well as a color-coded heatmap.
-    
+
     Parameters
     ----------
 
@@ -37,7 +37,7 @@ class ClassificationReport(ClassificationScoreVisualizer):
         Use sequential heatmap.
 
     kwargs : keyword arguments passed to the super class.
-    
+
     Examples
     --------
 
@@ -97,6 +97,7 @@ def score(self, X, y=None, **kwargs):
         self.scores = precision_recall_fscore_support(y, y_pred)
         self.scores = map(lambda s: dict(zip(self.classes_, s)), self.scores[0:3])
         self.scores = dict(zip(keys, self.scores))
+
         return self.draw(y, y_pred)
 
     def draw(self, y, y_pred):
@@ -112,10 +113,6 @@ def draw(self, y, y_pred):
         y_pred : ndarray or Series of length n
             An array or series of predicted target values
         """
-        # Create the axis if it doesn't exist
-        if self.ax is None:
-            self.ax = plt.gca()
-
         self.matrix = []
         for cls in self.classes_:
             self.matrix.append([self.scores['precision'][cls],self.scores['recall'][cls],self.scores['f1'][cls]])
@@ -126,9 +123,15 @@ def draw(self, y, y_pred):
                 base_color = self.cmap(current_score)
                 text_color= find_text_color(base_color)
 
+                # Limit the current score to a precision of 3
+                current_score = "{:0.3f}".format(current_score)
+
                 self.ax.text(column,row,current_score,va='center',ha='center', color=text_color)
 
-        fig = plt.imshow(self.matrix, interpolation='nearest', cmap=self.cmap, vmin=0, vmax=1,aspect='auto')
+        fig = plt.imshow(self.matrix, interpolation='nearest', cmap=self.cmap, vmin=0, vmax=1, aspect='auto')
+
+        # Add the color bar
+        plt.colorbar()
 
         return self.ax
 
@@ -145,9 +148,6 @@ def finalize(self, **kwargs):
         # Set the title of the classifiation report
         self.set_title('{} Classification Report'.format(self.name))
 
-        # Add the color bar
-        plt.colorbar()
-
         # Compute the tick marks for both x and y
         x_tick_marks = np.arange(len(self.classes_)+1)
         y_tick_marks = np.arange(len(self.classes_))
@@ -205,4 +205,4 @@ def classification_report(model, X, y=None, ax=None, classes=None, **kwargs):
     visualizer.score(X_test, y_test)
 
     # Return the axes object on the visualizer
-    return visualizer.ax
\ No newline at end of file
+    return visualizer.ax
diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py
index 4a9b7198a..522bf75f3 100644
--- a/yellowbrick/classifier/confusion_matrix.py
+++ b/yellowbrick/classifier/confusion_matrix.py
@@ -17,12 +17,12 @@
 class ConfusionMatrix(ClassificationScoreVisualizer):
     """
     Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). A confusion
-    matrix shows each combination of the true and predicted classes for a test data set. 
+    matrix shows each combination of the true and predicted classes for a test data set.
 
     The default color map uses a yellow/orange/red color scale. The user can choose between
     displaying values as the percent of true (cell value divided by sum of row) or as direct
     counts. If percent of true mode is selected, 100% accurate predictions are highlighted in green.
-    
+
     Requires a classification model
 
     Parameters
@@ -37,7 +37,7 @@ class ConfusionMatrix(ClassificationScoreVisualizer):
         indicated by that function. It may be used to reorder or select a subset of labels.
         If None, values that appear at least once in y_true or y_pred are used in sorted order.
         Default: None
-    
+
     Examples
     --------
 
@@ -48,8 +48,8 @@ class ConfusionMatrix(ClassificationScoreVisualizer):
     >>> viz.score(X_test, y_test)
     >>> viz.poof()
     """
-    
-        
+
+
     def __init__(self, model, ax=None, classes=None, **kwargs):
 
         super(ConfusionMatrix, self).__init__(model, ax=ax, classes=None,**kwargs)
@@ -69,7 +69,7 @@ def __init__(self, model, ax=None, classes=None, **kwargs):
         self.cmap.set_over(color='#2a7d4f')
         self.edgecolors=[] #used to draw diagonal line for predicted class = true class
 
-        
+
         #Convert list to array if necessary, since estimator.classes_ returns nparray
         self._classes = None if classes == None else np.array(classes)
 
@@ -136,8 +136,8 @@ def score(self, X, y, sample_weight=None, percent=True):
 
         self.confusion_matrix = confusion_matrix(y_true = y, y_pred = y_pred, labels=self.classes, sample_weight=sample_weight)
         self._class_counts = self.class_counts(y)
-        
-        #Make array of only the classes actually being used. 
+
+        #Make array of only the classes actually being used.
         #Needed because sklearn confusion_matrix only returns counts for selected classes
             #but percent should be calculated based on all classes
         selected_class_counts = []
@@ -163,10 +163,6 @@ def draw(self, percent=True):
                     Whether the heatmap should represent "% of True" or raw counts
 
         """
-        # Create the axis if it doesn't exist
-        if self.ax is None:
-            self.ax = plt.gca()
-
         if percent == True:
             #Convert confusion matrix to percent of each row, i.e. the predicted as a percent of true in each class
             #div_safe function returns 0 instead of NAN.
@@ -221,7 +217,7 @@ def draw(self, percent=True):
 
                 #extract the value
                 grid_val = self._confusion_matrix_plottable[x_int,y_int]
-                
+
                 #Determine text color
                 scaled_grid_val = grid_val / self.max
                 base_color = self.cmap(scaled_grid_val)
@@ -264,4 +260,4 @@ def draw(self, percent=True):
     def finalize(self, **kwargs):
         self.set_title('{} Confusion Matrix'.format(self.name))
         self.ax.set_ylabel('True Class')
-        self.ax.set_xlabel('Predicted Class')
\ No newline at end of file
+        self.ax.set_xlabel('Predicted Class')
diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py
index ee4e37945..b9d956af8 100644
--- a/yellowbrick/classifier/rocauc.py
+++ b/yellowbrick/classifier/rocauc.py
@@ -6,7 +6,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import auc, roc_auc_score, roc_curve
 
 
@@ -57,10 +57,10 @@ class ROCAUC(ClassificationScoreVisualizer):
     >>> viz.fit(X_train, y_train)
     >>> viz.score(X_test, y_test)
     >>> viz.poof()
-    
+
     """
     def __init__(self, model, ax=None, **kwargs):
-        
+
         super(ROCAUC, self).__init__(model, ax=ax, **kwargs)
 
