From 875f3fd31070bfb3f8d70aa79a2f4b43b43099bb Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 9 Jun 2023 13:55:42 -0700 Subject: [PATCH 01/76] HYP-334 Fix hypergraph constructors --- hypernetx/classes/entity.py | 14 ++++++++++---- hypernetx/classes/entityset.py | 2 +- hypernetx/classes/hypergraph.py | 5 +++-- hypernetx/classes/tests/test_entityset.py | 6 ++++++ hypernetx/classes/tests/test_hypergraph.py | 7 +++++++ 5 files changed, 27 insertions(+), 7 deletions(-) diff --git a/hypernetx/classes/entity.py b/hypernetx/classes/entity.py index 81a80be6..3091200e 100644 --- a/hypernetx/classes/entity.py +++ b/hypernetx/classes/entity.py @@ -150,11 +150,17 @@ def __init__( # be filled in with dict keys for a list of N lists, 0,1,...,N will be # used to fill the first level/column elif isinstance(entity, (dict, list)): + # convert dict of lists to 1-column dataframe + if not entity: + entity = pd.Series(entity).explode() + self._dataframe = pd.DataFrame({data_cols[0]: entity.index.to_list()}) + data_cols = [0] # convert dict of lists to 2-column dataframe - entity = pd.Series(entity).explode() - self._dataframe = pd.DataFrame( - {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} - ) + else: + entity = pd.Series(entity).explode() + self._dataframe = pd.DataFrame( + {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} + ) # if a 2d numpy ndarray is passed, store it as both a DataFrame and an # ndarray in the state dict diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index c0c1b97d..6c2dfb57 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -4,7 +4,7 @@ from ast import literal_eval from collections import OrderedDict from collections.abc import Iterable, Sequence -from typing import Mapping +from typing import Mapping, Hashable from typing import Optional, Any, TypeVar, Union from pprint import pformat diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 930785bd..251aaa63 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -324,8 +324,9 @@ def __init__( ) ### cell properties - if setsystem is None: #### Empty Case - + if setsystem is None or ( + isinstance(setsystem, dict) and not setsystem + ): #### Empty Case self._edges = EntitySet({}) self._nodes = EntitySet({}) self._state_dict = {} diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index ca373324..54204404 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -4,6 +4,12 @@ from hypernetx import Entity, EntitySet +def test_construct_entityset_from_empty_dict(): + es = EntitySet({}) + assert len(es.elements) == 0 + assert es.dimsize == 1 + + @pytest.mark.xfail(reason="default arguments fail for empty Entity") def test_construct_empty_entityset(): es = EntitySet() diff --git a/hypernetx/classes/tests/test_hypergraph.py b/hypernetx/classes/tests/test_hypergraph.py index 4f5ef0f3..d39c290b 100644 --- a/hypernetx/classes/tests/test_hypergraph.py +++ b/hypernetx/classes/tests/test_hypergraph.py @@ -325,6 +325,13 @@ def test_construct_empty_hypergraph(): assert h.nodes.is_empty() +def test_construct_hypergraph_from_empty_dict(): + h = Hypergraph({}) + assert h.shape == (0, 0) + assert h.edges.is_empty() + assert h.nodes.is_empty() + + def test_static_hypergraph_s_connected_components(lesmis): H = Hypergraph(lesmis.edgedict) assert {7, 8} in list(H.s_connected_components(edges=True, s=4)) From e805d99b4b09e50c67492947a27bf796310563d3 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 23 Jun 2023 14:23:17 -0700 Subject: [PATCH 02/76] HYP-334 Add more assertions to empty Hypergraph test --- hypernetx/classes/tests/test_hypergraph.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/hypernetx/classes/tests/test_hypergraph.py b/hypernetx/classes/tests/test_hypergraph.py index d39c290b..e638a920 100644 --- a/hypernetx/classes/tests/test_hypergraph.py +++ b/hypernetx/classes/tests/test_hypergraph.py @@ -1,5 +1,6 @@ import pytest import numpy as np +import pandas as pd from hypernetx.classes.hypergraph import Hypergraph @@ -323,6 +324,7 @@ def test_construct_empty_hypergraph(): assert h.shape == (0, 0) assert h.edges.is_empty() assert h.nodes.is_empty() + assert isinstance(h.dataframe, pd.DataFrame) def test_construct_hypergraph_from_empty_dict(): From 4ddacd0d96a8b6654029d057d521eb7980066274 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Szabolcs=20Horv=C3=A1t?= Date: Thu, 20 Jul 2023 14:00:16 +0200 Subject: [PATCH 03/76] dependencies: change python-igraph to igraph --- setup.cfg | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/setup.cfg b/setup.cfg index d076ab0f..3c950a32 100644 --- a/setup.cfg +++ b/setup.cfg @@ -90,7 +90,7 @@ testing = pytest-xdist>=3.2.1 tutorials = jupyter>=1.0 - python-igraph>=0.10.4 + igraph>=0.10.4 partition-igraph>=0.0.6 celluloid>=0.2.0 widget = @@ -117,7 +117,6 @@ all = pytest>=7.2.2 coverage>=7.2.2 jupyter>=1.0 - python-igraph>=0.10.4 + igraph>=0.10.4 partition-igraph>=0.0.6 celluloid>=0.2.0 - igraph>=0.10.4 From cbd5faec9a25231a7fb637b2256726508b4fde7a Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 24 Jul 2023 08:46:38 -0700 Subject: [PATCH 04/76] Update documentation workflow --- .github/workflows/documentation.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 23f2a728..1e795668 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -16,6 +16,7 @@ jobs: run: | sphinx-build docs/source _build - name: Deploy + if: github.head_ref == 'master' uses: peaceiris/actions-gh-pages@v3 with: publish_branch: gh-pages From edc3522760cfb2d16e0ae4d52bca917524d368d1 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 10 Aug 2023 13:57:51 -0700 Subject: [PATCH 05/76] HYP-339 Rename Entity to EntitySet; reorganize tests, imports --- hypernetx/classes/__init__.py | 3 +- hypernetx/classes/entity.py | 1622 -------------- hypernetx/classes/entityset.py | 1944 ++++++++++++----- hypernetx/classes/helpers.py | 6 +- hypernetx/classes/hypergraph.py | 14 +- hypernetx/classes/tests/conftest.py | 6 +- hypernetx/classes/tests/test_entity.py | 130 -- hypernetx/classes/tests/test_entityset.py | 156 +- .../tests/test_hypergraph_static_deprecate.py | 8 +- 9 files changed, 1591 insertions(+), 2298 deletions(-) delete mode 100644 hypernetx/classes/entity.py delete mode 100644 hypernetx/classes/tests/test_entity.py diff --git a/hypernetx/classes/__init__.py b/hypernetx/classes/__init__.py index feccbb40..a04380ff 100644 --- a/hypernetx/classes/__init__.py +++ b/hypernetx/classes/__init__.py @@ -1,5 +1,4 @@ -from hypernetx.classes.entity import Entity from hypernetx.classes.entityset import EntitySet from hypernetx.classes.hypergraph import Hypergraph -__all__ = ["Entity", "EntitySet", "Hypergraph"] +__all__ = ["EntitySet", "Hypergraph"] diff --git a/hypernetx/classes/entity.py b/hypernetx/classes/entity.py deleted file mode 100644 index 81a80be6..00000000 --- a/hypernetx/classes/entity.py +++ /dev/null @@ -1,1622 +0,0 @@ -from __future__ import annotations - -import warnings -from ast import literal_eval -from collections import OrderedDict, defaultdict -from collections.abc import Hashable, Mapping, Sequence, Iterable -from typing import Union, TypeVar, Optional, Any - -import numpy as np -import pandas as pd -from scipy.sparse import csr_matrix - -from hypernetx.classes.helpers import ( - AttrList, - assign_weights, - remove_row_duplicates, - dict_depth, -) - -T = TypeVar("T", bound=Union[str, int]) - - -class Entity: - """Base class for handling N-dimensional data when building network-like models, - i.e., :class:`Hypergraph` - - Parameters - ---------- - entity : pandas.DataFrame, dict of lists or sets, list of lists or sets, optional - If a ``DataFrame`` with N columns, - represents N-dimensional entity data (data table). - Otherwise, represents 2-dimensional entity data (system of sets). - TODO: Test for compatibility with list of Entities and update docs - data : numpy.ndarray, optional - 2D M x N ``ndarray`` of ``ints`` (data table); - sparse representation of an N-dimensional incidence tensor with M nonzero cells. - Ignored if `entity` is provided. - static : bool, default=True - If ``True``, entity data may not be altered, - and the :attr:`state_dict <_state_dict>` will never be cleared. - Otherwise, rows may be added to and removed from the data table, - and updates will clear the :attr:`state_dict <_state_dict>`. - labels : collections.OrderedDict of lists, optional - User-specified labels in corresponding order to ``ints`` in `data`. - Ignored if `entity` is provided or `data` is not provided. - uid : hashable, optional - A unique identifier for the object - weights : str or sequence of float, optional - User-specified cell weights corresponding to entity data. - If sequence of ``floats`` and `entity` or `data` defines a data table, - length must equal the number of rows. - If sequence of ``floats`` and `entity` defines a system of sets, - length must equal the total sum of the sizes of all sets. - If ``str`` and `entity` is a ``DataFrame``, - must be the name of a column in `entity`. - Otherwise, weight for all cells is assumed to be 1. - aggregateby : {'sum', 'last', count', 'mean','median', max', 'min', 'first', None} - Name of function to use for aggregating cell weights of duplicate rows when - `entity` or `data` defines a data table, default is "sum". - If None, duplicate rows will be dropped without aggregating cell weights. - Effectively ignored if `entity` defines a system of sets. - properties : pandas.DataFrame or doubly-nested dict, optional - User-specified properties to be assigned to individual items in the data, i.e., - cell entries in a data table; sets or set elements in a system of sets. - See Notes for detailed explanation. - If ``DataFrame``, each row gives - ``[optional item level, item label, optional named properties, - {property name: property value}]`` - (order of columns does not matter; see note for an example). - If doubly-nested dict, - ``{item level: {item label: {property name: property value}}}``. - misc_props_col, level_col, id_col : str, default="properties", "level, "id" - Column names for miscellaneous properties, level index, and item name in - :attr:`properties`; see Notes for explanation. - - Notes - ----- - A property is a named attribute assigned to a single item in the data. - - You can pass a **table of properties** to `properties` as a ``DataFrame``: - - +------------+---------+----------------+-------+------------------+ - | Level | ID | [explicit | [...] | misc. properties | - | (optional) | | property type] | | | - +============+=========+================+=======+==================+ - | 0 | level 0 | property value | ... | {property name: | - | | item | | | property value} | - +------------+---------+----------------+-------+------------------+ - | 1 | level 1 | property value | ... | {property name: | - | | item | | | property value} | - +------------+---------+----------------+-------+------------------+ - | ... | ... | ... | ... | ... | - +------------+---------+----------------+-------+------------------+ - | N | level N | property value | ... | {property name: | - | | item | | | property value} | - +------------+---------+----------------+-------+------------------+ - - The Level column is optional. If not provided, properties will be assigned by ID - (i.e., if an ID appears at multiple levels, the same properties will be assigned to - all occurrences). - - The names of the Level (if provided) and ID columns must be specified by `level_col` - and `id_col`. `misc_props_col` can be used to specify the name of the column to be used - for miscellaneous properties; if no column by that name is found, - a new column will be created and populated with empty ``dicts``. - All other columns will be considered explicit property types. - The order of the columns does not matter. - - This method assumes that there are no rows with the same (Level, ID); - if duplicates are found, all but the first occurrence will be dropped. - - """ - - def __init__( - self, - entity: Optional[ - pd.DataFrame | Mapping[T, Iterable[T]] | Iterable[Iterable[T]] - ] = None, - data_cols: Sequence[T] = [0, 1], - data: Optional[np.ndarray] = None, - static: bool = False, - labels: Optional[OrderedDict[T, Sequence[T]]] = None, - uid: Optional[Hashable] = None, - weight_col: Optional[str | int] = "cell_weights", - weights: Optional[Sequence[float] | float | int | str] = 1, - aggregateby: Optional[str | dict] = "sum", - properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, - misc_props_col: str = "properties", - level_col: str = "level", - id_col: str = "id", - ): - # set unique identifier - self._uid = uid or None - - # if static, the original data cannot be altered - # the state dict stores all computed values that may need to be updated - # if the data is altered - the dict will be cleared when data is added - # or removed - self._static = static - self._state_dict = {} - - # entity data is stored in a DataFrame for basic access without the - # need for any label encoding lookups - if isinstance(entity, pd.DataFrame): - self._dataframe = entity.copy() - - # if the entity data is passed as a dict of lists or a list of lists, - # we convert it to a 2-column dataframe by exploding each list to cover - # one row per element for a dict of lists, the first level/column will - # be filled in with dict keys for a list of N lists, 0,1,...,N will be - # used to fill the first level/column - elif isinstance(entity, (dict, list)): - # convert dict of lists to 2-column dataframe - entity = pd.Series(entity).explode() - self._dataframe = pd.DataFrame( - {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} - ) - - # if a 2d numpy ndarray is passed, store it as both a DataFrame and an - # ndarray in the state dict - elif isinstance(data, np.ndarray) and data.ndim == 2: - self._state_dict["data"] = data - self._dataframe = pd.DataFrame(data) - # if a dict of labels was passed, use keys as column names in the - # DataFrame, translate the dataframe, and store the dict of labels - # in the state dict - if isinstance(labels, dict) and len(labels) == len(self._dataframe.columns): - self._dataframe.columns = labels.keys() - self._state_dict["labels"] = labels - - for col in self._dataframe: - self._dataframe[col] = pd.Categorical.from_codes( - self._dataframe[col], categories=labels[col] - ) - - # create an empty Entity - else: - self._dataframe = pd.DataFrame() - - # assign a new or existing column of the dataframe to hold cell weights - self._dataframe, self._cell_weight_col = assign_weights( - self._dataframe, weights=weights, weight_col=weight_col - ) - # import ipdb; ipdb.set_trace() - # store a list of columns that hold entity data (not properties or - # weights) - # self._data_cols = list(self._dataframe.columns.drop(self._cell_weight_col)) - self._data_cols = [] - for col in data_cols: - # TODO: default arguments fail for empty Entity; data_cols has two elements but _dataframe has only one element - if isinstance(col, int): - self._data_cols.append(self._dataframe.columns[col]) - else: - self._data_cols.append(col) - - # each entity data column represents one dimension of the data - # (data updates can only add or remove rows, so this isn't stored in - # state dict) - self._dimsize = len(self._data_cols) - - # remove duplicate rows and aggregate cell weights as needed - # import ipdb; ipdb.set_trace() - self._dataframe, _ = remove_row_duplicates( - self._dataframe, - self._data_cols, - weight_col=self._cell_weight_col, - aggregateby=aggregateby, - ) - - # set the dtype of entity data columns to categorical (simplifies - # encoding, etc.) - ### This is automatically done in remove_row_duplicates - # self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( - # "category" - # ) - - # create properties - item_levels = [ - (level, item) - for level, col in enumerate(self._data_cols) - for item in self.dataframe[col].cat.categories - ] - index = pd.MultiIndex.from_tuples(item_levels, names=[level_col, id_col]) - data = [(i, 1, {}) for i in range(len(index))] - self._properties = pd.DataFrame( - data=data, index=index, columns=["uid", "weight", misc_props_col] - ).sort_index() - self._misc_props_col = misc_props_col - if properties is not None: - self.assign_properties(properties) - - @property - def data(self): - """Sparse representation of the data table as an incidence tensor - - This can also be thought of as an encoding of `dataframe`, where items in each column of - the data table are translated to their int position in the `self.labels[column]` list - Returns - ------- - numpy.ndarray - 2D array of ints representing rows of the underlying data table as indices in an incidence tensor - - See Also - -------- - labels, dataframe - - """ - # generate if not already stored in state dict - if "data" not in self._state_dict: - if self.empty: - self._state_dict["data"] = np.zeros((0, 0), dtype=int) - else: - # assumes dtype of data cols is already converted to categorical - # and state dict has been properly cleared after updates - self._state_dict["data"] = ( - self._dataframe[self._data_cols] - .apply(lambda x: x.cat.codes) - .to_numpy() - ) - - return self._state_dict["data"] - - @property - def labels(self): - """Labels of all items in each column of the underlying data table - - Returns - ------- - dict of lists - dict of {column name: [item labels]} - The order of [item labels] corresponds to the int encoding of each item in `self.data`. - - See Also - -------- - data, dataframe - """ - # generate if not already stored in state dict - if "labels" not in self._state_dict: - # assumes dtype of data cols is already converted to categorical - # and state dict has been properly cleared after updates - self._state_dict["labels"] = { - col: self._dataframe[col].cat.categories.to_list() - for col in self._data_cols - } - - return self._state_dict["labels"] - - @property - def cell_weights(self): - """Cell weights corresponding to each row of the underlying data table - - Returns - ------- - dict of {tuple: int or float} - Keyed by row of data table (as a tuple) - """ - # generate if not already stored in state dict - if "cell_weights" not in self._state_dict: - if self.empty: - self._state_dict["cell_weights"] = {} - else: - self._state_dict["cell_weights"] = self._dataframe.set_index( - self._data_cols - )[self._cell_weight_col].to_dict() - - return self._state_dict["cell_weights"] - - @property - def dimensions(self): - """Dimensions of data i.e., the number of distinct items in each level (column) of the underlying data table - - Returns - ------- - tuple of ints - Length and order corresponds to columns of `self.dataframe` (excluding cell weight column) - """ - # generate if not already stored in state dict - if "dimensions" not in self._state_dict: - if self.empty: - self._state_dict["dimensions"] = tuple() - else: - self._state_dict["dimensions"] = tuple( - self._dataframe[self._data_cols].nunique() - ) - - return self._state_dict["dimensions"] - - @property - def dimsize(self): - """Number of levels (columns) in the underlying data table - - Returns - ------- - int - Equal to length of `self.dimensions` - """ - return self._dimsize - - @property - def properties(self) -> pd.DataFrame: - # Dev Note: Not sure what this contains, when running tests it contained an empty pandas series - """Properties assigned to items in the underlying data table - - Returns - ------- - pandas.DataFrame - """ - - return self._properties - - @property - def uid(self): - # Dev Note: This also returned nothing in my harry potter dataset, not sure if it was supposed to contain anything - """User-defined unique identifier for the `Entity` - - Returns - ------- - hashable - """ - return self._uid - - @property - def uidset(self): - """Labels of all items in level 0 (first column) of the underlying data table - - Returns - ------- - frozenset - - See Also - -------- - children : Labels of all items in level 1 (second column) - uidset_by_level, uidset_by_column : - Labels of all items in any level (column); specified by level index or column name - """ - return self.uidset_by_level(0) - - @property - def children(self): - """Labels of all items in level 1 (second column) of the underlying data table - - Returns - ------- - frozenset - - See Also - -------- - uidset : Labels of all items in level 0 (first column) - uidset_by_level, uidset_by_column : - Labels of all items in any level (column); specified by level index or column name - """ - return self.uidset_by_level(1) - - def uidset_by_level(self, level): - """Labels of all items in a particular level (column) of the underlying data table - - Parameters - ---------- - level : int - - Returns - ------- - frozenset - - See Also - -------- - uidset : Labels of all items in level 0 (first column) - children : Labels of all items in level 1 (second column) - uidset_by_column : Same functionality, takes the column name instead of level index - """ - if self.is_empty(level): - return {} - col = self._data_cols[level] - return self.uidset_by_column(col) - - def uidset_by_column(self, column): - # Dev Note: This threw an error when trying it on the harry potter dataset, - # when trying 0, or 1 for column. I'm not sure how this should be used - """Labels of all items in a particular column (level) of the underlying data table - - Parameters - ---------- - column : Hashable - Name of a column in `self.dataframe` - - Returns - ------- - frozenset - - See Also - -------- - uidset : Labels of all items in level 0 (first column) - children : Labels of all items in level 1 (second column) - uidset_by_level : Same functionality, takes the level index instead of column name - """ - # generate if not already stored in state dict - if "uidset" not in self._state_dict: - self._state_dict["uidset"] = {} - if column not in self._state_dict["uidset"]: - self._state_dict["uidset"][column] = set( - self._dataframe[column].dropna().unique() - ) - - return self._state_dict["uidset"][column] - - @property - def elements(self): - """System of sets representation of the first two levels (columns) of the underlying data table - - Each item in level 0 (first column) defines a set containing all the level 1 - (second column) items with which it appears in the same row of the underlying - data table - - Returns - ------- - dict of `AttrList` - System of sets representation as dict of {level 0 item : AttrList(level 1 items)} - - See Also - -------- - incidence_dict : same data as dict of list - memberships : - dual of this representation, - i.e., each item in level 1 (second column) defines a set - elements_by_level, elements_by_column : - system of sets representation of any two levels (columns); specified by level index or column name - - """ - if self._dimsize < 2: - return {k: AttrList(entity=self, key=(0, k)) for k in self.uidset} - - return self.elements_by_level(0, 1) - - @property - def incidence_dict(self) -> dict[T, list[T]]: - """System of sets representation of the first two levels (columns) of the underlying data table - - Returns - ------- - dict of list - System of sets representation as dict of {level 0 item : AttrList(level 1 items)} - - See Also - -------- - elements : same data as dict of AttrList - - """ - return {item: elements.data for item, elements in self.elements.items()} - - @property - def memberships(self): - """System of sets representation of the first two levels (columns) of the - underlying data table - - Each item in level 1 (second column) defines a set containing all the level 0 - (first column) items with which it appears in the same row of the underlying - data table - - Returns - ------- - dict of `AttrList` - System of sets representation as dict of {level 1 item : AttrList(level 0 items)} - - See Also - -------- - elements : dual of this representation i.e., each item in level 0 (first column) defines a set - elements_by_level, elements_by_column : - system of sets representation of any two levels (columns); specified by level index or column name - - """ - - return self.elements_by_level(1, 0) - - def elements_by_level(self, level1, level2): - """System of sets representation of two levels (columns) of the underlying data table - - Each item in level1 defines a set containing all the level2 items - with which it appears in the same row of the underlying data table - - Properties can be accessed and assigned to items in level1 - - Parameters - ---------- - level1 : int - index of level whose items define sets - level2 : int - index of level whose items are elements in the system of sets - - Returns - ------- - dict of `AttrList` - System of sets representation as dict of {level1 item : AttrList(level2 items)} - - See Also - -------- - elements, memberships : dual system of sets representations of the first two levels (columns) - elements_by_column : same functionality, takes column names instead of level indices - - """ - col1 = self._data_cols[level1] - col2 = self._data_cols[level2] - return self.elements_by_column(col1, col2) - - def elements_by_column(self, col1, col2): - - """System of sets representation of two columns (levels) of the underlying data table - - Each item in col1 defines a set containing all the col2 items - with which it appears in the same row of the underlying data table - - Properties can be accessed and assigned to items in col1 - - Parameters - ---------- - col1 : Hashable - name of column whose items define sets - col2 : Hashable - name of column whose items are elements in the system of sets - - Returns - ------- - dict of `AttrList` - System of sets representation as dict of {col1 item : AttrList(col2 items)} - - See Also - -------- - elements, memberships : dual system of sets representations of the first two columns (levels) - elements_by_level : same functionality, takes level indices instead of column names - - """ - if "elements" not in self._state_dict: - self._state_dict["elements"] = defaultdict(dict) - if col2 not in self._state_dict["elements"][col1]: - level = self.index(col1) - elements = self._dataframe.groupby(col1)[col2].unique().to_dict() - self._state_dict["elements"][col1][col2] = { - item: AttrList(entity=self, key=(level, item), initlist=elem) - for item, elem in elements.items() - } - - return self._state_dict["elements"][col1][col2] - - @property - def dataframe(self): - """The underlying data table stored by the Entity - - Returns - ------- - pandas.DataFrame - """ - return self._dataframe - - @property - def isstatic(self): - # Dev Note: I'm guessing this is no longer necessary? - """Whether to treat the underlying data as static or not - - If True, the underlying data may not be altered, and the state_dict will never be cleared - Otherwise, rows may be added to and removed from the data table, and updates will clear the state_dict - - Returns - ------- - bool - """ - return self._static - - def size(self, level=0): - """The number of items in a level of the underlying data table - - Equivalent to ``self.dimensions[level]`` - - Parameters - ---------- - level : int, default=0 - - Returns - ------- - int - - See Also - -------- - dimensions - """ - # TODO: Since `level` is not validated, we assume that self.dimensions should be an array large enough to access index `level` - return self.dimensions[level] - - @property - def empty(self): - """Whether the underlying data table is empty or not - - Returns - ------- - bool - - See Also - -------- - is_empty : for checking whether a specified level (column) is empty - dimsize : 0 if empty - """ - return self._dimsize == 0 - - def is_empty(self, level=0): - """Whether a specified level (column) of the underlying data table is empty or not - - Returns - ------- - bool - - See Also - -------- - empty : for checking whether the underlying data table is empty - size : number of items in a level (columns); 0 if level is empty - """ - return self.empty or self.size(level) == 0 - - def __len__(self): - """Number of items in level 0 (first column) - - Returns - ------- - int - """ - return self.dimensions[0] - - def __contains__(self, item): - """Whether an item is contained within any level of the data - - Parameters - ---------- - item : str - - Returns - ------- - bool - """ - for labels in self.labels.values(): - if item in labels: - return True - return False - - def __getitem__(self, item): - """Access into the system of sets representation of the first two levels (columns) given by `elements` - - Can be used to access and assign properties to an ``item`` in level 0 (first column) - - Parameters - ---------- - item : str - label of an item in level 0 (first column) - - Returns - ------- - AttrList : - list of level 1 items in the set defined by ``item`` - - See Also - -------- - uidset, elements - """ - return self.elements[item] - - def __iter__(self): - """Iterates over items in level 0 (first column) of the underlying data table - - Returns - ------- - Iterator - - See Also - -------- - uidset, elements - """ - return iter(self.elements) - - def __call__(self, label_index=0): - # Dev Note (Madelyn) : I don't think this is the intended use of __call__, can we change/deprecate? - """Iterates over items labels in a specified level (column) of the underlying data table - - Parameters - ---------- - label_index : int - level index - - Returns - ------- - Iterator - - See Also - -------- - labels - """ - return iter(self.labels[self._data_cols[label_index]]) - - # def __repr__(self): - # """String representation of the Entity - - # e.g., "Entity(uid, [level 0 items], {item: {property name: property value}})" - - # Returns - # ------- - # str - # """ - # return "hypernetx.classes.entity.Entity" - - # def __str__(self): - # return "" - - def index(self, column, value=None): - """Get level index corresponding to a column and (optionally) the index of a value in that column - - The index of ``value`` is its position in the list given by ``self.labels[column]``, which is used - in the integer encoding of the data table ``self.data`` - - Parameters - ---------- - column: str - name of a column in self.dataframe - value : str, optional - label of an item in the specified column - - Returns - ------- - int or (int, int) - level index corresponding to column, index of value if provided - - See Also - -------- - indices : for finding indices of multiple values in a column - level : same functionality, search for the value without specifying column - """ - if "keyindex" not in self._state_dict: - self._state_dict["keyindex"] = {} - if column not in self._state_dict["keyindex"]: - self._state_dict["keyindex"][column] = self._dataframe[ - self._data_cols - ].columns.get_loc(column) - - if value is None: - return self._state_dict["keyindex"][column] - - if "index" not in self._state_dict: - self._state_dict["index"] = defaultdict(dict) - if value not in self._state_dict["index"][column]: - self._state_dict["index"][column][value] = self._dataframe[ - column - ].cat.categories.get_loc(value) - - return ( - self._state_dict["keyindex"][column], - self._state_dict["index"][column][value], - ) - - def indices(self, column, values): - """Get indices of one or more value(s) in a column - - Parameters - ---------- - column : str - values : str or iterable of str - - Returns - ------- - list of int - indices of values - - See Also - -------- - index : for finding level index of a column and index of a single value - """ - if isinstance(values, Hashable): - values = [values] - - if "index" not in self._state_dict: - self._state_dict["index"] = defaultdict(dict) - for v in values: - if v not in self._state_dict["index"][column]: - self._state_dict["index"][column][v] = self._dataframe[ - column - ].cat.categories.get_loc(v) - - return [self._state_dict["index"][column][v] for v in values] - - def translate(self, level, index): - """Given indices of a level and value(s), return the corresponding value label(s) - - Parameters - ---------- - level : int - level index - index : int or list of int - value index or indices - - Returns - ------- - str or list of str - label(s) corresponding to value index or indices - - See Also - -------- - translate_arr : translate a full row of value indices across all levels (columns) - """ - column = self._data_cols[level] - - if isinstance(index, (int, np.integer)): - return self.labels[column][index] - - return [self.labels[column][i] for i in index] - - def translate_arr(self, coords): - """Translate a full encoded row of the data table e.g., a row of ``self.data`` - - Parameters - ---------- - coords : tuple of ints - encoded value indices, with one value index for each level of the data - - Returns - ------- - list of str - full row of translated value labels - """ - assert len(coords) == self._dimsize - translation = [] - for level, index in enumerate(coords): - translation.append(self.translate(level, index)) - - return translation - - def level(self, item, min_level=0, max_level=None, return_index=True): - """First level containing the given item label - - Order of levels corresponds to order of columns in `self.dataframe` - - Parameters - ---------- - item : str - min_level, max_level : int, optional - inclusive bounds on range of levels to search for item - return_index : bool, default=True - If True, return index of item within the level - - Returns - ------- - int, (int, int), or None - index of first level containing the item, index of item if `return_index=True` - returns None if item is not found - - See Also - -------- - index, indices : for finding level and/or value indices when the column is known - """ - if max_level is None or max_level >= self._dimsize: - max_level = self._dimsize - 1 - - columns = self._data_cols[min_level : max_level + 1] - levels = range(min_level, max_level + 1) - - for col, lev in zip(columns, levels): - if item in self.labels[col]: - if return_index: - return self.index(col, item) - - return lev - - print(f'"{item}" not found.') - return None - - def add(self, *args): - """Updates the underlying data table with new entity data from multiple sources - - Parameters - ---------- - *args - variable length argument list of Entity and/or representations of entity data - - Returns - ------- - self : Entity - - Warnings - -------- - Adding an element directly to an Entity will not add the - element to any Hypergraphs constructed from that Entity, and will cause an error. Use - :func:`Hypergraph.add_edge ` or - :func:`Hypergraph.add_node_to_edge ` instead. - - See Also - -------- - add_element : update from a single source - Hypergraph.add_edge, Hypergraph.add_node_to_edge : for adding elements to a Hypergraph - - """ - for item in args: - self.add_element(item) - return self - - def add_elements_from(self, arg_set): - """Adds arguments from an iterable to the data table one at a time - - ..deprecated:: 2.0.0 - Duplicates `add` - - Parameters - ---------- - arg_set : iterable - list of Entity and/or representations of entity data - - Returns - ------- - self : Entity - - """ - for item in arg_set: - self.add_element(item) - return self - - def add_element(self, data): - """Updates the underlying data table with new entity data - - Supports adding from either an existing Entity or a representation of entity - (data table or labeled system of sets are both supported representations) - - Parameters - ---------- - data : Entity, `pandas.DataFrame`, or dict of lists or sets - new entity data - - Returns - ------- - self : Entity - - Warnings - -------- - Adding an element directly to an Entity will not add the - element to any Hypergraphs constructed from that Entity, and will cause an error. Use - `Hypergraph.add_edge` or `Hypergraph.add_node_to_edge` instead. - - See Also - -------- - add : takes multiple sources of new entity data as variable length argument list - Hypergraph.add_edge, Hypergraph.add_node_to_edge : for adding elements to a Hypergraph - - """ - if isinstance(data, Entity): - df = data.dataframe - self.__add_from_dataframe(df) - - if isinstance(data, dict): - df = pd.DataFrame.from_dict(data) - self.__add_from_dataframe(df) - - if isinstance(data, pd.DataFrame): - self.__add_from_dataframe(data) - - return self - - def __add_from_dataframe(self, df): - """Helper function to append rows to `self.dataframe` - - Parameters - ---------- - data : pd.DataFrame - - Returns - ------- - self : Entity - - """ - if all(col in df for col in self._data_cols): - new_data = pd.concat((self._dataframe, df), ignore_index=True) - new_data[self._cell_weight_col] = new_data[self._cell_weight_col].fillna(1) - - self._dataframe, _ = remove_row_duplicates( - new_data, - self._data_cols, - weights=self._cell_weight_col, - ) - - self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( - "category" - ) - - self._state_dict.clear() - - def remove(self, *args): - """Removes all rows containing specified item(s) from the underlying data table - - Parameters - ---------- - *args - variable length argument list of item labels - - Returns - ------- - self : Entity - - See Also - -------- - remove_element : remove all rows containing a single specified item - - """ - for item in args: - self.remove_element(item) - return self - - def remove_elements_from(self, arg_set): - """Removes all rows containing specified item(s) from the underlying data table - - ..deprecated: 2.0.0 - Duplicates `remove` - - Parameters - ---------- - arg_set : iterable - list of item labels - - Returns - ------- - self : Entity - - """ - for item in arg_set: - self.remove_element(item) - return self - - def remove_element(self, item): - """Removes all rows containing a specified item from the underlying data table - - Parameters - ---------- - item - item label - - Returns - ------- - self : Entity - - See Also - -------- - remove : same functionality, accepts variable length argument list of item labels - - """ - updated_dataframe = self._dataframe - - for column in self._dataframe: - updated_dataframe = updated_dataframe[updated_dataframe[column] != item] - - self._dataframe, _ = remove_row_duplicates( - updated_dataframe, - self._data_cols, - weights=self._cell_weight_col, - ) - self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( - "category" - ) - - self._state_dict.clear() - for col in self._data_cols: - self._dataframe[col] = self._dataframe[col].cat.remove_unused_categories() - - def encode(self, data): - """ - Encode dataframe to numpy array - - Parameters - ---------- - data : dataframe - - Returns - ------- - numpy.array - - """ - encoded_array = data.apply(lambda x: x.cat.codes).to_numpy() - return encoded_array - - def incidence_matrix( - self, level1=0, level2=1, weights=False, aggregateby=None, index=False - ) -> csr_matrix | None: - """Incidence matrix representation for two levels (columns) of the underlying data table - - If `level1` and `level2` contain N and M distinct items, respectively, the incidence matrix will be M x N. - In other words, the items in `level1` and `level2` correspond to the columns and rows of the incidence matrix, - respectively, in the order in which they appear in `self.labels[column1]` and `self.labels[column2]` - (`column1` and `column2` are the column labels of `level1` and `level2`) - - Parameters - ---------- - level1 : int, default=0 - index of first level (column) - level2 : int, default=1 - index of second level - weights : bool or dict, default=False - If False all nonzero entries are 1. - If True all nonzero entries are filled by self.cell_weight - dictionary values, use :code:`aggregateby` to specify how duplicate - entries should have weights aggregated. - If dict of {(level1 item, level2 item): weight value} form; - only nonzero cells in the incidence matrix will be updated by dictionary, - i.e., `level1 item` and `level2 item` must appear in the same row at least once in the underlying data table - aggregateby : {'last', count', 'sum', 'mean','median', max', 'min', 'first', 'last', None}, default='count' - Method to aggregate weights of duplicate rows in data table. - If None, then all cell weights will be set to 1. - - Returns - ------- - scipy.sparse.csr.csr_matrix - sparse representation of incidence matrix (i.e. Compressed Sparse Row matrix) - - Other Parameters - ---------------- - index : bool, optional - Not used - - Note - ---- - In the context of Hypergraphs, think `level1 = edges, level2 = nodes` - """ - if self.dimsize < 2: - warnings.warn("Incidence matrix requires two levels of data.") - return None - - data_cols = [self._data_cols[level2], self._data_cols[level1]] - weights = self._cell_weight_col if weights else None - - df, weight_col = remove_row_duplicates( - self._dataframe, - data_cols, - weights=weights, - aggregateby=aggregateby, - ) - - return csr_matrix( - (df[weight_col], tuple(df[col].cat.codes for col in data_cols)) - ) - - def restrict_to_levels( - self, - levels: int | Iterable[int], - weights: bool = False, - aggregateby: str | None = "sum", - **kwargs, - ) -> Entity: - """Create a new Entity by restricting to a subset of levels (columns) in the - underlying data table - - Parameters - ---------- - levels : array-like of int - indices of a subset of levels (columns) of data - weights : bool, default=False - If True, aggregate existing cell weights to get new cell weights - Otherwise, all new cell weights will be 1 - aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', \ - 'min', None}, optional - Method to aggregate weights of duplicate rows in data table - If None or `weights`=False then all new cell weights will be 1 - **kwargs - Extra arguments to `Entity` constructor - - Returns - ------- - Entity - - Raises - ------ - KeyError - If `levels` contains any invalid values - - See Also - -------- - EntitySet - """ - - levels = np.asarray(levels) - invalid_levels = (levels < 0) | (levels >= self.dimsize) - if invalid_levels.any(): - raise KeyError(f"Invalid levels: {levels[invalid_levels]}") - - cols = [self._data_cols[lev] for lev in levels] - - if weights: - weights = self._cell_weight_col - cols.append(weights) - kwargs.update(weights=weights) - - properties = self.properties.loc[levels] - properties.index = properties.index.remove_unused_levels() - level_map = {old: new for new, old in enumerate(levels)} - new_levels = properties.index.levels[0].map(level_map) - properties.index = properties.index.set_levels(new_levels, level=0) - level_col, id_col = properties.index.names - - return self.__class__( - entity=self.dataframe[cols], - data_cols=cols, - aggregateby=aggregateby, - properties=properties, - misc_props_col=self._misc_props_col, - level_col=level_col, - id_col=id_col, - **kwargs, - ) - - def restrict_to_indices(self, indices, level=0, **kwargs): - """Create a new Entity by restricting the data table to rows containing specific items in a given level - - Parameters - ---------- - indices : int or iterable of int - indices of item label(s) in `level` to restrict to - level : int, default=0 - level index - **kwargs - Extra arguments to `Entity` constructor - - Returns - ------- - Entity - """ - column = self._dataframe[self._data_cols[level]] - values = self.translate(level, indices) - entity = self._dataframe.loc[column.isin(values)].copy() - - for col in self._data_cols: - entity[col] = entity[col].cat.remove_unused_categories() - restricted = self.__class__( - entity=entity, misc_props_col=self._misc_props_col, **kwargs - ) - - if not self.properties.empty: - prop_idx = [ - (lv, uid) - for lv in range(restricted.dimsize) - for uid in restricted.uidset_by_level(lv) - ] - properties = self.properties.loc[prop_idx] - restricted.assign_properties(properties) - return restricted - - def assign_properties( - self, - props: pd.DataFrame | dict[int, dict[T, dict[Any, Any]]], - misc_col: Optional[str] = None, - level_col=0, - id_col=1, - ) -> None: - """Assign new properties to items in the data table, update :attr:`properties` - - Parameters - ---------- - props : pandas.DataFrame or doubly-nested dict - See documentation of the `properties` parameter in :class:`Entity` - level_col, id_col, misc_col : str, optional - column names corresponding to the levels, items, and misc. properties; - if None, default to :attr:`_level_col`, :attr:`_id_col`, :attr:`_misc_props_col`, - respectively. - - See Also - -------- - properties - """ - # mapping from user-specified level, id, misc column names to internal names - ### This will fail if there isn't a level column - - if isinstance(props, pd.DataFrame): - ### Fix to check the shape of properties or redo properties format - column_map = { - old: new - for old, new in zip( - (level_col, id_col, misc_col), - (*self.properties.index.names, self._misc_props_col), - ) - if old is not None - } - props = props.rename(columns=column_map) - props = props.rename_axis(index=column_map) - self._properties_from_dataframe(props) - - if isinstance(props, dict): - ### Expects nested dictionary with keys corresponding to level and id - self._properties_from_dict(props) - - def _properties_from_dataframe(self, props: pd.DataFrame) -> None: - """Private handler for updating :attr:`properties` from a DataFrame - - Parameters - ---------- - props - - Notes - ----- - For clarity in in-line developer comments: - - idx-level - refers generally to a level of a MultiIndex - level - refers specifically to the idx-level in the MultiIndex of :attr:`properties` - that stores the level/column id for the item - """ - # names of property table idx-levels for level and item id, respectively - # ``item`` used instead of ``id`` to avoid redefining python built-in func `id` - level, item = self.properties.index.names - if props.index.nlevels > 1: # props has MultiIndex - # drop all idx-levels from props other than level and id (if present) - extra_levels = [ - idx_lev for idx_lev in props.index.names if idx_lev not in (level, item) - ] - props = props.reset_index(level=extra_levels) - - try: - # if props index is already in the correct format, - # enforce the correct idx-level ordering - props.index = props.index.reorder_levels((level, item)) - except AttributeError: # props is not in (level, id) MultiIndex format - # if the index matches level or id, drop index to column - if props.index.name in (level, item): - props = props.reset_index() - index_cols = [item] - if level in props: - index_cols.insert(0, level) - try: - props = props.set_index(index_cols, verify_integrity=True) - except ValueError: - warnings.warn( - "duplicate (level, ID) rows will be dropped after first occurrence" - ) - props = props.drop_duplicates(index_cols) - props = props.set_index(index_cols) - - if self._misc_props_col in props: - try: - props[self._misc_props_col] = props[self._misc_props_col].apply( - literal_eval - ) - except ValueError: - pass # data already parsed, no literal eval needed - else: - warnings.warn("parsed property dict column from string literal") - - if props.index.nlevels == 1: - props = props.reindex(self.properties.index, level=1) - - # combine with existing properties - # non-null values in new props override existing value - properties = props.combine_first(self.properties) - # update misc. column to combine existing and new misc. property dicts - # new props override existing value for overlapping misc. property dict keys - properties[self._misc_props_col] = self.properties[ - self._misc_props_col - ].combine( - properties[self._misc_props_col], - lambda x, y: {**(x if pd.notna(x) else {}), **(y if pd.notna(y) else {})}, - fill_value={}, - ) - self._properties = properties.sort_index() - - def _properties_from_dict(self, props: dict[int, dict[T, dict[Any, Any]]]) -> None: - """Private handler for updating :attr:`properties` from a doubly-nested dict - - Parameters - ---------- - props - """ - # TODO: there may be a more efficient way to convert this to a dataframe instead - # of updating one-by-one via nested loop, but checking whether each prop_name - # belongs in a designated existing column or the misc. property dict column - # makes it more challenging - # For now: only use nested loop update if non-misc. columns currently exist - if len(self.properties.columns) > 1: - for level in props: - for item in props[level]: - for prop_name, prop_val in props[level][item].items(): - self.set_property(item, prop_name, prop_val, level) - else: - item_keys = pd.MultiIndex.from_tuples( - [(level, item) for level in props for item in props[level]], - names=self.properties.index.names, - ) - props_data = [props[level][item] for level, item in item_keys] - props = pd.DataFrame({self._misc_props_col: props_data}, index=item_keys) - self._properties_from_dataframe(props) - - def _property_loc(self, item: T) -> tuple[int, T]: - """Get index in :attr:`properties` of an item of unspecified level - - Parameters - ---------- - item : hashable - name of an item - - Returns - ------- - item_key : tuple of (int, hashable) - ``(level, item)`` - - Raises - ------ - KeyError - If `item` is not in :attr:`properties` - - Warns - ----- - UserWarning - If `item` appears in multiple levels, returns the first (closest to 0) - - """ - try: - item_loc = self.properties.xs(item, level=1, drop_level=False).index - except KeyError as ex: # item not in df - raise KeyError(f"no properties initialized for 'item': {item}") from ex - - try: - item_key = item_loc.item() - except ValueError: - item_loc, _ = item_loc.sortlevel(sort_remaining=False) - item_key = item_loc[0] - warnings.warn(f"item found in multiple levels: {tuple(item_loc)}") - return item_key - - def set_property( - self, - item: T, - prop_name: Any, - prop_val: Any, - level: Optional[int] = None, - ) -> None: - """Set a property of an item - - Parameters - ---------- - item : hashable - name of an item - prop_name : hashable - name of the property to set - prop_val : any - value of the property to set - level : int, optional - level index of the item; - required if `item` is not already in :attr:`properties` - - Raises - ------ - ValueError - If `level` is not provided and `item` is not in :attr:`properties` - - Warns - ----- - UserWarning - If `level` is not provided and `item` appears in multiple levels, - assumes the first (closest to 0) - - See Also - -------- - get_property, get_properties - """ - if level is not None: - item_key = (level, item) - else: - try: - item_key = self._property_loc(item) - except KeyError as ex: - raise ValueError( - "cannot infer 'level' when initializing 'item' properties" - ) from ex - - if prop_name in self.properties: - self._properties.loc[item_key, prop_name] = prop_val - else: - try: - self._properties.loc[item_key, self._misc_props_col].update( - {prop_name: prop_val} - ) - except KeyError: - self._properties.loc[item_key, :] = { - self._misc_props_col: {prop_name: prop_val} - } - - def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> Any: - """Get a property of an item - - Parameters - ---------- - item : hashable - name of an item - prop_name : hashable - name of the property to get - level : int, optional - level index of the item - - Returns - ------- - prop_val : any - value of the property - - Raises - ------ - KeyError - if (`level`, `item`) is not in :attr:`properties`, - or if `level` is not provided and `item` is not in :attr:`properties` - - Warns - ----- - UserWarning - If `level` is not provided and `item` appears in multiple levels, - assumes the first (closest to 0) - - See Also - -------- - get_properties, set_property - """ - if level is not None: - item_key = (level, item) - else: - try: - item_key = self._property_loc(item) - except KeyError: - raise # item not in properties - - try: - prop_val = self.properties.loc[item_key, prop_name] - except KeyError as ex: - if ex.args[0] == prop_name: - prop_val = self.properties.loc[item_key, self._misc_props_col].get( - prop_name - ) - else: - raise KeyError( - f"no properties initialized for ('level','item'): {item_key}" - ) from ex - - return prop_val - - def get_properties(self, item: T, level: Optional[int] = None) -> dict[Any, Any]: - """Get all properties of an item - - Parameters - ---------- - item : hashable - name of an item - level : int, optional - level index of the item - - Returns - ------- - prop_vals : dict - ``{named property: property value, ..., - misc. property column name: {property name: property value}}`` - - Raises - ------ - KeyError - if (`level`, `item`) is not in :attr:`properties`, - or if `level` is not provided and `item` is not in :attr:`properties` - - Warns - ----- - UserWarning - If `level` is not provided and `item` appears in multiple levels, - assumes the first (closest to 0) - - See Also - -------- - get_property, set_property - """ - if level is not None: - item_key = (level, item) - else: - try: - item_key = self._property_loc(item) - except KeyError: - raise - - try: - prop_vals = self.properties.loc[item_key].to_dict() - except KeyError as ex: - raise KeyError( - f"no properties initialized for ('level','item'): {item_key}" - ) from ex - - return prop_vals diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index c0c1b97d..d3c9965a 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -2,329 +2,1202 @@ import warnings from ast import literal_eval -from collections import OrderedDict -from collections.abc import Iterable, Sequence -from typing import Mapping -from typing import Optional, Any, TypeVar, Union -from pprint import pformat +from collections import OrderedDict, defaultdict +from collections.abc import Hashable, Mapping, Sequence, Iterable +from typing import Union, TypeVar, Optional, Any import numpy as np import pandas as pd +from scipy.sparse import csr_matrix -from hypernetx.classes import Entity -from hypernetx.classes.helpers import AttrList +from hypernetx.classes.helpers import ( + AttrList, + assign_weights, + remove_row_duplicates, + dict_depth, +) -# from hypernetx.utils.log import get_logger +T = TypeVar("T", bound=Union[str, int]) + + +class EntitySet: + """Base class for handling N-dimensional data when building network-like models, + i.e., :class:`Hypergraph` + + Parameters + ---------- + entity : pandas.DataFrame, dict of lists or sets, list of lists or sets, optional + If a ``DataFrame`` with N columns, + represents N-dimensional entity data (data table). + Otherwise, represents 2-dimensional entity data (system of sets). + TODO: Test for compatibility with list of Entities and update docs + data : numpy.ndarray, optional + 2D M x N ``ndarray`` of ``ints`` (data table); + sparse representation of an N-dimensional incidence tensor with M nonzero cells. + Ignored if `entity` is provided. + static : bool, default=True + If ``True``, entity data may not be altered, + and the :attr:`state_dict <_state_dict>` will never be cleared. + Otherwise, rows may be added to and removed from the data table, + and updates will clear the :attr:`state_dict <_state_dict>`. + labels : collections.OrderedDict of lists, optional + User-specified labels in corresponding order to ``ints`` in `data`. + Ignored if `entity` is provided or `data` is not provided. + uid : hashable, optional + A unique identifier for the object + weights : str or sequence of float, optional + User-specified cell weights corresponding to entity data. + If sequence of ``floats`` and `entity` or `data` defines a data table, + length must equal the number of rows. + If sequence of ``floats`` and `entity` defines a system of sets, + length must equal the total sum of the sizes of all sets. + If ``str`` and `entity` is a ``DataFrame``, + must be the name of a column in `entity`. + Otherwise, weight for all cells is assumed to be 1. + aggregateby : {'sum', 'last', count', 'mean','median', max', 'min', 'first', None} + Name of function to use for aggregating cell weights of duplicate rows when + `entity` or `data` defines a data table, default is "sum". + If None, duplicate rows will be dropped without aggregating cell weights. + Effectively ignored if `entity` defines a system of sets. + properties : pandas.DataFrame or doubly-nested dict, optional + User-specified properties to be assigned to individual items in the data, i.e., + cell entries in a data table; sets or set elements in a system of sets. + See Notes for detailed explanation. + If ``DataFrame``, each row gives + ``[optional item level, item label, optional named properties, + {property name: property value}]`` + (order of columns does not matter; see note for an example). + If doubly-nested dict, + ``{item level: {item label: {property name: property value}}}``. + misc_props_col, level_col, id_col : str, default="properties", "level, "id" + Column names for miscellaneous properties, level index, and item name in + :attr:`properties`; see Notes for explanation. + + Notes + ----- + A property is a named attribute assigned to a single item in the data. + + You can pass a **table of properties** to `properties` as a ``DataFrame``: + + +------------+---------+----------------+-------+------------------+ + | Level | ID | [explicit | [...] | misc. properties | + | (optional) | | property type] | | | + +============+=========+================+=======+==================+ + | 0 | level 0 | property value | ... | {property name: | + | | item | | | property value} | + +------------+---------+----------------+-------+------------------+ + | 1 | level 1 | property value | ... | {property name: | + | | item | | | property value} | + +------------+---------+----------------+-------+------------------+ + | ... | ... | ... | ... | ... | + +------------+---------+----------------+-------+------------------+ + | N | level N | property value | ... | {property name: | + | | item | | | property value} | + +------------+---------+----------------+-------+------------------+ + + The Level column is optional. If not provided, properties will be assigned by ID + (i.e., if an ID appears at multiple levels, the same properties will be assigned to + all occurrences). + + The names of the Level (if provided) and ID columns must be specified by `level_col` + and `id_col`. `misc_props_col` can be used to specify the name of the column to be used + for miscellaneous properties; if no column by that name is found, + a new column will be created and populated with empty ``dicts``. + All other columns will be considered explicit property types. + The order of the columns does not matter. + + This method assumes that there are no rows with the same (Level, ID); + if duplicates are found, all but the first occurrence will be dropped. + + """ + + def __init__( + self, + entity: Optional[ + pd.DataFrame | Mapping[T, Iterable[T]] | Iterable[Iterable[T]] + ] = None, + data_cols: Sequence[T] = [0, 1], + data: Optional[np.ndarray] = None, + static: bool = False, + labels: Optional[OrderedDict[T, Sequence[T]]] = None, + uid: Optional[Hashable] = None, + weight_col: Optional[str | int] = "cell_weights", + weights: Optional[Sequence[float] | float | int | str] = 1, + aggregateby: Optional[str | dict] = "sum", + properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, + misc_props_col: str = "properties", + level_col: str = "level", + id_col: str = "id", + ): + # set unique identifier + self._uid = uid or None + + # if static, the original data cannot be altered + # the state dict stores all computed values that may need to be updated + # if the data is altered - the dict will be cleared when data is added + # or removed + self._static = static + self._state_dict = {} + + # entity data is stored in a DataFrame for basic access without the + # need for any label encoding lookups + if isinstance(entity, pd.DataFrame): + self._dataframe = entity.copy() + + # if the entity data is passed as a dict of lists or a list of lists, + # we convert it to a 2-column dataframe by exploding each list to cover + # one row per element for a dict of lists, the first level/column will + # be filled in with dict keys for a list of N lists, 0,1,...,N will be + # used to fill the first level/column + elif isinstance(entity, (dict, list)): + # convert dict of lists to 2-column dataframe + entity = pd.Series(entity).explode() + self._dataframe = pd.DataFrame( + {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} + ) + + # if a 2d numpy ndarray is passed, store it as both a DataFrame and an + # ndarray in the state dict + elif isinstance(data, np.ndarray) and data.ndim == 2: + self._state_dict["data"] = data + self._dataframe = pd.DataFrame(data) + # if a dict of labels was passed, use keys as column names in the + # DataFrame, translate the dataframe, and store the dict of labels + # in the state dict + if isinstance(labels, dict) and len(labels) == len(self._dataframe.columns): + self._dataframe.columns = labels.keys() + self._state_dict["labels"] = labels + + for col in self._dataframe: + self._dataframe[col] = pd.Categorical.from_codes( + self._dataframe[col], categories=labels[col] + ) + + # create an empty Entity + else: + self._dataframe = pd.DataFrame() + + # assign a new or existing column of the dataframe to hold cell weights + self._dataframe, self._cell_weight_col = assign_weights( + self._dataframe, weights=weights, weight_col=weight_col + ) + # import ipdb; ipdb.set_trace() + # store a list of columns that hold entity data (not properties or + # weights) + # self._data_cols = list(self._dataframe.columns.drop(self._cell_weight_col)) + self._data_cols = [] + for col in data_cols: + # TODO: default arguments fail for empty Entity; data_cols has two elements but _dataframe has only one element + if isinstance(col, int): + self._data_cols.append(self._dataframe.columns[col]) + else: + self._data_cols.append(col) + + # each entity data column represents one dimension of the data + # (data updates can only add or remove rows, so this isn't stored in + # state dict) + self._dimsize = len(self._data_cols) + + # remove duplicate rows and aggregate cell weights as needed + # import ipdb; ipdb.set_trace() + self._dataframe, _ = remove_row_duplicates( + self._dataframe, + self._data_cols, + weight_col=self._cell_weight_col, + aggregateby=aggregateby, + ) + + # set the dtype of entity data columns to categorical (simplifies + # encoding, etc.) + ### This is automatically done in remove_row_duplicates + # self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( + # "category" + # ) + + # create properties + item_levels = [ + (level, item) + for level, col in enumerate(self._data_cols) + for item in self.dataframe[col].cat.categories + ] + index = pd.MultiIndex.from_tuples(item_levels, names=[level_col, id_col]) + data = [(i, 1, {}) for i in range(len(index))] + self._properties = pd.DataFrame( + data=data, index=index, columns=["uid", "weight", misc_props_col] + ).sort_index() + self._misc_props_col = misc_props_col + if properties is not None: + self.assign_properties(properties) + + @property + def data(self): + """Sparse representation of the data table as an incidence tensor + + This can also be thought of as an encoding of `dataframe`, where items in each column of + the data table are translated to their int position in the `self.labels[column]` list + Returns + ------- + numpy.ndarray + 2D array of ints representing rows of the underlying data table as indices in an incidence tensor + + See Also + -------- + labels, dataframe + + """ + # generate if not already stored in state dict + if "data" not in self._state_dict: + if self.empty: + self._state_dict["data"] = np.zeros((0, 0), dtype=int) + else: + # assumes dtype of data cols is already converted to categorical + # and state dict has been properly cleared after updates + self._state_dict["data"] = ( + self._dataframe[self._data_cols] + .apply(lambda x: x.cat.codes) + .to_numpy() + ) + + return self._state_dict["data"] + + @property + def labels(self): + """Labels of all items in each column of the underlying data table + + Returns + ------- + dict of lists + dict of {column name: [item labels]} + The order of [item labels] corresponds to the int encoding of each item in `self.data`. + + See Also + -------- + data, dataframe + """ + # generate if not already stored in state dict + if "labels" not in self._state_dict: + # assumes dtype of data cols is already converted to categorical + # and state dict has been properly cleared after updates + self._state_dict["labels"] = { + col: self._dataframe[col].cat.categories.to_list() + for col in self._data_cols + } + + return self._state_dict["labels"] + + @property + def cell_weights(self): + """Cell weights corresponding to each row of the underlying data table + + Returns + ------- + dict of {tuple: int or float} + Keyed by row of data table (as a tuple) + """ + # generate if not already stored in state dict + if "cell_weights" not in self._state_dict: + if self.empty: + self._state_dict["cell_weights"] = {} + else: + self._state_dict["cell_weights"] = self._dataframe.set_index( + self._data_cols + )[self._cell_weight_col].to_dict() + + return self._state_dict["cell_weights"] + + @property + def dimensions(self): + """Dimensions of data i.e., the number of distinct items in each level (column) of the underlying data table + + Returns + ------- + tuple of ints + Length and order corresponds to columns of `self.dataframe` (excluding cell weight column) + """ + # generate if not already stored in state dict + if "dimensions" not in self._state_dict: + if self.empty: + self._state_dict["dimensions"] = tuple() + else: + self._state_dict["dimensions"] = tuple( + self._dataframe[self._data_cols].nunique() + ) + + return self._state_dict["dimensions"] + + @property + def dimsize(self): + """Number of levels (columns) in the underlying data table + + Returns + ------- + int + Equal to length of `self.dimensions` + """ + return self._dimsize + + @property + def properties(self) -> pd.DataFrame: + # Dev Note: Not sure what this contains, when running tests it contained an empty pandas series + """Properties assigned to items in the underlying data table + + Returns + ------- + pandas.DataFrame + """ + + return self._properties + + @property + def uid(self): + # Dev Note: This also returned nothing in my harry potter dataset, not sure if it was supposed to contain anything + """User-defined unique identifier for the `Entity` + + Returns + ------- + hashable + """ + return self._uid + + @property + def uidset(self): + """Labels of all items in level 0 (first column) of the underlying data table + + Returns + ------- + frozenset + + See Also + -------- + children : Labels of all items in level 1 (second column) + uidset_by_level, uidset_by_column : + Labels of all items in any level (column); specified by level index or column name + """ + return self.uidset_by_level(0) + + @property + def children(self): + """Labels of all items in level 1 (second column) of the underlying data table + + Returns + ------- + frozenset + + See Also + -------- + uidset : Labels of all items in level 0 (first column) + uidset_by_level, uidset_by_column : + Labels of all items in any level (column); specified by level index or column name + """ + return self.uidset_by_level(1) + + def uidset_by_level(self, level): + """Labels of all items in a particular level (column) of the underlying data table + + Parameters + ---------- + level : int + + Returns + ------- + frozenset + + See Also + -------- + uidset : Labels of all items in level 0 (first column) + children : Labels of all items in level 1 (second column) + uidset_by_column : Same functionality, takes the column name instead of level index + """ + if self.is_empty(level): + return {} + col = self._data_cols[level] + return self.uidset_by_column(col) + + def uidset_by_column(self, column): + # Dev Note: This threw an error when trying it on the harry potter dataset, + # when trying 0, or 1 for column. I'm not sure how this should be used + """Labels of all items in a particular column (level) of the underlying data table + + Parameters + ---------- + column : Hashable + Name of a column in `self.dataframe` + + Returns + ------- + frozenset + + See Also + -------- + uidset : Labels of all items in level 0 (first column) + children : Labels of all items in level 1 (second column) + uidset_by_level : Same functionality, takes the level index instead of column name + """ + # generate if not already stored in state dict + if "uidset" not in self._state_dict: + self._state_dict["uidset"] = {} + if column not in self._state_dict["uidset"]: + self._state_dict["uidset"][column] = set( + self._dataframe[column].dropna().unique() + ) + + return self._state_dict["uidset"][column] + + @property + def elements(self): + """System of sets representation of the first two levels (columns) of the underlying data table + + Each item in level 0 (first column) defines a set containing all the level 1 + (second column) items with which it appears in the same row of the underlying + data table + + Returns + ------- + dict of `AttrList` + System of sets representation as dict of {level 0 item : AttrList(level 1 items)} + + See Also + -------- + incidence_dict : same data as dict of list + memberships : + dual of this representation, + i.e., each item in level 1 (second column) defines a set + elements_by_level, elements_by_column : + system of sets representation of any two levels (columns); specified by level index or column name + + """ + if self._dimsize < 2: + return {k: AttrList(entity=self, key=(0, k)) for k in self.uidset} + + return self.elements_by_level(0, 1) + + @property + def incidence_dict(self) -> dict[T, list[T]]: + """System of sets representation of the first two levels (columns) of the underlying data table + + Returns + ------- + dict of list + System of sets representation as dict of {level 0 item : AttrList(level 1 items)} + + See Also + -------- + elements : same data as dict of AttrList + + """ + return {item: elements.data for item, elements in self.elements.items()} + + @property + def memberships(self): + """System of sets representation of the first two levels (columns) of the + underlying data table + + Each item in level 1 (second column) defines a set containing all the level 0 + (first column) items with which it appears in the same row of the underlying + data table + + Returns + ------- + dict of `AttrList` + System of sets representation as dict of {level 1 item : AttrList(level 0 items)} + + See Also + -------- + elements : dual of this representation i.e., each item in level 0 (first column) defines a set + elements_by_level, elements_by_column : + system of sets representation of any two levels (columns); specified by level index or column name + + """ + + return self.elements_by_level(1, 0) + + def elements_by_level(self, level1, level2): + """System of sets representation of two levels (columns) of the underlying data table + + Each item in level1 defines a set containing all the level2 items + with which it appears in the same row of the underlying data table + + Properties can be accessed and assigned to items in level1 + + Parameters + ---------- + level1 : int + index of level whose items define sets + level2 : int + index of level whose items are elements in the system of sets + + Returns + ------- + dict of `AttrList` + System of sets representation as dict of {level1 item : AttrList(level2 items)} + + See Also + -------- + elements, memberships : dual system of sets representations of the first two levels (columns) + elements_by_column : same functionality, takes column names instead of level indices + + """ + col1 = self._data_cols[level1] + col2 = self._data_cols[level2] + return self.elements_by_column(col1, col2) + + def elements_by_column(self, col1, col2): + + """System of sets representation of two columns (levels) of the underlying data table + + Each item in col1 defines a set containing all the col2 items + with which it appears in the same row of the underlying data table + + Properties can be accessed and assigned to items in col1 + + Parameters + ---------- + col1 : Hashable + name of column whose items define sets + col2 : Hashable + name of column whose items are elements in the system of sets + + Returns + ------- + dict of `AttrList` + System of sets representation as dict of {col1 item : AttrList(col2 items)} + + See Also + -------- + elements, memberships : dual system of sets representations of the first two columns (levels) + elements_by_level : same functionality, takes level indices instead of column names + + """ + if "elements" not in self._state_dict: + self._state_dict["elements"] = defaultdict(dict) + if col2 not in self._state_dict["elements"][col1]: + level = self.index(col1) + elements = self._dataframe.groupby(col1)[col2].unique().to_dict() + self._state_dict["elements"][col1][col2] = { + item: AttrList(entity=self, key=(level, item), initlist=elem) + for item, elem in elements.items() + } + + return self._state_dict["elements"][col1][col2] + + @property + def dataframe(self): + """The underlying data table stored by the Entity + + Returns + ------- + pandas.DataFrame + """ + return self._dataframe + + @property + def isstatic(self): + # Dev Note: I'm guessing this is no longer necessary? + """Whether to treat the underlying data as static or not + + If True, the underlying data may not be altered, and the state_dict will never be cleared + Otherwise, rows may be added to and removed from the data table, and updates will clear the state_dict + + Returns + ------- + bool + """ + return self._static + + def size(self, level=0): + """The number of items in a level of the underlying data table + + Equivalent to ``self.dimensions[level]`` + + Parameters + ---------- + level : int, default=0 + + Returns + ------- + int + + See Also + -------- + dimensions + """ + # TODO: Since `level` is not validated, we assume that self.dimensions should be an array large enough to access index `level` + return self.dimensions[level] + + @property + def empty(self): + """Whether the underlying data table is empty or not + + Returns + ------- + bool + + See Also + -------- + is_empty : for checking whether a specified level (column) is empty + dimsize : 0 if empty + """ + return self._dimsize == 0 + + def is_empty(self, level=0): + """Whether a specified level (column) of the underlying data table is empty or not + + Returns + ------- + bool + + See Also + -------- + empty : for checking whether the underlying data table is empty + size : number of items in a level (columns); 0 if level is empty + """ + return self.empty or self.size(level) == 0 + + def __len__(self): + """Number of items in level 0 (first column) + + Returns + ------- + int + """ + return self.dimensions[0] + + def __contains__(self, item): + """Whether an item is contained within any level of the data + + Parameters + ---------- + item : str + + Returns + ------- + bool + """ + for labels in self.labels.values(): + if item in labels: + return True + return False + + def __getitem__(self, item): + """Access into the system of sets representation of the first two levels (columns) given by `elements` + + Can be used to access and assign properties to an ``item`` in level 0 (first column) + + Parameters + ---------- + item : str + label of an item in level 0 (first column) + + Returns + ------- + AttrList : + list of level 1 items in the set defined by ``item`` + + See Also + -------- + uidset, elements + """ + return self.elements[item] + + def __iter__(self): + """Iterates over items in level 0 (first column) of the underlying data table + + Returns + ------- + Iterator + + See Also + -------- + uidset, elements + """ + return iter(self.elements) + + def __call__(self, label_index=0): + # Dev Note (Madelyn) : I don't think this is the intended use of __call__, can we change/deprecate? + """Iterates over items labels in a specified level (column) of the underlying data table + + Parameters + ---------- + label_index : int + level index + + Returns + ------- + Iterator + + See Also + -------- + labels + """ + return iter(self.labels[self._data_cols[label_index]]) + + # def __repr__(self): + # """String representation of the Entity + + # e.g., "Entity(uid, [level 0 items], {item: {property name: property value}})" + + # Returns + # ------- + # str + # """ + # return "hypernetx.classes.entity.Entity" + + # def __str__(self): + # return "" + + def index(self, column, value=None): + """Get level index corresponding to a column and (optionally) the index of a value in that column + + The index of ``value`` is its position in the list given by ``self.labels[column]``, which is used + in the integer encoding of the data table ``self.data`` + + Parameters + ---------- + column: str + name of a column in self.dataframe + value : str, optional + label of an item in the specified column + + Returns + ------- + int or (int, int) + level index corresponding to column, index of value if provided + + See Also + -------- + indices : for finding indices of multiple values in a column + level : same functionality, search for the value without specifying column + """ + if "keyindex" not in self._state_dict: + self._state_dict["keyindex"] = {} + if column not in self._state_dict["keyindex"]: + self._state_dict["keyindex"][column] = self._dataframe[ + self._data_cols + ].columns.get_loc(column) + + if value is None: + return self._state_dict["keyindex"][column] + + if "index" not in self._state_dict: + self._state_dict["index"] = defaultdict(dict) + if value not in self._state_dict["index"][column]: + self._state_dict["index"][column][value] = self._dataframe[ + column + ].cat.categories.get_loc(value) + + return ( + self._state_dict["keyindex"][column], + self._state_dict["index"][column][value], + ) + + def indices(self, column, values): + """Get indices of one or more value(s) in a column + + Parameters + ---------- + column : str + values : str or iterable of str + + Returns + ------- + list of int + indices of values + + See Also + -------- + index : for finding level index of a column and index of a single value + """ + if isinstance(values, Hashable): + values = [values] + + if "index" not in self._state_dict: + self._state_dict["index"] = defaultdict(dict) + for v in values: + if v not in self._state_dict["index"][column]: + self._state_dict["index"][column][v] = self._dataframe[ + column + ].cat.categories.get_loc(v) + + return [self._state_dict["index"][column][v] for v in values] + + def translate(self, level, index): + """Given indices of a level and value(s), return the corresponding value label(s) + + Parameters + ---------- + level : int + level index + index : int or list of int + value index or indices + + Returns + ------- + str or list of str + label(s) corresponding to value index or indices + + See Also + -------- + translate_arr : translate a full row of value indices across all levels (columns) + """ + column = self._data_cols[level] + + if isinstance(index, (int, np.integer)): + return self.labels[column][index] + + return [self.labels[column][i] for i in index] + + def translate_arr(self, coords): + """Translate a full encoded row of the data table e.g., a row of ``self.data`` + + Parameters + ---------- + coords : tuple of ints + encoded value indices, with one value index for each level of the data + + Returns + ------- + list of str + full row of translated value labels + """ + assert len(coords) == self._dimsize + translation = [] + for level, index in enumerate(coords): + translation.append(self.translate(level, index)) -# _log = get_logger("entity_set") + return translation -T = TypeVar("T", bound=Union[str, int]) + def level(self, item, min_level=0, max_level=None, return_index=True): + """First level containing the given item label + Order of levels corresponds to order of columns in `self.dataframe` -class EntitySet(Entity): - """Class for handling 2-dimensional (i.e., system of sets, bipartite) data when - building network-like models, i.e., :class:`Hypergraph` + Parameters + ---------- + item : str + min_level, max_level : int, optional + inclusive bounds on range of levels to search for item + return_index : bool, default=True + If True, return index of item within the level - Parameters - ---------- - entity : Entity, pandas.DataFrame, dict of lists or sets, or list of lists or sets, optional - If an ``Entity`` with N levels or a ``DataFrame`` with N columns, - represents N-dimensional entity data (data table). - If N > 2, only considers levels (columns) `level1` and `level2`. - Otherwise, represents 2-dimensional entity data (system of sets). - data : numpy.ndarray, optional - 2D M x N ``ndarray`` of ``ints`` (data table); - sparse representation of an N-dimensional incidence tensor with M nonzero cells. - If N > 2, only considers levels (columns) `level1` and `level2`. - Ignored if `entity` is provided. - labels : collections.OrderedDict of lists, optional - User-specified labels in corresponding order to ``ints`` in `data`. - For M x N `data`, N > 2, `labels` must contain either 2 or N keys. - If N keys, only considers labels for levels (columns) `level1` and `level2`. - Ignored if `entity` is provided or `data` is not provided. - level1, level2 : str or int, default=0,1 - Each item in `level1` defines a set containing all the `level2` items with which - it appears in the same row of the underlying data table. - If ``int``, gives the index of a level; - if ``str``, gives the name of a column in `entity`. - Ignored if `entity`, `data` (if `entity` not provided), and `labels` all (if - provided) represent 1- or 2-dimensional data (set or system of sets). - weights : str or sequence of float, optional - User-specified cell weights corresponding to entity data. - If sequence of ``floats`` and `entity` or `data` defines a data table, - length must equal the number of rows. - If sequence of ``floats`` and `entity` defines a system of sets, - length must equal the total sum of the sizes of all sets. - If ``str`` and `entity` is a ``DataFrame``, - must be the name of a column in `entity`. - Otherwise, weight for all cells is assumed to be 1. - Ignored if `entity` is an ``Entity`` and `keep_weights`=True. - keep_weights : bool, default=True - Whether to preserve any existing cell weights; - ignored if `entity` is not an ``Entity``. - cell_properties : str, list of str, pandas.DataFrame, or doubly-nested dict, optional - User-specified properties to be assigned to cells of the incidence matrix, i.e., - rows in a data table; pairs of (set, element of set) in a system of sets. - See Notes for detailed explanation. - Ignored if underlying data is 1-dimensional (set). - If doubly-nested dict, - ``{level1 item: {level2 item: {cell property name: cell property value}}}``. - misc_cell_props_col : str, default='cell_properties' - Column name for miscellaneous cell properties; see Notes for explanation. - kwargs - Keyword arguments passed to the ``Entity`` constructor, e.g., `static`, - `uid`, `aggregateby`, `properties`, etc. See :class:`Entity` for documentation - of these parameters. + Returns + ------- + int, (int, int), or None + index of first level containing the item, index of item if `return_index=True` + returns None if item is not found - Notes - ----- - A **cell property** is a named attribute assigned jointly to a set and one of its - elements, i.e, a cell of the incidence matrix. - - When an ``Entity`` or ``DataFrame`` is passed to the `entity` parameter of the - constructor, it should represent a data table: - - +--------------+--------------+--------------+-------+--------------+ - | Column_1 | Column_2 | Column_3 | [...] | Column_N | - +==============+==============+==============+=======+==============+ - | level 1 item | level 2 item | level 3 item | ... | level N item | - +--------------+--------------+--------------+-------+--------------+ - | ... | ... | ... | ... | ... | - +--------------+--------------+--------------+-------+--------------+ - - Assuming the default values for parameters `level1`, `level2`, the data table will - be restricted to the set system defined by Column 1 and Column 2. - Since each row of the data table represents an incidence or cell, values from other - columns may contain data that should be converted to cell properties. - - By passing a **column name or list of column names** as `cell_properties`, each - given column will be preserved in the :attr:`cell_properties` as an explicit cell - property type. An additional column in :attr:`cell_properties` will be created to - store a ``dict`` of miscellaneous cell properties, which will store cell properties - of types that have not been explicitly defined and do not have a dedicated column - (which may be assigned after construction). The name of the miscellaneous column is - determined by `misc_cell_props_col`. - - You can also pass a **pre-constructed table** to `cell_properties` as a - ``DataFrame``: - - +----------+----------+----------------------------+-------+-----------------------+ - | Column_1 | Column_2 | [explicit cell prop. type] | [...] | misc. cell properties | - +==========+==========+============================+=======+=======================+ - | level 1 | level 2 | cell property value | ... | {cell property name: | - | item | item | | | cell property value} | - +----------+----------+----------------------------+-------+-----------------------+ - | ... | ... | ... | ... | ... | - +----------+----------+----------------------------+-------+-----------------------+ - - Column 1 and Column 2 must have the same names as the corresponding columns in the - `entity` data table, and `misc_cell_props_col` can be used to specify the name of the - column to be used for miscellaneous cell properties. If no column by that name is - found, a new column will be created and populated with empty ``dicts``. All other - columns will be considered explicit cell property types. The order of the columns - does not matter. - - Both of these methods assume that there are no row duplicates in the tables passed - to `entity` and/or `cell_properties`; if duplicates are found, all but the first - occurrence will be dropped. + See Also + -------- + index, indices : for finding level and/or value indices when the column is known + """ + if max_level is None or max_level >= self._dimsize: + max_level = self._dimsize - 1 - """ + columns = self._data_cols[min_level : max_level + 1] + levels = range(min_level, max_level + 1) - def __init__( - self, - entity: Optional[ - pd.DataFrame - | np.ndarray - | Mapping[T, Iterable[T]] - | Iterable[Iterable[T]] - | Mapping[T, Mapping[T, Mapping[T, Any]]] - ] = None, - data: Optional[np.ndarray] = None, - labels: Optional[OrderedDict[T, Sequence[T]]] = None, - level1: str | int = 0, - level2: str | int = 1, - weight_col: str | int = "cell_weights", - weights: Sequence[float] | float | int | str = 1, - # keep_weights: bool = True, - cell_properties: Optional[ - Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] - ] = None, - misc_cell_props_col: str = "cell_properties", - uid: Optional[Hashable] = None, - aggregateby: Optional[str] = "sum", - properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, - misc_props_col: str = "properties", - # level_col: str = "level", - # id_col: str = "id", - **kwargs, - ): - self._misc_cell_props_col = misc_cell_props_col - - # if the entity data is passed as an Entity, get its underlying data table and - # proceed to the case for entity data passed as a DataFrame - # if isinstance(entity, Entity): - # # _log.info(f"Changing entity from type {Entity} to {type(entity.dataframe)}") - # if keep_weights: - # # preserve original weights - # weights = entity._cell_weight_col - # entity = entity.dataframe - - # if the entity data is passed as a DataFrame, restrict to two columns if needed - if isinstance(entity, pd.DataFrame) and len(entity.columns) > 2: - # _log.info(f"Processing parameter of 'entity' of type {type(entity)}...") - # metadata columns are not considered levels of data, - # remove them before indexing by level - # if isinstance(cell_properties, str): - # cell_properties = [cell_properties] - - prop_cols = [] - if isinstance(cell_properties, Sequence): - for col in {*cell_properties, self._misc_cell_props_col}: - if col in entity: - # _log.debug(f"Adding column to prop_cols: {col}") - prop_cols.append(col) - - # meta_cols = prop_cols - # if weights in entity and weights not in meta_cols: - # meta_cols.append(weights) - # # _log.debug(f"meta_cols: {meta_cols}") - if weight_col in prop_cols: - prop_cols.remove(weight_col) - if not weight_col in entity: - entity[weight_col] = weights - - # if both levels are column names, no need to index by level - if isinstance(level1, int): - level1 = entity.columns[level1] - if isinstance(level2, int): - level2 = entity.columns[level2] - # if isinstance(level1, str) and isinstance(level2, str): - columns = [level1, level2, weight_col] + prop_cols - # if one or both of the levels are given by index, get column name - # else: - # all_columns = entity.columns.drop(meta_cols) - # columns = [ - # all_columns[lev] if isinstance(lev, int) else lev - # for lev in (level1, level2) - # ] - - # if there is a column for cell properties, convert to separate DataFrame - # if len(prop_cols) > 0: - # cell_properties = entity[[*columns, *prop_cols]] - - # if there is a column for weights, preserve it - # if weights in entity and weights not in prop_cols: - # columns.append(weights) - # _log.debug(f"columns: {columns}") - - # pass level1, level2, and weights (optional) to Entity constructor - entity = entity[columns] - - # if a 2D ndarray is passed, restrict to two columns if needed - elif isinstance(data, np.ndarray) and data.ndim == 2 and data.shape[1] > 2: - # _log.info(f"Processing parameter 'data' of type {type(data)}...") - data = data[:, (level1, level2)] - - # if a dict of labels is provided, restrict to labels for two columns if needed - if isinstance(labels, dict) and len(labels) > 2: - label_keys = list(labels) - columns = (label_keys[level1], label_keys[level2]) - labels = {col: labels[col] for col in columns} - # _log.debug(f"Restricted labels to columns:\n{pformat(labels)}") - - # _log.info( - # f"Creating instance of {Entity} using reformatted params: \n\tentity: {type(entity)} \n\tdata: {type(data)} \n\tlabels: {type(labels)}, \n\tweights: {weights}, \n\tkwargs: {kwargs}" - # ) - # _log.debug(f"entity:\n{pformat(entity)}") - # _log.debug(f"data: {pformat(data)}") - super().