diff --git a/.travis.yml b/.travis.yml index 4147cd431..3516ae706 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,8 +5,9 @@ python: before_install: - sudo apt-get build-dep python-scipy - - pip install coveralls - pip install scipy + - pip install nose coverage mock + - pip install coveralls requests install: pip install -r requirements.txt diff --git a/Makefile b/Makefile index e0c7a5b86..c7334eef0 100644 --- a/Makefile +++ b/Makefile @@ -22,6 +22,7 @@ clean: -rm -rf site -rm -rf classes_$(PROJECT).png -rm -rf packages_$(PROJECT).png + -rm -rf docs/_build # Targets for testing test: diff --git a/README.md b/README.md index 375509cc7..43e71be37 100644 --- a/README.md +++ b/README.md @@ -64,13 +64,12 @@ visualizer.poof() # Draw/show/poof the data 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. ```python from sklearn.svm import LinearSVC -from yellowbrick import ROCAUC +from yellowbrick.classifier import ROCAUC model = LinearSVC() model.fit(X,y) -y_pred = model.predict(X) visualizer = ROCAUC(model) -visualizer.score(y,y_pred) +visualizer.score(X,y) visualizer.poof() ``` diff --git a/docs/about.rst b/docs/about.rst new file mode 100644 index 000000000..de2dcf4af --- /dev/null +++ b/docs/about.rst @@ -0,0 +1,201 @@ +===== +About +===== + + +About Yellowbrick +----------------- +Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow. + +The Model Selection Triple +^^^^^^^^^^^^^^^^^^^^^^^^^^ +Discussions of machine learning are frequently characterized by a singular focus on model selection. Be it logistic regression, random forests, Bayesian methods, or artificial neural networks, machine learning practitioners are often quick to express their preference. The reason for this is mostly historical. Though modern third-party machine learning libraries have made the deployment of multiple models appear nearly trivial, traditionally the application and tuning of even one of these algorithms required many years of study. As a result, machine learning practitioners tended to have strong preferences for particular (and likely more familiar) models over others. + +However, model selection is a bit more nuanced than simply picking the "right" or "wrong" algorithm. In practice, the workflow includes: + + 1. selecting and/or engineering the smallest and most predictive feature set + 2. choosing a set of algorithms from a model family, and + 3. tuning the algorithm hyperparameters to optimize performance. + +The **model selection triple** was first described in a 2015 SIGMOD_ paper by Kumar et al. In their paper, which concerns the development of next-generation database systems built to anticipate predictive modeling, the authors cogently express that such systems are badly needed due to the highly experimental nature of machine learning in practice. "Model selection," they explain, "is iterative and exploratory because the space of [model selection triples] is usually infinite, and it is generally impossible for analysts to know a priori which [combination] will yield satisfactory accuracy and/or insights." + +Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search. By visualizing the model selection process, data scientists can steer towards final, explainable models and avoid pitfalls and traps. + +The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. Yellowbrick extends the Scikit-Learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process, providing visual diagnostics throughout the transformation of high dimensional data. + + +Contributing +------------ + +If you would like to contribute to Yellowbrick, you can do so in the following ways: + +- Add issues or bugs to the bug tracker: https://github.com/DistrictDataLabs/yellowbrick/issues +- Work on a card on the dev board: https://waffle.io/DistrictDataLabs/yellowbrick +- Create a pull request in Github: https://github.com/DistrictDataLabs/yellowbrick/pulls + +Here's how to get set up with Yellowbrick in development mode (since the project is still under development). + +1. Fork and clone the repository. After clicking fork in the upper right corner +for your own copy of yellowbrick to your github account. Clone it in a directory +of your choice.:: + + $ git clone https://github.com/[YOURUSERNAME]/yellowbrick + $ cd yellowbrick + +2. Create virtualenv and create the dependencies.:: + + $ virtualenv venv + $ pip install -r requirements.txt + +3. Fetch and switch to development.:: + + $ git fetch + $ git checkout develop + + +Git Conventions +^^^^^^^^^^^^^^^ + +The Yellowbrick repository is set up in a typical production/release/development cycle_ as described in "A Successful Git Branching Model." A typical workflow is as follows: + +1. Select a card from the dev board_, preferably one that is "ready" then move it to "in-progress". + +2. Create a branch off of develop called "feature-[feature name]", work and commit into that branch.:: + + $ git checkout -b feature-myfeature develop + +3. Once you are done working (and everything is tested) merge your feature into develop.:: + + $ git checkout develop + $ git merge --no-ff feature-myfeature + $ git branch -d feature-myfeature + $ git push origin develop + +4. Repeat. Releases will be routinely pushed into master via release branches, then deployed to the server. + +Name Origin +----------- +The Yellowbrick package gets its name from the fictional element in the 1900 children's novel **The Wonderful Wizard of Oz** by American author L. Frank Baum. In the book, the yellow brick road is the path that the protagonist, Dorothy Gale, must travel in order to reach her destination in the Emerald City. + +From Wikipedia_: + "The road is first introduced in the third chapter of The Wonderful Wizard of Oz. The road begins in the heart of the eastern quadrant called Munchkin Country in the Land of Oz. It functions as a guideline that leads all who follow it, to the road's ultimate destination—the imperial capital of Oz called Emerald City that is located in the exact center of the entire continent. In the book, the novel's main protagonist, Dorothy, is forced to search for the road before she can begin her quest to seek the Wizard. This is because the cyclone from Kansas did not release her farmhouse closely near it as it did in the various film adaptations. After the council with the native Munchkins and their dear friend the Good Witch of the North, Dorothy begins looking for it and sees many pathways and roads nearby, (all of which lead in various directions). Thankfully it doesn't take her too long to spot the one paved with bright yellow bricks." + + +Changelog +--------- +Hardened the Yellowbrick API to elevate the idea of a Visualizer to a first principle. This included reconciling shifts in the development of the preliminary versions to the new API, formalizing Visualizer methods like `draw()` and `finalize()`, and adding utilities that revolve around Scikit-Learn. To that end we also performed administrative tasks like refreshing the documentation and preparing the repository for more and varied open source contributions. + +Version 0.3.2 +^^^^^^^^^^^^^ + +* Tag: v0.3.2_ +* Deployed: Friday, January 20, 2017 +* Contributors: Benjamin Bengfort, Rebecca Bilbro + +Changes: + - Converted Mkdocs documentation to Sphinx documentation + - Updated docstrings for all Visualizers and functions + - Created a DataVisualizer base class for dataset visualization + - Single call functions for simple visualizer interaction + - Added yellowbrick specific color sequences and palettes and env handling + - More robust examples with downloader from DDL host + - Better axes handling in visualizer, matplotlib/sklearn integration + - Added a finalize method to complete drawing before render + - Improved testing on real data sets from examples + - Bugfix: score visualizer renders in notebook but not in Python scripts. + - Bugfix: tests updated to support new API + +Hotfix 0.3.1 +^^^^^^^^^^^^ +Hotfix to solve pip install issues with Yellowbrick. + +* Tag: v0.3.1_ +* Deployed: Monday, October 10, 2016 +* Contributors: Benjamin Bengfort + + Changes: + - Modified packaging and wheel for Python 2.7 and 3.5 compatibility + - Modified deployment to PyPI and pip install ability + - Fixed Travis-CI tests with the backend failures. + +Version 0.3 +^^^^^^^^^^^ +This release marks a major change from the previous MVP releases as Yellowbrick moves towards direct integration with Scikit-Learn for visual diagnostics and steering of machine learning and could therefore be considered the first alpha release of the library. To that end we have created a Visualizer model which extends sklearn.base.BaseEstimator and can be used directly in the ML Pipeline. There are a number of visualizers that can be used throughout the model selection process, including for feature analysis, model selection, and hyperparameter tuning. + +In this release specifically we focused on visualizers in the data space for feature analysis and visualizers in the model space for scoring and evaluating models. Future releases will extend these base classes and add more functionality. + +* Tag: v0.3_ +* Deployed: Sunday, October 9, 2016 +* Contributors: Benjamin Bengfort, Rebecca Bilbro, Marius van Niekerk + + Enhancements: + - Created an API for visualization with machine learning: Visualizers that are BaseEstimators. + - Created a class hierarchy for Visualizers throughout the ML process particularly feature analysis and model evaluation + - Visualizer interface is draw method which can be called multiple times on data or model spaces and a poof method to finalize the figure and display or save to disk. + - ScoreVisualizers wrap Scikit-Learn estimators and implement fit and predict (pass-throughs to the estimator) and also score which calls draw in order to visually score the estimator. If the estimator isn't appropriate for the scoring method an exception is raised. + - ROCAUC is a ScoreVisualizer that plots the receiver operating characteristic curve and displays the area under the curve score. + - ClassificationReport is a ScoreVisualizer that renders the confusion matrix of a classifier as a heatmap. + - PredictionError is a ScoreVisualizer that plots the actual vs. predicted values and the 45 degree accuracy line for regressors. + - ResidualPlot is a ScoreVisualizer that plots the residuals (y - yhat) across the actual values (y) with the zero accuracy line for both train and test sets. + - ClassBalance is a ScoreVisualizer that displays the support for each class as a bar plot. + - FeatureVisualizers are Scikit-Learn Transformers that implement fit and transform and operate on the data space, calling draw to display instances. + - ParallelCoordinates plots instances with class across each feature dimension as line segments across a horizontal space. + - RadViz plots instances with class in a circular space where each feature dimension is an arc around the circumference and points are plotted relative to the weight of the feature. + - Rank2D plots pairwise scores of features as a heatmap in the space [-1, 1] to show relative importance of features. Currently implemented ranking functions are Pearson correlation and covariance. + - Coordinated and added palettes in the bgrmyck space and implemented a version of the Seaborn set_palette and set_color_codes functions as well as the ColorPalette object and other matplotlib.rc modifications. + - Inherited Seaborn's notebook context and whitegrid axes style but make them the default, don't allow user to modify (if they'd like to, they'll have to import Seaborn). This gives Yellowbrick a consistent look and feel without giving too much work to the user and prepares us for Matplotlib 2.0. + - Jupyter Notebook with Examples of all Visualizers and usage. + + Bug Fixes: + - Fixed Travis-CI test failures with matplotlib.use('Agg'). + - Fixed broken link to Quickstart on README + - Refactor of the original API to the Scikit-Learn Visualizer API + +Version 0.2 +^^^^^^^^^^^ +Intermediate steps towards a complete API for visualization. Preparatory stages for Scikit-Learn visual pipelines. + +* Tag: v0.2_ +* Deployed: Sunday, September 4, 2016 +* Contributors: Benjamin Bengfort, Rebecca Bilbro, Patrick O'Melveny, Ellen Lowy, Laura Lorenz + + Changes: + - Continued attempts to fix the Travis-CI Scipy install failure (broken tests) + - Utility function: get the name of the model + - Specified a class based API and the basic interface (render, draw, fit, predict, score) + - Added more documentation, converted to Sphinx, autodoc, docstrings for viz methods, and a quickstart + - How to contribute documentation, repo images etc. + - Prediction error plot for regressors (mvp) + - Residuals plot for regressors (mvp) + - Basic style settings a la seaborn + - ROC/AUC plot for classifiers (mvp) + - Best fit functions for "select best", linear, quadratic + - Several Jupyter notebooks for examples and demonstrations + + + +Version 0.1 +^^^^^^^^^^^ +Created the yellowbrick library MVP with two primary operations: a classification report heat map and a ROC/AUC curve model analysis for classifiers. This is the base package deployment for continuing yellowbrick development. + +* Tag: v0.1_ +* Deployed: Wednesday, May 18, 2016 +* Contributors: Benjamin Bengfort, Rebecca Bilbro + + Changes: + - Created the Anscombe quartet visualization example + - Added DDL specific color maps and a stub for more style handling + - Created crplot which visualizes the confusion matrix of a classifier + - Created rocplot_compare which compares two classifiers using ROC/AUC metrics + - Stub tests/stub documentation + + +.. _SIGMOD: http://cseweb.ucsd.edu/~arunkk/vision/SIGMODRecord15.pdf +.. _cycle: http://nvie.com/posts/a-successful-git-branching-model/ +.. _board: https://waffle.io/districtdatalabs/yellowbrick +.. _Wikipedia: https://en.wikipedia.org/wiki/Yellow_brick_road +.. _v0.3.2: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.2 +.. _v0.3.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.1a2 +.. _v0.3: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3 +.. _v0.2: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.2 +.. _v0.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.1 diff --git a/docs/api/modules.rst b/docs/api/modules.rst index 6814db03e..798061270 100644 --- a/docs/api/modules.rst +++ b/docs/api/modules.rst @@ -2,6 +2,8 @@ API Reference ============== .. toctree:: - :maxdepth: 4 + :maxdepth: 4 - yellowbrick + yellowbrick + yellowbrick.style + yellowbrick.features diff --git a/docs/api/yellowbrick.features.rst b/docs/api/yellowbrick.features.rst new file mode 100644 index 000000000..4dc15c809 --- /dev/null +++ b/docs/api/yellowbrick.features.rst @@ -0,0 +1,37 @@ +yellowbrick.features package +============================ + +Submodules +---------- + +yellowbrick.features.base module +-------------------------------- + +.. automodule:: yellowbrick.features.base + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.features.pcoords module +----------------------------------- + +.. automodule:: yellowbrick.features.pcoords + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.features.radviz module +---------------------------------- + +.. automodule:: yellowbrick.features.radviz + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.features.rankd module +--------------------------------- + +.. automodule:: yellowbrick.features.rankd + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/yellowbrick.rst b/docs/api/yellowbrick.rst index c7b063df2..bbdc9bd2b 100644 --- a/docs/api/yellowbrick.rst +++ b/docs/api/yellowbrick.rst @@ -20,18 +20,18 @@ yellowbrick.base module :undoc-members: :show-inheritance: -yellowbrick.classifier module ------------------------------ +yellowbrick.bestfit module +-------------------------- -.. automodule:: yellowbrick.classifier +.. automodule:: yellowbrick.bestfit :members: :undoc-members: :show-inheritance: -yellowbrick.color_utils module ------------------------------- +yellowbrick.classifier module +----------------------------- -.. automodule:: yellowbrick.color_utils +.. automodule:: yellowbrick.classifier :members: :undoc-members: :show-inheritance: @@ -44,6 +44,15 @@ yellowbrick.exceptions module :undoc-members: :show-inheritance: + +yellowbrick.pipeline module +--------------------------- + +.. automodule:: yellowbrick.pipeline + :members: + :undoc-members: + :show-inheritance: + yellowbrick.regressor module ---------------------------- @@ -68,22 +77,6 @@ yellowbrick.version module :undoc-members: :show-inheritance: -yellowbrick.yb_palettes module ------------------------------- - -.. automodule:: yellowbrick.yb_palettes - :members: - :undoc-members: - :show-inheritance: - -yellowbrick.yb_rcmod module ---------------------------- - -.. automodule:: yellowbrick.yb_rcmod - :members: - :undoc-members: - :show-inheritance: - Module contents --------------- diff --git a/docs/api/yellowbrick.style.rst b/docs/api/yellowbrick.style.rst new file mode 100644 index 000000000..e64a315e5 --- /dev/null +++ b/docs/api/yellowbrick.style.rst @@ -0,0 +1,29 @@ +yellowbrick.style package +=================== + +Submodules +---------- + +yellowbrick.style.colors module +--------------------------- + +.. automodule:: yellowbrick.style.colors + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.style.palettes module +--------------------------- + +.. automodule:: yellowbrick.style.palettes + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.style.rcmod module +--------------------------- + +.. automodule:: yellowbrick.style.rcmod + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/conf.py b/docs/conf.py index 4f14dbc3f..6180e0c05 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -21,6 +21,8 @@ import sys sys.path.insert(0, os.path.abspath('..')) +import yellowbrick as yb + # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. @@ -32,10 +34,13 @@ # ones. extensions = [ 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', + 'sphinx.ext.todo', + 'numpydoc', ] # Add any paths that contain templates here, relative to this directory. @@ -64,9 +69,9 @@ # built documents. # # The short X.Y version. -version = '0.1' +version = yb.__version__ # The full version, including alpha/beta/rc tags. -release = '0.1' +release = yb.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -338,6 +343,10 @@ # # texinfo_no_detailmenu = False +# Autodoc requires numpy to skip class members otherwise we get an exception: +# toctree contains reference to nonexisting document +# See: https://github.com/phn/pytpm/issues/3#issuecomment-12133978 +numpydoc_show_class_members = False # Locations of objects.inv files for intersphinx extension that auto links to external api docs. intersphinx_mapping = {'python': ('https://docs.python.org/3', None), @@ -345,4 +354,4 @@ 'scipy': ('http://scipy.github.io/devdocs/', None), 'numpy': ('https://docs.scipy.org/doc/numpy-dev/', None), 'cycler': ('http://matplotlib.org/cycler/', None), - 'seaborn': ('https://web.stanford.edu/~mwaskom/software/seaborn/', None)} + 'sklearn': ('http://scikit-learn.org/stable/', None)} diff --git a/docs/examples/examples.rst b/docs/examples/examples.rst new file mode 100644 index 000000000..cd1298b2f --- /dev/null +++ b/docs/examples/examples.rst @@ -0,0 +1,10 @@ +====================== +Examples and Tutorials +====================== + + +.. toctree:: + :maxdepth: 4 + + methods + palettes diff --git a/docs/examples/images/examples_11_0.png b/docs/examples/images/examples_11_0.png new file mode 100644 index 000000000..fc594728a Binary files /dev/null and b/docs/examples/images/examples_11_0.png differ diff --git a/docs/examples/images/examples_12_0.png b/docs/examples/images/examples_12_0.png new file mode 100644 index 000000000..99c0ba558 Binary files /dev/null and b/docs/examples/images/examples_12_0.png differ diff --git a/docs/examples/images/examples_15_0.png b/docs/examples/images/examples_15_0.png new file mode 100644 index 000000000..3ce671648 Binary files /dev/null and b/docs/examples/images/examples_15_0.png differ diff --git a/docs/examples/images/examples_18_0.png b/docs/examples/images/examples_18_0.png new file mode 100644 index 000000000..c72900978 Binary files /dev/null and b/docs/examples/images/examples_18_0.png 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import matplotlib.pyplot as plt + +Anscombe's Quartet +------------------ + +Yellowbrick has learned Anscombe's lesson - which is why we believe that +visual diagnostics are vital to machine learning. + +.. code:: python + + g = yb.anscombe() + + +.. image:: images/examples_4_1.png + + +Load Datasets for Example Code +------------------------------ + +Yellowbrick has provided several datasets wrangled from the `UCI Machine +Learning Repository `__ to present the +following examples. If you haven't downloaded the data, you can do so by +running: + +:: + + $ python download.py + +In the same directory as the example notebook. Note that this will +create a directory called ``data`` that contains subdirectories with the +given data. + +.. code:: python + + from download import download_all + + ## The path to the test data sets + FIXTURES = os.path.join(os.getcwd(), "data") + + ## Dataset loading mechanisms + datasets = { + "credit": os.path.join(FIXTURES, "credit", "credit.csv"), + "concrete": os.path.join(FIXTURES, "concrete", "concrete.csv"), + "occupancy": os.path.join(FIXTURES, "occupancy", "occupancy.csv"), + } + + + def load_data(name, download=True): + """ + Loads and wrangles the passed in dataset by name. + If download is specified, this method will download any missing files. + """ + + # Get the path from the datasets + path = datasets[name] + + # Check if the data exists, otherwise download or raise + if not os.path.exists(path): + if download: + download_all() + else: + raise ValueError(( + "'{}' dataset has not been downloaded, " + "use the download.py module to fetch datasets" + ).format(name)) + + + # Return the data frame + return pd.read_csv(path) + +Feature Analysis +---------------- + +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. + +At the moment we have three feature analysis visualizers implemented: + +- Rank2D: rank pairs of features to detect covariance +- RadViz: plot data points along axes ordered around a circle to detect + separability +- Parallel Coordinates: plot instances as lines along vertical axes to + detect clusters + +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. + +.. code:: python + + # Feature Analysis Imports + # NOTE that all these are available for import from the `yellowbrick.features` module + from yellowbrick.features.rankd import Rank2D + from yellowbrick.features.radviz import RadViz + from yellowbrick.features.pcoords import ParallelCoordinates + +Rank2D +~~~~~~ + +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. + +.. code:: python + + # Load the classification data set + data = load_data('credit') + + # Specify the features of interest + features = [ + 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay', + 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill', + 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay', + 'jul_pay', 'aug_pay', 'sep_pay', + ] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.default.as_matrix() + +.. code:: python + + # Instantiate the visualizer with the Covariance ranking algorithm + 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 + + + +.. image:: images/examples_11_0.png + + +.. code:: python + + # Instantiate the visualizer with the Pearson ranking algorithm + visualizer = Rank2D(features=features, algorithm='pearson') + + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_12_0.png + + +RadViz +~~~~~~ + +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. + +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? + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + +.. code:: python + + # Instantiate the visualizer + visualizer = visualizer = RadViz(classes=classes, features=features) + + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_15_0.png + + +For regression, the RadViz visualizer should use a color sequence to +display the target information, as opposed to discrete colors. + +Parallel Coordinates +~~~~~~~~~~~~~~~~~~~~ + +Parallel coordinates displays each feature as a vertical axis spaced +evenly along the horizontal, and each instance as a line drawn between +each individual axis. This allows many dimensions; in fact given +infinite horizontal space (e.g. a scrollbar) an infinite number of +dimensions can be displayed! + +Data scientists use this method to detect clusters of instances that +have similar classes, and to note features that have high varaince or +different distributions. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + +.. code:: python + + # Instantiate the visualizer + visualizer = visualizer = ParallelCoordinates(classes=classes, features=features) + + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_19_0.png + + +Regressor Evaluation +-------------------- + +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: + +- Residuals Plot: plot the difference between the expected and actual + values +- Prediction Error: plot expected vs. the actual values in model space + +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``. + +.. code:: python + + # Regression Evaluation Imports + + from sklearn.linear_model import Ridge, Lasso + from sklearn.cross_validation import train_test_split + + from yellowbrick.regressor import PredictionError, ResidualsPlot + +Residuals Plot +~~~~~~~~~~~~~~ + +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. + +.. code:: python + + # Load the data + df = load_data('concrete') + feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age'] + target_name = 'strength' + + # Get the X and y data from the DataFrame + X = df[feature_names].as_matrix() + y = df[target_name].as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the linear model and visualizer + ridge = Ridge() + visualizer = ResidualsPlot(ridge) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_24_0.png + + +Prediction Error Plot +~~~~~~~~~~~~~~~~~~~~~ + +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. + +.. code:: python + + # Load the data + df = load_data('concrete') + feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age'] + target_name = 'strength' + + # Get the X and y data from the DataFrame + X = df[feature_names].as_matrix() + y = df[target_name].as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the linear model and visualizer + lasso = Lasso() + visualizer = PredictionError(lasso) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_27_0.png + + +Classifier Evaluation +--------------------- + +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: + +- ClassificationReport: Presents the confusion matrix of the classifier + as a heatmap +- ROCAUC: Presents the graph of receiver operating characteristics + along with area under the curve +- ClassBalance: Displays the difference between the class balances and + support + +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. + +.. code:: python + + # Classifier Evaluation Imports + + 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 yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance + +Classification Report +~~~~~~~~~~~~~~~~~~~~~ + +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. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the classification model and visualizer + bayes = GaussianNB() + visualizer = ClassificationReport(bayes, classes=classes) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_32_0.png + + +ROCAUC +~~~~~~ + +Plot the ROC to visualize the tradeoff between the classifier's +sensitivity and specificity. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the classification model and visualizer + logistic = LogisticRegression() + visualizer = ROCAUC(logistic) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_35_0.png + + +ClassBalance +~~~~~~~~~~~~ + +Class balance chart that shows the support for each class in the fitted +classification model. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the classification model and visualizer + forest = RandomForestClassifier() + visualizer = ClassBalance(forest, classes=classes) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_38_0.png diff --git a/docs/examples/palettes.rst b/docs/examples/palettes.rst new file mode 100644 index 000000000..19c62872f --- /dev/null +++ b/docs/examples/palettes.rst @@ -0,0 +1,926 @@ +.. _examples/yellowbrick-palettes: + +========= +Palettes +========= + +Yellowbrick includes custom palettes as well as familiar ones from Matplotlib and Seaborn. + +.. code:: python + + import matplotlib.pyplot as plt + from yellowbrick.style.palettes import PALETTES, SEQUENCES, color_palette + +.. code:: python + + # ['blue', 'green', 'red', 'maroon', 'yellow', 'cyan'] + for palette in PALETTES.keys(): + color_palette(palette).plot() + plt.title(palette, loc='left') + + + +.. image:: images/palettes_2_0.png + + + +.. image:: images/palettes_2_1.png + + + +.. image:: images/palettes_2_2.png + + + +.. image:: images/palettes_2_3.png + + + +.. image:: images/palettes_2_4.png + + + +.. image:: images/palettes_2_5.png + + + +.. image:: images/palettes_2_6.png + + + +.. image:: images/palettes_2_7.png + + + +.. image:: images/palettes_2_8.png + + + +.. image:: images/palettes_2_9.png + + + +.. image:: images/palettes_2_10.png + + + +.. image:: images/palettes_2_11.png + + + +.. image:: images/palettes_2_12.png + + + +.. image:: images/palettes_2_13.png + + + +.. image:: images/palettes_2_14.png + + + +.. image:: images/palettes_2_15.png + + + +.. image:: images/palettes_2_16.png + + +.. code:: python + + for name, maps in SEQUENCES.items(): + for num, palette in maps.items(): + color_palette(palette).plot() + plt.title("{} - {}".format(name, num), loc='left') + + +.. image:: images/palettes_3_1.png + + + +.. image:: images/palettes_3_2.png + + + +.. image:: images/palettes_3_3.png + + + +.. image:: images/palettes_3_4.png + + + +.. image:: images/palettes_3_5.png + + + +.. image:: images/palettes_3_6.png + + + +.. image:: images/palettes_3_7.png + + + +.. image:: images/palettes_3_8.png + + + +.. image:: images/palettes_3_9.png + + + +.. image:: images/palettes_3_10.png + + + +.. image:: images/palettes_3_11.png + + + +.. image:: images/palettes_3_12.png + + + +.. image:: images/palettes_3_13.png + + + +.. image:: images/palettes_3_14.png + + + +.. image:: images/palettes_3_15.png + + + +.. image:: images/palettes_3_16.png + + + +.. image:: images/palettes_3_17.png + + + +.. image:: images/palettes_3_18.png + + + +.. image:: images/palettes_3_19.png + + + +.. image:: images/palettes_3_20.png + + + +.. image:: images/palettes_3_21.png + + + +.. image:: images/palettes_3_22.png + + + +.. image:: images/palettes_3_23.png + + + +.. image:: images/palettes_3_24.png + + + +.. image:: images/palettes_3_25.png + + + +.. image:: images/palettes_3_26.png + + + +.. image:: images/palettes_3_27.png + + + +.. image:: images/palettes_3_28.png + + + +.. image:: images/palettes_3_29.png + + + +.. image:: images/palettes_3_30.png + + + +.. image:: images/palettes_3_31.png + + + +.. image:: images/palettes_3_32.png + + + +.. image:: images/palettes_3_33.png + + + +.. image:: images/palettes_3_34.png + + + +.. image:: images/palettes_3_35.png + + + +.. image:: images/palettes_3_36.png + + + +.. image:: images/palettes_3_37.png + + + +.. image:: images/palettes_3_38.png + + + +.. image:: images/palettes_3_39.png + + + +.. image:: images/palettes_3_40.png + + + +.. image:: images/palettes_3_41.png + + + +.. image:: images/palettes_3_42.png + + + +.. image:: images/palettes_3_43.png + + + +.. image:: images/palettes_3_44.png + + + +.. image:: images/palettes_3_45.png + + + +.. image:: images/palettes_3_46.png + + + +.. image:: images/palettes_3_47.png + + + +.. image:: images/palettes_3_48.png + + + +.. image:: images/palettes_3_49.png + + + +.. image:: images/palettes_3_50.png + + + +.. image:: images/palettes_3_51.png + + + +.. image:: images/palettes_3_52.png + + + +.. image:: images/palettes_3_53.png + + + +.. image:: images/palettes_3_54.png + + + +.. image:: 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--git a/docs/images/yb_rank2d_pearson.png b/docs/images/yb_rank2d_pearson.png new file mode 100644 index 000000000..eb61e41a2 Binary files /dev/null and b/docs/images/yb_rank2d_pearson.png differ diff --git a/docs/api/yellowbrick_classhierarchy.png b/docs/images/yellowbrick_classhierarchy.png similarity index 100% rename from docs/api/yellowbrick_classhierarchy.png rename to docs/images/yellowbrick_classhierarchy.png diff --git a/docs/index.rst b/docs/index.rst index 67aa52988..9e6a6d4d6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,66 +3,72 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. +======================================= Welcome to yellowbrick's documentation! ======================================= -**Yellowbrick is a suite of visual analysis and diagnostic tools to facilitate feature selection, model selection, and parameter tuning for machine learning.** +Yellowbrick is a suite of visual analysis and diagnostic tools to facilitate feature selection, model selection, and parameter tuning for machine learning. All visualizations are generated in Matplotlib. + +Custom ``yellowbrick`` visualization tools include: Tools for feature analysis and selection ------------------------------------------- +---------------------------------------- -- boxplots (box-and-whisker plots) -- violinplots -- histograms -- scatter plot matrices (sploms) -- radial visualizations (radviz) -- parallel coordinates -- jointplots -- diagonal correlation matrix + - Boxplots ("box-and-whisker" plots) + - Violinplots + - Histograms + - Scatter plot matrices ("sploms") + - Radial visualizations ("radviz") + - Parallel coordinates + - Jointplots + - Rank 1D + - Rank 2D Tools for model evaluation ----------------------------- +-------------------------- Classification -^^^^^^^^^^^^^^^ - -- ROC curves -- classification heatmaps +^^^^^^^^^^^^^^ + - ROC-AUC curves + - Classification heatmaps + - Class balance charts Regression -^^^^^^^^^^^^ +^^^^^^^^^^ + - Prediction error plots + - Residual plots + - Most informative features -- prediction error plots -- residual plots +Clustering +^^^^^^^^^^ + - Silhouettes + - Density measures Tools for parameter tuning ----------------------------- - -- validation curves -- gridsearch heatmap - - - +-------------------------- +- Validation curves +- Gridsearch heatmap +========= Contents: -========== +========= .. toctree:: - :maxdepth: 2 + :maxdepth: 4 + introduction setup + examples/examples api/modules + about - - +================== Indices and tables ================== * :ref:`genindex` * :ref:`modindex` -* :ref:`search` - diff --git a/docs/introduction.rst b/docs/introduction.rst new file mode 100644 index 000000000..7dc263802 --- /dev/null +++ b/docs/introduction.rst @@ -0,0 +1,18 @@ +============================ +Introduction to Yellowbrick +============================ + +.. image:: images/yellowbrickroad.jpg +Image by QuatroCinco_, used with permission, Flickr Creative Commons. + +Yellowbrick is a new Python library that extends the Scikit-Learn API_ to incorporate visualizations into the machine learning workflow. + +Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow. + +Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search. By visualizing the model selection process, data scientists can steer towards final, explainable models and avoid pitfalls and traps. + +The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. Yellowbrick extends the Scikit-Learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process, providing visual diagnostics throughout the transformation of high dimensional data. + + +.. _QuatroCinco: https://flic.kr/p/2Yj9mj +.. _API: http://scikit-learn.org/stable/modules/classes.html diff --git a/docs/setup.rst b/docs/setup.rst index 0f8738742..9dcb2423c 100644 --- a/docs/setup.rst +++ b/docs/setup.rst @@ -1,22 +1,40 @@ +=========== Quick Start -============== +=========== -This quick start is intended to get you setup with Yellowbrick in development -mode (since the project is still under development). +Installation +------------ -1. Fork and clone the repository. After clicking fork in the upper right corner -for your own copy of yellowbrick to your github account. Clone it in a directory -of your choice.:: +To install the Yellowbrick library, the simplest thing to do is use ``pip`` as follows.:: - $ git clone https://github.com/[YOURUSERNAME]/yellowbrick - $ cd yellowbrick + $ pip install yellowbrick -2. Create virtualenv and create the dependencies:: +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: - $ virtualenv venv - $ pip install -r requirements.txt +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.:: -3. Fetch and switch to development.:: + 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 - $ git fetch - $ git checkout develop + +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 `. diff --git a/examples/Getting Started with Yellowbrick.ipynb b/examples/Getting Started with Yellowbrick.ipynb new file mode 100644 index 000000000..c525b8ec7 --- /dev/null +++ b/examples/Getting Started with Yellowbrick.ipynb @@ -0,0 +1,107 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Started with Yellowbrick \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Alpha Testing \n", + "\n", + "We are looking for people to help us Alpha test the Yellowbrick project! Helping is simple: simply create a notebook that applies the concepts in this *Getting Started* guide to a small-to-medium size dataset of your choice. Run through the examples with the dataset, and try to change options and customize as much as possible. After you've exercised the code with your examples, respond to our [alpha testing survey](https://goo.gl/forms/naoPUMFa1xNcafY83)!