         ## hoisted to ScoreVisualizer base class
@@ -119,14 +119,13 @@ def draw(self, y, y_pred):
         ax : the axis with the plotted figure
 
         """
-        # Create the axis if it doesn't exist
-        if self.ax is None:
-            self.ax = plt.gca()
-
-        plt.plot(self.fpr, self.tpr, c=self.colors['roc'], label='AUC = {:0.2f}'.format(self.roc_auc))
+        self.ax.plot(
+            self.fpr, self.tpr, c=self.colors['roc'],
+            label='AUC = {:0.2f}'.format(self.roc_auc)
+        )
 
         # Plot the line of no discrimination to compare the curve to.
-        plt.plot([0,1],[0,1],'m--',c=self.colors['diagonal'])
+        self.ax.plot([0,1],[0,1],'m--',c=self.colors['diagonal'])
 
         return self.ax
 
diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py
index 4bf3a1589..6092aea52 100644
--- a/yellowbrick/cluster/elbow.py
+++ b/yellowbrick/cluster/elbow.py
@@ -18,21 +18,93 @@
 ## Imports
 ##########################################################################
 
+import time
+import numpy as np
+
 from .base import ClusteringScoreVisualizer
 from ..exceptions import YellowbrickValueError
 
 from sklearn.metrics import silhouette_score
+from sklearn.metrics import calinski_harabaz_score
+from sklearn.metrics.pairwise import pairwise_distances
+from sklearn.preprocessing import LabelEncoder
+
 
 ## Packages for export
 __all__ = [
-    "KElbowVisualizer",
+    "KElbowVisualizer", "distortion_score"
 ]
 
 
+##########################################################################
+## Metrics
+##########################################################################
+
+def distortion_score(X, labels, metric='euclidean'):
+    """
+    Compute the mean distortion of all samples.
+
+    The distortion is computed as the the sum of the squared distances between
+    each observation and its closest centroid. Logically, this is the metric
+    that K-Means attempts to minimize as it is fitting the model.
+
+    .. seealso:: http://kldavenport.com/the-cost-function-of-k-means/
+
+    Parameters
+    ----------
+    X : array, shape = [n_samples, n_features] or [n_samples_a, n_samples_a]
+        Array of pairwise distances between samples if metric == "precomputed"
+        or a feature array for computing distances against the labels.
+
+    labels : array, shape = [n_samples]
+        Predicted labels for each sample
+
+    metric : string
+        The metric to use when calculating distance between instances in a
+        feature array. If metric is a string, it must be one of the options
+        allowed by `sklearn.metrics.pairwise.pairwise_distances
+        <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances.html#sklearn.metrics.pairwise.pairwise_distances>`_
+
+    .. todo:: add sample_size and random_state kwds similar to silhouette_score
+    """
+    # Encode labels to get unique centers and groups
+    le = LabelEncoder()
+    labels = le.fit_transform(labels)
+    unique_labels = le.classes_
+
+    # Sum of the distortions
+    distortion = 0
+
+    # Loop through each label (center) to compute the centroid
+    for current_label in unique_labels:
+        # Mask the instances that belong to the current label
+        mask = labels == current_label
+        instances = X[mask]
+
+        # Compute the center of these instances
+        center = instances.mean(axis=0)
+
+        # Compute the square distances from the instances to the center
+        distances = pairwise_distances(instances, [center], metric=metric)
+        distances = distances ** 2
+
+        # Add the mean square distance to the distortion
+        distortion += distances.mean()
+
+    return distortion
+
+
 ##########################################################################
 ## Elbow Method
 ##########################################################################
 
+KELBOW_SCOREMAP = {
+    "distortion": distortion_score,
+    "silhouette": silhouette_score,
+    "calinski_harabaz": calinski_harabaz_score,
+}
+
+
 class KElbowVisualizer(ClusteringScoreVisualizer):
     """
     The K-Elbow Visualizer implements the "elbow" method of selecting the
@@ -43,20 +115,20 @@ class KElbowVisualizer(ClusteringScoreVisualizer):
     clusters even if that is not the right k for the dataset.
 
     The elbow method runs k-means clustering on the dataset for a range of
-    values for k (say from 1-10) and then for each value of k computes the
-    ``silhouette_score``, the mean silhouette coefficient for all samples.
-    The silhouette coefficient of a cluster is computed by comparing the mean
-    intra-cluster distance (a) and the mean nearest-cluster distance (b) for
-    each sample in the dataset; the silhouette is then computed as
-    ``(b-a) / max(a,b)``. The score is a value between -1 and 1, values near
-    zero indicate overlapping clusters. Negative values imply that samples
-    have been assigned to the wrong cluster, and positive values mean that
-    there are discrete clusters.
-
-    Finally, the silhouette score for each k is plotted. If the line chart
-    looks like an arm, then the "elbow" (the point of inflection on the curve)
-    is the best value of k. The idea is that we want as small a k as possible
-    such that the clusters do not overlap.
+    values for k (say from 1-10) and then for each value of k computes an
+    average score for all clusters. By default, the ``distortion_score`` is
+    computed, the sum of square distances from each point to its assigned
+    center. Other metrics can also be used such as the ``silhouette_score``,
+    the mean silhouette  coefficient for all samples or the
+    ``calinski_harabaz_score``, which computes the ratio of dispersion between
+    and within clusters.
+
+    When these overall metrics for each model are plotted, it is possible to
+    visually determine the best value for K. If the line chart looks like an
+    arm, then the "elbow" (the point of inflection on the curve) is the best
+    value of k. The "arm" can be either up or down, but if there is a strong
+    inflection point, it is a good indication that the underlying model fits
+    best at that point.
 
     Parameters
     ----------
@@ -74,6 +146,19 @@ class KElbowVisualizer(ClusteringScoreVisualizer):
         is specified, then will compute the range (2,k) otherwise the
         specified range in the tuple is used.
 