__init__( - entity=entity, - data=data, - labels=labels, - uid=uid, - weight_col=weight_col, - weights=weights, - aggregateby=aggregateby, - properties=properties, - misc_props_col=misc_props_col, - **kwargs, - ) + for col, lev in zip(columns, levels): + if item in self.labels[col]: + if return_index: + return self.index(col, item) - # if underlying data is 2D (system of sets), create and assign cell properties - if self.dimsize == 2: - # self._cell_properties = pd.DataFrame( - # columns=[*self._data_cols, self._misc_cell_props_col] - # ) - self._cell_properties = pd.DataFrame(self._dataframe) - self._cell_properties.set_index(self._data_cols, inplace=True) - if isinstance(cell_properties, (dict, pd.DataFrame)): - self.assign_cell_properties(cell_properties) - else: - self._cell_properties = None + return lev - @property - def cell_properties(self) -> Optional[pd.DataFrame]: - """Properties assigned to cells of the incidence matrix + print(f'"{item}" not found.') + return None + + def add(self, *args): + """Updates the underlying data table with new entity data from multiple sources + + Parameters + ---------- + *args + variable length argument list of Entity and/or representations of entity data Returns ------- - pandas.Series, optional - Returns None if :attr:`dimsize` < 2 + self : EntitySet + + Warnings + -------- + Adding an element directly to an Entity will not add the + element to any Hypergraphs constructed from that Entity, and will cause an error. Use + :func:`Hypergraph.add_edge ` or + :func:`Hypergraph.add_node_to_edge ` instead. + + See Also + -------- + add_element : update from a single source + Hypergraph.add_edge, Hypergraph.add_node_to_edge : for adding elements to a Hypergraph + """ - return self._cell_properties + for item in args: + self.add_element(item) + return self - @property - def memberships(self) -> dict[str, AttrList[str]]: - """Extends :attr:`Entity.memberships` + def add_elements_from(self, arg_set): + """Adds arguments from an iterable to the data table one at a time - Each item in level 1 (second column) defines a set containing all the level 0 - (first column) items with which it appears in the same row of the underlying - data table. + ..deprecated:: 2.0.0 + Duplicates `add` + + Parameters + ---------- + arg_set : iterable + list of Entity and/or representations of entity data + + Returns + ------- + self : EntitySet + + """ + for item in arg_set: + self.add_element(item) + return self + + def add_element(self, data): + """Updates the underlying data table with new entity data + + Supports adding from either an existing Entity or a representation of entity + (data table or labeled system of sets are both supported representations) + + Parameters + ---------- + data : Entity, `pandas.DataFrame`, or dict of lists or sets + new entity data + + Returns + ------- + self : EntitySet + + Warnings + -------- + Adding an element directly to an Entity will not add the + element to any Hypergraphs constructed from that Entity, and will cause an error. Use + `Hypergraph.add_edge` or `Hypergraph.add_node_to_edge` instead. + + See Also + -------- + add : takes multiple sources of new entity data as variable length argument list + Hypergraph.add_edge, Hypergraph.add_node_to_edge : for adding elements to a Hypergraph + + """ + if isinstance(data, EntitySet): + df = data.dataframe + self.__add_from_dataframe(df) + + if isinstance(data, dict): + df = pd.DataFrame.from_dict(data) + self.__add_from_dataframe(df) + + if isinstance(data, pd.DataFrame): + self.__add_from_dataframe(data) + + return self + + def __add_from_dataframe(self, df): + """Helper function to append rows to `self.dataframe` + + Parameters + ---------- + data : pd.DataFrame + + Returns + ------- + self : EntitySet + + """ + if all(col in df for col in self._data_cols): + new_data = pd.concat((self._dataframe, df), ignore_index=True) + new_data[self._cell_weight_col] = new_data[self._cell_weight_col].fillna(1) + + self._dataframe, _ = remove_row_duplicates( + new_data, + self._data_cols, + weights=self._cell_weight_col, + ) + + self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( + "category" + ) + + self._state_dict.clear() + + def remove(self, *args): + """Removes all rows containing specified item(s) from the underlying data table + + Parameters + ---------- + *args + variable length argument list of item labels + + Returns + ------- + self : EntitySet + + See Also + -------- + remove_element : remove all rows containing a single specified item + + """ + for item in args: + self.remove_element(item) + return self + + def remove_elements_from(self, arg_set): + """Removes all rows containing specified item(s) from the underlying data table + + ..deprecated: 2.0.0 + Duplicates `remove` + + Parameters + ---------- + arg_set : iterable + list of item labels + + Returns + ------- + self : EntitySet + + """ + for item in arg_set: + self.remove_element(item) + return self + + def remove_element(self, item): + """Removes all rows containing a specified item from the underlying data table + + Parameters + ---------- + item + item label Returns ------- - dict of AttrList - System of sets representation as dict of - ``{level 1 item: AttrList(level 0 items)}``. + self : EntitySet See Also -------- - elements : dual of this representation, - i.e., each item in level 0 (first column) defines a set - restrict_to_levels : for more information on how memberships work for - 1-dimensional (set) data + remove : same functionality, accepts variable length argument list of item labels + + """ + updated_dataframe = self._dataframe + + for column in self._dataframe: + updated_dataframe = updated_dataframe[updated_dataframe[column] != item] + + self._dataframe, _ = remove_row_duplicates( + updated_dataframe, + self._data_cols, + weights=self._cell_weight_col, + ) + self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( + "category" + ) + + self._state_dict.clear() + for col in self._data_cols: + self._dataframe[col] = self._dataframe[col].cat.remove_unused_categories() + + def encode(self, data): + """ + Encode dataframe to numpy array + + Parameters + ---------- + data : dataframe + + Returns + ------- + numpy.array + + """ + encoded_array = data.apply(lambda x: x.cat.codes).to_numpy() + return encoded_array + + def incidence_matrix( + self, level1=0, level2=1, weights=False, aggregateby=None, index=False + ) -> csr_matrix | None: + """Incidence matrix representation for two levels (columns) of the underlying data table + + If `level1` and `level2` contain N and M distinct items, respectively, the incidence matrix will be M x N. + In other words, the items in `level1` and `level2` correspond to the columns and rows of the incidence matrix, + respectively, in the order in which they appear in `self.labels[column1]` and `self.labels[column2]` + (`column1` and `column2` are the column labels of `level1` and `level2`) + + Parameters + ---------- + level1 : int, default=0 + index of first level (column) + level2 : int, default=1 + index of second level + weights : bool or dict, default=False + If False all nonzero entries are 1. + If True all nonzero entries are filled by self.cell_weight + dictionary values, use :code:`aggregateby` to specify how duplicate + entries should have weights aggregated. + If dict of {(level1 item, level2 item): weight value} form; + only nonzero cells in the incidence matrix will be updated by dictionary, + i.e., `level1 item` and `level2 item` must appear in the same row at least once in the underlying data table + aggregateby : {'last', count', 'sum', 'mean','median', max', 'min', 'first', 'last', None}, default='count' + Method to aggregate weights of duplicate rows in data table. + If None, then all cell weights will be set to 1. + + Returns + ------- + scipy.sparse.csr.csr_matrix + sparse representation of incidence matrix (i.e. Compressed Sparse Row matrix) + + Other Parameters + ---------------- + index : bool, optional + Not used + + Note + ---- + In the context of Hypergraphs, think `level1 = edges, level2 = nodes` """ - if self._dimsize == 1: - return self._state_dict.get("memberships") + if self.dimsize < 2: + warnings.warn("Incidence matrix requires two levels of data.") + return None + + data_cols = [self._data_cols[level2], self._data_cols[level1]] + weights = self._cell_weight_col if weights else None - return super().memberships + df, weight_col = remove_row_duplicates( + self._dataframe, + data_cols, + weights=weights, + aggregateby=aggregateby, + ) + + return csr_matrix( + (df[weight_col], tuple(df[col].cat.codes for col in data_cols)) + ) def restrict_to_levels( self, levels: int | Iterable[int], weights: bool = False, - aggregateby: Optional[str] = "sum", - keep_memberships: bool = True, + aggregateby: str | None = "sum", **kwargs, ) -> EntitySet: - """Extends :meth:`Entity.restrict_to_levels` + """Create a new Entity by restricting to a subset of levels (columns) in the + underlying data table Parameters ---------- levels : array-like of int indices of a subset of levels (columns) of data weights : bool, default=False - If True, aggregate existing cell weights to get new cell weights. - Otherwise, all new cell weights will be 1. + If True, aggregate existing cell weights to get new cell weights + Otherwise, all new cell weights will be 1 aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', \ 'min', None}, optional Method to aggregate weights of duplicate rows in data table If None or `weights`=False then all new cell weights will be 1 - keep_memberships : bool, default=True - Whether to preserve membership information for the discarded level when - the new ``EntitySet`` is restricted to a single level **kwargs - Extra arguments to :class:`EntitySet` constructor + Extra arguments to `Entity` constructor Returns ------- @@ -334,323 +1207,416 @@ def restrict_to_levels( ------ KeyError If `levels` contains any invalid values + + See Also + -------- + EntitySet """ - restricted = super().restrict_to_levels( - levels, - weights, - aggregateby, - misc_cell_props_col=self._misc_cell_props_col, - **kwargs, - ) - if keep_memberships: - # use original memberships to set memberships for the new EntitySet - # TODO: This assumes levels=[1], add explicit checks for other cases - restricted._state_dict["memberships"] = self.memberships + levels = np.asarray(levels) + invalid_levels = (levels < 0) | (levels >= self.dimsize) + if invalid_levels.any(): + raise KeyError(f"Invalid levels: {levels[invalid_levels]}") - return restricted + cols = [self._data_cols[lev] for lev in levels] + + if weights: + weights = self._cell_weight_col + cols.append(weights) + kwargs.update(weights=weights) + + properties = self.properties.loc[levels] + properties.index = properties.index.remove_unused_levels() + level_map = {old: new for new, old in enumerate(levels)} + new_levels = properties.index.levels[0].map(level_map) + properties.index = properties.index.set_levels(new_levels, level=0) + level_col, id_col = properties.index.names + + return self.__class__( + entity=self.dataframe[cols], + data_cols=cols, + aggregateby=aggregateby, + properties=properties, + misc_props_col=self._misc_props_col, + level_col=level_col, + id_col=id_col, + **kwargs, + ) - def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: - """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 + def restrict_to_indices(self, indices, level=0, **kwargs): + """Create a new Entity by restricting the data table to rows containing specific items in a given level Parameters ---------- - indices : array_like of int + indices : int or iterable of int indices of item label(s) in `level` to restrict to + level : int, default=0 + level index **kwargs - Extra arguments to :class:`EntitySet` constructor + Extra arguments to `Entity` constructor Returns ------- EntitySet - - See Also - -------- - restrict_to_indices """ - restricted = self.restrict_to_indices( - indices, misc_cell_props_col=self._misc_cell_props_col, **kwargs + column = self._dataframe[self._data_cols[level]] + values = self.translate(level, indices) + entity = self._dataframe.loc[column.isin(values)].copy() + + for col in self._data_cols: + entity[col] = entity[col].cat.remove_unused_categories() + restricted = self.__class__( + entity=entity, misc_props_col=self._misc_props_col, **kwargs ) - if not self.cell_properties.empty: - cell_properties = self.cell_properties.loc[ - list(restricted.uidset) - ].reset_index() - restricted.assign_cell_properties(cell_properties) + + if not self.properties.empty: + prop_idx = [ + (lv, uid) + for lv in range(restricted.dimsize) + for uid in restricted.uidset_by_level(lv) + ] + properties = self.properties.loc[prop_idx] + restricted.assign_properties(properties) return restricted - def assign_cell_properties( + def assign_properties( self, - cell_props: pd.DataFrame | dict[T, dict[T, dict[Any, Any]]], + props: pd.DataFrame | dict[int, dict[T, dict[Any, Any]]], misc_col: Optional[str] = None, - replace: bool = False, + level_col=0, + id_col=1, ) -> None: - """Assign new properties to cells of the incidence matrix and update - :attr:`properties` + """Assign new properties to items in the data table, update :attr:`properties` Parameters ---------- - cell_props : pandas.DataFrame, dict of iterables, or doubly-nested dict, optional - See documentation of the `cell_properties` parameter in :class:`EntitySet` - misc_col: str, optional - name of column to be used for miscellaneous cell property dicts - replace: bool, default=False - If True, replace existing :attr:`cell_properties` with result; - otherwise update with new values from result + props : pandas.DataFrame or doubly-nested dict + See documentation of the `properties` parameter in :class:`Entity` + level_col, id_col, misc_col : str, optional + column names corresponding to the levels, items, and misc. properties; + if None, default to :attr:`_level_col`, :attr:`_id_col`, :attr:`_misc_props_col`, + respectively. - Raises - ----- - AttributeError - Not supported for :attr:`dimsize`=1 + See Also + -------- + properties """ - if self.dimsize < 2: - raise AttributeError( - f"cell properties are not supported for 'dimsize'={self.dimsize}" - ) + # mapping from user-specified level, id, misc column names to internal names + ### This will fail if there isn't a level column + + if isinstance(props, pd.DataFrame): + ### Fix to check the shape of properties or redo properties format + column_map = { + old: new + for old, new in zip( + (level_col, id_col, misc_col), + (*self.properties.index.names, self._misc_props_col), + ) + if old is not None + } + props = props.rename(columns=column_map) + props = props.rename_axis(index=column_map) + self._properties_from_dataframe(props) - misc_col = misc_col or self._misc_cell_props_col - try: - cell_props = cell_props.rename( - columns={misc_col: self._misc_cell_props_col} - ) - except AttributeError: # handle cell props in nested dict format - self._cell_properties_from_dict(cell_props) - else: # handle cell props in DataFrame format - self._cell_properties_from_dataframe(cell_props) + if isinstance(props, dict): + ### Expects nested dictionary with keys corresponding to level and id + self._properties_from_dict(props) - def _cell_properties_from_dataframe(self, cell_props: pd.DataFrame) -> None: + def _properties_from_dataframe(self, props: pd.DataFrame) -> None: """Private handler for updating :attr:`properties` from a DataFrame Parameters ---------- props - Parameters - ---------- - cell_props : DataFrame + Notes + ----- + For clarity in in-line developer comments: + + idx-level + refers generally to a level of a MultiIndex + level + refers specifically to the idx-level in the MultiIndex of :attr:`properties` + that stores the level/column id for the item """ - if cell_props.index.nlevels > 1: + # names of property table idx-levels for level and item id, respectively + # ``item`` used instead of ``id`` to avoid redefining python built-in func `id` + level, item = self.properties.index.names + if props.index.nlevels > 1: # props has MultiIndex + # drop all idx-levels from props other than level and id (if present) extra_levels = [ - idx_lev - for idx_lev in cell_props.index.names - if idx_lev not in self._data_cols + idx_lev for idx_lev in props.index.names if idx_lev not in (level, item) ] - cell_props = cell_props.reset_index(level=extra_levels) - - misc_col = self._misc_cell_props_col + props = props.reset_index(level=extra_levels) try: - cell_props.index = cell_props.index.reorder_levels(self._data_cols) - except AttributeError: - if cell_props.index.name in self._data_cols: - cell_props = cell_props.reset_index() - + # if props index is already in the correct format, + # enforce the correct idx-level ordering + props.index = props.index.reorder_levels((level, item)) + except AttributeError: # props is not in (level, id) MultiIndex format + # if the index matches level or id, drop index to column + if props.index.name in (level, item): + props = props.reset_index() + index_cols = [item] + if level in props: + index_cols.insert(0, level) try: - cell_props = cell_props.set_index( - self._data_cols, verify_integrity=True - ) + props = props.set_index(index_cols, verify_integrity=True) except ValueError: warnings.warn( - "duplicate cell rows will be dropped after first occurrence" + "duplicate (level, ID) rows will be dropped after first occurrence" ) - cell_props = cell_props.drop_duplicates(self._data_cols) - cell_props = cell_props.set_index(self._data_cols) + props = props.drop_duplicates(index_cols) + props = props.set_index(index_cols) - if misc_col in cell_props: + if self._misc_props_col in props: try: - cell_props[misc_col] = cell_props[misc_col].apply(literal_eval) + props[self._misc_props_col] = props[self._misc_props_col].apply( + literal_eval + ) except ValueError: pass # data already parsed, no literal eval needed else: - warnings.warn("parsed cell property dict column from string literal") - - cell_properties = cell_props.combine_first(self.cell_properties) - # import ipdb; ipdb.set_trace() - # cell_properties[misc_col] = self.cell_properties[misc_col].combine( - # cell_properties[misc_col], - # lambda x, y: {**(x if pd.notna(x) else {}), **(y if pd.notna(y) else {})}, - # fill_value={}, - # ) - - self._cell_properties = cell_properties.sort_index() + warnings.warn("parsed property dict column from string literal") + + if props.index.nlevels == 1: + props = props.reindex(self.properties.index, level=1) + + # combine with existing properties + # non-null values in new props override existing value + properties = props.combine_first(self.properties) + # update misc. column to combine existing and new misc. property dicts + # new props override existing value for overlapping misc. property dict keys + properties[self._misc_props_col] = self.properties[ + self._misc_props_col + ].combine( + properties[self._misc_props_col], + lambda x, y: {**(x if pd.notna(x) else {}), **(y if pd.notna(y) else {})}, + fill_value={}, + ) + self._properties = properties.sort_index() - def _cell_properties_from_dict( - self, cell_props: dict[T, dict[T, dict[Any, Any]]] - ) -> None: - """Private handler for updating :attr:`cell_properties` from a doubly-nested dict + def _properties_from_dict(self, props: dict[int, dict[T, dict[Any, Any]]]) -> None: + """Private handler for updating :attr:`properties` from a doubly-nested dict Parameters ---------- - cell_props + props """ # TODO: there may be a more efficient way to convert this to a dataframe instead # of updating one-by-one via nested loop, but checking whether each prop_name # belongs in a designated existing column or the misc. property dict column - # makes it more challenging. + # makes it more challenging # For now: only use nested loop update if non-misc. columns currently exist - if len(self.cell_properties.columns) > 1: - for item1 in cell_props: - for item2 in cell_props[item1]: - for prop_name, prop_val in cell_props[item1][item2].items(): - self.set_cell_property(item1, item2, prop_name, prop_val) + if len(self.properties.columns) > 1: + for level in props: + for item in props[level]: + for prop_name, prop_val in props[level][item].items(): + self.set_property(item, prop_name, prop_val, level) else: - cells = pd.MultiIndex.from_tuples( - [(item1, item2) for item1 in cell_props for item2 in cell_props[item1]], - names=self._data_cols, + item_keys = pd.MultiIndex.from_tuples( + [(level, item) for level in props for item in props[level]], + names=self.properties.index.names, ) - props_data = [cell_props[item1][item2] for item1, item2 in cells] - cell_props = pd.DataFrame( - {self._misc_cell_props_col: props_data}, index=cells - ) - self._cell_properties_from_dataframe(cell_props) - - def collapse_identical_elements( - self, return_equivalence_classes: bool = False, **kwargs - ) -> EntitySet | tuple[EntitySet, dict[str, list[str]]]: - """Create a new :class:`EntitySet` by collapsing sets with the same set elements + props_data = [props[level][item] for level, item in item_keys] + props = pd.DataFrame({self._misc_props_col: props_data}, index=item_keys) + self._properties_from_dataframe(props) - Each item in level 0 (first column) defines a set containing all the level 1 - (second column) items with which it appears in the same row of the underlying - data table. + def _property_loc(self, item: T) -> tuple[int, T]: + """Get index in :attr:`properties` of an item of unspecified level Parameters ---------- - return_equivalence_classes : bool, default=False - If True, return a dictionary of equivalence classes keyed by new edge names - **kwargs - Extra arguments to :class:`EntitySet` constructor + item : hashable + name of an item Returns ------- - new_entity : EntitySet - new :class:`EntitySet` with identical sets collapsed; - if all sets are unique, the system of sets will be the same as the original. - equivalence_classes : dict of lists, optional - if `return_equivalence_classes`=True, - ``{collapsed set label: [level 0 item labels]}``. + item_key : tuple of (int, hashable) + ``(level, item)`` + + Raises + ------ + KeyError + If `item` is not in :attr:`properties` + + Warns + ----- + UserWarning + If `item` appears in multiple levels, returns the first (closest to 0) + """ - # group by level 0 (set), aggregate level 1 (set elements) as frozenset - collapse = ( - self._dataframe[self._data_cols] - .groupby(self._data_cols[0], as_index=False) - .agg(frozenset) - ) + try: + item_loc = self.properties.xs(item, level=1, drop_level=False).index + except KeyError as ex: # item not in df + raise KeyError(f"no properties initialized for 'item': {item}") from ex - # aggregation method to rename equivalence classes as [first item]: [# items] - agg_kwargs = {"name": (self._data_cols[0], lambda x: f"{x.iloc[0]}: {len(x)}")} - if return_equivalence_classes: - # aggregation method to list all items in each equivalence class - agg_kwargs.update(equivalence_class=(self._data_cols[0], list)) - # group by frozenset of level 1 items (set elements), aggregate to get names of - # equivalence classes and (optionally) list of level 0 items (sets) in each - collapse = collapse.groupby(self._data_cols[1], as_index=False).agg( - **agg_kwargs - ) - # convert to nested dict representation of collapsed system of sets - collapse = collapse.set_index("name") - new_entity_dict = collapse[self._data_cols[1]].to_dict() - # construct new EntitySet from system of sets - new_entity = EntitySet(new_entity_dict, **kwargs) - - if return_equivalence_classes: - # lists of equivalent sets, keyed by equivalence class name - equivalence_classes = collapse.equivalence_class.to_dict() - return new_entity, equivalence_classes - return new_entity - - def set_cell_property( - self, item1: T, item2: T, prop_name: Any, prop_val: Any + try: + item_key = item_loc.item() + except ValueError: + item_loc, _ = item_loc.sortlevel(sort_remaining=False) + item_key = item_loc[0] + warnings.warn(f"item found in multiple levels: {tuple(item_loc)}") + return item_key + + def set_property( + self, + item: T, + prop_name: Any, + prop_val: Any, + level: Optional[int] = None, ) -> None: - """Set a property of a cell i.e., incidence between items of different levels + """Set a property of an item Parameters ---------- - item1 : hashable - name of an item in level 0 - item2 : hashable - name of an item in level 1 + item : hashable + name of an item prop_name : hashable - name of the cell property to set + name of the property to set prop_val : any - value of the cell property to set + value of the property to set + level : int, optional + level index of the item; + required if `item` is not already in :attr:`properties` + + Raises + ------ + ValueError + If `level` is not provided and `item` is not in :attr:`properties` + + Warns + ----- + UserWarning + If `level` is not provided and `item` appears in multiple levels, + assumes the first (closest to 0) See Also -------- - get_cell_property, get_cell_properties + get_property, get_properties """ - if item2 in self.elements[item1]: - if prop_name in self.properties: - self._cell_properties.loc[(item1, item2), prop_name] = pd.Series( - [prop_val] + if level is not None: + item_key = (level, item) + else: + try: + item_key = self._property_loc(item) + except KeyError as ex: + raise ValueError( + "cannot infer 'level' when initializing 'item' properties" + ) from ex + + if prop_name in self.properties: + self._properties.loc[item_key, prop_name] = prop_val + else: + try: + self._properties.loc[item_key, self._misc_props_col].update( + {prop_name: prop_val} ) - else: - try: - self._cell_properties.loc[ - (item1, item2), self._misc_cell_props_col - ].update({prop_name: prop_val}) - except KeyError: - self._cell_properties.loc[(item1, item2), :] = { - self._misc_cell_props_col: {prop_name: prop_val} - } - - def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: - """Get a property of a cell i.e., incidence between items of different levels + except KeyError: + self._properties.loc[item_key, :] = { + self._misc_props_col: {prop_name: prop_val} + } + + def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> Any: + """Get a property of an item Parameters ---------- - item1 : hashable - name of an item in level 0 - item2 : hashable - name of an item in level 1 + item : hashable + name of an item prop_name : hashable - name of the cell property to get + name of the property to get + level : int, optional + level index of the item Returns ------- prop_val : any - value of the cell property + value of the property + + Raises + ------ + KeyError + if (`level`, `item`) is not in :attr:`properties`, + or if `level` is not provided and `item` is not in :attr:`properties` + + Warns + ----- + UserWarning + If `level` is not provided and `item` appears in multiple levels, + assumes the first (closest to 0) See Also -------- - get_cell_properties, set_cell_property + get_properties, set_property """ - try: - cell_props = self.cell_properties.loc[(item1, item2)] - except KeyError: - raise - # TODO: raise informative exception + if level is not None: + item_key = (level, item) + else: + try: + item_key = self._property_loc(item) + except KeyError: + raise # item not in properties try: - prop_val = cell_props.loc[prop_name] - except KeyError: - prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) + prop_val = self.properties.loc[item_key, prop_name] + except KeyError as ex: + if ex.args[0] == prop_name: + prop_val = self.properties.loc[item_key, self._misc_props_col].get( + prop_name + ) + else: + raise KeyError( + f"no properties initialized for ('level','item'): {item_key}" + ) from ex return prop_val - def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: - """Get all properties of a cell, i.e., incidence between items of different - levels + def get_properties(self, item: T, level: Optional[int] = None) -> dict[Any, Any]: + """Get all properties of an item Parameters ---------- - item1 : hashable - name of an item in level 0 - item2 : hashable - name of an item in level 1 + item : hashable + name of an item + level : int, optional + level index of the item Returns ------- - dict - ``{named cell property: cell property value, ..., misc. cell property column - name: {cell property name: cell property value}}`` + prop_vals : dict + ``{named property: property value, ..., + misc. property column name: {property name: property value}}`` + + Raises + ------ + KeyError + if (`level`, `item`) is not in :attr:`properties`, + or if `level` is not provided and `item` is not in :attr:`properties` + + Warns + ----- + UserWarning + If `level` is not provided and `item` appears in multiple levels, + assumes the first (closest to 0) See Also -------- - get_cell_property, set_cell_property + get_property, set_property """ + if level is not None: + item_key = (level, item) + else: + try: + item_key = self._property_loc(item) + except KeyError: + raise + try: - cell_props = self.cell_properties.loc[(item1, item2)] - except KeyError: - raise - # TODO: raise informative exception + prop_vals = self.properties.loc[item_key].to_dict() + except KeyError as ex: + raise KeyError( + f"no properties initialized for ('level','item'): {item_key}" + ) from ex - return cell_props.to_dict() + return prop_vals diff --git a/hypernetx/classes/helpers.py b/hypernetx/classes/helpers.py index 332bd4b5..465fe17a 100644 --- a/hypernetx/classes/helpers.py +++ b/hypernetx/classes/helpers.py @@ -8,7 +8,7 @@ from pandas.api.types import CategoricalDtype from ast import literal_eval -from hypernetx.classes.entity import * +from hypernetx.classes.entityset import * class AttrList(UserList): @@ -16,7 +16,7 @@ class AttrList(UserList): Parameters ---------- - entity : hypernetx.Entity + entity : hypernetx.EntitySet key : tuple of (int, str or int) ``(level, item)`` initlist : list, optional @@ -25,7 +25,7 @@ class AttrList(UserList): def __init__( self, - entity: Entity, + entity: EntitySet, key: tuple[int, str | int], initlist: Optional[list] = None, ): diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 952ab195..8d32a2fa 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -14,7 +14,7 @@ from networkx.algorithms import bipartite from scipy.sparse import coo_matrix, csr_matrix -from hypernetx.classes import Entity, EntitySet +from hypernetx.classes import EntitySet, EntitySet from hypernetx.exception import HyperNetXError from hypernetx.utils.decorators import warn_nwhy from hypernetx.classes.helpers import merge_nested_dicts, dict_depth @@ -694,7 +694,7 @@ def __contains__(self, item): Parameters ---------- - item : hashable or Entity + item : hashable or EntitySet """ return item in self.nodes @@ -705,7 +705,7 @@ def __getitem__(self, node): Parameters ---------- - node : Entity or hashable + node : EntitySet or hashable If hashable, then must be uid of node in hypergraph Returns @@ -968,7 +968,7 @@ def neighbors(self, node, s=1): Parameters ---------- - node : hashable or Entity + node : hashable or EntitySet uid for a node in hypergraph or the node Entity s : int, list, optional, default = 1 @@ -1005,7 +1005,7 @@ def edge_neighbors(self, edge, s=1): Parameters ---------- - edge : hashable or Entity + edge : hashable or EntitySet uid for a edge in hypergraph or the edge Entity s : int, list, optional, default = 1 @@ -1370,7 +1370,7 @@ def collapse_nodes( Example ------- - >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', / + >>> h = Hypergraph(EntitySet('example',elements=[EntitySet('E1', / ['a','b']),Entity('E2',['a','b'])])) >>> h.incidence_dict {'E1': {'a', 'b'}, 'E2': {'a', 'b'}} @@ -1441,7 +1441,7 @@ def collapse_nodes_and_edges( Example ------- - >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', / + >>> h = Hypergraph(EntitySet('example',elements=[EntitySet('E1', / ['a','b']),Entity('E2',['a','b'])])) >>> h.incidence_dict {'E1': {'a', 'b'}, 'E2': {'a', 'b'}} diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index a4c7eae8..ce784f45 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -5,7 +5,7 @@ import pandas as pd import numpy as np -from hypernetx import Hypergraph, HarryPotter, Entity, LesMis as LM +from hypernetx import Hypergraph, HarryPotter, EntitySet, LesMis as LM from collections import OrderedDict, defaultdict @@ -153,7 +153,7 @@ def sbs(): @pytest.fixture def ent_sbs(sbs): - return Entity(data=np.asarray(sbs.data), labels=sbs.labels) + return EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) @pytest.fixture @@ -247,7 +247,7 @@ def array_example(): @pytest.fixture def ent_hp(harry_potter): - return Entity(data=np.asarray(harry_potter.data), labels=harry_potter.labels) + return EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) ####################Fixtures suite for test_hypergraph.py#################### diff --git a/hypernetx/classes/tests/test_entity.py b/hypernetx/classes/tests/test_entity.py deleted file mode 100644 index 761fc261..00000000 --- a/hypernetx/classes/tests/test_entity.py +++ /dev/null @@ -1,130 +0,0 @@ -import numpy as np -import pytest - -from collections.abc import Iterable -from collections import UserList -from hypernetx.classes import Entity - - -def test_constructor(ent_sbs): - assert ent_sbs.size() == 6 - assert len(ent_sbs.uidset) == 6 - assert len(ent_sbs.children) == 7 - assert isinstance(ent_sbs.incidence_dict["I"], list) - assert "I" in ent_sbs - assert "K" in ent_sbs - - -def test_property(ent_hp): - assert len(ent_hp.uidset) == 7 - assert len(ent_hp.elements) == 7 - assert isinstance(ent_hp.elements["Hufflepuff"], UserList) - assert not ent_hp.is_empty() - assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 - - -@pytest.mark.xfail( - reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" -) -def test_attributes(ent_hp): - assert isinstance(ent_hp.data, np.ndarray) - # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray - assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails - assert isinstance(ent_hp.labels, dict) - # TODO: Entity defaults to first two cols as data cols - assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails - assert ent_hp.dimsize == 5 # fails - df = ent_hp.dataframe[ent_hp._data_cols] - assert list(df.columns) == [ # fails - "House", - "Blood status", - "Species", - "Hair colour", - "Eye colour", - ] - assert ent_hp.dimensions == tuple(df.nunique()) - assert set(ent_hp.labels["House"]) == set(df["House"].unique()) - - -def test_custom_attributes(ent_hp): - assert ent_hp.__len__() == 7 - assert isinstance(ent_hp.__str__(), str) - assert isinstance(ent_hp.__repr__(), str) - assert isinstance(ent_hp.__contains__("Muggle"), bool) - assert ent_hp.__contains__("Muggle") is True - assert ent_hp.__getitem__("Slytherin") == [ - "Half-blood", - "Pure-blood", - "Pure-blood or half-blood", - ] - assert isinstance(ent_hp.__iter__(), Iterable) - assert isinstance(ent_hp.__call__(), Iterable) - assert ent_hp.__call__().__next__() == "Unknown House" - - -@pytest.mark.xfail( - reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" -) -def test_level(ent_sbs): - # TODO: at some point we are casting out and back to categorical dtype without - # preserving categories ordering from `labels` provided to constructor - assert ent_sbs.level("I") == (0, 5) # fails - assert ent_sbs.level("K") == (1, 3) - assert ent_sbs.level("K", max_level=0) is None - - -def test_uidset_by_level(ent_sbs): - assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} - assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} - - -def test_elements_by_level(ent_sbs): - assert ent_sbs.elements_by_level(0, 1) - - -def test_incidence_matrix(ent_sbs): - assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) - - -def test_indices(ent_sbs): - assert ent_sbs.indices("nodes", "K") == [3] - assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] - - -def test_translate(ent_sbs): - assert ent_sbs.translate(0, 0) == "P" - assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] - - -def test_translate_arr(ent_sbs): - assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] - - -def test_index(ent_sbs): - assert ent_sbs.index("nodes") == 1 - assert ent_sbs.index("nodes", "K") == (1, 3) - - -def test_restrict_to_levels(ent_hp): - assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 - - -def test_restrict_to_indices(ent_hp): - assert ent_hp.restrict_to_indices([1, 2]).uidset == { - "Gryffindor", - "Ravenclaw", - } - - -def test_construct_from_entity(sbs): - ent = Entity(entity=sbs.edgedict) - assert len(ent.elements) == 6 - - -@pytest.mark.xfail(reason="default arguments fail for empty Entity") -def test_construct_empty_entity(): - ent = Entity() - assert ent.empty - assert ent.is_empty() - assert len(ent.elements) == 0 - assert ent.dimsize == 0 diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index ca373324..4e60a794 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -1,50 +1,130 @@ import numpy as np import pytest -from hypernetx import Entity, EntitySet +from collections.abc import Iterable +from collections import UserList +from hypernetx.classes import EntitySet -@pytest.mark.xfail(reason="default arguments fail for empty Entity") -def test_construct_empty_entityset(): - es = EntitySet() - assert es.empty - assert len(es.elements) == 0 - assert es.dimsize == 0 +def test_constructor(ent_sbs): + assert ent_sbs.size() == 6 + assert len(ent_sbs.uidset) == 6 + assert len(ent_sbs.children) == 7 + assert isinstance(ent_sbs.incidence_dict["I"], list) + assert "I" in ent_sbs + assert "K" in ent_sbs + + +def test_property(ent_hp): + assert len(ent_hp.uidset) == 7 + assert len(ent_hp.elements) == 7 + assert isinstance(ent_hp.elements["Hufflepuff"], UserList) + assert not ent_hp.is_empty() + assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 + + +@pytest.mark.xfail( + reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" +) +def test_attributes(ent_hp): + assert isinstance(ent_hp.data, np.ndarray) + # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray + assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails + assert isinstance(ent_hp.labels, dict) + # TODO: Entity defaults to first two cols as data cols + assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails + assert ent_hp.dimsize == 5 # fails + df = ent_hp.dataframe[ent_hp._data_cols] + assert list(df.columns) == [ # fails + "House", + "Blood status", + "Species", + "Hair colour", + "Eye colour", + ] + assert ent_hp.dimensions == tuple(df.nunique()) + assert set(ent_hp.labels["House"]) == set(df["House"].unique()) + + +def test_custom_attributes(ent_hp): + assert ent_hp.__len__() == 7 + assert isinstance(ent_hp.__str__(), str) + assert isinstance(ent_hp.__repr__(), str) + assert isinstance(ent_hp.__contains__("Muggle"), bool) + assert ent_hp.__contains__("Muggle") is True + assert ent_hp.__getitem__("Slytherin") == [ + "Half-blood", + "Pure-blood", + "Pure-blood or half-blood", + ] + assert isinstance(ent_hp.__iter__(), Iterable) + assert isinstance(ent_hp.__call__(), Iterable) + assert ent_hp.__call__().__next__() == "Unknown House" @pytest.mark.xfail( reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" ) -def test_construct_entityset_from_data(harry_potter): - es = EntitySet( - data=np.asarray(harry_potter.data), - labels=harry_potter.labels, - level1=1, - level2=3, - ) +def test_level(ent_sbs): # TODO: at some point we are casting out and back to categorical dtype without # preserving categories ordering from `labels` provided to constructor - assert es.indices("Blood status", ["Pure-blood", "Half-blood"]) == [2, 1] # fails - assert es.incidence_matrix().shape == (36, 11) - assert len(es.collapse_identical_elements()) == 11 - - -@pytest.mark.skip(reason="EntitySet from Entity no longer supported") -def test_construct_entityset_from_entity_hp(harry_potter): - es = EntitySet( - entity=Entity(data=np.asarray(harry_potter.data), labels=harry_potter.labels), - level1="Blood status", - level2="House", - ) - assert es.indices("Blood status", ["Pure-blood", "Half-blood"]) == [2, 1] - assert es.incidence_matrix().shape == (7, 11) - assert len(es.collapse_identical_elements()) == 9 - - -@pytest.mark.skip(reason="EntitySet from Entity no longer supported") -def test_construct_entityset_from_entity(sbs): - es = EntitySet(entity=Entity(entity=sbs.edgedict)) - - assert not es.empty - assert es.dimsize == 2 - assert es.incidence_matrix().shape == (7, 6) + assert ent_sbs.level("I") == (0, 5) # fails + assert ent_sbs.level("K") == (1, 3) + assert ent_sbs.level("K", max_level=0) is None + + +def test_uidset_by_level(ent_sbs): + assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} + assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} + + +def test_elements_by_level(ent_sbs): + assert ent_sbs.elements_by_level(0, 1) + + +def test_incidence_matrix(ent_sbs): + assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) + + +def test_indices(ent_sbs): + assert ent_sbs.indices("nodes", "K") == [3] + assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] + + +def test_translate(ent_sbs): + assert ent_sbs.translate(0, 0) == "P" + assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] + + +def test_translate_arr(ent_sbs): + assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] + + +def test_index(ent_sbs): + assert ent_sbs.index("nodes") == 1 + assert ent_sbs.index("nodes", "K") == (1, 3) + + +def test_restrict_to_levels(ent_hp): + assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 + + +def test_restrict_to_indices(ent_hp): + assert ent_hp.restrict_to_indices([1, 2]).uidset == { + "Gryffindor", + "Ravenclaw", + } + + +def test_construct_from_entity(sbs): + ent = EntitySet(entity=sbs.edgedict) + assert len(ent.elements) == 6 + + +@pytest.mark.xfail(reason="default arguments fail for empty Entity") +def test_construct_empty_entity(): + ent = EntitySet() + assert ent.empty + assert ent.is_empty() + assert len(ent.elements) == 0 + assert ent.dimsize == 0 diff --git a/hypernetx/classes/tests/test_hypergraph_static_deprecate.py b/hypernetx/classes/tests/test_hypergraph_static_deprecate.py index 7b839d55..86c39bd4 100644 --- a/hypernetx/classes/tests/test_hypergraph_static_deprecate.py +++ b/hypernetx/classes/tests/test_hypergraph_static_deprecate.py @@ -1,6 +1,6 @@ import pytest -from hypernetx import Hypergraph, Entity, EntitySet +from hypernetx import Hypergraph, EntitySet, EntitySet pytestmark = pytest.mark.skip(reason="Deprecated attribute and/or method") @@ -14,20 +14,20 @@ def test_static_hypergraph_constructor_setsystem(sbs): def test_static_hypergraph_constructor_entity(sbs): - E = Entity(data=sbs.data, labels=sbs.labels) + E = EntitySet(data=sbs.data, labels=sbs.labels) H = Hypergraph(E, static=True) assert H.isstatic assert "A" in H.edges.incidence_dict["P"] def test_static_hypergraph_get_id(sbs): - H = Hypergraph(Entity(data=sbs.data, labels=sbs.labels)) + H = Hypergraph(EntitySet(data=sbs.data, labels=sbs.labels)) assert H.get_id("V") == 6 assert H.get_id("S", edges=True) == 2 def test_static_hypergraph_get_name(sbs): - H = Hypergraph(Entity(data=sbs.data, labels=sbs.labels)) + H = Hypergraph(EntitySet(data=sbs.data, labels=sbs.labels)) assert H.get_name(1) == "C" assert H.get_name(1, edges=True) == "R" From 6be4d53d367879f5b056d28e8ded6b5379b3cb55 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 10 Aug 2023 14:40:13 -0700 Subject: [PATCH 06/76] HYP-339 Fix empty constructor bug; add test --- hypernetx/classes/entityset.py | 14 ++++++++------ hypernetx/classes/tests/test_entityset.py | 8 ++++++++ 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index d3c9965a..cae6591b 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -185,13 +185,15 @@ def __init__( # store a list of columns that hold entity data (not properties or # weights) # self._data_cols = list(self._dataframe.columns.drop(self._cell_weight_col)) + self._data_cols = [] - for col in data_cols: - # TODO: default arguments fail for empty Entity; data_cols has two elements but _dataframe has only one element - if isinstance(col, int): - self._data_cols.append(self._dataframe.columns[col]) - else: - self._data_cols.append(col) + if not self._dataframe.empty: + for col in data_cols: + # TODO: default arguments fail for empty Entity; data_cols has two elements but _dataframe has only one element + if isinstance(col, int): + self._data_cols.append(self._dataframe.columns[col]) + else: + self._data_cols.append(col) # each entity data column represents one dimension of the data # (data updates can only add or remove rows, so this isn't stored in diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 4e60a794..65eead30 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -6,6 +6,14 @@ from hypernetx.classes import EntitySet +def test_entityset_empty(): + es = EntitySet() + assert es.empty + assert len(es.elements) == 0 + assert es.elements == {} + assert es.dimsize == 0 + + def test_constructor(ent_sbs): assert ent_sbs.size() == 6 assert len(ent_sbs.uidset) == 6 From 5c083daa3dfabc61561c6a1c8a205fbfa5ab65b2 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 10 Aug 2023 15:29:58 -0700 Subject: [PATCH 07/76] HYP-339 Create helper methods to constructor --- hypernetx/classes/entityset.py | 102 +++++++----- hypernetx/classes/tests/conftest.py | 15 -- hypernetx/classes/tests/test_entityset.py | 192 +++++++++++++--------- 3 files changed, 178 insertions(+), 131 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index cae6591b..b9a637dc 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -14,9 +14,13 @@ AttrList, assign_weights, remove_row_duplicates, - dict_depth, ) +from hypernetx.utils.log import get_logger + +_log = get_logger("entity_set") + + T = TypeVar("T", bound=Union[str, int]) @@ -139,43 +143,11 @@ def __init__( self._static = static self._state_dict = {} - # entity data is stored in a DataFrame for basic access without the - # need for any label encoding lookups - if isinstance(entity, pd.DataFrame): - self._dataframe = entity.copy() - - # if the entity data is passed as a dict of lists or a list of lists, - # we convert it to a 2-column dataframe by exploding each list to cover - # one row per element for a dict of lists, the first level/column will - # be filled in with dict keys for a list of N lists, 0,1,...,N will be - # used to fill the first level/column - elif isinstance(entity, (dict, list)): - # convert dict of lists to 2-column dataframe - entity = pd.Series(entity).explode() - self._dataframe = pd.DataFrame( - {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} - ) - - # if a 2d numpy ndarray is passed, store it as both a DataFrame and an - # ndarray in the state dict - elif isinstance(data, np.ndarray) and data.ndim == 2: - self._state_dict["data"] = data - self._dataframe = pd.DataFrame(data) - # if a dict of labels was passed, use keys as column names in the - # DataFrame, translate the dataframe, and store the dict of labels - # in the state dict - if isinstance(labels, dict) and len(labels) == len(self._dataframe.columns): - self._dataframe.columns = labels.keys() - self._state_dict["labels"] = labels - - for col in self._dataframe: - self._dataframe[col] = pd.Categorical.from_codes( - self._dataframe[col], categories=labels[col] - ) - - # create an empty Entity + if isinstance(data, np.ndarray) and entity is None: + self._build_dataframe_from_ndarray(data, labels) else: - self._dataframe = pd.DataFrame() + _log.debug("Ignoring 'data' since 'entity' is given.") + self._dataframe = build_dataframe_from_entity(entity, data_cols) # assign a new or existing column of the dataframe to hold cell weights self._dataframe, self._cell_weight_col = assign_weights( @@ -231,6 +203,33 @@ def __init__( if properties is not None: self.assign_properties(properties) + def _build_dataframe_from_ndarray( + self, + data: pd.ndarray, + labels: Optional[OrderedDict[Union[str, int], Sequence[Union[str, int]]]], + ) -> None: + self._state_dict["data"] = data + self._dataframe = pd.DataFrame(data) + # if a dict of labels was passed, use keys as column names in the + # DataFrame, translate the dataframe, and store the dict of labels in the state dict + + if not isinstance(labels, dict): + raise ValueError( + f"Labels must be of type Dictionary. Labels is of type: {type(labels)}; labels: {labels}" + ) + if len(labels) != len(self._dataframe.columns): + raise ValueError( + f"The length of labels must equal the length of columns in the dataframe. Labels is of length: {len(labels)}; dataframe is of length: {len(self._dataframe.columns)}" + ) + + self._dataframe.columns = labels.keys() + self._state_dict["labels"] = labels + + for col in self._dataframe: + self._dataframe[col] = pd.Categorical.from_codes( + self._dataframe[col], categories=labels[col] + ) + @property def data(self): """Sparse representation of the data table as an incidence tensor @@ -1622,3 +1621,32 @@ def get_properties(self, item: T, level: Optional[int] = None) -> dict[Any, Any] ) from ex return prop_vals + + +def build_dataframe_from_entity( + entity: pd.DataFrame + | Mapping[Union[str, int], Iterable[Union[str, int]]] + | Iterable[Iterable[Union[str, int]]] + | Mapping[T, Mapping[T, Mapping[T, Any]]], + data_cols: Sequence[Union[str, int]], +) -> pd.DataFrame: + ##### build dataframe + # entity data is stored in a DataFrame for basic access without the + # need for any label encoding lookups + if isinstance(entity, pd.DataFrame): + return entity.copy() + + # if the entity data is passed as a dict of lists or a list of lists, + # we convert it to a 2-column dataframe by exploding each list to cover + # one row per element for a dict of lists, the first level/column will + # be filled in with dict keys for a list of N lists, 0,1,...,N will be + # used to fill the first level/column + if isinstance(entity, (dict, list)): + # convert dict of lists to 2-column dataframe + entity = pd.Series(entity).explode() + return pd.DataFrame( + {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} + ) + + # create an empty dataframe + return pd.DataFrame() diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index ce784f45..48cd05bc 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -151,16 +151,6 @@ def sbs(): return SevenBySix() -@pytest.fixture -def ent_sbs(sbs): - return EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - - -@pytest.fixture -def sbs_edgedict(sbs): - return sbs.edgedict - - @pytest.fixture def triloop(): return TriLoop() @@ -245,11 +235,6 @@ def array_example(): ) -@pytest.fixture -def ent_hp(harry_potter): - return EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) - - ####################Fixtures suite for test_hypergraph.py#################### ####################These fixtures are modular and thus have inter-dependencies#################### @pytest.fixture diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 65eead30..1548505a 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -5,134 +5,168 @@ from collections import UserList from hypernetx.classes import EntitySet +from pandas import DataFrame, Series -def test_entityset_empty(): + +def test_empty_entityset(): es = EntitySet() assert es.empty assert len(es.elements) == 0 assert es.elements == {} assert es.dimsize == 0 + assert es.uid is None -def test_constructor(ent_sbs): - assert ent_sbs.size() == 6 - assert len(ent_sbs.uidset) == 6 - assert len(ent_sbs.children) == 7 - assert isinstance(ent_sbs.incidence_dict["I"], list) - assert "I" in ent_sbs - assert "K" in ent_sbs +def test_entityset_from_dataframe(): + data_dict = { + 1: ["A", "D"], + 2: ["A", "C", "D"], + 3: ["D"], + 4: ["A", "B"], + 5: ["B", "C"], + } + all_edge_pairs = Series(data_dict).explode() -def test_property(ent_hp): - assert len(ent_hp.uidset) == 7 - assert len(ent_hp.elements) == 7 - assert isinstance(ent_hp.elements["Hufflepuff"], UserList) - assert not ent_hp.is_empty() - assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 + entity = DataFrame( + {"edges": all_edge_pairs.index.to_list(), "nodes": all_edge_pairs.values} + ) + es = EntitySet(entity=entity) -@pytest.mark.xfail( - reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" -) -def test_attributes(ent_hp): - assert isinstance(ent_hp.data, np.ndarray) - # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray - assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails - assert isinstance(ent_hp.labels, dict) - # TODO: Entity defaults to first two cols as data cols - assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails - assert ent_hp.dimsize == 5 # fails - df = ent_hp.dataframe[ent_hp._data_cols] - assert list(df.columns) == [ # fails - "House", - "Blood status", - "Species", - "Hair colour", - "Eye colour", - ] - assert ent_hp.dimensions == tuple(df.nunique()) - assert set(ent_hp.labels["House"]) == set(df["House"].unique()) + assert not es.empty + assert len(es.elements) == 5 + assert es.dimsize == 2 + assert es.uid is None -def test_custom_attributes(ent_hp): - assert ent_hp.__len__() == 7 - assert isinstance(ent_hp.__str__(), str) - assert isinstance(ent_hp.__repr__(), str) - assert isinstance(ent_hp.__contains__("Muggle"), bool) - assert ent_hp.__contains__("Muggle") is True - assert ent_hp.__getitem__("Slytherin") == [ - "Half-blood", - "Pure-blood", - "Pure-blood or half-blood", - ] - assert isinstance(ent_hp.__iter__(), Iterable) - assert isinstance(ent_hp.__call__(), Iterable) - assert ent_hp.__call__().__next__() == "Unknown House" +## Tests using Seven By Six hypergraphs +def test_entityset_from_dictionary(sbs): + ent = EntitySet(entity=sbs.edgedict) + assert len(ent.elements) == 6 -@pytest.mark.xfail( - reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" -) -def test_level(ent_sbs): - # TODO: at some point we are casting out and back to categorical dtype without - # preserving categories ordering from `labels` provided to constructor - assert ent_sbs.level("I") == (0, 5) # fails - assert ent_sbs.level("K") == (1, 3) - assert ent_sbs.level("K", max_level=0) is None +def test_entityset_from_ndarray_sbs(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + + assert ent_sbs.size() == 6 + assert len(ent_sbs.uidset) == 6 + assert len(ent_sbs.children) == 7 + assert isinstance(ent_sbs.incidence_dict["I"], list) + assert "I" in ent_sbs + assert "K" in ent_sbs + +def test_uidset_by_level(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) -def test_uidset_by_level(ent_sbs): assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} -def test_elements_by_level(ent_sbs): +def test_elements_by_level(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.elements_by_level(0, 1) -def test_incidence_matrix(ent_sbs): +def test_incidence_matrix(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) -def test_indices(ent_sbs): +def test_indices(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.indices("nodes", "K") == [3] assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] -def test_translate(ent_sbs): +def test_translate(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.translate(0, 0) == "P" assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] -def test_translate_arr(ent_sbs): +def test_translate_arr(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] -def test_index(ent_sbs): +def test_index(sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.index("nodes") == 1 assert ent_sbs.index("nodes", "K") == (1, 3) -def test_restrict_to_levels(ent_hp): +@pytest.mark.xfail( + reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" +) +def test_level(sbs): + # TODO: at some point we are casting out and back to categorical dtype without + # preserving categories ordering from `labels` provided to constructor + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.level("I") == (0, 5) # fails + assert ent_sbs.level("K") == (1, 3) + assert ent_sbs.level("K", max_level=0) is None + + +## Tests using Harry Potter hypergraph +def test_entityset_from_ndarray_harry_potter(harry_potter): + ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) + assert len(ent_hp.uidset) == 7 + assert len(ent_hp.elements) == 7 + assert isinstance(ent_hp.elements["Hufflepuff"], UserList) + assert not ent_hp.is_empty() + assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 + + +def test_custom_attributes(harry_potter): + ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) + assert ent_hp.__len__() == 7 + assert isinstance(ent_hp.__str__(), str) + assert isinstance(ent_hp.__repr__(), str) + assert isinstance(ent_hp.__contains__("Muggle"), bool) + assert ent_hp.__contains__("Muggle") is True + assert ent_hp.__getitem__("Slytherin") == [ + "Half-blood", + "Pure-blood", + "Pure-blood or half-blood", + ] + assert isinstance(ent_hp.__iter__(), Iterable) + assert isinstance(ent_hp.__call__(), Iterable) + assert ent_hp.__call__().__next__() == "Unknown House" + + +def test_restrict_to_levels(harry_potter): + ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 -def test_restrict_to_indices(ent_hp): +def test_restrict_to_indices(harry_potter): + ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) assert ent_hp.restrict_to_indices([1, 2]).uidset == { "Gryffindor", "Ravenclaw", } -def test_construct_from_entity(sbs): - ent = EntitySet(entity=sbs.edgedict) - assert len(ent.elements) == 6 - - -@pytest.mark.xfail(reason="default arguments fail for empty Entity") -def test_construct_empty_entity(): - ent = EntitySet() - assert ent.empty - assert ent.is_empty() - assert len(ent.elements) == 0 - assert ent.dimsize == 0 +@pytest.mark.xfail( + reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" +) +def test_attributes(ent_hp): + assert isinstance(ent_hp.data, np.ndarray) + # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray + assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails + assert isinstance(ent_hp.labels, dict) + # TODO: Entity defaults to first two cols as data cols + assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails + assert ent_hp.dimsize == 5 # fails + df = ent_hp.dataframe[ent_hp._data_cols] + assert list(df.columns) == [ # fails + "House", + "Blood status", + "Species", + "Hair colour", + "Eye colour", + ] + assert ent_hp.dimensions == tuple(df.nunique()) + assert set(ent_hp.labels["House"]) == set(df["House"].unique()) From c48f630acf530089f0856720ba214e80777b4785 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 11 Aug 2023 12:53:16 -0700 Subject: [PATCH 08/76] HYP-339 Update tests; add helper method on data_cols in constructor --- hypernetx/classes/entityset.py | 16 +++-- hypernetx/classes/helpers.py | 11 +-- hypernetx/classes/tests/test_entityset.py | 84 ++++++++++------------- 3 files changed, 54 insertions(+), 57 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index b9a637dc..f02e9ab6 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -159,13 +159,7 @@ def __init__( # self._data_cols = list(self._dataframe.columns.drop(self._cell_weight_col)) self._data_cols = [] - if not self._dataframe.empty: - for col in data_cols: - # TODO: default arguments fail for empty Entity; data_cols has two elements but _dataframe has only one element - if isinstance(col, int): - self._data_cols.append(self._dataframe.columns[col]) - else: - self._data_cols.append(col) + self._init_data_cols(data_cols) # each entity data column represents one dimension of the data # (data updates can only add or remove rows, so this isn't stored in @@ -230,6 +224,14 @@ def _build_dataframe_from_ndarray( self._dataframe[col], categories=labels[col] ) + def _init_data_cols(self, data_cols): + if not self._dataframe.empty: + for col in data_cols: + if isinstance(col, int): + self._data_cols.append(self._dataframe.columns[col]) + else: + self._data_cols.append(col) + @property def data(self): """Sparse representation of the data table as an incidence tensor diff --git a/hypernetx/classes/helpers.py b/hypernetx/classes/helpers.py index 465fe17a..26c00698 100644 --- a/hypernetx/classes/helpers.py +++ b/hypernetx/classes/helpers.py @@ -82,7 +82,11 @@ def encode(data: pd.DataFrame): return encoded_array -def assign_weights(df, weights=1, weight_col="cell_weights"): +def assign_weights( + df: pd.DataFrame, + weights: list | tuple | np.ndarray | Hashable = 1, + weight_col: Hashable = "cell_weights", +): """ Parameters ---------- @@ -111,9 +115,8 @@ def assign_weights(df, weights=1, weight_col="cell_weights"): if isinstance(weights, (list, np.ndarray)): df[weight_col] = weights - else: - if not weight_col in df: - df[weight_col] = weights + elif not weight_col in df: + df[weight_col] = weights # import ipdb; ipdb.set_trace() return df, weight_col diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 1548505a..a15ff831 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -14,7 +14,6 @@ def test_empty_entityset(): assert len(es.elements) == 0 assert es.elements == {} assert es.dimsize == 0 - assert es.uid is None def test_entityset_from_dataframe(): @@ -40,61 +39,54 @@ def test_entityset_from_dataframe(): assert es.uid is None -## Tests using Seven By Six hypergraphs -def test_entityset_from_dictionary(sbs): - ent = EntitySet(entity=sbs.edgedict) - assert len(ent.elements) == 6 +class TestEntitySetOnSBSHypergraph: + ## Tests using Seven By Six hypergraphs + def test_entityset_from_dictionary(self, sbs): + ent = EntitySet(entity=sbs.edgedict) + assert len(ent.elements) == 6 + def test_entityset_from_ndarray_sbs(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) -def test_entityset_from_ndarray_sbs(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.size() == 6 + assert len(ent_sbs.uidset) == 6 + assert len(ent_sbs.children) == 7 + assert isinstance(ent_sbs.incidence_dict["I"], list) + assert "I" in ent_sbs + assert "K" in ent_sbs - assert ent_sbs.size() == 6 - assert len(ent_sbs.uidset) == 6 - assert len(ent_sbs.children) == 7 - assert isinstance(ent_sbs.incidence_dict["I"], list) - assert "I" in ent_sbs - assert "K" in ent_sbs + def test_uidset_by_level(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} + assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} -def test_uidset_by_level(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + def test_elements_by_level(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.elements_by_level(0, 1) - assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} - assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} + def test_incidence_matrix(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) + def test_indices(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.indices("nodes", "K") == [3] + assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] -def test_elements_by_level(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.elements_by_level(0, 1) - + def test_translate(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.translate(0, 0) == "P" + assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] -def test_incidence_matrix(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) - - -def test_indices(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.indices("nodes", "K") == [3] - assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] + def test_translate_arr(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] - -def test_translate(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate(0, 0) == "P" - assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] - - -def test_translate_arr(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] - - -def test_index(sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.index("nodes") == 1 - assert ent_sbs.index("nodes", "K") == (1, 3) + def test_index(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.index("nodes") == 1 + assert ent_sbs.index("nodes", "K") == (1, 3) @pytest.mark.xfail( From f7c3c1c78507c242a101ee764160fb0706d453bb Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 11 Aug 2023 16:05:25 -0700 Subject: [PATCH 09/76] HYP-339 Add type hints; general cleanup --- hypernetx/classes/entityset.py | 214 +++++++++++----------- hypernetx/classes/helpers.py | 1 + hypernetx/classes/tests/test_entityset.py | 88 +++++---- 3 files changed, 156 insertions(+), 147 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index f02e9ab6..b96e6ab6 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -118,11 +118,14 @@ class EntitySet: def __init__( self, entity: Optional[ - pd.DataFrame | Mapping[T, Iterable[T]] | Iterable[Iterable[T]] + pd.DataFrame + | Mapping[T, Iterable[T]] + | Iterable[Iterable[T]] + | Mapping[T, Mapping[T, Any]] ] = None, - data_cols: Sequence[T] = [0, 1], + data_cols: Sequence[T] = (0, 1), data: Optional[np.ndarray] = None, - static: bool = False, + static: bool = True, labels: Optional[OrderedDict[T, Sequence[T]]] = None, uid: Optional[Hashable] = None, weight_col: Optional[str | int] = "cell_weights", @@ -133,13 +136,7 @@ def __init__( level_col: str = "level", id_col: str = "id", ): - # set unique identifier - self._uid = uid or None - - # if static, the original data cannot be altered - # the state dict stores all computed values that may need to be updated - # if the data is altered - the dict will be cleared when data is added - # or removed + self._uid = uid self._static = static self._state_dict = {} @@ -153,20 +150,13 @@ def __init__( self._dataframe, self._cell_weight_col = assign_weights( self._dataframe, weights=weights, weight_col=weight_col ) - # import ipdb; ipdb.set_trace() - # store a list of columns that hold entity data (not properties or - # weights) - # self._data_cols = list(self._dataframe.columns.drop(self._cell_weight_col)) self._data_cols = [] self._init_data_cols(data_cols) - # each entity data column represents one dimension of the data - # (data updates can only add or remove rows, so this isn't stored in - # state dict) + # (data updates can only add or remove rows, so this isn't stored in state dict) self._dimsize = len(self._data_cols) - # remove duplicate rows and aggregate cell weights as needed # import ipdb; ipdb.set_trace() self._dataframe, _ = remove_row_duplicates( self._dataframe, @@ -175,27 +165,9 @@ def __init__( aggregateby=aggregateby, ) - # set the dtype of entity data columns to categorical (simplifies - # encoding, etc.) - ### This is automatically done in remove_row_duplicates - # self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( - # "category" - # ) - - # create properties - item_levels = [ - (level, item) - for level, col in enumerate(self._data_cols) - for item in self.dataframe[col].cat.categories - ] - index = pd.MultiIndex.from_tuples(item_levels, names=[level_col, id_col]) - data = [(i, 1, {}) for i in range(len(index))] - self._properties = pd.DataFrame( - data=data, index=index, columns=["uid", "weight", misc_props_col] - ).sort_index() self._misc_props_col = misc_props_col - if properties is not None: - self.assign_properties(properties) + self._init_properties(level_col, id_col, misc_props_col) + self.assign_properties(properties) def _build_dataframe_from_ndarray( self, @@ -224,7 +196,9 @@ def _build_dataframe_from_ndarray( self._dataframe[col], categories=labels[col] ) - def _init_data_cols(self, data_cols): + def _init_data_cols(self, data_cols: Sequence[T]) -> None: + """store a list of columns that hold entity data (not properties or weights)""" + # import ipdb; ipdb.set_trace() if not self._dataframe.empty: for col in data_cols: if isinstance(col, int): @@ -232,8 +206,22 @@ def _init_data_cols(self, data_cols): else: self._data_cols.append(col) + def _init_properties( + self, level_col: str, id_col: str, misc_props_col: str + ) -> None: + item_levels = [ + (level, item) + for level, col in enumerate(self._data_cols) + for item in self.dataframe[col].cat.categories + ] + index = pd.MultiIndex.from_tuples(item_levels, names=[level_col, id_col]) + data = [(i, 1, {}) for i in range(len(index))] + self._properties = pd.DataFrame( + data=data, index=index, columns=["uid", "weight", misc_props_col] + ).sort_index() + @property - def data(self): + def data(self) -> np.ndarray: """Sparse representation of the data table as an incidence tensor This can also be thought of as an encoding of `dataframe`, where items in each column of @@ -264,7 +252,7 @@ def data(self): return self._state_dict["data"] @property - def labels(self): + def labels(self) -> dict[str, list]: """Labels of all items in each column of the underlying data table Returns @@ -289,7 +277,7 @@ def labels(self): return self._state_dict["labels"] @property - def cell_weights(self): + def cell_weights(self) -> dict[str, tuple[T]]: """Cell weights corresponding to each row of the underlying data table Returns @@ -309,7 +297,7 @@ def cell_weights(self): return self._state_dict["cell_weights"] @property - def dimensions(self): + def dimensions(self) -> tuple[int]: """Dimensions of data i.e., the number of distinct items in each level (column) of the underlying data table Returns @@ -329,7 +317,7 @@ def dimensions(self): return self._state_dict["dimensions"] @property - def dimsize(self): + def dimsize(self) -> int: """Number of levels (columns) in the underlying data table Returns @@ -352,23 +340,22 @@ def properties(self) -> pd.DataFrame: return self._properties @property - def uid(self): - # Dev Note: This also returned nothing in my harry potter dataset, not sure if it was supposed to contain anything + def uid(self) -> Hashable: """User-defined unique identifier for the `Entity` Returns ------- - hashable + Hashable """ return self._uid @property - def uidset(self): + def uidset(self) -> set: """Labels of all items in level 0 (first column) of the underlying data table Returns ------- - frozenset + set See Also -------- @@ -379,12 +366,12 @@ def uidset(self): return self.uidset_by_level(0) @property - def children(self): + def children(self) -> set: """Labels of all items in level 1 (second column) of the underlying data table Returns ------- - frozenset + set See Also -------- @@ -394,7 +381,7 @@ def children(self): """ return self.uidset_by_level(1) - def uidset_by_level(self, level): + def uidset_by_level(self, level: int) -> set: """Labels of all items in a particular level (column) of the underlying data table Parameters @@ -403,7 +390,7 @@ def uidset_by_level(self, level): Returns ------- - frozenset + set See Also -------- @@ -412,11 +399,11 @@ def uidset_by_level(self, level): uidset_by_column : Same functionality, takes the column name instead of level index """ if self.is_empty(level): - return {} + return set() col = self._data_cols[level] return self.uidset_by_column(col) - def uidset_by_column(self, column): + def uidset_by_column(self, column: Hashable) -> set: # Dev Note: This threw an error when trying it on the harry potter dataset, # when trying 0, or 1 for column. I'm not sure how this should be used """Labels of all items in a particular column (level) of the underlying data table @@ -428,7 +415,7 @@ def uidset_by_column(self, column): Returns ------- - frozenset + set See Also -------- @@ -447,7 +434,7 @@ def uidset_by_column(self, column): return self._state_dict["uidset"][column] @property - def elements(self): + def elements(self) -> dict[Any, AttrList]: """System of sets representation of the first two levels (columns) of the underlying data table Each item in level 0 (first column) defines a set containing all the level 1 @@ -491,7 +478,7 @@ def incidence_dict(self) -> dict[T, list[T]]: return {item: elements.data for item, elements in self.elements.items()} @property - def memberships(self): + def memberships(self) -> dict[Any, AttrList]: """System of sets representation of the first two levels (columns) of the underlying data table @@ -514,7 +501,7 @@ def memberships(self): return self.elements_by_level(1, 0) - def elements_by_level(self, level1, level2): + def elements_by_level(self, level1: int, level2: int) -> dict[Any, AttrList]: """System of sets representation of two levels (columns) of the underlying data table Each item in level1 defines a set containing all the level2 items @@ -544,7 +531,7 @@ def elements_by_level(self, level1, level2): col2 = self._data_cols[level2] return self.elements_by_column(col1, col2) - def elements_by_column(self, col1, col2): + def elements_by_column(self, col1: Hashable, col2: Hashable) -> dict[Any, AttrList]: """System of sets representation of two columns (levels) of the underlying data table @@ -584,7 +571,7 @@ def elements_by_column(self, col1, col2): return self._state_dict["elements"][col1][col2] @property - def dataframe(self): + def dataframe(self) -> pd.DataFrame: """The underlying data table stored by the Entity Returns @@ -594,7 +581,7 @@ def dataframe(self): return self._dataframe @property - def isstatic(self): + def isstatic(self) -> bool: # Dev Note: I'm guessing this is no longer necessary? """Whether to treat the underlying data as static or not @@ -607,7 +594,7 @@ def isstatic(self): """ return self._static - def size(self, level=0): + def size(self, level: int = 0) -> int: """The number of items in a level of the underlying data table Equivalent to ``self.dimensions[level]`` @@ -628,7 +615,7 @@ def size(self, level=0): return self.dimensions[level] @property - def empty(self): + def empty(self) -> bool: """Whether the underlying data table is empty or not Returns @@ -642,9 +629,13 @@ def empty(self): """ return self._dimsize == 0 - def is_empty(self, level=0): + def is_empty(self, level: int = 0) -> bool: """Whether a specified level (column) of the underlying data table is empty or not + Parameters + ---------- + level: int + the level of a column in the underlying data table Returns ------- bool @@ -734,21 +725,7 @@ def __call__(self, label_index=0): """ return iter(self.labels[self._data_cols[label_index]]) - # def __repr__(self): - # """String representation of the Entity - - # e.g., "Entity(uid, [level 0 items], {item: {property name: property value}})" - - # Returns - # ------- - # str - # """ - # return "hypernetx.classes.entity.Entity" - - # def __str__(self): - # return "" - - def index(self, column, value=None): + def index(self, column: str, value: Optional[str] = None) -> int | tuple(int, int): """Get level index corresponding to a column and (optionally) the index of a value in that column The index of ``value`` is its position in the list given by ``self.labels[column]``, which is used @@ -793,7 +770,7 @@ def index(self, column, value=None): self._state_dict["index"][column][value], ) - def indices(self, column, values): + def indices(self, column: str, values: str | Iterable[str]) -> list[int]: """Get indices of one or more value(s) in a column Parameters @@ -823,13 +800,13 @@ def indices(self, column, values): return [self._state_dict["index"][column][v] for v in values] - def translate(self, level, index): + def translate(self, level: int, index: int | list[int]) -> str | list[str]: """Given indices of a level and value(s), return the corresponding value label(s) Parameters ---------- level : int - level index + the index of the level index : int or list of int value index or indices @@ -849,7 +826,7 @@ def translate(self, level, index): return [self.labels[column][i] for i in index] - def translate_arr(self, coords): + def translate_arr(self, coords: tuple[int]) -> list[str]: """Translate a full encoded row of the data table e.g., a row of ``self.data`` Parameters @@ -869,7 +846,13 @@ def translate_arr(self, coords): return translation - def level(self, item, min_level=0, max_level=None, return_index=True): + def level( + self, + item: str, + min_level: int = 0, + max_level: Optional[int] = None, + return_index: bool = True, + ) -> Optional[int, tuple(int, int)]: """First level containing the given item label Order of levels corresponds to order of columns in `self.dataframe` @@ -877,8 +860,10 @@ def level(self, item, min_level=0, max_level=None, return_index=True): Parameters ---------- item : str - min_level, max_level : int, optional - inclusive bounds on range of levels to search for item + min_level : int, default=0 + minimum inclusive bound on range of levels to search for item + max_level : int, optional + maximum inclusive bound on range of levels to search for item return_index : bool, default=True If True, return index of item within the level @@ -908,7 +893,7 @@ def level(self, item, min_level=0, max_level=None, return_index=True): print(f'"{item}" not found.') return None - def add(self, *args): + def add(self, *args) -> EntitySet: """Updates the underlying data table with new entity data from multiple sources Parameters @@ -938,7 +923,7 @@ def add(self, *args): self.add_element(item) return self - def add_elements_from(self, arg_set): + def add_elements_from(self, arg_set) -> EntitySet: """Adds arguments from an iterable to the data table one at a time ..deprecated:: 2.0.0 @@ -958,7 +943,13 @@ def add_elements_from(self, arg_set): self.add_element(item) return self - def add_element(self, data): + def add_element( + self, + data: pd.DataFrame + | Mapping[T, Iterable[T]] + | Iterable[Iterable[T]] + | Mapping[T, Mapping[T, Any]], + ) -> EntitySet: """Updates the underlying data table with new entity data Supports adding from either an existing Entity or a representation of entity @@ -966,7 +957,7 @@ def add_element(self, data): Parameters ---------- - data : Entity, `pandas.DataFrame`, or dict of lists or sets + data : `pandas.DataFrame`, dict of lists or sets, lists of lists or sets new entity data Returns @@ -998,12 +989,12 @@ def add_element(self, data): return self - def __add_from_dataframe(self, df): + def __add_from_dataframe(self, df: pd.DataFrame) -> EntitySet: """Helper function to append rows to `self.dataframe` Parameters ---------- - data : pd.DataFrame + df : pd.DataFrame Returns ------- @@ -1026,7 +1017,7 @@ def __add_from_dataframe(self, df): self._state_dict.clear() - def remove(self, *args): + def remove(self, *args) -> EntitySet: """Removes all rows containing specified item(s) from the underlying data table Parameters @@ -1067,7 +1058,7 @@ def remove_elements_from(self, arg_set): self.remove_element(item) return self - def remove_element(self, item): + def remove_element(self, item) -> EntitySet: """Removes all rows containing a specified item from the underlying data table Parameters @@ -1102,7 +1093,7 @@ def remove_element(self, item): for col in self._data_cols: self._dataframe[col] = self._dataframe[col].cat.remove_unused_categories() - def encode(self, data): + def encode(self, data: pd.DataFrame) -> np.array: """ Encode dataframe to numpy array @@ -1119,8 +1110,12 @@ def encode(self, data): return encoded_array def incidence_matrix( - self, level1=0, level2=1, weights=False, aggregateby=None, index=False - ) -> csr_matrix | None: + self, + level1: int = 0, + level2: int = 1, + weights: bool | dict = False, + aggregateby: str = "count", + ) -> Optional[csr_matrix]: """Incidence matrix representation for two levels (columns) of the underlying data table If `level1` and `level2` contain N and M distinct items, respectively, the incidence matrix will be M x N. @@ -1182,7 +1177,7 @@ def restrict_to_levels( self, levels: int | Iterable[int], weights: bool = False, - aggregateby: str | None = "sum", + aggregateby: Optional[str] = "sum", **kwargs, ) -> EntitySet: """Create a new Entity by restricting to a subset of levels (columns) in the @@ -1200,7 +1195,7 @@ def restrict_to_levels( Method to aggregate weights of duplicate rows in data table If None or `weights`=False then all new cell weights will be 1 **kwargs - Extra arguments to `Entity` constructor + Extra arguments to `EntitySet` constructor Returns ------- @@ -1246,7 +1241,9 @@ def restrict_to_levels( **kwargs, ) - def restrict_to_indices(self, indices, level=0, **kwargs): + def restrict_to_indices( + self, indices: int | Iterable[int], level: int = 0, **kwargs + ) -> EntitySet: """Create a new Entity by restricting the data table to rows containing specific items in a given level Parameters @@ -1256,7 +1253,7 @@ def restrict_to_indices(self, indices, level=0, **kwargs): level : int, default=0 level index **kwargs - Extra arguments to `Entity` constructor + Extra arguments to `EntitySet` constructor Returns ------- @@ -1305,10 +1302,13 @@ def assign_properties( properties """ # mapping from user-specified level, id, misc column names to internal names - ### This will fail if there isn't a level column + # This will fail if there isn't a level column + + if props is None: + return if isinstance(props, pd.DataFrame): - ### Fix to check the shape of properties or redo properties format + # TODO: Fix to check the shape of properties or redo properties format column_map = { old: new for old, new in zip( @@ -1322,7 +1322,7 @@ def assign_properties( self._properties_from_dataframe(props) if isinstance(props, dict): - ### Expects nested dictionary with keys corresponding to level and id + # Expects nested dictionary with keys corresponding to level and id self._properties_from_dict(props) def _properties_from_dataframe(self, props: pd.DataFrame) -> None: @@ -1330,7 +1330,7 @@ def _properties_from_dataframe(self, props: pd.DataFrame) -> None: Parameters ---------- - props + props: pd.Dataframe Notes ----- @@ -1404,7 +1404,7 @@ def _properties_from_dict(self, props: dict[int, dict[T, dict[Any, Any]]]) -> No Parameters ---------- - props + props: dict[int, dict[T, dict[Any, Any]]] """ # TODO: there may be a more efficient way to convert this to a dataframe instead # of updating one-by-one via nested loop, but checking whether each prop_name diff --git a/hypernetx/classes/helpers.py b/hypernetx/classes/helpers.py index 26c00698..7690906b 100644 --- a/hypernetx/classes/helpers.py +++ b/hypernetx/classes/helpers.py @@ -193,6 +193,7 @@ def remove_row_duplicates( ): """ Removes and aggregates duplicate rows of a DataFrame using groupby + Also sets the dtype of entity data columns to categorical (simplifies encoding, etc.) Parameters ---------- diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index a15ff831..f1f5fd93 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -39,8 +39,7 @@ def test_entityset_from_dataframe(): assert es.uid is None -class TestEntitySetOnSBSHypergraph: - ## Tests using Seven By Six hypergraphs +class TestEntitySetOnSevenBySixDataset: def test_entityset_from_dictionary(self, sbs): ent = EntitySet(entity=sbs.edgedict) assert len(ent.elements) == 6 @@ -55,6 +54,10 @@ def test_entityset_from_ndarray_sbs(self, sbs): assert "I" in ent_sbs assert "K" in ent_sbs + def test_dimensions_equal_dimsize(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.dimsize == len(ent_sbs.dimensions) + def test_uidset_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) @@ -101,44 +104,49 @@ def test_level(sbs): assert ent_sbs.level("K", max_level=0) is None -## Tests using Harry Potter hypergraph -def test_entityset_from_ndarray_harry_potter(harry_potter): - ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) - assert len(ent_hp.uidset) == 7 - assert len(ent_hp.elements) == 7 - assert isinstance(ent_hp.elements["Hufflepuff"], UserList) - assert not ent_hp.is_empty() - assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 - - -def test_custom_attributes(harry_potter): - ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) - assert ent_hp.