\n", + "\n", + "### Step One: Dataset \n", + "\n", + "Select a multivariate dataset of your own; the more (e.g. different) datasets that we can run through Yellowbrick, the more likely we'll discover edge cases and exceptions! Note that your dataset must be well-suited to modeling with Scikit-Learn. In particular we recommend you choose a dataset whose target is suited to the following supervised learning tasks:\n", + "\n", + "- [Regression](https://en.wikipedia.org/wiki/Regression_analysis) (target is a continuous variable)\n", + "- [Classification](https://en.wikipedia.org/wiki/Classification_in_machine_learning) (target is a discrete variable) \n", + "\n", + "There are datasets that are well suited to both types of analysis; either way you can use the testing methodology from this notebook for either type of task (or both). In order to find a dataset, we recommend you try the following places:\n", + "\n", + "- [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)\n", + "- [MLData.org](http://mldata.org/)\n", + "- [Awesome Public Datasets](https://github.com/caesar0301/awesome-public-datasets) \n", + "\n", + "You're more than welcome to choose a dataset of your own, but we do ask that you make at least the notebook containing your testing results publicly available for us to review. If the data is also public (or you're willing to share it with the primary contributors) that will help us figure out bugs and required features much more easily! \n", + "\n", + "### Step Two: Notebook \n", + "\n", + "Create a notebook in a GitHub repository. We suggest the following:\n", + "\n", + "1. Fork the Yellowbrick repository \n", + "2. Under the `examples` directory, create a directory named with your GitHub username \n", + "3. Create the notebook in examples/USERNAME/testing.ipynb \n", + "\n", + "Alternatively, you could just send us a notebook via Gist or your own repository. However, if you fork Yellowbrick, you can initiate a pull request to have your example added to our gallery! \n", + "\n", + "### Step Three: Model with Yellowbrick and Scikit-Learn\n", + "\n", + "Add the following to the notebook:\n", + "\n", + "- A title in markdown \n", + "- A description of the dataset and where it was obtained \n", + "- A section that loads the data into a Pandas dataframe or NumPy matrix \n", + "\n", + "Then conduct the following modeling activities:\n", + "\n", + "- Feature analysis using Scikit-Learn and Yellowbrick \n", + "- Estimator fitting using Scikit-Learn and Yellowbrick \n", + "\n", + "You can follow along with our `examples` directory (check out [examples.ipynb](https://github.com/DistrictDataLabs/yellowbrick/blob/master/examples/examples.ipynb)) or even create your own custom visualizers! The goal is that you create an end-to-end model from data loading to estimator(s) with visualizers along the way. \n", + "\n", + "**IMPORTANT**: please make sure you record all errors that you get and any tracebacks you receive for step four! \n", + "\n", + "### Step Four: Feedback \n", + "\n", + "Finally, submit feedback via the Google Form we have created:\n", + "\n", + "[https://goo.gl/forms/naoPUMFa1xNcafY83](https://goo.gl/forms/naoPUMFa1xNcafY83)\n", + "\n", + "This form is allowing us to aggregate multiple submissions and bugs so that we can coordinate the creation and management of issues. If you are the first to report a bug or feature request, we will make sure you're notified (we'll tag you using your Github username) about the created issue! \n", + "\n", + "### Step Five: Thanks! \n", + "\n", + "Thank you for helping us make Yellowbrick better! We'd love to see pull requests for features you think would be extend the library. We'll also be doing a user study that we would love for you to participate in. Stay tuned for more great things from Yellowbrick!" + ] + } + ], + "metadata": { + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/download.py b/examples/download.py index 5d11a86fb..a82e2a341 100644 --- a/examples/download.py +++ b/examples/download.py @@ -3,6 +3,7 @@ # Downloads the example datasets for running the examples. # # Author: Rebecca Bilbro +# Author: Benjamin Bengfort # Created: Wed May 18 11:54:45 2016 -0400 # # Copyright (C) 2016 District Data Labs @@ -20,6 +21,7 @@ import os import sys +import hashlib import zipfile try: @@ -31,31 +33,90 @@ )) sys.exit(1) + ########################################################################## -## Links to data sets +## Links and MD5 hash of datasets ########################################################################## -OCCUPANCY = ('http://bit.ly/ddl-occupancy-dataset', 'occupancy.zip') -CREDIT = ('http://bit.ly/ddl-credit-dataset', 'credit.xls') -CONCRETE = ('http://bit.ly/ddl-concrete-data', 'concrete.xls') +DATASETS = { + 'concrete': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/concrete.zip', + 'signature': 'b9ea5f26a7bb272a040e2f1a993b26babbf8dc4a04ab8198bb315ca66d71f10d', + }, + 'credit': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip', + 'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e', + }, + 'occupancy': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/occupancy.zip', + 'signature': '429cfe376dc9929a1fa528da89f0e1626e34e19695f3f555d8954025bbc522b8', + } +} + + +########################################################################## +## Download functions +########################################################################## +def sha256sum(path, blocksize=65536): + """ + Computes the SHA256 signature of a file to verify that the file has not + been modified in transit and that it is the correct version of the data. + """ + sig = hashlib.sha256() + with open(path, 'rb') as f: + buf = f.read(blocksize) + while len(buf) > 0: + sig.update(buf) + buf = f.read(blocksize) + return sig.hexdigest() -def download_data(url, name, path='data'): + +def download_data(url, path='data', signature=None, extract=True): + """ + Downloads the zipped data set specified at the given URL, saving it to + the output path specified. This function verifies the download with the + given signature (if supplied) and extracts the zip file if requested. + """ + # Create the output directory if it does not exist if not os.path.exists(path): os.mkdir(path) - response = requests.get(url) - with open(os.path.join(path, name), 'w') as f: - f.write(response.content) + # Get the name of the file from the URL + name = os.path.basename(url) + dlpath = os.path.join(path, name) + + # Fetch the response in a streaming fashion and write it to disk. + response = requests.get(url, stream=True) + with open(dlpath, 'wb') as f: + for chunk in response.iter_content(65536): + f.write(chunk) + + # If verify, compare the signature + if signature is not None: + dlsignature = sha256sum(dlpath) + if signature != dlsignature: + raise ValueError( + "Download signature does not match hardcoded signature!" + ) + + # If extract, extract the zipfile. + if extract: + zf = zipfile.ZipFile(dlpath) + zf.extractall(path) -def download_all(path='data'): - for href, name in (OCCUPANCY, CREDIT, CONCRETE): - download_data(href, name, path) +def download_all(path='data', verify=True, extract=True): + """ + Downloads all the example datasets. If verify is True then compare the + download signature with the hardcoded signature. If extract is True then + extract the contents of the zipfile to the given path. + """ + for name, meta in DATASETS.items(): + url = meta['url'] + signature = meta['signature'] if verify else None - # Extract the occupancy zip data - z = zipfile.ZipFile(os.path.join(path, 'occupancy.zip')) - z.extractall(os.path.join(path, 'occupancy')) + download_data(url, path=path, signature=signature, extract=extract) if __name__ == '__main__': diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 7f7b27e11..7431d24d8 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -67,7 +67,7 @@ "data": { "image/png": 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9e6oH28tN+N8Dx/Ft22mnsRmxYSjMM6Iw14jZUyJlSkhE3k6KI2EAcO89D2Lza8/Aph4+\nR21MORw2BMVZh7Oo1QiNAxyiDWqVBkmRMzAtLhcp0bOgDfCt82xZqBF5kfrOM9hRbkJReT3KTjiv\n+5MSGYzCXCNW5KUhd2qUIq9eIiLvIsWRMACIj08Y85E4h2jHye5jqGs7CFNHNZIzh9uviaKI/i4R\nN89bjtTYTOgDxpfLG7BQI7/R0tqKNU9tQdOAHclBGrz80C+QkBAvdyy3zrVwKiozodTU5jSWEBaI\npZnJuCzMhsJrL0NwMC8KIKJBUlxpee89D+KFV55Cv7ULQQGD54VNJHctnPq77eg4eRrmdh3uvWt8\n58tN9hWonmKhRn5jzVNb8LZmBhCmxjcOB/D0Zry16Um5Y7nUfsaMtyobsL28Hp8dc93CaUVeGq6d\nFg+rIKC6uhpqNY+gEdEwKa60jI9PwGP3PzuhHUdG38JpDjRioMdZJuMKVCmxUCO/0TRgB8LOXpKt\nVqPptF3eQN/Raz7bwqlssIWTzUULp4JcA27KSIQuYPjqJaurjRGR35Pq/LKJMN4WTmaz2ePnVvLr\n4goLNfIbyUGawSNpajXgcCA5SP5Ltc+1cCoqH2zhZLFd2MKpMNeIH85iCyciGhupzi+TUs9A21Bx\n5qqFU1pcNoyxOYgKmbiPIpX4ulwMCzXyGy8/9Avg6c1oOj18jpoc3LVw+t4lSSjINeC2zBSEBXr/\npeVEJA9Pr7SUivsWTjlIi8uetBZOSnldRouFGvmNhIR42c5Js9kd+KS2BUVlF7Zwgiji2rRY3Dkv\nA0vnsIUTEUljPFdaSqXf0ov69grUtVcoroWTnK/LeHhcqB04cAAbN25EfX094uPjsXbtWtx6661S\nZCPyag6HiC/qTqGovB7FBy9s4QRRBFQqQBQRdWwP/u3nP5QnKBGRBMzWPpg6qlDXdmELJ11AEIwx\nc5AWl40EL2vhJDePCjWHw4F169bh8ccfx0033YT9+/fjJz/5CebOnYukpCSpMhJ5DVEU8XVjB4rK\n6rHjoAknepxPUr38bAun//6f/0VlaMbgjQq8sIGIaDQEm/lsC6cKNHfXOLVw0mr0SI2ejbS4HCRG\npkOj5od44+HRq9bb24uuri5YrYMf46hUKmi1Wmg0POmZ/Icoiqg82Y2i8noUldWjrvOM0/icxEgU\n5A52CTjXwumfb4moVNiFDUSkDFL115woVruAps4jqGs/iKbOby9o4ZQSPRNpsb7Twmkkk/U+eVSo\nRUZG4kc/+hF+9atf4f7774coiti4cSMSEpSzQxFJraW1Faue3IRDvQ4ERMbDkZCGmg7nRRndtXBS\nyoUNRKQ8UvXXlJJDtONE9xE0dR9GY+dh2BzC0JhapcHUqAykxeUMtnDS+Md5tpP1PnlUqImiiMDA\nQGzZsgXXXXcdvvzyS9x3332YPXs2LrnkklFtQxAESdZF8YTFYnH6KidmcU0pWRq6+nDHM2/gsHYW\nEHv26qSzRVpyRBCWzUnB8uxU5CZFDl295Gr/jogIx/8884jTbeP9PVDKa3N+Bk+zCILg/k5eSAnz\nHeCb+4wUlJLFpu5zWufLquqTZb9xOOxoPV2HuvYKNJkPw1E7fBGUCiokhE9DanQWkiNnQhcw2BXF\nbhVht05cVqW8R4B075O7+c6jQm337t2orKzEAw88AABYuHAhFi5ciJKSEqxfv35U2zCZTJ5EkFRt\nba3cEYYwi2tyZGkfsOLjhl7sNvWisn0ACHA+YhzdfwrP3j4Pc2KDoFapgO5mHOpuHmFrE8ff3ydv\noKT5DlDW+8Qsw/q6bU7rfPV121BdXT0pzy2KIvod7ei2N6LH3gg7nIuIYHUsIjUpiNAkI8AaiIFW\noKb1+KRkO5/c7xEwee+TR4XayZMnL6gEtVottNrRfyZtMBgQGhrq/o4TyGKxoLa2Funp6dDr5T1k\nyyzKyNLRb8HbVSewo7IBX9S14bwmAdCKNlihGbxi0+HAVQHt+PGiyyc800h88X0SBAE1NTUSJlMG\nJcx3gG/uM76UZf3Pf4s//L/fwWzvQaA6HOt//lvExU1cX2JRFNHZdwINXVVo6KzGgPW003hUUCL0\n1jjkpl+DyNC4CcsxGkp5jwDp3id3851HhdqCBQvw/PPPY9euXVi6dCn27duHjz76CH/+859HvQ2d\nTjchvcPGQ6/XM4sL/pJltC2csiMD8POnN+NYVz+mRwXhlUd+qYjXx5fep8lY9FIOSprvAN/aZ6Qk\nd5aUlFQ8vv53E95fc7QtnHSqEFRXVyMyNI7v0Xmkep/czXceFWoZGRnYvHkzNm3ahI0bNyIxMRHP\nPvssMjMzPdks0aTpF2x491CTyxZOgQEa3JqZjIJcA344ayqCtMO/Ltt+9+iETqKTqaW1FWue2oKm\ngeELGxISJu5/70Qkn57+NtS1j62FkxLOq/RnHi9qsmjRIixatEiCKESTYzQtnArzjFg8O9kvWjit\neWoL3tbMAMLUg71Qn94sWwcHIpLeGXMX6torUNdWjs6+k05jgy2csmGMy0ZMyFSfPZrtzbj6HPmF\ni7VwUqtUuC49AYV5Riydk4poP2vh1DRgB8LOrhLOxXeJfEK/0DvUX/O7LZwCtaEwxs7BtLhcWVo4\n0diwUCOfdX4Lp50VJrSdcb6c+5pp8SjIMWJZTioSwoJkSim/5CDN4JE0Lr5L5NUu1sJJHxAMQ2wW\n0mJzkBCRxhZOXoSFGvmU0bRwKswzIj/bgJSoEJlSKgsX3yWSRuupVmx+9RnYNf3Q2INx7z0PTnhH\nAXctnFKiZ2NaXA6SImdAreZ/wrwRCzVy6dzq+8e6BoaubFTqCeaiKKKqpRtFZfUoKq/H8Q7nFk5Z\nUyJRmGdEQa4B6bHhMqVUroSEeJ6TRiSBza8+g+nX6qBW6yd0pXqbXUDjiC2cApASPcsvWjj5CxZq\n5NKap7bgHe1MIEGNQw4H1Ao8wfxoWy+2n+2veai1x2ksPTYMhblGFOYZkemihRMRkdTsmn6nlept\n6n43jxjDth02nOg6irr2g2jsGKGFU+zZFk4B/nWera9joUYuKfUE88bufrx9+BiKyutxoMl53Z+U\nyGAszzFiRZ4Rc5OjefUSEU0qjT3YaaV6jT3Yo+2JogMne2pxouEwTB3VsNqHl8lQQY3EyOlIi8tB\nasxs6AM8ey5SLhZq5JKSTjCvONaAOzdvwzFHGAZ0h5zG4kMDsTzHgMI8I+Yb4qBWszgjInnce8+D\n2PzaM7Cph89RGytRdKC1tx61LQdQZ66Avca5+09CuBHG2BwYY+cgSCd/lwuaeCzUyKWXH/oFHE9u\nGlp9/+VHfjmpz9/RZ8HOChO2l9fj05oWICBxaEwr2vF/51+CghwjFk5PQICGVy8Rkfzi4xPGdU6a\nKIpoP9OEuraDqG+vQL/Q6zQeG5pydiHabIToI6SKS16ChRq5lJAQP+mr7/eaBbxd1YRtZXXOLZy+\n8xFm1plv8cryn0x4HiKiieKuhVNkUAL01jhcPut6xEYkyZSSlICFGsnqXAun7eUm/O1w0wUtnG6Z\nPRUNZaX4WpU49DFsKtf5IiIvNdoWTkGaCFRXVyNUHy1TUlIKFmo06Sw2O3Z/24xtZa5bON18SSIK\nc424LTMFYYFatH5/Fn4m48ewRESeOG3uRH17BeraKtDZ1+w0FqKPhDE2G9PichAdkjR0ERT7a9I5\nLNRoUpxr4bS9vB67KhvRPXD+peUXb+Ekx8ewRESeuFgLpyBtGIyxc5AWl8MWTuQWCzWaMA6HiC/r\nT6GorB7FLlo4XWWMw4q8NL9v4UREvsFtC6eYLBjjsjElYhpbONGosVAjSYmiiP2NHSgqr8eOchOa\nvtPC6dLkaKzIS8PyHLZwIiLv566FU2pMJtJic5AUmc4WTjQuHhdqra2teOyxx/D1118jLCwMd911\nF+68804pspEXqTzZNWILp8wpEViRl8YWTkTkE6x2AU2dh1HXdhBNXd/CIQ4vCK5Ra5EcNRNpcdlI\njprJFk7kMY8LtTVr1mD+/Pl46aWXUFdXhx//+MeYM2cOcnNzpchHCnauhdP28npUt7hu4VSQa0BW\nYpRMCYmIpMEWTiQXjwq1gwcPoq2tDffddx9UKhWmT5+OoqIiREXxD7OvaujqG+yv6aKFU3JEMArz\njCjMZQsnIvIurada8cLWjRiwdSPovUj8atXDiI2NxcmeY6hrO+iihZMKUyKnIy02B4bYTLZwognj\nUaFWXV2N9PR0PPfcc3jnnXcQGhqKVatWYcmSJVLlIwVoOW3Gu18PFmd76tucxtjCiYh8weZXn0HG\noiCo1YP9Ov/3s/9EfGo4LLY+p/vFhxsxLS4Hhhi2cKLJ4VGh1tPTg6+++grz58/HZ599hsrKStx9\n991ISUnBpZdeKlVGkkFHnwXbDxzHf5fW48CpQ3AMX7yEqCAd7shORUGuEYvYwomIvJwoitBGWKBW\nD35kqdaoEZGEoSItNjQZxthspMXlsIUTTTqPCjWdTofIyEisXLkSAJCXl4ebb74ZH3/88agLNUEQ\nZF/Yz2KxOH2Vk5xZes1WvHu4GTsrG/FRTctwCycAoboA3Do7CflzUnBD+hToAgaLM5tVgM068dmU\n9B4Bysrji1kEQXB/Jy+khPkO8M19ZqxEUUTPQCtMnVVo6KyCMc/5QifzGQcuz7gRqdFZCAs82x1A\nnLyFaPkeuaakLIA0edzNdx4VamlpabDZbBBFceh8JIfDAVEU3TxymMlk8iSCpGpra+WOMGSyspht\nDnzRfAYfmXrwZfMZWOznrfujUeGqpFDcZIjAVUmhCAxQA/Yu1HzbNSnZXFHSewQoKw+zKJ+S5jtA\nWe/TZGWxOE6j296AHnsjLOJppzHzGRu6WgfQYRLwvfkFQFc0GrpOAjg5Kdlc8cf3aDSUlAWY2Dwe\nFWpXXXUVgoKC8Ic//AFr1qzBwYMH8dFHH+FPf/rTqLdhMBgQGirv5/wWiwW1tbVIT0+HXi/v1TpS\nZGk91YZ7f/cSTgyImBqkwub71yA+Pm74OWx2fFzTih2VjXjv8An0CcOXlms1KtyYPgX52Sm4cVos\nWhvrfeZ18dU8vphFEATU1NRImEwZlDDfAb65z1xMn6UbDZ1VMHVWodvS4jQWrItAanQWUqMyEayJ\nxrFjx5C+xD9eF2ZRRh53851HhZper8cbb7yBxx9/HAsWLEBoaCj+4z/+A9nZ2aPehk6nU0xLIL1e\n7xNZfvn7V/COdiagU+OAwwH1869g+++fwKe1LSgaQwsns9mMVg+zSE1JWQBl5fGlLL56xbCS5jvA\nt/aZ7xpNCydjXDbiw1KHWjid+1jTl18XTzDLyDzJ426+83gdtZSUFPzxj3/0dDMkoaYBOxB29gR/\ntRp7hDgkP1F8QQunq9PiUZBrQH6OgS2ciMjrXayFky4gCMaYwf6aCRFpbOFEXoMtpHyMKIoICQoG\nRBE4W6W36aKBs0XaZSkxKMgxYHmuEals4UREXs5tC6fo2UiLy0FiZDo0av7JI+/DvdZHOLVwCjA6\njWXEhOBfLk9HYZ6RLZyIyOvZ7AIaO4+grv0gmjq/hUO0DY1p1FqkRM9CWmw2pkZfggA1WziRd2Oh\n5sVqzrZwKhqhhVNBrgEFuUbMYQsnIvJyo27hFDMLWo38J5kTSYWFmpdp6OrDjrPF2TcuWjgV5Bqx\nIo8tnIjI+zkcdrctnKbF5iCVLZzIh7FQ8wItvQPYWWFCUVk9vnTRwik/x4AVbOFERD5AFB1o7a1H\nXVsF6tsrL2jhlBBuRFpcDgwxWQjShcmUkmjysFBTqM5+AX8rb8T28np8WtsKx3mLCEcG6XDHnFQU\n5rGFExF5P1EU0XGmCSeaj6C+vQL9Qq/T+HALp2yE6CNlSkkkDxZqCtJrFrCz3IQ/fdmAr7Yddm7h\npA/AbZkpKMg14HuXJEEXoJExKRGRZ0RRRFd/C2pOfoNaSxmqjjgfOYsKnoK0uBwYY7MRHhQjU0oi\n+bFQk9mA1Yb3Dp1AUXk9/nboBMy24S4B+gA1bpmdjIJcI26ZNRXBOr5dROTdevrbUNd+EHVtFegZ\nOOU0Fh4Yi7S4bBhjcxAVkiBTQiJl4V9+GQg2O3YfPYmisnr8tboRZyzDl5YHqFW4YkoIfnrVbCzL\nTUN4oE4L9LBJAAAgAElEQVTGpJ5raW3Fmqe2oGnAjuQgDV5+6BdISIiXOxYRTaIz5i7UtVegrq0c\nnX3OfTODdeEIdiRi7oyFSIxO40VQRN/BQm2S2OwOfHasFUVl9XirssFlC6eCXCN+mJGAk3U1yMw0\nINDLizQAWPPUFrytmQGEqfGNwwE8vRlvbXpS7lhENMEu1sIpUBuKtNhsGOOyEa6Nx6FDhxEdkjSu\nIq31VCs2v/oM7Jp+aOzBuPeeBxEfz6Nx5DtYqE0gh0PEnvo2FJXXo/igCafOmJ3GrzLGoTDPiGXZ\nBkwJH2zhZDabcdLVxrzUd9tZNZ22X/wBROS1RtfCKRsJEdOGWjid6685XptffQbTr9VBrdbDYXdg\n82vP4MmHX/Bom0RKwkJNYqIo4pumThSV1WN7eT2aevqdxucmR2NFrtFvWjglB2kGj6Sp1YDDgeQg\nXgRB5EvkbuFk1/RDrR5c4FatUcOm7nfzCCLvwkJNIlUnu1BUXo+iMhOOdZx2GpudEIEVeUYU5Box\nI86/Wji9/NAvgKc3o+n08DlqROTdrHYBTRdp4ZQcNRPT4nIwNeoSBGgmtoWTxh4Mh90BtUYNh90B\njZ0L35JvYaHmgYu1cJoeM9jCaUWeEVl+3MIpISGe56QR+YBRt3CKngVtwOS1cLr3ngex+bVnYFMP\nn6NG5EskK9Ta29tx22234emnn8bChQul2qziuGvhtDzXgBV5abiULZyIyMs5RDtOdo/UwkmNxMjp\nSIvLQWrMbNlaOMXHJ/CcNPJpkhVqDz/8MHp6etzf0Qu5a+G0LDsVK/LSsMDoWQunltZWrHpyE451\nDWB6VBBeeeSXXMqCiCbV+S2cTB2VMFsvbOFkjM2BMXYOgnShMqUk8h+SFGrbtm1DSEgIpkyZIsXm\nFKGz34K3Khou2sKpINeA69KnSNbCac1TW/COdiaQoMYhhwNqLmVBRJNAFEW0n2lCXdvBEVs4nesS\nEKKPkCklkX/yuFCrr6/Hn/70J+zYsQNLliyRIpNsTlusKK5uRlFZPT48ehJW+/DVS5PRwolLWRDR\nZBFFEd39LTjRcgR1bRU4Y3E+lYMtnIiUwaNCzW6344EHHsAjjzyC8HDvvJpxwGpDSVUT/t8Xjdiz\n/QjMtuHibLJbOHEpCyKaaD39bahp+QY1lm9Qdcj5CvVzLZzS4nIQGcxFY4mUwKPK48UXX8SsWbNw\nzTXXjHsbgiB4vODhmJ/T5sAnta3YXtGA9w4344zg3MLpxhlTkJ+dgltmJiE88Oyl5Q4bzGbbCFuU\nxqb7fgbbsy+irscMY4Qem9avm/TX5nwWi8Xpq5yUlAVQVh5fzCIIgvs7eSE55jsAOGPpQmNnNRq6\nqtDV3+I0FqyLQGpUJlKjsxAVnDh0EdRk5fTF/VcKzOKakrIA0uRxN9+pRPG8k6/G6Ac/+AHa29uH\nfrFPnz6NoKAgrF69GitXrrzoYy0WC6qqqsb71GNmc4g4cKoPu029+KyxF73C8JEzFYBLE4JxsyEC\ni1LCEKnnqiVESpCVlQW9fvKWepgokz3fAYBVHECPvQndtgYMiM4fawZAjwhNCiI0KQhWx/AKdSIF\nGGm+86gief/9952+v/766/HYY4+NaXkOg8GA0NCJuXLI4RDxVWMHiisasbOyEW19zhXvlakxWJ6d\nih9mxOF0SxPS09Nl/6NgsVhQW1vLLArOorQ8vphFEATU1NRImEwZJnK+AwCLtQ+N3YfR0FmFU6fr\nncZ0miAkR82CIToLEbpEHDt23Kf2GWZhFm/N426+k/TQ0Xj+V6bT6RAYGChZhnMtnLaXD7Zwaux2\nbieSNzV6qEvAuRZOZrMZ1S1N0Ov1kmbxBLO4pqQsgLLy+FIWXz3CI/V8B7hv4ZQSPRvT4nKQFDkD\navXgea/nPtb0pX1GSsziGrOMzJM87uY7SQu1jz/+WMrNjUl1Sze2ldWN2MKp8GxxluFnLZyIyPfY\n7AIaL9LCKSV6JtJiJ6eFExFNLK8+Gau2vRfbywcXoq1q6XYamxYTisJco9+3cCIi3+CuhVNS5AxM\ni8ud9BZORDSxvK5Qa+zqw46DJhSV12N/Y4fT2NSIYCzPGeyveVkKT5AlIu928RZOKiRGpsMYmw1D\nbKZsLZyIaGJ5RaHWenoAOw82oKi8Hl/UnXIaiw8NRH6OAQW5BlxljPeohRMRkdzYwomIzqfYQq2z\n34JdlQ0oKpu8Fk5ERHIYTQsnY+zgQrRs4UTkXxRVqJ02W/HX6kYUlddj97fOLZxCdAG4LXOwS8D3\nZiZBPwEtnIiIJosoiujqO4m69go3LZzmIDwoVqaURCQ32Qs1s82ODysGLwh479AJmG3D/S31AWr8\ncFYyCvMmp4UTEdFE6xloQ13bQdS1HUTPQJvTWFhgDKbF5cAYm4OoEN9v4dR6qhUvbN2IAVs3gt6L\nxK9WPYz4eN//uYnGQvbK5+oXP8Kx7uETZAPUKtx0SRIKc424PSsZ4YE6GdMREUnnnzV/QfuAyem2\nEH0k0mKzYYzLRkzIVL+6CGrzq88gY1EQ1OpgOOwObH7tGTz58AtyxyJSFNkLtX6rHWqVCgunx6Mw\nLw13zElFTAgvLSci39NjHjyCFqQNgzF2DtLichAXlgKVyj/Ps7Vr+qFWD873ao0aNnW/m0cQ+R/Z\nC7WHr8/E0rnpmBIeJHcUIqIJlRqVhfSkHCREpEHtp8XZ+TT2wSNpao0aDrsDGjuXGCH6LtkLtf8z\n14BwFmlE5Adykm9EeDi7o5xz7z0P4oVXnkK/tQtBAYPnqBGRM9kLNSIi8k/x8Ql47P5nUV1djczM\nTEX1biRSCh57JyIiIlIoFmpERERECsVCjYiIiEihPC7U9u/fj4KCAlx22WW4+eabUVRUJEUuIiIi\nIr/n0cUEvb29WLt2LR599FHccsstOHToEH76058iNTUV8+fPlyojERERkV/y6Ihac3MzFi1ahFtu\nuQUAMHv2bFxxxRUoKyuTJBwRERGRP/OoUJs5cyaeffbZoe97enqwf/9+zJo1y+NgRERERP5OsosJ\nTp8+jVWrVmHOnDm47rrrpNosERERkd+SZMHbxsZGrF69GgaDAS+8MLaGuoIgwGw2u7/jBLJYLE5f\n5cQsrikpC6CsPL6YRRAEKeIojhLmO8A39xkpMItrzDIyKfK4m+9UoiiK4946gOrqaqxcuRK33347\n1q9fP+rHWSwWVFVVefLUROTjsrKyoNfr5Y7hMc53ROTOSPOdR0fU2tvbsXLlSvzbv/0b7r777nFt\nw2AwIDQ01JMYHrNYLKitrUV6errsfxSYRflZlJbHF7MIgoCamhoJkymDEuY7wDf3GWZhFm/N426+\n86hQ27lzJ7q6uvDSSy/hxRdfBACoVCr867/+K/793/99VNvQ6XSK6e+m1+uZxQVmGZmS8vhSFpVK\nJWEa5VDSfAf41j4jJWZxjVlG5kked/OdR4Xaz372M/zsZz/zZBNERERENAK2kCIiIiJSKBZqRERE\nRArFQo2IiIhIoVioERERESkUCzUiIiIihWKhRkRERKRQLNSIiIiIFIqFGhEREZFCsVAjIiIiUigW\nakREREQKxUKNiIiISKFYqBEREREpFAs1IiIiIoVioUZERESkUCzUiIiIiBTK40Lt0KFDWL58OfLy\n8rB06VIcPHhQilxEREREfs+jQk0QBKxevRr5+fnYv38//uVf/gVr1qzBwMCAVPmIiIiI/JZHhdre\nvXuh0WhQWFgIjUaDZcuWITo6Gv/4xz+kykdERETktzwq1I4fP47p06c73ZaWlobjx497FIqIiIiI\ngABPHjwwMICgoCCn24KCgmA2m90+VhRFAEB/f78nESQhCAIA4MyZM0P/ZhZmuRgl5fHFLFarFcDw\nPOHtlDTfAb65zzALs8hBijzu5juPCjVXRdnAwACCg4NHHezEiROeRJCUyWSSO8IQZnFNSVkAZeXx\nxSxWqxWBgYGSbEtOSpzvAN/cZ6TALK4xy8ikyDPSfOdRoTZt2jT85S9/cbqtrq4Ot912m9vHhoSE\nICMjA1qtFiqVypMYRORjRFGE1WpFSEiI3FEkwfmOiEbibr7zqFC78sorIQgC/vKXv6CwsBAlJSXo\n7OzE1Vdf7faxarUaYWFhnjw9EfkwXziSdg7nOyK6mIvNdyrRw5NAjh49ikcffRQ1NTUwGAz47W9/\ni+zsbE82SURERESQoFAjIiIioonBFlJERERECsVCjYiIiEihWKgRERERKRQLNSIiIiKFYqFGRERE\npFAs1IiIiIgUioUaERERkUKxUCMiIiJSKBZqRERERArFQo2IiIhIoVioERERESkUCzUiIiIihWKh\nRkRERKRQLNSIiIiIFIqFGhEREZFCsVAjIiIiUigWakREREQKxUKNiIiISKFYqBEREREpFAs1IiIi\nIoVioUZERESkUCzUiIiIiBSKhRoRERGRQrFQIyIiIlIoFmpERERECsVCjYiIiEihWKgRERERKRQL\nNSIiIiKFYqFGREREpFAs1IiIiIgUKkCuJ3Y4HOjr64NWq4VKpZIrBhEpkCiKsFqtCAkJgVrt/f+f\n5HxHRCNxN9/JVqj19fXh6NGjcj09EXmBjIwMhIWFyR3DY5zviMidkeY72Qo1rVYLADAajQgNDZUr\nBgDAYrGgpqYGM2bMgF6vZxZm8ao8vphFEAQcPXp0aJ7wdkqa7wDf3GeYhVm8NY+7+U62Qu3c4X+t\nViv7iy2KIgBAp9MxC7OMipLy+HIWX/mYUEnzHeDb+wyzMMtkkjLPSPOd95/8QUREROSjWKgRERER\nKRQLNSIiIiKFYqFGREREpFBjKtQqKipwzTXXXHC7KIq488478dxzz0kWjIiIiMjfjbpQKy4uxl13\n3QWbzXbB2Ouvv44DBw5IGoyIiIjI342qUNu6dSvefPNNrF69+oKxI0eOYNeuXbjxxhslD0dERETk\nz0ZVqOXn56OkpARZWVlOtwuCgAcffBAbNmxAcHDwhAQkIiIi8lejWvA2NjbW5e3PP/88rr32Wsyd\nOxc7duwYVwBBEGA2m8f1WKlYLBanr3JiFteUlAVQVh5fzCIIghRxFEcJ8x3gm/uMFJjFNWYZmRR5\n3M134+5MUFpair1796K4uHi8mwAAmEwmjx4vpdraWrkjDGEW15SUBVBWHmZRPiXNd4Cy3idmcY1Z\nXFNSFmBi84y7UHv//ffR2NiIBQsWAAD6+/uh0Whw/PhxbN26ddTbMRgMsve+s1gsqK2tRXp6uuwt\nKZhF+VmUlscXswiCgJqaGgmTKYMS5jvAN/cZZmEWb83jbr4bd6H2xBNP4Iknnhj6/je/+Q2ioqLw\nwAMPjGk7Op0OgYGB440hKb1ezywuMMvIlJTHl7L4So/P71LSfAf41j4jJWZxjVlG5kked/MdF7wl\nIiIiUqgxHVGbN28eSktLXY49/fTTkgQiIiIiokE8okZERESkUCzUiIiIiBSKhRoRERGRQrFQIyIi\nIlIoFmpERERECsVCjYiIiEihWKgRERERKRQLNSIiIiKFYqFGREREpFDj7vVJRL7hVEsL/rhuDQKa\nm2BLSsbdL76M+IQEuWMRERFYqBH5vT+uW4MbPvgr1ADsFQfw+jrgNzvekjsWERGBH30S+b2A5qah\niUADQHOiSc44RER0HhZqRH7OlpQM+9l/289+T0REysCPPon83N0vvozX1w0eSTt3jhoRESnDmAq1\niooKrF27Fp9//jkAoLW1FU888QT2798PrVaL73//+1i/fj20Wu2EhCUi6cUnJPCcNCIihRr1R5/F\nxcW46667YLPZhm779a9/jcTERHzxxRd4++23UVlZiZdeemlCghIRERH5m1EValu3bsWbb76J1atX\nD91mtVoREhKC1atXQ6vVIiYmBosXL0ZZWdmEhSUiIiLyJ6Mq1PLz81FSUoKsrKyh27RaLbZu3YqY\nmJih2z799FPMnDlT+pREREREfmhUhVpsbKzb+zz55JOoq6vDPffc43EoIiIiIpLgqk+LxYL7778f\nNTU1ePPNNxEdHT2mxwuCALPZ7GkMj1gsFqevcmIW15SUBVBWHl/MIgiCFHEURwnzHeCb+4wUmMU1\nZhmZFHnczXcqURTF0W5s3759uPfee1FaWgoA6Onpwd13343Q0FBs3rwZYWFhow5msVhQVVU16vsT\nkf/JysqCXq+XO4bHON8RkTsjzXceHVFbt24d4uLisGXLFmg0mnFtw2AwIDQ01JMYHrNYLKitrUV6\nerrsfxSYRflZlJbHF7MIgoCamhoJkymDEuY7wDf3GWZhFm/N426+G3ehVlZWhv3790Ov1+Oyyy6D\nSqUCAGRmZuKNN94Y9XZ0Oh0CAwPHG0NSer2eWVxglpEpKY8vZTk3n/gaJc13gG/tM1JiFteYZWSe\n5HE3342pUJs3b97Qx555eXk4fPjwuEIRERERkXvs9UlERESkUCzUiIiIiBSKhRoRERGRQrFQIyIi\nIlIoFmpERERECsVCjYiIiEihWKgRERERKRQLNSIiIiKFYqFGREREpFAs1IiIiIgUioUaERERkUKx\nUCMiIiJSKBZqRKQo1vZ2nP7yn3LHICKacKLDgc5vjl/0PgGTlIWIaES2nh50v/s2OouL0PPJR0Bc\nPNRFJXLHIiKSnCiK6Pj6GExFpWjYsRdWhw3T375nxPuPqVCrqKjA2rVr8fnnnwMAent78dBDD2Hv\n3r0IDw/HmjVrkJ+f79lPQER+wd7Xh+733xsszna/D1EQhsbUgYEyJiMikpYoiuiubICpqBSmolL0\n1Z0aGgtIDL/oY0ddqBUXF+PZZ59FQMDwQx555BGEhISgtLQUhw8fxsqVK5GRkYHs7Oxx/BhE5Osc\nFgt6Pvw7OouL0P23d+Do7x8aU2m1iLjpe4jOX4Ggm76Hw8cv/nEAEZHS9X7bDNP2weKs9/AJp7HQ\n9CkwrliAxPzL0SB0jLiNURVqW7duxQcffIDVq1fjtddeAwD09/fj448/xu7du6HVapGdnY3Fixej\npKSEhRoRDXFYrTj92cfoKN6O7ndKYO/tHR7UaBC+6HrELF+ByFtvR0BkJADAYrHIlJaIyDN9prah\n4qyrrN5pLDg1FoblV8KwYgGi8oxQqVSD812Vh4Vafn4+Vq1ahX379g3dVl9fD61Wi6lTpw7dlpaW\nhg8//HCMPxIR+RrRbkfvPz9D584d6CrZCVtH+/CgSoWwBVcjOr8QUUuWQRsXJ19QIiIJDJzshund\ncpiK9qC9tMZpLDAhAqn5V8Cw4irEXpkOlXps13GOqlCLjY29MNTAAPR6vXOYwECYzeYxBSAi3yCK\nIvr374Pjj6/gyOf/gK21xWk85PJ5iF5WgOg7lkOXNHWErRAReQdzey/qtu9B439/gm8PNAGiODSm\niw5FytLLYVixAPELZ0OtGf8iG+O+6jMoKOiCjyfMZjOCg4PHtB1BEGQv7s79HEr4uIVZXFNSFkBZ\neeTMIooizFWV6NlVjO6SnbA2mAAAtrPjgVlzELE0H5G33wGdMQ0A4ADc/s4L511Y4EuUMN8B3H9H\nwiyuMcswa08/mt8tQ1PxVzj1ySGINvvQWEBYIJJunYup+fOQcH0m1LrBEkuwCoB15G26m+/GXagZ\nDAbYbDa0tLRgypQpAIC6ujpMnz59TNsxmUzjjSC52tpauSMMYRbXlJQFUFaeycwimuohfvIh8MmH\nQMN3fodTUoEbbobquhshGNPQBqCtrx+orp60fEqlpPkO8N/91x1mcc1fszjMVpz5/BhOf/gt+vYc\nhygMF2cqfQBCr56GsJtmImRBGtSBWnQC6Kz5VrLnH3ehFhISguuvvx6///3vsWHDBhw9ehTvvvsu\nXn311TFtx2AwIDQ0dLwxJGGxWFBbW4v09PQLPs5lFmZRep7JyiLU16G7ZCd6SnbCXFXpNKZNSUXE\nkmUIvvV2NOoDMWPGDI+yCIKAmpoa93f0MkqY7wD/3H+ZhVnGwm6xovWjKjTt+Aon/1YOe9/wETyV\nVoOEm+Ygedk8xNw4G/WtTR7lcTffebTg7YYNG/DYY49h4cKFCAkJwfr168d8xadOp0OgQtZM0uv1\nzOICs4xMSXkmIovQfAKdu4rRuWMb+vZ/7TSmnZKI6DuWI3rZcoTMuxIqlQpmsxlN1dUeZ1GpVJ5G\nVyQlzXeA7++/48Usrvl6FofVhpZPqtFQVIrGkq9h7Tlv+SCNGgnXZcJQOB8pSy+HLmrwP1xmsxlo\nbfIoj7v5bkyF2rx581BaWjr0fUREBDZt2jSuYESkTNa2NnSV7ETnzu04/eXnTifIBkTHIGrJMkQv\nW46wq6+FSqORMSkRkWdEhwNtX3yL+m170LjzK1jaTzuNx10zE4bC+UhddgUC4yNkycgWUkQEW3c3\nut4pQWdxEXo/+wSwD5+DoQkPR+TiJYjJL0DYohug1mplTEpE5BlRFNGx7xhMRXvQsGMvBpq7nMaj\nL58OQ8GVMBTMR3ByjEwph7FQI/JT9jN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NoVQi/L65iE7LQOTDKfAPC7vm+Ty3jIh6kytl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\nNYmoFzJfMuJcbiHK9QW4/K8TLmuq/mGISUtEjI4jnIh6M48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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -84,7 +84,15 @@ "collapsed": false }, "source": [ - "## Load Datasets for Example Code" + "## Load Datasets for Example Code\n", + "\n", + "Yellowbrick has provided several datasets wrangled from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/) to present the following examples. If you haven't downloaded the data, you can do so by running:\n", + "\n", + "```\n", + "$ python download.py\n", + "```\n", + "\n", + "In the same directory as the example notebook. Note that this will create a directory called `data` that contains subdirectories with the given data. " ] }, { @@ -95,48 +103,41 @@ }, "outputs": [], "source": [ + "from download import download_all \n", + "\n", "## The path to the test data sets\n", "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", "\n", "## Dataset loading mechanisms\n", "datasets = {\n", - " \"credit\": os.path.join(FIXTURES, \"credit.xls\"),\n", - " \"concrete\": os.path.join(FIXTURES, \"concrete.xls\"),\n", - " \"occupancy\": os.path.join(FIXTURES, 'occupancy', 'datatraining.txt'),\n", - "}\n", - "\n", - "## Human readable column names\n", - "columns = {\n", - " \"credit\": [\n", - " 'id', 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n", - " 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n", - " 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n", - " 'jul_pay', 'aug_pay', 'sep_pay', 'default'\n", - " ],\n", - " \"concrete\": [\n", - " 'cement', 'slag', 'ash', 'water', 'splast',\n", - " 'coarse', 'fine', 'age', 'strength'\n", - " ],\n", - " \"occupancy\": [\n", - " 'date', 'temp', 'humid', 'light', 'co2', 'hratio', 'occupied'\n", - " ],\n", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", "}\n", "\n", "\n", - "def load_data(name):\n", + "def load_data(name, download=True):\n", " \"\"\"\n", - " Loads and wrangles the passed in dataset.\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files. \n", " \"\"\"\n", - "\n", + " \n", + " # Get the path from the datasets \n", " path = datasets[name]\n", - " data = {\n", - " 'credit': lambda p: pd.read_excel(p, header=1),\n", - " 'concrete': lambda p: pd.read_excel(p),\n", - " 'occupancy': lambda p: pd.read_csv(p),\n", - " }[name](path)\n", - "\n", - " data.columns = columns[name]\n", - " return data" + " \n", + " # Check if the data exists, otherwise download or raise \n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all() \n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + " \n", + " \n", + " # Return the data frame\n", + " return pd.read_csv(path)" ] }, { @@ -215,7 +216,7 @@ "data": { "image/png": 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Uq1fPOl31Z6dOnao0sSjP+++/rwkTJqhTp06aM2eOtdwvnfusffbZZ7rrrrvk7e1tbW/d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\nJNHUwuEYaZYjDsfA5UGCAACAAXesILB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xcT5tsbGxcjqdfuvOmzdPF154oXr37m3J/kokCAAA1Esul0sRERE+bRERESot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\nlXJychQVFaV27dopMzNTb7zxRq2vMTV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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -273,7 +274,7 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -281,12 +282,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", "classes = ['unoccupied', 'occupied']\n", "\n", "# Extract the numpy arrays from the data frame \n", "X = data[features].as_matrix()\n", - "y = data.occupied.as_matrix()" + "y = data.occupancy.as_matrix()" ] }, { @@ -298,9 +299,9 @@ "outputs": [ { "data": { - "image/png": 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RM2qf3Xn44Yfx3Xff4fz586iursaHH35o8kg8e3t7lJeXm7SvVqvFF198gTlz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\nJkCpAwAAMAFKHQAAgAlQ6gAAAEzg/wHcNIV6m0tTZAAAAABJRU5ErkJggg==\n", 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gx44dEIvFKvtDVqds9feOHTtW2OJHHSQSCR48eIAOHTrUaQFS0l28J6g83hNUHu8JKk/T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\ngwFbtmzBnDlzOGE3URkMdURmKJVKTJkyBe+88w5++OEHGAwGFBQUYP3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OoaSkBFeuXMGx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\nO/D/AVvwztGQIY1uAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -316,6 +317,13 @@ "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": {}, @@ -339,12 +347,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", "classes = ['unoccupied', 'occupied']\n", "\n", "# Extract the numpy arrays from the data frame \n", "X = data[features].as_matrix()\n", - "y = data.occupied.as_matrix()" + "y = data.occupancy.as_matrix()" ] }, { @@ -356,9 +364,9 @@ "outputs": [ { "data": { - "image/png": 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kuv21NPpKm/tS28YAkYiI2oyDKaudOn78ZX9cLPNtvqFZXbAnlwkY0bWsXnHJ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\ngwcZOXIk06dPx+l0Ap5/VzVfUmpqKgsWLKC0tJR9+/bxyiuve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Fx8eH06dP884773Ds2DH8/PwYO3Ys/fr1IyIiglOnTjF37lwATp8+Tb9+/Thx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Cspii8uyg247oX9JeTXtSMg8fjCFkTJS6g+kpSoSf71Z6SuwByWzGhotdZyrGqb/+dYUxoCApIk\n0mKt5kTNDt49+QKvHfkJHxW/SllLPjan9SIrXhrxphC2P7KaeJMBq8PNzX/eSYVlqNjlzulp/kzt\nxymzL4bRP2f767452wJ9zm4Olm9iw8Hvj2rNTruGxh5ZPa9RieSlWPnApyRekt6FSe/ldGsYC1K6\nyYwayPRKEmw4bw70DdntLEi1BlwD4PYK/GhXFh5RTYTezSPz61GroNGq52/Hx4dZ+Gj4sNxMXXdw\nO6KbJ7YTYwwU1BxrCKfC5yX5ienNFzUXd3jU/sC7Pwu5JL2L/TWRiBKsyOxAr/Hi8qrYVRnNJ6YN\nZMi/vOnopWxL4RpBCSAVFBSuOidqPsAretBrQpmWvDzgnM1p5WTtLgBc0gR2VciZjOfvno9aNfC/\nsJqOXr7/njy55qmV4PLIGab5mbdxpPJtQLbwmZG6alTP6HD3saf4FUTJS7hB7rm8ZeZj3L/w+6yY\n9CC58fMI1cviAqfHRpXlJAfKNvLGsZ/x9vHfcKzqXzR2lgWUfMeCLLOJbV9YTbhBS0uPg5v+tJOW\nICMPR6rMvhhhhiiW5K7jjjn/j4wY2VpI+phJJhfi5cJE+gO4VVntHK2PoMuhRUBiTU47kgSlbUZ/\n32N/UHi4LjygZ3FybI9PsIH/un67nl/sT6fTofOZhdcTafDgcKv43dFUnN5Aj9F/J7ySwMvnlfj7\nf38qAR6ae76gRuD1UwkApEY4WZJ+cQunLWfj/EpsrwgalURecg/5DeEYdSLLM2Tz8F2V0cxM7PH7\nUJa0WantuHiGU+HaRwkgFRQUriqdfc2Utx4HYFbaKnQaQ8D5gur38IgutGoDT++UA4ZP5WWxJHOg\nPCdJEo9vPkqfy8PEGIncKLlXMiduLk3d5fQ4OhAEFUtHWboWJZG9Ja9hc3WjVmlZOXm9/5xeYyQj\nZjpLcu9hXd63uXPO15ifeSvJURNR+xTenbZmzjTsY8eZF3n18I/YcfolzjTso8vWMuJycjBmJUez\n5XMr0GtUVLT3cMufd2F1BGaXRqvMvhiRxjhWTHpg1Pe7vQJFzSbfTxK3TbL4rXvmJFmJD3NR2WHg\npgmyKKPPN+9ZkuDVQUrjuFAnTy6uHaK4liT4W0ESFR1yf+M9U1uYECMLif56PImmnvFlFj4aKjqM\n/n7SwUp0gIkxdqbFW4dcn+/zerxrcit69dCpR/24vGr/mMn+LOSyjE72VEUhSnJGWC1I9Lo0HKqL\n5Ppsi//efpcEhfGLEkAqKChcVQqq3wMkTAYzExIWBJxrtdZQ0XYCgOL2XBp7JMINWv7nljkB171R\nWMN75xoQkPjW8k5EyUOI1kSaeQrFTYcAefqMOSwwGzNciup2+SfjLMq+k6jQhKDXCYJApDGOKclL\nuWHq5/jkwv9mzbTPMy15GVFG+R6v6KGxq5RjVf/irePP8eax/+FA2Uaq2opwuoenkg7GipwENjx4\nHSpB4ERDB3f/dQ8Od+CH//nK7Lv+umfYyuwLUdt2btT3vlscg+TLPk6N7aHMYqSxR/4C0R80GnUi\nRp2IzaXyT53ZVxNJn1v+IhCi9fKd5VVogiiu3y8zc6BODp7ykrq50edRuKPcTH5jxKife7yx6Uw8\n3Q51UEHNF+Y2DhHUbDwTj0cUiAzx+H9nF2JrcSweUV7YK4JWLTEzoZeTTSaijR7mp8g+kDvKY7hl\nosVfFn+/pHFEgi6Faw8lgFRQULhqNHVVUN8p23rMSV8TkB2UJJEjle8AoNfG8D975GDohzfOJCF8\nwDS60+bkybeOAfDoQhFJlBv08zJv8QtvokOTmJGyclTP2NBZSmHtTgAmxM8nJ37usO/VqLQkReaQ\nl3kzd8x5kk/M/w5Lc+8lK3YWeo2cFbO5uilryeejkn/y2pEf8+7J33KiZgct1mpE6cLZn2DcPSON\n394jjzzcXd7Cp/65f8jIw8HK7IZu24iV2eezq+Tvo7pPkmBHRb/9ksSnZjf7s1kTY/rIjHLQ5VCT\naJIzqY09en9J+p++3keVIPGNpVWY9EN/T0XNYbx5Rg7aE8KcfM5nFl5qMbLx9Pg2Cx8pNrfaX5o+\nnzC9l1smBHo/tvXp2OUzF1+bayHScOG2C7eoYmtxYBZyRWYHu6uikKSB+eVtfTpOt5qY7st4ihL8\ncPvJS9qXwtVFCSAVFBSuCoNHFprDUsiImR5wvqylgPZe2bLl9VOJiJLA1IQIHlsyMeC6b249Tmuv\ng6RwLwuSZRPrjJgZtFqr6HV2ohLUXDfhE0PmaQ+HPmcXe0teAySiQ5OYn33bKHY6gFEXTk78XJZN\nvJ/7F3yX22Z9hTnpNxIfnokgqJCQsPTUcbJuF+8V/YFXD/+Y3edepqTpCD2OoQrrYHxx0QR+tHYm\nAJuLavnK5mNDyuTnK7Mffu3QqErpO0/9Y8T39JPfYMLllT+C4kJdWJ0aSn1WOv1BR6RBDgz3VUeS\nFS33dW46HYNblO/73JwG0iOHKq6benQ8f0g2W9erfWbhWpFuh4Y/HE3BO87NwkfDkfoIzrYGtyq6\ndZKF6JDAlod3i2Poc6nRayTunDx0BOJg3iuNwe0dyELqNRITY2ycaQ0lJcLJtPge/3UPzmym31j8\ntwdKxqSFQ+HqoASQCgoKV4Vqyynae+Um/ryMmxCEgf8dOT12jtfI865FMth6Ts6iPX/XfLSD3KH3\nlDfz0tFyQOL7K3vwii70GiPp5mmUNMs9VjPTVl+w5HwxvKKH3cWv4PTY0KoNrJz84JhOrREEFeaw\nZGakruSmGY/wwIIfsHryp5mUuMg/k9rtdVDTfoZDFVvYlP8smwt+yeGKt6nrOHdBI2+A714/3R9o\n//FQKU8H8d4brMx+vbD6gmMRL4QkidR1nx3RPYN5/dRAD+P6mU3+7GNyuIPp8b1+i5iydiN6tReV\nAA43fFgpG16vympncVr3kHX7XCp+vDvTVxqX+OycRpLCnXhF+MPRFLr/TczCR47AhpOJ/kAPAgU1\nD89tCLi6z63hnWL5d70kvYuU8At7nXolFVvOxfnXAlid1eFXad/k+0JQ0xWCxabzT6qxu728dqLq\n0remcFVQAkgFBYUrzuCRhSlRE0mMzA44f7J2Jw53HypBw88/krMm983KYEXOQCDocHv50kY5SLx/\nhgedSv4AnJO+lvzqfwFgDktmekqgqnu45Fdtw9JTB8B1Ez7hD+ouF1qNnlTzFBZm38E9ed/gnrxv\nsDD7TtKip6BVy2IPq91CcdMhdp79O68efprtp/5EUd0e2nsbArwnBUHgN3fmce/MdACe3lHE7w8O\nnQAyWJn9g+0jU2ZvOPCjUe+1waqj09Hfw+ghJtTNiUZZTLM2pw1BkAORHqeazadjyUvpQZLgz/nJ\neEQVE8y9PDBjwBZmwK4Hnt6dhdMrr319dgfzU+SS6cYz8f4M538qLb16tpXKgfr5gppJsTamxAZa\nQO2ujKKlV4dKwGfDc+Fs4QflZr9xuVcEg1YkM8pBicXIxBibfxrN9rIYPjVrwFj8G1uV+djjFSWA\nVFBQuOKUNveXZAXmZtwUcK7L1sq5poMA1PXkUNkBoToNz94WKJz52YenKG2zEmHwsjZXHuWXEjUJ\nS28dfc5uVIKapbmfQCWMvHRd1XbS/wzTUpaTZp4yil1eGiaDmUmJC1k15dN8csF/c9P0R5mRuoqY\nsBRAQJS8NHdXcrxmO1sL/4/Xj/6MvSWvU9F6HLurB7VKxd8fWMLqXDno/srmo0MCxNEqs/vsVrxc\nOAP6cfx9kPfi7RPbeL/MjIRAlMHNwrQBVfCLBcksyehGJUC5RcuZ1nBijC7+a2nNEMW1KMGvD6Rj\nscnBdk60jXt93oP5DeHsKL+8XwDGC9tKY2ju1QUV1HxxXgNqYeCLiFcaMBefGt/HtLgLW++IksBG\n37X9Wcjrs9vZUW5GEAbaEk61mAjTiYRq5b7bJquDosbOsdiawhVGCSAVFBSuKC6Pg0Kfr2NO3JyA\n8nL/mEBJEtGqw/npbvmT6Ps3zCAlciB7dLqpk2d2nQbgB6t78YoOtGo9mbEzKWuRBTWz0m4gKnTk\nYokuWysHyjcBEB+eyZz0NaPb6BiiUqmJj8hgTvoabp31Ze5f8D2WT/wkOXFzMepkyxWHu5fKthPs\nK32D14/+lHdO/C+n6nfw+7vTmJcaiSTBp1/ZP2QKyGiU2W8W/GzUe7G5VFR0GuV9CSJ5yd0cqJXH\n4j0ws8kffLxXaqbBqmdhaheiBOcskahVEt9bUelXXA+269lQmEhpexgA4XoPj86vQ6OS+yH/+m9s\nFj5SPKKKl08kBj1n0nu5+TxBTUGjibJ2+f36xPQWVMKFs5C7K6Oxu1X+gD5UJ5JkclLZEcKcJCux\noXKf5ftlMdw5ZcCz8wtvHLrUbSlcBZQAUkFB4YpyumEvTk8fapWG2ek3BJyr6zjrt8t5rywFp0fF\nhNhwvrpskv8aUZR45M3DeESJtROcROjkPspZqdf7LIEgJiyVaSnXjfjZ3F4Xe4pfweN1EaI1sXzS\nA6PKYF5uDNpQMmNnsnTCvdw77ynumP0k8zJvISky169k7+hr4nT9R+wr/SuPzz/KU8sauC69jUfe\n2E5BnSVgvZEos8uaTlzSs792KoH+YG5RahcfVZvxiCpSTHZmJ8ol1PL2ELacjefmCRY0KvCKAttK\nzXxjaXWA4ro/i7azMoqPquV+O5Ug8ci8OqJCPDg9Ar87korDc+29h1eTYksYh2plG6PzNSy3TbIQ\nFaC6Fnj9lPxFLDncydL0C2cLpUHX9ofra3La2VEWjVol/zfIgp7p8b3+bGd+XTsdfaPPaCtcHZQA\nUkFB4Yphc1o507APgMmJSwjVR/rPeUQ3Ryvl3kWERF71OXz8713z0GkGAoA/HirlcI2FUK2H+6bJ\nvVQJEVl02pqxuayoBA1LJ9w74sBPkiQOlW+my9aCgIrlkz6JUWf6+BuvMoIgEBWawNTk61gz7WE+\nueAH3DD1IaYmLSXSKH+Ye0U3OeYuHpjZzHeXl7C3+Hm2n36NasspnB65N224yuwDFa+P+llFCQ7X\n9b/nsrp3V2UUakHkqz4jcIdH4A/HUgnTe1ia3oUkwdvnYnlwZjMZUUOFHGdaQwNMxe+e0sKkWDmD\n+rcTyX5fSYVA3jid4DdmH4xaNXRCTVWnkSN1cqb7zsltGDQXtpfaXxNFn3MgC2nSe4k2eqjr1rMk\nrZMwnQevJLCr0hwQjH71LWW84XhDCSAVFBSuGIW1H+IV3eg1Rqanrgg4d7ZhP71OuS/yuYORgMBd\n09NYM3HA/Lu+q4+n/iVnwL69og9RsqFWacmKnU15q9yMPyd9DZHGOEZKSfMRKtvkUYhzMm4kISJr\nVHu82mjUWpKjJjAv61bunPP/uHfeUyzJXUdGzAzUKjmYigpx0dxVyJ7iV3jt8I/ZdvL3FNZ+yOJ0\n+NnNsgVQMGX2eyf+fEnPtqsiym+hkxVt43hTBDa3hnuntRBtlDOefz6WTKddy4057WjVEm5RwOZW\ncV3G0LF6Lb1a/vdgOv35rjlJVm6aIGe5PqyI5mj9f45Z+EixOjX+nsXzmRJnY/J5gprNZ+NxewUi\nDB5/P2MwJAReKQoskd+Y28720hj0GolVWbId1UfVUdw80UK/MOeNwho8XvH85RSuYZQAUkFB4YrQ\nZWv19yfOSF2JXjNgBt7n7KaoTu6LbLNncrpZhUGj5le3B5p2P7HlGD1ONwtTHSSFyVmSGSkrKaz9\nAIBYUxpTkpeO+NksPfUcrdwKQGr0ZKYlLxv5Bq9RQvUR5MbnsWLSAzy46L9JMn+Sd4rjKbUYESWQ\nEGntqaGw9kO2Ff2O1NAt/HxNB9eld/Kbj/L9whtR9NLSV3FJz/J28UDA8qmZjbxfZmZWopUbfHOu\nz7aGUtgcQZjOw4rMDiQJ9lRG8qlZzUPW6nOp+PGeLH9AGh/m5KE5shK/vD2EN079Z5mFj4Z91VGU\nt4cEPXe+oMZi0/ltedbktJ9X5g7kSH0EPU41Kl8WMsLgIVTnpalHx6qsDnRqEeMTexIAACAASURB\nVKdHzdG6SHLNcrbYI0r8797RTzVSuPIoAaSCgsIVoaB6OxISofpIJiUuOu/ce3hEN2qVgZ/s0gHw\n1PXTSI8O81+zuaiWt0/XYdB4+UKe3IAfY0ql296GzWVFreovXY/sf2tOt43dxRsQJS9h+miWTvgE\nQjCJ6r8BKkHFmskz+czCdfxifxZPvDuJ3dXTyYmbT5heDg5cHjtxoU18dk4jv1xbSnnjS2w9+Sb/\nOPj9S3rtc61GbG65rSDS4KaxJwRBwB/0eUR44XAqIAcoeo2EywursjsDFNf91/5kTxZ23zhDnVrk\nsfl1hGhFuh1qfn80Fa+kfLx9HBICLxcmESzxF673+sdJ9vOv0lh6nLK5+F1TLmYuLvCP84Q6aydY\n2F4ag0nv9ZeuP6yI5sGZjfRnIX8yQi9ShauL8i9MQUHhstPSXU1dh2w6PSf9xoCRhS3d1f7S8f7a\nNHpdarLMYXx9xVT/Nd12F09skXuknljcC/SiEtRkxc6m0jcre076jUSExI7ouSRJZF/pG/Q5u1AJ\nGlZOfjAgM/rvyh3TUvn9ugXYPWo2nBD59YFo7pj9X9w99+ssyLqd1OjJqH2m6YkmJ+09BcCllRdf\nPjkQUNw/vYkPyqN5dF49oTp53e1lMTi9akK0Xlb6ypyiJARVXD9/MI3WPr1vNYnPzG4kJcKJKMEf\nj6XS5fhPNQsfOfVWg9/i6PyW19sntQWMMbS71X5z8UVpXaRF2C+47vGmcLocchZSkiA6xINGLdHW\np2VNTjsCEt1OLdWdRqJD5NewOtx8UNJ4wTUVri2UAFJBQeGyIkkS+dXbAIgOTSQrdqb/nCiJHKl8\nGwBBMPPiMTmwfO7OeRi0AyKYp/51giarnalxDiaa5dL11KTrOFW3G4C48AwmJy0Z8bOdqv+I+s5i\nABZm3445LHkUOxyffH5hLj+5aRYAb5+u4/HNRzEZzExOWszqKZ/hgYU/ICv+HgoaIocEFiOl3aah\npVcO+HQqLyFakQWpVv94Qrtb5Te4vj6rHaNWxCtCiHbghfuzkK8WxXOmbUDctDKzg4Wp8kSazWfi\nKLH8Z5uFj4Z3iuNot2mDC2rmBE6o+agqmuZ+c/HpLVzYXFzgb8fl/uX+qUJrcy1sLzUTG+omL1n2\n+9xeFsMD0wespR7fdGQstqRwBVACSAUFhctKbfsZ2npko++8jJsDRhaWtRyjo0/+8PhzfgwSAjdP\nTubWKSn+a/ZXtvLHQ6VoVSJPLJLLZlHGBHqdndjdPahVWpbmrhtx6bqpq5wTNTsAyI6bQ278vEva\n53jk26un8ZXrZIukF4+U8fT7+yhvKeBQ+RbeLXyBypbNzE3uCmo6PRJePjHgw3hjbjvlHUa/pYsk\nwfvlZpweNXq1l+uz5eyjOsjb+VFVJDsrY/w/Z0XZuH+G3M5wvNHEe2UxQ29S+FhcXhWvnAzuDTk1\nvo+JMQMG4l5JYONpub90cmwfMxIubC5+qsVEh02DWiW/z7GhbkQEOuwavxCnuVcPgoBeLSu7K9p7\nqe7oueCaCtcOSgCpoKBw2RBFLwW+mdZJkbkkReX6zzk9No5Xy+MMu12pHKrVoFOr+M2d8wZd4+XR\njYcB+Pw8KxpVDwIqsuJmU2WRfX7yMtYSHjKywKHP2c1HJa8hIRFlTGBR9p3/tn2PF8LjddNqreah\nOX38/EYLz91cQnrYNvaXvUlJ8xE6bRcfXTdc3F6B061yL6uAxOwkKzdky8Fjr0uF3SOws0Iuoa7I\n7CRMH9wiptRi5B+FA4p8k87DlxbUoVFJNPfqeKkgmfFkFh7it8K59N/xWHCy2URBY3Dbqkfn1aMe\nZCB+oslEiUU2F793WvNFzMUFXjouZ/UHspDt7CgzkxHlYJIvMN1eZg7ot3zkDSULOR7QfPwlCgoK\nCqOjtOUYVrv8wTA3Y23AucKaD3F6bPK8691y3+E3Vk4lO2bgQ+zZXWc419JNRpSdvCS5N2pi4kK/\nl2R8eOYQQc7HIYpePip5FYe7F61az4rJD6JR60a9x/FCn7ObVmsNrT01tFlrae8bmJ8dZxy4zukR\nCNElMi15IpVNhfR6Lm3M3JazsUh+m51uDBqRUJ2Iza3CqBV5rzQGm1uNRiVy04Tgwoy2Pg2/OjBg\n1yObhdcTPcgs3D6OzMK1guh/Xlmp3B+AXd0A+NWiRKbG9mHQBva7hhu8rM1t41+l/fZYAq+fSuC/\nV1aSZHKxLKOTPVXRQdc81xZKa6+WuDC5zzE+zIXNrabboWbthHaKLWGUd4Ry99RWBCQkBHaWNWFz\nuTHqlF7WaxklgFRQULgsuL1OCms/BCArdlZAf2FnXzPFTXJmsbA5jTablrSoUL69epr/muKWbn72\n4SnUgsjXl8p+ceEhMdhdVhzuXjQqHUsnrAsoiQ+HgprttFqrAViSu27EwpvxgCh66ehr9AWMtbT1\n1NDn7A56bZg+irjwdMJDUvjue43sKHOgVql567MT6fXsvqTnkCTYVdk/g1rigRnNRIbImbfK9hBy\nY2x+AceanDZM+qGZLLtbxU/2ZOERB97nOye3MjmuD4B/FCbRYB0/ZuECIu5BCnFREtCqRNzi1S8I\ndtq1vHUu1t8WMJg7JrexvyaKbqcc1NV0hXCoNoJFad3cMbmVw3URF5j4I/BiQTJPLa/GI4JGJWch\nPyiP5p6pbaSEO6i3Gvig3MzcpG7yGyORgO9uK+S5O//z2krGE0oAqaCgcFk407APh1tWS88eNE9a\nnnf9LhIigmDid4dlccWvbs/DqJP/lySKEo9uPIzLK/LgzG5CNFZAICt2tt/zMS/zJkwG85DXvRg1\nltP+7OWUpKVkxEwfg51efRzuXlqttb7sYg2W3ga84lCfPpWgxhyWTFx4OnGmdGLD0/yztAH+9qCD\nZS+8T2mblcrmP2C8xMTs0XqTPzBaldXhDx53VUZxXXoXe6qi6HVp0Ks93DV5oITZr7j2ivDzvZn0\nugYyUbMSrdwy0eJfZ2CyzXhAYmiWUcItqtCoxIAg+Wqxs9LM4rRu0iIDp/7IE2oaeO5ghv/Y5rNx\n5CVbCdd7uXmChc1ng3tvlncYaerRkWiSZ2EnhTvpdGjpc6u5MdfCiwUpFDaZ+PZ1VeQ3RgACfzxU\nyq/vyPuPay0ZTygBpIKCwphjd/Vwun4vAJMTF2EyDJS3atvP0NRdDsiKWreo4voJidw1PdV/zYtH\ny9lX2UqyycGqLNlEOiduLucaDwKQGJHNxIQFI3omq93C/rI3AYgzpZOXcdPoN3gVESWRLlsLbb6A\nsdVaQ4+jPei1IVoTceHpxJrSiAtPxxyWHGChdD6xYQa2f3E1d7/4JiFjUD18/ZQszDCHuLh/uvw+\nVnSEYHfLgdL2shhA4kerK1EFsev57ZHUgOxiXKiTh+c2+Nd5/VTCpT/kFUNCq5J8AfXgQFIAJDyi\nChUi4lWWJoiSwD8KE/nO8ipU58Vu0+L7yDX3UtYu97R22HXsKDdzy0QLN+S0s6cqig57sG8dAn/J\nT+b7K6v8WcgbczrYWRHFLRMtbDkbR4ddx4G6SFLDHdRZQ3B6RDbkV/KpedmXf9MKo0IJIBUUFMac\nk3U78YgutGoD01NX+o97vG6OVr0LgM0TzwflOrRqFc/fNc+faWiy2vjW1gIEJL621AKIGHUROD02\nnJ4+NGodi3PvGVHp2uN1s/vcBtxeJwZtKMsnPYBKNT565pweO5aeOlqtNbT11NLWU4vb6xxynYCK\n6NBEYsPT/NnFMH3UiDM46dFhfGVRySU/d123jm6nBrUg8tiCOtQq6HOpeakgiaeWV3OgNpIuh5b1\nMxuIDR3IlvY/7sYzcZxsHsiO6tTyOkatSI9TNgu/FjJ2w0PCqPVic2sYmoUc+FkOHoNlKa8sVZ1G\n9lRFsSpraP/rl+bX8/XtExF9E4C2lcZwXUYn4Xovd09p5S8FKUPuAajuCqGuS09qpPy3mxbp4L0y\nM26viuuzO3jjdAIHayN5YkEtvz6UAcA33z2uBJDXMEoAqaCgMKZ029soaZZNv2ekrsCgHfDlO9Ow\nlz5nFyDwy33hgMCTyyYzMW5gZvGTb+XT7XBz+6QuIg1y31523GxO1e8BYF7GLQEZzeFwuOItn6pY\nYNnETxKqvzZnJEuShNVhkYNFX4axy9ZKMKWuXmP0ZxZjTWnEmFLQqvVDFx0h+eXvXfIaAH87Lqui\n75naQkaUXA59sSCZ6fF9GDRetpXGsDitkxWZQ2dcH6yNYHvZ4N5UiU/NaiTVbxaeQqd9/AgsTDoP\nPS4tA8Hh+RnIwfSfD3buyrH5TDxzknqINHgCjkcYvNyYY+E93/vj8Kh551wc62c1sSitmw8qzNR0\nBTPjF/hzQTJPr67E7RXQqiXW5LSzuyqKlZmdbC2Jxe5WU9we6vt9aWjtdXC8rp05qSNrVVG4MigB\npIKCwphyvPp9JEnOGk5OHDD37nV0UeQLAss6Uqjp0pEUHsJ3rx/oQ9x6po6NJ2uIC3Vy+2S5kT/D\nPINSX0CaGJnDhIT5I3qesuZjlLcWADA77XqSInMuZXtjisfrwtJbL4tdfBlGp8cW9NpIYxyxpnR/\nwBgREjNiAdHHIUkip5s/uuR1el0qqrtCmJlg5cZcubz+fpmZUy1h/HxNGUfqIgjTenhoTuMQj8mK\n9hCfJc8AyzM6WZwmf5nYcjaOc21hjBdCtf3BIwwNHoMjX3V1s5B2j5rXixJ4ZH79kHN3TWnlQG0U\nVqccQnxUHcWq7HaSTC7um97Ms/syCLbHBquByg4DWdHyF4rMKAfvl8WgEjpYmdnBttJY9lRFc8+U\nZl4+Kf8NfOGNQxT8162XbZ8Ko0cJIBUUFMaMVmstNe2nAZidfgMa9UCWKL96G17RjSAYeP6gnJX8\nxe1zMRnka3ocbr686SgCEl9dbEHAi0EThkd04vTY0Kr1LMlZN6KSbHtvI4d9k26SoyYyY1A5/Uoj\nSZJspeMTurT21NDR1+S30hmMRq0jNiyNuPA0YsPTiTWlotcYg6w6trx5+NkxWeefJxMwh7h5eK5s\nvVTbpWfTmXjmp3QTHeJmT1UU31lRNSR47LBp+OWBjIDgKTPKxidnyP2ThU0m3isdP2bhWpWHPvf5\nH7Mf9/crW9moBfGqz/M+2hDOkpZQpsX3BRyXJ9TU8xtfqVmUBN48ncBXF9UyMcbGrMQeCpvCg6wo\n90L+bE2FPwu5OrudvdWRrM7uYEe5GZtbjdOr9ouKChs7ae91YA4bP0r7/xSUAFJBQWFMkCSJAt/I\nwihjAtlxc/znmrsrqbYUAfBucSI2t5rl2fHcNyvDf833txdS321jdVYXCWFyWTMrbhZnG/cDMC/z\nVsIMw1fcOj129hS/glf0EKqP5LoJnxjzjN3F8IqeASsdq2ylY3NZg15rMpiJM8nBYpwpjcjQhBFP\n1rlU7K5ebN6h5eSR4hXheGME37iumlCdF49X4IXDaXglgbW5FgoaTHxlYZ1/xnU/Do/ATz/KwuUd\nOBGm8/Cl+fVo1RKtvVr+UpB81TNzw8eLWxzNR6ycf/RKqmsgiBR45WQiT68uR3tey/D0hD6yo/uo\n6JC/DBY1h3GuLZTJsX3cO7WFU80mvNLQ96qlz0CpxciEGDnTnmu282G5mRWZXSxK7WZfTRQfVkSz\nPKPDP3Xo8c1HeO3Tyy/vVhVGzIj+uouKinj88cfZt0+2wbBarXznO9/h8OHDhIeH89hjj7Fu3ToA\nXC4XP/zhD9m5cydarZb169fz6KOP+tf61a9+xcaNGxFFkTvuuIOnnnpKkesrKIxj6jvO0eLzV5yb\nsdYfAImSlyMV7wDgEqPYcjYEtUoIEM4cqWnjhf3FRIW4uX+GbCadEj3JX3pOjppAbnzesJ9FkiQO\nlL5Jj6MdlaBm5aQHA3oxLwVJkvz9mLXtZ0iNnoIgCNhdPbLnoi+7aOlpQJQ8Q+5XCRpiTCl+oUuc\nKY0QXfAJIFeS14/+ZEzW2VFu5q4prWT75lxvOhtHu13H1LheksNlAUW4QbbzGWzX8+zeDLocAxlr\nAYkv5tVjNrpxeQV+dzQVu3t8CJ9kxbWAe2hyeVj3Dg4i+821rxatfXreLYnjrimt/vern8fm1/H1\n7RN9zyfwxql4vr+ykgSTi+WZHYM8QAP5S0Eyz95Y5s9Crsjq5GBtJGtzLeyviaTDriMlwkn/72Jz\nUR0er4gm2HxLhavGsAPIjRs38swzz6DRDNzyve99j9DQUA4dOsS5c+f4whe+wIQJE5gxYwbPPfcc\nzc3N7Nq1C4vFwkMPPURGRgZr165lw4YN7N27l3ffldWYX/ziF3nppZd4+OGHx36HCgoKlx1RGhhZ\nmBCRRXLURP+50uZjPgEL/N/BaCQEvrx0ItMSowBwe0UeefMwkiTx2IJWVIIHnToEUfTi8tjRqg0s\nzrlnRF8wzzTso7bjLADzs24lxpT6MXcMn6K6XZzweVHuOvcyOrUBlUqDwx18JrBRF+7rW0wnLjyN\n6NCki1rpXA3q20vHbK3qLgNfmi9b7TRZdewol7NIa3MtWPo0pEYMKMj77Xr+dCyZmu7AEv0dk1uZ\n6iudvlyYRF13MGHGtYhEmM5LryuY4vrC95xv69P/s3QNiGq2l5lZkNpFks/HsZ/IEC9rciy8Xy4L\namq7QzhUF8GStG5un9TGobrIoEF/u03H6RYj0+LlLOTk2D52V0axJL2Tmb7y984KM5NjejlnMeGV\nJJ7dfYbvXP/v4dv678Kwwvk//OEPbNiwgS996Uv+YzabjZ07d/LEE0+g1WqZMWMGt912G2+99RYA\nW7du5dFHHyU0NJT09HTWr1/Pli1bAHjnnXf4zGc+g9lsxmw288gjj7B58+bLsD0FBYUrQXlLgU8t\nDHkZN/mDPafbxomaHQDUWxM522Yg3mTgB2tm+u/99Z6znGqSy1dZUXIJNSN2Oo1dZYAcAI5ENd3c\nXUlBtRzMZsXOYmLCwkvfoI+SpiP+4LEfl9cxKHgUiA5NZnLiYpZP/CTr8r7NvfOeYsWkB5mavJRY\nU9o1FzwCfHjupTFZp7TNwKdnyV8Warv0bPIZS6dF2JkU00ds6NCM7DvnYshvDGxNmJHQw22TZLPw\nPVVRHKwdL2bhElEh7mEFj3dPkUViSSYHgcprgtzX//PVmZvtEVVsGDSHfDD3TG0lTDfwvm45E4/T\nI2DSe7llQtsF1/zrcdnux+WV93ZdRhdH6yO4KVd+3+utBhand9G/52d3nR6LrSiMIcMKINetW8db\nb73FtGkDY8aqq6vRarUkJw+o5TIzM6msrMRqtWKxWMjOzh5yDqCyspKcnJyAc9XV1Ze6FwUFhauA\nx+vyjyzMiJkRkO07UfsBTo8NAS3PHZDLtP9z6xwiQmSz4XKLlad3FBGu9/DZOXIAmhiRRVWb3C+Z\nEjWJnLi5w34Wm6uHj4pfRUIkIiSORTl3jVlrTLXlFIcq3go4plUbziuNS7i9DqLDkkg3TyPMEHnN\nt+bsOf36mK2l10qE6rz0uVQ09Og54RNS3D21GUFgiGjmWIOJd0riAo7FGF18fq6s/K3qNPBq0fgx\nC482uOn0G2mfHxQOsCKzg5snyIHSk4triTC4L3p9/2pX09anxBLKgZqhgbwsqGnw/9zp0PrHU16f\n3YHZ6BpyD0CXQ0thUxg6tbzn6fG9nGwykRVtJydazkweqI0iLlS+v8fp4b2zQxXhClePYQWQMTFD\nVW92ux29PtBzzGAw4HA4sNvt/p8Hn+s/brfbh5wTRRGXK/gfmoKCwrXL2cYD2FxWVIKaOek3+o93\n9DVR4pt3vbsqgS6HlsUZsayfkwXIvYRfevMIDo+Xh+a2olG50ah0SAi4vQ50agOLc+4edgAmSl72\nlryK3d2DRqVj5eT1Y+KLCNDUVcHektc4/wP+gYU/4P4F3+fWmY+TGj0ZgB5HOwfKNrK54JeUNB3B\nKw7Nul0reL0eqrtOjMladreKdJ9J9MuFiZxrla120sJtTIuzDQkeazr1/PlYKoODIq1K5PEFdYTq\nRHpdan5/ZPyYhYfp3HQ4Bk9hCZ6BnJlg5YEZTf7fh9no5slFteg13qDXD6wmoBKuTgaynzdOx9Pr\nGvp+zEzsJStqQKm9vSyGbocGrVrinilD52r387fjyUjSQBZyUVo3BQ3hrPVlIYvbwliTMzDi8vHN\nR8dqKwpjwKj/ZYaEhOB0Bk5DcDgcGI1Gf3A4+LzD4SA0VP6m3h9oDj6nVqvR6S5x8KqCgsIVxeHu\n8wtKJiYsIDxEzjxIksSRineQkPBIYbxaFIZKEHj+rvmofPPR/n6skl3lzcxOtDI9Xp54kW6eRnN3\nBQALsu/AqA9mBRKcEzUf0NwtVzkW595DpDHuY+4YHu29Dew69w9Eyes/NivtegB/cBtjSmX1lM9w\n+6wnSDdPBwR6nZ0cqtjCpvxfcK7xAB7v0NnUV5tXDo2NcAYgRCsrRt4vMxNrdHG4LgKVIPHU8uoh\nwWOXXcNvD6eep9KVWD+ribRIB6IEfzqWQnvQsXjXHmq8AfO6Zc6fNiNbEj0yr57ztSBpkQ4em1+H\n+qIBooQoCQhXqYwN0OvS8Obp4BnhxxfU+5/N4VHz1jm5L3JBqpXMqODepj0uDccawv1ZyFmJPeQ3\nhDMjoYeEMDl+KLGEEaKR/+3VdPZR2d4zpntSGD2jDiDT09PxeDw0Nzf7j1VVVZGdnU1ERARms9lf\nsh58DiA7O5uqqir/ucrKyoByt4KCwvjgZN0u3F4nWrWemWmr/Mdr2k/RYpX/jb+Yb8YjqnhkUS6z\nU+QJMq09dr6xNR+j1svn8+QMRawpjdqOMwCkRk8mK3bWsJ+jrv2sP5CdlLiIrNiZF79hmFjtFj44\n89eA0YGTExczM3V10Oujw5JYOflB7pzzJFmxsxAQsLm6OVK5lU35z3K6fi9u77VRabHaLYg4Pv7C\nYSD5YpqKjhC2nInDI6nwSiq+s7wCnSYw4HF6BP6Sn0S7IzA7vCyjk6Xpcg/s2+diOdM6XszCRf+X\nouDImci4UCdPLKpFrwkeAE6L7+MzsxuCnpORS9wDopqrE0geqImk1DLUkzQyxMP12QMz2ffXRNFg\nld/j+6a3cKHn/ceJxIAs5LwUK6dawrjRl4XMbwhn1aB1H3790FhtReESGXUAGRoayqpVq/jVr36F\nw+GgqKiId999l9tvvx2A22+/nRdeeIHu7m6qq6vZsGEDd955p//ciy++SEtLCxaLhT/96U/+cwoK\nCuODHke7v0Q9LXkZBq38ge/xujhW9S8ALLYYjtaHYjbqefqmgYDwa2/n02FzsX5WKwaNC5WgQRBU\nuL1O9Boji0ZQuu5xdLCv9A0AYsJSmZd5y5jsz+aysuP0SwHq6qzYWczPuvVjny3SGM+yifdz19z/\nIiduLgIq7O4e8qu3sfHYMxTV7cblGZvgbbRsLvjlmK0lCNDnUvHHYylMT7SytzqKB2Y0kBklB979\nAaYowfOHUmnoCTSFTo+084DPLLyoOYx/lcQyPpAI04m4xf4Z1sEQCNN5eHJxLeF67wWukVmS3s2d\nky9U8g02/vDKB5ESAi8XJuIJYlF077QWQn2CGlESeOO0LKLKNduYmxQ8c2j3aDhQG+nPQs5JsnK4\nLoKFKd1E6N1ICNhcan/5fl9FC33Oay+b/5/IJTWX/PjHP8btdrN8+XKefPJJvvWtbzF9uiyzf/LJ\nJ8nIyOCmm25i/fr13HfffaxZswaABx54gNWrV7Nu3TpuvfVW8vLy+OxnP3vJm1FQULhyHK/egSh5\nCdGZmJJ8nf/4qfqP6HN2Ayp+czASEPjpLbOJNsrZiO3FDbx6opqpcb0sSOkAIM08mVafh+SC7Nsx\nDtMX0SO62XPuFVxeB3qNkRWTHhwTlbPTY+eD0y/R6+zwH0uJmsTS3HtHZEYeHhLD0gn3cnfe15mQ\nMB+VoMbp6eN4zftszH+GwtoPLzi68HJS2nRszNd86Xgy7TYtKeFOZiVaWZU1YEreH2//9XgSCSYX\nVudAuTdU5+Gx+XVo1RJtfVr+kj9ezMIlzMaPV1zr1CJfXVRLfNjwMs+3TrSwLKMjyJmPU2pfORp7\nDLxfNlQbcb6g5nRLGGda5Na1e6a2oBaCG2P+82SCPwupEmBWYi9lHUauz5Z/Dwdqo5ibKI+ylIBv\nvnt8jHekMBoESZKublfuCHE6nZw+fZpp06YNEfEoKAwXQRAYZ3/61xSWnnrePfkCAItz7vbPp+5x\ndPDW8V/jFT3kNyby+yPRzEs1c/CJm1CpBPqcbmb8civNViv/s6YKk95JVGgivY4O3F4n6eaprJi0\nftjZx4PlWyhtPgIIXD/1s6QM8p8cLR6vmw/OvOQvwQPEhaezZurDaNQDPXmj+Rvqc3Zxqv4jSpuP\n+U3GtWo9kxMXMyV56ZiZnV8MSZL4+4GnxnTNHeVmXj+VQFq4jVC9h/9aUjek73FbiZkSSyg1XQb/\nbGgBia8uqmV6Qi9ur8DPPsqkdlz4PUrEhbpo7dNzcbseiS8vqGP2BbJvn7vuGf6671tDjntF+L/D\naZxqMX3s+lcrkNSpRZ5eXU5saGA2UJLgJ3syqe6Sy9wp4Q5+sKoClQCvFiXwYUVwc/H1MxtZkdmJ\nIMiZ6r8cS2b9rCa+/v4EnB41N+W28V5ZDCCgU6uwPfPANe9wcLm52vHQ+JC3KSgoXDNIkkS+b2Rh\nREgsOfEDNjv5Vdvwih5EycDfCmTvxufvHhDO/PD9Iqo7+lg3tRWT3omACrWg8ZWuQ1mYPXzbnYrW\n477gEWamrhqT4FGUvHxU8mpA8BhlTGD1lM8GBI+jJVQfycLsO1g375tMTb4OjUqL2+ukqH43G489\nw7Gqbdhcl08k4BU9bD723JiuWdNpYONpWbA0P6WLJxcPDR4Lm8LYVmpmdpLVHzwC3DapjekJcovA\nhpOJ4yR4hChDf/AIF7PfeXBm8wWDx4uhVsGj8+tJj7RfdH24eqIal1fFR02X3wAAIABJREFUKycT\nhxwXBPjywjr/c9VbDX77n9sntWHUBi/jv34qAYmBLOSU+D7qrQaWZ8gCu301UaRH2nyvLfLi4bLL\nsCuFkaAEkAoKCiOiobPUr3aWRxbKkyaausqpaZfNfl8/FYvdo+ah+TnMT5NLXcfr2/nN3nPkmvtY\nmSWXplKiJmDprQNgYfYdhOiGJ5zo7GvmYLk8mCApMpeZacFFLSNBkiQOlW+hzjfBBsBkiOaGaQ+h\n14xtYGPUhTMv8xbWzfsW01NWoFXr8YguzjTsZVP+MxypeMfXBjA2dPY1c7RyK28c/Rk9rtYxW9cr\nwm+PpuCVVITrnCzL7B4y47q+W8/vj6byqVnNbDoT7z8+Lb6H2ybJRtN7qyPZXxM1Zs91OdGrPHQG\nCICCZwFvym1jVVawUvTwMGhEvrqohhijK+j6/a8tXTTAvLycajGR3zDUKSEqxMOqrAHhy1vn4nB6\nBEJ1Xm6dGNxc3C2q+LA8Gq1K3sui1C72VUdyQ3Y7akGi16VhRnwf/Xv9zrbCsd+QwohQAkgFBYVh\nI0oiBdXvAXJZNzV6iu+4lyOVWwGwOiPYWWEiMkTHz26ZDYDHK/LFNw6jFrx8MU8WS4QbYmjyBaIZ\nMdPJjJ0xrGdweRzsLt6AV3Rj1IWzbOJ9/rnbl8Lxmvcpa8n3/2zQhLFm2sMYdcO3EhopBm0YczPW\nsi7vW8xMXY1ObcArejjXdJBN+c9ysHwLPY7RBSFOj53ipsNsLXyBt0/8hrONB8a83/L1U/G02+Rg\n6nsrqgnVBfa4WR1qfrE/g9mJVkQJbG65P9VsdPHFvAZUAlR3GoJmsq5NRLxDPjaHBncLUrq4e+ql\nB+oRBi//b3ENodoLeYkOHnt4dZTZrxYlYA+iablveov/ubscWrb7eiZXZ3cQGxq8H3TT2XgkwO0V\nUKsg22yn26lhfor8ZepwfSQRevnF2m1OjtZceNKNwuVHCSAVFBSGTWXrCf9c67yMm/3l5pKmI3TZ\nZPXoC4fledc/XjuL2DBZbfv8vmJONHRwx+RWoo3y6DatRs66GbShLMy+Y1ivL0kSB8s3YbVbEAQV\nKyY96Fd/XwpnGvb7bYAAtCo9a6Y/jMkQvF9rrNFrjcxOv4F1877NnPQb0WuMiJKX0uYjbC74JftL\n38Rqt3zsOpIk0thVzt6S13jj6E85XPEW7b3y9I4Q7fCEScPF5YGdlfLv51Mz6zGfN6bQ5YVn9mXi\nFQXWz2zyj8LTqEQem1/nm1ij5vdHx4tZuESoTrzIs8rB26SYXj43p5GLOvuMgASTiycW1aK5gADl\naiuzuxxatpwd6g2pVsHnBglqtpfF0GXXoFFd2FzcI6rYVhKD2peFXJrexe6qKL+xeFufjoWpA5n5\nzyuWPleV8fCvVkFB4RrAI7o5USvPtU43TyUuPB2QzcT7512fa4ulosPIrKQoHlmcC0BVew8/eL+Q\n9Eg7a3PlslZiZDbtvfKHy8LsO4cdBJ5rPEC15RQA8zJu9j/DpVDReoJjVe/6f1YJGq6f9jmiQ698\nVkynMTAjdSXr5n2LvIybMWjDkCSR8tYCthT8ir0lr/lnjg+m19FJYe2HbMx/lh2n/0JlWyFe0YNK\n0JAVO4s10z6P3T22vZV/PZ4MCCxM6WB5ZmC5XZLgfw+m0dyr5/Nz6yloDMfhlVsdHpzZREaUzyw8\nPxmLbTyYhUvEGN30+RXXQ8+DQHK4g8cXyIrysSTHbOeL8+ov8tr9CFwNUc2uymiqOw1Djs9K7CU9\nor9vUcWWc3Kv7LwUK9nRwbPhbxfHIUpyFlKjkkiLcCIhtzyAbCyuUcl9lGdaumntsV+GHSkMByWA\nVFBQGBbFjQfpc3YjoGJO+lr/8eM1O3B5HUho+Eu+3Mf2/N3zUatUSJLEY5uO4nS7+UKePL4tTB9F\nq7UGgMyYmWTETB/W67daazjmE+9kxExnctKSS95TfUcJ+8veHHREYNXk9cSHZ1zy2peCVq1nWsoy\n1uV9i/lZt2HUhSMhUdlWyFvHn2NP8Su09dRR2VbI+6f/wsb8Zyms/ZA+p2ydYw5LYWH2ndy34Dss\nm3g/zV1VH/OKI8MrQn5jBKnhNj6f1zRENLOhMIFii4mc6D5mJPby2ik5Q7U0vZNlGfIzvlscy+mW\nsc2KXh4k4kOdvkD3QqpngSiDmycX1WDUXShTeGnMTe7hkzOag5w5/3mujjfkPwqTEM97aUGALy8a\nENQcqImkrrvfXLyZYM8qSgLvnIv1T+VZltHJrspobvJlIau7QshLtvqvf/TNI5dhRwrD4dIN0xQU\nFP7tcbptFNXtBmBCwjwijLLRc3tvA6XN8nzaf5XE0eXQsn5uFksy5UzDqyeq2VHSyG0TLSSa5EyB\nVm2g19mJQRvGguzbh/X6Dncve4r/iSSJhIfEsDjnnku28Gi11rK7eAOSNPCBv2zCfaRET7qkdccS\njVrLlKQlTExYQFlLPqfq99Dn7KLacsqfie1HrzGSHTebnPi8gOypKIkU1e8c0+faVhqDQePl+yur\nhgSPe6oi2VNtRkDisQW17KqIxuVVkxZh58GZTQCcagnjneLxYRYebXDR0tefXQv+N2fQePnq4hqi\njZd37vn12R2027TsKB/qwTjA4L7IK0dNVwi7KqP93o39RId4WJHZwe4qMxICb55O4GtLasiOtpOX\nbCW/IWLIWttKY7ltUhter4BOLZEQ5iRC7yE90k5NVwi9Tg2CT0C09Ww9bq+I9vz5kAqXHeU3rqCg\n8LEU1e/G5XWgUemY6ZsDLUmSTzgjYfeEsrU4ApNeyzO3zgGgvc/J194+RrLJwe2T5exBXHgGnTY5\niFicc9ewfA9FSWRvyevYXN2oVVpWTlqPTjO0XDYSumwtfHj2r3jFge7/BVm3kxU3/PGJVxK314ko\netCqgnu9RYcmsXLyeuZn3Tak9P76gWfH/Hm2l0Xz8xvKhsx0bujW+nsdl2V0YtKLvHkmHqPWy2ML\n6tCpJdptWv48TszCDWoPHUMU14GokHh8QR2pEc4h5y4H905rYV7yxyn0r46oZsvZODrt6iHH75/R\n7LfvOdMaxqlmuWVl3dQWNKqhGVsJgU1n4v29kCsyO9ldFeXPQp5uNTHB3AeAKEn89IOiy7IfhYuj\nBJAKCgoXpdfRybnGgwBMTb7OPyWmylLknx7zp6PyvOsf3jiDhHDZ8uYbWwuw9Dp4aG4TKkEiRGvy\n9z1mxc4mzTx1WK9/8v+z997xcZTn+vd3ZntR773LvTdcsQFjAiFACKRACiGFdAjJL4WclJND4CSk\nkJw3hCQQCAFCDSRUY9y7LVuWLVm9d+2uyvY28/4xq5VkFUtGMsbe7x/+eEc7M8+zba65n/u+7uZ3\nae9TPN/WFN5EnGl0wv5UcHr72HrqcXyBodypxdlXMSd9zXs67nQjyRKttip2nH6a5w//gsMNr9Hn\nVooPzLp4chIWhIt8bM523jr5Z94++dewxZLLZ+fdiifx0jfuOc6Fsk4T313XhFk38sLf71HxP7sK\nkRFQixIfm9fFWzUJBCWBLy5vJcnkxx8U+OOhrFAu4YWNgIRfOlMMjRa9dyxrY26yc8rHP9c+BqIA\ndy5rCwuoiXIyx//7zOAJqHi2bHTusPqMgpoXTqUgyZBk8o9rdfROXQIBCQIS6NQysfoA2THucAV3\nlD7I4Nx+s+v09E8mwlm58L/FESJEeF853qS0LNRrzMzP2ACAP+jjaKjfdVNfHGVdUcxLjeFr65Tl\n33erO3jySB1XF1rJjVOS5XUaA26/HYM2ilX510/q3K29VZxo2Q5AcepKCpKXvqe5ePxOtp56DJdv\nKIIzO201i7Leu4/kdDHgtlDbVUJtdwku31Cul1rUkJu4kKKU5SRH5yIIApIs0WQ5RVnLdnpdnXT0\n19JxsjZkLz0zwsHlE1mYOjLa5vSJ/GZvDr6gEpPYXGhFp5b4V3ky182ysDBkFv5MWSqNfR8Es3AZ\ng0YK2w6Nx0fndrEme+p+nf4g/LNMuRGSZUalAZwNjUrm65c188DuPDrsY0Xjh1dmn98oZEl7NGWd\n5vB7PsiSNDvZMW6a+w202fXsaYzj8rxePjyrh33NsWPcVAg8V5bKpxYpeZ+b8nv5d2USVxdaefpE\nGsfbo0kxeely6nH6Arx6qpkb5mefp1lGgEgEMkKECBNgdbRT16MY9i7OvhKNWlnOO9m6A5dvABmR\nR48oUbCHb1qJRiXi9gf4youHSDZ5uXme4tOWYM4MVw+vKfwoOo3xrOd2ePrYU/UcIJNgymDlJEXn\nePiDPrZVPEG/e8g7Li9pEavyr3/fW6L5gz5qu0p4s+xRXi55iLLQ6wuQHJXDmsKb+fjKH7Gu+BZS\nYvLC4xUFkZzEecxJW4NOPZQOMFPiccAjsCprZDV3QIK/laTTaleEoUaU2FJo5T+VScxJdnHDHOV9\n39sUy+7GD4JZuEy8wX9W8bgxz8a1xWe3VhpOUFJaOn7l33NxB5To5olOwzlFI01aibvXNBOjH8OE\ncQTn+7Mt8PSJNLxnpIMKAnzjsmYGBe0rp5PwBERMWonrxzEX39kYjz+ofMYMGgmjJsi8ZDtmbYCg\nLJAb5wk/95svT39/9wgTE4lARogQYVwU03CZaH0ixSmD/a6tnGrdA8CuhkS6HDpuXZzDpkIlovLz\nrWXUWwf47vp21KKETm2kz6lEEQqSl5IVP+es5w1KAXZWPo034EKr0rNxzm2oRc1Z95voeDtO/wOL\nvSW8LSNuFuuKbkGYBhPyc0GWZXrszdR0HaXRUoY/OBTV02vMFCYvozBlGbHG5FH7SrJEm62K0uZt\nWJ1to/4+U0Tp5BHRMlmGRw5lUto5VAixKd+GQSOxvzmG/9rUgChAc5+ef5Sm8X71bZ4KSQYfPe6J\ne1wvSrXzqYWjq8/HQ5LhSGsUfzmawbIMB/99VS3pUT4OAAlGiZOdehakeqYciUw0+rl7dTMP7M7F\nFzxzuf3965NtcWn5T1UyHzvDTD3eGGBjro2djQkMeDW8WZ3ATXN72JRvY3t9/LD2kIMoYvQzS5S8\n6SvybbxZncgV+Tb+XZlMaWcUJk0Ap19Na7+LWssAhYkzZ/wfYSQRARkhQoQxae+rCeceLs3dgigq\nF6gj9a8jyQH8QR0vnIrHpFXzq+uVfthl7b08tLOCDbm9zEoMLV2rjXgDLoza6ElHEY80vB5ucbi+\n+Fai9PHnPA9ZlthX8yLtfdXhbclROWyafRsq8fz/BLp8duq7j1HTdXRENFQQRLLiZlOUspyMuFnh\n13sQWZboHmiirHUnbb3VnO+lybGWWp8/lTLCikctSmwpsvJKRSJ3rWwjShfE5RP54+Es/B8As/AY\n3aB4hLHtcQTy4lx8eUXLqAKisZBlqLbo+e2+HOanuvjJFQ2jim2yYryIgo6Kbj1zk6cuIrNjFe/J\nh/fnII0Y83j2PudHVG6tSWB1Vh8Z0SO7znxyUSeHWmNxB1RsrU3k8rxe4g0BPja/iz8eGr0Evb8l\njk8u7EQUFCN3tSizMrOft2oS8QZUzEvr51iHcgPzuWf2sfebHzov84sQEZARIkQYA1mWONqgtCxM\nisomJ2E+AO29NTSHekU/VZqIJ6DigesWkBlrIihJfPmFA0TrfHx8gVLsEWdMDXeuWVN486R6Stf3\nnKCyQ+kwsSBzI1kJc9/DPGQON7xOfc9Q39w4YypXzvssatX5M7CWpCCtvZXUdB2l1VaFzFABSowh\nmaKU5RQkL8GgHemLKMsyFkcrle0HaLCcQJKD523MZ3KmsNnTGMPW2gSG28asz+nDpAmSYAyQH68U\nKf2lJJMe54VvFq5VBen3TjROgSST0hVGp55YvMsydAyo+d+9eeTFefn+5U0jllu7HRpiQ0vPkgwZ\n0V4EdJzu0TEnyTtlETk/xclnl7aHzN3HH/9QZfbMi8igLPL30nR+sKFxxHa1CJ9b2sYjh7MVc/GK\nFO5c1saydDtFCU5qrGc6Mwg8eTydLyxvQ4XSCvHdunjW5fSyvT6BOpsREQkJkYNNFpxePybdua9W\nRJg8EQEZIUKEUTT0lGFztgOwPPdDSsGGNNTvutsZxf7mGIqTorl7g7Ik/cd9VRxutvCt1e3o1BIa\nlT4cYStKWU5m/KyznrfP1c3+mpcASI3JZ0nO5vc0j5OtOzndvi/82KSNY/P8z6NTnz0Hczroc3VT\n03WUuu5jePxDRQUalY68pEUUpSwn0Zw1IgdTlmVsznZqu45R03WEgDR23+D3k8ZeHX8vVTrRKAio\nBJlrii0cbo1mU34vAK9VJlLWeeGbhQuc2aJwtMgyaQLcs6aJaN34Il6Wod8t8Ov9ucToJb65upWC\n+KFqf4tTQ4zeT7J5KG9RFBQRmR7tRRC0VFu1FCf4piwi1+X00evW8Mrp0SkPI+d0/kRkrdXE7sbY\nsHn8IMvS7WRGu2kdMHCgOYbNBVayYz3cOr+LX+zKG2XxdLQ9hk8H2lEJMlG6IJKsmNLvqI+n36th\nVqKDKosZGfjWK0f468cvLEeFi5WIgIwQIcIIglKAY01vA5AVP4eUmDwAKjsO0O9Wcpr+fCQRGYGH\nb1qBVq2iudfJfW+UcllWf7j6Uqc24vDaMGpjWJH34bOe1x/0suP0PwhIPgzaKDbM+iSiMNpTbrJU\ndx4Oz0MZj4ktC76AUTuzOVK+gIdGSxk1XUfpsTeP+FtqTD5FKcvJSZg/KgLa6+ykvruUqq7D+AJj\nt3m7ELC5VDy0Nw9JVi7yGlHCL4mszu4jSuNnRaZS/FPebZpAzFxIyOjUMp7AcAE5UsCoBYlvrWkm\nxTy+mHf54E9HsvEHRW5f3BVO4QDodauJ0gZINI1d8DIoItOifAiCljqbhoJ4/5RF5Idn9WB1adjT\nNFax0vtTmf1ieQpL0vqJ0g2dUxDgW6ub+e7bxcgIPHcyhe+ubyI/3s2KzAEOt55pLi7wWEkmX1nV\nihaZKwt62dMUy/KMAY60xdDn1jAoiv9RUs+fb1mNOF3NyCOMS0RARogQYQSVHQdweHsREFiWq7Qs\ndPscHG/eBsDRtgQaeo3cuCCLq2elI8syX3/5ECrBw6dCrdai9YkMeJQK1bVFN5/V+FuWZQ7U/ot+\ndzcCIpfP+mTYb/JcaLKWs7/2X+HHalHHlvl3Em1IOOdjToQsy3QNNIQKYk6OMCg3amMoTFlGYfKy\nUefvd/VQ31NKTdeREZY9Fyoun8CzZanhCmKdKoA3qHQFua64B29QRbQmiNWl4c9HMj8AZuEy0doA\nA76Jljxl7lo5MpI4HH8QnjmRSuuAgRvndjNvmCdkv0eFWRskzjCyJHmsfNJBEZlq9tGNhnqbhvwp\nikhBgE8vbqfPo+ZkVxQTtV48Xzh9ap4/lc6dy0YWe8UbA1ye28uuxngqLWZOdJpZlOrg5rldHGuP\nOiMiDGVdUXgCSn/sGH0At19kY56NI20xdDl1pEW56bAb8Adl/rS/iq+uu3A6Sl2sRARkhAgRwngD\n7nDLwsKU5cQaUwA41vQ2/qCHoKTm6RMJ6NUqfv2R5QC8WNbM6xVt3LWiA5M2iFrUYPcq5sDFqSvJ\niCs+63mrOg+F8xSX5W4hNSb/nOfQ2V/PrspnGIyyiIKKzfPuIN6cfs7HHA+nt5/a7hJqu0qwe6zh\n7aKgIjthHkUpy0mLLUQcVuk94LbS0HOCmu4SHMP2udAJSvDo0UxOdQ1FcHVq8AZhZWYfCUY/KhEC\nksAjhzNxfADMwmN1fvq8E/W4htsWdbIk3T5qe1BSOvKUtMdw45wePrt0qE+13avCqAkSox+53D0o\nHMcThYMiMtnsRxA0NPaqyYufWntElQh3rWzll3tyaTqr5+b5Wcre3xzD2uxeZieNjKx/alEHh1pj\n8ARUvHAqlfnJtSSa/GwusPJmzZmtLgUePZwVzkG9Mt/GkbZoZic6qLSY0amGluZ//NaJiIA8D1z4\n3/AIESKcN0617sIbcKESNSzJVvIPLY5WarqOAvByRSIDXg0/u2Y+ufFmel1evvWvwyxJGwgvXeo0\nJpzePky6WFbkXnfWc1rsLRwO5VZmx89lXsis/FywOtp5t+LJYcUmApvmfJqUmNxzPuZYDHZ7efHI\ngyM8F+NMaRSnLCcvafGINo0OT29oWbsknAbwQeMvRzMY8AxdMooTHFRblZZ0H1/QFa5KfrYslYbe\n85Nj+l4wagbFI4wnoq4psozqlCLJcLAliq01iXxkjoUfb2oI/83hEzGoJaJ0UxOOwxkUkUkmP6Kg\noblXRXbc1Iqn9GqJb61u5he78rC4ziwMOlM0ng8RKfBUaTo/u7IW9bDAolqEzy1p509Hsuiw69jd\nGMem/F6unWVhT1PcqJuQSosJl19EowoSbwzQ79FwVaGVSouZxj4jMTof/V4tvW4f+xu6WZP3QUih\n+OBy4fsqRIgQ4bzg9PZT0b4XgHnp6zDqopUq5jql33W/x8g7tXHkJ5j5zkalDeH3Xz+G3ePk04sV\nnzazLg6nV0mYX1v0sbDx+Hh4/E52VD6NJAeJ0iewtviWczb1tnusvFP++Ag/xfXFt5IVP72RiJqu\no7x98q+AYtitVRuYnbaa6xd/gxuWfIs56WvRa0w4vf2Ut+3l1eMP8+LR/+Vo45sfWPH4r4okSjui\naO5XIlpmrXLxBvjIrO5wYcn+5hh2Nlz4ZuEqIYjLP3Gl7qrMPm6e1xV+LMtQ2a3nv7fnoRYFfnpl\nA0tDkUmXX0SSwayVRtj7DBqET/UjPSgiE4x+zDqB1r6pX6pj9ErRj1F9ZgRzLKufmc+J7HToeLM6\ncdT25RkDpEcpFeqvnk7G7RcxaiQ+Mnssc3GlFaYvqIx7U74Nm1NDZrSy//Ac0y88t3/6JxFhBBEB\nGSFCBEBpWRiUAujURuZnXg5AfU8p3fYmAB4vSSQoi/zmhuXoNSp213Xx14O1fHxBJzH6AKKgxhlq\nETgr9TLSYwsnPJ8sS+ypfh6ntw9RULNx9m2TsvkZC7fPztZTj42odF6Zfz0FyUvO6XhjIcsyZS07\n2FfzYtiG5/JZn+TWlT/ksoIbSDBn4PbZOd1+gNdK/8ALRx7gSMNr9Do7pm0M7weHWqN4rSoJoybI\nYAHGxjwrXU4dcXo/H57dgyBAS7+Op0rTufDNwmUkeeJL36xEJ3csbWewDsPiFLh/Vw69Hh0/vqKB\nlaFouycgIstg1EgMr9k4V+E4nEERGW8MYNKJdAxM/XKdGuXjm2uaUQnSBM86f+/X61VJdDlGRhWV\ngpomQMbuU/NGSGRuzLORYvaOOkZ9rxGnT8nBTTL56XFruapASQWptxnRisrNTFWPnS772HmrEaaH\niICMECECvc5O6rqPAbAo6wq0aj3+gJejjW8AUNkTw6nuKK6dk8H187Lw+IPc9cJB5iU7WJejRBz1\nGiOyLGHWxbE87+xmvmUtO2jrrQIICbBzy1H0BTy8U/44ds/QUuOirCuZm772nI43FpIscaj+3+Gq\n7rYBJQ/w2/+x8U5VM5Xth3jjxCM8d/h+DtW/isVx/rrDzCQNNj2Pl2QC0O9VInZFCS5KO2JQCRL3\nrmtEJSoROCUydKFfUmQ0ojxhcU96lIevr2pGo1JU4H8q4zndE8MPNjSxOrsfUQBvQECWlaXisUTi\ndHXGHBSRcYYABq1Il33qBy5KcPOlFa2MH2U8fx1r/JLI0ydGe1UmmgKsz1Gsn96pTcDq0qAS4Zb5\nXaOeCwJ/OJiFx6+MeWNeLwEJ4g0+ZATSo4dE553/jEQhZ5IL/dseIUKE80BJ41vIyETp45mVdhkA\nZa07cPvsSLLIk8eT0KpEfnujUjjz4LunaOrt5bNLFK9IozY6XEW8tuhjaFQTL12399WEq7oLk5dR\nnLrinMYdkPxsP/13bMOifLPTLmNx9lXndLyxCEoBdlc9GzY3r+iOpcehRDmuzttKR89fOFj/r3Ck\n9mLB5RP47YEcApKIKCgiQ0BmfW4vrQN6Pr6gi7QoxdbmsZKMMdrQXXgY1cEJO+LE6PzKsq9WidhV\nWwx8qNjG+tw+VCLhpVOdWp42kXg2BkVkrD6AXqOix3H2fc5keYadTy7sHOevZ+ZDzizl3WYOtYy2\n0rp9cQc6lfL+vFyh5C4uSbMzK9E56rkt/QbsoShkqtlHp0PHlaFc1dYBPUJoHm9XteMPThR9jfBe\niAjICBEucTr762ntrQRgSc4WVKKaAbeF8jal3/XbNUqP2u9smkthYjTlnX08uP0UH53bTYLRj4CI\n26dc1eakrSEttmDC8zm9/eyu+icgE2dM5bKCG85p3JIssafqn+GCFoC8xEWsyv/IOedRnslgdLPR\nchKAgy0JZETZWZyuXNQMGhnxIv0V/eWeXJw+NcOXfFdm9rOjPp5VmX1cWaBcsF+vSqC048LvP2zU\nBHAFxq8b1amC3LO2iXijkjMYlKA40Y1aBH9IOGpV57d95CCDIjJGH0CnUWE5BxF5VYGNqwstZ3nW\n+fGIfO5kKq4zLDEHO9QAHGqJobFXsf66dX5nWBAOH+fv9ufg9iufyw25feg1AQyaIAFJJDW09C3J\n8OM3S4kwM1ykP30RIkSYDLIsc7RBWaZOMGeQl7gAUHpRS3IQl1/La1WJZMeZ+MGVC5AkmbteOEhu\nrD0sIHQaIzISUfoEloZ8I8dDkoLsqnwGj9+JRqVj05zbz6mloCzLHKx7hSZreXhbRlwx64pvQRCm\n52fN5RvgzZOPhgXqWzXxLE2zEmN4/9oJni9qLHpaBgYrqYXQvzILUuz4gmI48tzYq+eV0ynv0ygn\nj1YM4vKPLx4FZL5+WcuIPtWKJZHyf837JByHMygio3VBdBoVttGBubNyy/wuVmT0T+KZMzvffq+G\nl8vTRm1fkWEnzewJmYunApAb5+GyrNFj7nTo6PcqUciMaC/tAwY25im/SX2eQWNx+MPeyhmaRYSI\ngIwQ4RKm0XISi6MVGGxZKNLaW0WL7TQAT59IwhNQ8dBHlmHUqvk2X3zRAAAgAElEQVTzwRqONHfx\nuaWKgNCrTaHCFYF1RR9DcxYxeLTxzfBS77qiW4g2jK7KnAzHm9+huvNw+HGiOYuNs29HJU6PM1m/\nu4c3TjxCr7MDWRb49+kENhfY0F4ixmePHskatW1lZj8HW2L56qoWdGqZoAS/258d7khzoSIg4ZMm\n7mj0+WVtzB1mAD646qmeoSvkqU4Tv9mXDSiicLIMisgoXRCtRkXfFBsWiQLcuayN4oSJ1Of5eT93\nNsRRbxvZYEAQ4O41SkFNtdXE8XalmcBH53ahEc9cihb47b6hKOS6nD6SjF7UooQ7oAr3Gnf7g7xQ\n2jjDs7k0iQjICBEuUYa3LMyIKyYttpCgFOBw/WsAtPSbONgSw1XFaXx0QTZt/S5+8PoxbpjTTWqo\npZs31HJvbvqacMvD8Wi0nByyCcpYT07i/HMa9+n2fZS1bA8/jjEks3neHWcVr5Olx97CGyf+hMPb\niySLvFETx/WzrSPsWS5mOu0aej2jLW7mJjnYkNdLqtmHJMOTx9KwT9jB5UJg4oIZUMTJmmwlwjVY\nPT0T77U/CDsbYvnRtkJ+eyCX8m5FHD1yOIuppOkNikizNohGLdI/xUJjjUrma5e1kBayzhmbmV/K\nlkPekGcK6ERTgHXZSmHeC+UpSoGMMcDmwtGm+xaXFptbuavLjvXQNqBnddbgezk0h3tePTpzE7mE\nuUR+EiNEiHAm1Z2HQ91TBJblKlXTp9v3M+DuQZbhb8dSUIsiD9+4AkEQ+Na/jhCnH2BLkfJDrlMb\nkZGJ1ieyNGfLhOfqd/ewr+ZFAJKjc1mWM/FS93jU95RyKGQ6DmDSxrJl/hfQaabHuLrVVsXbJ/+M\nN+DEH9SwtzGKa4ts561g4kLgsZJMzoxCzUt2ICOwLOR7aPeI7Gu50P0eZdTCxBXGl+fauLZ4KC9w\nJt5nu1fkpfIk7n1rFk+VZtBh1xFv8IU9Jo+1R/PQ3ly8U2g4MygiTVoJjUpkqm41Zm2Qu9c0E60d\nuze3wsx/6Jv7DWyrG91e9NNL2tGqJLocOnY1xANwXbGFaN1oT8vf7MvBFYpCXpY1QE6sEl3t92rQ\nq5V0k44BN5WdfTM3kUuUiICMEOESxBfwcKLlXQAKkpcQb0rD5bOHt+1rjqOpz8DdG+YwOyWGV042\n8+9TjdyxtA1RAK3KEIo+CqwrvmXCPMZA0MfO00/jD3rRa8xsnPUpRHHiJcWxaOutZk/18+HHOrWJ\nLQu+gFE3PQUctV0lvHv6SQKSH6dfR1WPhvW5/ZeUePQGBOp7R3txbsyzsjZk12R1qfnV3lwudL9H\nrSgRmMDvcVGqndsWdczY+9var+HPRzK4983ZvFGdjNOnZnaSg6+tauZ/t9TwoSJFuN44p4tqq4n7\ndxXg8E5+MKKgREyNWgm1WsAxUUBxDBKNfu5e24xGPFtO78xGIl89nYTNNfJ9UjrUKKk1/65MwuUX\n0WskPjJ7tBF/n0eL1alEwvPj3XTa9SxOVRwhjJqh0O5nnt03U1O4ZIkIyAgRLkHK23bj8TsRBTVL\nsq8G4FjjW/iDXnxBNS+eSiY92sCPNi9kwOPjGy8f5tpiS7jIwBdUrlbzMtaTHJ0z7nlkWeZA3Sv0\nujoRELh81ifOSfD12FvYfvopZFm5IKhFLVfPv/OccyjPHOPJ1l3srXkBWZawugz0uWXmp7ouKfEI\n8I8TaQwvmgGYl2RndqILUYAuhwa3T6DDoZ/gKO8/GjE4Yd5jXpyLL69omfalalmGEx1mHtiVx0+2\nF3GoNRa1KLExz8bPr6zlu+uaWJhqRxzW1vD62RbuWNpGx4COn+0oxOKa/M2VEBKRBo2MWgXO0b7b\nE5IT6+Hrl7WMUeU84izMpIj0BFQ8UzbaG3Jlpp1UsweHT83rVUpf7Mvzesdcev/1vmxcPuXNXJ5p\nZ3aSUqZuc2vDJuolrTbsnokirhGmSkRARohwieHyDYQteuamr8Gsj6XH3kJtdwkAL5UnYvep+eX1\ny4jSa/jh68cR5F4+PFuJmGhVBkAm2pAU7pc9HjVdR8IG5UtyribtLN1pxqLP1c075Y8TlJQff1FQ\nsXneHedsPD4cWZY40vAaJY1vAkrep0blJSPa956P/UFDluFAc+zQYwRUgsTnlrVj1Eq4/SLtAzr+\n71AOF3L0UUTCP4F4TDT6+ObqZnTq6RNFviC8WxfH97YW8fuDOdTajCSbfHxiQQcPXVPNbYs6wkUd\nalFZft7fHBPef11OH99a04TDp+K/dxTS1Dv5fN5BEanXgFoF7il+dOenOMNFcRMzcyLyeEcUpR3m\nEdsEAe5Z0wzIbKuLx+LUIApjm4vbfVq6nUNG9xaXlsJ4JT87Rj+07P21lw7N2BwuRSICMkKES4zS\n5m0EJD9atYEFWRuRQ11WAHqcenbUx3N5QQqfWJLL/oZuHj1QxeeWtqMWZTQqHb6gGwGBdUW3oFaN\nX0RhdbRxsE45bmbcbBaE2iNOBae3n62nHsMXGEzyEtg05/azFuxMBsUg/Dkq2pWlrcoeEykmJ9G6\nS9N4+OXypHDByWDF62eWdBBvUC7AL5UnE60L0OOanmKlmUFGmuCyZtIo/aEHe3e/V/o9Is+WpXDP\nG7N5piwdq0vLghQ7d69p4v7NtazO7sPlFxEFwubkJzrN/Gx7QSjXVHldQRFy39/QiAqZB/cUcLJr\n8nm9gyJSpwZRnLqIXJfTxw1zJurTPtM3DALPnEgblQeaaPKzJquPgCTyUrliF7Uo1cGcpNFGmA/t\nzQlHIRenOliYquTr2txaBsXvc6WNSFMpe48wIREBGSHCJUSfq5uaTqUicWHmJnRqI3Xdx7HYWwD4\n+/EUEEQevmkF/qDEXS8e5KoCK/nxioDzB5Ur07zMDSRHZ497Hm/AxY7TTyPJAUy6WNYX3zplf0av\n38XWU4/h8g15wK0vvoWs+DlTOs5Y+AIetpU/QYPlBADH2qIoTnReMjY9Z+IPCrxZkxR6JCPJAisz\n+sNtKt+qSSA71sMjh7O4cKOPExfMqJD45upmUqPee3S5tV/D7w9kcu+bs9lWl4ggwOYCC7/YXMPd\na5oxa/009eoxayUSTYoqqrEaeHB3Lr8/kEPrwFAKwBvVSTx6JBN/UFlSvm9jA/EGP78/kMu+psmn\newwXkSoVTHW19vpZPeF2guOcgZmMQlrdWl6tHO0p+tmlSkHN4bZo6m1Kfu6t87tGLbu7Axo67MrN\nzZxkJwNeVdhQfLD4JiDJPLynYsbmcKkREZARIlxCHGt8CxkJky6W2emr8QU8lDS+BUBpRzQVPWa+\ntnYWC9Li+NWOcnrs3dwYikwo7QllYo3JE7YKlGWJvdUv4PDaEAUVm2bfPuUq6UDQx7aKJ+h3D0VF\nVuZ9mILkpVOf9Bm4fXbeOvlnOvprATjaamZJupKXdqnyxLG0cPRRq5JINnvDXUEqe4xsr48nyeQd\n097nQmHii5nMXataKUyYYrnyMCQZyjrN/HR7Pj/ZXsyJzhjSorx8enE7D11TzQ1zuqm1GjjWHkVO\nrJe8eCVXr7Vfx+8PZPPg7jxqrKYxj324NYZf78vF4RNJNPn5wYYG8uNcPH4sk9cqR1cpj8egiNSq\nFCuiqYhIQYDbF7ezIMU+0bOYSRG5rTaBlv6RnzG1CJ9d0gYIPHdKEZjZsR7WZI+uqn5o71BF9vxk\nJ8vTlZtPu1cdHvfPt56csfFfakQEZIQIlwhdA40025S776U5V6MWNZS1bMfttxOUlKW4ZLOen25Z\nRFV3P/e/U8Znl7SjU8uoRQ3+oBcBUVm6FscXEqfa9oSNyFfmX09iVOaUxilJQXZUPk2PvTm8bWHW\nFczNWHcOsx7JgNvCG2WPYHO2I8tQ2mFiWYbjkiuWGU6N1cDB1qHcR71a4mshs/A+t5pHj2SypdDK\nny7g6KNakJAmGNttizpZmj6RMBofXxC218Vy75vFPHwgh9Z+PUvTB/juugb++8o6CuKdvHo6iX1N\ncazMHGBp6GbE4tTw16MZ/HR7ASc6ozjba1djNfGLXfl0ObSYdUHuXdfE0nQ7/zqdyj9KU8MelWdj\nUERqQiLSNwV7ILUId61sJTtmIqE9cyIyKAs8VTr692JV5gApJi+1VhMlbYp/5k1zu9GqRqab+CQ1\nrX1KT/YFqQ68QYEYnR8ZAYNaeW6/x8+u2vH6gkeYChEBGSHCJcDwloVxpjTykxbT7+4J5/+9WR2P\nxaXlwQ8vJUqn4SsvHuKyLCuzk5RE9ECogGVB5uUkRo3uUjJIZ389x0IRzfykxcxKXTXFcUrsq3mR\ntt6q8LZZqavOWqwzGSz2Vt4oewS7x4Yki9TbdCxKdV7S4tHtF/nzkSHfR7UY5LZFnaRFKWbhjxxR\nLuZJJi+OC9Q0XBQmtuu5psjCFfm2KR/X7oF/lqXwjdfm8HRZBpIscG1xD/+7pYYvr2ihz6Ph1/ty\nONIaw/Wze7iq0IZGJWP3qni2LJX7thVyoCX2rEbmw+ly6PjFrjxqrAa0KpmvrGzhygIrOxoSeORw\n5qQNx4eLSHGKIlKvlrh7TTMJhomW+mdORNbZjOxsGOkxKghwz1qlQ82L5SkEJIE4Q2DM3t6/GVaR\nPSvJzfJMxdLHLw29D198/sCMjP1SIyIgI0S4BGi2locjeoMtC4/Uv4YkBxnwanm9OonVOUl8elk+\nfztSS1l7a7jaUS0qeUWxxhQWZV857jlcvgF2Vj6DjEysMYXVhR9FmII6GxS5dT3Hw9tyExeyquCG\nKR1nLNp6q3nr5J/x+J34giosThUFCd5LWjwCvHAqJVRkoLCl0MbyDOWC++KpFGqtJrYUWkMi80JE\nRppAPK7M7A8bdk+W9gE1vz+Qxd1vzuWdukQyor3csbSNh66pZlOejZ0NcXzvrSJqrQa+uLyNj87r\nwaiR8PhFXj2dxPe3FrGtLoGAdG6XV4dPzUN7czncGo0owCcXdHLL/E5K2mN4aG8u/kmKwUERqRYV\nETnZ/UCpXP722iYMqinsNI28VJ6C/QxPzCSTn8sy++l26therwjMDxVbidGNXKf3y2oae5Uc0yVp\ndlSChE4dJCCJqEOel3VWBx39U+wDGWEUEQEZIcJFjiQHKQm1LEyLLSQjrpgWWyWtoSjfs2XJ+CWR\nP3x0Jd0OD//vPyV8ZnE7Bo2ESlATkHwIiKwvvnXcXtOSHGRX5bN4/A7UKi0bZ9825daCp9p2Ux5q\ndQiQFlPI+uJbEadYfHMmdd3H2VbxBAHJh8OnxheQSTZH/OBOdJrZ1Rgfflyc4AhX4tbb9Lxdm4BJ\nEyDe6MMduBCriyYumpmV6OTzIeP7syHJUNGt57+2FfBf787iZKeZVZn9/GBDPfddXk+0zs8fD2fx\nvbeLsLo0fG9DI7cv7iRGHyAgCWyrjef7W4v4d2UynsDUTfLPJCApkeHXq5QCnWuKrHxpRQt1NgM/\n31WAc5J1QKNE5BSKz1OjfHxrbTMi44U9Zy4K6fKr+OfJ0TZddyxtQyNKvFaZhNMnoldL3Dh3dPX4\n7w9kh3Mh8+M8LE9XbopUwz4Ln40Yi79nIgIyQoSLnJrOowy4ewAl+hiUAhwJ9buusxk53BrNl1cX\nsyQznntePcLsxB4Wpio2GUFZiUAszNpEgnm02e8gx5q20jXQAMDawpuJNSZPbYxdR8NejACJ5iyu\nmPuZcQXrZClv28Oe6ucUg3CnBp0qgPkStekZjt2r4oljQxfoGJ2fu1a2ohLB5lLz+wOK1+PmQhtP\nHBv/fX//mFg8ppk9fH1VMxrVxAInEIQ9jTHc/fosfr2vAKdfxQ1zuvnVh2q4bVEHNVYj920r4uED\nOUgy/NemBr60oo1ksx9Jhn3NMfzwnUKePZmG3Te9IltG4OWKFJ48nkZQUvIAv722CatLw8+2F2F1\nTS58PigiVaLSvSYwBRFZlODmyytbGV8ozpyIPNgSQ0X3yK5IapVSUOP0q3ktZC6+LqePjOiR5uJ+\nWUWdVYlCLssYwKAOoBJkvEEVQkgQb6/pxDeVFyPCKCICMkKEixh/0Mvx5ncAJScxwZxBRfs+BjwW\nZBn+UZpKglHPzz+0mNcrWnnrdB2fXKgkmKtChTJxpjQWZm0a9xzN1gpOte4CYE7aGvKSFk1pjM3W\nCvbXvBR+HGNIZvO8O6YcwRyOYhD+OkcaXgegY0BHnNGP5r0Hhz7wyDI8VZrGgFcDyKgEmbtWthCj\nD+Lxi2yvj8PuU2NQB4nT+/AGP1gvWrTWzz1rm8O+i2Ph8SuG+V/5z1yeOJ5JWpSXL61o4Zdbapif\nbOfFUyl8+81ZvFieSpQ2wHfXNXLPmmayYxWhUtoRxU+3F/B4SSbWGfbF3N0Yz8MHcnD7BWYnufjB\n5Q0EZYGf7SimtX9yeamDqRqqUCRyKrppeYadTyyYqOhkpkSkwD9OZIwa62VZAyQZvWyvj6fbMb65\n+P8dzMYd8uDMi/eyKOQLOWggLwPfe+3YDIz70iEiICNEuIgpb9uDx+9AFFQsybkal28g3O96V2Mc\nzf0G7r9uCVqVyNdfPsynFnZg1gYRBZGg5EcQlKrr8SKBdo813J86MSqL5XnXTml8Xf0N7Kx8Gjl0\nATJqo7l6/p1Ttv0ZTlAKsKf6+XC3naZePalR3kvapmc4B1piKGkf7IIicPO8LooTlarbN6sT2NGg\n2MZsyrPxVOl77/Yz3QgTRB+1YpBvr2siwTg6RUGWweYWeeRQJl97bR5v1ySxOquPH2+q4961jXgD\nAvfvzOP+XQUcaIklyeTja6uauW9jQ7iYrMZq5IHdufzhYDZtA+evnWN5t5kHdudjdanJjPZy38Z6\n4vQB7t9VSEXX6N7lEyEKUxeRmwttYxasDDEzIrLLoeP16qQR2wQB7l3XNMJcfEGKg3nJI83FA7KK\naovy2qzM7CcuVBSkpBgoY330QDXyZMvbI4ziQkxsiRAhwjTg9jk41bYbgNlpq4nSx7On+nkCQR+e\ngIp/VSSzPCuBz68s4Lv/KSFB38GKUMWiFOo5vSjrinFbBgYkPztOP40/6EGnNrJx1m1TWnK2OTvY\nVvEEkqxcyXRqI9cs+BImXcxZ9hwff8DLjsp/0N5XA0BLn5bsWM+0F8u0hSI/33htNglGHwlGP6lm\nL5nRXlLMXhIMfoza4AUX8bS51DxzIg0AtSixONXOliIrAFtrE7C4tXgCKrQqCYdXnLC6+f1BHreq\nWUDmG6tbwv3aw3vI0NKv4fFjWbT0G4g3+Lh5Xhfrc3px+UW21yewrzkWt195s+INPm6Y08Oa7L7w\nTUdrv46XKlIo6zTzflkZtQ3o+cWufL65upmcWA/f29DA/3cwi98eyOPOpS1clj15myJRAEQIBhXT\n8clwy/wuet0ajrSN9/0cFJHT+/q8UZ3IZVm9pJiHCnqSTH5WZfZyqDWWWquBwgQ3t8zvpGJ7wYjP\nxx8PZ/G7a6sxaCRyYj3MSnRQZTGjVUn4giq8AYlnjzXwqWX50zrmS4WIgIwQ4SLlRMu7BII+NCo9\nC7M20T3QHO5L/VJ5Eg6fmt/ftIJjrTYeO1jBz67sAEAlqAnKAeJN6SzMHH/p+nDdf7A52wGBDbM+\ngVkfO+5zz8TusbH11GP4g8rFXi1quHr+nUQbEs95vm6fg20Vf8PqUAywOwc0ZMb4plU8yrLS8/if\nJxURlmr2YnFpaOkfOwqkESXijX5STF6yYz1kRrtJj/IRawigV0uIAuetElyS4bFjGbgDKvRigDhT\ngDtCPZCrLUb2NsVgcynCOEbnY3dz/ESHex+YWJx8flkbc5Od4ceSBGVdJp44loHdp2F2koOvrWpm\nQYqdss4o/nQ4k0qLKXxMszbAtcWK5c9g7qTFqeGV08kcbImZkh3PTNHn0fC/u3P58spWFqU6uHtN\nM387ls5fSrKwuTu5dtbk7YpEAVBNXkSKAty5rI1et5pa29iG6DNBQBJ5qjSD76xrGrH9jqXtlLTH\n8PypVH54eQNZMV7W5fSxpylu2L4qKrsNLMlwsiqrnwabgSqLGV9wMAopcO+/SyIC8hyJCMgIES5C\nBtwWqjoPAbAgcyM6tSHc77rDrmdnQzyfX1nI0swEVv3uDW6Z30GsPoCASFAOIAoqpQJaHPvKUttV\nQnXXYUCJUmbEFU96bG6fg62n/orHryw5iYKKq+bdMWGRztnna+Wd8sexe6zIMvR7VKRGT2+ldSCo\n5FWVd5v4+IJOngHu26gUDvmCAjaXBotLg9WlxerSYHVrsDg1WN1aTnZFUdY1ui2dgEycwU9enIvs\nGDe5cR5So3xEaQNoVIq0mS6B+W5dPJU9ZgDiTX6+srINvUai36PiT4czKUp00jZgAORQNO79F0xD\nTCweb5rbxZpspetIIAjb6+N4qTwVUZBZk9PPlfk2zNoAOxriefpEGn3DOuroVEE2F9rYUmTBqFEi\n7wNeFa9VJrGrMe6c7XhmCm9Qxf8dzOYTCzq5ssDGF5a3EW/w81JFGr1uDZ9a1DWlz4xqCiJSo1Ki\nvA/syqXTMdYS/sxEIU/3mDnQHM3q7IFhY1EKah4ryeJwazQrMwe4cW43h1ujR+TtPno0i9+lVKFX\ny2THesiKdtMyYEAtygQkgW6Hh1PtNuanX2g3TBc+EQEZIcJFSEnj28iyhFEbzdz0tdR2lWB1tALw\nj9IUovU6fnHdEn67q4JAoCXc81gOVSguyr6SOFPqmMe2OTs4UPcKAOmxRRN6Q56JL+DhnfLHsHsG\nIyUCG2ffRmrMuUcArI423in/Gx6/g6CkiLlYw/RWV1qcKh7YXYBKkPnhxgYMGuX4VRYjCUY/cXo/\nKWZfqM+yc9T+AUmg163G6tJgGRSYw/5f2hFDSfvICK5WJfGLzdXETcNc2ge04XyxZWm9LM90kB7t\nJSjBnw5nIQhwqlPp8JFo9GF1XWim4eMLkstzbVxXbMHtV7wrdzYmkGzy8bH5XazN6aOlT88rp5Mp\n7YgiKA8dRyVIbMjt5frZFmL0yvKoxy/ydm0CW2sTpsWOZ6aQZIFnylLpdmr5+IJOPja/m3hjgKdP\npNLvVXPXysnZFw2iUkFQUopszoZZG+Setc38z/Y87P6xPiczIyKfP5XG4rQBDMNOuTprgFcqvLxU\nnsKSNDux+gDXFFl5tXLIBcIvqTjVZWZ5hp3V2f3U9RpoGTCMuDH49DP7OP6d66d1vJcCEQEZIcJF\nRo+9mSar0u91SfbmET6QR9uiqbSY+cNNixnw+Hlw23Hu26gsY4qCCkkOkmDOYEHm5WMe2xfwsPP0\n0wQlP0ZtDBtmfWLSPo1BKcD203/H5uwIb1tX9DGyE+ae81zb+2rYfvopAkEf3iCoAINmepPi9zdH\n8cSxLBal2vn04jYOtcbyfKgn7y/35AEgIhNr8JNs8pFk8pNo8pFo9JNo9JFo8hOjC5Bk8pNk8gOj\nDYwlGfrcaqwurRLFdGvIi3NNi3gMSPDXkkz8kkic3s/CVCcrQ7muL1WkUG01EaX14wmqEJBZntHP\nWzVTs2GaWcYXIwtT7Fxb3Mlv9mVT0WNmQYqyrFsQ72R/Uyy/2JVPh103Yh8BmZWZ/dw4pzvsB+oP\nCuxsiOO1KiW144OBwLa6BKwuDV9c3sIV+TZi9X4ePZzJr/Zo+Pbaxinl4KpEZdlfnMTXOdHo5551\nzTywKxe/NNZJpl9EDnjVvHAqjc8sGfr9EAT4zromfvBOMdvr49lSZGVLkYVdjXEjosyPl2SwIKUK\nnVomN9ZDZY8Pi0uLKEhIskhZRx8DHh/R+pmtqL/Y+KB8UyJEiDAJlG4uip9irDGFgpRllDS8gcfv\nICCJPH8qhUXpcXzxskKu++t2rpvVEapYFZDkIKKgYl3RrYjC6IuCLMvsq3mRAY8FQRDZOPs29JrJ\n5UJJssTuqn/S2V8f3rYi7zoKU5ad81zre06wt/p5JDmI0ydi1EjTmk8oSfCXo+kcbYvh1gWdLE/v\n589HMzndEzX6uQjY3Fpsbi2VYxSrqgSJOH2AZLOXZLOfpJCwTDQqQtOkDRJvDBBvDFA0fVMA4LWq\nJJr6lBzNr64asqI52hbF2zUJgBz2MFyd1cdbNUnjHep9YHwRkmb20OnQ8JPts1if08vtizsJSPBO\nbSJ/PJSFL3imEpKZn+Lg5rnd4ddAkpWq9FdPJ8+4Hc9Mcbwjml/tzeMblym9s7+7vpGH92fzPzsL\n+P76OgxTmJY4BRGZE+vh65e18Nv9imfoaKZfRO5ujGNtto2ChKFCqWSznxUZfbxWlcTanD7M2iA3\nze3mb8P8S71BFWWdJlZkOlib00et1YDFpUUeFpH+4vMHeO4zY984RxibiICMEOEiorW3MmzovSz3\nQwy4LVR07AfgjaoErC4tL9+xkmdLG2m21fGpDYNLyUrUbknOZuJMKWMeu6J9L03WU4Ai/pKjsyc1\nJlmWOVT3anhfgIWZm5iXsf5cpghAedtejjQoZuj9bhXR+uC0iscBj8j9u/KRZYEfXN6I06fix9sL\ncfmVn8ycWDcNwMqMfmweNb1uDX1uNcFxqpaDsojFrcXi1lLRM/rvGlEiVu8nyeQjPcrLDXO6MWrf\neyS13mbg9ZDh8nXF3aRHe1GL0GHX8uSxdEDAqAng8qsRkNGpg1w4uY8TiQ+ZoARbim2syOjnZJeZ\nvx7NoL7XMOY+BfEubp7XxazEoehvaUcUL5cn02Y/f3Y8M0VDr5Ff7CrgW6ubKExwc9/GBn6zL4ef\nbC/iB5fXEmeY/GdpKiJyfoqTO5a2jxBrI5nez5KMwN9LM/nxproRy+13Lmvn66/N5j+VSXxyYSdr\nsvvYVhc/orjtyeMZLEytRqeWyYtzc7IrCqdfzeDn7F8nW5AkGTHi9zVpIgIyQoSLBEkOhru5pETn\nkRFbzLaKvyHLEr1uDW/WJHL7snxmJUdz65Pb+eZqZelaEERkWSIxKmtcUdc10MjR0LFzExcyJ23N\npMd1ouXdcEEPQFHKSpbkXH1Oc5RlmZLGtzjVphiX97nVxAzzqkMAACAASURBVOgD0yoeSztMPHI4\nmwUpDm5b2M5bNUlsq1e8EdWixKcWdrAht4/PQ6hLxxCegIDTp6LfoyxHdzu1dDq0WF1aet1q+jya\nMSJj4JdEelw6elw6ChJc0yIevQGBv5ZkIMkC6VEebpzTgygq2/94KBNXICQaVRIuPyzPGAh7QL7/\njC8eBSQ+v6yN5ekDvFmdyA/fKRp32Tk9ysNH53azJH3I4qbaYuSl8hRqbefuNXohYnFpeWB3Pl9d\n1cKcJCf3bWzgd/uz+cn2Yr6/vo706Mn3tZ6KiFyX04fNreHV0+OlPUxvFLJ1QM87dfFcUzRUca5R\nyXxmcRtPHs/ginwbKWYft87v4tf7hqKj7oCaEx1mVmbZWZfbT7XFRElHDKKgRKKDkswvt5/i+1ct\nmLaxXuxEBGSECBcJtV3H6HMpfWGX511La29l2A/xnydT0Kl1PPjhJdz776Osz2kh1awY68qyhCio\nWVd0y5hL126fg12VzyDLEtGGJNYW3owwScVW2XGQ0uZt4cc5CfNZXXjjpPcfjiQF2Vf7UtiKaMCj\nmlbxKMnw1PEU9jbFc+uCLuYmOfj1/lw6QhGq9Cg396xpJt44/oVYr5bRqwMkGAPkx3vGfI4/KODy\ni9i9avo8aiwuLZ12Le12HaIg85HZExk2T54Xy1PocijHvHddY1gMPHE8g/bQnIoSXFRblTQEtXih\nRB/HFxwiErfO78SkCXL368V4pbGLfRIMPj5yAXo5zjQuv4rf7svms0s6WJvTx/c2NPLI4Ux+vrOI\nu9c0MCtx7M/kWIii8p2YTEDu+lk92FyaERY6Q0z/Uvarp1O4LLN/RLHcmuwBXj2dwovlKXxtVQtz\nk50sSHFwsmso5eQfJ9JZkl6NXi1RmOiirDsKf1AMj+/BiICcEhEBGSHCRUAg6KM01LIwN3EBcaZU\ndlc9C0Blj5GjbdE89JGFnOzoY29dBT/aaB2x/9Kcq8fsXz2Yu+jyDaAWNWyafTsatW7U88ai0VLG\nwVC1NkBaTMGUim6G4w962Vn5NG291QC4fALR+umrtHb54IHd+XgDKr6/oZGWAT0/21FAUBYRkLlu\nVg83zOmZdGWrLI9vv6NRycSogsTog2TGeBmravu9cqrLxPZQ1PSLy1uJDb1W2+riOdwazeDF3OZW\nBNji1AEOtIx18X8/GH/Z2qgNcrI7ivJTJsZqpGbWBrhuloVNeUNejj0hL8dDF4iX40wTlEUeP5ZO\nt1PDTXN7+MZlzfz9eDq/3pvPF5e3sCJzaobjkxGRggC3L27H5lZT3j06R3i6RaQvKPL30gy+ubp5\nxBi+s66RH7xTRLXFSHGii1vmd1HebUYK5To6/WpK2qO4LGuA9Tl9lHebONUVHZ6n3Rvgnap2Ns+6\n8DowXYhcWAZXESJEOCcq2vfh8g0gCCJLc7ZQ0bYXu8eGJMOzZWnMTYnljpWFfP2lA9yxVLH4EEI/\n5klR2czNWDfmcUubt9HRXwvA6sKPjpsfeSbtfTXsqvxn+HG8KYMr5n5mSp1qBvH4Hbx98i9h8egL\nCNOyxDtIjcXAd96aTYrZz71rG3i9OpGnStMJyiKxej8/3lTHTXMnLx5h8t6NM9FFzekTwzlpS9P6\nWZGhVFzXWg08fzIlfAnPiXVjCRWOiML0266cGxO9IAIBSQwJlJGXLp0qyIdndfPg1TVcXWhFo5IZ\n8Kp4+kQqP9pWyMGW2EtCPA4h8FpVMn85qnwO7ljaznWzevjTkSzerp7ajcKguDobahG+uqqVrGj3\nuGOaznaHJzqjON4+sogv2exneXp/2CUhI9rL+pzeEc95tiwVf1DAoJGYnehCQA4JTGVsX37h4LSN\n8WInIiAjRPiA4/E7Odm6E4BZqatQiRrKWnYAsLMhjtYBPQ/ftIIHtp1kTmJjuNWbjIxKVLOu+JYx\no4KttirKWraHj1uQvGRS47HYW3m34u9hT8loQyJXz/88GtXkIpfDsXtsvHHiT1hCHpZBCbTq6bkI\nyTK8eCqJX+7J4aa53VyZb+WB3QWc6FQMv9dk9fLg1TVkx3rPcqRzZya60AwaZZs0Ab60og1BUIzV\nHzmcRVAWw0LK7Vfe8zlJdo51jDY5P/+cXcQO72MMSnX7FflWHri6hpvm9mDQSHj8Iq9UJPGDrUVs\nr0+44IzAzycHW2L59d5cXH6RG+b08Lkl7bxQnsazZclTunmZrIjUqyXuWdtMnN43zjOmV0Q+XZaB\n74yMki8sb6e5T8/BFqXl4g1zutGrh1YrHD4NJe1KlHRDbh9FCc7wyACaep209E7/qsDFyKX7zYoQ\n4SLhRMt2/EEvapWWRVlXUtL4JgHJh9On4pXTydyyKIc4g45/Hj/O9Wfk1y3NuYYYw2jbFoenlz3V\nzwGQYM5gRf6HJzWWfncPW8sfIygp/noGbRRb5n9x0nY/w7E52nnjxCMMeCwEJUXwTcboeDL4AnD/\nrjwOt8by/9Y3MeBR89C+XOw+NQZ1kHvWNHLn8vbwMugHhcOt0RxqjQVkvr+hAY1KRpLh0SOZIV88\nZT6pZi/dTkXQCzK8/9HHyUZAlecJyKzK7OP+zbXctqiTGH0Qf1Bga20839taxH+qki9oI/DzSbXV\nxP278ul2qFmf28c3VzezqyGBPx3OmJQoHEQUJhcxj9EHuHddEzrVeLnC0/dZ63VreLli5KqIUlDT\nzssVyfiDAjH6IB8qGvm79/zJFAISmLRB5qcoHbGGR6hvf3rPtI3xYiYiICNE+ABj91ip6lCWXBZk\nXM6Ax0J9TykA/6pIRkYpnLnrhf18ZnEbanHoQp0cncvc9NHV1EEpwM7Kp/EGXGjVBjbOvg21ePbO\nJC7vAG+f/Au+gLKEpVUbuGbBlzDpYqY8r46+Ot48+Shuvx1fgGntGd3Sp+Xet2YTqw/w1VXNPH0i\njTdqkgCB2YkOfnlNNfNTPngRiF63mqdKlR7dH5vXRXq0EgU61h5FlcUcepbyIg4KgcJ4JxXhv71f\nnF08GtUBBsXvghQ7P7miji+taCPJ5EeSYV9TLPdtK+S5k2kfICPw80eXQ8f9uwqos+lZmOrg+xsa\nqLKYeWhvDoEppBILkxSRaVE+7lnTghhahRjN9N2YvVufQFv/SLPLtTn9yJKS8wtwdZGVOMNQa9N+\nr5ajbUrUfUNuH9kxg/ZOyrj2NfTg9U++av1SJSIgI0T4AHOsaSuSHMSgiWJ2+loO1/0HgJZ+Hbsa\n4/jRVQt55WQL8foaCuIHc5NkVKKGdUUfQxhj6fpIw2vhJeP1xR8nSn/2HrHegIu3T/0Fl0/Jt1OJ\nGrbM/8KY0c2z0Wgp453yx/EHvXj9Ss/b6RCPsgxvVsfx3zvy+cjsbpamDfDg7vxQX1yJzy1p4zvr\nmsL9kD9IyDI8cSwdl19NcYKDzYWKxYkkE86DG7w4xun9dIWij7J8IeQ+TnR+mbmJdlwBFQXxbr63\nvpG71zSH0zCOd0Tx03cLePxYxgfWCPx84fCp+dWePI62RZMb5+G+jfX0ujT8fGcBnim0jZ+siCxK\ndIVsrsZ68vQtZUuywN+Oj4ymCgJ8d30jr1clYfeq0KpkbprbNWK/F8tTCEoQpQsOs3kK3WAB97x6\ndFrGdzETEZARInxAsThaaeg5AcDinKtospRhdbYB8MyJNAoTY7hpQRYP7z7EjXO6R+y7PPcaog2J\no45Z311KZSiiuTBzE1nxs886jkDQxzvlf6PfrThkC4hcNfdzJJjHMxcen9Pt+9lZ+SySHMQbENCq\np0c8BiR4aF82/z975xkYR3Wu4WerVlr13ntz7924UmxsXCgh1IQLN0AghJAQLiW5lEAgtOSGhGZI\nwYSEXtx773KR1XvvdSVt35n7Y6SV1pJs2bKxJM/zAzP9zK72zDvfOd/7bS8K5LE5ZRQ0ufPBcam8\nX4SXiZevzeeq2JZLMifx+2BXsR8ZdV54aaV5j1KkGY5WeGE/o9ScVi0J5BhfI4XN5z+14PtDRK2w\n02JR8/DMcp6aX0xypxF4XoMHv98dx1uHokeEEfj3hU1Q8s6RSDbmBRCkt/HUgmK0KoHf7kiideAO\nPwMWkVMj2vjhuJr+zsLFEpHFzR7sKnYd6Qj2tDEqqJ1vsqWX2DnRrUT7dCf4NJu0HKvsngsZrO/6\nAKQ2/e1IYecLlkx/yAJSRmYYIooiaZ0lC33cg4gJGENaiVTv+kiFN3mNet5cOZVHvznKrWPLcVN3\nR5pCvONIDZvV65wtxloOFHwBQKhPPBNjrj5nOwTRwc6ctTS0lTvXLRx1B2G+Ced9P8dLNnO46FtA\nxGoHN7V4UQRdfbuaX21MwU0l8uOJlbx/LJJjlb4oEFmRWsezi4vwcx++w1W17Vo+zQhFqRB5YHq5\ny718kt5lRyJ9/x4aB7XtUvSxvl3D5Y0+9v9wViscPDitjLsn1fLc4iImhUkRovJWN/54IJpX9saO\nOCPw7wsRBZ9nhvLPE2F4aBz8am4J0T5mfrs9hZq2gc8bHaiIvCaxiWsS+vM2vXgi8susMNqtrpLm\nJ9Mq2V/qR3WbFJ2+dVyNy/W+zApBEMFXZ3fWh+/C6hD4+5HCi9K2kYosIGVkhiGVLXlUt0qd2+TY\nJZwq34nF3oHVoeCzjBBWjo2ixWzFbM4kNah7fo9aqWVucu+ha5vDws7stdgFG+5aL+an3NanqXhP\nRFFkf94XTnsdgDlJNxMdMOa87kUQHezP/5z0Cilz3O4A7UWYxiaKsLvYh6e3JrI0uZ5YHxN/PBhL\nq0WDn87Gs4sKz8vbcSjiEOCdI5FYHUpuHF3b47uGilYtbVbX79BTK4lLrdKB0X7uea2Xjv6Hzj3U\ndn6zsIgJYR3MiWlFqZC8HN8/FsFzOxI6jaGH8Zc2RNhd4s//HYpGFOGnM8qZFmHguZ3JFDYOfCrA\nQEXkD8bVMiW8tb+zDPh6Z8NkU/HxSVf/Ro1K5I4JVXzWaeuTGmRkQmi7c3uD0Y20HlFIHzcbPUXt\nE+uOX5S2jVRkASkjM8wQRMEZfQz2isFLF0BO1UEANuQGYrS589trx/PsxgPcMtZ13s/UuKV46VzL\n1YmiyIH8L2k11aNAyYKU23HX9mUG7EpayUYK67s72Kmx15MUMvW87sXusLIj6yMK6tI67w3UFyF5\n1iHAXw5H8l1uMD+dUcGxSh++yQ1BRMHc6GZevi6v08R7ePPRyVDKWt2ZGGZgabKrOfz7xyLpfjgr\n0CgFZ+a1VbicGcr9i8eJoQbeXJZLpI/Vxcvx6a1XopfjpSej1ovf74mn1azijgnV3JBazyt7Ezle\nOfDEqoGISKUC/ntqJfF+lzY57UilNzl17i7r5sS0UtriRk69FLG+ZWwNKkV3g7/KliyNAjxszIpu\ncTm20WjmRIXr70qmG1lAysgMM4rqTtBslOYVTYldytGidYgINBg1bMoP5H8Wj+Wtfdlcn1yMe4+E\nkDCfBFJCZ/Q6X071IYobTnWebwkhPnHnbENGxR4yKvc4l8dGzGds5Lzzug+zrYPNGWuoaM4BpIfQ\nxYgGGsxKntichCAqWD2qjnePRVLU7IG72sFjc0q4Z0oV6mHe84ki/Ds9mL2l/gTrLdw/zbUmt8mm\noMLQNTdQelh2RR97rrs89P0lX59cz89mlaNWSh6VPb0cHeIw/8KGMBUGHS/uSqC81Y3rkxu4Z3Il\nbx+JZFvhwA3HByIiNSqRn88uJ9jjUr64KfjHyQgcPfLgFAp44qpSPs0IBaQM8Xmx3ebite06jnf6\nQi6Ia8Zd3bOkp4K7P95/Cds7vJF/lTIywwi7YONEZ8nC6IAxmG3tzkoxn54OJdLXm2nRAWRXH2d8\nj6EatUrL7KSbeg1d17eVcbR4nfN8YyKuOmcbCmrTOFaywbmcFDyVKbFLzus+2s3NbEh/h/o2qRTZ\n2Ur/nQ9plZ78elMyixOa0GvsfHg8EqtDxeigNl5dkseY4OFnz3MmogjvHolgV3EAWpXII7PK0J7h\nVylVoul+CCoQaTZ3DU0OhcxrV66KaeLG0XWdXo4B/I/s5fi90mzW8MreONJr9MyKbuUXc8r5MjOE\nT08HDdhwfCAi0lPr4JdzS/HU9Jf2PfgXm7oON77NcXV/CPa04e9u5UBZt7m4ew9z8a5EmyC9rUfl\nGqktWXWttBiH/2jFpUAWkDIyw4icqoN0WFpQoGRi1GKOFq8HILtOT1qVFy8vn8ST6/Zz23jXzMdp\ncct62fGYbR3syvkYQXTgpQtgbtItKM6h4sqbctiX/7lzOcp/NLOSbjzncT1p7qhhQ/rbGEz1ToPw\nwYpHUYQPjoXxyekw7p5czc4