+    metric : string, default: ``"distortion"``
+        Select the scoring metric to evaluate the clusters. The default is the
+        mean distortion, defined by the sum of squared distances between each
+        observation and its closest centroid. Other metrics include:
+
+        - **distortion**: mean sum of squared distances to centers
+        - **silhouette**: mean ratio of intra-cluster and nearest-cluster distance
+        - **calinski_harabaz**: ratio of within to between cluster dispersion
+
+    timings : bool, default: True
+        Display the fitting time per k to evaluate the amount of time required
+        to train the clustering model.
+
     kwargs : dict
         Keyword arguments that are passed to the base class and may influence
         the visualization as defined in other Visualizers.
@@ -99,17 +184,29 @@ class KElbowVisualizer(ClusteringScoreVisualizer):
     For a discussion on the Elbow method, read more at
     `Robert Gove's Block <https://bl.ocks.org/rpgove/0060ff3b656618e9136b>`_.
 
-    .. todo:: add parallelization ooption for performance
+    .. todo:: add parallelization option for performance
     .. todo:: add different metrics for scores and silhoutte
     .. todo:: add timing information about how long its taking
     """
 
-    def __init__(self, model, ax=None, k=10, **kwargs):
+    def __init__(self, model, ax=None, k=10,
+                 metric="distortion", timings=True, **kwargs):
         super(KElbowVisualizer, self).__init__(model, ax=ax, **kwargs)
 
+        # Get the scoring method
+        if metric not in KELBOW_SCOREMAP:
+            raise YellowbrickValueError(
+                "'{}' is not a defined metric "
+                "use one of distortion, silhouette, or calinski_harabaz"
+            )
+
+        # Store the arguments
+        self.scoring_metric = KELBOW_SCOREMAP[metric]
+        self.timings = timings
+
         # Convert K into a tuple argument if an integer
         if isinstance(k, int):
-            k = (2, k)
+            k = (2, k+1)
 
         # Expand k in to the values we will use, capturing exceptions
         try:
@@ -133,12 +230,20 @@ def fit(self, X, y=None, **kwargs):
         """
 
         self.k_scores_ = []
+        self.k_timers_ = []
 
         for k in self.k_values_:
+            # Compute the start time for each  model
+            start = time.time()
+
+            # Set the k value and fit the model
             self.estimator.set_params(n_clusters=k)
             self.estimator.fit(X)
+
+            # Append the time and score to our plottable metrics
+            self.k_timers_.append(time.time() - start)
             self.k_scores_.append(
-                silhouette_score(X, self.estimator.labels_, metric='euclidean')
+                self.scoring_metric(X, self.estimator.labels_)
             )
 
         self.draw()
@@ -147,12 +252,16 @@ def draw(self):
         """
         Draw the elbow curve for the specified scores and values of K.
         """
-
-        if self.ax is None:
-            self.ax = self.gca()
-
         # Plot the silhouette score against k
-        self.ax.plot(self.k_values_, self.k_scores_)
+        self.ax.plot(self.k_values_, self.k_scores_, marker="D", label="score")
+
+        # If we're going to plot the timings, create a twinx axis
+        if self.timings:
+            self.axes = [self.ax, self.ax.twinx()]
+            self.axes[1].plot(
+                self.k_values_, self.k_timers_, label="fit time",
+                c='g', marker="o", linestyle="--", alpha=0.75,
+            )
 
         return self.ax
 
@@ -161,12 +270,20 @@ def finalize(self):
         Prepare the figure for rendering by setting the title as well as the
         X and Y axis labels and adding the legend.
         """
+        # Get the metric name
+        metric = self.scoring_metric.__name__.replace("_", " ").title()
 
         # Set the title
         self.set_title(
-            'Silhoutte for {} Clustering Behavior'.format(self.name)
+            '{} Elbow for {} Clustering'.format(metric, self.name)
         )
 
         # Set the x and y labels
         self.ax.set_xlabel('k')
-        self.ax.set_ylabel('silhouette')
+        self.ax.set_ylabel(metric.lower())
+
+        # Set the second y axis labels
+        if self.timings:
+            self.axes[1].grid(False)
+            self.axes[1].set_ylabel("fit time (seconds)", color='g')
+            self.axes[1].tick_params('y', colors='g')
diff --git a/yellowbrick/cluster/silhouette.py b/yellowbrick/cluster/silhouette.py
index 17c298c7f..f5a57917b 100644
--- a/yellowbrick/cluster/silhouette.py
+++ b/yellowbrick/cluster/silhouette.py
@@ -93,9 +93,6 @@ def draw(self, labels):
             visualizer so that the figure can be redrawn with new data.
         """
 
-        if self.ax is None:
-            self.ax = self.gca()
-
         # Track the positions of the lines being drawn
         y_upper = 10 # the top of the silhouette
         y_lower = 10 # The bottom of the silhouette
diff --git a/yellowbrick/features/__init__.py b/yellowbrick/features/__init__.py
index 3e97568b2..109d1a89d 100644
--- a/yellowbrick/features/__init__.py
+++ b/yellowbrick/features/__init__.py
@@ -21,4 +21,5 @@
 from .pcoords import ParallelCoordinates, parallel_coordinates
 from .radviz import RadialVisualizer, RadViz, radviz
 from .rankd import Rank2D, rank2d
-from .scatter import ScatterViz, scatterviz
+from .scatter import ScatterViz, ScatterVisualizer, scatterviz
+from .jointplot import  JointPlotVisualizer
diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py
index fb92c9389..77c7bcc44 100644
--- a/yellowbrick/features/pcoords.py
+++ b/yellowbrick/features/pcoords.py
@@ -102,7 +102,7 @@ class ParallelCoordinates(DataVisualizer):
 
     Parameters
     ----------
-    
+
     ax : matplotlib Axes, default: None
         The axis to plot the figure on. If None is passed in the current axes
         will be used (or generated if required).
@@ -177,9 +177,6 @@ def draw(self, X, y, **kwargs):
         # TODO: Allow the user to specify this feature
         x = list(range(ncols))
 
-        # Create the axis if it doesn't exist
-        if self.ax is None: self.ax = plt.gca()
-
         # Create the colors
         # TODO: Allow both colormap, listed colors, and palette definition
         # TODO: Make this an independent function or property for override!
diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py
index 0a0e652ca..bf40f9d01 100644
--- a/yellowbrick/features/radviz.py
+++ b/yellowbrick/features/radviz.py
@@ -162,9 +162,9 @@ def draw(self, X, y, **kwargs):
         # Get the shape of the data
         nrows, ncols = X.shape
 
-        # Create the axes if they don't exist
-        if self.ax is None:
-            self.ax = plt.gca(xlim=[-1,1], ylim=[-1,1])
+        # Set the axes limits
+        self.ax.set_xlim([-1,1])
+        self.ax.set_ylim([-1,1])
 