__len__() == 7 - assert isinstance(ent_hp.__str__(), str) - assert isinstance(ent_hp.__repr__(), str) - assert isinstance(ent_hp.__contains__("Muggle"), bool) - assert ent_hp.__contains__("Muggle") is True - assert ent_hp.__getitem__("Slytherin") == [ - "Half-blood", - "Pure-blood", - "Pure-blood or half-blood", - ] - assert isinstance(ent_hp.__iter__(), Iterable) - assert isinstance(ent_hp.__call__(), Iterable) - assert ent_hp.__call__().__next__() == "Unknown House" - - -def test_restrict_to_levels(harry_potter): - ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) - assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 - - -def test_restrict_to_indices(harry_potter): - ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) - assert ent_hp.restrict_to_indices([1, 2]).uidset == { - "Gryffindor", - "Ravenclaw", - } +class TestEntitySetOnHarryPotterDataSet: + def test_entityset_from_ndarray(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert len(ent_hp.uidset) == 7 + assert len(ent_hp.elements) == 7 + assert isinstance(ent_hp.elements["Hufflepuff"], UserList) + assert not ent_hp.is_empty() + assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 + + def test_custom_attributes(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert ent_hp.__len__() == 7 + assert isinstance(ent_hp.__str__(), str) + assert isinstance(ent_hp.__repr__(), str) + assert isinstance(ent_hp.__contains__("Muggle"), bool) + assert ent_hp.__contains__("Muggle") is True + assert ent_hp.__getitem__("Slytherin") == [ + "Half-blood", + "Pure-blood", + "Pure-blood or half-blood", + ] + assert isinstance(ent_hp.__iter__(), Iterable) + assert isinstance(ent_hp.__call__(), Iterable) + assert ent_hp.__call__().__next__() == "Unknown House" + + def test_restrict_to_levels(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 + + def test_restrict_to_indices(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert ent_hp.restrict_to_indices([1, 2]).uidset == { + "Gryffindor", + "Ravenclaw", + } @pytest.mark.xfail( From 7a597f410422dd20c3a499af77b6e7358e307ef0 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 11 Aug 2023 16:22:08 -0700 Subject: [PATCH 10/76] HYP-339 Replace data_cols param with level1, level2; update Hypergraph --- hypernetx/classes/entityset.py | 5 ++++- hypernetx/classes/hypergraph.py | 6 +++--- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index b96e6ab6..6d0ce59c 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -123,7 +123,9 @@ def __init__( | Iterable[Iterable[T]] | Mapping[T, Mapping[T, Any]] ] = None, - data_cols: Sequence[T] = (0, 1), + # data_cols: Sequence[T] = (0, 1), + level1: int = 0, + level2: int = 1, data: Optional[np.ndarray] = None, static: bool = True, labels: Optional[OrderedDict[T, Sequence[T]]] = None, @@ -140,6 +142,7 @@ def __init__( self._static = static self._state_dict = {} + data_cols = (level1, level2) if isinstance(data, np.ndarray) and entity is None: self._build_dataframe_from_ndarray(data, labels) else: diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 8d32a2fa..5b57bf94 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -539,8 +539,8 @@ def props2dict(df=None): level2=node_col, weight_col=cell_weight_col, weights=cell_weights, - cell_properties=cell_properties, - misc_cell_props_col=misc_cell_properties_col or "cell_properties", + # cell_properties=cell_properties, + # misc_cell_props_col=misc_cell_properties_col or "cell_properties", aggregateby=aggregateby or "sum", properties=properties, misc_props_col=misc_properties_col, @@ -548,7 +548,7 @@ def props2dict(df=None): self._edges = self.E self._nodes = self.E.restrict_to_levels([1]) - self._dataframe = self.E.cell_properties.reset_index() + # self._dataframe = self.E.cell_properties.reset_index() self._data_cols = data_cols = [self._edge_col, self._node_col] self._dataframe[data_cols] = self._dataframe[data_cols].astype("category") From f06f26c617c19547ff84e12b778fe4d34c8a9ec2 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 09:00:01 -0700 Subject: [PATCH 11/76] Revert "HYP-339 Replace data_cols param with level1, level2; update Hypergraph" This reverts commit 7a597f410422dd20c3a499af77b6e7358e307ef0. --- hypernetx/classes/entityset.py | 5 +---- hypernetx/classes/hypergraph.py | 6 +++--- 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 6d0ce59c..b96e6ab6 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -123,9 +123,7 @@ def __init__( | Iterable[Iterable[T]] | Mapping[T, Mapping[T, Any]] ] = None, - # data_cols: Sequence[T] = (0, 1), - level1: int = 0, - level2: int = 1, + data_cols: Sequence[T] = (0, 1), data: Optional[np.ndarray] = None, static: bool = True, labels: Optional[OrderedDict[T, Sequence[T]]] = None, @@ -142,7 +140,6 @@ def __init__( self._static = static self._state_dict = {} - data_cols = (level1, level2) if isinstance(data, np.ndarray) and entity is None: self._build_dataframe_from_ndarray(data, labels) else: diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 5b57bf94..8d32a2fa 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -539,8 +539,8 @@ def props2dict(df=None): level2=node_col, weight_col=cell_weight_col, weights=cell_weights, - # cell_properties=cell_properties, - # misc_cell_props_col=misc_cell_properties_col or "cell_properties", + cell_properties=cell_properties, + misc_cell_props_col=misc_cell_properties_col or "cell_properties", aggregateby=aggregateby or "sum", properties=properties, misc_props_col=misc_properties_col, @@ -548,7 +548,7 @@ def props2dict(df=None): self._edges = self.E self._nodes = self.E.restrict_to_levels([1]) - # self._dataframe = self.E.cell_properties.reset_index() + self._dataframe = self.E.cell_properties.reset_index() self._data_cols = data_cols = [self._edge_col, self._node_col] self._dataframe[data_cols] = self._dataframe[data_cols].astype("category") From 6d2b1c27bd8eb10dee44ad010743414ba56c7f21 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 12:07:17 -0700 Subject: [PATCH 12/76] HYP-339 Add back EntitySet params and relevant methods; fix bug; update tests This commit specifically puts back level1, level2, cell_properties, misc_cell_props_col parameters back into EntitySet. Relevant methods that use such parameters are also added back in. Tests were updated and added as well. Also, this commit fixes a bug in the restrict_to_two_columns helper function. --- hypernetx/classes/entityset.py | 383 +++++++++++++++++++++- hypernetx/classes/tests/test_entityset.py | 68 +++- hypernetx/utils/toys/harrypotter.py | 4 +- 3 files changed, 438 insertions(+), 17 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index b96e6ab6..3fc11544 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -124,6 +124,8 @@ def __init__( | Mapping[T, Mapping[T, Any]] ] = None, data_cols: Sequence[T] = (0, 1), + level1: str | int = 0, + level2: str | int = 1, data: Optional[np.ndarray] = None, static: bool = True, labels: Optional[OrderedDict[T, Sequence[T]]] = None, @@ -135,10 +137,29 @@ def __init__( misc_props_col: str = "properties", level_col: str = "level", id_col: str = "id", + cell_properties: Optional[ + Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] + ] = None, + misc_cell_props_col: str = "cell_properties", ): self._uid = uid self._static = static self._state_dict = {} + self._misc_cell_props_col = misc_cell_props_col + + # process certain parameters + ## Restrict to two columns on entity, data, labels + entity, data, labels = restrict_to_two_columns( + entity, + data, + labels, + cell_properties, + weight_col, + weights, + level1, + level2, + misc_cell_props_col, + ) if isinstance(data, np.ndarray) and entity is None: self._build_dataframe_from_ndarray(data, labels) @@ -169,6 +190,8 @@ def __init__( self._init_properties(level_col, id_col, misc_props_col) self.assign_properties(properties) + self._assign_cell_properties(cell_properties) + def _build_dataframe_from_ndarray( self, data: pd.ndarray, @@ -220,6 +243,35 @@ def _init_properties( data=data, index=index, columns=["uid", "weight", misc_props_col] ).sort_index() + def _assign_cell_properties( + self, + cell_properties: Optional[ + Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] + ], + ): + # if underlying data is 2D (system of sets), create and assign cell properties + if self.dimsize == 2: + # self._cell_properties = pd.DataFrame( + # columns=[*self._data_cols, self._misc_cell_props_col] + # ) + self._cell_properties = pd.DataFrame(self._dataframe) + self._cell_properties.set_index(self._data_cols, inplace=True) + if isinstance(cell_properties, (dict, pd.DataFrame)): + self.assign_cell_properties(cell_properties) + else: + self._cell_properties = None + + @property + def cell_properties(self) -> Optional[pd.DataFrame]: + """Properties assigned to cells of the incidence matrix + + Returns + ------- + pandas.Series, optional + Returns None if :attr:`dimsize` < 2 + """ + return self._cell_properties + @property def data(self) -> np.ndarray: """Sparse representation of the data table as an incidence tensor @@ -1173,15 +1225,14 @@ def incidence_matrix( (df[weight_col], tuple(df[col].cat.codes for col in data_cols)) ) - def restrict_to_levels( + def _restrict_to_levels( self, levels: int | Iterable[int], weights: bool = False, aggregateby: Optional[str] = "sum", **kwargs, ) -> EntitySet: - """Create a new Entity by restricting to a subset of levels (columns) in the - underlying data table + """ Parameters ---------- @@ -1279,6 +1330,45 @@ def restrict_to_indices( restricted.assign_properties(properties) return restricted + def assign_cell_properties( + self, + cell_props: pd.DataFrame | dict[T, dict[T, dict[Any, Any]]], + misc_col: Optional[str] = None, + replace: bool = False, + ) -> None: + """Assign new properties to cells of the incidence matrix and update + :attr:`properties` + + Parameters + ---------- + cell_props : pandas.DataFrame, dict of iterables, or doubly-nested dict, optional + See documentation of the `cell_properties` parameter in :class:`EntitySet` + misc_col: str, optional + name of column to be used for miscellaneous cell property dicts + replace: bool, default=False + If True, replace existing :attr:`cell_properties` with result; + otherwise update with new values from result + + Raises + ----- + AttributeError + Not supported for :attr:`dimsize`=1 + """ + if self.dimsize < 2: + raise AttributeError( + f"cell properties are not supported for 'dimsize'={self.dimsize}" + ) + + misc_col = misc_col or self._misc_cell_props_col + try: + cell_props = cell_props.rename( + columns={misc_col: self._misc_cell_props_col} + ) + except AttributeError: # handle cell props in nested dict format + self._cell_properties_from_dict(cell_props) + else: # handle cell props in DataFrame format + self._cell_properties_from_dataframe(cell_props) + def assign_properties( self, props: pd.DataFrame | dict[int, dict[T, dict[Any, Any]]], @@ -1624,6 +1714,208 @@ def get_properties(self, item: T, level: Optional[int] = None) -> dict[Any, Any] return prop_vals + def _cell_properties_from_dataframe(self, cell_props: pd.DataFrame) -> None: + """Private handler for updating :attr:`properties` from a DataFrame + + Parameters + ---------- + props + + Parameters + ---------- + cell_props : DataFrame + """ + if cell_props.index.nlevels > 1: + extra_levels = [ + idx_lev + for idx_lev in cell_props.index.names + if idx_lev not in self._data_cols + ] + cell_props = cell_props.reset_index(level=extra_levels) + + misc_col = self._misc_cell_props_col + + try: + cell_props.index = cell_props.index.reorder_levels(self._data_cols) + except AttributeError: + if cell_props.index.name in self._data_cols: + cell_props = cell_props.reset_index() + + try: + cell_props = cell_props.set_index( + self._data_cols, verify_integrity=True + ) + except ValueError: + warnings.warn( + "duplicate cell rows will be dropped after first occurrence" + ) + cell_props = cell_props.drop_duplicates(self._data_cols) + cell_props = cell_props.set_index(self._data_cols) + + if misc_col in cell_props: + try: + cell_props[misc_col] = cell_props[misc_col].apply(literal_eval) + except ValueError: + pass # data already parsed, no literal eval needed + else: + warnings.warn("parsed cell property dict column from string literal") + + cell_properties = cell_props.combine_first(self.cell_properties) + # import ipdb; ipdb.set_trace() + # cell_properties[misc_col] = self.cell_properties[misc_col].combine( + # cell_properties[misc_col], + # lambda x, y: {**(x if pd.notna(x) else {}), **(y if pd.notna(y) else {})}, + # fill_value={}, + # ) + + self._cell_properties = cell_properties.sort_index() + + def _cell_properties_from_dict( + self, cell_props: dict[T, dict[T, dict[Any, Any]]] + ) -> None: + """Private handler for updating :attr:`cell_properties` from a doubly-nested dict + + Parameters + ---------- + cell_props + """ + # TODO: there may be a more efficient way to convert this to a dataframe instead + # of updating one-by-one via nested loop, but checking whether each prop_name + # belongs in a designated existing column or the misc. property dict column + # makes it more challenging. + # For now: only use nested loop update if non-misc. columns currently exist + if len(self.cell_properties.columns) > 1: + for item1 in cell_props: + for item2 in cell_props[item1]: + for prop_name, prop_val in cell_props[item1][item2].items(): + self.set_cell_property(item1, item2, prop_name, prop_val) + else: + cells = pd.MultiIndex.from_tuples( + [(item1, item2) for item1 in cell_props for item2 in cell_props[item1]], + names=self._data_cols, + ) + props_data = [cell_props[item1][item2] for item1, item2 in cells] + cell_props = pd.DataFrame( + {self._misc_cell_props_col: props_data}, index=cells + ) + self._cell_properties_from_dataframe(cell_props) + + def set_cell_property( + self, item1: T, item2: T, prop_name: Any, prop_val: Any + ) -> None: + """Set a property of a cell i.e., incidence between items of different levels + + Parameters + ---------- + item1 : hashable + name of an item in level 0 + item2 : hashable + name of an item in level 1 + prop_name : hashable + name of the cell property to set + prop_val : any + value of the cell property to set + + See Also + -------- + get_cell_property, get_cell_properties + """ + if item2 in self.elements[item1]: + if prop_name in self.properties: + self._cell_properties.loc[(item1, item2), prop_name] = pd.Series( + [prop_val] + ) + else: + try: + self._cell_properties.loc[ + (item1, item2), self._misc_cell_props_col + ].update({prop_name: prop_val}) + except KeyError: + self._cell_properties.loc[(item1, item2), :] = { + self._misc_cell_props_col: {prop_name: prop_val} + } + + def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: + """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 + + Parameters + ---------- + indices : array_like of int + indices of item label(s) in `level` to restrict to + **kwargs + Extra arguments to :class:`EntitySet` constructor + + Returns + ------- + EntitySet + + See Also + -------- + restrict_to_indices + """ + restricted = self.restrict_to_indices( + indices, misc_cell_props_col=self._misc_cell_props_col, **kwargs + ) + if not self.cell_properties.empty: + cell_properties = self.cell_properties.loc[ + list(restricted.uidset) + ].reset_index() + restricted.assign_cell_properties(cell_properties) + return restricted + + def restrict_to_levels( + self, + levels: int | Iterable[int], + weights: bool = False, + aggregateby: Optional[str] = "sum", + keep_memberships: bool = True, + **kwargs, + ) -> EntitySet: + """Create a new EntitySet by restricting to a subset of levels (columns) in the + underlying data table + + + Parameters + ---------- + levels : array-like of int + indices of a subset of levels (columns) of data + weights : bool, default=False + If True, aggregate existing cell weights to get new cell weights. + Otherwise, all new cell weights will be 1. + aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', \ + 'min', None}, optional + Method to aggregate weights of duplicate rows in data table + If None or `weights`=False then all new cell weights will be 1 + keep_memberships : bool, default=True + Whether to preserve membership information for the discarded level when + the new ``EntitySet`` is restricted to a single level + **kwargs + Extra arguments to :class:`EntitySet` constructor + + Returns + ------- + EntitySet + + Raises + ------ + KeyError + If `levels` contains any invalid values + """ + restricted = self._restrict_to_levels( + levels, + weights, + aggregateby, + misc_cell_props_col=self._misc_cell_props_col, + **kwargs, + ) + + if keep_memberships: + # use original memberships to set memberships for the new EntitySet + # TODO: This assumes levels=[1], add explicit checks for other cases + restricted._state_dict["memberships"] = self.memberships + + return restricted + def build_dataframe_from_entity( entity: pd.DataFrame @@ -1652,3 +1944,88 @@ def build_dataframe_from_entity( # create an empty dataframe return pd.DataFrame() + + +def restrict_to_two_columns( + entity: Optional[ + pd.DataFrame + | Mapping[T, Iterable[T]] + | Iterable[Iterable[T]] + | Mapping[T, Mapping[T, Any]] + ], + data: Optional[np.ndarray], + labels: Optional[OrderedDict[T, Sequence[T]]], + cell_properties: Optional[ + Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] + ], + weight_col: str | int, + weights: Optional[Sequence[float] | float | int | str], + level1: str | int, + level2: str | int, + misc_cell_props_col: str, +): + """Restrict columns on entity or data as needed; if data is restricted, also restrict labels""" + if isinstance(entity, pd.DataFrame) and len(entity.columns) > 2: + _log.info(f"Processing parameter of 'entity' of type {type(entity)}...") + # metadata columns are not considered levels of data, + # remove them before indexing by level + # if isinstance(cell_properties, str): + # cell_properties = [cell_properties] + + prop_cols = [] + if isinstance(cell_properties, Sequence): + for col in {*cell_properties, misc_cell_props_col}: + if col in entity: + _log.debug(f"Adding column to prop_cols: {col}") + prop_cols.append(col) + + # meta_cols = prop_cols + # if weights in entity and weights not in meta_cols: + # meta_cols.append(weights) + # # _log.debug(f"meta_cols: {meta_cols}") + if weight_col in prop_cols: + prop_cols.remove(weight_col) + if weight_col not in entity: + entity[weight_col] = weights + + # if both levels are column names, no need to index by level + if isinstance(level1, int): + level1 = entity.columns[level1] + if isinstance(level2, int): + level2 = entity.columns[level2] + # if isinstance(level1, str) and isinstance(level2, str): + columns = [level1, level2, weight_col] + prop_cols + # if one or both of the levels are given by index, get column name + # else: + # all_columns = entity.columns.drop(meta_cols) + # columns = [ + # all_columns[lev] if isinstance(lev, int) else lev + # for lev in (level1, level2) + # ] + + # if there is a column for cell properties, convert to separate DataFrame + # if len(prop_cols) > 0: + # cell_properties = entity[[*columns, *prop_cols]] + + # if there is a column for weights, preserve it + # if weights in entity and weights not in prop_cols: + # columns.append(weights) + # _log.debug(f"columns: {columns}") + + # pass level1, level2, and weights (optional) to Entity constructor + entity = entity[columns] + + # if a 2D ndarray is passed, restrict to two columns if needed + elif isinstance(data, np.ndarray): + + if data.ndim == 2 and data.shape[1] > 2: + data = data[:, (level1, level2)] + + # should only change labels if 'data' is passed + # if a dict of labels is provided, restrict to labels for two columns if needed + if isinstance(labels, dict) and len(labels) > 2: + labels = { + col: labels[col] for col in [level1, level2] + } # example: { 0: ['e1', 'e2', ...], 1: ['n1', ...] } + + return entity, data, labels diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index f1f5fd93..b6fb2a8d 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -4,6 +4,7 @@ from collections.abc import Iterable from collections import UserList from hypernetx.classes import EntitySet +from hypernetx.classes.entityset import restrict_to_two_columns from pandas import DataFrame, Series @@ -92,18 +93,6 @@ def test_index(self, sbs): assert ent_sbs.index("nodes", "K") == (1, 3) -@pytest.mark.xfail( - reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" -) -def test_level(sbs): - # TODO: at some point we are casting out and back to categorical dtype without - # preserving categories ordering from `labels` provided to constructor - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.level("I") == (0, 5) # fails - assert ent_sbs.level("K") == (1, 3) - assert ent_sbs.level("K", max_level=0) is None - - class TestEntitySetOnHarryPotterDataSet: def test_entityset_from_ndarray(self, harry_potter): ent_hp = EntitySet( @@ -149,6 +138,61 @@ def test_restrict_to_indices(self, harry_potter): } +#### testing entityset helpers + + +def test_restrict_to_two_columns_on_ndarray(harry_potter): + data = np.asarray(harry_potter.data) + labels = harry_potter.labels + expected_num_cols = 2 + expected_ndarray_first_row = np.array([1, 1]) + + entity, data, labels = restrict_to_two_columns( + entity=None, + data=data, + labels=labels, + cell_properties=None, + weight_col="cell_weights", + weights=1, + level1=0, + level2=1, + misc_cell_props_col="properties", + ) + + assert entity is None + assert len(labels) == 2 + assert 0 in labels + assert 1 in labels + + print(data) + print(type(data[0])) + + assert data.shape[1] == expected_num_cols + assert np.array_equal(data[0], expected_ndarray_first_row) + + +@pytest.mark.skip(reason="TODO: implement") +def test_restrict_to_two_columns_on_dataframe(sbs): + pass + + +@pytest.mark.skip(reason="TODO: implement") +def build_dataframe_from_entity_on_dataframe(sbs): + pass + + +@pytest.mark.xfail( + reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" +) +def test_level(sbs): + # TODO: at some point we are casting out and back to categorical dtype without + # preserving categories ordering from `labels` provided to constructor + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.level("I") == (0, 5) # fails + assert ent_sbs.level("K") == (1, 3) + assert ent_sbs.level("K", max_level=0) is None + + @pytest.mark.xfail( reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" ) diff --git a/hypernetx/utils/toys/harrypotter.py b/hypernetx/utils/toys/harrypotter.py index 69eec2eb..637b5299 100644 --- a/hypernetx/utils/toys/harrypotter.py +++ b/hypernetx/utils/toys/harrypotter.py @@ -74,6 +74,6 @@ def __init__(self, cols=None): self.arr = imat slabels = OrderedDict() - for cdx, c in enumerate(list(ldict.keys())): - slabels.update({c: np.array(list(ldict[c].keys()))}) + for col_idx, col in enumerate(list(ldict.keys())): + slabels.update({col_idx: np.array(list(ldict[col].keys()))}) self.labels = slabels From 87804559615a0b15e4bad518a13ae07399f38913 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 12:31:56 -0700 Subject: [PATCH 13/76] HYP-339 Cleanup constructor; add comments --- hypernetx/classes/entityset.py | 32 ++++++++++++++--------- hypernetx/classes/tests/test_entityset.py | 2 +- 2 files changed, 21 insertions(+), 13 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 3fc11544..9dcb327b 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -147,8 +147,7 @@ def __init__( self._state_dict = {} self._misc_cell_props_col = misc_cell_props_col - # process certain parameters - ## Restrict to two columns on entity, data, labels + # Restrict to two columns on entity, data, labels entity, data, labels = restrict_to_two_columns( entity, data, @@ -161,6 +160,7 @@ def __init__( misc_cell_props_col, ) + # build initial dataframe if isinstance(data, np.ndarray) and entity is None: self._build_dataframe_from_ndarray(data, labels) else: @@ -172,12 +172,13 @@ def __init__( self._dataframe, weights=weights, weight_col=weight_col ) - self._data_cols = [] - self._init_data_cols(data_cols) + # create data_cols + self._create_data_cols(data_cols) # each entity data column represents one dimension of the data # (data updates can only add or remove rows, so this isn't stored in state dict) self._dimsize = len(self._data_cols) + # remove any row dupes # import ipdb; ipdb.set_trace() self._dataframe, _ = remove_row_duplicates( self._dataframe, @@ -186,11 +187,11 @@ def __init__( aggregateby=aggregateby, ) - self._misc_props_col = misc_props_col - self._init_properties(level_col, id_col, misc_props_col) - self.assign_properties(properties) + # create properties + self._create_properties(level_col, id_col, misc_props_col, properties) - self._assign_cell_properties(cell_properties) + # create cell properties (From old EntitySet) + self._create_assign_cell_properties(cell_properties) def _build_dataframe_from_ndarray( self, @@ -219,9 +220,10 @@ def _build_dataframe_from_ndarray( self._dataframe[col], categories=labels[col] ) - def _init_data_cols(self, data_cols: Sequence[T]) -> None: + def _create_data_cols(self, data_cols: Sequence[T]) -> None: """store a list of columns that hold entity data (not properties or weights)""" # import ipdb; ipdb.set_trace() + self._data_cols = [] if not self._dataframe.empty: for col in data_cols: if isinstance(col, int): @@ -229,8 +231,12 @@ def _init_data_cols(self, data_cols: Sequence[T]) -> None: else: self._data_cols.append(col) - def _init_properties( - self, level_col: str, id_col: str, misc_props_col: str + def _create_properties( + self, + level_col: str, + id_col: str, + misc_props_col: str, + properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]], ) -> None: item_levels = [ (level, item) @@ -242,8 +248,10 @@ def _init_properties( self._properties = pd.DataFrame( data=data, index=index, columns=["uid", "weight", misc_props_col] ).sort_index() + self._misc_props_col = misc_props_col + self.assign_properties(properties) - def _assign_cell_properties( + def _create_assign_cell_properties( self, cell_properties: Optional[ Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index b6fb2a8d..701c480e 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -138,7 +138,7 @@ def test_restrict_to_indices(self, harry_potter): } -#### testing entityset helpers +# testing entityset helpers def test_restrict_to_two_columns_on_ndarray(harry_potter): From f97a7b6a3b0ceffd605828ba4a119d401e3358d0 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 12:42:16 -0700 Subject: [PATCH 14/76] HYP-339 Add back collapse_identitcal_elements --- hypernetx/classes/entityset.py | 54 ++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 9dcb327b..c6d343ed 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1924,6 +1924,60 @@ def restrict_to_levels( return restricted + def collapse_identical_elements( + self, return_equivalence_classes: bool = False, **kwargs + ) -> EntitySet | tuple[EntitySet, dict[str, list[str]]]: + """Create a new :class:`EntitySet` by collapsing sets with the same set elements + + Each item in level 0 (first column) defines a set containing all the level 1 + (second column) items with which it appears in the same row of the underlying + data table. + + Parameters + ---------- + return_equivalence_classes : bool, default=False + If True, return a dictionary of equivalence classes keyed by new edge names + **kwargs + Extra arguments to :class:`EntitySet` constructor + + Returns + ------- + new_entity : EntitySet + new :class:`EntitySet` with identical sets collapsed; + if all sets are unique, the system of sets will be the same as the original. + equivalence_classes : dict of lists, optional + if `return_equivalence_classes`=True, + ``{collapsed set label: [level 0 item labels]}``. + """ + # group by level 0 (set), aggregate level 1 (set elements) as frozenset + collapse = ( + self._dataframe[self._data_cols] + .groupby(self._data_cols[0], as_index=False) + .agg(frozenset) + ) + + # aggregation method to rename equivalence classes as [first item]: [# items] + agg_kwargs = {"name": (self._data_cols[0], lambda x: f"{x.iloc[0]}: {len(x)}")} + if return_equivalence_classes: + # aggregation method to list all items in each equivalence class + agg_kwargs.update(equivalence_class=(self._data_cols[0], list)) + # group by frozenset of level 1 items (set elements), aggregate to get names of + # equivalence classes and (optionally) list of level 0 items (sets) in each + collapse = collapse.groupby(self._data_cols[1], as_index=False).agg( + **agg_kwargs + ) + # convert to nested dict representation of collapsed system of sets + collapse = collapse.set_index("name") + new_entity_dict = collapse[self._data_cols[1]].to_dict() + # construct new EntitySet from system of sets + new_entity = EntitySet(new_entity_dict, **kwargs) + + if return_equivalence_classes: + # lists of equivalent sets, keyed by equivalence class name + equivalence_classes = collapse.equivalence_class.to_dict() + return new_entity, equivalence_classes + return new_entity + def build_dataframe_from_entity( entity: pd.DataFrame From 8704e8e25949d02b75f81635cdd5644607c87037 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 13:02:01 -0700 Subject: [PATCH 15/76] HYP-339 Remove logs; cleanup type hints --- hypernetx/classes/entityset.py | 27 +++++---------------------- 1 file changed, 5 insertions(+), 22 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index c6d343ed..f16082d1 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -16,11 +16,6 @@ remove_row_duplicates, ) -from hypernetx.utils.log import get_logger - -_log = get_logger("entity_set") - - T = TypeVar("T", bound=Union[str, int]) @@ -164,7 +159,6 @@ def __init__( if isinstance(data, np.ndarray) and entity is None: self._build_dataframe_from_ndarray(data, labels) else: - _log.debug("Ignoring 'data' since 'entity' is given.") self._dataframe = build_dataframe_from_entity(entity, data_cols) # assign a new or existing column of the dataframe to hold cell weights @@ -785,7 +779,7 @@ def __call__(self, label_index=0): """ return iter(self.labels[self._data_cols[label_index]]) - def index(self, column: str, value: Optional[str] = None) -> int | tuple(int, int): + def index(self, column: str, value: Optional[str] = None) -> int | tuple[int, int]: """Get level index corresponding to a column and (optionally) the index of a value in that column The index of ``value`` is its position in the list given by ``self.labels[column]``, which is used @@ -912,7 +906,7 @@ def level( min_level: int = 0, max_level: Optional[int] = None, return_index: bool = True, - ) -> Optional[int, tuple(int, int)]: + ) -> Optional[int, tuple[int, int]]: """First level containing the given item label Order of levels corresponds to order of columns in `self.dataframe` @@ -1049,17 +1043,13 @@ def add_element( return self - def __add_from_dataframe(self, df: pd.DataFrame) -> EntitySet: + def __add_from_dataframe(self, df: pd.DataFrame) -> None: """Helper function to append rows to `self.dataframe` Parameters ---------- df : pd.DataFrame - Returns - ------- - self : EntitySet - """ if all(col in df for col in self._data_cols): new_data = pd.concat((self._dataframe, df), ignore_index=True) @@ -1118,7 +1108,7 @@ def remove_elements_from(self, arg_set): self.remove_element(item) return self - def remove_element(self, item) -> EntitySet: + def remove_element(self, item) -> None: """Removes all rows containing a specified item from the underlying data table Parameters @@ -1126,10 +1116,6 @@ def remove_element(self, item) -> EntitySet: item item label - Returns - ------- - self : EntitySet - See Also -------- remove : same functionality, accepts variable length argument list of item labels @@ -2008,6 +1994,7 @@ def build_dataframe_from_entity( return pd.DataFrame() +# TODO: Consider refactoring for simplicity; SonarLint states this function has a Cognitive Complexity of 26; recommends lowering to 15 def restrict_to_two_columns( entity: Optional[ pd.DataFrame @@ -2028,7 +2015,6 @@ def restrict_to_two_columns( ): """Restrict columns on entity or data as needed; if data is restricted, also restrict labels""" if isinstance(entity, pd.DataFrame) and len(entity.columns) > 2: - _log.info(f"Processing parameter of 'entity' of type {type(entity)}...") # metadata columns are not considered levels of data, # remove them before indexing by level # if isinstance(cell_properties, str): @@ -2038,13 +2024,11 @@ def restrict_to_two_columns( if isinstance(cell_properties, Sequence): for col in {*cell_properties, misc_cell_props_col}: if col in entity: - _log.debug(f"Adding column to prop_cols: {col}") prop_cols.append(col) # meta_cols = prop_cols # if weights in entity and weights not in meta_cols: # meta_cols.append(weights) - # # _log.debug(f"meta_cols: {meta_cols}") if weight_col in prop_cols: prop_cols.remove(weight_col) if weight_col not in entity: @@ -2072,7 +2056,6 @@ def restrict_to_two_columns( # if there is a column for weights, preserve it # if weights in entity and weights not in prop_cols: # columns.append(weights) - # _log.debug(f"columns: {columns}") # pass level1, level2, and weights (optional) to Entity constructor entity = entity[columns] From b00e209adfc1fa5a1f0a2c3ef327f3ac1dae13db Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 13:06:42 -0700 Subject: [PATCH 16/76] HYP-339 Add back get_cell_property and get_cell_properties methods --- hypernetx/classes/entityset.py | 61 ++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index f16082d1..5d333892 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1829,6 +1829,67 @@ def set_cell_property( self._misc_cell_props_col: {prop_name: prop_val} } + def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: + """Get a property of a cell i.e., incidence between items of different levels + + Parameters + ---------- + item1 : hashable + name of an item in level 0 + item2 : hashable + name of an item in level 1 + prop_name : hashable + name of the cell property to get + + Returns + ------- + prop_val : any + value of the cell property + + See Also + -------- + get_cell_properties, set_cell_property + """ + try: + cell_props = self.cell_properties.loc[(item1, item2)] + except KeyError: + raise + # TODO: raise informative exception + + try: + prop_val = cell_props.loc[prop_name] + except KeyError: + prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) + + return prop_val + + def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: + """Get all properties of a cell, i.e., incidence between items of different + levels + + Parameters + ---------- + item1 : hashable + name of an item in level 0 + item2 : hashable + name of an item in level 1 + + Returns + ------- + dict + ``{named cell property: cell property value, ..., misc. cell property column + name: {cell property name: cell property value}}`` + + See Also + -------- + get_cell_property, set_cell_property + """ + try: + cell_props = self.cell_properties.loc[(item1, item2)] + except KeyError: + raise + # TODO: raise informative exception + def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 From 6ebd5e8d120f7875eb63b4c7a0b5fa265fad42ad Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 13:28:50 -0700 Subject: [PATCH 17/76] HYP-339 Remove skipped tests on deprecated methods --- .../tests/test_hypergraph_nwhy_deprecate.py | 56 ------------------- .../tests/test_hypergraph_static_deprecate.py | 44 --------------- 2 files changed, 100 deletions(-) delete mode 100644 hypernetx/classes/tests/test_hypergraph_nwhy_deprecate.py delete mode 100644 hypernetx/classes/tests/test_hypergraph_static_deprecate.py diff --git a/hypernetx/classes/tests/test_hypergraph_nwhy_deprecate.py b/hypernetx/classes/tests/test_hypergraph_nwhy_deprecate.py deleted file mode 100644 index 7e7fbdc6..00000000 --- a/hypernetx/classes/tests/test_hypergraph_nwhy_deprecate.py +++ /dev/null @@ -1,56 +0,0 @@ -import re - -import pytest - -from hypernetx import Hypergraph -from hypernetx.exception import NWHY_WARNING - -pytestmark = pytest.mark.skip(reason="Deprecated attribute and/or method") - - -def test_get_linegraph_warn_nwhy(sbs): - H = Hypergraph(sbs.edgedict) - lg = H.get_linegraph(s=1, use_nwhy=False) - with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): - lg_nwhy = H.get_linegraph(s=1, use_nwhy=True) - assert lg == lg_nwhy - - -def test_recover_from_state_warn_nwhy(): - with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): - with pytest.raises(FileNotFoundError): - Hypergraph.recover_from_state(use_nwhy=True) - - -def test_convert_to_static_warn_nwhy(sbs): - H = Hypergraph(sbs.edgedict, static=False) - H_static = H.convert_to_static(use_nwhy=False) - with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): - H_static_nwhy = H.convert_to_static(use_nwhy=True) - - assert not H_static_nwhy.nwhy - assert H_static_nwhy.isstatic - assert H_static.incidence_dict == H_static_nwhy.incidence_dict - - -@pytest.mark.parametrize( - "constructor, example", - [ - (Hypergraph, "sbs_edgedict"), - (Hypergraph.from_bipartite, "complete_bipartite_example"), - # (Hypergraph.from_numpy_array, "array_example"), - # (Hypergraph.from_dataframe, "dataframe_example"), - ], -) -def test_constructors_warn_nwhy(constructor, example, request): - example = request.getfixturevalue(example) - H = constructor(example, use_nwhy=False) - with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): - H_nwhy = constructor(example, use_nwhy=True) - assert not H_nwhy.nwhy - assert H.incidence_dict == H_nwhy.incidence_dict - - -def test_add_nwhy_deprecated(sbs_hypergraph): - with pytest.deprecated_call(): - Hypergraph.add_nwhy(sbs_hypergraph) diff --git a/hypernetx/classes/tests/test_hypergraph_static_deprecate.py b/hypernetx/classes/tests/test_hypergraph_static_deprecate.py deleted file mode 100644 index 86c39bd4..00000000 --- a/hypernetx/classes/tests/test_hypergraph_static_deprecate.py +++ /dev/null @@ -1,44 +0,0 @@ -import pytest - -from hypernetx import Hypergraph, EntitySet, EntitySet - -pytestmark = pytest.mark.skip(reason="Deprecated attribute and/or method") - - -def test_static_hypergraph_constructor_setsystem(sbs): - H = Hypergraph(sbs.edgedict, static=True) - assert isinstance(H.edges, EntitySet) - assert H.isstatic == True - assert H.nwhy == False - assert H.shape == (7, 6) - - -def test_static_hypergraph_constructor_entity(sbs): - E = EntitySet(data=sbs.data, labels=sbs.labels) - H = Hypergraph(E, static=True) - assert H.isstatic - assert "A" in H.edges.incidence_dict["P"] - - -def test_static_hypergraph_get_id(sbs): - H = Hypergraph(EntitySet(data=sbs.data, labels=sbs.labels)) - assert H.get_id("V") == 6 - assert H.get_id("S", edges=True) == 2 - - -def test_static_hypergraph_get_name(sbs): - H = Hypergraph(EntitySet(data=sbs.data, labels=sbs.labels)) - assert H.get_name(1) == "C" - assert H.get_name(1, edges=True) == "R" - - -def test_static_hypergraph_get_linegraph(lesmis): - H = Hypergraph(lesmis.edgedict, static=True) - assert H.shape == (40, 8) - G = H.get_linegraph(edges=True, s=2) - assert G.number_of_edges, G.number_of_nodes == (8, 8) - - -def test_static_hypergraph_s_connected_components(lesmis): - H = Hypergraph(lesmis.edgedict, static=True) - assert {7, 8} in list(H.s_connected_components(edges=True, s=4)) From d44947b03320b06da9b0603acb01ce752a41a7ae Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 17 Aug 2023 13:39:52 -0700 Subject: [PATCH 18/76] HYP-339 Cleanup tests --- .../tests/test_hypergraph_factory_methods.py | 55 +++++-------------- .../classes/tests/test_nx_hnx_agreement.py | 12 ++-- 2 files changed, 19 insertions(+), 48 deletions(-) diff --git a/hypernetx/classes/tests/test_hypergraph_factory_methods.py b/hypernetx/classes/tests/test_hypergraph_factory_methods.py index a72af049..36c67068 100644 --- a/hypernetx/classes/tests/test_hypergraph_factory_methods.py +++ b/hypernetx/classes/tests/test_hypergraph_factory_methods.py @@ -1,10 +1,8 @@ -from collections import OrderedDict - import pytest import numpy as np import pandas as pd import networkx as nx -from hypernetx import Hypergraph, EntitySet +from hypernetx import Hypergraph def test_from_bipartite(): @@ -21,37 +19,14 @@ def test_from_bipartite(): assert "Hypergraph is not s-connected." in str(excinfo.value) -@pytest.mark.skip(reason="Deprecated attribute and/or method") -@pytest.mark.parametrize("static", [(True), (False)]) -def test_hypergraph_from_bipartite_and_from_constructor_should_be_equal(sbs, static): - edgedict = OrderedDict(sbs.edgedict) - - bipartite_graph = Hypergraph(edgedict).bipartite() - hg_from_bipartite = Hypergraph.from_bipartite(bipartite_graph, static=static) - - hg_from_constructor = Hypergraph(EntitySet(edgedict), static=static) - - assert hg_from_bipartite.isstatic == hg_from_constructor.isstatic - - assert hg_from_bipartite.shape == hg_from_constructor.shape - - incidence_dict_hg_from_bipartite = { - key: sorted(value) for key, value in hg_from_bipartite.incidence_dict.items() - } - incidence_dict_hg_from_constructor = { - key: sorted(value) for key, value in hg_from_constructor.incidence_dict.items() - } - assert incidence_dict_hg_from_bipartite == incidence_dict_hg_from_constructor - - -@pytest.mark.skip(reason="Deprecated attribute and/or method") +# TODO: Fails when Hypergraph calls entitySet.elements_by_level def test_from_numpy_array(): M = np.array([[0, 1, 1, 0, 1], [1, 1, 1, 1, 1], [1, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) h = Hypergraph.from_numpy_array(M) assert "v1" in h.edges["e0"] assert "e1" not in h.nodes.memberships["v2"] with pytest.raises(Exception) as excinfo: - h = Hypergraph.from_numpy_array(M, node_names=["A"]) + Hypergraph.from_numpy_array(M, node_names=["A"]) assert "Number of node names does not match number of rows" in str(excinfo.value) node_names = ["A", "B", "C", "D"] edge_names = ["a", "b", "c", "d", "e"] @@ -61,7 +36,6 @@ def test_from_numpy_array(): assert "B" in h.edges["a"] -@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_numpy_array_with_key(): M = np.array([[5, 0, 7, 2], [6, 8, 1, 1], [2, 5, 1, 9]]) h = Hypergraph.from_numpy_array( @@ -74,7 +48,6 @@ def test_from_numpy_array_with_key(): assert "C" not in h.edges["a"] -@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_dataframe(): M = np.array([[1, 1, 0, 0], [0, 1, 1, 0], [1, 0, 1, 0]]) index = ["A", "B", "C"] @@ -86,7 +59,6 @@ def test_from_dataframe(): assert "C" in h.edges["a"] -@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_dataframe_with_key(): M = np.array([[5, 0, 7, 2], [6, 8, 1, 1], [2, 5, 1, 9]]) index = ["A", "B", "C"] @@ -97,7 +69,6 @@ def test_from_dataframe_with_key(): assert "C" not in h.edges["a"] -@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_dataframe_with_transforms_and_fillna(dataframe): df = dataframe.df @@ -116,13 +87,13 @@ def test_from_dataframe_with_transforms_and_fillna(dataframe): assert "A" not in h.edges["b"] h = Hypergraph.from_incidence_dataframe(df, fillna=1) assert "A" in h.edges["b"] - h = Hypergraph.from_incidence_dataframe(df, transforms=[key1, key2]) - assert "A" in h.edges["c"] - assert "C" not in h.edges["b"] - h = Hypergraph.from_incidence_dataframe(df, transforms=[key2, key3]) - assert "C" in h.edges["b"] - h = Hypergraph.from_incidence_dataframe(df, transforms=[key3, key1], key=key2) - assert "A" not in h.edges["a"] - assert "B" in h.edges["b"] - assert "C" not in h.edges["c"] - assert "C" in h.edges["a"] + # h = Hypergraph.from_incidence_dataframe(df, transforms=[key1, key2]) + # assert "A" in h.edges["c"] + # assert "C" not in h.edges["b"] + # h = Hypergraph.from_incidence_dataframe(df, transforms=[key2, key3]) + # assert "C" in h.edges["b"] + # h = Hypergraph.from_incidence_dataframe(df, transforms=[key3, key1], key=key2) + # assert "A" not in h.edges["a"] + # assert "B" in h.edges["b"] + # assert "C" not in h.edges["c"] + # assert "C" in h.edges["a"] diff --git a/hypernetx/classes/tests/test_nx_hnx_agreement.py b/hypernetx/classes/tests/test_nx_hnx_agreement.py index 8f027923..edc2d34a 100644 --- a/hypernetx/classes/tests/test_nx_hnx_agreement.py +++ b/hypernetx/classes/tests/test_nx_hnx_agreement.py @@ -54,9 +54,9 @@ def test_neighbors(G, H): assert_are_same_sets(G[v], H[v]) -# def test_edges_iter(G, H): -# """ -# Confirm that the edges() function returns an iterator over the edges -# """ -# breakpoint() -# assert_are_same_set_of_sets(G.edges(), H.edges()) +@pytest.mark.xfail( + reason="Confirm that the edges() function returns an iterator over the edges" +) +def test_edges_iter(G, H): + # breakpoint() + assert_are_same_set_of_sets(G.edges(), H.edges()) From fd12f9257af5c7bad314cb77cf017a7ded9a8f5b Mon Sep 17 00:00:00 2001 From: Brenda Praggastis <39808911+brendapraggastis@users.noreply.github.com> Date: Mon, 21 Aug 2023 10:49:11 -0700 Subject: [PATCH 19/76] updated typing error --- hypernetx/classes/entityset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 5d333892..807e657f 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -906,7 +906,7 @@ def level( min_level: int = 0, max_level: Optional[int] = None, return_index: bool = True, - ) -> Optional[int, tuple[int, int]]: + ) -> int | tuple[int, int] | None : """First level containing the given item label Order of levels corresponds to order of columns in `self.dataframe` From 7a8c1edbc2350218504d278f9eb4b66dc5a98fd0 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 21 Aug 2023 12:48:37 -0700 Subject: [PATCH 20/76] HYP-339 Fix memberships property to account for level 1 entitysets; cleanup --- hypernetx/classes/entityset.py | 4 +++- hypernetx/classes/hypergraph.py | 2 +- hypernetx/classes/tests/test_hypergraph_factory_methods.py | 1 - hypernetx/classes/tests/test_nx_hnx_agreement.py | 4 +--- 4 files changed, 5 insertions(+), 6 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 807e657f..b5149beb 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -581,6 +581,8 @@ def elements_by_level(self, level1: int, level2: int) -> dict[Any, AttrList]: elements_by_column : same functionality, takes column names instead of level indices """ + if len(self._data_cols) == 1: + return self._state_dict["memberships"] col1 = self._data_cols[level1] col2 = self._data_cols[level2] return self.elements_by_column(col1, col2) @@ -906,7 +908,7 @@ def level( min_level: int = 0, max_level: Optional[int] = None, return_index: bool = True, - ) -> int | tuple[int, int] | None : + ) -> int | tuple[int, int] | None: """First level containing the given item label Order of levels corresponds to order of columns in `self.dataframe` diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 8d32a2fa..9e4de7ba 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -2243,7 +2243,7 @@ def from_numpy_array( # Validate the size of the node and edge arrays M = np.array(M) - if len(M.shape) != (2): + if len(M.shape) != 2: raise HyperNetXError("Input requires a 2 dimensional numpy array") # apply boolean key if available if key is not None: diff --git a/hypernetx/classes/tests/test_hypergraph_factory_methods.py b/hypernetx/classes/tests/test_hypergraph_factory_methods.py index 36c67068..72ccea8d 100644 --- a/hypernetx/classes/tests/test_hypergraph_factory_methods.py +++ b/hypernetx/classes/tests/test_hypergraph_factory_methods.py @@ -19,7 +19,6 @@ def test_from_bipartite(): assert "Hypergraph is not s-connected." in str(excinfo.value) -# TODO: Fails when Hypergraph calls entitySet.elements_by_level def test_from_numpy_array(): M = np.array([[0, 1, 1, 0, 1], [1, 1, 1, 1, 1], [1, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) h = Hypergraph.from_numpy_array(M) diff --git a/hypernetx/classes/tests/test_nx_hnx_agreement.py b/hypernetx/classes/tests/test_nx_hnx_agreement.py index edc2d34a..79b90167 100644 --- a/hypernetx/classes/tests/test_nx_hnx_agreement.py +++ b/hypernetx/classes/tests/test_nx_hnx_agreement.py @@ -54,9 +54,7 @@ def test_neighbors(G, H): assert_are_same_sets(G[v], H[v]) -@pytest.mark.xfail( - reason="Confirm that the edges() function returns an iterator over the edges" -) +@pytest.mark.xfail(reason="Hypergraph edges do not match edges in nx graph") def test_edges_iter(G, H): # breakpoint() assert_are_same_set_of_sets(G.edges(), H.edges()) From 9b64403997071ce1d9b195df5b977059b552a51b Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 22 Aug 2023 12:19:11 -0700 Subject: [PATCH 21/76] HYP-334 Fix Hypergraph constructor on empty dictionary; add test --- hypernetx/classes/entityset.py | 8 ++++++++ hypernetx/classes/hypergraph.py | 11 ++++++++--- hypernetx/classes/tests/test_hypergraph.py | 7 +++++++ 3 files changed, 23 insertions(+), 3 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index b5149beb..adf47c21 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1,5 +1,6 @@ from __future__ import annotations +import copy import warnings from ast import literal_eval from collections import OrderedDict, defaultdict @@ -1259,6 +1260,9 @@ def _restrict_to_levels( """ levels = np.asarray(levels) + # the following line of code returns an array of boolean values + # numpy compares arrays using element-wise operations, meaning that it will compare the value in each index + # in one array to the corresponding index in the other array and save the result in a numpy array invalid_levels = (levels < 0) | (levels >= self.dimsize) if invalid_levels.any(): raise KeyError(f"Invalid levels: {levels[invalid_levels]}") @@ -1958,6 +1962,10 @@ def restrict_to_levels( KeyError If `levels` contains any invalid values """ + # check for an empty EntitySet and return a copy + if self.empty: + return copy.deepcopy(self) + restricted = self._restrict_to_levels( levels, weights, diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 9e4de7ba..ef7581cd 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -548,12 +548,17 @@ def props2dict(df=None): self._edges = self.E self._nodes = self.E.restrict_to_levels([1]) - self._dataframe = self.E.cell_properties.reset_index() self._data_cols = data_cols = [self._edge_col, self._node_col] - self._dataframe[data_cols] = self._dataframe[data_cols].astype("category") + + self._dataframe = self.E.cell_properties + if self._dataframe is not None: + self._dataframe = self._dataframe.reset_index() + self._dataframe[data_cols] = self._dataframe[data_cols].astype( + "category" + ) + self._set_default_state() self.__dict__.update(locals()) - self._set_default_state() @property def edges(self): diff --git a/hypernetx/classes/tests/test_hypergraph.py b/hypernetx/classes/tests/test_hypergraph.py index 3f8f5228..81181278 100644 --- a/hypernetx/classes/tests/test_hypergraph.py +++ b/hypernetx/classes/tests/test_hypergraph.py @@ -341,6 +341,13 @@ def test_construct_empty_hypergraph(): assert h.nodes.is_empty() +def test_construct_hypergraph_empty_dict(): + h = Hypergraph(dict()) + assert h.shape == (0, 0) + assert h.edges.is_empty() + assert h.nodes.is_empty() + + def test_static_hypergraph_s_connected_components(lesmis): H = Hypergraph(lesmis.edgedict) assert {7, 8} in list(H.s_connected_components(edges=True, s=4)) From 33c7dda8f7e9254db21f8d4f25188c6ffbf6320b Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 22 Aug 2023 13:32:42 -0700 Subject: [PATCH 22/76] HYP-339 Add tests stubs for all properties and methods of EntitySet --- Makefile | 6 +- hypernetx/classes/tests/test_entityset.py | 169 +++++++++++++++++++++- 2 files changed, 167 insertions(+), 8 deletions(-) diff --git a/Makefile b/Makefile index 17933a8a..786d1b77 100644 --- a/Makefile +++ b/Makefile @@ -21,7 +21,11 @@ test-ci-github: test-deps @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' @$(PYTHON3) -m tox -.PHONY: test, test-ci, test-ci-github +test-coverage: test-deps + coverage run --source=hypernetx -m pytest + coverage html + +.PHONY: test, test-ci, test-ci-github, test-coverage ## Continuous Deployment ## Assumes that scripts are run on a container or test server VM diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 701c480e..ff9e1f37 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -41,6 +41,7 @@ def test_entityset_from_dataframe(): class TestEntitySetOnSevenBySixDataset: + # Tests on different inputs for entity and data def test_entityset_from_dictionary(self, sbs): ent = EntitySet(entity=sbs.edgedict) assert len(ent.elements) == 6 @@ -55,29 +56,178 @@ def test_entityset_from_ndarray_sbs(self, sbs): assert "I" in ent_sbs assert "K" in ent_sbs + # Tests for properties + @pytest.mark.skip(reason="TODO: implement") + def test_cell_properties(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_cell_weights(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_children(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_data(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_dataframe(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_dimensions(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_dimsize(self): + pass + def test_dimensions_equal_dimsize(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.dimsize == len(ent_sbs.dimensions) - def test_uidset_by_level(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + @pytest.mark.skip(reason="TODO: implement") + def test_elements(self): + pass - assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} - assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} + @pytest.mark.skip(reason="TODO: implement") + def test_empty(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_incidence_dict(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_isstatic(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_labels(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_memberships(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_properties(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_uid(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_uidset(self): + pass + + # Tests for methods + @pytest.mark.skip(reason="TODO: implement") + def test_add(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_add_element(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_add_elements_from(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_assign_properties(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_collapse_identitical_elements(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_elements_by_column(self): + pass def test_elements_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.elements_by_level(0, 1) + @pytest.mark.skip(reason="TODO: implement") + def test_encode(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_get_cell_properties(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_get_cell_property(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_get_properties(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_get_property(self): + pass + def test_incidence_matrix(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) + def test_index(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.index("nodes") == 1 + assert ent_sbs.index("nodes", "K") == (1, 3) + def test_indices(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.indices("nodes", "K") == [3] assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] + @pytest.mark.skip(reason="TODO: implement") + def test_is_empty(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_level(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_remove(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_remove_elements(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_restrict_to(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_restrict_to_indices(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_restrict_to_levels(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_set_cell_property(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_set_property(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_size(self): + pass + def test_translate(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.translate(0, 0) == "P" @@ -87,10 +237,15 @@ def test_translate_arr(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] - def test_index(self, sbs): + @pytest.mark.skip(reason="TODO: implement") + def test_uidset_by_column(self): + pass + + def test_uidset_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.index("nodes") == 1 - assert ent_sbs.index("nodes", "K") == (1, 3) + + assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} + assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} class TestEntitySetOnHarryPotterDataSet: From 42dfcd838f191ba7cb11623b704fe392fc4b2f71 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 23 Aug 2023 16:24:56 -0700 Subject: [PATCH 23/76] HYP-342 Update GH Workflows --- .github/workflows/ci.yml | 7 +++++++ .github/workflows/documentation.yml | 2 +- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index fd4246d0..b8826ea6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -59,3 +59,10 @@ jobs: - name: Run tests run: | make test-ci-github + - name: Install documentation dependencies + run: | + pip install sphinx sphinx_rtd_theme + pip install .'[documentation]' + - name: Check documentation build + run: | + sphinx-build docs/source _build diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 1e795668..745e289a 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -1,5 +1,5 @@ name: Docs -on: [push, pull_request, workflow_dispatch] +on: [push, workflow_dispatch] permissions: contents: write jobs: From 779a2c124a07b0bb808f4daefab423be8ff248b9 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 31 Aug 2023 11:07:15 -0700 Subject: [PATCH 24/76] HYP-347 Fix test fixture reference --- hypernetx/classes/tests/test_hypergraph.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hypernetx/classes/tests/test_hypergraph.py b/hypernetx/classes/tests/test_hypergraph.py index 7d4f594a..60774faa 100644 --- a/hypernetx/classes/tests/test_hypergraph.py +++ b/hypernetx/classes/tests/test_hypergraph.py @@ -311,8 +311,8 @@ def test_dual(sbs_hypergraph): assert list(H.dataframe.columns) == list(HD.dataframe.columns) -def test_dual_again(sbs_edgedict): - H = Hypergraph(sbs_edgedict, edge_col="Types", node_col="Values") +def test_dual_again(sbs): + H = Hypergraph(sbs.edgedict, edge_col="Types", node_col="Values") assert list(H.dataframe.columns[0:2]) == ["Types", "Values"] assert list(H.dual().dataframe.columns[0:2]) == ["Values", "Types"] assert list(H.dual(switch_names=False).dataframe.columns[0:2]) == [ From 4b69ca5ee6a2dd0fad9447a655b89878631c1554 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 19 Sep 2023 16:18:23 -0700 Subject: [PATCH 25/76] HYP-344 Reorganize tutorials --- ... 10 - Hypergraph Modularity and Clustering.ipynb | 0 .../{ => advanced}/Tutorial 5 - s-Centrality.ipynb | 0 ...ial 6 - Homology mod 2 for TriLoop Example.ipynb | 0 .../Tutorial 7 - Laplacians and Clustering.ipynb | 0 .../advanced}/Tutorial 8 - Generative Models.ipynb | 0 .../Tutorial 9 - Contagion on Hypergraphs.ipynb | 0 tutorials/{ => basic}/Tutorial 1 - HNX Basics.ipynb | 0 .../Tutorial 2 - Visualization Methods.ipynb | 0 .../Tutorial 3 - LesMis Case Study.ipynb | 0 ...utorial 4 - LesMis Visualizations-BookTour.ipynb | 0 {tutorials-jupyter => tutorials}/images/chunglu.png | Bin .../images/clus_workflow.png | Bin .../images/erdosrenyi.png | Bin .../images/genmodels_hypergraph.png | Bin .../widget}/Demo 1 - HNXWidget.ipynb | 0 ...- HNX Constructor and More Widget Examples.ipynb | 0 16 files changed, 0 insertions(+), 0 deletions(-) rename {tutorials-jupyter => tutorials/advanced}/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb (100%) rename tutorials/{ => advanced}/Tutorial 5 - s-Centrality.ipynb (100%) rename tutorials/{ => advanced}/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb (100%) rename {tutorials-jupyter => tutorials/advanced}/Tutorial 7 - Laplacians and Clustering.ipynb (100%) rename {tutorials-jupyter => tutorials/advanced}/Tutorial 8 - Generative Models.ipynb (100%) rename {tutorials-jupyter => tutorials/advanced}/Tutorial 9 - Contagion on Hypergraphs.ipynb (100%) rename tutorials/{ => basic}/Tutorial 1 - HNX Basics.ipynb (100%) rename tutorials/{ => basic}/Tutorial 2 - Visualization Methods.ipynb (100%) rename tutorials/{ => basic}/Tutorial 3 - LesMis Case Study.ipynb (100%) rename tutorials/{ => basic}/Tutorial 4 - LesMis Visualizations-BookTour.ipynb (100%) rename {tutorials-jupyter => tutorials}/images/chunglu.png (100%) rename {tutorials-jupyter => tutorials}/images/clus_workflow.png (100%) rename {tutorials-jupyter => tutorials}/images/erdosrenyi.png (100%) rename {tutorials-jupyter => tutorials}/images/genmodels_hypergraph.png (100%) rename {tutorials-jupyter => tutorials/widget}/Demo 1 - HNXWidget.ipynb (100%) rename {tutorials-jupyter => tutorials/widget}/Demo 2 - HNX Constructor and More Widget Examples.ipynb (100%) diff --git a/tutorials-jupyter/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb b/tutorials/advanced/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb similarity index 100% rename from tutorials-jupyter/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb rename to tutorials/advanced/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb diff --git a/tutorials/Tutorial 5 - s-Centrality.ipynb b/tutorials/advanced/Tutorial 5 - s-Centrality.ipynb similarity index 100% rename from tutorials/Tutorial 5 - s-Centrality.ipynb rename to tutorials/advanced/Tutorial 5 - s-Centrality.ipynb diff --git a/tutorials/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb b/tutorials/advanced/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb similarity index 100% rename from tutorials/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb rename to tutorials/advanced/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb diff --git a/tutorials-jupyter/Tutorial 7 - Laplacians and Clustering.ipynb b/tutorials/advanced/Tutorial 7 - Laplacians and Clustering.ipynb similarity index 100% rename from tutorials-jupyter/Tutorial 7 - Laplacians and Clustering.ipynb rename to tutorials/advanced/Tutorial 7 - Laplacians and Clustering.ipynb diff --git a/tutorials-jupyter/Tutorial 8 - Generative Models.ipynb b/tutorials/advanced/Tutorial 8 - Generative Models.ipynb similarity index 100% rename from tutorials-jupyter/Tutorial 8 - Generative Models.ipynb rename to tutorials/advanced/Tutorial 8 - Generative Models.ipynb diff --git a/tutorials-jupyter/Tutorial 9 - Contagion on Hypergraphs.ipynb b/tutorials/advanced/Tutorial 9 - Contagion on Hypergraphs.ipynb similarity index 100% rename from tutorials-jupyter/Tutorial 9 - Contagion on Hypergraphs.ipynb rename to tutorials/advanced/Tutorial 9 - Contagion on Hypergraphs.ipynb diff --git a/tutorials/Tutorial 1 - HNX Basics.ipynb b/tutorials/basic/Tutorial 1 - HNX Basics.ipynb similarity index 100% rename from tutorials/Tutorial 1 - HNX Basics.ipynb rename to tutorials/basic/Tutorial 1 - HNX Basics.ipynb diff --git a/tutorials/Tutorial 2 - Visualization Methods.ipynb b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb similarity index 100% rename from tutorials/Tutorial 2 - Visualization Methods.ipynb rename to tutorials/basic/Tutorial 2 - Visualization Methods.ipynb diff --git a/tutorials/Tutorial 3 - LesMis Case Study.ipynb b/tutorials/basic/Tutorial 3 - LesMis Case Study.ipynb similarity index 100% rename from tutorials/Tutorial 3 - LesMis Case Study.ipynb rename to tutorials/basic/Tutorial 3 - LesMis Case Study.ipynb diff --git a/tutorials/Tutorial 4 - LesMis Visualizations-BookTour.ipynb b/tutorials/basic/Tutorial 4 - LesMis Visualizations-BookTour.ipynb similarity index 100% rename from tutorials/Tutorial 4 - LesMis Visualizations-BookTour.ipynb rename to tutorials/basic/Tutorial 4 - LesMis Visualizations-BookTour.ipynb diff --git a/tutorials-jupyter/images/chunglu.png b/tutorials/images/chunglu.png similarity index 100% rename from tutorials-jupyter/images/chunglu.png rename to tutorials/images/chunglu.png diff --git a/tutorials-jupyter/images/clus_workflow.png b/tutorials/images/clus_workflow.png similarity index 100% rename from tutorials-jupyter/images/clus_workflow.png rename to tutorials/images/clus_workflow.png diff --git a/tutorials-jupyter/images/erdosrenyi.png b/tutorials/images/erdosrenyi.png similarity index 100% rename from tutorials-jupyter/images/erdosrenyi.png rename to tutorials/images/erdosrenyi.png diff --git a/tutorials-jupyter/images/genmodels_hypergraph.png b/tutorials/images/genmodels_hypergraph.png similarity index 100% rename from tutorials-jupyter/images/genmodels_hypergraph.png rename to tutorials/images/genmodels_hypergraph.png diff --git a/tutorials-jupyter/Demo 1 - HNXWidget.ipynb b/tutorials/widget/Demo 1 - HNXWidget.ipynb similarity index 100% rename from tutorials-jupyter/Demo 1 - HNXWidget.ipynb rename to tutorials/widget/Demo 1 - HNXWidget.ipynb diff --git a/tutorials-jupyter/Demo 2 - HNX Constructor and More Widget Examples.ipynb b/tutorials/widget/Demo 2 - HNX Constructor and More Widget Examples.ipynb similarity index 100% rename from tutorials-jupyter/Demo 2 - HNX Constructor and More Widget Examples.ipynb rename to tutorials/widget/Demo 2 - HNX Constructor and More Widget Examples.ipynb From c9d4e926ec4ec6c55a5230a4dbbb5295b656cf0a Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 11 Oct 2023 14:18:58 -0700 Subject: [PATCH 26/76] HYP-344 Add tutorial readmes, update Collab links --- Makefile | 15 ++++++-- README.md | 12 +++---- tutorials/advanced/README.md | 29 ++++++++++++++++ tutorials/basic/README.md | 32 ++++++++++++++++++ .../images/jupyter_notebook_screenshot.png | Bin 0 -> 21650 bytes tutorials/widget/README.md | 31 +++++++++++++++++ 6 files changed, 111 insertions(+), 8 deletions(-) create mode 100644 tutorials/advanced/README.md create mode 100644 tutorials/basic/README.md create mode 100644 tutorials/images/jupyter_notebook_screenshot.png create mode 100644 tutorials/widget/README.md diff --git a/Makefile b/Makefile index 786d1b77..0c7be1a9 100644 --- a/Makefile +++ b/Makefile @@ -15,7 +15,6 @@ test-ci: test-deps pre-commit install pre-commit run --all-files @$(PYTHON3) -m tox -e py38 -r - @$(PYTHON3) -m tox -e py38-notebooks -r test-ci-github: test-deps @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' @@ -53,13 +52,25 @@ version-deps: .PHONY: version-deps -#### Documentation +### Documentation docs-deps: @$(PYTHON3) -m pip install -e .'[documentation]' --use-pep517 .PHONY: docs-deps +## Tutorials + +.PHONY: tutorial-deps +tutorial-deps: + @$(PYTHON3) -m pip install .'[tutorials]' .'[widget]' --use-pep517 + +.PHONY: tutorials +tutorials: + jupyter notebook tutorials + + + ## Environment clean-venv: diff --git a/README.md b/README.md index e5098c1b..dae06123 100644 --- a/README.md +++ b/README.md @@ -72,25 +72,25 @@ Google Colab ------------ - + Open In Colab Tutorial 1 - HNX Basics
- + Open In Colab Tutorial 2 - Visualization Methods
- + Open In Colab Tutorial 3 - LesMis Case Study
- + Open In Colab Tutorial 4 - LesMis Visualizations-Book Tour @@ -102,7 +102,7 @@ Google Colab
- + Open In Colab Tutorial 6 - Homology mod2 for TriLoop Example @@ -112,7 +112,7 @@ Google Colab Jupyter Notebooks ----------------- -Additional tutorials that can be run as Jupyter Notebooks can be found in the 'tutorials-jupyter' folder. +Additional tutorials that can be run as Jupyter Notebooks are found under [tutorials](./tutorials). Installation ==================== diff --git a/tutorials/advanced/README.md b/tutorials/advanced/README.md new file mode 100644 index 00000000..09d36ab7 --- /dev/null +++ b/tutorials/advanced/README.md @@ -0,0 +1,29 @@ +# Overview + +These tutorials cover advanced topics in hypergraphs such as hypergraph metrics, homology, generating hypergraphs from +random models, modeling contagion with hypergraphs, and hypergraph modularity. + +# How to run the tutorials on Jupyter Notebook + +Create a virtual environment: + +`make venv` + +Activate the environment: + +`source venv-hnx/bin/activate` + +Navigate to the root of this repository. Install the required dependencies in order to run the Jupyter Notebooks: + +`make tutorials-deps` + +Once the dependencies have been installed, run the notebooks: + +`make tutorials` + +This command will open up the notebooks on a browser at the following URL: http://localhost:8888/tree + +Below is a screenshot of what to expect to see on the browser. Click a folder and open the desired +tutorial on your browser: + +![](../images/jupyter_notebook_screenshot.png) diff --git a/tutorials/basic/README.md b/tutorials/basic/README.md new file mode 100644 index 00000000..3db4f888 --- /dev/null +++ b/tutorials/basic/README.md @@ -0,0 +1,32 @@ +# Overview + +These tutorials provide an introduction to the HyperNetX library using graph data such as the [Les Miserables dataset from the +Stanford GraphBase](https://cs.stanford.edu/pub/sgb/sgb.tar.gz). The tutorials also show how to use the library's visualization tools +to visualize and analyze hypergraphs. + +# How to run the tutorials on Jupyter Notebook + +Create a virtual environment: + +`make venv` + + +Activate the environment: + +`source venv-hnx/bin/activate` + + +Navigate to the root of this repository. Install the required dependencies in order to run the Jupyter Notebooks: + +`make tutorials-deps` + +Once the dependencies have been installed, run the notebooks: + +`make tutorials` + +This command will open up the notebooks on a browser at the following URL: http://localhost:8888/tree + +Below is a screenshot of what to expect to see on the browser. Click a folder and open the desired +tutorial on your browser: + +![](../images/jupyter_notebook_screenshot.png) diff --git a/tutorials/images/jupyter_notebook_screenshot.png b/tutorials/images/jupyter_notebook_screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..6a47bd1347f192095b12669536f075784cce1dea GIT binary patch literal 21650 zcmdSBby!qg_%)0OQX(iI(ujykD%~I@D5Z1^A>BE2I3l2uigbyTbk`u=l2Sv5G($)? zynCKU;Q7Ay_wW0K*9DGe&N+LZefGZZwbs419~9-E;p39yqM@PT%ScPSLPJB3LqoeF zg?$YiX}P9ghlYk*Y$h(QC?hUTt7vayY-VYMh9>is%oev0mcHU?;u1gUzUCVuy)%`yN}>QAMJz1g%h&S)H&BopJFkGu<-0fR-_0 z@2Y=(<@%4kuGl#D`SxQ6lljvH`7R7JT%QNwX$_fZhSG&b(^rH$hRR(StO*IP;7na1 z>2Wz`ek8!mj6utuamGr+e}yw`T{EQVaBtyE#S~rtHVxXt>(lg4Z;M==4r_h&C$-QL zP4Vbv<*(nCOLug|qwb31N|c)fN)lz?nMtveN^bF_Yw)}|h=HaszhOv+*4q3&KDvSQ zu3anTJFENX1sM9X0)IMMTZw4{XEEpBhY(K}>}RLcG~PU6G5*ZX5c1&FW}`+kX7^1K znLgJSXWfX6n)~-4!qE-H8neV2dc5C0!M|deMb|x#pzp+qH!RQ_^Nv%Rg}n^x=-v!U zG`tc*lsy(J{J`*YSktMtQJkgqEInS)F&%?O1X*rJIP;c~0=oitxLU&P)Z%pPil6%x zk3KyKi@O(`z|p2*cC$`NxcYgeEOlj-x?N23jC;oE64w~JyJ9nS zAL=VKb$nDAWFWLZPV@No<+Rt5#)#F-Evi>Hiu!A@$(+Q)J>z?%_U1NGn^b9=Z|fPz zY~RpeKNXfG#H2*S8@d~C^>v}|i{`b5e>%UghJTQJG%U;AYb3^?R$mrfS6o)&jHZ`u z@*uad^3R6OJI*e-VfIhduRTQ%4Y$6qWDvjc6QkcreiXaGiuN57t)FLWt}qT~R9)c8 z^-T*=%S|pG%0Vc(_RgDDy)u)`}D~WM(JDJ ze2SH;{6FPbaa97$#Y(=eeRF%6rlcoy0in59t z0uz3h`%G7AQgSbeWYOjht`wGmuU&)flY%=pji00uD*5rf;q$F{TM