ifw6U+6NWCvzX5Aoem1PmEo0drtgFeHZnPNXtOmyCgrsmVhHm5Vr1\nQxDhRFVXZRmxx39d/2+oMCaojTsmVHO43Juntybyn9OhspfjZcBsV/HnQzHsLPJjdHAHT84v5mC5\nH+8dDR+w4fhARGSg3sZjc8tQKfoyoLw4STWb8gNp6HD9G7p/WiXfZAdjdSjwcnNwfUp3Yk9lmzsn\nq6Vh+/lxzagVAj1fsn787wODbtNIRBaQMjLDBIvN6CwtmBQ6jfKmbNotzTgEqb7r0lGRnKhsZnZk\nAZ7a7s453DeJ5JDpLucSRYG9ef+hw9KKSqlmYeodaNVnt0OpM5SyM/sjujr4EK84FqTe3mcZxP6o\naS1iQ/o7GK0GHMLFMQg3WuGprYm0W9XMi2nmw7RwGk1aIrxMvHJtPnNiWoetPU9P2i0Kfr0piXEh\n7VQYdMyPbWZ2dO/EhA25AQg9oo89/9Vr7Ayl6GOUt4lrkhp4Y38U76dF0WiSvRwvJ4KoYO2pMD49\nHUKUj4VnFhRT3urOa/tiXIaFz8ZARGSMr5lHZpVzqTwi7YKSD49HuqzTqESWJ9expUCaA35NQiMB\n7t0vX11RyFAvK1fFuUYh12dVIJxP2Z4rBFlAysgME9IrdmF1mFErNSSHTCO9s/71zmJ/6o0ePDg7\nmW05B5nWw45Co3JjduJNvSKE6eU7ndnTMxNW4e/pmr14Js0dtWzJ+ABBlISpn0coV4/5MSrlwCNF\npQ0ZbMn4EJvDjEOQyhIOVtjl1On45cZUZke1YLYr+SYnBFCwalQtzy4uwncY2/P0pLjZjZd2x/Hg\njAoOl/sQ52fk9gnVfe67Ia9r+M71geeltdFhuxyRvb4fvJ5aG0F6K5+cCiWvcSjU4paRULC5IJC/\nHo7E283Gk/OLEUUFz+9M6FV3ut8zDEBEjg3p4MeTqvptw2DJbdBzqMz172pubCv7S31oNavQqERu\nHNPtj1vW6kF6jeSLujCuGUWPqR6CCM9vOTXoNo00ZAEpIzMMaDe3kF0lDaOMiZhHZuVeHIKNdouK\nb7KDeWzBKF7dcZzbxrt2yNPiluOp83VZV9Wcz4mybQAkhUw9Z+Z0u7mZzRnvYRekt3VPN3+uG/ff\naNRuA25/TvUhduZ8jCDaEYTB17QWRfgkPZg1x6NYPbqOzQWB5DV64qez8vziAm5IbRjW9jxdiCLs\nKvbhu5xgnllQwp5if6wOJT+dXt5nIlBmrR6Lo2veYNcHID3J7YKC7z/62N98SwElIo1GNbUd7n1s\nl7ncpFX58Ie9cQgiPDanlFBPC89sT6JjgNMBByIir4ptYUVq3dl3GgSfnA7DbO/++1Mo4NHZZXyT\nHQzAzKhWYn27zcW75k5GeFuYc8ZcyDd2ZV2ydg5XZAEpIzMMOFG2BUG0o9N4EuQV7cya/jIrmAC9\nFwEebiT6ZeGr6w4RRPgl9xKHHZZWduf+GxDx04cxI37lWa9rtrWz8fR7mG1S8olO48nS8fej0wys\ngokoipwo3cKhwq8BUcq0HmSvY7XDszviqW13Y3RQO//JCMNkV3FVTBMvX5fvrAE93JHKE4bRbtXw\n8Mxysuv1HCz34SfTKvD36DsU9OHxM3zwlNJcLr3GjumyJKT0JR5FtCqRME8Lpa1DuRKOTFGzB7/b\nlUCjUc390yuYEm7gqW0pNHYM7EVkICJyRWo9c6Kbz77TBdJuVfNJepjLuhBPGwaziiqD9ALc01y8\nuFlPZq1k97MovpmeL0AdNgfrs1zdDq50ZAEpIzPEaWqvorDuBADjIxeRVroJgLIWHXtK/Hjy6rH8\n5/g+5sZ0e5hpVLpeQ9cOwc6unI+x2DvQqNxYmHoHalX/ZtI2h4XNp9fQYZE6d41Sx9Jx96N38+n3\nmJ4IooODBV9xqnPe5sVIlilr0fDYhlTGhbRT26Zlf5k/7hoHj88t5seTq4e9PU8XDgFe2xfFtEgD\ny1MaaLOo+efJcFaOqus3kzyr1oMWs+v36eh8eNsclyP62DdKhUi4l4ncpoF7DcpcPhqMWl7aHU9e\ngzu3jqtleUo9z2xPobR5YEb05xKRCgXcPamKUUFt/ewxuLmH+0t9KWl2HS25f3oln2dKUcjkQKOz\n0hHAd7lSFDLG18yMyK45xlIbHvz80KDaMtIYId2tjMzI5VjJJkDESxeAQgHNHdLct4/TQ1mYGMrW\nnDJ+MNb1zXhG/A29hF5ayUanbc7c5B/0WQu7C4dgZ1vm351+k0qFiuvG34ePR1C/x/TE7rCxM3st\nebVHgMGLR1GE77IC+OOBGObFNrE5P4A6o44xwW28tiTXpQLLcMdkgzf3x3DPlBrGhnQgivCPE+HE\n+5u4IbXvknA59R68cTCGniJRp3YgiEp0agdWYah09SKCqKCkxZOhImhlzk2HTc2bB2I5WObNNYlN\n3Dulipf3JHCqemDTD84lItVKeHhmBRFepj62Di6pRkTBB2mRCD2SgDQqkQmhBrLqpAj4LWNrUSmk\nHfIbPcntNB2/OqGRnlHIylYjJY39Cd0rj0H3Kh988AFjx45l8uTJTJo0icmTJ5OWlobBYOChhx5i\n6tSpLFq0iM8/77b+sFqtPPXUU8yYMYO5c+fyzjvvDLYZMjIjkqqWAqpapGSX8VELONk5d/FQuQ8l\nzZ7cMCYKd+UJAjy6fdUi/VJJCJ7scp6ShnSyqiRD3DER84g5S7lBURTYnftvag3FAChQcs2Y/yLQ\nM7LfY3pisRnZkrGG8qbszvMNTjzaBfj9nhjym/WEeVnZXBiEUgn3TangF7PL0KlHTnZktUHD5xkh\n/Hx2GYGd3+neUl8qDW7cN6Xv4bND5T68ti8G0Wm2LX0eUtQR7N979PFs38fQiYTKnB92QcmatEi+\nzQ5iaoSBX8wp471jUewsGtiIxLlEpE4t8Mu5Zfi49TUFZXB/M1VtOjblu9qYzYttZX1uAIIIIZ5W\nFsR1D6N3RSHj/c0u0UlQcNvafYNqy0hi0AIyOzubX/3qVxw/fpwTJ05w/PhxpkyZwjPPPIOnpycH\nDx7kj3/8I6+++irp6ekAvPnmm9TU1LBjxw4+/vhjPvvsMzZt2jTom5GRGUmIPUoWBnpG0dhehcVu\nxGJX8HlGCA/MSubfaYdYnNDkPEar0jE70dWXsdVY7/RuDPGOZUrMdWe5psihwm8oa8xwrlsw6g7C\nfBMG1OYOSwsbT79DXVtp5/kGJx7r21X8ckMK0T4WSprdyWnwItrHxB+uy2NW9Miw5wHpczpa7klh\ns567JtWi6TQGr+/Q8EVmMA/NKEevFVwewKII63MDef9YJKJLVy6VLXSISrRKB3bx+xaPI+RLkekD\nBd/kBPNBWgQJ/kaenl/C+rxgPs8cmOH4uUSkj87O41eVolFcfPeE73JDMFi65wFLQ+fVHCiTkgxX\njKrHXSO5TGTX6ylolKKr1yY2IP1dSw0/UtaAeaDp6COciyIgU1JSXNYZjUa2b9/OI488gkajYfz4\n8dxwww18/fXXAHz33Xc88MAD6PV6YmJiuPPOO/nqq68G2xQZmRFFcUM6jR2VAKSEzSC3Wpp/sz43\nCJ3WG5PVworUYpdjZiSsxMOt27rC5rCyM2ctdocVncaT+Sm3o1T2n0xxqmw7uTWHncuzE286a7Sy\nJ80dtaw/9TYtxjrnQ+JCBZ4ows4ib36/J47RwW3sLPbHaFNx4+gafruwCB9dXybEwxNRhG9z/An2\nsrvMYxVE+CAtnFvG1hHta3YR4w4BPjoZxpdZIV1ncfm36/O3fe+Z17J4vBI4UObLG/tj8HO38cyC\nYtJrvHj/WPhFEZFhXlZ+ObccuLjG/1aHkg/TIlzWhXjayKl3x2JX4Kl1sDylvquVzihkcqCJMcHt\n9PzbfujLIxe1bcOVQQlIs9lMSUkJ//znP5k7dy7Lli3jiy++oLS0FI1GQ0RE95cVFxdHUVERBoOB\nhoYGEhISem2TkZGRcAh2jpdsBiDCN4XCuuOIiNR3aNhcEMC9MxJpMR4m1Mvq7Iyj/EcTHzTReQ5R\nFDlU8BUtxloUKJifcpuLuDyT3OpDnCzf5lyeErOU5NBpA2pvraGEjelvY7S2DjrqKAjw5oEojlb4\n4q4ROVLph5+7jReuLmBZSuOIiTqCdK+fng5icUIrMb5ml22b8wMI87I6RWXXfZvtSv58KJrdJdKQ\nXJSPkZ6G4UqFiF1UolIIiLKgk7lE5DR48uLuBBwCPDmvhFaLhtf2RbvMNeyPc4nIpEAjD06v5GJX\nTjpd6+X0euziR5Oqnebii+ObCPSQhtAzaj0paZaKKyxJ6pp7LLVnbVoR4kBrPI5gBiUgGxoamDx5\nMrfffju7du3iueee4+WXX2bnzp24ublmPel0OsxmMyaTybncc1vXehkZGck3sd3SjAIF4b6J1LRK\nL1j/OR3KzJgQ9hZkcl2i1KkpFOCm9mBW4mqXoeu82iMU1kvZ25Nirj3rMHRJ/WkOFn7tXB4dcRXj\nouYPqK1ljZlsyViD1WEetHg0mOCXG5PQawTyG/XUtOuYH9vIK9fm9yrZN9yx2mFLoR+3jKt3qRwE\nUN7qxolqL24/oyRlq1nNH/bGcrrWC5CqaTQZe9a47v5XysCWBaTMpaO6zY3f7Uqgtl3LL2aX4udu\n5/ldcdgHMEBwLhE5NcLArWNr+tl64eLtHyciXNqna47rSwAAIABJREFUUYGvzkqrWY1GJXLTmNqu\nFrKuMwo5OthIkn93FNIuiLy9P/eC2zBSGJSAjIyM5KOPPuKqq65CrVYzdepUVq5cybFjx7BYXN1G\nzWYzHh4eTuHYc7vZbEavl/3AZGQArHazs2RhfPBEsqql5JfMWj3ptd6MC/VhYVyeixn3jIQVeGi9\nnMsN7RUcLvwWkJJqxkX2LwarWwrZnfsv53J80CSmxV4/oLbm1RxhZ/ZaHIJ9UOJRFOFYhZ7ndiYS\n4mXlSKUvbhqRJ64q5u5JNYM2Hh9qNBlV5DZ4sCSpuZfhuc2h4OOTYfxkaiUaleisQ1xl0PLi7jhK\nW9xRIPLDcdXMjGqiw9bTOFxEEJUoEZBNNmS+DwwWNa/sjSO9xpN7p1QxIbSDp7clYR7A+965ROS1\nSU0sjuvLeeDCM7NbzBo+zQh2WTc3xsDGPCmiPz3SQLyf5OpwstqL8lYpGLYspbFzb+m6v9l48oKu\nP5IYVA+TlZXFe++957LOYrEQHh6O3W6npqb77aG4uJiEhAR8fHwICAhwGbLu2iYjIwOnK3ZhsRtR\nKTW4qfV0WFqketenw/jhpDiqWg4S5dP9AhYTMIa4wAnOZYvdyK7sjxFEB55uflyV8gMU/dSrbmyv\nZFvm3xA7O8VIvxTmJt/cq/ThmYiiyMmybRwo+BKx0yB8MOLx3aPhbCkIwi6oyG/0ZHyIgdeX5JAc\nOHLseUC614IGNxyiknGhfd/b19lBLEtpIFBvQxCleuG5DR78fk88jUYtGqXAA9PLuTqhiTXHouj5\nMFUrpX8FOfIo8z1idSj56+FothT4s3p0HUuTG3hiazItAxhYPJeI/OGEWiaG9q75Ppjo+o6iQGrb\nust6KhSwMK6Jik6x+INOc3GxRxRyXGg7sb7d00VazFaOlNafeeorikEJSA8PD/7yl7+wZcsWRFHk\n4MGDbNiwgTvuuINFixbx+uuvYzabSU9PZ926daxYsQKAFStW8NZbb9Ha2kpJSQlr165l1apVF+WG\nZGSGMx2WVrKqJJuIxOAp5NZIiTPbi/yxC940tddwfXJ36S83tZ6ZCd1D16IosDf3U9otzSgVKhaM\nugM3tUef1zKYGth0+j0copRRGOQVw8JRd6FUnL1iiSAKHCr82mkpNBjxaLLCrzcl0m5VU9jsgdmh\n5P6p5Twyqxy3y1G2+RIiinCqWk+Mn5Ugva3PffIbPHBTCYwLbQck8Xi4wps39sdgtKnw1Nr51dwS\nJoa2sfZEINXtXVOFuobWlMiZ0DKXAxEFn2aEsfZkKPNim7lnchW/2Z5CZeu5KyCdTUQqFfDA9Epi\nfPs20L/Qtr57LNrlmsGedo5WSqM4SQEmpoQbAEir9KaqTZomckNqvfMMoODuf+2/aG0ajgxKQMbG\nxvKnP/2Jt956i8mTJ/P888/z8ssvM2rUKF544QVsNhvz58/n0Ucf5YknnmDcuHEAPProo8TGxrJ0\n6VLuvPNObr31Vq699tqLckMyMsOZk2VbcQh23NQemKwGHIIdg0XFtznBLEkNZ1Jopku1lVmJK3HX\ndlf0OF2xh4rmHABmxK/o17vRaDWwMf1dbA4pkunjHsy1Y/4LlfLsqs3usLEre60zU3sw4jG7TsfT\n25LQqESy6yV7nleX5DE9yjCiEmVA+pwyat2ZGN7htOg5E5NNyd5SX5Z3moU7BNiYF8B7R6OwC0qC\n9RZ+PaeYY5Ue/PVwJHvKgnAVivKkfpnLz87iAP58KJrRwe38YnYZf9ibQHbtuQ3HzyYiNSqRx+aU\n46e7ePOgS1vc2VPi63L95SmNZNRK0+luHlOLWikloq3vjEJODGsn0ttM1+8uv8FAU4e517mvFBTi\nMEslslgsZGRkMHbs2F6JOjIyA0WhUAy5LLrmjlq+PfFHRERSQmc6o49/Px6O0Z5AuFchq0ZVOfeP\nDRzHgtQ7nMvVLYVsyViDiEhC0CTmJv+gz6Foi93EupNv0WaW5vR4aH1YMemRc9a3ttiN7Mj6J7WG\nkkHdpyjCx6dCKGjwoLpdh0NUcPOYWq5LGl4Z1vdc9Qp/2/vEOfezC1BtUBPle3bvuM8zglma3Ihe\n65C8PjND2FEkZYfG+RqJ82un0uBBh0XgmsQW/naya/haZngiIrzxI5SP/YOR+D1G+5j4xexSTHYV\nr++L4obUOq6KPXcVl7O9lDZ0aPjf7fGYHRdneEKndvD6khx0PaoyHiv3YnJkG0oF/Ds9hK2FgSgV\nIr+7Op8QTxtHK7x452i0c/9rU8LY+JOrL0p7zpfLrYfkWdYyMkOEtJKNiIjotX7OKjAlzTr2l/kS\n6WtnaVK3eNRp9MxMWOlcNloN7M79BBERX48QZp6Rkd2F3WFjS8Yap3h0U3uwbMKD5xSPHZZWNqa/\nO2jxaLPD01sTKGjUU97mgbfOzovXFLAkeXiJx4FitCkw2RTnFI+nqvVMjTCg1zqwOhR8kh7qFI9h\nnmaSA9qwC0oajFoq273428loRqLouDIQ0dLBmlVZALx+XQ6S5+HQeqEdLGWt7jy/S7L5+c3CEnYU\nBfJNdsA5vSLPFokM1Nv49bzSziSxMzn/z89sV/G3467ekFMi2zhcLtmd3ZDagF5jRxAVbMiTopBT\nItoI8+qeg741twqH4+J6Vg4XZAEpIzMEqGktcg49h/nG02KUrCT+lR7GNcnhJPudcpkTODNhFTqN\nNHQtiA525/wLs60dtUrLwtQ70ai0va4hiA52ZP+TxnbJnFyt1LJ0/APo3Xx77duTFmMdG9L/Soux\ndkBGwf1R1qLmiS1JtFvUlBvcWRTfyMvX5hPiObLseUB6ADYblejUIl5uZ//Q2iwqjDY1sX6SDdLn\nGcHsLZUyQoM8LDQa1WwuDGFPaSCNJq3s7ThsEQEb8f5GRKWO+76WDPrVavhgdTa/mFrASBOSzSYN\nL+6Op7zVjf+ZV0JRk56/Hw8blIiM8TXzyOxyen9OF/a7OFblQ2Fjt62gQgGJ/kbMdiV6rcM5peRg\nmQ+NRg1KBSxP6Z6HLqLg6Q0nLujawx1ZQMrIXGZEUeRYZ8lCP30oZU1SZOJgmQ9NJl/clPmkBnVn\n7MYFTSA2cJxz+XjJZmdkcG7Szfh4BPV5jb15n1LVkg+AUqFiybif4OsR3GvfntQZStmQ/jYdlgs3\nCBdF+CrTn7ePxNBq0SAq4Ml5RdwxYeTZ84B0v40dSvw8hF4WPX3te7TCm1nRUpbp1gJ/thcFAqBW\nCtQb3bAKZw7XnfngHDmCY2TS9f0oAA1FTXpsQndiyc/Xj+LdI+EkhNr5YHU2q5PL6Fk6b7hjtqv4\n44FYDpf78MisMpQKeHN/1DkNx88mIseFtPOjiVV9bzxvFLx3LMpplwUQqLdzokp6QV8U30Sw3oJD\nVLIhT/ptTo80EKzvjkL+377si9SW4cUI7L5lZIYXpY2naWgvB0Dv5ofVbsJsV/J5ZgizYty5NqHM\nua9O48mM+BU9js0ko3IPAKPC5xAbOL7PaxwtXk9x/anOJQVXj/4xgV59J9h0Ud6YxeaMNVjtpgsW\njw4HPLs9lv3lAdR16JgYauC1JbkkBozMwgGiKJmhB3oObEgrvcaTq2KlSjOZtXo+zQilSzhIGdUA\nIjq1g3g/I1PCWlAqLk7kReb74tzfz5FKPx5eN4q1p0K5NrWDD1ZnMdqrlpEiJB2ign+cDOerrCDu\nnlRNcqCJF3bF4jiH4fjZROS8uBaW9XCkGAwNRi3fZge6XHdqhIFmkxq1UuSmMdJ19pX60mxSo1TA\nspRuf0qLXeTLUyUXpS3DCVlAyshcRgTBQVpnycIgrxgqm6Rh7HW5gYR4+ZPkl467prsHnZ242jlf\n0WBqZF/eZ53HRjM1dmmf1zhdsdtpDQSwIOV2wv2SztquvJqj7Mhei0OwXbB4bOhQ8suNyVS36Wiz\nqHlwehk/m1Ux4ux5uhBFsDrAp2/XpF40GdVEeFvQqEQaOjS8eyzSOTztpbUxMbSVaxLqGR9sABGK\nmj1Iq/ZFEOVue/gjkuzflVDS/bKxsyiAB78dxddZgfx8YSMfrM7CW2FgJIhIULAxP5h3j0ayJLmB\naxKbeXpbApazTw8+q4hcPbqe6ZHNF6V1G/KCaDV3/7Y0Kqhtl7JrpkYYSPQ3YheUbMqXhObMqBYC\nPKx0fTdXYn1suSeSkbmM5NYccSa0CIINEZHadi1bCwJICaxnfKcfIEgVYqIDpHlTdoeNnTlrsTnM\nuKn1LEi9vU8LnoLaNNJKNjqXZyasJjZoXK/9uhBFkVPlOzhQ8AUiwgWJR1GELfne/H53PG02DVG+\nFl5bksvUiHNnYA53BiqOBREaTRoC9TZsDgV/PRKFQ1CwLKWWBbFNaFUiJ2u82VoYRHqdD2bHub30\nZIYDIuNDWnl3ZRZPzJdGFt5dmc3ypFq6haSC73JDuP/bUWwp8Oe1FRV8sDoLsDAShOTRSh9e3RvH\nxFADd06s4amtibRZzn5MfyJSoYB7p1SR4Nvee+N54hCVvH0k2mVdSqCJSoM0n7zLXHxPiR+tZhVq\nJVyf1EBXhLmu3UxBfV+G5yMXWUDKyFwmbHYLp8olM+4Q71gaO6Q5Pf8+HcqMaG8Wxxc793XXeDEj\n/gbn8uGib2juqAYUzE/5YZ+JMBVNOezL/8y5PDH6GlLDZvTbHkEUOFz0LSdKtzjXna94FER4cWcM\n32SH0WrRcuu4ap5ZUIyX28jPUhzoZ9VqVnGozIekzmH8f6WHUt3mhsWuYH1uCLtKAmg0aZGHpkcS\nknB8Y2kO8+Na+SIzhOd3xgPQZNSwemwD767MZpmLkFTyn9Nh3P/NKPaX+PD+qgLeX5kF2BnuQrKg\nyYMXdiUQ6mXm57MqeG5HEnXtZ/97709EqpXwi7nlBLoP3o8xv1FPWmW3r65CAR4aaZw9wd/EtAgD\nVoeSzQVSFHJOTAu+uq6iAAp++NG+M085opEFpIzMZSKjcg9mWwcKVBhMUhTydI0nxc1+TAjOwVPb\nLbpmJ92Im0YaG82vPUZ+7TEAJkYv7nM4us5QyvbsfzqXU0NnMTF6cb9tsQs2dud8Qk71wQu+H4MJ\nfrEuieJWPXo3gZeuyePaxKYRac9zIZhssLvYl1NVemZ2Js3sK/VlT4kfVocSsd/ueGTMg7syEYny\n7mBRXBMtZi2/3JjKnw9Fs6UgkNIWyVz7ya3J/H53LHXtWlaP6S0kRZR8eCKSB74ZxckaT9asyuWv\ny7MY7hnbdR1uPL8zEbsA/zO/hDcPxFPQ2Ns9oif9iUh3jcCTC0pxV/Vd4el8+MfJCGw95mb66hwU\nNUlZ2jd1movvKvKj3apCoxJZktQ9F/JEZSNm6znG5EcQsoCUkbkMGK0GMjuTXwK9IjDZ2rAL8Mnp\nUObFmpgS0T0Ukhg8hSj/UQA0tVdxqPBrAMJ9k5kQtajXuVuMdWzOWIMoSg+g2MDxzEhY0Wu/Lqx2\nM1szPqS08fQF3Ysowr4SL57elkK7XcvVCY38/pp8gj2vnI70bNgcsLfEm7cORrM+N5BJER0oFVDW\nomPtyTD6jjT2fEoq+tnnzP1khg4iWoX0919u0LOjOICyVvd+LZgKmvT8ZnsSL+yMp6qtW0guT+4W\nkg5RyV8Ox/DT71IpbPZgzapsXrlmeHtIdljVvLI3juImd55eUMK/08M5VnF2T9r+RKSvzs5TC0pQ\nKQY32tFhVbP2ZKjL9aJ8zAgiBOltLE5owuJQsbVA8mmdH9eMt1tXX6fg3k8PDOr6wwlZQMrIXAZO\nlm3HLtjQKLVOX8bthQF4uXmxKL7QuZ+H1ptp8csBSejtzPkYh2BH7+bDvJRbUShcf8IdlhY2nHob\nhyC9iYf5JHTu1/eDy2gxsDH9Hadx+fkiivDy7mj+cSISFPD0/CJuG187Iu15zhdBgMNlnry6L5a/\nn4iioFnPA9Mr8XJz0GFV8pfDUdh6ZFr3Fo0DQQ7vDi26LXusoprz/X5KW915bkcSv92RQFmLjlWj\newtJq0PF6/vjeGR9MgarljWrhreHpF1Q8u7RSHYX+fLEvFIOlPuxKc/vrMf0JyLDva08PreUwX4O\n+8r8qTJ0l6fRqKCmsx728uR6PLV2thf6Y7Qq0apErk3sjkJ+erJkyFU5u1TI3byMzPdMq7Ge/Jqj\nAOh1fgiig1azmm9zglgUV4Kvrnv8ZHbiTbip3RFFkX35n9FmbkSpULEg9Y5e1WPMtg7Wn/orVoc0\nt85fH87iMT9Gqeg7AaPVWM/69L/SbKy5IIPwDis8si6ZgmZPJoa18frSXOL9R6Y9z/kgiHC6xoM/\nHYzivbQYCpuk7+m28TXOz2dNWgQNxh71084aZeyPK+MhNby4OIK+yqDjxd0JPLU1gaImd1aNbuC9\nldksT6qhS0gabRpe3J3ArzYl4ecNa1ZlsyppeHpIiij4MjuUtadCeXB6BQ1GN9aeDDprv9SfiEwK\nNHL/tAoG9xko+OuRGJfzh3lZMdsVeGgFbkitx2RXsa2zWtTC+GY8tVIUUhAV/HHPleELKQtIGZnv\nmbTSTYgI6DR6Z8WZLzKDmRUtMiOq25IiKWQqkf4pAGRW7qWsMROAaXHLCPJyzRa0OaxsTH8Ho9UA\ngJcugCXjfoJaqaEv6tvKOg3CWy4o0/pQmZ7HNozCKqh4eEYZD82sQCsnClPYqOOdI5H88WAsGXXe\nzvWzo1tYECd9t+tyAkmv8WagYkOn6i9pQo4+Dld+NDkOw0s/BOCFpX17twLUdeh4ZW88v96URE69\nB6vGNPLeymxuSO4Wki1mLb/dkcQzWxOYEWthzaoskjzrGI5Ccl+pP386GMMtY2sI8BD4v4ORLgbf\nZ9KfiJweaeCm0bWDakt1mxs7CruTExUKsDuk39yCuCZCPC1sK/DHbFOiUwtck9Do3Pd/N6YN6trD\nBVlAysh8j9QaSpxCsCsyWNTkTmatL0sSew5d+zAtThq6rm0tIa1kEwBxgRNIDZvlck5BcLAlYw2t\npnoA3DWeLJvwIFq1jr6oaMph8+n3sdiN5y0eBQFe3hXF+2kxRPuaeGNpDpPCB2+hMdypNqj5+/Ew\nXtoTT1qVDz3FXaS3mbs6q2Zk1un5Ovvs1X+60KkdrEytw8vNQW+xOLyEgQzo1UrW37sQ+2t38uEd\nc9G7SS93T109Acfrd/Hlj+b1e2yTWcsbB+J4bGMyp2v0rBgtCcnlPYRkTYeOJ7cm87tdsdw/t5k1\nq7LwpJ3h9reSXe/Ji7sSmBPdzMwoAy/tjjlr1Zr+ROT1KY3Mi2kaVFs+ywzD2CMvR68VaLNIFj43\nj6mlw6ZmR7FUdnRRQhPunRnbHTaRvYU1g7r2cEAWkDIy3xOiKJLWWbLQXePljBb+Kz2U1WPqCNR3\nJ53MTboZrVqHydrGrtyPERHwcQ9iduKNLvMZRVFgR/ZH1LdJnnIalY5lEx521sk+k/zaY2zL+gf2\nzjmS5yMeDWYlD32XQkGzF7ePr+Gp+SXotcPr4XSxaTYp+c/pYJ7ZnuSsX90Td42Dn84oR6sSaTRq\neO9oZL+JFF2olQJXJ0h1whfFN1Jv7CszVY4+DhfmxgZS/tubMLxyB0tGR/Y7H3nl+Bgcr99F+q+W\n463pex+DRcOfD8fy8/UpHK/yZOWo3kKypEXPrzal8vq+aH53fQVrVg0/D8nKNh3P7kgk0tvELWPr\n+d8d8djPUrWmPxF516RqUgMMF9wOm6DknSNRLtfpEomTw9tIDuhgS34AFrsCD43A4viuKKSCu/+1\n94KvO1yQBaSMzPdEWVMWdW2lANgckmfZ/lJf/NzVzI3pnoSdHDqdcL8kBFFgd+4nmKxtqJUaFqTe\niUbt5txPFEX2539BRbNUvUalULNswk/x1PX2hBRFkfTyXezP/5wLeZDsL/XkFxtT8NEJvHxtHosT\nrmx7nopWKXr0+KZkthQE0t2Vdn8oCkTum1JJiKcVu6Dg7SORtFv7dxpXIDI7uoWXringtvE1eLk5\neOdoJLJYHJ48e904zH+4g90/W0r4QMsTAWPC/Gh++U7qn7+F5AD3Pvcx2tS8czSGR9ancLjcmxWj\nGnl/VTY3pHQLyewGLx7dMIq3j0by1vKiYech2WrR8LvdidgdCh6YXslvd8RjOotLT18iUqmAR+dU\nEOp54R6RmXXe5DV0j+aoldBmkUaPfjCuhnarit0l0svjNYlN6NSSwCxrMVHXNrLnhMsCUkbme0AQ\nHRzvHIb20HpjF2yYbEq+yQ7kptElzv30br5Mi10GwMnSrdS0FgFSMo2fPsTlnCdKt1BQJ821UaBk\nybif4OvRe3hUFAWOFH3H8dJN591uhyAZg394PJrrEpt46Zp8l0jplYTFDkfKPXllTzT/uyMZABEV\n/Qm8pckNTAyTqu98kh5KcXN/IkJkQqiB5xYXcu+USgI8bAgi7C7yJbu+70iyzNBEr4LdP70Gx+t3\n8ZtrJ6IZhB2Bv15H9lM3Y/nD7aweHdHnPia7mg+OR/HwulT2FPuyPLVLSHZnbadV+vDwulF8dDKU\nv96Qx1+WDR8PSatDyZsHYsir9+DxuWW8vCeeFmP/+/clIjUqkSfnl+CpufB+650j0Th6DKN7aiWR\nGOdnZnpkK5vzA7A5FHhqHSyM6xo2V3DrP3df8DWHAyO0Kq2MzNAiv/aYc45i19D1dzlBLEkyEOrV\n/Vo9J+lmNGo3yptySK/YCUBK6Ezigye6nC+rar9zO8DiMT8iyNs1sQbAIdjZm/cpJQ3p593m+g41\nT29JRKcR+c2CQmL9zlFvbIRSaVBzuNyPbYV+WBx9JyWdyaigdlaPrgPgQJkPu4r7tiVJCujgpjG1\nzqo0AGlVXnyVFUJ1m1ufx8gMPa6KDeCLexYR4Nn3vOPBoFap+PzeRYiiyItbTvG/W3r7tVodKj46\nFcEnp0O5aXQty1MbWDGqgW+yAlmXFwQo2VMawJ7SAK5PquOdFdkYzAp+vSWVC3MA+P4QUbA2PZya\n9gZ+fVUJfz4Uw50Tyoj06VsAd4nIniMknloHv1lYxJNbEhA4/2y/VouGLzODuWVcnfMaZrsCnVrk\npjF1PL01kb2lviyKb+bapEa2FwVgdSjZU1SHwyGgGqG+ZrKAlJG5xNgcVk6WSiUL3dQeWOxGatq0\nFDXruWlMkXO/1LCZhPsm0mZuYm/efwAI8IxkeqcPZBfF9ac4UvSdc/mq5FuJ9EvpdV2r3czO7I+o\nbi3ste1cbMnz5T+Z4UyLMHDvlAo0V1iGtdUOp6o92VniT26D13kd6+du4/5pFSgVUN7qxkcnwznz\nAR3pbebGMbVM6FHrPKfeg88zQ84SqZQZavx+6QQeXzyu33mNFxOFQsEz103kmesm8u3pUlb/fU+v\nfeyCiv9khPN5ZhgrU2tYntrAytENfJsVwHd5wYCSDfnBbMgP4uYxNby3MpvTpRr+fDKRoS4ktxUF\nUm/U8OisMj48Hs68mAbGhvb9UtuXiAzU23hmYUlnCcnzv8/NBYFcndiAn7sUinRTiQgiBHjYuDqx\nkY15gcyLbcbbzcH82Ca2FgYCCh779hh/Wj39Au546KMQh5njpcViISMjg7Fjx+LmJr+hy1wYCoXi\nezN7PVW2nRNlW1GgQOwcNvq/A1HcOKaOSB+pA/R082Pl5EdRKlRsSH+bxvZKtGp3bpj4M7x03ckZ\n1S0FbM74gK7hp+lxyxkdMbfXNY3WNrZlfkhTR/V5tdVsU/Di7ljqOnT8dEYZE0I7LuymhynVBjWH\nyv3YUuCPVTj7+7Xwxt0oH/unyzqVQuCJeSUk+Jsw2pS8sDOeuo7ufirQw8qqUXXMiGpF2fkMK23R\n8UVmCJl1eobyA1ymm6M/X8Lk6KBBn2ew/VBWdTMz3liHsZ8sZQUi16fUsyy5AY1K5NvsAL7LlYSk\ntF3g7knVzI1pYe0hT3bXRDuPHKrE+Jr45exi1uUFEexhZmFC/0kyfblMpFd78qdD0VzIPUb7GPnt\nwmLnOR0CqJRgsil5cksSN46pZV5sCy0mNU9sScIuKFErBSyv/ui8rzUQLrceGplxVRmZIYLJ2s7p\nSmkejKrTk/FUjSfxAVaneITOoWuVG0eKvnNWppmXfKuLeGxsr2Rr5od0icfxkQv6FI8GUwMbTr1N\nU0f1eRmEFzfpeGhdKh4akTeXZl8x4tFqh6PlnvxhbzTPbE9hXV7wOcVjf9w6rpaETrPwD9IinOLR\n283ObeOrefGaAmZFS+Kxtl3Lu0cieWFnPJl1ngzlh7YMJHhpaH3xhzhev+uiiMeLwegwP9pevYv6\n528hzrf38LmIgvW5wTz03Sg+PR3MkqQm3l/VnbUtouQfJyK4/+tRjI5SsGZVFtFuTQxlD8nSFnee\n25nI4vgmHKj4LD2w336urzmR48PauWN81QVdu6zVgyOV3SMSKiXYBakW94pRdazPDcIhgK+7nati\nWgCp0s6/0or6O+WwRh7ClpG5hJwq347dYUWpUGEXpGzcHYV+/GxWuXOfUWGzCfNNoLDuBLk1hwEY\nH7WQSP9U5z4GUyMb099B6KxvnRgyjUkx1/W6XkNbOVsy/obVIc00H8jImiDCZ6eD2FoYyJ0TalgQ\n13xFZFhXt3VGG/MDsAqDH6OfEdnC4gRpAv363EBOVnujUzu4LrGRa5Ma0am7jJ/VfJcTxN4SPxzi\nFfBBD3MemZvEm6tnXu5mnBV/vY6C39yC3eFg9Yc72JDj6kEoomBrofQbnx/bxM1j6lg5utE5tC2g\n5O2j0WjSHPxsZjmjg2u47+tYwIOh+GLTaNLy7I5EHp1dgr9OwztHQnlgek2f/VZf6xYltFDbrmVb\n0fm/CPzzRASTQnPQdqonVef558c2s6MogEMVPsyJbmVpcj17SnxxiEoe/mIft0+JP+9rDXVkASkj\nc4kwmBqcglDsFH5bC/xZNaoedWfs39PNj8mxS2juqOVgwZeAVL96YvQ1zvOYrG2sP/WW07sxym8U\nc87wgwSobM5jW+Y/EDmLYdoZtJjUvLAzFo2OewjtAAAgAElEQVQaXr0uFz+Pszj2jgBsdkiv9WR7\n0fnPbTwbEd5mfjRJimpk1+v5OjuI+bFNrB7dZQQORpuSjXmBbCuUJtjLDG22P7CYBUnhl7sZ54Va\npeK7/5b6jmc3nOCF7Rln7KFgd0kAu0v8mRXVzA/H1bJidCPfZAWwLi8Ym6DijQOxuKttPDmvjAR/\nM/d9nQhoGWpC0mRX8Ye98dw7pYJrElt5dV8Uv5pTjnKAP60fjq+jtt2N0z0qRg0Es13F345HcP90\naaRIoegeyr55TC2fZYQwK6qVAA87s6Nb2VvqR6tFmm4wOuzsNb6HG7KAlJG5RBwv3YwoCqgUahyi\nnRaTGpNNRZx/tyfZVck/AFFkZ85a7IIND60381JuQ6mQekGr3cy6U29hsUvDokFeMSwcfWcv8VhY\nd8KZeDNQjpR78e6xSJYmNXLjmDrnnLyRSG2bmgMXMdrYE3e1ZBbuphZpMql590gk90yqYnZMKwA2\nh4LtRf5syAuk4yw+kDJDg4bnb8ZP37f/4nDi2esn8ez1k/g6o4yb/namnYyCg+X+HCz3Z0p4C3dM\nqHGJSJrsGn6/JwEfNyvPLioj3NPCT75NRZr1NnQ6Coeo4L1jkaxMrePuibW8tDuaJ+eXMZCkZ4UC\nHp5VzrM74qhuO7/EtSOVPixvrSXCR7IG6uo7J4a1saUggGOV3kyPNHB9cj37y3wRRAU3/X0b2U/e\ncr63OKSRezMZmUtAfVs5JQ2S3YZDlDqZLQX+rBpV59xndPgcgr1j2Z37CQZTPQqUzE+9HXet5P1n\nF2yd9aolIeLjHsx14+5zlkDsIqNiD8dKNgy4bTaHgncOR5DfpOfZhYVE+VoHda9Dle5oYwC5DZfK\nT1HknimVhHpasQvwzpEopkQYmB3TiiDCvlJfvs0Jptk0MPsfmctDoBaqX7wD5UDDV8OIVWOjcbx+\nF9k1zUx6dR1nenGnVfmSVuXLuOA27p5U6RKRbLVIQ8XBHmZeuqYQb42NhzeMYmhlbCv4JieE2g43\nHp5RySt7Ynj8qtIBOUeolfD0/FJ+vTER4wAturqu+aeDcbx8XT5KhWvW963javgwLZzpkQaCPW3M\niGzlYLkveQ0mOixW9G59VZYansgCUkbmIiOKIseKJUGnVKgQRAeFTe5MDG1zzpvxdPNncsx15FQf\ncHo0To1bSoh3LACCKLDl9Ae0GGsB0Gt9WTbhp6iVmh7XEThWvIHMqn0Dblt5ixsv7Y5lUng7byzN\nRT0C7Xnq29XsK/VhS0HQRY82nsmSpEamhEtm4f85HUq7RcUPxtaQVunFl1kh1LTLThFDmRtTQvjs\nJ9de7mZ8L4wK9cP8+l20GM2Mf+UrKttdjbVP13nx+OZUUgLbuWdyJStGNfJttiQk64w6ntqaTJS3\nkVevy8dgcPDCwaHlIXmo3JdGo5qHZ5bzfweieWB6GfoB/PzcNQIvXFvE4xuTEM4jr7jRpGVrgT/X\nJUnznrsGhWJ8zUT5WEir8mJKeBvLU+o5VO6DiII71u7h63uvvpDbG5LINj4yVySX0sanvCmH7Vl/\ndy4LImzMC2BZSqNz3fXjHwQUbDr9LoLoICZgDAtS73S2a2fOR5Q1ZgGSd+SqyY85I5MgGYTvz/+c\novqTA2qTKMK63EDW5Qbw8MxyxoWcpZzDMMTugNM1erYWBV7CaKMrwht38+GeJ1Ao4FC5D2uOhXPb\n+FoOlvvIXo5DnPdvmsp/zR51uZvxvdqJnYnd4WDZO1vZVlTf5/Z4fyP3Tq4gSG/ju+xuH0mA5IA2\nHpxRSWa5kjWnh5aHZIinhcfnFPFZZig3jqoicIDdQZVBy2+2J3A+5jQqhcgfr8/CozOo2BWFbDKp\needIJE/NLwHg3SORHKn0QYmI9bW7Lppv6OXWQyMvXi8jcxkRRIG0ko0AKDo71KMV3s7sXIAxEfPw\ndg9kV87HCKIDL10Ac5JucXYqhwq/dopHtVLD8gkPu4hHm93C9qx/DFg8tltU/O/2OLLq9Pzp+twR\nJR4bOlR8mRnAz9al8taR2EsuHv3cbcyPbeKXc4oB6WFRaXDjHyfCCfCw86+zliyUudwcf3Qpjtfv\nGhLi8XKjVqnY/NASHK/fxf8sHN1re1GTB09vS+aFXfFMi2zjvZXd9j95jV78YkMqafXB/HlZLrOC\nyxkq1j+17W48uzOJ65Pq2VUaRGHjwGROuLeVx+eUcj734BAV/OlgjHO5ayjb391OaqCRUzVSf7Qs\npR4FIgIKXtrWu5LQcEWOQMpckVyqN//8mqPsL/jCuWy0KSlu1jEmWBJtnm4BrJj0CLty1lLVko9K\nqWbZhIfw14cBcLJ0GyfLpao1SoWKGyb+DD99qPN8Jmsbm09/QIvJ1aajP05We/KXQ5GdZsGtI8Ke\nx+6AjFoPthYGkXOJBaMCkTg/ExNC25gQ2kaUb7d35z1XvcLbO57k+V3x1LYPvSxVmW7qn7sF/0tQ\nZnCwXM4IZF98daqEm/+5t89tYZ5m7ptaQZSPxTm03RWDmhPdyB0TannyuzBa8e084vL+HtRKgYdn\nlNJo0uKhMjM92nzug4D9pT58eDzyvK71q7mFjAqSzt8VhTTblLxzNIJHZ0uWbW8diuJEtTduKgfG\nP/z4vM7fH5dbD8lzIGVkLhJ2h5UTZVtd1p2q9mJWdKtzeX7qrWRV7aWqJR+AmQmrnOIxt/qwUzwq\nUHDd2PtcxKPB1Mim9Pcw2lo5F3YB1hwNp9zgzmtLc/HRDZ2H1IXS2KFiT4nvJZ/bqFM7GBPcwfjQ\nNiaEGvBy69/a6IPjEdTK8xyHJCqg/eXb0Grkx9xAWT0hFsfrseTUNDPm1XUu26rbdbywK5FAdwv/\nPa2ClaOy+aZTSO4vC2B/WQDXJdazenQ2D3wbC7hzOUWkXVDyp4Ox/HBcFW7uatbleLMsxXDOl+g5\nMa1Ut2nZmB884Gu9fTiGN6/PRaXsjkLqNAITw9rJrNMzJriDG1LrOVHthcWhZEtuJdemRAzyDi8/\ncgRS5orkUrz5p5fv4njpJudybbsWT60NvVa6ztiI+YT5JrA182+ASFLINOYk3QRAaUMmO3M+ch67\nKPUuogPHOJcb2yvZdOp9bOK536Kr27S8tCuGxYnNrEhtGNb2PA4HZNS5s6UgiJyL6Nt4JkF6KxNC\n2xgfaiAl0Oj06TwTQYTCRndO1Xqx/oFf9iplKHP5SfXXkfHUzd9LferBMtQikGfSYjST9OLnNJl7\nt9FHZ+W/p1SQEmjim5wA1jlLJIrcOLqWJUmN/OSbJEDD5Y5GLopvYFZUK/tLvLhzUv2ARmL+fDCC\nkzW+596xk6vjG7htQq3LOkGED9PCuW+q5BH7pwPRpNd6EeBup+5395zXPfTF5dZD8quZjMxFwGzr\n4HTFTpd1LSYVIZ6SRY7ezZ/k0GmsP/VXQMRfH8aM+BUA1LQWsytnrfO42Yk3uYjHqpZ8tmT8DTi7\nybcowuZ8fzbmB/Lk/GLCvc807Bg+NBuV7Cr2Y2tBEJZLEG1UKkQS/Y2MD21jYlgbYV79WxmZbEpO\n1XiSXuNFRp2n7OU4RLlrfAR//9Giy92MEYWvh476F+/E4RBY+OcN7C9vdm5rNWt5bX88nlo7906u\n4P2VXRHJEL7MCuXLrGDumiDV2b7/MntI7igKpK7djdvHV/PXw2H8dEb1OUXkQzMr+e1WLdUdA5vT\nvK0ogKVJtfh27i6Kkj/k9EgDuQ0epAQaWZ5aT3qtJ40mFZUtHUT46gd5Z5cXOQIpc0Vysd/8jxR9\nR1bVfudyUZM78Z01kQGWjnuAoyXraWgrR6PSccPEn+HtHkBzRw3fnfwzgihVK5kccx3joxZ2n6fu\nJHvy/n3O6xttSl7eHUO0r4V7JlcNyEh3qGF3QOYljDbqNXbGhrQzPrSN8aFteGj6//6r27ScrPYi\nvcaLgiYPhD5KDgpv3C1HIIcA7984hf+a0zsJZDgw1COQffH414d5Y29er/UeGjv3TKpkQlg7X2cG\nsKEgBGkyjsBPplYyLthw2T0kI71NPDyzjH+dDOFnsyrPWbXG6lDwyw2JGO0D824M9TTxu6uLeonT\nz04Hc8s4yQP49X0xZNV7Mi5YzcknbruQ23ByufWQLCBlrkguZsfdZm7iq7TXnSLQ5lBgsSvw7Jw7\nNy5yAXaHlezqAwAsGnUX0QFjaDe38PXxN7ALUvRrVNgcZiTc4DxvZuVejhavP+f1M2s9eOtQJD+f\nXUZq0MAmig8lWkxKdhZdimijSJiXVYoyhhpI8Df1K6ztAuTU6zlVI4nGBuO5HxiygLy8HP/5EiZE\nn38t46HEcBSQXXx+oohb1+7vtd5N5eDuSVVMizC4CEmVQqqz7YXxsnpI+upsPD63mM8zgnlwRuU5\nX7bbLSp+sSEJgYH1TfdNLWVWVDvQnVBT3qrF6lCR4G8ir8GDV/bGASKWP9yBWnXhfd7l1kOygJS5\nIrmYHfee3H+7WOpUtLoR6SNl67pr/ZkaczV78z8FpHmQU+OWYrZ28NXx17HYpezsuMDxzEu5rbNd\nAsdKNpFZuees1xVE+OBYGC1mDT+fVeY0KR8OOATIrnNnU34Q2Rcx2qhSCCQHGpkQ2sakMAOBenu/\n+xrMKk52CsasOj3/3955x0dVpX/4udMy6QUCCYEUCE1qgvRILyId1wq4Iu6Koqi4FlQEVl1BEUVx\nRRTX3yprYV1AmvTeMfQAISQESCW9Tr3398ckQyYFkkAYIOf5fGBy75x7z7kzd8753vec932N1pp1\n5EJAOoe02Q/S0PPuCJV0JwvIUmJSs+lQzuEGQKOyMr5jCr2Cc1l1ugHrztmEpIvawsu9LxKfauGX\nWOfEkHRRW5kelcCuBB/Gd0xDd50kNOkFWmZsakl12qlRySwacbpCJpxNcX4MDreFc5u3M5TYTHcm\nRjbju/H9ancROF8PCQEpqJfcrI47syCJ1Uc/t28XGNV4uNgskYoC/duOZ/e55VisJhp7hTG0w9PI\nspUV0QsoNOYAEODdgqHtJyNJKmTZyo4zP5GYde1YYVcKtfx9ayiPd0qlR7P8OyY8T06xih3xvmw8\n74+hhoKtKjx1Fvu0dPvGBeg1VX+vCdl6jqd6cizVk4s5epQbGLiEgLy1FM97HN1dljrpbhCQpeQW\nGWky8xfKz4GoJZmH26fQNyyH38oISQ+tmVeiEll9xIXonNKwObeuI1NJCpMjLpNWqGNgiww8riMn\nzmfo+ceu5lSnjZGBOUztkeSwL6dYTZ5RS7CPgVPp7izYE4oKGfPHf671NThbD91BNguB4PbClrJw\nvcM+Sbrq6NIuqA9HL27GYjWh13rQt81joMDaY/+0i0c/90CGtH8KSVJhthrZfPI70vITrlnv5nO+\n7Ej0Ze6Qc9VK1eVsrDKcveLKutibZW1UaOplpFNAPp0D8wj1NVTpaW60SJxM8+B4mifHUz3IM4qc\n1HcSjbSQ/MGEO8Kjur7j7eZC4ccTsVplus5fwbF02+yKVVHx44kgfjoRyNh2aXw5MoY1Zxqw9lxj\n5mwLp4GbgfcHxTF7sz9mvEvOVvfft6xIfB3dlJGt04hO9qZNw1waXaN7atHQwDNdLvHVH82u277o\nFB+S81Jo4mUbDxQFfFytxGa6E+xjoF2jQpr7FhGf7cbS/eeY3KPlTbyyW4ewQArqJTfjyT8pO5ZN\np761b+ca1HjrbdZHncaXIJ9mJGQcR0JiSPunCfAO4/cTX5OWZxOIHi5+jIl8GY1aS7GpgHXHFpNv\nzKiyPqNF4h87QugalM8DrTNv+/A8uQYV2+N92Rh349ZGrUqmrb8tNmNEYB4+rtYqy14p1HIsxWZl\njM10wyLXjUeRsEDWHcNaNmTNlGHObkadczdZICtj6s97WHww3mGfhMKI1ld4oFUGv532Y31cYwCa\nehqY1usir21oxq2OIdktKIdezbLRqYy0blx13wKwKqYBv50NuGYZAC8XMx8Pi3Xop40WyCrWEehp\n4niqBwv3heCmsZA/r3YhfZyth4QFUiCoBbZ1iuvLbGMXj4oCrQM628P6RIYOJdCnBdvOLLOLR73W\ng1ER09CoteQbMll5+HOsFSZ/rhKb4criA02Z0fc8/h7XDudzqyhdIF4WqwyxGXrWnm10w9ZGH725\nxAEmn7aNCtBVoUGtMsRlunGsZGo6VWSFuWN5b1AbZgzr6uxmCG4SXzzSmy8e6c1Ph+MY/+M+ABQk\nVp9txOqz/gxpkckXI06z5rQv688H8NqGVrT0K+TZ7meZvr4FNolS97/lg0k+ZBRrebxDCnsvaOkZ\nYqhyWdDoezK5lKPnSNq1Y0TmGbVsiPVjWGvbukdFARcN5BRrSpz7CgjxKSYxR8+RpEwighrc7Muq\nc4QFUlAvudEn//Pp0eyK/cW+bTBL6EvCwoQ17EJi5lFkxUozv7YMaDuRA/GrOZNi60C1ahfGRv4N\nNxfPCmsoyyMr8K8/AlCpFJ7onHbbhucpMEhsu+DH77G1tzZKKIT4GOgUaLMyNvM2Vlm2wKTmRKoH\nx0piMxabb/3aOGGBvHnsmDKQqJZNnN2MW87dboEsz6nkbDp+XN7hRqFvaBZ/apfG6lN+bLxgs0h2\nDMjjqYgkXlrfmlsVQ7Khm5EXul/kSLKeEW2rzlqjKDBzU3NSCl2veT4JhUUjYtCXWTkjK5BZpMXf\n3Ux0sidfHAgmwMNA0py/1Li9ztZDwgIpENQQi2wmOnGjfdtsvSoe1Spv0vLikBUrHi5+RLV6iOOX\nd9jFo0rSMKLzC7i5eJKSE8eGk99UWU92sYZ3t4XwfPeLNG9w+wUFl2WIy9Tz25nGnK5lTmoXtZV7\nGhXSKSCPzoEFeLpUPX10KdfF7gATn+V6Qw4wgtuDpLdHE+Dr5exmCG4R7Zr4Yv14IrlFRvxmlj6A\nS+y40IAdFxrQs1kWCx84w+pTvmxObMxL673o1SyHB+9J5pUNdR9DMqPIhXm7mzO91wWWHfNnfKfK\ns9ZIEsweFM/La1tRZKl6XbWCxPzdYbzVL8F+HpVkW44EENkkn6ZeBi7nuZBXbMLLtXrxJm8XhAVS\nUC+5kSf/k5d3cvjCOvu2XJJxQFEgwDuUtLwLqCQNwzs9S2ZBMnvjfrXViYoHOj2Hv2dTEq4cY8fZ\nH6usY1u8N0eSvXih56UK4SBuJZVNUxcaYXuCH+tiG9XK2tjAzZY2sHNp2sAqTmGySmViM3qQVXx7\nda7CAll7iuY+joszb+zbhPpmgSyP1SoT/vefuFjg+ODYJTCHCZ1T+e2kL9su2XJSD2qeSWe/dOYf\nrvsYkmpJ5vkeiRxJdueJa6Q+LDareGFNK5TrxIh8Peocrfwds13lFGvwcbVw8LIXXx1qRpcACwdf\nrdlaSGfrISEgBfWS2nbcRksR/z30IWarbb2iRcaeN7mBRyiZBRcA6BU+DledB1tiSgWGxOB7JhHk\n14oTF3fwx8X1FU+OzZr53rYQRrVNJ7JJ0W0TnkeWIT5Lx8rTgTW2NkootGhQRKeAAiID8wi4RtrA\nrGKN3cp45oo7JuttOmePEJC1wTJfeFSXpb4LyLJM+PdWfjzmGPqmvX8ek7oks/qEL9uTbEJydJt0\nZEMeqy+0oG6FpMLjHZJJK9TwaIeMKrPWZBdr+Nvvra7ZDp1a5osRpx3OkV2swdfVgqzAzM3hpBbo\nsMyfWKPfh7P1kBCQgnpJbTvuwwnrOFlJgG8FdyQKAWjRKJJWjbvx+4mvULDVcV+rR2nu34n9cas4\nm7a/0nPHZer54Wggb/RJcFgzc6uozNpYZILt8X6sraG1Ua+x0qFxAZ0C8ugUWICbtnLHH1mBhGxX\nu9f05TwX7hQHGCEgq0drbw0x79xYyra7FSEgK/Ld/rNMXn7QYV+rBgU83SWJdad8SoSkzOMd09h+\nXCKZUo/ouuk3BoRl4K03MaxlNlUljUnM1vH37aVB0Sunf9gVJnROd9hXYFLjobOy96I3S/9oypR7\nA/jiscHVbpuz9ZBYAykQVJMCQw6nkq6m7ioVXIoCLloFkwV83QK4p0lv1h77p108dg0bQVjDDmw4\n8S2peecqnFdR4NvDjQn2NTBrQMIttzqWXkdpvbJs6xD/dzqAmCvV96Ru6GYksonNASbcr7jKJ/Yi\ns4pTaTYHmBNpHhSYRDd0NzKlazBfPNrX2c0Q3GE82aM1T/ZozfGkDCIW2GZqYjM9eG1ja0J9Cvlg\nUCy/n/bhP8cDAZm/dLnM1394A6V91c3tQLcmNKSdfx7eLla6N8urNONXiK+J57pe5J+Hgqusf1uC\nP2PapOOhv7pPLdnGiO5Nc/ntTCOWRifzxR30rCV6boGgmhy5uBGFq6F6SgWXi9YLkyUPrdqF7i1G\ns/74V/a82O2D+tIqoBs/7f0HphILZVlyDWrmbg/h9b4J+LjeOktE2faXvhabYMcFH1afCaiWtVFC\npoVfMV2C8ogIzMff3Vzh3KWk5uvsYXbiMt2wKneGlVFQc75/uAuPd7/H2c0Q3OF0DGpY4nBjwG/m\ncgAu5LgzY3NrgryKeHfgOTbFePP1H01RYeX5Hol8tj8AuPmzGKeueJFt0OGtt9Dcr6jSrDVdmhYw\nIjeNNbFVx4h8d0c4c4fE2ftHV62MwaxCr5V5oNUV/u9IE1YciWdsRPOb2v66QghIgaAaZBUkcz49\n2r5tt9YpOkyWPAC6NR/FttPfY5Fta/xaNOpC+6Z9WLbvnUrPuSPei8t5et4fGn/LgoKXtzYqCiTm\naPn1VGNirnhf+2BsXtMdGhfQNSiX9gGVpw2UJLDIErEZbnYHmPRCsdzkbufs34YTHujn7GYI7jK8\n3fRYSzLc+L62jEIgKc+NmVta0di9mDkDzrPhlBef7Q9Bp7LyUq8EPtwdDKi5mUIyOV/Pt9FNmdLt\nEg30xfhXMjkztl0mSblVx4jMKHIh+rIbXZoV2fdpVLblPb2Cc1hzxp+nftkuBKRAcDdRPmh46dS1\nSrKJxTaBPTl6cRNGi61jCPJtTedmA/npwLsVzmWR4R/bQ/jrvUn0bZ53S9pfXjgazLArwZuVpwMw\nyNfqBhR89BbuDcqle9Nce9rAyqyMeUY1x1M9OZ7qyal0dwwW4WVbH8h97yE8XPXXLygQ3ABqtYq8\njycCcP/nq9l0IYe0QldmbQ3Hz9XIzH7n2XjKiw93h+GuNfNSzwTe39mcmxlDMt+k5dO9obzQI5Es\nQxGtGlbsB6f2TOKtTS6kVREjcvEfoSwOirHH9NWobREndGqFYa0y+OFYIImZ+YQ0uBlpX+sW4UQj\nqJfUZPF6VfEaJdQoWPH3CMYsG8kpSgOgoUczeoaPqTRAeHymnjVn/ZjaI7nOg4KXF3mKAkm5GpYd\nDyQ2s+rYexIKTTwN9AzOpmdwHj56a6WCESAxR2+3Ml7Irl+xGeu7E43pw/Gob9fI9ncIwonmxvhi\nx3Gm/XbMvu3tYubZbpfYetqDgxn++OpNPN81kXd3lTq43Jz+SULh6S6XUCkmugYbK/SNsgJTf2uF\nSa7cG7JTQA4v9EhymAmSJFsUjjc2tsTbxUz8O89ctx3O1kPCAikQXANFkTmUsK7CfllRoZKs6NRu\nIGEXj176hnQMGlBBPCoKfHOoEX3C8nihZ3KdOsqUtzYaLbD5nC+rzzbGrFRuFdSoZFr4FtMvLJOI\nJvlo1VfjW0LZc0nEXPHgeKoHx1M9yTE4wV1c4FSsJVYggcDZTO3bkal9OxKdmE7XzzaQa9Qyd1dz\nPHRm/tb7AjvOuPPurpY08TAwKCCRf8eVhtu5sQ5YQeLrP4IZ3SaVTXFqBoc7hlxTSbBwxDme/a0N\nNguoI8dSfcguSsHP3TZ9bVv2A1q1wtCWGfx8IgCT2YJOe3tLNGGBFNRLqvvkH3/lGDtLAn6Xnbq2\ndRYSjb1C7fmtXbWetA28j+iLjoKzwKTisz1BvHbfJTR11B+UXkrZJ9rYK1r+fawZqQWVTaUouGll\n2vgXMCz8Cs0bGLHKtuPLr8fMKNTarYxnMtyxyMLqBPXLAtnZT+KPtyY4uxl3HcICeXNxzHBjCyf2\nTNfL7DrjSnS2Py18C/EsTuWooTlXReSNicnuTbNp6pnHsNYFFZf1FKt5+ffWldbhqjXz+fDYCscY\nLRKvb2hFu8b57Hlp2jXrdrYeur3lrUDgRKyyhcMJV9c+lvda9vdoZhePWrUeP5egCuJx23kvXLQy\nM/pdqhOrY3lrY3q+hl9jGhGd7INcodOyrWfsHJDH8NZX8HG1YrFK6DSlaRhtpWQF4jLdOF6Sazo5\n/86JzSi4ubzWO5QPxt3n7GYIBNXC280F68cTkWUZ7avLMFjULNwXglZl5bmul9h2Rs9RQwvaN8rn\nZLoB8AcUbqR/O3DZlyu+WrQqFYNaOubP9nK1Mrv/OWZva1mhjmKzli3nvRkUnmvfJyvgorFZIf93\nqlGt23SrEAJSIKiCsyn7KTLlOOwrFWxe+oZcKbgIgFrSYLYaSCo4Yy8nK/Dhzqa81OtynQQFLysc\nMws17LjgxbYEf4rM5X/SCo3cTfQKzmFQ80yQJDQqBa3aJhpLxWOhScWJNJuV8WS6B4UiNmO9ZutT\n99G3XaizmyEQ1AqVSmVfatF29vfE5qv556FgNJItbuTOMy6AP92DcjiQpAXcS46snZCMz/Ygp1iH\nq9ZKr5BChxi4zXzMTOmSyOI/Qiqc/8cTQfQNy7Wnqy2d/enfPJv15xry4o/LWfjYQ7Vq061AjBIC\nQSWYLAaOXNzksK9UtOnUruQZMgBbfmurYnEol5jtwtEUd17vc/mmWh3LTlNnFmk5dNmLHRd8uVLk\nOHUhodDUu5gh4Zl0CihAViTcddaSzunqdFlSnovdyng+yw1ZxGas92TMHoevp/v1CwoEdwinZ9uE\n5KxVe3lv53m+/qMZKknmic7J7DmnAXzo3zyTbfFegJbaisgsg44fTzbFRXOJyKAiByfJrsGFXMxL\nY925xuXOL/HBjhBm9k90WH6k18gMbiW377YAACAASURBVJHJ10ckFt7GgcWFgBQIKuHEpe2YrUaH\nfaXrHk3W4tI9KDim6Pv6YGMe7pDG6Hscj70RSoVrZpGGg0neHLjkxeU8N4cyakmhZYMChrXKIMDT\nhJtGxk3n2DazVeJMhrvdASajSHfT2ii4sxEe1YK7nTmjezFndC8OJKTQa9Fm/n00CAmZRzuksj9O\nDWgZ2Tqd1WcbUNsYkgaLhiV/hPK0kkiXJoUOa94fbJ/JhSxXYjId4+0m5nqQnKcmyNuWfKJUSA5s\nkcWGuAZsPBHHkA7htbvoOkYISIGgHIXGXE4m76pGyavWvGKzxM/HGjK5a1qVQcGrCoVT5dkVyDWo\n2HPRl8NJ3lzMdXSG0ahkugTmEhWaQwM3M36uFvvUdCk5xRqOl6QNPJ3ujrEG+awFdz/Co1pQ3+ge\nFmjLcFNowO+d5fx0IhBQGHtPGvtOawAVj7RL5udTAVz1oK5+xy0rEkv+COExUzK9g3NwLfOc/kqf\ny7y23oVMg2Pc1L9vb8XiUacdrJBuWpmBzbOZ8PN20oWAFAjuDP64sB6lJBUhOHpdlxWNpWw+50n7\nxvn8ucuVSgVieUeXa6EotrA7ey/6sPOCH5fyHEWji9pKn9AsOjQupLGHiYYl6QPLEp/larMypnly\nMUdfr2IzCqqHEI6C+o63u97B4WZFTACg8ECrK2w8ZVsWNKrZJX671LTMUdXtSyV+PBFERqGWoS2v\n4ON6tf+fd/95pqxqjUW5Kr8ssorlJxvycIeSpVElZQeHZ7L5vC9ZBQb8PG6/YP1CQAoEZcgpSiP+\nylGHfVeFn6N4lBVYvL8RU7qnOyyatpeuoXCMzdCz/FQgCdmO09PeLib6hWXTqmERTb2NeOisDu8b\nzCpOpbtzLNWTE2ke5BlFbEZBRXo0UrPn9ced3QyB4LairMON+pXvWRfbCFAY3DyD9fHuqJDprL5I\ntDW0zFHVE5Kb4htxpVDPmLaXaOpzdTz4YtRZnlnVlrIxIjfENWZM2wx0ZVSZh85Kv7Acei/8F6ff\nevaGr/VmIwSkQFCG/edXVatcQpYGs9XCsz3SKwjE6gpHm7VR4qcTjdmV6EfZTinYq4ieIbmE+xUR\n7GNAU06gphfYYjMeS/XkXKabiM0oqJLPH2jLcwPvdXYzBILbnlIh+eAXq1gZb5txui8ki12JPmhV\nMmo5GQOlFsnqicijaV6kFTZnfId42jS2jQsaFSx84CwvrmvjcJ43N4Xz0f1xDmPHkPAM3ohviSzL\nqCqzVDgRISAFghJSc+JJzY2/brlvD/vzROSVCqKuJsLxfKYLXx4MJsdoWyCjlmTaN84nMjCflg0L\naezhODVtlW2xGY+W5JpOLdAhYjMKrsXJFwfRNjjQ2c0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CCOGxtm/fzujRo21mML/44ovs3bvXboFuIYRwlce1PP7jH/9Aq9Vag2F2djbfffcdK1eu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\nb6qgKAI6zXn4h4O88ccqNFoIeOXcduNdfPGBB2P2f+/gr+K+9gSdAk3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ZAAAg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\nUYAhFOIAAAAASUVORK5CYII=\n", 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WWFyG8T//+Q9OTk6aYPD8+fOkpqayZMkSrK2tmTRpEiNHjuS7774zc0mFEMKy7d69W5M1\nPHr0KBs2bODTTz9l7ty55i6aEKKbsagxjBkZGWzYsIEvvviC2267DYD09HT69eunNVsvNDS0xUV2\nhRBCNFixYoXmCSslJSUEBgayfPly/vjHP5q7aEKIbsZiAsa6ujqefPJJnnnmGa3lHSoqKnQeMeXg\n4EB+fn5XF1EIIboVZ2fnFh95KIQQxrCYgHHt2rUMGjRI56kPjWuqNVVZWan32ar61NfXc+3aNWxt\nbQ1aIkIIIYQQoidSq9XU1NTg5OSk9RQlQ1hMwPjf//6XgoIC/vvf/wJQVlbGX//6Vx588EGys7Op\nqanRLK+Qnp7OmDFjDDrutWvXOHfuXKeVWwghhBCiO4mIiGjxsaMtsaiAsalp06bx/PPPM3nyZHbs\n2MGbb77JsmXLSExM5MiRI7zwwgsGHbcxyOzXr5/BWcmeprq6mosXLxIcHGzQkxt6mt5ef5A26O31\nB2mD3l5/kDbo7fWHhmF+2dnZWuubGspiAsbmmnYfv/322zzzzDOMGzcOHx8f1qxZY9Dju5oex9HR\nUe+jr3qDqqoqoGE8U/PxoL1Bb68/SBv09vqDtEFvrz9IG/T2+jfVniF6Fhsw7t69W/Nvf39/Pvzw\nQzOWRgghhBCi97K4dRiFEEIIIYRlsdgMoxBCCCE6Rq1Wa56p3rjiSGVlJfX19eYslln0pvo7ODiY\nfGUYCRiFEEKIHqqyspLKykocHBxQKpUMGjTI3EUym95S/8YvCKae6CsBoxBCCNGDOTg49NpVQoTp\nyBhGIYQQQgjRKgkYhRBCCCFEqyRgFEIIIYQQrZKAUQghhBDCAuXk5Ji7CBoy6UUIIYQQZrdw4UKO\nHj2KQqFApVKhUCiwtbVFoVAwZ84cVq5cadTxnn/+eTw8PHjkkUda3e+9997jwoULvPLKKx0ovbat\nW7eyYsUKzRNl1Go1wcHB3H333dx5550GHeOVV15BoVDw5JNPmqxcHSEBoxBCCCHMbv369Zp/L1u2\njIiICJYuXdru473wwgsG7bdo0aJ2n6M1gwcPZsuWLZqfExMTefTRR6mrq2Pu3Lltvr64uBgPD49O\nKVt7SMAohBBCCL3UajV7Dh/n+MU8Kmvr8XdWMntMLH6+Pl1elsOHD7Ny5UoCAgI4efIkb7/9Nk5O\nTrz22mukpqZy7do1hg0bxmuvvYanpyfLly/Hw8ODJ598knnz5jF8+HASEhLIzMxk8ODBvPLKK/Tt\n25e3336b1NRU3nzzTZYvX46TkxMpKSn8+uuvhIWFsWrVKs36jW+//Tb//ve/USqVzJ8/n3/84x/8\n8MMP9O3bt83yjx07lqeeeopXX31VEzB+9913fPjhh2RlZaFQKLj55ptZuXIlGzduJD4+HoVCQU5O\nDm+88YbOvrNmzTI4KDYFGcMohBBCCL1+OHCYXblVlLn1o9YrkEylL+/tSKSsrMws5blw4QKzZ89m\n7969DBs2jEceeYTp06ezf/9+EhISKCsr41//+pfe127fvp1169axZ88e1Go17733nt794uPjef75\n5zl06BBBQUGsXr0agC1btrB161Y2b97Mt99+y5EjR4x+YszEiRO5evUqFy5cIDs7m2effZYXX3yR\nw4cP8+9//5v4+HgOHjzIggULiIuLY968ebzxxht69/322285ePCgcQ3YARIwCiGEEEJHXV0dRy5d\nwdbBWWt7vVcA+078YpYyWVlZccstt6BUKrG2tubDDz/kz3/+M5WVleTm5uLh4UFeXp7e186ZM4e+\nffvi7OzM9OnTuXjxot79pk2bRkREBHZ2dsyePVuzX3x8PPfccw+BgYE4OTnxxBNPGF1+Nzc3AEpK\nSujTpw/ffvst0dHRFBcXU1RUhJubm97y+/r6GrxvZ5EuaSGEEELoUKlUVKqtsG223cramuLKGrOU\nydXVFVvb6yU6efIkCxcupKKigoiICEpLS/H09NT72qbjAW1tbVvMDjbdz8bGRrNffn4+/v7+mt/1\n69cPtVptVPmLioo057C2tmbz5s18+eWXODk5MXjwYGpra/Ue08bGxuB9O4sEjEIIIYTQ4eDggJu1\nmopm22urq/H3cTJLmRQKhebfeXl5/O///i+bNm3ihhtuAODpp5/utCDK399fa5mb3NxcrfIYYu/e\nvfj6+hISEkJ8fDzff/8933zzjSbInT59ut7Xffvttwbv21mkS1oIIYQQOhQKBVMGh1BbXKDZVl9X\nh1tZDuOHxZixZA2uXbsGoFm6Zs+ePXz//ffU1tZ2yvluv/12Pv30Uy5dukRFRQWvv/66wa+tr69n\nz549vP766/z1r38FGspvY2ODjY0N1dXVrF+/nuzsbGpqGrK3tra2lJeXG7RvV5AMoxBCCCH0GhkT\nhZuTI4lnLlBZW0+AmxM3Tp2JnZ1dp57XkMxd//79eeihh5g/fz719fWEhYUxd+5cvRNBjM0E6hMX\nF0daWhp33nknjo6OzJkzB0Cri7yplJQUhg0bptknODiYFStWcPPNNwMNAWhiYiJTp07FwcGBkSNH\nMmPGDC5cuADArFmz+Otf/0pOTg7r1q1rdd+uoFB3ZQe4GahUKk6fPs2AAQNwdXU1d3HMoqqqiuTk\nZKKiojTfxHqT3l5/kDbo7fUHaYPeWv+KioYOZUdHRzOXpPs7c+YMXl5e+Pg0LCl0/vx55syZw4kT\nJzo9gDZGa+95aWkpqampREdHo1QqjTqudEkLIYQQQrRh7969PPHEE1y7do2qqirWr1/PqFGjLCpY\n7EzSJS2EEEII0YYFCxaQmZnJ9OnTqa2tZdSoUSZ9nKClk4BRCCGEEKINdnZ2rFq1ilWrVpm7KGYh\nXdJCCCGEEKJVEjAKIYQQQohWScAohBBCCCFaJQGjEEIIIYRolQSMQgghhBCiVRIwCiGEEKJHy8rK\n6tbHtwSyrI4QQgghzG7hwoUcPXoUhUKBSqVCoVBga2uLQqFgzpw5rFy5sl3HPX36NEuXLiUhIUHn\nd3V1dURFReHg4IBCoUCtVuPi4sK0adN47LHHcHFx6dDxexIJGIUQQgjRovr6evb/vI+c3EwmT7gR\nf3//TjnP+vXrNf9etmwZERERLF26tMPHLS0tpba2tsXfKxQKtm7dSkhICACXL1/m2WefZdGiRfz7\n3//u8PF7CumSFkIIIYRe2dlZPLHyQX6++B+uuhzjn5ue4Z0P3zRbeQ4ePMjvf/97Ro4cydy5czl9\n+rTmdx9++CFTp05l7NixzJs3j5SUFPLz81m8eDGFhYUMGzaM8vJynWOq1WrUarXmZz8/P15//XVS\nUlLYt28fAJmZmTz44INMmTKFIUOG8Oc//5lLly7pPX7TfWNjYzX7dncSMAohhBBCr3c/XkPUdA/8\ngt1wdFYSMcqHq1a/snf/ni4vS1ZWFg899BDLli3j0KFDzJ8/n4ULF1JeXk56ejrr1q3jP//5D4mJ\niYwcOZJXXnkFX19f3n33Xby8vDh+/DjOzs4GncvZ2ZmhQ4dy7NgxAJ5++mkiIyP56aefOHjwIK6u\nrrz77rt6j99038TERM2+3Z10SQshhBBCR3l5OSrrq1hZ+Wlt7zfAg8RjPzFpwuQuLU98fDwTJkxg\n8uSG886ePZvPPvuMnTt3MnLkSKqrq9m0aROzZs3i//2//4dCoejQ+dzc3CgpKQHgtddew9PTk5qa\nGrKzs3F3dycvL0/v64zZtzuRgFEIIYQQLWgh6OpYLNYuubm5JCQkMGrUKKChK7m2tpZx48YRGBjI\n+++/z4cffsjGjRvx8PDg4Ycf5rbbbmv3+YqKiggNDQUgNTWV1atXc+XKFQYMGIBarcba2lrv64zZ\ntzuRgFEIIYQQOpydnbGrc0etVmtl67LTipgwbG6Xl8fHx4e4uDj+9re/abZlZWXh6enJ1atXcXJy\n4oMPPqC6uprvvvuOp59+mkmTJrXrXKWlpZw8eZLFixdTXV3NsmXLWL16NdOmTQPgrbfeIikpSed1\nxuzb3cgYRiGEEELo9eD8Rzm18yr5WaVUVVRz7sgVXFURTJ4wtcvLcsstt7Br1y4OHz4MwNGjR4mL\ni+PXX38lMzOT//mf/+HMmTPY2dnh7u6Ovb09Dg4O2NraolKpDJ7JnJmZyWOPPcawYcMYPXo01dXV\nVFdX4+DgAMCJEyf4/PPPqampAdAcv66urs19uzPJMAohhBBCr4CAQFa/8B4Je3eTczmbJX+cQb9+\n/Tr9vPrGH/bv3581a9bw97//nYsXL+Lt7c2zzz7LiBEjAHjkkUd46KGHKCoqIiAggLfeegsHBwcG\nDRpEcHAwo0ePJj4+nr59++qc64477kChUGBlZYWHhwczZ85k2bJlQEOm9bnnnuPJJ5+kqqqKoKAg\n5s6dy+bNm1Gr1Zrjjxo1ivj4+Fb37ei4SnNSqJvOJe+BVCoVp0+fZsCAAbi6upq7OGZRVVVFcnIy\nUVFR2Nvbm7s4Xa631x+kDXp7/UHaoLfWv6KiAgBHR0czl0R0ldbe89LSUlJTU4mOjkapVBp1XOmS\nFkIIIYQQrZKAUQghhBBCtEoCRiGEEEII0SqLChi3b9/O7NmzGTp0KHFxcezatQto6HNfunQpI0aM\nYNq0aWzZssXMJRVCCCGE6D0sZpZ0RkYGK1asYOPGjQwZMoTExEQeeOAB9u3bx3PPPYeTkxOJiYmk\npKSwcOFCIiIiiImJMXexhRBCCCF6PIsJGENCQvj5559xcHCgtraWK1eu4OzsjI2NDbt372bnzp3Y\n2toSExNDXFwc27Ztk4BRCCGEEKILWEzACODg4EBWVhY33XQTarWalStXkpmZia2trda6T6Ghofzw\nww9mLKkQQgjRPVRWVpq7CKILVVZWahYONyWLGsMI0LdvX06dOsWGDRv4v//7P3788UedtYLs7e2p\nqqoyUwmFEEKI7sHBwUETPKhUKlJSUlCpVGYulXn0lvo3fc9NyaIyjABWVg0x7OjRo7nppps4ffq0\nzptbVVVl9CKk1dXVvTbIbGy/nv4haUlvrz9IG/T2+oO0QW+uf+N9ten/G//dm/Sm+rd0nVdXV7f7\nmBYTMO7Zs4eNGzeyYcMGzbaamhqCg4PZt28fly9fxs/PD4D09HTCwsKMOv7FixdNWt7uKC0tzdxF\nMKveXn+QNujt9Qdpg95ef5A26O31by+LCRijoqJITk7mm2++IS4ujr1797J3714+//xzcnJyWL16\nNatWreLcuXN8++23vP/++0YdPzg4GGdn504qvWVTqVSkpaURHh5u9KOAeoLeXn+QNujt9Qdpg95e\nf5A26O31BygvL293As1iAkZvb2/eeecdXn75ZV588UVCQkJYt24doaGhrFq1iueff57Jkyfj5OTE\nU089ZfQMaTs7u171/FB9lEplr26D3l5/kDbo7fWHttsgPTOLhKQzFKlq8VDaMCU2ktDAAIOOfeho\nIjsStlGtvoYtTsyaehujR4w1VdFNQq4BaYPeXP8e0SUNMHz4cL788kud7W5ubrzxxhtmKJEQQvQe\n6ZlZbNx3Gisvf7CDMmDjvtMsmEibQeOho4nEH/iI8DFeQENvTvyBjwAsLmgUQhivZ4/6FEIIYbCE\npDMNwWITVl7+JCSdafO1OxK2ET7cS2tb+HAvdiRsM2kZhRDmIQGjEEIIAIpUtUZtb6pafU3v9hp1\nRYfKJISwDBbVJS2EEMJ8PJQ2lLWwvS12Cie9220Vxi2BJkyvcVzqlfIqKgrzcHJzJzLcuJVGhJAM\noxBCCACmxEZSX5irta2+MJcpsZFtvvamKbeRdqxQa1vasUJumnKbScsojNM4LvWSnQ9lrv7kOvXl\nk8RfSc/MMnfRRDcjGUYhhBBAw8SWBRPRniU9MdqgWdKNE1t2JGyjRl2BDY7ETb1PJryYmb5xqdae\nfUlIOmPw7HchQAJGIYTotVpaQqe9gcToEWMlQLQwRapasGthuxBGkIBRCCF6oY4sodMZZWnv2o+9\nRXvXuOzIuFQhmpIrRgjRIzUNQpyt1PjZK4iKMnepLEdrS+h0ZbBmSYGrperIGpdTYiOvt+9v6q7m\nMGXK0E4rr+iZJGAUQvQ4zYOQotpajpw5RUREtswO/Y2ldFVaSuBqDoZmVnckbPstWLyucY3LtgLG\npuNSr5TwDEuXAAAgAElEQVRXYX0tj7mzpvT4thWmJwGjEKLH0RuEePix99Q5CRh/YyldlZYSuHY1\nYzKrDWtcOuscw9A1LhvHpVZVVZGcnExIQD8T1ED0NrKsjhCix2kp2ChW1XVxSSxXS0vohPVxY0P8\nLtZs+Z4N8bs6ffmVlgLUnj7Gzpin6sgal8IS9OxPpBCiV2ope+autO7ysnQlYyZG6FtCJyzci91p\nhV06nlDfGLv6wlymTIzulPNZCmMyq0MGj2f3/i8YNuF6ZvD4/mxujLmzM4sohBYJGIUQPY7eIKTo\nMpMmTjJjqTpXeyZGNF9CZ0P8ri4fT9iRtR+7M2OGBFytc6TOdRY7v96H0q4aVbUtHqGzuFonGUbR\ndSRgFEK0m6Uuh9I8CHG2UjMy0r9Hj91qz8SI5hnJcqt+OEdP1tmvs8cTdmTtx+7KmMxqkaoW37Bo\nfMO0f1dUlquzrxCdRQJGIUS7WPpyKE2DkMbB/j2ZsRMj9GUkTx04Sv55L53ApKePJzQHYzKrljJB\nSfRucrUJIdqlNy+HYomMnRihLyMZM74vO7/YpRUw9obxhOZiaGbVmGykpWb9Rfcns6SFEO3SUjdl\nT18OxVLdNOU20o4Vam1LO1bITVNu07t/Q0ZSVz8fO4Kqr+BSlktQ9RUW9ILxhN2Bc20pZw/t5Uzi\nTzjmnNH7vjRm/S/Z+VDm4s8lOx827jvd6TPdRe8gGUYhRLtIN5llaRynuCNhGzXqCmxwJG7qfS2O\nX2wpI+li7869cdNNWrb2PtZONBn60WcgA/sMBKC8UP/YRcn6i84kf9mFEO3SW5dDsWSjR4w1OBC7\nacptDWMYh1/vlk47VkjclPtMWqaOPNZOGBcE9tZF0EXXkIBRCNEuvXU5lJ7C2Ixke3XksXadrTuM\n92stCGxe/rqKUnDx19lXsv7CFOQqEkK0W29cDqUnMSYj2V4dfaxdZzHFLP+uCDhbGvpRV1GqU/6r\nZflQeRbPkIGa/STrL0xFAkYhhOgmuuNYQFM/1s5UQVpHx/t11bJSLQ39oE6NVR/t8nuGR+GYcwbv\n6iuS9RcmJwGjEEJ0A/rGAn705Wr+s9UDJ3dllwSQ7QnWTDlW0pRBWkfH+3XVBJOWhn5sPXRa7/7W\nLm4mn7QkBEjAKIQQ3ULzsYCZqVdw8rQmZuz1bZ05maS9wZopx0qaMkjr6Cz/rpxgom/oh0fSGVml\nQHQpubKEEKIbaD4W8OK5fCbcEqW1T2dOJulIsGaqsZKmDNJam+VvSCbV3MtKySoFoqvJwt1CCNEN\nNB8LaGNrrXe/XzPP8+cX/8lrn3xu0gWbLWGh9paCsfYEaQ1dvdE6i5QDBi1+PSU2smEsYRP1hblM\niY00uizt0VL5Zbyi6CySYRRCiG6g+VjA2po6vftdrXGhxiuCnPxysrb/zCOzx5kkiDB3Rg1Mn1XT\n19W7IX6XQZlUS1hWSlYpEF1JAkYhhOgGmo8FVNb4kHIwl0Fjrgc3B/dmU+E5EltA4eDMRVWFySZh\nWEIXaFcEacZ0e0vAJnoTCRiFEKKbaD4WsHGZnUsFOWQV1lDeZxy2/tfX4KuqqzdZl7ElZNQay9GZ\n57SETKoQlkg+AUII0U3om4zx3OOvsCF+F98kZ6Jy6au1v721lUkDnd6QUetIJrU7PDlGiPaSgFEI\nIbqB1pa1mRIbSdKlfIqLLoOHHwDqynKCXepanIQhwY1+7c2kdtVC3kKYiwSMQggNCSIsV2vL2twb\nN51H5kxiy64DHL+YgsLKmqEhffjDVP0TXiS4aV17MqldtZC3EOYiAaMQApAgwlwMDdLbmowRGhjA\nE/feZdA5Jbgxva5cyFsIc5B1GIUQQOtBhOgcjUF6W2v+gWnXILSENRV7GlO+P0JYIgkYhRCABBHm\nYEyQbsqFoiW4MT1zL+QtRGeTvw5CCECWEzEHY9f8M9WyNl25puLm+O2s+24/RXVWeFjX89AtE7gr\nbrbJz2NulrLskBCdRe4EQnQDXTEZxRIWZu5tjA3STbWsTVcFN5vjt7M8/jjqwOFAw7jY5fHHAXps\n0CgBouipJGAUwsJ11WQUyZB0PXMG6V0R3Kz7br8mWGykDhzEuu/2d6uAUVYPEEICRiEsXlfOaJUM\nScs6I2jo6UF6UZ3+YfItbbdEsnqAEA0kYBTCwslyHebXmUFDTw7SPazr9Xe5W9d3eVnaS5YgEqKB\nRX3NO3r0KH/84x8ZMWIEM2fOZPPmzQCUlpaydOlSRowYwbRp09iyZYuZSypE12kcz1ZckE/SieMc\nOnqcpBPHqSsr6bRzpmdmsSF+F2u2fM+G+F16l3npTWTJofZ56JYJKDJTtLYpMlN46JYJZiqR8WT1\nACEaWEyGsbS0lCVLlvDcc89xyy238Ouvv3LvvfcSFBTEpk2bcHJyIjExkZSUFBYuXEhERAQxMTHm\nLrYQnW5KbCRvfLOXU0U14NHwrOCKynKyqutIz8wyeZZDuuB0WWKW99DRRHYkbKNafQ1bnJg19TYA\nnW2jR4w1WxkbxylqzZKOs4xZ0oYOMZDVA4RoYDFXfE5ODlOmTOGWW24BYPDgwYwePZrjx4/z448/\nsmPHDmxtbYmJiSEuLo5t27ZJwCh6hdDAAAJcbLhY50BVdTn21laEBPni4ebaKd1i0gWny9KChkNH\nE4k/8BHhY7wAZwA++nI17l5ODBrjr9kWf+AjALMHjZYQIDZlzJciWT1AiAYWEzBGRkbyyiuvaH4u\nKSnh6NGjDBw4EBsbG/r166f5XWhoKD/88IM5iimEWVg7ujK0j7/O9s7IcFliNs3cTBU0mGrizI6E\nbb8Fi9fVoWLQmHCtbeHDvdiRsM2sAaMlMuZLUU+fmCSEoSwmYGyqrKyMxYsXc8MNNzB69Gg++eQT\nrd/b29tTVVVl1DGrq6uNfk1PoVKptP7f2/SE+jtbqSmq1Q3YnK3UBl3XxrSBs5Wa9KtXuZhXSFWt\nGnsbBcF9vPBXGnYuS9TRa8Dfx5s/jRrA3lPnKFbV4a60ZtKoCPx9vA1uk4ysbD5J/BVrz77gAEVA\nasIJ5o9VERLQr83XN1VVVwa4am2zsbXWu6+qrpyqqqoe8TnoiKb1v1JeRa2r7ufpSnmV3vfT38eb\nP83QHnfZHT8Lcg307vpDQyzUXhYXMGZmZrJ48WKCg4N5/fXXSUtL03lzq6qqcHR0NOq4Fy9eNGUx\nu6W0tDRzF8GsunP9/ewVHDlzCisPP822+qLLjIz0Jzk52eDjGNIGivIiEo9ngl8YoIBayDl0mJhh\ngTrnys67wvEL2ZTVgosNDOvfj359fAwuT1fr6DUwPNhX8+9rJcUklxQb/Nr4xCQKnfpCfn6TrTb8\n5/sE4sbGGlWOqtI6nW21NbrbACpLa7Xet+78OTCFtLQ0KgrzyK/SDbCtr+UZ9XnqruQa6N31by+L\nChiTk5NZuHAht956K0899RQAwcHB1NbWcvnyZfz8Gm6W6enphIWFGXXs4OBgnJ2dTV7m7kClUpGW\nlkZ4eDhKpdLcxelyPaH+UVEQEZGtneGaOMngzJQxbXDsYj5jh3pyKSubqnqwt4KgoUNQO6uJiorS\n7JeRlc3BX3OwDo3FEagDDhbmMD/C3eiMWWezhGtg97lcfF19dbY7ltZptash7qj6M/898ikDRnhr\nttWqFBzfc5Fhk4M121KPFnDH7HlERUVZRBs0yshqdi3HRHT6NdO0/nPd3K9ne39TdzWHubOmWNy1\na0qWdA2YQ2+vP0B5eXm7E2gWEzAWFBSwcOFC7rvvPu6//37NdicnJ6ZNm8bq1atZtWoV586d49tv\nv+X999836vh2dnbY29ubutjdilKp7NVt0N3rHxkeRmS4cV+UmjOkDcrrFXj7+ePtpz3Gq7wsV+u1\niSnpKH2DtPax8Q0iMSW9w+XsLOa8Bnyc7am00f2T6+Nsb3SZJoyfjK3Sjh0J2yirKiYrX4Vn5F3U\nATu/3ofS6hq+jh7cccv/6IxfNPfnID0zi02HU7Hy6gtOUAlsOpzKAqWyS8YFKpVKIsPDWKhUao9L\nnDK03efvbk+CMfc1YG69uf49okv6yy+/pKioiHXr1rF27VoAFAoF8+fP56WXXuK5555j8uTJODk5\n8dRTT8kMadEjWNqNJj0zi+SzqeSSi9IaQgIDcPduyIo1nxEsk2OMY+rZtqNHjGX0iLFsiN+Fs931\nYQC+YQ3HC6q+0uHJLp1xfVrKLHxTLZguy1CJ3sJiAsZFixaxaNGiFn//xhtvdGFphOh8lnajaSyP\ny4BYzl/Kp9LBmaT0HGIBV0WdTmBjaUvNWLrOmm3bWYF7Z12fPe2LhqUEwEJ0NvnLLoSZWNqNprE8\nHkBsEGRcLqDKyZnSjDMsm3+7rE9nAp3xGMDOCtwTks5QYuNExtkLVNXVN6z/6efd4euzp33R6GkB\nsBAtsahHAwrRm1jaI8eantfDzZWhA/szdnA4UQMH6A0QGjJm0QRVX8GlLJeg6isskPXputyU2Ejq\nC3O1ttUX5jIlNrJDxz2fV0jSpXxKrB1R2TlTYu1I0qV8zucVdui4nVVec2kp0O2uAbAQLZErWggz\nsbRMS3vK0xkZM2Gczurqzs3LR+EeobVN4eBMbt65Dh23py2ELZl20VtIwCiEmVjajcbSyiMM1xmB\nu7+3FxeuXIYma39SdBl/H6+WX2SgnvRFo6cFwEK0RAJGIczE0m40llYeYV5hfX255qwmIzMLVR0N\ns+ZDAwhzVZi7aBanJwXAQrREAkYhzKjxRnPoaCI7Erbx4Zlt2OLErKm3meX5v6a88TXWqVp9zax1\nEu0zJTaSjH2niR06TLOtYaxhz844W9pSV0JYCgkYhTCzQ0cTiT/wEeFjvICGpxHFH/gIwCwBlqE3\nzNb2s7Q6CeP1xoyzpS11JYQlkYBRCDPbkbDtt8DquvDhXry/aT2+fQJNeqPKyMomMSW9xWDQ0Btm\nW/t1ZZ1E5+ltXa2WttSVEJZEltURwsyq1df0bq+3bwjC0jOzTHKe7LwrfJL4K5fsfChz8eeSnY/O\n8Vu7YTbV1n5dVSdoCF43xO9izZbv2RC/y6THFr2LpS11JYQlkQyjEGZmp3DSu11VbWfS7MbxC9lY\nh8ZqbWt+/MZFiItKShsW7v5tweYaR+3nj7a1WHFX1Um6EDuup4zZM8WYWUtb6koISyKfAiHawZQ3\n2Zum3NYw3m/49S7cEz9fxiN0BmC67EZZLTjq2a61YLfShkslpSRdykfh4AzWoAKSL14kPTNLU8e2\nbqxdVafe3oX49fZv+Gb3NhS2NairbZgz/XZunT3H4Nf3lIDbVGNmZWkpIVomAaMQRuroTVZfsBk3\n/j7e37SeentQVdviEToD37CGm5SpshsuNlCnZ3vT40+JjeTrj+JR2bhTmpNCjVqBbWUJ/aMjtIKw\ntm6sjTfpzq5Tb34s29fbv2H3qS+YeHs/zbbd+78AMDho7IqAuysymC2Nmd2RsM2ogLE3TvQRwlAS\nMAphpI7cZFsONqN55pEXOzW7Max/Pw4W5mDjG9Ti8UMDAwiwV3Mu6xK1rn5YW4Fr30AuFJfhb5uv\ntV9bN9bRI8bi2yewU+vUni7EntIF+83ubVrBIsCwCf34Zus2bp09R6eL9sZxs3Gwd9bavzHgPrj5\nTag4i5uXI8UFFRyxDeTeuOkdLqMxX670vS/5eZkGdTM3jJl11tleo64wusy9baKPEIaSgFEII3Uk\nq9VasHlv3PROzW706+PD/Ah3zSzpurISsFaw9dBpPJLOaG7Q2Wk/McjVntKrKVR6DcPW0Q8cncgt\n0H4knCE31s7O2BjbhZiRlc2mw6ndvgsWQGFb0+J2fV20/z3yKYN8JxEVFaXZ10Npww+b3yS8fwnD\np0zUbD+WkMqTKx7j1b+t7lAZDf1ypS+wfO2zr3CzS2LgGF/a6mZuacysrULfIIyO6SlfOBr1tPqI\nziMBoxBG6sjA+LaCzc7OboQE9CMyPEz7Bo32DfrWe8I0+x/ce4TTuWDj3g//Pr7tOmdn1snYgHTv\nqXNYefXV2tZdxzyqa2xb3K6vi3bACG+O//cAf7jjj5ptU2Ij+fxfZ7WCRYDhUwbwxdoDHS6joV+u\n9AWWxYW/MupW7WuupW5mfWNm044VEjflvg7WQFtPGfPZqKfVR3QuCRiFMFJHBsZbyixMQ2/QYyb1\n49JnR4mIGUqYg8oisxHGBKTFqjrQk4zqjmMe59x4G7v3f8GwCde7pY/vz2bOjXdyJHkn+rpo1Xqy\nkm5e+rNwrp72HS6jode7vsBSaac9M7+Rvm7mxgByR8I2atQV2OBI3NT7TL5IfE+bZNXT6iM6lwSM\nQhjJmKxW8wArrI8bGWm5Zp+FeT4nn+SizOvPCA4MaPEG3cfTBrfaa4T18er22Qh3pTWVerZ3x2VT\nGie2fLO16SzpO7l19hxO/qo/O6hokpVszC6VXNU/zq/0alWL5zb0i4OhX670BZaqaj2pSVruZh49\nYmynP0Wop02y6mn1EZ2r+/2VFMICGJLV0tfdk5GWy43hXpzPu2K2WZjpmVmcvlxCiXd/ACqBpPQc\nvMv1729VBQsmRveIbMSkmIjrYxh/052XTbl19hy9M6L1ddGmHi1gWNQkzc+a99NxIMcSUhk+ZYDm\nd8cSUonqP0rvOY3pxjT0y5W+wNLdazBnDycxcNT1rHdjN7O5Mt2W0kNgKj2tPqJzyVUhRCdpKcA6\nn3fFJDNQ2ysh6Qwhg6Kvr7UI4OFHWY4nZw/n69yg59x4GwlJZ/j6SDIKR1dCAgNw976+T3fKRoQE\n9GOBUtnjl03R10V787h5WrOkG7NLY+56mIOb3yTt9D7cPB0pLqwgJnxMixNejP3i0N7JUfPvvoP8\nvJE63cxaM++7ONPd09Zp7Gn1EZ1LAkYhOomldvcUqWrxcPMhNgitp7lEDxtJXGyQ1g16ZFQcx4vt\nsPLyAe8giq0dSUrPIRY0QaMpshHtfUpHezJNvWXZlOZdtFVVVSQnJ2t+bppdGnPXw5rtQdWtf6Hp\nrOta3/sSGhigcx1siN9ltkx3T1unsafVR3QuCRiFMJCxwYmldvc0lsvDzRUPN1fN9qDqKzpBxob4\nXZTYKMk4e4Gismvkl1/F3ceHjMwsYr19TZKNaO9TOmSGZ8e0N7uk77ouKinlUmoqa7bQ6V3E5v4i\n1tO+cPS0+ojOY2XuAghhKdIzs9gQv4s1W75nQ/wu0jOztH63cd9pLtn5UObizyU7HzbuO621T3NT\nYiOpL8zV2lZfmMuU2MhOq0Oj5nXJyMpuV7nO5xWSdCmfEmtHrNx9cPf0oDgvl4rCfIKqr7DABNmI\nHQnbtMbawfXlU1rTWteoaFtDdimaoOoruJTlGvx+Nr9+ikpKOXnyFK4hkQZ/NjqipS9c5v4iJkRP\nJ58wIdDNVl0qKeXrj+KJ9nMjrK8vBUVXseozUOs1Vl7+bPnpZ7w9PPVmHU3R3dOeLld9mbfUxF8Z\n42VLVJRx5crNy0fhHqH52d7RCb8gJwKKz5lsHGZ7n9Jh7kxTT9BWdqml66/p9XMpNZXYwZFa41o7\ns4tYxt0JYR4SMAqBdraqqKS0YUKId3+Si3KwDfEh8Wwykfb+Wl24RSWlJJ7NZeykgS12iXaku6e9\nXa76Mm/Wnn05fiGJmdMwqlz+3l5cuHIZPPyubyy6jL+Pl6aMHZ2t2t6ndBja5Z+emcUPR37h7KUc\nBl7MZ8bIG3pcF1xnzBpu6/prPP6aLVDmoruoe2cF7jLuTgjzkIBRCLSzVRmXCzSzh1V1DdscXFzJ\nuFygFTBmXC7AwcVV6zimzKy0dxmbljJvZc3u34ZMNAnr68s1ZzUZmVnX12wMDSDMVdFmQGFoENPe\np3QYkmlqLGO9my/lbpCp9O1x4xw7ayynodefOcbqyrg7IbqeBIxCoH3Tq6qrB+uGfyt/+39IYAC/\nnj0HA/trXlNZkMPggRE019HMSmOg9fWRZPAOIsTPWzuz2cbxW7qBuzT5tBs60WRKbCQZ+04TO3SY\nZlvDeMfW12UEDA5i2vuUDkMyTY1lrK+93mbdbe1IfZoG48lnU3EZEItHk9+boo6GdvlLF3HvYYlP\nehJdRwJGIdC+6dlbW6ECKLpMSGjDH0N3b1+mV1/Fu/r6gtuOod5UeOt2xXUks9I0W6RwdG1YxuZS\nPrFBaILGto6v7wZedzWHkf2vP0JO37OG9T2nt7WgbOuh0y0GFMZmR9v7lI62Mk09cZxj84xiLrmc\nb3aNgPF1bJ5xViiDYKC/zn7Nrz/pIu4dZFUCIQGjEGjf9Gocq0m+eJGQyCjNQP76wlz+MH281h9G\nrT+gv2lvZiU9M4tPt37N0VMJODhbUVfviFvf4RQXXUbh4dfQTV5TRcaZZGqC/dkQv6vFb/f6buBj\nxw7mWkmxZh9jJpq0FJS11hVpKYGapS5t1BHNg3GlNVQ6OOsMmTCmjvoyzmcPH6PkRB19hk7W7NfS\n9S1dxF3LHJm+nvCkJ9Ex3fevphAm1vSmp/mDXJbbYsbEVJmV9MwsXvvsK2ytjnPHgjDN9mP7DxNs\nM4qSsnqu5WeTXuRFyA3DsXVz5RKtf7tvfgOvqqoiuUnA2N6JJk211hWZkHTGIgK1xjLi5qPZ1t27\nS5sH4yGBASSl51DldP0LgLF11JdxHjjKl5Qf0wiqHiyZQwtirkyfpXwJFOYjAaOweOb4Nm1oxsQU\nmZWEpDMUF/7KzFv9tLYPn9CXnV+fJnb6Yi4dLSdoxDiKSkr5ec9OFFdP4epUz3OJsPSepUZ35xoy\n0aStdm8tYM7MzeWb+J9Q2bs1TJQJDMBVUdflgVpjGX848gsVJTkEusGMid17lnRj1jT//GmK0veh\ntKvGq0yNwsoXl0AnzfsADQuva7LMg0JbPGZLGWc7B7VZH2MpdJkr09cTs/XCOPJOC4vWXcfNGBPk\nFqlqUdpV6/2d0q6a+sJc/Pv4UlRSypEjiQTW7sfZQ42NrTU1trV88MVrwBNGBY1tTTQxtN31Bczp\nmVnsTisk+IbhZFwuoLKunvS0NB69eYTB75kpvySEBgYw38eb5ORkoqKisLe3b9dxLMWU2EhNRrrp\nl4yzh/MZH+LG6BFj21yLszlTZJxF1zBXpk8mNwkJGIVF647jZloKtm4Mz+V8XolOEOShtEFVrecO\nAFhVwYLfuni/Ti/AJjeRvkMciBl7PVt0KjGdTzavNzrL2NpEk460e+NrPWg6CSOc83lXmPTbT/oC\nQoAtP/3M/uR0MgpL6RMcxuCBAyizc+0WXxK6SmhgAC7VaQyapp2RHjjKVzNpyZC1OJtq79JGouuZ\nK9Mnk5uEBIzCohnybdrSlnpISDpDiY0TGWcvUFVXj721FZ7OSl6PP0DshKk6GbspsZEcSR7MiZ+P\nM3ScdsbogT8t1NTlP0e+xsW6jJixI7TOFzM2lB2fnjRpHTqSxWjrtfoC6je+2UtJZRUZuJFn50dV\ncDhlxZcpPX2WMdED8bDwLwldzdahXu/2xklLhq7F2Wj0iLGcOfsrOz7dip2TgupyNTdNvr1dM9dF\n5zJnpk8mN/VuEjAKi9bWt+nWuk6hIXi7Ul5FRWEeTm7uRIaH6TmaaZ3PKySp2KZh8W9rKLyUTH7O\nz3i71HNo2zF8+k+kf8xoTcbu3rjpPHH3HXy61ZofvzyKg0Md7kp37rhloeaGHRoYwPRQb44U6P/I\n2jvpz1C2V0eyGHUVpZzIqdQEy43rSDa+Vl/261KZiotldbj1daauqBwAtbsfZUWZmtm/Mrj+ura6\nkA1Zi7OpQ0cTSS04yMx514OO1GMHOXR0sASNFkYyfcJcJGAUFq2tb9MtdZ1u2XWAcqUbVl7+1LrW\nkl9lzSeJv7JQqez0P6xNn79cfimZWJfjjF8Urvn9wT37uHAK+seM1gRBoYEBPLdsSavH/cP08RxM\n/ELv79wddNeD7Ij2ZjHSM7PIKqulpLQSPPxQAUmX8hniksv82eMA/dkvVR1U1qlxA6ytoOa37TVq\nRcNC6sjg+qba6kI2ZC3Opgxdl1NYBsn0CXOQv8DCoun7Nh0W7kVC0hm2HjrNkdNn8Btwfb3ERscv\n5hI5NlJrm7Vn3y7p1mz6/GWHwmOMjwvU+v2Yyf34/LP99I8ZbVQQFBoYwPzb5vPj3v8wdFKQZnvK\nwVzm3v6g0eVsrSvfkCyGvtcnJJ3BMzyK2IJ8rccJBtg5al6rL/ultAYHawUA7q4uXC4uB3snbBVq\n7K2tes3gekOHV7Q2aanxGHUVJWSlp+Lv7UVYX1+dtTibMmZdTiFE7yQBo7B4zddHbNoFXelZSVJ6\nDrGgFTQqrKz1HqsrujWbPn+51vaa3n2UNlXtCoJunT0HP18frUDhjqkPGp0FMmQWdGtZjJZeX1d2\nFdsQH9y9fYlt8n5Yl+Vq/q0v+xXkosTNRk1GZTn2js74ASWXUvG2qmG8ew1/mDiyx2dUjF0RQN+k\npabHsA30ISgw8rdHOUbi7+OttRZnUzJLWgjRFgkYRbfSvAs6xM+bpOp6MjKzNAFKfWEuQ0P6oC83\nYspuzZayQU2fv/zDqW16X3s1v4wF7Rx31N7H6DXV0dnnLb0+Kz2VoBDd/Zu2u77s5fw5DfOnt/z0\nMycyzqGur2PYsBCdp+v0ZB19T9Izs3jpk61kO/bFvuCCZuxo4zH+NGNCi6+1pFnSzR9ROGvqbdIt\nLoQFkIBRdCvNx795uLkSGwT5Z3JwafJUFkCTaSkuLeVcVh7paWm4hvuSnpkFYPTM6qYBYl1FKVll\ntXiGR+nNBjUGRFervdi/+yITbgzWHOfAT5lU2wZputXrykrAWoG1o2uXzfLu6Fpu+l6ff/4013IP\nkfHjYVT1jth53UAJTlSWleI40J/0zKw2s5dPzP+jzjZLmwXfWTrynjRmFnNxRmXnrBk72vh86baO\n0da6nF1F3yMK4w98pFVGIYR5SMAouhV949883FwZMnSQzhMpFkxsyFidTclCobZi4KBIKvz8eeOb\nvd5WGsAAACAASURBVGBjg2fIQIMXA2/eXXgip5KS0kpiC/I1XeFNs0GN/+0/lcLJoirOfXQCN+d6\nSsoV5KoDKczPZcXmXdhWleHiH4R3cDixQU5dtuagIbOgWwrU0jOzSD6bSi65mqe4VJfkY1P+A7Pm\nXi/zoYSfya0YyqDYMVS4ta9evWHh9kYdmZnemJ1UXsql8rdtiibPlzbkGKbIXHeUTL4RwnJZmbsA\n+pw6dYqJEydqfi4tLWXp0qWMGDGCadOmsWXLFjOWTpjTlNiGMVlNNY7Rai40MABvD09GT5iMn68P\nl7KyOXT0OAd+TSUlv1xr38ZgT5/Grr5Dedc4cfYCRSWlDTN3PfzI+C1b2ah5JueeGWNwVjpiPfwe\nygfey9V+N5FdrqY2YgyVQUMocu3HhVoHCkrKyLhc0GZZTKWtdmwM1C7Z+VDm4s8lOx827jvN3sNH\n2LjvNC4DYqlQulLs0pek9BxyTm/XWkMSYPSUILxqzmsW725PvZp20xaVlHLi7AUO5V3jpU+2ajLF\nlqaltmurvMZc2801XnchgQFQdFmzvaqu3uBjWIKGyTe6ZPKNEOZncRnGLVu28Morr2Bjc71ozzzz\nDE5OTiQmJpKSksLChQuJiIggJibGjCUV5mDsGmRFqlqKr10hNb8Ix35hWFlZUVphRfn5U7gV7MfZ\nGVTVtniETsLF10vn9Y03//NVNuTV1VBbr+Zs7jl8lGDt44yqTnv/5pmcSaNG8gLw8Q8HKVTVk5aU\nhF3AEOxrigm+/DMeyioKKpVkn+9DgOf1MWadPTmnrXZsaTzdxz/sJWjEJDyA2CDIuFxAkcIaaGFy\nT7NHHhpbr8Zu2qKSUpIu5WvWtsytcLbYTGN7xyJ2ZH29xuyku7cvsaCZoe5POQsm3k5oYABVVVUd\nrFnnk8k3QlguiwoY3333Xb7//nsWL17M+vXrAaioqGD37t3s3LkTW1tbYmJiiIuLY9u2bRIwmpE5\nx5UZswaZh9KGPWcvona/nv2yLspgQnge46dcP8aJn3+gvHakzusbn9qSX1xKla8vWEOtox05xZfx\ntr6Cu21DMJNxuYDKghwcQ721xuoBKK1qCVZm4W93jWKXQs5fPsXM2EpCghy5eK6UEJ86ArJPUXxG\nDYPDNeXubK21Y0vj6QpV9TQu6OPh5oqHmysnzl6gqkb/85mbP/LQ2Ho1BkIZlwsagsXfKK1bD8Ka\nT5y4cdxsHOx1l43pDB0Zi9je9fWazjxvnKFeX5hr0MQqSxojakmTb4QQ2iyqS/oPf/gD27ZtIzr6\n+lIjGRkZ2Nra0q/f9QVnQ0NDuXDhgjmKKGh/l5s5TImNpLK8RGtb35qzWsEiwNBxflwrPqvz+iJV\nLRmXC3DzC0JRfL2rz8reGdeKKwxyqObMyeMoKooZPDCCir6RWm3ROIg/aIyC8LHOLHhoMCM8LxIS\n5EhRQTkTbolizMxIbrt3JIP7ZZF//rRFdCE2D+wau4Pz8/JIOnGc4oJ8ze+q6upRuwzkxM+XtV5z\nKOESHqHXh5a0p16N3bSNi3c3FOZyQ9cr+oOw5m0ePFbBf498SsrZZKPO3V4tBcWd+SWgITsZTVD1\nFVzKcgmqvmJwsGhJn+XRI8YSN/4+Lh1Ucz7xGhcT1cSN7/rJN0IIXRaVYfT29tbZVllZiVKp1Npm\nb29vdPdKdXV1t+iS6QwqlUrr/x31w5FfqHfzpb62yc3azYcfjvzCfB/d99Cc/H28mRjiw4/55VhX\n1eFoa42bp/7L3kZZp3ONOFupqaiuxc7VHR91PWWFmdSqFbhUFTE8pj8hfj5Yh19fb7C2tlarLbbv\n/pLw8dpd3X36uXDxXD4TbonS2j5qWjh7v/qBPz30LP4+3ia9Xo29BsYOCiU18VesPftSXFrKycwr\nKKquETFkGBfK67l6PouY2jrcvX2wLc4lZMhYVKVh7Nh2AKVdDVUqG/q4DCbKz5Piq5m4K62ZNCrC\n6Hr5+3jzp1EDuPRZPDlWLthbQVBgP5zdPamtrcXZSq1zPH1tPmCEN8f/e4C4W241+NwAGVnZ7D11\njmJVXUMdYiIICdD/tJRGTduuUd3VHMaOHWzS91Rf2ZovndP0fPquAUv8LA+JHsqQ6KFa20zRbqb+\nO2gpjLlGe2obGKq31x8aYqH2sqiAUR8HBwedN7eqqgpHR+PGtFy8eNGUxeqW0tLSTHKcs5dyKHfT\n3V5RkkNycjLZeVc4fiGbstqGZ9cO69+Pfn18THLu9hgS6MOla7lYeTRMvrhyXn9ivbK0luRk7SyU\nn72CmpzzXPMNBxsljn0CUBRfZkDQAJT1VW22RWllIdBH63e1NXXY2OpfWNzN1YprJcUtLrDcUcZc\nA2O8bDl+IYm089nYKF3x8/VB6eBEQH0FuSoFaccPMnpAIHPCPDlckIuVhx+uUbcBYFt0mRsj/bXe\n947U6/aRkcSfaThHdb2a/Px86osuMzLSX+c909fmAGrbGqPqn513RXNOsIYqOPLNXuKa1UufxrZr\n/AyM7N/PpO9rR8rWtA3aun6NKY8lfeZbk5aW1q3K25r2Xgemuhd0V729/u1l8QFjcHAwtbW1XL58\nGT+/hnFo6enphIWFGX0cZ+euGcNkaVQqFWlpaYSHh+tka9tj4MV8MpW6zy4OdAMnN3cO/pqDdWgs\njkAdcLAwh/kR7m1mZjpLeLgKSCSzvJprdVb06T+UM4eSiBx9PahIPVrAHbPnERWlnfWLigJHFxfe\n/O9Bqh3cGzJcg8Jxsaph7tgx7D11rsW2iIqKwnWX7kSa4Ahfkg6k6y2rq4OnThlMoT3XQFQUzJwG\nb23dRZnr9UkcvkBYKLiUerPs9oaljCY2z3JMnGTS9zsqCiIiDDuHvjYHUNTYGlX/Yxd/wm9gs3HS\nvr5crspnZhvvUWPbdZb2lE3fNdDaZ9nQ6zAjK7tDn/n2ZHHbo7H+tvaOHCyssai/Ue1l7HVg6ntB\nd9Pb6w9QXl7e7gSaxQeMTk5OTJs2jdWrV7Nq1SrOnTvHt99+y/vvv2/Ucezs7LC31z8wv7dQKpUm\naYMZI2/QebRbfWEuMybeQELSGZS+QVr72/gGkZiSTmS4cUF+ezUfxD92UCj9+vgwc1qUpv6NkyIa\nFymeM/V/WhwnNX3ieMJCgrWO6Wldzefb3qesqois/Go8I2fgG9Yw9raxLezt7Zl94+91BvFfyaum\n1m4QR346z8ip19sk7VghcTfe16nXaXuuAR9neyptdP9U+Djba44VGR7W6e+voefQ1+apRwsYFjXJ\nqPqX1yu0VmvQbK9UmP1vSUfK1rQNWvssG1rHxJT0dn/m0zOz2HQ4FSuvvvx/9t4zsM3zPvf+YQ8S\nIMC9hyiJWpaoTU1LluU4cmRZjkeT1iPJ66R1z/Hx2yYnTZvaPU3SvDlt3DQ9PU7qOo7rOrZjxZas\nRLasRVOD1CQlkRKHKJAEJ7hAggSx8X6AsAiABJdIWbg+kTee537uZ9339fzH9ScORoB3zjXwrEw2\nY4k3FXVNsz5HTRcm+xxM11pwp+JuPv/PtUsa4Ac/+AEvv/wy9957L3FxcXz3u9+NZUjPIsaS//jw\nbPWUKohMFeGEnhvKr1GSJCEuoY3y6zoau3rp6DKQkbyCwszUiFmhkUqUeZIq3rslMJzIUuDK6cMM\nVfeyZOGSICmUcBU0dix/nL6lSq7VX+Pkh1UkaiXEyzRRVdaYjYzWcLWfJ1MH+3Yh3DX/4sanJpwl\nPRUh7ZnGZMZ26vxF/u3DI7iUZSQrxDyzs4St69ZOWsrHi6lkhU+1HOJkYLQ6IYx6z+2Yo6b7/Z3L\nz2gMnz/Myadq3bp1lJeX+/5PSEjgZz/72SyOKIbRiCT/MdsTWLgFSJSYybGqk1T02jHLNFQZxQg0\nC7nZ3clwvJumMHp+Y5UoC1eNYvmmTFoq2vja7hdCxhS5goa/Mo13ITm975OIC0k0VU9mglBORR9w\ntjD6mlsslgnF5MHcJsrhxtZ3owalSswrYZ6hsnPn+fuPL2FOXUZcXByDQiEvHzjH/8KjFTpbpGWq\nJSonA41M5KuGE4iZnqNmomrRXH5GY/j8YU7J6sRw52Mq1SqmA5EWmvruQUSJmcF6frcqtYSrQHK4\ndH+QSxP8JcqmuxpFtNImY1ljJtLPZMY3V3T6bicmK1UzE9DpW3nj4FG+89Of842//Dr/982/x9Jw\nCEHdKVSmDpTttSAWY04rCnvv3zxSgSBjfnCnmfN580jFlMc2lXd+NiSIti5fOCtz1Hjv72Qwl57R\nicL7TL+y7xPeOHh0TsqyxRCMOWlhjOHORSRrlKFLz1vv/WuIe3ciiIa4RLJ24PLo+FmcLghIUPZW\nahlNND2kMNSFaXebI1ajqK7vpOzcebauCxUAHwuRFpJ9J86QrE30nW/FpfOYhpqRSW2+6jSphct8\nYx9rQRottxItJmIV+TwSy8kKaU8nvPegx9iL2HmJLXv9IvQ3LlaxKX8V1zrEmKXBWbGBrt1eq2t0\ntwAR2yeCqVigZ8NClp+dxbMy2W23mM+UNXUuPKMTxZ1aI/5uR4wwxjCtCEcaDF36iO7daElj2bnz\n/PPB01jlCchEnpq5TSer2TG/g8auAd/xCtMSqDpfQ4vJitXpqQiSoRRSlOKR1JGLhASKNDnNA5Qf\n/gDp4DXqLv0GjUzDow99JYgU6hu6aa43IJaIGOx08IV799JwsSLIAnm6VM910TL+9PWPebmrmyd3\n74rqvM5eKOfE8bcRKQXBJHBgkPK6DjZsLQIpNDZWIxJd4YE9eb59K88cwdAIuTmebO9+qwPjoMFX\nFs57nVQyZ6TDj4toY8xiC4Af002cvfegv3I/D+wJrtfttXrH528fk4wkyYT0h+k7STY9TqbJkpbZ\nCneYDZI12+E6cwmzEbsaw9Rx9z2pdyGaWts4WF7FsfoOUuLlM2b5iUQaLDcOsfi+8O7daAijTt/K\nKx9fYCB9MeDJpKzStTNPI+OVj3WsLNnoO17V+RoGRiy4lVpcThdukRCByMLS3HR0fe3kpyf7ahJb\nWuoYNnaxKreVkkf92ZH7y15jUcYmGi5WIFO7fBVZvKg9d5rBnlTe+sU1lGoJvUMCDNp1OLM8ffzj\nwTOsK14+7jX2xkne92W/vI+XBLY5lChUal97v+5kEFkET3WaT98/yrbdPwDAaRqgqs0C2syg65SZ\nNflswGhJaGwB8GAmiLPXMjW6LrcXdrd5XDLyzM4Svr+/AgLkkWi/wTN7SiY1punEnWghmwxi8YZ+\nzEbsagxTR4wwfs6h07fyn+XX6I3LJFWdyohYPGOWn0ikwVhtBEKF1k0WI3//83/jhu4CCoXTZ90b\nTSJLq2qxqD1acRZjH4O9XdjdAlqqb5K9+J6gbVtMVtxKLSuL5vnaHA4Hbboqnn5wPeXXdcRrHHR0\n1dPjMmEXtlFyb7D2WtG6VJor6tm97ev8y2s/4oGnloX8/ukBI+3ZX6LHLcdFL0qZyvcymeUJPqIU\nLtO609DNR8f2Y7Z2oowXImtwkbPA405cuTGdTw+cZERxD0uKFvqOGYksZKVI/fdRJMAtj0cQ8Ltb\nHg+iyVsYoyWhsQXAg5kgzl4yOLoutxcSgXJcMrJ13VpestpuZUkneLKk95RMOHziTsf1uho+OvoO\nTqFl0qExk8WdmDw2U4hZW+9MxO7O5xylVbWeEmUGf+3faBawybjVIpGGkZHwVU1utvSSPf88933Z\n72bbX/Ya4HFVe8dw4HwNPYM23PI4+u1u3NocAIadQrqNQxh7DGiSPYTS6gSXMzQuy+TwxC4F6qy9\nsu8TrlY2hh2b3W1m/ZoNzDuRH/Z3mdSGAgESQxM5zjqSsTDQp6Qz7h6UQgH9VkfYTOv/eP8fkalk\nbNmbh0cCG66Ue0S8vaRRJTGzsiAZc7JfUDkSWVDJNb6/RUo1xblxNHX2YHG6kIuE5OemIhKGT9KJ\nClGS0NgC4MFMEGcvGdQWbKHyzBFWbgyMYexl97avR0VGNq9djVYpZ+nSpXelBt2Fi2e52n6MxZsy\nmGxozFRxt1hTx0PM2npn4u6aze9C9FsdoIjQHgGTdatFIg3zC9Zw42JVUMzfldPt2F2ioMUPPNa7\nw6X7SU3L8Y8hOReJRoTu4mkk+ff4HlqJ00ZC/gKa9K0U3yJXMhG4RaFxWaowT7pWJh7TagNETHCx\n2qTMUw+zKvs6m3f4XcWnjlVS1Z5HTV0DXdcafK54bxxkX4+Jh3cHuwGXbyjg1B9qfIQxKzGDx+7f\nFDShagu2cOnUJ6za7LeGeslC4PmYpGq0Ceqg/rW2yddNjZaE3ikLQFOrR4tzphJzZoI4B5LBa/Wr\nImp3znUyMttJUUdOHbxFFv2YSGhMDNOHmLX1zkSMMH7OoZWJwwa7j7WATdatFok0PLt3D4auVUFC\nyiLxGpLSqsL2Y3ebg8aQn56MscWAMikNq9mEWCJCbDWzpiiPbpvdl+kMkKuSgdge1J+zr52180JL\nfm0rXsT5miVUnrkURFzrzhl4ZNtzAHxh2yMhVUMqz3SiLdhJv+4km0fFFW7ekUfLmy2o83dguHKO\nxSjRN3T74iArPg0voeGtLR3JYpSbk0ai6HEuV5z2XcPRQt8zQdqiJaFzdQHwkpTuIQvtjfXYtRmk\nzl8yY4k5M0Wc/WTw/pDfys6d580jFfRaXSTJhD5B7rmEuZAUZccMqEPbJymHFcPUMNkPnNn+8Lib\nESOMn3NsK15EQ2klgbd6vAVssm61sUhDQU52ELl54+BRjpRdC9uPRKAMGoM2QU1xLgy13mRYJqIo\nUU5+ejbaBDX9A4OYGtpRmTrQysQ8/fBWIHgMGzYsYXjAGHa83/njR/k/bw3zwa8voYxzI3HJeOyL\nT/rGOrpqiM0swC5bRVrhMsxtx8KOPz1RjCY5lS6XxzrZXG/wJc047OHjCQ2twzSXu8e1GO3Z9XDY\n/b3bR7r+FoslZPtoJt6JEKDptnBNdWEIJCkOtYMrQzdwSEWsGhj0EeDpTsy53cS57Nx5Xj5wDjI9\n8a4DECTIPRZu58I7F5KiJGHiqMHvTYhh7mMufHjczYgRxs85CnKyeXqDlXc/KUU56PRkSQcsYOEW\njam41aIlDeNZ9651DAeNQZugZsvaYnQ3bgQltCQ4hvkfT+8NOWbg+R05f5W6lnaKmg3sXHtPyLbx\n+StZvdovg3Opt4Pl+tag8n6BZNd7zRrN7rDn5nB5FiBtwRaqTn3isx4C5C1M5Uq5juUbCvzHO9XG\nM4/99zHJYLSI9vpHO/FOlgBNJ9mb7MIwmqT0dnXR19qJ7qqQNLWMFYuLyF2weNoTc26na/jNIxU+\nsujDLUHunIyMkJrqXtzuhXcuJEXt3Lybg+W/YnGJ/5kYHdYRw9zGXPjwuJsRI4x3AfKzs9i9oTgk\n2D3SorFjfhJNNzpmNB7Na91760MRx38XmCX9HKlpORwo+z2/bzgD2gzSNXEsyc8hQeDkL764hsau\n7qjIS9m58/zww1O0u+OwWkfIM/dQ032GF3dt9O0zEZ3B0QRoScY3Q9zVZ44302ZbSOrAIKmFyxDU\nGanVfez73RuneOoPNQwNOLDY40jLXk2fU4kugKROFaPHG0gWJnLeED0B8rpG9b0D9FrdrFi9mrys\nrGkhe2ONLxICSYq+oY42mxBn3jKEbie9SgUnrjWyHdiclxxVf3MRkYS3W43miDXVly69/QvvXEiK\nWrN6PU3NzTScvoJLaA0b1hEtYm7R2cFc+PC4mxEjjHcxIi0ajV2e8lIz7VYryMnmpRf+PKhNp2/l\nZx+VccWsIC57AYO9XTS1GBF0t/A3j+9g67q1bI2ib52+lR+9f4wGZR5ukQS7xM6NIRtDThv7Tpzh\nO08/AYSfgIw9BmobaoLEwI/d6A1jjVnG2gW7eeud3+IUO+kehE71SlziOKzVdazPUvPi3j0YNq4K\nIpY5C1Iw97uRJhSTtvJeAFqYPgtPuA+BQLIQ6bx97ZNAoGvU0Hsdc3oOJ2rb2Q7kZWVNiexNdnyB\nJOVqbT3i3EU4HXaEYo/F151RyOXK83x/z3+Lus+5hiSZkIEw7RbTQNia6pduVvHAfbd/4Z0rSVGL\ni5by2KNPTClLPOYWnT3MhQ+Puxmxq3wXY6xFY7YyLkuramkxWUGbiRyQaxIBUDjNNHYNBJHFsb7y\nS6tq6bBLQCwF9y3XsTyOAdswlU1dvj68E5ChsZp+3UnEQjMdvXaEqaswqTIwAQc+PkPB/PloAo7t\nrwGrxFH0ZQZESqy6K2T2VaKJd2HSjSCWlFCQ84RvTB8cegejxcjIiJARdxpZG1cGnXs4UhVOx3E8\ni0i4D4FAshB43qMx2Yk30DVqd98S4VFpqWpsIy/Lk3A0WbI32fEFkpQRhIjEYuIEduRiNwKnDbFQ\nQFKidtYW+UjP70SsV8/sLLlF1APqRLff4J6C8Nubbt2CofZG6vT7Q8pMTuT6TmSc3tCGf3j1F9xs\nuYJaJcRugWznIzz31LNRH3MuIOYWnT3MlQ+PuxUxwngXYzpJw3S5aPqtjqCsZ3tHHcq+SiRyGyd0\nEpZkxPk0GgO/8q9UnmTfh78gJUmKyClDrCpE4JaE9G+x2mjqbOOVfZ/4rIfnj3+GRHjpVtk1Dy28\neKqaK4d7sAw1I5LYqbrxCXlLd1KwckvQWMFTn9puqGN14hVKHsn1/V516hpnL5Szfs0GUtNykM/f\nReatie7shUtUtRiYl2iib8jqk6yxK/0C3WcvlLO/7DWKSlLx6sYF6lSOdQ3DfQiYAvjadEy8gff8\nsq4dSUEyck0iEoEbb576iNMf5zlZshfN+CI9f974S7V9iBGnjdTEBJQKv85Utqg76jFNJyKHg3RE\nsGaHWq/OXiintGw/y8UGmqtOMCItIDszl2f2lNDYNUBLmOOqxPD7w4foGy7ngT3+/irPHKGrspen\n//jRKY1/LCvb0dKjiBQ3eepPl/jaKk58Cm9xR5HGmFt09jBX1RjuFsQI412M6fpam04XjVYmRiby\nVBSxd9SxTHmekgf9kjheod1rHcO+cesqTyJ3lPP41/1WlnOl11CaMzAZ43EneErvOex2bG036UtI\n4I2LzQgEQtLdQ+TbLrNub7AeZGqaGIe1kfWP+vs8ffw0xw91s3LT/WgT1D4CJBcJsXadpeSZnKA+\nijdn+TTeRlslZCLodwsob+ggPTsbRGAFapqbfbGMHxx6h8X3pQb1WbQulQ8OvROWMHpJ0/nqWkYS\nR8hPTw6SwwnUopzqxDv6nrvS5tHV10caoE5KY6SvE7cmHYXIY22c6HM1kfGN9/wV5GSTo1HwV787\nhTwj4HreptJ44chsJCvVm0fKyF2zNaR934kzJGsTfX0kisycbzjI/JIkckljJWmeBI5Nj7J+zVpy\nAq/JLTj72slVK9h37AAPPhF8HVduTOf68RszGmN6qOwP7HmmMKitZHsuB978wx1FGGNu0dnFRL1f\nge9fvNBNulzgC82JYWKIPeF3Mabra206XTTbihdR1WLA2N+Jsq8yiCyCX2g3Pn+77yu/++YpHn86\nP2i7ddtyufkfdbjjFjPY34rZZsfW3Y5QGcewMgmx0hNPaHLEox46BQQTxkApHC823ZdH/Ws1VLUs\nZ4Wqg6d3bQTg0KX9yMeo8wuhVon8nGzqKi7hSPJbJOnvJH/RUt91M1rCl1T0tAcjkDQpk9O5XFdP\nTWs3BcnxLMnPQWk1BmlRTtUiPPqeFxdmcaK2ncHeLlILF5MGDN68xMal+eTauiM+V2O53KNdGKJ5\n/javXc23dM2c0dXR7xR49AojlMabzoSGwPtiHDRw8oae9ypqkDktFKzaGKJv2Wt1kTuqj/6BQcrr\nOtiwtchHiI8c+hkPPJ4WtF2gCHW4d3vDhiW8+0kp8vhQYXsAqcId9blPxsomUYZXFYjUPh4mE64x\nHYi5Re8cjP6Y7Hc4OF97hYUL24KqfsUQHWKE8S5H4KLsXSw+PFs9oYVyOl00BTnZvPjwVvYdPc2l\nbhOQFrKN3W0O+spXRlhw1Coh82QmLnX1IhwyMWwz485egCDBn9WMWEpPmKyBQCmcQCTEuRA5BsmW\nKn3XRjgywLApfLaqV+NttFVCk5xKWpwMk7EVt9PIkGmIOK0WXe8QcSMe8hmppGK4di9pamm4Tlll\nNSaLE7ujG0uLC0FHI9959D60Sk+g/3RYhEff87ysLLYD18+Xk9BVyzyZkGe+9ciYWoDhSidOplRb\ntM/fqqVFPPXEo2MmPEx3QoP3vhh7DFTp2j01uVWZ9DRcYaDFQHGuRzLKG0MbZ++l7ug1X0whQFNn\nDwpVMLGUxQvCHS5IhHo04bZYLJgckctM2kYEYcntjqIMHtu+MaivyVjZ7OYIY47QPham69mZDGJu\n0TsHYT8mtemUXamPEcZJIEYYYwCmtlBO1UUTzlLwna89yd//06Ww20sESt9X/qBbRG9vqCg1QHef\nHb3QwtqNm7G53FRUnKXNKUDhdCIW+UlXnzCbqlNtFAeU3evpCHdG4HRIWb1yFSJTh6/NKo3HlXsv\nFWXnKdnq7+NcaTPPPPgCQNB4m/StGI0DtNyoIyF7HgbjIAnpuQg1iQzgd0vPL1hD5ZnzQTqVlWc6\nmV+wNsQS1NhuYDjezcnzlfTFpeNK8+xjtlsYlNs5V9vEF1YtAqbHIhzunudlZbEl5V6+tju0Gkk4\nHC7df2vB92Mypdqm00U43QkNXjLbpG/1kMVbiNNqEViGaOoUYu9pQTx0JCiGtvLMEQyNkFq4jJGe\ndpYUBWstjlfSMhJUYkjI20TlmeNBz9WV0+1oNOtxhyG3p41mhkbNBZOxsu3a+hAVJz6lZLvfhlpx\nooVdWx8ac8zhMF3PzmQx18swxuBBpI9JozV8AYUYxkZ430QY2O123nrrLVpbWwF45ZVXuP/++3nx\nxRcxGkNdZDHcWRhroRwP24oX4ertCGpz9XawrXjRuPt6LQW5JQLmb4gnb4OA/WWv8fc//zfqIpHF\nVQAAIABJREFUu0RUHNMFbX/jYi9f2PYIBTnZ7JifhO7GDSya5Zw53hy03enjLbQpltIlVlN33VNR\nJk2rRmTqxmLzlw4UGDtJL1xBorKElgo3jeWeiiubVjzEpVNtQX1WlOpJmbcZCCYjSTIhkowiqs1r\n+c1vujj4YTtvv91Jd09ukHvVO94+q5MuKySsvJdWpwxTUgFdfX1YjH1Bbumn9u7B7lrFpweMfPax\ngU8P9GN3rWJbyTp+fbKaFmkKJlUGLdIUqjsHuHathiGnEJfGTwSEEilGiZrLLf7McK/lrX9gkMq6\nm5Rfu0Fl3U0au3rHvV9eTOWee2FzD4dtn2iptsCxGHsMVFVeoryslJ7+PnT61gn1FckqPtmEBu9z\nMnp90mqTKC7IRNHXjKnhaEhN9ZUb0zE1HCXX1s39BclokoNjWbUFW7hyuj2ozftujIVV87JI1Ghx\nxO/0PVeHf9vJvfc8Rnymx+LiIbf+8VicrpC5wGNlW0aurRuVqYNcm0eKaywS9dxTz7I6/wEOvNnI\nofdvsP/NG6zOf2BS8YvT9ezE8PlGpI9GjSy89yaGsRH1J/hPfvITPvnkE9asWcOxY8f41a9+xQsv\nvEBZWRk/+MEP+OlPfzqT44xhhjEVt3IkFw14SgBGiofS6Vv593deY8ve0MSOt14vpVq8GuVIMdW/\nukpKgoAho40n7nvIR8IauwZYWeKJI7xy+De8/a9n0CQp6e8doV9xD+785bhFUrpar1IAFC1eQq/p\nAh0tV5CkZCIRuFHFx7M4NZ6ndj0Qsti99tav+d2vDiCQuTENuUnOXc+yrVuCLCkHDn3EcOspVK3H\nGRgW0p+4Cvu8NdB+g/++Z11Qf97xVlVeQjh/sef6GgcY6TdgF4rpuHaRhYuW4JbI6bcO+8TNS6uW\nhE2Y6B8YpKmzB4vThUuRQGv9eRDH+Q/ociKXS3G4nPR2NPHT18+hUInp7bMgzlhPj3oeAkV82ISb\naBDvGKTybANul5NVeRk8dv+mCVldpII49A3dNNcbEEtE2G0O8ovSkAiS0elbPWEJzR0IhCJW5qeF\nuEW98D5/+46e5oyuB0VyJovmzcecoPZZyTNSohPnnu6EBq8lzpvIBeAeGSI/NxVNgprlagGNtTfC\n7puXGc/Xdt8fbP2/hWRNEqvyHxuztng4ZKWl8PRCDeXXdfSnPhL0Xr5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RAAAg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\n21DACWgC8K/M63jezk4rM5QN8RzokqG2P8DfDwH+Dyb2ZWSl40jWHvhPc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -501,9 +509,9 @@ "outputs": [ { "data": { - "image/png": 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iDvb2Vl79KkGYc28I2kerUTeMMURKbH0iIvJL3LJHWpkmO0xWAVHBpXv5Hmup\nl6AiKolrboiIyC9xEYc0bpntSNyfjQfWX8dbP2VLXQ6Vgz2BRETkl7iIw7tyLQI2HcvF2sO5yDIL\njtssNgFqXuFDlhgCiYjIL3ERh/dYbAL6b76Oi7cKru/bvpYaEzsZ0a6WRuLKqDwMgURE5Je4iMN7\n1CoF7m+sw6FL+ZjYKRj31FZDoWDvn9wxBBIREVGlTexkhFppZPjzIVwYQkRERE7ZBQE7T5ow+5us\nMu/XqBQMgD6GPYFEREQeIsbehFITBAF7zuZj2f5snMoouL5v32Y6dIjmfD9fxxBIRETkIf62N+G+\n8/lYvDcbJ9KtAICawUqM7WBE2yi1xJWRJzAEEhEReYi/7U14It2CE+lWVDcoMaa9AQNa6qHhdi9+\ngyGQiIjIQ/xtb8LHWxugVikwsKUeejXDn79hCCQiIvIQX92b8OR1KxqHq6AssbDDoFZgWBuDRFWR\n2BgCiYgCiNwXLsi9Pmd8bW/CU9etWHYgB1+fMWPh/aHo00QndUnkRQyBREQBRO4LF+Ren784l2nF\nigM52HnKDAGAVgWk59qlLou8jCGQiCiAyH3hgtzr8weHLuVj5PZM2AQgSAn8X4weo9sZEGFUSV0a\neRlDIBGRH3E2nCr3hQtyr88ftIlSo14VFeJqqjGmnRG1QisW/nx16N5X6xYDQyARkR9xNpwq94UL\nYtfHAAAEKRX4z+DwSm/1Ioeh+4q8nnKoWy4YAomI/Iiz4VS5L1wQu75ACQA3TXasO5yLOmEqDGip\nL3W/J/b6k8PQfUVeTznULRcMgUTkFeyB8Q4Op5bP3wNAdr4d64/kYf3RXGTnC6hhVOKRZjqoRdjg\nWQ7nWkVeTznULRcMgUTkFVL3wARKCJX7cK/Ucm0RuJrxFz75KQ/BOgUOp/6BrieOo0XLVlKXVikW\nm4D1R3Ox9rdc3DQLAIDOdTSY2MkoSgAE5HGuVSTQyaFuuWAIJCKvkLoHRuoQ6i1yH+6V2pAxCzF7\nQie8OTYYSqUCg+8T8MpbQ/H2B8ekLq1SgpTA7tNm3DQLaFdTjYn3GNG+lkbU55TDuVaRQCeHuuWC\nIZCIvELqIRipQyjJQ40akWhQS1vsXIgKM0tcVeUpFAq81DUEuRY7OtfRQKEIjEu8MdBVDkMgEXmF\n1EMwUodQko8rmVrYbHbHuXAlUyt1SS6z2gWczrChWfXSH99xNdUSVES+jCGQiLxC6m/sUodQko9n\np23CK2/X7vqmAAAgAElEQVQNRVSYGVcytXh22iapS3LKLgjYlWrG8gM5SM+1Y9ewagjTKaUui3wc\nQyARBQSpQyjJR4uWrXxmDqAgCPj2bD6WHsjGqes2AEDtUCUuZNkYAqnSGAKJiIhk6q2fs/Hh0TwA\nQGSwEuPaG9GvuThbvlDgYQgkIiKSqX821eGLU2aMbmfAwJZ6aIMY/shzGAKJiIhkqlUNNb56qhp7\n/kgUnFBAREQkoZR0CybtzMTlW7Yy72cAJLGwJ5CIiEgCF3KDkLQnF9+cswIAahhzMbNHiMRVUSBh\nCCSqgEC5BBmRP5PqfXz5lg2Lf87Dl6ejIMAKrQp4vLUeo+KNoj83icvXPhsYAokqIFAuQUbkz6R6\nH1tsAnafsUCpAB5rrsbYjqGIDFaJ/rwkPl/7bGAIJKoAXoLMN/nat3QSl1Tv47pVgjCrmw7BWWdw\nb7vm0OkYAP2Fr302MAQSVQAvQeY9ngxuvvYtncQl9vs4I88Os1VAzZDSIe/hJhokJ5e9EIR8l699\nNjAEElUAL0HmPZ4Mbr72LZ3EJdb7+KbJjg+O5GLD0Tx0qavBkgfDPHJcoPwvRezplp6vfTYwBBJV\ngD9cgqzkB8ZjI16XuqQyeTK4+dq3dBKXp9/H2fl2bDiah6QjubiVLwAArHYBFpvgsW1eyvtSxJ5u\n6fnaZ4PXQ+D777+Pt99+GxqNBoIgQKFQYM2aNWjSpAmmT5+O/fv3IzQ0FOPHj8fAgQO9XR5RwCj5\ngbFu7XR0f/hfUpdViieDm699SyffYbEJ6L85A5ez7QCAe2qrMbFTMNpEqT36POV9KWJPN7nL6yEw\nJSUFU6ZMwYgRI4rdPmnSJAQHB2Pv3r1ISUnB6NGj0bRpU8TGxnq7RKKAUPIDw6iS5weGJ4Obr31L\nJ9+hVinwUFMdfrtswcRORnSM1ojyPOV9KWJPN7lLkhA4YMCAYrfl5uZiz5492L17N9RqNWJjY9G3\nb19s376dIZBIJCU/MLJl+oHB4Ea+4rmORgQpAYVCvCt8lPeliD3d5C6vhkCTyYRz585h/fr1mDp1\nKsLCwjBy5Ei0bNkSarUa0dHRjsc2aNAAX331lTfLIwooJT8wBo18HWlp8uwNDGSc7C8fNruAz0+Z\nsO+8Ba/fF1Iq7Hnj8m7lfSniFyZyl1dD4LVr1xAfH4+hQ4ciISEBR44cwbhx4/D0009Dq9UWe6xO\np4PJZPJmeUQBpeQHhslkYgiUIU7296yKhGq7IGB3qhnLDubg7I2CbV0eba5Dx9riDPkSeYtXQ2Dt\n2rXx4Yd3/vFq3749+vXrh19//RVms7nYY00mEwwGg1vHN5vNEATBI7X6isJ2K9l+VBzbyTm2kXNS\ntJFecbXY3E09rsr+C7Kcz6UNK17EqK5H7yyIWvEvjJv2/l0fv++CFe8eNOFkRsGCj1rBCjwbr0XL\ncFulXgc5t5GcsJ2cK9pG7k5F8GoIPHHiBH766Sc8++yzjtvMZjNq1aqFAwcO4MqVK4iKigIAnD17\nFo0aNXLr+KdOnfJovb4kNTVV6hJ8AtvJObaRc95soyuZOthsgmPu5pVMHZKTk732/JUhx3MpyHq+\nWKhWWc+X254/XgrByYwqqKqxol90FrpF5CAoH/gzxTP1yLGN5Ijt5FxF2sirIdBgMGDZsmWoX78+\nevfujX379mHnzp3YsGEDsrKysGjRIsybNw8nT57Ejh07sHr1areO36RJE2g0gdU9bzabkZqaisaN\nG5caUqc72E7OsY2ck6KNakxOxLq102FUpSPbFoGnJ89HREQNrzx3RYnZTunpV7Ft7QwE326PQSPd\na4/vd9SBzZbhCNXWoDqIiYm56+MbNRNQ/6QFjzRVQxsU7ok/AQDfb65iOzlXtI0UCoVbHWJeDYH1\n69fHO++8g8WLF2PatGmIiorCggUL0KJFC8ybNw9z5sxBjx49YDQaMW3aNLdXBmu12oA9SbRaLXQ6\nndRlyB7byTm2kXPebKM6derihTkbvfJcniZGO/33g5lFhnMvISnpZbfmSD45bnGxBVFPjlsInU6H\nE+kWNK8eBGWJ4TQdgGHxeo/+DUXx/eYatpNzWq1W3sPBAHDvvffi3nvvLXV7WFgYlixZ4u1yiIjI\nQwoXXegVV3ElU4cakxNRp05djz5HZTdELrkg6o90C179PBPfncvHW/eH4qEmDBoUOHjZOCIi8oji\nK5kFrFs73eO9mJ7aEPl0hhXLD+Rg1+mCSfX6IOBGnt2TpRLJHkMgEZEfkmJ/QW9chaYiGyKXbIuW\njy3Ei98rYBcAjQoY3EqPUfFGVDcoPV4vkZwxBBIR3eZPGzNLsb+gN65CU5ENkUtdJ/t//0ajRosR\nV1ODZ9sbEBWs8nidRL6AIZCI6DZPBycpQ2Vl585VRGEvnR4FcwKfnjxf9Od0Rcm2CFZexdb/C/fK\nFT6k5E9fakgcDIFERLd5OjhJebUPT82dc0dhL53JZEJycrKkW9ncyLNj7eFc1AtTldkW/h4AAV5t\nhpxjCCQius3TwUmK3rhCFZk75w+yzHasP5KL9UfzkGsREGFQYsPohUh6L/DaQsrzj3wDQyAR0W2e\nDk5S9MYVqsjcOV9msQlYdzgXHxzJRZa54PKh3epqMKGTEdE11AHVFoWkPP/INzAEEhHdVpHgVN68\nq0DtjZNCkBL49qwZWWYBHaPVmNgpGHE11VKXJSmef+QMQyARUSWUN+8q0HrjpKRQKPBStxCYrAI6\n1Q6sy4feDc8/cqbcEHj27FmXD9SgQYNKF0NE5Gs478q7LDYBqRlWtIgo3cvXJkq6nj+uxCVfVG4I\nfPDBB6FQKCAIQrHr0QlCwXyLorelpKSIVCIRkXxx3pV32OwCdp4yY/mBHGTk2bHrqWqoopPP5s5c\niUu+qNwQuGfPHsd///DDD0hKSsL06dPRunVrqNVqHD9+HAsWLMDQoUNFL5SISApFe3huWaohtvOI\nYvdz3pW47IKAr0+bkXggB2du2AAA9auocPmWTVYhkD3C5IvKDYHR0dGO/16zZg0WLlyI+Ph4x20J\nCQl49dVX8dxzz2Hw4MHiVUlEJJHNq6bi4Zhfsf3HPATrFPjkg8No1vxL1KlTFwDnXYntzZ+ysfFY\nHgCgVogS4zoY0beZDkFKee3zxx5h8kUuLwzJysqCWl16voXFYoHJZPJoUUREgDzmWRlU6dj+Yx6e\neTj49lCfgHVrp+OFORu9WkegeqSZDl+fNmN0ewMGtNDLdpNn9giTL3I5BPbu3RszZszAjBkz0Lx5\ncwiCgGPHjmH+/Pno16+fmDUSUYCSwzyrXFsEgnV/FhvqM6o41OctMTXU+HJYNdmGv0LsESZf5PKE\nitmzZ6NZs2Z45pln0LlzZ3Tp0gUTJ05EQkICpk2bJmaNRBSg5DDPasiYhfjjcghstoIFcTabgGwO\n9XnU71ctmPB5Ji5l2cq8X+4BkMhXudwTqNfrsXDhQsydO9exdUzDhg1hNBpFK46IApsc5lnVqBGJ\n6W99haTVU6DHVVzJ1OHpyfO9Xoc/+vOaFcsOZOObs/kAgKjgXMzqESJxVUSBw63Noq9fv45t27bh\n3LlzmDp1Kr7//ns0btwYTZs2Fas+IgpgcplnVTjUZzKZkJycjIiIGpLU4S8uZdmweG82vkw1AwB0\nQcDQ1gaMjDdIXBlRYHE5BJ44cQLDhw9Ho0aNcPz4cUyYMAG//PILpk+fjpUrVyIhIUHMOokoAHGe\nlX+yCcDXZ8xQK4HBrfR4Jt6A6kaV1GURBRyXQ+D8+fPx1FNPYeLEiYiLiwMAvPbaa6hSpQoWLlyI\njz/+WLQiiYjIf9QJU+G1nqFoH61GVDDDH5FUXF4YkpycjEceeaTU7YMHD8bp06c9WhQREfm+a7n2\nuy72eLiZjgGQSGIu9wSGhYXh0qVLqFevXrHbk5OTER4e7vHCiIjIN2Wa7NjyVxi++TUbnevk492H\nqkhdklfIYV9LIne4HAKHDBmC2bNnY8qUKQCAP//8Ez/88AOWLl2Kp59+WrQCiYjIN9wy25F0JBcf\nHs1FjiXUcbvFJnh9mxcpApkc9rUkcofLIfDZZ5+F0WjEggULkJeXh+eeew7Vq1fH2LFjMWLECBFL\nJCIiubPYBPT/KANXsu0AgNZheZjSozra1ZFmGzEpApkc9rUkcofLIfDSpUsYMmQInnjiCeTm5sJu\ntyM4OBg2mw2///47WrduLWadREQkY2qVAg831eHIFQvGxqmhvn4eMRHSDYVKEcjksK8lkTtcDoH3\n3Xcffv75Z4SHh8NguLOX019//YUnn3wSR48eFaVAIiJyTg7z0Z7rZIRKAZjNZiRf9+pTlyJFIJPL\nvpZErio3BH700UdITEwEAAiCgL59+0KhKD6vIycnB02aNBGvQiIikckhQFWWN4Y/rXYBn/1pwr4L\n+VjQK7TU50GQUj6Xd5MikHFfS/I15YbAgQMHQqfTwW63Y8aMGRgzZgxCQu5c0kehUMBgMOCee+4R\nvVAiIrH4w4R+MYc/bXYBX6aasfxADv66WbDly4AWFnSsrfHYc3gaAxmRc+WGwKCgIDz66KMAgNq1\nayM+Ph75+fmO4eAzZ86gYcOG4ldJRCQif5jQL9bw5w/nzFi8NxupGQXhr26YChM6GtGultojxyci\n6bg8JzAqKgr9+/dH9+7dMXXqVADAE088gZo1a2LZsmWoWbOmaEUSEYnJHyb0izX8eTbThtQMG2oG\nKzG2gxH9mutkNexLRBXncgh85ZVX0KBBA4wcOdJx25dffolZs2bh1VdfxYoVK0QpkIhIbP4woV+s\n4c/BrfQwqBXo11wHjZf3+iMicbkcAg8fPozt27ejWrVqjtvCwsLw/PPPY9CgQaIUR0TkDZw/Bvx+\n1YKWEUFQlejl0wUpMChGL1FVRCQml0NgaGgoTp8+jbp16xa7/eLFi9Dr+Q8EEZEvSk6zIHF/Dn78\nOx9v9g7FP5vqKnW8tLQr2LDiRQRZz+P7HXXw5LjFLq20ltMKbTnVQiQml0PgoEGDMHPmTEycOBGt\nWrUCAKSkpCAxMREDBgwQrUAiIvK8k9etWHYgB3vOmAEABrUCWWZ7pY+7edVUjOp69PZK6wyXV1rL\naYW2nGohEpPLIXD8+PGw2Wx49913kZGRAQCoVq0ahg8fjlGjRolWIBERedavl/Lx9P8yIQDQBQFD\nWhswMs6AqnplpY9d0ZXWclqhLadaiMTkcghUKBSYNGkSJk2ahIyMDGg0GgQHB4tZGxERiSAuSo2m\n1YLQPlqN0fEGVDeqPHbsiq60ltMKbTnVQiSmckPgli1b0L9/f2g0GmzZsqXcAw0ePNijhRERkThU\nSgW2/F9VUbZ6GTJmIdat+BdU1vOwBtXBk+NcW2ktpxXacqqFSEzlhsBVq1bh/vvvh0ajwapVq+76\nOIVCwRBIRCSSiixUuJZrx/u/5aBBlSD8X6vSi/fE2uuvRo1IjJv2PpKTkxETEwOdzrWFJnJaoS2n\nWojEVG4I/Oabb8r8byIi8h53FircNNmx9nAuNh3LRZ4ViDAo0b+FDmru8UdEJZQbAvPz810+kEYj\n32tIEhH5MlcWKlhsAtYcysX6o7nIzhcAAP+or8GEjkYGQCIqU7khMDY2FgqFa/94pKSkeKQgIiIq\nztlChbS0K9i0aio+Dx2L7KAmaBcpYEq3cLSO5PV9iejuyg2B69evd/z38ePHsXbtWowfPx6tW7eG\nWq3G8ePHsXz5cgwfPlz0QomIApWzhQqbV03F050PI8H8NvKsGhw9pkLrgb49p40bNhOJr9wQ2LFj\nR8d/z507F/Pnz0e3bt0ctzVv3hzR0dGYNWsWgyARkUiKLlSw2AT8ed2KGkXuLxwubqn/EwBwShkt\nQZWexQ2bicTn8s6gly9fRlRUVKnbw8LCHJtHExGROKx2AZ/8kYe+m65j5PZM3MizQxAK5v4VDBcX\n/Le/7GvHDZuJxOdyCExISMCrr76K8+fPO247deoU5syZg3/84x+iFEdEFOjsgoCdp0zovzkDL++5\nhQtZdkQFK3E12+Z4zJAxC5G0Lw4f7Y1G0t44v9jXzh+DLZHcuHzFkNdeew3PPfccevfuDb2+YM8p\nk8mErl274pVXXhGtQCLyfZzfVXELfszGpt/zAAB1QlUY39GIh5pooSqyz58/7mvHDZuJxOdyCAwP\nD8emTZuQmpqK1NRUAECzZs3QoEED0YojIv/A+V0V17+FDt+eNWNMByP6NQuc/f6kCrb8wkKBxK2r\nhefn5yMlJQUnT57EPffcg7S0NFy7dk2s2ojIT3B+V8W1iFDji2HVMLClPmACoJQKv7A8nnARwxMO\nY/PqKVKXRCQal0Pg+fPn0adPHyxcuBCrVq3CrVu3sHHjRvzzn//EiRMnxKyRiHwc53eV78gVC8Z8\nlomLWbYy7xfrEm9UGr+wUCBxOQS+/vrr6Nq1K7799lvH1UEWL16Me++9F/Pnz3frSa9du4bOnTvj\n+++/BwBcvHgRI0aMQHx8PPr06YPvvvvOreMRkbz548IFTziRbsG4HZl48uMb+PnvfLz/W67UJQU8\nfmGhQOLynMBDhw5hy5YtUCrv5MagoCCMGzcO/fv3d+tJX375Zdy8edPx8+TJk9GlSxe8//77+Pnn\nn/HCCy/g888/L3NLGiJv4Lwgz/LHhQuVcSHLhoU/Z+PrM2YAgD5IgWFt9Bje1iBxZcQFKRRIXA6B\nGo0GWVlZpW6/cOECjEajy0/40UcfwWg0OgLe6dOncerUKWzatAkqlQrdu3dHhw4d8Pnnn2PUqFEu\nH5fIk7iQgcT23TkztCrg8dZ6jIo3Ilzv1hRtEgm/sFAgcTkEPvLII5g3b55jO5ibN2/izJkzeOWV\nV/Dwww+7dIxz585h3bp12LZtGx599FEAwNmzZxEdHe0YYgaABg0a4MyZM+78HUQexXlBJKbaoSrM\n7xWKdrXUqGFUSV0OOcGRAfJXLofAF198EYsXL8bQoUORn5+PgQMHIigoCEOGDMG//vUvp79vs9nw\n73//GzNnzkRoaKjj9tzcXOh0umKP1ev1SEtLc+PPIPKsgnlBF6BSKTgviCosLceGfBugMaWXChEP\nNmGIKI+cghdHBshfuRwCf//9dzz//POYPHky/v77b9hsNtStWxcGg2tzWJYtW4YWLVoUu/YwUBD4\nzGZzsdvy8vJcPm5RZrPZcRmlQFHYdiXbkIpzt50eG/E61q2dDqMqHdm2CAwa+TpMJpOYJUqO55Jz\nrrZRRp4dScfy8Z+UfHSKDkLj317EqK5HHSFi3Yp/Ydy0971RsiQ8cS5tWCGfNtMrrhYbGdDjaqX/\nPeD7zTVsJ+eKtpFC4d5OAi6HwHHjxmHdunVo0aIFmjRp4l6FAL744gtcu3YNX3zxBQDg1q1beOGF\nFzB27FhcvHgRFosFarUaQMEQ8T333OP2c5w6dcrt3/EXhRt4U/ncaafuD9/p4U5LS0daWmAMCfNc\ncq5kG93IyMAvu5ZCZ7DioHYQTlftg3yhYJg3LzsLCuvlYiFCZT2P5ORkr9ftbZU5l4Ks52XTZlcy\ndbDZBMfIwJVMncdq4fvNNWwn5yrSRi6HwNq1a+Ps2bNo0aKF208CwBH+CvXs2RNz5sxBjx49sGvX\nLrzzzjuYNGkS9u7di4MHD1boUnRNmjQpNrcwEJjNZqSmpqJx48bQarVSlyNbbCfn2EbO3a2Nlr85\nChP6/IVBpzcizVoDEIBudYIwJl6L5tVDsfxkTdhsaY4QYQ2qg5iYGAn/EnF54lz6fkcd2GwZsmiz\nGpMTi40MPD15PiIialTqmHy/uYbt5FzRNlIoFG51iLkcAhs1aoQpU6Zg5cqVqFOnTql5fIsWLXK9\nYqBYl2ViYiJmzpyJzp07IyIiAosXL0ZkpPtzP7RabcCeJFqtttRrQqWxnZxjGzlXso1C1NehUdnx\nUJVdSM5riQZXd2D2I8sc9z85bnGxbUeeHLcwINq4MueSnNqsTp26eGHORlGOzfeba9hOzmm1WvGG\ng5VKJfr16+d2UXezZ88ex3/XrFkT77/vv/NjiMi/FS4keiZiPWC3Iel8XLH7ue2I+8pqMzktFpEr\nthG5o9wQaLPZsHr1auzevRsajQY9e/bEyJEjHXP3iIgCidUu4NurRmy4movFD9751s0Nhr2Dq3Sd\nYxuRO8oNgW+//TY2bdqEvn37IigoCGvWrMGFCxcwb948b9VHRCQ5m13AjpMmLD+QjYu3wgFYcfCi\nBR1rF8xBlrKnL5B6frh/p3NsI3JHuSHw888/x//7f/8P9913HwCgV69eGDt2LObOnQuVihucEpH/\nKgxXV7TR+FH3BDJVBcGqps6CSQmhaB8tjxERf+r5cRZouX+nc2wjcke5ITA9PR2tWrVy/NypUydY\nrVZcu3atQgs3iIjKI6dercJwtfVGQ3x2NRIhtut48R/RqGs+j9iG1aB0cwK2WPyp58dZoOWwu3Ns\nI3JHuSHQarUiKOjOQ5RKJTQaDfLz80UvjIgCj5x6tQrDVf+qn8GozEXWHyfwcJOPIbft/fyp58dZ\noOUCG+fYRuQOl1cHE1HFyKl3S+6k7NU6fNmC2MggqG4/f2G40qoseCj0CyTZ4pwcQRr+1PPjT4GW\nyBc4DYGffPIJjEaj42e73Y4dO3YgPDy82OMGDx7s+eqI/ICcerfkTooQcOyKBe/uz8a+Cxa80SsE\njzTTA/CdcOVPPT9F2/xGXiisVhPWvNGHX56IRFJuCKxVqxY2bNhQ7LZq1aph27ZtxW5TKBQMgeRX\nPNl7509ztsTmzeD1R7oFSw/k4PtzBdNbgjUK5Fnu3F9WuPL360dLrWibvzNvGMbcyy9PRGIqNwR+\n88033qqDSFY82XvHIS7XeatX6+DFfDy9PRMAoA9S4IlYPZ6OMyBMpxT9uSsrUKYX8MsTkfg4J5Co\nDJ78APKVYcVAEl9TjZYRQehQS42R8UZUM8g//BUKlOkF/PJEJD6GQKIyePIDyJ/mbPkLlVKBzQOr\nOhaB+BKpesi83QPpypenQOkVJRILQyBRGdh75/vSc2xYcygXDauq8HhrQ6n7fTEAAtL1kHm7B9KV\nL0+B0itKJBaGQKIysPfOd2Xk2fH+bzn46Pc8mG1AdYMSA1rqoVZJF/o82WMl1RcUOc7Rk2NNRL6E\nIZCI/ILFJmDFwRx8eDQPeVYBANCroRYTOhpFD4DOQp4ne6yk+oIixzl6cqyJyJcwBBKRJDw9nytI\nCRy4aEGeVUD3eho818mIlhHeub6vs5DnDz1WcpwiIceaiHwJQyARScLT87kUCgVe6hYMqx1oG+Wd\n8FfIWcjzhx4rOU6RkGNNRL6EIZCIJFHR3rF8m4CUdCvalBH0WtXwbvgr5CzksceKiOSIIZCIJOFu\n75jFJuCTP01YeTAHN00Cdj1VDeF6cfb3c3eo2lnIc7fHSuytT1w9PrdgIfJvDIEke/wg8k+u9o7Z\n7AJ2njJj+YEcnM+yAQCahKuQnmMXLQS6O1Tt6WFJsbc+cfX43IKFyL8xBJLs8YPIP7kanN78KRub\nfs8DANQLU2FCRyP6NNFCqRBvxa/UCznEfn5Xjy91OxCRuBgCSfb4QRTYHmupww9/mTG2vREPN9Mh\nyAubPIuxkMOdHm2xF5K4enx/WNBCRHfHEEiyxw+iwNa8uhqfP1HNq1f4EGMhhzs92p54/vJCp6vH\n54IWIv/GEEiyxw8i/3foUj5WHMzBnHtDUSdMVep+b1/iTYytR9zp0fbE85cXOl09PrdgIfJvDIEk\ne/wg8l+/X7Vg6f4c/HI+HwCw7nAuZt8bInFV4vB2jzanURCRMwyB5FVc6UsAcP6mDW/9dAvfnisI\nf0a1AsPbGjCsjV7iysRT2KOttV3Gn2fSULf2Rbwzb5ho7wFOoyAiZxgCyau40pcAQKkAfvw7H/og\nYGisAU/HGVBFJ852L3JR2KP9zrxhmPvUFSiV6bDZ0kR7D3AaBRE5wxBIXsUhKgKA6FAV3uodivha\nGlQ3+Gf4u1uvtyvvAWc95q70qHMaBRE5wxBIXsUhKv9TXiC5fMsGi11A3bDS/9Tc31jn7VK9qrDX\nO+OWHR9/9wfef/0fMFRvh5zcENhsQrnvAWc95uxRJyJPYAgkr+IQlf8pK5AMfeEDrDmUi23Jeehc\nV4Nl/6widZleV9jj99/vczG6b/Dt9jmMNd/HIGlfXLnvgfJ6C9PSruD86YPYkpePW3l2PNbDwB51\nIqoQhkDyKg5R+Z+igeWWEIbfDD3x4YbrMFlv369WwGIToFZ5d5uXivLE4qW0tCs48eclbLqVjUvp\nNmTcsqN6mAoqlQJV9DcxesaX5f5+eT3mm1dNxaxhQVAq1bDZBLy/IxtB1crvUeeCLCIqC0MgEVVK\nYWARlEF46sxqpGsjACvQs4EGEzoGo1n14v/MyD2QeGKodfOqqZj7lB1KZbAjqD3bL8TlKRDl9ZiX\n7CXMFwwY5qRHncPHRFQWhkCiACFW+CoaWGprDqNBg954oWs4Wkeqy3z8B0snYcy9JxyBZM3SSZg6\nb0ul6/AUTyxeKiuofbQ32uUpEOX1mJfsJTRUb+f0deSCLCIqC0MgUYAQqzeoaGAZJQhQKsof9rVl\nHYdSWXBVEJVKAWvW75WuwZM8sXiprKA2eoZnet4qMq+WC7KIqCwMgUQBwhO9Qfk2Af89kYf9Fy1Y\n/EAoFCUCn7MACADXb9phsykdgeT6TbvbdYjJE4uXxFwAVZF5tVyQRURlYQikYuQ+X4sqrjK9QVa7\ngE//MGHlrzm4dKsgtB28aEHH2pq7/s7dzqXwmq3x/o6DCDEokZVrR3jNDpX+2zxLAITKHUFuC6Dk\nVg8RyQNDIBXDCeT+q6K9QXvOmLHol2z8fdMGAGhUVYXnOhnRIbrsOX+F7nYuPfNCIt5b8hwuX/od\n1cKUCApSIC3tqmy+bMj1PcAvaETkaQyBVAwnkPuvivYGXb5lw983bagbpsKEjkb0aayFSul82Pdu\n50LDlSAAACAASURBVFKNGpEw6nWYMCzodtA64fWgVV6gkut7QK7hlIh8F0MgFcMJ5FTSoBg9QrQK\nPNRE59Zef+WdS1IHrfIClVzfA1K3GRH5H4ZAKkZOE8jlPPwl59oqQhAE/HbZiqAy5sJpgxTo11xf\n6nZnbVDeuSR10CovUMnpPVCU1G1GRP6HITBAuBpa5DSBXM7DX3KuzV2/Xc7H0v05OHjRgtGNDIh1\n8fectUF555LUQau8QCWn90BRUrcZEfkfhsAA4YuhRc7DX3KuzVXH0yxI3J+Dn/7OBwCEaACr3fXh\n3sq0gdRByxcDldRtRkT+hyEwQPhKaCnaY3niz0uwdRRkOfzlytCcnIeMD1zMx8jtmQAAo1qBYW30\nGNxchb9Tz7t8DF8enmSgIiJiCAwYUn9guxqIivZYXm1mxSsb1GjZtJbsemtc6UmSc+9ru5pqtKoR\nhA7RGoyMM6CqXgmTyeTWMXyxN42IiO5gCAwQUn9guxqIivZYRoYHoWXTWhg940uv1uoKV3qS5Nz7\nqlIqsGlg1WJX+EhPv4rt62diN/7C9Zt2hNdsjWdeSCwW1uXcu1mSL9VKRCQFhsAAIfXwl6uBSOoe\nS0+S+m+5km3Dql9z0KhqEJ5sYyh1f8lLvG1bOwNTHjkPpVIFm02J93ccxOYSYV3OvZsl+VKtRERS\nYAgkj7pb74urgUjqHktPkupvuZZrx3uHcrA1OQ/5NqC6QYnBrfRO9/gLLhHUQwxKKEqEdTn3bpbk\nS7USEUmBIZA86m69L64GIql7LD3J23+LxSYg8UAONh3LRZ614LYHGmsxoaPRpU2es20RsNkuOYJ6\nVq4dan3xsC5176Y7fKlWIiIpMASSR5V3qTB/CXdyFaQEDl+2IM8K9GygwYSOwWhW3fW3+KCR87F4\n0RjohcI5gR3wVImw7ks9ta7WyrmDRBSoGALJo9j7Ih2FQoHp3YJhtQOtI9Vu/35ERA30G/YaYmJi\noNPpynyML4V5V2vl3EEiClReD4E7d+5EYmIiLl++jNq1a2Py5Mno1asXsrKyMGPGDOzbtw+hoaEY\nP348Bg4c6O3yqJJ8qadILtztiTJbBZxItyKuZumg1yLC/fAX6Dh30DvY40okP14NgefOncPLL7+M\nDz74AG3atMHevXvx7LPP4scff8Ts2bNhNBqxd+9epKSkYPTo0WjatCliY129iBXJgS/1FMmFqz1R\nFpuAj1PysObXXGSZBXw5rBqqGZQSVOzbioaR9FshuHL+POy3cpFtEtCvq5691yJhjyuR/Hg1BNav\nXx+//PIL9Ho9rFYr0tPTERwcjKCgIOzZswe7d++GWq1GbGws+vbti+3btzMEkt9z1hNltQv47E8T\nVh7MwcVbdgBAs2pBuJ5nZwisgKJhZNUntzDv6WAolcGw2QS8skGJ6W+x91oM7HElkh+vDwfr9Xpc\nuHABDzzwAARBwNy5c3H+/Hmo1WpER0c7HtegQQN89dVX3i6PyOuczaN886dsbP49DwDQsKoKz3U0\nolcjbal9/sg1RcNIqEFZLJi0bFqLQ5Qi4XxhIvmRZGFIrVq1cOzYMfz6668YO3YsnnnmGWi12mKP\n0el0bl/Gymw2QxAET5Yqe2azudj/U9nk3E6PjXgd69ZOh1GVjmxbBAaNfL3Yud+vsRI//6XEM3Ea\nPNBQDZUSyBfh73C3jdLTr2Lb2hkIdtQ9HxERNTxel6fdslRzhJGsXDtstjvXp75lqVbuvztinke+\n2p5lKaudnJ3ngUbO/ybJCdvJuaJtpHCzc0AhSJyaXnrpJdy8eRN79+7FkSNHHLdv3LgRe/bswdq1\na50ew2w24/jx42KWSSQpuwAoZdbx9/nmVzD5wTO353gJWLqrIR56fI7UZTl1IyMDe3cvRZguE5cy\n9NColagekoPMvCro/MBEVA0Pl6QuX21PIpKfVq1alepcK4tXewK///57fPDBB1i3bp3jNovFgnr1\n6uHHH3/ElStXEBUVBQA4e/YsGjVq5NbxmzRpAo1G49Ga5c5sNiM1NRWNGzd26QUPVHJvp18vWbH6\nsBmzu+lRO1SaeX7uttGhKqZiQ6mRYSbExMSIXaZHdO3WzfHfhT1wUVXSceyXD8rtgRPzPPLl9ixJ\n7u83OWAbuYbt5FzRNlIoFDh16pTLv+vVEBgTE4Pk5GR8+umn6Nu3L3744Qf88MMP2Lp1Ky5duoRF\nixZh3rx5OHnyJHbs2IHVq1e7dXytVhuwJ4lWq73r3m50h9za6fBlCxL3Z2P/RQsAYNMJG2bfW/o6\nv97kahvlCZHFrjCSJ0TKqm1d9d8PZmJU16O3e+AuISnpZaerVsU4j/ylPYuS2/tNjthGrmE7OafV\nat0eDvZqCKxevTpWrFiBN954A6+++irq16+P5cuXo0GDBpg3bx7mzJmDHj16wGg0Ytq0aVwZTH7r\n70wr5v+UjR//ygcAhGgUGBFnwJOxeokrc52/7Akpl1Wr/tKeROQ7vL4wpF27dvj4449L3R4WFoYl\nS5Z4uxwiSQQpFdh7Ph8GtQJPxuoxvK0BYTrf2u7FX/aElMuqVX9pTyLyHbxsHJEEaoWqsOiBMMTV\nVCNc71vhz1PkcgUJ9sARUaBiCCRRyOUDXmoXs2yw/v/27jwuqnr/H/hrNmbDXUnFJUUFRRFQh1y4\nGmZZin1vWqZluaa4lS0u5bV6qJmmN/2ZpVh51cq6VprZpqZSN00kDRXMFY1FFkt2BmbOnN8fyMg+\nAwyzvp7/KGdmznzmzRFffLZjEtG5edV/asO7uu/8VWu+/5XvILFy0VB06DrA7tcKe+CIyFN5ZhdE\nI8rMTMeG5ZOw9Y2R2LB8EjIzMxzdJIco+w/+8YGpeHrgaeyKftHRTbKrjHwBy2PyMOrjv/Dm//Id\n3Ry7s+b7X3kuXvd2JR55rRAROQp7Am2M98cs5SyT7e3tr0ITPjhVgE/PFaFEACQAmqukMAgiFDIn\n2+ivEVnz/a88Fy+vyORR1woRkaMxBNqYp4afypxlsr09