         # Create the colors
         # TODO: Allow both colormap, listed colors, and palette definition
diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py
index 8e0eef9ea..4186d811d 100644
--- a/yellowbrick/features/rankd.py
+++ b/yellowbrick/features/rankd.py
@@ -246,10 +246,8 @@ def draw(self, X, **kwargs):
         """
         Draws the heatmap of the ranking matrix of variables.
         """
-        # Create the axes if they don't exist
-        if self.ax is None:
-            self.ax = plt.gca()
-            self.ax.set_aspect("equal")
+        # Set the axes aspect to be equal
+        self.ax.set_aspect("equal")
 
         # Generate a mask for the upper triangle
         mask = np.zeros_like(X, dtype=np.bool)
@@ -285,7 +283,7 @@ def finalize(self, **kwargs):
 
         """
         # Set the title
-        self.ax.set_title(
+        self.set_title(
             "{} Ranking of {} Features".format(
                 self.ranking_.title(), len(self.features_)
             )
diff --git a/yellowbrick/features/scatter.py b/yellowbrick/features/scatter.py
index 0b6de468d..51a00e2fa 100644
--- a/yellowbrick/features/scatter.py
+++ b/yellowbrick/features/scatter.py
@@ -243,9 +243,9 @@ def draw(self, X, y, **kwargs):
         draws each instance as a class or target colored point, whose location
         is determined by the feature data set.
         """
-        # Create the axes if they don't exist
-        if self.ax is None:
-            self.ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1])
+        # Set the axes limits
+        self.ax.set_xlim([-1,1])
+        self.ax.set_ylim([-1,1])
 
         # set the colors
         if self.colormap is not None or self.color is not None:
diff --git a/yellowbrick/pipeline.py b/yellowbrick/pipeline.py
index a282b71e9..0eb81592f 100644
--- a/yellowbrick/pipeline.py
+++ b/yellowbrick/pipeline.py
@@ -17,7 +17,9 @@
 ## Imports
 ##########################################################################
 
+from os import path
 from .base import Visualizer
+from .utils.helpers import slugify
 from sklearn.pipeline import Pipeline
 
 
@@ -71,25 +73,36 @@ def visual_steps(self):
             if isinstance(step[1], Visualizer)
         )
 
-    def draw(self, *args, **kwargs):
+    def poof(self, outdir=None, ext=".pdf", **kwargs):
         """
-        Calls draw on steps (including the final estimator) that has a draw
-        method and passes the args and kwargs to that draw function.
+        A single entry point to rendering all visualizations in the visual
+        pipeline. The rendering for the output depends on the backend context,
+        but for path based renderings (e.g. saving to a file), specify a
+        directory and extension to compse an outpath to save each
+        visualization (file names will be based on the  named step).
+
+        Parameters
+        ----------
+        outdir : path
+            The directory to save visualizations to.
+
+        ext : string, default = ".pdf"
+            The extension of the file to save the visualization to.
+
+        kwargs : dict
+            Keyword arguments to pass to the ``poof()`` method of all steps.
         """
         for name, step in self.visual_steps.items():
-            step.draw(*args, **kwargs)
+            if outdir is not None:
+                outpath = path.join(outdir, slugify(name) + ext)
+            else:
+                outpath = None
 
-    def poof(self, *args, **kwargs):
-        """
-        Calls poof on steps (including the final estimator) that has a poof
-        method and passes the args and kwargs to that poof function.
-        """
-        for name, step in self.visual_steps.items():
-            step.poof(*args, **kwargs)
+            step.poof(outpath=outpath, **kwargs)
 
-    def fit_transform_poof(self, X, y=None, **kwargs):
+    def fit_transform_poof(self, X, y=None, outpath=None, **kwargs):
         """
         Fit the model and transforms and then call poof.
         """
         self.fit_transform(X, y, **kwargs)
-        self.poof(**kwargs)
+        self.poof(outpath, **kwargs)
diff --git a/yellowbrick/regressor/alphas.py b/yellowbrick/regressor/alphas.py
index 958bcbeb6..4715da4ed 100644
--- a/yellowbrick/regressor/alphas.py
+++ b/yellowbrick/regressor/alphas.py
@@ -139,9 +139,6 @@ def draw(self):
         """
         Draws the alpha plot based on the values on the estimator.
         """
-        if self.ax is None:
-            self.ax = plt.gca()
-
         # Search for the correct parameters on the estimator.
         alphas = self._find_alphas_param()
         errors = self._find_errors_param()
@@ -250,10 +247,11 @@ class ManualAlphaSelection(AlphaSelection):
     cv : int, cross-validation generator or an iterable, optional
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
-          - None, to use the default 3-fold cross validation,
-          - integer, to specify the number of folds in a `(Stratified)KFold`,
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train, test splits.
+
+        - None, to use the default 3-fold cross validation,
+        - integer, to specify the number of folds in a `(Stratified)KFold`,
+        - An object to be used as a cross-validation generator.
+        - An iterable yielding train, test splits.
 
         This argument is passed to the
         ``sklearn.model_selection.cross_val_score`` method to produce the
@@ -337,9 +335,6 @@ def draw(self):
         Draws the alphas values against their associated error in a similar
         fashion to the AlphaSelection visualizer.
         """
-        if self.ax is None:
-            self.ax = plt.gca()
-
         # Plot the alpha against the error
         self.ax.plot(self.alphas, self.errors, label=self.name.lower())
 
diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py
index fca400537..1d38424da 100644
--- a/yellowbrick/regressor/residuals.py
+++ b/yellowbrick/regressor/residuals.py
@@ -20,7 +20,7 @@
 
 import matplotlib.pyplot as plt
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 from ..bestfit import draw_best_fit
 from ..style.palettes import LINE_COLOR
@@ -129,10 +129,6 @@ def draw(self, y, y_pred):
         ------
         ax : the axis with the plotted figure
         """
-        # Create the axis if it doesn't exist
-        if self.ax is None:
-            self.ax = plt.gca()
-
         self.ax.scatter(y, y_pred, c=self.colors['point'])
 
         # TODO If score is happening inside a loop, draw would get called multiple times.
@@ -339,10 +335,6 @@ def draw(self, y_pred, residuals, train=False, **kwargs):
         ax : the axis with the plotted figure
 
         """
-        # Create the axis if it doesn't exist
-        if self.ax is None:
-            self.ax = plt.gca()
-
         color = self.colors['train_point'] if train else self.colors['test_point']
         alpha = 0.5 if train else 1.0
 
diff --git a/yellowbrick/text/freqdist.py b/yellowbrick/text/freqdist.py
index 412636ee4..dad332afb 100644
--- a/yellowbrick/text/freqdist.py
+++ b/yellowbrick/text/freqdist.py
@@ -179,9 +179,6 @@ def draw(self, **kwargs):
         kwargs: generic keyword arguments.
 