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--- /dev/null +++ b/tutorials/widget/README.md @@ -0,0 +1,31 @@ +# Overview + +These tutorials demonstrate how to use [hnxwidget](https://pypi.org/project/hnxwidget/), an interactive visualization tool +of HyperNetX. + +# How to run the tutorials on Jupyter Notebook + +Create a virtual environment: + +`make venv` + + +Activate the environment: + +`source venv-hnx/bin/activate` + + +Navigate to the root of this repository. Install the required dependencies in order to run the Jupyter Notebooks: + +`make tutorials-deps` + +Once the dependencies have been installed, run the notebooks: + +`make tutorials` + +This command will open up the notebooks on a browser at the following URL: http://localhost:8888/tree + +Below is a screenshot of what to expect to see on the browser. Click a folder and open the desired +tutorial on your browser: + +![](../images/jupyter_notebook_screenshot.png) From 83b492db610f360b85a5a51abfdb6d4fae16d0ec Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 31 Aug 2023 13:03:25 -0700 Subject: [PATCH 27/76] HYP-177 Refactor assign_cell_properties method --- hypernetx/classes/entityset.py | 36 ++++++++++++++++++++++------------ 1 file changed, 23 insertions(+), 13 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index bfded939..8bfe4673 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -26,11 +26,13 @@ class EntitySet: Parameters ---------- - entity : pandas.DataFrame, dict of lists or sets, list of lists or sets, optional + entity : pandas.DataFrame, dict of lists or sets, dict of dicts, list of lists or sets, optional If a ``DataFrame`` with N columns, represents N-dimensional entity data (data table). Otherwise, represents 2-dimensional entity data (system of sets). - TODO: Test for compatibility with list of Entities and update docs + data_cols : sequence of ints or strings, default=(0,1) + level1: str or int, default = 0 + level2: str or int, default = 1 data : numpy.ndarray, optional 2D M x N ``ndarray`` of ``ints`` (data table); sparse representation of an N-dimensional incidence tensor with M nonzero cells. @@ -45,7 +47,8 @@ class EntitySet: Ignored if `entity` is provided or `data` is not provided. uid : hashable, optional A unique identifier for the object - weights : str or sequence of float, optional + weight_col: string or int, default="cell_weights" + weights : sequence of float, float, int, str, default=1 User-specified cell weights corresponding to entity data. If sequence of ``floats`` and `entity` or `data` defines a data table, length must equal the number of rows. @@ -54,11 +57,11 @@ class EntitySet: If ``str`` and `entity` is a ``DataFrame``, must be the name of a column in `entity`. Otherwise, weight for all cells is assumed to be 1. - aggregateby : {'sum', 'last', count', 'mean','median', max', 'min', 'first', None} + aggregateby : {'sum', 'last', count', 'mean','median', max', 'min', 'first', None}, default="sum" Name of function to use for aggregating cell weights of duplicate rows when - `entity` or `data` defines a data table, default is "sum". + `entity` or `data` defines a data table. If None, duplicate rows will be dropped without aggregating cell weights. - Effectively ignored if `entity` defines a system of sets. + Ignored if `entity` defines a system of sets. properties : pandas.DataFrame or doubly-nested dict, optional User-specified properties to be assigned to individual items in the data, i.e., cell entries in a data table; sets or set elements in a system of sets. @@ -69,9 +72,13 @@ class EntitySet: (order of columns does not matter; see note for an example). If doubly-nested dict, ``{item level: {item label: {property name: property value}}}``. - misc_props_col, level_col, id_col : str, default="properties", "level, "id" + misc_props_col: str, default="properties" Column names for miscellaneous properties, level index, and item name in :attr:`properties`; see Notes for explanation. + level_col: str, default="level" + id_col : str, default="id" + cell_properties: sequence of int or str, pandas.DataFrame, or doubly-nested dict, optional + misc_cell_props_col: str, default="cell_properties" Notes ----- @@ -199,6 +206,9 @@ def _build_dataframe_from_ndarray( # DataFrame, translate the dataframe, and store the dict of labels in the state dict if not isinstance(labels, dict): + print( + f"Labels must be of type Dictionary. Labels is of type: {type(labels)}; labels: {labels}" + ) raise ValueError( f"Labels must be of type Dictionary. Labels is of type: {type(labels)}; labels: {labels}" ) @@ -259,6 +269,7 @@ def _create_assign_cell_properties( # ) self._cell_properties = pd.DataFrame(self._dataframe) self._cell_properties.set_index(self._data_cols, inplace=True) + # TODO: What about when cell_properties is a Sequence[T]? if isinstance(cell_properties, (dict, pd.DataFrame)): self.assign_cell_properties(cell_properties) else: @@ -270,7 +281,7 @@ def cell_properties(self) -> Optional[pd.DataFrame]: Returns ------- - pandas.Series, optional + pandas.DataFrame, optional Returns None if :attr:`dimsize` < 2 """ return self._cell_properties @@ -1358,15 +1369,14 @@ def assign_cell_properties( f"cell properties are not supported for 'dimsize'={self.dimsize}" ) - misc_col = misc_col or self._misc_cell_props_col - try: + if isinstance(cell_props, pd.DataFrame): + misc_col = misc_col or self._misc_cell_props_col cell_props = cell_props.rename( columns={misc_col: self._misc_cell_props_col} ) - except AttributeError: # handle cell props in nested dict format - self._cell_properties_from_dict(cell_props) - else: # handle cell props in DataFrame format self._cell_properties_from_dataframe(cell_props) + elif isinstance(cell_props, dict): + self._cell_properties_from_dict(cell_props) def assign_properties( self, From fb5f633671b38247875713391733a98c266fdd39 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 31 Aug 2023 15:50:10 -0700 Subject: [PATCH 28/76] HYP-177 Update tests --- hypernetx/classes/tests/test_entityset.py | 81 +++++++++++++++------- hypernetx/classes/tests/test_hypergraph.py | 8 +-- 2 files changed, 58 insertions(+), 31 deletions(-) diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index ff9e1f37..c4f1dd31 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -7,6 +7,7 @@ from hypernetx.classes.entityset import restrict_to_two_columns from pandas import DataFrame, Series +import pandas as pd def test_empty_entityset(): @@ -16,37 +17,63 @@ def test_empty_entityset(): assert es.elements == {} assert es.dimsize == 0 + assert isinstance(es.data, np.ndarray) + assert es.data.shape == (0, 0) -def test_entityset_from_dataframe(): - data_dict = { - 1: ["A", "D"], - 2: ["A", "C", "D"], - 3: ["D"], - 4: ["A", "B"], - 5: ["B", "C"], - } + assert es.labels == {} + assert es.cell_weights == {} + assert es.isstatic + assert es.incidence_dict == {} + assert "foo" not in es + assert es.incidence_matrix() is None - all_edge_pairs = Series(data_dict).explode() + # TODO: results in bound method issue + # assert es.size == 0 - entity = DataFrame( - {"edges": all_edge_pairs.index.to_list(), "nodes": all_edge_pairs.values} - ) + with (pytest.raises(AttributeError)): + es.get_cell_property("foo", "bar", "roma") + with (pytest.raises(AttributeError)): + es.get_cell_properties("foo", "bar") + with (pytest.raises(KeyError)): + es.set_cell_property("foo", "bar", "roma", "ff") + with (pytest.raises(KeyError)): + es.get_properties("foo") + # with(pytest.raises(KeyError)): + # es.get_property("foo", "bar") + with (pytest.raises(ValueError)): + es.set_property("foo", "bar", "roma") + + +class TestEntitySetOnDataframe: + def test_cell_properties(self, dataframe_example): + es = EntitySet(entity=dataframe_example) + + assert es.cell_properties.shape == (3, 1) - es = EntitySet(entity=entity) + def test_data(self, dataframe_example): + es = EntitySet(entity=dataframe_example) - assert not es.empty - assert len(es.elements) == 5 - assert es.dimsize == 2 - assert es.uid is None + data = es.data + + assert isinstance(data, np.ndarray) + assert data.shape == (3, 2) + assert not es.empty + assert len(es.elements) == 2 + assert es.dimsize == 2 + assert es.uid is None class TestEntitySetOnSevenBySixDataset: # Tests on different inputs for entity and data - def test_entityset_from_dictionary(self, sbs): + def test_entityset_with_dict(self, sbs): ent = EntitySet(entity=sbs.edgedict) assert len(ent.elements) == 6 - def test_entityset_from_ndarray_sbs(self, sbs): + def test_entityset_with_dict_data_cols(self, sbs): + ent = EntitySet(entity=sbs.edgedict, data_cols=["edges", "nodes"]) + assert len(ent.elements) == 6 + + def test_entityset_with_ndarray(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.size() == 6 @@ -56,10 +83,16 @@ def test_entityset_from_ndarray_sbs(self, sbs): assert "I" in ent_sbs assert "K" in ent_sbs + def test_entityset_with_ndarray_fail_on_labels(self, sbs): + with (pytest.raises(ValueError, match="Labels must be of type Dictionary.")): + EntitySet(data=np.asarray(sbs.data), labels=[]) + + def test_entityset_with_ndarray_fail_on_length_labels(self, sbs): + with (pytest.raises(ValueError, match="The length of labels must equal the length of columns in the dataframe.")): + EntitySet(data=np.asarray(sbs.data), labels=dict()) + + # Tests for properties - @pytest.mark.skip(reason="TODO: implement") - def test_cell_properties(self): - pass @pytest.mark.skip(reason="TODO: implement") def test_cell_weights(self): @@ -69,10 +102,6 @@ def test_cell_weights(self): def test_children(self): pass - @pytest.mark.skip(reason="TODO: implement") - def test_data(self): - pass - @pytest.mark.skip(reason="TODO: implement") def test_dataframe(self): pass diff --git a/hypernetx/classes/tests/test_hypergraph.py b/hypernetx/classes/tests/test_hypergraph.py index 60774faa..b183a01e 100644 --- a/hypernetx/classes/tests/test_hypergraph.py +++ b/hypernetx/classes/tests/test_hypergraph.py @@ -2,6 +2,8 @@ import numpy as np from hypernetx.classes.hypergraph import Hypergraph +from networkx.algorithms import bipartite + def test_hypergraph_from_iterable_of_sets(sbs): H = Hypergraph(sbs.edges) @@ -296,11 +298,7 @@ def test_edge_diameter(sbs): def test_bipartite(sbs_hypergraph): - from networkx.algorithms import bipartite - - h = sbs_hypergraph - b = h.bipartite() - assert bipartite.is_bipartite(b) + assert bipartite.is_bipartite(sbs_hypergraph.bipartite()) def test_dual(sbs_hypergraph): From d62ff4ce2047119dc438d03be88bb726e9700d4e Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 19 Sep 2023 16:43:09 -0700 Subject: [PATCH 29/76] HYP-177 Add helpers; update tests --- hypernetx/classes/entityset.py | 42 ++++---- hypernetx/classes/helpers.py | 26 +++++ hypernetx/classes/tests/conftest.py | 25 +++++ hypernetx/classes/tests/test_entityset.py | 112 +++++++++++----------- 4 files changed, 123 insertions(+), 82 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 8bfe4673..ce6dd83e 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -9,7 +9,7 @@ import numpy as np import pandas as pd -from scipy.sparse import csr_matrix +import scipy.sparse as sp from hypernetx.classes.helpers import ( AttrList, @@ -198,17 +198,12 @@ def __init__( def _build_dataframe_from_ndarray( self, data: pd.ndarray, - labels: Optional[OrderedDict[Union[str, int], Sequence[Union[str, int]]]], + labels: Optional[OrderedDict[T, Sequence[T]]], ) -> None: self._state_dict["data"] = data self._dataframe = pd.DataFrame(data) - # if a dict of labels was passed, use keys as column names in the - # DataFrame, translate the dataframe, and store the dict of labels in the state dict if not isinstance(labels, dict): - print( - f"Labels must be of type Dictionary. Labels is of type: {type(labels)}; labels: {labels}" - ) raise ValueError( f"Labels must be of type Dictionary. Labels is of type: {type(labels)}; labels: {labels}" ) @@ -216,10 +211,11 @@ def _build_dataframe_from_ndarray( raise ValueError( f"The length of labels must equal the length of columns in the dataframe. Labels is of length: {len(labels)}; dataframe is of length: {len(self._dataframe.columns)}" ) - + # use dict keys of 'labels' as column names in the DataFrame and store the dict of labels in the state dict self._dataframe.columns = labels.keys() self._state_dict["labels"] = labels + # translate the dataframe for col in self._dataframe: self._dataframe[col] = pd.Categorical.from_codes( self._dataframe[col], categories=labels[col] @@ -264,9 +260,6 @@ def _create_assign_cell_properties( ): # if underlying data is 2D (system of sets), create and assign cell properties if self.dimsize == 2: - # self._cell_properties = pd.DataFrame( - # columns=[*self._data_cols, self._misc_cell_props_col] - # ) self._cell_properties = pd.DataFrame(self._dataframe) self._cell_properties.set_index(self._data_cols, inplace=True) # TODO: What about when cell_properties is a Sequence[T]? @@ -678,7 +671,8 @@ def size(self, level: int = 0) -> int: -------- dimensions """ - # TODO: Since `level` is not validated, we assume that self.dimensions should be an array large enough to access index `level` + if self.empty: + return 0 return self.dimensions[level] @property @@ -1174,7 +1168,7 @@ def incidence_matrix( level2: int = 1, weights: bool | dict = False, aggregateby: str = "count", - ) -> Optional[csr_matrix]: + ) -> Optional[sp.csr_matrix]: """Incidence matrix representation for two levels (columns) of the underlying data table If `level1` and `level2` contain N and M distinct items, respectively, the incidence matrix will be M x N. @@ -1228,7 +1222,7 @@ def incidence_matrix( aggregateby=aggregateby, ) - return csr_matrix( + return sp.csr_matrix( (df[weight_col], tuple(df[col].cat.codes for col in data_cols)) ) @@ -1726,10 +1720,6 @@ def get_properties(self, item: T, level: Optional[int] = None) -> dict[Any, Any] def _cell_properties_from_dataframe(self, cell_props: pd.DataFrame) -> None: """Private handler for updating :attr:`properties` from a DataFrame - Parameters - ---------- - props - Parameters ---------- cell_props : DataFrame @@ -1868,8 +1858,9 @@ def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: try: cell_props = self.cell_properties.loc[(item1, item2)] except KeyError: - raise - # TODO: raise informative exception + raise KeyError( + f"cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" + ) try: prop_val = cell_props.loc[prop_name] @@ -1902,8 +1893,11 @@ def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: try: cell_props = self.cell_properties.loc[(item1, item2)] except KeyError: - raise - # TODO: raise informative exception + raise KeyError( + f"cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" + ) + + return cell_props def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 @@ -1952,8 +1946,7 @@ def restrict_to_levels( weights : bool, default=False If True, aggregate existing cell weights to get new cell weights. Otherwise, all new cell weights will be 1. - aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', \ - 'min', None}, optional + aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', 'min', None}, optional Method to aggregate weights of duplicate rows in data table If None or `weights`=False then all new cell weights will be 1 keep_memberships : bool, default=True @@ -2070,7 +2063,6 @@ def build_dataframe_from_entity( {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} ) - # create an empty dataframe return pd.DataFrame() diff --git a/hypernetx/classes/helpers.py b/hypernetx/classes/helpers.py index 7690906b..84365f4c 100644 --- a/hypernetx/classes/helpers.py +++ b/hypernetx/classes/helpers.py @@ -272,3 +272,29 @@ def dict_depth(dic, level=0): if not isinstance(dic, dict) or not dic: return level return min(dict_depth(dic[key], level + 1) for key in dic) + + +def create_dataframe(data: Mapping[str | int, Iterable[str | int]]) -> pd.DataFrame: + """Create a valid pandas Dataframe that can be used for the 'entity' param in EntitySet""" + + validate_mapping_for_dataframe(data) + + # creates a Series of all edge-node pairs (i.e. all the non-zero cells from an incidence matrix) + data_t = pd.Series(data=data).explode() + return pd.DataFrame(data={0: data_t.index.to_list(), 1: data_t.values}) + + +def validate_mapping_for_dataframe( + data: Mapping[str | int, Iterable[str | int]] +) -> None: + if not isinstance(data, Mapping): + raise TypeError("data must be a Mapping type, i.e. dictionary") + key_types = set(type(key) for key in data.keys()) + if key_types != {str} and key_types != {int}: + raise TypeError("keys must be a string or int") + for val in data.values(): + if not isinstance(val, Iterable): + raise TypeError("The value of a key must be an Iterable type, i.e. list") + val_types = set(type(v) for v in val) + if val_types != {str} and val_types != {int}: + raise TypeError("The items in each value must be a string or int") diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index 25ba8294..8059554a 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -6,6 +6,8 @@ import numpy as np from hypernetx import Hypergraph, HarryPotter, EntitySet, LesMis as LM +from hypernetx.classes.helpers import create_dataframe + from collections import OrderedDict, defaultdict @@ -65,6 +67,8 @@ def __init__(self, static=False): ] ) + self.dataframe = create_dataframe(self.edgedict) + class TriLoop: """Example hypergraph with 2 two 1-cells and 1 2-cell forming a loop""" @@ -151,6 +155,26 @@ def sbs(): return SevenBySix() +@pytest.fixture +def sbs_dataframe(sbs): + return sbs.dataframe + + +@pytest.fixture +def sbs_dict(sbs): + return sbs.edgedict + + +@pytest.fixture +def sbs_data(sbs): + return np.asarray(sbs.data) + + +@pytest.fixture +def sbs_labels(sbs): + return sbs.labels + + @pytest.fixture def triloop(): return TriLoop() @@ -217,6 +241,7 @@ def dataframe(): @pytest.fixture def dataframe_example(): + """NOTE: Do not use this dataframe as an input for 'entity' when creating an EntitySet object""" M = np.array([[1, 1, 0, 0], [0, 1, 1, 0], [1, 0, 1, 0]]) index = ["A", "B", "C"] columns = ["a", "b", "c", "d"] diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index c4f1dd31..a257ee34 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -1,5 +1,6 @@ import numpy as np import pytest +from pytest_lazyfixture import lazy_fixture from collections.abc import Iterable from collections import UserList @@ -7,7 +8,6 @@ from hypernetx.classes.entityset import restrict_to_two_columns from pandas import DataFrame, Series -import pandas as pd def test_empty_entityset(): @@ -27,8 +27,7 @@ def test_empty_entityset(): assert "foo" not in es assert es.incidence_matrix() is None - # TODO: results in bound method issue - # assert es.size == 0 + assert es.size() == 0 with (pytest.raises(AttributeError)): es.get_cell_property("foo", "bar", "roma") @@ -38,60 +37,75 @@ def test_empty_entityset(): es.set_cell_property("foo", "bar", "roma", "ff") with (pytest.raises(KeyError)): es.get_properties("foo") - # with(pytest.raises(KeyError)): - # es.get_property("foo", "bar") + with (pytest.raises(KeyError)): + es.get_property("foo", "bar") with (pytest.raises(ValueError)): es.set_property("foo", "bar", "roma") -class TestEntitySetOnDataframe: - def test_cell_properties(self, dataframe_example): - es = EntitySet(entity=dataframe_example) - - assert es.cell_properties.shape == (3, 1) +class TestEntitySetOnSevenBySixDataset: + # Tests on different use cases for combination of the following params: entity, data, data_cols, labels + + @pytest.mark.parametrize( + "entity, data, data_cols, labels", + [ + (lazy_fixture("sbs_dataframe"), None, (0, 1), None), + (lazy_fixture("sbs_dict"), None, (0, 1), None), + (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), + (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), + ], + ) + def test_all_properties_on_entity_as_dataframe( + self, entity, data, data_cols, labels, sbs + ): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - def test_data(self, dataframe_example): - es = EntitySet(entity=dataframe_example) + assert len(es.elements) == 6 - data = es.data + assert es.size() == len(sbs.edgedict) + assert len(es.uidset) == 6 + assert len(es.children) == 7 + assert isinstance(es.incidence_dict["I"], list) + assert "I" in es + assert "K" in es - assert isinstance(data, np.ndarray) - assert data.shape == (3, 2) assert not es.empty - assert len(es.elements) == 2 - assert es.dimsize == 2 - assert es.uid is None + assert es.dimsize == 2 + assert len(es.dimensions) == es.dimsize -class TestEntitySetOnSevenBySixDataset: - # Tests on different inputs for entity and data - def test_entityset_with_dict(self, sbs): - ent = EntitySet(entity=sbs.edgedict) - assert len(ent.elements) == 6 - - def test_entityset_with_dict_data_cols(self, sbs): - ent = EntitySet(entity=sbs.edgedict, data_cols=["edges", "nodes"]) - assert len(ent.elements) == 6 - - def test_entityset_with_ndarray(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - - assert ent_sbs.size() == 6 - assert len(ent_sbs.uidset) == 6 - assert len(ent_sbs.children) == 7 - assert isinstance(ent_sbs.incidence_dict["I"], list) - assert "I" in ent_sbs - assert "K" in ent_sbs + assert es.isstatic - def test_entityset_with_ndarray_fail_on_labels(self, sbs): + assert es.uid is None + assert es.uidset == {"I", "R", "S", "P", "O", "L"} + assert es.dimensions == (6, 7) + + # cell_weights # dict of tuples, ints: pairs to weights # basically the simplest dataframe as a dictionary + # children # set of nodes + # dataframe # the pandas dataframe + # elements # dict of str to list that summarizes the edge node pairs + # incidence_dict # same as elements + # labels # the list of all unique elements in the first two columns of the dataframe, basically the edge, nodes + # memberships # the opposite of elements; it is the node to edges pairs + # properties: a pandas dataframe of all the nodes and edges. The index is fomratted as /. The columns from left to right are uid, weight, and properties + # uidset: the set of all edges + # cell properties: a pandas dataframe of one column of all the cells. A cell is an edge-node pair. And we are saving the weight of each pair + + # assert es.cell_properties.shape == (3, 1) + + def test_ndarray_fail_on_labels(self, sbs): with (pytest.raises(ValueError, match="Labels must be of type Dictionary.")): EntitySet(data=np.asarray(sbs.data), labels=[]) - def test_entityset_with_ndarray_fail_on_length_labels(self, sbs): - with (pytest.raises(ValueError, match="The length of labels must equal the length of columns in the dataframe.")): + def test_ndarray_fail_on_length_labels(self, sbs): + with ( + pytest.raises( + ValueError, + match="The length of labels must equal the length of columns in the dataframe.", + ) + ): EntitySet(data=np.asarray(sbs.data), labels=dict()) - # Tests for properties @pytest.mark.skip(reason="TODO: implement") @@ -343,22 +357,6 @@ def test_restrict_to_two_columns_on_ndarray(harry_potter): misc_cell_props_col="properties", ) - assert entity is None - assert len(labels) == 2 - assert 0 in labels - assert 1 in labels - - print(data) - print(type(data[0])) - - assert data.shape[1] == expected_num_cols - assert np.array_equal(data[0], expected_ndarray_first_row) - - -@pytest.mark.skip(reason="TODO: implement") -def test_restrict_to_two_columns_on_dataframe(sbs): - pass - @pytest.mark.skip(reason="TODO: implement") def build_dataframe_from_entity_on_dataframe(sbs): From a5721cb9f02378a8f97c7db9d15325701357230b Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 20 Sep 2023 12:16:36 -0700 Subject: [PATCH 30/76] HYP-177 Remove restrict_to_two columns helper --- hypernetx/classes/entityset.py | 96 ----------------------- hypernetx/classes/hypergraph.py | 3 +- hypernetx/classes/tests/test_entityset.py | 22 ------ 3 files changed, 1 insertion(+), 120 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index ce6dd83e..cbdb8c79 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -127,8 +127,6 @@ def __init__( | Mapping[T, Mapping[T, Any]] ] = None, data_cols: Sequence[T] = (0, 1), - level1: str | int = 0, - level2: str | int = 1, data: Optional[np.ndarray] = None, static: bool = True, labels: Optional[OrderedDict[T, Sequence[T]]] = None, @@ -150,19 +148,6 @@ def __init__( self._state_dict = {} self._misc_cell_props_col = misc_cell_props_col - # Restrict to two columns on entity, data, labels - entity, data, labels = restrict_to_two_columns( - entity, - data, - labels, - cell_properties, - weight_col, - weights, - level1, - level2, - misc_cell_props_col, - ) - # build initial dataframe if isinstance(data, np.ndarray) and entity is None: self._build_dataframe_from_ndarray(data, labels) @@ -2064,84 +2049,3 @@ def build_dataframe_from_entity( ) return pd.DataFrame() - - -# TODO: Consider refactoring for simplicity; SonarLint states this function has a Cognitive Complexity of 26; recommends lowering to 15 -def restrict_to_two_columns( - entity: Optional[ - pd.DataFrame - | Mapping[T, Iterable[T]] - | Iterable[Iterable[T]] - | Mapping[T, Mapping[T, Any]] - ], - data: Optional[np.ndarray], - labels: Optional[OrderedDict[T, Sequence[T]]], - cell_properties: Optional[ - Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] - ], - weight_col: str | int, - weights: Optional[Sequence[float] | float | int | str], - level1: str | int, - level2: str | int, - misc_cell_props_col: str, -): - """Restrict columns on entity or data as needed; if data is restricted, also restrict labels""" - if isinstance(entity, pd.DataFrame) and len(entity.columns) > 2: - # metadata columns are not considered levels of data, - # remove them before indexing by level - # if isinstance(cell_properties, str): - # cell_properties = [cell_properties] - - prop_cols = [] - if isinstance(cell_properties, Sequence): - for col in {*cell_properties, misc_cell_props_col}: - if col in entity: - prop_cols.append(col) - - # meta_cols = prop_cols - # if weights in entity and weights not in meta_cols: - # meta_cols.append(weights) - if weight_col in prop_cols: - prop_cols.remove(weight_col) - if weight_col not in entity: - entity[weight_col] = weights - - # if both levels are column names, no need to index by level - if isinstance(level1, int): - level1 = entity.columns[level1] - if isinstance(level2, int): - level2 = entity.columns[level2] - # if isinstance(level1, str) and isinstance(level2, str): - columns = [level1, level2, weight_col] + prop_cols - # if one or both of the levels are given by index, get column name - # else: - # all_columns = entity.columns.drop(meta_cols) - # columns = [ - # all_columns[lev] if isinstance(lev, int) else lev - # for lev in (level1, level2) - # ] - - # if there is a column for cell properties, convert to separate DataFrame - # if len(prop_cols) > 0: - # cell_properties = entity[[*columns, *prop_cols]] - - # if there is a column for weights, preserve it - # if weights in entity and weights not in prop_cols: - # columns.append(weights) - - # pass level1, level2, and weights (optional) to Entity constructor - entity = entity[columns] - - # if a 2D ndarray is passed, restrict to two columns if needed - elif isinstance(data, np.ndarray): - if data.ndim == 2 and data.shape[1] > 2: - data = data[:, (level1, level2)] - - # should only change labels if 'data' is passed - # if a dict of labels is provided, restrict to labels for two columns if needed - if isinstance(labels, dict) and len(labels) > 2: - labels = { - col: labels[col] for col in [level1, level2] - } # example: { 0: ['e1', 'e2', ...], 1: ['n1', ...] } - - return entity, data, labels diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 63821d08..a79cde0c 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -538,8 +538,7 @@ def props2dict(df=None): self.E = EntitySet( entity=entity, - level1=edge_col, - level2=node_col, + data_cols=(edge_col, node_col), weight_col=cell_weight_col, weights=cell_weights, cell_properties=cell_properties, diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index a257ee34..611c03a0 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -5,9 +5,6 @@ from collections.abc import Iterable from collections import UserList from hypernetx.classes import EntitySet -from hypernetx.classes.entityset import restrict_to_two_columns - -from pandas import DataFrame, Series def test_empty_entityset(): @@ -339,25 +336,6 @@ def test_restrict_to_indices(self, harry_potter): # testing entityset helpers -def test_restrict_to_two_columns_on_ndarray(harry_potter): - data = np.asarray(harry_potter.data) - labels = harry_potter.labels - expected_num_cols = 2 - expected_ndarray_first_row = np.array([1, 1]) - - entity, data, labels = restrict_to_two_columns( - entity=None, - data=data, - labels=labels, - cell_properties=None, - weight_col="cell_weights", - weights=1, - level1=0, - level2=1, - misc_cell_props_col="properties", - ) - - @pytest.mark.skip(reason="TODO: implement") def build_dataframe_from_entity_on_dataframe(sbs): pass From 6cbb49a5c6b33b34c6345c25d5c8a00500d02064 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 27 Sep 2023 12:41:15 -0700 Subject: [PATCH 31/76] HYP-177 Update comments; add tests for remove and add methods; cleanup tests --- hypernetx/classes/entityset.py | 26 +-- hypernetx/classes/tests/test_entityset.py | 272 ++++++++++++---------- 2 files changed, 166 insertions(+), 132 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index cbdb8c79..b3de1751 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -6,6 +6,7 @@ from collections import OrderedDict, defaultdict from collections.abc import Hashable, Mapping, Sequence, Iterable from typing import Union, TypeVar, Optional, Any +from typing_extensions import Self import numpy as np import pandas as pd @@ -373,7 +374,8 @@ def dimsize(self) -> int: @property def properties(self) -> pd.DataFrame: - # Dev Note: Not sure what this contains, when running tests it contained an empty pandas series + # TODO: Not sure what this contains, when running tests it contained an empty pandas series + # Update: returns a dataframe columns: edge/node, a number, weight, misc attributes """Properties assigned to items in the underlying data table Returns @@ -448,7 +450,7 @@ def uidset_by_level(self, level: int) -> set: return self.uidset_by_column(col) def uidset_by_column(self, column: Hashable) -> set: - # Dev Note: This threw an error when trying it on the harry potter dataset, + # TODO: This threw an error when trying it on the harry potter dataset, # when trying 0, or 1 for column. I'm not sure how this should be used """Labels of all items in a particular column (level) of the underlying data table @@ -627,7 +629,7 @@ def dataframe(self) -> pd.DataFrame: @property def isstatic(self) -> bool: - # Dev Note: I'm guessing this is no longer necessary? + # TODO: I'm guessing this is no longer necessary? """Whether to treat the underlying data as static or not If True, the underlying data may not be altered, and the state_dict will never be cleared @@ -753,7 +755,7 @@ def __iter__(self): return iter(self.elements) def __call__(self, label_index=0): - # Dev Note (Madelyn) : I don't think this is the intended use of __call__, can we change/deprecate? + # TODO: (Madelyn) : I don't think this is the intended use of __call__, can we change/deprecate? """Iterates over items labels in a specified level (column) of the underlying data table Parameters @@ -939,7 +941,7 @@ def level( print(f'"{item}" not found.') return None - def add(self, *args) -> EntitySet: + def add(self, *args) -> Self: """Updates the underlying data table with new entity data from multiple sources Parameters @@ -969,7 +971,7 @@ def add(self, *args) -> EntitySet: self.add_element(item) return self - def add_elements_from(self, arg_set) -> EntitySet: + def add_elements_from(self, arg_set) -> Self: """Adds arguments from an iterable to the data table one at a time ..deprecated:: 2.0.0 @@ -995,16 +997,15 @@ def add_element( | Mapping[T, Iterable[T]] | Iterable[Iterable[T]] | Mapping[T, Mapping[T, Any]], - ) -> EntitySet: + ) -> Self: """Updates the underlying data table with new entity data - Supports adding from either an existing Entity or a representation of entity + Supports adding from either an existing EntitySet or a representation of entity (data table or labeled system of sets are both supported representations) Parameters ---------- - data : `pandas.DataFrame`, dict of lists or sets, lists of lists or sets - new entity data + data : `pandas.DataFrame`, dict of lists or sets, lists of lists, or nested dict Returns ------- @@ -1137,15 +1138,14 @@ def encode(self, data: pd.DataFrame) -> np.array: Parameters ---------- - data : dataframe + data : dataframe, dataframe columns must have dtype set to 'category' Returns ------- numpy.array """ - encoded_array = data.apply(lambda x: x.cat.codes).to_numpy() - return encoded_array + return data.apply(lambda x: x.cat.codes).to_numpy() def incidence_matrix( self, diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 611c03a0..9bfbf39b 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -1,4 +1,5 @@ import numpy as np +import pandas as pd import pytest from pytest_lazyfixture import lazy_fixture @@ -26,17 +27,17 @@ def test_empty_entityset(): assert es.size() == 0 - with (pytest.raises(AttributeError)): + with pytest.raises(AttributeError): es.get_cell_property("foo", "bar", "roma") - with (pytest.raises(AttributeError)): + with pytest.raises(AttributeError): es.get_cell_properties("foo", "bar") - with (pytest.raises(KeyError)): + with pytest.raises(KeyError): es.set_cell_property("foo", "bar", "roma", "ff") - with (pytest.raises(KeyError)): + with pytest.raises(KeyError): es.get_properties("foo") - with (pytest.raises(KeyError)): + with pytest.raises(KeyError): es.get_property("foo", "bar") - with (pytest.raises(ValueError)): + with pytest.raises(ValueError): es.set_property("foo", "bar", "roma") @@ -49,7 +50,7 @@ class TestEntitySetOnSevenBySixDataset: (lazy_fixture("sbs_dataframe"), None, (0, 1), None), (lazy_fixture("sbs_dict"), None, (0, 1), None), (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), - (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), + # (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), ], ) def test_all_properties_on_entity_as_dataframe( @@ -57,126 +58,163 @@ def test_all_properties_on_entity_as_dataframe( ): es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert len(es.elements) == 6 + assert es.isstatic + assert es.uid is None + assert not es.empty + assert es.uidset == {"I", "R", "S", "P", "O", "L"} assert es.size() == len(sbs.edgedict) - assert len(es.uidset) == 6 - assert len(es.children) == 7 + assert es.dimsize == 2 + assert es.dimensions == (6, 7) + assert es.data.shape == (15, 2) + assert es.data.ndim == 2 + + assert len(es.elements) == 6 + expected_elements = { + "I": ["K", "T2"], + "L": ["E", "C"], + "O": ["T1", "T2"], + "P": ["C", "K", "A"], + "R": ["E", "A"], + "S": ["K", "V", "A", "T2"], + } + for expected_edge, expected_nodes in expected_elements.items(): + assert expected_edge in es.elements + assert es.elements[expected_edge].sort() == expected_nodes.sort() + + expected_incident_dict = { + "I": ["K", "T2"], + "L": ["E", "C"], + "O": ["T1", "T2"], + "P": ["C", "K", "A"], + "R": ["E", "A"], + "S": ["K", "V", "A", "T2"], + } + for expected_edge, expected_nodes in expected_incident_dict.items(): + assert expected_edge in es.incidence_dict + assert es.incidence_dict[expected_edge].sort() == expected_nodes.sort() + + # check dunder methods assert isinstance(es.incidence_dict["I"], list) assert "I" in es assert "K" in es - assert not es.empty - - assert es.dimsize == 2 - assert len(es.dimensions) == es.dimsize - - assert es.isstatic - - assert es.uid is None - assert es.uidset == {"I", "R", "S", "P", "O", "L"} - assert es.dimensions == (6, 7) + assert es.children == {"C", "T1", "A", "K", "T2", "V", "E"} + assert es.memberships == { + "A": ["P", "R", "S"], + "C": ["P", "L"], + "E": ["R", "L"], + "K": ["P", "S", "I"], + "T1": ["O"], + "T2": ["S", "O", "I"], + "V": ["S"], + } - # cell_weights # dict of tuples, ints: pairs to weights # basically the simplest dataframe as a dictionary - # children # set of nodes - # dataframe # the pandas dataframe - # elements # dict of str to list that summarizes the edge node pairs - # incidence_dict # same as elements - # labels # the list of all unique elements in the first two columns of the dataframe, basically the edge, nodes - # memberships # the opposite of elements; it is the node to edges pairs - # properties: a pandas dataframe of all the nodes and edges. The index is fomratted as /. The columns from left to right are uid, weight, and properties - # uidset: the set of all edges - # cell properties: a pandas dataframe of one column of all the cells. A cell is an edge-node pair. And we are saving the weight of each pair + assert es.cell_properties.shape == ( + 15, + 1, + ) # cell properties: a pandas dataframe of one column of all the cells. A cell is an edge-node pair. And we are saving the weight of each pair + assert es.cell_weights == { + ("P", "C"): 1, + ("P", "K"): 1, + ("P", "A"): 1, + ("R", "E"): 1, + ("R", "A"): 1, + ("S", "K"): 1, + ("S", "V"): 1, + ("S", "A"): 1, + ("S", "T2"): 1, + ("L", "E"): 1, + ("L", "C"): 1, + ("O", "T1"): 1, + ("O", "T2"): 1, + ("I", "K"): 1, + ("I", "T2"): 1, + } - # assert es.cell_properties.shape == (3, 1) + # check labeling based on given attributes for EntitySet + if data_cols == [ + "edges", + "nodes", + ]: # labels should use the data_cols as keys for labels + assert es.labels == { + "edges": ["I", "L", "O", "P", "R", "S"], + "nodes": ["A", "C", "E", "K", "T1", "T2", "V"], + } + elif labels is not None: # labels should match the labels explicity given + assert es.labels == labels + else: # if data_cols or labels not given, labels should conform to default format + assert es.labels == { + 0: ["I", "L", "O", "P", "R", "S"], + 1: ["A", "C", "E", "K", "T1", "T2", "V"], + } + + # check dataframe + # size should be the number of rows times the number of columns, i.e 15 x 3 + assert es.dataframe.size == 45 + + actual_edge_row0 = es.dataframe.iloc[0, 0] + actual_node_row0 = es.dataframe.iloc[0, 1] + actual_cell_weight_row0 = es.dataframe.loc[0, "cell_weights"] + + assert actual_edge_row0 == "P" + assert actual_node_row0 in ["A", "C", "K"] + assert actual_cell_weight_row0 == 1 + + # print(es.data) + # print(es.properties) + assert len(es.data) == 15 # TODO: validate state of 'data' + + assert ( + es.properties.size == 39 + ) # Properties has three columns and 13 rows of data (i.e. edges + nodes) + assert list(es.properties.columns) == ["uid", "weight", "properties"] def test_ndarray_fail_on_labels(self, sbs): - with (pytest.raises(ValueError, match="Labels must be of type Dictionary.")): + with pytest.raises(ValueError, match="Labels must be of type Dictionary."): EntitySet(data=np.asarray(sbs.data), labels=[]) def test_ndarray_fail_on_length_labels(self, sbs): - with ( - pytest.raises( - ValueError, - match="The length of labels must equal the length of columns in the dataframe.", - ) + with pytest.raises( + ValueError, + match="The length of labels must equal the length of columns in the dataframe.", ): EntitySet(data=np.asarray(sbs.data), labels=dict()) - # Tests for properties - - @pytest.mark.skip(reason="TODO: implement") - def test_cell_weights(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_children(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_dataframe(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_dimensions(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_dimsize(self): - pass - def test_dimensions_equal_dimsize(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.dimsize == len(ent_sbs.dimensions) - @pytest.mark.skip(reason="TODO: implement") - def test_elements(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_empty(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_incidence_dict(self): - pass + # Tests for methods + @pytest.mark.parametrize( + "data", + [ + pd.DataFrame({0: ["P"], 1: ["E"]}), + {0: ["P"], 1: ["E"]}, + EntitySet(entity={"P": ["E"]}), + ], + ) + def test_add(self, sbs_dataframe, data): + es = EntitySet(entity=sbs_dataframe) - @pytest.mark.skip(reason="TODO: implement") - def test_isstatic(self): - pass + assert es.data.shape == (15, 2) + assert es.dataframe.size == 45 - @pytest.mark.skip(reason="TODO: implement") - def test_labels(self): - pass + es.add(data) - @pytest.mark.skip(reason="TODO: implement") - def test_memberships(self): - pass + assert es.data.shape == (16, 2) + assert es.dataframe.size == 48 - @pytest.mark.skip(reason="TODO: implement") - def test_properties(self): - pass + def test_remove(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) + assert es.data.shape == (15, 2) + assert es.dataframe.size == 45 - @pytest.mark.skip(reason="TODO: implement") - def test_uid(self): - pass + es.remove("P") - @pytest.mark.skip(reason="TODO: implement") - def test_uidset(self): - pass - - # Tests for methods - @pytest.mark.skip(reason="TODO: implement") - def test_add(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_add_element(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_add_elements_from(self): - pass + assert es.data.shape == (12, 2) + assert es.dataframe.size == 36 + assert "P" not in es.elements @pytest.mark.skip(reason="TODO: implement") def test_assign_properties(self): @@ -194,9 +232,17 @@ def test_elements_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.elements_by_level(0, 1) - @pytest.mark.skip(reason="TODO: implement") - def test_encode(self): - pass + def test_encode(self, sbs_dataframe): + es = EntitySet() + + df = pd.DataFrame({"Category": ["A", "B", "A", "C", "B"]}) + # Convert 'Category' column to categorical + df["Category"] = df["Category"].astype("category") + + expected_arr = np.array([[0], [1], [0], [2], [1]]) + actual_arr = es.encode(df) + + assert np.array_equal(actual_arr, expected_arr) @pytest.mark.skip(reason="TODO: implement") def test_get_cell_properties(self): @@ -228,22 +274,14 @@ def test_indices(self, sbs): assert ent_sbs.indices("nodes", "K") == [3] assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] - @pytest.mark.skip(reason="TODO: implement") - def test_is_empty(self): - pass + def test_is_empty(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) + assert not es.is_empty() @pytest.mark.skip(reason="TODO: implement") def test_level(self): pass - @pytest.mark.skip(reason="TODO: implement") - def test_remove(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_remove_elements(self): - pass - @pytest.mark.skip(reason="TODO: implement") def test_restrict_to(self): pass @@ -264,10 +302,6 @@ def test_set_cell_property(self): def test_set_property(self): pass - @pytest.mark.skip(reason="TODO: implement") - def test_size(self): - pass - def test_translate(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.translate(0, 0) == "P" From fbde6b790c6254c131e27d9bde70e6e157fa3407 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 27 Sep 2023 15:15:25 -0700 Subject: [PATCH 32/76] HYP-177 Add tests for get_property(s) and get_cell_property(s); fix methods --- hypernetx/classes/entityset.py | 35 ++++--- hypernetx/classes/tests/test_entityset.py | 107 +++++++++++++++++++--- 2 files changed, 115 insertions(+), 27 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index b3de1751..e25c3d8c 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1060,13 +1060,13 @@ def __add_from_dataframe(self, df: pd.DataFrame) -> None: self._state_dict.clear() - def remove(self, *args) -> EntitySet: + def remove(self, *args: T) -> EntitySet: """Removes all rows containing specified item(s) from the underlying data table Parameters ---------- *args - variable length argument list of item labels + variable length argument list of items which are of type string or int Returns ------- @@ -1101,13 +1101,13 @@ def remove_elements_from(self, arg_set): self.remove_element(item) return self - def remove_element(self, item) -> None: + def remove_element(self, item: T) -> None: """Removes all rows containing a specified item from the underlying data table Parameters ---------- - item - item label + item : Union[str, int] + the label of an edge See Also -------- @@ -1637,19 +1637,19 @@ def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> try: item_key = self._property_loc(item) except KeyError: - raise # item not in properties + raise KeyError(f"item does not exist: {item}") try: prop_val = self.properties.loc[item_key, prop_name] - except KeyError as ex: - if ex.args[0] == prop_name: - prop_val = self.properties.loc[item_key, self._misc_props_col].get( + except KeyError: + try: + prop_val = self.properties.loc[item_key, self._misc_props_col][ prop_name - ) - else: + ] + except KeyError as e: raise KeyError( f"no properties initialized for ('level','item'): {item_key}" - ) from ex + ) from e return prop_val @@ -1844,13 +1844,18 @@ def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: cell_props = self.cell_properties.loc[(item1, item2)] except KeyError: raise KeyError( - f"cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" + f"Item not exists. cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" ) try: prop_val = cell_props.loc[prop_name] except KeyError: - prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) + try: + prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) + except KeyError: + raise KeyError( + f"Item exists but property does not exist. cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" + ) return prop_val @@ -1882,7 +1887,7 @@ def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: f"cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" ) - return cell_props + return cell_props.to_dict() def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 9bfbf39b..3a98a39e 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -244,21 +244,104 @@ def test_encode(self, sbs_dataframe): assert np.array_equal(actual_arr, expected_arr) - @pytest.mark.skip(reason="TODO: implement") - def test_get_cell_properties(self): - pass + def test_get_cell_properties(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) - @pytest.mark.skip(reason="TODO: implement") - def test_get_cell_property(self): - pass + props = es.get_cell_properties("P", "A") - @pytest.mark.skip(reason="TODO: implement") - def test_get_properties(self): - pass + assert props == {"cell_weights": 1} - @pytest.mark.skip(reason="TODO: implement") - def test_get_property(self): - pass + def test_get_cell_properties_raises_keyerror(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) + + with pytest.raises(KeyError, match="cell_properties:"): + es.get_cell_properties("P", "FOOBAR") + + def test_get_cell_property(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) + props = es.get_cell_property("P", "A", "cell_weights") + assert props == 1 + + @pytest.mark.parametrize( + "item1, item2, prop_name, err_msg", + [ + ("P", "FOO", "cell_weights", "Item not exists. cell_properties:"), + ( + "P", + "A", + "Not a real property", + "Item exists but property does not exist. cell_properties:", + ), + ], + ) + def test_get_cell_property_raises_keyerror( + self, sbs_dataframe, item1, item2, prop_name, err_msg + ): + es = EntitySet(entity=sbs_dataframe) + + with pytest.raises(KeyError, match=err_msg): + es.get_cell_property(item1, item2, prop_name) + + @pytest.mark.parametrize("item, level", [("P", 0), ("P", None), ("A", 1)]) + def test_get_properties(self, sbs_dataframe, item, level): + es = EntitySet(entity=sbs_dataframe) + + # to avoid duplicate test code, reuse 'level' to get the item_uid + # but if level is None, assume it to be 0 and that the item exists at level 0 + if level is None: + item_uid = es.properties.loc[(0, item), "uid"] + else: + item_uid = es.properties.loc[(level, item), "uid"] + + props = es.get_properties(item, level=level) + + assert props == {"uid": item_uid, "weight": 1, "properties": {}} + + @pytest.mark.parametrize( + "item, level, err_msg", + [ + ("Not a valid item", None, ""), + ("Not a valid item", 0, "no properties initialized for"), + ], + ) + def test_get_properties_raises_keyerror(self, sbs_dataframe, item, level, err_msg): + es = EntitySet(entity=sbs_dataframe) + + with pytest.raises(KeyError, match=err_msg): + es.get_properties(item, level=level) + + @pytest.mark.parametrize( + "item, prop_name, level, expected_prop", + [ + ("P", "weight", 0, 1), + ("P", "properties", 0, {}), + ("P", "uid", 0, 3), + ("A", "weight", 1, 1), + ("A", "properties", 1, {}), + ("A", "uid", 1, 6), + ], + ) + def test_get_property(self, sbs_dataframe, item, prop_name, level, expected_prop): + es = EntitySet(entity=sbs_dataframe) + + prop = es.get_property(item, prop_name, level) + + assert prop == expected_prop + + @pytest.mark.parametrize( + "item, prop_name, err_msg", + [ + ("XXX", "weight", "item does not exist:"), + ("P", "not a real prop name", "no properties initialized for"), + ], + ) + def test_get_property_raises_keyerror( + self, sbs_dataframe, item, prop_name, err_msg + ): + es = EntitySet(entity=sbs_dataframe) + + with pytest.raises(KeyError, match=err_msg): + es.get_property(item, prop_name) def test_incidence_matrix(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) From d0afa855d80d745d2e8c93c1b4ecefb237e610b4 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 27 Sep 2023 16:51:10 -0700 Subject: [PATCH 33/76] HYP-177 Add tests for set_property --- hypernetx/classes/entityset.py | 1 + hypernetx/classes/tests/test_entityset.py | 53 +++++++++++++++++++---- 2 files changed, 45 insertions(+), 9 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index e25c3d8c..77d60ccd 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1593,6 +1593,7 @@ def set_property( self._properties.loc[item_key, self._misc_props_col].update( {prop_name: prop_val} ) + # TODO: Is it possible to ever hit this case given that misc_props_col will always be set in the dataframe? except KeyError: self._properties.loc[item_key, :] = { self._misc_props_col: {prop_name: prop_val} diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 3a98a39e..ab3b5961 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -185,7 +185,6 @@ def test_dimensions_equal_dimsize(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.dimsize == len(ent_sbs.dimensions) - # Tests for methods @pytest.mark.parametrize( "data", [ @@ -343,6 +342,50 @@ def test_get_property_raises_keyerror( with pytest.raises(KeyError, match=err_msg): es.get_property(item, prop_name) + @pytest.mark.parametrize( + "item, prop_name, prop_val, level", + [ + ("P", "weight", 42, 0), + ], + ) + def test_set_property(self, sbs_dataframe, item, prop_name, prop_val, level): + es = EntitySet(entity=sbs_dataframe) + + orig_prop_val = es.get_property(item, prop_name, level) + + es.set_property(item, prop_name, prop_val, level) + + new_prop_val = es.get_property(item, prop_name, level) + + assert new_prop_val != orig_prop_val + assert new_prop_val == prop_val + + @pytest.mark.parametrize( + "item, prop_name, prop_val, level, misc_props_col", + [ + ("P", "new_prop", "foobar", 0, "properties"), + ("P", "new_prop", "foobar", 0, "some_new_miscellaneaus_col"), + ], + ) + def test_set_property_on_non_existing_property( + self, sbs_dataframe, item, prop_name, prop_val, level, misc_props_col + ): + es = EntitySet(entity=sbs_dataframe, misc_props_col=misc_props_col) + + es.set_property(item, prop_name, prop_val, level) + + new_prop_val = es.get_property(item, prop_name, level) + + assert new_prop_val == prop_val + + def test_set_property_raises_keyerror(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) + + with pytest.raises( + ValueError, match="cannot infer 'level' when initializing 'item' properties" + ): + es.set_property("XXXX", "weight", 42) + def test_incidence_matrix(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) @@ -377,14 +420,6 @@ def test_restrict_to_indices(self): def test_restrict_to_levels(self): pass - @pytest.mark.skip(reason="TODO: implement") - def test_set_cell_property(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_set_property(self): - pass - def test_translate(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.translate(0, 0) == "P" From 14df743d983ebff72124fc06e629ba8865e0cc1a Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 29 Sep 2023 13:40:35 -0700 Subject: [PATCH 34/76] HYP-177 Add tests for assign_properties, update docs --- hypernetx/classes/entityset.py | 10 ++- hypernetx/classes/tests/conftest.py | 9 +++ hypernetx/classes/tests/test_entityset.py | 78 +++++++++++++++++------ 3 files changed, 73 insertions(+), 24 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 77d60ccd..b8657aed 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -70,7 +70,7 @@ class EntitySet: If ``DataFrame``, each row gives ``[optional item level, item label, optional named properties, {property name: property value}]`` - (order of columns does not matter; see note for an example). + (order of columns does not matter; see Notes for an example). If doubly-nested dict, ``{item level: {item label: {property name: property value}}}``. misc_props_col: str, default="properties" @@ -374,13 +374,11 @@ def dimsize(self) -> int: @property def properties(self) -> pd.DataFrame: - # TODO: Not sure what this contains, when running tests it contained an empty pandas series - # Update: returns a dataframe columns: edge/node, a number, weight, misc attributes """Properties assigned to items in the underlying data table Returns ------- - pandas.DataFrame + pandas.DataFrame a dataframe with the following columns: level/(edge|node), uid, weight, properties """ return self._properties @@ -1284,7 +1282,7 @@ def _restrict_to_levels( def restrict_to_indices( self, indices: int | Iterable[int], level: int = 0, **kwargs ) -> EntitySet: - """Create a new Entity by restricting the data table to rows containing specific items in a given level + """Create a new EntitySet by restricting the data table to rows containing specific items in a given level Parameters ---------- @@ -1369,7 +1367,7 @@ def assign_properties( Parameters ---------- props : pandas.DataFrame or doubly-nested dict - See documentation of the `properties` parameter in :class:`Entity` + See documentation of the `properties` parameter in :class:`EntitySet` level_col, id_col, misc_col : str, optional column names corresponding to the levels, items, and misc. properties; if None, default to :attr:`_level_col`, :attr:`_id_col`, :attr:`_misc_props_col`, diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index 8059554a..0aaf0468 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -150,6 +150,15 @@ def __init__(self, n1, n2): self.left, self.right = nx.bipartite.sets(self.g) +@pytest.fixture +def props_dataframe(): + multi_index = pd.MultiIndex.from_tuples([(0, "P")], names=["level", "id"]) + data = { + "properties": [{"prop1": "propval1", "prop2": "propval2"}], + } + return pd.DataFrame(data, index=multi_index) + + @pytest.fixture def sbs(): return SevenBySix() diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index ab3b5961..dcf53f50 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -53,7 +53,7 @@ class TestEntitySetOnSevenBySixDataset: # (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), ], ) - def test_all_properties_on_entity_as_dataframe( + def test_all_attribute_properties_on_common_entityset_instances( self, entity, data, data_cols, labels, sbs ): es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) @@ -215,8 +215,39 @@ def test_remove(self, sbs_dataframe): assert es.dataframe.size == 36 assert "P" not in es.elements + @pytest.mark.parametrize( + "props, multidx, expected_props", + [ + ( + lazy_fixture("props_dataframe"), + (0, "P"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + {0: {"P": {"prop1": "propval1", "prop2": "propval2"}}}, + (0, "P"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + {1: {"A": {"prop1": "propval1", "prop2": "propval2"}}}, + (1, "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ], + ) + def test_assign_properties(self, sbs_dataframe, props, multidx, expected_props): + es = EntitySet(entity=sbs_dataframe) + print(es.properties) + original_prop = es.properties.loc[multidx] + assert original_prop.properties == {} + + es.assign_properties(props) + + updated_prop = es.properties.loc[multidx] + assert updated_prop.properties == expected_props + @pytest.mark.skip(reason="TODO: implement") - def test_assign_properties(self): + def test_assign_cell_properties(self): pass @pytest.mark.skip(reason="TODO: implement") @@ -227,6 +258,30 @@ def test_collapse_identitical_elements(self): def test_elements_by_column(self): pass + @pytest.mark.skip(reason="TODO: implement") + def test_level(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_index(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_indices(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_translate(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_translate_arr(self): + pass + + @pytest.mark.skip(reason="TODO: implement") + def test_incidence_matrix(self): + pass + def test_elements_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.elements_by_level(0, 1) @@ -400,26 +455,15 @@ def test_indices(self, sbs): assert ent_sbs.indices("nodes", "K") == [3] assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] - def test_is_empty(self, sbs_dataframe): + @pytest.mark.parametrize("level", [0, 1]) + def test_is_empty(self, sbs_dataframe, level): es = EntitySet(entity=sbs_dataframe) - assert not es.is_empty() + assert not es.is_empty(level) @pytest.mark.skip(reason="TODO: implement") def test_level(self): pass - @pytest.mark.skip(reason="TODO: implement") - def test_restrict_to(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_restrict_to_indices(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_restrict_to_levels(self): - pass - def test_translate(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.translate(0, 0) == "P" @@ -486,8 +530,6 @@ def test_restrict_to_indices(self, harry_potter): # testing entityset helpers - - @pytest.mark.skip(reason="TODO: implement") def build_dataframe_from_entity_on_dataframe(sbs): pass From 97830b3eb1ba7ef0c724edfaa764de0bd25b6f3a Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 29 Sep 2023 15:26:21 -0700 Subject: [PATCH 35/76] Add tests for assign_cell_properties --- hypernetx/classes/entityset.py | 1 + hypernetx/classes/tests/conftest.py | 26 +++++++++ hypernetx/classes/tests/test_entityset.py | 64 +++++++++++++++++++++-- 3 files changed, 87 insertions(+), 4 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index b8657aed..d66410c1 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1777,6 +1777,7 @@ def _cell_properties_from_dict( [(item1, item2) for item1 in cell_props for item2 in cell_props[item1]], names=self._data_cols, ) + # This will create a MultiIndex dataframe with exactly one column named from _misc_cell_props_col (default is cell_properties) props_data = [cell_props[item1][item2] for item1, item2 in cells] cell_props = pd.DataFrame( {self._misc_cell_props_col: props_data}, index=cells diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index 0aaf0468..2fb031a1 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -159,6 +159,32 @@ def props_dataframe(): return pd.DataFrame(data, index=multi_index) +@pytest.fixture +def cell_props_dataframe_multidx(): + multi_index = pd.MultiIndex.from_tuples([("P", "A"), ("P", "C")], names=[0, 1]) + data = { + "cell_properties": [ + {"prop1": "propval1", "prop2": "propval2"}, + {"prop1": "propval1", "prop2": "propval2"}, + ] + } + + return pd.DataFrame(data, index=multi_index) + + +@pytest.fixture +def cell_props_dataframe(): + data = { + 0: ["P", "P"], + 1: ["A", "C"], + "cell_properties": [ + {"prop1": "propval1", "prop2": "propval2"}, + {"prop1": "propval1", "prop2": "propval2"}, + ], + } + return pd.DataFrame(data) + + @pytest.fixture def sbs(): return SevenBySix() diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index dcf53f50..4c548e0e 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -237,7 +237,7 @@ def test_remove(self, sbs_dataframe): ) def test_assign_properties(self, sbs_dataframe, props, multidx, expected_props): es = EntitySet(entity=sbs_dataframe) - print(es.properties) + original_prop = es.properties.loc[multidx] assert original_prop.properties == {} @@ -246,9 +246,65 @@ def test_assign_properties(self, sbs_dataframe, props, multidx, expected_props): updated_prop = es.properties.loc[multidx] assert updated_prop.properties == expected_props - @pytest.mark.skip(reason="TODO: implement") - def test_assign_cell_properties(self): - pass + @pytest.mark.parametrize( + "cell_props, multidx, expected_cell_properties", + [ + ( + lazy_fixture("cell_props_dataframe"), + ("P", "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + lazy_fixture("cell_props_dataframe_multidx"), + ("P", "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + {"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}}, + ("P", "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ], + ) + def test_assign_cell_properties_on_default_cell_properties( + self, sbs_dataframe, cell_props, multidx, expected_cell_properties + ): + es = EntitySet(entity=sbs_dataframe) + + es.assign_cell_properties(cell_props=cell_props) + + updated_cell_prop = es.cell_properties.loc[multidx] + + assert updated_cell_prop.cell_properties == expected_cell_properties + + def test_assign_cell_properties_on_multiple_properties(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) + multidx = ("P", "A") + + es.assign_cell_properties( + cell_props={"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}} + ) + + updated_cell_prop = es.cell_properties.loc[multidx] + assert updated_cell_prop.cell_properties == { + "prop1": "propval1", + "prop2": "propval2", + } + + es.assign_cell_properties( + cell_props={ + "P": { + "A": {"prop1": "propval1", "prop2": "propval2", "prop3": "propval3"} + } + } + ) + + updated_cell_prop = es.cell_properties.loc[multidx] + assert updated_cell_prop.cell_properties == { + "prop1": "propval1", + "prop2": "propval2", + "prop3": "propval3", + } @pytest.mark.skip(reason="TODO: implement") def test_collapse_identitical_elements(self): From 289677e93d7c94ca2bfa52c4f81ec65ad4b6b9c8 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 29 Sep 2023 15:32:27 -0700 Subject: [PATCH 36/76] HYP-177 Minor cleanup on assign_properties --- hypernetx/classes/entityset.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index d66410c1..11080b27 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1396,8 +1396,7 @@ def assign_properties( props = props.rename(columns=column_map) props = props.rename_axis(index=column_map) self._properties_from_dataframe(props) - - if isinstance(props, dict): + elif isinstance(props, dict): # Expects nested dictionary with keys corresponding to level and id self._properties_from_dict(props) From a6cbee16e84a5d13582a8d3b72d6787c31e8c3f6 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 29 Sep 2023 16:43:26 -0700 Subject: [PATCH 37/76] HYP-177 Fix set_cell_property bug --- hypernetx/classes/entityset.py | 29 ++++++++++++----------- hypernetx/classes/tests/test_entityset.py | 25 ++++--------------- 2 files changed, 20 insertions(+), 34 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 11080b27..a4c3c92f 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1803,20 +1803,21 @@ def set_cell_property( -------- get_cell_property, get_cell_properties """ - if item2 in self.elements[item1]: - if prop_name in self.properties: - self._cell_properties.loc[(item1, item2), prop_name] = pd.Series( - [prop_val] - ) - else: - try: - self._cell_properties.loc[ - (item1, item2), self._misc_cell_props_col - ].update({prop_name: prop_val}) - except KeyError: - self._cell_properties.loc[(item1, item2), :] = { - self._misc_cell_props_col: {prop_name: prop_val} - } + if item2 not in self.elements[item1]: + return + + if prop_name in self._cell_properties: + self._cell_properties.loc[(item1, item2), prop_name] = prop_val + else: + try: + self._cell_properties.loc[ + (item1, item2), self._misc_cell_props_col + ].update({prop_name: prop_val}) + except KeyError: + # TODO: this will set the existing values in row's columns to Nan; the property name and value are not captured + self._cell_properties.loc[(item1, item2), :] = { + self._misc_cell_props_col: {prop_name: prop_val} + } def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: """Get a property of a cell i.e., incidence between items of different levels diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 4c548e0e..09ebdec6 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -306,6 +306,11 @@ def test_assign_cell_properties_on_multiple_properties(self, sbs_dataframe): "prop3": "propval3", } + def test_set_cell_property_from_existing_properties(self, sbs_dataframe): + es = EntitySet(entity=sbs_dataframe) + es.set_cell_property("P", "A", "cell_weights", 42) + assert es.cell_properties.loc[("P", "A")].cell_weights == 42.0 + @pytest.mark.skip(reason="TODO: implement") def test_collapse_identitical_elements(self): pass @@ -318,26 +323,6 @@ def test_elements_by_column(self): def test_level(self): pass - @pytest.mark.skip(reason="TODO: implement") - def test_index(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_indices(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_translate(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_translate_arr(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_incidence_matrix(self): - pass - def test_elements_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.elements_by_level(0, 1) From 36b805de58723047401a89b88d3e0aae34310c96 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 29 Sep 2023 17:17:07 -0700 Subject: [PATCH 38/76] HYP-177 Add tests for level method --- hypernetx/classes/tests/test_entityset.py | 37 +++++++++++++++-------- hypernetx/utils/toys/harrypotter.py | 3 +- 2 files changed, 26 insertions(+), 14 deletions(-) diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 09ebdec6..c2fbb069 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -319,10 +319,6 @@ def test_collapse_identitical_elements(self): def test_elements_by_column(self): pass - @pytest.mark.skip(reason="TODO: implement") - def test_level(self): - pass - def test_elements_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.elements_by_level(0, 1) @@ -501,9 +497,28 @@ def test_is_empty(self, sbs_dataframe, level): es = EntitySet(entity=sbs_dataframe) assert not es.is_empty(level) - @pytest.mark.skip(reason="TODO: implement") - def test_level(self): - pass + @pytest.mark.parametrize( + "item_level, item, min_level, max_level, expected_lidx", + [ + (0, "P", 0, None, (0, 3)), + (0, "P", 0, 0, (0, 3)), + (0, "P", 1, 1, None), + (1, "A", 0, None, (1, 0)), + (1, "A", 0, 0, None), + (1, "K", 0, None, (1, 3)), + ], + ) + def test_level( + self, sbs_dataframe, item_level, item, min_level, max_level, expected_lidx + ): + es = EntitySet(sbs_dataframe) + + actual_lidx = es.level(item, min_level=min_level, max_level=max_level) + + assert actual_lidx == expected_lidx + + if actual_lidx is not None: + actual_lidx[0] == es.labels[item_level].index(item) def test_translate(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) @@ -571,9 +586,6 @@ def test_restrict_to_indices(self, harry_potter): # testing entityset helpers -@pytest.mark.skip(reason="TODO: implement") -def build_dataframe_from_entity_on_dataframe(sbs): - pass @pytest.mark.xfail( @@ -591,8 +603,9 @@ def test_level(sbs): @pytest.mark.xfail( reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" ) -def test_attributes(ent_hp): - assert isinstance(ent_hp.data, np.ndarray) +def test_attributes(harry_potter): + assert isinstance(harry_potter.data, np.ndarray) + ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails assert isinstance(ent_hp.labels, dict) diff --git a/hypernetx/utils/toys/harrypotter.py b/hypernetx/utils/toys/harrypotter.py index 637b5299..a23cba0f 100644 --- a/hypernetx/utils/toys/harrypotter.py +++ b/hypernetx/utils/toys/harrypotter.py @@ -11,7 +11,6 @@ class HarryPotter(object): def __init__(self, cols=None): - # Read dataset in using pandas. Fix index column or use default pandas index. try: @@ -21,7 +20,7 @@ def __init__(self, cols=None): fname = f"{current_dir}/HarryPotter_Characters.csv" harrydata = pd.read_csv(fname, encoding="unicode_escape") - self.harrydata = pd.DataFrame(harrydata) + self.harryxdata = pd.DataFrame(harrydata) # Choose string to fill NaN. These will be set to 0 in system id = sid columns = cols or [ From ee57955dfc87a345bd3494aa9efe8eee659a6c0c Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Sat, 30 Sep 2023 20:21:19 -0700 Subject: [PATCH 39/76] HYP-177 Update test config for CI --- .coveragerc | 6 +++++- .gitignore | 2 +- MANIFEST.in | 1 + Makefile | 13 ++++--------- pytest.ini | 9 ++++++--- setup.cfg | 22 ++++++++++------------ tox.ini | 26 ++++++++++++++------------ 7 files changed, 41 insertions(+), 38 deletions(-) create mode 100644 MANIFEST.in diff --git a/.coveragerc b/.coveragerc index 40c661b7..124c7c86 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,5 +1,9 @@ [run] -omit = */tests/* +omit = + */tests/* + */utils/toys/* + */utils/log.py + [report] exclude_lines = _log diff --git a/.gitignore b/.gitignore index c22f5005..75d1a1a4 100644 --- a/.gitignore +++ b/.gitignore @@ -27,7 +27,7 @@ dist/ *.egg-info* .tox/ venv* -.coverage +.coverage* .idea *env* .venv* diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..122da47b --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1 @@ +include hypernetx/utils/toys/HarryPotter_Characters.csv diff --git a/Makefile b/Makefile index 0c7be1a9..83b59381 100644 --- a/Makefile +++ b/Makefile @@ -11,27 +11,22 @@ test: test-deps @$(PYTHON3) -m tox test-ci: test-deps - @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' pre-commit install pre-commit run --all-files - @$(PYTHON3) -m tox -e py38 -r + @$(PYTHON3) -m tox test-ci-github: test-deps @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' @$(PYTHON3) -m tox -test-coverage: test-deps - coverage run --source=hypernetx -m pytest - coverage html - -.PHONY: test, test-ci, test-ci-github, test-coverage +.PHONY: test, test-ci, test-ci-github ## Continuous Deployment ## Assumes that scripts are run on a container or test server VM ### Publish to PyPi publish-deps: - @$(PYTHON3) -m pip install -e .'[packaging]' + @$(PYTHON3) -m pip install -e .'[packaging]' --use-pep517 build-dist: publish-deps clean @$(PYTHON3) -m build --wheel --sdist @@ -48,7 +43,7 @@ publish-to-pypi: publish-deps build-dist ### Update version version-deps: - @$(PYTHON3) -m pip install .'[releases]' + @$(PYTHON3) -m pip install .'[releases]' --use-pep517 .PHONY: version-deps diff --git a/pytest.ini b/pytest.ini index 286a2cb1..2363bdb2 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,5 +1,8 @@ [pytest] minversion = 6.0 -; addopts are a set of command line arguments given to pytest: -; '-r A' will show all extra test summary as indicated by 'a' -addopts = -r A +; addopts are a set of optional arguments given to pytest: +; '-rA' will show a short test summary with the results for every test' +addopts = -rA -n auto --cov=hypernetx --cov-report term --cov-report html --junit-xml=pytest.xml --cov-fail-under=45 +testpaths = + hypernetx/classes/tests + hypernetx/classes/algorithms diff --git a/setup.cfg b/setup.cfg index 3c950a32..8204a7e5 100644 --- a/setup.cfg +++ b/setup.cfg @@ -50,6 +50,7 @@ license_files = LICENSE.rst [options] +include_package_data=True packages = hypernetx hypernetx.algorithms @@ -66,28 +67,25 @@ install_requires = scikit-learn>=0.20.0 pandas>=1.5.3 decorator>=5.1.1 + typing-extensions>=4.8.0 [options.extras_require] releases = commitizen>=3.2.1 -linting = - pre-commit>=3.2.2 - pylint>=2.17.2 - pylint-exit>=1.2.0 - black>=23.3.0 testing = + pytest>=7.2.2 + pytest-cov>=4.1.0 + pytest-lazy-fixture>=0.6.3 + pytest-xdist>=3.2.1 + pytest-env tox>=4.4.11 - pre-commit>=3.2.2 + nbmake>=1.4.1 + pre-commit>=3.2.2 pylint>=2.17.2 pylint-exit>=1.2.0 black>=23.3.0 - pytest>=7.2.2 - coverage>=7.2.2 celluloid>=0.2.0 igraph>=0.10.4 - nbmake>=1.4.1 - pytest-lazy-fixture>=0.6.3 - pytest-xdist>=3.2.1 tutorials = jupyter>=1.0 igraph>=0.10.4 @@ -115,7 +113,7 @@ all = sphinx-autobuild>=2021.3.14 sphinx-copybutton>=0.5.1 pytest>=7.2.2 - coverage>=7.2.2 + pytest-cov>=4.1.0 jupyter>=1.0 igraph>=0.10.4 partition-igraph>=0.0.6 diff --git a/tox.ini b/tox.ini index a840d36b..2bf91b4a 100644 --- a/tox.ini +++ b/tox.ini @@ -6,35 +6,37 @@ [tox] min_version = 4.4.11 -envlist = py{38,39,310,311} +envlist = clean, py{38,39,310,311} isolated_build = True skip_missing_interpreters = true [testenv] deps = pytest>=7.2.2 - coverage>=7.2.2 - celluloid>=0.2.0 - igraph>=0.10.4 - nbmake>=1.4.1 + pytest-cov>=4.1.0 pytest-lazy-fixture>=0.6.3 pytest-xdist>=3.2.1 + celluloid>=0.2.0 + igraph>=0.10.4 partition-igraph>=0.0.6 allowlist_externals = env commands = env - python --version - coverage run --source=hypernetx -m pytest - coverage report -m + coverage run -m pytest [testenv:py38-notebooks] description = run tests on jupyter notebooks deps = - hnxwidget>=0.1.1b3 + nbmake>=1.4.1 + hnxwidget>=0.1.1b3 jupyter-contrib-nbextensions>=0.7.0 jupyter-nbextensions-configurator>=0.6.2 allowlist_externals = env commands = - env - python --version - pytest --nbmake "tutorials/" --junitxml=pytest.xml -n=auto --nbmake-timeout=20 --nbmake-find-import-errors + env + pytest --nbmake "tutorials/" -n=auto --nbmake-timeout=20 --nbmake-find-import-errors + +[testenv:clean] +deps = coverage +skip_install = true +commands = coverage erase From a2e906aad0e6ceacf3545c7628b7b477cd0c5913 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 2 Oct 2023 15:06:53 -0700 Subject: [PATCH 40/76] HYP-177 Add tests for collapse_identical_elements --- hypernetx/classes/tests/conftest.py | 7 +++++ hypernetx/classes/tests/test_entityset.py | 33 ++++++++++++++++++++--- 2 files changed, 37 insertions(+), 3 deletions(-) diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index 2fb031a1..65041ac6 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -104,6 +104,8 @@ def __init__(self): ] ) + self.dataframe = create_dataframe(self.edgedict) + class LesMis: def __init__(self): @@ -241,6 +243,11 @@ def sbsd_hypergraph(): return Hypergraph(sbsd.edgedict) +@pytest.fixture +def sbsd_dataframe(): + return SBSDupes().dataframe + + @pytest.fixture def lesmis(): return LesMis() diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index c2fbb069..6c6ea72c 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -311,9 +311,36 @@ def test_set_cell_property_from_existing_properties(self, sbs_dataframe): es.set_cell_property("P", "A", "cell_weights", 42) assert es.cell_properties.loc[("P", "A")].cell_weights == 42.0 - @pytest.mark.skip(reason="TODO: implement") - def test_collapse_identitical_elements(self): - pass + @pytest.mark.parametrize("ret_ec", [True, False]) + def test_collapse_identical_elements_on_duplicates(self, sbsd_dataframe, ret_ec): + # There are two edges that share the same set of 3 (three) nodes + es = EntitySet(entity=sbsd_dataframe) + new_es = es.collapse_identical_elements(return_equivalence_classes=ret_ec) + + es_temp = new_es + if isinstance(new_es, tuple): + # reset variable for actual EntitySet + es_temp = new_es[0] + + # check equiv classes + collapsed_edge_key = "L: 2" + assert "M: 2" not in es_temp.elements + assert collapsed_edge_key in es_temp.elements + assert set(es_temp.elements.get(collapsed_edge_key)) == {"F", "C", "E"} + + equiv_classes = new_es[1] + assert equiv_classes == { + "I: 1": ["I"], + "L: 2": ["L", "M"], + "O: 1": ["O"], + "P: 1": ["P"], + "R: 1": ["R"], + "S: 1": ["S"], + } + + # check dataframe + assert len(es_temp.dataframe) != len(es.dataframe) + assert len(es_temp.dataframe) == len(es.dataframe) - 3 @pytest.mark.skip(reason="TODO: implement") def test_elements_by_column(self): From 296e571badd733d8cc73cebbb3ba6be390f92eab Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 2 Oct 2023 15:36:21 -0700 Subject: [PATCH 41/76] HYP-177 Add tests for elements_by_column --- hypernetx/classes/tests/test_entityset.py | 42 +++++++++++++++++++++-- 1 file changed, 39 insertions(+), 3 deletions(-) diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py index 6c6ea72c..0c25ea8a 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset.py @@ -342,9 +342,45 @@ def test_collapse_identical_elements_on_duplicates(self, sbsd_dataframe, ret_ec) assert len(es_temp.dataframe) != len(es.dataframe) assert len(es_temp.dataframe) == len(es.dataframe) - 3 - @pytest.mark.skip(reason="TODO: implement") - def test_elements_by_column(self): - pass + @pytest.mark.parametrize( + "col1, col2, expected_elements", + [ + ( + 0, + 1, + { + "I": {"K", "T2"}, + "L": {"C", "E"}, + "O": {"T1", "T2"}, + "P": {"K", "A", "C"}, + "R": {"A", "E"}, + "S": {"K", "A", "V", "T2"}, + }, + ), + ( + 1, + 0, + { + "A": {"P", "R", "S"}, + "C": {"P", "L"}, + "E": {"R", "L"}, + "K": {"P", "S", "I"}, + "T1": {"O"}, + "T2": {"S", "O", "I"}, + "V": {"S"}, + }, + ), + ], + ) + def test_elements_by_column(self, sbs_dataframe, col1, col2, expected_elements): + es = EntitySet(entity=sbs_dataframe) + + elements_temps = es.elements_by_column(col1, col2) + actual_elements = { + elements_temps[k]._key[1]: set(v) for k, v in elements_temps.items() + } + + assert actual_elements == expected_elements def test_elements_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) From 7cf1f5a098ef8c43f83141381926008fac3a712c Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 2 Oct 2023 16:52:08 -0700 Subject: [PATCH 42/76] HYP-177 Reorganize tests; cleanup fixtures --- hypernetx/classes/tests/conftest.py | 10 +- .../classes/tests/test_entityset_empty.py | 37 ++ .../tests/test_entityset_harry_potter_data.py | 75 ++++ ...ntityset.py => test_entityset_sbs_data.py} | 337 ++++++------------ 4 files changed, 220 insertions(+), 239 deletions(-) create mode 100644 hypernetx/classes/tests/test_entityset_empty.py create mode 100644 hypernetx/classes/tests/test_entityset_harry_potter_data.py rename hypernetx/classes/tests/{test_entityset.py => test_entityset_sbs_data.py} (64%) diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index 65041ac6..7c21ad8a 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -238,14 +238,14 @@ def sbs_graph(sbs): @pytest.fixture -def sbsd_hypergraph(): - sbsd = SBSDupes() - return Hypergraph(sbsd.edgedict) +def sbsd(): + return SBSDupes() @pytest.fixture -def sbsd_dataframe(): - return SBSDupes().dataframe +def sbsd_hypergraph(): + sbsd = SBSDupes() + return Hypergraph(sbsd.edgedict) @pytest.fixture diff --git a/hypernetx/classes/tests/test_entityset_empty.py b/hypernetx/classes/tests/test_entityset_empty.py new file mode 100644 index 00000000..67271c21 --- /dev/null +++ b/hypernetx/classes/tests/test_entityset_empty.py @@ -0,0 +1,37 @@ +import numpy as np +import pytest + +from hypernetx.classes import EntitySet + + +def test_empty_entityset(): + es = EntitySet() + assert es.empty + assert len(es.elements) == 0 + assert es.elements == {} + assert es.dimsize == 0 + + assert isinstance(es.data, np.ndarray) + assert es.data.shape == (0, 0) + + assert es.labels == {} + assert es.cell_weights == {} + assert es.isstatic + assert es.incidence_dict == {} + assert "foo" not in es + assert es.incidence_matrix() is None + + assert es.size() == 0 + + with pytest.raises(AttributeError): + es.get_cell_property("foo", "bar", "roma") + with pytest.raises(AttributeError): + es.get_cell_properties("foo", "bar") + with pytest.raises(KeyError): + es.set_cell_property("foo", "bar", "roma", "ff") + with pytest.raises(KeyError): + es.get_properties("foo") + with pytest.raises(KeyError): + es.get_property("foo", "bar") + with pytest.raises(ValueError): + es.set_property("foo", "bar", "roma") diff --git a/hypernetx/classes/tests/test_entityset_harry_potter_data.py b/hypernetx/classes/tests/test_entityset_harry_potter_data.py new file mode 100644 index 00000000..63bdb684 --- /dev/null +++ b/hypernetx/classes/tests/test_entityset_harry_potter_data.py @@ -0,0 +1,75 @@ +import numpy as np +import pytest + +from collections.abc import Iterable +from collections import UserList +from hypernetx.classes import EntitySet + + +@pytest.mark.xfail( + reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" +) +def test_attributes(harry_potter): + assert isinstance(harry_potter.data, np.ndarray) + ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) + # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray + assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails + assert isinstance(ent_hp.labels, dict) + # TODO: Entity defaults to first two cols as data cols + assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails + assert ent_hp.dimsize == 5 # fails + df = ent_hp.dataframe[ent_hp._data_cols] + assert list(df.columns) == [ # fails + "House", + "Blood status", + "Species", + "Hair colour", + "Eye colour", + ] + assert ent_hp.dimensions == tuple(df.nunique()) + assert set(ent_hp.labels["House"]) == set(df["House"].unique()) + + +class TestEntitySetOnHarryPotterDataSet: + def test_entityset_from_ndarray(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert len(ent_hp.uidset) == 7 + assert len(ent_hp.elements) == 7 + assert isinstance(ent_hp.elements["Hufflepuff"], UserList) + assert not ent_hp.is_empty() + assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 + + def test_custom_attributes(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert ent_hp.__len__() == 7 + assert isinstance(ent_hp.__str__(), str) + assert isinstance(ent_hp.__repr__(), str) + assert isinstance(ent_hp.__contains__("Muggle"), bool) + assert ent_hp.__contains__("Muggle") is True + assert ent_hp.__getitem__("Slytherin") == [ + "Half-blood", + "Pure-blood", + "Pure-blood or half-blood", + ] + assert isinstance(ent_hp.__iter__(), Iterable) + assert isinstance(ent_hp.__call__(), Iterable) + assert ent_hp.__call__().__next__() == "Unknown House" + + def test_restrict_to_levels(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 + + def test_restrict_to_indices(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert ent_hp.restrict_to_indices([1, 2]).uidset == { + "Gryffindor", + "Ravenclaw", + } diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset_sbs_data.py similarity index 64% rename from hypernetx/classes/tests/test_entityset.py rename to hypernetx/classes/tests/test_entityset_sbs_data.py index 0c25ea8a..26332e9b 100644 --- a/hypernetx/classes/tests/test_entityset.py +++ b/hypernetx/classes/tests/test_entityset_sbs_data.py @@ -1,49 +1,14 @@ import numpy as np import pandas as pd import pytest + from pytest_lazyfixture import lazy_fixture -from collections.abc import Iterable -from collections import UserList from hypernetx.classes import EntitySet -def test_empty_entityset(): - es = EntitySet() - assert es.empty - assert len(es.elements) == 0 - assert es.elements == {} - assert es.dimsize == 0 - - assert isinstance(es.data, np.ndarray) - assert es.data.shape == (0, 0) - - assert es.labels == {} - assert es.cell_weights == {} - assert es.isstatic - assert es.incidence_dict == {} - assert "foo" not in es - assert es.incidence_matrix() is None - - assert es.size() == 0 - - with pytest.raises(AttributeError): - es.get_cell_property("foo", "bar", "roma") - with pytest.raises(AttributeError): - es.get_cell_properties("foo", "bar") - with pytest.raises(KeyError): - es.set_cell_property("foo", "bar", "roma", "ff") - with pytest.raises(KeyError): - es.get_properties("foo") - with pytest.raises(KeyError): - es.get_property("foo", "bar") - with pytest.raises(ValueError): - es.set_property("foo", "bar", "roma") - - -class TestEntitySetOnSevenBySixDataset: +class TestEntitySetUseCases: # Tests on different use cases for combination of the following params: entity, data, data_cols, labels - @pytest.mark.parametrize( "entity, data, data_cols, labels", [ @@ -170,6 +135,8 @@ def test_all_attribute_properties_on_common_entityset_instances( ) # Properties has three columns and 13 rows of data (i.e. edges + nodes) assert list(es.properties.columns) == ["uid", "weight", "properties"] + +class TestEntitySetOnSevenBySixDataset: def test_ndarray_fail_on_labels(self, sbs): with pytest.raises(ValueError, match="Labels must be of type Dictionary."): EntitySet(data=np.asarray(sbs.data), labels=[]) @@ -185,6 +152,31 @@ def test_dimensions_equal_dimsize(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.dimsize == len(ent_sbs.dimensions) + def test_translate(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.translate(0, 0) == "P" + assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] + + def test_translate_arr(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] + + def test_uidset_by_level(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + + assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} + assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} + + +class TestEntitySetOnSBSDataframe: + @pytest.fixture + def es_from_sbsdf(self, sbs): + return EntitySet(entity=sbs.dataframe) + + @pytest.fixture + def es_from_sbs_dupe_df(self, sbsd): + return EntitySet(entity=sbsd.dataframe) + @pytest.mark.parametrize( "data", [ @@ -193,27 +185,24 @@ def test_dimensions_equal_dimsize(self, sbs): EntitySet(entity={"P": ["E"]}), ], ) - def test_add(self, sbs_dataframe, data): - es = EntitySet(entity=sbs_dataframe) - - assert es.data.shape == (15, 2) - assert es.dataframe.size == 45 + def test_add(self, es_from_sbsdf, data): + assert es_from_sbsdf.data.shape == (15, 2) + assert es_from_sbsdf.dataframe.size == 45 - es.add(data) + es_from_sbsdf.add(data) - assert es.data.shape == (16, 2) - assert es.dataframe.size == 48 + assert es_from_sbsdf.data.shape == (16, 2) + assert es_from_sbsdf.dataframe.size == 48 - def test_remove(self, sbs_dataframe): - es = EntitySet(entity=sbs_dataframe) - assert es.data.shape == (15, 2) - assert es.dataframe.size == 45 + def test_remove(self, es_from_sbsdf): + assert es_from_sbsdf.data.shape == (15, 2) + assert es_from_sbsdf.dataframe.size == 45 - es.remove("P") + es_from_sbsdf.remove("P") - assert es.data.shape == (12, 2) - assert es.dataframe.size == 36 - assert "P" not in es.elements + assert es_from_sbsdf.data.shape == (12, 2) + assert es_from_sbsdf.dataframe.size == 36 + assert "P" not in es_from_sbsdf.elements @pytest.mark.parametrize( "props, multidx, expected_props", @@ -235,15 +224,13 @@ def test_remove(self, sbs_dataframe): ), ], ) - def test_assign_properties(self, sbs_dataframe, props, multidx, expected_props): - es = EntitySet(entity=sbs_dataframe) - - original_prop = es.properties.loc[multidx] + def test_assign_properties(self, es_from_sbsdf, props, multidx, expected_props): + original_prop = es_from_sbsdf.properties.loc[multidx] assert original_prop.properties == {} - es.assign_properties(props) + es_from_sbsdf.assign_properties(props) - updated_prop = es.properties.loc[multidx] + updated_prop = es_from_sbsdf.properties.loc[multidx] assert updated_prop.properties == expected_props @pytest.mark.parametrize( @@ -267,31 +254,28 @@ def test_assign_properties(self, sbs_dataframe, props, multidx, expected_props): ], ) def test_assign_cell_properties_on_default_cell_properties( - self, sbs_dataframe, cell_props, multidx, expected_cell_properties + self, es_from_sbsdf, cell_props, multidx, expected_cell_properties ): - es = EntitySet(entity=sbs_dataframe) - - es.assign_cell_properties(cell_props=cell_props) + es_from_sbsdf.assign_cell_properties(cell_props=cell_props) - updated_cell_prop = es.cell_properties.loc[multidx] + updated_cell_prop = es_from_sbsdf.cell_properties.loc[multidx] assert updated_cell_prop.cell_properties == expected_cell_properties - def test_assign_cell_properties_on_multiple_properties(self, sbs_dataframe): - es = EntitySet(entity=sbs_dataframe) + def test_assign_cell_properties_on_multiple_properties(self, es_from_sbsdf): multidx = ("P", "A") - es.assign_cell_properties( + es_from_sbsdf.assign_cell_properties( cell_props={"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}} ) - updated_cell_prop = es.cell_properties.loc[multidx] + updated_cell_prop = es_from_sbsdf.cell_properties.loc[multidx] assert updated_cell_prop.cell_properties == { "prop1": "propval1", "prop2": "propval2", } - es.assign_cell_properties( + es_from_sbsdf.assign_cell_properties( cell_props={ "P": { "A": {"prop1": "propval1", "prop2": "propval2", "prop3": "propval3"} @@ -299,23 +283,25 @@ def test_assign_cell_properties_on_multiple_properties(self, sbs_dataframe): } ) - updated_cell_prop = es.cell_properties.loc[multidx] + updated_cell_prop = es_from_sbsdf.cell_properties.loc[multidx] assert updated_cell_prop.cell_properties == { "prop1": "propval1", "prop2": "propval2", "prop3": "propval3", } - def test_set_cell_property_from_existing_properties(self, sbs_dataframe): - es = EntitySet(entity=sbs_dataframe) - es.set_cell_property("P", "A", "cell_weights", 42) - assert es.cell_properties.loc[("P", "A")].cell_weights == 42.0 + def test_set_cell_property_from_existing_properties(self, es_from_sbsdf): + es_from_sbsdf.set_cell_property("P", "A", "cell_weights", 42) + assert es_from_sbsdf.cell_properties.loc[("P", "A")].cell_weights == 42.0 @pytest.mark.parametrize("ret_ec", [True, False]) - def test_collapse_identical_elements_on_duplicates(self, sbsd_dataframe, ret_ec): + def test_collapse_identical_elements_on_duplicates( + self, es_from_sbs_dupe_df, ret_ec + ): # There are two edges that share the same set of 3 (three) nodes - es = EntitySet(entity=sbsd_dataframe) - new_es = es.collapse_identical_elements(return_equivalence_classes=ret_ec) + new_es = es_from_sbs_dupe_df.collapse_identical_elements( + return_equivalence_classes=ret_ec + ) es_temp = new_es if isinstance(new_es, tuple): @@ -339,8 +325,8 @@ def test_collapse_identical_elements_on_duplicates(self, sbsd_dataframe, ret_ec) } # check dataframe - assert len(es_temp.dataframe) != len(es.dataframe) - assert len(es_temp.dataframe) == len(es.dataframe) - 3 + assert len(es_temp.dataframe) != len(es_from_sbs_dupe_df.dataframe) + assert len(es_temp.dataframe) == len(es_from_sbs_dupe_df.dataframe) - 3 @pytest.mark.parametrize( "col1, col2, expected_elements", @@ -372,10 +358,8 @@ def test_collapse_identical_elements_on_duplicates(self, sbsd_dataframe, ret_ec) ), ], ) - def test_elements_by_column(self, sbs_dataframe, col1, col2, expected_elements): - es = EntitySet(entity=sbs_dataframe) - - elements_temps = es.elements_by_column(col1, col2) + def test_elements_by_column(self, es_from_sbsdf, col1, col2, expected_elements): + elements_temps = es_from_sbsdf.elements_by_column(col1, col2) actual_elements = { elements_temps[k]._key[1]: set(v) for k, v in elements_temps.items() } @@ -386,34 +370,27 @@ def test_elements_by_level(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) assert ent_sbs.elements_by_level(0, 1) - def test_encode(self, sbs_dataframe): - es = EntitySet() - + def test_encode(self, es_from_sbsdf): df = pd.DataFrame({"Category": ["A", "B", "A", "C", "B"]}) # Convert 'Category' column to categorical df["Category"] = df["Category"].astype("category") expected_arr = np.array([[0], [1], [0], [2], [1]]) - actual_arr = es.encode(df) + actual_arr = es_from_sbsdf.encode(df) assert np.array_equal(actual_arr, expected_arr) - def test_get_cell_properties(self, sbs_dataframe): - es = EntitySet(entity=sbs_dataframe) - - props = es.get_cell_properties("P", "A") + def test_get_cell_properties(self, es_from_sbsdf): + props = es_from_sbsdf.get_cell_properties("P", "A") assert props == {"cell_weights": 1} - def test_get_cell_properties_raises_keyerror(self, sbs_dataframe): - es = EntitySet(entity=sbs_dataframe) - + def test_get_cell_properties_raises_keyerror(self, es_from_sbsdf): with pytest.raises(KeyError, match="cell_properties:"): - es.get_cell_properties("P", "FOOBAR") + es_from_sbsdf.get_cell_properties("P", "FOOBAR") - def test_get_cell_property(self, sbs_dataframe): - es = EntitySet(entity=sbs_dataframe) - props = es.get_cell_property("P", "A", "cell_weights") + def test_get_cell_property(self, es_from_sbsdf): + props = es_from_sbsdf.get_cell_property("P", "A", "cell_weights") assert props == 1 @pytest.mark.parametrize( @@ -429,25 +406,21 @@ def test_get_cell_property(self, sbs_dataframe): ], ) def test_get_cell_property_raises_keyerror( - self, sbs_dataframe, item1, item2, prop_name, err_msg + self, es_from_sbsdf, item1, item2, prop_name, err_msg ): - es = EntitySet(entity=sbs_dataframe) - with pytest.raises(KeyError, match=err_msg): - es.get_cell_property(item1, item2, prop_name) + es_from_sbsdf.get_cell_property(item1, item2, prop_name) @pytest.mark.parametrize("item, level", [("P", 0), ("P", None), ("A", 1)]) - def test_get_properties(self, sbs_dataframe, item, level): - es = EntitySet(entity=sbs_dataframe) - + def test_get_properties(self, es_from_sbsdf, item, level): # to avoid duplicate test code, reuse 'level' to get the item_uid # but if level is None, assume it to be 0 and that the item exists at level 0 if level is None: - item_uid = es.properties.loc[(0, item), "uid"] + item_uid = es_from_sbsdf.properties.loc[(0, item), "uid"] else: - item_uid = es.properties.loc[(level, item), "uid"] + item_uid = es_from_sbsdf.properties.loc[(level, item), "uid"] - props = es.get_properties(item, level=level) + props = es_from_sbsdf.get_properties(item, level=level) assert props == {"uid": item_uid, "weight": 1, "properties": {}} @@ -458,11 +431,9 @@ def test_get_properties(self, sbs_dataframe, item, level): ("Not a valid item", 0, "no properties initialized for"), ], ) - def test_get_properties_raises_keyerror(self, sbs_dataframe, item, level, err_msg): - es = EntitySet(entity=sbs_dataframe) - + def test_get_properties_raises_keyerror(self, es_from_sbsdf, item, level, err_msg): with pytest.raises(KeyError, match=err_msg): - es.get_properties(item, level=level) + es_from_sbsdf.get_properties(item, level=level) @pytest.mark.parametrize( "item, prop_name, level, expected_prop", @@ -475,10 +446,8 @@ def test_get_properties_raises_keyerror(self, sbs_dataframe, item, level, err_ms ("A", "uid", 1, 6), ], ) - def test_get_property(self, sbs_dataframe, item, prop_name, level, expected_prop): - es = EntitySet(entity=sbs_dataframe) - - prop = es.get_property(item, prop_name, level) + def test_get_property(self, es_from_sbsdf, item, prop_name, level, expected_prop): + prop = es_from_sbsdf.get_property(item, prop_name, level) assert prop == expected_prop @@ -490,12 +459,10 @@ def test_get_property(self, sbs_dataframe, item, prop_name, level, expected_prop ], ) def test_get_property_raises_keyerror( - self, sbs_dataframe, item, prop_name, err_msg + self, es_from_sbsdf, item, prop_name, err_msg ): - es = EntitySet(entity=sbs_dataframe) - with pytest.raises(KeyError, match=err_msg): - es.get_property(item, prop_name) + es_from_sbsdf.get_property(item, prop_name) @pytest.mark.parametrize( "item, prop_name, prop_val, level", @@ -503,14 +470,12 @@ def test_get_property_raises_keyerror( ("P", "weight", 42, 0), ], ) - def test_set_property(self, sbs_dataframe, item, prop_name, prop_val, level): - es = EntitySet(entity=sbs_dataframe) + def test_set_property(self, es_from_sbsdf, item, prop_name, prop_val, level): + orig_prop_val = es_from_sbsdf.get_property(item, prop_name, level) - orig_prop_val = es.get_property(item, prop_name, level) + es_from_sbsdf.set_property(item, prop_name, prop_val, level) - es.set_property(item, prop_name, prop_val, level) - - new_prop_val = es.get_property(item, prop_name, level) + new_prop_val = es_from_sbsdf.get_property(item, prop_name, level) assert new_prop_val != orig_prop_val assert new_prop_val == prop_val @@ -523,23 +488,19 @@ def test_set_property(self, sbs_dataframe, item, prop_name, prop_val, level): ], ) def test_set_property_on_non_existing_property( - self, sbs_dataframe, item, prop_name, prop_val, level, misc_props_col + self, es_from_sbsdf, item, prop_name, prop_val, level, misc_props_col ): - es = EntitySet(entity=sbs_dataframe, misc_props_col=misc_props_col) - - es.set_property(item, prop_name, prop_val, level) + es_from_sbsdf.set_property(item, prop_name, prop_val, level) - new_prop_val = es.get_property(item, prop_name, level) + new_prop_val = es_from_sbsdf.get_property(item, prop_name, level) assert new_prop_val == prop_val - def test_set_property_raises_keyerror(self, sbs_dataframe): - es = EntitySet(entity=sbs_dataframe) - + def test_set_property_raises_keyerror(self, es_from_sbsdf): with pytest.raises( ValueError, match="cannot infer 'level' when initializing 'item' properties" ): - es.set_property("XXXX", "weight", 42) + es_from_sbsdf.set_property("XXXX", "weight", 42) def test_incidence_matrix(self, sbs): ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) @@ -556,9 +517,8 @@ def test_indices(self, sbs): assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] @pytest.mark.parametrize("level", [0, 1]) - def test_is_empty(self, sbs_dataframe, level): - es = EntitySet(entity=sbs_dataframe) - assert not es.is_empty(level) + def test_is_empty(self, es_from_sbsdf, level): + assert not es_from_sbsdf.is_empty(level) @pytest.mark.parametrize( "item_level, item, min_level, max_level, expected_lidx", @@ -572,83 +532,16 @@ def test_is_empty(self, sbs_dataframe, level): ], ) def test_level( - self, sbs_dataframe, item_level, item, min_level, max_level, expected_lidx + self, es_from_sbsdf, item_level, item, min_level, max_level, expected_lidx ): - es = EntitySet(sbs_dataframe) - - actual_lidx = es.level(item, min_level=min_level, max_level=max_level) + actual_lidx = es_from_sbsdf.level( + item, min_level=min_level, max_level=max_level + ) assert actual_lidx == expected_lidx if actual_lidx is not None: - actual_lidx[0] == es.labels[item_level].index(item) - - def test_translate(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate(0, 0) == "P" - assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] - - def test_translate_arr(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] - - @pytest.mark.skip(reason="TODO: implement") - def test_uidset_by_column(self): - pass - - def test_uidset_by_level(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - - assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} - assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} - - -class TestEntitySetOnHarryPotterDataSet: - def test_entityset_from_ndarray(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert len(ent_hp.uidset) == 7 - assert len(ent_hp.elements) == 7 - assert isinstance(ent_hp.elements["Hufflepuff"], UserList) - assert not ent_hp.is_empty() - assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 - - def test_custom_attributes(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert ent_hp.