GQQRj/73b2QWlN7f9wE/JWbrtPBrabt/\nZq4yzG7N979sLl5h1m9QSAoxdpjGY64VT5GVlYEv/7PU6a9XIk/FEGhjnhh+quOJk+0VMgnG9VIj\nMcuAuTotAtoobP4ertLTbM33v2wuXmZmBnZFv4hDiZ5zrXiK3R++XG4PRue9Xok8FUOgjXli+KmO\np062nzVAA2kdN+usC1v3NJfvWcwztELQoMk2aGXdvv+eeq14Am+OjBA5NYZAG+N/aO6t2Chid0IR\nYlNLsOHBZlV2Z69rAKzr8G5deprrs0J34w8bK9xSjagh8oU2Fe6C4qkjI0TOiiGQ3E514adp02YN\nOqdBELH3Dz02xxUgI790zl9sqgFhHRp2r+q6Du/WpafZmnNX7llspsq2qt2uMjeRHOvRqauwffsr\nHj8yQuSsGALJ7VQXfma+uLXe5zt4RY91x/KRklsa/nq0kmNemBY634bP+avr8G5deprrs0I3u6h5\nrecsC3+FN0sXc9w3TIMW3pzrRdVr08aH1wWRE2MIJLfTkHlz1fVwZRU0QUquCV1ayDB7gBYPdFM2\neN7fnTD1J7Z8VboytoW31KbDZXVZoauRls4JHPTA5FrPeSdgyyAI3vjwm3zMGNOEc72IiFwQQyC5\nnYas0K6uFzFqyQ40V0nxQDclZFLbLPqoHKZWfmxEh679bDpcVpcVugCg1+uRkJBQ6zkrB2xvtZRz\nvYiIXBRDILmd+qzQFkURJ1IMUMn+qtKL6CWT4KEeKpu2sXKY6tH9bsx42bbDZo2xSKlywL6Y5oXt\nx0M414uIyAUxBJLbqS786PX6Gp8fm1qCjScKcPqGAUNlIzFeiDaHnMQLadj6xkibL35w1f0kKwfs\nhWu4IISIyFUxBJLH+j3dgHdO5OPXFAMAoJlSAl3/8dh+8CQ00iwkXkjD3IcN8GmRavONbl1xP0mu\nCCYici8MgeSRYlNKMPWr0u1QvL0kmByswZN91fD2kgJhpUFv6xsj4dMiFYDtN7qtz1Cto0OYq9yt\nhIiIrMMQSB6pv68CfdvKofP1wuRgDZqppFWe42xDto4OYbwvNhGRe2EIdEGNdasvW3B0b5W1pBIJ\ndj7SotatXpxtyNbRIczZQjERETUMQ6Cd2SIkOfOtvhzdW1VeWq6AzXEF6N5Kjkf9q/b0Wdrrz9lu\nAejoEOZsoZiIiBqGIdDObBGS6nurL3twdG8VAGQWCNj6WyF2JxTBaAJaqaX4Z3et3dtRV5Z+Qbh/\n7EK8vuYJtG1WjPRsJZ5ZtMiu7XO2UExERA3DEGhntghJdb3Vlz1VblvihTRkZmbYZUjYIIhY/2s+\nPj1bhGIBkAAY1UOJ2QO0kEuNjf7+DWXpF4QDX6zBa5NMkEq9IAgmbP9yNXr22ukUQ/DO0AYiIqob\nhkA7s8WQXl1v9WVPE2auxesLRyCgXR7y9SLmjFFjl52GhOVSICHTiGIBGOGnxJwBWnRrVXqJ6/U1\nh0BbBZiGnCczMx3JV07is6IS5BWZ8MhQTZVfEGr6BcIZhuB3bXkJowPjsPfnInirLmDVwhFYsuYg\ngyARkRNjCLQzW8yrquutvuzJx+cu9OrRHo8PTDUfs6a30xZBTCKRYPEQb5gA9GqjsPp1tgpRDTnP\nri0v4V+T5JBKFRAEER/sz4e8VcVfEGr6BcIZhuA1sizs/bkI00d73/78Jm4hQ0Tk5BgC7cwT5lXV\np7ezLgGqyCAiIcuA/u29qjwWUIfwV8ZWIaoh56n82mJBBaGkuMLdSmr6BcLRC0bK2uCtuuDwMEpE\nRNZjCCSbq09vpzUBqkQQsTuhCFt/K0RBiQnfTWqN1pqqq37rylYhqiHnqfzarFwvvPZ/iVVCcXXB\n2BlW7U6YuRarFo6AIJicZgsZzlMkIqodQyDZXH16O2sLUAZBxFd/6LE5rgDp+SYAQKCPHLeKTDYJ\ngbYKUQ05T+XXduqQCuntIGypV80evcuWApWPz11Ysuagw8Noec4wV5KIyJkxBJJTqC1Avfm/fHx2\nrggA0L2VDPN03ri3ixckFvb5s5atQlRDzlP5tRuWT4IgZDpNr5o1gcrZpjo4w1xJIiJnxhBITqG2\nAPF4bzVOppYgaoAWD3RTWtzk2R04wxBvea4YqJxhriQRkTNjCCSn172VHF9NaGmznj9X4Gy9aq4Y\nqJwtSBMRORuGQALg2En0oijieIoB750swMqIJujUvOpl2RgB0J6f2dUXKbhioHK2IE1E5GwYAgmA\n4ybRx6WV4J0TBYhLMwAA/vN7EZYNa9Lo7wvY9zO7+iIFBioiIvfDEEgA7D/n61q2EW/8lI9jySUA\ngGZKCaaGajChj6ZR37c8e35mV5xTR0RE7o0hkADYf86XUibBydQSeHtJ8HSwBpP6quHt1fDtXurC\nnp/ZFefUERGRe2MIJAD2n/PVrokMb49shpB2CjRT2Tf8lbHnZ7b0Xq4+Z5CIiFwPQyABaLw5X3/m\nGCGYgC4tql5qw7oobf5+dWHPeW6W3svV5wwSEZHrYQikRnEjT8CWuALsOa/HwI5e2BzZ3NFNcmrW\nzhm0R49hVlYGvvzPUvZKEhG5OYZAsqmsAgFbfyvE7oQiGEyAVAK01khhEEQoZI2zz587DKVaO2fQ\nHj2Guz98GdOGxLNXkojIzTEEks0YBBGP/vcWbhaaIAHwUHclHvMrwi+fzMF/TjReQHOHoVRr5yfa\nY5WxN1cyExF5BIZAshmFTILxvdX446YRc3Ra9Gglx4blcxs9oNkjGDV2b6O18xPtsco4X2gDQUjj\nSmYiIjfHEEg2Nau/psLdPWoLaLYKVpaCUWZmOj567wXIjcmI2d8RT0b9u87v4yy9jfZY0fzo1FXY\nvv0Vl7o7CBER1R1DINWJ3iji07NFOJFSgndHN6tyO7fKX9cW0GwVrCwFo11bXio3x+3ver1PY/Q2\n1icE22NFc5s2Pi43nE5ERHXHEEhWKRFEfJFYhOi4QmQVmgAAsakGhHXwqvV1tQU0WwUrS8HIFu/T\nGMOwztK7SEREnokhkCz6/pIe647l40Z+afjr1UaOeWFa6HwVFl9bW0Cz1100bPE+jTEMy1vJERGR\nIzEEkkXZehNu5JvQraUMc8O8MbyLV5Vh3/qw1x07Jsxci23vPQ+ZMRlGeUc8GWXd+zT2YhDeSo6I\niByJIZAseqSXGi3UUtzXVQmZ1HZ7/dnrjh0+PnchatEHSEhIQGBgIFQqlVWvqzxc++76udCqVTYL\nhfa+VR8REVF5DIFuqD49WKIo4lhyCcI6eEFeKeh5ySR4oJt1wcmdVB6u/TvtLOZMkttsDp89b1tH\nRERUmdTRDSDbK+vBenxgKp4eeBq7ol+s8bmiKOLX5BI8+cUtzPw6B/v+0Nuxpc6tdLhWBAAIgohW\nzaScw0dERG6DPYFuyNKCg7KewlteLfCL8nHcUHQDALRUSyDjrwVmlYdrZU2LIQiJnMNHRERugSHQ\nDVlacLBry0vo01fE/OSlAAClqRAzB7bBk0FqaLyYAstUHq7NzMzgHD4iInIbdg+BcXFxWLNmDa5e\nvYqWLVti2rRpGD9+PHJzc/Hyyy/j119/RdOmTTF79myMGzfO3s1zStXN8QNE7NryEtSSDKRnq+Dz\n7Dvo2LETAMsLDjSyLPTzTkOw5gz6aU5DceUYnum/xwGfzLVwDh95gsZeFU9EzsOuITA3Nxdz5szB\nsmXLMGrUKCQmJmLKlCno1KkTdu3aBa1Wi+PHj+P8+fOYMWMGevTogaCgIHs20SlVt6kwRJQ7JmLb\nh0uw4NWPAVgOK4VCG4imFLzbeQFMJhHbL4bY66MQkZPjJuZEnsOuITAtLQ3Dhg3DqFGjAAC9evVC\nWFgYTp06hcOHD+OHH36AQqFAUFAQIiMjsXfvXoZA1DzHr/wxrazqIoXkHAGb4wrQvaUck0M05uPc\nmoSIasJNzIk8h11DYEBAAFavXm3+OicnB3FxcfD394dcLoevr6/5sS5duuDgwYP2bJ7DWBp+qWmO\nX/lj+eXm/aXnC9gSV4A95/UwmoBWaimeCFJDISv9wc5hTSKqCTcxJ/IcDlsYkpeXh6ioKPTp0wdh\nYWHYsWNHhcdVKhX0+rptV1JcXAxRFG3ZTLv46L0XMG1IvHn4Zdt7zyNq0Qfmxx+ZvBLbPlwCrSwL\n+UIbPDp1JQBg24dLoJFmIiNHjSdmvYb8wiJsiC3GF3+UoEQApBJgdHcFpgcrIRiKIRgc9Qkdr7i4\nuMKfVBVrZJkn1Ki6nzf1+Vlc/k+qijWyDutkWfka1fVuXg4JgcnJyYiKikLnzp3x9ttv4/Lly1W+\nwXq9HhqNpoYzVO/SpUu2bKbdyI3JFYZfZMZkJCQkVHjOP0Y/b/57ZmZWlWPZObm4lZ2I08ltUCKo\nENaqEP/XIQft1UbkJAM5dvgcruDy5cuOboLTY40sc/caVf55U/Yzp67cvU62wBpZh3WyrD41snsI\nTEhIwIwZM/Dwww9j0aJFAIDOnTvDaDQiPT0dbdu2BQAkJSXBz8+vTufu3r07vLy8bN7mxhazvyME\n4W/z8ItR3hGBgYFWvba4uBiXL19Gt27doFQq8Wp7AQDQvWVTAG0bsdWupXKdqCrWyDLWyDqsk2Ws\nkXVYJ8vK10gikdSpQ8yuIfDmzZuYMWMGpk6diunTp5uPa7VaREREYN26dVi+fDkuXryI/fv3Izo6\nuk7nVyqVLnmRPBn17woLNZ6MWlvr/W0LS0w4m2lEWIc7gVepVEKlUqFPe9u1q/xcxay8JpDLJWih\nznXpbSPK6kQ1Y40sY42swzpZxhpZh3WyTKlUOvdw8BdffIFbt27h3XffxaZNmwAAEokETz31FFas\nWIFly5Zh6NCh0Gq1WLRokcesDLZ2oYbeKOK/54rw/qkCFBpEfPdkKzSRNV67ym8VseWrPMyI9DbP\nW3x3/Vxo1SruJUZEROSi7BoCZ86ciZkzZ9b4+Pr16+3YGtdhEER8cb4I0XGFyCwwAQD63iVHjl5E\nE23jvW/5rSKaaireN/fvtLOYM0nOvcSIiIhcFG8b18hssfv+m//Lx2fnigAAPVvLMTdMi3909oJE\nIoFeb2yMZgOouFVEbqEJgiCa5y22aiblXmJEREQujCGwkdli9/2JfdQ4daMEswdoMbyrEtI6jvnX\nV/lNpUvUTbA1Rorm6hwUCm0ga1oMQUjkXmJEREQuiiGwkdli932/lnJ8Ob5lnSd8NlRtcxUzMzN4\n1xEiIiIXxhDYyKzZfV8URfx8vQTvnSzAqhFNcXfzqt8WewdAS1zhriPlh+LzDK0QNGiyo5tERETk\nNBgCG5ml+/SeSCnB/zuRj/j00rl9O34vwrJhTRzR1CrOJ55B9Oon0bZ5MdKzlXhm0Sfo2au3o5tl\ntcpD8Rt/2Igh4eGObhYREZFTYAhsZDX1mCXdMmJ5TB5iU0vv5dZCJcH0flqM76226rxlvVxqSQbS\ns1XwefYddOzYyaZtj179JF57ygSp1AuCYMLraybi7f+csel7NKbKQ/HNVNkObhEREZHzYAh0ELVC\ngt/TDWiqlGBysAZPBKmh9ZJa/fqKvVwitn24BAte/dimbWzbvBhSaemG1DKZBG2buda9GysPxWcX\nNa/xubZYxU1ERORKGAIdpK23DBsebIa+bRVoqrQ+/JWp3Mulldl+i5b0bCUEwWQOUenZrnU3lvJD\n8XmGVhj0wOQan2uLVdxERESuhCGwkV3LNkIwla7wrSy8c/1DVeVervxG2KLlmUWf4PU1E9G22Z05\nga6k/FC8Xq9HQkJCjc+1xSpuIiIiV8IQ2EhScgVsPlmAfRf0uKeDF6LH1DwUWR9lvVxqlM4JnPLs\nKpueHwB69urtUnMAG8KaVdxERETuhCHQxtLzBUTHFeLL80UwmgCZBGjfRAqDIEIhs902L2W9XGU9\nXG3a+HBeWwNYWsVNRETkbhgCbcggiHh89y3cLDRBKgHG+Kswa4AGnZrZp8yeNq/NlqHXFfY9JCIi\nsiWGQBtSyCSY0EeNizeNmK3TVjsPsDHVd16bs/YgWmqXp4VeIiIiW2IItLFn+mkcdneP+s5rc7Yw\nVRb+CrLi4CUtwn3DNGjhnYKtG+fjpeWfmZ/HxRxERET1xxBYR4UGEbvOFuJEigFbIptVCXyOvL1b\nfee1OVuYuhNK5RAEb3z4TT5mjGkCY+7ZCs/jYg4iIqL6Ywi0UrFRxH8TirD1twL8XSQCAE6mGqDr\n4OXglt1R33ltzhamKodSb7UUgiDirxxThedxMQcREVH9MQRa4dtLeqz7JR8ZBaUhpLePHPPCtBjg\nq3Bwy2zD2cJU5VB64U8D3t+fj5btBlR4HhdzEBER1R9DoBXyikVkFJjg36o0/A2928uhw771Udsi\nC2cLU2WhVCncwIWkTHTq3B1SlS+eYk8fERGRzTAEWuGRniq00kgR0cULUhcLf2WcbfFHbZwtlBIR\nEbmjut+01k2JooiYa8UwCGKVxxQyCe7rqnTZAAg43+IPIiIiciyPD4GiKOJ/14sxfvctzPkmB19d\n0Du6SY2idJ5dacB1hsUfRERE5FgePRwcm1qCjScKcPqGAQDQSi2Fl8zBjWokzrb4g4iIiBzLY0Ng\nbEoJpn6VDQBorpJgWqgWj/dWQ61w3SHf2nCeHREREZXnsSGwv68CA3wVuKeDF54MUkPr5fEj40RE\nRORBPCIEiqJYZUsXqUSCDx9u7nJbvRARERHZglt3f13PNmLRwRxsO11Y7eMMgEREROSp3LInMDVX\nwOa4Auz7Qw9BBE5oDJjUVwOFjKHPHdS28TURERFZx61CoFEQ8VZMHj5PLILRBMgkwD97qhDVX8sA\n6EZcaeNrIiIiZ+VWIVAuk+BathGCCRjdQ4nZA7To1NytPiKBG18TERHZgtslpMVDmgAAurVyu49G\nt5VufJ0CmUzCja+JiIjqye2SEsOf++PG10RERA3HxEQuhxtfExERNZxbbxFDRERERNVjCCQiIiLy\nQAyBRERERB6IIZCIiIjIAzEEEhEREXkghkAiIiIiD8QQSEREROSBGAKJiIiIPBBDIBEREZEHYggk\nIiIi8kAMgUREREQeiCGQiIiIyAPJHd0AZ5aZmY5dW16CRpaFQqENJsxcCx+fuxzdLCIiIqIGY09g\nLXZteQmTB53G4wNT8fTA09gV/aKjm0RERERkEwyBtdDIsiCVSgAAMpkEGmmWg1tEREREZBscDq5F\nodAGgpACmUwCQRBRKLSx6fk53ExERESOwhBYiwkz12J79IvQSO+ENFsqG26WSiUQhBRsj34Rzy7d\nadP3ICIiIqqOw0LgmTNnMGfOHPz8888AgNzcXLz88sv49ddf0bRpU8yePRvjxo1zVPMAAD4+dzVq\nKONwMxERETmKQ0Lg559/jtWrV0Muv/P2S5cuhVarxfHjx3H+/HnMmDEDPXr0QFBQUL3fx9mHWxt7\nuJmIiIioJnZfGLJ582Z89NFHiIqKMh8rLCzEjz/+iPnz50OhUCAoKAiRkZHYu3dvg97L2Vf3Tpi5\nFtt/DcGnx32x/XiIzYebiYiIiGpi957AcePGYdasWYiNjTUfu3btGhQKBXx9fc3HunTpgoMHDzbo\nvZx9uLWxh5uJiIiIamL3ENi6desqx4qKiqBUKiscU6lU0Ov1dTp3cXExRFE0f51naFVhuDXP0KrO\n53R2xcXFFf6k6rFOlrFGlrFG1mGdLGONrMM6WVa+RhKJpE6vdYrVwWq1uso3WK/XQ6PR1Ok8ly5d\nqvB10KDJ2PjDRjRTZSO7qDkGPTAZCQkJDW6vM7p8+bKjm+ASWCfLWCPLWCPrsE6WsUbWYZ0sq0+N\nnCIEdu7cGUajEenp6Wjbti0AICkpCX5+fnU6T/fu3eHl5VXh2JDwcJu10xkVFxfj8uXL6NatW5Xe\nVLqDdbKMNbKMNbIO62QZa2Qd1smy8jWSSCRVOsRq4xQhUKvVIiIiAuvWrcPy5ctx8eJF7N+/nAC0\nKAAADlhJREFUH9HR0XU6j1Kp9NiLRKlUQqVSOboZTo91sow1sow1sg7rZBlrZB3WyTKlUumaw8EA\nsHz5crz66qsYOnQotFotFi1aZPX2MGXzAEtKShqziU6p7DOXlJTU+ZvvSVgny1gjy1gj67BOlrFG\n1mGdLKuuRuXXR9RGIlr7TCeWl5eHixcvOroZRERERA7Xo0cPNGnSxOLz3CIEmkwmFBQUQKFQ8DcF\nIiIi8kiiKMJgMECr1UIqtbwVtFuEQCIiIiKqG7vfMYSIiIiIHI8hkIiIiMgDMQQSEREReSCGQCIi\nIiIPxBBIRERE5IEYAomIiIg8EEOgizpz5gzCy90XOTc3F3PnzkX//v0RERGBzz//3IGtc6y4uDg8\n9thj6N+/P+6//3589tlnAFij8r799ls89NBDCAkJQWRkJA4dOgSANarOzZs3MWjQIMTExAAAUlNT\nMXnyZISGhmLkyJE4evSoYxvoYB988AF69+6N0NBQhISEIDQ0FL/99htyc3MxZ84cXku3ZWRkYNas\nWejXrx+GDRuGnTt3AuC/uTJff/21+fopu5Z69uyJZcuW8Voq59SpUxg7diz69euHBx98EPv37wfQ\ngOtIJJeze/dusX///uI999xjPjZv3jxx4cKFYklJiRgfHy/qdDoxPj7ega10jJycHFGn04n79+8X\nRVEUExISRJ1OJx47dow1ui0pKUkMDg4Wf//9d1EURfHYsWNi7969xVu3brFG1XjmmWfEXr16iUeP\nHhVFURTHjh0r/vvf/xaNRqMYExMjhoaGijdu3HBwKx3nhRdeELdt21blOK+lih555BHxrbfeEgVB\nEC9fvizqdDrx9OnTrFMNjh07JoaHh4vp6ems0W2CIIgDBw4UDxw4IIqiKJ48eVIMDAwUU1NT610j\n9gS6mM2bN+Ojjz5CVFSU+VhhYSF+/PFHzJ8/HwqFAkFBQYiMjMTevXsd2FLHSEtLw7BhwzBq1CgA\nQK9evRAWFoZTp07h8OHDrBGAu+++G8eOHUPfvn1hNBqRlZUFb29vyOVyXkeVfPrpp9BqtWjbti0A\n4MqVK7h06RLmzJkDmUyGf/zjHxgwYAC++eYbB7fUcc6fPw9/f/8Kx/gzqaL4+HhkZWXhhRdegFQq\nhZ+fHz777DP4+PiwTtUoKCjA4sWL8dprr6FJkyas0W25ubm4desWDAYDAEAikUChUEAqlda7RgyB\nLmbcuHHYu3cvevfubT527do1KBQK+Pr6mo916dIFV69edUQTHSogIACrV682f52Tk4O4uDgAgFwu\nZ41uU6vVSElJQd++fbF48WIsWLAAycnJvI7KuXbtGrZt24bXXnvNfDP2pKQk+Pr6wsvLy/w8T66R\nXq/HtWvXsGPHDgwZMgSjRo3CF198gevXr/NaKichIQHdunXDmjVrMGTIEIwcORK///47cnJyWKdq\nvP/++/D390dERASvpXKaN2+OCRMm4Pnnn0dgYCAmTZqEZcuW4datW/WuEUOgi2ndunWVY0VFRVAq\nlRWOqVQq6PV6ezXLKeXl5SEqKgp9+vRBWFgYa1RJ+/btcebMGWzbtg2rVq3C4cOHWaPbBEHAwoUL\nsXTpUjRt2tR8vLCwECqVqsJz1Wq1R9YIKJ0vGRoaiokTJ+Lo0aN4/fXX8eabb+LIkSO8lsrJycnB\niRMn0LJlSxw9ehSrVq3CihUrUFBQwDpVUlhYiI8//hhz5841f80alRJFESqVChs3bkR8fDzee+89\nrFy5Evn5+fWuEUOgG1Cr1SguLq5wTK/XQ6PROKhFjpecnIwJEyagRYsW2LhxIzQaDWtUiVQqhUwm\nQ1hYGB544AGcO3eONbpt06ZN6NmzZ4XFV0D1/9aKioo8skYA0KFDB+zcuRPh4eGQy+Xo378/Hn74\nYcTFxfFaKsfLywvNmzfHjBkzIJfLERISghEjRmDjxo2sUyWHDh2Cr68vgoKCAPD/t/IOHDiAs2fP\nYsSIEZDL5Rg6dCiGDh3aoOuIIdANdO7cGUajEenp6eZjSUlJ8PPzc2CrHCchIQHjx49HeHg4Nm3a\nBC8vL9aonJiYGEyZMqXCMYPBwBqV89133+Hbb7+FTqeDTqfDjRs3sGDBAiQlJSE1NdU8Jwfw3BoB\nQGJiIqKjoyscKy4uRvv27XktldOlSxcYjUbztAIAMJlM6NWrF+tUyZEjR/Dggw+av+bPpTtu3LiB\nkpKSCscUCgUCAwPrXSOGQDeg1WoRERGBdevWQa/X48yZM9i/fz8iIyMd3TS7u3nzJmbMmIGpU6di\n0aJF5uOs0R2BgYFISEjAvn37IIoiYmJi8NNPP2H8+PGs0W3fffcdTp48idjYWMTGxqJdu3Z4++23\n8cwzz8DPzw8bNmxASUkJYmJicPLkyQr/aXkSjUaDTZs24cCBAxBFEcePH8e3336LJ554gtdSOYMH\nD4ZarcY777wDQRBw6tQpHDp0CA8++CDrVEl8fDyCg4PNX/Nn9x2DBg3C+fPnsWfPHgBAbGwsDh06\nhNGjR9e/Ro24mpka0YkTJypsEZOdnS0+++yzok6nE++9917xyy+/dGDrHGfz5s1iQECAGBISIgYH\nB4vBwcFiSEiI+Pbbb4s5OTms0W1xcXHiI488Ivbr108cO3asGBsbK4oir6OaREREmLeISUtLE6dO\nnSr269dPHDlypPm4pzpy5IgYGRkpBgcHiyNHjjRvX8FrqaI///xTnDZtmqjT6cSIiAhxz549oiiy\nTuUJgiD26tVLvHr1aoXjrNEdR44cER9++GGxX79+4ujRo8VDhw6Jolj/GklEsVz/NBERERF5BA4H\nExEREXkghkAiIiIiD8QQSEREROSBGAKJiIiIPBBDIBEREZEHYggkIiIi8kAMgUREREQeiCGQiNxK\nQEAAAgICcOXKlSqPnTlzBgEBAXjqqacc0DLbS01NRUBAAJKSkhzdFCJyQQyBROR2FAoFDh48WOX4\nwYMHIZW61489iUTi6CYQkYtyr5+GREQAdDodDh06VOX4wYMHK9yX1B3wpk9EVF8MgUTkdkaMGIHE\nxERkZGSYj128eBH5+fkIDQ2t8NyrV69i2rRpCA4Oxn333YcNGzbAaDSaH9+zZw8iIyPRp08fDBgw\nAM899xzy8/MBAPn5+ViwYAHCwsIQGhqK2bNnm99zz549GDJkSIX3ev7557FkyRIAwDvvvINZs2Zh\n6tSp0Ol0+P777wEA0dHRuPfeexEaGopJkyYhMTHR/PqioiIsWbIE/fv3R0REBH766Sf2BBJRvTEE\nEpHb6dChA/z9/Sv0Bh44cAAjRoyoEJpKSkowffp0+Pv7Y9++fXjjjTfwww8/YP369QCA3377DcuW\nLUNUVBQOHDiA9evXIzY2Fp988gkAYMOGDUhJScFHH32Ezz//HHl5eVixYoXV7Tx69CjCw8Oxa9cu\nDBo0CB9//DF2796NN954A3v27IFOp8PTTz+Nv/76CwDwr3/9C+fOncO2bduwdu1abN++3RblIiIP\nxRBIRG5p+PDh+PHHH81fHzx4EPfff3+F5+zbtw9arRYLFy5Ep06doNPp8Morr2DHjh0wmUxQqVRY\nuXIlHnroIbRr1w6DBw/G4MGDcenSJQClCzPUajXat2+Prl27YvXq1Zg1a5bVbfT29saUKVPg5+eH\npk2b4v3338eLL76IgQMHonPnzpg3bx78/Pywe/du5Ofn47vvvsPLL7+MPn36IDQ01NyrSERUH3JH\nN4CIqDGMGDECmzdvRn5+Pv7++29kZGQgLCwMx44dMz/nypUruHr1KkJCQiq81mg0IjU1FYGBgdBo\nNNi0aROuXLmCS5cu4erVqxg5ciQAYPLkyZg9ezYGDhwInU6H++67D//85z+tbmOHDh3Mfy8sLMSN\nGzewePFiLF682HzcYDDg7rvvRlJSEkwmEwICAsyP9enTh3MCiajeGAKJyC35+/vD19cXR44cQWZm\nJoYPH15lZbAgCOjXrx9WrlxZ5fXt2rXDL7/8gqioKERGRuKee+7BtGnTsG3bNnPw0ul0iImJwdGj\nRxETE4N169Zh3759+OSTT6qdq2c0GqFUKs1fq1Qq899NJhMA4K233oK/v3+F12k0GqSlpQGouBBE\noVDUtSxERGYcDiYitzV8+HAcPny42qFgAPDz88P169fRtm1bdOzYER07dkRqairWrl0Lk8mEnTt3\nYsyYMVi5ciUee+wxBAYG4vr16+Ygtn37dpw6dQqjRo3CmjVr8P777+P06dO4ceMGFAoFCgsLK7xf\nSkpKjW319vZG69atkZGRYW5Lx44dsWXLFpw4cQJdu3aFXC7HmTNnzK85d+4cF4YQUb0xBBKR2xox\nYgRiYmKQlJSEwYMHV3l8zJgxAIDFixfj8uXLOHnyJJYuXQqFQgEvLy/cddddiI+Pxx9//IGrV69i\nxYoVOHv2LEpKSgAA6enpWLFiBU6dOoXk5GR89dVXaNOmDXx8fNCnTx8UFRVh69atSElJwbvvvlvt\nBtblTZ8+HRs3bsT333+P5ORkrF27Fl9//TW6desGrVaLsWPHYtWqVYiLi0N8fDzefPNN2xeNiDwG\nQyARuZXyPWMhISHw9vbG0KFDIZdXnf2iVqvxwQcfIDs7G48++iiee+45hIeHm1f4zp8/H+3bt8fE\niRPx1FNP4a+//sK8efPM27Y899xzGDhwIObPn4/Ro0fjwoUL2LJlC2QyGTp16oQlS5Zgx44dGDNm\nDNLS0vD444/X2vann34aU6ZMwZo1axAZGYnjx49j8+bN6NGjBwDglVdeQXh4OKKiojB37lxMnDjR\nVmUjIg8kETmrmIiIiMjjsCeQiIiIyAMxBBIRERF5IIZAIiIiIg/EEEhERETkgRgCiYiIiDwQQyAR\nERGRB2IIJCIiIvJADIFEREREHoghkIiIiMgD/X9UyGe6U0XVFQAAAABJRU5ErkJggg==\n", 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V2EaqYTtVjW2kGrZT1dhGqtF0OxUUFCA5OfmFU90ECX2//fYbRo4cqfjZ1tYWffr0werV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\nLl68aMRWGs+9e/cEuVwuHD58WBAEQUhKShLkcrlw+vRp1umh1NRUwdv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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -574,12 +582,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", - "classes = ['unoccupied', 'occupied']\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "classes = ['unoccupied', 'occupied']\n", "\n", "# Extract the numpy arrays from the data frame \n", "X = data[features].as_matrix()\n", - "y = data.occupied.as_matrix()\n", + "y = data.occupancy.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)" @@ -594,9 +602,9 @@ "outputs": [ { "data": { - "image/png": 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hhBBCeJEgUQghhBBCeJEgUQghhBBCeJEgUQghhBBCeJEgUQghhBBCeJEgUQgh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\nVPF/JSvnh4w/KwwAAAAASUVORK5CYII=\n", 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iIuJFQaKIiIiIeNHAFREREZHfqW6VbKyWrN+djqNyNkmXIT+Xg1oSRURERMSL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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -624,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -634,12 +642,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", - "classes = ['unoccupied', 'occupied']\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "classes = ['unoccupied', 'occupied']\n", "\n", "# Extract the numpy arrays from the data frame \n", "X = data[features].as_matrix()\n", - "y = data.occupied.as_matrix()\n", + "y = data.occupancy.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)" @@ -647,16 +655,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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4nU4HBweHQsefeeYZEaqhspDddieciiUiIiqZ7OxsxMXF4eOPP0ZOTo7Y5VA5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\nVCpotVqZqiZ7c8hgV1hYiN69e9sc8/f3R2Fh4V3H+fv7o6q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ffPABJk+eDCsrK8TGxsLKyuqZAbRTp05o0KAB3n33XQQFBcHFxQUbN27EDz/8YBj00aJF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\ngFWrVqG5uVnqWERERPSIZDkVy5Mneq+goACJiYm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ERESdY2FHREREZCVY2BERERFZCRZ2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\nrAQLOyIiIiIrwcKOiIiIyEr8H1Y93s0jTGVFAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -684,9 +692,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -694,12 +702,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", - "classes = ['unoccupied', 'occupied']\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "classes = ['unoccupied', 'occupied']\n", "\n", "# Extract the numpy arrays from the data frame \n", "X = data[features].as_matrix()\n", - "y = data.occupied.as_matrix()\n", + "y = data.occupancy.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)" @@ -707,16 +715,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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AMJlSBbwtW7bo0UcfVfPmzdWtWzd9/PHHkqScnByNGjVK4eHhio6O1tq1a53T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\nD1qK7viOAMG2AAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -750,7 +758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/examples/examples.py b/examples/examples.py deleted file mode 100644 index 1c99474d7..000000000 --- a/examples/examples.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python -# Ben's scratchpad for testing - -## Imports -import os -import pandas as pd -import yellowbrick as yb -import matplotlib.pyplot as plt - -from pandas.tools.plotting import radviz, parallel_coordinates -from yellowbrick.features import ParallelCoordinates, RadViz, Rank2D - -## Module Constants - the path to the test data sets -FIXTURES = os.path.join(os.path.dirname(__file__), "examples", "data") - -## Dataset loading mechanisms -datasets = { - "credit": os.path.join(FIXTURES, "credit.xls"), - "concrete": os.path.join(FIXTURES, "concrete.xls"), - "occupancy": os.path.join(FIXTURES, 'occupancy', 'datatraining.txt'), -} - -## Human readable column names -columns = { - "credit": [ - 'id', 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay', - 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill', - 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay', - 'jul_pay', 'aug_pay', 'sep_pay', 'default' - ], - "concrete": [ - 'cement', 'slag', 'ash', 'water', 'splast', - 'coarse', 'fine', 'age', 'strength' - ], - "occupancy": [ - 'date', 'temp', 'humid', 'light', 'co2', 'hratio', 'occupied' - ], -} - - -def load_data(name): - """ - Loads and wrangls the passed in dataset. - """ - - path = datasets[name] - data = { - 'credit': lambda p: pd.read_excel(p, header=1), - 'concrete': lambda p: pd.read_excel(p), - 'occupancy': lambda p: pd.read_csv(p), - }[name](path) - - data.columns = columns[name] - return data - - -def test_parallel_coords(pandas=False, outpath=None): - """ - Runs the parallel coordinates visualizer on the dataset. - - Parameters - ---------- - pandas : bool - Run the pandas version of the function - outpath : path or None - Save the figure to disk rather than show (if None) - """ - data = load_data('occupancy') # Load the data - features = ['temp', 'humid', 'light', 'co2', 'hratio'] - classes = ['unoccupied', 'occupied'] - X = data[features].as_matrix() - y = data.occupied.as_matrix() - - if pandas: - parallel_coordinates(data[features + ['occupied']], 'occupied') - if outpath: - plt.savefig(outpath) - else: - plt.show() - - else: - visualizer = ParallelCoordinates( # Instantiate the visualizer - classes=classes, features=features - ) - visualizer.fit(X, y) # Fit the data to the visualizer - visualizer.transform(X) # Transform the data - visualizer.poof(outpath=outpath) # Draw/show/poof the data - - -def test_radviz(pandas=False, outpath=None): - """ - Runs the radviz visualizer on the dataset. - - Parameters - ---------- - pandas : bool - Run the pandas version of the function - outpath : path or None - Save the figure to disk rather than show (if None) - """ - data = load_data('occupancy') # Load the data - features = ['temp', 'humid', 'light', 'co2', 'hratio'] - classes = ['unoccupied', 'occupied'] - X = data[features].as_matrix() - y = data.occupied.as_matrix() - - if pandas: - radviz(data[features + ['occupied']], 'occupied') - if outpath: - plt.savefig(outpath) - else: - plt.show() - - else: - visualizer = RadViz( # Instantiate the visualizer - classes=classes, features=features - ) - visualizer.fit(X, y) # Fit the data to the visualizer - visualizer.transform(X) # Transform the data - visualizer.poof(outpath=outpath) # Draw/show/poof the data - - -def test_rank2d(seaborn=False, outpath=None): - """ - Runs the radviz visualizer on the dataset. - - Parameters - ---------- - pandas : bool - Run the pandas version of the function - outpath : path or None - Save the figure to disk rather than show (if None) - """ - data = load_data('occupancy') # Load the data - features = ['temp', 'humid', 'light', 'co2', 'hratio'] - classes = ['unoccupied', 'occupied'] - X = data[features].as_matrix() - y = data.occupied.as_matrix() - - if seaborn: - raise NotImplementedError("Not yet!") - - else: - visualizer = Rank2D(features=features, algorithm='covariance') - visualizer.fit(X, y) # Fit the data to the visualizer - visualizer.transform(X) # Transform the data - visualizer.poof(outpath=outpath) # Draw/show/poof the data - - -if __name__ == '__main__': - # test_parallel_coords(pandas=True) - # test_radviz(pandas=False, outpath='/Users/benjamin/Desktop/yb_radviz.png') - test_rank2d(outpath='/Users/benjamin/Desktop/yb_rank2d_covariance.png') diff --git a/examples/palettes.ipynb b/examples/palettes.ipynb index e8faf5ce7..17336a97b 100644 --- a/examples/palettes.ipynb +++ b/examples/palettes.ipynb @@ -20,7 +20,7 @@ "%matplotlib inline \n", "\n", "import matplotlib.pyplot as plt\n", - "from yellowbrick.style.palettes import PALETTES, color_palette" + "from yellowbrick.style.palettes import PALETTES, SEQUENCES, color_palette" ] }, { @@ -34,7 +34,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -44,7 +44,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -54,7 +54,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -64,7 +64,7 @@ "data": { "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ['blue', 'green', 'red', 'maroon', 'yellow', 'cyan']\n", + "for palette in PALETTES.keys():\n", + " color_palette(palette).plot()\n", + " plt.title(palette, loc='left')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " max_open_warning, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -212,10 +2306,10 @@ } ], "source": [ - "# ['blue', 'green', 'red', 'maroon', 'yellow', 'cyan']\n", - "for palette in PALETTES.keys():\n", - " color_palette(palette).plot()\n", - " plt.title(palette, loc='left')" + "for name, maps in SEQUENCES.items():\n", + " for num, palette in maps.items():\n", + " color_palette(palette).plot()\n", + " plt.title(\"{} - {}\".format(name, num), loc='left')" ] } ], @@ -235,7 +2329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt index af932dd2e..964d7e1c6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,11 +10,11 @@ cycler>=0.10.0 #pytz==2016.7 #python-dateutil==2.5.3 #six==1.10.0 -#requests==2.10.0 ## Testing Requirements (uncomment for development) #nose>=1.3.7 #coverage>=4.1 +#requests>=2.10.0 ## Python 2 Testing Requirements #mock>=2.0.0 @@ -25,6 +25,7 @@ cycler>=0.10.0 ## Documentation (uncomment to build documentation) #Sphinx>=1.4.4 #sphinx-rtd-theme>=0.1.9 +#numpydoc>=0.6.0 ## Documentation Dependencies (installed from above) #alabaster==0.7.9 diff --git a/setup.py b/setup.py index a29bf6040..3da905580 100755 --- a/setup.py +++ b/setup.py @@ -45,9 +45,10 @@ ## Define the classifiers ## See https://pypi.python.org/pypi?%3Aaction=list_classifiers CLASSIFIERS = ( - 'Development Status :: 4 - Beta', - 'Environment :: Console', + 'Development Status :: 3 - Alpha', + 'Environment :: Other Environment', 'Intended Audience :: Developers', + 'Intended Audience :: Science/Research', 'License :: OSI Approved :: Apache Software License', 'Natural Language :: English', 'Operating System :: OS Independent', diff --git a/tests/.gitignore b/tests/.gitignore new file mode 100644 index 000000000..a2ded98bb --- /dev/null +++ b/tests/.gitignore @@ -0,0 +1 @@ +fixtures/* diff --git a/tests/__init__.py b/tests/__init__.py index 5013fc95e..241a2df75 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -28,7 +28,7 @@ ## Test Constants ########################################################################## -EXPECTED_VERSION = "0.3.1" +EXPECTED_VERSION = "0.3.2" ########################################################################## diff --git a/tests/dataset.py b/tests/dataset.py new file mode 100644 index 000000000..45e8b7dfc --- /dev/null +++ b/tests/dataset.py @@ -0,0 +1,154 @@ +# tests.dataset +# Helper functions for tests that utilize downloadable datasets. +# +# Author: Benjamin Bengfort +# Created: Thu Oct 13 19:55:53 2016 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: dataset.py [] benjamin@bengfort.com $ + +""" +Helper functions for tests that utilize downloadable datasets. +""" + +########################################################################## +## Imports +########################################################################## + +import os +import sys +import shutil +import hashlib +import zipfile +import numpy as np + +try: + import requests +except ImportError: + requests = None + + +########################################################################## +## Fixtures +########################################################################## + +DATASETS = { + 'concrete': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/concrete.zip', + 'signature': 'b9ea5f26a7bb272a040e2f1a993b26babbf8dc4a04ab8198bb315ca66d71f10d', + }, + 'credit': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip', + 'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e', + }, + 'occupancy': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/occupancy.zip', + 'signature': '429cfe376dc9929a1fa528da89f0e1626e34e19695f3f555d8954025bbc522b8', + } +} + +FIXTURES = os.path.join(os.path.dirname(__file__), "fixtures") + + +########################################################################## +## Test Cases that Require Download +########################################################################## + +class DatasetMixin(object): + """ + Mixin for unittest.TestCase class to download datasets from S3 for + testing real world machine learning visual diagnostics. + """ + + @staticmethod + def sha256sum(path, blocksize=65536): + """ + Computes the SHA256 signature of a file to verify that the file has not + been modified in transit and that it is the correct version of the data. + """ + sig = hashlib.sha256() + with open(path, 'rb') as f: + buf = f.read(blocksize) + while len(buf) > 0: + sig.update(buf) + buf = f.read(blocksize) + return sig.hexdigest() + + + @staticmethod + def download_data(url, path=FIXTURES, signature=None, extract=True): + """ + Downloads the zipped data set specified at the given URL, saving it to + the output path specified. This function verifies the download with the + given signature (if supplied) and extracts the zip file if requested. + """ + if requests is None: + raise ImportError( + "The requests module is required to download data --\n" + "please install it with pip install requests." + ) + + # Create the output directory if it does not exist + if not os.path.exists(path): + os.mkdir(path) + + # Get the name of the file from the URL + name = os.path.basename(url) + dlpath = os.path.join(path, name) + + # Fetch the response in a streaming fashion and write it to disk. + response = requests.get(url, stream=True) + with open(dlpath, 'wb') as f: + for chunk in response.iter_content(65536): + f.write(chunk) + + # If verify, compare the signature + if signature is not None: + dlsignature = DatasetMixin.sha256sum(dlpath) + if signature != dlsignature: + raise ValueError( + "Download signature does not match hardcoded signature!" + ) + + # If extract, extract the zipfile. + if extract: + zf = zipfile.ZipFile(dlpath) + zf.extractall(path) + + + @staticmethod + def download_all(path=FIXTURES, verify=True, extract=True): + """ + Downloads all the example datasets. If verify is True then compare the + download signature with the hardcoded signature. If extract is True then + extract the contents of the zipfile to the given path. + """ + for name, meta in DATASETS.items(): + url = meta['url'] + signature = meta['signature'] if verify else None + + DatasetMixin.download_data( + url, path=path, signature=signature, extract=extract + ) + + @staticmethod + def remove_all(fixtures=FIXTURES): + """ + Removes all the downloaded datasets as clean up + """ + shutil.rmtree(fixtures) + + @staticmethod + def load_data(name, fixtures=FIXTURES): + """ + Loads the numpy matrix from the specified data set, downloads it if + it hasn't already been downloaded. + """ + + path = os.path.join(fixtures, name, "{}.csv".format(name)) + if not os.path.exists(path): + DatasetMixin.download_all(path=fixtures) + + return np.genfromtxt(path, dtype=float, delimiter=',', names=True) diff --git a/tests/test_base.py b/tests/test_base.py index 336bcdf4c..fe948c74b 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -44,13 +44,13 @@ def test_visualizer_returns_self(self): visualizer = Visualizer() self.assertIs(visualizer.fit([]), visualizer) - def test_base_poof(self): - """ - Assert that the base visualizer implements poof interface - """ - with self.assertRaises(NotImplementedError): - visualizer = Visualizer() - visualizer.poof() + # def test_base_poof(self): + # """ + # Assert that the base visualizer implements poof interface + # """ + # with self.assertRaises(NotImplementedError): + # visualizer = Visualizer() + # visualizer.poof() def test_fit_draw(self): """ diff --git a/tests/test_features/test_base.py b/tests/test_features/test_base.py index 4f61f3548..09a2558d2 100644 --- a/tests/test_features/test_base.py +++ b/tests/test_features/test_base.py @@ -39,11 +39,11 @@ def test_subclass(self): self.assertIsInstance(visualizer, BaseEstimator) self.assertIsInstance(visualizer, Visualizer) - def test_interface(self): - """ - Test the feature visualizer interface - """ - - visualizer = FeatureVisualizer() - with self.assertRaises(NotImplementedError): - visualizer.poof() + # def test_interface(self): + # """ + # Test the feature visualizer interface + # """ + # + # visualizer = FeatureVisualizer() + # with self.assertRaises(NotImplementedError): + # visualizer.poof() diff --git a/tests/test_features/test_radviz.py b/tests/test_features/test_radviz.py index f9541657d..d2be595e1 100644 --- a/tests/test_features/test_radviz.py +++ b/tests/test_features/test_radviz.py @@ -21,13 +21,14 @@ import numpy as np import numpy.testing as npt +from tests.dataset import DatasetMixin from yellowbrick.features.radviz import * ########################################################################## ## RadViz Base Tests ########################################################################## -class RadVizTests(unittest.TestCase): +class RadVizTests(unittest.TestCase, DatasetMixin): X = np.array( [[ 2.318, 2.727, 4.260, 7.212, 4.792], @@ -40,6 +41,12 @@ class RadVizTests(unittest.TestCase): y = np.array([1, 1, 0, 1, 0, 0]) + def setUp(self): + self.occupancy = self.load_data('occupancy') + + def tearDown(self): + self.occupancy = None + def test_normalize_x(self): """ Test the static normalization method on the RadViz class @@ -62,3 +69,21 @@ def test_radviz(self): """ visualizer = RadViz() visualizer.fit_transform(self.X, self.y) + + def test_integrated_radviz(self): + """ + Test radviz on the real, occupancy data set + """ + + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = self.occupancy['occupancy'].astype(int) + + # Convert X to an ndarray + X = X.view((float, len(X.dtype.names))) + + # Test the visualizer + visualizer = RadViz() + visualizer.fit_transform(X, y) diff --git a/tests/test_style/test_palettes.py b/tests/test_style/test_palettes.py index 0419790cd..acfb045eb 100644 --- a/tests/test_style/test_palettes.py +++ b/tests/test_style/test_palettes.py @@ -26,7 +26,8 @@ from yellowbrick.style.palettes import * from yellowbrick.style.colors import get_color_cycle from yellowbrick.style.rcmod import set_aesthetic, set_palette -from yellowbrick.style.palettes import ColorPalette, PALETTES +from yellowbrick.style.palettes import color_sequence, color_palette +from yellowbrick.style.palettes import ColorPalette, PALETTES, SEQUENCES from tests.base import VisualTestCase @@ -278,6 +279,40 @@ def test_preserved_palette_length(self): pal_out = color_palette(pal_in) self.assertEqual(pal_in, pal_out) + def test_color_sequence(self): + """ + Ensure the color sequence returns listed colors. + """ + for name, ncols in SEQUENCES.items(): + for n in ncols.keys(): + cmap = color_sequence(name, n) + self.assertEqual(name, cmap.name) + self.assertEqual(n, cmap.N) + + def test_color_sequence_default(self): + """ + Assert the default color sequence is RdBu + """ + cmap = color_sequence() + self.assertEqual(cmap.name, "RdBu") + self.assertEqual(cmap.N, 11) + + def test_color_sequence_unrecocognized(self): + """ + Test value errors for unrecognized sequences + """ + with self.assertRaises(YellowbrickValueError): + cmap = color_sequence('PepperBucks', 3) + + def test_color_sequence_bounds(self): + """ + Test color sequence out of bounds value error + """ + with self.assertRaises(YellowbrickValueError): + cmap = color_sequence('RdBu', 18) + + with self.assertRaises(YellowbrickValueError): + cmap = color_sequence('RdBu', 2) if __name__ == "__main__": unittest.main() diff --git a/tests/test_utils.py b/tests/test_utils.py index 1b041de86..48ae79f65 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,33 +1,49 @@ # tests.test_utils -# Test the export module - to generate a corpus for machine learning. +# Test the yellowbrick utilities module. # # Author: Jason Keung -# Patrick O'Melveny -# Created: Thurs Jun 2 15:33:18 2016 -0500 +# Author: Patrick O'Melveny +# Author: Benjamin Bengfort +# Created: Thu Jun 02 15:33:18 2016 -0500 # +# Copyright (C) 2016 District Data LAbs # For license information, see LICENSE.txt # - +# ID: test_utils.py [] jason.s.keung@gmail.com $ """ -Test the utils module - to generate a corpus for machine learning. +Test the yellowbrick utilities module. """ ########################################################################## ## Imports ########################################################################## +import inspect +import unittest + +from sklearn.cluster import KMeans +from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA +from sklearn.neighbors import LSHForest from sklearn.linear_model import LassoCV from sklearn.linear_model import LinearRegression -from sklearn.neighbors import LSHForest -from sklearn.pipeline import Pipeline -import unittest +from sklearn.linear_model import LogisticRegression +from sklearn.ensemble import RandomForestClassifier + +from yellowbrick.utils import * +from yellowbrick.base import Visualizer, ScoreVisualizer, ModelVisualizer + -from yellowbrick.utils import get_model_name, isestimator +########################################################################## +## Model Utility Tests +########################################################################## +class ModelUtilityTests(unittest.TestCase): -class ModelNameTests(unittest.TestCase): + ##//////////////////////////////////////////////////////////////////// + ## get_model_name testing + ##//////////////////////////////////////////////////////////////////// def test_real_model(self): """ @@ -35,8 +51,10 @@ def test_real_model(self): """ model1 = LassoCV() model2 = LSHForest() + model3 = KMeans() self.assertEqual(get_model_name(model1), 'LassoCV') self.assertEqual(get_model_name(model2), 'LSHForest') + self.assertEqual(get_model_name(model3), 'KMeans') def test_pipeline(self): """ @@ -58,12 +76,35 @@ def test_str_input(self): """ self.assertRaises(TypeError, get_model_name, 'helloworld') + ##//////////////////////////////////////////////////////////////////// + ## isestimator testing + ##//////////////////////////////////////////////////////////////////// + + def test_estimator_alias(self): + """ + Assert is_estimator aliases isestimator + """ + self.assertEqual( + is_estimator(LinearRegression), isestimator(LinearRegression) + ) + def test_estimator_instance(self): """ Test that isestimator works for instances """ - model = LinearRegression() - self.assertTrue(isestimator(model)) + + models = ( + LinearRegression(), + LogisticRegression(), + KMeans(), + LSHForest(), + PCA(), + LassoCV(), + RandomForestClassifier(), + ) + + for model in models: + self.assertTrue(isestimator(model)) def test_pipeline_instance(self): """ @@ -80,7 +121,19 @@ def test_estimator_class(self): """ Test that isestimator works for classes """ - self.assertTrue(LinearRegression) + models = ( + LinearRegression, + LogisticRegression, + KMeans, + LSHForest, + PCA, + LassoCV, + RandomForestClassifier, + ) + + for model in models: + self.assertTrue(inspect.isclass(model)) + self.assertTrue(isestimator(model)) def test_collection_not_estimator(self): """ @@ -92,6 +145,245 @@ def test_collection_not_estimator(self): things = ['pepper', 'sauce', 'queen'] self.assertFalse(isestimator(things)) + def test_visualizer_is_estimator(self): + """ + Assert that a Visualizer is an estimator + """ + self.assertTrue(is_estimator(Visualizer)) + self.assertTrue(is_estimator(Visualizer())) + self.assertTrue(is_estimator(ScoreVisualizer)) + self.assertTrue(is_estimator(ScoreVisualizer(LinearRegression()))) + self.assertTrue(is_estimator(ModelVisualizer)) + self.assertTrue(is_estimator(ModelVisualizer(LogisticRegression()))) + + ##//////////////////////////////////////////////////////////////////// + ## isregressor testing + ##//////////////////////////////////////////////////////////////////// + + def test_regressor_alias(self): + """ + Assert is_regressor aliases isregressor + """ + instance = LinearRegression() + self.assertEqual(is_regressor(instance), isregressor(instance)) + + def test_regressor_instance(self): + """ + Test that is_regressor works for instances + """ + + # Test regressors are identified correctly + regressors = ( + LassoCV, + LinearRegression, + ) + + for model in regressors: + instance = model() + self.assertTrue(is_regressor(instance)) + + # Test that non-regressors are identified correctly + notregressors = ( + KMeans, + PCA, + LSHForest, + LogisticRegression, + RandomForestClassifier, + ) + + for model in notregressors: + instance = model() + self.assertFalse(is_regressor(instance)) + + def test_regressor_class(self): + """ + Test that is_regressor works for classes + """ + + # Test regressors are identified correctly + regressors = ( + LassoCV, + LinearRegression, + ) + + for klass in regressors: + self.assertTrue(inspect.isclass(klass)) + self.assertTrue(is_regressor(klass)) + + # Test that non-regressors are identified correctly + notregressors = ( + KMeans, + PCA, + LSHForest, + LogisticRegression, + RandomForestClassifier, + ) + + for klass in notregressors: + self.assertTrue(inspect.isclass(klass)) + self.assertFalse(is_regressor(klass)) + + def test_regressor_pipeline(self): + """ + Test that is_regressor works for pipelines + """ + model = Pipeline([ + ('reduce_dim', PCA()), + ('linreg', LinearRegression()) + ]) + + self.assertTrue(is_regressor(model)) + + def test_regressor_visualizer(self): + """ + Test that is_regressor works on visualizers + """ + model = ScoreVisualizer(LinearRegression()) + self.assertTrue(is_regressor(model)) + + ##//////////////////////////////////////////////////////////////////// + ## isclassifier testing + ##//////////////////////////////////////////////////////////////////// + + def test_classifier_alias(self): + """ + Assert is_classifier aliases isclassifier + """ + instance = LogisticRegression() + self.assertEqual(is_classifier(instance), isclassifier(instance)) + + def test_classifier_instance(self): + """ + Test that is_classifier works for instances + """ + + # Test classifiers are identified correctly + classifiers = ( + LogisticRegression, + RandomForestClassifier, + ) + + for model in classifiers: + instance = model() + self.assertTrue(is_classifier(instance)) + + # Test that non-classifiers are identified correctly + notclassifiers = ( + KMeans, + PCA, + LSHForest, + LinearRegression, + LassoCV, + ) + + for model in notclassifiers: + instance = model() + self.assertFalse(is_classifier(instance)) + + def test_classifier_class(self): + """ + Test that is_classifier works for classes + """ + + # Test classifiers are identified correctly + classifiers = ( + RandomForestClassifier, + LogisticRegression, + ) + + for klass in classifiers: + self.assertTrue(inspect.isclass(klass)) + self.assertTrue(is_classifier(klass)) + + # Test that non-regressors are identified correctly + notclassifiers = ( + KMeans, + PCA, + LSHForest, + LassoCV, + LinearRegression, + ) + + for klass in notclassifiers: + self.assertTrue(inspect.isclass(klass)) + self.assertFalse(is_classifier(klass)) + + def test_classifier_pipeline(self): + """ + Test that is_regressor works for pipelines + """ + model = Pipeline([ + ('reduce_dim', PCA()), + ('linreg', LogisticRegression()) + ]) + + self.assertTrue(is_classifier(model)) + + def test_classifier_visualizer(self): + """ + Test that is_classifier works on visualizers + """ + model = ScoreVisualizer(RandomForestClassifier()) + self.assertTrue(is_classifier(model)) + +########################################################################## +## 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 +########################################################################## if __name__ == "__main__": unittest.main() diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 485e0e9f8..4e806f8a5 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -37,29 +37,94 @@ class Visualizer(BaseEstimator): primarily ensures that styling arguments are passed in. """ - def __init__(self, **kwargs): + def __init__(self, ax=None, **kwargs): + """ + Parameters + ---------- + :param ax: the axis to plot the figure on. + + :param kwargs: keyword arguments passed to the super class. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + self.ax = ax self.size = kwargs.pop('size', None) self.color = kwargs.pop('color', None) + self.title = kwargs.pop('title', None) def fit(self, X, y=None, **kwargs): """ Fits a transformer to X and y + + 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: keyword arguments passed to Scikit-Learn API. """ 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 + def draw(self, **kwargs): - pass + """ + Rendering function + """ + ax = self.gca() - def poof(self, **kwargs): + def poof(self, outpath=None, **kwargs): """ The user calls poof, which is the primary entry point for producing a visualization. Visualizes either data features or fitted model scores + + Parameters + ---------- + outpath: path or None + Save the figure to disk or if None show in a window + + kwargs: generic keyword arguments. """ - raise NotImplementedError( - "All visualizations must specify their own poof methodology" - ) + if self.ax is None: return + + self.finalize() + + if outpath is not None: + plt.savefig(outpath, **kwargs) + else: + plt.show() + + def set_title(self, title=None): + """ + Sets the title on the current axes. + """ + title = self.title or title + 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: generic keyword arguments. + """ + pass def fit_draw(self, X, y=None, **kwargs): """ @@ -85,37 +150,55 @@ class ScoreVisualizer(Visualizer): Draws the score for the fitted model. """ - def __init__(self, model, **kwargs): + def __init__(self, model, ax=None, **kwargs): + """ + Parameters + ---------- + :param models: the Scikit-Learn models being compared with each other. + + :param ax: the axis to plot the figure on. + + :param kwargs: keyword arguments. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) + self.estimator = model - super(ScoreVisualizer, self).__init__(**kwargs) + self.name = get_model_name(self.estimator) def fit(self, X, y=None, **kwargs): + """ + 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: keyword arguments passed to Scikit-Learn API. + """ self.estimator.fit(X, y, **kwargs) return self def predict(self, X): return self.estimator.predict(X) - def score(self, X, y=None): - """ - Score will call draw to visualize model performance. - If y_pred is None, call fit-predict on the model to get a y_pred. - - Score calls draw + def draw(self, X, y): """ - y_pred = self.predict(X) - return self.draw(y,y_pred) + Parameters + ---------- - def draw(self, X, y): - pass + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features - def poof(self, **kwargs): + y : ndarray or Series of length n + An array or series of target or class values """ - The user calls poof - """ - raise NotImplementedError( - "Please specify how to render the feature visualization" - ) + pass ########################################################################## @@ -128,23 +211,38 @@ class ModelVisualizer(Visualizer): and enables the user to visualize the performance of models across a range of hyperparameter values (e.g. using VisualGridsearch and ValidationCurve). """ + def __init__(self, model, ax=None, **kwargs): + """ + Parameters + ---------- + :param ax: the axis to plot the figure on. - def fit(self, X, y=None, **kwargs): - pass + :param kwargs: 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 - def predict(self, X): - pass - def poof(self, model=None): + def fit(self, X, y=None, **kwargs): """ - The user calls poof. + Parameters + ---------- + + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features - A model visualization renders a model + y : ndarray or Series of length n + An array or series of target or class values + + kwargs: keyword arguments passed to Scikit-Learn API. """ - raise NotImplementedError( - "Please specify how to render the model visualization" - ) + pass + def predict(self, X): + pass ########################################################################## ## Multiple Models and Mixins @@ -155,10 +253,23 @@ class MultiModelMixin(object): Does predict for each of the models and generates subplots. """ - def __init__(self, models, **kwargs): + def __init__(self, models, ax=None, **kwargs): # Ensure models is a collection, if it's a single estimator then we # wrap it in a list so that the API doesn't break during render. - if isestimator(models): + """ + Parameters + ---------- + :param models: the Scikit-Learn models being compared with each other. + + :param kwargs: keyword arguments. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + # TODO: How to handle the axes in this mixin? + self.ax = ax + + if all(isestimator, models): models = [models] # Keep track of the models diff --git a/yellowbrick/bestfit.py b/yellowbrick/bestfit.py index bbc027bbf..f98c0b899 100644 --- a/yellowbrick/bestfit.py +++ b/yellowbrick/bestfit.py @@ -25,6 +25,7 @@ from sklearn.metrics import mean_squared_error as mse from operator import itemgetter +from yellowbrick.style.palettes import LINE_COLOR from yellowbrick.exceptions import YellowbrickValueError @@ -112,8 +113,12 @@ def draw_best_fit(X, y, ax, estimator='linear', **kwargs): # Uses the estimator to fit the data and get the model back. model = estimator(X, y) + # Set the color if not passed in. + if 'c' not in kwargs and 'color' not in kwargs: + kwargs['color'] = LINE_COLOR + # Plot line of best fit onto the axes that were passed in. - # TODO: determin if xlim or X.min(), X.max() are better params + # TODO: determine if xlim or X.min(), X.max() are better params xr = np.linspace(*ax.get_xlim(), num=100) ax.plot(xr, model.predict(xr[:,np.newaxis]), **kwargs) return ax diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 40f88ae04..417979b15 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -22,14 +22,15 @@ import matplotlib.pyplot as plt from sklearn.pipeline import Pipeline +from sklearn.cross_validation import train_test_split from sklearn.metrics import auc, roc_auc_score, roc_curve from sklearn.metrics import precision_recall_fscore_support -from .style.palettes import ddlheatmap from .exceptions import YellowbrickTypeError -from .style.palettes import PALETTES as YELLOWBRICK_PALETTES from .utils import get_model_name, isestimator, isclassifier from .base import Visualizer, ScoreVisualizer, MultiModelMixin +from .style.palettes import color_sequence, color_palette, LINE_COLOR + ########################################################################## ## Classification Visualization Base Object @@ -37,7 +38,7 @@ class ClassificationScoreVisualizer(ScoreVisualizer): - def __init__(self, model, **kwargs): + def __init__(self, model, ax=None, **kwargs): """ Check to see if model is an instance of a classifer. Should return an error if it isn't. @@ -47,7 +48,8 @@ def __init__(self, model, **kwargs): "This estimator is not a classifier; try a regression or clustering score visualizer instead!" ) - super(ClassificationScoreVisualizer, self).__init__(model, **kwargs) + super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) + ########################################################################## ## Classification Report @@ -57,22 +59,55 @@ class ClassificationReport(ClassificationScoreVisualizer): """ Classification report that shows the precision, recall, and F1 scores for the model. Integrates numerical scores as well color-coded heatmap. + """ - def __init__(self, model, classes=None, **kwargs): + def __init__(self, model, ax=None, classes=None, **kwargs): """ - Pass in a fitted model to generate a ROC curve. + Pass in a fitted model to generate a classification report. + + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param model: the Scikit-Learn estimator + Should be an instance of a classifier, else the __init__ will + return an error. + + :param classes: a list of class names for the legend + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + :param colormap: optional string or matplotlib cmap to colorize lines + Use sequential heatmap. + + :param kwargs: keyword arguments passed to the super class. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. """ - super(ClassificationReport, self).__init__(model, **kwargs) + super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) - # TODO: hoist + ## hoisted to ScoreVisualizer base class self.estimator = model - self.ax = None - self.name = get_model_name(self.estimator) - self.cmap = kwargs.pop('cmap', ddlheatmap) + + self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) self.classes_ = classes def fit(self, X, y=None, **kwargs): + """ + 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: keyword arguments passed to Scikit-Learn API. + """ super(ClassificationReport, self).fit(X, y, **kwargs) if self.classes_ is None: self.classes_ = self.estimator.classes_ @@ -81,6 +116,16 @@ def fit(self, X, y=None, **kwargs): def score(self, X, y=None, **kwargs): """ Generates the Scikit-Learn classification_report + + 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 + """ y_pred = self.predict(X) keys = ('precision', 'recall', 'f1') @@ -92,7 +137,17 @@ def score(self, X, y=None, **kwargs): def draw(self, y, y_pred): """ Renders the classification report across each axis. + + Parameters + ---------- + + y : ndarray or Series of length n + An array or series of target or class values + + 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() @@ -108,25 +163,79 @@ def draw(self, y, y_pred): return self.ax - - def poof(self): + def finalize(self, **kwargs): """ - Plots a classification report as a heatmap. + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + """ - if self.ax is None: return + # Set the title of the classifiation report + self.set_title('{} Classification Report'.format(self.name)) - plt.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_)) + + # Set the tick marks appropriately + # TODO: make sure this goes through self.ax not plt plt.xticks(x_tick_marks, ['precision', 'recall', 'f1-score'], rotation=45) plt.yticks(y_tick_marks, self.classes_) - plt.ylabel('Classes') - plt.xlabel('Measures') - return self.ax + # Set the labels for the two axes + self.ax.set_ylabel('Classes') + self.ax.set_xlabel('Measures') +def classification_report(model, X, y=None, ax=None, classes=None, **kwargs): + """Quick method: + + Displays precision, recall, and F1 scores for the model. + Integrates numerical scores as well color-coded heatmap. + + This helper function is a quick wrapper to utilize the ClassificationReport + ScoreVisualizer for one-off analysis. + + 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. + + ax : matplotlib axes + The axes to plot the figure on. + + model : the Scikit-Learn estimator (should be a classifier) + + classes : list of strings + The names of the classes in the target + + Returns + ------- + ax : matplotlib axes + Returns the axes that the classification report was drawn on. + """ + # Instantiate the visualizer + visualizer = ClassificationReport(model, ax, classes, **kwargs) + + # Create the train and test splits + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X_train, y_train, **kwargs) + visualizer.score(X_test, y_test) + + # Return the axes object on the visualizer + return visualizer.ax + ########################################################################## ## Receiver Operating Characteristics ########################################################################## @@ -136,23 +245,70 @@ class ROCAUC(ClassificationScoreVisualizer): Plot the ROC to visualize the tradeoff between the classifier's sensitivity and specificity. """ - def __init__(self, model, **kwargs): + def __init__(self, model, ax=None, **kwargs): """ - Pass in a model to generate a ROC curve. + Pass in a fitted model to generate a ROC curve. + + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param model: the Scikit-Learn estimator + Should be an instance of a classifier, else the __init__ will + return an error. + + :param roc_color: color of the ROC curve + Specify the color as a matplotlib color: you can specify colors in + many weird and wonderful ways, including full names ('green'), hex + strings ('#008000'), RGB or RGBA tuples ((0,1,0,1)) or grayscale + intensities as a string ('0.8'). + + :param diagonal_color: color of the diagonal + Specify the color as a matplotlib color. + + :param kwargs: keyword arguments passed to the super class. + Currently passing in hard-coded colors for the Receiver Operating + Characteristic curve and the diagonal. + These will be refactored to a default Yellowbrick style. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ - super(ROCAUC, self).__init__(model, **kwargs) + super(ROCAUC, self).__init__(model, ax=ax, **kwargs) - # TODO hoist to main + ## hoisted to ScoreVisualizer base class self.name = get_model_name(self.estimator) - self.ax = None - super(ROCAUC, self).__init__(model, **kwargs) + # Color map defaults as follows: + # ROC color is the current color in the cycle + # Diagonal color is the default LINE_COLOR self.colors = { - 'roc': kwargs.pop('roc_color', '#2B94E9'), - 'diagonal': kwargs.pop('diagonal_color', '#666666'), + 'roc': kwargs.pop('roc_color', None), + 'diagonal': kwargs.pop('diagonal_color', LINE_COLOR), } def score(self, X, y=None, **kwargs): + """ + Generates the predicted target values using the Scikit-Learn + estimator. + + 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 + + Returns + ------ + + ax : the axis with the plotted figure + + """ y_pred = self.predict(X) self.fpr, self.tpr, self.thresholds = roc_curve(y, y_pred) self.roc_auc = auc(self.fpr, self.tpr) @@ -162,9 +318,26 @@ def draw(self, y, y_pred): """ Renders ROC-AUC plot. Called internally by score, possibly more than once + + Parameters + ---------- + + y : ndarray or Series of length n + An array or series of target or class values + + y_pred : ndarray or Series of length n + An array or series of predicted target values + + 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() + plt.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. @@ -172,24 +345,65 @@ def draw(self, y, y_pred): return self.ax - def poof(self, **kwargs): + def finalize(self, **kwargs): """ - Called by user. + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. - Only takes self. + Parameters + ---------- + kwargs: generic keyword arguments. - Take in the model as input and generates a plot of - the ROC plots with AUC metrics embedded. """ - if self.ax is None: return + # Set the title and add the legend + self.set_title('ROC for {}'.format(self.name)) + self.ax.legend(loc='lower right') - plt.title('ROC for {}'.format(self.name)) - plt.legend(loc='lower right') + # Set the limits for the ROC/AUC (always between 0 and 1) + self.ax.set_xlim([-0.02, 1.0]) + self.ax.set_ylim([ 0.00, 1.1]) - plt.xlim([-0.02,1]) - plt.ylim([0,1.1]) - return self.ax +def roc_auc(model, X, y=None, ax=None, **kwargs): + """Quick method: + + Displays the tradeoff between the classifier's + sensitivity and specificity. + + This helper function is a quick wrapper to utilize the ROCAUC + ScoreVisualizer for one-off analysis. + + 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. + + ax : matplotlib axes + The axes to plot the figure on. + + model : the Scikit-Learn estimator (should be a classifier) + + Returns + ------- + ax : matplotlib axes + Returns the axes that the roc-auc curve was drawn on. + """ + # Instantiate the visualizer + visualizer = ROCAUC(model, ax, **kwargs) + + # Create the train and test splits + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X_train, y_train, **kwargs) + visualizer.score(X_test, y_test) + + # Return the axes object on the visualizer + return visualizer.ax + ########################################################################## ## Class Balance Chart @@ -198,23 +412,56 @@ def poof(self, **kwargs): class ClassBalance(ClassificationScoreVisualizer): """ Class balance chart that shows the support for each class in the - fitted classification model. - """ - def __init__(self, model, classes=None, **kwargs): - """ - Pass in a fitted model to generate a class balance chart. - """ + fitted classification model displayed as a bar plot. It is initialized + with a fitted model and generates a class balance chart on draw. - # TODO: hoist - self.estimator = model - self.ax = None + Parameters + ---------- + ax: axes + the axis to plot the figure on. - self.name = get_model_name(self.estimator) - self.colors = kwargs.pop('colors', YELLOWBRICK_PALETTES['paired']) + model: estimator + Scikit-Learn estimator object. Should be an instance of a classifier, + else ``__init__()`` will raise an exception. + + classes: list + A list of class names for the legend. If classes is None and a y value + is passed to fit then the classes are selected from the target vector. + + kwargs: dict + Keyword arguments passed to the super class. Here, used + to colorize the bars in the histogram. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + def __init__(self, model, ax=None, classes=None, **kwargs): + + super(ClassBalance, self).__init__(model, ax=ax, **kwargs) + + self.colors = color_palette(kwargs.pop('colors', None)) self.classes_ = classes def fit(self, X, y=None, **kwargs): + """ + 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: keyword arguments passed to Scikit-Learn API. + + Returns + ------- + self : instance + Returns the instance of the classification score visualizer + + """ super(ClassBalance, self).fit(X, y, **kwargs) if self.classes_ is None: self.classes_ = self.estimator.classes_ @@ -223,6 +470,20 @@ def fit(self, X, y=None, **kwargs): def score(self, X, y=None, **kwargs): """ Generates the Scikit-Learn precision_recall_fscore_support + + 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 + + Returns + ------- + + ax : the axis with the plotted figure """ y_pred = self.predict(X) self.scores = precision_recall_fscore_support(y, y_pred) @@ -233,29 +494,85 @@ def draw(self): """ Renders the class balance chart across the axis. - TODO: Would rather not have to set the colors with this method. - Refactor to make better use of yb_palettes module? + 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) return self.ax - def poof(self): + def finalize(self, **kwargs): """ - Plots a class balance chart + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + """ - if self.ax is None: return + # Set the title + self.set_title('Class Balance for {}'.format(self.name)) + + # Set the x ticks with the class names + # TODO: change to the self.ax method rather than plt.xticks plt.xticks(np.arange(len(self.support)), self.support.keys()) + + # Compute the ceiling for the y limit cmax, cmin = max(self.support.values()), min(self.support.values()) - ceiling = cmax + cmax*0.1 - span = cmax - cmin - plt.ylim(0, ceiling) - plt.show() + self.ax.set_ylim(0, cmax + cmax* 0.1) - return self.ax +def class_balance(model, X, y=None, ax=None, classes=None, **kwargs): + """Quick method: + + Displays the support for each class in the + fitted classification model displayed as a bar plot. + + This helper function is a quick wrapper to utilize the ClassBalance + ScoreVisualizer for one-off analysis. + + 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. + + ax : matplotlib axes + The axes to plot the figure on. + + model : the Scikit-Learn estimator (should be a classifier) + + classes : list of strings + The names of the classes in the target + + Returns + ------- + ax : matplotlib axes + Returns the axes that the class balance plot was drawn on. + """ + # Instantiate the visualizer + visualizer = ClassBalance(model, ax, classes, **kwargs) + + # Create the train and test splits + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X_train, y_train, **kwargs) + visualizer.score(X_test, y_test) + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index c7b380a26..dadbd6ba8 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -19,6 +19,7 @@ ########################################################################## from yellowbrick.base import Visualizer +from yellowbrick.utils import is_dataframe from sklearn.base import TransformerMixin @@ -37,8 +38,8 @@ class FeatureVisualizer(Visualizer, TransformerMixin): Accepts as input a DataFrame or Numpy array. """ - def __init__(self, **kwargs): - super(FeatureVisualizer, self).__init__(**kwargs) + def __init__(self, ax=None, **kwargs): + super(FeatureVisualizer, self).__init__(ax=ax, **kwargs) def fit(self, X, y=None, **fit_params): """ @@ -60,16 +61,6 @@ def transform(self, X): """ return X - def poof(self, **kwargs): - """ - The user calls poof in order to draw the feature visualization. - - Visualize data features individually or together - """ - raise NotImplementedError( - "Please specify how to render the feature visualization" - ) - def fit_transform_poof(self, X, y=None, **kwargs): """ Fit to data, transform it, then visualize it. @@ -81,3 +72,115 @@ def fit_transform_poof(self, X, y=None, **kwargs): Xp = self.fit_transform(X, y, **kwargs) self.poof(**kwargs) return Xp + + +########################################################################## +## Data Visualizers +########################################################################## + +class DataVisualizer(FeatureVisualizer): + """ + Data Visualizers are a subclass of Feature Visualiers which plot the + instances in feature space (also called data space, hence the name of the + visualizer). Feature space is a multi-dimensional space defined by the + columns of the instance dependent vector input, X which is passed to + `fit()` and `transform()`. Instances can also be labeled by the target + independent vector input, y which is only passed to `fit()`. For that + reason most Data Visualizers perform their drawing in `fit()`. + + This class provides helper functionality related to target identification: + whether or not the target is sequential or categorical, and mapping a + color sequence or color set to the targets as appropriate. It also uses + the fit method to call the drawing utilities. + """ + + def __init__(self, ax=None, features=None, classes=None, color=None, + colormap=None, **kwargs): + """ + Initialize the data visualization with many of the options required + in order to make most visualizations work. + + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param features: a list of feature names to use + If a DataFrame is passed to fit and features is None, feature + names are selected as the columns of the DataFrame. + + :param classes: a list of class names for the legend + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + :param color: optional list or tuple of colors to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + :param colormap: optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + :param kwargs: keyword arguments passed to the super class. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + super(DataVisualizer, self).__init__(ax=ax, **kwargs) + + # Data Parameters + self.features_ = features + self.classes_ = classes + + # Visual Parameters + self.color = color + self.colormap = colormap + + def fit(self, X, y=None, **kwargs): + """ + The fit method is the primary drawing input for the parallel coords + visualization since it has both the X and y data required for the + viz and the transform method does not. + + 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 + Pass generic arguments to the drawing method + + Returns + ------ + self : instance + Returns the instance of the transformer/visualizer + """ + # Get the shape of the data + nrows, ncols = X.shape + + # Store the classes for the legend if they're None. + if self.classes_ is None: + # TODO: Is this the most efficient method? + self.classes_ = [str(label) for label in set(y)] + + # Handle the feature names if they're None. + if self.features_ is None: + + # If X is a data frame, get the columns off it. + if is_dataframe(X): + self.features_ = X.columns + + # Otherwise create numeric labels for each column. + else: + self.features_ = [ + str(cdx) for cdx in range(ncols) + ] + + # Draw the instances + self.draw(X, y, **kwargs) + + # Fit always returns self. + return self diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index e2e3be389..be69cce8c 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -22,12 +22,10 @@ import numpy as np import matplotlib.pyplot as plt -from yellowbrick.utils import is_dataframe -from yellowbrick.features.base import FeatureVisualizer +from yellowbrick.features.base import DataVisualizer from yellowbrick.exceptions import YellowbrickTypeError from yellowbrick.style.colors import resolve_colors, get_color_cycle - ########################################################################## ## Quick Methods ########################################################################## @@ -74,7 +72,7 @@ def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, ax : matplotlib axes Returns the axes that the parallel coordinates were drawn on. """ - # Insantiate the visualizer + # Instantiate the visualizer visualizer = ParallelCoordinates( ax, features, classes, color, colormap, vlines, vlines_kwds, **kwargs ) @@ -91,7 +89,7 @@ def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, ## Static Parallel Coordinates Visualizer ########################################################################## -class ParallelCoordinates(FeatureVisualizer): +class ParallelCoordinates(DataVisualizer): """ Parallel coordinates displays each feature as a vertical axis spaced evenly along the horizontal, and each instance as a line drawn between @@ -134,73 +132,16 @@ def __init__(self, ax=None, features=None, classes=None, color=None, These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - super(ParallelCoordinates, self).__init__(**kwargs) - - # The figure params - # TODO: hoist to a higher level base class - self.ax = ax - - # Data Parameters - self.features_ = features - self.classes_ = classes + super(ParallelCoordinates, self).__init__( + ax, features, classes, color, colormap, **kwargs + ) # Visual Parameters - self.color = color - self.colormap = colormap self.show_vlines = vlines self.vlines_kwds = vlines_kwds or { 'linewidth': 1, 'color': 'black' } - def fit(self, X, y=None, **kwargs): - """ - The fit method is the primary drawing input for the parallel coords - visualization since it has both the X and y data required for the - viz and the transform method does not. - - 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 - Pass generic arguments to the drawing method - - Returns - ------ - self : instance - Returns the instance of the transformer/visualizer - """ - # Get the shape of the data - nrows, ncols = X.shape - - # Store the classes for the legend if they're None. - if self.classes_ is None: - # TODO: Is this the most efficient method? - self.classes_ = [str(label) for label in set(y)] - - # Handle the feature names if they're None. - if self.features_ is None: - - # If X is a data frame, get the columns off it. - if is_dataframe(X): - self.features_ = X.columns - - # Otherwise create numeric labels for each column. - else: - self.features_ = [ - str(cdx) for cdx in range(ncols) - ] - - # Draw the instances - self.draw(X, y, **kwargs) - - # Fit always returns self. - return self - def draw(self, X, y, **kwargs): """ Called from the fit method, this method creates the parallel @@ -247,27 +188,26 @@ def draw(self, X, y, **kwargs): for idx in x: self.ax.axvline(idx, **self.vlines_kwds) - # Finalize the plot + # Set the limits self.ax.set_xticks(x) self.ax.set_xticklabels(self.features_) self.ax.set_xlim(x[0], x[-1]) - def poof(self, outpath=None, **kwargs): + def finalize(self, **kwargs): """ - Display the parallel coordinates. + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. Parameters ---------- - outpath: path or None - Save the figure to disk or if None show in a window + kwargs: generic keyword arguments. """ - if self.ax is None: return + # Set the title + self.set_title( + 'Parallel Coordinates for {} Features'.format(len(self.features_)) + ) + # Set the legend and the grid self.ax.legend(loc='best') self.ax.grid() - - if outpath is not None: - plt.savefig(outpath, **kwargs) - else: - plt.show() diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 1493615fb..89ada2519 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -21,8 +21,7 @@ import matplotlib.pyplot as plt import matplotlib.patches as patches -from yellowbrick.utils import is_dataframe -from yellowbrick.features.base import FeatureVisualizer +from yellowbrick.features.base import DataVisualizer from yellowbrick.exceptions import YellowbrickTypeError from yellowbrick.style.colors import resolve_colors, get_color_cycle @@ -85,7 +84,7 @@ def radviz(X, y=None, ax=None, features=None, classes=None, ## Static RadViz Visualizer ########################################################################## -class RadialVisualizer(FeatureVisualizer): +class RadialVisualizer(DataVisualizer): """ RadViz is a multivariate data visualization algorithm that plots each axis uniformely around the circumference of a circle then plots points on @@ -125,19 +124,9 @@ def __init__(self, ax=None, features=None, classes=None, color=None, These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - super(RadialVisualizer, self).__init__(**kwargs) - - # The figure params - # TODO: hoist to a higher level base class - self.ax = ax - - # Data Parameters - self.features_ = features - self.classes_ = classes - - # Visual Parameters - self.color = color - self.colormap = colormap + super(RadialVisualizer, self).__init__( + ax, features, classes, color, colormap, **kwargs + ) @staticmethod def normalize(X): @@ -148,63 +137,11 @@ def normalize(X): b = X.max(axis=0) return (X - a[np.newaxis, :]) / ((b - a)[np.newaxis, :]) - def fit(self, X, y=None, **kwargs): - """ - The fit method is the primary drawing input for the parallel coords - visualization since it has both the X and y data required for the - viz and the transform method does not. - - 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 - Pass generic arguments to the drawing method - - Returns - ------ - self : instance - Returns the instance of the transformer/visualizer - """ - # TODO: This class is identical to the Parallel Coordinates version, - # so hoist this functionality to a higher level class that is extended - # by both RadViz and ParallelCoordinates. - - # Get the shape of the data - nrows, ncols = X.shape - - # Store the classes for the legend if they're None. - if self.classes_ is None: - # TODO: Is this the most efficient method? - self.classes_ = [str(label) for label in set(y)] - - # Handle the feature names if they're None. - if self.features_ is None: - - # If X is a data frame, get the columns off it. - if is_dataframe(X): - self.features_ = X.columns - - # Otherwise create numeric labels for each column. - else: - self.features_ = [ - str(cdx) for cdx in range(ncols) - ] - - # Draw the instances - self.draw(X, y, **kwargs) - - # Fit always returns self. - return self - def draw(self, X, y, **kwargs): """ - Called from the fit method, this method creates the parallel - coordinates canvas and draws each instance and vertical lines on it. + Called from the fit method, this method creates the radviz canvas and + draws each instance as a class or target colored point, whose location + is determined by the feature data set. """ # Get the shape of the data @@ -275,22 +212,24 @@ def draw(self, X, y, **kwargs): self.ax.axis('equal') - def poof(self, outpath=None, **kwargs): + def finalize(self, **kwargs): """ - Display the radial visualization + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. Parameters ---------- - outpath: path or None - Save the figure to disk or if None show in a window + kwargs: generic keyword arguments. + """ - if self.ax is None: return + # Set the title + self.set_title( + 'RadViz for {} Features'.format(len(self.features_)) + ) + + # Add the legend self.ax.legend(loc='best') - if outpath is not None: - plt.savefig(outpath, **kwargs) - else: - plt.show() # Alias for RadViz RadViz = RadialVisualizer diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 91abd55ea..314dc479d 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -122,11 +122,7 @@ def __init__(self, ax=None, algorithm='pearson', features=None, kwargs : dict keyword arguments passed to the super class. """ - super(Rank2D, self).__init__(**kwargs) - - # The figure params - # TODO: hoist to a higher level base class - self.ax = ax + super(Rank2D, self).