         """
-        # Create the axis if it doesn't exist
-        if self.ax is None: self.ax = plt.gca()
-
         # Plot the top 50 most frequent words
         y_pos = np.arange(self.N)
         self.ax.bar(y_pos, self.counts[:self.N], align='center', alpha=0.5)
diff --git a/yellowbrick/text/tsne.py b/yellowbrick/text/tsne.py
index e9dd4647c..ef3c0165c 100644
--- a/yellowbrick/text/tsne.py
+++ b/yellowbrick/text/tsne.py
@@ -277,10 +277,6 @@ def draw(self, points, target=None, **kwargs):
         of each of the points. If the target is not specified, then the points
         are plotted as a single cloud to show similar documents.
         """
-
-        # Create the axis if it doesn't exist
-        if self.ax is None: self.ax = plt.gca()
-
         # Create the color mapping for the classes.
         # TODO: Allow both colormap, listed colors, and palette definition
         # See the FeatureVisualizer for more on this.
diff --git a/yellowbrick/utils.py b/yellowbrick/utils.py
deleted file mode 100644
index da4475122..000000000
--- a/yellowbrick/utils.py
+++ /dev/null
@@ -1,320 +0,0 @@
-# yellowbrick.utils
-# Utility functions and helpers for the Yellowbrick library.
-#
-# Author:   Jason Keung <jason.s.keung@gmail.com>
-# Author:   Patrick O'Melveny <pvomelveny@gmail.com>
-# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
-# Author:   Rebecca Bilbro <rbilbro@districtdatalabs.com>
-# Created:  Thu Jun 02 15:33:18 2016 -0500
-#
-# Copyright (C) 2016 District Data LAbs
-# For license information, see LICENSE.txt
-#
-# ID: utils.py [] jason.s.keung@gmail.com $
-
-"""
-Utility functions and helpers for the Yellowbrick library.
-"""
-
-##########################################################################
-## Imports
-##########################################################################
-
-import inspect
-import math
-import numpy as np
-
-from functools import wraps
-from sklearn.pipeline import Pipeline
-from sklearn.base import BaseEstimator
-
-from yellowbrick.exceptions import YellowbrickTypeError
-
-
-##########################################################################
-## Model detection utilities
-##########################################################################
-
-def get_model_name(model):
-    """
-    Detects the model name for a Scikit-Learn model or pipeline
-
-    Parameters
-    ----------
-    model: class or instance
-        The object to determine the name for
-    """
-    if not is_estimator(model):
-        raise YellowbrickTypeError(
-            "Cannot detect the model name for non estimator: '{}'".format(
-                type(model)
-            )
-        )
-
-    else:
-        if isinstance(model, Pipeline):
-            return model.steps[-1][-1].__class__.__name__
-        else:
-            return model.__class__.__name__
-
-
-##########################################################################
-## Type checking utilities
-##########################################################################
-
-def is_estimator(model):
-    """
-    Determines if a model is an estimator using issubclass and isinstance.
-
-    Parameters
-    ----------
-    estimator : class or instance
-        The object to test if it is a Scikit-Learn clusterer, especially a
-        Scikit-Learn estimator or Yellowbrick visualizer
-    """
-    if inspect.isclass(model):
-        return issubclass(model, BaseEstimator)
-
-    return isinstance(model, BaseEstimator)
-
-# Alias for closer name to isinstance and issubclass
-isestimator = is_estimator
-
-
-def is_classifier(estimator):
-    """
-    Returns True if the given estimator is (probably) a classifier.
-
-    Parameters
-    ----------
-    estimator : class or instance
-        The object to test if it is a Scikit-Learn clusterer, especially a
-        Scikit-Learn estimator or Yellowbrick visualizer
-
-    See also
-    --------
-    is_classifier
-        `sklearn.is_classifier() <https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L518>`_
-    """
-    # TODO: once we make ScoreVisualizer and ModelVisualizer pass through
-    # wrappers as in Issue #90, these three lines become unnecessary.
-    # NOTE: This must be imported here to avoid recursive import.
-    from yellowbrick.base import Visualizer
-    if isinstance(estimator, Visualizer):
-        return is_classifier(estimator.estimator)
-
-    # Test the _estimator_type property
-    return getattr(estimator, "_estimator_type", None) == "classifier"
-
-# Alias for closer name to isinstance and issubclass
-isclassifier = is_classifier
-
-
-def is_regressor(estimator):
-    """
-    Returns True if the given estimator is (probably) a regressor.
-
-    Parameters
-    ----------
-    estimator : class or instance
-        The object to test if it is a Scikit-Learn clusterer, especially a
-        Scikit-Learn estimator or Yellowbrick visualizer
-
-    See also
-    --------
-    is_regressor
-        `sklearn.is_regressor() <https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L531>`_
-    """
-    # TODO: once we make ScoreVisualizer and ModelVisualizer pass through
-    # wrappers as in Issue #90, these three lines become unnecessary.
-    # NOTE: This must be imported here to avoid recursive import.
-    from yellowbrick.base import Visualizer
-    if isinstance(estimator, Visualizer):
-        return is_regressor(estimator.estimator)
-
-    # Test the _estimator_type property
-    return getattr(estimator, "_estimator_type", None) == "regressor"
-
-# Alias for closer name to isinstance and issubclass
-isregressor = is_regressor
-
-
-def is_clusterer(estimator):
-    """
-    Returns True if the given estimator is a clusterer.
-
-    Parameters
-    ----------
-    estimator : class or instance
-        The object to test if it is a Scikit-Learn clusterer, especially a
-        Scikit-Learn estimator or Yellowbrick visualizer
-    """
-    # TODO: once we make ScoreVisualizer and ModelVisualizer pass through
-    # wrappers as in Issue #90, these three lines become unnecessary.
-    # NOTE: This must be imported here to avoid recursive import.
-    from yellowbrick.base import Visualizer
-    if isinstance(estimator, Visualizer):
-        return is_clusterer(estimator.estimator)
-
-    # Test the _estimator_type property
-    return getattr(estimator, "_estimator_type", None) == "clusterer"
-
-# Alias for closer name to isinstance and issubclass
-isclusterer = is_clusterer
-
-
-def is_dataframe(obj):
-    """
-    Returns True if the given object is a Pandas Data Frame.
-
-    Parameters
-    ----------
-    obj: instance
-        The object to test whether or not is a Pandas DataFrame.
-    """
-    try:
-        # This is the best method of type checking
-        from pandas import DataFrame
-        return isinstance(obj, DataFrame)
-    except ImportError:
-        # Pandas is not a dependency, so this is scary
-        return obj.__class__.__name__ == "DataFrame"
-
-# Alias for closer name to isinstance and issubclass
-isdataframe = is_dataframe
-
-
-def is_structured_array(obj):
-    """
-    Returns True if the given object is a Numpy Structured Array.
-
-    Parameters
-    ----------
-    obj: instance
-        The object to test whether or not is a Numpy Structured Array.
-    """
-    if isinstance(obj, np.ndarray) and hasattr(obj, 'dtype'):
-        if obj.dtype.names is not None:
-            return True
-    return False
-
-
-# Alias for closer name to isinstance and issubclass
-isstructuredarray = is_structured_array
-
-
-def has_ndarray_int_columns(features, X):
-    """ Checks if numeric feature columns exist in ndarray """
-    _, ncols = X.shape
-    if not all(d.isdigit() for d in features if isinstance(d, str)) or not isinstance(X, np.ndarray):
-        return False
-    ndarray_columns = np.arange(0, ncols)
-    feature_cols = np.unique([int(d) for d in features])
-    return all(np.in1d(feature_cols, ndarray_columns))
-
-# Alias for closer name to isinstance and issubclass
-hasndarrayintcolumns = has_ndarray_int_columns
-
-
-#From here: http://stackoverflow.com/questions/26248654/numpy-return-0-with-divide-by-zero
-def div_safe( numerator, denominator ):
-    """
-    Ufunc-extension that returns 0 instead of nan when dividing numpy arrays
-
-    Parameters
-    ----------
-    numerator: array-like
-
-    denominator: scalar or array-like that can be validly divided by the numerator
-
-    returns a numpy array
-
-    example: div_safe( [-1, 0, 1], 0 ) == [0, 0, 0]
-    """
-    #First handle scalars
-    if np.isscalar(numerator):
-        raise ValueError("div_safe should only be used with an array-like numerator")
-
-    #Then numpy arrays
-    try:
-        with np.errstate(divide='ignore', invalid='ignore'):
-            result = np.true_divide( numerator, denominator )
-            result[ ~ np.isfinite( result )] = 0  # -inf inf NaN
-        return result
-    except ValueError as e:
-        raise e
-
-##########################################################################
-## Decorators
-##########################################################################
-
-def memoized(fget):
-    """
-    Return a property attribute for new-style classes that only calls its
-    getter on the first access. The result is stored and on subsequent
-    accesses is returned, preventing the need to call the getter any more.
-
-    Parameters
-    ----------
-    fget: function
-        The getter method to memoize for subsequent access.
-
-    See also
-    --------
-    python-memoized-property
-        `python-memoized-property <https://github.com/estebistec/python-memoized-property>`_
-    """
-    attr_name = '_{0}'.format(fget.__name__)
-
-    @wraps(fget)
-    def fget_memoized(self):
-        if not hasattr(self, attr_name):
-            setattr(self, attr_name, fget(self))
-        return getattr(self, attr_name)
-
-    return property(fget_memoized)
-
-
-class docutil(object):
-    """
-    This decorator can be used to apply the doc string from another function
-    to the decorated function. This is used for our single call wrapper
-    functions who implement the visualizer API without forcing the user to
-    jump through all the hoops. The docstring of both the visualizer and the
-    single call wrapper should be identical, this decorator ensures that we
-    only have to edit one doc string.
-
-    Usage::
-
-        @docutil(Visualizer.__init__)
-        def visualize(*args, **kwargs):
-            pass
-
-    The basic usage is that you instantiate the decorator with the function
-    whose docstring you want to copy, then apply that decorator to the the
-    function whose docstring you would like modified.
-
-    Note that this decorator performs no wrapping of the target function.
-    """
-
-    def __init__(self, func):
-        """Create a decorator to document other functions with the specified
-        function's doc string.
-
-        Parameters
-        ----------
-        func : function
-            The function whose doc string we should decorate with
-        """
-        self.doc = func.__doc__
-
-    def __call__(self, func):
-        """Modify the decorated function with the stored doc string.
-
-        Parameters
-        ----------
-        func: function
-            The function to apply the saved doc string to.
-        """
-        func.__doc__ = self.doc
-        return func
diff --git a/yellowbrick/utils/__init__.py b/yellowbrick/utils/__init__.py
new file mode 100644
index 000000000..e9e6acaee
--- /dev/null
+++ b/yellowbrick/utils/__init__.py
@@ -0,0 +1,24 @@
+# yellowbrick.utils
+# Utility functions and helpers for the Yellowbrick library.
+#
+# Author:   Jason Keung <jason.s.keung@gmail.com>
+# Author:   Patrick O'Melveny <pvomelveny@gmail.com>
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Author:   Rebecca Bilbro <rbilbro@districtdatalabs.com>
+# Created:  Thu Jun 02 15:33:18 2016 -0500
+#
+# Copyright (C) 2016 District Data LAbs
+# For license information, see LICENSE.txt
+#
+# ID: __init__.py [] jason.s.keung@gmail.com $
+
+"""
+Utility functions and helpers for the Yellowbrick library.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+from .helpers import *
+from .types import *
diff --git a/yellowbrick/utils/decorators.py b/yellowbrick/utils/decorators.py
new file mode 100644
index 000000000..9e04dce2b
--- /dev/null
+++ b/yellowbrick/utils/decorators.py
@@ -0,0 +1,96 @@
+# yellowbrick.utils.decorators
+# Decorators and descriptors for annotating yellowbrick library functions.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Thu May 18 15:13:33 2017 -0400
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: decorators.py [] benjamin@bengfort.com $
+
+"""
+Decorators and descriptors for annotating yellowbrick library functions.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+from functools import wraps
+
+
+##########################################################################
+## Decorators
+##########################################################################
+
+def memoized(fget):
+    """
+    Return a property attribute for new-style classes that only calls its
+    getter on the first access. The result is stored and on subsequent
+    accesses is returned, preventing the need to call the getter any more.
+
+    Parameters
+    ----------
+    fget: function