__len__() == 7 - assert isinstance(ent_hp.__str__(), str) - assert isinstance(ent_hp.__repr__(), str) - assert isinstance(ent_hp.__contains__("Muggle"), bool) - assert ent_hp.__contains__("Muggle") is True - assert ent_hp.__getitem__("Slytherin") == [ - "Half-blood", - "Pure-blood", - "Pure-blood or half-blood", - ] - assert isinstance(ent_hp.__iter__(), Iterable) - assert isinstance(ent_hp.__call__(), Iterable) - assert ent_hp.__call__().__next__() == "Unknown House" - - def test_restrict_to_levels(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 - - def test_restrict_to_indices(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert ent_hp.restrict_to_indices([1, 2]).uidset == { - "Gryffindor", - "Ravenclaw", - } - - -# testing entityset helpers + actual_lidx[0] == es_from_sbsdf.labels[item_level].index(item) @pytest.mark.xfail( @@ -661,27 +554,3 @@ def test_level(sbs): assert ent_sbs.level("I") == (0, 5) # fails assert ent_sbs.level("K") == (1, 3) assert ent_sbs.level("K", max_level=0) is None - - -@pytest.mark.xfail( - reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" -) -def test_attributes(harry_potter): - assert isinstance(harry_potter.data, np.ndarray) - ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) - # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray - assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails - assert isinstance(ent_hp.labels, dict) - # TODO: Entity defaults to first two cols as data cols - assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails - assert ent_hp.dimsize == 5 # fails - df = ent_hp.dataframe[ent_hp._data_cols] - assert list(df.columns) == [ # fails - "House", - "Blood status", - "Species", - "Hair colour", - "Eye colour", - ] - assert ent_hp.dimensions == tuple(df.nunique()) - assert set(ent_hp.labels["House"]) == set(df["House"].unique()) From d6be744a874734c6cc95d9026c6fe5ac735c738e Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 2 Oct 2023 16:53:36 -0700 Subject: [PATCH 43/76] HYP-177 Update pytest and tox config --- hypernetx/utils/toys/harrypotter.py | 3 +-- pytest.ini | 2 +- tox.ini | 2 +- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/hypernetx/utils/toys/harrypotter.py b/hypernetx/utils/toys/harrypotter.py index a23cba0f..6d575c7e 100644 --- a/hypernetx/utils/toys/harrypotter.py +++ b/hypernetx/utils/toys/harrypotter.py @@ -12,7 +12,6 @@ class HarryPotter(object): def __init__(self, cols=None): # Read dataset in using pandas. Fix index column or use default pandas index. - try: fname = "https://raw.githubusercontent.com/pnnl/HyperNetX/master/hypernetx/utils/toys/HarryPotter_Characters.csv" harrydata = pd.read_csv(fname, encoding="unicode_escape") @@ -20,7 +19,7 @@ def __init__(self, cols=None): fname = f"{current_dir}/HarryPotter_Characters.csv" harrydata = pd.read_csv(fname, encoding="unicode_escape") - self.harryxdata = pd.DataFrame(harrydata) + self.harrydata = pd.DataFrame(harrydata) # Choose string to fill NaN. These will be set to 0 in system id = sid columns = cols or [ diff --git a/pytest.ini b/pytest.ini index 2363bdb2..de71beaa 100644 --- a/pytest.ini +++ b/pytest.ini @@ -2,7 +2,7 @@ minversion = 6.0 ; addopts are a set of optional arguments given to pytest: ; '-rA' will show a short test summary with the results for every test' -addopts = -rA -n auto --cov=hypernetx --cov-report term --cov-report html --junit-xml=pytest.xml --cov-fail-under=45 +addopts = -rA -n auto testpaths = hypernetx/classes/tests hypernetx/classes/algorithms diff --git a/tox.ini b/tox.ini index 2bf91b4a..edeccc86 100644 --- a/tox.ini +++ b/tox.ini @@ -22,7 +22,7 @@ deps = allowlist_externals = env commands = env - coverage run -m pytest + coverage run -m pytest --cov=hypernetx --cov-report term --cov-report html --junit-xml=pytest.xml --cov-fail-under=45 [testenv:py38-notebooks] description = run tests on jupyter notebooks From 4fedb4ed1f530869c04be4092d6aaf0c1aa94929 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 3 Oct 2023 13:46:02 -0700 Subject: [PATCH 44/76] HYP-177 Modify helper method --- hypernetx/classes/helpers.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/hypernetx/classes/helpers.py b/hypernetx/classes/helpers.py index 84365f4c..6edde0e8 100644 --- a/hypernetx/classes/helpers.py +++ b/hypernetx/classes/helpers.py @@ -214,6 +214,9 @@ def remove_row_duplicates( weight_col : Hashable The name of the column holding aggregated weights, or None if aggregateby=None """ + if df.empty: + return df, None + df = df.copy() categories = {} for col in data_cols: From 7da3e76c2fdcb3875d5585ff928ddce27cae18e4 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 3 Oct 2023 15:14:46 -0700 Subject: [PATCH 45/76] HYP-177 Cleanup tests --- hypernetx/classes/entityset.py | 2 +- .../classes/tests/test_entityset_sbs_data.py | 83 +++++++++++++------ 2 files changed, 59 insertions(+), 26 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index a4c3c92f..20e688b3 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -872,7 +872,7 @@ def translate(self, level: int, index: int | list[int]) -> str | list[str]: return [self.labels[column][i] for i in index] - def translate_arr(self, coords: tuple[int]) -> list[str]: + def translate_arr(self, coords: tuple[int, int]) -> list[str]: """Translate a full encoded row of the data table e.g., a row of ``self.data`` Parameters diff --git a/hypernetx/classes/tests/test_entityset_sbs_data.py b/hypernetx/classes/tests/test_entityset_sbs_data.py index 26332e9b..9082c78b 100644 --- a/hypernetx/classes/tests/test_entityset_sbs_data.py +++ b/hypernetx/classes/tests/test_entityset_sbs_data.py @@ -1,3 +1,5 @@ +from collections import OrderedDict + import numpy as np import pandas as pd import pytest @@ -7,33 +9,45 @@ from hypernetx.classes import EntitySet -class TestEntitySetUseCases: +@pytest.mark.parametrize( + "entity, data, data_cols, labels", + [ + (lazy_fixture("sbs_dataframe"), None, (0, 1), None), + (lazy_fixture("sbs_dict"), None, (0, 1), None), + (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), + # (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), + ], +) +class TestEntitySetUseCasesOnSBS: # Tests on different use cases for combination of the following params: entity, data, data_cols, labels - @pytest.mark.parametrize( - "entity, data, data_cols, labels", - [ - (lazy_fixture("sbs_dataframe"), None, (0, 1), None), - (lazy_fixture("sbs_dict"), None, (0, 1), None), - (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), - # (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), - ], - ) - def test_all_attribute_properties_on_common_entityset_instances( - self, entity, data, data_cols, labels, sbs - ): + + def test_size(self, entity, data, data_cols, labels, sbs): es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.size() == len(sbs.edgedict) + # check all the EntitySet properties + def test_isstatic(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.isstatic + + def test_uid(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.uid is None + + def test_empty(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert not es.empty + def test_uidset(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.uidset == {"I", "R", "S", "P", "O", "L"} - assert es.size() == len(sbs.edgedict) + + def test_dimsize(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.dimsize == 2 - assert es.dimensions == (6, 7) - assert es.data.shape == (15, 2) - assert es.data.ndim == 2 + def test_elements(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert len(es.elements) == 6 expected_elements = { "I": ["K", "T2"], @@ -47,6 +61,8 @@ def test_all_attribute_properties_on_common_entityset_instances( assert expected_edge in es.elements assert es.elements[expected_edge].sort() == expected_nodes.sort() + def test_incident_dict(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) expected_incident_dict = { "I": ["K", "T2"], "L": ["E", "C"], @@ -58,13 +74,16 @@ def test_all_attribute_properties_on_common_entityset_instances( for expected_edge, expected_nodes in expected_incident_dict.items(): assert expected_edge in es.incidence_dict assert es.incidence_dict[expected_edge].sort() == expected_nodes.sort() - - # check dunder methods assert isinstance(es.incidence_dict["I"], list) assert "I" in es assert "K" in es + def test_children(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.children == {"C", "T1", "A", "K", "T2", "V", "E"} + + def test_memberships(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.memberships == { "A": ["P", "R", "S"], "C": ["P", "L"], @@ -75,10 +94,15 @@ def test_all_attribute_properties_on_common_entityset_instances( "V": ["S"], } + def test_cell_properties(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.cell_properties.shape == ( 15, 1, - ) # cell properties: a pandas dataframe of one column of all the cells. A cell is an edge-node pair. And we are saving the weight of each pair + ) + + def test_cell_weights(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert es.cell_weights == { ("P", "C"): 1, ("P", "K"): 1, @@ -97,6 +121,8 @@ def test_all_attribute_properties_on_common_entityset_instances( ("I", "T2"): 1, } + def test_labels(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) # check labeling based on given attributes for EntitySet if data_cols == [ "edges", @@ -114,6 +140,8 @@ def test_all_attribute_properties_on_common_entityset_instances( 1: ["A", "C", "E", "K", "T1", "T2", "V"], } + def test_dataframe(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) # check dataframe # size should be the number of rows times the number of columns, i.e 15 x 3 assert es.dataframe.size == 45 @@ -126,17 +154,20 @@ def test_all_attribute_properties_on_common_entityset_instances( assert actual_node_row0 in ["A", "C", "K"] assert actual_cell_weight_row0 == 1 - # print(es.data) - # print(es.properties) + def test_data(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert len(es.data) == 15 # TODO: validate state of 'data' + def test_properties(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) assert ( es.properties.size == 39 ) # Properties has three columns and 13 rows of data (i.e. edges + nodes) assert list(es.properties.columns) == ["uid", "weight", "properties"] -class TestEntitySetOnSevenBySixDataset: +class TestEntitySetOnSBSasNDArray: + # Check all methods def test_ndarray_fail_on_labels(self, sbs): with pytest.raises(ValueError, match="Labels must be of type Dictionary."): EntitySet(data=np.asarray(sbs.data), labels=[]) @@ -177,6 +208,7 @@ def es_from_sbsdf(self, sbs): def es_from_sbs_dupe_df(self, sbsd): return EntitySet(entity=sbsd.dataframe) + # check all methods @pytest.mark.parametrize( "data", [ @@ -540,8 +572,9 @@ def test_level( assert actual_lidx == expected_lidx - if actual_lidx is not None: - actual_lidx[0] == es_from_sbsdf.labels[item_level].index(item) + if isinstance(actual_lidx, tuple): + index_item_in_labels = actual_lidx[1] + assert index_item_in_labels == es_from_sbsdf.labels[item_level].index(item) @pytest.mark.xfail( From 714e868ed729e5b919408c73e0266645ddd16c31 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 5 Oct 2023 16:01:09 -0700 Subject: [PATCH 46/76] HYP-177 Refactor and fix set_cell_property --- hypernetx/classes/entityset.py | 26 +++++++----- .../classes/tests/test_entityset_sbs_data.py | 42 ++++++++++++++++--- pytest.ini | 2 +- tox.ini | 2 +- 4 files changed, 55 insertions(+), 17 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 20e688b3..7a14725d 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1808,16 +1808,22 @@ def set_cell_property( if prop_name in self._cell_properties: self._cell_properties.loc[(item1, item2), prop_name] = prop_val - else: - try: - self._cell_properties.loc[ - (item1, item2), self._misc_cell_props_col - ].update({prop_name: prop_val}) - except KeyError: - # TODO: this will set the existing values in row's columns to Nan; the property name and value are not captured - self._cell_properties.loc[(item1, item2), :] = { - self._misc_cell_props_col: {prop_name: prop_val} - } + return + + try: + # assumes that _misc_cell_props already exists in cell_properties + self._cell_properties.loc[(item1, item2), self._misc_cell_props_col].update( + {prop_name: prop_val} + ) + except KeyError: + # creates the _misc_cell_props with a defualt empty dict + self._cell_properties[self._misc_cell_props_col] = [ + {} for _ in range(len(self._cell_properties)) + ] + # insert the property name and value as a dictionary in _misc_cell_props for the target incident pair + self._cell_properties.loc[(item1, item2), self._misc_cell_props_col].update( + {prop_name: prop_val} + ) def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: """Get a property of a cell i.e., incidence between items of different levels diff --git a/hypernetx/classes/tests/test_entityset_sbs_data.py b/hypernetx/classes/tests/test_entityset_sbs_data.py index 9082c78b..d63e6757 100644 --- a/hypernetx/classes/tests/test_entityset_sbs_data.py +++ b/hypernetx/classes/tests/test_entityset_sbs_data.py @@ -1,5 +1,3 @@ -from collections import OrderedDict - import numpy as np import pandas as pd import pytest @@ -322,9 +320,43 @@ def test_assign_cell_properties_on_multiple_properties(self, es_from_sbsdf): "prop3": "propval3", } - def test_set_cell_property_from_existing_properties(self, es_from_sbsdf): - es_from_sbsdf.set_cell_property("P", "A", "cell_weights", 42) - assert es_from_sbsdf.cell_properties.loc[("P", "A")].cell_weights == 42.0 + def test_set_cell_property_on_cell_weights(self, es_from_sbsdf): + item1 = "P" + item2 = "A" + prop_name = "cell_weights" + prop_val = 42 + + es_from_sbsdf.set_cell_property(item1, item2, prop_name, prop_val) + + assert es_from_sbsdf.cell_properties.loc[(item1, item2), prop_name] == 42.0 + + # Check that the other cell_weights were not changed and retained the default value of 1 + for row in es_from_sbsdf.cell_properties.itertuples(): + if row.Index != (item1, item2): + assert row.cell_weights == 1 + + def test_set_cell_property_on_non_exisiting_cell_property(self, es_from_sbsdf): + item1 = "P" + item2 = "A" + prop_name = "non_existing_cell_property" + prop_val = {"foo": "bar"} + es_from_sbsdf.set_cell_property(item1, item2, prop_name, prop_val) + + assert es_from_sbsdf.cell_properties.loc[(item1, item2), "cell_properties"] == { + prop_name: prop_val + } + + # Check that the other rows received the default empty dictionary + for row in es_from_sbsdf.cell_properties.itertuples(): + if row.Index != (item1, item2): + assert row.cell_properties == {} + + item2 = "K" + es_from_sbsdf.set_cell_property(item1, item2, prop_name, prop_val) + + assert es_from_sbsdf.cell_properties.loc[(item1, item2), "cell_properties"] == { + prop_name: prop_val + } @pytest.mark.parametrize("ret_ec", [True, False]) def test_collapse_identical_elements_on_duplicates( diff --git a/pytest.ini b/pytest.ini index de71beaa..937fc3a8 100644 --- a/pytest.ini +++ b/pytest.ini @@ -2,7 +2,7 @@ minversion = 6.0 ; addopts are a set of optional arguments given to pytest: ; '-rA' will show a short test summary with the results for every test' -addopts = -rA -n auto +addopts = -rA testpaths = hypernetx/classes/tests hypernetx/classes/algorithms diff --git a/tox.ini b/tox.ini index edeccc86..9fa2d7f6 100644 --- a/tox.ini +++ b/tox.ini @@ -22,7 +22,7 @@ deps = allowlist_externals = env commands = env - coverage run -m pytest --cov=hypernetx --cov-report term --cov-report html --junit-xml=pytest.xml --cov-fail-under=45 + coverage run -m pytest -n auto --cov=hypernetx --cov-report term --cov-report html --junit-xml=pytest.xml --cov-fail-under=45 [testenv:py38-notebooks] description = run tests on jupyter notebooks From a44d424da64a4ec14fb8041970b7ffaa1a60b359 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 5 Oct 2023 16:31:31 -0700 Subject: [PATCH 47/76] HYP-177 Return none when property not found; update tests --- hypernetx/classes/entityset.py | 34 +++++++++++++------ .../classes/tests/test_entityset_sbs_data.py | 16 ++++----- 2 files changed, 30 insertions(+), 20 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 7a14725d..9181b388 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -1613,6 +1613,9 @@ def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> prop_val : any value of the property + None + if property not found + Raises ------ KeyError @@ -1644,10 +1647,10 @@ def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> prop_val = self.properties.loc[item_key, self._misc_props_col][ prop_name ] - except KeyError as e: - raise KeyError( - f"no properties initialized for ('level','item'): {item_key}" - ) from e + except KeyError: + # prop_name is not a key in the dictionary in the _misc_props_col; + # in other words, property was not found + return None return prop_val @@ -1842,6 +1845,14 @@ def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: prop_val : any value of the cell property + None + If prop_name not found + + Raises + ------ + KeyError + If `(item1, item2)` is not in :attr:`cell_properties` + See Also -------- get_cell_properties, set_cell_property @@ -1859,13 +1870,13 @@ def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: try: prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) except KeyError: - raise KeyError( - f"Item exists but property does not exist. cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" - ) + # prop_name is not a key in the dictionary in the _misc_cell_props_col; + # in other words, property was not found + return None return prop_val - def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: + def get_cell_properties(self, item1: T, item2: T) -> Optional[dict[Any, Any]]: """Get all properties of a cell, i.e., incidence between items of different levels @@ -1882,6 +1893,9 @@ def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: ``{named cell property: cell property value, ..., misc. cell property column name: {cell property name: cell property value}}`` + None + If properties do not exist + See Also -------- get_cell_property, set_cell_property @@ -1889,9 +1903,7 @@ def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: try: cell_props = self.cell_properties.loc[(item1, item2)] except KeyError: - raise KeyError( - f"cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" - ) + return None return cell_props.to_dict() diff --git a/hypernetx/classes/tests/test_entityset_sbs_data.py b/hypernetx/classes/tests/test_entityset_sbs_data.py index d63e6757..ccdb79a4 100644 --- a/hypernetx/classes/tests/test_entityset_sbs_data.py +++ b/hypernetx/classes/tests/test_entityset_sbs_data.py @@ -450,8 +450,7 @@ def test_get_cell_properties(self, es_from_sbsdf): assert props == {"cell_weights": 1} def test_get_cell_properties_raises_keyerror(self, es_from_sbsdf): - with pytest.raises(KeyError, match="cell_properties:"): - es_from_sbsdf.get_cell_properties("P", "FOOBAR") + assert es_from_sbsdf.get_cell_properties("P", "FOOBAR") is None def test_get_cell_property(self, es_from_sbsdf): props = es_from_sbsdf.get_cell_property("P", "A", "cell_weights") @@ -461,12 +460,6 @@ def test_get_cell_property(self, es_from_sbsdf): "item1, item2, prop_name, err_msg", [ ("P", "FOO", "cell_weights", "Item not exists. cell_properties:"), - ( - "P", - "A", - "Not a real property", - "Item exists but property does not exist. cell_properties:", - ), ], ) def test_get_cell_property_raises_keyerror( @@ -475,6 +468,9 @@ def test_get_cell_property_raises_keyerror( with pytest.raises(KeyError, match=err_msg): es_from_sbsdf.get_cell_property(item1, item2, prop_name) + def test_get_cell_property_returns_none_on_prop(self, es_from_sbsdf): + assert es_from_sbsdf.get_cell_property("P", "A", "Not a real property") is None + @pytest.mark.parametrize("item, level", [("P", 0), ("P", None), ("A", 1)]) def test_get_properties(self, es_from_sbsdf, item, level): # to avoid duplicate test code, reuse 'level' to get the item_uid @@ -519,7 +515,6 @@ def test_get_property(self, es_from_sbsdf, item, prop_name, level, expected_prop "item, prop_name, err_msg", [ ("XXX", "weight", "item does not exist:"), - ("P", "not a real prop name", "no properties initialized for"), ], ) def test_get_property_raises_keyerror( @@ -528,6 +523,9 @@ def test_get_property_raises_keyerror( with pytest.raises(KeyError, match=err_msg): es_from_sbsdf.get_property(item, prop_name) + def test_get_property_returns_none_on_no_property(self, es_from_sbsdf): + assert es_from_sbsdf.get_property("P", "non-existing property") is None + @pytest.mark.parametrize( "item, prop_name, prop_val, level", [ From 69f88019b7b34db8aceca3ff85ed9be0732f6cc7 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 13 Oct 2023 10:22:06 -0700 Subject: [PATCH 48/76] HYP-177 Update tox.ini script test deps --- tox.ini | 15 ++------------- 1 file changed, 2 insertions(+), 13 deletions(-) diff --git a/tox.ini b/tox.ini index 9fa2d7f6..29a92bcc 100644 --- a/tox.ini +++ b/tox.ini @@ -11,14 +11,7 @@ isolated_build = True skip_missing_interpreters = true [testenv] -deps = - pytest>=7.2.2 - pytest-cov>=4.1.0 - pytest-lazy-fixture>=0.6.3 - pytest-xdist>=3.2.1 - celluloid>=0.2.0 - igraph>=0.10.4 - partition-igraph>=0.0.6 +extras = testing allowlist_externals = env commands = env @@ -26,11 +19,7 @@ commands = [testenv:py38-notebooks] description = run tests on jupyter notebooks -deps = - nbmake>=1.4.1 - hnxwidget>=0.1.1b3 - jupyter-contrib-nbextensions>=0.7.0 - jupyter-nbextensions-configurator>=0.6.2 +extras = widget allowlist_externals = env commands = env From 02892739b77fffd91f59928a9316823eba29407e Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 18 Oct 2023 16:02:15 -0700 Subject: [PATCH 49/76] HYP-356 Add deprecate warnings to certain ES methods --- hypernetx/classes/entityset.py | 37 ++++++++++++++++++++++++++++++--- hypernetx/classes/hypergraph.py | 2 +- hypernetx/utils/decorators.py | 31 +++++++++++++++++++++++---- 3 files changed, 62 insertions(+), 8 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 9181b388..c0a5e3fd 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -18,6 +18,8 @@ remove_row_duplicates, ) +from hypernetx.utils.decorators import warn_to_be_deprecated + T = TypeVar("T", bound=Union[str, int]) @@ -626,10 +628,11 @@ def dataframe(self) -> pd.DataFrame: return self._dataframe @property + @warn_to_be_deprecated def isstatic(self) -> bool: - # TODO: I'm guessing this is no longer necessary? """Whether to treat the underlying data as static or not + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] If True, the underlying data may not be altered, and the state_dict will never be cleared Otherwise, rows may be added to and removed from the data table, and updates will clear the state_dict @@ -637,6 +640,7 @@ def isstatic(self) -> bool: ------- bool """ + return self._static def size(self, level: int = 0) -> int: @@ -816,9 +820,12 @@ def index(self, column: str, value: Optional[str] = None) -> int | tuple[int, in self._state_dict["index"][column][value], ) + @warn_to_be_deprecated def indices(self, column: str, values: str | Iterable[str]) -> list[int]: """Get indices of one or more value(s) in a column + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- column : str @@ -846,9 +853,12 @@ def indices(self, column: str, values: str | Iterable[str]) -> list[int]: return [self._state_dict["index"][column][v] for v in values] + @warn_to_be_deprecated def translate(self, level: int, index: int | list[int]) -> str | list[str]: """Given indices of a level and value(s), return the corresponding value label(s) + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- level : int @@ -872,9 +882,12 @@ def translate(self, level: int, index: int | list[int]) -> str | list[str]: return [self.labels[column][i] for i in index] + @warn_to_be_deprecated def translate_arr(self, coords: tuple[int, int]) -> list[str]: """Translate a full encoded row of the data table e.g., a row of ``self.data`` + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- coords : tuple of ints @@ -892,6 +905,7 @@ def translate_arr(self, coords: tuple[int, int]) -> list[str]: return translation + @warn_to_be_deprecated def level( self, item: str, @@ -901,6 +915,8 @@ def level( ) -> int | tuple[int, int] | None: """First level containing the given item label + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Order of levels corresponds to order of columns in `self.dataframe` Parameters @@ -969,10 +985,11 @@ def add(self, *args) -> Self: self.add_element(item) return self + @warn_to_be_deprecated def add_elements_from(self, arg_set) -> Self: """Adds arguments from an iterable to the data table one at a time - ..deprecated:: 2.0.0 + DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] Duplicates `add` Parameters @@ -1079,10 +1096,12 @@ def remove(self, *args: T) -> EntitySet: self.remove_element(item) return self + @warn_to_be_deprecated def remove_elements_from(self, arg_set): """Removes all rows containing specified item(s) from the underlying data table - ..deprecated: 2.0.0 + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Duplicates `remove` Parameters @@ -1130,6 +1149,7 @@ def remove_element(self, item: T) -> None: for col in self._data_cols: self._dataframe[col] = self._dataframe[col].cat.remove_unused_categories() + @warn_to_be_deprecated def encode(self, data: pd.DataFrame) -> np.array: """ Encode dataframe to numpy array @@ -1145,6 +1165,7 @@ def encode(self, data: pd.DataFrame) -> np.array: """ return data.apply(lambda x: x.cat.codes).to_numpy() + @warn_to_be_deprecated def incidence_matrix( self, level1: int = 0, @@ -1154,6 +1175,8 @@ def incidence_matrix( ) -> Optional[sp.csr_matrix]: """Incidence matrix representation for two levels (columns) of the underlying data table + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + If `level1` and `level2` contain N and M distinct items, respectively, the incidence matrix will be M x N. In other words, the items in `level1` and `level2` correspond to the columns and rows of the incidence matrix, respectively, in the order in which they appear in `self.labels[column1]` and `self.labels[column2]` @@ -1279,11 +1302,14 @@ def _restrict_to_levels( **kwargs, ) + @warn_to_be_deprecated def restrict_to_indices( self, indices: int | Iterable[int], level: int = 0, **kwargs ) -> EntitySet: """Create a new EntitySet by restricting the data table to rows containing specific items in a given level + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- indices : int or iterable of int @@ -1907,9 +1933,12 @@ def get_cell_properties(self, item1: T, item2: T) -> Optional[dict[Any, Any]]: return cell_props.to_dict() + @warn_to_be_deprecated def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- indices : array_like of int @@ -1935,6 +1964,7 @@ def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: restricted.assign_cell_properties(cell_properties) return restricted + @warn_to_be_deprecated def restrict_to_levels( self, levels: int | Iterable[int], @@ -1946,6 +1976,7 @@ def restrict_to_levels( """Create a new EntitySet by restricting to a subset of levels (columns) in the underlying data table + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] Parameters ---------- diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index a79cde0c..02001416 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -766,7 +766,7 @@ def get_properties(self, id, level=None, prop_name=None): : str or dict single property or dictionary of properties """ - if prop_name == None: + if prop_name is None: return self.E.get_properties(id, level=level) else: return self.E.get_property(id, prop_name, level=level) diff --git a/hypernetx/utils/decorators.py b/hypernetx/utils/decorators.py index 5652bf30..28cfcaac 100644 --- a/hypernetx/utils/decorators.py +++ b/hypernetx/utils/decorators.py @@ -6,10 +6,7 @@ import hypernetx as hnx from hypernetx.exception import NWHY_WARNING -__all__ = [ - "not_implemented_for", - "warn_nwhy", -] +__all__ = ["not_implemented_for", "warn_nwhy", "warn_to_be_deprecated"] def not_implemented_for(*object_types): @@ -89,3 +86,29 @@ def wrapper(*args, **kwargs): return func(*args, **kwargs) return wrapper + + +def warn_to_be_deprecated(func): + """Decorator for methods that are to be deprecated + + Public references to deprecated methods or functions will be removed from the Hypergraph API in a future release. + + Warns + ----- + FutureWarning + """ + + deprecation_warning_msg = ( + "This method or function will be deprecated in a future release. " + "Public references to this method or function will be removed from the " + "Hypergraph API in a future release." + ) + + @wraps(func) + def wrapper(*args, **kwargs): + warnings.simplefilter("always", FutureWarning) + warnings.warn(deprecation_warning_msg, FutureWarning, stacklevel=2) + warnings.simplefilter("default", FutureWarning) + return func(*args, **kwargs) + + return wrapper From 05789210297a8b8262046a15f4180bfb9da6b6a6 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 13 Oct 2023 17:14:16 -0700 Subject: [PATCH 50/76] HYP-353 Remove option to customize misc props column --- hypernetx/classes/entityset.py | 23 +++++------------------ hypernetx/classes/hypergraph.py | 2 -- 2 files changed, 5 insertions(+), 20 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index c0a5e3fd..37385353 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -34,8 +34,6 @@ class EntitySet: represents N-dimensional entity data (data table). Otherwise, represents 2-dimensional entity data (system of sets). data_cols : sequence of ints or strings, default=(0,1) - level1: str or int, default = 0 - level2: str or int, default = 1 data : numpy.ndarray, optional 2D M x N ``ndarray`` of ``ints`` (data table); sparse representation of an N-dimensional incidence tensor with M nonzero cells. @@ -75,9 +73,6 @@ class EntitySet: (order of columns does not matter; see Notes for an example). If doubly-nested dict, ``{item level: {item label: {property name: property value}}}``. - misc_props_col: str, default="properties" - Column names for miscellaneous properties, level index, and item name in - :attr:`properties`; see Notes for explanation. level_col: str, default="level" id_col : str, default="id" cell_properties: sequence of int or str, pandas.DataFrame, or doubly-nested dict, optional @@ -110,10 +105,7 @@ class EntitySet: all occurrences). The names of the Level (if provided) and ID columns must be specified by `level_col` - and `id_col`. `misc_props_col` can be used to specify the name of the column to be used - for miscellaneous properties; if no column by that name is found, - a new column will be created and populated with empty ``dicts``. - All other columns will be considered explicit property types. + and `id_col`. All other columns will be considered explicit property types. The order of the columns does not matter. This method assumes that there are no rows with the same (Level, ID); @@ -138,7 +130,6 @@ def __init__( weights: Optional[Sequence[float] | float | int | str] = 1, aggregateby: Optional[str | dict] = "sum", properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, - misc_props_col: str = "properties", level_col: str = "level", id_col: str = "id", cell_properties: Optional[ @@ -150,6 +141,7 @@ def __init__( self._static = static self._state_dict = {} self._misc_cell_props_col = misc_cell_props_col + self._misc_props_col = "properties" # build initial dataframe if isinstance(data, np.ndarray) and entity is None: @@ -178,7 +170,7 @@ def __init__( ) # create properties - self._create_properties(level_col, id_col, misc_props_col, properties) + self._create_properties(level_col, id_col, properties) # create cell properties (From old EntitySet) self._create_assign_cell_properties(cell_properties) @@ -224,7 +216,6 @@ def _create_properties( self, level_col: str, id_col: str, - misc_props_col: str, properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]], ) -> None: item_levels = [ @@ -235,9 +226,8 @@ def _create_properties( index = pd.MultiIndex.from_tuples(item_levels, names=[level_col, id_col]) data = [(i, 1, {}) for i in range(len(index))] self._properties = pd.DataFrame( - data=data, index=index, columns=["uid", "weight", misc_props_col] + data=data, index=index, columns=["uid", "weight", self._misc_props_col] ).sort_index() - self._misc_props_col = misc_props_col self.assign_properties(properties) def _create_assign_cell_properties( @@ -1296,7 +1286,6 @@ def _restrict_to_levels( data_cols=cols, aggregateby=aggregateby, properties=properties, - misc_props_col=self._misc_props_col, level_col=level_col, id_col=id_col, **kwargs, @@ -1329,9 +1318,7 @@ def restrict_to_indices( for col in self._data_cols: entity[col] = entity[col].cat.remove_unused_categories() - restricted = self.__class__( - entity=entity, misc_props_col=self._misc_props_col, **kwargs - ) + restricted = self.__class__(entity=entity, **kwargs) if not self.properties.empty: prop_idx = [ diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 02001416..5eca748b 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -328,7 +328,6 @@ def __init__( ### cell properties if setsystem is None: #### Empty Case - self._edges = EntitySet({}) self._nodes = EntitySet({}) self._state_dict = {} @@ -545,7 +544,6 @@ def props2dict(df=None): misc_cell_props_col=misc_cell_properties_col or "cell_properties", aggregateby=aggregateby or "sum", properties=properties, - misc_props_col=misc_properties_col, ) self._edges = self.E From 119295c8bb1bb085e9536cbcb1f597bfb343adb6 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 13 Oct 2023 17:24:50 -0700 Subject: [PATCH 51/76] HYP-353 Remove option to customize misc cell props col --- hypernetx/classes/entityset.py | 5 +---- hypernetx/classes/hypergraph.py | 1 - 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index 37385353..fff5b405 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -76,7 +76,6 @@ class EntitySet: level_col: str, default="level" id_col : str, default="id" cell_properties: sequence of int or str, pandas.DataFrame, or doubly-nested dict, optional - misc_cell_props_col: str, default="cell_properties" Notes ----- @@ -135,12 +134,11 @@ def __init__( cell_properties: Optional[ Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] ] = None, - misc_cell_props_col: str = "cell_properties", ): self._uid = uid self._static = static self._state_dict = {} - self._misc_cell_props_col = misc_cell_props_col + self._misc_cell_props_col = "cell_properties" self._misc_props_col = "properties" # build initial dataframe @@ -1998,7 +1996,6 @@ def restrict_to_levels( levels, weights, aggregateby, - misc_cell_props_col=self._misc_cell_props_col, **kwargs, ) diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 5eca748b..7c077112 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -541,7 +541,6 @@ def props2dict(df=None): weight_col=cell_weight_col, weights=cell_weights, cell_properties=cell_properties, - misc_cell_props_col=misc_cell_properties_col or "cell_properties", aggregateby=aggregateby or "sum", properties=properties, ) From eb78a61815c909ed40c74fc8b2268ef0ba6c8256 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 18 Oct 2023 16:20:22 -0700 Subject: [PATCH 52/76] HYP-353 Add deprecation warnings for property column args --- hypernetx/classes/entityset.py | 20 +++++++++++++++++++- hypernetx/classes/hypergraph.py | 2 ++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index fff5b405..46c4fc66 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -34,6 +34,8 @@ class EntitySet: represents N-dimensional entity data (data table). Otherwise, represents 2-dimensional entity data (system of sets). data_cols : sequence of ints or strings, default=(0,1) + level1: str or int, default = 0 + level2: str or int, default = 1 data : numpy.ndarray, optional 2D M x N ``ndarray`` of ``ints`` (data table); sparse representation of an N-dimensional incidence tensor with M nonzero cells. @@ -73,9 +75,13 @@ class EntitySet: (order of columns does not matter; see Notes for an example). If doubly-nested dict, ``{item level: {item label: {property name: property value}}}``. + misc_props_col: str, default="properties" + Column names for miscellaneous properties, level index, and item name in + :attr:`properties`; see Notes for explanation. level_col: str, default="level" id_col : str, default="id" cell_properties: sequence of int or str, pandas.DataFrame, or doubly-nested dict, optional + misc_cell_props_col: str, default="cell_properties" Notes ----- @@ -104,7 +110,10 @@ class EntitySet: all occurrences). The names of the Level (if provided) and ID columns must be specified by `level_col` - and `id_col`. All other columns will be considered explicit property types. + and `id_col`. `misc_props_col` can be used to specify the name of the column to be used + for miscellaneous properties; if no column by that name is found, + a new column will be created and populated with empty ``dicts``. + All other columns will be considered explicit property types. The order of the columns does not matter. This method assumes that there are no rows with the same (Level, ID); @@ -129,12 +138,21 @@ def __init__( weights: Optional[Sequence[float] | float | int | str] = 1, aggregateby: Optional[str | dict] = "sum", properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, + misc_props_col: Optional[str] = None, level_col: str = "level", id_col: str = "id", cell_properties: Optional[ Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] ] = None, + misc_cell_props_col: Optional[str] = None, ): + if misc_props_col or misc_cell_props_col: + warnings.warn( + "misc_props_col and misc_cell_props_col will be deprecated; all public references to these " + "arguments will be removed in a future release.", + DeprecationWarning, + ) + self._uid = uid self._static = static self._state_dict = {} diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 7c077112..2a3c3037 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -541,8 +541,10 @@ def props2dict(df=None): weight_col=cell_weight_col, weights=cell_weights, cell_properties=cell_properties, + misc_cell_props_col=misc_cell_properties_col or "cell_properties", aggregateby=aggregateby or "sum", properties=properties, + misc_props_col=misc_properties_col, ) self._edges = self.E From fd25bb5ff6d9b9b8f53159c4185db232667606d2 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 18 Oct 2023 16:38:35 -0700 Subject: [PATCH 53/76] Update classes based on changes from testing --- hypernetx/classes/entityset.py | 352 ++++++++++++++------------------ hypernetx/classes/helpers.py | 29 +++ hypernetx/classes/hypergraph.py | 6 +- hypernetx/utils/decorators.py | 31 ++- 4 files changed, 215 insertions(+), 203 deletions(-) diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py index bfded939..46c4fc66 100644 --- a/hypernetx/classes/entityset.py +++ b/hypernetx/classes/entityset.py @@ -6,10 +6,11 @@ from collections import OrderedDict, defaultdict from collections.abc import Hashable, Mapping, Sequence, Iterable from typing import Union, TypeVar, Optional, Any +from typing_extensions import Self import numpy as np import pandas as pd -from scipy.sparse import csr_matrix +import scipy.sparse as sp from hypernetx.classes.helpers import ( AttrList, @@ -17,6 +18,8 @@ remove_row_duplicates, ) +from hypernetx.utils.decorators import warn_to_be_deprecated + T = TypeVar("T", bound=Union[str, int]) @@ -26,11 +29,13 @@ class EntitySet: Parameters ---------- - entity : pandas.DataFrame, dict of lists or sets, list of lists or sets, optional + entity : pandas.DataFrame, dict of lists or sets, dict of dicts, list of lists or sets, optional If a ``DataFrame`` with N columns, represents N-dimensional entity data (data table). Otherwise, represents 2-dimensional entity data (system of sets). - TODO: Test for compatibility with list of Entities and update docs + data_cols : sequence of ints or strings, default=(0,1) + level1: str or int, default = 0 + level2: str or int, default = 1 data : numpy.ndarray, optional 2D M x N ``ndarray`` of ``ints`` (data table); sparse representation of an N-dimensional incidence tensor with M nonzero cells. @@ -45,7 +50,8 @@ class EntitySet: Ignored if `entity` is provided or `data` is not provided. uid : hashable, optional A unique identifier for the object - weights : str or sequence of float, optional + weight_col: string or int, default="cell_weights" + weights : sequence of float, float, int, str, default=1 User-specified cell weights corresponding to entity data. If sequence of ``floats`` and `entity` or `data` defines a data table, length must equal the number of rows. @@ -54,11 +60,11 @@ class EntitySet: If ``str`` and `entity` is a ``DataFrame``, must be the name of a column in `entity`. Otherwise, weight for all cells is assumed to be 1. - aggregateby : {'sum', 'last', count', 'mean','median', max', 'min', 'first', None} + aggregateby : {'sum', 'last', count', 'mean','median', max', 'min', 'first', None}, default="sum" Name of function to use for aggregating cell weights of duplicate rows when - `entity` or `data` defines a data table, default is "sum". + `entity` or `data` defines a data table. If None, duplicate rows will be dropped without aggregating cell weights. - Effectively ignored if `entity` defines a system of sets. + Ignored if `entity` defines a system of sets. properties : pandas.DataFrame or doubly-nested dict, optional User-specified properties to be assigned to individual items in the data, i.e., cell entries in a data table; sets or set elements in a system of sets. @@ -66,12 +72,16 @@ class EntitySet: If ``DataFrame``, each row gives ``[optional item level, item label, optional named properties, {property name: property value}]`` - (order of columns does not matter; see note for an example). + (order of columns does not matter; see Notes for an example). If doubly-nested dict, ``{item level: {item label: {property name: property value}}}``. - misc_props_col, level_col, id_col : str, default="properties", "level, "id" + misc_props_col: str, default="properties" Column names for miscellaneous properties, level index, and item name in :attr:`properties`; see Notes for explanation. + level_col: str, default="level" + id_col : str, default="id" + cell_properties: sequence of int or str, pandas.DataFrame, or doubly-nested dict, optional + misc_cell_props_col: str, default="cell_properties" Notes ----- @@ -120,8 +130,6 @@ def __init__( | Mapping[T, Mapping[T, Any]] ] = None, data_cols: Sequence[T] = (0, 1), - level1: str | int = 0, - level2: str | int = 1, data: Optional[np.ndarray] = None, static: bool = True, labels: Optional[OrderedDict[T, Sequence[T]]] = None, @@ -130,31 +138,26 @@ def __init__( weights: Optional[Sequence[float] | float | int | str] = 1, aggregateby: Optional[str | dict] = "sum", properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, - misc_props_col: str = "properties", + misc_props_col: Optional[str] = None, level_col: str = "level", id_col: str = "id", cell_properties: Optional[ Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] ] = None, - misc_cell_props_col: str = "cell_properties", + misc_cell_props_col: Optional[str] = None, ): + if misc_props_col or misc_cell_props_col: + warnings.warn( + "misc_props_col and misc_cell_props_col will be deprecated; all public references to these " + "arguments will be removed in a future release.", + DeprecationWarning, + ) + self._uid = uid self._static = static self._state_dict = {} - self._misc_cell_props_col = misc_cell_props_col - - # Restrict to two columns on entity, data, labels - entity, data, labels = restrict_to_two_columns( - entity, - data, - labels, - cell_properties, - weight_col, - weights, - level1, - level2, - misc_cell_props_col, - ) + self._misc_cell_props_col = "cell_properties" + self._misc_props_col = "properties" # build initial dataframe if isinstance(data, np.ndarray) and entity is None: @@ -183,7 +186,7 @@ def __init__( ) # create properties - self._create_properties(level_col, id_col, misc_props_col, properties) + self._create_properties(level_col, id_col, properties) # create cell properties (From old EntitySet) self._create_assign_cell_properties(cell_properties) @@ -191,12 +194,10 @@ def __init__( def _build_dataframe_from_ndarray( self, data: pd.ndarray, - labels: Optional[OrderedDict[Union[str, int], Sequence[Union[str, int]]]], + labels: Optional[OrderedDict[T, Sequence[T]]], ) -> None: self._state_dict["data"] = data self._dataframe = pd.DataFrame(data) - # if a dict of labels was passed, use keys as column names in the - # DataFrame, translate the dataframe, and store the dict of labels in the state dict if not isinstance(labels, dict): raise ValueError( @@ -206,10 +207,11 @@ def _build_dataframe_from_ndarray( raise ValueError( f"The length of labels must equal the length of columns in the dataframe. Labels is of length: {len(labels)}; dataframe is of length: {len(self._dataframe.columns)}" ) - + # use dict keys of 'labels' as column names in the DataFrame and store the dict of labels in the state dict self._dataframe.columns = labels.keys() self._state_dict["labels"] = labels + # translate the dataframe for col in self._dataframe: self._dataframe[col] = pd.Categorical.from_codes( self._dataframe[col], categories=labels[col] @@ -230,7 +232,6 @@ def _create_properties( self, level_col: str, id_col: str, - misc_props_col: str, properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]], ) -> None: item_levels = [ @@ -241,9 +242,8 @@ def _create_properties( index = pd.MultiIndex.from_tuples(item_levels, names=[level_col, id_col]) data = [(i, 1, {}) for i in range(len(index))] self._properties = pd.DataFrame( - data=data, index=index, columns=["uid", "weight", misc_props_col] + data=data, index=index, columns=["uid", "weight", self._misc_props_col] ).sort_index() - self._misc_props_col = misc_props_col self.assign_properties(properties) def _create_assign_cell_properties( @@ -254,11 +254,9 @@ def _create_assign_cell_properties( ): # if underlying data is 2D (system of sets), create and assign cell properties if self.dimsize == 2: - # self._cell_properties = pd.DataFrame( - # columns=[*self._data_cols, self._misc_cell_props_col] - # ) self._cell_properties = pd.DataFrame(self._dataframe) self._cell_properties.set_index(self._data_cols, inplace=True) + # TODO: What about when cell_properties is a Sequence[T]? if isinstance(cell_properties, (dict, pd.DataFrame)): self.assign_cell_properties(cell_properties) else: @@ -270,7 +268,7 @@ def cell_properties(self) -> Optional[pd.DataFrame]: Returns ------- - pandas.Series, optional + pandas.DataFrame, optional Returns None if :attr:`dimsize` < 2 """ return self._cell_properties @@ -384,12 +382,11 @@ def dimsize(self) -> int: @property def properties(self) -> pd.DataFrame: - # Dev Note: Not sure what this contains, when running tests it contained an empty pandas series """Properties assigned to items in the underlying data table Returns ------- - pandas.DataFrame + pandas.DataFrame a dataframe with the following columns: level/(edge|node), uid, weight, properties """ return self._properties @@ -459,7 +456,7 @@ def uidset_by_level(self, level: int) -> set: return self.uidset_by_column(col) def uidset_by_column(self, column: Hashable) -> set: - # Dev Note: This threw an error when trying it on the harry potter dataset, + # TODO: This threw an error when trying it on the harry potter dataset, # when trying 0, or 1 for column. I'm not sure how this should be used """Labels of all items in a particular column (level) of the underlying data table @@ -637,10 +634,11 @@ def dataframe(self) -> pd.DataFrame: return self._dataframe @property + @warn_to_be_deprecated def isstatic(self) -> bool: - # Dev Note: I'm guessing this is no longer necessary? """Whether to treat the underlying data as static or not + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] If True, the underlying data may not be altered, and the state_dict will never be cleared Otherwise, rows may be added to and removed from the data table, and updates will clear the state_dict @@ -648,6 +646,7 @@ def isstatic(self) -> bool: ------- bool """ + return self._static def size(self, level: int = 0) -> int: @@ -667,7 +666,8 @@ def size(self, level: int = 0) -> int: -------- dimensions """ - # TODO: Since `level` is not validated, we assume that self.dimensions should be an array large enough to access index `level` + if self.empty: + return 0 return self.dimensions[level] @property @@ -763,7 +763,7 @@ def __iter__(self): return iter(self.elements) def __call__(self, label_index=0): - # Dev Note (Madelyn) : I don't think this is the intended use of __call__, can we change/deprecate? + # TODO: (Madelyn) : I don't think this is the intended use of __call__, can we change/deprecate? """Iterates over items labels in a specified level (column) of the underlying data table Parameters @@ -826,9 +826,12 @@ def index(self, column: str, value: Optional[str] = None) -> int | tuple[int, in self._state_dict["index"][column][value], ) + @warn_to_be_deprecated def indices(self, column: str, values: str | Iterable[str]) -> list[int]: """Get indices of one or more value(s) in a column + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- column : str @@ -856,9 +859,12 @@ def indices(self, column: str, values: str | Iterable[str]) -> list[int]: return [self._state_dict["index"][column][v] for v in values] + @warn_to_be_deprecated def translate(self, level: int, index: int | list[int]) -> str | list[str]: """Given indices of a level and value(s), return the corresponding value label(s) + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- level : int @@ -882,9 +888,12 @@ def translate(self, level: int, index: int | list[int]) -> str | list[str]: return [self.labels[column][i] for i in index] - def translate_arr(self, coords: tuple[int]) -> list[str]: + @warn_to_be_deprecated + def translate_arr(self, coords: tuple[int, int]) -> list[str]: """Translate a full encoded row of the data table e.g., a row of ``self.data`` + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- coords : tuple of ints @@ -902,6 +911,7 @@ def translate_arr(self, coords: tuple[int]) -> list[str]: return translation + @warn_to_be_deprecated def level( self, item: str, @@ -911,6 +921,8 @@ def level( ) -> int | tuple[int, int] | None: """First level containing the given item label + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Order of levels corresponds to order of columns in `self.dataframe` Parameters @@ -949,7 +961,7 @@ def level( print(f'"{item}" not found.') return None - def add(self, *args) -> EntitySet: + def add(self, *args) -> Self: """Updates the underlying data table with new entity data from multiple sources Parameters @@ -979,10 +991,11 @@ def add(self, *args) -> EntitySet: self.add_element(item) return self - def add_elements_from(self, arg_set) -> EntitySet: + @warn_to_be_deprecated + def add_elements_from(self, arg_set) -> Self: """Adds arguments from an iterable to the data table one at a time - ..deprecated:: 2.0.0 + DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] Duplicates `add` Parameters @@ -1005,16 +1018,15 @@ def add_element( | Mapping[T, Iterable[T]] | Iterable[Iterable[T]] | Mapping[T, Mapping[T, Any]], - ) -> EntitySet: + ) -> Self: """Updates the underlying data table with new entity data - Supports adding from either an existing Entity or a representation of entity + Supports adding from either an existing EntitySet or a representation of entity (data table or labeled system of sets are both supported representations) Parameters ---------- - data : `pandas.DataFrame`, dict of lists or sets, lists of lists or sets - new entity data + data : `pandas.DataFrame`, dict of lists or sets, lists of lists, or nested dict Returns ------- @@ -1069,13 +1081,13 @@ def __add_from_dataframe(self, df: pd.DataFrame) -> None: self._state_dict.clear() - def remove(self, *args) -> EntitySet: + def remove(self, *args: T) -> EntitySet: """Removes all rows containing specified item(s) from the underlying data table Parameters ---------- *args - variable length argument list of item labels + variable length argument list of items which are of type string or int Returns ------- @@ -1090,10 +1102,12 @@ def remove(self, *args) -> EntitySet: self.remove_element(item) return self + @warn_to_be_deprecated def remove_elements_from(self, arg_set): """Removes all rows containing specified item(s) from the underlying data table - ..deprecated: 2.0.0 + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Duplicates `remove` Parameters @@ -1110,13 +1124,13 @@ def remove_elements_from(self, arg_set): self.remove_element(item) return self - def remove_element(self, item) -> None: + def remove_element(self, item: T) -> None: """Removes all rows containing a specified item from the underlying data table Parameters ---------- - item - item label + item : Union[str, int] + the label of an edge See Also -------- @@ -1141,31 +1155,34 @@ def remove_element(self, item) -> None: for col in self._data_cols: self._dataframe[col] = self._dataframe[col].cat.remove_unused_categories() + @warn_to_be_deprecated def encode(self, data: pd.DataFrame) -> np.array: """ Encode dataframe to numpy array Parameters ---------- - data : dataframe + data : dataframe, dataframe columns must have dtype set to 'category' Returns ------- numpy.array """ - encoded_array = data.apply(lambda x: x.cat.codes).to_numpy() - return encoded_array + return data.apply(lambda x: x.cat.codes).to_numpy() + @warn_to_be_deprecated def incidence_matrix( self, level1: int = 0, level2: int = 1, weights: bool | dict = False, aggregateby: str = "count", - ) -> Optional[csr_matrix]: + ) -> Optional[sp.csr_matrix]: """Incidence matrix representation for two levels (columns) of the underlying data table + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + If `level1` and `level2` contain N and M distinct items, respectively, the incidence matrix will be M x N. In other words, the items in `level1` and `level2` correspond to the columns and rows of the incidence matrix, respectively, in the order in which they appear in `self.labels[column1]` and `self.labels[column2]` @@ -1217,7 +1234,7 @@ def incidence_matrix( aggregateby=aggregateby, ) - return csr_matrix( + return sp.csr_matrix( (df[weight_col], tuple(df[col].cat.codes for col in data_cols)) ) @@ -1285,16 +1302,18 @@ def _restrict_to_levels( data_cols=cols, aggregateby=aggregateby, properties=properties, - misc_props_col=self._misc_props_col, level_col=level_col, id_col=id_col, **kwargs, ) + @warn_to_be_deprecated def restrict_to_indices( self, indices: int | Iterable[int], level: int = 0, **kwargs ) -> EntitySet: - """Create a new Entity by restricting the data table to rows containing specific items in a given level + """Create a new EntitySet by restricting the data table to rows containing specific items in a given level + + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] Parameters ---------- @@ -1315,9 +1334,7 @@ def restrict_to_indices( for col in self._data_cols: entity[col] = entity[col].cat.remove_unused_categories() - restricted = self.__class__( - entity=entity, misc_props_col=self._misc_props_col, **kwargs - ) + restricted = self.__class__(entity=entity, **kwargs) if not self.properties.empty: prop_idx = [ @@ -1358,15 +1375,14 @@ def assign_cell_properties( f"cell properties are not supported for 'dimsize'={self.dimsize}" ) - misc_col = misc_col or self._misc_cell_props_col - try: + if isinstance(cell_props, pd.DataFrame): + misc_col = misc_col or self._misc_cell_props_col cell_props = cell_props.rename( columns={misc_col: self._misc_cell_props_col} ) - except AttributeError: # handle cell props in nested dict format - self._cell_properties_from_dict(cell_props) - else: # handle cell props in DataFrame format self._cell_properties_from_dataframe(cell_props) + elif isinstance(cell_props, dict): + self._cell_properties_from_dict(cell_props) def assign_properties( self, @@ -1380,7 +1396,7 @@ def assign_properties( Parameters ---------- props : pandas.DataFrame or doubly-nested dict - See documentation of the `properties` parameter in :class:`Entity` + See documentation of the `properties` parameter in :class:`EntitySet` level_col, id_col, misc_col : str, optional column names corresponding to the levels, items, and misc. properties; if None, default to :attr:`_level_col`, :attr:`_id_col`, :attr:`_misc_props_col`, @@ -1409,8 +1425,7 @@ def assign_properties( props = props.rename(columns=column_map) props = props.rename_axis(index=column_map) self._properties_from_dataframe(props) - - if isinstance(props, dict): + elif isinstance(props, dict): # Expects nested dictionary with keys corresponding to level and id self._properties_from_dict(props) @@ -1604,6 +1619,7 @@ def set_property( self._properties.loc[item_key, self._misc_props_col].update( {prop_name: prop_val} ) + # TODO: Is it possible to ever hit this case given that misc_props_col will always be set in the dataframe? except KeyError: self._properties.loc[item_key, :] = { self._misc_props_col: {prop_name: prop_val} @@ -1626,6 +1642,9 @@ def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> prop_val : any value of the property + None + if property not found + Raises ------ KeyError @@ -1648,19 +1667,19 @@ def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> try: item_key = self._property_loc(item) except KeyError: - raise # item not in properties + raise KeyError(f"item does not exist: {item}") try: prop_val = self.properties.loc[item_key, prop_name] - except KeyError as ex: - if ex.args[0] == prop_name: - prop_val = self.properties.loc[item_key, self._misc_props_col].get( + except KeyError: + try: + prop_val = self.properties.loc[item_key, self._misc_props_col][ prop_name - ) - else: - raise KeyError( - f"no properties initialized for ('level','item'): {item_key}" - ) from ex + ] + except KeyError: + # prop_name is not a key in the dictionary in the _misc_props_col; + # in other words, property was not found + return None return prop_val @@ -1716,10 +1735,6 @@ def get_properties(self, item: T, level: Optional[int] = None) -> dict[Any, Any] def _cell_properties_from_dataframe(self, cell_props: pd.DataFrame) -> None: """Private handler for updating :attr:`properties` from a DataFrame - Parameters - ---------- - props - Parameters ---------- cell_props : DataFrame @@ -1793,6 +1808,7 @@ def _cell_properties_from_dict( [(item1, item2) for item1 in cell_props for item2 in cell_props[item1]], names=self._data_cols, ) + # This will create a MultiIndex dataframe with exactly one column named from _misc_cell_props_col (default is cell_properties) props_data = [cell_props[item1][item2] for item1, item2 in cells] cell_props = pd.DataFrame( {self._misc_cell_props_col: props_data}, index=cells @@ -1819,20 +1835,27 @@ def set_cell_property( -------- get_cell_property, get_cell_properties """ - if item2 in self.elements[item1]: - if prop_name in self.properties: - self._cell_properties.loc[(item1, item2), prop_name] = pd.Series( - [prop_val] - ) - else: - try: - self._cell_properties.loc[ - (item1, item2), self._misc_cell_props_col - ].update({prop_name: prop_val}) - except KeyError: - self._cell_properties.loc[(item1, item2), :] = { - self._misc_cell_props_col: {prop_name: prop_val} - } + if item2 not in self.elements[item1]: + return + + if prop_name in self._cell_properties: + self._cell_properties.loc[(item1, item2), prop_name] = prop_val + return + + try: + # assumes that _misc_cell_props already exists in cell_properties + self._cell_properties.loc[(item1, item2), self._misc_cell_props_col].update( + {prop_name: prop_val} + ) + except KeyError: + # creates the _misc_cell_props with a defualt empty dict + self._cell_properties[self._misc_cell_props_col] = [ + {} for _ in range(len(self._cell_properties)) + ] + # insert the property name and value as a dictionary in _misc_cell_props for the target incident pair + self._cell_properties.loc[(item1, item2), self._misc_cell_props_col].update( + {prop_name: prop_val} + ) def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: """Get a property of a cell i.e., incidence between items of different levels @@ -1851,6 +1874,14 @@ def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: prop_val : any value of the cell property + None + If prop_name not found + + Raises + ------ + KeyError + If `(item1, item2)` is not in :attr:`cell_properties` + See Also -------- get_cell_properties, set_cell_property @@ -1858,17 +1889,23 @@ def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: try: cell_props = self.cell_properties.loc[(item1, item2)] except KeyError: - raise - # TODO: raise informative exception + raise KeyError( + f"Item not exists. cell_properties: {self.cell_properties}; item1: {item1}, item2: {item2}" + ) try: prop_val = cell_props.loc[prop_name] except KeyError: - prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) + try: + prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) + except KeyError: + # prop_name is not a key in the dictionary in the _misc_cell_props_col; + # in other words, property was not found + return None return prop_val - def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: + def get_cell_properties(self, item1: T, item2: T) -> Optional[dict[Any, Any]]: """Get all properties of a cell, i.e., incidence between items of different levels @@ -1885,6 +1922,9 @@ def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: ``{named cell property: cell property value, ..., misc. cell property column name: {cell property name: cell property value}}`` + None + If properties do not exist + See Also -------- get_cell_property, set_cell_property @@ -1892,12 +1932,16 @@ def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: try: cell_props = self.cell_properties.loc[(item1, item2)] except KeyError: - raise - # TODO: raise informative exception + return None + + return cell_props.to_dict() + @warn_to_be_deprecated def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] + Parameters ---------- indices : array_like of int @@ -1923,6 +1967,7 @@ def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: restricted.assign_cell_properties(cell_properties) return restricted + @warn_to_be_deprecated def restrict_to_levels( self, levels: int | Iterable[int], @@ -1934,6 +1979,7 @@ def restrict_to_levels( """Create a new EntitySet by restricting to a subset of levels (columns) in the underlying data table + [DEPRECATED; WILL BE REMOVED IN NEXT RELEASE] Parameters ---------- @@ -1942,8 +1988,7 @@ def restrict_to_levels( weights : bool, default=False If True, aggregate existing cell weights to get new cell weights. Otherwise, all new cell weights will be 1. - aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', \ - 'min', None}, optional + aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', 'min', None}, optional Method to aggregate weights of duplicate rows in data table If None or `weights`=False then all new cell weights will be 1 keep_memberships : bool, default=True @@ -1969,7 +2014,6 @@ def restrict_to_levels( levels, weights, aggregateby, - misc_cell_props_col=self._misc_cell_props_col, **kwargs, ) @@ -2060,86 +2104,4 @@ def build_dataframe_from_entity( {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} ) - # create an empty dataframe return pd.DataFrame() - - -# TODO: Consider refactoring for simplicity; SonarLint states this function has a Cognitive Complexity of 26; recommends lowering to 15 -def restrict_to_two_columns( - entity: Optional[ - pd.DataFrame - | Mapping[T, Iterable[T]] - | Iterable[Iterable[T]] - | Mapping[T, Mapping[T, Any]] - ], - data: Optional[np.ndarray], - labels: Optional[OrderedDict[T, Sequence[T]]], - cell_properties: Optional[ - Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] - ], - weight_col: str | int, - weights: Optional[Sequence[float] | float | int | str], - level1: str | int, - level2: str | int, - misc_cell_props_col: str, -): - """Restrict columns on entity or data as needed; if data is restricted, also restrict labels""" - if isinstance(entity, pd.DataFrame) and len(entity.columns) > 2: - # metadata columns are not considered levels of data, - # remove them before indexing by level - # if isinstance(cell_properties, str): - # cell_properties = [cell_properties] - - prop_cols = [] - if isinstance(cell_properties, Sequence): - for col in {*cell_properties, misc_cell_props_col}: - if col in entity: - prop_cols.append(col) - - # meta_cols = prop_cols - # if weights in entity and weights not in meta_cols: - # meta_cols.append(weights) - if weight_col in prop_cols: - prop_cols.remove(weight_col) - if weight_col not in entity: - entity[weight_col] = weights - - # if both levels are column names, no need to index by level - if isinstance(level1, int): - level1 = entity.columns[level1] - if isinstance(level2, int): - level2 = entity.columns[level2] - # if isinstance(level1, str) and isinstance(level2, str): - columns = [level1, level2, weight_col] + prop_cols - # if one or both of the levels are given by index, get column name - # else: - # all_columns = entity.columns.drop(meta_cols) - # columns = [ - # all_columns[lev] if isinstance(lev, int) else lev - # for lev in (level1, level2) - # ] - - # if there is a column for cell properties, convert to separate DataFrame - # if len(prop_cols) > 0: - # cell_properties = entity[[*columns, *prop_cols]] - - # if there is a column for weights, preserve it - # if weights in entity and weights not in prop_cols: - # columns.append(weights) - - # pass level1, level2, and weights (optional) to Entity constructor - entity = entity[columns] - - # if a 2D ndarray is passed, restrict to two columns if needed - elif isinstance(data, np.ndarray): - if data.ndim == 2 and data.shape[1] > 2: - data = data[:, (level1, level2)] - - # should only change labels if 'data' is passed - # if a dict of labels is provided, restrict to labels for two columns if needed - if isinstance(labels, dict) and len(labels) > 2: - labels = { - col: labels[col] for col in [level1, level2] - } # example: { 0: ['e1', 'e2', ...], 1: ['n1', ...] } - - return entity, data, labels diff --git a/hypernetx/classes/helpers.py b/hypernetx/classes/helpers.py index 7690906b..6edde0e8 100644 --- a/hypernetx/classes/helpers.py +++ b/hypernetx/classes/helpers.py @@ -214,6 +214,9 @@ def remove_row_duplicates( weight_col : Hashable The name of the column holding aggregated weights, or None if aggregateby=None """ + if df.empty: + return df, None + df = df.copy() categories = {} for col in data_cols: @@ -272,3 +275,29 @@ def dict_depth(dic, level=0): if not isinstance(dic, dict) or not dic: return level return min(dict_depth(dic[key], level + 1) for key in dic) + + +def create_dataframe(data: Mapping[str | int, Iterable[str | int]]) -> pd.DataFrame: + """Create a valid pandas Dataframe that can be used for the 'entity' param in EntitySet""" + + validate_mapping_for_dataframe(data) + + # creates a Series of all edge-node pairs (i.e. all the non-zero cells from an incidence matrix) + data_t = pd.Series(data=data).explode() + return pd.DataFrame(data={0: data_t.index.to_list(), 1: data_t.values}) + + +def validate_mapping_for_dataframe( + data: Mapping[str | int, Iterable[str | int]] +) -> None: + if not isinstance(data, Mapping): + raise TypeError("data must be a Mapping type, i.e. dictionary") + key_types = set(type(key) for key in data.keys()) + if key_types != {str} and key_types != {int}: + raise TypeError("keys must be a string or int") + for val in data.values(): + if not isinstance(val, Iterable): + raise TypeError("The value of a key must be an Iterable type, i.e. list") + val_types = set(type(v) for v in val) + if val_types != {str} and val_types != {int}: + raise TypeError("The items in each value must be a string or int") diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 63821d08..2a3c3037 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -328,7 +328,6 @@ def __init__( ### cell properties if setsystem is None: #### Empty Case - self._edges = EntitySet({}) self._nodes = EntitySet({}) self._state_dict = {} @@ -538,8 +537,7 @@ def props2dict(df=None): self.E = EntitySet( entity=entity, - level1=edge_col, - level2=node_col, + data_cols=(edge_col, node_col), weight_col=cell_weight_col, weights=cell_weights, cell_properties=cell_properties, @@ -767,7 +765,7 @@ def get_properties(self, id, level=None, prop_name=None): : str or dict single property or dictionary of properties """ - if prop_name == None: + if prop_name is None: return self.E.get_properties(id, level=level) else: return self.E.get_property(id, prop_name, level=level) diff --git a/hypernetx/utils/decorators.py b/hypernetx/utils/decorators.py index 5652bf30..28cfcaac 100644 --- a/hypernetx/utils/decorators.py +++ b/hypernetx/utils/decorators.py @@ -6,10 +6,7 @@ import hypernetx as hnx from hypernetx.exception import NWHY_WARNING -__all__ = [ - "not_implemented_for", - "warn_nwhy", -] +__all__ = ["not_implemented_for", "warn_nwhy", "warn_to_be_deprecated"] def not_implemented_for(*object_types): @@ -89,3 +86,29 @@ def wrapper(*args, **kwargs): return func(*args, **kwargs) return wrapper + + +def warn_to_be_deprecated(func): + """Decorator for methods that are to be deprecated + + Public references to deprecated methods or functions will be removed from the Hypergraph API in a future release. + + Warns + ----- + FutureWarning + """ + + deprecation_warning_msg = ( + "This method or function will be deprecated in a future release. " + "Public references to this method or function will be removed from the " + "Hypergraph API in a future release." + ) + + @wraps(func) + def wrapper(*args, **kwargs): + warnings.simplefilter("always", FutureWarning) + warnings.warn(deprecation_warning_msg, FutureWarning, stacklevel=2) + warnings.simplefilter("default", FutureWarning) + return func(*args, **kwargs) + + return wrapper From a249417bb8efe6d14e91e18b617a4af460f77d70 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 25 Oct 2023 16:59:47 -0700 Subject: [PATCH 54/76] HYP-177 Reorg entityset tests --- hypernetx/classes/tests/conftest.py | 18 +- .../tests/test_entityset_on_dataframe.py | 412 ++++++++++++ .../classes/tests/test_entityset_on_dict.py | 177 +++++ .../tests/test_entityset_on_np_array.py | 108 +++ .../classes/tests/test_entityset_sbs_data.py | 619 ------------------ 5 files changed, 706 insertions(+), 628 deletions(-) create mode 100644 hypernetx/classes/tests/test_entityset_on_dataframe.py create mode 100644 hypernetx/classes/tests/test_entityset_on_dict.py create mode 100644 hypernetx/classes/tests/test_entityset_on_np_array.py delete mode 100644 hypernetx/classes/tests/test_entityset_sbs_data.py diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index 7c21ad8a..dca99432 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -42,8 +42,8 @@ def __init__(self, static=False): ) self.labels = OrderedDict( [ - ("edges", ["P", "R", "S", "L", "O", "I"]), - ("nodes", ["A", "C", "E", "K", "T1", "T2", "V"]), + ("edges", [p, r, s, l, o, i]), + ("nodes", [a, c, e, k, t1, t2, v]), ] ) @@ -51,18 +51,18 @@ def __init__(self, static=False): [ [0, 0], [0, 1], - [0, 2], + [0, 3], + [1, 0], [1, 2], - [1, 3], [2, 0], - [2, 2], - [2, 4], + [2, 3], [2, 5], + [2, 6], [3, 1], - [3, 3], + [3, 2], + [4, 4], [4, 5], - [4, 6], - [5, 0], + [5, 3], [5, 5], ] ) diff --git a/hypernetx/classes/tests/test_entityset_on_dataframe.py b/hypernetx/classes/tests/test_entityset_on_dataframe.py new file mode 100644 index 00000000..d49ee408 --- /dev/null +++ b/hypernetx/classes/tests/test_entityset_on_dataframe.py @@ -0,0 +1,412 @@ +import pytest + +import pandas as pd +import numpy as np + +from pytest_lazyfixture import lazy_fixture + +from hypernetx import EntitySet + + +class TestEntitySetOnSBSDataframe: + @pytest.fixture + def es_from_df(self, sbs): + return EntitySet(entity=sbs.dataframe) + + @pytest.fixture + def es_from_dupe_df(self, sbsd): + return EntitySet(entity=sbsd.dataframe) + + # check all methods + @pytest.mark.parametrize( + "data", + [ + pd.DataFrame({0: ["P"], 1: ["E"]}), + {0: ["P"], 1: ["E"]}, + EntitySet(entity={"P": ["E"]}), + ], + ) + def test_add(self, es_from_df, data): + assert es_from_df.data.shape == (15, 2) + assert es_from_df.dataframe.size == 45 + + es_from_df.add(data) + + assert es_from_df.data.shape == (16, 2) + assert es_from_df.dataframe.size == 48 + + def test_remove(self, es_from_df): + assert es_from_df.data.shape == (15, 2) + assert es_from_df.dataframe.size == 45 + + es_from_df.remove("P") + + assert es_from_df.data.shape == (12, 2) + assert es_from_df.dataframe.size == 36 + assert "P" not in es_from_df.elements + + @pytest.mark.parametrize( + "props, multidx, expected_props", + [ + ( + lazy_fixture("props_dataframe"), + (0, "P"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + {0: {"P": {"prop1": "propval1", "prop2": "propval2"}}}, + (0, "P"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + {1: {"A": {"prop1": "propval1", "prop2": "propval2"}}}, + (1, "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ], + ) + def test_assign_properties(self, es_from_df, props, multidx, expected_props): + original_prop = es_from_df.properties.loc[multidx] + assert original_prop.properties == {} + + es_from_df.assign_properties(props) + + updated_prop = es_from_df.properties.loc[multidx] + assert updated_prop.properties == expected_props + + @pytest.mark.parametrize( + "cell_props, multidx, expected_cell_properties", + [ + ( + lazy_fixture("cell_props_dataframe"), + ("P", "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + lazy_fixture("cell_props_dataframe_multidx"), + ("P", "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ( + {"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}}, + ("P", "A"), + {"prop1": "propval1", "prop2": "propval2"}, + ), + ], + ) + def test_assign_cell_properties_on_default_cell_properties( + self, es_from_df, cell_props, multidx, expected_cell_properties + ): + es_from_df.assign_cell_properties(cell_props=cell_props) + + updated_cell_prop = es_from_df.cell_properties.loc[multidx] + + assert updated_cell_prop.cell_properties == expected_cell_properties + + def test_assign_cell_properties_on_multiple_properties(self, es_from_df): + multidx = ("P", "A") + + es_from_df.assign_cell_properties( + cell_props={"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}} + ) + + updated_cell_prop = es_from_df.cell_properties.loc[multidx] + assert updated_cell_prop.cell_properties == { + "prop1": "propval1", + "prop2": "propval2", + } + + es_from_df.assign_cell_properties( + cell_props={ + "P": { + "A": {"prop1": "propval1", "prop2": "propval2", "prop3": "propval3"} + } + } + ) + + updated_cell_prop = es_from_df.cell_properties.loc[multidx] + assert updated_cell_prop.cell_properties == { + "prop1": "propval1", + "prop2": "propval2", + "prop3": "propval3", + } + + def test_set_cell_property_on_cell_weights(self, es_from_df): + item1 = "P" + item2 = "A" + prop_name = "cell_weights" + prop_val = 42 + + es_from_df.set_cell_property(item1, item2, prop_name, prop_val) + + assert es_from_df.cell_properties.loc[(item1, item2), prop_name] == 42.0 + + # Check that the other cell_weights were not changed and retained the default value of 1 + for row in es_from_df.cell_properties.itertuples(): + if row.Index != (item1, item2): + assert row.cell_weights == 1 + + def test_set_cell_property_on_non_exisiting_cell_property(self, es_from_df): + item1 = "P" + item2 = "A" + prop_name = "non_existing_cell_property" + prop_val = {"foo": "bar"} + es_from_df.set_cell_property(item1, item2, prop_name, prop_val) + + assert es_from_df.cell_properties.loc[(item1, item2), "cell_properties"] == { + prop_name: prop_val + } + + # Check that the other rows received the default empty dictionary + for row in es_from_df.cell_properties.itertuples(): + if row.Index != (item1, item2): + assert row.cell_properties == {} + + item2 = "K" + es_from_df.set_cell_property(item1, item2, prop_name, prop_val) + + assert es_from_df.cell_properties.loc[(item1, item2), "cell_properties"] == { + prop_name: prop_val + } + + @pytest.mark.parametrize("ret_ec", [True, False]) + def test_collapse_identical_elements_on_duplicates(self, es_from_dupe_df, ret_ec): + # There are two edges that share the same set of 3 (three) nodes + new_es = es_from_dupe_df.collapse_identical_elements( + return_equivalence_classes=ret_ec + ) + + es_temp = new_es + if isinstance(new_es, tuple): + # reset variable for actual EntitySet + es_temp = new_es[0] + + # check equiv classes + collapsed_edge_key = "L: 2" + assert "M: 2" not in es_temp.elements + assert collapsed_edge_key in es_temp.elements + assert set(es_temp.elements.get(collapsed_edge_key)) == {"F", "C", "E"} + + equiv_classes = new_es[1] + assert equiv_classes == { + "I: 1": ["I"], + "L: 2": ["L", "M"], + "O: 1": ["O"], + "P: 1": ["P"], + "R: 1": ["R"], + "S: 1": ["S"], + } + + # check dataframe + assert len(es_temp.dataframe) != len(es_from_dupe_df.dataframe) + assert len(es_temp.dataframe) == len(es_from_dupe_df.dataframe) - 3 + + @pytest.mark.parametrize( + "col1, col2, expected_elements", + [ + ( + 0, + 1, + { + "I": {"K", "T2"}, + "L": {"C", "E"}, + "O": {"T1", "T2"}, + "P": {"K", "A", "C"}, + "R": {"A", "E"}, + "S": {"K", "A", "V", "T2"}, + }, + ), + ( + 1, + 0, + { + "A": {"P", "R", "S"}, + "C": {"P", "L"}, + "E": {"R", "L"}, + "K": {"P", "S", "I"}, + "T1": {"O"}, + "T2": {"S", "O", "I"}, + "V": {"S"}, + }, + ), + ], + ) + def test_elements_by_column(self, es_from_df, col1, col2, expected_elements): + elements_temps = es_from_df.elements_by_column(col1, col2) + actual_elements = { + elements_temps[k]._key[1]: set(v) for k, v in elements_temps.items() + } + + assert actual_elements == expected_elements + + def test_elements_by_level(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.elements_by_level(0, 1) + + def test_encode(self, es_from_df): + df = pd.DataFrame({"Category": ["A", "B", "A", "C", "B"]}) + # Convert 'Category' column to categorical + df["Category"] = df["Category"].astype("category") + + expected_arr = np.array([[0], [1], [0], [2], [1]]) + actual_arr = es_from_df.encode(df) + + assert np.array_equal(actual_arr, expected_arr) + + def test_get_cell_properties(self, es_from_df): + props = es_from_df.get_cell_properties("P", "A") + + assert props == {"cell_weights": 1} + + def test_get_cell_properties_raises_keyerror(self, es_from_df): + assert es_from_df.get_cell_properties("P", "FOOBAR") is None + + def test_get_cell_property(self, es_from_df): + props = es_from_df.get_cell_property("P", "A", "cell_weights") + assert props == 1 + + @pytest.mark.parametrize( + "item1, item2, prop_name, err_msg", + [ + ("P", "FOO", "cell_weights", "Item not exists. cell_properties:"), + ], + ) + def test_get_cell_property_raises_keyerror( + self, es_from_df, item1, item2, prop_name, err_msg + ): + with pytest.raises(KeyError, match=err_msg): + es_from_df.get_cell_property(item1, item2, prop_name) + + def test_get_cell_property_returns_none_on_prop(self, es_from_df): + assert es_from_df.get_cell_property("P", "A", "Not a real property") is None + + @pytest.mark.parametrize("item, level", [("P", 0), ("P", None), ("A", 1)]) + def test_get_properties(self, es_from_df, item, level): + # to avoid duplicate test code, reuse 'level' to get the item_uid + # but if level is None, assume it to be 0 and that the item exists at level 0 + if level is None: + item_uid = es_from_df.properties.loc[(0, item), "uid"] + else: + item_uid = es_from_df.properties.loc[(level, item), "uid"] + + props = es_from_df.get_properties(item, level=level) + + assert props == {"uid": item_uid, "weight": 1, "properties": {}} + + @pytest.mark.parametrize( + "item, level, err_msg", + [ + ("Not a valid item", None, ""), + ("Not a valid item", 0, "no properties initialized for"), + ], + ) + def test_get_properties_raises_keyerror(self, es_from_df, item, level, err_msg): + with pytest.raises(KeyError, match=err_msg): + es_from_df.get_properties(item, level=level) + + @pytest.mark.parametrize( + "item, prop_name, level, expected_prop", + [ + ("P", "weight", 0, 1), + ("P", "properties", 0, {}), + ("P", "uid", 0, 3), + ("A", "weight", 1, 1), + ("A", "properties", 1, {}), + ("A", "uid", 1, 6), + ], + ) + def test_get_property(self, es_from_df, item, prop_name, level, expected_prop): + prop = es_from_df.get_property(item, prop_name, level) + + assert prop == expected_prop + + @pytest.mark.parametrize( + "item, prop_name, err_msg", + [ + ("XXX", "weight", "item does not exist:"), + ], + ) + def test_get_property_raises_keyerror(self, es_from_df, item, prop_name, err_msg): + with pytest.raises(KeyError, match=err_msg): + es_from_df.get_property(item, prop_name) + + def test_get_property_returns_none_on_no_property(self, es_from_df): + assert es_from_df.get_property("P", "non-existing property") is None + + @pytest.mark.parametrize( + "item, prop_name, prop_val, level", + [ + ("P", "weight", 42, 0), + ], + ) + def test_set_property(self, es_from_df, item, prop_name, prop_val, level): + orig_prop_val = es_from_df.get_property(item, prop_name, level) + + es_from_df.set_property(item, prop_name, prop_val, level) + + new_prop_val = es_from_df.get_property(item, prop_name, level) + + assert new_prop_val != orig_prop_val + assert new_prop_val == prop_val + + @pytest.mark.parametrize( + "item, prop_name, prop_val, level, misc_props_col", + [ + ("P", "new_prop", "foobar", 0, "properties"), + ("P", "new_prop", "foobar", 0, "some_new_miscellaneaus_col"), + ], + ) + def test_set_property_on_non_existing_property( + self, es_from_df, item, prop_name, prop_val, level, misc_props_col + ): + es_from_df.set_property(item, prop_name, prop_val, level) + + new_prop_val = es_from_df.get_property(item, prop_name, level) + + assert new_prop_val == prop_val + + def test_set_property_raises_keyerror(self, es_from_df): + with pytest.raises( + ValueError, match="cannot infer 'level' when initializing 'item' properties" + ): + es_from_df.set_property("XXXX", "weight", 42) + + def test_incidence_matrix(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) + + def test_index(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.index("nodes") == 1 + assert ent_sbs.index("nodes", "K") == (1, 3) + + def test_indices(self, sbs): + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.indices("nodes", "K") == [3] + assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] + + @pytest.mark.parametrize("level", [0, 1]) + def test_is_empty(self, es_from_df, level): + assert not es_from_df.is_empty(level) + + @pytest.mark.parametrize( + "item_level, item, min_level, max_level, expected_lidx", + [ + (0, "P", 0, None, (0, 3)), + (0, "P", 0, 0, (0, 3)), + (0, "P", 1, 1, None), + (1, "A", 0, None, (1, 0)), + (1, "A", 0, 0, None), + (1, "K", 0, None, (1, 3)), + ], + ) + def test_level( + self, es_from_df, item_level, item, min_level, max_level, expected_lidx + ): + actual_lidx = es_from_df.level(item, min_level=min_level, max_level=max_level) + + assert actual_lidx == expected_lidx + + if isinstance(actual_lidx, tuple): + index_item_in_labels = actual_lidx[1] + assert index_item_in_labels == es_from_df.labels[item_level].index(item) diff --git a/hypernetx/classes/tests/test_entityset_on_dict.py b/hypernetx/classes/tests/test_entityset_on_dict.py new file mode 100644 index 00000000..9b0e8982 --- /dev/null +++ b/hypernetx/classes/tests/test_entityset_on_dict.py @@ -0,0 +1,177 @@ +import numpy as np +import pytest + +from pytest_lazyfixture import lazy_fixture + +from hypernetx.classes import EntitySet + + +@pytest.mark.parametrize( + "entity, data, data_cols, labels", + [ + (lazy_fixture("sbs_dict"), None, (0, 1), None), + (lazy_fixture("sbs_dict"), None, (0, 1), lazy_fixture("sbs_labels")), + (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), + (lazy_fixture("sbs_dict"), lazy_fixture("sbs_data"), (0, 1), None), + (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), + ], +) +class TestEntitySBSDict: + """Tests on different use cases for combination of the following params: entity, data, data_cols, labels""" + + def test_size(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.size() == len(sbs.edgedict) + + # check all the EntitySet properties + def test_isstatic(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.isstatic + + def test_uid(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.uid is None + + def test_empty(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert not es.empty + + def test_uidset(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.uidset == {"I", "R", "S", "P", "O", "L"} + + def test_dimsize(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.dimsize == 2 + + def test_elements(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert len(es.elements) == 6 + expected_elements = { + "I": ["K", "T2"], + "L": ["E", "C"], + "O": ["T1", "T2"], + "P": ["C", "K", "A"], + "R": ["E", "A"], + "S": ["K", "V", "A", "T2"], + } + for expected_edge, expected_nodes in expected_elements.items(): + assert expected_edge in es.elements + assert es.elements[expected_edge].sort() == expected_nodes.sort() + + def test_incident_dict(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + expected_incident_dict = { + "I": ["K", "T2"], + "L": ["E", "C"], + "O": ["T1", "T2"], + "P": ["C", "K", "A"], + "R": ["E", "A"], + "S": ["K", "V", "A", "T2"], + } + for expected_edge, expected_nodes in expected_incident_dict.items(): + assert expected_edge in es.incidence_dict + assert es.incidence_dict[expected_edge].sort() == expected_nodes.sort() + assert isinstance(es.incidence_dict["I"], list) + assert "I" in es + assert "K" in es + + def test_children(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.children == {"C", "T1", "A", "K", "T2", "V", "E"} + + def test_memberships(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.memberships == { + "A": ["P", "R", "S"], + "C": ["P", "L"], + "E": ["R", "L"], + "K": ["P", "S", "I"], + "T1": ["O"], + "T2": ["S", "O", "I"], + "V": ["S"], + } + + def test_cell_properties(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.cell_properties.shape == ( + 15, + 1, + ) + + def test_cell_weights(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert es.cell_weights == { + ("P", "C"): 1, + ("P", "K"): 1, + ("P", "A"): 1, + ("R", "E"): 1, + ("R", "A"): 1, + ("S", "K"): 1, + ("S", "V"): 1, + ("S", "A"): 1, + ("S", "T2"): 1, + ("L", "E"): 1, + ("L", "C"): 1, + ("O", "T1"): 1, + ("O", "T2"): 1, + ("I", "K"): 1, + ("I", "T2"): 1, + } + + def test_labels(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + # check labeling based on given attributes for EntitySet + if data_cols == [ + "edges", + "nodes", + ]: # labels should use the data_cols as keys for labels + assert es.labels == { + "edges": ["I", "L", "O", "P", "R", "S"], + "nodes": ["A", "C", "E", "K", "T1", "T2", "V"], + } + elif (labels is not None and not entity) or ( + labels is not None and data + ): # labels should match the labels explicitly given + assert es.labels == labels + else: # if data_cols or labels not given, labels should conform to default format + assert es.labels == { + 0: ["I", "L", "O", "P", "R", "S"], + 1: ["A", "C", "E", "K", "T1", "T2", "V"], + } + + def test_dataframe(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + # check dataframe + # size should be the number of rows times the number of columns, i.e 15 x 3 + assert es.dataframe.size == 45 + + actual_edge_row0 = es.dataframe.iloc[0, 0] + actual_node_row0 = es.dataframe.iloc[0, 1] + actual_cell_weight_row0 = es.dataframe.loc[0, "cell_weights"] + + assert actual_edge_row0 == "P" + assert actual_node_row0 in ["A", "C", "K"] + assert actual_cell_weight_row0 == 1 + + # TODO: validate state of 'data' + def test_data(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert len(es.data) == 15 + + def test_properties(self, entity, data, data_cols, labels, sbs): + es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + assert ( + es.properties.size == 39 + ) # Properties has three columns and 13 rows of data (i.e. edges + nodes) + assert list(es.properties.columns) == ["uid", "weight", "properties"] + + +@pytest.mark.xfail(reason="Deprecated; to be removed in next released") +def test_level(sbs): + # at some point we are casting out and back to categorical dtype without + # preserving categories ordering from `labels` provided to constructor + ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) + assert ent_sbs.level("I") == (0, 5) # fails + assert ent_sbs.level("K") == (1, 3) + assert ent_sbs.level("K", max_level=0) is None diff --git a/hypernetx/classes/tests/test_entityset_on_np_array.py b/hypernetx/classes/tests/test_entityset_on_np_array.py new file mode 100644 index 00000000..f4fd04de --- /dev/null +++ b/hypernetx/classes/tests/test_entityset_on_np_array.py @@ -0,0 +1,108 @@ +import pytest +import numpy as np + +from collections.abc import Iterable +from collections import UserList + +from hypernetx import EntitySet + + +class TestEntitySetOnSBSasNDArray: + def test_ndarray_fail_on_labels(self, sbs_data): + with pytest.raises(ValueError, match="Labels must be of type Dictionary."): + EntitySet(data=np.asarray(sbs_data), labels=[]) + + def test_ndarray_fail_on_length_labels(self, sbs_data): + with pytest.raises( + ValueError, + match="The length of labels must equal the length of columns in the dataframe.", + ): + EntitySet(data=np.asarray(sbs_data), labels=dict()) + + def test_dimensions_equal_dimsize(self, sbs_data, sbs_labels): + ent_sbs = EntitySet(data=np.asarray(sbs_data), labels=sbs_labels) + assert ent_sbs.dimsize == len(ent_sbs.dimensions) + + def test_translate(self, sbs_data, sbs_labels): + ent_sbs = EntitySet(data=np.asarray(sbs_data), labels=sbs_labels) + assert ent_sbs.translate(0, 0) == "P" + assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] + + def test_translate_arr(self, sbs_data, sbs_labels): + ent_sbs = EntitySet(data=np.asarray(sbs_data), labels=sbs_labels) + assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] + + def test_uidset_by_level(self, sbs_data, sbs_labels): + ent_sbs = EntitySet(data=np.asarray(sbs_data), labels=sbs_labels) + + assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} + assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} + + +class TestEntitySetOnHarryPotterDataSet: + def test_entityset_from_ndarray(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert len(ent_hp.uidset) == 7 + assert len(ent_hp.elements) == 7 + assert isinstance(ent_hp.elements["Hufflepuff"], UserList) + assert not ent_hp.is_empty() + assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 + + def test_custom_attributes(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert ent_hp.