__init__(ax=ax, **kwargs) # Data Parameters self.ranking_ = algorithm @@ -249,6 +245,7 @@ def draw(self, X, **kwargs): mask = mask[::-1] # Draw the heatmap + # TODO: Move mesh to a property so the colorbar can be finalized data = np.ma.masked_where(mask, X) mesh = self.ax.pcolormesh(data, cmap=self.colormap, vmin=-1, vmax=1) @@ -261,26 +258,19 @@ def draw(self, X, **kwargs): cb = self.ax.figure.colorbar(mesh, None, self.ax) cb.outline.set_linewidth(0) - - def poof(self, outpath=None, **kwargs): + def finalize(self, **kwargs): """ - Display the Rank2D visualization + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. Parameters ---------- - outpath: path or None - Save the figure to disk or if None show in a window - """ - if self.ax is None: return + kwargs: generic keyword arguments. + """ # Set the title self.ax.set_title( "{} Ranking of {} Features".format( self.ranking_.title(), len(self.features_) ) ) - - if outpath is not None: - plt.savefig(outpath, **kwargs) - else: - plt.show() diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 6be8a221e..a59f645aa 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -21,17 +21,22 @@ import matplotlib as mpl import matplotlib.pyplot as plt +from sklearn.cross_validation import train_test_split + from .bestfit import draw_best_fit +from .style.palettes import LINE_COLOR from .exceptions import YellowbrickTypeError from .utils import get_model_name, isestimator, isregressor from .base import Visualizer, ScoreVisualizer, MultiModelMixin + ########################################################################## ## Regression Visualization Base Object ########################################################################## class RegressionScoreVisualizer(ScoreVisualizer): - def __init__(self, model, **kwargs): + + def __init__(self, model, ax=None, **kwargs): """ Check to see if model is an instance of a regressor. Should return an error if it isn't. @@ -42,7 +47,7 @@ def __init__(self, model, **kwargs): "clustering score visualizer instead!" ) - super(RegressionScoreVisualizer, self).__init__(model, **kwargs) + super(RegressionScoreVisualizer, self).__init__(model, ax=ax, **kwargs) ########################################################################## @@ -54,16 +59,39 @@ class PredictionError(RegressionScoreVisualizer): Plot the actual targets from the dataset against the predicted values generated by our model(s). """ - def __init__(self, model, **kwargs): - super(PredictionError, self).__init__(model, **kwargs) + def __init__(self, model, ax=None, **kwargs): + """ + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param model: the Scikit-Learn estimator + Should be an instance of a regressor, else the __init__ will + return an error. + + :param point_color: color of the error points + Any matplotlib color + + :param line_color: color of the best fit line + Any matplotlib color + + :param kwargs: keyword arguments passed to the super class. + Currently passing in hard-coded colors for the prediction error + points and the line of best fit. + These will be refactored to a default Yellowbrick style. + + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + + super(PredictionError, self).__init__(model, ax=ax, **kwargs) - self.ax = None - self.name = get_model_name(self.estimator) self.colors = { - 'point': kwargs.pop('point_color', '#F2BE2C'), - 'line': kwargs.pop('line_color', '#2B94E9'), + 'point': kwargs.pop('point_color', None), + 'line': kwargs.pop('line_color', LINE_COLOR), } - # self.fig, self.ax = plt.subplots() def score(self, X, y=None, **kwargs): """ @@ -83,20 +111,39 @@ def score(self, X, y=None, **kwargs): y : array-like y (also y_test) is the independent actual variables to score against + Returns + ------ + + ax : the axis with the plotted figure + """ y_pred = self.predict(X) return self.draw(y, y_pred) def draw(self, y, y_pred): """ - If score is happening inside a loop, draw would get called multiple times. + Parameters + ---------- + + y : ndarray or Series of length n + An array or series of target or class values - But, ideally we'd want the best fit line to be drawn only once? + y_pred : ndarray or Series of length n + An array or series of predicted target values + + 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() - self.ax.scatter(y, y_pred, c='#F2BE2C') + self.ax.scatter(y, y_pred, c=self.colors['point']) + + # TODO If score is happening inside a loop, draw would get called multiple times. + # Ideally we'd want the best fit line to be drawn only once draw_best_fit(y, y_pred, self.ax, 'linear', ls='--', lw=2, c=self.colors['line']) self.ax.set_xlim(y.min()-1, y.max()+1) @@ -104,11 +151,64 @@ def draw(self, y, y_pred): return self.ax - def poof(self): - self.ax.set_title('Prediction Error for {}'.format(self.name)) + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + + """ + # Set the title on the plot + self.set_title('Prediction Error for {}'.format(self.name)) + + # Set the axes labels self.ax.set_ylabel('Predicted') - plt.xlabel('Measured') - return self.ax + self.ax.set_xlabel('Measured') + + +def prediction_error(model, X, y=None, ax=None, **kwargs): + """Quick method: + + Plot the actual targets from the dataset against the + predicted values generated by our model(s). + + This helper function is a quick wrapper to utilize the PredictionError + ScoreVisualizer for one-off analysis. + + 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. + + ax : matplotlib axes + The axes to plot the figure on. + + model : the Scikit-Learn estimator (should be a regressor) + + Returns + ------- + ax : matplotlib axes + Returns the axes that the prediction error plot was drawn on. + """ + # Instantiate the visualizer + visualizer = PredictionError(model, ax, **kwargs) + + # Create the train and test splits + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X_train, y_train, **kwargs) + visualizer.score(X_test, y_test) + + # Return the axes object on the visualizer + return visualizer.ax + ########################################################################## ## Residuals Plots @@ -123,28 +223,91 @@ class ResidualsPlot(RegressionScoreVisualizer): a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate. """ - def __init__(self, model, **kwargs): - super(ResidualsPlot, self).__init__(model, **kwargs) + def __init__(self, model, ax=None, **kwargs): + """ + Parameters + ---------- + + :param model: the Scikit-Learn estimator + Should be an instance of a regressor, else the __init__ will + return an error. + + :param ax: the axis to plot the figure on. - self.ax = None - self.name = get_model_name(self.estimator) + :param train_color: color of the training data residuals + Residuals for training data are ploted with this color but also + given an opacity of 0.5 to ensure that the test data residuals + are more visible. Default color is 'b' for training data. + + :param test_color: color of test data residuals + Residuals for test data are plotted with this color. In order to + create generalizable models, reserved test data residuals are of + the most analytical interest, so these points are highlighted by + hvaing full opacity. Default color is 'g' for test data. + + :param line_color: color of the zero error line + Any matplotlib color. Default is a dark grey. + + :param kwargs: keyword arguments passed to the super class. + Currently passing in hard-coded colors for the residual train and + test points and the horizontal line. + These will be refactored to a default Yellowbrick style. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + + """ + + super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) + + # TODO Is there a better way to differentiate between train and test points? + # We'd like to color them differently in draw... + # Can the user pass those in as keyword arguments? self.colors = { - 'train_point': kwargs.pop('train_point_color', '#2B94E9'), - 'test_point': kwargs.pop('test_point_color', '#94BA65'), - 'line': kwargs.pop('line_color', '#333333'), + 'train_point': kwargs.pop('train_color', 'b'), + 'test_point': kwargs.pop('test_color', 'g'), + 'line': kwargs.pop('line_color', LINE_COLOR), } - def fit(self, X, y=None, **kwargs): + """ + 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 values + + kwargs: keyword arguments passed to Scikit-Learn API. + """ super(ResidualsPlot, self).fit(X, y, **kwargs) self.score(X, y, train=True) def score(self, X, y=None, train=False, **kwargs): """ - Is there a better way to differentiate between train and test points? + Generates predicted target values using the Scikit-Learn + estimator. + + Parameters + ---------- + X : array-like + X (also X_test) are the dependent variables of test set to predict + + y : array-like + y (also y_test) is the independent actual variables to score against + + train : boolean + If False, `score` assumes that the residual points being plotted + are from the test data; if True, `score` assumes the residuals + are the train data. + + Returns + ------ + + ax : the axis with the plotted figure - We'd like to color them differently in draw... - Can the user pass those in as keyword arguments? """ y_pred = self.predict(X) scores = y_pred - y @@ -152,8 +315,27 @@ def score(self, X, y=None, train=False, **kwargs): def draw(self, y_pred, residuals, train=False, **kwargs): """ + Parameters + ---------- + y_pred : ndarray or Series of length n + An array or series of predicted target values + + residuals : ndarray or Series of length n + An array or series of the difference between the predicted and the + target values + + train : boolean + If False, `draw` assumes that the residual points being plotted + are from the test data; if True, `draw` assumes the residuals + are the train data. + + 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() @@ -164,15 +346,68 @@ def draw(self, y_pred, residuals, train=False, **kwargs): return self.ax - def poof(self): + def finalize(self, **kwargs): """ - The user calls poof + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + """ - if self.ax is None: return + # Add the title to the plot + self.set_title('Residuals for {} Model'.format(self.name)) + + # Set the legend + # Assumes that the first set of points are training data, and the next are test + # Assumes that you want a box around legend + self.ax.legend(['Training Data', 'Test Data'], loc = 'best', frameon = True) + + # Create a full line across the figure at zero error. + self.ax.axhline(y=0, c=self.colors['line']) - self.ax.hlines(y=0, xmin=0, xmax=100) - self.ax.set_title('Residuals for {} Model'.format(self.name)) + # Set the axes labels self.ax.set_ylabel('Residuals') - plt.xlabel("Predicted Value") + self.ax.set_xlabel("Predicted Value") - return self.ax + +def residuals_plot(model, X, y=None, ax=None, **kwargs): + """Quick method: + + Plot the residuals on the vertical axis and the + independent variable on the horizontal axis. + + This helper function is a quick wrapper to utilize the ResidualsPlot + ScoreVisualizer for one-off analysis. + + 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. + + ax : matplotlib axes + The axes to plot the figure on. + + model : the Scikit-Learn estimator (should be a regressor) + + Returns + ------- + ax : matplotlib axes + Returns the axes that the residuals plot was drawn on. + """ + # Instantiate the visualizer + visualizer = ResidualsPlot(model, ax, **kwargs) + + # Create the train and test splits + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X_train, y_train, **kwargs) + visualizer.score(X_test, y_test) + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/style/palettes.py b/yellowbrick/style/palettes.py index 26b488256..7008a9bd9 100644 --- a/yellowbrick/style/palettes.py +++ b/yellowbrick/style/palettes.py @@ -1,27 +1,6 @@ -# yellowbrick.style.palettes -# Implements the variety of colors that yellowbrick allows access to by name. -# -# Author: Patrick O'Melveny -# Author: Benjamin Bengfort -# Created: Tue Oct 04 15:30:15 2016 -0400 -# -# Copyright (C) 2016 District Data Labs -# For license information, see LICENSE.txt -# -# ID: palettes.py [] pvomelveny@gmail.com $ - -""" -Implements the variety of colors that yellowbrick allows access to by name. -This code was originally based on Seaborn's rcmody.py but has since been -cleaned up to be Yellowbrick-specific and to dereference tools we don't use. - -Note that these functions alter the matplotlib rc dictionary on the fly. -""" - -########################################################################## -## Imports -########################################################################## - +# yellowbrick.style.palettes # Implements the variety of colors that yellowbrick allows access to by nam # +# Author: Patrick O'Melveny # Copyright (C) 2016 District Data Lab# For license information, see LICENSE.txt +# D: palettes.py [] pvomelveny@gmail.com "" Implements the variety of colors that yellowbrick allows access to by name This code was originally based on Seaborn's rcmody.py but has since beecleaned up to be Yellowbrick-specific and to dereference tools we don't use. Note that these functions alter the matplotlib rc dictionary on the fly. ######################################################################### ## Import ######################################################################### from __future__ import division from itertools import cycle @@ -48,6 +27,7 @@ ########################################################################## YB_KEY = '#111111' # The yellowbrick key (black) color is very dark grey +LINE_COLOR = YB_KEY # Colors for best fit lines, diagonals, etc. ########################################################################## @@ -62,8 +42,8 @@ "yellowbrick": ['#0272a2', '#9fc377', '#ca0b03', '#a50258', '#d7c703', '#88cada'], # The following are from ColorBrewer - "accent": ['#7fc97f', '#beaed4', '#fdc086', '#ffff99', '#386cb0', '#f0027f'], - "dark": ['#1b9e77', '#d95f02', '#7570b3', '#e7298a', '#66a61e', '#e6ab02'], + "accent": ['#386cb0', '#7fc97f', '#f0027f', '#beaed4', '#ffff99', '#fdc086'], + "dark": ['#7570b3', '#66a61e', '#d95f02', '#e7298a', '#e6ab02', '#1b9e77'], "pastel": ['#cbd5e8', '#b3e2cd', '#fdcdac', '#f4cae4', '#fff2ae', '#e6f5c9'], "bold": ['#377eb8', '#4daf4a', '#e41a1c', '#984ea3', '#ffff33', '#ff7f00'], "muted": ['#80b1d3', '#8dd3c7', '#fb8072', '#bebada', '#ffffb3', '#fdb462'], @@ -85,19 +65,287 @@ # Other palettes "flatui": ["#34495e", "#2ecc71", "#e74c3c", "#9b59b6", "#f4d03f", "#3498db"], - # Longer palettes that do not map to bgrmyck color space. - "ddl_heat": ['#DBDBDB', '#DCD5CC', '#DCCEBE', '#DDC8AF', '#DEC2A0', '#DEBB91', - '#DFB583', '#DFAE74', '#E0A865', '#E1A256', '#E19B48', '#E29539'], - - "paired": ["#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", - "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#a6cee3", "#1f78b4"], + "paired": ["#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", + "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", "#fdbf6f", "#ff7f00"], - "set1": ["#e41a1c", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#ffff33", + "set1": ["#377eb8", "#4daf4a", "#e41a1c", "#984ea3", "#ffff33", "#ff7f00", "#a65628", "#f781bf", "#999999"], } + +SEQUENCES = { + "ddl_heat": { + 12: ['#DBDBDB', '#DCD5CC', '#DCCEBE', '#DDC8AF', '#DEC2A0', '#DEBB91', '#DFB583', '#DFAE74', '#E0A865', '#E1A256', '#E19B48', '#E29539'], + }, + "YlGn": { + 3: ["#f7fcb9", "#addd8e", "#31a354"], + 4: ["#ffffcc", "#c2e699", "#78c679", "#238443"], + 5: ["#ffffcc", "#c2e699", "#78c679", "#31a354", "#006837"], + 6: ["#ffffcc", "#d9f0a3", "#addd8e", "#78c679", "#31a354", "#006837"], + 7: ["#ffffcc", "#d9f0a3", "#addd8e", "#78c679", "#41ab5d", "#238443", "#005a32"], + + 8: ["#ffffe5", "#f7fcb9", "#d9f0a3", "#addd8e", "#78c679", "#41ab5d", "#238443", "#005a32"], + 9: ["#ffffe5", "#f7fcb9", "#d9f0a3", "#addd8e", "#78c679", "#41ab5d", "#238443", "#006837", "#004529"], + }, + "YlGnBu": { + 3: ["#edf8b1", "#7fcdbb", "#2c7fb8"], + 4: ["#ffffcc", "#a1dab4", "#41b6c4", "#225ea8"], + 5: ["#ffffcc", "#a1dab4", "#41b6c4", "#2c7fb8", "#253494"], + 6: ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#2c7fb8", "#253494"], + 7: ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"], + 8: ["#ffffd9", "#edf8b1", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"], + 9: ["#ffffd9", "#edf8b1", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#253494", "#081d58"], + }, + "GnBu": { + 3: ["#e0f3db", "#a8ddb5", "#43a2ca"], + 4: ["#f0f9e8", "#bae4bc", "#7bccc4", "#2b8cbe"], + 5: ["#f0f9e8", "#bae4bc", "#7bccc4", "#43a2ca", "#0868ac"], + 6: ["#f0f9e8", "#ccebc5", "#a8ddb5", "#7bccc4", "#43a2ca", "#0868ac"], + 7: ["#f0f9e8", "#ccebc5", "#a8ddb5", "#7bccc4", "#4eb3d3", "#2b8cbe", "#08589e"], + 8: ["#f7fcf0", "#e0f3db", "#ccebc5", "#a8ddb5", "#7bccc4", "#4eb3d3", "#2b8cbe", "#08589e"], + 9: ["#f7fcf0", "#e0f3db", "#ccebc5", "#a8ddb5", "#7bccc4", "#4eb3d3", "#2b8cbe", "#0868ac", "#084081"], + }, + "BuGn": { + 3: ["#e5f5f9", "#99d8c9", "#2ca25f"], + 4: ["#edf8fb", "#b2e2e2", "#66c2a4", "#238b45"], + 5: ["#edf8fb", "#b2e2e2", "#66c2a4", "#2ca25f", "#006d2c"], + 6: ["#edf8fb", "#ccece6", "#99d8c9", "#66c2a4", "#2ca25f", "#006d2c"], + 7: ["#edf8fb", "#ccece6", "#99d8c9", "#66c2a4", "#41ae76", "#238b45", "#005824"], + 8: ["#f7fcfd", "#e5f5f9", "#ccece6", "#99d8c9", "#66c2a4", "#41ae76", "#238b45", "#005824"], + 9: ["#f7fcfd", "#e5f5f9", "#ccece6", "#99d8c9", "#66c2a4", "#41ae76", "#238b45", "#006d2c", "#00441b"], + }, + "PuBuGn": { + 3: ["#ece2f0", "#a6bddb", "#1c9099"], + 4: ["#f6eff7", "#bdc9e1", "#67a9cf", "#02818a"], + 5: ["#f6eff7", "#bdc9e1", "#67a9cf", "#1c9099", "#016c59"], + 6: ["#f6eff7", "#d0d1e6", "#a6bddb", "#67a9cf", "#1c9099", "#016c59"], + 7: ["#f6eff7", "#d0d1e6", "#a6bddb", "#67a9cf", "#3690c0", "#02818a", "#016450"], + 8: ["#fff7fb", "#ece2f0", "#d0d1e6", "#a6bddb", "#67a9cf", "#3690c0", "#02818a", "#016450"], + 9: ["#fff7fb", "#ece2f0", "#d0d1e6", "#a6bddb", "#67a9cf", "#3690c0", "#02818a", "#016c59", "#014636"], + }, + "PuBu": { + 3: ["#ece7f2", "#a6bddb", "#2b8cbe"], + 4: ["#f1eef6", "#bdc9e1", "#74a9cf", "#0570b0"], + 5: ["#f1eef6", "#bdc9e1", "#74a9cf", "#2b8cbe", "#045a8d"], + 6: ["#f1eef6", "#d0d1e6", "#a6bddb", "#74a9cf", "#2b8cbe", "#045a8d"], + 7: ["#f1eef6", "#d0d1e6", "#a6bddb", "#74a9cf", "#3690c0", "#0570b0", "#034e7b"], + 8: ["#fff7fb", "#ece7f2", "#d0d1e6", "#a6bddb", "#74a9cf", "#3690c0", "#0570b0", "#034e7b"], + 9: ["#fff7fb", "#ece7f2", "#d0d1e6", "#a6bddb", "#74a9cf", "#3690c0", "#0570b0", "#045a8d", "#023858"], + }, + "BuPu": { + 3: ["#e0ecf4", "#9ebcda", "#8856a7"], + 4: ["#edf8fb", "#b3cde3", "#8c96c6", "#88419d"], + 5: ["#edf8fb", "#b3cde3", "#8c96c6", "#8856a7", "#810f7c"], + 6: ["#edf8fb", "#bfd3e6", "#9ebcda", "#8c96c6", "#8856a7", "#810f7c"], + 7: ["#edf8fb", "#bfd3e6", "#9ebcda", "#8c96c6", "#8c6bb1", "#88419d", "#6e016b"], + 8: ["#f7fcfd", "#e0ecf4", "#bfd3e6", "#9ebcda", "#8c96c6", "#8c6bb1", "#88419d", "#6e016b"], + 9: ["#f7fcfd", "#e0ecf4", "#bfd3e6", "#9ebcda", "#8c96c6", "#8c6bb1", "#88419d", "#810f7c", "#4d004b"], + }, + "RdPu": { + 3: ["#fde0dd", "#fa9fb5", "#c51b8a"], + 4: ["#feebe2", "#fbb4b9", "#f768a1", "#ae017e"], + 5: ["#feebe2", "#fbb4b9", "#f768a1", "#c51b8a", "#7a0177"], + 6: ["#feebe2", "#fcc5c0", "#fa9fb5", "#f768a1", "#c51b8a", "#7a0177"], + 7: ["#feebe2", "#fcc5c0", "#fa9fb5", "#f768a1", "#dd3497", "#ae017e", "#7a0177"], + 8: ["#fff7f3", "#fde0dd", "#fcc5c0", "#fa9fb5", "#f768a1", "#dd3497", "#ae017e", "#7a0177"], + 9: ["#fff7f3", "#fde0dd", "#fcc5c0", "#fa9fb5", "#f768a1", "#dd3497", "#ae017e", "#7a0177", "#49006a"], + }, + "PuRd": { + 3: ["#e7e1ef", "#c994c7", "#dd1c77"], + 4: ["#f1eef6", "#d7b5d8", "#df65b0", "#ce1256"], + 5: ["#f1eef6", "#d7b5d8", "#df65b0", "#dd1c77", "#980043"], + 6: ["#f1eef6", "#d4b9da", "#c994c7", "#df65b0", "#dd1c77", "#980043"], + 7: ["#f1eef6", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#91003f"], + 8: ["#f7f4f9", "#e7e1ef", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#91003f"], + 9: ["#f7f4f9", "#e7e1ef", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#980043", "#67001f"], + }, + "OrRd": { + 3: ["#fee8c8", "#fdbb84", "#e34a33"], + 4: ["#fef0d9", "#fdcc8a", "#fc8d59", "#d7301f"], + 5: ["#fef0d9", "#fdcc8a", "#fc8d59", "#e34a33", "#b30000"], + 6: ["#fef0d9", "#fdd49e", "#fdbb84", "#fc8d59", "#e34a33", "#b30000"], + 7: ["#fef0d9", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#990000"], + 8: ["#fff7ec", "#fee8c8", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#990000"], + 9: ["#fff7ec", "#fee8c8", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#b30000", "#7f0000"], + }, + "YlOrRd": { + 3: ["#ffeda0", "#feb24c", "#f03b20"], + 4: ["#ffffb2", "#fecc5c", "#fd8d3c", "#e31a1c"], + 5: ["#ffffb2", "#fecc5c", "#fd8d3c", "#f03b20", "#bd0026"], + 6: ["#ffffb2", "#fed976", "#feb24c", "#fd8d3c", "#f03b20", "#bd0026"], + 7: ["#ffffb2", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#b10026"], + 8: ["#ffffcc", "#ffeda0", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#b10026"], + 9: ["#ffffcc", "#ffeda0", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#bd0026", "#800026"], + }, + "YlOrBr": { + 3: ["#fff7bc", "#fec44f", "#d95f0e"], + 4: ["#ffffd4", "#fed98e", "#fe9929", "#cc4c02"], + 5: ["#ffffd4", "#fed98e", "#fe9929", "#d95f0e", "#993404"], + 6: ["#ffffd4", "#fee391", "#fec44f", "#fe9929", "#d95f0e", "#993404"], + 7: ["#ffffd4", "#fee391", "#fec44f", "#fe9929", "#ec7014", "#cc4c02", "#8c2d04"], + 8: ["#ffffe5", "#fff7bc", "#fee391", "#fec44f", "#fe9929", "#ec7014", "#cc4c02", "#8c2d04"], + 9: ["#ffffe5", "#fff7bc", "#fee391", "#fec44f", "#fe9929", "#ec7014", "#cc4c02", "#993404", "#662506"], + }, + "Purples": { + 3: ["#efedf5", "#bcbddc", "#756bb1"], + 4: ["#f2f0f7", "#cbc9e2", "#9e9ac8", "#6a51a3"], + 5: ["#f2f0f7", "#cbc9e2", "#9e9ac8", "#756bb1", "#54278f"], + 6: ["#f2f0f7", "#dadaeb", "#bcbddc", "#9e9ac8", "#756bb1", "#54278f"], + 7: ["#f2f0f7", "#dadaeb", "#bcbddc", "#9e9ac8", "#807dba", "#6a51a3", "#4a1486"], + 8: ["#fcfbfd", "#efedf5", "#dadaeb", "#bcbddc", "#9e9ac8", "#807dba", "#6a51a3", "#4a1486"], + 9: ["#fcfbfd", "#efedf5", "#dadaeb", "#bcbddc", "#9e9ac8", "#807dba", "#6a51a3", "#54278f", "#3f007d"], + }, + "Blues": { + 3: ["#deebf7", "#9ecae1", "#3182bd"], + 4: ["#eff3ff", "#bdd7e7", "#6baed6", "#2171b5"], + 5: ["#eff3ff", "#bdd7e7", "#6baed6", "#3182bd", "#08519c"], + 6: ["#eff3ff", "#c6dbef", "#9ecae1", "#6baed6", "#3182bd", "#08519c"], + 7: ["#eff3ff", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#084594"], + 8: ["#f7fbff", "#deebf7", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#084594"], + 9: ["#f7fbff", "#deebf7", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#08519c", "#08306b"], + }, + "Greens": { + 3: ["#e5f5e0", "#a1d99b", "#31a354"], + 4: ["#edf8e9", "#bae4b3", "#74c476", "#238b45"], + 5: ["#edf8e9", "#bae4b3", "#74c476", "#31a354", "#006d2c"], + 6: ["#edf8e9", "#c7e9c0", "#a1d99b", "#74c476", "#31a354", "#006d2c"], + 7: ["#edf8e9", "#c7e9c0", "#a1d99b", "#74c476", "#41ab5d", "#238b45", "#005a32"], + 8: ["#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476", "#41ab5d", "#238b45", "#005a32"], + 9: ["#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476", "#41ab5d", "#238b45", "#006d2c", "#00441b"], + }, + "Oranges": { + 3: ["#fee6ce", "#fdae6b", "#e6550d"], + 4: ["#feedde", "#fdbe85", "#fd8d3c", "#d94701"], + 5: ["#feedde", "#fdbe85", "#fd8d3c", "#e6550d", "#a63603"], + 6: ["#feedde", "#fdd0a2", "#fdae6b", "#fd8d3c", "#e6550d", "#a63603"], + 7: ["#feedde", "#fdd0a2", "#fdae6b", "#fd8d3c", "#f16913", "#d94801", "#8c2d04"], + 8: ["#fff5eb", "#fee6ce", "#fdd0a2", "#fdae6b", "#fd8d3c", "#f16913", "#d94801", "#8c2d04"], + 9: ["#fff5eb", "#fee6ce", "#fdd0a2", "#fdae6b", "#fd8d3c", "#f16913", "#d94801", "#a63603", "#7f2704"], + }, + "Reds": { + 3: ["#fee0d2", "#fc9272", "#de2d26"], + 4: ["#fee5d9", "#fcae91", "#fb6a4a", "#cb181d"], + 5: ["#fee5d9", "#fcae91", "#fb6a4a", "#de2d26", "#a50f15"], + 6: ["#fee5d9", "#fcbba1", "#fc9272", "#fb6a4a", "#de2d26", "#a50f15"], + 7: ["#fee5d9", "#fcbba1", "#fc9272", "#fb6a4a", "#ef3b2c", "#cb181d", "#99000d"], + 8: ["#fff5f0", "#fee0d2", "#fcbba1", "#fc9272", "#fb6a4a", "#ef3b2c", "#cb181d", "#99000d"], + 9: ["#fff5f0", "#fee0d2", "#fcbba1", "#fc9272", "#fb6a4a", "#ef3b2c", "#cb181d", "#a50f15", "#67000d"], + }, + "Greys": { + 3: ["#f0f0f0", "#bdbdbd", "#636363"], + 4: ["#f7f7f7", "#cccccc", "#969696", "#525252"], + 5: ["#f7f7f7", "#cccccc", "#969696", "#636363", "#252525"], + 6: ["#f7f7f7", "#d9d9d9", "#bdbdbd", "#969696", "#636363", "#252525"], + 7: ["#f7f7f7", "#d9d9d9", "#bdbdbd", "#969696", "#737373", "#525252", "#252525"], + 8: ["#ffffff", "#f0f0f0", "#d9d9d9", "#bdbdbd", "#969696", "#737373", "#525252", "#252525"], + 9: ["#ffffff", "#f0f0f0", "#d9d9d9", "#bdbdbd", "#969696", "#737373", "#525252", "#252525", "#000000"], + }, + "PuOr": { + 3: ["#f1a340", "#f7f7f7", "#998ec3"], + 4: ["#e66101", "#fdb863", "#b2abd2", "#5e3c99"], + 5: ["#e66101", "#fdb863", "#f7f7f7", "#b2abd2", "#5e3c99"], + 6: ["#b35806", "#f1a340", "#fee0b6", "#d8daeb", "#998ec3", "#542788"], + 7: ["#b35806", "#f1a340", "#fee0b6", "#f7f7f7", "#d8daeb", "#998ec3", "#542788"], + 8: ["#b35806", "#e08214", "#fdb863", "#fee0b6", "#d8daeb", "#b2abd2", "#8073ac", "#542788"], + 9: ["#b35806", "#e08214", "#fdb863", "#fee0b6", "#f7f7f7", "#d8daeb", "#b2abd2", "#8073ac", "#542788"], + 10: ["#7f3b08", "#b35806", "#e08214", "#fdb863", "#fee0b6", "#d8daeb", "#b2abd2", "#8073ac", "#542788", "#2d004b"], + 11: ["#7f3b08", "#b35806", "#e08214", "#fdb863", "#fee0b6", "#f7f7f7", "#d8daeb", "#b2abd2", "#8073ac", "#542788", "#2d004b"], + }, + "BrBG": { + 3: ["#d8b365", "#f5f5f5", "#5ab4ac"], + 4: ["#a6611a", "#dfc27d", "#80cdc1", "#018571"], + 5: ["#a6611a", "#dfc27d", "#f5f5f5", "#80cdc1", "#018571"], + 6: ["#8c510a", "#d8b365", "#f6e8c3", "#c7eae5", "#5ab4ac", "#01665e"], + 7: ["#8c510a", "#d8b365", "#f6e8c3", "#f5f5f5", "#c7eae5", "#5ab4ac", "#01665e"], + 8: ["#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#c7eae5", "#80cdc1", "#35978f", "#01665e"], + 9: ["#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#f5f5f5", "#c7eae5", "#80cdc1", "#35978f", "#01665e"], + 10: ["#543005", "#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#c7eae5", "#80cdc1", "#35978f", "#01665e", "#003c30"], + 11: ["#543005", "#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#f5f5f5", "#c7eae5", "#80cdc1", "#35978f", "#01665e", "#003c30"], + }, + "PRGn": { + 3: ["#af8dc3", "#f7f7f7", "#7fbf7b"], + 4: ["#7b3294", "#c2a5cf", "#a6dba0", "#008837"], + 5: ["#7b3294", "#c2a5cf", "#f7f7f7", "#a6dba0", "#008837"], + 6: ["#762a83", "#af8dc3", "#e7d4e8", "#d9f0d3", "#7fbf7b", "#1b7837"], + 7: ["#762a83", "#af8dc3", "#e7d4e8", "#f7f7f7", "#d9f0d3", "#7fbf7b", "#1b7837"], + 8: ["#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837"], + 9: ["#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#f7f7f7", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837"], + 10: ["#40004b", "#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837", "#00441b"], + 11: ["#40004b", "#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#f7f7f7", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837", "#00441b"], + }, + "PiYG": { + 3: ["#e9a3c9", "#f7f7f7", "#a1d76a"], + 4: ["#d01c8b", "#f1b6da", "#b8e186", "#4dac26"], + 5: ["#d01c8b", "#f1b6da", "#f7f7f7", "#b8e186", "#4dac26"], + 6: ["#c51b7d", "#e9a3c9", "#fde0ef", "#e6f5d0", "#a1d76a", "#4d9221"], + 7: ["#c51b7d", "#e9a3c9", "#fde0ef", "#f7f7f7", "#e6f5d0", "#a1d76a", "#4d9221"], + 8: ["#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221"], + 9: ["#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#f7f7f7", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221"], + 10: ["#8e0152", "#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221", "#276419"], + 11: ["#8e0152", "#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#f7f7f7", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221", "#276419"], + }, + "RdBu": { + 3: ["#ef8a62", "#f7f7f7", "#67a9cf"], + 4: ["#ca0020", "#f4a582", "#92c5de", "#0571b0"], + 5: ["#ca0020", "#f4a582", "#f7f7f7", "#92c5de", "#0571b0"], + 6: ["#b2182b", "#ef8a62", "#fddbc7", "#d1e5f0", "#67a9cf", "#2166ac"], + 7: ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"], + 8: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac"], + 9: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#f7f7f7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac"], + 10: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac", "#053061"], + 11: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#f7f7f7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac", "#053061"], + }, + "RdGy": { + 3: ["#ef8a62", "#ffffff", "#999999"], + 4: ["#ca0020", "#f4a582", "#bababa", "#404040"], + 5: ["#ca0020", "#f4a582", "#ffffff", "#bababa", "#404040"], + 6: ["#b2182b", "#ef8a62", "#fddbc7", "#e0e0e0", "#999999", "#4d4d4d"], + 7: ["#b2182b", "#ef8a62", "#fddbc7", "#ffffff", "#e0e0e0", "#999999", "#4d4d4d"], + 8: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#e0e0e0", "#bababa", "#878787", "#4d4d4d"], + 9: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#ffffff", "#e0e0e0", "#bababa", "#878787", "#4d4d4d"], + 10: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#e0e0e0", "#bababa", "#878787", "#4d4d4d", "#1a1a1a"], + 11: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#ffffff", "#e0e0e0", "#bababa", "#878787", "#4d4d4d", "#1a1a1a"], + }, + "RdYlBu": { + 3: ["#fc8d59", "#ffffbf", "#91bfdb"], + 4: ["#d7191c", "#fdae61", "#abd9e9", "#2c7bb6"], + 5: ["#d7191c", "#fdae61", "#ffffbf", "#abd9e9", "#2c7bb6"], + 6: ["#d73027", "#fc8d59", "#fee090", "#e0f3f8", "#91bfdb", "#4575b4"], + 7: ["#d73027", "#fc8d59", "#fee090", "#ffffbf", "#e0f3f8", "#91bfdb", "#4575b4"], + 8: ["#d73027", "#f46d43", "#fdae61", "#fee090", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4"], + 9: ["#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4"], + 10: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4", "#313695"], + 11: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4", "#313695"], + }, + "Spectral": { + 3: ["#fc8d59", "#ffffbf", "#99d594"], + 4: ["#d7191c", "#fdae61", "#abdda4", "#2b83ba"], + 5: ["#d7191c", "#fdae61", "#ffffbf", "#abdda4", "#2b83ba"], + 6: ["#d53e4f", "#fc8d59", "#fee08b", "#e6f598", "#99d594", "#3288bd"], + 7: ["#d53e4f", "#fc8d59", "#fee08b", "#ffffbf", "#e6f598", "#99d594", "#3288bd"], + 8: ["#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#66c2a5", "#3288bd"], + 9: ["#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#e6f598", "#abdda4", "#66c2a5", "#3288bd"], + 10: ["#9e0142", "#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#66c2a5", "#3288bd", "#5e4fa2"], + 11: ["#9e0142", "#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#e6f598", "#abdda4", "#66c2a5", "#3288bd", "#5e4fa2"], + }, + "RdYlGn": { + 3: ["#fc8d59", "#ffffbf", "#91cf60"], + 4: ["#d7191c", "#fdae61", "#a6d96a", "#1a9641"], + 5: ["#d7191c", "#fdae61", "#ffffbf", "#a6d96a", "#1a9641"], + 6: ["#d73027", "#fc8d59", "#fee08b", "#d9ef8b", "#91cf60", "#1a9850"], + 7: ["#d73027", "#fc8d59", "#fee08b", "#ffffbf", "#d9ef8b", "#91cf60", "#1a9850"], + 8: ["#d73027", "#f46d43", "#fdae61", "#fee08b", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850"], + 9: ["#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850"], + 10: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"], + 11: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"], + }, +} + ## Special, backward compatible color map. -ddlheatmap = mplcol.ListedColormap(PALETTES["ddl_heat"]) +ddlheatmap = mplcol.ListedColormap(SEQUENCES["ddl_heat"][12], "DDL Heat", 12) + +## Default Color Sequence +DEFAULT_SEQUENCE = "RdBu" ########################################################################## @@ -163,7 +411,7 @@ def as_rgb(self): def plot(self, size=1): """ - Plot the values in the color palatte as a horizontal array. + Plot the values in the color palette as a horizontal array. See Seaborn's palplot function for inspiration. Parameters @@ -324,3 +572,102 @@ def set_color_codes(palette="accent"): rgb = mpl.colors.colorConverter.to_rgb(color) mpl.colors.colorConverter.colors[code] = rgb mpl.colors.colorConverter.cache[code] = rgb + + +########################################################################## +## Sequence Functions +########################################################################## + +def color_sequence(palette=None, n_colors=None): + """ + Return a `ListedColormap` object from a named sequence palette. Useful + for continuous color scheme values and color maps. + + Calling this function with ``palette=None`` will return the default + color sequence: Color Brewer RdBu. + + Parameters + ---------- + + palette : None or str or sequence + Name of a palette or ``None`` to return the default palette. If a + sequence the input colors are used to create a ListedColormap. + + The currently implemented color sequences are from Color Brewer. + + Available palette names from :py:mod:`yellowbrick.colors.palettes` are: + + .. hlist:: + :columns: 3 + + * :py:const: Blues + * :py:const: BrBG + * :py:const: BuGn + * :py:const: BuPu + * :py:const: GnBu + * :py:const: Greens + * :py:const: Greys + * :py:const: OrRd + * :py:const: Oranges + * :py:const: PRGn + * :py:const: PiYG + * :py:const: PuBu + * :py:const: PuBuGn + * :py:const: PuOr + * :py:const: PuRd + * :py:const: Purples + * :py:const: RdBu + * :py:const: RdGy + * :py:const: RdPu + * :py:const: RdYlBu + * :py:const: RdYlGn + * :py:const: Reds + * :py:const: Spectral + * :py:const: YlGn + * :py:const: YlGnBu + * :py:const: YlOrBr + * :py:const: YlOrRd + * :py:const: ddl_heat + + n_colors : None or int + Number of colors in the palette. If ``None``, the default will depend + on how ``palette`` is specified - selecting the largest sequence for + that palette name. Note that sequences have a minimum lenght of 3 - if + a number of colors is specified that is not available for the sequence + a ``ValueError`` is raised. + + Returns + ------- + colormap + Returns a ListedColormap object, an artist object from the matplotlib + library that can be used wherever a colormap is necessary. + """ + # Select the default colormap if None is passed in. + palette = palette or DEFAULT_SEQUENCE + + # Create a listed color map from the sequence + if not isinstance(palette, string_types): + return mplcol.ListedColormap(palette) + + # Otherwise perform a case-insensitive lookup + sequences = {key.lower(): key for key in SEQUENCES.keys()} + if palette.lower() not in sequences: + raise YellowbrickValueError( + "'{}' is not a recognized palette!".format(palette) + ) + + # Collect the palette into the dictionary of lists. + n_palettes = SEQUENCES[sequences[palette.lower()]] + + # If no definitive color is passed in, maximize it. + if n_colors is None: + n_colors = max(n_palettes.keys()) + + else: + if n_colors not in n_palettes.keys(): + raise YellowbrickValueError( + "No {} palette of length {}".format(palette, n_colors) + ) + + # Return the color map from the sequence + return mplcol.ListedColormap(n_palettes[n_colors], name=palette, N=n_colors) diff --git a/yellowbrick/utils.py b/yellowbrick/utils.py index 4f873d8ea..bea7cf504 100644 --- a/yellowbrick/utils.py +++ b/yellowbrick/utils.py @@ -1,24 +1,34 @@ -# utils +# yellowbrick.utils +# Utility functions and helpers for the Yellowbrick library. # # Author: Jason Keung -# Patrick O'Melveny -# Created: Thurs Jun 2 15:33:18 2016 -0500 +# Author: Patrick O'Melveny +# Author: Benjamin Bengfort +# Author: Rebecca Bilbro +# 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 for yellowbrick +Utility functions and helpers for the Yellowbrick library. """ ########################################################################## ## Imports ########################################################################## +import inspect + +from functools import wraps from sklearn.pipeline import Pipeline from sklearn.base import BaseEstimator from yellowbrick.exceptions import YellowbrickTypeError + ########################################################################## ## Model detection utilities ########################################################################## @@ -26,6 +36,11 @@ 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( @@ -48,8 +63,13 @@ def get_model_name(model): def is_estimator(model): """ Determines if a model is an estimator using issubclass and isinstance. + + Parameters + ---------- + model: class or instance + The object to test whether or not is a Scikit-Learn estimator. """ - if type(model) == type: + if inspect.isclass(model): return issubclass(model, BaseEstimator) return isinstance(model, BaseEstimator) @@ -61,8 +81,25 @@ def is_estimator(model): def is_classifier(estimator): """ Returns True if the given estimator is (probably) a classifier. - From: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L526 + + Parameters + ---------- + estimator: class or instance + The object to test whether or not is a Scikit-Learn classifier. + + See also + -------- + is_classifier + `sklearn.is_classifier() `_ """ + # 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 @@ -72,11 +109,25 @@ def is_classifier(estimator): def is_regressor(estimator): """ Returns True if the given estimator is (probably) a regressor. - From: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L531 + + Parameters + ---------- + model: class or instance + The object to test whether or not is a Scikit-Learn regressor. + + See also + -------- + is_regressor + `sklearn.is_regressor() `_ """ + # 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 @@ -86,6 +137,11 @@ def is_regressor(estimator): 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 @@ -97,3 +153,79 @@ def is_dataframe(obj): # Alias for closer name to isinstance and issubclass isdataframe = is_dataframe + + +########################################################################## +## 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 `_ + """ + 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/version.py b/yellowbrick/version.py index 7c1cbad93..876e75fdc 100644 --- a/yellowbrick/version.py +++ b/yellowbrick/version.py @@ -20,9 +20,9 @@ __version_info__ = { 'major': 0, 'minor': 3, - 'micro': 1, + 'micro': 2, 'releaselevel': 'alpha', - 'serial': 2, + 'serial': 3, } ##########################################################################