+        The getter method to memoize for subsequent access.
+
+    See also
+    --------
+    python-memoized-property
+        `python-memoized-property <https://github.com/estebistec/python-memoized-property>`_
+    """
+    attr_name = '_{0}'.format(fget.__name__)
+
+    @wraps(fget)
+    def fget_memoized(self):
+        if not hasattr(self, attr_name):
+            setattr(self, attr_name, fget(self))
+        return getattr(self, attr_name)
+
+    return property(fget_memoized)
+
+
+class docutil(object):
+    """
+    This decorator can be used to apply the doc string from another function
+    to the decorated function. This is used for our single call wrapper
+    functions who implement the visualizer API without forcing the user to
+    jump through all the hoops. The docstring of both the visualizer and the
+    single call wrapper should be identical, this decorator ensures that we
+    only have to edit one doc string.
+
+    Usage::
+
+        @docutil(Visualizer.__init__)
+        def visualize(*args, **kwargs):
+            pass
+
+    The basic usage is that you instantiate the decorator with the function
+    whose docstring you want to copy, then apply that decorator to the the
+    function whose docstring you would like modified.
+
+    Note that this decorator performs no wrapping of the target function.
+    """
+
+    def __init__(self, func):
+        """Create a decorator to document other functions with the specified
+        function's doc string.
+
+        Parameters
+        ----------
+        func : function
+            The function whose doc string we should decorate with
+        """
+        self.doc = func.__doc__
+
+    def __call__(self, func):
+        """Modify the decorated function with the stored doc string.
+
+        Parameters
+        ----------
+        func: function
+            The function to apply the saved doc string to.
+        """
+        func.__doc__ = self.doc
+        return func
diff --git a/yellowbrick/utils/helpers.py b/yellowbrick/utils/helpers.py
new file mode 100644
index 000000000..28bd2775b
--- /dev/null
+++ b/yellowbrick/utils/helpers.py
@@ -0,0 +1,133 @@
+# yellowbrick.utils.helpers
+# Helper functions and generic utilities for use in Yellowbrick code.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Fri May 19 10:39:30 2017 -0700
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: helpers.py [] benjamin@bengfort.com $
+
+"""
+Helper functions and generic utilities for use in Yellowbrick code.
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import re
+import unicodedata
+import numpy as np
+
+from sklearn.pipeline import Pipeline
+
+from .types import is_estimator
+from yellowbrick.exceptions import YellowbrickTypeError
+
+
+##########################################################################
+## Model and Feature Information
+##########################################################################
+
+def get_model_name(model):
+    """
+    Detects the model name for a Scikit-Learn model or pipeline.
+
+    Parameters
+    ----------
+    model: class or instance
+        The object to determine the name for. If the model is an estimator it
+        returns the class name; if it is a Pipeline it returns the class name
+        of the final transformer or estimator in the Pipeline.
+
+    Returns
+    -------
+    name : string
+        The name of the model or pipeline.
+    """
+    if not is_estimator(model):
+        raise YellowbrickTypeError(
+            "Cannot detect the model name for non estimator: '{}'".format(
+                type(model)
+            )
+        )
+
+    else:
+        if isinstance(model, Pipeline):
+            return get_model_name(model.steps[-1][-1])
+        else:
+            return model.__class__.__name__
+
+
+def has_ndarray_int_columns(features, X):
+    """ Checks if numeric feature columns exist in ndarray """
+    _, ncols = X.shape
+    if not all(d.isdigit() for d in features if isinstance(d, str)) or not isinstance(X, np.ndarray):
+        return False
+    ndarray_columns = np.arange(0, ncols)
+    feature_cols = np.unique([int(d) for d in features])
+    return all(np.in1d(feature_cols, ndarray_columns))
+
+# Alias for closer name to isinstance and issubclass
+hasndarrayintcolumns = has_ndarray_int_columns
+
+
+##########################################################################
+## Numeric Computations
+##########################################################################
+
+#From here: http://stackoverflow.com/questions/26248654/numpy-return-0-with-divide-by-zero
+def div_safe( numerator, denominator ):
+    """
+    Ufunc-extension that returns 0 instead of nan when dividing numpy arrays
+
+    Parameters
+    ----------
+    numerator: array-like
+
+    denominator: scalar or array-like that can be validly divided by the numerator
+
+    returns a numpy array
+
+    example: div_safe( [-1, 0, 1], 0 ) == [0, 0, 0]
+    """
+    #First handle scalars
+    if np.isscalar(numerator):
+        raise ValueError("div_safe should only be used with an array-like numerator")
+
+    #Then numpy arrays
+    try:
+        with np.errstate(divide='ignore', invalid='ignore'):
+            result = np.true_divide( numerator, denominator )
+            result[ ~ np.isfinite( result )] = 0  # -inf inf NaN
+        return result
+    except ValueError as e:
+        raise e
+
+
+##########################################################################
+## String Computations
+##########################################################################
+
+def slugify(text):
+    """
+    Returns a slug of given text, normalizing unicode data for file-safe
+    strings. Used for deciding where to write images to disk.
+
+    Parameters
+    ----------
+    text : string
+        The string to slugify
+
+    Returns
+    -------
+    slug : string
+        A normalized slug representation of the text
+
+    .. seealso:: http://yashchandra.com/2014/05/08/how-to-generate-clean-url-or-a-slug-in-python/
+    """
+    slug = re.sub(r'[^\w]+', ' ', text)
+    slug = "-".join(slug.lower().strip().split())
+    return slug
diff --git a/yellowbrick/utils/types.py b/yellowbrick/utils/types.py
new file mode 100644
index 000000000..7e3808a51
--- /dev/null
+++ b/yellowbrick/utils/types.py
@@ -0,0 +1,169 @@
+# yellowbrick.utils.types
+# Detection utilities for Scikit-Learn and Numpy types for flexibility
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Fri May 19 10:51:13 2017 -0700
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: types.py [] benjamin@bengfort.com $
+
+"""
+Detection utilities for Scikit-Learn and Numpy types for flexibility
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import inspect