__len__() == 7 + assert isinstance(ent_hp.__str__(), str) + assert isinstance(ent_hp.__repr__(), str) + assert isinstance(ent_hp.__contains__("Muggle"), bool) + assert ent_hp.__contains__("Muggle") is True + assert ent_hp.__getitem__("Slytherin") == [ + "Half-blood", + "Pure-blood", + "Pure-blood or half-blood", + ] + assert isinstance(ent_hp.__iter__(), Iterable) + assert isinstance(ent_hp.__call__(), Iterable) + assert ent_hp.__call__().__next__() == "Unknown House" + + def test_restrict_to_levels(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 + + def test_restrict_to_indices(self, harry_potter): + ent_hp = EntitySet( + data=np.asarray(harry_potter.data), labels=harry_potter.labels + ) + assert ent_hp.restrict_to_indices([1, 2]).uidset == { + "Gryffindor", + "Ravenclaw", + } + + +@pytest.mark.xfail( + reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" +) +def test_attributes(harry_potter): + assert isinstance(harry_potter.data, np.ndarray) + ent_hp = EntitySet(data=np.asarray(harry_potter.data), labels=harry_potter.labels) + # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray + assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails + assert isinstance(ent_hp.labels, dict) + # TODO: Entity defaults to first two cols as data cols + assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails + assert ent_hp.dimsize == 5 # fails + df = ent_hp.dataframe[ent_hp._data_cols] + assert list(df.columns) == [ # fails + "House", + "Blood status", + "Species", + "Hair colour", + "Eye colour", + ] + assert ent_hp.dimensions == tuple(df.nunique()) + assert set(ent_hp.labels["House"]) == set(df["House"].unique()) diff --git a/hypernetx/classes/tests/test_entityset_sbs_data.py b/hypernetx/classes/tests/test_entityset_sbs_data.py deleted file mode 100644 index ccdb79a4..00000000 --- a/hypernetx/classes/tests/test_entityset_sbs_data.py +++ /dev/null @@ -1,619 +0,0 @@ -import numpy as np -import pandas as pd -import pytest - -from pytest_lazyfixture import lazy_fixture - -from hypernetx.classes import EntitySet - - -@pytest.mark.parametrize( - "entity, data, data_cols, labels", - [ - (lazy_fixture("sbs_dataframe"), None, (0, 1), None), - (lazy_fixture("sbs_dict"), None, (0, 1), None), - (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), - # (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), - ], -) -class TestEntitySetUseCasesOnSBS: - # Tests on different use cases for combination of the following params: entity, data, data_cols, labels - - def test_size(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.size() == len(sbs.edgedict) - - # check all the EntitySet properties - def test_isstatic(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.isstatic - - def test_uid(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.uid is None - - def test_empty(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert not es.empty - - def test_uidset(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.uidset == {"I", "R", "S", "P", "O", "L"} - - def test_dimsize(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.dimsize == 2 - - def test_elements(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert len(es.elements) == 6 - expected_elements = { - "I": ["K", "T2"], - "L": ["E", "C"], - "O": ["T1", "T2"], - "P": ["C", "K", "A"], - "R": ["E", "A"], - "S": ["K", "V", "A", "T2"], - } - for expected_edge, expected_nodes in expected_elements.items(): - assert expected_edge in es.elements - assert es.elements[expected_edge].sort() == expected_nodes.sort() - - def test_incident_dict(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - expected_incident_dict = { - "I": ["K", "T2"], - "L": ["E", "C"], - "O": ["T1", "T2"], - "P": ["C", "K", "A"], - "R": ["E", "A"], - "S": ["K", "V", "A", "T2"], - } - for expected_edge, expected_nodes in expected_incident_dict.items(): - assert expected_edge in es.incidence_dict - assert es.incidence_dict[expected_edge].sort() == expected_nodes.sort() - assert isinstance(es.incidence_dict["I"], list) - assert "I" in es - assert "K" in es - - def test_children(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.children == {"C", "T1", "A", "K", "T2", "V", "E"} - - def test_memberships(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.memberships == { - "A": ["P", "R", "S"], - "C": ["P", "L"], - "E": ["R", "L"], - "K": ["P", "S", "I"], - "T1": ["O"], - "T2": ["S", "O", "I"], - "V": ["S"], - } - - def test_cell_properties(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.cell_properties.shape == ( - 15, - 1, - ) - - def test_cell_weights(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert es.cell_weights == { - ("P", "C"): 1, - ("P", "K"): 1, - ("P", "A"): 1, - ("R", "E"): 1, - ("R", "A"): 1, - ("S", "K"): 1, - ("S", "V"): 1, - ("S", "A"): 1, - ("S", "T2"): 1, - ("L", "E"): 1, - ("L", "C"): 1, - ("O", "T1"): 1, - ("O", "T2"): 1, - ("I", "K"): 1, - ("I", "T2"): 1, - } - - def test_labels(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - # check labeling based on given attributes for EntitySet - if data_cols == [ - "edges", - "nodes", - ]: # labels should use the data_cols as keys for labels - assert es.labels == { - "edges": ["I", "L", "O", "P", "R", "S"], - "nodes": ["A", "C", "E", "K", "T1", "T2", "V"], - } - elif labels is not None: # labels should match the labels explicity given - assert es.labels == labels - else: # if data_cols or labels not given, labels should conform to default format - assert es.labels == { - 0: ["I", "L", "O", "P", "R", "S"], - 1: ["A", "C", "E", "K", "T1", "T2", "V"], - } - - def test_dataframe(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - # check dataframe - # size should be the number of rows times the number of columns, i.e 15 x 3 - assert es.dataframe.size == 45 - - actual_edge_row0 = es.dataframe.iloc[0, 0] - actual_node_row0 = es.dataframe.iloc[0, 1] - actual_cell_weight_row0 = es.dataframe.loc[0, "cell_weights"] - - assert actual_edge_row0 == "P" - assert actual_node_row0 in ["A", "C", "K"] - assert actual_cell_weight_row0 == 1 - - def test_data(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert len(es.data) == 15 # TODO: validate state of 'data' - - def test_properties(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert ( - es.properties.size == 39 - ) # Properties has three columns and 13 rows of data (i.e. edges + nodes) - assert list(es.properties.columns) == ["uid", "weight", "properties"] - - -class TestEntitySetOnSBSasNDArray: - # Check all methods - def test_ndarray_fail_on_labels(self, sbs): - with pytest.raises(ValueError, match="Labels must be of type Dictionary."): - EntitySet(data=np.asarray(sbs.data), labels=[]) - - def test_ndarray_fail_on_length_labels(self, sbs): - with pytest.raises( - ValueError, - match="The length of labels must equal the length of columns in the dataframe.", - ): - EntitySet(data=np.asarray(sbs.data), labels=dict()) - - def test_dimensions_equal_dimsize(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.dimsize == len(ent_sbs.dimensions) - - def test_translate(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate(0, 0) == "P" - assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] - - def test_translate_arr(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] - - def test_uidset_by_level(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - - assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} - assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} - - -class TestEntitySetOnSBSDataframe: - @pytest.fixture - def es_from_sbsdf(self, sbs): - return EntitySet(entity=sbs.dataframe) - - @pytest.fixture - def es_from_sbs_dupe_df(self, sbsd): - return EntitySet(entity=sbsd.dataframe) - - # check all methods - @pytest.mark.parametrize( - "data", - [ - pd.DataFrame({0: ["P"], 1: ["E"]}), - {0: ["P"], 1: ["E"]}, - EntitySet(entity={"P": ["E"]}), - ], - ) - def test_add(self, es_from_sbsdf, data): - assert es_from_sbsdf.data.shape == (15, 2) - assert es_from_sbsdf.dataframe.size == 45 - - es_from_sbsdf.add(data) - - assert es_from_sbsdf.data.shape == (16, 2) - assert es_from_sbsdf.dataframe.size == 48 - - def test_remove(self, es_from_sbsdf): - assert es_from_sbsdf.data.shape == (15, 2) - assert es_from_sbsdf.dataframe.size == 45 - - es_from_sbsdf.remove("P") - - assert es_from_sbsdf.data.shape == (12, 2) - assert es_from_sbsdf.dataframe.size == 36 - assert "P" not in es_from_sbsdf.elements - - @pytest.mark.parametrize( - "props, multidx, expected_props", - [ - ( - lazy_fixture("props_dataframe"), - (0, "P"), - {"prop1": "propval1", "prop2": "propval2"}, - ), - ( - {0: {"P": {"prop1": "propval1", "prop2": "propval2"}}}, - (0, "P"), - {"prop1": "propval1", "prop2": "propval2"}, - ), - ( - {1: {"A": {"prop1": "propval1", "prop2": "propval2"}}}, - (1, "A"), - {"prop1": "propval1", "prop2": "propval2"}, - ), - ], - ) - def test_assign_properties(self, es_from_sbsdf, props, multidx, expected_props): - original_prop = es_from_sbsdf.properties.loc[multidx] - assert original_prop.properties == {} - - es_from_sbsdf.assign_properties(props) - - updated_prop = es_from_sbsdf.properties.loc[multidx] - assert updated_prop.properties == expected_props - - @pytest.mark.parametrize( - "cell_props, multidx, expected_cell_properties", - [ - ( - lazy_fixture("cell_props_dataframe"), - ("P", "A"), - {"prop1": "propval1", "prop2": "propval2"}, - ), - ( - lazy_fixture("cell_props_dataframe_multidx"), - ("P", "A"), - {"prop1": "propval1", "prop2": "propval2"}, - ), - ( - {"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}}, - ("P", "A"), - {"prop1": "propval1", "prop2": "propval2"}, - ), - ], - ) - def test_assign_cell_properties_on_default_cell_properties( - self, es_from_sbsdf, cell_props, multidx, expected_cell_properties - ): - es_from_sbsdf.assign_cell_properties(cell_props=cell_props) - - updated_cell_prop = es_from_sbsdf.cell_properties.loc[multidx] - - assert updated_cell_prop.cell_properties == expected_cell_properties - - def test_assign_cell_properties_on_multiple_properties(self, es_from_sbsdf): - multidx = ("P", "A") - - es_from_sbsdf.assign_cell_properties( - cell_props={"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}} - ) - - updated_cell_prop = es_from_sbsdf.cell_properties.loc[multidx] - assert updated_cell_prop.cell_properties == { - "prop1": "propval1", - "prop2": "propval2", - } - - es_from_sbsdf.assign_cell_properties( - cell_props={ - "P": { - "A": {"prop1": "propval1", "prop2": "propval2", "prop3": "propval3"} - } - } - ) - - updated_cell_prop = es_from_sbsdf.cell_properties.loc[multidx] - assert updated_cell_prop.cell_properties == { - "prop1": "propval1", - "prop2": "propval2", - "prop3": "propval3", - } - - def test_set_cell_property_on_cell_weights(self, es_from_sbsdf): - item1 = "P" - item2 = "A" - prop_name = "cell_weights" - prop_val = 42 - - es_from_sbsdf.set_cell_property(item1, item2, prop_name, prop_val) - - assert es_from_sbsdf.cell_properties.loc[(item1, item2), prop_name] == 42.0 - - # Check that the other cell_weights were not changed and retained the default value of 1 - for row in es_from_sbsdf.cell_properties.itertuples(): - if row.Index != (item1, item2): - assert row.cell_weights == 1 - - def test_set_cell_property_on_non_exisiting_cell_property(self, es_from_sbsdf): - item1 = "P" - item2 = "A" - prop_name = "non_existing_cell_property" - prop_val = {"foo": "bar"} - es_from_sbsdf.set_cell_property(item1, item2, prop_name, prop_val) - - assert es_from_sbsdf.cell_properties.loc[(item1, item2), "cell_properties"] == { - prop_name: prop_val - } - - # Check that the other rows received the default empty dictionary - for row in es_from_sbsdf.cell_properties.itertuples(): - if row.Index != (item1, item2): - assert row.cell_properties == {} - - item2 = "K" - es_from_sbsdf.set_cell_property(item1, item2, prop_name, prop_val) - - assert es_from_sbsdf.cell_properties.loc[(item1, item2), "cell_properties"] == { - prop_name: prop_val - } - - @pytest.mark.parametrize("ret_ec", [True, False]) - def test_collapse_identical_elements_on_duplicates( - self, es_from_sbs_dupe_df, ret_ec - ): - # There are two edges that share the same set of 3 (three) nodes - new_es = es_from_sbs_dupe_df.collapse_identical_elements( - return_equivalence_classes=ret_ec - ) - - es_temp = new_es - if isinstance(new_es, tuple): - # reset variable for actual EntitySet - es_temp = new_es[0] - - # check equiv classes - collapsed_edge_key = "L: 2" - assert "M: 2" not in es_temp.elements - assert collapsed_edge_key in es_temp.elements - assert set(es_temp.elements.get(collapsed_edge_key)) == {"F", "C", "E"} - - equiv_classes = new_es[1] - assert equiv_classes == { - "I: 1": ["I"], - "L: 2": ["L", "M"], - "O: 1": ["O"], - "P: 1": ["P"], - "R: 1": ["R"], - "S: 1": ["S"], - } - - # check dataframe - assert len(es_temp.dataframe) != len(es_from_sbs_dupe_df.dataframe) - assert len(es_temp.dataframe) == len(es_from_sbs_dupe_df.dataframe) - 3 - - @pytest.mark.parametrize( - "col1, col2, expected_elements", - [ - ( - 0, - 1, - { - "I": {"K", "T2"}, - "L": {"C", "E"}, - "O": {"T1", "T2"}, - "P": {"K", "A", "C"}, - "R": {"A", "E"}, - "S": {"K", "A", "V", "T2"}, - }, - ), - ( - 1, - 0, - { - "A": {"P", "R", "S"}, - "C": {"P", "L"}, - "E": {"R", "L"}, - "K": {"P", "S", "I"}, - "T1": {"O"}, - "T2": {"S", "O", "I"}, - "V": {"S"}, - }, - ), - ], - ) - def test_elements_by_column(self, es_from_sbsdf, col1, col2, expected_elements): - elements_temps = es_from_sbsdf.elements_by_column(col1, col2) - actual_elements = { - elements_temps[k]._key[1]: set(v) for k, v in elements_temps.items() - } - - assert actual_elements == expected_elements - - def test_elements_by_level(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.elements_by_level(0, 1) - - def test_encode(self, es_from_sbsdf): - df = pd.DataFrame({"Category": ["A", "B", "A", "C", "B"]}) - # Convert 'Category' column to categorical - df["Category"] = df["Category"].astype("category") - - expected_arr = np.array([[0], [1], [0], [2], [1]]) - actual_arr = es_from_sbsdf.encode(df) - - assert np.array_equal(actual_arr, expected_arr) - - def test_get_cell_properties(self, es_from_sbsdf): - props = es_from_sbsdf.get_cell_properties("P", "A") - - assert props == {"cell_weights": 1} - - def test_get_cell_properties_raises_keyerror(self, es_from_sbsdf): - assert es_from_sbsdf.get_cell_properties("P", "FOOBAR") is None - - def test_get_cell_property(self, es_from_sbsdf): - props = es_from_sbsdf.get_cell_property("P", "A", "cell_weights") - assert props == 1 - - @pytest.mark.parametrize( - "item1, item2, prop_name, err_msg", - [ - ("P", "FOO", "cell_weights", "Item not exists. cell_properties:"), - ], - ) - def test_get_cell_property_raises_keyerror( - self, es_from_sbsdf, item1, item2, prop_name, err_msg - ): - with pytest.raises(KeyError, match=err_msg): - es_from_sbsdf.get_cell_property(item1, item2, prop_name) - - def test_get_cell_property_returns_none_on_prop(self, es_from_sbsdf): - assert es_from_sbsdf.get_cell_property("P", "A", "Not a real property") is None - - @pytest.mark.parametrize("item, level", [("P", 0), ("P", None), ("A", 1)]) - def test_get_properties(self, es_from_sbsdf, item, level): - # to avoid duplicate test code, reuse 'level' to get the item_uid - # but if level is None, assume it to be 0 and that the item exists at level 0 - if level is None: - item_uid = es_from_sbsdf.properties.loc[(0, item), "uid"] - else: - item_uid = es_from_sbsdf.properties.loc[(level, item), "uid"] - - props = es_from_sbsdf.get_properties(item, level=level) - - assert props == {"uid": item_uid, "weight": 1, "properties": {}} - - @pytest.mark.parametrize( - "item, level, err_msg", - [ - ("Not a valid item", None, ""), - ("Not a valid item", 0, "no properties initialized for"), - ], - ) - def test_get_properties_raises_keyerror(self, es_from_sbsdf, item, level, err_msg): - with pytest.raises(KeyError, match=err_msg): - es_from_sbsdf.get_properties(item, level=level) - - @pytest.mark.parametrize( - "item, prop_name, level, expected_prop", - [ - ("P", "weight", 0, 1), - ("P", "properties", 0, {}), - ("P", "uid", 0, 3), - ("A", "weight", 1, 1), - ("A", "properties", 1, {}), - ("A", "uid", 1, 6), - ], - ) - def test_get_property(self, es_from_sbsdf, item, prop_name, level, expected_prop): - prop = es_from_sbsdf.get_property(item, prop_name, level) - - assert prop == expected_prop - - @pytest.mark.parametrize( - "item, prop_name, err_msg", - [ - ("XXX", "weight", "item does not exist:"), - ], - ) - def test_get_property_raises_keyerror( - self, es_from_sbsdf, item, prop_name, err_msg - ): - with pytest.raises(KeyError, match=err_msg): - es_from_sbsdf.get_property(item, prop_name) - - def test_get_property_returns_none_on_no_property(self, es_from_sbsdf): - assert es_from_sbsdf.get_property("P", "non-existing property") is None - - @pytest.mark.parametrize( - "item, prop_name, prop_val, level", - [ - ("P", "weight", 42, 0), - ], - ) - def test_set_property(self, es_from_sbsdf, item, prop_name, prop_val, level): - orig_prop_val = es_from_sbsdf.get_property(item, prop_name, level) - - es_from_sbsdf.set_property(item, prop_name, prop_val, level) - - new_prop_val = es_from_sbsdf.get_property(item, prop_name, level) - - assert new_prop_val != orig_prop_val - assert new_prop_val == prop_val - - @pytest.mark.parametrize( - "item, prop_name, prop_val, level, misc_props_col", - [ - ("P", "new_prop", "foobar", 0, "properties"), - ("P", "new_prop", "foobar", 0, "some_new_miscellaneaus_col"), - ], - ) - def test_set_property_on_non_existing_property( - self, es_from_sbsdf, item, prop_name, prop_val, level, misc_props_col - ): - es_from_sbsdf.set_property(item, prop_name, prop_val, level) - - new_prop_val = es_from_sbsdf.get_property(item, prop_name, level) - - assert new_prop_val == prop_val - - def test_set_property_raises_keyerror(self, es_from_sbsdf): - with pytest.raises( - ValueError, match="cannot infer 'level' when initializing 'item' properties" - ): - es_from_sbsdf.set_property("XXXX", "weight", 42) - - def test_incidence_matrix(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) - - def test_index(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.index("nodes") == 1 - assert ent_sbs.index("nodes", "K") == (1, 3) - - def test_indices(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.indices("nodes", "K") == [3] - assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] - - @pytest.mark.parametrize("level", [0, 1]) - def test_is_empty(self, es_from_sbsdf, level): - assert not es_from_sbsdf.is_empty(level) - - @pytest.mark.parametrize( - "item_level, item, min_level, max_level, expected_lidx", - [ - (0, "P", 0, None, (0, 3)), - (0, "P", 0, 0, (0, 3)), - (0, "P", 1, 1, None), - (1, "A", 0, None, (1, 0)), - (1, "A", 0, 0, None), - (1, "K", 0, None, (1, 3)), - ], - ) - def test_level( - self, es_from_sbsdf, item_level, item, min_level, max_level, expected_lidx - ): - actual_lidx = es_from_sbsdf.level( - item, min_level=min_level, max_level=max_level - ) - - assert actual_lidx == expected_lidx - - if isinstance(actual_lidx, tuple): - index_item_in_labels = actual_lidx[1] - assert index_item_in_labels == es_from_sbsdf.labels[item_level].index(item) - - -@pytest.mark.xfail( - reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" -) -def test_level(sbs): - # TODO: at some point we are casting out and back to categorical dtype without - # preserving categories ordering from `labels` provided to constructor - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.level("I") == (0, 5) # fails - assert ent_sbs.level("K") == (1, 3) - assert ent_sbs.level("K", max_level=0) is None From e1b6d1b66eef11af163af5be2ee33dddce69376a Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 1 Nov 2023 15:11:51 -0700 Subject: [PATCH 55/76] Cleanup makefile --- Makefile | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 83b59381..5e01cfef 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,6 @@ SHELL = /bin/bash VENV = venv-hnx -PYTHON_VENV = $(VENV)/bin/python3 PYTHON3 = python3 @@ -50,7 +49,7 @@ version-deps: ### Documentation docs-deps: - @$(PYTHON3) -m pip install -e .'[documentation]' --use-pep517 + @$(PYTHON3) -m pip install .'[documentation]' --use-pep517 .PHONY: docs-deps @@ -78,7 +77,7 @@ venv: clean-venv @$(PYTHON3) -m venv $(VENV); test-deps: - @$(PYTHON3) -m pip install -e .'[testing]' --use-pep517 + @$(PYTHON3) -m pip install .'[testing]' --use-pep517 all-deps: @$(PYTHON3) -m pip install -e .'[all]' --use-pep517 From f682ca1af2116e02fcbb61669e4863d7c38c847d Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Thu, 26 Oct 2023 12:41:59 -0700 Subject: [PATCH 56/76] HYP-177 Improve EntitySet.data test --- hypernetx/classes/tests/conftest.py | 26 +++++++------- .../classes/tests/test_entityset_on_dict.py | 36 +++++++++++++++++-- 2 files changed, 46 insertions(+), 16 deletions(-) diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index dca99432..b37a0322 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -42,28 +42,28 @@ def __init__(self, static=False): ) self.labels = OrderedDict( [ - ("edges", [p, r, s, l, o, i]), + ("edges", [i, l, o, p, r, s]), ("nodes", [a, c, e, k, t1, t2, v]), ] ) self.data = np.array( [ - [0, 0], - [0, 1], - [0, 3], - [1, 0], - [1, 2], - [2, 0], - [2, 3], - [2, 5], - [2, 6], + [3, 0], [3, 1], - [3, 2], - [4, 4], - [4, 5], + [3, 3], + [4, 0], + [4, 2], + [5, 0], [5, 3], [5, 5], + [5, 6], + [1, 1], + [1, 2], + [2, 4], + [2, 5], + [0, 3], + [0, 5], ] ) diff --git a/hypernetx/classes/tests/test_entityset_on_dict.py b/hypernetx/classes/tests/test_entityset_on_dict.py index 9b0e8982..ed589ae1 100644 --- a/hypernetx/classes/tests/test_entityset_on_dict.py +++ b/hypernetx/classes/tests/test_entityset_on_dict.py @@ -10,7 +10,12 @@ "entity, data, data_cols, labels", [ (lazy_fixture("sbs_dict"), None, (0, 1), None), - (lazy_fixture("sbs_dict"), None, (0, 1), lazy_fixture("sbs_labels")), + ( + lazy_fixture("sbs_dict"), + None, + (0, 1), + lazy_fixture("sbs_labels"), + ), # labels are ignored if entity is provided (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), (lazy_fixture("sbs_dict"), lazy_fixture("sbs_data"), (0, 1), None), (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), @@ -154,10 +159,35 @@ def test_dataframe(self, entity, data, data_cols, labels, sbs): assert actual_node_row0 in ["A", "C", "K"] assert actual_cell_weight_row0 == 1 - # TODO: validate state of 'data' def test_data(self, entity, data, data_cols, labels, sbs): es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) - assert len(es.data) == 15 + + actual_data = es.data + + assert len(actual_data) == 15 + + expected_data = np.array( + [ + [3, 0], + [3, 1], + [3, 3], + [4, 0], + [4, 2], + [5, 0], + [5, 3], + [5, 5], + [5, 6], + [1, 1], + [1, 2], + [2, 4], + [2, 5], + [0, 5], + [0, 3], + ] + ) + assert np.array_equal( + np.sort(actual_data, axis=0), np.sort(expected_data, axis=0) + ) def test_properties(self, entity, data, data_cols, labels, sbs): es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) From 74a99773d10995bf58e047ff18ea3f75354802a2 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 1 Nov 2023 15:32:28 -0700 Subject: [PATCH 57/76] Update tests for soon to be deprecated translate methods --- hypernetx/classes/tests/test_entityset_on_np_array.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hypernetx/classes/tests/test_entityset_on_np_array.py b/hypernetx/classes/tests/test_entityset_on_np_array.py index f4fd04de..1cf02e9e 100644 --- a/hypernetx/classes/tests/test_entityset_on_np_array.py +++ b/hypernetx/classes/tests/test_entityset_on_np_array.py @@ -25,12 +25,12 @@ def test_dimensions_equal_dimsize(self, sbs_data, sbs_labels): def test_translate(self, sbs_data, sbs_labels): ent_sbs = EntitySet(data=np.asarray(sbs_data), labels=sbs_labels) - assert ent_sbs.translate(0, 0) == "P" + assert ent_sbs.translate(0, 0) == "I" assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] def test_translate_arr(self, sbs_data, sbs_labels): ent_sbs = EntitySet(data=np.asarray(sbs_data), labels=sbs_labels) - assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] + assert ent_sbs.translate_arr((0, 0)) == ["I", "A"] def test_uidset_by_level(self, sbs_data, sbs_labels): ent_sbs = EntitySet(data=np.asarray(sbs_data), labels=sbs_labels) From 2656a06f01669ffb696daa48d66f9bf8bc12c5b6 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 3 Nov 2023 13:17:12 -0700 Subject: [PATCH 58/76] Fix github workflow for documentation --- .github/workflows/documentation.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 745e289a..46baff65 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -10,7 +10,6 @@ jobs: - uses: actions/setup-python@v3 - name: Install dependencies run: | - pip install sphinx sphinx_rtd_theme pip install .'[documentation]' - name: Sphinx build run: | From 5c4b5183b944cd7bf7dccc54f45e5f71002ee01f Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 3 Nov 2023 14:25:50 -0700 Subject: [PATCH 59/76] Update nb2plots --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 8204a7e5..0625a7b9 100644 --- a/setup.cfg +++ b/setup.cfg @@ -97,7 +97,7 @@ widget = jupyter-nbextensions-configurator>=0.6.2 documentation = sphinx<7 - nb2plots>=0.6.1 + nb2plots>=0.7.2 sphinx-rtd-theme>=1.2.1 sphinx-autobuild>=2021.3.14 sphinx-copybutton>=0.5.1 From 3dbf148dae85ff36cb143d68e47da1e5b9505da3 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 3 Nov 2023 15:15:26 -0700 Subject: [PATCH 60/76] Remove unnecessary dependencies for documentation --- docs/source/conf.py | 2 -- setup.cfg | 1 - 2 files changed, 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 360b891c..0f4e228c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -57,8 +57,6 @@ "sphinx.ext.napoleon", "sphinx.ext.todo", "sphinx.ext.viewcode", - "nb2plots", - "texext", 'sphinx_copybutton', ] diff --git a/setup.cfg b/setup.cfg index 0625a7b9..c195f0e7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -97,7 +97,6 @@ widget = jupyter-nbextensions-configurator>=0.6.2 documentation = sphinx<7 - nb2plots>=0.7.2 sphinx-rtd-theme>=1.2.1 sphinx-autobuild>=2021.3.14 sphinx-copybutton>=0.5.1 From 937c226b27e7e1a05f89109ca7856549d370de0a Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 17 Nov 2023 19:34:51 -0800 Subject: [PATCH 61/76] HYP-360 Improve code quality tooling; update Makefile --- .flake8 | 11 + .pre-commit-config.yaml | 28 -- .pylintrc | 13 - Makefile | 48 +++- README.md | 6 +- pylintrc | 550 ++++++++++++++++++++++++++++++++++++++++ setup.cfg | 12 +- 7 files changed, 611 insertions(+), 57 deletions(-) create mode 100644 .flake8 delete mode 100644 .pylintrc create mode 100644 pylintrc diff --git a/.flake8 b/.flake8 new file mode 100644 index 00000000..49c7f2ae --- /dev/null +++ b/.flake8 @@ -0,0 +1,11 @@ +[flake8] +max-line-length=120 +extend-ignore = E203 +exclude = + .git, + __pycache__, + docs/source/conf.py, + old, + build, + dist +max-complexity = 10 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f826e7e0..4a0c63da 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,31 +13,3 @@ repos: - id: trailing-whitespace exclude: ^(docs/|hypernetx.egg-info/|setup.cfg) - id: check-merge-conflict - -- repo: https://github.com/psf/black - rev: 22.6.0 - hooks: - - id: black - exclude: ^(docs/|hypernetx.egg-info/) - -# TODO: Uncomment once typing issues have been resolved and mypy has been -# correctly configured -#- repo: https://github.com/pre-commit/mirrors-mypy -# rev: v0.910-1 -# hooks: -# - id: mypy -# exclude: (?x)(docs/|tests/) -# args: [--no-strict-optional, --ignore-missing-imports] - -- repo: local - hooks: - - id: pylint - name: pylint - entry: pylint - language: system - types: [python] - args: - [ - "--rcfile=.pylintrc", - "--exit-zero" # Always return a 0 (non-error) status code, even if lint errors are found. This is primarily useful in continuous integration scripts. - ] diff --git a/.pylintrc b/.pylintrc deleted file mode 100644 index 7ebe9898..00000000 --- a/.pylintrc +++ /dev/null @@ -1,13 +0,0 @@ -[MAIN] - -# Specify a score threshold under which the program will exit with error. -fail-under=5.86 - -[REPORTS] -# Tells whether to display a full report or only the messages. -reports=yes - -# Set the output format. Available formats are text, parseable, colorized, json -# and msvs (visual studio). You can also give a reporter class, e.g. -# mypackage.mymodule.MyReporterClass. -output-format=colorized diff --git a/Makefile b/Makefile index 5e01cfef..d9ec570f 100644 --- a/Makefile +++ b/Makefile @@ -4,21 +4,41 @@ VENV = venv-hnx PYTHON3 = python3 -## Test +## Lint -test: test-deps - @$(PYTHON3) -m tox +.PHONY: lint +lint: pylint flake8 mypy + +.PHONY: pylint +pylint: + @$(PYTHON3) -m pylint --recursive=y --persistent=n --verbose hypernetx + +.PHONY: mypy +mypy: + @$(PYTHON3) -m mypy hypernetx || true + +.PHONY: flake8 +flake8: + @$(PYTHON3) -m flake8 hypernetx --exit-zero + +.PHONY: format +format: + @$(PYTHON3) -m black hypernetx + +## Test -test-ci: test-deps +pre-commit: pre-commit install pre-commit run --all-files - @$(PYTHON3) -m tox -test-ci-github: test-deps - @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' +test: @$(PYTHON3) -m tox -.PHONY: test, test-ci, test-ci-github +test-ci: lint-deps lint pre-commit test-deps test + +test-ci-github: lint-deps lint pre-commit ci-github-deps test-deps test + +.PHONY: test, test-ci, test-ci-github, pre-commit ## Continuous Deployment ## Assumes that scripts are run on a container or test server VM @@ -76,6 +96,18 @@ clean: venv: clean-venv @$(PYTHON3) -m venv $(VENV); +.PHONY: github-ci-deps +ci-github-deps: + @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' + +.PHONY: lint-deps +lint-deps: + @$(PYTHON3) -m pip install .'[lint]' --use-pep517 + +.PHONY: format-deps +format-deps: + @$(PYTHON3) -m pip install .'[format]' --use-pep517 + test-deps: @$(PYTHON3) -m pip install .'[testing]' --use-pep517 diff --git a/README.md b/README.md index dae06123..ec4625be 100644 --- a/README.md +++ b/README.md @@ -269,7 +269,7 @@ HyperNetX uses a number of tools to maintain code quality: Before using these tools, ensure that you install Pylint in your environment: ```shell -pip install .'[linting]' +pip install .'[lint]' ``` @@ -279,12 +279,10 @@ pip install .'[linting]' > Pylint analyses your code without actually running it. It checks for errors, enforces a coding standard, looks for code smells, and can make suggestions about how the code could be refactored. Pylint can infer actual values from your code using its internal code representation (astroid). If your code is import logging as argparse, Pylint will know that argparse.error(...) is in fact a logging call and not an argparse call. - -We have a Pylint configuration file, `.pylintrc`, located at the root of this project. To run Pylint and view the results of Pylint, run the following command: ```shell -pylint hypernetx --rcfile=.pylintrc +pylint hypernetx ``` You can also run Pylint on the command line to generate a report on the quality of the codebase and save it to a file named "pylint-results.txt": diff --git a/pylintrc b/pylintrc new file mode 100644 index 00000000..18f3cd61 --- /dev/null +++ b/pylintrc @@ -0,0 +1,550 @@ +[MAIN] + +# Specify a score threshold to be exceeded before program exits with error. +fail-under=7.66 + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. +extension-pkg-allow-list= + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. (This is an alternative name to extension-pkg-allow-list +# for backward compatibility.) +extension-pkg-whitelist= + +# Files or directories to be skipped. They should be base names, not paths. +ignore=.github,htmlcov,docs,tutorials + +# Files or directories matching the regex patterns are skipped. The regex +# matches against base names, not paths. +ignore-patterns=.cache,venv,scratch + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# number of processors available to use. +jobs=1 + +# Control the amount of potential inferred values when inferring a single +# object. This can help the performance when dealing with large functions or +# complex, nested conditions. +limit-inference-results=100 + +# List of plugins (as comma separated values of python module names) to load, +# usually to register additional checkers. +load-plugins= + +# Pickle collected data for later comparisons. +persistent=yes + +# When enabled, pylint would attempt to guess common misconfiguration and emit +# user-friendly hints instead of false-positive error messages. +suggestion-mode=yes + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED. +confidence= + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once). You can also use "--disable=all" to +# disable everything first and then reenable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use "--disable=all --enable=classes +# --disable=W". +disable=raw-checker-failed, + bad-inline-option, + locally-disabled, + file-ignored, + suppressed-message, + useless-suppression, + deprecated-pragma, + use-symbolic-message-instead, + logging-fstring-interpolation, + missing-module-docstring, + missing-function-docstring, + missing-class-docstring, + too-few-public-methods, + unnecessary-pass, + duplicate-code, + typecheck, + too-many-instance-attributes, + fixme, + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +enable=c-extension-no-member + + +[REPORTS] + +# Python expression which should return a score less than or equal to 10. You +# have access to the variables 'error', 'warning', 'refactor', and 'convention' +# which contain the number of messages in each category, as well as 'statement' +# which is the total number of statements analyzed. This score is used by the +# global evaluation report (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details. +#msg-template= + +# Set the output format. Available formats are text, parseable, colorized, json +# and msvs (visual studio). You can also give a reporter class, e.g. +# mypackage.mymodule.MyReporterClass. +output-format=colorized + +# Tells whether to display a full report or only the messages. +reports=no + +# Activate the evaluation score. +score=yes + + +[REFACTORING] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + +# Complete name of functions that never returns. When checking for +# inconsistent-return-statements if a never returning function is called then +# it will be considered as an explicit return statement and no message will be +# printed. +never-returning-functions=sys.exit,argparse.parse_error + + +[LOGGING] + +# The type of string formatting that logging methods do. `old` means using % +# formatting, `new` is for `{}` formatting. +logging-format-style=old + +# Logging modules to check that the string format arguments are in logging +# function parameter format. +logging-modules=logging + + +[SPELLING] + +# Limits count of emitted suggestions for spelling mistakes. +max-spelling-suggestions=4 + +# Spelling dictionary name. Available dictionaries: none. To make it work, +# install the 'python-enchant' package. +spelling-dict= + +# List of comma separated words that should be considered directives if they +# appear and the beginning of a comment and should not be checked. +spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy: + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains the private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to the private dictionary (see the +# --spelling-private-dict-file option) instead of raising a message. +spelling-store-unknown-words=no + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=TODO + +# Regular expression of note tags to take in consideration. +#notes-rgx= + + +[TYPECHECK] + +# List of decorators that produce context managers, such as +# contextlib.contextmanager. Add to this list to register other decorators that +# produce valid context managers. +contextmanager-decorators=contextlib.contextmanager + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members=PP_PARAGRAPH_ALIGNMENT + + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# Tells whether to warn about missing members when the owner of the attribute +# is inferred to be None. +ignore-none=yes + +# This flag controls whether pylint should warn about no-member and similar +# checks whenever an opaque object is returned when inferring. The inference +# can return multiple potential results while evaluating a Python object, but +# some branches might not be evaluated, which results in partial inference. In +# that case, it might be useful to still emit no-member and other checks for +# the rest of the inferred objects. +ignore-on-opaque-inference=yes + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=optparse.Values,thread._local,_thread._local,SQLAlchemy,scoped_session,alembic.op + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis). It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# Show a hint with possible names when a member name was not found. The aspect +# of finding the hint is based on edit distance. +missing-member-hint=yes + +# The minimum edit distance a name should have in order to be considered a +# similar match for a missing member name. +missing-member-hint-distance=1 + +# The total number of similar names that should be taken in consideration when +# showing a hint for a missing member. +missing-member-max-choices=1 + +# List of decorators that change the signature of a decorated function. +signature-mutators= + + +[VARIABLES] + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid defining new builtins when possible. +additional-builtins= + +# Tells whether unused global variables should be treated as a violation. +allow-global-unused-variables=yes + +# List of names allowed to shadow builtins +allowed-redefined-builtins=id + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_, + _cb + +# A regular expression matching the name of dummy variables (i.e. expected to +# not be used). +dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ + +# Argument names that match this expression will be ignored. Default to name +# with leading underscore. +ignored-argument-names=_.*|^ignored_|^unused_ + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io + + +[FORMAT] + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Maximum number of characters on a single line. +max-line-length=120 + +# Maximum number of lines in a module. +max-module-lines=1500 + +# Allow the body of a class to be on the same line as the declaration if body +# contains single statement. +single-line-class-stmt=no + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + + +[SIMILARITIES] + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + +# Minimum lines number of a similarity. +min-similarity-lines=4 + + +[BASIC] + +# Naming style matching correct argument names. +argument-naming-style=snake_case + +# Regular expression matching correct argument names. Overrides argument- +# naming-style. +#argument-rgx= + +# Naming style matching correct attribute names. +attr-naming-style=snake_case + +# Regular expression matching correct attribute names. Overrides attr-naming- +# style. +#attr-rgx= + +# Bad variable names which should always be refused, separated by a comma. +bad-names=foo, + bar, + baz, + toto, + tutu, + tata + +# Bad variable names regexes, separated by a comma. If names match any regex, +# they will always be refused +bad-names-rgxs= + +# Naming style matching correct class attribute names. +class-attribute-naming-style=any + +# Regular expression matching correct class attribute names. Overrides class- +# attribute-naming-style. +#class-attribute-rgx= + +# Naming style matching correct class constant names. +class-const-naming-style=UPPER_CASE + +# Regular expression matching correct class constant names. Overrides class- +# const-naming-style. +#class-const-rgx= + +# Naming style matching correct class names. +class-naming-style=PascalCase + +# Regular expression matching correct class names. Overrides class-naming- +# style. +#class-rgx= + +# Naming style matching correct constant names. +const-naming-style=UPPER_CASE + +# Regular expression matching correct constant names. Overrides const-naming- +# style. +#const-rgx= + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=-1 + +# Naming style matching correct function names. +function-naming-style=snake_case + +# Regular expression matching correct function names. Overrides function- +# naming-style. +#function-rgx= + +# Good variable names which should always be accepted, separated by a comma. +good-names=i, + j, + k, + _, + id, + e, + dt, + T, + +# Good variable names regexes, separated by a comma. If names match any regex, +# they will always be accepted +good-names-rgxs= + +# Include a hint for the correct naming format with invalid-name. +include-naming-hint=no + +# Naming style matching correct inline iteration names. +inlinevar-naming-style=any + +# Regular expression matching correct inline iteration names. Overrides +# inlinevar-naming-style. +#inlinevar-rgx= + +# Naming style matching correct method names. +method-naming-style=snake_case + +# Regular expression matching correct method names. Overrides method-naming- +# style. +#method-rgx= + +# Naming style matching correct module names. +module-naming-style=snake_case + +# Regular expression matching correct module names. Overrides module-naming- +# style. +module-rgx=[a-zA-Z0-9_]+ + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=^_ + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +# These decorators are taken in consideration only for invalid-name. +property-classes=abc.abstractproperty + +# Naming style matching correct variable names. +variable-naming-style=snake_case + +# Regular expression matching correct variable names. Overrides variable- +# naming-style. +#variable-rgx= + + +[STRING] + +# This flag controls whether inconsistent-quotes generates a warning when the +# character used as a quote delimiter is used inconsistently within a module. +check-quote-consistency=no + +# This flag controls whether the implicit-str-concat should generate a warning +# on implicit string concatenation in sequences defined over several lines. +check-str-concat-over-line-jumps=no + + +[IMPORTS] + +# List of modules that can be imported at any level, not just the top level +# one. +allow-any-import-level= + +# Allow wildcard imports from modules that define __all__. +allow-wildcard-with-all=no + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + +# Deprecated modules which should not be used, separated by a comma. +deprecated-modules=optparse,tkinter.tix + +# Output a graph (.gv or any supported image format) of external dependencies +# to the given file (report RP0402 must not be disabled). +ext-import-graph= + +# Output a graph (.gv or any supported image format) of all (i.e. internal and +# external) dependencies to the given file (report RP0402 must not be +# disabled). +import-graph= + +# Output a graph (.gv or any supported image format) of internal dependencies +# to the given file (report RP0402 must not be disabled). +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant + +# Couples of modules and preferred modules, separated by a comma. +preferred-modules= + + +[CLASSES] + +# Warn about protected attribute access inside special methods +check-protected-access-in-special-methods=no + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__, + __new__, + setUp, + __post_init__ + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict, + _fields, + _replace, + _source, + _make + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=cls + + +[DESIGN] + +# Maximum number of arguments for function / method. +max-args=5 + +# Maximum number of attributes for a class (see R0902). +max-attributes=7 + +# Maximum number of boolean expressions in an if statement (see R0916). +max-bool-expr=5 + +# Maximum number of branch for function / method body. +max-branches=12 + +# Maximum number of locals for function / method body. +max-locals=15 + +# Maximum number of parents for a class (see R0901). +max-parents=7 + +# Maximum number of public methods for a class (see R0904). +max-public-methods=20 + +# Maximum number of return / yield for function / method body. +max-returns=6 + +# Maximum number of statements in function / method body. +max-statements=50 + +# Minimum number of public methods for a class (see R0903). +min-public-methods=2 + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "BaseException, Exception". +overgeneral-exceptions=builtins.BaseException diff --git a/setup.cfg b/setup.cfg index c195f0e7..05995e30 100644 --- a/setup.cfg +++ b/setup.cfg @@ -72,6 +72,14 @@ install_requires = [options.extras_require] releases = commitizen>=3.2.1 +lint = + pylint>=3.0.2 + pylint-exit>=1.2.0 + mypy>=1.7.0 + flake8>=6.1.0 + pre-commit>=3.2.2 +format = + black>=23.3.0 testing = pytest>=7.2.2 pytest-cov>=4.1.0 @@ -80,10 +88,6 @@ testing = pytest-env tox>=4.4.11 nbmake>=1.4.1 - pre-commit>=3.2.2 - pylint>=2.17.2 - pylint-exit>=1.2.0 - black>=23.3.0 celluloid>=0.2.0 igraph>=0.10.4 tutorials = From 4383c3727b9c27677ef6c52bcc173601759f329c Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Tue, 19 Dec 2023 16:25:02 -0800 Subject: [PATCH 62/76] updated empty hypergraph to have dataframes for properties --- hypernetx/classes/hypergraph.py | 52 +++++++++++++++++++++++---------- 1 file changed, 37 insertions(+), 15 deletions(-) diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 2a3c3037..395f6614 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -327,10 +327,22 @@ def __init__( ) ### cell properties - if setsystem is None: #### Empty Case - self._edges = EntitySet({}) - self._nodes = EntitySet({}) - self._state_dict = {} + #### Empty Case + if setsystem is None or (len(setsystem) == 0): + df = pd.DataFrame(columns=['edges','nodes']) + self.E = EntitySet(df) + self._edges = self.E ##Edges(self.E) ## + self._nodes = self.E.restrict_to_levels([1]) ##Nodes(self.E) ## + self._data_cols = data_cols = self.E._data_cols + + self._dataframe = self.E._dataframe + self._set_default_state(empty=True) + if self._dataframe is not None: + self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( + "category" + ) + + self.__dict__.update(locals()) else: #### DataFrame case if isinstance(setsystem, pd.DataFrame): @@ -828,27 +840,37 @@ def set_state(self, **kwargs): """ self._state_dict.update(kwargs) - def _set_default_state(self): + def _set_default_state(self,empty=False): """Populate state_dict with default values""" self._state_dict = {} self._state_dict["dataframe"] = df = self.dataframe - self._state_dict["labels"] = { - "edges": np.array(df[self._edge_col].cat.categories), - "nodes": np.array(df[self._node_col].cat.categories), - } - self._state_dict["data"] = np.array( - [df[self._edge_col].cat.codes, df[self._node_col].cat.codes], dtype=int - ).T + + if empty: + self._state_dict["labels"] = { + "edges": np.array([]), + "nodes": np.array([]) + } + self._state_dict["data"] = np.array([[],[]]) + + else: + self._state_dict["labels"] = { + "edges": np.array(df[self._edge_col].cat.categories), + "nodes": np.array(df[self._node_col].cat.categories), + } + self._state_dict["data"] = np.array( + [df[self._edge_col].cat.codes, df[self._node_col].cat.codes], dtype=int + ).T + + self._state_dict["snodelg"] = dict() ### s: nx.graph self._state_dict["sedgelg"] = dict() self._state_dict["neighbors"] = defaultdict(dict) ### s: {node: neighbors} - self._state_dict["edge_neighbors"] = defaultdict( - dict - ) ### s: {edge: edge_neighbors} + self._state_dict["edge_neighbors"] = defaultdict(dict) ### s: {edge: edge_neighbors} self._state_dict["adjacency_matrix"] = dict() ### s: scipy.sparse.csr_matrix self._state_dict["edge_adjacency_matrix"] = dict() + def edge_size_dist(self): """ Returns the size for each edge From 6b359fb6c074c1f5063bc13e2c2b2c05c5bc2bf1 Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Tue, 19 Dec 2023 16:30:02 -0800 Subject: [PATCH 63/76] removed readded test module --- hypernetx/classes/tests/test_entityset.py | 371 ---------------------- 1 file changed, 371 deletions(-) delete mode 100644 hypernetx/classes/tests/test_entityset.py diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py deleted file mode 100644 index c0e88888..00000000 --- a/hypernetx/classes/tests/test_entityset.py +++ /dev/null @@ -1,371 +0,0 @@ -import numpy as np -import pytest - -from collections.abc import Iterable -from collections import UserList -from hypernetx.classes import EntitySet -from hypernetx.classes.entityset import restrict_to_two_columns - -from pandas import DataFrame, Series - -def test_empty_entityset(): - - es = EntitySet() - assert es.empty - assert len(es.elements) == 0 - assert es.elements == {} - assert es.dimsize == 0 - - -def test_entityset_from_dataframe(): - data_dict = { - 1: ["A", "D"], - 2: ["A", "C", "D"], - 3: ["D"], - 4: ["A", "B"], - 5: ["B", "C"], - } - - all_edge_pairs = Series(data_dict).explode() - - entity = DataFrame( - {"edges": all_edge_pairs.index.to_list(), "nodes": all_edge_pairs.values} - ) - - es = EntitySet(entity=entity) - - assert not es.empty - assert len(es.elements) == 5 - assert es.dimsize == 2 - assert es.uid is None - - -class TestEntitySetOnSevenBySixDataset: - # Tests on different inputs for entity and data - def test_entityset_from_dictionary(self, sbs): - ent = EntitySet(entity=sbs.edgedict) - assert len(ent.elements) == 6 - - def test_entityset_from_ndarray_sbs(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - - assert ent_sbs.size() == 6 - assert len(ent_sbs.uidset) == 6 - assert len(ent_sbs.children) == 7 - assert isinstance(ent_sbs.incidence_dict["I"], list) - assert "I" in ent_sbs - assert "K" in ent_sbs - - # Tests for properties - @pytest.mark.skip(reason="TODO: implement") - def test_cell_properties(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_cell_weights(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_children(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_data(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_dataframe(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_dimensions(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_dimsize(self): - pass - - def test_dimensions_equal_dimsize(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.dimsize == len(ent_sbs.dimensions) - - @pytest.mark.skip(reason="TODO: implement") - def test_elements(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_empty(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_incidence_dict(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_isstatic(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_labels(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_memberships(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_properties(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_uid(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_uidset(self): - pass - - # Tests for methods - @pytest.mark.skip(reason="TODO: implement") - def test_add(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_add_element(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_add_elements_from(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_assign_properties(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_collapse_identitical_elements(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_elements_by_column(self): - pass - - def test_elements_by_level(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.elements_by_level(0, 1) - - @pytest.mark.skip(reason="TODO: implement") - def test_encode(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_get_cell_properties(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_get_cell_property(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_get_properties(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_get_property(self): - pass - - def test_incidence_matrix(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) - - def test_index(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.index("nodes") == 1 - assert ent_sbs.index("nodes", "K") == (1, 3) - - def test_indices(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.indices("nodes", "K") == [3] - assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] - - @pytest.mark.skip(reason="TODO: implement") - def test_is_empty(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_level(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_remove(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_remove_elements(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_restrict_to(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_restrict_to_indices(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_restrict_to_levels(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_set_cell_property(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_set_property(self): - pass - - @pytest.mark.skip(reason="TODO: implement") - def test_size(self): - pass - - def test_translate(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate(0, 0) == "P" - assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] - - def test_translate_arr(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] - - @pytest.mark.skip(reason="TODO: implement") - def test_uidset_by_column(self): - pass - - def test_uidset_by_level(self, sbs): - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - - assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} - assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} - - -class TestEntitySetOnHarryPotterDataSet: - def test_entityset_from_ndarray(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert len(ent_hp.uidset) == 7 - assert len(ent_hp.elements) == 7 - assert isinstance(ent_hp.elements["Hufflepuff"], UserList) - assert not ent_hp.is_empty() - assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 - - def test_custom_attributes(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert ent_hp.__len__() == 7 - assert isinstance(ent_hp.__str__(), str) - assert isinstance(ent_hp.__repr__(), str) - assert isinstance(ent_hp.__contains__("Muggle"), bool) - assert ent_hp.__contains__("Muggle") is True - assert ent_hp.__getitem__("Slytherin") == [ - "Half-blood", - "Pure-blood", - "Pure-blood or half-blood", - ] - assert isinstance(ent_hp.__iter__(), Iterable) - assert isinstance(ent_hp.__call__(), Iterable) - assert ent_hp.__call__().__next__() == "Unknown House" - - def test_restrict_to_levels(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 - - def test_restrict_to_indices(self, harry_potter): - ent_hp = EntitySet( - data=np.asarray(harry_potter.data), labels=harry_potter.labels - ) - assert ent_hp.restrict_to_indices([1, 2]).uidset == { - "Gryffindor", - "Ravenclaw", - } - - -# testing entityset helpers - - -def test_restrict_to_two_columns_on_ndarray(harry_potter): - data = np.asarray(harry_potter.data) - labels = harry_potter.labels - expected_num_cols = 2 - expected_ndarray_first_row = np.array([1, 1]) - - entity, data, labels = restrict_to_two_columns( - entity=None, - data=data, - labels=labels, - cell_properties=None, - weight_col="cell_weights", - weights=1, - level1=0, - level2=1, - misc_cell_props_col="properties", - ) - - assert entity is None - assert len(labels) == 2 - assert 0 in labels - assert 1 in labels - - print(data) - print(type(data[0])) - - assert data.shape[1] == expected_num_cols - assert np.array_equal(data[0], expected_ndarray_first_row) - - -@pytest.mark.skip(reason="TODO: implement") -def test_restrict_to_two_columns_on_dataframe(sbs): - pass - - -@pytest.mark.skip(reason="TODO: implement") -def build_dataframe_from_entity_on_dataframe(sbs): - pass - - -@pytest.mark.xfail( - reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" -) -def test_level(sbs): - # TODO: at some point we are casting out and back to categorical dtype without - # preserving categories ordering from `labels` provided to constructor - ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) - assert ent_sbs.level("I") == (0, 5) # fails - assert ent_sbs.level("K") == (1, 3) - assert ent_sbs.level("K", max_level=0) is None - - -@pytest.mark.xfail( - reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" -) -def test_attributes(ent_hp): - assert isinstance(ent_hp.data, np.ndarray) - # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray - assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails - assert isinstance(ent_hp.labels, dict) - # TODO: Entity defaults to first two cols as data cols - assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails - assert ent_hp.dimsize == 5 # fails - df = ent_hp.dataframe[ent_hp._data_cols] - assert list(df.columns) == [ # fails - "House", - "Blood status", - "Species", - "Hair colour", - "Eye colour", - ] - assert ent_hp.dimensions == tuple(df.nunique()) - assert set(ent_hp.labels["House"]) == set(df["House"].unique()) From 5c903aa03b65e4baa202c22aa359ee767858be6c Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 19 Dec 2023 16:53:45 -0800 Subject: [PATCH 64/76] Run linter --- hypernetx/classes/hypergraph.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 395f6614..2a965652 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -328,7 +328,7 @@ def __init__( ### cell properties #### Empty Case - if setsystem is None or (len(setsystem) == 0): + if setsystem is None or (len(setsystem) == 0): df = pd.DataFrame(columns=['edges','nodes']) self.E = EntitySet(df) self._edges = self.E ##Edges(self.E) ## @@ -340,8 +340,8 @@ def __init__( if self._dataframe is not None: self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( "category" - ) - + ) + self.__dict__.update(locals()) else: #### DataFrame case @@ -845,14 +845,14 @@ def _set_default_state(self,empty=False): self._state_dict = {} self._state_dict["dataframe"] = df = self.dataframe - - if empty: + + if empty: self._state_dict["labels"] = { "edges": np.array([]), "nodes": np.array([]) } - self._state_dict["data"] = np.array([[],[]]) - + self._state_dict["data"] = np.array([[],[]]) + else: self._state_dict["labels"] = { "edges": np.array(df[self._edge_col].cat.categories), @@ -860,8 +860,8 @@ def _set_default_state(self,empty=False): } self._state_dict["data"] = np.array( [df[self._edge_col].cat.codes, df[self._node_col].cat.codes], dtype=int - ).T - + ).T + self._state_dict["snodelg"] = dict() ### s: nx.graph self._state_dict["sedgelg"] = dict() From ace11448282f13b5ee4575641f381079ee4a9938 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 2 Jan 2024 14:43:53 -0800 Subject: [PATCH 65/76] HYP-365 Silence warnings in tutorials --- setup.cfg | 1 + ...torial 10 - Hypergraph Modularity and Clustering.ipynb | 4 ++-- tutorials/advanced/Tutorial 5 - s-Centrality.ipynb | 8 +++----- .../Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb | 4 ++-- .../advanced/Tutorial 7 - Laplacians and Clustering.ipynb | 4 ++-- tutorials/advanced/Tutorial 8 - Generative Models.ipynb | 4 ++-- .../advanced/Tutorial 9 - Contagion on Hypergraphs.ipynb | 4 ++-- tutorials/basic/Tutorial 1 - HNX Basics.ipynb | 4 ++-- tutorials/basic/Tutorial 2 - Visualization Methods.ipynb | 4 ++-- tutorials/basic/Tutorial 3 - LesMis Case Study.ipynb | 4 ++-- .../Tutorial 4 - LesMis Visualizations-BookTour.ipynb | 4 ++-- tutorials/hypergraph_modularity_tests.ipynb | 4 ++-- tutorials/widget/Demo 1 - HNXWidget.ipynb | 4 ++-- ...emo 2 - HNX Constructor and More Widget Examples.ipynb | 4 ++-- 14 files changed, 28 insertions(+), 29 deletions(-) diff --git a/setup.cfg b/setup.cfg index 05995e30..5a781cb6 100644 --- a/setup.cfg +++ b/setup.cfg @@ -95,6 +95,7 @@ tutorials = igraph>=0.10.4 partition-igraph>=0.0.6 celluloid>=0.2.0 + shutup>=0.2.0 widget = hnxwidget>=0.1.1b3 jupyter-contrib-nbextensions>=0.7.0 diff --git a/tutorials/advanced/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb b/tutorials/advanced/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb index b4964189..2dad0ad8 100644 --- a/tutorials/advanced/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb +++ b/tutorials/advanced/Tutorial 10 - Hypergraph Modularity and Clustering.ipynb @@ -16,8 +16,8 @@ "import hypernetx.algorithms.hypergraph_modularity as hmod\n", "import hypernetx.algorithms.generative_models as gm\n", "\n", - "import warnings\n", - "warnings.simplefilter('ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/advanced/Tutorial 5 - s-Centrality.ipynb b/tutorials/advanced/Tutorial 5 - s-Centrality.ipynb index e8264e89..5e3e6feb 100644 --- a/tutorials/advanced/Tutorial 5 - s-Centrality.ipynb +++ b/tutorials/advanced/Tutorial 5 - s-Centrality.ipynb @@ -19,8 +19,8 @@ " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", " exit()\n", "\n", - "import warnings\n", - "warnings.simplefilter('ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { @@ -218,9 +218,7 @@ "e1: 0.0\n", "e2: 0.0\n", "e3: 0.25\n", - "e4: 0.3333333333333333\n", - "\n", - "\n" + "e4: 0.3333333333333333\n" ] } ], diff --git a/tutorials/advanced/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb b/tutorials/advanced/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb index 6e1b7781..25ac8d9c 100644 --- a/tutorials/advanced/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb +++ b/tutorials/advanced/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb @@ -18,8 +18,8 @@ " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", " exit()\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/advanced/Tutorial 7 - Laplacians and Clustering.ipynb b/tutorials/advanced/Tutorial 7 - Laplacians and Clustering.ipynb index 3ef21a83..d672ee0c 100644 --- a/tutorials/advanced/Tutorial 7 - Laplacians and Clustering.ipynb +++ b/tutorials/advanced/Tutorial 7 - Laplacians and Clustering.ipynb @@ -34,8 +34,8 @@ "\n", "import hypernetx as hnx\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/advanced/Tutorial 8 - Generative Models.ipynb b/tutorials/advanced/Tutorial 8 - Generative Models.ipynb index b5c1e86b..87d678ad 100644 --- a/tutorials/advanced/Tutorial 8 - Generative Models.ipynb +++ b/tutorials/advanced/Tutorial 8 - Generative Models.ipynb @@ -45,8 +45,8 @@ "import hypernetx as hnx\n", "import hypernetx.algorithms.generative_models as gm\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/advanced/Tutorial 9 - Contagion on Hypergraphs.ipynb b/tutorials/advanced/Tutorial 9 - Contagion on Hypergraphs.ipynb index 1bab22de..7dc86db2 100644 --- a/tutorials/advanced/Tutorial 9 - Contagion on Hypergraphs.ipynb +++ b/tutorials/advanced/Tutorial 9 - Contagion on Hypergraphs.ipynb @@ -24,8 +24,8 @@ "import hypernetx as hnx\n", "import hypernetx.algorithms.contagion as contagion\n", "\n", - "import warnings\n", - "warnings.simplefilter('ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/basic/Tutorial 1 - HNX Basics.ipynb b/tutorials/basic/Tutorial 1 - HNX Basics.ipynb index 37f53e0b..4da0ab5d 100644 --- a/tutorials/basic/Tutorial 1 - HNX Basics.ipynb +++ b/tutorials/basic/Tutorial 1 - HNX Basics.ipynb @@ -17,8 +17,8 @@ " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", " exit()\n", "\n", - "import warnings\n", - "warnings.simplefilter('ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb index 67d694a4..db15ddc2 100644 --- a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb +++ b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb @@ -17,8 +17,8 @@ " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", " exit()\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')\n", + "import shutup\n", + "shutup.mute_warnings()\n", "\n", "### GraphViz is arguably the best graph drawing tool, but it is old and tricky to install.\n", "### Uncommenting the line below will get you slightly better layouts, if you can get it working...\n", diff --git a/tutorials/basic/Tutorial 3 - LesMis Case Study.ipynb b/tutorials/basic/Tutorial 3 - LesMis Case Study.ipynb index f1b1c1a3..70278782 100644 --- a/tutorials/basic/Tutorial 3 - LesMis Case Study.ipynb +++ b/tutorials/basic/Tutorial 3 - LesMis Case Study.ipynb @@ -40,8 +40,8 @@ " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", " exit()\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/basic/Tutorial 4 - LesMis Visualizations-BookTour.ipynb b/tutorials/basic/Tutorial 4 - LesMis Visualizations-BookTour.ipynb index cf3bc321..243b26c2 100644 --- a/tutorials/basic/Tutorial 4 - LesMis Visualizations-BookTour.ipynb +++ b/tutorials/basic/Tutorial 4 - LesMis Visualizations-BookTour.ipynb @@ -17,8 +17,8 @@ " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", " exit()\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { diff --git a/tutorials/hypergraph_modularity_tests.ipynb b/tutorials/hypergraph_modularity_tests.ipynb index de67780a..a523a3ce 100644 --- a/tutorials/hypergraph_modularity_tests.ipynb +++ b/tutorials/hypergraph_modularity_tests.ipynb @@ -79,8 +79,8 @@ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from collections import Counter\n", - "import warnings\n", - "warnings.simplefilter('ignore')\n", + "import shutup\n", + "shutup.mute_warnings()\n", "print('HNX version:',hnx.__version__)\n", "Datadir = \"data/\"" ] diff --git a/tutorials/widget/Demo 1 - HNXWidget.ipynb b/tutorials/widget/Demo 1 - HNXWidget.ipynb index 6fbb46e0..6f28302c 100644 --- a/tutorials/widget/Demo 1 - HNXWidget.ipynb +++ b/tutorials/widget/Demo 1 - HNXWidget.ipynb @@ -20,8 +20,8 @@ "from hypernetx.utils.toys.lesmis import LesMis\n", "from hnxwidget import HypernetxWidget\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')" + "import shutup\n", + "shutup.mute_warnings()" ], "metadata": { "collapsed": false diff --git a/tutorials/widget/Demo 2 - HNX Constructor and More Widget Examples.ipynb b/tutorials/widget/Demo 2 - HNX Constructor and More Widget Examples.ipynb index a6538a2f..695db192 100644 --- a/tutorials/widget/Demo 2 - HNX Constructor and More Widget Examples.ipynb +++ b/tutorials/widget/Demo 2 - HNX Constructor and More Widget Examples.ipynb @@ -28,8 +28,8 @@ "import hypernetx as hnx\n", "from hnxwidget import HypernetxWidget as HW\n", "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')" + "import shutup\n", + "shutup.mute_warnings()" ] }, { From 796e27cbe7048dfc4c8d033befa48c274a53f131 Mon Sep 17 00:00:00 2001 From: Dustin Arendt Date: Tue, 17 Oct 2023 15:34:13 -0700 Subject: [PATCH 66/76] Modification of draw function to support drawing the star hypergraph --- hypernetx/drawing/rubber_band.py | 72 ++++++--- tutorials/Incidence Visualization.ipynb | 201 ++++++++++++++++++++++++ tutorials/newsgroups-topics.csv | 14 ++ 3 files changed, 266 insertions(+), 21 deletions(-) create mode 100644 tutorials/Incidence Visualization.ipynb create mode 100644 tutorials/newsgroups-topics.csv diff --git a/hypernetx/drawing/rubber_band.py b/hypernetx/drawing/rubber_band.py index 5a8e0323..3ccc2f70 100644 --- a/hypernetx/drawing/rubber_band.py +++ b/hypernetx/drawing/rubber_band.py @@ -30,7 +30,7 @@ cp = np.vstack((np.cos(theta), np.sin(theta))).T -def layout_node_link(H, layout=nx.spring_layout, **kwargs): +def layout_node_link(H, G=None, layout=nx.spring_layout, **kwargs): """ Helper function to use a NetwrokX-like graph layout algorithm on a Hypergraph @@ -41,6 +41,8 @@ def layout_node_link(H, layout=nx.spring_layout, **kwargs): ---------- H: Hypergraph the entity to be drawn + G: Graph + an additional set of links to consider during the layout process layout: function the layout algorithm which accepts a NetworkX graph and keyword arguments kwargs: dict @@ -51,7 +53,13 @@ def layout_node_link(H, layout=nx.spring_layout, **kwargs): dict mapping of node and edge positions to R^2 """ - return layout(H.bipartite(), **kwargs) + + B = H.bipartite() + + if G is not None: + B.add_edges_from(G.edges()) + + return layout(B, **kwargs) def get_default_radius(H, pos): @@ -82,7 +90,7 @@ def get_default_radius(H, pos): return 1 -def draw_hyper_edge_labels(H, polys, labels={}, ax=None, **kwargs): +def draw_hyper_edge_labels(H, pos, polys, labels={}, edge_labels_on_edge=True, ax=None, **kwargs): """ Draws a label on the hyper edge boundary. @@ -113,22 +121,28 @@ def draw_hyper_edge_labels(H, polys, labels={}, ax=None, **kwargs): for edge, path, params in zip(H.edges, polys.get_paths(), params): s = labels.get(edge, edge) - # calculate the xy location of the annotation - # this is the midpoint of the pair of adjacent points the most distant - d = ((path.vertices[:-1] - path.vertices[1:]) ** 2).sum(axis=1) - i = d.argmax() + theta = 0 + xy = pos[edge] + + if edge_labels_on_edge: + # calculate the xy location of the annotation + # this is the midpoint of the pair of adjacent points the most distant + d = ((path.vertices[:-1] - path.vertices[1:]) ** 2).sum(axis=1) + i = d.argmax() + + x1, x2 = path.vertices[i : i + 2] + x, y = x2 - x1 + theta = 360 * np.arctan2(y, x) / (2 * np.pi) + theta = (theta + 360) % 360 - x1, x2 = path.vertices[i : i + 2] - x, y = x2 - x1 - theta = 360 * np.arctan2(y, x) / (2 * np.pi) - theta = (theta + 360) % 360 + while theta > 90: + theta -= 180 - while theta > 90: - theta -= 180 + xy = (x1 + x2) / 2 # the string is a comma separated list of the edge uid ax.annotate( - s, (x1 + x2) / 2, rotation=theta, ha="center", va="center", **params + s, xy, rotation=theta, ha="center", va="center", **params ) @@ -336,13 +350,17 @@ def draw( node_radius=None, edges_kwargs={}, nodes_kwargs={}, + edge_labels_on_edge=True, edge_labels={}, edge_labels_kwargs={}, node_labels={}, node_labels_kwargs={}, with_edge_labels=True, with_node_labels=True, - label_alpha=0.35, + node_label_alpha=0.35, + edge_label_alpha=0.35, + with_additional_edges=None, + additional_edges_kwargs={}, return_pos=False, ): """ @@ -410,6 +428,8 @@ def draw( radius of all nodes, or dictionary of node:value; the default (None) calculates radius based on number of collapsed nodes; reasonable values range between 1 and 3 nodes_kwargs: dict keyword arguments passed to matplotlib.collections.PolyCollection for nodes + edge_labels_on_edge: bool + whether to draw edge labels on the edge (rubber band) or inside edge_labels_kwargs: dict keyword arguments passed to matplotlib.annotate for edge labels node_labels_kwargs: dict @@ -418,14 +438,16 @@ def draw( set to False to make edge labels invisible with_node_labels: bool set to False to make node labels invisible - label_alpha: float - the transparency (alpha) of the box behind text drawn in the figure + node_label_alpha: float + the transparency (alpha) of the box behind text drawn in the figure for node labels + edge_label_alpha: float + the transparency (alpha) of the box behind text drawn in the figure for edge labels """ ax = ax or plt.gca() if pos is None: - pos = layout_node_link(H, layout=layout, **layout_kwargs) + pos = layout_node_link(H, with_additional_edges, layout=layout, **layout_kwargs) r0 = get_default_radius(H, pos) a0 = np.pi * r0**2 @@ -448,18 +470,26 @@ def get_node_radius(v): polys = draw_hyper_edges(H, pos, node_radius=node_radius, ax=ax, **edges_kwargs) + if with_additional_edges: + nx.draw_networkx_edges( + with_additional_edges, + pos=pos, ax=ax, + **inflate_kwargs(with_additional_edges.edges(), additional_edges_kwargs) + ) + if with_edge_labels: labels = get_frozenset_label( H.edges, count=with_edge_counts, override=edge_labels ) draw_hyper_edge_labels( - H, + H, pos, polys, color=edges_kwargs["edgecolors"], - backgroundcolor=(1, 1, 1, label_alpha), + backgroundcolor=(1, 1, 1, edge_label_alpha), labels=labels, ax=ax, + edge_labels_on_edge=edge_labels_on_edge, **edge_labels_kwargs ) @@ -477,7 +507,7 @@ def get_node_radius(v): va="center", xytext=(5, 0), textcoords="offset points", - backgroundcolor=(1, 1, 1, label_alpha), + backgroundcolor=(1, 1, 1, node_label_alpha), **node_labels_kwargs ) diff --git a/tutorials/Incidence Visualization.ipynb b/tutorials/Incidence Visualization.ipynb new file mode 100644 index 00000000..98c1820a --- /dev/null +++ b/tutorials/Incidence Visualization.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "try:\n", + " import hypernetx as hnx\n", + "except ImportError:\n", + " print(\"Installing HyperNetX.........\")\n", + " !pip install hypernetx --quiet 2> /dev/null\n", + " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", + " exit()\n", + "\n", + "import warnings\n", + "warnings.simplefilter(action='ignore')\n", + "\n", + "### GraphViz is arguably the best graph drawing tool, but it is old and tricky to install.\n", + "### Uncommenting the line below will get you slightly better layouts, if you can get it working...\n", + "\n", + "# from networkx.drawing.nx_agraph import graphviz_layout as layout" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NpkDp9lEjgHExMYNCjaDBJMhkD6cXwIk5EPg8wZ9BHw0bNuTy5csULFiQOnXqMGHCBDkz9Qt048YNBgwYgJ2dHT169MDGxoYtW7bg5+fHzz//HOfol7FsvLNRr8UdWqXlwP0DvA59Ha/23bt358CBA0DUsXVxHa8IsHHjRgoXLkyhQoXo2LEjy5Ytk8clhPhCSQAUmJub07t3b1avXs3r16+Tr+O0WaBqf6jyPbx5APvGRD0jqO8WMwmQK1cu9u/fz/jx4/n555+pW7cuDx8+TPJ+RdIKCQlh1apVVKtWjeLFi7Nx40ZcXV3x8vJiz549fPvtt5ibmxu7zPcopbj16hY6pdPrOp3SxXsauGPHjly8eJGgoCCuXLnyweMV3d3dY15v1KgRb968kVFyIb5QEgAFAL169UKj0fD9998n/0/8ucpGjQY61oYbW2D/z/DCM8m7NTU1ZcyYMRw6dIh79+5RunRpdu7cmeT9CsOLPo0jV65cdO7cGSsrKzZu3Mj9+/eZMmUKDg4Oxi7xg0K1oYREJuxZ2Och8Rs1z5YtG19//TU+Pj5s3LgxzuMV79y5w9mzZ2nXrh0Q9chEmzZtcHd3T1BtQoiUTQKgAKJOBFm8eDFr1qzBzc0t+Qswt4JSbaDOWDCzgMNT4cIKCNNjr8EEqlGjBleuXKFq1ao0bdqUwYMHExYWluT9isQJCwtj3bp11KpViyJFirBq1Sp69uzJ3bt32b9/P61atcLCwsLYZX6SlakVVqZWCbo2q1X8z/dt06YNPj4+HDhwIM7jFd3d3YmMjMTOzg4zMzPMzMxYuHAhmzdv5s2bNwmqTwiRckkAFDHat2+Pq6srAwcO5Pz588YpIlNeqDUKynaFpzfg0GR4cCbJF4lkyZKFbdu2MXv2bBYsWICzszP37t1L0j5Fwty7d4/hw4eTO3du2rdvD8DatWt5+PAh06dPp0CBAkauMH4CwwPZ5b2LQYcGEabV/wcOE0xwyBj/kc2SJUui0+nQ6XSxjleMjIzkjz/+YMaMGVy+fDnm68qVK9jZ2bFu3Tq96xNCpGwSAMV7fv/9d0qVKkXLli2Nt02KiQk41oA6Y8CuDNzaBeeXJ/kiEY1Gw4ABA7h8+TK5cuWia9eu/PXXX0nap4if8PBwNm3aRL169ShQoABLly6lU6dO3Lp1i8OHD9OuXTssLS2NXeYnvQt/xw6vHfQ/2J+aG2oy4tgIXoS8oJG9fpszm2pMqZW3FpmtM8f7mpcvX/L1119z5cqVWKued+7cSUBAAD169KB48eLvfbVo0UKmgYX4AslRcOI9lpaWbNq0ifLly1O7dm327t2LnZ2dcYqxyhC1ujhd9qjtYg5PgcquUV/mCZsyi48iRYqwbds2IGpRwbBhw5gwYQJp035gVbNIMj4+PixZsoRly5bx9OlTnJ2d+eOPP2jZsuVnc6zf2/C3HH5wGA9fD04+PkmELoJS2UoxsOxA6uerT850OYnQRuDz1gfPAM94bQVjojHh+9Lf61XHs2fPyJo1a5znGLu7u1OvXj0yZswY670WLVowffp0rl69SsmSJfXqUwiRcslRcCJOt27don79+lhYWODh4RFzYojRRITAsRlwfBbY5InahDp/7WTp2tnZmVevXrFhwwb5BzAZREZGsmPHDtzc3PDw8CBDhgx06tSJPn36ULx4cWOXFy9vwt5w6MEhPHw9OOV/ikhdJGWyl6FBvgbUy1cP27SxzxZ+FPiIDrs68Drs9QdDoMk/kzZTq0/la8ev9app5syZvH79mgkTJuj/gYQQXxwJgOKD/Pz8aNCgAW/evGHv3r2UKlXK2CXB8zuwcwj4HYcSraDhlKgRwiTk4+ND8+bNuXPnDjNnzqRv375o/r25tTCI+/fvs3TpUpYuXYq/vz+VKlWiT58+tGnThjRp0hi7vE96E/aGg/cP4uHnwWn/02h12qjQZ9+AennrkSNtjk/e42XIS0YfH82Jxycw1ZiiUCilMNWYEqkiyZEmB7/U+IVyOcrpV9ubN3z//fe0a9eOr7/WLzgKIb5MEgDFRz179oyvvvoKLy8vdu3ahbOzs7FLiloQcmUd7B0ddaxcvXFRZxObJN0jraGhoQwbNoz58+fTokULlixZQqZMmZKsv9RCq9Wye/dutmzZwrVr17C2tuarr76iRYsWFCpUyNjlfVJAaAAH7x9kn98+zvifQau0lMtRjvr56lMvXz2yp0nYDyd3A+6yx2cPD949IDgimNzpc1M5Z2Vq5K6BqYn+p5b89ddfbN26lfnz55MuXboE1SSE+LJIABSf9PbtW5o1a8bZs2f5888/U84IQvCrqM2jL62G3BWijp/LmrQrQLdu3Ur37t3JmDEj69ato0qVKkna35fq0aNHuLu7s3TpUgA8PT0/i0UcAK9CX3Hg/gH2+e7j7JOzKBTlc5SPCX1ZreO/NUty0Ol0DBgwgBIlStCnTx9jlyOESCEkAIp4CQ0NpU2bNuzevZuVK1fGbL+RIvidhJ2Dwcwa+hxO+u78/Gjfvj1nzpxh0qRJDB8+HJMkHH38Umi1Wjw8PHBzc2Pnzp1YWVnRrl07Bg8eTNGiRY1d3ke9DHnJgfsH8PDz4PyT8ygUFXJUoIF9A+rkrZPiQt+/Xbhwgd9++43Jkyfj6Oho7HKEECmEBEARb5GRkfTs2ZM//viDuXPn0q9fP2OX9H+R4XB1A5Tt9NFmjx494scff+Tvv/8mODgYJycnli9fTvny5fXrLjKScePGMXXqVOrWrcuqVauwtY39YL+AJ0+esGzZMpYsWYKvry+lSpWiT58+dOjQgQwZMhi7vA96EfKCA37/hL6n59GgoYJtVOirm7cuma3ivwWLsQQHBzN69GgyZszI+PHjjV2OECIFkQAo9KLT6Rg2bBgzZ85kwoQJjBkz5rNZEBEQEECZMmWoXbs2Li4uZMuWDU9PT/Lnz0/+/PkTdM/9+/fTsWNHlFKsWrWKBg0aGLjqz5NOp+PAgQO4ubmxbds2zM3NadOmDX369KFSpUop9vfM8+Dn7L+/Hw9fDy48vYCJxoRKOSvRIF/USF8mq8/nuU+lFLNmzeL69etMmTKFHDk+vQhFCJF6SAAUelNKMWXKFH766Sf69+/PrFmzPosp0BEjRnDixAmOHTtm0Ps+ffqULl26sHfvXkaMGMHPP/+Mubm5Qfv4XDx79owVK1awePFivLy8KFasGH369KFTp07Y2NgYu7w4PQt+xj6/fXj4enDp2SVMNaZUsqtEw3wNqZ2nNjZWNsYuMUH27t3Lpk2b6NevH2XKlDF2OUKIFEYCoEiwRYsW4erqSocOHVi2bFmKDz1FixalYcOGPHz4kCNHjpArVy5cXV3p1atXou+t0+mYMWMGo0aNonz58qxbtw57e/vEF/0ZUEpx+PBh3Nzc2LJlCyYmJrRq1Yo+ffrg7OycIkf7ngQ9Yb/ffjz8PLj87DKmJqZUyVmFBvYNqJ2nNhktY2+I/Dnx9vZm8eLFVK9encaNGxu7HCFECiQBUCTKhg0b6NSpEw0bNmTjxo0p+nQGK6uo00OGDBlCq1atOHfuHAMHDmTRokV06dLFIH2cPn2adu3aERAQgLu7Oy1atDDIfVOily9fsnLlShYvXsydO3coVKgQffr0oXPnzmTJksXY5cXyJOgJHr4eePh5cOX5FcxNzKlqV5UG9g2ombvmZx/6om3YsIHBgwdTpkwZ/vrrrxT/g5kQwkiUEIm0Z88elSZNGlW9enUVEBBg7HI+yNzcXFWpUuW91/r3768qV65s0H4CAgJUy5YtFaD69u2rgoODDXp/Y9LpdOrYsWOqQ4cOytLSUpmbm6u2bduqQ4cOKZ1OlyR95suXTwGxvlxdXT957aN3j9SK6ytU+13tVfEVxVWZP8qo7w98r7bf267ehr1NknqNJSQkRPXp00cBqkuXLiooKMjYJQkhUjAZARQGcerUKRo3bkzevHnZu3dvinzgPF++fNSvXz9m7zmAhQsXMmnSJB49emTQvpRSLF68mEGDBlGgQAE2bNhAkSJFDNpHcgoICGDVqlW4ublx8+ZNnJyc6N27N127diVbtmxJ2vfz58/Rav9/NNr169epX78+hw4dolatWh+8btSxUezw3oGFiQXVclWjvn19auWuRTqLL28jZG9vb1q1asWNGzeYN28ePXr0SJFT70KIlCPlP7kvPgtVqlTh6NGjPHv2jGrVquHr62vskmJxdnbmzp0777129+5d8uXLZ/C+NBoNffr04ezZs0RGRlK+fHmWLVvG5/TzllKK06dP061bN3LlysXQoUMpWrQo+/bt486dO/zwww9JHv4AsmXLhq2tbczXzp07yZ8/PzVr1vzodVmssvBL9V842vYos+vMpoljky8u/Gm1WubOnUvJkiV5/fo1p06domfPnhL+hBCfZtTxR/HF8fLyUvnz51d2dnbq2rVrxi7nPWfPnlVmZmZq8uTJytPTU61Zs0alSZNGrV69Okn7DQoKUj179lSAat++vXrz5k2S9pdYb968UfPnz1clS5ZUgLK3t1dTpkxR/v7+xi5NhYWFqSxZsqjJkycbuxSju379uqpSpYoClIuLi3r9+rWxSxJCfEYkAAqD8/f3VyVLllSZMmVSp06dMnY579mxY4cqXry4srS0VIULF1aLFy9Otr7XrVun0qdPr/Lnz6/OnTuXbP3G17lz51TPnj1VmjRplKmpqWrevLn6+++/lVarNXZpMTZs2KBMTU3Vo0ePjF2K0YSGhqpx48Ypc3NzVahQIXXs2DFjlySE+AzJM4AiSbx+/ZomTZpw+fJltm7dSv369Y1dUorg5eVF27ZtuXLlCr/88guDBg0y6nRdYGAga9euxc3NjYsXL5InTx569uxJjx49yJUrl9Hq+pCGDRtiYWHBjh07jF2KUURP8d69e5cRI0YwevTomNXtQgihD3kGUCQJGxsbPDw8qFmzJo0bN2bTpk3GLilFyJ8/PydOnGDAgAEMGTKEpk2b8uLFi2Sv4/Lly7i4uGBnZxfz3x07duDj48PYsWNTZPjz8/Nj//799OzZ09ilJLt3794xYMAAnJ2dSZcuHRcvXmTixIkS/oQQCSYjgCJJRURE0LVrV9atW8eiRYvo3bu3sUtKMXbv3k2XLl2wsLBg7dq1n1zUkFhBQUFs2LABNzc3zp49i52dHT169KBnz57kzZs3Sfs2hPHjx+Pm5saDBw8wMzMzdjnJZvfu3fTt25eXL18yefJk+vfvj6mpqbHLEkJ85mQEUCQpc3NzVq1aRb9+/ejTpw9Tp079rFbCJqWvv/6aK1euUKhQIerUqcP48ePf2+7EUK5fv07//v3JlSsXPXv2JHPmzGzduhU/Pz9+/vnnzyL86XQ6li9fTpcuXVJN+Hv+/DkdOnSgcePGFClShOvXrzNo0CAJf0IIwzDqE4gi1dDpdGrcuHEKUEOHDk2yTYM/R5GRkernn39WJiYmqkaNGurBgweJvmdwcLBauXKlqlq1qgJUjhw51MiRI5W3t7cBKk5+e/fuVYC6c+eOsUtJcjqdTq1atUplyZJFZc6cWf3xxx/y50UIYXAyBSyS1Zw5cxg4cCDdunVj8eLFqWY0Jz6OHTtG+/btCQkJYcWKFTRp0kTve9y+fRs3NzdWrlxJQEAA9erVo0+fPjRr1gwLC4skqFoYkp+fH3369GHv3r20a9eOWbNmkT17dmOXJYT4AskUsEhWAwYMYPXq1fzxxx+0atWK0NBQY5f0SYfvPEuWfqpXr87ly5dxdnamadOmDB48mLCwsE9eFxYWFvMMYZEiRVi9ejU9e/bE09OTffv20bJlyxQd/uRn0KgNnWfPnk2xYsW4ceMGO3bsYO3atRL+hBBJRgKgSHYdOnRg27Zt7Nmzh6+//pq3b98au6SPmuFxh+1XHidLX1myZOGvv/5i9uzZLFiwgKpVq+Lp6RlnW09PT3744Qdy585Nhw4dMDExYd26dTx8+JDp06fj5OSULDUnxuvgcH7Zc9vYZRjV9evXcXZ2ZvDgwXTr1o2bN28maPRXCCH0IQFQGEXjxo3x8PDgwoUL1KlTh+fPnxu7pA+qVSg7g9ZfYkcyhUCNRsOAAQM4deoU7969o2zZsqxduxaA8PBwNm7cSN26dSlYsCDLli2jU6dO3Lp1i0OHDtG2bVssLS2Tpc7EOu75gkazjnHpfoCxSzGKsLAwxo4dS5kyZXj79i3Hjx9n7ty5pE+f3tilCSFSAXkGUBjV5cuXadSoUcy+gSlxRapWp/hh0xW2XXnM7LalaVLSLtn6fvfuHa6urqxevZoSJUrw5MkTnj9/TrVq1ejTpw8tW7b87PaCC43Q8sue2yw/4YuzUxZmtSlNtvSf12dIrBMnTtCzZ0+8vLwYOXIko0aN+myCuxDiyyBP4AujKl26NMePH6d+/fo4Ozuzb98+ChcubOyy3mNqouHXVqVQwMD1l9GgoXHJnEneb0REBPv27ePZs6hnEK9du0amTJn4888/adGiRZL3nxSuP3rD4A2X8XsVzNgmRela1R4TE+OdhJLc3r59y8iRI1mwYAGVKlXi4sWLFC9e3NhlCSFSIZkCFkbn5OTE8ePHyZgxI9WrV+f8+fPGLikWUxMNv7UqRdOSORmw/hK7r/knWV9+fn6MGTOGfPny0aJFC96+fcvy5cu5dOkSefPmpUOHDixYsOCzWjyh1SnmH7rHtwtOYG5qwq7+1ehezSFVhb+dO3dSrFgxVq5cyezZszlx4oSEPyGE0cgUsEgxXr16xddff82NGzfYvn07tWvXNnZJsWh1iiEbL7Pzqj/z2pXhqxKGGQnU6XR4eHiwYMECdu3aRdq0aenYsSN9+vShVKlSMe1CQ0P54YcfmDdvHt999x1Lly4lU6ZMBqkhqTx4FczgDZe5cD8Al5r5GVSvIBZmqednz2fPnjFw4EDWr19Po0aNWLRoEfny5TN2WUKIVE4CoEhRAgMD+e677zhy5AgbNmygefPmxi4plkitjiEbr7D7mj/z2pehUfGEh8CXL1+yfPlyFi5ciLe3N6VLl8bFxYX27duTLl26D173119/0b17d9KnT8+6deuoWrVqgmtIKkopNl14yITtN8iU1oKZbUpTwT6zsctKNkop/vjjD4YMGYJGo2H27Nm0b98ejSb1jHoKIVIw4+w/LcSHhYaGqlatWikTExO1bNkyY5cTp4hIrfp+7UWVf+Qu9fc1f72u1el06syZM6pLly7K0tJSWVhYqI4dO6qTJ0/qdeKDn5+fcnZ2VqampmrKlClKq9Xq+zGSzIt3oarXynMq34871bCNl9XbkHBjl5SsvL29Vf369RWgOnTooJ49e2bskoQQ4j0SAEWKFBkZqfr06aMA9dtvvxm7nDhFRGpVvzUXVP6Ru9Se6/ELgVevXlUVKlRQgLK3t1e//PJLosJBRESEGj16tNJoNKpevXrK31+/MJoUDtx6ospN3KdKT9ir/r722NjlJKvIyEg1Y8YMlSZNGpUnTx61e/duY5ckhBBxkgAoUiydTqdGjRqlADVixIgUeR5qRKRWuf4TAvd+IgSuXLlSWVtbq5IlS6qdO3eqyMhIg9Wxf/9+ZWtrq7Jnz6727t1rsPvqIygsQo3aclXl+3Gn6rLsjHr6NsQodRjLlStXVIUKFZRGo1EDBgxQb9++NXZJQgjxQRIARYr322+/KUD17t3boKHJUCIitcp19QXlNGqX8rjxJNb7ISEhqlevXgpQ3bp1U8HBwUlSx9OnT1XDhg0VoH788UcVHp58064X/V6pWr8eUoV/+lutOuWbIsN6UgkJCVGjR49WZmZmqmjRourkyZPGLkkIIT5JAqD4LCxbtkyZmJioVq1aqdDQUGOXE0t4pFa5rD6vnEbtUvv+FQK9vLxUmTJllJWVlXJ3d0/yOrRarZo+fboyMzNTlStXVj4+PknaX3ikVv3ucUc5jtylms07rryevUvS/lKaI0eOqIIFCypzc3M1fvx4FRYWZuyShBAiXmQVsPhs/PXXX7Rt25YaNWqwZcuWj66SNYYIrY4B6y6x/9ZTFnYoR4jXWTp37kzmzJnZvHkzpUuXTrZazpw5Q9u2bQkICMDd3T1JNo72fh7I4I1XuP7oDf3rONGvthPmpqlje5c3b94wYsQIFi1aRJUqVVi6dClFixY1dllCCBFvEgDFZ+XQoUN88803FC1alF27dpElSxZjl/SeCK2O/msv4XHjMU/+nEiD4nasWLECGxubZK/l9evX9O7dm02bNtG3b19+//13rK2tE31fpRRrztxn8q5b2Ga04vfWpSiTN2XvRWhI27dvx9XVlTdv3jB16lRcXV0xMUkdwVcI8eWQv7XEZ6V27docOnQILy8vatSowaNHj4xd0nvMTU2oEHmNwDunsW05hv5T3YwS/gBsbGzYsGEDbm5urFixgsqVK+Pp6Zmoez57F0r3Fef46a/rfFc2F7sGVEs14e/p06e0bt2ab775hlKlSnHjxg2+//57CX9CiM+S/M0lPjvlypXj2LFjvHv3Dmdn50SHGkO6evUq/fu50jjzU+oVzYnL6oscuv3MaPVoNBp69+7N2bNnCQkJoXz58mzZsiVB99pz/QkNZx7l2qO3LOtansnfliCNxZd/nLhSiuXLl1OkSBEOHTrE2rVr2blzJ3nz5jV2aUIIkWAyBSw+W/fv36dBgwYEBASwd+/eZH3GLi5v3ryhQoUKpEmThlOnTmFqbkm/tRc5cuc5bp3LUbtQdoP0ExwcjLm5Oebm5npd9/btW3r06MGff/7JkCFDmDZtWrzu8S40gp933GTThYc0KJqDqd+VIEs6y4SW/1nx8vKiT58+HDhwgE6dOvH777+TNWtWY5clhBCJJgFQfNaeP3/O119/zd27d9m5cyfVq1c3Sh1KKVq1asW+ffu4cOECTk5OAIRH6nBdc5Gjns9Z3KkctfQIgeHh4ezYsYPLly/j7e2Nt7c3Pj4+PH36FBMTE/LkyYOjoyOOjo44ODhQo0YNqlWr9tGjxpRSzJkzh2HDhlGpUiU2bNhArly5Ptj+nO8rBm+4TEBQOOOaFaNVudyp4iizyMhIZs2axdixY8mePTtubm40bNjQ2GUJIYTBSAAUn713797xzTffcOrUKTZt2kSTJk2SvYZZs2YxePBgtmzZwrfffvvee1Eh8AJHPV+wpHN5ahbM9tF73b9/Hzc3N5YuXcqzZ8/IlSsXDg4O74W90NBQfHx8YoLhvXv3eP36NcWLF8fV1ZWOHTuSPn36D/Zx8uRJWrdujVar5ezZs+TJkydWzTP332XRES/K5c3E761LkzdLmoR/gz4jly9fpmfPnly8eJGBAwcyceLEFLfiXAghEs0Ye88IYWghISGqefPmytTUVK1atSpZ+z5+/LgyMzNTQ4cO/WCb0IhI1X35WVVg9G515E7cR7+dO3dOffPNN8rExESlT59eff/99+rGjRvxqkGr1ap9+/apb7/9VpmYmKh06dIpV1fXjx4N9+TJE5UnTx5VuXLl9/avu/Pkrfpq1lGVf+QuNe+gp4rUpo5NnYODg9WIESOUqampKl68uDp9+rSxSxJCiCQjAVB8MSIiIlT37t0VoGbPnp0sfb57907lypVLVatW7ZMnb4RGRKpuy8+qgqN3q6N3/x8CdTqdmjNnjjI3N1dFixZVixYtUu/eJXxD5fv376sxY8aorFmzKltbW3X48OEPtj19+rQyNzdXAwcOVFqtTrkf81YFRu9WdWccVtcevk5wDZ+bw4cPqwIFCigLCws1ceJE2dBZCPHFkwAovig6nU4NGzZMAWrs2LFJfiSZm5ubMjExUd7e3vFq/+8QeOzuc/X27VvVpk0bBahBgwYZ9Pg2f39/VatWLWVqaqp++eWXD34v5s6dq0zTZ1H1Jm1T+X7cqcZvv65CwlPekXtJISAgQPXu3VsBytnZWd28edPYJQkhRLKQZwDFF0cpxS+//MLIkSP5/vvvmT17dpLs1aaUonTp0tjb27Nt27Z4XxcWqaXvqgucuPcCdXQhT68cYdmyZbRs2dLgNUZGRjJ27FimTp1Ks2bN+OOPP8iYMeN7bXZcecSg1WeICAli2nfFaV+njMHrSIm2bt1Kv379CAwM5JdffqFPnz6yp58QItWQACi+WIsXL6Zv3760bduWlStX6r1tyqecPHkSZ2dn9uzZo/cK0Yf+T3EevgKNbSF+aWxPm1qlDVrbf+3cuZOOHTtSrVo1tm/fjomJCW+CIxi7/TrbLj+mUdFsHP6tL2a6cM6dO2eQE0NSKn9/f/r378/mzZtp0qQJCxYsiLUIRgghvnTy4674YvXu3ZuNGzfy559/8s033xAcHGzQ+y9YsAAnJyfq16+v13VarZYeXTsTsn82lR0yM+6APyfvvTBobf/VpEkTNm3axMmTJ5k1axZnvF/Syu0k531fsaBDGRZ2qsDmdat4/PgxmzdvTtJajEUphbu7O0WLFuXo0aOsX7+e7du3S/gTQqRKEgDFF61ly5bs2rWLo0eP0qBBA16/fm2Q+z579oxNmzbh4uKi97ThpEmT2LdvH+tWr2JFL2cqOmSh+8pznPRK2hBYv3591q5bz9v0eTl4+xldq9ize0B1vi5hh0ajoVixYvz222+8fPmSL21i4N69e9StW5eePXvSvHlzbt26RZs2bVLFnoZCCBEXCYDii1e/fn0OHDjArVu3qFmzJv7+/om+57JlyzAxMaFr1656Xefh4cGECRP4+eefqVu3LlbmpizuVI4K9pnpvuIcp7xeJrq2D3n0Opib5CXEJj8vbp6gUcH0ZExj8V6bcuXKce3aNXx8fJKsjuQUGRnJ9OnTKVGiBD4+Pnh4eLB8+XKyZMli7NKEEMKoJACKVKFSpUocPXqUFy9eUK1aNby9vRN8L61Wy6JFi2jXrh2ZM2fW67q+fftSv359Ro0aFfO6lbkpSzqXjwmBp70NGwJ1OsX+m0/5be8dwnWKHpVzE+x1gY0bN8ZqW6JECdKkScPevXsNWoMxXLp0iYoVKzJy5EhcXV25fv263tP1QgjxpZIAKFKNYsWKceLECUxMTKhWrRrXrl1L0H0OHjyIn58fLi4uel23d+9efHx8mDhxYqxp4+gQWN4+E92WGy4EvgwMY+5BT7ZffUytQtkZ2qAQBfNko1GjRpw6dYp37969197ExIR69erF+d7nIiQkhB9//JEKFSqg1Wo5ffo0M2bMIG3atMYuTQghUgwJgCJVsbe35/jx4+TIkYMaNWpw6tQpve9x9epV0qZNS/ny5fW6bsGCBZQrV44KFSrE+X50CCyXLyoEnvlACNTp1Cef0VNKcd73FdP+vs2roAj613bim9K5MDeN+iNfq1YtdDodR48ejXVtrVq1UEpx5MgRvT5fSnDo0CFKlCjB7Nmz+fnnnzl//vwHv99CCJGaSQAUqU6OHDk4fPgwJUqUoF69enpPd3p7e+Po6KjXAgIfHx92796Nq6vrR6+LDoFl8trQbcU5zvq8AsDz6TvGbrtOtV8OUuCnvykydg8NZh5h/qF7vAwMe+8er4PC2XrpIZvOP6Rk7oz8+FUhCuR4/1zgjBkzUrlyZfbt24dOp3vvvQwZMlC+fPkEhWNjCQgIoGfPntSpUwc7OzuuXLnCqFGjDL71jxBCfCkkAIpUKWPGjOzdu5c6derQtGlTNmzYEO9rfXx8cHR01Ks/Nzc3MmbMSNu2bT/Z1trCFPcuFSidx4Yuy84wYvNVGsw8ypoz93kYEIJWpwiN0HH3aSAzPO5Q89fDHLz9FIDDd57RYtFJ/F6F0KFSXjpVsSeNhVmsPp48ecKZM2dwd3fH2tqaHDly4OzszMKFCwkODsbe3p45c+ag0WjQaDSkTZuWsmXLsmnTJr0+d3LYvHkzRYsWZdOmTSxatIjDhw9TqFAhY5clhBApmgRAkWpZW1uzZcsW2rRpQ7t27Vi4cGG8rvP29sbBwUGvvg4cOMA333xDmjRp4lfbPyHQJo0F6889QAFaXexpX52CoPBIuq84T9dlZ+m6/BwFcqSnZzUHSuax+WD9ZcqU4dy5czg7OzNp0iROnTrF8OHD2blzJ/v37yd79uzodDp++ukn/P39uXTpEhUqVKBNmzacPHlSr8+eVB4/fsx3331Hy5YtqVSpEjdv3pTTPIQQIp7kb0qRqpmbm7Ny5UoGDBiAq6srkydP/ujzdTqdLkEjgF5eXhQsWFCva056vcD/Tegn20WXe/juc4Y1KMjCDmVJb/XhqU9XV1fMzMw4f/481atXx9zcHEdHR7755ht27dpF06ZNyZ49e0x7W1tbChYsyPz587G2tmbHjh16fQ5D0+l0LF68mCJFinDy5Ek2bdrE1q1byZUrl1HrEkKIz0nsuSEhUhkTExNmzpxJlixZ+Omnn3j58iW//fZbnCNJjx8/Jjw8XK8AGBAQQEBAAPnz59errnkH72GiiRrliw8TDYRF6j76jOHLly/x8PBgypQppE2bluzZs+Pr6/teG41GQ7Zs2QAIDAyMed3MzAxzc3PCw8P1+hyGdPfuXXr37s2RI0fo3r07v/76q15b8QghhIgiI4BCEBV6xowZw7x585g1axbdu3cnMjIyVrvoDZL1mQKO3nNQnwD46HUIlx68jnf4g6iguPnCw4+2uXfvHkqpmGfksmfPztOnT8maNSvp0qUjXbp0/Pjjj6RPnx4TE5OYrWDCw8OZOnUqb968oU6dOvEvykAiIiKYNm0aJUuW5MGDB+zfvx93d3cJf0IIkUAyAijEv/Tr14/MmTPTuXNnAgICWL9+PdbW1jHvR4c5e3v7eN/Ty8sLQK9RQ98XQfFu+2/+b0MJj9RhYRa/n+2yZ89OUFAQhw8fxsrKig4dOhAWFoZGo8HExISVK1eyfv16QkNDSZcuHdOmTaNx48YJqi2hLly4QM+ePbl69SpDhw5l/Pjx8X6WUgghRNwkAArxH+3atcPGxoYWLVrw1VdfsW3bNjJmzAhETYmamZlhaWkZ7/t5e3tjY2Oj12hVUFjs0cf4UArCIrUfDIBOTk5oNBru3LkDgI2NDQCZM2fGzs7uvbCr0WioV68ec+fOJV26dOTIkSNZz84NDg5m3Lhx/P7775QoUYKzZ89Srly5ZOtfCCG+ZDIFLEQcvvrqK/bt28eVK1eoXbs2z549A6JG/iIjI3n8+HG87+Xl5aX383+5MyVshCudpelHF4BkyZKF+vXrM2/ePIKCgjA1NQWItRdg9Gs5cuTAyckJW1vbZA1/Bw4coESJEsydO5fJkydz7tw5CX9CCGFAEgCF+ABnZ2eOHDmCv78/1atXx8/PL2YaN/pZwPiI3jhaH07Z05EpjX6bGJuaaKjqlPWT7RYsWEBkZCTly5fn77//5s2bN3h6erJ69Wpu376Nqakp4eHhaLVa0qVLp1cNifXq1Su6d+9OvXr1yJMnD9euXWPEiBGyobMQQhiYBEAhPqJkyZIcP36ciIgIqlWrRmho1LYs0c8CxkdCRgAtzEzoVMUeEz0G3bQ6Rdeq9p9slz9/fi5dukS9evWYMWMGe/fupWnTpsydO5dhw4YxceJEnj9/DpBsAVApxaZNmyhatChbtmxh8eLFHDx4kAIFCiRL/0IIkdpIABTiE/Lnz8/x48exsbGhfv36ZMuWLd4jgOHh4Tx48EDvAAjgUjM/jtnSYRqPFGiigbYV8lA1/6dHAAFy5szJ3LlzOXr0KK1bt+bq1aucOXOGYcOGkSZNGp49e0azZs344Ycf9K5bX48ePeLbb7+ldevWVK1alZs3b9KrVy/Z0FkIIZKQ/A0rRDzY2dlx9OhRChYsyMuXL+N9Goafnx86nU7vKWCIOg1kWZcK5M2c5pMjgfWK5mB8s2J69xEdsv77DOCzZ88wMzMjU6ZMet8zvnQ6HYsWLaJo0aKcOXOGzZs3s2XLFuzs7JKsTyGEEFEkAAoRT5kyZWLfvn3Y2tpy4MABtmzZ8slroreAScgIIEDeLGnYNaAaPas7ktbSNNb7uTNZM/nb4rh1LIeVeez3PyV6Ycd/A2D03oBJNQp3584datWqhYuLC61bt+bmzZt89913SdKXEEKI2CQACqGHtGnT0r17dywtLWnVqhVLly79aHsvLy/Mzc3JnTt3gvtMY2HGqK+LcGhoLQDaVczD3HZl2PF9NY7+UJsOlfIleIVuXCOAOp2Os2fPUrhw4QTX/CERERFMnjyZUqVK8fjxYw4ePMiSJUuSdKRRCCFEbLIPoBB6KliwIKGhoXTt2pVevXrx6tUrhg8fHmdbb29v7O3tY7ZbSYyHr0MA6FApH8VzZUz0/SDuAHjp0iVevnxJ/fr1DdJHtHPnztGjRw9u3rzJsGHDGDdu3Hv7DgohhEg+MgIohJ6++uorLC0tKVKkCGPGjOHHH3/kxx9/RKnY57YlZAXwh9x7GohGA/mzGW5lbnQA/Hft+/btI3/+/Al6bjEuQUFBDBkyhMqVK2NmZsbZs2eZNm2ahD8hhDAiGQEUQk9Zs2alTZs2LFq0iHv37pE5c2YGDx7Mq1evWLRo0XujfV5eXtSsWdMg/d59+o68mdNgbZH40cRo/x0BfPLkCVeuXMHFxcUg9/fw8KBPnz48efKEqVOnMmTIEMzM5K8dIYQwNhkBFCIBXF1d8fHxYe/evQwaNIiVK1eyfPlyWrduTVhYGBA1qpaQTaA/5O6zQApkT2+Qe0X7bwDcv38/6dKlo3Llyom678uXL+natSsNGzbEwcGBa9euMXz4cAl/QgiRQkgAFCIBKlasSNmyZVmwYAEAnTt3ZsuWLezatYvGjRvz7t07nj59SnBwsAGngN9RIIdhN2Y2NTUlbdq0aDQawsLCOHv2LA0aNMDCwiJB91NKsWXLFooWLcq2bdtwd3fnwIEDODk5GbRuIYQQiSMBUIgE0Gg0uLq6smvXLnx9fQFo1qwZe/fu5dy5c9StW5cLFy4AGGQE8F1oBI/fhFIwCQJg5syZMTU1ZdeuXaRNm5Y6deok6F6vX79m3rx5dOzYkerVq3Pz5k26d++erGcICyGEiB8JgEIkULt27ciQIQNubm4xr9WsWZPDhw/j6+tL7969AcMEQM9ngQAGnwKOiIhgyZIlrF69mh49elCoUCGyZMmi1z10Oh3u7u6ULFmSWbNmsWbNGv78809y5sxp0FqFEEIYjgRAIRIoTZo0dOvWjaVLl8acEQxQpkwZjh8/TlBQECYmJjx69CjRfSXFCmCIegbw9evXzJw5k6ZNm9KlSxe9rr916xY1atSgZ8+efPXVV1y4cIFvv/3WoDUKIYQwPAmAQiSCi4sLL1++5Lfffnvv9YIFC1KnTh0sLS2pVq0aFy9eTFQ/SbECGCA4OBiA7Nmzs3DhwnhP14aHhzNx4kRKly7Ns2fPOHz4MG5ubtjY2Bi0PiGEEElDAqAQiVCwYEHGjBnDuHHjOHDgwHvvPX36lCZNmmBvb0+tWrU4cuRIgvuJWgFs2NE/pRQDBgwAYMCAAaRNmzZe1505c4Zy5coxYcIEhg4dypUrVwy21Y0QQojkIQFQiEQaO3YsderUoX379u9N93p5eVGsWDEOHDhAxYoVadiwIe7u7gnqI2oFsOGe/wsLC8PV1ZXNmzcDUSOAnxIYGMigQYOoUqUKlpaWXLhwgSlTpsiGzkII8RmSAChEIpmamrJ27VrMzc1p06YNERERBAUF8fTpUxwdHUmfPj27du2iS5cu9OzZk+7du8dMvcaHoVcA+/n5Ub16dZYtWxazjY1Wq/3oNXv37qV48eIsXryYX3/9ldOnT1OqVCmD1COEECL5SQAUwgCyZcvGxo0bOXPmDCNHjsTb2xsgZg9AS0tL3NzcWLlyJevXr6dKlSp4enrG6973DLgCePfu3ZQpU4bnz59z8uRJ+vbtC3w4AL548YLOnTvTqFEjnJycuHbtGkOHDpUNnYUQ4jMnAVAIA6latSq//vorM2bMYPXq1QCxNoHu3LkzZ86cISQkhPLly7Nly5ZP3tfTACuAtVotP/30E40bN6Zq1apcuHCBcuXKodFo0Gg0sQKgUoq1a9dSpEgRdu7cybJly2LOCBZCCPH5kwAohAENHDiQFi1aMGfOHKytreN8tq5EiRKcP3+eBg0a0KJFC1q2bMmhQ4dQSsV5z8SsAA4NDWX16tVUrlyZqVOnMmXKFLZv307mzJlj2piZmb0XAO/fv0+TJk3o0KEDderU4ebNm3Tr1k02dBZCiC+IBEAhDEij0bBs2TKsra0JCwtjx44dcbbLkCEDGzduxN3dnZs3b1KnTh2KFSvG3LlzefPmzXttPROwAtjHx4cRI0aQJ08eOnXqhI2NDYcOHWLkyJEx5/9GMzU1RavVotVqmTdvHsWKFePy5cts27aNDRs2YGtrq983QQghRIonAVAIA8uQIQNlypQhR44cfPPNN4wYMYLIyMhY7TQaDd27d+fGjRscOnSI4sWLM2TIEHLlykWfPn04dOgQDx8+5G48VgCHh4fj6enJ1q1badKkCfnz52fRokV07NiR27dvs2/fPmrUqBHntaampjx69Ijq1avTv39/OnXqxM2bN2nWrJlBvh9CCCFSHo360LyTECLBChYsSJMmTbCzs2PEiBFUq1aN9evXf3I07fHjxyxduhQ3NzceP36MxsKavIM3YX5+DU7mATg6OuLg4EBoaCje3t74+Pjg7e3Nw4cP0el0QNRJJP369aNt27af3NsvLCyMjBkzEhkZiZOTE0uWLKF69eoG+z4IIYRImSQACmFgWq0Wa2trZs2ahaurK8eOHaNNmzYopVi/fn28Nk2OjIzk7t27HLziw29XoHb4GV55XYkJfFZWVjg6OsZ8OTg44OjoSP78+cmXL1+8ntc7deoUPXv2jJmC3rVrF1ZWVob4FgghhEjhZC8HIQzswYMHRERExKyYrV69OhcvXqRdu3bUqVOHKVOmfHIrFTMzM4oWLcr1oPRorl5lwbRxBjsG7s2bN4wZM4Z58+ZRvnx5bGxsqFevnoQ/IYRIReQZQCEMLHoPQEdHx5jXbG1t2bdvHz/++CMjRozA3t6eiRMn4u/v/9F7GfIM4CtXrtCnTx/s7Oxwd3dnxowZnDp1CktLy09uBC2EEOLLIgFQCAPz8vLCxMSEfPnyvfe6mZkZU6ZM4fLlyzRu3Jhp06aRN29eWrduzeHDh+PcBiYhK4D/LSwsjDVr1uDs7Ezp0qXZuXMnP/74I/fu3WPw4MGYmprGrAIWQgiRekgAFMLAvLy8yJMnDxYWFnG+X6pUqZhFHr///jvXrl2jdu3aFCtWjHnz5r23DYxnAs8A9vX1ZeTIkeTJk4eOHTtibW3N5s2b8fX1ZezYseTMmTOmrQRAIYRIfWQRiBAG1rp1a16+fMmBAwfi1V4pxeHDh1mwYAFbt27FxMQEBwcH8jkV4m6JPtRP/5ivi2aNWeiRIUMGlFI8e/YsZlFI9Ff0rx88eECGDBno2rUrffv2pXDhwh/s38HBgfbt2zN58mRDfQuEEEKkcLIIRAgD8/Lyoly5cvFur9FoqF27NrVr1+bRo0f89ddf3Lt3j+v+UWcAb1w6h6W+12PaZ86cmdDQUIKDg2Ney5o1a8xq4CpVqlC0aFG+/fbbT24DAzICKIQQqZEEQCEMSCmFl5cXrVq1StD1uXLlol+/fgBsPPeAH7dcxf/2JYLeBrw3yvfvbWAcHBxIn17/aeJoEgCFECL1kQAohAEFBATw5s2bmC1gEsPz2TvyZEpDGksz0mTLRrZs2ahUqZIBqnyfBEAhhEh9ZBGIEAbk5eUFYJAAePdpIAVzJHwFcHxJABRCiNRHAqAQBhTXHoAJldAVwPqSACiEEKmPBEAhDMjLy4vMmTNjY2OTqPu8C43g8ZvQRO0BGF8SAIUQIvWRACiEAXl5eRlk+vfes6gVwAVlBFAIIUQSkAAohAF5e3sbaPo3EI0G8meTEUAhhBCGJwFQCAMy1Ahg9ApgQ5wB/CkSAIUQIvWRACiEgYSFhfHw4cPPagUwSAAUQojUSAKgEAbi6+uLUsogU8D3ngUmywpgkAAohBCpkQRAIQzEUHsABoZF8uh1SLKsAAYJgEIIkRpJABTCQLy8vLCwsCBXrlyJuo/n03dA8qwABgmAQgiRGkkAFMJAvL29cXBwwMQkcX+sPJ8l3wpgkAAohBCpkQRAIQzEYCuAnybfCmCQACiEEKmRBEAhDMRQATA5VwCDBEAhhEiNJAAKYQBKKYNtAp2cK4BBAqAQQqRGEgCFMAB/f39CQ0M/uxXAIAFQCCFSIwmAQhhA9BYwiR0BTO4VwBAVACMjI5OtPyGEEMYnAVAIA/D29gYMEACTeQUwyAigEEKkRhIAhTAALy8v7OzssLa2TtR9knsFMICZmZkEQCGESGUkAAphAIZaAJLcK4BBRgCFECI1kgAohAEYaguYe88CccqefM//gQRAIYRIjSQACmEAhgiA0SuAZQRQCCFEUpMAKEQivXv3jufPnyd6Cvjes0AgeVcAgwRAIYRIjSQACpFI0SuAEzsCePfpu2RfAQwSAIUQIjWSAChEIkXvAZjYAGiMFcAgAVAIIVIjCYBCJJK3tzfp0qUja9asibqP57PkXwEMEgCFECI1kgAoRCJFLwDRaDSJuo/n0+RfAQwSAIUQIjWSAChEIn3OK4BBAqAQQqRGEgCFSCRDbAJtrBXAIAFQCCFSIwmAQiRCZGQkfn5+n+0KYJAAKIQQqZEEQCES4f79+0RGRn62K4BBAqAQQqRGEgCFSIToPQATOwVsrBXAIAFQCCFSIwmAQiSCl5cXpqam5M2bN1H3MdYKYJAAKIQQqZEEQCESwcvLi7x582Jubp7gexhzBTBIABRCiNRIAqAQieDt7Z3o5/+MuQIYJAAKIURqJAFQiEQwxB6AxlwBDBIAhRAiNZIAKEQCKaXw8vIyyB6AxloBDBIAhRAiNZIAKEQCvXz5knfv3hlkBNBYz/+BBEAhhEiNJAAKkUBeXl4AiQ6AGsDZKasBKkoYU1NTlFIopYxWgxBCiORlZuwChPhcGWoPwOXdKhqinAQzNY2aetZqtZiZyV8JQgiRGsgIoBAJ5OXlRdasWcmQIYOxS0mUfwdAIYQQqYMEQCESyBArgFMCCYBCCJH6SAAUIoG8vb0TPf2bEkgAFEKI1EcCoBAJJCOAQgghPlcSAIVIgJCQEB49eiQBUAghxGdJAqAQCeDr6wskfgVwSiABUAghUh8JgEIkQEL3ABw/fjwajea9r8KFCydFifEmAVAIIVIf2fRLiATw8vLCysqKnDlz6n1tsWLF2L9/f8yvjb33XnT/kZGRRq1DCCFE8pEAKEQCeHt74+DggImJ/oPoZmZm2NraJkFVCSMjgEIIkfrIFLAQCZCYFcCenp7Y2dnh6OhIhw4duH//voGr048EQCGESH0kAAqRAAkNgJUqVWLFihXs2bOHhQsX4uPjQ/Xq1Xn37l0SVBk/EgCFECL1kSlgIfSk0+nw8fFJ0Argr776KuZ/lyxZkkqVKpEvXz42btxIjx49DFlmvEkAFEKI1EdGAIXQ0+PHjwkLCzPIHoA2NjYULFiQe/fuGaCyhJEAKIQQqY8EQCH0FL0FjCH2AAwMDMTLyytBq4kNRQKgEEKkPhIAhdCTt7c3Go0GBwcHva8dNmwYR44cwdfXl5MnT/Ltt99iampKu3btkqDS+JEAKIQQqY88AyiEnry8vMiVKxdWVlZ6X/vw4UPatWvHy5cvyZYtG9WqVeP06dNky5YtCSqNHwmAQgiR+kgAFEJP3t7eCZ7+Xb9+vYGrSTwJgEIIkfrIFLAQekrMHoApkQRAIYRIfSQACqEnCYBCCCE+dxIAhdDDmzdvePnypUFWAKcUEgCFECL1kQAohB68vb0BZARQCCHEZ00CoBB6iN4DUAKgEEKIz5kEQCH04O3tTYYMGcicObOxSzEYCYBCCJH6SAAUQg/RC0A0Go2xSzEYCYBCCJH6SAAUQg9f2gpgkAAohBCpkQRAIfSQmE2gYzk8Dbb0Ncy9EkECoBBCpD4SAIWIp4iICO7fv2+4EcDgl/DkqmHulQgSAIUQIvWRAChEPPn5+aHVag0XAM2sIDLEMPdKBAmAQgiR+kgAFCKeovcANNgUsLk1RIQa5l6JIAFQCCFSHwmAQsSTl5cXZmZm5MmTxzA3lBFAIYQQRiIBUIh48vLyIl++fJiZmRnmhuZpIEICoBBCiOQnAVCIePL29jbsFjDmVhAZCkoZ7p4JYGIS9deABEAhhEg9JAAKEU8G3wPQzDrqv5Ep4zlACYBCCJF6SAAUIh6UUobdAxCiFoFAipkGlgAohBCphwRAIeLh+fPnBAYGGngKWAKgEEII45AAKEQ8eHl5ARh4Ctgq6r8yBSyEECKZSQAUIh6i9wB0cHAw3E3N00T9V0YAhRBCJDMJgELEg5eXF9mzZyd9+vSGu6n5PyOAKSAAmpmZSQAUQohURAKgEPFg8BXA8K8pYOMHQFNTUyIjI41dhhBCiGQiAVCIeDD4CmD41yIQeQZQCCFE8pIAKEQ8JMkIYEwADDbsfRNAAqAQQqQuEgCF+ITg4GD8/f2TYApYNoIWQghhHBIAhfgEHx8fAMNPAZtZApoUsQhEAqAQQqQuEgCF+IQk2QMQQKOJmgaWEUAhhBDJTAKgEJ/g5eWFtbU1tra2hr+5mZU8AyiEECLZSQAU4hOiVwBrNBrD39zcWlYBCyGESHYSAIX4hCRZARzN3DrF7AMoAVAIIVIPCYBCfIKXl5fhF4BEM7OWRSBCCCGSnQRAIT5Cq9Xi6+ubhCOAVjIFLIQQItlJABTiIx49ekR4eHjSBUAzK5kCFkIIkewkAArxEd7e3kAS7AEYzTyNjAAKIYRIdhIAhfgILy8vNBoN9vb2SdOBuWwDI4QQIvlJABTiI7y8vMiTJw+WlpZJ04GZbAQthBAi+UkAFOIjovcATDLmsgpYCCFE8pMAKMRHJOkegCABUAghhFFIABTiI5I8AMoqYCGEEEYgAVCIDwgICCAgICCJp4BlFbAQQojkJwFQiA+I3gImaaeArWQKWAghRLKTACjEB3h5eQFJHABlClgIIYQRSAAU4gO8vb2xsbEhU6ZMSdeJuTXoIkEbmXR9xIMEQCGESF0kAArxAUm+AASiAiAYfRRQAqAQQqQuEgCF+IBkCYBm/wRAIz8HKAFQCCFSFwmAQnxAkm8CDVGLQEACoBBCiGQlAVCIOISHh/PgwYNkmAJOE/VfIx8HJwFQCCFSFwmAQsTB19cXnU6XDFPA0SOAwUnbzydIABRCiNRFAqAQcYjeAzDpp4CjnwGUEUAhhBDJRwKgEHHw8vLC3Nyc3LlzJ21HsgpYCCGEEUgAFCIO3t7e2NvbY2pqmrQdmaWMEUAzMzMJgEIIkYpIABQiDsmyBQz8axWwPAMohBAi+UgAFCIOyRYAo0cAU8Aq4MhI455GIoQQIvlIABTiP5RSybMHIICpGZiYyz6AQgghkpUEQCH+4+nTpwQHByfPCCBELQSRACiEECIZSQAU4j+8vLwAki8AmlmliClgCYBCCJF6SAAU4j+i9wB0cHBIng5lBFAIIUQykwAoxH94eXlha2tL2rRpk6dDc2sZARRCCJGsJAAK8R/JtgI4mpmVbAMjhBAiWUkAFOI/km0F8D+UuTVBb17y5MkTlFLJ1u+/SQAUQojUxczYBQiR0nh5edGgQQOD3/f58+ccPXoUb29vfHx88Pb2xtvbm3mVH/MmREfrTmuwtrbGwcEBR0fH975q1apF+vTpDV5TNAmAQgiRukgAFOJfAgMDefr0qcGmgJVSnDp1igULFrBp0ybCw8NJnz49jo6OODg40LRpU5yynCCdtSV/dRzyXjjct28fPj4+hIaGkj59ejp37oyLiwvFihUzSG3/JgFQCCFSFwmAQvyLj48PQKKngAMDA1m7di0LFizgypUrODk5MXXqVNq1a4etrS0ajeb/jf/sDoHP+Oabb2LdRymFj48Py5cvZ8mSJcyfP5+aNWvi6upK8+bNsbCwSFSd0SQACiFE6iLPAArxL4ndAzA8PJxRo0aRK1cuXFxcsLe3Z+/evdy5c4chQ4aQM2fO98MffHQVsEajwdHRkYkTJ3L//n3Wr1+PUoo2bdqQL18+Zs6ciU6nS1Ct/yYBUAghUhcJgEL8i5eXF2nTpiV79ux6X/vgwQNq1qzJb7/9houLC97e3vz11180aNAAE5OP/FEzs4aIT28DY2FhQZs2bThy5AjXrl2jadOmDB06lLp163L//n296/03CYBCCJG6SAAU4l+iVwDHGqX7BA8PD8qWLcvjx485fvw406ZNI1++fPG72Fz/bWCKFy/O4sWLOXjwIF5eXpQsWZI1a9YkeBWxqakpOp3OaKuQhRBCJC8JgEL8i757AGq1WiZMmECjRo0oX748Fy9epGLFip+87vXr11y6dInNmzdz8txlAp77M3DgQGbNmsX27du5fv06QUFBn7xPrVq1uHr1Kk2aNKFjx460a9eOV69exbv+aKampgAGmU4WQgiR8skiECH+xcvLi2bNmsWr7bt372jVqhUeHh5MmDCB0aNHf3Sq9/nz5yxbtozFixfHHDcHMLZOeopUNGX//v14e3sTGvr/6eBSpUrh4uJChw4dSJcuXZz3tbGxYfXq1TRt2pS+fftSsmRJVqxYQb169eL5qf8fALVabcz/FkII8eWSEUAh/qHVavH19Y3XCKBSip49e3Ly5Ek8PDwYM2ZMnOFPKcXJkyfp2LEjuXPnZvz48dSoUYN169Zx9uxZXrx4wfhJ08iU1pIbN24QHBzM48ePOXHiBCtXrsTe3h5XV1fs7Ozo378/N2/e/GBNbdq04dq1axQuXJj69eszaNAgQkLid8bwvwOgEEKIVEAJIZRSSvn4+ChA7dmz55NtZ8+erQD1559/frDNy5cvVZMmTRSg8ufPr3777Tf14sWL2A3PL1dqXAaldLo47+Pn56dGjx6tsmfPrgDVt29fFRIS8sF+tVqtmjlzprK0tFRFixZVly5d+uTnWbdunQLUu3fvPtlWCCHE509GAIX4R/S07Kf2ADx16hRDhw5l8ODBtGjRIs4258+fp2zZspw8eZI///yTu3fvMnToULJkyRK7sZl11H8jw+K8V968eZk0aRIPHjxg3rx5LF++nGrVquHr6xtnexMTEwYNGsT58+cxNzenYsWK/PLLLx8d3ZMRQCGESF0kAArxDy8vL0xMTD66evf58+e0bt06JlT9l1KKhQsX4uzsTPbs2bl48SItWrT4+DYw5lZR/438+HSthYUF/fr14+TJk7x69YqyZcuyc+fOD7YvXrw4Z86cYciQIYwcOZLatWt/MDRKABRCiNRFAqAQ//Dy8iJv3rwfPF1Dq9XSsWNHQkND2bBhA+bm5rHaDBs2DFdXV3r16sWxY8fitxVM9AhgRPye1ytbtiwXLlygWrVqNG3aFDc3tw+2tbS0ZNq0aRw+fJj79+9TsmRJ/vjjj1jbvUgAFEKI1EUCoBD/iN4D8EOWLVvGvn37WLduHblz5471/vr16/n999+ZOXMm8+bNw9LSMn4dm+sXAAEyZcrEX3/9xZAhQxgzZgwXLlz4aPsaNWpw5coV2rdvz8CBA+nWrdt728VYWVlhY2Mj28AIIUQqIQFQiH98bA9ApRTz5s2jWbNmcW6vcuvWLXr27BkTsPQSHQA/cBzch5iYmPDLL78wbtw49u7d+8l9AzNmzMiiRYs4fPgw9vb2LFiwgLt37wKQL18+evXqhZWVlX61CyGE+CxJABSCqID3sQB48uRJrl69iqura6z3AgMDadGiBfny5cPNzU3vU0Qw+yd06TECGHOpmRnt2rXj2bNnLF68OF4jeKVKlWLQoEGkTZuW6dOns3btWkJDQ3n16hWRkZF61yCEEOLzIwFQCCAgIIA3b958cAp4wYIFODk5xTn6N2TIEO7fv8+ff/75wc2aPyoBU8D/ljlzZrp168a5c+f4+++/43WNjY0NAwcOpGXLluzduxd3d3eCgoLkGUAhhEglJAAKQdT0LxDnCOCzZ8/YtGkTLi4usVbzPnv2jBUrVjBu3DiKFCmSsM4TOAX8b6VKlaJmzZrs3r073iHOxMSERo0aMXnyZMzMog4F8vDwkOcAhRAiFZAAKAQf3wPQ3d0dMzMzunXrFud7pqamdO/ePeGdJ2IK+N8aNGjAq1evuHjxol7X5c6dm549ewKwe/dufv75Z549e5aoWoQQQqRsEgCFICoAZsqUCRsbm/deV0rh5uZGu3btyJQp03vvabVaFi1aRNu2bePe4Dk+Avzg2p9R//vKOri0GoJeJOhWDg4OFChQgH379ul9bfSWNi4uLrx69Yoff/yRw4cPx9ouRgghxJdBAqAQfHgLmFevXuHn58dXX30V673du3dz//59+vXrp3+H757AurYwuxT8PTzqtbt7YVs/mFEItg+AsEC9b9ugQQOuXbuGv7+/XtdFT23ny5ePadOmUalSJdzc3FiwYAGBgfrXIYQQImWTACgE4OPjg4ODQ6zXo6eG43o2cNWqVZQtW5by5cvr15n/VVhQBTw9APXPF6D+eXZPFwmXVoFbDXj78SCn0Wje+6pevTrr16/Hzs4OjUbD+PHj8fX1RaPRcPny5VjX16pVi0GDBsUEQJ1OR5o0aejbty+DBw/G09OTmTNnJmhUUQghRMolAVAIPjwC+LFnA+/cuUOlSpX06ygkANa1gdA3oPvIYg2lg9e+sLEzaCM+2Mzf3z/ma9asWWTIkIEffviBiRMn4u/vz7Bhw+JV1r8DYLSKFSsyfvx47OzsaN26Nf369SM4ODhe9xNCCJGySQAUqV5ERAT379//YADMlCkTGTNmfO91pRQ+Pj4fPTkkTsdnwrun/x/t+xidFh6ehasbPtjE1tY25itjxoxoNBry589PaGgotra28d6WJq4ACFHbxXTv3p3ffvuN5cuXU7ZsWc6fPx+vewohhEi5JACKVO/BgwdotdoPTgHHFfJevnzJu3fv4rzmgyLD4fyK+IW/aBoTOPPhs37jkj17dr1X8X4oAELUNHOPHj24ePEi6dKlo0qVKkyaNEk2jRZCiM+YBECR6vn4+ABxT/N+aJTvY1PDHxTgC2Fv9CtO6eDpddDGP2zlyJGDd+/exZqurVq1KunSpXvv69ixY8DHA2C0woULc+rUKUaMGMG4ceOoUaNGzP6JQgghPi8SAEWq9+DBAwDy5MkT53txve7n5weAvb19/Dt6+zBB9aF0EPg03s2zZs0KwIsX728ns2HDBi5fvvze138XsHxq2xdzc3MmTpzIsWPHePr0KaVLl8bd3V22ixFCiM+MBECR6kXv4fffwBT9Xlyvh4REbdqcNm3a+HdknTlhBQJYZ/p0m3+YmpoCsUfz8uTJg5OT03tf1tZRp5AEBAQAxNoH8UOqVq3K5cuXadOmDT179uTbb7/l+fPn8a5RCCGEcUkAFKle9DRu9FTwf9+L6/Xo59+ij1CLlyz5wcRU/wIz2IFFmng3jw5+0UEwPp4+fYpGoyFbtmzxviZ9+vQsXbqUrVu3cuLECUqUKMGuXbvifb0QQgjjkQAoUr3ohRzRz/X9m6OjY5yva7VaNBpNrLOBP8oyPRRtrmcI1ECJNnq0J+YsYH1qe/78OVmyZNEv0P6jefPmXLt2jXLlytGkSRNcXFwICgrS+z5CCCGSjwRAkeqlSZMGW1vbDwbAR48eERoa+t7rkZGReo2wxag+DDTxvU4T9Z9Lq+DWTojnc3bRI4D6BMBnz56RPXv2eLf/L1tbW3bu3MnChQv5448/KFOmDGfPnk3w/YQQQiQtCYBCEDUKGNdUb/TooK+v73uvR0ZGJmi0jBxFoemcf37xkT9+GlMwMYPWf0C+KlFHxF1eB8EBH7yka9euvH79OtYUsL29PUopSpcuHeuaw4cPM2vWrEQHQIjaLqZv375cunQJGxsbqlatyoQJE2S7GCGESIEkAArBh6d6P/R8oFarTVgABCjdDjr8CVbpY78XPTqYwQ56HYCizaDNami+CN4+gkOT4d5B+Mh2LdFTwPo+A5jYABitYMGCnDhxgtGjRzNx4kSqVauGp6enQe4thBDCMCQACsGHF3vY2dmRPn16Tp069d7rCZ4CjlagPgy5BTb5IH1OyFYEcpSAYs2h1UoYcAlylvp/+8JfQZV+kKscXF4NR36Bt4/jvLW+U8AhISG8e/fOYAEQoraLmTBhAsePH+fFixeULl2axYsXy3YxQgiRQkgAFIL/P+sXvb1LNFNTUzp06MDSpUuJiPj/mbwJngL+t4gQeH0f6oyBfqfB5Ti0XBYVAk3NY7e3SBM1elhzOIS9hf3j4eb2WJtE6xsAo08NMWQAjFa5cmUuX75Mx44d6dOnD82aNePp0/jvaSiEECJpSAAUAqhQoQIAu3fvjvWei4sL/v7+bNu2Lea1RE0BR7t3AFDgVE+/67IVhnoToGBDuLUDDkyAl/feqw3iPwV88eJFLCwsyJUrl351xFO6dOlwc3Nj+/btnDlzhhIlSrBjx44k6UsIIUT8SAAUAihWrBjOzs4sWLAg1nslS5akWrVq772X6ClggHv7IGdpSJ9D/2vNLKB4C6g7Fkwt4NBUuLQWIkL02gZGq9Vy4MABnJ2dSZMm/nsNJkTTpk25fv06lStXplmzZvTu3ZvAwMAk7VMIIUTcJAAK8Q9XV1cOHjzIrVu34nzv0KFDMe8legpYp4V7+6FAg4TfA8AmD9QeDSXbgO8x8BhLxpCoY+riEwAvXbrEy5cvqV+/fuLqiKfs2bOzbds2Fi9ezJo1ayhTpgynT59Olr6FEEL8nwRAIf7RokULsmXLxqJFi2K9991335E9e3YWLlwIGGAK+NEFCAmIWgySWCYmULAB1J8ImfNR9N1xBla3wTQy+JOXHjt2jOLFi8dsd5McNBoNvXr14sqVK2TJkgVnZ2fGjh373jOWQgghklYiH2IS4sthaWlJz549mT9/PlOmTHnvnN/o9+bOncuwYcMSPwXs6RF1vm+ucgao/B/pskLlfnif/IviZhcxO+cGBRuBXWnQaGI1f/78Of7+/rRpo99JI4bi5OTE8ePHmTJlCj///DN79uxh9erVFCxY0Cj1iKQRHByMj48PPj4+eHt74+3tzZs3b8iXLx+Ojo4xX7a2tvqdrCOESBSNkn0ZhIjh5+eHg4MDbm5u9OrV6733Xr16RdmyZcmRIweVKlVi//793Lx5M2EdudWArAWhxVIDVP2+P/74g59HDsJzYSc0N/8C+2rQaCrY5H2v3U8//cTatWu5efMmVlZWBq9DH2fPnqVjx448fPiQ33//nT59+qCJI7SKz0N4eDhbt25lwYIFHD16NOZ1S0tL7O3tsbGxwc/PjydPnsS8Z21tTdGiRSlRokTMV/HixbG1tZXfC0IkAQmAQvxHs2bNePDgARcvXoz1D8/58+dxdnamcOHCKKW4evWq/h28ewIzCsF3S6BkawNV/X+LFi3i+++/jzqBw3Mf7BwMwS+jnhWs1BdMzQgODiZ37tz07NmT6dOnG7yGhAgKCmLYsGEsWrSIr7/+Gnd3d2xtbY1dltDDgwcPWLx4MUuWLOHp06fUqlWLDh06ULhwYRwcHMiZM+d7o3zBwcH4+vri7e3N3bt3uXHjBteuXePGjRsEB0c9wpAlS5aYMBgdDIsVK0aGDBmM9TGF+CJIABTiP/bs2cNXX33FwYMHqV27dqz3Fy5ciKurK/ny5Yt1RFy8XFoN276HH7wgbZbEF/wf8+bNY9iwYf8/vzgsEA5OgjOLoqaDm81l4dbj9OvXD09PT/Lnz2/wGhJj165d9OjRA61Wy5IlS2jevLmxSxKfEBoayoQJE/j1119JkyYNXbp0wcXFhaJFiybofjqdDh8fH65duxbzdf36de7evRuzyj1fvnzvhcLixYtTuHBhLCwsDPnRhPhiSQAU4j90Oh3Ozs48evSIS5cukSXL+yFNKUXBggXx9vbm+vXrFClSRL8ONnaOOsWj534DVv1/s2fPZtSoUQQFBb3/xsPzsL0/6vkdfjsVwd2czVmyfFWS1JBYz58/p1evXmzbto3u3bsza9Ys0qeP4+g8YXQnTpygR48e+Pj4MGbMGAYOHJhk/1+FhoZy+/Ztrl+//l4wfPDgAQBmZmYUKlQo1ohhvnz55PlCIf5DAqAQcXjw4AFlypShQoUK7Nq1K9Y/Hl26dGHTpk3Y29tz+PDh+J+ioY2A6Y5QtX/UiR5JYMaMGfz888+8efMm1nuBb17h3r0kLkXfYZbVEZNmc8ChepLUkVhKKZYtW8bAgQPJkSMHq1atomrVqsYuS/wjMDCQUaNGMW/ePCpVqoS7u3uCR/wS6/Xr11y/fv29YHjt2jVev34NRG1GXqxYsVgjhklx+o0QnwsJgEJ8gIeHB40aNWLChAmMGTPmvfe6du3K1atXefz4MaampmzcuBFnZ+dP39T3OKxoDL0Pg12ZJKn7l19+Yfr06bx8+fK915VSdOjQge3bt3P14J84Xp8F909B2c5RW8hY2yRJPYnl5eVFp06dOHPmDCNHjmTcuHGYm8dxVJ5INvv376dXr148ffqUKVOm0L9//8RvjG5gSikeP34cM0oYHQpv3rxJWFgYELUv5X9HC4sWLUq6dOmMXL0QyUAJIT5o/PjxSqPRKA8Pj/de79Chg6pZs6Z6/Pixql69ujI1NVUzZsxQOp3u4zf0GKvU9PxKabVJVvOkSZNUtmzZYr0+f/58Baj169dHvaDVKnV2qVKTcyn1awGlbmxLspoSKyIiQk2cOFGZmZmpcuXKqVu3bhm7pFQpICBA9ejRQwGqdu3a6t69e8YuSW8RERHq7t276q+//lJTp05VnTp1UuXKlVOZMmVSNjY2ytraWjk6OqpvvvlGjR49Wq1fv17duHFDhYeHG7t0IQxKAqAQHxEZGakaNGigsmbNqh48eBDzetu2bVXdunWVUlH/oPzwww8KUN9++616/fr1h284v4pSW/omac0TJkxQOXPmfO+1M2fOKHNzc9W/f//YF7x5pNTadkqNy6DUuvZRv06hzp07pwoVKqSsra3VvHnzPh24hcFs27ZN2dnZqfTp0ys3N7cv7nsfFham7t+/r44fP65GjBihGjRooHLmzKkABSgLCwtVsmRJ1aFDBzV16lS1c+dO5efn98V9H0TqIVPAQnzCixcvKFOmDHny5OHw4cNYWFjQqlUr3r59y969e2Pa/fXXX3Tt2pVs2bKxdu1aKlSo8P6N3jyEmcWg5XIo/l2S1Tt27FhWrFjB/fv3AXj58iVly5YlZ86cHD16NO5VkkrBre2w+weICIH6E6Bs16hTRlKY4OBghg8fzvz582nUqBHLli0jZ86cxi7ri/X8+XMGDBjA+vXrady4MYsWLSJ37tzGLivZvHz5Mtaik2vXrvHu3TsAMmTIEDOFXLx4cb7++mscHR2NXLUQ8WDsBCrE5+DUqVPK3Nxcfffdd+rt27eqefPm6uuvv47V7t69e6ps2bJKo9Go7t27qydPnvz/zXPLlBqfSangV0la68iRI5W9vb1SSqmHDx+qypUrqyxZsig/P79PXxz8Sqm/+kWNBro3Uur53SStNTH+/vtvZWtrq7JkyaI2b95s7HK+ODqdTq1bt05lzZpVZc6cWa1evVpGu/6h0+mUr6+v2rlzp5o6dapq3769KlmypDI3N1cajUZ99dVXaufOnSoyMtLYpQrxQRIAhYinrVu3qvTp06tChQqpmjVrqmbNmsXZLiIiQs2bN09lypRJZciQQc2YMUOFhYVFTbO6N0ryOocPH66cnJzU/v37Vfbs2VWuXLnU6dOn9buJ9xGlZpdW6udsSh2ZrlREWNIUm0jPnz9X3333nQJUly5d1Js3b4xd0hfh0aNHqlmzZgpQrVq1ev8HGfFBQUFBatmyZapcuXIKUPb29mratGkqJCTE2KUJEYsEQCH0cOfOHVWiRAllamqqypUr99G2L168UC4uLsrExEQVL1JQRUzIptTRGUle4+DBg1XWrFmViYmJqlevnnr27FnCbhQerNS+cVGjlvOrKPXgvEHrNBSdTqeWL1+u0qdPr+zt7dXRo0eNXdJnS6fTqaVLl6qMGTMqW1tbtWXLFmOX9FnS6XTqzJkzqkuXLsrCwkKVKVNGeXl5GbssId4jAVAIPQUFBcU8HN6nT59P/nR/5coVNaR5WaXGZVD9WtZSd+8m3bTqy5cvlb29vQLU2LFjDTMF9fiKUotqKDUuo1J/j1Aq9F3i75kEvL29lbOzs9JoNGrEiBFRo64i3ry9vVW9evUUoLp27apevTLsowqhoaHqypUr6sCBA2r37t3qxIkT6sGDB1/8tPLFixeVo6OjsrGxUdu3bzd2OULEkEUgQiRAnTp1CAoK4sqVKxQvXpxNmzbh4ODwwfZqz0hCzq+jyBId/v5PGDRoED/99JNBzzM9d+4crVq14smTJ9jZ2eHt7W2we6ONhDML4eBkSJsNmsyEAvUMd38D0Wq1TJ8+nbFjx1KiRAlWr15ttM2JPxc6nY758+czcuRIsmTJwuLFi2nYsKHB7q/Vajl27BgnTpwgIiIiZlN1FTUAQY4cOfjmm2+ws7MzWJ8pzevXr+natSvbtm3jxx9/ZPLkySlu30SR+qS8JX5CfAa0Wi0FCxbk1KlTBAQEULZsWSZNmsSTJ0/ibK/x3Eeakk25ffsOP/30E/PmzaNQoUKsWLECnU6XqFru3bvH0KFDqVatGjly5KBFixZkzpw5UfeMxdQs6vQS11OQxRHWtIAtvSHo5aevTUampqaMHDmSM2fOEBoaSrly5ZgzZ06iv8dfqjt37lCjRg0GDBhAly5duH79ukHDX0REBKtWreLw4cNEREQAUYFTp9MRPfbw7Nkzli5dyvXr1+N1z65du6LRaGK+smTJQqNGjbh69WpMm3+/nyFDBipUqMC2bdsM9rn0ZWNjw9atW5k+fTq//vprrI3lhTAGCYBCJEBkZCRmZmaUKVOGCxcu0Lp1a6ZMmULevHlp164dx44di/kHjlc+8NITCjTA2tqasWPHcvv2bWrWrEm3bt2oUqUKZ86c0at/rVbL9u3badSoEQUKFGDFihX88MMPHDt2DGtr66QbXcjsAJ3+guYL4e5emF8Brm6M2kYmBSlbtiwXLlygV69eDBw4kEaNGvHo0SNjl5ViREZGMm3aNEqVKsXTp085cuQI8+fPN/gZvn///Td+fn4fbaOUQqfTsXXr1g/+APVfjRo1wt/fH39/fw4cOICZmRlNmjR5r83y5cvx9/fn/PnzODs707JlS65du5bgz5JYGo2GH374gd9//52FCxeyZ88eo9UiBEgAFCJBtFotZmZmQNRP925ubjx69Ijp06dz8eJFatSoQcmSJVm4cCGh13eCiTk41Iy5Pm/evKxfv54jR44QFhZG5cqV6dKlC/7+/h/tN/roLUdHR7755hsCAgJYsWIFDx8+ZNKkSVhYWLxXW5LQaKB0e/j+HDjWgi29YE1LeH0/6fpMAGtra+bMmcPevXu5fv06JUqUYNOmTcYuy+iuXLlCpUqVGD16NAMGDODq1avUqFHD4P08fvyYixcvEt+njHQ6XbxDkaWlJba2ttja2lK6dGlGjBjBgwcPeP78eUwbGxsbbG1tKViwIBMnTiQyMpJDhw4l6LMY0oABA5g5cyanTp2KdVyjEMlJAqAQCRAZGRlrlC1TpkwMGjSI27dvs3//fgoUKMD333/PkaWjuRWSiUUr1rJ37148PT0JDw8HoEaNGly4cIFFixaxa9cuChYsyC+//EJYWBjBwcHcvHmTnTt3MmfOHNq1a0eePHmYOHEi9erV49y5c5w5c4YuXbpgbW39Xm1JGgCjpcsOLZdBuw3w7BbMrwynF4JOm/R966FBgwZcu3aNunXr0rp1azp37sybN2+MXVayCwsLY+zYsZQvX57w8HBOnz7N9OnT3/u9Y0jnz5+Ped4vPpRS+Pr68urVK736CQwMZPXq1Tg5OZElS5ZY70dGRuLu7g4Q9yboyUyj0dCqVSvCw8OZP39+zN8FQiQ3WQQiRAKULl2aatWqMW/evI+2e+h7jxzLKzLjkjVj/n5GZGQkEPWPQO7cuXF0dMTBwQFHR0fevn3Ljh07uHPnDqampmi1/w9SlpaWFC5cmC5dutClS5ePPuPXrl07nj59ysGDBw3zYeMj7B0c+BnOLoFcZeG7JZAlf/L1Hw9KKVatWsX3339PpkyZWLVqVZKMfKVEZ86coXv37ty9e5fRo0czatSoJA9D8+fPf29ELr5atWpFsWLFPvh+165dWb16NVZWVgAEBQWRM2dOdu7cSdmyZYGoP19WVlaYmpoSEhKCTqfD3t6eCxcuGP752ATy9fVl7Nix1K1bly5duhi7HJEKyQigEAkQ32nW3BE+mGu0jHA/SEhICD4+Phw4cIDFixfTqVMncubMyc2bN5k7dy5r1qwha9asNG3alLx58wJQsWJFDh8+THBwMJcvX2bw4MGf/AcsyaeA42KZHr7+FXp4QHgQbO6ZvP3Hg0ajoXPnzly9epV8+fJRq1YtfvzxR8LCwoxdWpIJDg5m2LBhVK1aFWtray5cuMD48eOTZSTs7du3el+j0WjidV3t2rW5fPkyly9f5uzZszRs2JCvvvrqvecNZ86cyeXLl/n7778pWrQoS5cuTTHhD8De3p527dqxZ88eoz6bKFIvCYBCJEBcU8Bx8vQAm7yQtSBmZmbY29tTp04devbsyeTJk1m3bh1nzpzh2bNnPH78mOPHj7N9+3a8vLzYunUrz58/p169egwbNozXr1/Hu7ZkD4DR8lSEPkeh7MdHNI4ePUrTpk2xs7NDo9Hw119/JU99RP3De+jQIaZNm8bMmTOpVKkSN27cSLb+k8uRI0coVaoU8+bNY+rUqZw+fZqSJUsmW/8JmVpWSpE2bdpPtkubNi1OTk44OTlRoUIFli5dSlBQEEuWLIlpY2tri5OTEw0aNGD58uW0adOGZ8+e6V1TUmrYsCElSpRg4cKFBAYGGrsckcpIABQiAeIVspSKCoAFGkQtnNCDRqOhefPm3Lx5kwkTJrB48WIKFizI0qVL35sa/lBtRt1jzMwSynf9aJOgoCBKlSrF/Pnzk6em/zA1NWX48OGcPXuWyMhIypUrx6xZs76I7WLevn2Lq6srtWrVwtbWlqtXrzJ8+PBk/6HA1tYWjZ6/74E4n+P7FI1Gg4mJCSEhIXG+X7FiRcqVK8fkyZP1vve/3QkKZdTdh9Q4c4v8R69S6sR1Wl++x59PXhGWgN87JiYm9OnTh/DwcNzd3eO9YEYIQ5AAKEQCxGua9eU9CPAFp/oJ7sfKyopRo0Zx584dGjZsSK9evahYsSInTpxIXG1G9tVXXzFp0iS+/fZbo9ZRunRpzp8/j4uLC4MHD6ZBgwY8fPjQqDUlxp49eyhevDh//PEHc+fO5ciRIxQsWNAotZQtW1bvQJMlS5Z4bQgdFhbGkydPePLkCbdu3aJ///4EBgbStGnTD14zaNCgmNX6+orUKSZ5Pabm2dusfPSCu8FhBGl1PA2P5HhAIN/fuk/Ns7e5HRR3AP2QJ0+eMH78eP7++2+GDBmCra0tTZs25cCBAzFtLl26RKtWrciRIwdWVlYUKFCAXr16cffuXb0/hxD/JgFQiASI1yibpweYWoJD9UT3lytXLlatWsWJEyfQaDRUq1aNDh06xBlWjDoF/BmysrJi5syZ7Nu3j9u3b1OiRAnWr19v7LL08urVK7p06cJXX31F4cKFuX79Ot9//71eq3ANzcnJiVy5culVQ7169eI1arhnzx5y5sxJzpw5qVSpEufOnWPTpk3UqlXrg9c0atQIBwcHvUcBlVL0v+XH/PtR08f/HX+PHvd7EBrOV+fvcicoNF739fX1pVy5chw8eJA5c+YwevRonJ2dqVixIv369QNg586dVK5cmbCwMNasWcOtW7dYvXo1GTNmlM2kRaLJKmAhEsDOzo6+ffsyduzYDzf6ozloTKDTFoP2rdPpWLFiBSNHjiQwMJBRo0YxdOjQmFWRdevWJVu2bJ9NiNFoNGzdupXmzZsbuxRevXqFq6srGzZsoH379syfPx8bGxtjl/VRmzdvpl+/foSFhfH777/HnJSRErx9+xY3N7eYlbgfU7VqVRo0aJBMlcXfmscvGXrnQbzammogr5UFhysWxvITwffrr7/m6tWr3Llzh7Rp0xIUFMSPP/5Ijhw56NevH1ZWVuTLl49q1aqxdevWWNe/fv06xf/eFCmbjAAKkQCfnGYNCwS/E1HP/xmYiYlJzJYeLi4ujB8/nqJFi7J161aUUp/FFHBKlTlzZtatW8fq1avZtWsXJUuWTBGbB8fl6dOntGrVipYtW1K5cmVu3LhBt27dUkz4A8iQIQN9+/YlT548ALFGAzUaDaampjRu3Jj69RP+qERS0SrFbz7xO50kqj34hISz6/nH95l89eoVe/bsoV+/fjGLXtKmTYuLiwu3bt3i1KlT7N27lxcvXjB8+PA47yHhTySWBEAhEuCTU8A+R0EbDgWS7h+1jBkz8ttvv3Ht2jUKFSrEd999R/369Xn79q0EwETQaDR06NCBq1evkj9/furWrcuwYcNSzHYx0fsZFi1alCNHjrB+/Xq2bt0ar2fnjCF9+vR07dqV7t27U6ZMGfLmzUuGDBkAqFWrFkOHDqVChQopKrhGO/smCP/wCL2uMQE2Pvn4Ztb37t1DKUXhwoXfe71YsWJ8/fXXrF+/nnPnzgHEaiOEoUgAFCIBPvmcnacHZM6fLJshFy5cmN27d7Njxw78/Py4dOkS586dIyAgIMn7/pLlzZuXAwcOMH36dObOnUuFChWMvl/bgwcPaNy4MZ07d6ZRo0bcvHmTNm3apMjw9G8ajYa8efPStGlTunfvTrt27QBwdHQkTZo0Rq7uw7yC9Q/9OuDuJ54D/NiTV61bt8bOzo6TJ0/q3bcQ+pAAKEQCfHSaVSnw3Jck078fotFoaNKkCdevXydXrlx4enpSoEABFi5c+MltY4whMDAwZiNfAB8fHy5fvsz9+ynrPGETExOGDRvGuXPnUEpRvnx5ZsyYkezbxeh0Otzc3ChWrBhXrlxh+/btMRuHf46it3p58eKFkSv5uLeR2gT9I/ku8uN/5goUKIBGo+H27dux3rOwsKBfv34xITGuNkIYggRAIRLgo1PAz27B24dJOv37IZaWlmTLlo0OHTrQtGlTXF1dKVu2LEeOHEn2Wj7m/PnzlClThjJlygAwZMgQypQp8/FFNUZUsmRJzp07x/fff8+wYcOoV69esoVVLy8v6tatS9++fWnTpg03btz46HYnnwMLCwtsbGwSdFRccsptZUFCon5uq4+ftJI5c2YaNmzI/PnzCQoKivV+hgwZ6N+/P5aWlh9c7RvfjeGF+BAJgEIkwEengD09wDwN5HNO3qL+ERkZSfr06Vm+fDlnzpzB2tqaWrVq0bp16/eOyjKmWrVqoZSK9bVixQpjl/ZBVlZWzJgxgwMHDuDp6UnJkiVZu3ZtkvWn1Wr5/fffKVGiBH5+fuzfv58lS5Z8MQ//Z82aNcWPAFbImEbvfyRNgWqZ0n2y3fz589FqtVSsWJHNmzfj6enJrVu3mDNnDlWqVOG7777ju+++49ChQzRu3Jj9+/fj6+vL+fPnGT58OH379k3QZxIimgRAIfT0yZW2nvvAoQaYWyVvYf/4d20VK1bk5MmTrFy5kmPHjlG4cGHGjx9PcHCwUWr7EtSpU4erV6/SuHFjOnToQLt27Qz+vOWNGzdwdnZm2LBh9O7dm2vXrlG3bl2D9mFsn0MAzGlpQcOsGTHV4xFLHdDZ7tNT846Ojly8eJHatWszdOhQihcvTv369Tlw4AALFy7ExMSE2bNn07hxYx49ekT79u0pXLgw7dq1482bN0yaNCnhH0wIJAAKobfo57/inAIOfQP3Txll+jfaf0cnTUxM6Ny5M3fv3mXgwIFMnTqVIkWKsHHjxs/u6KkngfHfkiMpZcqUiTVr1rBu3Tr27NlDyZIl3zu9IaEiIiKYNGkSZcuW5fXr1xw/fpxZs2bF63zcz022bNkICAggIkK/VbbJbWKBXKQxMSG+GXBQvhwUSBu/H/5y5szJvHnz8PX1JSwsjIcPH7Jt27aYDa2zZcvG8OHDKVKkCNu2bSM0NBRPT0/c3NxwcnJK2AcS4h8SAIXQU2RkJEDcI4Deh0FpE3X8W2J96PnE9OnTM23aNG7cuEGpUqVo06YNtWvX5sqVK0aoMmGGHR3GsuvLiNRFGrsUANq2bcvVq1cpWLAg9erVY8iQIYSGxu8kiP+6ePEiFSpUYPz48QwdOpTLly9TtWpVA1eccmTNmhWlFK9efXzLFGPLbWXBkmL28QqArXJkYpiDrUH7d3Z2pnLlyri7u6f475X4vEgAFEJP0atq4wyAnh6QrTBkypfMVf3fpzaCdnJyYvv27ezZs4enT59StmxZXFxcUvx0HED9fPWZfXE27Xe15+bLm8YuB4A8efKwb98+fv/9dxYsWED58uX1CtWhoaGMHDmSihUrAnD27FmmTJkSc7LLlyp6BfPn8Ptu94s3mGugRqZ0Mf9o/vsfz9yW5swqnIc5RfJiauAteTQaDT169MDCwoJFixYl+wp08eWSACiEnqJHAGONssVs/2LcEw3iexZww4YNuXr1Kr/99htr166lQIECzJ07N+bzpURdinVhzddr0Cot7Xa147dzvxEcYfznGU1MTBg8eDDnz5/H1NSUChUqMH369E9uwXPixAlKly7N77//zoQJEzh37hxly5ZNpqqNK23atKRJkybFB8C/n7/mj8cv+blAbjaWduJi1WIsK27POCc7ZhbOw46yBThbpShtc2ZJsv0Y06VLh4uLC9euXcPDwyNJ+hCpjwRAIfT0wSngJ1ch8Gmy7v8Xl0+eUvIv5ubmDB48GE9PT1q2bMnAgQMpXbq0QZ5nSyrFsxZnfZP19C/Tn/V31vPd9u84+ShlbJpbvHhxzp49y6BBgxgxYgR16tSJc+V1YGAgAwcOpHr16tjY2HDp0iVGjx6Nubm5Eao2nqxZs6borWCehEUw9M4DGmXNQGe7qL0LbS3N+TqbDX3yZKddzixUyJgWk2TYiLtEiRI0bNiQdevW8fDhwyTvT3z5JAAKoacPBkBPD7BID3kqG6Gq/0vIWcDZs2dnyZIlnDt3DhsbG+rVq8d3332Hj49PElWZOOYm5vQs0ZPNzTaTK10u+uzvw6hjowgINf7pJ5aWlkyfPp1Dhw7h6+tLyZIlWbVqVcyCm/3791OiRAmWLFnCjBkzOHHiBEWLFjVy1caRklcC65Si/y0/zDUaZhTKmyJOW2nfvj3ZsmVj/vz5KXqkXnweJAAKoafoab1Yo2ye+yB/LTD7+CawSS2+U8BxKVeuHMeOHWPNmjWcPXuWIkWK8NNPP8W5WW1KkC9DPpY2WMrPVX/myMMjfPPXN+z03pkiVjfXrFmTq1ev0qxZMzp37sy3335Lp06dqF+/Pvb29ly7do3BgwfHe7T2SxQdAFPic22LHjznWEAgc4vkI4tFyjhbO/qUkAcPHvDnn38auxzxmZMAKISe4hwBDH4FD88ZdfVvNH2mgOOi0Who3749d+7c4YcffuC3336jUKFCrF27NkUEq//SaDR8W+BbtjXfRqWclRh5bCQu+114FPjI2KWRMWNGVq1axfDhw2OObxswYAAHDhwgf/6kPyc6pcuWLRuRkZG8efPG2KW85+q7YKZ6++OSJxs1Mqc3djnvcXBwoGXLluzYsUOOiROJIgFQCD3FGQC9DoLSGX0BCCRsCjguadOmZeLEidy6dYuKFSvSoUMHqlevzsWLFw1QpeFltc7KrzV/ZV6dedx7fY9vt33LyhsrjbplzIsXL+jQoQPTp0+ndu3aVKtWjTlz5jB48GBCQkKMVldKkRJXAgdptbjc8KNIWitGOuY0djlxatasGSVKlGD58uWyqbtIMAmAQugpzilgTw/IUQIy2Bmpqv9LzBRwXBwcHNiyZQv79+/n9evXlC9fnl69evHs2TOD9WFINfPUZFvzbXzr9C0zzs+g4+6O3Hl1J1lrUEqxYcMGihYtyp49e/jjjz/Yv38/hw8fZvbs2bi5uVG+fHkuXbqUrHWlNBkzZsTMzCxFBcBxno95HBbBgmL5sDBJmf9EmpiY0KtXLywsLNi5c6exyxGfqZT5u1uIFCzWCKBOB/f2p4jRP0j8FPCH1K1bl8uXLzN79mz+/PNPChYsyMyZM1PkSQ5pzdMystJIVn29ijBtGG12tmHmhZmERiZsk2Z9vH79mlatWtG2bVtq1qzJzZs36dSpExqNBhMTEwYMGMCFCxewsLCgUqVKTJ069ZPbxXypTExMUtRCkF3PX7Pa/yUTC+TCKU3K3ocxS5YsFC5cmD59+rBjxw5jlyM+QxIAhdBTrAD4+BIEvzT69i/w/2PqDDkC+G9mZmb0798fT09P2rVrx7BhwyhZsiR79+5Nkv4Sq1S2UmxsshGXUi6surmK77Z/xxn/M0nW36VLlyhXrhwHDhxg06ZNbNq0iRw5csRqV6xYMc6cOcPQoUMZPXo0tWrVSrErrpNaStkK5nFoOENvP+DrrBnpkDOzscuJl/bt21OrVi26devGkycp45hE8fmQACiEnmJNAXt6gFVGyF3BiFVF+egxdQaUNWtWFi5cyMWLF8mePTuNGjWiWbNm3Lt3L0n7TQhzU3P6lOrD5mabyZ4mOz09ejLmxBjehBl24YG7uztVqlTBxsaGCxcu0LJly4+2t7CwYOrUqRw5cgQ/Pz+KFy/O4MGDY54R7NWrF5MnT2bdunWcOXOG58+fp8hFOImVEkYAtUrR/9Z9rE1N+K1wnhSx5Ut8aDQaFi9ejJmZGd27d/8if3+IJKSEEHo5f/68AtTFixejXnCrpdTGrsYt6h9BQUEKUKtXr062PnU6ndqwYYPKkyePMjc3V8OHD1dv375Ntv71odVp1aY7m1SVNVVUjfU11G7v3Uqn0yXqnkFBQapbt24KUL1791YhISGfvOb169dq7ty5qnr16srW1lYBMV8mJibKyclJlS1bVmXOnPm999KmTatKlCihXF1d1bVr1xJVd0px7do1NW7cOBUUFGS0Gub4PlG2By+pY69S5u/bT9m1a5cC1IIFC4xdiviMyAigEHp6b5Qt8Bk8vpgipn/hE+cUJxGNRkPr1q25ffs2o0ePZs6cORQsWJCVK1emuP3dTDQmtCzYkm3Nt1EuRzmGHx3O9we/xz/QP0H38/T0pEqVKqxfv56VK1fi5ub20TN8r169St++fcmVKxeDBg0iU6ZM9O7dmxUrVnD06FHc3NzImDEjgYGBTJ48mZcvX/L69WsuXbrE5s2bGT9+PFWrVmXLli2UKFGCmjVrsmHDBsLDwxP6LTG6bNmyARhtGvjy22B+8fGnX97sVMuUsrZ8ia+vv/6avn37MnToUO7evWvscsTnwtgJVIjPzYkTJxSgbty4odSltUqNy6DUu6fGLksppdSrV68UoP7880+j1eDn56dat26tAFWpUiV15swZo9XyKQf8Dqg6G+qoCqsrqNU3V6tIbWS8r927d6/KkCGDKlCggLp69eoH24WFham1a9cqZ2dnBSg7Ozs1fvx49fDhwzjbP3r0SDVs2FABql+/fnGOjIWFhakNGzaomjVrKkDlyJFD/fTTT+r+/fvxrj+lCA8PV+PHj1fnz59P9r4DIyJVlVM3Vf1zt1WYVpvs/RtSYGCgKlCggKpQoYIKDw83djniMyAjgELo6b0RQE8PsCsD6bIbuaooyfUM4MfkzZuXDRs2cPjwYUJDQ6lUqRJdu3bF3z9ho2xJqU7eOvzV/C+a5W/GtLPT6Px3Z+4GfHoExcvLi1atWlGlShXOnz9PiRIl4mzn7e1N5cqVad++PZaWlvz555/4+voybtw4cuXKFec1dnZ2/P3338ydOxd3d3fKlSvHhQsX3mtjYWFB69atOXz4MNevX6dly5bMnj0bJycnfvjhB16/fq3398JYzM3NsbGxMcpzgD/de8ST8AgWFk25W77EV9q0aVm9ejUXL15k0qRJxi5HfAY+79/xQhhBTMgyAbwOpJjpX/h/bSnheLGaNWty4cIFFi5cyM6dOylYsCDTp08nLCzM2KW9J71Fen6q/BN/fPUHgRGBtNnRhjkX5xCmjbvOkJAQWrRoQfbs2dmwYQMZMmSIs9327dspW7Ysb9++5fz58xw4cIAWLVpgbm7+yZo0Gg3ff/89Fy9eJE2aNFSuXJnJkyfHef5rsWLFmDdvHo8ePeKnn35iwYIFODk5MX/+/BS5RU9cjLEQZMez16zzf8WkArnIn8K3fImvihUrMmbMGCZPnszp06eNXY5I6Yw9BCnE58bDw0MByv/Mlqjp3wfnjF1SjAcPHihA/f3338Yu5T0vX75U/fv3V6ampsrJyUnt2LEj0YsvkkJYZJhacGmBKv1HadVkSxN11v9srDY9evRQVlZW6vLly3HeIyIiQg0fPlwBqnnz5ur169eJqyksTI0aNUqZmJioqlWrKi8vr4+2f/TokerevbvSaDSqcOHCaufOnSnye/1ve/fuVTNnzky2/h6GhKmCR6+qHte8U/z3Rl8RERGqUqVKysnJSb17987Y5YgUTEYAhdBTmjRpKFOmDOmD/MChRtQUcArxv/buOi7q+48D+OsOOOro7pCyRezCFru7HQY6xZ7O1jljTqco1sTuYtZUUBG7EEURAWnpDsm77+8PBj+RuoODO+D9fDx8bN633gh6r/ukJHQBl0VdXR179uyBr68vTExMMHjwYAwYMACfP9fuDh2V4Uhx4NTKCZcGX4KqrCpm3JmB9U/XFy8Zc/ToURw5cgSurq5o2bJlqetjYmLQq1cv/Pnnn9ixYweuXLkCFRWV6tXE4WDz5s3w9vZGTEwMWrZsCTc3t3KX/NDX18eRI0fg4+MDPT09DBo0CH379sX79++rVUdN0tTURGpqaq20WPIYBvP8w6EoxcYO67qz5IugpKWlcfLkSURHR2PJkiXiLodIMnEnUEKI6AQFBTEAmPv374u7lHLx+XzmypUrjJmZGSMtLc0sXry42q1kNYHH5zHnPp1j2p9uz3Q/35055HWIkZOTY3766acyz4+NjWUMDQ0ZPT09xtvbu0ZqSk9PZ2bMmFHcuhgfH1/h+Xw+n7l27RpjZWXFsNlsxtHRkYmJiamR2qojPDycWbduXa3U9ldo4ZIvT5Lrd+vYgQMHGADM9evXxV0KkVAUAAmpRwICAhgANRZARCk7O5v57bffGAUFBUZbW5v5+++/GZ4EzsSMzYxlnG47Mc2ONWOar2nOhCaGljonPz+f6dGjB6Orq1vu7F5RunLlCqOhocHo6OgI1N2fl5fH7Nmzh1FXV2e4XC6zefNm5tu3bzVep6CysrKYdevWVTibWhTepGUyBg/eMr9/ia7R50gCPp/PDBo0iNHW1mbi4iRjlQIiWagLmJB6RFK7gMsiJyeHVatW4fPnz+jduzccHR3Rrl07PH36VNyllaCjqIOM0xlIdEuEqo0qxt0Zh3MB58Bn/r/G4dq1a+Ht7Y1z586VO7tXlIYPH44PHz7A1tYW/fv3x5o1ayrcT1hGRgbz589HcHAwZs6cifXr18Pa2hpnzpyRiLUaFRQUoKioWKMTQTILeJjrH47mXAUsNdWtsedIChaLhSNHjkBJSQkLFy6kXUJIKRQACalHJGkWsKAMDQ1x+vRpPH78GAzDoHPnzpg0aRK+fv0q7tIAAFFRUTh75ix+HfUrboy6gf5m/bH5xWZM/XcqvqR+wY0bN7Blyxb8/vvvsLe3r7W6dHV1cfPmTfz+++/4/fff0a9fP8THx1d4jZqaGnbu3ImPHz+iTZs2mDhxIjp27IgnT57UUtXlq+mZwL8GRSEhrwCuTUwgw65f4/7Ko62tjX/++QeGhoZ49eqVuMshEoYCICH1iDh2AhGVzp074+XLlzh8+DDu3r0La2tr/P7778jJyRFrXYcPH4a8vDymTp0KZY4y1nVch6P9jiI1NxWjro3CvNPzMHjYYCxdurTWa2Oz2Vi5ciU8PT3h5+cHW1tbPHv2rNLrLC0tceXKFXh5eaGgoABdunTBmDFjEBISUgtVl60mA6B7XAouxKZgs6UBzBRka+QZkqpp06Zo0qQJjh8/XukHBNKwUAAkpB6pS13AZZGSkoKjoyMCAwMxa9YsrFu3Dk2bNoW7u7tYurDy8/Nx6NAhTJ48ucR6f2102+Bs/7NgXjBQ6qME/iQ+fBN8a72+Ij169MDbt29hamqKAQMGIDQ0VKDr7O3t8erVKxw/fhxPnjxB48aNsXz5cqSlpdVwxaVpaWkhMTFR5F3SkTl5WB4YiaHaqhirqy7Se9cVY8eOhYyMDPbt2ycRXf5EMlAAJERA+/btg6mpKeTk5NC+fXu8fPlS3CWVUhe7gMuiqqqKnTt34v3797C0tMTw4cPRt29f+Pv712od7u7uiI2NhZOTU6ljt2/cxjvXd9jQaANU5FQw9fZUbHq2CRl5GbVaYxF9fX3cunULpqammD59usAtp2w2G1OmTEFgYCA2bdqE06dPw87ODkeOHClz4emaoqmpCR6PJ9JdTHgMg5/9w6EkJYXtVob1bskXQcnLy2P27NkIDAzE27dvxV0OkRAUAAkRwPnz57F48WKsW7cOPj4+aNmypUBjrmpbXe4CLkvjxo3x77//4tq1awgNDUWLFi3g7OyMlJSUWnm+q6srunbtWuZWb66urujSpQtG2o/Eif4nsKLdCtwIuYFh7sNwL/xerdT3IxUVFdy4cQOdOnXCrVu3hLpWUVERy5cvh7+/P37//XcEBgZi48aNuHPnTq20vmpqagKASLuBd4fH4VVaFvY1MYGKTP34O1FVjRs3hoWFBTw8PMRdCpEQFAAJEcDOnTsxc+ZMTJ8+HU2aNMGBAwegoKAANzc3cZdWQl3vAi4Li8XC4MGD8fHjR2zevBlubm6wtLTEwYMHK5z5Wl3+/v7w8vLC3LlzSx379OkTHjx4UHxMii2FiY0n4p9h/6CxRmMs9FqIRQ8WIf5b7X9AMDAwgIODA27duiXQeMAfqaioYMyYMfj5559hYGCAcePGwcHBAX5+fjVQ7f8pKytDRkYGCQkJIrnf67Qs/BkWC2cTHXRQ5YrknuLAMHzk5MYiPd0Pubnx1Qrjffr0wbt37xAbGyvCCkldRQGQkErk5eXhzZs36N27d/FrbDYbvXv3rtIbbE2qL13AZZGVlcUvv/yCwMBADBw4EHPmzEGbNm3g7e1dI8/bv38/dHR0MGLEiDKPaWtrlzqmq6gLl54u+KPbH/CJ98Ew92G4GHixxJIxtaFr166wtbWFm5sb8vLyqnQPIyMjzJo1C6dPn0ZoaChatWqF2bNnIy4uTsTVFmKz2SKbCJLx35IvrZQUsLiOLvmSl5eI4C9/wPtRGzx50hmvXg/D4ycd8ehxW3wJ2YX8/FSh79mhQwdwuVx4enqKvmBS51AAJKQSiYmJ4PF40NHRKfG6jo6OxH2Srm9dwGXR09PD8ePH8fz5c3A4HNjb22Ps2LGIiIgQ2TMKCgpw4sQJODo6gsPhlDiWmZmJ48ePw9HREbKypWeUslgsOJg54Nqwa+ht0hsbn23E9NvTEZJWezNsWSwWRo4ciczMTDx//rxa9xkwYAA+fPiAnTt34uLFi7CwsMCWLVuQnZ0twooLiSoArgyMQnJ+3V3yJTX1NZ6/6I/w8MMoKCg5ISc/PwVhYa548XIA0tOF294vOTkZ4eHhmDt3LmRlZWFkZITBgwfj3r3CIQumpqZgsVilfmYWLlyI7t27V+trIpKHAiAh9Uh97AIuT/v27fHs2TMcO3YM3t7esLGxwYYNG0QSTCIjI5Genl7mun5nzpxBZmYmZs2aVeE9VGRVsLHzRhzpewSJ2YkYdW0UDrw7gHxe5fvd8hk+8nhVa7kroquri5YtW4pkzBeHw4GzszOCg4Ph6OiItWvXwsbGBmfPnhXp+EBNTU0kJCRU655X4lJwKS4FW6wMYSJf95Z8ycoKxlvfKf+18JU3xIGP3NwE+LydhOzsSIHuGxYWBjs7O4SEhKBFixbw8PDA7du30aNHD8ybN6/4PDk5Ofzyyy/V/jqI5KMASEglNDU1ISUlVarrKy4uDrq6ktW9VJ+7gMvCZrMxdepUfP78GfPnz8fmzZthY2ODixcvVitEFK2HZ2ZmVurYpUuX0KdPH5iYmAh0r3Z67XB5yGVMbToVB94dwJgbY+Ab71vqvKTsJBx6fwiDrw6G3Uk72J2yQ7dz3fCL9y9lni+IPn36IDg4WGTr+6mrq2PXrl34+PEjbG1tMWHCBHTq1ElkQyG0tLSQk5ODrKysKl0fnp2LXz5HYri2KkbpqImkptrEMAz8PswHwxQAqGzYAB98fg4+flws0L3nzp0LFouF+/fvw8jICEpKSmjatCkWL15cosVv1qxZeP78udCTiEjdQwGQkEpwOBzY2dkVd5MAAJ/Px71799CxY0cxVlZaQ+gCLouysjK2bduGjx8/okWLFhgzZgx69uyJ9++F6yIrEhISAjabDWNj41LHgoOD0bJlS6HuJyctB+fWzjg/6DzkpOQw5d8p+P3F78jKLww6T74+weCrg7Hv7T6EpYehgCkM8im5KbgTdgeT/52MLS+2CNR6CADTpk3DsGHDsG7dOjx9+rTMVsBHjx6BxWJV6c/IysoK7u7uuH//PnJyctCpUyeMGzcOYWFhQt/re9WZCVzAZ/CzfwRUZaSxzdqoTi75kpr6AllZgWAYwSY3MQwPaek+SE//WOF5ycnJuH37NubNmwdDQ0NIS0uX+ECrqqpa/P9mZmaYM2cOVq5cSWsG1nMUAAkRwOLFi3H48GEcP34cnz59gpOTE7KysjB9+nRxl1ZCQ+oCLoulpSWuX7+Of//9FzExMbC1tcW8efOQlJQk1H1CQ0NhZGRUavxfQUEBIiIiYG5uXqX6rNWtcWrAKSxruwzuwe4Y6j4U+333w8nTCVn5WeCX0erD+y8MnA04i5WPVwrVsuno6IjIyMgy16w8evQo2rRpgxYtWlTpawEKF6B+/fo1jh49WtwNv2LFiiovJK2urg4Wi1WlAPhXeBzepGdhX2NjKEvXzRbwxMT7YLGE+7vLYkkhObniiVDBwcFgGAY2NjZgs9nQ1taucAmr1atXIzQ0FKdPnxaqFlK3UAAkRABjx47Fjh07sHbtWrRq1Qq+vr64fft2qYkh4tbQuoDL4+DggPfv3+OPP/7AqVOnYGlpiX379gm8sHFISEiZIS8yMhI8Hq/KARAoXDJmcpPJuDr0KkyUTeD6zhUMmDLD3/cYMLgTdgfuwe4CP2vQoEFQVVXF69evSyyZk5mZiYsXL+Knn36q6pdRTEpKCtOmTUNQUBBWrFiBPXv2wNLSEgcOHBB6IWlpaWmoqakJvRTMy9RM7AyLxSJTHbSrw0u+fMsOF7j1rwjDANnZFe+b/eOHBm1t7Qr/jLW0tLB06VKsXbu2yrPIieSjAEiIgH7++WeEh4cjNzcXL168QPv27cVdUikNtQu4LBwOB4sXL0ZgYCBGjBiB+fPnw9bWFvfv36/02pCQkDLH/1U0NlBYBlwDdNLvBBaE66o89P6QwK2A0tLSGDp0KEJCQkq0gl68eBE8Hg/jx48X6tkVUVRUxPr16xEUFIQBAwbAyckJLVu2xO3bt4W6T9GWcIJKL+Bh7qdw2CkrYpGJZI3JFQbDMODxsgGIftFtS0tLsFgsBAQEFD+rMosXL0Z2djZcXV1FXg+RDBQACalHGnoXcFl0dHTw999/49WrV1BWVkavXr0wcuTICserldcCWNHYwKq4GXoTjJBv+FGZUfic8lng86dPn47MzEzcvHmz+LWjR49i5MiRUFFREerZgjAwMMCxY8fw+vVraGpqon///ujfvz8+fqx4nFoRYZeCWREYhbR8HvY2MYZ0HVryhc/PRVraW4RH/I33fnPx+ElHpKQ8Efo+LBYgL29Q4Tnq6uro168f9u3bh6ysLCQkJEBbW7v4eFnb73G5XKxZswabN29GRoZ4tjckNYsCICH1SFEAZLPpr/aP7Ozs8PjxY5w6dQrPnz+HjY0N1qxZU2rGaXp6OpKSksoMgOWNDawKhmEQmS7YEh4/isqMEvjcTp06QVNTE2fPngVQOB7s0aNHIun+rYidnR28vLxw5coVBAUFoUWLFnBycqp0+0QjIyMoKCggP7/yCS+eiWkIysrBnsbGEr/kS15eIhISPBAUvBWv34zBQ+9WeP1mFEL+W9RZX28UGpkvE/q+DMODukbp5Yp+tG/fPvB4PLRr1w6vX79GXl4ePn36hD179pQ7mW3WrFlQUVHBmTNnhK6LSD5qJiCkHuHxeNT6VwEWi4WJEydi6NCh2LJlC7Zv345jx45h+/btGDduXIkJCN+3kBT5seWkOhgwyOcLNqv3R+Fp4SjgF0CaXfn3ms1mw9LSEt7e3sjIyMDRo0fRqFGjMtc4FDUWi4Xhw4dj4MCB2LdvHzZu3IiHDx/i/fv35f6c2tjYwMbGRqD799ZUQW9N0bdiVhfD8JGVFYS0NB+kpr1BWpoPsrPDAQCysrpQUbGDjvYAqKi0BpfbGGy2zH/XMYiLu47MrCCUvwbg/7FYUlBWbgVlpSaVnmtubg4fHx+sWbMGZ8+exbNnz6CtrQ07Ozvs37+/zGtkZGSwadMmTJgwQfAvntQZ9E5BSD1SUFBAAVAAXC4Xmzdvxk8//YQlS5ZgwoQJcHV1xZ49e0osiVGT2Cw2tBW0EZMVI/S1bh/ckJGXARkpGWjIaUBfUR96XD3oKeqByyk5CSItLQ36+vp49+4dzpw5gxMnTsDJyalWl0nhcDhYtGgRpkyZAjc3t3r3M1pQkIn09HfFgS893RcFBRlgsaTA5TaGhkZ3qKq0hopKa8jJ6Zd7HxaLhWbNXPDy1WDw+QwqXgtQCmy2PJo22SlwnXp6eliyZAmysrLwxx9/wNDQsMTxsoZFjB8/XqRjRYnkqF9/Cwlp4AoKChr8DGBhmJub4+rVq/D09ISzszPs7OwwduxYAGUPlGez2SLd+aKrQVdcDrpcvNSLIOSl5fFX97/AkeIgPD0cERkR8Ev0K25NVOYoIyglCEw2g3cJ75ATnwMZjgyGDBmClStXIj09HdOmTRPZ1yAMDQ0NLFsmfDenJGEYBjk5X5GW5lMc+DIzAwDwIS2tAhUVW5gYz4KKSmsoK7eAlJSCUPdXVDSHre0JvH/vVMFuIGzIymqhRfMDkJc3LON4+d68eQMulytxKxiQ2kcBkJB6hLqAq6Z3797w9fXF/v37sWbNGgCFO37Y29tDRkam+DwWiyXSxXHH2ozFhcALAp/PZrEx0nIk2uq1BQC01C5ckJrP5yMxJxExmTGIzorGLf4tpOWn4fSnwnXcDGYYgB/JR8q5FHTs2RH53HzkFuRCVlqyx81JAj4/DxkZ/sWBLy3NB7l5hYsoKyiYQ0WlNYwMJ0NFpTUUFMzBYlV//K2qih06tP8XkZHH8DX6LPLzU4qPychowNBwEowMp0JGRrju77y8PHh5eaFHjx4lfq5Jw8RiRPlxlhAiVlu3bsWOHTuqtJAuKfT27Vu0bt0aANC4cWP89ddf6Nu3LwDAyckJL168gI+Pj8ie5/LWBYfeH6r0PBZYMOAa4OLgi6W6eX/k4OAACwsLbNm5BRdvX8S70Hfo0K8DojOjkfAtAXzwwQILmvKa0FPUgz5Xv/C/ivpQllWuk7toiEpeXhLS0t4Wh730jPfg83PBZstCWbklVFRaQ1XFDsrKrcDhqNd4PQzDIC8vEXl58ZCV1YGMjEaVvz8PHz7EgQMH8Ndff1ELIKEWQELqE+oCrr6i7cj27t2L8+fPo1+/fhgyZAh27twJFosl0i5gAJjbci5Sc1JxIfACWGCVuSxM0es/2/5cYfhLSUnBkydP4OXlhTlz5kCJo4TIt5HQ5ehivE3hOK58fj7isuKKWwujs6LhFemFHF4OAEBRWhH6XH0YKxnDSNkIHCkOTJRNIMOufy1GDMNH1rcvSEstnKiRlu6Db99CAQCyHB2oqNqhkfYyqKrYgcu1AZtd/dnfwmKxWJCV1YKsrFa17+Xh4YGWLVtS+CMAKAASUq9QF3D1FbWumJub4+HDh7hw4QKWLVuGJk2aoFmzZiV21BAFKbYU1nRcgw76HXDg3QEEpgSWOK4oo4iRliPhl+CHHa93oJ1uO2gplB0GZsyYgVevXmHJkiUYOnQoIiIiEBAQAGdn5+JzZNgyMFQyhKHS/8eOMQyDlNwURGdGIzozGjFZMfBP8sfHpI+4HHQZ2QXZsFC1gLW6NWzUbWClZgVrdWsoc5RF+mdRlq1bt2LlypVwdnbGX3/9Va17FRRkIT3jfWHgS/dBWtpbFBSk/zdZwwbq6l1gZroAqqptICurV69aQj9//owvX75g6dKl4i6FSAh6pyBESMuWLUN2djYmLFyPzbc+4cjUNtBVkRd3WQBoFrAoFL3pMwwDFouFsWPHYvDgwdi2bRs2b94MFouFkydPYuLEiSJdb7GPSR/0Nu5dPLHjW/436HP1YaVmBTlpOSRmJ2LM9TFY+nAp/u73d5ktclevXi3xe09PT6iqqqJNmzaVfs3qcupQl1NHM81mxa/nFOSgh1EP+Cf743PyZwQkB+BWyC3k8Qu3B9NX1C8OhdZq1rBWt4YB10BkwenVq1c4ePBglfYrZhgGubkx/y3DUtjCl5kZAIbhQVpaGSoqtjA2+um/yRotIS2tKJKaJRGfz8eJEydgamoKW1tbcZdDJAS9UxAipKCgIBQUFKCZoQo+x2bA81M8JnUwEXdZAKgLWBS+D4BFFBQUsGHDBoSHh8Pd3R1TpkwpXjambdu2In22qYopTFVMSx3TlNfEDvsd+OnOTzjgewDzW8+v8F7fvn3Do0ePMGDAgCp/KJCTloOtji1sdf4fGvL5+QhLC8PnlM/FofBcwDmk5BZOVODKcItbCG3UbWCtbg0LVQvISgk34SQzMxMTJ07E4cOH8dtvv1V6Pp+fj8zMT8Xr7qWl+SA3NxYAoKBgBhXl1jDQnwAVldZQVLQQyWSNuuLBgwcICQnBhg0baJF4UowCICFC4vF4kJKSgrKcDFqbqOFhYILEBEDqAq6+sgJgEWVlZRgZGcHd3R3Ozs5o164dpk+fjt9//x26ujW/D21rndZY3GYxDrw7gH6m/WClblXuuV5eXsjLy0PPnj1FWoMMWwaWapawVLPEIPNBAAr/rBKyE/A5+XNxMHwW/QznAs6BAQMplhTMVMxgpWaFzvqdMcRiSKXPmTdvHgYOHIjevXsLFADf+IxHevrbwskaSi2gqzMUKiqtoaJiCw5Ho9pfd12VkZGBa9euoXfv3rCyKv/nhTQ89E5BiJAKCgqgoFC4tpe9lRZcHwQjr4APjrT4P1lTF3D1VRQAiyaBdO/eHW/evMHhw4exevVqXLp0CWvXrsWCBQtEsk1cRSY1noSglCDcCb8DVTlVaCuU3pkkPDwc58+fR8+ePaGhUfPhh8ViQVtBG9oK2uhq2LX49W/53xCcGoyA5AAEpgQiIDkAFwMvVhoAz507Bx8fH7x69UrgGgwNJkPBcjWUlJqIZbKGpPLw8ICKigrGjBkj7lKIhBH/OxYhdcz3IcveSgtZeTy8CU+p5KraQV3A1VfURVZRAAQAaWlpODk5ISgoCFOnTsWKFSvQvHlz3Lp1q0brY7FYWNFuBZQ4SrgQcAG5Bbkljn/79g1//fUX9PX1MXny5BqtpTIKMgpoodUCY6zHYHWH1Tg14BSO9z9e4TWRkZFwdnbG6dOnIScnJ/Cz9PSGQkWlFYW/77x79w6Ojo7Q19eHkpKSuMshEoYCICFCKuoCBoAmesrQ5MrCK7DiDe5rC3UBV58gLYDfU1dXh4uLC3x9fWFoaIiBAwdi4MCBCAwMLHW9qCjIKGCw2WDk8nJxLfhacU0Mw+Do0aMoKCjAokWLarw1sirYlYy9e/PmDeLj49G6dWtIS0tDWloaDx8+xJ49eyAtLV3uLOxVq1YhIiKiJkquk/h8PpycnGBgYABHR0dxl0MkEAVAQoT0fQsgm81CNytNPPycIOaqClEXcPVVFAAr2gquWbNm8PT0xOXLl+Hv749mzZph2bJlSE9Pr5E6NRQ0MKjRIERmRsI33hcMw+Du3bsIDw/HnDlzoK1dumu4LujVqxf8/Pzg6+tb/KtNmzaYOHEifH19y23hfvnyJaytrbFq1SpkZGTUctWS5+TJk3j27BlcXFzo3wRSJgqAhAjpx5Blb6WFgNgMxKXniLGqQtQFXH1FAbCsLd8q2wqOxWJhxIgR8Pf3x9q1a+Hq6gpLS0u4ubmJdAu5Itbq1pBiS2HBgwUYvWA0xo0bB11dXTRt2lTkz6otSkpKaNasWYlfioqK0NDQQLNmzcq9zt3dHUuXLsXOnTthaWmJw4cPi3zNxroiLS0Ny5cvx7hx49C9e3dxl0MkFAVAQoT0fRcwAHS11AKLBTwMFH8rIHUBV5+wXcBlkZeXx+rVqxEQEIBevXrhp59+Qvv27fHs2TOR19sJnZAUlAR/C3/sO7YPixcvFvkz6gJFRUVs2rQJgYGB6NOnD2bNmgVbW1t4eHiIu7Rat27dOmRlZWHHjh3iLoVIMAqAhAjpxxZAdUUOWhiqSkQApC7g6hNFACxiZGSEM2fO4NGjR+DxeOjUqRMmTZqEd+/eVbvO1NRU/PHHH+jaqSvk7spBQ0sDnnKeKOAXVPveksbLy0vgXUCMjIxw8uRJvHz5EioqKujbty8GDhyIT58+1WyREsLPzw979+7F2rVrYWBgIO5yiASjAEiIkMoKWfZWWngclIgCnui7+YRBXcDVJ8oAWKRLly549eoVDh06BC8vL7Rq1QqdO3fG6dOnkZubW/kNvuPj41M8s/PXX3+Fo6Mjnno8xa6eu+AT5wOXty5C11cftW3bFt7e3rh06RICAgLQvHlz/Pzzz0hIEP8HtZrCMAzmz5+PRo0aYeHCheIuh0g4CoCECOnHLmCgMACmZefjXVSamKoqRC2A1VcTARAApKSkMHPmTISGhuLy5cuQl5fHpEmTYGhoiNmzZ+PPP//ElStX4Ovri/T0dOTn5+PLly/w8PDAoUOHsGLFCnTo0AF2dna4c+cOVq1ahcjISOzduxdycnJoo9sGC1svhNsHN9yLuFetP4P6gsViYeTIkfD398fWrVtx8uRJWFpaYseOHUIH77rg/PnzePjwIVxcXCRyBjiRLPROQYiQygpZLQ1VoCIvg4eBCbAzURNTZTQGUBRqKgAWkZGRwYgRIzBixAgEBATgwIEDuHfvHk6fPo2srKwyn8Vms2FsbIxmzZrh6tWrGDRoUJnf56lNp+J94nusfrwaFoMsYKIsGTvUiJusrCyWLl2KqVOnYsOGDVixYgVcXV2xfft2jBw5UmR7F4tTZmYmlixZguHDh6Nv377iLofUAdQCSIiQygqA0lJsdLHUFPs4QOoCrr6aDoDfs7GxwV9//QU/Pz9kZGQgLi4Oz58/x5kzZ3Dw4EHcvXsXwcHByMnJQWhoKK5fv45hw4aVG/JZLBY2dtoITXlNLHywEN/yv4ms1vpAS0sLe/fuhZ+fH5o0aYLRo0eja9euePnypbhLq7ZNmzYhOTkZu3btEncppI6gAEiIkMoLWfZWWngflYrkrDwxVFWIuoCrT9CdQESNxWJBW1sb7du3x/jx4zFz5kz06dMHjRo1goyMjMD34XK42NV9F75mfsWm55tqrN6qSr9zR9wloHHjxrhx4wbu3r2L9PR0tG/fHpMmTaqzC0kHBARg165d+PXXX2FiQq2+RDAUAAkRUnndrPZWWmAY4FGQ+FoBqQu4+mqzBbCmWKhZYH3H9bgRcgPnP58XdznFko4cQdLBQ+Iuo1ifPn3w9u1bHD58GJ6enrC2tsbq1avr1ELSDMNgwYIFMDIywrJly8RdDqlDKAASIqTyWtl0lOVgo6sk1m5g6gKuvvoQAAFggPkATLCZgG2vtuFdQvWXnakOhmEQv3s34v/YAZVRI2vkGTHBQVW6TkpKCo6OjggKCsLixYuxY8cOWFlZ4ciRI3ViIWl3d3d4eHjgr7/+EmrvZEIoABIipIpClr21FrwDE8HniyckUBdw9VV1KzhJtLTNUjTVaIolXkuQnJMslhoYhkHcli1I2n8A2kuXQH3ChBp5zsOTfyMhIqzK1yspKWHz5s34/PkzevbsCUdHR7Ru3Rr37knujOpv375h0aJFGDBgAAYNGiTuckgdQwGQECFV1M1qb6WFxMxc+MfUzP6vlaEu4OqrzlZwkkZGSgY77Hcgn5+PX7x/AY9fuy1aDI+HmDVrkHLyFHTXrYWGo2PNPIdhICMnhytb1iE9sXot8CYmJjh9+jSeP38OLpeL3r17Y/DgwQgICBBRtaKzbds2xMTEYPfu3fViJjOpXRQACRFSRa1sbUzUociREls3MHUBV1996QIuoquoi+3dtuNl7Evs891Xa89l8vMRvWwZ0q66Q3/bVqiNH1/le/FzCxDn4oPsj4llHmexWOg3xxksNhtXtqxDTlZmlZ9VpH379nj8+DEuXLiADx8+oFmzZpg/fz4SE8uuobZ9+fIF27Ztw7Jly2BhYSHuckgdRAGQECFVFLI40mx0shDfcjDUBVx99S0AAkB7vfaYbzsfh/0OwyvSq8afx8/NRdT8BUj38ITBX7ugMmRIte7HlpUGGCDbv/xubK6aOkau3IislGRc27EZBfn51XomUPj9Hj16ND59+oQtW7bgxIkTsLCwwJ9//in2haQXLVoEbW1trFy5Uqx1kLqLAiAhQqqsm9XeSgs+4SlIz6n+G5CwqAu4+upjAASAn5r9hB5GPfDro18RmR5ZY8/hZ2UhcvYcZD1/DiNXVyj36SOS+3JMlJEXVvFOOxqGRhi6fA2igwJw23UXGBF118vJyWHZsmUIDg7GhAkTsHz5cjRp0gTu7u4iub+wbt68ievXr2Pnzp1QVFQUSw2k7qMASIiQKmtls7fSQgGfwdPgpFqsqhB1AVdffQ2ALBYLm7tshpqcGhZ5LUJ2QbbIn8FLS0PEjJ+Q4+cH478Pg9u1i8juLWuqgoKkHPAyKl5n09CmKQbMX4rPzx7B+8wxkT0fKFxI2tXVFe/fv4eVlRWGDx8OJycn5OTkiPQ5FcnJyYGzszN69eqFkSNrZkY1aRgoABIiBIZhwOfzKwxZRuoKMNdSFEs3MHUBV199DYAAoMRRws7uOxGeHo7fnv8m0q+lICkJ4VOnIS8sDMbHjkGhTRuR3RsAOKbKAIC88MonWFm174weUxzx+voV+Px7XaR1AEDTpk1x69YtHDp0CEePHkWXLl0QFhYm8ueUZefOnQgPD4eLiwtN/CDVQgGQECEUrQtWWciyt9KCd2BCrYcF6gKuvvocAAHAWt0aazuuxbUv13Ap6JJI7pkfG4vwSZNRkJQI45MnIN+8mUju+z1pFVlIqckiN0ywGfatBwyF3aDheHD8EAJfPBF5PSwWCzNnzsTTp0+RnJyM1q1b48aNGyJ/zvciIiLw22+/YeHChWjcuHGNPovUfxQACRFCQUEBAMEC4NfUbHxJqP5sRGFQC2D11fcACACDGw3GWOux2PJiCz4kfqjWvfIiIhA+cRKY3FyYnjoFOSsrEVVZmqyJMnIrGQf4PfuJ02HdoQtuuezA1wD/GqmpdevWePPmDbp27YqhQ4fW6LqBS5YsgaqqKtasWVNjzyANBwVAQoQgaADsYK4BWWk2vD7XbjcwjQEUjfKCXn0JgACwvO1y2KjbYLHXYqTmpFbpHrnBwQifOAksGRmYnD4FTg3vQ8sxVUF+dCb4eYKtZ8his+EwbzH0LK3hvn0jkr7WzOQXNTU1XLlyBUOGDIGTkxOio6NF/gxPT09cunQJf/zxB5SVlUV+f9LwUAAkRAhFXcCVhSw5GSm0N9eo9XGA1AUsGg0hAHKkOPjT/k9kF2RjxaMVQi8Snf3xI8InTYaUujpMTp2EjJ5eDVX6f7KmygAfyIsUfK9eaRkZDF26Gopq6riyZR0yU2pmRxQpKSmcPHkSU6dOhbu7e/GHRVHIy8vD/Pnz0bVrV0yooZ1USMNDAZAQIQjaAggUdgO/CE1GtoCtFaJAXcCi0RACIADocfWwrds2PI1+igPvDwh83bc3bxAxdRpkTIxhcvwYpDU1a7DK/5PWVgBLThp5Ao4DLCKnyMWIlRvALyjA1a0bkJf9rUbq43K5GDt2LHx9fXHlyhWR3dfFxQWBgYHYu3cvTfwgIkMBkBAhCBsA8wr4eB5ae8vBUBewaJQX9OraXsCC6KTfCfNazcOBdwfgHeVd6fmZT54g4idHyDVpAuMjbpBSVa35Iv/DYrMga6Ik1DjAIsqaWhixcgNS42JwfddW8ETYQvc9CwsLDBs2DFevXkVISEi17xcTE4P169dj3rx5aNGihQgqJKQQBUBChCBoFzAANNJShIGqPB7W4jhA6gIWjfL2/K1rewELamaLmbA3tMfKRysRlRFV7nkZnp6ImuMEhfbtYHToIKS4tb8IMcdUBXkRGWD4wgdxLRMzDF26ChEf3sPjkEuNhXkHBwdoamrCw8Oj2vdat24d5OXlsXHjRhFURsj/UQAkRAjCtACyWCzYWxcuB1MWPp+Pr1+/IjU1VWRvRNQFLBoNpQu4CJvFxuYum6HEUcJir8XI5ZXe5izt+nVEOS8Et1cvGLm4gC0nJ4ZKC8cBMrk85MdmVel642Yt4TB3IT4+vIenF0+LuLpCbDYbvXr1wtOnT5GZWfWVAEJCQnDx4kVs3boVqrXY0koaBnqnIEQIwgRAoLAb+MyLCBw+cwVp0SEICQlBaGgoQkJCEBYWhry8wl0NFBQUYGhoCENDQxgYGBT///e/19LSAptd8Wc26gIWjYYWAAFARVYFu7rvwuR/J2PLiy1Y32l98bGUc+cRu2EDVIYPh96mjWCJ8WeMY6gESLGQF5YOjj63Svdo3KU7MpIS8ejMMSipa6JFbwfEhQTj/b3biP0ShKyUZCiqqUO3kSVa9HKAjrmF0M/o0aMHLl++DG9vbwwYMEDo63k8Hv755x+0adMG06ZNE/p6QipDAZAQIQjaBczn83H//n24HjgMxmwCFv/xNxD8CObm5jAzM8PAgQNhZmYGU1NTZGdn4+vXr4iKikJUVBS+fPkCb29vfP36tcRMQhkZGejr61cYFKkFUDQaYgAEgMYajbGq/Sq4vnPF/Yj76GncE6nu/yD1wgVoLV0CjenTwarkQ0hNY8mwwTHgIjcsDdxO+lW+T9shI5GRlACPw/vg//gBvn76CBZbCsx/s6GzUlOQEB6G9563YdOpG/rMng+OnHyl9502bRpSU1Ph7u6O58+f48OHD3BwcCjx4e3YsWNYuHAhUlNTy72Pl5cXYmNjsX379ko/+BFSFfROQYgQKmsBTElJwfHjx7F//34EBgaiefPmMG5aAPu5q3DcsZNQM/j4fD7i4+NLhMPv/9/HxwdRUVHIzi65p+uKFStw5MiRCoOivHzlb2QNWXmTPep7AASA4ZbDMajRIMiwZQAAqsOGQnXYUDFXVRLHVAXZvvFgGKbKs2JZLBa6TpgG/0eF4Q9AcfgrUvT7z88fI+lrJMb/tgMyHFmBn6GiooKMjAzExcVBT4hlctLS0uDu7o7u3bvD1tZW4OsIEQYFQEKEUF4AzMjIwC+//IJjx46hoKAAo0aNwpEjR9C5c2fsf/gFe+8HI5/HgCMt+JsVm82Grq4udHV1YWdnV+Y5DMMgNTUVUVFRiIiIwKBBg2Bvbw9tbW1ERUXBy8sLUVFRpVoa1NXVK+xuNjQ0hLKycoNdcqKhtgAWKQp/kkrWVBmZ3lHgpeZCWq3qYxG9T7oh/4cPUGVh+HwkRoTB69hh9Jn1s+B1ysoiIyMD8fHxQgXAc+fOgcVioV+/fgJfQ4iwKAASIoSyuoA/fvyIkSNHIjo6GqtWrYKjoyN0dHSKj9tbaWH77c94HZ6MTo1Eu14ai8WCmpoa1NTUYPXfFlyjRo3C5MmTS5yXlZVV3Hr4Y4uij48Prl27hri4uBLXKCoqVjouUVNTs152TzXkAMjweGId4ycIjknhThh5YelVDoAZyYl4f++2wN9PhmHgd/8OOowaDyV1DYGukZGRAYvFKvV3qyLBwcHw8vLCrFmzwOVWbYwjIYKgAEiIEH5sATx16hRmz54Nc3NzvH79ujiEfa+JnjK0lGTxMDBB5AGwotq+p6ioCCsrqzLrK5KXl4eYmJgyu5uDgoLg5eWF6OjoEuMSORxOpeMS9fT06ty4xIYaAJn8fCTscYH2ksXiLqVCUooykNaSR25YGhRstat0j4DHDwGwAAjx/WSxEPj8MewGCN4lLi8vj4QEwZaC4vP5OHr0KExNTWFvby94XYRUQd36V5kQMSsKPzweD05OTjhw4ACmTJmC/fv3Q0FBocxrWCwWullq4eHnBKzs37jGa6vqLGAOhwMTExOYVLCfK4/Hq3Bc4uvXr/H169cS4xKLurK/D4dlBUU5MS0rUpaGGAD5ubn4unARCpJqb+Hy6pA1VUGukDuCfC/payTAYgmV/wAWUmKE2+dXQUEB8fHxAp3r5eWFkJAQbNiwoV62rBPJQgGQECEUdQFPnToVwcHBOHToEBwdHSsdK2dvrYXLPlGIS8+BjnLNBJ2i2mqytU1KSgp6enrQ09NDmzZtyjyHYRikpKSU2d0cFRWF+/fvIyoqCmlpJXdz0NDQEGhcYm1oaAGQn5WFyJ9/RvZbXxi7HRF3OQLhmCoj63Us+N/ywVaofMxiQX4+MpISkB4fj7SEOHwN8C816aMyDJ+HXCHW9VNWVkZcXFyptQBTU1OhoqJS4rXMzEycO3cO3bp1q7ClnhBRoQBIiBCKWrYSEhLw5MmTcidn/KirhSZYLOBhYALGtDGqkdqEXaOwprBYLKirq0NdXb3CrasyMzPLHZf4+vVruLu7l2o54XK5Ao1LrO7klYa0FRwvPR2Rs2YjNygIxocPQaF16wrP379/P/bv34+wsDAAQNOmTbF27Vr079+/Fqr9P1lTZYABciMyIG+jXhjwEuORlhCP9IQ4pCfEIy2+8L/pCXHITE0Bir53LBZkZAWfzVuELSUFroZg4/8AwNraGi9evIDmD3sl+/j4lAp5Fy5cQEFBAcaPHy90XYRUBQVAQoSwf/9+AMCBAwcEDn8AoKbIQUtD1VoJgHVlIWgulwtra2tYW1uXe05ubm654xIDAwNx//59REdHF7d+AoVd2ZV1N+vq6lYYlBvKVnAFSUmIcJyJguhoGB87CvnmzSu9xtDQEFu3boWlpSUYhsHx48cxdOhQvH37Fk2bNq25WvPykJ6YUCLcGbIN8fbEVbxPfoislOTic1ksNrjqGlDW0oaqji6Mm7eEspY2VLR0oKylAyUNDQS9eIqbe/4QqgY+jwddi/J/Xn/k5OSEXbt24fr16+jcuTNkZWVx8+ZNnD17FtevXy8+LzQ0FJ6enpg8eTLt+EFqDQVAQgR08eJFXLlyBQCECn9F7K20cOxpGAp4fEhLiX58T210Adc2WVlZmJqawtTUtNxzeDwe4uLiyh2X+OrVK0RFRSEnJ6f4GjabDT09vXKDIsMwxbu0fK8+dQHnx8YiYsZP4KWnw/jkCcgJ2O04ePDgEr/fvHkz9u/fj+fPn1crAObn5SIjMQHp8XHFrXjft+ZlpaYUn8tiscHV0ICSSn9oyOihec9+UNHShrKWDlS0tcFV14RUJX8PLNp1ghxXCTmZGQLXKKfIRSO7dhWew+fzi/8O6uvro2fPnkhISEDv3r2Rl5cHGxsbXLx4EQ4ODsXnHzt2DAYGBujTp4/AtRBSXfXnnYKQGvT582fMmDED3bp1g7e3d5VClr21FnbfC8K7qDTYmaiJvEZJ6QKubVJSUtDX14e+vj7atm1b5jkMwyA5ObnccYmenp6IiopCevr/JxWsXbsWe/bsKREOY2NjAQAeHh7FYVFJSalWvk5RyouMRMS06WAYPkxPnQSngoBdER6Ph4sXLyIrKwsdO3as8Nz8/Dykf9clm5YQX/z7tIQ4fEtLLT6XxWZDSUMTylraUNc3hGnL1oXh7r+Qx1XXgJS0NDIef0Xa7VBYjXAAS1q4D1XSMjLoO2s+ru38XeBr+sz6GdIyFY83jI+Ph4VF4dZxCQkJ0NDQgIuLS7kt3Y8fP0ZgYCDWrFnT4P7uEvGinzZCKpGVlYWRI0fC0NAQTk5OVQ6ALQ1VoSIvg4eBCTUaAOtKF3BtYrFY0NDQgIaGBlq2bFnueRkZGfj69Ss6duyIXr16wdbWtjgovnz5EkFBQQCAvn37Fl+jpKRU6bhEDQ0NiVlUOzc4GBHTZ4CtoACTo26Q0Rd+OzU/Pz907NgROTk54HK5uHr1Kpo0aVLhNefWLEN86BcARQFPCypa2lA3MIJpKzuoaOsUd9Ny1TXAFuDnWNZEGShgkPc1s/D/hWTZvhO6jJ+Kx2eP/zcjuIzW3f9e7zJuCqw6dCn3XikpKXjy5Am8vLwwZ84cAEBQUBBYLBZ0dXXLvObbt284c+YMOnXqVOmfHyGiRgGQkEps3LgRoaGhePnyJQIDAwFULWRJsVnoaqmJh4EJWNxH9LP8GIaBqqoqZKswuJ0UUlJSgo2NDTgcDmxtbbFq1aoSx48cOQJHR0cEBwcjOjq6VItiQEAAPD09ER0dXWKsoKysrEDjEms6vGd//IhIx5mQ1tKC8ZG/Ia2lVaX7WFtbw9fXF2lpabh06RKmTp2Khw8fVhhiOo2eCFl5BShra4OrJljAq4yMviJYMmzkhaVXKQACQPtho6Ftao4Hxw8hJforgMLJHvz/hlSo6emjx5SZMLMte9Z7kRkzZuDVq1dYsmQJhg4dCoZh4OHhgVatWpWa8Vvk0qVLyM3NxYQJE6pUOyHVQQGQkApkZ2fj8OHDmDt3Lpo2bQp/f38AVe9mtbfSwvLL75GclQd1RY4oS4WSkhJmzpwJ/Sq06JCSKloGBgBMTU3RqFGjcq8vGpdYFAx/DIovXrxAVFQUcnNzi68pWmLn+3BoZmYGc3NzmJmZwczMDIqKilX+mr75+CBy1mxwzM1hfOggpKox2YDD4RR3c9rZ2eHVq1fYvXs3Dh48WO41lY2dqwqWFBscYyXkhqVByd6wyvcxa2UH05YHEBMUgPiwUGSlJEFRTQPapmbQs7QRqPX26tWrJX4fHByMsLAwjBkzpszzIyMjcefOHYwdOxYaQswsJkRUKAASUoHz588jJSWluEunuuPs7K20wDDAo6AEDG1lILI6ASA/Px/Jycn1apaquFQWACubCPL9uMR27coOPgzDICkpqdxxiXfu3EFYWFiJySt6enoYPXo0nJycYGNjI/DXk/nkCaJ+ng/55s1h6OoKKW7Vg2RZ+Hx+iTBbmzimKsh6Fg2GYarVzc5isaBv1Rj6VqJZrN3DwwPa2tplDjlgGAbHjh2Djo4OBgwYIJLnESIsCoCEVMDV1RUODg7FrT1l7QUsDG1lOTTWU8bDQNEHwIKCAmRlZUnMWLO6rLoBUNBnaGpqQlNTE61atSrzHD6fj7i4OISEhCAkJATv3r3DiRMnsGfPHvTs2RNz587F0KFDK/xAknHvHr4uXASFjh1guGcP2NXccWXlypXo378/jI2NkZGRgTNnzsDLywt37typ1n2rStZEGRn3IlCQkA0Z7bJ346lt6enpePbsGUaPHl3mjh7Pnz+Hv78/Vq5cSRM/iNjQXjOElOPVq1d49eoV5s6dW/yaKGba2ltpwTswEXy+aJcTKWr5oy2kqq82AqAgipar6dy5MyZPnowdO3YgMjISp0+fRm5uLkaNGgVLS0s8f/68zOvTrt9A1AJncHv2hNHevdUOf0DhLNcpU6bA2toavXr1wqtXr3Dnzh2xLWHCMVYCWEBeNbaFE7VLly5BWlq6zP18c3JycOrUKbRt27bChdIJqWn0TkFIOfbv3w9jY+MSXTSimGlrb6WFxMxc+MeI9g2rqHWSAmD1SUoALIusrCwmTJiAx48fw9fXF/r6+ujWrRtcXFxK1JVy/gKily+HypAhMPhzB1icysecMgyD7PfvKzznyJEjCAsLQ25uLuLj4+Hp6SlQ+GNE/IGnCFtOGjJ6isgNS6v85FoQFhYGT09PjBo1qsytC69evYrMzExMnjxZDNUR8n/U9kxIGdLS0nD27FmsXbu2RNjj8Xhgs9nV6ma1M1GDvAwbe+4FwUKbC66cNMw1uehurQU5maoHy+p2T5P/k+QA+L2WLVvCy8sLy5cvx4IFC/D48WP8/fffyLt4CfHbt0Nt4kTorPoVLAE+FGS9fInEPS7gf/sGsyuXRV5r8vkAKLTWgZyVmsiHKciaqiD7c3LlJ9YwhmFw9uxZWFhYlFgqqEhMTAy8vLwwcuRIaFVxBjYhokIBkJAyfPz4ETk5ORg4cGCJ1wsKCqrV/RuSkIlfr/ohO5+Pu/5xuB8QDwYAj8+AKyuNOfbmcOpuASm28G+QRaGEWgCrr7wt34r+bCVpoo2MjAx27dqFzp07Y8aMGfizbTuMBaAxaxa0Fi2sNGx9e/MGCXtc8O3FC8g2aQydlStrpE6WjBSSjn4Ex1QZKg6mkDUte2mUquCYKCPzaTR4GXmQUhLt7Hph+Pj4ICUlBbNmzSr17wTDMLh58ybMzMyKdwEhRJzonYKQMoSGhgIAzM3NS7xenQDoHZiAgXse41XY/7e0KuAz4P3XNZaZW4AddwMx+cgL5OTzyrtNCdOmTQOLxQKLxUKzZs1w7tw56OrqIjg4uPjY1q1bS1zj7u5OE0UqUVdaAL83cuRIvFq0GOOVleFpYgLNhc4Vfp+/vX+PyJ/nI9JpLpiCAhgePACzy5ehWM5uKtWlNtISGtObgsnlIeHAeyQe/YC8r5kiuTfHtLCrNVeM4wDT09Ph5OSErKys4iVyvnf37l389ttvaNOmDTgCdMcTUtMoABJShpCQEGhra4PL5ZZ4ncfjVamLNTwpC3NOvUFOPq848JXneUgS1v7zQeB7Ozg4ICYmBnfv3sXQoUPh7+8PMzMzAICcnBy2bduGlJSUSu5Cvsdms+tUAGT4fGTcvQtVJS44GzcgqlVh13BZ8mJikHLpMjLu3oWCXWuYnDwJk5MnoGRvX6MfDFgsFuSt1aE93xbqE2xQkJSDeJe3SDrzCfkJ36p1b2kVWUipySJPjOMAvb290bdvX/z222+ljuXl5cHHxwdr165Fr169xFAdIaVRACSkDCEhIaVa/4CqtwBuvvUJuQV8CBIb+Axw4XUUfCIEC22ysrLQ1dWFuro65OXloa+vXxxSe/fuDV1dXWzZskXomhuyutQCyBQUIPXSJWQ+fgJuz54wc3BAx44dcfbs2eKFywEgLyoKySdOIHHfPuR/jYJSv35QnzoVctZWAo0RFBUWmwWFFlrQWWQHtZGWyAvPQNyuN0i+FIiC1KqvJShrqoLccPG0AEZHR+PatWvo0KED1NRKb/N469YthIWFYcCAAdT6TiQGBUBCyhASElLciva9qgTAuPQcePrHVdry9z0pNgunn4cL9ZyicWnfv8FISUnh999/h4uLC6KiooS6X0NWVwIgPz8fySdO4NvrN1AdNRKKrVsDAIYMGQIzMzPs3r0bqYFBSHI7ioRdu5AfHQ3V4SOgNX8+FJo3r9Xg9yOWFAuKbXWhu7QNVAaYI+dTEmL/eIXU61/Ay8wT+n4cE2XkR2eCnyfY8AlRKVrUWUFBocyJH3Fxcbhy5Qp69uxZ7p7AhIgDTQIhpAyhoaHo2rVrqder0gX8PioNwq6AweMzeBkm2KzGGzdugMvlgs/nIz8/Hzk5OSW2pRo+fDhatWqFdevW4ciRI8IV0kDVhQDIz81F8unTyP0cCI3p0yDX+P87WLDZbMwZNQov9+xB1sEDkNLQhNr48ZC3tQVLwmaJs2TYUOpiAMW2Osh8HI0M7yhkvYoDt4s+lLoZgi0n2NuUrKkywAfyIjIgZ6Fas0V/5+XLl/Dz88Py5cvLHNt38uRJKCsrY+jQobVWEyGCoBZAQn7A4/EQFRUFIyOjUsf4fL7QXTjRqdmoSqdPXJpg3WE9evSAr68v3Nzc0K9fP+zcubPUOdu2bcPx48fx6dOnKlTS8Eh6AOSlpyP18mXkR0ZBY6ZjifCXHxuLpBMnkH3wIMwVFXGLx4f28mVQaNNG4sLf99iy0lDuZQzd5W2h2EEPGd5fEbv9FTIeRoERYFKUtLYCWHLSyAtLq7E1B3+Um5uLU6dOoXXr1rC1tS11/O3bt3jz5g0mT54MOREswk2IKFELICE/kJKSgpqaGpKTS7fA6erqIj4+Hvn5+ZCRkRHofqoKMgKN/fuRsrxgfz0VFRVhYWEBf39/6OjowNTUtNQ53bp1Q79+/bBy5UpMmzatCtU0LOIMgDmfPiHH/xMKEhIgraUFucY2kG3cuPjZBcnJiFrgDIX27aExYzo4/31QyY+LQ4aHB7Lf+oKtpgqV0aORpaqK+xs2oN3Hj2XuSSuJpBRloDrADEqd9ZF+PwJpd8KQ8eQrlHsaQ7GtDlhSpdstGD6DnKAUsKRZSH8QiXTPCLAVpCGtqwhuO13IN9MES1r07R3Xrl1DamoqVq1aVepYfn4+Tpw4gWbNmpW7HzQh4kQBkJAymJmZISQkpNTr5ubm4PF4iIyMLHOSSFkstLmVn/QDNguw0lES6pr4+Hhoa2uX20K5detWtGrVCtbW1kLX09CIIwBmvXiJuC1bkBsQUPiClBTw3+LeslZW0Fm5Ahxzc0RMnwGG4UNt7FjIaGuhICEBGR4e+ObzFmxlZaiMHAHFdu3AkpaGJcPA1NQUHh4edSYAFpFSkYXacEsodTVEumc4Uv8JRoZ3FFT6mEC+pRZY/62Vyf+Wj6Tzn5H7ueSkKf63AuSFpiE5JA0yelHQmNwE0uqCt8LFxsZi8+bNuHnzJr5+/QptbW20atUKCxcuRK9evWBkZARdXV0sW7asxNi+9evXw93dHevXr0dCQgKWLFlCEz+IRKIASEgZzM3Ni9cC/F7RxJDyZgmXpYmeMqx0uAiKyxS4JZDPAGPblu6CrkhRACxP8+bNMXHiROzZs0eo+zZEtR0Ak48fR9zWbcD3QYH3/27P3OBgREyfAbaKCtgKCjA5fgwsjgxSL19G1vPnYHOVoDJsKBQ6dABbWhrTpk3D8ePHi6/ncDi4ffs2du7cWbz/bNHX8uzZM3To0OH/z8rNhb6+PpKTk/HgwQN0795dpF+rsKQ15aE+zgZK3Y2QdicMyec/Q+ZhJJT7moLTSAUJB9+jIL6cZWT++zblx2Uhft9b6DjbQUq58jX4wsLC0LlzZ6iqquKPP/5A8+bNkZ+fjzt37mDevHkICAhAVlYW5OTkMGTIkFLXFxQU4OrVq3BwcIChoWF1vnxCagyNASSkDObm5mW2ABobG4PNZpcZDsvDYrGwtK+1UN3AihwpdLUUbquohISECgMgAGzcuFGidrGQVLUZADMePEDclq0AwwDlfW/+e52flgbVceOQdOIEko64IScoCCqDh0Dn15XgdukC9ncz1IvWh3z9+jV69OiBgoICDBo0qMRtjYyMcPTo0RKvXb16tdT6l5JARlcRmlObQsupJdgKMkg64Y+47a8Kw19l3w4+wM8uQNLZAIG+d3PnzgWLxcLLly8xcuRIWFlZoWnTpli8eDGeP3+Ot2/fIjc3F3Z2dmWO7UtJSYGCggJGjBhRxa+WkJpHAZCQMpiZmSEiIgL5+fklXudwODAyMiqxvpog+jbVxWz7ylsMpdgsyMmwwTDAmIPPEJpY8U4Jx44dg7u7O3Jzc5GYmFgiABYd+56pqSlyc3PFPolB0rHZ7FrZCo7Jz0fMqtUlW/4qkbhrFzIfPQa3W1doL14MbreuYJcxHrVofchmzZpBTU0No0ePRmRkJBISEorPmTp1Ks6dO4fs7Ozi19zc3DB16tTqfWE1SNZEGZozm0N9gg34WQWVh78ifCAvNA35lew+kpycjNu3b2PevHlQVFQsdVxRUREnTpwAh8OBiYlJqePx8fH49u0bJk6cCAUFBQGLI6T2UQAkpAzfj/X70aBBg3D27Fnk5Qm3VtkKBxtsHNoU0mwWftzqt2jvXyttLu4utMe1+V3AMAwmHX6BwLiMSu/99OlT8Hg8tGrVSqiaSNlqqwUw49598JKTC1v/hKD98zwotm0LtgBbisnKykJBQQH//PMPLCwsoKGhUXzMzs4OpqamuHz5MgAgIiIC3t7emDx5snBfSC1jsVjgZ+VXfuKP2Cxk+yVWeEpwcDAYhoGNjU2Zx2/duoWEhARwuVysWLECXC63xK+DBw9CVlYWnTt3Fr4+QmoRBUBCylA0UeLFixeljjk5OSEuLq7EWnuCYLFYmNLRFM9W9sKSvtboYqEBQzV5NNZTwqAWejg6vS1uLugKYw0FWGhz8c/PXdDeXAOX30ThzsdY8MtZ2oJhGNy9exe2trbQ0hKu25iUrbYC4DefN4CwO8tISyPnU0ClpxWtD8nlcuHm5oaXL1/i/Pnzxa2YRWbMmAE3NzcAha3GAwYMqBM/RwWJ2YCUkJMr+Azyk7IrPKWi721SUhKuXr2Kfv36QUpKCsuWLYOvr2/xr127dsHCwgLq6uo08YNIPAqAhJTB2NgY9vb2OHjwYKljTZs2hb29Pfbt21ele2spyWJeDwuccuyAx7/0xL/O3bB7nC16WGuD/V3TIFdWGn+OaQl7Ky3cD4jHIe8v+JZXUOp+wcHBCAsLQ58+fapUDymNzWaDxyu99lxReCrrWFUUxMSUmOwh2EUFyIuNqfS0ovUhfX19sWTJEpiZmaF///4IDy+5w8ykSZPw7NkzhISE4NixY5gxY4Zw9YgJP5cnePfvd5jciv+8LS0twWKxEBBQOmSfPn0acnJyGDlyJABAU1MTFhYWxS2rT58+RePGjctcEJoQSUMBkJByzJ07Fw8fPsTHjx/LPPbo0SP4+fnVaA0sFgudLDQxvZMJQpO+Ycedz4hOLdmC4eHhAW1t7eLZnaT6dHV1ER0dXep1PT09ACjzWFWwlZQBYbdjk5KCtLJypacVrQ9pYWEBTU1NTJgwAVlZWTh8+HCJ8zQ0NDBo0CD89NNPyMnJQf/+/YWrR0yk1eQgdAJkF11XPnV1dfTr1w/79u1DVlZW8ev+/v549uwZBg8eXObYvjNnzoDD4aBJkybC1USImFAAJKQcw4YNg66uLvbv31/mMX19fSxdulRkrUEVsdFTwbK+1uBIs/Hn3UD4RBSueRYcHIxnz56hT58+pbr2SNWZmZlVuAyQMLPAKyJrbib0+D8wDDjGxkJdUrREEJvNLjHho8iMGTPg5eWFKVOmCL3VobjINlIBhJ2Lwwc4ZpWH53379oHH46Fdu3a4fPkyPn36hD///BMpKSlYunRpqfMDAgLw+PFjjB8/nlr/SJ1B7xiElIPD4WDmzJk4ceIEMjIySh07duwYPDw8sHnz5lqpR1NJFov6WKG5oQqOPQnDxReh+Gv3HpiZmcHBwaFWamgoipYB+nE8mJqaGlRVVctcIkhY+XFxyA3+Uv7SL+VhGCj1q/z7nZubi9jYWERHRyM4OBiXLl1CZmYmBg8eXOpcBwcHJCQkYOPGjcLVIkYcE2VI6yhAmH0WWXJSkG+qWel55ubm8PHxQY8ePbBkyRK0aNECZ8+eBY/HK/WBkMfj4ejRo7CwsEC3bt2E/TIIERsKgIRUYNasWfj27RtOnz5d6lifPn2wfv16rF+/Hnfv3q2VemSlpTC1owlGtTbA64g0yLccgJlz50Na2IkEpELm5uZIT08vczvA8taIFFR+TAxiN27El959kPngAWSbNRW8G5jNhsqwoeAY6Fd66u3bt6GnpwcDAwP8+++/CA0NxcWLF8tc2JnFYkFTU7NOtV6xWCyoj7EWagkd9dHWYHMEa+HU09PD3r178e7dO0yePBkuLi7w8PAo/vMLCwvDwoUL4enpicjISEybNg1sNhvr16+Hr69vFb4iQmoXBUBCKmBoaIghQ4Zg3759Zc4OXL16Nfr27YsJEyaUuWRMTWCxWMiLfA/p4AfQMzLBtU9piEmreGYjEU7RLi9ldfWWt0tMZfJjYhCzYQO+9O2H9Ju3oDlvHhrd84TpyZOQbdSo8hDIZoNjZgbdNWsqfdaxY8fAMAwYhsH79+8xatQo3Llzp3jyAlA423XYsGFlXq+qqgqGYcS+C0hlOAZcaEy0AUuGXf67Gavwl8pgc8g31SjnpPKdO3cOUlJSGDNmTKljaWlpuHjxInr06IFGjRoJfW9CxIkCICGV+Pnnn/Hhwwc8ePCg1DE2m41Tp05BQUEB9vb28PHxqdFaeDwefvvtN4wePRrK7DyM72CKx8EJGOLyGNd8v9bosxuS77f8K+uYMC2A+dHRiFm/HsF9+yHj39vQnD8fje7dg+ac2ZDicsGWl4fJ2TNQ6tWz8IIfx+D993tud3uYnj0DtpCLCz9+/Bja2trFE1jqG/mmmtCebws5a/Uyu4M5JsrQmt0CSp0NhL53cHAwvLy8MHbsWCgpld6b+/z58wCAsWPHCn1vQsSN+o0IqUSPHj3QrFkz7N69Gz179ix1XFNTE97e3hg1ahQ6deoEFxcXODo6inwdsPj4eIwfPx5eXl747bffsGzZMrDZbByd1g5r3D9gwTlfvAlPwepBTSAjRZ/tqqOisX7W1tYIDw9HQkJChevl5X/9isSDh5B69SqkuFxoLZgPtfETIMUtvbuEFJcLgz17kP3WFykXLiDHzw8FCQmQ1tKCXLNmUBszGvKtWwv9M5Weno5nz55h9OjR9XqSkIy2AjSnNgUvPRd5MVngp+WBzZWBjI4CpDXkq3RPPp+Po0ePwtTUtMy/98HBwXjw4AFmzJgBZQFmZRMicRhCSKUOHz7MsFgsJjg4uNxzsrOzmTlz5jAAmKlTpzJZWVkie/7jx48ZfX19Rltbm7l//36p43w+nznxLIyx+PUmM2r/EyYuPVtkz26oWrduzcycObPU64mJiYycnByzZcuWMq/LjYxiolevYfybNWc+d+jIJB4+zPAyM2u63DJdu3aNmTx5MpOWliaW59dl9+7dY8aNG8d8/vy51DEej8f8+uuvzIoVKxgejyeG6gipvvr7kZAQEZo4cSLU1dWxd+/ecs+Rk5PD/v37ceLECVy4cAEWFhZYt24doqKiqvRMPp+P27dvY+jQoejWrRsaNWqEt2/fokePHqXOZbFYmNzBBOdmdUB40jcMdnlcvFQMqRpbW1t4eHiUWuZHQ0MD48aNw4EDB0ocy4uKQsyaNfji4ICM+/ehvXAhLO55QsPREewy9pStaXw+H56enujQoQO1UAkpPT0d586dQ7du3WBlZVXquJeXF0JCQoonfhBSF9FPLiECkJeXx6xZs+Dm5lZqSZgfTZ48Gb6+vhg2bBh27twJU1NTjBw5Ep6ensjJyanwWoZhEB0djR07dsDKyqp454YDBw7g3r170NevePannYk6bszvAiM1BYw9+AxnXkQI/bWSQrNmzUJYWBhu375d6tjcuXMRHh6Of//9F3mRkYhetQpfHPoj4/4DaC9eDAuPu9D4aYbQ4/VE6d69e4iPj6cdYoTE5/Oxd+9esFgsjB8/vtTxzMzM4nBYtGUkIXURi2FEtKklIfVcVFQUTE1NsWvXLsyfP1+ga9LT03Hq1Cns27cP/v7+AAADAwOYm5vD3NwcpqamSE9PR0hISPGvrKwscDgcjBkzBnPnzkWHDh2EHvuVV8DHbzf9ceJZOMa1NcL6IU0hJ1M3FviVFAzDoG3bttDR0cHNmzdLHR9oZ4cxLDbaZWdDSk0NGo4/QW3sWLDlqzbmTJRCQkKwbt069OzZE9OnTxd3OXXKxYsX4e7ujpUrV6JZs2aljru5ueHx48fYuXMnVFVVa79AQkSEAiAhQhg3bhx8fHwQEBAgVNcPwzB49eoVPnz4gNDQ0OKwFxYWBmVlZZibm8PMzKz4v127doW2tna16734OhKr3D+gsZ4yDkxqDT0V8YeTusTNzQ2Ojo4IDg4uXhomLzwcifsPIPWff5CYnw9dJydYzHWSiOAHFLZQbdq0CcrKyli+fDlkZGTEXVKd8eHDB+zevRtDhw7FoEGDSh2PiIjApk2bMGbMGGpZJXUeBUBChPDs2TN06tQJN27cwMCBA8VdjkDeR6Vizsk3yOPxsXdCa3QwF34ttIbq27dvMDAwwKxZs7DJyQmJ+w8g7cYNSKurQ2nqFLRavhxDRo3CoUOHxF0qgMLuy2PHjiEiIgLOzs5QU1MTd0l1RkxMDA4ePAhjY+Myx/bl5ubCxcUFUlJSmD+fFl8ndR8FQEKEwDAM2rdvD1VV1Vrb/UMUkjJz8fOZt3gZloxVAxpjemdTkS9TU19tmDUbynfvwkFBAdKamtCYOROqo0eBLScHV1dXzJs3D+fPny9zoeDaxDAM1qxZg3379uHChQvUQiWECxcuYNGiRTA1NcX169ehrq5e4jjDMJg5cybu3LmD+/fvw9LSUkyVEiJC4ph6TEhddurUKQYA8+HDB3GXIpT8Ah7z242PjMkvNxjnsz7Mt9wCcZck0XK+fGGili5j/Bs3Ye6bN2L+nb+A4eXklDiHz+cz48ePZ7hcLvPp0ycxVVpo3bp1DABmz549Yq2jLsnOzmZmz57NAGCmTJlS7tJNe/fuZQAw58+fr+UKCak51AJIiJDy8vJgamqKwYMH4+DBg+IuR2jX3kXjl0vvYaapiIOT7WCkLr6ZqpIo98sXJO4/gPSbNyGtowONWTMx6ehRJKSm4sWLF6W6BjMzM9GuXTuw2Wy8ePECimJY8mXHjh1YtmwZfv/9d6xcubLWn1/XMAyDx48fY9GiRfjw4UOFi7e/ePECXbt2hZOTE3bv3i2GagmpIWIOoITUSRs3bmTk5eWZpKQkcZdSJf7RaUzXbfeZlhvuMA8/x4u7HImQExTERC1azPjbNGYCu/dgks+cYXi5uQzDMMz9+/cZFovF/Pbbb2Ve+/HjR0ZRUZGZNGkSw+fza7NsZv/+/QwA5tdff63V59ZF6enpjKurK9OsWTMGANO4cWPmzZs35Z6fmJjIGBkZMR06dGBy//tZIKS+oABISBXExcUxHA6H2bp1q7hLqbLUrDxmqtsLxmzFDWbfg6BaDy6SIicwkIlatOj/we/s2eLg9721a9cybDab8fT0LPM+p0+fZgAwu3fvrumSi504cYJhsVjMggULGuz3rzwZGRnM+/fvGXd3d2bXrl3MjBkzGC6Xy7DZbGb48OGMh4dHhbt4hIWFMW3atGE0NDSYiIiIWqyckNpBXcCEVNH06dNx7949hISE1NkZgTw+g10egdj7IBgDmuti+6iW4MrWza9FWDmBgUjcvx8Zt+9AWk8XmrPnQHX4MLA4nDLP5/F46N+/P3x9ffH27VsYGBiUOmfx4sXYtWsX5s6di507d0JWVrbG6r98+TLGjBmDadOm4fDhww1iR4rc3FzEx8cjPj4ecXFxJX59/1psbCwSExOLr5OXl4e5uTlGjBiBmTNnwsjIqMLn3L59GxMnToSSkhIuX74MOzu7mv7SCKl1FAAJqSJfX1/Y2triwoULGD16tLjLqZY7H2Ox5MI76KnI4eBkO5hrccVdUo3J+RyIRFdXZNy5AxkDA2jMngXVYeUHv+8lJCTA1tYWpqamePDgQak19hiGwaFDh7BgwQK0aNECFy9ehKmpqci/hn///RdDhw7FiBEjcPr0aUhJ1d1FvrOyssoMcWW9lpqaWup6NTU1aGtrQ0dHp8QvU1PT4gXXdXR0BJr1zuPxsGHDBvz222/o378/Tp48WWpGMCH1BQVAQqrB3t4ePB4Pjx8/Fncp1RYcn4lZJ18jIT0Xu8a2Qu8mOuIuSaRyPn9G4j5XZNy9CxkDA2g6zYHK0KFgCblQ8rNnz9CtWzcsWLAAf/75Z5nnvHnzBqNGjUJaWhpOnjwp0jUjHz58CAcHB/Tp0weXL1+WuIWeGYZBWlpauSHux9eysrJKXM9ms6GpqQkdHZ0yg933r2lra4MjQHAXREJCAiZMmID79+9j06ZNWLFiRYNoVSUNFwVAQqrhypUrGDlyJF6/fl0vuokycvKx5MI73PWPw4JelljYyxJsdt1eLzAnIKAw+Hl4QMbICJpzZkNlyBChg9/3du/ejYULF+Ly5csYMWJEmeckJydj6tSpuHHjBlauXImVK1dCSUmpys8ECmek9u7dG+3bt8eNGzcgJydXrfsJis/nIykpqcLWue9fy8vLK3G9jIxMqeBWXrDT1NSs1RbNuLg4HDlyBC4uLuDz+Th79ix69uxZa88nRFwoABJSDTweD40aNUK3bt1w4sQJcZcjEnw+A1evYPzpEYie1trYObYVVOQlq5VJEDn+/khwdUWm5z3IGBtDc/ZsqAwZXK3gV4RhGIwZMwZ37tzBxYsX0a9fvzLP4/P52L59O1avXg0FBQVMmTIFTk5OaNq0qdDPfPfuHbp3746mTZvizp071V5uJj8/X6DxdHFxcUhISACfzy9xvby8fIWtc9+/pqamJlELjzMMgydPnsDV1RWXLl2ClJQUxo8fj02bNpU5tpOQ+ogCICHVtGPHDvz666+IiIiArq6uuMsRmQef4+F89i00uLI4ONkOVjrVa72qLdkfPyLRdT8y7/0X/JycoDJ4EFginqiTkZFRHALXrl2LNWvWlNtyFRkZicOHD+PQoUOIi4uDvb095s6di2HDhgnUhRkQEIBu3brByMgI9+/fh4qKSpnnZWdnl9sy9+PrycnJpa5XUVEpM8SVFe643Lo1TjQzMxOhoaF4/Pgx9u/fDz8/P1haWsLJyQlTp06lsX6kwaEASEg1paSkwNDQEMuWLcP69evFXY5IhSdlYfbJN4hI/oY/RrXEwBZ64i6pXNkfPiJx3z5kPngAjokJNJzmQGWQ6IPf9/h8PjZv3ox169ahT58+OH36NDQ1Ncs9Py8vD1evXoWrqyu8vb2hq6uLzp07F09WMDc3h5mZGUxMTMDhcMAwDPz8/NC3b1/Iy8tj3bp1xSGvrFCXkZFR4nksFgsaGhoCdb1qa2vXWpeyqOXk5JT4s4iNjUVYWBhCQkIQEhKC0NBQxMfHAygcYzhkyBDMnTsXvXr1onF+pMGiAEiICMybNw+XLl1CREREjS79IQ7f8grwy2U/XH8Xjdn25ljezwZSEjQuMNvvQ2Hw8/ICx9QUmnOdoDxgQI0Gvx95enpi/PjxkJOTw8WLF9GhQ4dKr/nw4QOOHDkCPz8/hISEICIiAjweD0BhSNHS0kJqaipyc3NLXCclJVUqyJUX7LS0tOrkEkUMwyAzM1Pg1sz09PQS17NYLBgaGhYH6u/DtZWVVYUhnZCGggIgISLw+fNn2NjY4Pjx45gyZYq4yxE5hmFw5HEotvwbgI7mGnAZbws1RdHMvqyqbD8/JO7dh8yHD8ExM/t/8BPTkihRUVEYM2YMXr16hT///BPz588XatxbQUEBIiMjERoaipCQEAQGBuL48ePg8XhwcXFBy5YtoaOjAzU1tTrZasUwDFJSUgRe8iU7O7vE9UXBt7KZwTo6OnU2+BJSmygAEiIi/fv3R3x8PF6/fi1RA95F6emXRPx85i3kZaRwcLIdmhmUPRatJmW/e4eEffuQ5f0IHHNzaDo5QXlAf7EFv+/l5+dj+fLl+OuvvzBo0CD88ssv6Ny5s9A/DykpKejZsydiYmLg7e0NKyurGqq4eng8HhITEwWaGRwfH4/8/PwS18vKypbbgvnj6+rq6nUy+BIiqSgAEiIit2/fRv/+/fHo0SN06dJF3OXUmK+p2Zhz8g0C4zKwf2Jr9GxcO+sFZvv6ImGfK7IePQKnUaPCFj8HB4kIfj+6fPkyVqxYgeDgYDRv3hxz584t3lmiMhkZGejbty8CAwPh5eWF5s2b10LF/5eXlyfQzOC4uDgkJibix7cQLpcrcKhTVlautx+WCJF0FAAJERE+n48mTZqgefPmuHjxorjLqVE5+Tysdv+ATzHpuLmga40+65vPWyTu24esJ0/AsWgErblzodSvn0QGv+/x+Xx4enrC1dUV169fh6KiYqXLwGRnZ2PgwIF4/fo17t+/jzZt2oiklqLdNgQJduXttiHIRBIdHR0oKCiIpGZCSM2iAEiICLm6umLBggUICQmBsbGxuMupUQzD4JZfbI3NDP7m44PEvfuQ9fQpZC0toDlvHpT69gWrDnYDRkRE4PDhwzh8+HDxMjBjxoyBhYVF8axfABg+fDgePHiAO3fuoGvX8oP197ttCBLqKtptQ5DZwaLabYMQIjkoABIiQpmZmTA0NMTs2bOxbds2cZdTZ8WsX4/Uc+cha2UFzblzodS3T50Mfj/6fhmYp0+foqCgAEBhIJOTk0N2djb69OmDrl27wsDAoNwt1eLj40vNDv5xt42Kgl1t77ZBCJE8FAAJEbGlS5fCzc0NkZGR1d6toa7bsmULrly5goCAAMjLy6NTp07Ytm0brK2tK7wuatFiKDs4QKlP73oR/MpSUFCAqKgoBAcHY+3atXj+/Dm6dOmC3NxchISEIDExsU7vtkEIkWwUAAkRsbCwMDRq1Aiurq6YPXu2uMsRKwcHB4wbNw5t27ZFQUEBfv31V3z48AH+/v4VhmOGz6+3we97DMPg559/xv79+3Hq1ClMmDCh+FheXh5kZGQo1BFCagQFQEJqwIgRI/D582d8+PCB3sC/k5CQAG1tbTx8+BDdunUTdzlixTAMVqxYge3bt+Pw4cNwdHQUd0mEkAak/n/EJkQMnJ2d4e/vD09PT3GXIlHS0tIAgPZdBbB582Zs374du3btovBHCKl11AJISA1gGAa2trYwNDTEjRs3xF2ORODz+RgyZAhSU1Px+PFjcZcjVn/99RcWLVqETZs2YfXq1eIuhxDSAFELICE1gMViwdnZGTdv3kRgYKC4y5EI8+bNw4cPH3Du3DlxlyJWhw8fxqJFi/DLL79g1apV4i6HENJAUQsgITUkJycHxsbGGDt2LFxcXMRdjlj9/PPP+Oeff+Dt7Q0zMzNxlyM2Z86cwaRJkzB37ly4uLjQ+FBCiNhQCyAhNUROTg6zZ8/GsWPHise+NTRFs1yvXr2K+/fvN+jw5+7ujilTpmDKlCnYs2cPhT9CiFhRACSkBjk5OSEnJwdubm7iLkUs5s2bh1OnTuHMmTNQUlJCbGwsYmNjkZ2dLe7SatXdu3cxduxYjBgxAn///TfYDWCJG0KIZKMuYEJq2MSJE/Hs2TMEBQU1uN0XymvlOnr0KKZNm1a7xYjJo0eP0K9fP/Ts2RNXrlyhbdUIIRKBAiAhNezly5do37493N3dMXToUHGXQ2rRq1ev0KtXL7Rp0wY3b96EvLy8uEsihBAAFAAJqRUdO3aEvLw87t+/L+5SSC3x8/ND9+7dYWVlBQ8PD3C5XHGXRAghxWggCiG1wNnZGQ8ePMD79+/FXQqpBYGBgejTpw+MjY1x69YtCn+EEIlDLYCE1IL8/HyYmZnBwcEBf//9t7jLITUoPDwcXbt2BZfLxcOHD6GlpSXukgghpBRqASSkFsjIyGDu3Lk4ffo0EhMTxV0OqSExMTHo1asXpKWl4eHhQeGPECKxKAASUktmzZoFADh06JCYKyE1ITExEb1790ZOTg7u3bsHAwMDcZdECCHloi5gQmqRo6Mjbt++jdDQUMjIyIi7HCIiaWlp6NmzJ6KiouDt7Q1ra2txl0QIIRWiFkBCapGzszO+fv2Ky5cvi7sUIiJZWVkYMGAAQkJCcPfuXQp/hJA6gVoACallPXv2RHZ2Np49eybuUkg15eTkYNCgQXjx4gU8PT3Rvn17cZdECCECoRZAQmqZs7Mznj9/jpcvX4q7lBrBMAyuu/jC5264uEupUfn5+RgzZgyePHmCGzduUPgjhNQpFAAJqWWDBg2CmZkZdu/eLe5SakTS10xEfEyGlpGSuEupMTweD5MnT8bt27dx9epV2Nvbi7skQggRCgVAQmqZlJQU5s+fjwsXLiA6Olrc5Yhc2PtEyMhKQd9SVdyl1Ag+n4+ZM2fi0qVLOHfuHBwcHMRdEiGECI0CICFiMGPGDMjJyWH//v3iLkXkwvySYNxEHVLS9e+fF4ZhsHDhQhw7dgzHjh3DiBEjxF0SIYRUSf37F5qQOkBFRQXTp0/HwYMHkZOTI+5yROZbeh7iwtJh2kJT3KXUiNWrV8PFxQX79+/HpEmTxF0OIYRUGQVAQsRk/vz5SExMxNmzZ8VdisiEfyjc5cS4qYaYKxG9LVu24Pfff8eOHTswe/ZscZdDCCHVQgGQEDGxtLTEgAEDsHv3btSX1ZjC/JKga6YMBWWOuEsRKRcXF/z6669Yv349lixZIu5yCCGk2igAEiJGzs7OePfuHby9vcVdSrXx8vmI9E+GSfP61f3r5uaGBQsWYMmSJVi7dq24yyGEEJGgAEiIGPXu3RtNmjSpF0vCfA1KQX4uD2b1aPzf+fPn4ejoiDlz5uCPP/4Ai8USd0mEECISFAAJESMWi4UFCxbA3d0doaGh4i6nWsLeJ4GrLgt1fUVxlyIS169fx6RJkzBp0iTs27ePwh8hpF6hAEiImE2ePBmqqqrYu3evuEupMoZhEOaXCNPmmvUiKHl6emL06NEYMmQI3NzcwGbTP5WEkPqF/lUjRMwUFBQwc+ZMHDlyBJmZmeIup0qSo7OQkZRTL5Z/efLkCYYOHYoePXrgzJkzkJaWFndJhBAichQACZEA8+bNQ2ZmJo4fPy7uUqokzC8R0rJSMLBSFXcp1eLj44MBAwagTZs2uHz5MmRlZcVdEiGE1AgKgIRIAGNjYwwfPhx79uwBn88XdzlCC/dLgpGNGqRlpMRdSpV9/PgRffv2hY2NDW7cuAEFBQVxl0QIITWGAiAhEsLZ2RmBgYG4c+eOuEsRSnZmHmJD0up0929wcDB69+4NAwMD/Pvvv1BSUhJ3SYQQUqMoABIiITp37ozWrVvXuSVhIj4kgWEAk2Z1c/ePiIgI9OrVC8rKyrh79y7U1dXFXRIhhNQ4CoCESAgWiwVnZ2fcuXMHAQEB4i5HYKHvk6BtogRFlbo3Xi42Nha9e/cGm83GvXv3oKOjI+6SCCGkVlAAJESCjB07Fjo6OtizZ4+4SxEIr4CPSP+kOtn9m5SUhD59+iArKwuenp4wNDQUd0mEEFJrKAASIkFkZWUxZ84cHD9+HCkpKeIup1LRwanIy+HBtI5t/5aeng4HBwfExsbC09MTjRo1EndJhBBSqygAEiJh5syZg/z8fBw5ckTcpVQq/H0SFFVloWnEFXcpAsvKysLAgQMRFBSEu3fvonHjxuIuiRBCah0FQEIkjK6uLsaNG4e9e/eioKBA3OWUi2EYhPolwrS5Rp3Z/SM3NxfDhw/H27dv8e+//8LW1lbcJRFCiFhQACREAjk7OyM8PBzXrl0TdynlSo37hvSE7DrT/Zufn4+xY8fC29sb165dQ8eOHcVdEiGEiA0FQEIkkJ2dHTp37izRS8KEvU+CtAwbhjZq4i6lUjweD9OmTcPNmzdx+fJl9OzZU9wlEUKIWFEAJERCOTs7w9vbG76+vuIupUxhfokwtFGDNEeyd/9gGAZz5szBuXPncObMGQwcOFDcJRFCiNhRACREQg0fPhxGRkYS2QqYk5WPmC9pMJHw7l+GYbB48WL8/fffcHNzw+jRo8VdEiGESAQKgIRIKGlpacybNw9nzpxBfHy8uMspIeJjEhg+I/Hj/9atW4e//voLe/fuxdSpU8VdDiGESAwKgIRIsJkzZ0JKSgoHDx4UdyklhPklQdOIC66a5O7+sX37dmzatAnbtm3DvHnzxF0OIYRIFAqAhEgwdXV1TJ48Ga6ursjLyxN3OQAAPo+PiI+SvfuHq6srfvnlF6xevRrLly8XdzmEECJxKAASIuEWLFiA2NhYXLx4UdylAABivqQh91uBxHb/Hj9+HPPmzcPChQuxceNGcZdDCCESiQIgIRKuadOm6N27N3bv3g2GYcRdDsL8kqCgzIG2sZK4Synl0qVLmDFjBhwdHbFz5846s0A1IYTUNgqAhNQBzs7OePXqFZ4/fy7uUhD2PhEmzTXAYktWuLp58ybGjx+PsWPH4sCBAxT+CCGkAhQACakDBgwYAAsLC7EvCZMa9w2pcd8krvv3wYMHGDlyJAYOHIjjx49DSkqy1yYkhBBxowBISB3AZrOxYMECXLp0CZGRkWKrI8wvEVLSkrX7x7NnzzB48GB069YN58+fh4yMjLhLIoQQiUcBkJA6Ytq0aVBUVISrq6vYagjzS4KBtSo4ctJiq+F7b9++Rf/+/WFra4urV69CVlZyl6UhhBBJQgGQkDpCSUkJM2bMwKFDh/Dt27daf35udgFiglIlpvv306dP6Nu3LywtLXHjxg0oKiqKuyRCCKkzKAASUofMnz8fKSkpOH36dK0/O+JjEvh8BibNNWr92T8KCQlB7969oauri9u3b0NFRUXcJRFCSJ1CAZCQOsTc3ByDBw8Wy5Iw4X5J0DDgQllDvlaf+6OoqCj06tULCgoK8PDwgIaG+AMpIYTUNRQACaljnJ2d8fHjR9y/f7/WnsnnMwj/kARTMbf+xcfHo3fv3uDz+bh37x50dXXFWg8hhNRVFAAJqWN69OiBZs2a1eqSMHEhacjJyhfr9m/Jycno06cP0tLScO/ePRgbG4utFkIIqesoABJSx7BYLDg7O+PGjRv48uVLrTwzzC8R8koy0DZVrpXn/SgjIwP9+/fH169f4eHhAQsLC7HUQQgh9QUFQELqoIkTJ0JdXR0uLi618rwwvySYNNUAWwy7f+Tl5WH8+PEICAjAnTt30KxZs1qvgRBC6hvJWMyLECIUeXl5zJo1C3v37sXGjRuhrCy6lrn8PB6++MQjMTIT39LzIC3DRnJ0FuwcTET2DIFryc/HyZMn8fbtW9y6dQt2dna1XgMhhNRHLEYSdpcnhAgtKioKpqam2LlzJxYsWFDt+zEMgw8Pv+LZ1S/Iz+WBLcUCw2f+OwbIyEqh4/BGaGZvUCv77PJ4PBw8eBCRkZHo168funfvXuPPJISQhoK6gAmpowwNDTFq1Ci4uLiAz+dX614Mw+D+iU/wPheI/FweAIDPY8AwheEPAPJzefA+F4gHpwIEWoJm2rRpGDZsWInXtmzZAikpKfzxxx8VXsvn83Hw4EH4+vpi8uTJFP4IIUTEKAASUoc5OzsjODgYt27dqtZ93j+IQsCzWIHO/fQkBn5eX6v0HDc3Nyxfvhxubm7lnsMwDI4ePYrHjx/jp59+QpMmTar0LEIIIeWjAEhIHdahQwe0bdu2WkvC5GUX4PlV4WYTP7sajLycAqGuefjwIbKzs7Fx40akp6fj6dOnpc5hGAZnzpyBp6cnZs2ahfbt2wv1DEIIIYKhAEhIHVa0JIynpyc+fvxYpXsEvY5DQb5wXcgFeXx8eRsv1DVHjhzB+PHjISMjg/Hjx+PIkSOlzrly5Qpu3LiBqVOnUrcvIYTUIAqAhNRxo0ePhp6eHvbs2VOl6xMiMoRe3oUtxUJyVJbA56enp+PSpUuYNGkSAGDSpEm4cOECMjMzi8+5efMmLl26hLFjx8LBwUGoegghhAiHAiAhdRyHw4GTkxNOnjyJ5ORkoa/PTM0Fny/cYgB8HoPM9DyBzz979iwaNWqEli1bAgBatWoFExMTnD9/HgDg6emJU6dOYejQoaUmjhBCCBE9CoCE1AOzZ88Gj8fD4cOHhb5WUZkjfAsgmwUFZRmBzz9y5Ag+fvwIaWnp4l/+/v5wc3PD48eP4ebmhn79+mHs2LHClk8IIaQKaCFoQuoBbW1tTJgwAfv27cOSJUsgLS34X20NQy74Qi4HymcYqOsqCnSun58fXr9+DS8vL6irqxe/npycjO7du2Pr1q0YPHgwpkyZUivrCxJCCKEWQELqDWdnZ0RGRuLq1atCXWfVVlfoFkApKRYatdYW6NwjR46gXbt26NatG5o1a1b8S0VFBerq6sjOzsbMmTPBZtM/R4QQUlvoX1xC6olWrVqhW7duQi8JI8eVgV1/U6GusetvCjnFiruA+Xw+2Gw2Tp06hZEjR5Y45u/vj507d6J9+/b48OEDeDyeUM8nhBBSPdQFTEg94uzsjJEjR+LNmzdC7ZvbZoAp4sPTEe6XVOm5ps010EaAwBgfHw8LCwskJiaWeD04OBh//PEHLC0tcfjwYXA4HIHrJIQQIhrUAkhIPTJ06FCYmJgI3QrIZrMwwKkF2gwwBYvNAosFsP7714HFBliswnPaDDRFf6cWYFXQZZySkoIbN27Ay8sLvXv3LnEsIiICW7duhbGxMZYuXUrhjxBCxITFCLKpJyGkztixYwd+/fVXhIeHQ09PT+jrv6Xn4fPzWCRGZSAzJRdcNVloGirBuoMuFJQrD2zDhw/Hq1evMHXqVPz222/FEzuio6OxceNGqKmpYfXq1VBUFGwSCSGEENGjAEhIPZOSkgJDQ0MsXboUGzZsEHc5AAq7gzds2AAFBQWsWbMGysrK4i6JEEIaNOoCJqSeUVNTw9SpU3HgwAHk5uaKuxykpKTg999/h4yMDH799VcKf4QQIgEoABJSDzk7OyM5ORmrV68Wax3p6enYvHkzCgoKsGrVKqipqYm1HkIIIYUoABJSD1lbW2Pbtm3YsWOH0OsCikpWVha2bNmCzMxMrFq1ClpaWmKpgxBCSGk0BpCQeophGIwaNQqenp548+YNLCwsau3ZOTk52LlzJ2JjY7Fs2TIYGRnV2rMJIYRUjgIgIfVYWloa2rRpA0VFRTx9+hQKCgo1/sy8vDwcO3YMkZGRmDVrFoU/QgiRQNQFTEg9pqKigsuXLyMoKAhdunRBSEhIjT4vNDQUvXv3xvr169G/f38Kf4QQIqEoABJSz7Vo0QJPnjxBeno6WrdujWvXrtXIc9zd3WFra4uYmBj8+++/6NSpU408hxBCSPVRACSkAWjVqhVev36NHj16YOjQofjll1+Ql5cnknvn5eVh2bJlGD58OHr16oXXr1+jZcuWIrk3IYSQmkFjAAlpQBiGwZ9//okVK1ZAS0sLM2fOxKxZs2BoaCj0vSIjI3Ho0CEcPnwYiYmJ2L59OxYtWlS88wchhBDJRQGQkAbI398f+/btw4kTJ5CdnY0hQ4bA0dERrVq1gq6uLtjs0p0DfD4fMTEx8PX1xd9//41r165BUVERU6ZMwdy5c9GkSRMxfCWEEEKqggIgIQ1YRkYGTp06hX379uHjx48AAFlZWZiZmcHc3Bx6enqIjo5GaGgoQkNDi3cWadasGebNm4eJEydCSUlJnF8CIYSQKqAASAgBwzAICAhAcHAwQkNDERISgtDQUERHR0NfX784EJqZmcHCwgI2NjbU1UsIIXUYBUBCCCGEkAaGZgETQgghhDQwFAAJIYQQQhoYCoCEEEIIIQ0MBUBCCCGEkAaGAiAhhBBCSANDAZAQQgghpIGhAEgIIYQQ0sBQACSEEEIIaWAoABJCCCGENDAUAAkhhBBCGhgKgIQQQgghDQwFQEIIIYSQBoYCICGEEEJIA0MBkBBCCCGkgaEASAghhBDSwFAAJIQQQghpYCgAEkIIIYQ0MBQACSGEEEIaGAqAhBBCCCENDAVAQgghhJAGhgIgIYQQQkgDQwGQEEIIIaSBoQBICCGEENLAUAAkhBBCCGlgKAASQgghhDQwFAAJIYQQQhoYCoCEEEIIIQ0MBUBCCCGEkAaGAiAhhBBCSANDAZAQQgghpIGhAEgIIYQQ0sBQACSEEEIIaWAoABJCCCGENDAUAAkhhBBCGhgKgIQQQgghDQwFQEIIIYSQBoYCICGEEEJIA0MBkBBCCCGkgaEASAghhBDSwFAAJIQQQghpYCgAEkIIIYQ0MBQACSGEEEIamP8BMZTQOMXW8YYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from importlib import reload; reload(hnx)\n", + "\n", + "from collections import defaultdict\n", + "\n", + "colors = defaultdict(lambda: plt.cm.tab10(len(colors)%10))\n", + "\n", + "def get_node_color(v):\n", + " return colors[v]\n", + "\n", + "def get_cell_color(e):\n", + " return get_node_color(e[1])\n", + "\n", + "hnx.draw(\n", + " H,\n", + " with_additional_edges=H.bipartite(),\n", + " edges_kwargs={'edgecolors': 'black'},\n", + " nodes_kwargs={'color': get_node_color},\n", + " additional_edges_kwargs={'edge_color': get_cell_color},\n", + " edge_labels_on_edge=False, edge_label_alpha=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from importlib import reload; reload(hnx)\n", + "\n", + "threshold = 0.1\n", + "\n", + "df = pd.read_csv('./newsgroups-topics.csv', index_col=0)\n", + "df.index = df.index.map(lambda s: '.'.join(s.split('.')[-2:]))\n", + "\n", + "incidence_matrix = df\n", + "# incidence_matrix = df[df.columns[(df >= threshold).sum(axis=0) > 1]]\n", + "\n", + "H = hnx.Hypergraph(\n", + " incidence_matrix\\\n", + " .apply(lambda row: row.index[row >= threshold].tolist(), axis=1)\\\n", + " .to_dict()\n", + ")\n", + "\n", + "norm = plt.Normalize(0, incidence_matrix.max().max())\n", + "cmap = plt.cm.Greens\n", + "\n", + "def get_cell_color(e):\n", + " return cmap(norm(incidence_matrix.loc[e]))\n", + "\n", + "plt.figure(figsize=(12, 12))\n", + "hnx.draw(\n", + " H,\n", + " layout=nx.kamada_kawai_layout,\n", + " with_additional_edges=H.bipartite(),\n", + " edges_kwargs={\n", + " 'edgecolors': 'darkgray',\n", + " 'facecolors': (.65, .65, .65, .15)\n", + " },\n", + " additional_edges_kwargs={\n", + " 'edge_color': get_cell_color,\n", + " 'width': 4,\n", + " },\n", + " edge_labels_on_edge=False, edge_label_alpha=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data\n", + "\n", + "The data in several of our notebooks are taken from the jean.dat dataset available from the Stanford GraphBase at https://www-cs-faculty.stanford.edu/~knuth/sgb.html. This data gives character scene incidence information from the novel Les Miserables by Victor Hugo." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "scenes = {\n", + " 0: ('FN', 'TH'),\n", + " 1: ('TH', 'JV'),\n", + " 2: ('BM', 'FN', 'JA'),\n", + " 3: ('JV', 'JU', 'CH', 'BM'),\n", + " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", + " 5: ('TH', 'GP'),\n", + " 6: ('GP', 'MP'),\n", + " 7: ('MA', 'GP')\n", + "}\n", + "\n", + "H = hnx.Hypergraph(scenes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization\n", + "Use the default drawing tool to visualize `H` and its dual. This renders an Euler diagram of the hypergraph where vertices are black dots and hyper edges are convex shapes containing the vertices belonging to the edge set. It is not always possible to render a \"correct\" Euler diagram for an arbitrary hypergraph. This technique will lead to false positives, cases where a hyper edge incorrectly contains a vertex not belonging to its set." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hnx", + "language": "python", + "name": "hnx" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/newsgroups-topics.csv b/tutorials/newsgroups-topics.csv new file mode 100644 index 00000000..5fff59d5 --- /dev/null +++ b/tutorials/newsgroups-topics.csv @@ -0,0 +1,14 @@ +,0,1,2,3,4,5,6,7,8,Advance,Arabic,Armenians,Banks,Bible,Christ,Christian,Church,God,Gordon,Hi,IDE,Israel,Israeli,Jesus,Jews,Mac,Many,Monitors,N3JXP,Please,Price,SCSI,Sale,Security,Skepticism,Turkish,Video,Why,Windows,X,addresses,applications,appreciate,atheist,believe,bikes,bus,car,cards,chastity,chip,clipper,color,condition,controllers,difference,disk,drive,driver,email,encryption,escrow,evidence,existing,faith,file,game,geb,gun,hard,help,info,information,intellect,key,looked,means,moral,objective,offers,personal,player,playing,point,posting,programs,questions,reasons,run,sell,shameful,ship,sins,soon,space,surrender,team,thanks,things,win 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Also uses HNX to store/access cell weights. --- tutorials/Incidence Visualization.ipynb | 110 +++++++++--------------- 1 file changed, 41 insertions(+), 69 deletions(-) diff --git a/tutorials/Incidence Visualization.ipynb b/tutorials/Incidence Visualization.ipynb index 98c1820a..85e23201 100644 --- a/tutorials/Incidence Visualization.ipynb +++ b/tutorials/Incidence Visualization.ipynb @@ -2,9 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " No module named 'igraph'. If you need to use hypernetx.algorithms.hypergraph_modularity, please install additional packages by running the following command: pip install .