+import numpy as np
+
+from sklearn.base import BaseEstimator
+from yellowbrick.exceptions import YellowbrickTypeError
+
+
+##########################################################################
+## Type checking utilities
+##########################################################################
+
+def is_estimator(model):
+    """
+    Determines if a model is an estimator using issubclass and isinstance.
+
+    Parameters
+    ----------
+    estimator : class or instance
+        The object to test if it is a Scikit-Learn clusterer, especially a
+        Scikit-Learn estimator or Yellowbrick visualizer
+    """
+    if inspect.isclass(model):
+        return issubclass(model, BaseEstimator)
+
+    return isinstance(model, BaseEstimator)
+
+# Alias for closer name to isinstance and issubclass
+isestimator = is_estimator
+
+
+def is_classifier(estimator):
+    """
+    Returns True if the given estimator is (probably) a classifier.
+
+    Parameters
+    ----------
+    estimator : class or instance
+        The object to test if it is a Scikit-Learn clusterer, especially a
+        Scikit-Learn estimator or Yellowbrick visualizer
+
+    See also
+    --------
+    is_classifier
+        `sklearn.is_classifier() <https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L518>`_
+    """
+    # TODO: once we make ScoreVisualizer and ModelVisualizer pass through
+    # wrappers as in Issue #90, these three lines become unnecessary.
+    # NOTE: This must be imported here to avoid recursive import.
+    from yellowbrick.base import Visualizer
+    if isinstance(estimator, Visualizer):
+        return is_classifier(estimator.estimator)
+
+    # Test the _estimator_type property
+    return getattr(estimator, "_estimator_type", None) == "classifier"
+
+# Alias for closer name to isinstance and issubclass
+isclassifier = is_classifier
+
+
+def is_regressor(estimator):
+    """
+    Returns True if the given estimator is (probably) a regressor.
+
+    Parameters
+    ----------
+    estimator : class or instance
+        The object to test if it is a Scikit-Learn clusterer, especially a
+        Scikit-Learn estimator or Yellowbrick visualizer
+
+    See also
+    --------
+    is_regressor
+        `sklearn.is_regressor() <https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L531>`_
+    """
+    # TODO: once we make ScoreVisualizer and ModelVisualizer pass through
+    # wrappers as in Issue #90, these three lines become unnecessary.
+    # NOTE: This must be imported here to avoid recursive import.
+    from yellowbrick.base import Visualizer
+    if isinstance(estimator, Visualizer):
+        return is_regressor(estimator.estimator)
+
+    # Test the _estimator_type property
+    return getattr(estimator, "_estimator_type", None) == "regressor"
+
+# Alias for closer name to isinstance and issubclass
+isregressor = is_regressor
+
+
+def is_clusterer(estimator):
+    """
+    Returns True if the given estimator is a clusterer.
+
+    Parameters
+    ----------
+    estimator : class or instance
+        The object to test if it is a Scikit-Learn clusterer, especially a
+        Scikit-Learn estimator or Yellowbrick visualizer
+    """
+    # TODO: once we make ScoreVisualizer and ModelVisualizer pass through
+    # wrappers as in Issue #90, these three lines become unnecessary.
+    # NOTE: This must be imported here to avoid recursive import.
+    from yellowbrick.base import Visualizer
+    if isinstance(estimator, Visualizer):
+        return is_clusterer(estimator.estimator)
+
+    # Test the _estimator_type property
+    return getattr(estimator, "_estimator_type", None) == "clusterer"
+
+# Alias for closer name to isinstance and issubclass
+isclusterer = is_clusterer
+
+
+def is_dataframe(obj):
+    """
+    Returns True if the given object is a Pandas Data Frame.
+
+    Parameters
+    ----------
+    obj: instance
+        The object to test whether or not is a Pandas DataFrame.
+    """
+    try:
+        # This is the best method of type checking
+        from pandas import DataFrame
+        return isinstance(obj, DataFrame)
+    except ImportError:
+        # Pandas is not a dependency, so this is scary
+        return obj.__class__.__name__ == "DataFrame"
+
+# Alias for closer name to isinstance and issubclass
+isdataframe = is_dataframe
+
+
+def is_structured_array(obj):
+    """
+    Returns True if the given object is a Numpy Structured Array.
+
+    Parameters
+    ----------
+    obj: instance
+        The object to test whether or not is a Numpy Structured Array.
+    """
+    if isinstance(obj, np.ndarray) and hasattr(obj, 'dtype'):
+        if obj.dtype.names is not None:
+            return True
+    return False
+
+
+# Alias for closer name to isinstance and issubclass
+isstructuredarray = is_structured_array
diff --git a/yellowbrick/utils/wrapper.py b/yellowbrick/utils/wrapper.py
new file mode 100644
index 000000000..7dc93b4db
--- /dev/null
+++ b/yellowbrick/utils/wrapper.py
@@ -0,0 +1,42 @@
+# yellowbrick.utils.wrapper
+# Utility package that provides a wrapper for new style classes.
+#
+# Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
+# Created:  Sun May 21 20:27:32 2017 -0700
+#
+# Copyright (C) 2017 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: wrapper.py [] benjamin@bengfort.com $
+
+"""
+Utility package that provides a wrapper for new style classes.
+"""
+
+##########################################################################
+## Wrapper Class
+##########################################################################
+
+
+class Wrapper(object):
+    """
+    Object wrapper class.
+
+    An object wrapper is initialized with the object it wraps then proxies
+    any unhandled getattribute methods to it. If no attribute is found either
+    on the wrapper or the wrapped object, an AttributeError is raised.
+
+    .. seealso:: http://code.activestate.com/recipes/577555-object-wrapper-class/
+
+    Parameters
+    ----------
+    obj : object
+        The object to wrap with the wrapper class
+    """
+
+    def __init__(self, obj):
+        self._wrapped = obj
+
+    def __getattr__(self, attr):
+        # proxy to the wrapped object
+        return getattr(self._wrapped, attr)
diff --git a/yellowbrick/version.py b/yellowbrick/version.py
index 21aa9c456..461db9471 100644
--- a/yellowbrick/version.py
+++ b/yellowbrick/version.py
@@ -20,9 +20,9 @@
 __version_info__ = {
     'major': 0,
     'minor': 4,
-    'micro': 0,
-    'releaselevel': 'alpha',
-    'serial': 3,
+    'micro': 1,
+    'releaselevel': 'final',
+    'serial': 4,
 }
 
 ##########################################################################