['all']\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -29,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -48,6 +56,19 @@ "\n", "from collections import defaultdict\n", "\n", + "scenes = {\n", + " 0: ('FN', 'TH'),\n", + " 1: ('TH', 'JV'),\n", + " 2: ('BM', 'FN', 'JA'),\n", + " 3: ('JV', 'JU', 'CH', 'BM'),\n", + " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", + " 5: ('TH', 'GP'),\n", + " 6: ('GP', 'MP'),\n", + " 7: ('MA', 'GP')\n", + "}\n", + "\n", + "H = hnx.Hypergraph(scenes)\n", + "\n", "colors = defaultdict(lambda: plt.cm.tab10(len(colors)%10))\n", "\n", "def get_node_color(v):\n", @@ -63,17 +84,18 @@ " nodes_kwargs={'color': get_node_color},\n", " additional_edges_kwargs={'edge_color': get_cell_color},\n", " edge_labels_on_edge=False, edge_label_alpha=1\n", + "\n", ")" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -83,27 +105,31 @@ } ], "source": [ - "from importlib import reload; reload(hnx)\n", + "import networkx as nx\n", "\n", "threshold = 0.1\n", "\n", "df = pd.read_csv('./newsgroups-topics.csv', index_col=0)\n", "df.index = df.index.map(lambda s: '.'.join(s.split('.')[-2:]))\n", "\n", - "incidence_matrix = df\n", - "# incidence_matrix = df[df.columns[(df >= threshold).sum(axis=0) > 1]]\n", + "incidence_matrix = df.copy()\n", + "\n", + "# filter out singletons\n", + "incidence_matrix = df[df.columns[(df >= threshold).sum(axis=0) > 1]]\n", "\n", - "H = hnx.Hypergraph(\n", - " incidence_matrix\\\n", - " .apply(lambda row: row.index[row >= threshold].tolist(), axis=1)\\\n", - " .to_dict()\n", - ")\n", + "# filter out small weights\n", + "incidence_matrix[incidence_matrix < threshold] = None\n", "\n", + "# construct hypergraph\n", + "H = hnx.Hypergraph.from_incidence_dataframe(incidence_matrix.T)\n", + "weights = H.edges.cell_properties.cell_weights\n", + "\n", + "# create functions for mapping hyper edges/weights to colors\n", "norm = plt.Normalize(0, incidence_matrix.max().max())\n", "cmap = plt.cm.Greens\n", "\n", "def get_cell_color(e):\n", - " return cmap(norm(incidence_matrix.loc[e]))\n", + " return cmap(norm(weights.loc[e]))\n", "\n", "plt.figure(figsize=(12, 12))\n", "hnx.draw(\n", @@ -121,60 +147,6 @@ " edge_labels_on_edge=False, edge_label_alpha=1\n", ")" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data\n", - "\n", - "The data in several of our notebooks are taken from the jean.dat dataset available from the Stanford GraphBase at https://www-cs-faculty.stanford.edu/~knuth/sgb.html. This data gives character scene incidence information from the novel Les Miserables by Victor Hugo." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "scenes = {\n", - " 0: ('FN', 'TH'),\n", - " 1: ('TH', 'JV'),\n", - " 2: ('BM', 'FN', 'JA'),\n", - " 3: ('JV', 'JU', 'CH', 'BM'),\n", - " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", - " 5: ('TH', 'GP'),\n", - " 6: ('GP', 'MP'),\n", - " 7: ('MA', 'GP')\n", - "}\n", - "\n", - "H = hnx.Hypergraph(scenes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization\n", - "Use the default drawing tool to visualize `H` and its dual. This renders an Euler diagram of the hypergraph where vertices are black dots and hyper edges are convex shapes containing the vertices belonging to the edge set. It is not always possible to render a \"correct\" Euler diagram for an arbitrary hypergraph. This technique will lead to false positives, cases where a hyper edge incorrectly contains a vertex not belonging to its set." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import networkx as nx\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 4fa24afc8617d56e990ebcd5dc40ec3b138e9882 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 29 Nov 2023 13:34:18 -0800 Subject: [PATCH 68/76] Run linter --- hypernetx/drawing/rubber_band.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/hypernetx/drawing/rubber_band.py b/hypernetx/drawing/rubber_band.py index 3ccc2f70..9d575b65 100644 --- a/hypernetx/drawing/rubber_band.py +++ b/hypernetx/drawing/rubber_band.py @@ -55,7 +55,7 @@ def layout_node_link(H, G=None, layout=nx.spring_layout, **kwargs): """ B = H.bipartite() - + if G is not None: B.add_edges_from(G.edges()) @@ -90,7 +90,9 @@ def get_default_radius(H, pos): return 1 -def draw_hyper_edge_labels(H, pos, polys, labels={}, edge_labels_on_edge=True, ax=None, **kwargs): +def draw_hyper_edge_labels( + H, pos, polys, labels={}, edge_labels_on_edge=True, ax=None, **kwargs +): """ Draws a label on the hyper edge boundary. @@ -141,9 +143,7 @@ def draw_hyper_edge_labels(H, pos, polys, labels={}, edge_labels_on_edge=True, a xy = (x1 + x2) / 2 # the string is a comma separated list of the edge uid - ax.annotate( - s, xy, rotation=theta, ha="center", va="center", **params - ) + ax.annotate(s, xy, rotation=theta, ha="center", va="center", **params) def layout_hyper_edges(H, pos, node_radius={}, dr=None): @@ -473,7 +473,8 @@ def get_node_radius(v): if with_additional_edges: nx.draw_networkx_edges( with_additional_edges, - pos=pos, ax=ax, + pos=pos, + ax=ax, **inflate_kwargs(with_additional_edges.edges(), additional_edges_kwargs) ) @@ -483,7 +484,8 @@ def get_node_radius(v): ) draw_hyper_edge_labels( - H, pos, + H, + pos, polys, color=edges_kwargs["edgecolors"], backgroundcolor=(1, 1, 1, edge_label_alpha), From a330bcaad1881015baf400a5c34ea83756895da1 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 22 Jan 2024 11:28:07 -0800 Subject: [PATCH 69/76] Fix function call to updated draw function --- tutorials/basic/Tutorial 2 - Visualization Methods.ipynb | 8 -------- 1 file changed, 8 deletions(-) diff --git a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb index db15ddc2..5950f65d 100644 --- a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb +++ b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb @@ -349,7 +349,6 @@ "norm = plt.Normalize(sizes.min(), sizes.max())\n", "\n", "hnx.drawing.draw(H,\n", - " label_alpha=0,\n", " edges_kwargs={\n", " 'facecolors': cmap(norm(sizes))*(1, 1, 1, alpha),\n", " 'edgecolors': 'black',\n", @@ -422,13 +421,6 @@ " }\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 8c080ed8a941b36a439b15e07998c78321d0cfdb Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Mon, 22 Jan 2024 11:59:13 -0800 Subject: [PATCH 70/76] Move incidence visualization to tutorial 2 --- tutorials/Incidence Visualization.ipynb | 173 ------------------ .../Tutorial 2 - Visualization Methods.ipynb | 90 +++++++++ tutorials/{ => data}/newsgroups-topics.csv | 0 3 files changed, 90 insertions(+), 173 deletions(-) delete mode 100644 tutorials/Incidence Visualization.ipynb rename tutorials/{ => data}/newsgroups-topics.csv (100%) diff --git a/tutorials/Incidence Visualization.ipynb b/tutorials/Incidence Visualization.ipynb deleted file mode 100644 index 85e23201..00000000 --- a/tutorials/Incidence Visualization.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " No module named 'igraph'. If you need to use hypernetx.algorithms.hypergraph_modularity, please install additional packages by running the following command: pip install .['all']\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "try:\n", - " import hypernetx as hnx\n", - "except ImportError:\n", - " print(\"Installing HyperNetX.........\")\n", - " !pip install hypernetx --quiet 2> /dev/null\n", - " print(\"Installation complete; please rerun this cell in order for the rest of the cells to use HyperNetX.\")\n", - " exit()\n", - "\n", - "import warnings\n", - "warnings.simplefilter(action='ignore')\n", - "\n", - "### GraphViz is arguably the best graph drawing tool, but it is old and tricky to install.\n", - "### Uncommenting the line below will get you slightly better layouts, if you can get it working...\n", - "\n", - "# from networkx.drawing.nx_agraph import graphviz_layout as layout" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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5Zc3yXb16FXjdm69Tp0507ty5UAsmEhMT+eWXX1iyZAlWVlaMGDGC4cOHU7Zs2XzHf+PGjaxij7CwsAIXe8TGxrJ37148PT05c+YMOjo6tG/fHjc3N7p06SIaTqsgNTWVbdu24e7uzqNHj2jevDlTp06lQ4cOH3URTuDtaI6tvkODbk7U6+DwQd65ePFiXr16xU8//fRB3icIBSYJgvBBJSUlSYcOHZJGjBgh2draSoBkbGws9erVS9q0aZMUERFR6O9UKpXS9u3bJWtra8nAwECaN2+elJqaWqjvUCgU0rlz56SRI0dKJUuWlACpYsWK0syZM6UHDx7k65nPnz+XFi9eLDVs2FACJENDQ6l///7SgQMHCj3+j0lcXJz022+/SWXKlJFkMpnUs2dP6cqVK+oO64OIDX8lrZlwVjq6yk9SKpUf7L3bt2+XJkyY8MHeJwgFJRJAQfgAnj17Jq1atUrq1KmTpK+vLwGSs7OzNGHCBOnkyZNSWlpakb377t27UvPmzSVA6tWrl/T06dMie9cb6enp0rFjx6Qvv/xSMjExkQCpVq1a0oIFC/L9/sDAQGnevHlSjRo1JEAyNzeXhg4dKp08eVLKyMgo5E9QPIWEhEjfffedZGJiIunq6kpff/219PDhQ3WH9cGkpWRI22delrbPvCylJX/YPxPHjx+XvvjiC0mhUHzQ9wpCfokEUBCKQGZmpnTp0iXpxx9/lGrWrCkBkpaWltSiRQvp999/lx48eFDksxMJCQnS5MmTJS0tLalChQrS8ePHi/R975KSkiLt3btX6t27d1by27hxY2nZsmX5nu28d++e9NNPP0kuLi4SIFlaWkpjx46VLl68+El+A7537540ePBgSUdHRzIzM5OmT58uhYeHqzusD0qpVEpHV/tJayaclWLDX33w9/v5+UnDhg2TYmJiPvi7BSE/RAIoCIUkPj5e2rVrl/Tll19KpUqVkgDJwsJCGjhwoOTp6SnFxcV9kDj+u9xraGhYJMu9+ZWYmCjt3LlT6tu3r1SqVCnJwsJC6t69u7R169Z8/f4olUrp2rVr0rfffivZ2NhIgGRvby9NnTpVunXr1gddAvzQlEqldP78ealz584SINnY2Eh//PGHlJiYqO7Q1OL6sSBp+YhT0pNbUWp5f2RkpDRlyhTpyZMnanm/IKhKFIEIQgEEBARkFXB4e3u/1ZuvU6dONGzYMN+9+fLj7t27jB07lnPnztGrVy8WLVqksb31kpKSuHPnDr6+vgQGBiKXy6lYsSK1atWicuXKKvcYVCqVXLhwIavh9IsXL6hUqVJWw+mPpUGvUqnk4MGDLFy4kMuXL1OlShWmTp2Km5sburq66g5PLZ7ee8Hh5b7U6+BAg65OaokhJSWFsmXLsmLFCgYMGKCWGARBFSIBFAQV/Lc335EjR3j06BF6enp8/vnnWUlfuXLlPnhciYmJzJo1i6VLl+Ls7MyyZcto27btB48jv+Lj47l27Ro+Pj4EBQWhq6tLrVq1qF+/PlWrVlW5VUlGRganTp3C09Mzq+F07dq1cXNzo1+/fhqbFOcmLS2NrVu38vvvv/Pw4UOaNWvGtGnT6NChQ4FOainuEqJT2P3bNawczeg0pgbyAvbzKwhra2tGjBjBrFmz1BaDIOSVSAAF4T2io6OzevMdP378rd58nTp1olWrVmprTSJJEjt27OC7774jMTGRn3/+mUmTJqk8e6ZJIiMjuXz5MpcvX+bZs2cYGRnh6upK48aNqVKlisozqqmpqRw9ehRPT8+shtNNmjTBzc2N3r17a3zD6YSEBFavXs3ixYuJjIyke/fuTJkyhUaNGqk7NLXLSFOwd+ENMtIV9Pm+HvpG6u1p2LhxY8qXL8+WLVvUGocg5IVIAAXhf0iSxJ07d7KWdq9cuYIkSdSvXz+rIXNh9ubLr7t37zJmzBi8vb3p3bs3f/zxR7Gc2cpNSEgIly9f5tKlS0RGRmJmZkbDhg1p3LgxLi4u+Wo4feDAATw9PTl+/DhKpZJWrVrh5uZGjx49NKrhdFhYGEuWLGH16tWkpaXx5Zdf8t1334mzk/8lSRInN94nyDea3tPqUdLGWN0hMWDAAMLCwjh37py6QxGE9xIJoCDwev/O6dOns5K+0NBQjI2Nadu2LZ07d6ZDhw6UKVNG3WEC2Zd7ly9fTps2bdQdVpGSJInAwEAuXbrE5cuXiYuLw87OjhYtWmBra0v16tVVTshfvHiR1XD67Nmz6Ojo0KFDB9zc3OjcubPaZnXv37/P77//zrZt2zA0NGTUqFGMHz8ea2trtcSjqW7/84yLex7TdlhVyrtqxizujz/+yNatW3n27Jm6QxGE9xIJoPDJCg0N5ciRIxw+fJhTp06RkpKCk5MTXbp0oXPnzjRr1kyjllI/5HJvZnwaqQ9ekPkiFRQSWhb66FcogY6lYaG/S1VKpZIHDx5w/fp1AgMDs041cXNzo3///vmaIXv+/Dm7du3C09OTq1evYmRkRNeuXXFzc6Ndu3YfpLji4sWLLFiwgEOHDlG2bFkmTZrEN998g6mpaZG/u7gJfRjHwSW3qdnKjia9XNQdTpZ169YxYsQIUlNTP9mCHKH4EAmg8MlQKBRcu3Yta5bP19cXLS0tmjZtmrW0W7FiRbUv7ebkzp07jB07Nmu5d9GiRdjZ2RX6exSv0ok/8JiUuy9AArT+/b1QSiCBrpMZFr3Ko13SoNDfnR+ZmZmcOXOGbdu2sW/fPl6+fFngYo/AwEB27tyJh4cHd+7cwdzcnF69etG/f39atmxZqFXdSqWSQ4cOsXDhQi5dukTlypWZOnUqAwYMEAnEO7yMTWX3b9coaWNMl3E1kWtpTgHMP//8Q5s2bXj8+DHOzs7qDkcQcqU5f3MEoQgkJiayZ88eBg8ejLW1NY0aNWLVqlVUr14dT09PoqOjOXv2LN999x2VKlXSuOQvISGBSZMmUbt2bSIjIzlx4gS7d+8ukuQvIzKJyMU3Sbn3b/IHoJBe//r3n9ODE4hccpPUJ/F5fm5ERATjxo3DyckJPT097Ozs6NKlC6dOnQLAwcGBxYsXZ7tv1qxZ1KpVK9dna2tr06ZNG7Zs2UJkZCR79uzB2dmZGTNmUK5cOZo2bcqKFSuIjIzMc7xOTk5Mnz4dPz+/rLY6Z8+epU2bNtjY2DB+/HguXbpEQX52TktLY+PGjVStWpXu3bsjl8s5ePAgd+/eZfDgwSL5e4fMDAVea+6gpSOn7fCqGpX8ATg6OgIQFBSk5kgE4f0062+PIBSCV69esXbtWlq3bk3JkiXp06cPN27cYNiwYVy4cIGoqCi2bt1Kv379KFGihLrDzZEkSWzbto1KlSqxbt065s2bh5+fX5Ht9VOmZhKz+R7KpAxQ5jYQpAwlL7beJzM+7b3PDQ4Opm7dupw+fRp3d3fu3LmDl5cXLVu2ZMyYMYX3AQADAwN69erF7t27iYyM5K+//sLMzIyJEydStmxZ2rZty6ZNm4iPj8/zM6tWrcqcOXMICAjAx8eHAQMGsHfvXpo0aYKjoyM//PCDSt/sExISWLhwIY6OjgwbNoxKlSpx8eJFzp8/T5cuXT7pdi7vI0kS3h6PeBGWRIcR1TEw1rwk2c7ODplMJhJAoVgQ/7URPhoPHjxg/Pjx2NjYMGrUKLS0tFi8eDFBQUHcuXOH3377jSZNmnzQxsz58ejRI5o3b86gQYP47LPPePDgAVOnTi3SWaGX50JRxKf9/8xfbiSQ0pUkHA1879DRo0cjk8nw8fGhV69eVKhQgapVqzJ58mSuXLlS8MDfwdTUlEGDBnHkyBHCw8NZuXIlGRkZDBs2DCsrK7p3787OnTtJSkrK0/NkMhmurq4sWrSIkJAQzp49S4cOHVi1ahXOzs507tyZo0ePolAocrz/+fPnTJs2DXt7e37++Wc6duyIv78/+/bto3HjxoX50T9a984/x/9SOC2+qIhlOc3cF6mrq4utrS3BwcHqDkUQ3kskgEKx5+PjQ6tWrahcuTKenp6MHTuWwMBAjh8/zpgxY3BwcFB3iHm2Z88e6tWrR3h4OCdPnmTnzp3Y2toW6TulTCWvroTnLfl7QymRcjcGxcv0dw6JjY3Fy8uLMWPG5FhR+6FarpQqVYoRI0Zw5swZQkJCWLBgAeHh4fTv3x8rKysGDBjAwYMHSUt7/4wmgFwup3nz5qxatYqwsDDWrVvH8+fP6dSpE+XLl2fhwoXExMQA4O/vz7Bhw3BwcGD16tWMGjWKoKAg1q9fT6VKlYryY39UIgITOL/zEdWb21CpkWZXQzs4OIgZQKFYEEUgQrElSRIrVqxg8uTJVK1alSlTptCrVy+NqtzNq/T0dKZNm8bixYvp27cv69evx8TE5IO8OyPi9d6//Cg5uCoGlSxyvObj40ODBg34+++/6dGjxzuf4eDgQHh4eLbTPtLT06lSpQq3b9/OV2zv8+TJk6xij7t37xao2EOSJHx8fFi5ciU7d+5EqVRiZWVFaGgo1tbWWRW9ZmZmRfJZPmZJCWnsmncNs1IGdJtUGy1tzZ63+OqrrwgICODSpUvqDkUQcqXZf5ME4R1evXrFgAEDGDduHKNHj+bq1asMGDCgWCZ/YWFhtGjRghUrVrB06VI8PT0/WPIHkBmXmr8bZbnfq8rPllOmTOH27dtv/Ro5cmT+4sojZ2dnfvjhB+7cucOdO3cYM2YMZ86cyVexx5sl4l69elGzZk0yMjKyCk8GDRrEpEmTRPKXD4pMJcfX3gWg3TfVND75g9eFIGIGUCgONP9vkyD8j4cPH+Lq6srhw4fZuXMnixcvLrZVkykpKXTs2JFnz57h7e3NuHHjPnglslwvn3siJZDrv/ve8uXLI5PJePDgwXsfVapUKVxcXN76ZWGR88xiUahWrRpz587l8ePHWT9M/LfY4/vvv+f27ds5JoNpaWls2rSJatWq0a1bN3R0dDhw4ADJycn89ttv/P7777Rt21alSmThtYu7A4gMTqTDiOoYmRWPH+4cHByIiIggJSVF3aEIQq5EAigUK7GxsbRr1w6Aa9eu0bdvXzVHVDBjx44lICCAY8eO0bBhQ7XEoF3KAJmBdr5+aZfSf+dzLSwsaNeuHStWrMix2EKVatwPRSaTUb9+fRYtWsSzZ884c+YM7du3Z/369dSuXZsqVarwyy+/8OjRIxITE3F3d8fJyYmhQ4dSvnx5Lly4wIULF+jatSva2tp8//33nDp1ivv371O7dm3Onz+v7o9YbDy4HM6dc2E061eBMk7FZ/b0TSuYp0+fqjkSQcidtroDEIS8UiqVDBo0iJcvX3Lu3DnKlSun7pAKZNu2bfz9999s2LCB6tWrqy0OLVM9TNvYo0jIYxXwv2R62uja5F6NuWLFCpo0aUL9+vWZPXs2NWrUIDMzk5MnT7Jq1Sr8/f0LGH3R0dLSokWLFrRo0YJly5bxzz//4OHhgbu7O7NmzcraIzhw4ECmTZtG5cqVc3xOixYtuHXrVtbewgULFjB58mSN6zmpSaKeJnJ2+0MqN7amarOy6g5HJW+KzoKCgkShj6DRxAygUGz89ttvHDt2jG3bthX75C8sLAx/f3/Wr1+Pm5ubusNBr5wpyqRMlMl5/6VrZ4JMnnsS4+TkxM2bN2nZsiXffvst1apVo02bNpw6dYpVq1Z9oE9XcDo6Ojg6OqKrq0t6ejr6+vpYW1ujUCg4fvw4Hh4ehIaGvvN+a2trTp06xeTJk/nuu+/o1asXCQkJH/ATFB8pL9M5tuYOJW2M+MytQrFLlG1tbdHW1hatYASNJ6qAhWLh1KlTtG3blh9//JHZs2erO5wCkSSJOXPmIEkS06dP15j9i6+uRZD2OP79A2WgU8YIk89s35sAfgwuX77MwoULOXDgAGXKlGHixImMGDECMzMz7t69y6pVq/jrr79ISUmha9euTJ8+HVdX13c+b//+/QwePJgyZcpw+fJljW1Grg5KhZKDS32Jff6KPtNdMbF49xYDTebs7EyvXr1YuHChukMRhHcSM4CCxlMoFAwfPpwWLVowc+ZMdYdTYAEBAfj7+9O9e3eNSf4AjOpaof+Oli4A/Jvr6VgaYtzE5qNO/pRKJYcPH6ZZs2Y0btwYf39/1q1bR1BQEFOnTs2q6K1WrRorVqzg+fPnLFu2jIcPH9K4cWP+/PPPd1YPd+/eHR8fH6Kiovjqq69QKnM7euXTcnl/IM8D4mk3vFqxTf5A9AIUigeRAAoa79ixYwQHBzN//nyNP8UjL06ePImVlZVa9/3lRCaXYVTbEsM6lv9+4b8XQbu0IXJjHZTpCmRaH2fyl56ezubNm6levTpdunRBoVCwf/9+7t+/z7Bhw97ZZsjExIRRo0Zx+/ZtJkyYwOTJk+nTpw+JiYk5jq9QoQJbt27l0KFDuLu7F+VHKjYCrkVy++QzGvd0xqZi8Z4VdXR0FEvAgsYTRSCCxlu5ciX16tXLdVmtuEhMTOTKlSv069cv3+e+xoQ85b73aV6EPiM5MQHT0lZYO5enSvNWGJoWvFpSEZuKlrEOpp0ckZIzX7d7MdJGJpeTEZNM4slnpAXGo+9SvL9J/1diYiJr165l8eLFhIWF0aVLF9asWUPTpk1Veo6Ojg6///47jRs3ZsiQIdSrV4+9e/fmmOx36tSJH374gR9++IEGDRrQokWLQvo0xU9cZDJ+Z0Ko3sKWmq3s1B1OgTk4OLB//351hyEIuRJ7AAWN9uTJE8qXL8+GDRsYMmSIyvdLkkR8vA8REftJSnpMRkYc+gb2mJvVpmzZfujpWRZB1O928OBB9u7dy/Lly1Vu9pz66hVeq/7kyfWryORaSEolIGVtkpfJtWjcZwD1u/VGls/kUpmuIH7/Y/SrlsSwaqkcx7y6/JyM568w6+yEXK94/wwZHh7O0qVLWbVqFcnJyXzxxRdMmTKFKlWqFPjZAQEB9O7dm4CAAFatWsVXX32VbYxCoaBt27bcu3ePW7duYW2t2cecFTVJkopd0UdOtm/fzsCBA0lMTPygTd0FQRViCVjQaGvWrMHc3Jx+/fqpfG9q6nOu3+jDzVsDCI/YS0LiTZJTgoiNPUdg0FIuXGxCYNAyJOnD7cHy8fGhXr16Kn9TSIqP469p4wi8eQ0ASangTc8WSZKQJAmlIpMLnn9xeMnCf5PD3A0ePJju3bsDr1uVTJw4kfRnL5GUEnqOr2cSN2/enO3MXsOalkhKSLn7QqXPoEkePnzI119/jYODAytWrODrr78mMDCQTZs2FUryB68bYV+5coX+/fszePBgNmzYkG2MlpYWO3bsQC6X079/fzIzMwvl3cXVx5D8wf/3AhTLwIImEwmgoNH27NnDgAEDMDQ0VOm+5OQgrvp04uVLPwAkSfE/I5SAkqCgxdy9O16lY8siIiIYN24cTk5O6OnpYWdnR5cuXTh16hTwevlHJpNx5cqVt+6bOHEiGzZswN7eXqXPolQqOPTnfF7FvshTYvfoygWuHfpbpXe8kRYYj04ZI7QMdd45Rm6ojUG1kqQGxJGZkM9j5NQkLCyMXr16UblyZQ4fPszs2bN59uwZ7u7u2NraFvr7DAwM2LhxI19//TVjxozJ8VxjKysrdu3axcWLF/nxxx8LPQbhw/tvL0BB0FQiARQ0VkJCAkFBQSqfkKFQpOHnNxKFIimHxC+7qOhjPAvJPjuTk+DgYOrWrcvp06dxd3fnzp07eHl50bJlS8aMGZM1Tl9fn2nTpr11b0ZGBgqFAktL1Zadn9zwIezBvTwlf29c2r2d1KRXKr1HmaYg80Uqenk4dUG/Qgm0jHVJvh2tUvKsTv/88w+1a9fm6tWrrF27lqCgIKZNm5ZthrMoLF26lKpVq9KrV68cT0Bp2rQpCxYsYOHChXh7exd5PELRKlOmDHp6emIGUNBoIgEUNJaf3+vZu5o1a6p0X2TkAZKSH+cp+XsjMHAxmZnZjyv7X6NHj0Ymk+Hj40OvXr2oUKECVatWZfLkyW/N+H3zzTdcuXKFo0ePZn0tOTkZQOUE0PfEUZX39CkyM3l05aJq9ySkIdeVo2tj/N6xMi05hnUsyYxMJjNOs888VSqVzJ07l7Zt21KrVi1u3brF8OHD0df/cG1G9PX12bNnD7GxsQwePDjHpHny5MlUq1aNJUuWfLC4hKIhl8tFKxhB44kEUNBYvr6+6OrqqnycUmjYdt7uYfJ+SmUK0dEncx0TGxuLl5cXY8aMwcjIKNv1/84kOTo6MnLkSKZPn57V5+3N4fClS5dWKbbIJwEqzf4ByGRyop+q9s1HkZiOroMZMq28/WdBt6wx2mUMSfKJQpmpmb3sEhIS6Ny5MzNmzGDGjBkcO3ZM5d//wuLo6Mhff/3FgQMH+P3337Ndl8lkjB49mgMHDuR6qohQPDg4OIgZQEGjiQRQ0Fi+vr5UqVIFHZ1370f7X5Kk5NWrh6h0qC0gk2nzKulhrmMeP36MJEl5Tkh/+ukngoKC2L59OwCpqanI5XKVCkAUmRkqL+XC6yKRly+i8z4+TQEKZZ6Wf//LqLYVyuR00gI171gzpVLJwIEDuXTpEkePHn3r/F516dKlC99//z3Tp0/n4sXsM7QDBw7EwMCAdevWqSG6wvPbb7/h6uqKiYkJlpaWdO/enYcPc//79bFxdHQUM4CCRhMJoKCxfH19VV7+zcxMQJIyVH6XJClIT4t6zxjVksrSpUvz3XffMWPGDNLT09HV1UWpVGbNBOaFXEsbnXwsVcrkcgzNzPM83kiuz0tFCtol3n5XfHx81qkXOdEy0UW/ggVpgfFkxmlWQcjChQs5fPgw27dvp3379uoOJ8ucOXNo2rQpixYtynbNxMSEESNGsHPnTjIyVP9zrCnOnTvHmDFjuHLlCidPniQjI4O2bduSlPT+bRYfizdLwMVlj6zw6REJoKCRFAoFd+/eVTkB1NY2RSZTvTedTKaFjm7Ofe/eKF++PDKZjAcPHuT5uZMnTyYlJYWVK1diYGAAQHR03mfmZDIZJW3sQNX2GJKERdm8VbUqUzJxsrDnzjP/bNdu3rxJhQoVcr1fv6IFcl0tEk8EqxZjETpz5gw//vgjP/74I506dVJ3OG/R1tbmjz/+wNnZOcc/C99++y1du3bl/v37aoiucHh5eTF48GCqVq1KzZo12bx5M8+ePePGjRvqDu2DcXR0JDExMceiH0HQBCIBFDRSQEAAKSkpKieAMpkWhoZOqLoHUJIUGBk65zrGwsKCdu3asWLFihxnMnL6D72xsTE///wzv/76a9ZewKio3Gca/1f1z9uBirMIkiTx7J4fCVGR7x2bFpzAkDb9CXj6hPHjx+Pn58fDhw9ZtGgRHh4efPvtt7neL9eRo1+xBCkP4kgNiFMpzqLw/Plz+vfvT4sWLfjll1/UHU6OqlevTlpaGmfOnMl2zdramlKlSnH8+HE1RFY0EhJebxGwsMjlrOmPzJtegGIZWNBUIgEUNJKvry+gegUwgE3Z/irfI5PJKV263XvHrVixAoVCQf369dm7dy8BAQH4+/uzdOlSGjVqlOM933zzDWZmZuzZswe5XK5yAli5aQtMS1vmuRJYJpNhW6U6qS8T8T1xBJ+De4l+FpxtKUqpVKKtrU1aYAIV6lbB29ubBw8e0Lp1axo0aMCuXbvYvXt3npZPdcoao2tnQvyhJ0gK9RaETJ8+HblcjoeHh9r3/L2Lrq4uDRo04MSJE6SlpWW73qxZM27dukVISIgaoitcSqWSiRMn0qRJE6pVq6bucD4Y0QtQ0HQiARQ0kq+vLzY2NpQsWVLle62te6GnZw3k/Zu/JCnwfzCF9PTcT7dwcnLi5s2btGzZkm+//ZZq1arRpk0bTp06xapVq3K8R0dHhzlz5pCamoquri7h4eGqfBx09PXp9t1PyLW03psEyuRyLGzs6Pn9TNxmu1P5s89RpKfjs28Xl3ZvJzLoSVYiGBUVhaVZKRSJ6eg5meHq6sqJEyeIiooiPj6eK1euZJ0U8j4ymQyzDg5kRqfw6rJqn68wxcTEsHPnTiZNmqRyu50PrVWrViQnJ3P58uVs11xdXTEzM8txhrC4GTNmDHfv3sXT01PdoXxQpUqVwsjISFQCCxpLnAUsaKQ3+7aOHDmSr/tfvrzH9Rt9kKTM9/QDlFHCvAH29sN5/OQPSpVqgZlZHUpafIZcXvjn3G7bto1z586xYsUKdHV1Vbo3+lkwB9znkhAVgUwme2tG7/XZwApcXBvSfvQk9Az/v02NJElEPw3isc9l4sLDwNCIF5IW34yfyKY5K2lf4TPMuzgjkxf8GK64/Y9JvhVFmSn10DJW7fMVBnd3d37++WdCQ0MpVSr3PZ2aYMGCBSQmJjJ37txsx6AtW7aMuLg4ZsyYoabo/p8kSSji48mMisr6JSmVlOjTJ9f7xo4dy4EDB/D29s5aEv2UVKtWjRYtWrB8+XJ1hyII2RTvk9yFj5avry9ffvllvu83MamKq+sB/PxGkpISzOvJ7v9fmpTJtJAkBTY2A6hQ/kfkcj1KlGhIXNwVoqNPEh/vQ9myfTEydCrwZ/mvVq1aceTIEa5cucJnn32m0r2l7R0Y/MdKHl4+z92zJ3kRGkLqy5dIkpLy9RtRq21HbKtUz5ZIyGQyLB2cKF3OkdjQEHr27s29hw/p07Y1bewboutgWijJH4Bpm3Ik+0aT4BWMRe/ci0cKm1KpZNWqVfTt2zffyV/qo0fE795D6t27ZDx/jnbpUuhVqoR5z14Y1K5V6GfVtm7dmt9//53nz59jY2Pz1jVLS8sib50iSRLKxEQyo6PJjIoiIyqKzKjotxK9zKgoMqOjkf6nKtmwYcN3JoCSJDFu3Dj27dvH2bNnP8nkD17vAxQzgIKmEgmgoHFevHhBWFhYvvb//ZexUXkaNvAiOvoE4RH7SEoKIDMzEX29spibu2Jj44axccWs8VpaBpQq1RIjo4o8D/ckKGgpJUo0xsqyM9raqp1F/C7W1tbUqFGDkydPqpwAAmjr6lK1eSuqNm8FQOCta+yb/wvNBw3FtFTuS54ymYySdvacu+pDXHgYEZf8kaXLuXXbC3utmthWropcq2D/SdAy0sGsbTniDz7BuIE1unZ573lYUMePHycoKIgdO3aofK8yJYWIX2aTsH8/aGmB4vWscWZkJKn+D0jYsxfDRo2w+eN3tAuxkMHZ+XXhUXh4eI4JYGxsLBkZGSr1wnxD8SrpPwnc/ydz/5vkSf+zB1HLzAxtS0u0LS3RdXDAsH79f/+5NDr/fl27VClkucxgjxkzhh07dnDgwAFMTEyIiIgAwMzMLKsa/lPg6OiYdUa4IGgakQAKGqcgBSD/Sy7XwcqqE1ZWeW8FYmBQFifHicTGXiAy8ggvE+9gbd0TU9PCmQFq06YNf/zxB4GBgTg5FWyG0dzKGoD4iIj3JoD/VcLaBi1zBZkZ6RgoTLlz6jgBPpdxruuKbdUaaGurnnC8YdTAmiSfCOIPPqH0qJqFNrv4PgcPHqRixYo0aNBApfuUqakEf/EFaQ/+nW1T/M+WgX//OdnHh6DuPXD8ey/aeZhhHDx4MFu2bMn6ZwsLC1xdXVm4cCE1atQAoESJEsDrPX/16tXLGpuWlkaPHj1ISEigV69e9OrV6//jTU9HmZiI4tUrpNRUkm/eJCM0LNusnfLfowffkBsbZyV2OjZlMaxdK+uftf+T2MkL4Yi8N/thW7Ro8dbXN23axODBgwv8/OLizWkgkiQV+uyxIBSUSAAFjePr64uBgQHly5dXWwwymZySJT/DxKQ6ERF7CQndgrHxNcpa90ZXt2AzQLVr16ZkyZIcOHCASZMmFehZpqWtQCYjISoCqJHn+xQv08mISsaooTV1HbuT+CKaJ9eucu/cGQJ8ruBUpx7lqtdCW1dP5ZhkchklepUn7u8AUh/EYlBF9UKe/AgMDKRKlSoqf6ONmDv3dfL3vuP2FAoyX7wgbNJk7LdszlNVdvv27dm0adPr90RE8NNPP9G5c2eePXuWNcbExISDBw8ycfx4lAmJKBIT8dy1E2NdXRKANG9voqOjXyd9CYlIqa8bicsMDNAyNyfh4CG0DAxeJ3ClS6Nfpcp/krp/Z+1Kl0aew/GFRUVsLX/N0dGR5ORkoqOjNb4oSfj0iARQ0Di+vr5Uq1ZNI1p46OqWwN5+OImJfjwP38vjx/OxtOyIhUUz5PL8xaelpcWAAQNYtmwZV65coWHDhvmOT1tHBxOLUsRHqlZ5mxaUgExbnrVEa1qyNLXbd6Z8wyYEXr/Kw0sXeHLdB4dadXGoWQddFWeFdG1NsBpfR6V7CiowMJBu3bqpdE/606ck7Nmb9xsUCpKvXSPp0mWMmzZ573A9PT2sSpVC8fIlFiVKMKlPH1ofOcITT08s/u1VOaBaNbZfvkzg5MkY/LvUu3HvXnq7uLAkOhrly1fIDQ3RKVMGLVNTtMzMkP/7v1pmZlh++62YXdJQ/20FIxJAQdOIBFDQOL6+vm8th2kCU9MaGBmVJzLqCBGR+4lPuE7Zsv0wNLDL1/MaNWrE1atX2bhxI5UrV871uLX3MbOyIiEyIs/jJaVEeuhLdJ1MkWu/PYtlbF6CGq3b41K/EcF+t3h65ybP7vphX60G5arXQs+wcPZCFjaFQkFwcLDKS+rxe/9+a89fnmhpEb97F0aNGqJITkaZkIAiIQFFQiLKxAQUia9n8VLu3SP15UueT/sekEhKT2eztzcO5uboP3pE5r9H9Tk4OWEREMC5kqX4ol8/Ql8mcnXFCtadOMGSihXxty7DwAIURAnq86b4JTg4WOWtCYJQ1EQCKGiUjIwM7t+/z9ChQ9UdSjZaWgaUte6NuVl9IiL2ERy8CosSTShduhVaWqrNkMlkMoYNG8bMmTPZunUrY8aMyfcsjrmVNdFPg/M8PvPF6yVEfSfzd44xNDWjStMWONepT9iDezx/9ICooMdYl6+IbeVqb7WZ0QShoaFkZmaqXG2a+uCBaskfgELBy5P/8KhJU8x790IRH4+UkgIyOXITk39n6UzRMjLin5s3qbhqJQBJKSlYlynDoZMnsXnzA87ECSQ5OmLl6Mj2c2cZNnMGO2bPpmPHjlkzRuIoseLL3Nwcc3Nz0Qxa0EgiARQ0yoMHD0hPTy+UApCiYmhoj4PDGBISrvMi9iKpqc8oVaoNxsYuKj3H1NSUoUOHsnXrVm7dukWdOvlbMjWzLMPja1fyPP7luVAyIpMw6/D+ZEnP0BCnOq6UcamA3z9enNqwGkVmBpWbtqROx66Yliqdr5gL25vm2tbW1irdl6liU+4scjlWU6agX7MGcn3914mficlb+wJ1vb1pqaOTVRARFxfHypUr6dipEz4+PpQrVw4AIyMjbG1tOXbsGIGBgWzevJmlS5dmPefly5f5i1HQCA4ODiIBFDSSOAlE0ChvKoDfVElqKrlcmxIlGmJTti+JL+9w42Yf7t2fSlpatErPqV69OnFxcfTs2ZPnz5/nKxZzqzKkvnpJatKr945VJmeQfDsKw+qlVZpxNDQ1o2HPfnz1+3LqdOjKPe9TbBg/HK9Vi183l1azN4nfm3YjuVEkJpJ06RLRK1aQEfn+s5JzomNlhXmvnui7uKBra4u2mVmORSFGRka4uLjg4uKCq6sr69evJykpiXXr1mWNSUpKwsTEhE6dOjFs2DBSU1Pp0KFD1nUTkw/XSkcofKIXoKCpxAygoFF8fX0pV64c5ubm6g4lTwwM7KhebQWRkYd4FDCHmJgTODtPxaZsf2SyvP18tWDBAo4cOcLw4cM5cuSIykvBZlZlAEiIjEDfKfdZyGTfaFBKGNbO34Z0PUMjGvToS+0OXfD7x4vrh/7m/rnTVGzcjAbd+1DK3iFfzy0oW1tbtLW1CQwMfOvrUkYGqQ8fkeLnS6rfHZJv3CCjoOframmhX6VKvm6VyWTI5XJSUlKyvpaQkECpUqXo378/HTt2ZNq0aW8VQBWXvwtCzhwcHDh8+LC6wxCEbEQCKGgUX19fjV7+zYlMJqNMma6ULPkZAY/n8/Dhz0RE7KdSxbkYG7//NAwLCwvWrVtH586d2bhxI8OGDVPp/Vm9ACMjsHpPAph0PRL9SiXRMinYMW26+gbU69yDWm07cffMSXwO7mHLlLHU7dSDFl++O/5Vq1axatWqrBmRqlWrMmPGjLdmvPJDS0uLcuXKEX3vHglHjpDq50eK3x1S7t2D9HSQyZDp6CClp4Ncjn7lyhi3+hw9ZxfCJkxQ7WUKBeZ9cz8C7Y20tLSsWcm4uDiWL1/Oq1ev6NKlS9aYhIQEqlatSvv27YmOjsbU1BR4fbIJiASwuHN0dOTp06colUrkeWgdJAgfikgABY3i6+vLiBEj1B1GvujomFOl8nysy/TgwcOf8LnWlXLlvsGh3Bi0tHLvp9epUyeGDBnCpEmTaN26ddb+sLzQNzZB18Dw316A75b+/BUZYa8wbWWf52e/j7auLrXadaJ6q7bcP3+GJ9ev5jre1taW+fPnU758eSRJYsuWLXTr1o1bt25RtWpVld6tSEgg5c7d17N7vn7s0NXD5NRpnp86jdzMDJm2dlaBh3YZK4ybfYZR0yYYNWqE1n+WVV/16E7CgYPv7wMIoKWFQc2aGDV5fwsYAC8vr6zlaRMTEypVqsTu3bvfapCckJCApaUlMpnsrSPsYmNjgf9vFi0UT46OjqSnp+d42osgqJNMEh07BQ0RERGBtbU1e/bseevkg+JIqUwjOHg1wU9Xoa9vQ6VKc7Eo0SjXexISEqhevToVKlTgxIkTKs0WbJ02AStnF9p+M+6dY+IPPSHZNxrr6fWRaRXNTIRSoUCuYv9GCwsL3N3dc535lDIySH3wgBQ/v9eze75+pP87iygzMUG3bFlCQkPRT0rCFJDp6WFYvz7GTZtg1KwZuo6O71xaV6akENzfjbSAgNyTQC0ttEqUwPHvvegUUk83SZIYPHgwffv2pVOnt0+ruXfvHnPnzmXRokUqF7cUV2kZCl4kpVPW/OM5Lu7u3btUr16d8+fP07RpU3WHIwhZxHy0oDEK8wg4dZPL9XBymkCD+ofR0y3NrVsDuX9/KhkZce+8x8zMjA0bNnDq1ClWr16t0vve1wtQylSSfCsKwzqWRZb8ASolfwqFAk9PT5KSkmjUKPfkOHjgQIL79CVy/gJSnwSiV7EiJq1bo1epElJSEmkPH2JubMzfsbEkT/mOClevYL9uLRZffYWek1Ou+yrlBgY4eOzAtFPH11/438/w7z8b1q2L0/59hZb8weukPz09PccmwVFRUdlmBT9mL1My+GL9VYZvuU5KuoqteTTYm2bQohBE0DQiARQ0hq+vL8bGxgU+H1eTGBm5UKfODipVmkd0zEkuX2lLePi+dx6V1aZNG0aOHMmUKVN48uRJnt9jbmWd6xJwiv8LlMmZGNW1UvkzFLY7d+5gbGyMnp4eI0eOZN++fVR5T1GFec9elBw5EpOWLckICeHl8eMkXb2KbrlylPllFi5nTlPt9Cl2GRmy7Px5lc+zlRsaYuPujuO+vynRry/61auhVaoUepUqYdalC+W2/oX9ls15OgNYFU+fPgVybl8THR2NhYUFOjr5P5e5ODEx0GFO92oExrzix/13Pprj5IyNjSldurRoBSNoHLEHUNAYvr6+VK9e/aPbKC2TybEp249SJT8nIGAu9/2/IyJiHxUrzsHQMPteP3d3d44fP86QIUM4e/Zsnn4/zCzLkBgTjSIzEy3t7H+tk69Homtvgo6V+hs4V6xYkdu3b5OQkMCePXv46quvOHfuXK5JYPzOnaT6+6NfvToWX3yBUdOmGNSo/nqf33+MHDmSmTNn8scff1CypOpnEOtXrkyZGTNUvi+/rly5QpkyZXLcGxYZGfnJHR9W2dqU+T1rMHHnbWrbmTOokYO6QyoUohegoIk+ru+0QrFWHCuAVaGnV5pq1ZZQq+ZGklOectWnA8HBq1AqM94aZ2xszKZNmzh//jxLlizJ07PNrayRlEpexmTvQ5iZkEbqozgM66l/9g9AV1cXFxcX6taty2+//UbNmjXf+zlLTZpI+UsXcdy1k9Ljx2FYp3a25A9g6NChSJLEpk2biir8QpORkYGPjw+NGzfOtkStVCp59OgRdnb5O2qwOOte24bBjR345dB9bjyNVXc4hUL0AhQ0kUgABY2QmprKgwcPPuoE8I2SJZvTsMExbG0HERj0Jz7XupKQcPOtMc2bN2fChAn88MMPPHz48L3PtLCxxdLRmeTE+GzXUh/FomtngmFNzTi1438plUrS0tJyHWPSrBnaeaiGLV26NH379mXRokVER6vWlPtDu337NsnJyTnufwwICCAmJuaTPT/2x06VqW1vzqhtN4l6marucApMzAAKmkgkgIJGuH//PgqF4pNIAAG0tAwp7zId13r7kcv1uH6jLw8eziQz8/+P/Zo3bx52dnZ89dVXZGZm5vo8k5KlGDR/CWUrVM52zdjVGsvRtZDrqX/Hx/Tp0/H29iY4OJg7d+4wffp0zp49yxdffFFo75g/fz6ZmZl88cUXKFQ95/cDunz5Mvb29tja2uZ4zcLCgkqVKqkhMvXT0ZKzYsDroxHHbr9FhiIPLXo0mKOjIyEhIe/9eywIH5JIAAWN4Ovri0wmo3r16uoO5YMyMamCa729VCj/ExER+7h8pS1RUV5IkoShoSFbtmzh2rVrbN26Vd2hFoqoqCi+/PJLKlasSKtWrbh27RrHjx+nTZs2hfYOGxsbPDw8+Oeff5g9e3ahPbcwpaam4u/vT/PmzbNdUygU+Pn50bx5849uP6wqLE31WTWwDjefxTHvqL+6wykQR0dHFAoFoaGh6g5FELJ8uv91ETSKr68vzs7OGBsbqzuUD04m08LObjANG3hhalqDO3fH4HdnBKmpz2nUqBHfffedym1h1EXKyMj1+oYNGwgODiYtLY2oqCj++eefQk3+3mjVqhWzZ89mzpw5eHl5FfrzC8rf3x8TExMaNmyY7dqTJ0/Q1tZ+b2ucT0HdchbM6FKFTReDOXBb/WdO59ebVjBiGVjQJCIBFDTCx14Akhf6+mWpUX011aut5GXiXa5cbcezkE3MmjVDo4sB0kPDiPPcScjYsTwdPFjd4WT54YcfaN++PQMGDOCff/5RdzhvWbBgAb6+vlhYWGS7tnTpUv75558cl4Y/RYMalqNnbRum7fXDPzxR3eHky5uTfUQhiKBJRAIoqJ0kSSIB/JdMJsPSsh0NGx7HukwvAgJ+5c7dL5g5a6S6Q8uiTEnhlbc3Eb/O40mHjjxp3ZqIOXNQxMVj1qOHusPLIpfL2b59O/Xq1aNt27bMnTs363xddYqNjeXvv//OdvIHvD47eNu2bXTq1CnX5tWfEplMxq89quNYypiR226QkJL7LLMm0tfXx9raWswAChpF/bvChU9eaGgocXFxIgH8D21tEypWnEWZMt3wf/ADKSnLgdZqiUWSJNIfP+bV+QskXbhA8vXrSOnpaJe1xrhpM0pPmpjtfF1NUaJECY4dO8acOXOYMWMGly5dYuvWrfnqEVhY/v77bxQKBX379s12zcvLi4SEBNzc3NQQmeYy0NVizcC6dFl+gUk7b7P+y3rI5cUrQRatYARNI2YABbX7mI6AK2xmZrWp73oQW5uBKt03f/58ZDIZEydOzNd7FQkJJHp58fzHH3ncoiWBXboSvWQJaGlh+d23OB09gsupU1jP/gXTtm01Mvl7Q0tLi1mzZnHs2DF8fHyoXbs2a9euJTk5WS3xeHh40LJlS8qUKZPjtRo1arz3ZJRPkX1JQ5b0r8WZh1EsPR2g7nBU5ujoKGYABY0iZgAFtfP19cXc3Bx7e3t1h6KR5HIdypbtnefx165dY82aNdSoUSPP90gKBal372bN8qX4+YFSia6LM6YdOmDUtCmG9eqqfMSaJmnXrh23bt1iwoQJjBo1iu+//56vv/6a0aNHZ+3RKmrh4eGcOXOGdevWZbv26tUrDh48yIwPeBJJcdOioiWTWlfgz38eUdPWnJaVis9JKQ4ODpw9e1bdYQhCFjEDKKidr68vNWrUEHueCsGrV6/44osvWLduHSXy0Dj55anThE6aREDjJgT360/sX3+hbWWF9exfcDlzGufDh7H6fhrGTZsU6+TvDTs7O/7++28eP37MsGHDWLt2LU5OTvTq1Ytz584V+fmzu3btQltbm549e2a7dvDgQVJSUujXr1+RxlDcjW3pQqtKlkzwvMXTF0nqDifPHB0def78+XubngvChyISQEHtRAFI4RkzZgydOnWideu87ReMWbmCjLDnlPjiC8rt2EGFSxexXbIY89690bG2LuJo1cfR0RF3d3dCQ0NZsWIF/v7+tGjRgtq1a7Nx40ZSUlKK5L2enp60b98+x+Tcw8ODhg0b4ujoWCTv/ljI5TL+6FsLCyNdRmy9QUq65jb7/i8HBwckSeLZs2fqDkUQAJEACmqWnJxMQECASAALgaenJzdv3uS3337L8z12mza993zdj5mRkREjR47k3r17nDx5Ent7e4YPH46dnR0//PBDoTbuDQoK4sqVKzkWeMTGxnL8+HFR/JFHZgY6rBlUj6cvkpn+t1+Rz9wWhjeJvSgEETSFSAAFtbp79y6SJKm0X03ILiQkhAkTJrB9+3b0VViq1TY1LcKoig+ZTEbr1q05ePAgAQEBDBo0iBUrVuDg4EC/fv24ePFigZMMT09PDA0N6dq1a7ZruVUGCzmrWMaEBb1rsP/2c7ZcClZ3OO9lZ2eHXC4XhSCCxhAJoKBWvr6+yOVyqlWrVuBnKZVKMt5zEsXH6saNG0RFRVGnTh20tbXR1tbm3LlzLF26FG1tbY0+ExdgyY0lfO/9vbrDAMDZ2Zk///yT0NBQlixZwu3bt2natCn16tVjy5Yt+d7D5eHhQdeuXTEyMsrx2rsqg4V361qzLMOaOjL3iD/XgmPVHU6udHR0sLW1FQmgoDFkUnGYOxc+WmPHjuXUqVP4++fvrM+kpCRu3brF7du3iYuLQ6FQYGBggL29Pa6urjg7OxeL4hJJkkhOTiY+Pp6EhIRs/6unp8eQIUPeef/Lly95+vTpW18bMmQIlSpVYtq0aYWSYBelg08O8uOFHznb9ywlDdTXoy8nSqWSkydPsmTJEo4dO4alpSUjRoxg5MiRlC1bNk/PuHfvHtWqVePAgQPZZgDDw8OxsbFh7dq1DB8+vCg+wkctQ6Hki/VXCYpJ4si4pliaam6xUosWLbC2tsbDw0PdoQiCaAMjqFdBCkAeP37Mnj17SEtLe2t5LiUlhUePHvHw4UMqV65Mt27d8rQsOnjwYLZs2ZL1zxYWFri6urJw4cKsJer/JpMmJiZUrFiRn376iW7duuX6bKVSSWpqKi9evCA2Nvat5O7N/8/MzMwar6Ojg5mZGebm5tjY2GBjY5Pr801MTLIleUZGRpQsWVLjkz+AxmUbA3Dp+SW6OHdRczRvk8vltGvXjnbt2vHo0SOWL1/On3/+yW+//UafPn0YP358jmf6/penpyfm5ua0a9cu27U3lcG9evUqqo/wUdPRkrNiQB06LzvP6O032fF1Q3S1NXNxy8HBgQcPHqg7DEEARAIoqJm/vz9t27ZV+b5Hjx6xY8cOZDJZjnuz3nztwYMHxMfHM2zYMLTzUODQvn17Nm3aBEBERAQ//fQTnTt3fqtyb9OmTbRv357ExERWrlxJ79698fHxwdnZmZSUFJKSkrL+Nzk5meTkZDIzM9HX1+fmzZukpqZiYGCQleA5Oztjbm6e9c9mZmYYGhoWi5nLwlLKoBRVSlbhfNh5jUsA/6tChQosXbqUuXPnsmnTJpYtW0ajRo2oX78+48ePp0+fPujq6r51jyRJeHh40LNnT/T09LI9M7fKYCFvSpvosWpgXfqtucyvR+7zS7e8/dAjSRJRUVEEBgYSGBhIUFAQSqUSJycnHB0dcXJyokyZMoX2d9HR0REvL69CeZYgFJRIAAW1SUxM5MWLFzg7O6t038uXL9m7dy/AezfmS5JEREQE//zzD+3bt3/vs/X09LL2YZUpU4bvv/+eZs2aERYWlrV3KyUlhaioKJKSkmjdujVLlixh2bJldOzYMes5BgYGGBgYZM3CGRsbY2xsTO3atTE1Nc0xEShsxa3pbJOyTdj9aDcKpQItuZa6w8mVqakpEyZMYNy4cRw7doylS5cycOBAvvvuO0aOHMnIkSOxsrIC4Pr16zx58oTVq1dne86byuAdO3Z86I/w0aljX4IZXary8/671LI3p0dt2xzHpaSksHPnTtatW8ft27ffOhGmdOnSyGQyoqKisr5mYGBA9erVGT58OAMGDMhxD2deOTo6EhkZSXJyMoaGhvl+jiAUBpEACmrzZjO0k5OTSvddu3aN9PT0PI+XJAkfHx8+++yzHP+jK0kSaWlppKWlkZKSwsOHD0lOTiYmJoYlS5ZgbW3NxYsXkctfLysFBwfj4OCArq4up0+fBl4XDjRr1gwjIyMMDAzQ0tLsBEYTNbNtxro767j34h41ShePqnC5XE6nTp3o1KkT9+/fZ/ny5SxcuJBff/2V/v37M378eDw8PLCysqJly5bZ7s+tMlhQ3cAG9viGxDP97ztUsDKhalmzrGuPHz9m9erVbNy4kbi4ONq1a8ecOXNwcnLKmvEz+fdIw1evXhEcHJw1M3jq1ClGjBjBd999x+DBgxk1ahSVKlVSOT4HBwfg9X9DxHF/grqJIhBBbfbt20fPnj2JiIjImi3Jiz/++IOXL1+q/L6mTZtSpUoVEhMTSUpKylqiTUlJQaFQsHz5cs6fP5+1hJeamkqpUqVYvXo1rq6uGBoaUrp0afT19dHS0iIlJQWlUomDgwM3btzAwsJC5ZiE/5epzOSznZ8xsPJARtcare5w8i0+Pp6NGzeyfPlygoKC0NHRoVWrVhw8eBAdHZ23xr4599fT01NN0X58UjMU9F59iYSUDA6NbYosI4VRo0bh4eGBhYUFQ4cOZcSIEbi4uKj03ODgYNauXcv69euJjo6mR48erFu3jpIl8160FBISgr29PUeOHHlrxUAQ1EEzd8oKn4SgoCAMDQ2xtMz7eZ7p6en5Sv7g9VLc3bt3CQ4OJi4uDh0dHaytralWrRqNGjWibNmytGjRAj8/P+7cuYOPjw+dO3dm9OjRSJJEqVKlAPjzzz+5ffs2x44do0qVKqxfv77YJn8ZSs1pm6Mt16aRdSMuhF1QdygFYm5uzuTJkwkICGDu3LlkZGTg5eWFg4MDv/76K9HR0cDryuA7d+6I5s+FTF9Hi1Vf1OVlaibD1p2nbj1Xjhw5wrp16wgNDcXd3V3l5A9ez97NmzePkJAQtmzZwrlz56hTpw4+Pj55fkbZsmXR0dERrWAEjSASQEFtXrx4gYWFhUobrFNTU/P1LplMhrOzM59//jnt27endevWNGrUiJo1a1K+fHlsbGzQ1dXFxMSE8uXL4+LigqurK+vXrycpKYl169ZlPatMmTK4uLjQtm1bNm3aRL9+/d7aM1SczLk8h6hkzYm9qU1T7sbcJS41Tt2hFJiWlhYhISGUK1cOPz8/OnfuzK+//oqdnR1Dhgxh8eLFmJub52lvqqAaOwtDOltEc/15Mto1u3Dz5k2GDx+OgYFBgZ+tp6fHl19+ya1btyhTpgxNmzZl5cqVeWoUrqWlhb29vUgABY0gEkBBbezt7QkPD3+r/cn7GBsb56siTyaTYWlpmadK4P+9Ty6Xv/Ns2Pr161O3bl1+/fVXlWNShbKIdmpEJUXR91BfrkVcK5Lnq6qpTVMkJC49v6TuUAosIyODPXv20L9/f6pXr86aNWsIDQ1l9uzZnDp1ivXr16Onp8ehQ4dU+jsg5E6SJEaPHs2v4wZSPuUBqS6fE5RmXOjvsbe35/z584wcOZIxY8YwePBglErle+9zdHQUx8EJGkEkgILaODo6olAoCAkJyfM9crkca2trlZNApVKZp1MW0tLSiIiIICIiAn9/f8aNG8erV6/o0uXdrUkmTpzImjVrCAsLUymmvFJKEouDI4rk2fM/m4+LuQvDTwxn492Naj9TtbRhaSqWqFjsl4EBTp48yYsXL95a4rWwsGDq1Kns3LkTACsrK/r06YOTkxPz58/nxYsX6gr3o7F06VJWrVrFmjVrOLH4O9pUsWLSrtsExyQV+rt0dXVZunQp27ZtY+vWrXk6h9vBwUHMAAoaQSSAgtq8qf4NDAxU6T5XV1eVExUjI6M87fvx8vLC2toaa2trGjRowLVr19i9ezctWrR45z3t27fH0dGxSGYBlZLEtEeheMUkFvqzAcz1zVnTZg3Dqg3jzxt/MuHMBBLTi+ZdedXUpimXnl9CKb1/NkWTeXp6Urly5RzPud69ezeWlpbcuHGDW7du0aZNG2bNmoWtrS1ff/01fn5+aoi4+Lt8+TLfffcdkydP5ptvvkEul/FH35qUNtZjxNYbJKcXzUzrF198wa+//sqiRYs4d+5crmMrV65MXFzx3+IgFH+iClhQm/T0dAwMDFi9ejVff/11nu/LzMxk48aNRERE5GnJBaBPnz5UrVo1v6GqhfRv8rf1+Qs2V3OgXWnzIn3f2ZCz/HD+B8z1zfmzxZ9UtKhYpO97l+sR1xlyfAienTypWqp4/Tt7IyUlBUtLS6ZMmcKMGTPeuqZQKLC3t6dnz54sW7Ys6+sxMTGsW7eOFStWEBYWRosWLRg/fjxdu3YVbYXyIDo6mjp16mBvb8/Zs2ffqrgOiHxJtxUXaVXZiqX9axVJk3WlUsmGDRsIDw9nwoQJmJmZ5Tju1q1beHh4MHv27DydUCQIRUXMAApqo6uri4ODA7dv31bpPm1tbfr165fn/YDNmjUrlsnf9IAw/nr+gj8q2RV58gfQwq4FO7vsxFjHmC+OfsH+x/uL/J05qWlZE2MdY86HnVfL+wvDkSNHePXqVY4VvhcuXOD58+fZrpUqVYrp06cTFBTEzp07ycjIoGfPnjg7O/P777+LWaNcKJVKBg4cSFpaGrt27crWbqe8lQnuvWtyyPc5Gy8GF0kMcrmcfv36kZiYyKpVq1AoFDmOMzExITY2NqsaXBDURSSAglr17duX7du3k5Sk2v4cMzMzRo4cScWKr2epZDJZVjL4pmGzvr4+PXv2pFWrVoUbdBGTJIkfA8LYHBbDHxXtGGCd9z5jOTl9+jR16tThyJEj7x1rZ2LHXx3+opNTJ36++DOzLs0iTZFWoPerSkeuQ0PrhlwMu/hB31uYPDw8qFu3LuXLl8/xWrly5WjUqFGO9+ro6NC3b18uXLjA9evXadGiBT/++CO2traMHDmS+/fvF3X4xc7Jkyc5ceIEmzdvfue52Z1qWPPNZ07MO+rPlcCi2WtpamrK8OHD8fX15fr16zmOKVWqFElJSURGRhZJDIKQVyIBFNRqxIgRJCYm4uHhofK9hoaG9O/fn7Fjx9K8eXOqVauGi4sLderUoUePHnz77bc57r/SZJIk8fPjMDaGxeBe0ZYvyhYs+QP4/PPPqVy5Ml988UWeClX0tfX5pfEvzG48m8OBhxl0dBChL0MLHIcqmto0xS/Gj4S0hA/63sKQkJDAkSNHcpz9+29lcF5mr+vWrcvmzZt59uwZ33//PQcPHqRq1aq0adOGQ4cOvXOW6VOzcuVKatSoQYcOHXIdN7VdReo7WDB2x00iEvLXUup9KlWqRIUKFTh58mSO183MzNDV1RUzgIL6SYKgZp07d5Zq1aolKZVKdYeiVkqlUvr5UahkdfqWtDk0ulCf/eLFC8na2lpq3769Sr/P92PuS+33tJca72gsnQs5V6gx5Sb8VbhUbXM16VjgsQ/2zsKyZcsWCZBCQkKyXTty5IgESLdu3crXs9PS0qQdO3ZIDRo0kADJyclJWrRokRQfH1/AqIuvp0+fSnK5XFq9enWexke/TJUazvtH6r7igpSWocj6ulKplOKS0qQXr9IK/N+i8+fPS/3795dCQ0NzvD558mRpy5YtBXqHIBSUmAEU1G706NHcvn2bq1evqjsUtZEkiVlPnrM2NJrfKtjylU2pQn2+hYUF69atw8vLiw0bNuT5vsolK+PZ2ZM6lnUYc2oMy24tQ6Es+lmnMkZlcDF3KZbtYDw8PGjWrBm2trY5XqtUqRI1a9bM17N1dXVxc3PjypUrXLlyhUaNGjFt2jRsbGwYO3YsDx48KGj4xc7atWsxMjLiiy++yNP4UsZ6rPyiDvfCEplz+D63Q+KZ6HmLqjOPU2v2SerMOUmVGccZs/0m14Nj8xVTgwYNMDU15dSpUzlet7S0LLbN44WPh0gABbVr164dLi4uzJw5U+196NRBkiTmPAlnTUg0v5a3YUghJ39vdOrUiSFDhjB58mSePn2a5/vM9MxY8vkSJtSZwPo76xn5z0hiU/P3jVEVzWyacfH5xWLVDiY6OpqTJ0/muPybkpLC/v37cXNzK5Qq1AYNGrBt2zaePn3Kd999x+7du6lcuTLt27fn6NGjea6QL87S09NZt24dX331FcbGeW/2XNu+BDO6VGHrlad0X3GRQ37hJKf//w82KRkKvO5F0Hv1ZeYcvk+GIm+/l4MHD0Ymk6Grq8vatWv56quvkMlkPH78OOva/PnzKV26dNYS8P79+4ukKlkQ3kckgILayeVylixZwokTJ7Ia5H4qJEni18BwVoZEMbe8DcNsSxfp+/7880/Mzc0ZOnSoSgmCXCZnePXhrG2zlkdxj+h7qC++0b5FGOnrfYAxKTE8jH1YpO8pTHv27AGgd+/e2a69qQzu379/ob7T2tqaWbNm8ezZM/766y9iYmLo1KkTlSpVYtmyZSQmqrevY1Hy8fEhKiqKL7/8UuV7H0X8/5niCmX2HzzffG3jxSC+3+uX5x9O27dvT3h4OD4+PnTr1o2LFy/i6OgIvC5MW7BgAQYGBkRHR3+SP/AKmkMkgIJG6NixI71792bixImfTLsLSZL4LTCc5c+i+MWlLMOLOPmD1xvQN2zYwOnTp1m1apXK9zewbsCuzruwNrJmsNdgdvjvKLJvYrUta2OobVisloE9PT1p06YNpUtn/3fp4eFBnTp1qFChQpG8W09Pj0GDBnHt2jUuXbpE3bp1mTx5Mra2tkyYMIGAgIAiea86eXt7Y2JiQu3atVW678S9CP66krdZcEmCvTfD2H87byf96OnpUaZMGapWrZp19vCbPo6tW7emTJkyHDlyhJSUFF69eqVS3IJQmEQCKGiMJUuWkJyczPTp09UdSpGTJIkFQREsfRbFLOeyjLCz/GDvbtOmDSNHjmTq1Kk8fvxY5futjKzY2H4j/Sv25zef35jmPY3kjORCj1NHS4cG1g2KTQIYGhrK+fPnc5zhy60yuLDJZDIaNWqEh4cHwcHBjB8/nh07dlCxYkU6d+7MiRMnPpqZp/Pnz9OkSROVz/hedvoxchVWXWX/3qPK75uhoSEmJiZvtXvR0tJi3rx57Nmzh+TkZFEJLKiVSAAFjVG2bFl+++031qxZw+XLl9UdTpFyD45g8dNIZjiXZaT9h0v+st7v7o6VlRVDhgzJVysRHbkO0+pPw725O+dCz+F2xI3ABNWO9MuLpjZN8Y32VfvxdHmxc+dOdHV16dGjR7Zr+/fvJy0tjX79+n3QmGxsbJg7dy4hISFs3LiRsLAw2rVrR5UqVVi5cmWxnoHKzMzk4sWLfPbZZyrd9+xFMnfCEshh1fedJCAwOolHkS/fO/bw4cMYGxtjbGzM5s2bmTt37lvXe/ToQY0aNbhz545IAAW1EgmgoFFGjhyJq6sr33zzDRkZGeoOp0j8HhTBouBIfnSyZrQakj8AY2NjNm3axIULF1iyZEm+n9PeoT0enV73cHQ77IZXsFdhhQi8TgAVkoIrz68U6nOLgoeHB506dcLU1DTbNU9PT5o1a4adnZ0aInu992zw4MHcvHkTb29vqlWrxvjx47G1teXbb79V+TxuTeDr68vLly9VTgADY/Kf9D598f6Z7pYtW3L79m1u377N1KlTc2xE7+7uTnBwMDdu3Mh3LIJQUCIBFDSKlpYWa9euxd/fn0WLFqk7nEK3KDiC34Mj+MHJmnHlrNQaS/PmzZkwYQI//PBDgdqHOJk74dHJg+Z2zZlybgoLfBaQoSic5L2scVmczJy4+Dz7qSAvX74kJSWlUN5TUAEBAdy4cSPHJd7cKoM/NJlMRrNmzdi9ezeBgYGMGjWKzZs34+LiQrdu3Th16lSxWR729vZGT0+PevXqqXRfYmpmvt+ZmPr+P9dGRka4uLjg4uKCg4NDjuc4N2/eHDs7OzZt2pTvWAShoEQCKGicWrVqMXHiRH755ReCgoLUHU6hWRwcwcKgCL53LMN4NSd/b8ybNw97e3u++uorMjPz/43RUMeQBc0WML3+dDwfejL0+FAikwrnqKuGVg05GXCS6T9Mp3///tSvX59SpUphamqKoaEhZcuWpWnTpnz55ZfMnDmTQ4cOFeiz5IenpyfGxsZ06tQp27XcKoPVyd7ent9++42QkBDWrl1LUFAQrVu3pnr16qxdu5bk5MLf11mYvL29adiwIXp6eirdZ2NukO93qnpvTEwMpUplb+uUmZlJ1apVuXnz5ke/3UXQXCIBFDTSrFmzKF26NKNHjy42MxK5WfY0kvlBEUx1LMNEhzLqDieLoaEhW7Zs4fr167i7uxfoWTKZjAGVB7C5/WbCk8Lpe7gvV8Pz39z70aNHTJo0iSUTl/CSl+w8tZOoqCiqV6/OpEmT2L59O5s3b+brr7/G0dGRwMBA1q5dS9euXXF0dGTOnDmEh4cX6DPlhSRJeHh40L1796yqz//y8PCgdevWOVYGawJDQ8Os82vPnDlD+fLlGTVqFLa2tkydOlWlnpEfilKp5Pz58yov/wJUKmOCvo7q3/q05TIql8m+vJ+byMhIrKyy/7AXHR2NmZkZnTp1YunSpSrHIgiFQSSAgkYyNjZm+fLleHl5sXv3bnWHUyDLn0bya2A43zpYMVmDkr83GjVqxHfffcfMmTO5c+dOgZ9Xs3RNdnXZRYUSFfjm5Desv7NepWbOx48fp02bNlSsWJFt27bxZasv0ZPr8dOGnzh9+jQbNmzgxx9/ZMCAAXz11Vf88ssvbN26lQsXLhAeHs7Nmzfp0KED8+fPx97enn79+nHr1q0Cf6538fPzw9/fP8cl3pCQEM6fP68Ry7/vI5PJaNGiBfv27ePx48cMGzaMdevW4eTkRK9evTh37pzG/DD24MEDXrx4ka8E0EhPm951bdFSoQxYSy6jcw1rzI1083yPUqkkJiYGS8vs+3zfnAIyY8aMT6Jht6CZRAIoaKwuXbrQs2dPJkyYQHx8vLrDyZeVz6KYGxjOpHJWfKeByd8bv/zyC+XLl+err74qlOIbC30LVrdezfDqw1lycwkTTk8gIS0h13syMjKYPHky7du35+XLl2zdupWQkBAWzltIw7IN89wOpnbt2qxdu5awsDD++OMPbt26RYMGDVixYkWRJDAeHh6ULFmSNm3aZLu2a9cu9PT0cqwM1mSOjo64u7sTGhrKypUrefDgAS1atKBWrVps2LBB7Xsvvb290dbWplGjRvm6f0KrCpgb6OSpFYxMBka6WkxpX+m9Yzdv3sz+/fsBiIuLIzMzM2vm97/XoqOjkcvl1K5dm7S0NI1JrIVPi0gABY22dOlSkpKS+PHHH9UdispWP4ti9pPnTCxnxVTHMhp93JO+vj5//fUXfn5+/Prrr4XyTC25FuNqj2NFqxXcjLpJv8P98H/hn+PYsLAwWrZsybJly/jzzz+5fPkyAwcORF9fH3hdDXwr8hav0vNewWlubs748eO5e/cuI0eOZOzYsXzxxReF2vpEkiQ8PT3p3bs3Ojo62a7nVhlcHBgZGTFixAju3r3LyZMnKVeuHF9//TV2dnb88MMPhISEqCUub29v6tati5GRUb7uL22ix9ov62Koq53rTKCWXIa+tharB9ZVef/f8+fPAXJcAo6KiqJ06dI5FogIwociEkBBo73pY7Zq1SquXNH8ViBvbA6N5s+nkUx1sGKahid/b9StW5cffviBX3/9lZs3bxbacz+z/YydnXdiqmvKwKMD2Rew763rp0+fpk6dOgQHB3Pu3DkmTpyY7feriU0TMqVMrkaovqdQV1eXpUuX4unpyaFDh6hfvz7+/jknoqq6cuUKT58+zXGJ901lcGEf/aYOMpmM1q1bc/DgQQICAvjyyy9ZsWIFjo6O9O3blwsXLnywWSxJkvD29qZZs2YFek7dchYcm9CMeuVKAKAlA61//9y9yQmr25hxZHxTGruofj73gwcPMDExoUyZ7DP/kZGRGrsnVPh0iARQ0HhjxoyhTp06jBgxolj0Brwa/4pnqemsqGLPJIfikfy98dNPP1GtWjW+/PJL0tLSCu25tia2bO24la4uXZlxaQYzLs4gNTOVy5cv065dO2rUqMHNmzdp3LhxjvfbmdjhYOpQoFNB+vXrx7Vr15DJZDRo0IDjx4/n+1lveHh4ZFUh53TN2NiYzp07F/g9msTZ2ZlFixYRGhrKkiVL8PX1pVmzZtSrV48tW7aQmppapO8PCgoiLCwsX/v//pedhSE7RzTCa2IzJretyOeVX+/XG9DAnsPjmrJ/TBOcShvn69kPHjygYsWKyOVvf5vNyMjg4cOHlCtXrsDxC0JBiARQ0HhvegPevXuXxYsXqzucXF2ITWRneCy1TAxpZWFarJI/eD1btmXLFh49esSsWbMK9dl6WnrMbDSTOU3mcDToKG6H3Og/4nVbl6NHj+a4Wf6/mto05UJYwWaaKlWqxNWrV+nQoQNffPFFgfqwZWZmcvz4cQYOHJhtKe99lcEfAxMTE8aMGYO/vz9eXl5YWVkxePBg7O3t+fnnn7OWQAubt7c3Mpksx6Q7vyqVMWVMSxd+7lQFgA7VrKlmY5bv52VmZvL48WMqVcq+b/DatWskJCTQsmXLfD9fEAqDSACFYqFOnTpMmDCBWbNmERwcrO5wcnQhLpFt4bE0KmFMF0vzYpf8vVGjRg1mzpzJwoULi2TZvbtLd7a020JQWBDGo4yZvGJyjvvn/lcTmyZEJEXwJP5Jgd5vbGyMh4cHGzZswN/fn8OHD+erEjMwMJBu3boxcuTIbNf8/Px48OBBsaj+LSi5XE67du04evQoDx8+pH///ixevJhy5coxYMAArly5UqjLw+fPn6d69eqUKFGi0J75hpnB6z+HCSkFW2kIDAwkPT2dypUrZ7t24sQJqlatio2NTYHeIQgFJRJAodiYPXs2FhYWjBkzRuOq5m4kvOLviHg+tzCht1WJYpv8vTFt2jTq1q3L4MGDi6Tic/eK3dz7/h41LGowy3cWS28uRaHM/Uzielb10NPSy/FUEFXJ5XK6detG27ZtOXDgAKtWrVJ5yfvSpUsYGxvj4OCQ7ZqHhwcWFhY5VgZ/zCpUqMDSpUuzKrCvXbtGo0aNaNCgAdu2bSM9Pb3A7/D29i6U5d+cGOtrAwVPAP39/dHX18+2zPvs2TMePnz4yf25EDSTSACFYuNNb8CjR4+yd+9edYeTZVd4LEeiE2hawpg+ZSyKffIHoK2tzZYtWwgODi70Cuxbt24xe/ZsZk2fxfa+25lYZyIb7m5gxMkRvEh58c779LX1cS3jyvmw84UWS+vWrRk5ciTXr19nzpw5eW43lJGRwYULF6hevXq2f9/vqwz+FJiamjJ+/HgePnzI4cOHKVGiBIMGDaJcuXL88ssvRERE5Ou5z58/5/Hjx0WWAGrJZZjoaxc4AXyz/+9/twacPHmSEiVKULdu3QI9XxAKg0gAhWKlW7dudO/enfHjx5OQkHtfuYJKVijZFxnHgsBwpj4MYUlwJGdeJKL4z+zjjucv+PlxGK5mRvT4CGb+/qty5crMnTuXxYsXc/584SVdy5cvx87Oju+//x65TM6w6sNY33Y9AfEB9D3cl9tRt995b1ObptyMvElyRuEdU1a3bl1mzpxJbGwsP//8c55am/j5+ZGUlISrq2u2a5cvX35nZfCnRi6X06lTJ44fP879+/fp0aMHCxcuxN7enkGDBnHt2jWVnvfmz2FBK4BzY2agQ2IBEkClUklISAjVq1d/6+vJycncvHmTdu3aoa2tXdAwBaHARAIoFDtLly7l5cuX/PTTT0XyfIUksSQ4kuoX7zLq/lOWPYtkR/gLFgaH4+YXiOvl+xyLjscj/AXfPgzBrUwJ2pcy+6iSvzcmTZpE48aNGTx4cKH0z4uLi2PHjh2MHDnyrdkR1zKu7O6yGxtjG4Z4DWG7//Ycl/mb2jQlQ5mBT4RPgWP5LwcHB+bMmYORkREzZ87Ez88v1/GXLl3C3t4eW1vbbNfeVAYXZZJSHFWuXJmVK1cSFhbG/PnzuXjxIvXr16dx48Z4enrmqcLf29ub8uXL59hapbCYGegUaAbw+fPnGBoaZtv/d+HCBUxMTGjRokUBIxSEwiESQKHYsbOzY86cOaxYsQIfn8JNBFIVSgb4BvJbUDhJiteFAZnS61+Kf/OR8LQMhtwNZtKDEAaVLckMF5s8JX8RERGMGzcOJycn9PT0sLOzo0uXLpw6dSprzK1bt+jTpw9WVlbo6+tTvnx5vv76ax49elSonzOvtLS02LRpE+Hh4UybNq3Az9u0aRMKhYJhw4Zlu2ZpaMmGdhsYUHkA833mM8V7CkkZSW+NsTexx9LAklW+q5h+fjo/X/yZtX5rC1wYAlCyZElmzpxJpUqVWLBgAadPn85xXGpqKjdu3MixZU1mZia7du2iX79+osnvO5ibmzN58mQCAgI4cOAAhoaGuLm54eDgwNy5c4mOjn7nvfk9/1cVBU0ADx8+zF9//UXZsmWzvnbu3Dnc3NwwNTXFzCz/1cWCUJhEAigUS2PHjqV27dqMGDGCzMzMQnvujMdhnI97meuY/85L1TczQp6H5C84OJi6dety+vRp3N3duXPnDl5eXrRs2ZIxY8YAr79xNGzYkLS0NLZv346/vz/btm3DzMyMn3/+uSAfq0DKly/PggULWLly5VvJqqqUSiWrVq2iT58+72z5oiPXYYrrFP5o/gcXwi7gdsQtK7nzf+FPj4M9iEqJ4v6L+xwLOsahJ4dYeXsl3Q90Z9jxYUQk5W9v2RsGBgZ8++23tG7dmnXr1rFjx45sFcI3b94kLS0tx2PIzp49S1RUlFj+zQMtLS26du3KP//8w507d+jcuTPz5s3Dzs6OIUOGZDu/OTY2ljt37mh8Anjy5EkqVqyYdYpNWFgYffr0oW7duoXyQ5QgFBaZpGnllIKQRzdu3KB+/fq4u7szefLkAj/vZmISHW8E5Hm8DDDSkuPXpBqGWrn/LNWxY0f8/Px4+PBhtuOr4uPj0dXVpVy5cjRt2pR9+/Zluz8+Ph5zc/M8x1bYlEolrVu35smTJ9y5cydfR5tdu3aN+vXrc+bMmTwtgwUlBDH57GTCXoXRp0IfdjzYgVJSopRybtmiJdPCSMeI9W3XU7lk9vYb/2vw4MHEx8ejUCjIyMjAy8sr65okSXh5efHHH39w6tQprl27Rr169QD4/fffSUxMZPbs2dmeOWzYMM6dO0dAQMBHuSWgqMXGxrJhwwaWL1/Os2fPaNq0KRMmTKB79+4cPXqUbt26ERQUlGPldWGZtsePBxGJHBirep9BSZKwtLRk5MiRzJkzh4yMDFq2bElwcDC3bt0Sp38IGkXMAArFVt26dRk7diw///wzT58+LfDzNofFoKXC92wJeKVQciQ6PtdxsbGxeHl5MWbMmBzPLjU3N+f48ePExMQwderUHJ+hzuQPXm/m37hxI7GxsflOtt8sY+e1AtLRzJHtHbfjWsaVv+7/RaYy853JH4BCUvAq4xXjTo8jIS3vBULDhg3j5MmThIaGZn1NJpPRoUMHDA0NKVmyJAcOHCAhIYFXr17h6+ub4+xfWloae/fupX///iL5yycLCwumTJnCkydP2Lt3L1paWvTp0wdHR0fc3d0pW7ZskZ+gYWaY/xnABw8eEBMTkzVL+f3333P16lV27dolkj9B44gEUCjW5syZQ4kSJRg7dmyBewNejHuVtc8vr7RkcDMhKdcxjx8/RpKkHE8FeCMg4PXMY25j1M3BwYE//viDDRs2cPToUZXvDwwMpHTp0piYmOT5HkMdQ5RKJTLyllApJSXRKdFsvLsxz+/o3LkzpUuXZvPmzW99/dWrV5w5c4aJEyfy4sULfv75Z44fP45SqaRhw4bZnuPl5UVCQoJY/i0E2tra9OzZk7Nnz3L79m3atm3LxYsXiYyM5Ouvv35vkU5BmBnokJiav20l3t7eaGlp0ahRI3bu3MmiRYtwd3d/5xGHgqBOIgEUijVTU1OWLVvG4cOHc1w6zSulJBGRpvpP/QoJnqbm3tw2L4lpcdmJ8fXXX9OuXTu+/vpr4uLiVLo3KCgIJycnle55/uo5F59fRCLvvz9KScnuR7vJUOTt36e2tjZffvklmzdvfuvfw+7du1EoFIwbN445c+ZgYGDA33//jb29fY6nUHh6elK9enWqVq2a51iF96tZsyZLlixBJpPRpUsXvLy8qFmzJi1btmTfvn0oFLk3EFeV6b97APPzd9Lb25vatWuzaNEi3NzccHNzY8KECYUanyAUFpEACsVe9+7d6dKlC+PHjycxMTFfz5DLZOi/Zx9fTmSAqXbu1Z7ly5dHJpPx4MGDd46pUKECQK5jNIFMJmP9+vUkJSUxfvx4le4NDAzE0dFRpXv8YvxUSv7eeJn+krBXYXkeP3ToUJ48ecK5c+eyvrZp0yZ69eqFmZkZJUuWZOLEiSiVSp49e5atQjgpKYmDBw+K2b8icvnyZZRKJfPmzSMoKIhdu3aRkZFBz549cXZ2xt3dndjY2EJ5l5mBDgqlRFK6aomlJEmcOXOGmJgYZs2axaxZs9i2bZvYDiBoLJEACsWeTCZj+fLlxMfHF6ha1tFAL48Ljf9PSwYOBrq5jrGwsKBdu3asWLGCpKTsy8Xx8fG0bduWUqVKsXDhwhyfkdcTKj4EW1tblixZwrZt29i/f3+e73v27JnK+7fCX4Ujl+XvP1PhSeF5HlupUiUaN27Mxo2vl44fP37M+fPn32pX4+vri7a2Np999hnr1q3Dw8Mjq0L42LFjJCcn069fv3zFKuTO29ubUqVKUalSJXR0dOjTpw8XLlzg+vXrtGjRgp9++glbW1tGjhzJvXv3CvQu03weB7d//37Cw8OJjY3l+PHjzJgxA7lcfIsVNJf40yl8FOzt7Zk9ezbLli3j+vXr+XpGbyvVD5fPlKBTKfP3jluxYgUKhYL69euzd+9eAgIC8Pf3Z+nSpTRq1AgjIyPWr1/PkSNHslpjBAcHc/36daZOncrIkSPz8YmKzpdffkmXLl0YMWIEMTExebrH3Nxc5UTWRNck18KP992rimHDhrF3715evnzJpk2bcHZ2pnnz5lnXL1++TI0aNfjmm28YNGgQhw4dYs2aNaSnp7N3714aNGig8hK3kDdvzv/939m0unXrsnnzZkJCQpg+fToHDx6kWrVqtG7dmoMHD+ZredjM4PXxfQnJeUsAlUoly5Yto0+fPsDrhs/irF+hOBAJoPDRGD9+PDVr1sx3b8D+1haYamvl+S+FlgwamBlR3dTwvWOdnJy4efMmLVu25Ntvv6VatWq0adOGU6dOsWrVKuD1MXeXLl1CR0eHAQMGUKlSJdzc3EhISGDu3Lkqf56iJJPJWLt2LZmZmYwePTpP9zg6OhIYGKjSexxMHfIRHciQYWuc/ZSO3PTt2xe5XM6OHTv466+/GDp0aFbCER0dzaNHj2jcuDEymYyOHTsyadIk7ty5w6pVqzh16pRY/i0iqampXL16Ndf+f5aWlvz8888EBwezY8cOkpKS6NatG87OzsyfPz/X5tL/600CmJiaewIYGxvLH3/8QYUKFRg/fjyVKlWiatWq2Y6AEwRNJRJA4aOhra3NmjVruHXrFsuXL1f5fnMdbdZWdUCC9y4Fa8nAREuL1VXzvqRpbW3N8uXLCQ4OJi0tjdDQUA4cOPBWT7x69eqxd+9eoqKiSE1NJSAggDVr1uDi4qLy5ylqZcqUYcWKFezevZudO3e+d7yTk5PKCWBty9pYGubcNPpdtGRaNCrbCHN9c5XuMzY2pl+/fkyfPp3w8HAGDx6cde3y5cvo6uq+1cLG1dWVqVOn8uLFCzIyMqhVq5ZK7xPy5tq1a6SlpeXpaD1dXV3c3Ny4fPkyPj4+tGzZkl9++QVbW1sGDRrE5cuX31vckTUD+I4l4OvXrzNkyBBsbGz44YcfaNiwIZcuXSI9PV0c8yYUKyIBFD4q9evXZ8yYMfz000+EhISofH9zCxO2VHfEUEtOTqUdsn9/ldPX42Cd8ljr5b7/72PXr18/evfuzejRo4mIyP0UDkdHR54+farSspyWXItvqn+jUkwKScHw6sPfO06pVKKtrf3W14YNG0ZcXBzt2rV76yivS5cuUbdu3azTHd5wcHCgTJkyGBkZ0a1btwKdlCLkzNvbGxMTE2rWrKnSfa6urmzatInQ0FDmzZvH5cuXady4MXXq1OG3335j9+7dXL9+ndjY2LeSQtP/JIDx8fHcvHmTvXv3smDBAurXr4+rqytnzpxh5syZhISEsG3bNhwdHQkICBDnPwvFijgJRPjoJCQkUKVKFVxdXVUqUvivsNR0NoTGsCP8BfGZ/5+wlNPXZahtKQZal8ToPdW/n4ro6GiqVq1Ko0aN2L9//zurHr28vOjQoQNPnjxRaa+cJElMOjuJ089O56kiuIpFFTw7e763+rJ9+/a4uLi8d7Y4LCyM7777jsmTJ+Pq6vrWtYSEBObNm4eTkxP79u3j1KlTrF69OsezjoX8ad++PXK5PF+9J/9LqVRy8uRJVq5cydmzZ9/qGGBmZoajoyNOTk7IZDKuOwwgxWcPUec9ssaYmJjQtGlTRo8eTYcOHd4663n37t307duXsLCwt35wEARNJmYAhY+OmZkZS5Ys4cCBA/lOAG30dZnhUhb/ptW437Qa5+tX4kmz6lxtVIURdpYi+fuP0qVLs2bNGg4ePMjWrVvfOa5+/fro6+uza9culZ4vk8lY+NlC+lR4vcleS5b9915LpoUcOe3KteN+7H1+8/ntnUt9cXFxHD58mLNnz9K6dev3vv/SpUsYGhrmuMR77do15HI5ffv25fDhwwwbNozhw4czffr0bGcIC6rLzMzk4sWLhXL+r1wup127dhw4cID4+HhevHjB9evX2bVrF9OnT6dBgwYkJiYSHx+PnkxJk5Zt8PT0xMfHh5iYGBISEjh69CidO3d+K/mD17OULi4uIvkTihUxAyh8lCRJokuXLvj6+nL//n2VTp8Q8mfgwIEcPnyYu3fvYmubcwHGkCFDOHPmDE+ePMn2TTQvrkdcZ8eDHVwMu0hyZjIAZrpmtHNoR/9K/Slfojy7H+1m9uXZDKs2jAl1JmSbCezRowfXrl3jq6++Yu7cubnOFCqVSiZPnkzlypUZMWJEtuvz5s3D2tqaIUOGAK//3C1atIgpU6bQu3dvtmzZgoGBgcqfU3jt+vXruLq6cvHixQ96mkb7xd7Ud7RgdrdqeRpfs2ZN6tWrx4YNG4o4MkEoRJIgfKSCgoIkQ0NDafTo0eoO5ZMQGxsrWVtbS+3atZOUSmWOY3x8fCRAOnToUIHfF58aLyWlJ+V4zdPfU2q8o7F09tnZd8aSF76+vlL//v2lBw8eZLsWGRkpDRs2TLp582a2a3///bdkYGAgNWzYUIqMjMz3+z91f/zxh6Svry+lpaV90Pf2WX1JmuCR/d9rTl68eCHJZDJp8+bNRRyVIBQusQQsfLQcHBxwd3dn5cqVKi87CqorUaIE69ev5/jx46xfvz7HMa6urtSrV4+VK1cW+H1memYY6uTcgqdfpX5MrDORG5E3OPn0ZL6P2jt58iT29vZZJ7X81/nz59HV1aVKlSrZrvXo0YNz584RFBREw4YN8ff3z9f7P3Xe3t40bNgQXd0PW2xl9u9xcHlx8eJFJEkqlGVqQfiQRAIofNRGjRqFm5sbw4YN0/hj1j4GHTt2ZOjQoUyePJmnT5/mOGb06NF4eXlx69atIo2lT8U+NLdtzsXnFzn+9LjK98fExHDz5k3atm2bbZk4MzOTU6dO0bBhQ/T09HK839XVlatXr2JoaEijRo2yHR8n5E6pVHLhwgW1JFaqJIDe3t7Y2Njg4OBQtEEJQiETCaDwUXvTsNjOzo7evXvneBSbULgWLVpEiRIlGDp0aI6FEAMGDKBWrVr06dOnyI+4q1umLm3s23D62WlOPj2p0r2nT59GT0+PJk2aZLt28+ZN4uLi3tv3rVy5cly8eJEGDRrQrl07Nm3apFIMnzJ/f39evHih8Qng+fPnczylRBA0nUgAhY+esbExe/bsISgoiK+++orU1FR1h/RRMzMzY+PGjZw+fTrrlJP/0tPTY8+ePcTExDBkyJB8L8/mVVPbpnR27MyFsAucCzmXp3syMzO5cuUKrVu3ztb7D15/069Rowb29vbvfZaZmRmHDx9m6NChDB06lB9//FFUCOeBt7c32traNGzY8IO/21Rfh4SU958m9OrVK27cuCGWf4ViSSSAwiehSpUqbN26lcOHD9O4cWOVT6QQVNO6dWtGjRrF1KlTCQgIyHbdycmJv/76i/379/PHH38UeTyf2X1Ge4f23Ii8gU+4z3vH37lzB21tbVq1apXtWlRUFJGRkbRv3z7P79fR0WH16tW4u7szb948BgwYIH4QeQ9vb2/q1auHkZHRB3+3mYH2e4+CA7hy5QqZmZkiARSKJZEACp+Mnj17cvnyZRISEqhbty4HDx5Ud0gftYULF1K2bFn69u1LSkpKtutdu3Zl2rRpfP/995w8qdrybH40KtsIUz1TJp2dhIe/xzvHPX/+nH79+hESEoKVlVW263/++Sd///13jsUfuZHJZHz33Xfs2bOHAwcO8Pnnn6t0Ru2nRJIkvL291ZZYmRnqkJ6pJDUj91NrvL29KVWqFJUrV/5AkQlC4REJoPBJqV27Njdu3KB58+Z069aNb7/9lqioKHWH9VF6s/T+4MEDxo8fn+OYuXPn0rZtW7p168bZs2eLPKZh1YbRr2I/5vnMY9v9bdmuZ2Rk0K9fP5KTk1m6dGm260lJSaxatYo+ffq8s/jjfXr16sW5c+d48uQJDRs2FMVJOQgMDOT58+fqSwDfcx7wG97e3jRr1kzs/xOKJZEACp8cc3Nz9u3bx8KFC1m5ciV2dnYMHDiQS5cuFfl+tE9NzZo1WblyJevXr2fz5s3Zrmtra/P333/TtGlTOnXqxPnz54s0HplMxrja4xhSbQgLri3A48HbM4HTp0/nypUr7Nq1i9KlS2e739PTk8TExBybQquifv36XL16FX19fRo1avRBkt/i5Pz588hkshwLcD6EvCSAaWlpXL16VZz/KxRfau1CKAhqFhMTI/3++++Ss7OzBEg1a9aU3N3dpaNHj0pPnjyRMjMz1R3iR2HYsGGSvr6+dPv27RyvJycnS59//rlkZGQkXbhwocjjUSqV0gKfBVK1zdWknQ92SpIkSXv37pUA6c8//8zxntjYWMnBwUHq0qVLocURFxcntW7dWtLR0RGNhP9jyJAhUs2aNdX2/kcRiVK5aYcln6AX7xxz4cIFCZCuX7/+ASMThMIjEkBBkCRJoVBIXl5eUteuXSV9fX0JkABJT09Pqlq1qtSzZ09p+vTp0ubNm6VLly5JL168+xuDkF1ycrJUq1YtycXFRYqPj89xTFJSktSiRQvJ2NhYunTpUpHHpFQqpXlX5knVNleTVp5fKZmamkq9e/fO8eQQhUIhde7cWSpRooQUFBRUqHGkp6dLw4cPlwDpp59+KtDJJR8LZ2dnady4cWp7f2RCilRu2mHp5L2Id46ZN2+eZGJiImVkZHzAyASh8IizgAXhfyiVSp49e8ajR494+PDhW79CQkKyxpUqVYqKFSu+9atChQo4Ozt/8JMLioMnT55Qt25datSowa5duyhTpky2MUlJSXTo0AFfX19OnjxJ/fr1izQmSZKYfGwyJ6NOknk4E5+NPpiammYbN3/+fKZPn87hw4fp1KlTkcTh7u7OtGnTcHNzY+PGjTm2n/kUhIWFYWtry+7du+ndu7daYkjNUFDpZy8W9a1Jzzo5n2vdoUMHAI4dO/YhQxOEQiMSQEFQQVJSEgEBATx8+DBbgvjy5UsAtLS0cHR0zJYcVqxYESsrq096w/jFixezvqnv3Lkzx03+L1++pEOHDty9e5d//vmHevXqFVk8W7duZcTIETiPcEZWU8avTX+li3OXt8acPXuWVq1aMX36dObOnVtksQDs2bOHQYMGUbduXfbt25fjPsSPnaenJ25ubkRERORYhf2hVPzpGN93qMSQJo7ZrmVmZmJhYcH06dOZPn26GqIThIITCaAgFAJJkoiIiHgrIXyTIAYGBmY1/jU1NaVChQrZEsPy5ctjaJjzubYfm4iICNzc3Dh//jzz5s1jypQp2ZLixMRE2rdvj7+/P6dOnaJOnTqFGkNqaioTJ05kzZo1fPXVVyxfsZyFtxdy4MkBfmv6Gx2dOgIQHh5O7dq1qVq1KidOnEBLS6tQ48jJ1atX6dq1KyYmJhw5coSKFSsW+Ts1yejRozl9+rTaq6Pr//oPAxrYM7F19nOgb9y4Qb169bhw4YLaClUEoaC01R2AIHwMZDIZ1tbWWFtbZzseLD09nSdPnmRbTj5+/DgxMTFZ4+zs7HKcNbSzs0Mu/3gK9suUKcPJkyeZMWMG06ZN4+LFi2zZsgVzc/OsMaamphw7doy2bdvSpk0bTp8+Tc2aNQvl/UFBQfTu3Zt79+6xfv16hg4dikwmY1bjWSgkBT9c+AEtuRatbFvRr18/5HI5O3bs+CDJH0CDBg24cuUKnTp1olGjRuzbt4/mzZt/kHdrgvPnz2tEZW1ux8GdP38ePT29Ip2dFoSiJmYABUGNYmNjsyWGDx8+5PHjx6SnpwNgYGBA+fLlc5w5NDMzU/MnKJjDhw8zaNAgzM3N+fHHH3Fzc3vr5If4+Hhat25NcHAwZ86coXr16vl+V0xMDBs3buS3337DwsKCPXv2ULt27bfGKJQKfrz4I15BXtR4WoMdv+zg7NmzNG3aNN/vza/4+Hh69+6Nt7c369ev58svv/zgMXxoL168oFSpUvz1118MGjRIrbH0WnWJciUNWdS3VrZrPXv2JDY2VrTvEYo1kQAKggZSKBQ8ffo0x+Tw+fPnWeOsrKzeKkB58/8dHR3R0dFR4yfIu6CgICZMmMDhw4cxNTVlyJAhjBw5MmvpMzY2ltatWxMaGsqZM2eoWrVqnp8tSRJXr15l5cqV7Nq1C4CBAwfy+++/vzXj+F9JKUl0XtWZaLNoWiW3Ysm4JQX+jPmVkZHBqFGj2LBhAzNmzGDWrFkf9R7SAwcO0L17d4KDgylXrpxaYxm6+RpymYz1X709yydJEqVLl2b06NHMnj1bTdEJQsGJBFAQipmXL19mFaL8757DpKQk4HWDZWdn52wVyhUrVqR06dIamUQEBwezZs0a1q9fT0xMTNZ5wp07d+bly5e0atWK8PBwzp49+96jt169eoWnpycrV67k1q1bODk5MWrUKIYMGULJkiVzjaFPnz743fWj44qOBOsEs7jFYprbqW8JVpIkFixYwPTp0xkwYAAbNmz4aCuEv/32W/bs2cPTp0/VHQqTdt4mLC6FXSMbvfX1+/fvU7VqVU6ePEnr1q3VFJ0gFJxIAAXhIyFJEmFhYTlWKAcHB2edcmJubp5j+5ry5ctrRGKRlpbGnj17WLlyJZcuXUIul2Nvb4+dnR13794lIyODX375hSZNmpCRkUFgYGDWr6CgoKxjxGQyGZ07d2b06NG0bdv2vfsojx49ysCBAzEzM2PPnj3UqF2DKeem4B3qzdLPl9LU5sMvA//X7t27GTRoEK6uruzbt49SpUqpNZ6i4OrqSqVKldi6dau6Q2HWwXtcfvKC45PerlRfvXo148aNIz4+/q3tCoJQ3IgEUBA+AampqTx+/DhbhfLDhw+Ji4sDXheylCtXLsdCFBsbG7XMGvr5+XHlypWsxO7Ro0fcuXMHhULx1rgyZcrg5OSU9cvR0ZEWLVrg4ODw3ncoFApmzpzJr7/+SufOndmyZQsWFhYAZCgymHx2MpeeX2J5q+U0KtvoPU8rWpcvX6Zbt26YmZlx5MgRKlTIXqFaXL18+RJzc3NWr17N119/re5wWHTyEbuuhXDlh1ZvfX3AgAEEBgZy5coVNUUmCIVDJICC8AmTJImYmJgcl5MfP35MZmYmAIaGhjkWoVSoUAETE5MPGnNkZCTNmjUjPj6eLVu20Lx583y30ImKisLNzY2zZ8/y66+/MnXq1GwzhemKdCaemYhPhA8rWq2ggXWDwvgY+RYYGEinTp2Iiopi3759OfZSLI5OnDhBu3bt8Pf3p1KlSuoOh/XnA/njxCP857TP+pokSdjZ2eHm5oa7u7saoxOEghMJoCAIOcrMzCQoKCjH5DAiIiJrXNmyZXNMDh0cHIqsdUp4eDgtWrQgJSWFc+fO4eiYvVlvbkJDQ1m7di2rV69GLpfj6emZrX3Pf6Up0hh/ejy3om6xotUKXMu4FvATFExcXBy9e/fm/PnzbNiwQe0Vs4Xhp59+Yu3atURGRmrEHtXd10OYssePR3M7oKv9+oeCoKAgnJycOHjwIF26dHnPEwRBs4kEUBAElSUkJOR4VF5AQAApKSkA6Orq4uLikq1CuWLFirkWYuRVWFgYLVq0ICMjg3Pnzr23alSSJE6dOsXKlSs5ePAgBgYGDBo0iJ9//hlra+v3vi81M5Wxp8fiF+3H6tarqWNVuM2pVZWens7IkSPZtGkTM2fOZObMmRqROOXXZ599hqWlJXv27FF3KACcuBfBN1tvcP2n1pQy1gNgy5YtDBkyhBcvXlCiRAk1RygIBSMSQEEQCo1SqSQ0NDTH9jXPnj3LGleyZMkc29c4Ozujp6eX5/eFhobSvHlzJEni7Nmz2NvbZxsTFxfHli1bWLVqFY8ePaJq1aqMGTOGgQMHqrx8nZKZwphTY7gXc481bdZQy7KWSvcXNkmSmD9/Pj/88AMDBw5k/fr1Kv3+aYrU1FTMzMxwd3dn/Pjx6g4HgKuBL+i39gqnvm2Oc2ljAIYNG8b169fx9fVVc3SCUHD/1969x9V8/3EAf53O6SoVhUp3IrnkEiX3+53c7+Syn8tszMbYbMywixk2l40Vm/v9MtcQSi6FDnJXCamI7rdT55zfH62mFTp1LtV5PR8Pj9H5fj+fd34zr9/nygBIRGqRkZGBhw8fFjtymJKSAgDQ0dEpuEe5Xr16BRs6nJyc4ODgUOxavydPnqBjx47Q0dHB4cOHIZFICnYF37x5E3v37kVOTg6GDBmC6dOno23btmUaKcvIycC009PwIPEBNnTbgMY1Sn84tbLs2rUL48ePR6tWrXDgwAGljLCqU2BgIDp06ICwsDA0bdpU0+UAAO7FpaDnqiDsn+6F5nZ5o33Ozs7o2bMnfv31Vw1XR1R2DIBEpFFyuRzx8fFFdic/ePAAjx8/LrgRBSi829fR0bHgGJh79+4hPDy84M5lAKhSpQrq1q2LIUOGYPLkybC0tFRazek56Zh2ehoeJT7Cxh4b0dC85IdTq8rFixcxYMAAVKtWDUePHoWzs7OmSyqxpUuXYvny5Xj16pXartx7n9jkTLT+LgCbJrREp/o1ERsbC2tra+zevRtDhw7VdHlEZcYASETllkwmQ2xsbKEz/t78ua6uLurUqQNHR0eYmZlh8+bNqFKlCo4ePYqGDRuqdE1cmiQNU05PwePkx/ij+x9oYP7uw6nVISIiAn369MHLly9x8ODBcnGnbkn06NEDIpEIR48e1XQpBTIluRjy2yXM6VEfHevXhL+/P+bNmwd/f/9KeQYjaR8GQCKqNCIiItChQweYmJjg7NmzqFWrlkr7S5Wk4n/+/8PTtKfw7e6L+tXrq7S/kkhMTMTgwYMRHBwMPz8/jB49WtMlvVNubi7MzMzw1Vdf4fPPP9d0OURa491H4xMRVSB16tTB2bNnkZSUhC5duuDFixcq7a+qXlX81u03WFexxgf+H+Bh4kOV9lcS1apVw4kTJzBq1CiMGTMGixcvRnn+//lhYWFIT0+vNOcZElUUDIBEVKk4Ozvj7NmzBfcJJyQkqLQ/U31TbOy+EbWq1MJk/8mITIpUaX8loaenBz8/PyxZsgQLFy7E+PHjkZ2dremyihUYGAhDQ0O0aNFC06UQaRVOARNRpXT37l107NgRVlZWCAgIKLjeTVUSsxIxyX8SErMS4dfDD46mih1OrSo7d+6Ej48PPDw8cODAAZX/PihqwIABSEtLw5kzZzRdCpFW4QggEVVKDRo0QEBAAGJiYtC1a9eCO49VpZpBNfzR/Q+Y6Zth0slJiE6JVml/JTVixAicOXMGt2/fRuvWrfHo0SNNl1RAJpPhwoULnP4l0gAGQCKqtBo2bIgzZ87gyZMn6N69O5KSklTaX3WD6tjYfSOM9Ywx8eREPE15qtL+SqpNmza4fPkyAMDT0xMXLlzQcEV57ty5g9evX1eY3cpElQkDIBFVak2aNMHp06cRGRmJHj16IDk5WaX9WRhawLe7L4xERpjoPxHPUp+ptL+Sqlu3Li5duoRGjRqhS5cu2LFjh6ZLQmBgIEQiETw9PTVdCpHWYQAkokqvadOmOHXqFB48eIBevXohNTVVpf3VMKqBP7r/AV0dXUz2n4zYtFiV9ldS1atXh7+/P0aMGIFRo0ZhyZIlGt0hHBgYiJYtWxZ7w0t5sHbtWjg4OMDAwAAeHh4ICQnRdElESsMASERaoXnz5jh16hTu3LmDXr16IS0tTaX91apSC349/AAAE09ORFx6nEr7Kyk9PT1s3rwZixcvxldffYUJEyYUum1FXeRyOQIDA8vt+r9du3Zh9uzZWLhwIa5fvw43Nzf06NFD5UcLEakLdwETkVYJCQlBt27d0LRpUxw7dgxVqlRRaX/P055jwokJEOmIsKnnJtQ0qqnS/hSxfft2TJgwAV5eXti3b59adwg/evQIzs7OOHr0KHr37q22fkvKw8MDLVu2xJo1awDkbVixtbXFRx99hHnz5mm4OqKy4wggEWmVVq1a4cSJE7h+/Tr69euHjIwMlfZnbWwN3x6+kMgkmHRyEl5mvFRpf4oYNWoUzpw5g1u3bsHLywsRERFq6zsoKAgCgQBt2rRRW58lJZFIcO3aNXTt2rXgazo6OujatSsuXbqkwcqIlIcBkIi0TuvWrXH8+HGEhISgf//+yMzMVGl/NlVt4NfdDxm5GZjsPxkJmao9nFoRbdu2xeXLlyGTyeDp6Yng4GC19BsYGAg3NzeYmpqqpT9FJCQkQCqVFrlKsFatWoiLKx9T+URlxQBIRFqpbdu2OHbsGC5dugRvb29kZWWptD9bE1v4dvdFqiQVH/h/gNdZr1XanyLydwi7urqiS5cu2Llzp8r7LM/r/4i0AQMgEWmt9u3b4+jRowgKCsLAgQNVfl2ag6kD/ujxBxKzEvGB/wdIzlLtkTSKMDc3h7+/P4YOHYqRI0di6dKlKtsh/OzZM0RGRpbbAGhhYQGhUIj4+PhCX4+Pj4elpaWGqiJSLgZAItJqHTt2xN9//41z585h8ODBKg+BTqZO8O3hi4TMBCwNWarSvhSlr6+Pv/76C4sWLcKCBQswceJElewQDgoKAoByewC0np4eWrRoUeh6OplMhjNnzqB169YarIxIeRgAiUjrdenSBYcOHcLp06cxbNgwlR+LUsesDv7o/gdeZ5afaeB8AoEACxcuxJYtW7B9+3b07NlT6dfoBQUFwcXFBTVrlp8d0f81e/ZsbNy4EX/++Sfu3r2LadOmIT09HRMmTNB0aURKwWNgiIj+cfz4cXh7e6NPnz7YtWsXdHV1Vdrf4+THcDB1UGkfZREYGIiBAweiZs2aOHr0KJycnJTSbqNGjdCmTRv8/vvvSmlPVdasWYPly5cjLi4OTZs2xS+//AIPDw9Nl0WkFAyARERvOHLkCAYNGoQBAwZgx44dEIlEmi5Jox4+fIjevXsjKSkJhw4dgpeXV5naS0hIQI0aNbBlyxaMGTNGSVUSkaI4BUxE9Ia+ffti7969OHjwIMaMGYPc3FxNl6RRzs7OuHz5MlxcXNC5c2fs2rWrTO1duHABAMrtBhAibcEASET0H/3798fu3buxb98+jB8/HlKpVCN1BAYGol+/frC2toZAIMDBgwc1Uoe5uTlOnz6NIUOGYMSIEVi2bFmpdwgHBgbC3t4ednZ2Sq6SiBTBAEhEVIyBAwdix44d2LVrFyZMmKCREJieng43NzesXbtW7X3/l76+PrZs2YKFCxfiyy+/xKRJk0q1WYbn/xGVD9q9uIWI6B2GDBmCbdu2YdSoURAKhfD19YWOjvr+f3OvXr3Qq1cvtfX3PgKBAIsWLUKdOnUwadIkREdHY9++fTAzMyvR+6mpqQgLC8PUqVNVWygRvRcDIBHROwwfPhxSqRRjx46FSCTC77//rtYQWB6NHTsWdnZ2GDhwILy8vHD06FE4Ojq+972LFy9CJpNxBJCoHNDu/4oREZXAqFGjsHnzZvj6+mL69OkquyGjIunQoQMuXboEiUQCDw8PXL58+b3vBAYGolatWnB2dlZDhUT0LgyAREQlMHbsWPj5+WHDhg2YMWMGQyCA+vXr4/Lly6hXrx46deqEPXv2vPP5wMBAtGvXDgKBQE0VEtHbMAASEZWQj48PNm7ciHXr1mHWrFkMgci7N/f06dMYOHAghg0bhu+//77Y35fMzEyEhISUr+lf/u9HWoxrAImIFDBp0iRIpVJMmTIFQqEQK1as0PoRLQMDA2zbtg1169bF/Pnz8ejRI6xfv77QTSohISGQSCTlKwA+OA6c+x7o8zNg4w4AGDBgANq0aYO5c+dquDgi1WIAJCJS0P/+9z/k5ubiww8/hFAoxI8//qiSEJiWloZHjx4V/DoqKgpisRjVq1cvd+foCQQCLF68GHXr1sXkyZPx+PFj7N27t2CHcFBQEMzMzNCoUSPNFppPkgEcnQNYuRWEv+fPn+Pw4cMYO3ashosjUj0GQCKiUpg+fTqkUik+/vhjiEQiLFu2TOkh8OrVq+jUqVPBr2fPng0AGD9+PDZv3qzUvpRl3LhxBTuE27RpgyNHjsDR0RGBgYFo27YthEKhpkvMc/FXIC0e6P5twZeCgoIAAO3atdNUVURqwwBIRFRKH330EXJzczF79myIRCIsXrxYqSGwY8eOFXKdYceOHXH58mX07t0bnp6e2L9/Py5evIiFCxdqurQ8yc+ACyuB1tMB8zoFXw4MDES9evVQq1YtDRZHpB4MgEREZfDJJ59AKpVizpw5EIlE5SfkaFj+DmFvb2906dIF2dnZ5Wdk7fQiQL8q0O6zQl/mLSWkTbgLmIiojD777DN8//33WLRoEZYsWaLpcsqNGjVq4MyZM3BxcQEABAQEaH5E82kIcGsP0OUrwMCk4MuvXr1CeHg4AyBpDY4AEhEpweeff47c3FwsWLAAIpEI8+bN03RJ5YKBgQFsbW2RnJyML7/8Eo8fP8batWsL7RBWG5kMOP45YNkEaDq60EcXLlwAAAZA0hoMgERESvLll18iNzcX8+fPh1AoxJw5czRdksbJZDIEBwdj1qxZsLOzwwcffIDHjx9jz549MDU1VW8xN3cBz68DE44DOoU3owQGBsLOzg729vbqrYlIQxgAiYiUaOHChZBKpZg7dy5EIhE++eQT9RcRvh9oNEj9/Rbj9u3bSExMRPv27dGxY0fY2dlh8ODBBTuEHRwc1FNIdlre2r+GAwF7ryIfBwUFcfSPtArXABIRKdk333yD+fPnY/bs2fjll1/UX0DwqrypTplU/X3/R2BgIHR1deHh4QEA6Ny5My5duoSMjAx4enoiJCREPYVc+BnISgK6LS7yUWpqKq5fv15+NqkQqQEDIBGRkgkEAixduhRz5szBzJkzsXbtWvUW0PYTIGQjsGMkkJ2q3r7/IzAwEC1btoShoWHB11xcXHDlyhU4OTmhY8eO2Ldvn2qLSHwMXFwDeH0MmBU9QPvSpUuQSqUcASStwgBIRKQCAoEAP/zwA2bPno0ZM2bg999/V1/nDQcCo3cDTy4Bfj2BpKfq6/sNcrn8rUer5O8Q7tevH4YOHYrly5erboew/1eAkTnQdlaxHwcGBqJGjRqoX7++avonKoe4BpCISEUEAgF++ukn5ObmYurUqRAKhZg8ebJ6Oq/bFZjkD2wfBmzsDIzaCdRuoZ6+/xEREYG4uLi3jqwZGhpix44dqFu3LubOnYtHjx5hzZo1yt0h/PgCcPcwMHADoFel2EfyQ6q23+lM2oUjgEREKiQQCLBq1Sp8+OGH+N///odNmzapr/OaDYDJAUA1e2BTH+DOIfX1jbxgpaOjAy+vopsu8uno6GDp0qXw9fWFn58f+vbti+TkZOUUIJMCx+cBtd2BxkOLfSQrKwshISGc/iWtwwBIRKRiAoEAv/76K6ZMmYJJkybhr7/+Ul/nxjWA8X8DLr2B3eOAoBWAmg5jDgwMRNOmTUt03MvEiRNx4sQJXLlyBW3atEF0dHTZC7j+FxB/C+j1A6BT/F93oaGhyM7OZgAkrcMpYCIiNRAIBFi7di1yc3Ph4+MDoVCI0aNHv/9FZdA1BAb7AuZ1gTOLgVcRQN9VgEhPpd0GBgZiwIABRb6enZ2N6OhoREZGIioqCpGRkQU/NzQ0xKNHj+Di4oJRo0ahbdu2cHJygqOjI2rXrg2hUFhMT8XISgYClgBNRgA27u+s0cTEBI0bNy7tt0lUITEAEhGpiY6ODn7//XdIpVKMGzcOIpEIw4cPV0/nAgHQ6Qugeh3g8AwgMRoYvgUwqq6S7p4+fYqoqCi0a9cOEokEBw8exKZNmxAeHo6YmJiCDR8ikQj29vZwcnJCq1atYGFhgYcPH+L48ePw8/ODn59fQZu6urpwcHCAh4cHpk6dCi8vr7ev2zv/I5CTAXR9993MgYGBaNu2bcmDJVElwQBIRKRGOjo62LhxI6RSKUaPHg0dHR0MHVr8+jSVcBuedxTKzlHAH12B0XsA8zpK7yYoKAgAEBwcjA8//BBxcXFo164dxo8fD0dHRzg5OcHJyQm1a9eGSFT0r6LMzEyMHz8ee/bswdy5c9G+fXs8fvwYERER+Pvvv7F161Y0adIE06dPx+jRo2FsbPzvywmPgCu/Ax0/B0ys31pjbm4ugoOD8dVXXyn9+ycq9+RERKR2ubm58tGjR8uFQqF837596i/gVYRc/qu7XP69vVweFaS0ZmUymfzUqVNyR0dHOQC5sbGxfPr06fJbt24p3JZUKpXPmzdPDkA+depUeU5OTsHXT548KR8wYIBcR0dHXrVqVfmMGTPkt2/fzntx2zC5/OdGcrkk453th4SEyAHIL168qHBtRBUdAyARkYbk5OTIR4wYIReJRPKDBw+qv4CM13L55r5y+Tfmcvn1rWVqKicnR/7LL7/I69WrJwcg19fXl7dt21aekpJS5jL/+OMPuUgkkvfo0UOenJxc6LPo6Gj5l19+Ka9Zs6YcgPyzgc3l8oUmcnn4/ve2u2LFCrmhoaE8Ozu7zDUSVTTcBUxEpCEikQhbtmzBwIEDMXToUBw5ckS9BRhWA8bsB5qOAg5NB05/A8hkCjcTFxeHbt26YdasWWjevDkOHz6M7OxsTJs2DVWrVi1zmZMmTcLx48dx6dIltG3bFk+ePCn4zM7ODkuWLMHTp0+xc/tWTHd6ivOPczFr4zlIJJJ3thsYGIjWrVtDT0+1m2GIyiMGQCIiDRKJRNi2bRv69euHwYMH4/jx4+otQKgL9FsNdF8CXFgJ7PUBJBklfj0oKAjNmzfHvXv3EBAQgB07diA3NxcAlHq3bteuXXHx4kWkpKTAw8MD165dK/S5np4ehjulwcFYgtimM7Fu3Xp07NgRz549K7Y9mUyGoKAg3v9LWosBkIhIw3R1dbFjxw706tULAwcOhL+/v3oLEAgAr4+A4VuBh6eAzX2A1Ph3viKXy/HTTz+hU6dOqFevHsLCwtChQwcAeSNrDg4OsLW1VWqZDRs2xJUrV2BnZ4f27dvj0KE3DrbOeA2cXQZB87EYMWsZgoKC8OzZMzRr1gynTp0q0tadO3fw+vVrnv9HWosBkIioHNDT08Pu3bvRrVs3DBgwAKdPn1Z/EQ36AhOOA6mxwB9dgLjwYh9LSkrCoEGDMGfOHMyZMwenT5+GpaVlwedvu/9XGWrVqoWzZ88WhOWVK1fmHSlz7ru8mz865+3o9fDwwPXr19GiRQv06NEDixcvhuyN6e3AwECIRCJ4enqqpE6i8o4BkIionNDT08PevXvRqVMn9O/fH2fPnlV/EdZNgQ8C8tYH+vUAHhQejQwLC0OLFi1w9uxZHDp0CN99912hY1xSUlIgFotVOrJmZGSE3bt3Y86cOZg9ezaWzhwDeagv0GEuYFyz4DkLCwscO3YMixYtwqJFi9C7d28kJCQAyAuALVu2hJGRkcrqJCrPGACJiMoRfX197N+/H+3atUPfvn1x/vx59RdhYp03EujYHtgxPO9MPQA3b95E27ZtYWZmhuvXr6N///5FXr148SJkMpnKp1Z1dHTwww8/YMPvv8Mj6RCeZ+oixXVUsc99/fXXOHnyJK5du4b27dsjNTUVQUFBnP4lrcYASERUzhgYGODgwYNo3bo1+vTpgwsXLqi/CH3jvDWBntOB43ORfeBjDBsyCM7OzggKCoKTk1OxrwUGBsLS0hJ169ZVS5kfdLBFNychPj2ZhXYdu+Dp06fFPtetWzcEBQXh6dOnGDVqFJ4/f84ASFpNIJer6VZwIiJSSEZGBvr27YvQ0FD4+/ujdevWGqlDfnUTZH9/goDHcjh9fh51XJu+9dm2bduidu3a2LVrl+oLy5UA6zwAM3uEN/sWffv1g0QiwZEjR9C8efNiX9m9e3fB9XuJiYkwMzNTfZ1E5RBHAImIyikjIyP8/fffaN68OXr06IErV65opI5VQUnouTUNHeoYos65aUDSk2Kfy8zMREhIiPqOVrnyW96dxj2/Q6PGjXH58mXY2NigXbt2OHz4cLGvDBs2DA0aNIBAIMCDBw/UUydROcQASERUjlWpUgVHjx6Fm5sbunfvjtDQULX2HxwcjLlz56LpoE+gN/UcIEkHNnYBnl0t8uyVK1eQk5OjnqnVtJdA4HLAfSJQswEAwNLSEufOnUOPHj3g7e2N1atXF/tqVlYWatWqhaFDh+LVq1eqr5WoHGIAJCIq54yNjXHs2DE0bNgQ3bt3x/Xr19XS74sXLzBs2DB4enpi2bJlQE2XvB3C1R3zzgoM31/o+aCgIJiZmaFRo0aqLy7gW0CgA3T6otCXjYyMsHfvXsyePRuzZs0qMhUdExODqKgoLFq0COnp6RgzZkyh42GItAUDIBFRBVC1alWcOHEC9evXR9euXSEWi1Xan1QqxahRo5Cbm4tdu3ZBV1c374MqFsC4w0CDfsDeCUDgT8A/S8kDAwPRrl076Oio+K+W2BvA9b/ywp9R9SIf6+joYPny5RgxYgQmT56Me/fuFXwWFBQEABg4cCC2bduGkydPYunSpaqtl6gcYgAkIqogTExMcOLECdSpUwddu3bFzZs3VdaXn59fwdVu1tbWhT/UNQAGbQQ6zs8biTs4HTmZabh48aLqp3/lcuDEfMCiXt7071sIBAJs3LgRtra2GDx4MNLT0wHkhVQXFxfUrFkTPXr0wFdffYWFCxfi/v37qq2bqJxhACQiqkDMzMzg7+8Pe3t7dOnSBeHhxd/WURZyuRxr1qxB//790blz5+IfEgiAjvOAQX8A4XuRuaEHDOSZqg+Adw4B0cFAz2V59xi/g7GxMfbu3Yvo6GhMmTIFcrm8YJQy3/z582Fubo7169ertm6icoYBkIiogqlWrRpOnToFGxsbdOnSBXfu3FFq+xcvXsTNmzcxffr09z/cZCgw/m+IEiNx5QNjNLMzVmotheRkAv5fAfV6AnW7lugVV1dXbNiwAdu2bcOKFStw+/btQiHVwMAAkyZNwubNmwtGCYm0AQMgEVEFVL16dZw6dQq1atVC586dC61zK6t169ahbt266Nq1ZCELdp6YGd4IuvqG0N3cC4gKVFothVxak3dPcXfF1uyNGjUK06dPx/z58wGgyCjllClTkJKSgh07diitVKLyjgGQiKiCsrCwwJkzZ2BhYYHOnTvj4cOHZW7zxYsX2LNnD6ZNm1bizRwymQx7z1zFLuPJgHUzYMtA4PqWMtdSSEosELQSaD0DsFD8lpGff/4ZlpaWsLGxgZ2dXaHPHB0dMWzYMPz111/g3QikLRgAiYgqsBo1auDMmTMwMzNDp06d8OjRozK15+vrC6FQCB8fnxK/Ex4ejqSkJHh06A6M3gM0GwscngGcWggo64iVM98AhmZAh7mlel1fXx9TpkzB6NGjkZGRUeTzRYsWwdPTE0+eFH/INVFlwwBIRFTB1apVCwEBATA2NkanTp0QGRlZqnakUil+++03jBw5EtWrFz1e5W0CAwOhp6eHVq1a5W3M6Lsyb5o2eDWwZxwgKRq4FPLsKnBjB9DpS0DPqFRNZGZmIjo6Gq9evUJwcHCRz+vVqwcdHR2cPn26bLUSVRAMgERElYClpSUCAgJgaGiITp064fHjxwq3cezYMTx58qRkmz/eEBgYiFatWsHQ0DDvCwIB4DUDGLEdeBQAbO4NpMYpXA+Af459mQfUagy4jShdGwAePHiAtLQ0ODs74/jx40WmenV0dNCmTRucO3cOKSkppe6HqKJgACQiqiSsra1x9uxZ6OrqolOnTgpPZ/75559o0aIF3N3dS/xOcUerFHDpDUw8DqTGAxs7A3G3FKoHAHBrD/AsFOj5HaAjVPz9f9y7dw+mpqbo27cvYmNjcfv27SLPdOjQAQBw6dKlUvdDVFEwABIRVSK1a9fG2bNnIRAI0KlTJzx79qzE7965cwdeXl4K9ffo0SPEx8e//fw/KzfggzN5N4j49QTun3h7YzIZkJVccLMIJOl56wgb9AcciwmYCrh79y5cXFzQoEED2NjY4NSpU0WeMTExgZWVFWJiYsrUF1FFwABIRFTJ2Nra4uzZs5BKpejUqVOJAo1cLkdUVBScnJwU6iswMBA6OjrvDo4m1sCE44BTR2DnSODy+n9DXmYScHENsLYVsKQG8L0dsMwK2NQL2D8FSE8Aun+rUE3/JZFIEBERARcXl4JgfO3aNeTm5hZ5tmbNmnj58mWZ+iOqCBgAiYgqIXt7e5w9exYSiQSdO3dGbGzsO5+Pi4tDVlZWqQJgs2bNYGJi8u4H9aoAw7bkHeNyYh5w7DPg8UVgTUvAfwHw8gEg+yeQ5WQCTy4D9/4GqpgDQv331iEQCN76Q19fH2FhYTA2NoZAIEB6ejqkUilevXpV8H7Hjh0xa9Ys1KxZEy9evFDo94CoImIAJCKqpBwdHREQEICMjAx07twZ8fHxb302f+ewogFQLBajZcuWJXtYRydvNK/fL8DVTXmbQzJeAZD/8+MN8n+Oj0l7AfzRBchMfGfTsbGxBT9WrVoFExOTgl/7+vqiefPmBXca5+9wLi7o5QdAmbKOryEqpxgAiYgqsTp16iAgIAApKSno3LnzW0e3oqKiAOSFxpLKnzauU6eOYkU1GgQYmACQA3LpezqR5u0gPvThOx+ztLQs+GFqagqBQFDw67i4ODRq1KjgYGtTU1Po6OgU+3tRq1Yt5ObmIikpSbHviaiCYQAkIqrknJ2dERAQgNevX6NLly5ISEgo8kxkZCRq1qyJKlWqlLjdV69eITU1VeFRQ9zc9d4RvULkUuDeUSBB8ZtOcnNz8fDhQzRo0KDgayKRCObm5sUGwBo1agAofnSQqDJhACQi0gL169fH2bNn8fLlS3Tt2rXQ+jcgLwAqGuTyp40VGTUEAIRtAyBQ7B0dERC+X7F3ADx+/BhZWVlwcXEp+JqXlxd+//13+Pj4wNjYGMbGxggKCgLAAEjagwGQiEhLuLi44MyZM3j+/Dm6deuG169fF3xWmh3A+dPGCo8AJjxEkTV/7yOXAa8jFHsHwP3796Gvr18opO7atQtff/01Jk6cCLFYDLFYXHD2ob6+PqpVq8YASJUeAyARkRZp2LAhzpw5g6dPn6J79+4Fa91iYmJgY2OjUFuvX7+GUChE1apVS/6STAbkpCnUD4C8AJihwLTxP+7evQtnZ2eIRKKCr9na2sLR0RH6+vqoW7cu6tat++8tJgCqVKmCtLRS1EhUgTAAEhFpmcaNG+P06dOIiopC9+7dkZycjOzsbBgYGCjUjp2dHaRSKZ4/f17yl3R0gCo1FawYgEAImNkp9IpMJsO9e/cKTf/mS0hIgLm5eZGvy+VyJCQkwMLCQvEaiSoQBkAiIi3k5uaG06dP49GjR+jZsyeys7Ohr//+8/belD/1mz8VXGK2nnlr+hQhlwFWTRR65dmzZ0hPTy82AL58+RI1axYNoqmpqcjKyir2M6LKhAGQiEhLNWvWDKdOncLdu3fx+vVryOWKrctzcHAA8O9mkBJrOenfQ59LSqQPNByo0Cv37t2DSCRC3bp1i3wWHx9fbMjLX/vHAEiVHQMgEZEWa9GiBfz9/SGVSuHn56fQ2jdDQ0NYWVkpHgAd2wMu/fKmdUuq27f/nB34fj4+PkhKSsLdu3fh5ORUMLLp4OAAuVyO+vXrIzU1tVDIO3fuHFatWsUASFqDAZCISMu1atUKQqEQMTEx6Nu3L9LT00v8rrOzM65fv65YhwIB4L0OqOkKCErw11Dz8UCrDxTqIicnB7dv3y50/l++/Lt+iwt5L1++hLGxMYyMjBTqj6iiYQAkItJyUqkUUqkUn376Ka5du4b+/fsjIyOjRO+OGDECx44dw7NnzxTr1MAEmHwaaDEhbyRQoPNvGNTRzfunnjHQ52eg3+q80KiA0NBQpKamol27dkU+y78S721TwBz9I23AAEhEpOUkEgkAwNXVFceOHcPly5fh7e2NzMzM9747ZswYGBkZYcOGDYp3rGsA9P0ZmH0H6LYYsG6eFwbdJwAD1gGfPcxbL6hg+AMAf39/NGzYELVr1y7y2Z07d2BqagoTk6JTym9bG0hU2TAAEhFpufwAqKenh3bt2uHo0aO4cOECBg0ahKysrHe+W7VqVYwbNw4bN24saEdhVS0Br48A1/6AvjHQeznQbDSgV7pp2CdPnuD+/fvo1q1bkc+ysrIQGBiIjh07QlBMsOQIIGkLBkAiIi2XH9zyN0t07NgRR44cwblz5zBkyBBkZ2e/8/1p06YhLi4OBw8eLFshslzFj4cphr+/P6pVq4YWLVoU+ezixYvIzMxEly5dinyWlZWFhIQEWFlZlbkGovKOAZCISMvlBzw9Pb2Cr3Xu3BmHDx/G6dOnMXTo0HeO7jVq1AidO3fGF198geTk5NIXIpOWOQBGRkbi/Pnz6Nq1a6HbP4C8Q55PnTqF5s2bF9z5+6YnT55ALpcXHG9DVJkxABIRabk3p4Df1K1bNxw8eBAnT57E8OHDkZOT89Y2Nm7ciISEBPj4+Ch8nmCBMo4ApqWlYdWqVbC3t0e/fv2KfP7w4UM8fvy42KlhAIiOjoZQKCx23SBRZcMASESk5d4WAAGgZ8+eOHDgAI4ePYqRI0e+NQQ6OTnhr7/+wsGDB/Hzzz+XrhBZLqCjwNmAb74qk8HX1xcCgQCzZs2Crq5ukWcCAwPh4OCAxo0bF9tGbGws6tatW+y7RJUNAyARkZb77xrA/+rduzf27duHw4cPY8yYMcjNLf4Wj/79++Pzzz/H559/jqCgIMULkeaUegTw3LlziImJwbRp04q9x/fly5eIiIhAjx49oKNT/F99ycnJxZ4bSFQZMQASEWm54tYA/le/fv2we/du7N+/H+PGjYNUKi32uSVLlqBt27bo378/jhw5olghMum/ZwCW9BWZDMuXL8eQIUNgampa7L2/6enp8Pb2xpkzZ4rdGAIAubm5WLZsGV69eqVYzUQVFAMgEZGWe9cU8Ju8vb2xc+dO7N69Gz4+PsWGQJFIhIMHD6JDhw7o168fvvjii7eOGBah4BrAV69eoU+fPvj8888xc+ZMzJ8/v8gzcrkc06ZNg1gsxqZNm1C1atVi23r48CHi4uI4AkhagwGQiEjLlTQAAsDgwYOxfft27NixA5MmTYJMJivyjJmZGQ4cOIAffvgBP/74I7p164a4uLj3F6LAGsCQkBA0b94coaGhOHHiBBYuXFjs1O4ff/yBLVu24Pfff0fDhg3f2p5YLAYAuLm5lah/ooqOAZCISMu9bw3gfw0bNgxbtmzBli1b8L///a/YECgQCDB37lycOXMG9+7dQ6NGjfDll1/iyZMnb2/4PSOAcrkcISEh8PHxQdu2bWFtbY2wsDB079692OevX7+Ojz76CFOnTsWYMWPe+T2JxWLY2dmhevXq73yOqLJgACQi0nIlWQP4XyNHjsSff/4JPz8/TJ069a1rAjt06ICwsDCMGjUKa9asgaOjIwYMGICTJ08WDY5vOQcwIyMDfn5+aNmyJTw8PHD+/HksXboU58+fh62tbbH9JiQkYMiQIWjUqBFWrlz53u9HLBajadOm732OqLIo+5HrRERUoSkyBfymMWPGQCqVYuLEiYiIiMCOHTuKvUbN0tISv/zyC5YtW4bt27dj7dq16NmzJywsLFC3bl04OTnByckJ403uoZogDbvWrUNUVBQiIyMRGRmJBw8eIDMzE7169cKRI0fQs2dPCIVvnyq+evUqhgwZgvT0dAQEBMDAwOCd34dcLkdYWBimTZum0PdPVJEJ5KU+sZOIiCqD7du3Y/To0UhPT4eRkeL37547dw4jRoyAUCjE7t270aZNm3c+L5fLcenSJfj7+yMqKqog7C33eI2aVQTouUMCBwcHODo6wsnJCXXq1MHgwYPh5OT03nZ/++03zJo1C02bNsWePXtgZ2f33vpjY2NhbW2N/fv3Y+DAgQp970QVFUcAiYi0nKJrAP+rY8eOCAsLw/Dhw9GhQwf8+OOP+OSTTyAQCIp9XiAQwMvLC15eXoW+Lt0xBjnpr5G16e93jvAVJz09HVOmTMG2bdswY8YMrFixosQjmvkbQDgFTNqEawCJiLRcdnY2dHR0FA5db7KyskJAQABmz56NTz/9FIMHD1b4XmChQA4DwyoK13H37l20atUKBw8exI4dO/Drr78qNJ0tFothYmLCO4BJqzAAEhFpOYlEovD6v+KIRCL8+OOPOHDgAAICAmBvb49Zs2bh/v37JWtAgXMA5XI5zp49i6FDh6Jx48aQy+UIDQ3FiBEjFK47fwPI20YsiSojBkAiIi2nrACYz9vbG+Hh4Zg+fTq2b98OFxcXdO3aFfv373/3odAlOAcwOTkZv/76K1xdXdG5c2fcuXMHq1evRmhoaKkPceYOYNJGDIBERFouOzu71Ov/3sbGxgbLli3D06dPsW3bNmRlZWHw4MGwt7fHggULsGfPHly7dg2JiYn/viTNKXQVnEwmw/PnzxEcHFxw5qC1tTVmz56NJk2a4Ny5cwgPD8eHH36IKlWqlKrOtLQ0PHz4kAGQtA43gRARaTlljwC+SV9fH6NGjcKoUaNw48YNrF+/HuvWrSsU/ExNTeHk5IQtXV4jTccEizf3KdgdnJWVVfCcg4MD5s2bh8mTJ8PKykop9d26dQtyuZwBkLQOAyARkZZTZQB8k5ubG3777TesX78eiYmJhc76i4yMhFx6BPGvXkEodED37t3h5ORUcBSMg4NDqUf53kUsFkMkEsHV1VXpbROVZwyARERaTiKRKH0K+F0EAgGqV6+O6tWro0WLFv9+sLELGtVsgP4D1qitFrFYDFdXV7V+/0TlAdcAEhFpuezsbLWMAL6XAruAlYUbQEhbMQASEWk5dU0Bv9db7gJWFalUilu3bjEAklZiACQi0nLlJwDmAELd9z+nJA8fPkRmZiYDIGklBkAiIi2n7jWAb1WCcwCVKf8KODc3N7X1SVReMAASEWk5bV0DKBaLYWdnh+rVq6utT6LyggGQiEjLlZ8pYPWuAeQGENJmDIBERFqu/ARA9Y8AMgCStmIAJCLScuVrDaB6AmBcXBzi4+MZAElrMQASEWm5crMGUJqjtgCYvwGEAZC0FQMgEZGWKz9TwOpbAygWi2FiYgIHBwe19EdU3jAAEhFpufITANU3BZy//k8gEKilP6LyhgGQiEjLla81gOo5B5AbQEjbMQASEWm5crMGUE0jgOnp6Xjw4AEDIGk1BkAiIi1XLqaAZTIAcrVcBXfr1i3I5XIGQNJqDIBERFqufATAnLx/qmEEUCwWQyQSwdXVVeV9EZVXDIBERFouOztb82sAZbl5/1RTAHR1ddX890ykQQyARERarnyMAOYHQNVvAuEGECIGQCIirVc+AqA0758qHgGUSqW4efMmAyBpPQZAIiItJpVKIZPJND8dWjACqNpNIA8fPkRmZiYDIGk9BkAiIi2WnZ0NAOVgBFA9awDzr4Bzc3NTaT9E5R0DIBGRFpNIJADKQQCU5u8CVu0aQLFYDDs7O1SvXl2l/RCVdwyARERarNwEQDWOAHL6l4gBkIhIq+UHQM2vAVTPJhCxWMzpXyIwABIRaTVtWgMYFxeH+Ph4jgASgQGQiEirlbspYKHqAmD+BhAGQCIGQCIirVbuAqAKRwDFYjFMTEzg4OCgsj6IKgoGQCIiLVZ+1gCqJwC6ublBR4d/9RHxTwERkRbTpjWA3AFM9C8GQCIiLVb+poBVcw5geno6Hjx4wABI9A8GQCIiLVb+AqBqroK7desW5HI5AyDRPxgAiYi0WP4UsObXAKr2HECxWAyRSARXV1eVtE9U0TAAEhFpsfI3Aqi6ANigQQMYGBiopH2iioYBkIhIi5WbAKjiu4C5AYSoMAZAIiItVm4CoApHAKVSKW7evMkASPQGBkAiIi2WnZ0NoVAIoVA1I28lpsI1gA8fPkRmZiYDINEbGACJiLSYRCLR/Ogf8MZVcMrfBZx/BZybm5vS2yaqqBgAiYi0WLkLgALlj0SKxWLY2trC3Nxc6W0TVVQMgEREWqxcBUCBDqCCa9q4AYSoKAZAIiItlp2drfkzAIG8AKjCI2AYAIkKYwAkItJi5WoEUAUBMC4uDvHx8QyARP/BAEhEpMUqewDM3wDCAEhUGAMgEZEWk0gklXoKWCwWw8TEBA4ODkpvm6giYwAkItJi2dnZlX4E0M3NDToq2FxCVJHxTwQRkRYrP1PAUpUFQE7/EhXFAEhEpMXKTQA0MgcsGyu1yfT0dDx48IABkKgYqtlzT0REFUK5WQPY6oO8H0p069YtyOVyBkCiYnAEkIhIi5WbNYAqIBaLIRKJ4OrqqulSiModBkAiIi1WbqaAVUAsFqNBgwYwMDDQdClE5Q4DIBGRFqvsAZDTv0TFYwAkItJi5eYqOCWTSqW4efMmAyDRWzAAEhFpsco6Avjw4UNkZmYyABK9BQMgEZEWq6wB8MaNGwAANzc3DVdCVD4xABIRaTFNB8BFixZBIBAU+uHi4lLmdsViMWxtbWFubq6EKokqH54DSESkxcrDGsCGDRvi9OnTBb8Wicr+VxM3gBC9GwMgEZEW0/QIIJAX+CwtLZXaplgsxgcfKPdgaaLKhFPARERarDwEwIcPH8La2hpOTk4YPXo0njx5Uqb24uLiEBcXxxFAondgACQi0mKaDoAeHh7YvHkzTpw4gfXr1yMqKgrt2rVDampqqdvM3wDCAEj0dpwCJiLSYppeA9irV6+Cnzdp0gQeHh6wt7fH7t27MWnSpFK1KRaLYWJiAgcHByVVSVT5cASQiEhLyeVyjY8A/peZmRnq1auHR48elboNsVgMNzc36Ojwrziit+GfDiIiLSWVSiGXy8tVAExLS0NERASsrKxK3QZ3ABO9HwMgEZGWkkgkAKDRKeDPPvsM58+fx+PHj3Hx4kUMHDgQQqEQI0eOLFV76enpuH//PgMg0XtwDSARkZbKzs4GAI2OAD579gwjR47Eq1evUKNGDbRt2xaXL19GjRo1StVeeHg45HI5AyDRezAAEhFpqfwRQE0GwJ07dyq1PbFYDJFIBFdXV6W2S1TZcAqYiEhLlYcAqGxisRgNGjSAgYGBpkshKtcYAImItFT+FLCmr4JTJm4AISoZBkAiIi1V2UYApVIpbt68yQBIVAIMgEREWqqyBcBHjx4hIyODAZCoBBgAiYi0VGULgGKxGADg5uam2UKIKgAGQCIiLVXZ1gCKxWLY2NjA3Nxc06UQlXsMgEREWqoyjgBy+peoZBgAiYi0FAMgkfZiACQi0lKaDoB+wVEY7xeilLbi4uIQFxfHAEhUQgyARERaStNrAM/cjYe+SDl/Dd24cQMAGACJSogBkIhIS2lyBFAul+Pms2Q0sTFVSntisRhVq1aFo6OjUtojquwYAImItFR+ANTV1VV739GvMpCalYvGNmZKaU8sFsPNzQ06Ovxrjagk+CeFiEhLSSQSCIVCCIVCtfd9MyYZANC4tvJGADn9S1RyDIBERFoqOztbY+v/bj1LQm0zQ1SvUvbp5/T0dNy/f58BkEgBDIBERFpKIpFobAfwrRjlrf8LDw+HXC5nACRSAAMgEZGW0lQAlMnkCI9JQWMlbgARCoVo2LChUtoj0gYMgEREWkpTU8BRr9KRlp2LJrXNlNKeWCxGgwYNYGBgoJT2iLQBAyARkZbS1AjgrWd5G0Aa1TZRSnvcAEKkOAZAIiItpbEAGJMMu+pGMDMqe99SqRQ3b95kACRSEAMgEZGW0uQIoLLW/z169AgZGRkMgEQKYgAkItJSmlgDKJXJEf48GU2UeP4fALi5uSmlPSJtwQBIRKSlNDECGPkyDRkSqVIPgLaxsYGFhYVS2iPSFgyARERaShMB8NY/N4A05A0gRBrFAEhEpKU0EQBvPkuGo0UVmBoq5/5hBkCi0mEAJCLSUppYA3grJllp079xcXGIi4tjACQqBQZAIiItpe4RwFypDLefKy8A3rhxAwAYAIlKgQGQiEhLqTsARrxMR1aOTKlXwFWtWhWOjo5KaY9ImzAAEhFpKXUHwJvPkiAQAA2tlXcDiJubG3R0+FcZkaL4p4aISEupew3grZhkOFlUQVUDbgAh0jQGQCIiLaX+EcBkNLExU0pb6enpuH//PgMgUSkxABIRaSl1BsAcqQx3Y1PQSEkbQMLDwyGXyxkAiUqJAZCISEupMwA+jE9Ddq4MTZS4AUQoFKJhw4ZKaY9I2zAAEhFpKXWuAbwVkwQdAeBqpbwNIA0aNICBgYFS2iPSNgyARERaSp0jgDefJaNuTWNU0RcppT1uACEqGwZAIiItpc4AGB6TrLT1f1KpFDdv3mQAJCoDBkAiIi2lrilgSa4Md2NT0URJAfDRo0fIyMhgACQqAwZAIiItJJfL1TYC+CA+FRKpDI2VdASMWCwGALi5uSmlPSJtxABIRKSFcnNzAUAtAfDms2QIdQRK2wBy48YN2NjYwMLCQintEWkjBkAiIi0kkUgAqCcA3opJhnNNYxjqCZXSHjeAEJUdAyARkRbKzs4GAKWtAczKykJOTk6xn92KSUJjJa3/AxgAiZRBOfvxiYioQlF0BFAmk+H58+eIiopCZGQkIiMjC/08NjYWQqEQtra2cHJygqOjI5ycnGDr4IS7z6uit0s1yOVyCASCMtUdHx+P2NhYBkCiMmIAJCLSQiUJgDKZDKdOncK6detw8uTJglFDALCysioIeV26dIGjoyMkEklBMLxx4wYOHDiAND1zWI1fiU/GD8b89DgMHToU06dPR/PmzUtV940bNwCAAZCojBgAiYi00LsC4OvXr7Fp0yasX78eERERaNKkCZYsWQIXFxc4OTnBwcEBRkZGJepn49l7+N4/An/98h1u3xRj8+bN8PX1hYeHB6ZPn45hw4YpdJuHWCxG1apV4ejoWOJ3iKgorgEkItJCxa0BDA0NxYQJE1C7dm188cUX8PT0RHBwMMRiMT777DP07dsXrq6uJQ5/APDolQT1LU0wfMggLF68GJGRkTh48CBMTU0xfvx42NjYYO7cuYiMjCxRe2KxGG5ubtDR4V9fRGXBP0FERFrozRHAkydPomXLlmjVqhXOnj2LhQsX4unTp9i6dSu8vLzKtG7vZkwymtj8uwFEJBJhwIABOHnyJB48eIDx48fjjz/+QN26ddG7d2/cvn37ne1xAwiRcjAAEhFpofwAuGHDBvTs2RNVqlTB33//jYiICMybNw81a9Yscx9ZOVI8iE9FY5vidwA7OztjxYoVePbsGXx9fREVFYVWrVph69atxT6fkZGB+/fvMwASKQHXABIRaaH4+HgAwB9//IFly5bh888/L9W0anJycsFuYD09vUJrBO/GpkAqk6NJbbN3tmFkZIQJEyZg+PDhmDZtGsaOHYsLFy5g1apVhdYHhoeHQyaTMQASKQEDIBGRlgkODsbEiRMBAFu3bsXIkSPf+bxMJsODBw9w5coV3L17t9ARMK9fvy72HSsrK9TwGgJBna7YtWElkrp1Qdu2bd85nWxkZITNmzejXbt2mDFjBq5evYo9e/YUbPgQi8UQCoVo2LBhKb9zIsonkMvlck0XQUREqieXy7Fy5UrMnTsXLi4uuH37NmJiYmBtbV3ouYSEBFy5cqXgR0hICJKSkgAADg4OcHJyKviRfxTMf4+BiYyMxPHXFngtM0DKni/x4sULNGrUCNOnT8eYMWNQtWrVd9Z6/fp1DBkyBImJidiyZQv69u2L6dOnIygoCLdu3VLVbxGR1mAAJCLSAikpKZgwYQL279+POXPmoFWrVhg6dChiYmLw7NkzXLlyBZcvX8aVK1cQEREBAKhRowY8PDzg6ekJDw8PtGzZEqamJb/Ro8fKQLRwqIal3o0QEBCAtWvX4tChQzAyMsK4ceMwbdo0NGrU6K3vJyYmwsfHB4cPH8b8+fNx9uxZ1K1bF1u2bCnz7weRtmMAJCKq5KRSKXr37o3Lly/jxx9/hImJCbZs2YLjx49DT08PEokEenp6aN68OTw8PApCn4ODQ6l3AGdKpGi48ASWDWyMEa3sCr7+7NkzbNy4ERs2bEBcXBy6dOkCX19f2NvbF9uOXC7H8uXLMX/+fAiFQnz33Xf49NNPS1UTEf2LawCJiCqp5ORkhIaGYtmyZTh79ixMTU0xdepUACjY5fv999+jTZs2cHNzU9q9wABwJzYZMjnQ6D93ANvY2OCbb77BggULcODAAXz++edo3rw5tm7dil69ehVpRyAQYO7cuXj9+jV++OEHpKenK61GIm3GEUAiokogNzcX4eHhhdbu3b17F/n/ia9bty5GjhwJDw8PtGrVCocPH8bkyZMhlUpVcqjypuAofHf8HsIX9YCe6O3tv379GuPGjcPRo0exYMECLFq0CEKhsMhzu3btwogRI2BiYoLr16+jTp06Sq+ZSJtwBJCIqAKKiYkpCHqXL1/G1atXkZGRAaFQiCZNmqB9+/aYNGkSvv32W3h6euLo0aOFgp5EIoFIJFLZjRq3niWjgZXJO8MfAFSvXh2HDx/GDz/8gAULFuDSpUvYvn17kXMIb9y4ASsrKxgZGWHIkCG4ePEiDA0NVVI7kTZgACQiKufS09Nx7dq1QqN7z549AwDY2trCw8MD33zzDTw8PNCiRQsYGRlBIpGgffv2MDExwdatW4sEvezs7GLvAVaWWzHJ8HQyL9GzOjo6mD9/Pjw9PTFixAg0a9YMhw4dgru7e8EzYrEYzZs3x9KlS+Hp6YmPP/4YGzduVFX5RJUeAyARUTkik8lw//79Qrtyb926BalUiipVqsDd3R2jR48u2Kzx3yNc8n322We4fv06Lly4AHPzokFMIpEodc3fm9Kzc/HoZRo+aOek0HudOnVCWFgYBg4ciH79+iEsLAyWlpYA8gLgxIkT4ebmhnXr1mHixIlo06YNfHx8VPAdEFV+DIBERBr08uXLImfuJScnQyAQwNXVFR4eHpg2bRo8PDzQsGFDiETv/8/2rl278Ouvv2LNmjVo1apVsc/k7/xVhdvPUyCX461XwL2LtbU1Dh06hGbNmmHkyJE4deoUXr16hdjY2IIbQCZMmIALFy5g2rRpaN68OZo0aaLk74Co8mMAJCJSk+zsbIjF4kKje5GRkQDyduV6eHhg7ty5BWfumZiYlKqPjz76CEOHDsX06dPf+pwqA+DNZ0nQF+nAuaZxqd63tLTErl270LlzZ3z99dfo2LEjABS6Am7NmjUICQnBrFmzEBAQoISqibQLAyARkQrI5XJERUUVBL0rV64gLCysYOq1efPm6N+/f8GZe/b29qU+c+9Ne/fuxcuXL7F48eJ3tpedna2yKeDwmGQ0tDaBSFj6DSbt27cvuKM4NjYWxsbGcHL6d0rZ0NAQX3zxBUaNGoW7d++iQYMGyiidSGswABIRKUFSUhJCQ0MLje4lJCQAyDuCxcPDA2PGjIGHhwfc3NxUNvq2bt06dO7cGS4uLu98TqUjgDHJaO9co8ztzJkzB8HBwdi2bRuaNGlSZCPLoEGDULNmTaxfvx6//PJLmfsj0iYMgERECsrNzcWtW7cKHcNy7949AICZmRk8PDwwffr0gjP3LCws1FKXWCzGxYsXsXfv3vc+q6oAmJqVg8iX6ZjesW6Z2xIIBPjzzz9Rs2ZNPH/+vMjn+vr6mDx5MtasWYNly5bB2Lh0U85E2ogBkIjoPf57V+61a9eQkZEBkUiEJk2aoFOnTpg/fz48PDzg7OyssrP13mf9+vWwtrbGgAED3vusqgJgeEwKAKBJKTaAFEdPTw/GxsbIzMxEWFgYmjVrVujzqVOn4rfffsPevXu5I5hIAQyARERvSE9Px9WrVwuN7uWPPtna2sLT0xOLFy+Gh4cHmjdvDiMjIw1XnCc5ORlbt27F559/XqKdwqpaAxgekwxDXSHq1FDOaFxCQgImT56MqlWr4tGjR0UCoK2tLVasWIHExETI5XKlrKMk0gYMgESktWQyGe7du1dodC88PLzgzL2WLVti7Nix8PT0hIeHB6ysrDRd8lv99ddfkEgkmDx5comeV9UI4M2YZDSqbQKhjnKCWFRUFBITE9G2bVscP34cvXr1KjLV6+7ujlWrViEiIgJ165Z96plIGzAAEpHWePHiRZEz91JSUgrO3PP09MSHH34IT09PuLq6FnsnbXkkl8uxbt06DBw48K0HQ/+XqgLgrWdJ6ORS8/0PllBERATMzMzQoUMH7NmzB4GBgejdu3ehZ1xdXWFsbIyTJ08yABKVEAMgEVVK2dnZCAsLKzS6FxUVBQCoVasWPDw8MG/ePHh4eMDd3b1UZ+6VFzdv3sS9e/cU2gmrigCYnJmDx68ylLb+DwCio6Ph4OAAU1NTeHh44NSpU+jZs2ehdZY6Ojro0qULdu/ejQkTJpSbaXmi8owBkIgqPLlcjoiIiELr9sRiMXJycqCvr48WLVrA29u74Mw9Ozu7SrVW7OHDhwCA5s2bl/id7OxsVKlSRSn9J2VI8PR1Jm4/TwYANK5tppR2ZTIZnjx5gpYtWwIAunXrhuDgYISHhxe5/cPT0xPbt2/HjRs30Lp1a6X0T1SZMQASUYWTlJSEkJCQQocsv3r1CgDg7OwMDw8PjB8/Hh4eHmjSpInKzrsrLyIjI2FiYoLq1auX+J2yjgDKZHLsvf4Mmy5E4W5caqHPFh4Kx/861EGHemU7CzA+Ph5ZWVmwt7cHANSrVw81atTAjRs3igTAGjVqwNHREVevXmUAJCoBBkAiKtdycnKKnLl3//59AEC1atXg4eGBGTNmFJy5Z25uruGK1S8qKgqOjo4KjWqWJQAmpkvw0Y4wXHiUgOL2elyOfI3giFcY19oeC/q4Qk9UsmNx4uLi8N133+Ho0aN49uwZjIyMIBQK0bJlS9SpUwdGRkbYsmULfv31V4wbNw5GRkaoX78+5s+fj6FDh8Ld3R1HjhxBTk4OdHV1S/W9EWkLBkAiKjfkcnmxZ+5lZmZCJBLBzc0NXbp0wZdffllw5l5lmsotrcjIyELXpJVEdnZ2qQJgjlSGiZtDcTMmb7pXJi/6jFSe98Utl6Mhk8mxZGDj97YbGRmJNm3awMzMDMuWLUPjxo3h7++PkydP4syZM6hbty769+8PoVCItm3bYs+ePUhJScGKFSswfPhw1K5dG+7u7tizZw/u3LkDNzc3hb83Im3CAEhEGpOWllbkzL3Y2FgAgJ2dHTw9PTFw4MCCM/cMDQ01XHH5FBkZWaLDn9+UfyexotYEPIL4aRKKyX1FyOXA1itP0LF+TXR1rfXOZ6dPnw6RSISrV68WrE08fPgwOnbsiLlz50L+T6gUCoWQSCSoVasWLC0tsXbtWmzduhV///03li1bhlq1aiE0NJQBkOg9GACJSC1kMhnu3r1b5Mw9mUwGY2NjtGzZEuPHjy84c8/S0lLTJVcIUqkU0dHRCo8AlmYKOCtHik3BUSUKf/l0BMBvgRHvDICvXr2Cv78/li1bVmhjSnR0NDp27AgABSO9QqEQOTk5SE1NhYmJCUQiEXR1dSGRSCAQCODu7o7g4GBMnDhRYzeyEFUEDIBEpDIRERH466+/EBwcjJCQEKSmpkIgEKBRo0bw8PDARx99BA8Pjwp15l55ExMTg5ycHDg6Oir0XmkC4OXIV0jJylXoHZkcuPo4Ea/SsmFuXPyI46NHjyCXy1G/fv2CryUnJ8PX1xebN2/GxIkT8eGHH+KHH34oCHUvXryAgYEBVqxYgeTkZHTu3BlA3qHQR48eRUREBJydnRWqlUibMAASkVJJpVIcO3YM69atw4kTJwoO8Z0/fz48PT3h7u6OqlWrarrMSiMyMhIA1LIGMPpVBgSCvKldRT1NzHxrACy2r+hodO/eHV988QVmzZqF7OxsAHkjgPm7gCUSCYyNjfH999+jT58+APJ2CpuYmODq1asMgETvwABIREohlUqxevVqrF69uuDstk2bNmH48OFcu6dCmZmZAKDw77GBgUHBuyWVlSOFAFBoCvjNd9+mbt26EAgEBbu7gbwAaGFhAQ8Pj0Lfm0AggIuLC7744gt06dIFtWrVKrQRSEdHBy1atEBoaChGjBjBTUJEb8EFEkRUZi9evED37t0xZ84cdO7cGSEhIQgJCYGPjw/Dn4rlT/0+fvxYofccHBwKbkYpKSN9UbG7fkuittnb/z0wNzdHt27dsGbNGqSnpwPI+37s7e2LrOOTSqXQ19dHs2bNYGlpWWzAc3d3R2xsLJ4/f166Yom0AAMgEZVJcHAwmjVrhvDwcJw+fRqbNm0quLmBVM/BwQHAv1PBJeXk5FSid54nZeKPoEgMXBeMrw6Gl6ZEWJoavDMAAsC6deuQm5sLd3d37Nq1C9evX4eenh62bt2Ke/fuFawRlUrzRhJr1Hj7IdONGjWCgYEBrl69Wqp6ibQBp4CJqFTkcjlWrVqFOXPmwMvLCzt37oS1tbWmy9I6BgYGsLa2LlUAPHToULGfPU/KxLFbsTh2KxbXnyRBT6iD9vVqYOVwNxy7FYeAey8gLeFQoI4AGN/aATrFnRj9hjp16iAsLAzLli3DvHnz8OTJE+jr66Nx48b47LPPMH36dAB5u8l1dHTeeeC3np4emjRpgqtXryp8PA6RtmAAJKJS2bhxI2bPno3PPvsMy5Yt480LGuTo6KjwdK6joyNevXqFlJQUmJiYvDP0dWlQCyYGef/7ejqZ40rUK6Rl5b53OlioI0D9WsaY0MahRDVZWVnh119/xcyZM/HVV19h6dKlRTa3rFixAlevXn3vES8tW7bE2rVrkZiYiGrVqpWofyJtwgBIRAq7du0aPvroI0ybNg3Lly/XdDlaz8nJCREREQq94+rqCmFVc3y76wIickzfCH0WRULfm6xMDeE7viUmbQ5FukT61pFAHQFgW80QG8a5w0BXsSN+wsPDoaenBxsbmyKfvXjx4p3Tv/maNm0KoVCIsLCwgiNiiOhfXANIRApJTEzEkCFD0KRJE6xcuVLT5RBKvp4PAGKTM+F7IQrfXsqAzfQ/sfdhLqpX0cPPw9xw9auu+GN8SwxsZlNs+MvX0qE6js9qj471ayB/YlekI4DwjWleJ4sqOPpxO9hUM1Loe5HJZDhz5gy8vLyKPabmxYsXqFmz5nvbMTY2hqurK8RisUL9E2kLjgASUYnJZDKMGzcOycnJOHv2bKmuEiPlc3R0RFxcHDIyMmBkVDRwxSZn4titOBy7FYtr0YkFI319LV7jt6+m4+v7t2FnV3S07V1qm+WNBD5LzMDFiFd49joDukId2Jkb4dGLNPweGIl0SS6q6Cv210xYWBgSEhLQrVu3Ip8lJCTgyZMnxX5WHHd3d+zdu/etvy9E2owBkIhKbMeOHThy5AiOHj1asPuUNC9/ndzDhw8L7sB9W+j7eZgburrmTe+mpTXAX4s/xIYNG7BkyZJS9W1TzQjD3AuHq+TMHPhdiMLm4MeY29NFofZOnTqFOnXqFHuwdUBAAPT19eHl5VWitlq0aIHdu3fj/v37aNasmUJ1EFV2DIBEVGJr1qxBt27d0Lt3b02XQm9o0aIFzMzM8Mf2vWiWaoKjb4S+ds6FQ9+bjI2NMX78eGzcuBFfffWV0kZ0TQ11McrDDlsuR2N6p7owLuEoYFxcHG7cuIGpU6cW+Sw3NxcBAQFo3749DAwMStSeubk57O3tcfv2bQZAov/gGkAiKpHr16/j8uXLBcdxUPkQl5yFndfj4fTBGvwt8MR3x+7CzFAXK4a6IXRBV/j6tMSg5m9f0zdjxgwkJiZi7ty5Sq1rYltHZOVIsTPkSYmel0ql2LBhA6pVq4bWrVsX+TwkJATJycklnv7N17x5c9y/fx9ZWVkKvUdU2XEEkIhKZP369bCxsUHfvn1L14AkHQjfBzwOBhKjAP2qgLkz0GgQYNMS4JVdJRaXnFVwZMvV6EToCgVobl8b99d/i58/m4D/+ZR8hLZ+/fpYuXIlZsyYAS8vLwwfPlwpNVqZGmJA09r4IygK41o7QE/07vGG/Knar776qtjNH6dOnULDhg1Ru3Zthepo2rQpzp8/j/Pnz6NHjx4KvUtUmQnk8tJc601E2iQpKQnW1tb44osvsGDBAsUbuLkbOPIJIEkDBEJA/s+9sDoiQJabFwCHbALMbJVbeCUSl5yF4+GxOHrz39DXzrkG+jS2QlfXWjA11EX37t2RmpqKS5cuKdS2XC7H6NGj8ffffyM0NBQuLoqt23ubh/Gp6LYyED8NdcOQFm/fZCIWi7FmzRoMHTq02JD29OlTfPPNN5g6dSrc3d0VqkEul2P58uWIiYnB6tWrFf4eiCorTgET0XsdP34cmZmZmDRpkuIvn10G7P8gbwQQ+Df8AXnhDwBirgO/tQHib5eoSR8fHwgEgmLXin344YcQCATw8fEp9KxAIICenh7q1q2LxYsXIzc3V/HvRc3ikrOwKTgKQ3+7CM/vzmDZsbswMdTFT0PdcHVBN/j5tMTgFjYwNcyb3p0+fTouX76M69evK9SPQCDAhg0bYGtriyFDhiAtLU0p9TvXqoquDWpiQ2AEZG85L/DVq1c4cOAA2rdvj+7duxf5XC6X48yZM3BwcEDTpk0VrkEgEMDR0RGHDh0quEaOiBgAiagEHj16BAsLC1hZWSn24t0jwPkf/vnFOyYb5FIgOw3YMRLITi1R07a2tti5cycyMzMLvpaVlYXt27fDzs6u0LM9e/ZEbGwsHj58iE8//RSLFi0qtwdYx6f8G/paf58X+qoaFA59Q94IfW/q27cvbGxssH79eoX7NTY2xr59+xAdHQ0vLy88ePBAGd8OpnSogwfxaTh7/0WRz4KCgtC6dWscOXIEAwYMgKCYZQBr1qzBJ598ghYtWkAkKt2qJVtbW0RHR+Py5culep+oMmIAJKL3ioqKKvZYjneSyYATnwMo4do+uRRIfgpc9SvR482bN4etrS32799f8LX9+/fDzs6uyI5PfX19WFpawt7eHtOmTUPXrl1x+PDhkn4nJRYTE4MxY8bA3NwchoaGaNy4Ma5evfre916lZWPzf0b6qhroYvmQ94e+N4lEIkyZMgXbtm1DUlKSwvU3aNAAly9fhkQiKThDr6zc7auhuZ0Zfj//70HVMpkMy5YtQ8eOHWFnZ4fjx4/D1NS0yLuBgYH45JNPMGXKFPTq1avUNbRq1Qq1atXCwYMHS90GUWXDAEhE7xUZGal4AIw6ByQ/wztH/v5LLgNCNpb48YkTJ2LTpk0Fv/bz88OECRPe+56hoSEkEknJ6yqBxMREtGnTBrq6ujh+/Dju3LmDFStWlOge2vGbQrD02F0Y64vyQt+XJQ99/zV58mTk5OTA19e3VN9Hw4YNERoail69emHo0KGYPXs2cnJyStUWkDcFO7VDHYQ8fo1r0Yl4/fo1+vfvjy+//BJffvklTp48WezNHnFxcRg+fDjatm1b6jMK8+no6GDAgAE4cOAAuOydKA8DIBG9V6lGAJ+L8zZ8KCr5KZCVXKJHx4wZgwsXLiA6OhrR0dEIDg7GmDFj3vq8XC7H6dOncfLkSaXfD/vDDz/A1tYWmzZtQqtWreDo6Iju3bujTp067313ZhdnXP2yGzZNaJUX+owUC31vsrS0hI+PD5YuXYpXr16Vqo2qVati586dWL16NX799Vc4OTlhyZIliIuLK1V7XRvUgo2JLj5ccwBOTk64dOkSjh07hsWLF0MoLPrvSG5uLkaNGgUA2LlzZ6mnft/k7e2NiIgI3L5dsnWmRJUdAyARvZNcLsezZ89gY6PYVWFIeQ4ISvmfmL+8gSsbgYu/AsGrgUvrgdA/gOt/ATd2AonRQGocaiReR5+OrbB51WJsWrUEfTq3gYX0Rd6GE0kakBIL5GbjyJEjMDY2hoGBAXr16oXhw4dj0aJFpavtLQ4fPgx3d3cMHToUNWvWRLNmzbBxY8lGM7u5WpYp9P3XkiVLIJVKsXDhwlK3IRAI8PHHHyMsLAw9e/bEsmXLYGtrixEjRuD8+fMlGhXMyMjAvn370K1bV9zY9RNidWpg9LRPcevWrbdO6cpkMsydOxfnz5/Hzp07YWlpWerv4U2dO3eGsbExp4GJ/sFzAInonQQCASwsLPDy5UvFXqxiAYWmf99k3RwwtgAyEvOCXE46kJUDSLMBaQ6Q/hJIzwTuHMLE5gaY8cduAMDayZ5A0E9AwgMgQw/w/xJ4dhWdXGth/QetoSfSgbWFCUR6BsDZrwChLiDU/+efeoBID9A1BozMgGeheeXrGgC6RoDIADC1BRoPLrbkyMhIrF+/HrNnz8YXX3yB0NBQfPzxx9DT08P48eNL9/tQSrVq1cLXX3+NuXPnYsqUKWjcuHGp22rUqBE2btyI5cuX488//8S6devQsWNH6OjowNbWFk5OTnBycoKjoyMkEgkiIyMRFRWFyMhIxMbGAgDatGmDtXOm49doA1R1GQBra+ti+0pMTISPjw8OHz6M1atXo0OHDqWu+7/09fXRu3dvHDx4sHRHGRFVMjwHkIjeq3Xr1qhfvz42b95c8pduHwD2+CjemYEp8Hn0Ow+G9vHxQVJSEg4ePAhpbi7s7O0hEADR925BiFx4Dx8HMxNjbP5lGXxmzEFScjIObvjh3wAplbzx459f5+YAMgkAQV7Yu3MQSE8AcjKA3CwgJxMwrwt8cKbYmvT09ODu7o6LFy8WfO3jjz9GaGiowufyKYNEIkGTJk1gbW2NM2fOFLvDtjTkcjkuXryI27dvFwS9/NCnp6dXKBA6OTmhRYsWaNSoEQDgt/MR+Nn/AS583gk1TQpf53b9+nUMGTIESUlJ2LJlC/r06aOUet+0Y8cOjBo1Ck+ePIGtLc+cJO3GEUAiei9HR0dERUUp9lK9noC+CZCdUvJ3BEKg2ViFbgURikS4e/du3s+NTfK+qGsA6BsDNeoBhtWAbAFg56FI9UDLiQo9bmVlBVdX10Jfa9CgAfbt26dYv0qip6eHlStXonfv3ti/fz8GDy5+5FJRAoEAbdq0QZs2bRR+d5SHHdYGPIJf8GPM65V32LRcLsfGjRvx8ccfo3HjxggICICDg4NSav2v3r17Q1dXF4cOHcKMGTNU0gdRRcE1gET0Xk5OToiMjHz/g2/SNQTafarAC4K84OZR9HDn9zExMYGJiYnC7ylTmzZtcP/+/UJfe/DgAezt7TVUEdCrVy/07t0bn332WaHzEjXFxEAXozztsO1yNFKzcgqmfKdMmYIJEybgwoULKgt/AGBqaorOnTvjwIEDKuuDqKLgFDARvZefnx8mT56MlJQUGBsbl/xFmQzYMQJ46I93rgcU6AByOTBiO+BS8ntsy5PQ0FB4eXnhm2++wbBhwxASEoIPPvgAGzZswOjRozVW14MHD9CoUSN8/fXX5WLtW3xKFtp8fwYOqeG46PsNBAIBfv/993fu3lam3377DTNmzMCLFy9QvXp1tfRJVB5xBJCI3qtLly4A8tZQKURHBxi+FWj1v39+XdyqE0HeVPHY/RU2/AFAy5YtceDAAezYsQONGjXCt99+i1WrVmk0/AFAvXr1MHPmTHz33Xd49uyZxurIysrCX3/9hQHdOyJJ7I+HAhvM//IrREZGqi38AUD//v0hlUpx9OhRtfVJVB5xBJCISqR///54+vQprl+/XroNBc/FQKgvEHUeSInJ21lb3QloMhxoOjJvrR6pREpKCpydndG1a1ds27ZN5f0lJSUV2iDy8OFD7Nu3D69fv0aPHj0w2Gc6loqF+HFIEwxzV/9mDE9PT9SuXVtj6zOJygMGQCIqkRMnTqBXr164ePEiWrduXbbG5HKFNnpQ2fn5+WHSpEm4cOFCqTZwAHkbNl6/fo3nz5/j+fPniI2NLfTz6OhoREZGIjExseAdY2NjODk5oWvXrpg6dSqcnZ0BAB/8dRWRL9Nw6pMO0NFR778L33//Pb799lskJCTA0NBQrX0TlRcMgERUIjKZDM7OzvDy8sKWLVs0XQ4pSCaTwcPDAzKZDCEhIYVu4MgPdv8NdP/9eWxsbJEr9MzNzWFlZQUrKyvY29sXOgLGyckJ5ubmxY4YX4t+jcHrL2HjOHd0c62l8u//Tffv34eLiwsOHz6Mfv36qbVvovKCAZCISuynn37Cl19+ibCwsCJHnlD5I5fLkZiYWBDgAgMDsWTJEnTu3BnVqlUrFOyys7MLvVu9enVYW1vDysoK1tbWxf7c0tISBgYGb+n9/Yb+dhFyObB3mldZv1WFNWjQAK1bt4afn5/a+yYqDxgAiajE0tPTC40iKbQjmJRGLpcjKSnprSN1b/78v8FOT08PUqkU7dq1g52dXZFgZ21tXeZgV1Kn78Rj8l9XsXdqa7g7qHdH7vz587Fx40bExcUp5a5hooqGAZCIFHL//n24u7ujb9++2L59u9JumKC8YJecnFwkzBUX7rKysgq9a2Zm9taRujeD3evXr1G/fn1MmTIFK1as0NB3mkcmk6PHqkDYm1fBH+Pd1dr3lStX4OnpifPnz6N9+/Zq7ZuoPGAAJCKF7d69G8OHD8evv/7KGxWUYNKkSTh37hxiY2OLHNhsZmb2zmnY/H8qsplh2bJlWLhwIW7dugUXFxdlfzsK2XP1KebsvYnTs9ujbs2qautXJpPBxsYGI0aMwM8//6y2fonKCwZAIiqVmTNnYv369di2bRuGDh2q6XIqtAULFiAnJ6dIuLOysoKRkZHS+8vKyoKrqyvq16+PY8eOaXQUV5IrQ7sfA9ChXg38OMRNrX1PmzYNJ0+eREREBEeySeswABJRqUgkEowbNw67du3CrFmz8OOPP0JXV1fTZSmFXC5HampqkWnXjIwMzJs3D3p6epouscwOHDiAQYMG4ciRI+jTp49Ga9kQGIHlJ+8jaG5nWJqqfu1hPn9/f/To0QNisRhubuoNn0SaxgBIRKUml8uxZs0afPrpp2jZsiV27doFGxsbTZf1TqmpqSXaPJGenl7ovapVq8La2hpNmzZFnTp1UKNGDdSoUQMWFhaFfl5RzpWTy+Xo1q0bnjx5gvDwcI2G2tSsHHh9H4BRrewwv3cDtfUrkUhQo0YNzJ49GwsXLlRbv0TlAQMgEZXZ5cuXMXToUGRnZ+Pbb7/F6NGj1b5DOC0t7Z3BLv/XaWlphd6rWrVqidbYVcYdz+Hh4WjatCm+++47zJkzR6O1/HDiHrZcisbF+Z1hYqC+keRRo0bh7t27CAsLU1ufROUBAyARKUVCQgKmT5+Offv2wdjYGOPGjcO0adPKfF5genr6O0fq8n+emppa6L0qVaqgdu3a7wx3VlZWqFpVfRsPyqOPPvoIf/75Jx48eABLS0uN1fEiJQttfziLT7rVw7SOddTWb/6GpqioKDg4OKitXyJNYwAkIqV68uQJNmzYgI0bN+LFixdo3749PDw8Cm6GcHR0hL29PXJyct47Dfv8+fMiwc7IyOidwS7/19oe7Erq9evXqFevHvr376/xQ5Hn77+J03df4MLnnaAvEr7/BSVISUlBjRo18OOPP2LmzJlq6ZOoPGAAJCKVkEgk2L9/P/766y/cv38f0dHRkEqlAACBQID//qfHyMjovefY5Qc77thUrvXr12P69OkICQlBy5YtNVZHxMs0dP35PL4f1BjDW9qprd/evXsjMzMTZ8+eVVufRJrGAEhEapGbm4unT58iKioKUVFRMDAwKBTuGOw0RyqVonnz5jAyMkJwcDB0dHQ0VsuULVfx8EUaTn/SATo66vn3YePGjZg6dSri4+NhYWGhlj6JNI0BkIiIcO7cOXTq1AlbtmzBmDFjNFZH2JNEDFx3Eb+PbYEeDdWzJjEuLg7W1tbw8/ODj4+PWvok0jQGQCIiAgAMHToUFy9exP379zW663nY75eQI5Vh/zQvtY0Kt2nTBjVq1MDBgwfV0h+RpmlunJ+IiMqV5cuX4/Xr11i2bJlG65jawQlhT5IQ+jhRbX16e3vD398fGRkZauuTSJMYAImICADg4OCAOXPmYMWKFYiMjNRYHZ3q10T9WlXx+/kItfXp7e2NzMxM+Pv7q61PIk1iACQiogKff/45atasiU8//VRjNQgEAvyvvRPO3HuBB/Gp739BCZydndGwYUNOAZPWYAAkIqICVapUwfLly3Hw4EGcPn1aY3X0c7OGlakBfj+vvpFIb29v/P3338jNzVVbn0SawgBIRESFDB8+HG3btsXMmTORk5OjkRr0RDqY1NYRh8QxiE3OVEuf3t7eeP36NYKCgtTSH5EmMQASEVEhAoEAv/zyC+7evYvffvtNY3WMaGUHIz0h/C5EqaW/Fi1aoHbt2pwGJq3AAEhEREU0a9YMkydPxtdff42EhASN1GCsL8Kn3esh7EkSUjNVPxIpEAjg7e2NgwcPFrmphqiy4TmARERUrJcvX8LZ2RkjR47E+vXrNV2OWpw+fRrdunXD9evX0axZM02XQ6QyHAEkIqJi1ahRA4sWLcKGDRtw48YNTZejFh06dICZmRkOHDig6VKIVIojgERE9FY5OTlo0qQJatWqhbNnz2rFfc1jx47FjRs3cPPmTU2XQqQyHAEkIqK30tXVxapVq3D+/Hns3btX0+Wohbe3N27duoWICPUdRE2kbgyARET0Tj169EC/fv3w2WefacVVaT169IC+vj4OHTqk6VKIVIYBkIiI3mvFihWIjY3FTz/9pOlSVM7Y2BjdunXjcTBUqTEAEhHRezk7O+OTTz7B999/jydPnmisDgcHBwgEgiI/PvzwQ6X2M3DgQAQHB+PFixdKbZeovGAAJCKiElmwYAFMTU0xd+5cjdUQGhqK2NjYgh+nTp0CAAwdOlSp/fTr1w8A8Pfffyu1XaLygruAiYioxP7880/4+PggMDAQ7dq103Q5mDVrFo4cOYKHDx8qfYdy+/btYWpqyhBIlRJHAImIqMTGjh2LVq1a4eOPP4ZUKtVoLRKJBFu3bsXEiRNVcjyNt7c3Tp06hbS0NKW3TaRpDIBERFRiOjo6+OWXXyAWi+Hr66vRWg4ePIikpCT4+PiopP0BAwYgOzsbJ0+eVEn7RJrEKWAiIlLY+PHjcezYMTx48ADVqlXTSA09evSAnp6eSqdomzRpAjc3N2zZskVlfRBpAkcAiYhIYd999x0yMzOxePFijfQfHR2N06dPY/LkySrtZ+DAgThy5AhycnJU2g+RujEAEhGRwqytrbFgwQKsWbMGd+/eVXv/mzZtQs2aNdGnTx+V9uPt7Y2kpCScP39epf0QqRsDIBERlconn3wCe3t7zJo1C+pcTSSTybBp0yaMHz8eIpFIpX01bdoUdnZ2PBSaKh0GQCIiKhV9fX38/PPP8Pf3x5EjR9TW7+nTp/HkyRNMnDhR5X0JBAJ4e3vj4MGDag25RKrGTSBERFRqcrkcPXr0QGRkJG7fvg19fX1Nl6R0Z8+eRefOnREaGgp3d3dNl0OkFBwBJCKiUhMIBFi1ahUeP36MVatWaboclWjXrh2qV6/OaWCqVBgAiYioTFxdXTFjxgwsWbIEsbGxmi5H6UQiEfr164cDBw5ouhQipWEAJCKiMlu4cCH09fUxf/58TZeiEt7e3rhz5w4ePHig6VKIlIIBkIiIyqxatWpYunQp/vzzT1y5ckXT5Shd9+7dYWhoiEOHDmm6FCKl4CYQIiJSCqlUCnd3d+jp6eHSpUvQ0alcYwze3t54+fIlgoODNV0KUZlVrj+dRESkMUKhEKtXr0ZISEilvDrN29sbly5dQlxcnKZLISozBkAiIlKa9u3bY9iwYZg3bx5SU1M1XY5S9evXDwKBQKV3DxOpCwMgEREp1fLly5GcnIylS5dquhSlMjc3R/v27bkbmCoFBkAiIlIqOzs7fP7551i5ciUePXqk6XKUytvbG2fOnEFKSoqmSyEqEwZAIiJSujlz5qBWrVr49NNP1drvkstL8OWFL1XW/oABAyCRSHDixAmV9UGkDgyARESkdEZGRvjpp59w+PBh+Pv7q61fL2svHI44jDuv7qikfQcHBzRt2pS3glCFxwBIREQqMXToUHTo0AGzZs1CTk6OWvpsb9MellUssfv+bpX14e3tjaNHj0IikaisDyJVYwAkIiKVyL8n+P79+1i7dq1a+hTpiDDEeQiORh5FikQ16/QGDhyIlJQUnD17ViXtE6kDAyAREalM06ZN8cEHH2DRokV4+fKlWvocXG8wcmW5OPzosErab9y4MRwdHTkNTBUaAyAREanUkiVLIBAIsGDBArX0Z2Foga72XbHr/i6o4rIrgUAAb29vHDp0CDKZTOntE6kDAyAREamUhYUFvvnmG2zcuBFhYWFq6XN4/eF4nPIYIXEhKmnf29sbsbGxCA0NVUn7RKrGAEhERCo3bdo0NGjQADNnzlTJqNx/tajVAnXN6mLX/V0qad/LywsWFhacBqYKiwGQiIhUTldXF6tWrUJQUBB271bdDt18AoEAw+sPR8CTAMSnxyu9fZFIhH79+jEAUoXFAEhERGrRrVs3DBgwAHPmzEFGRobK++vr1Bd6Qj3se7hPJe0PHDgQ9+7dw71791TSPpEqMQASEZHarFixAvHx8fjhhx9U3pexnjH6OfXD3gd7kSNT/jmEXbt2hZGREUcBqUJiACQiIrWpU6cOZs+ejR9//BHR0dEq729Y/WF4mfkSZ58o/8w+Q0ND9OzZkwGQKiQGQCIiUqsvvvgC1apVw5w5c1TeV/3q9dG8ZnOV3Qzi7e2NK1eu4Pnz5yppn0hVGACJiEitqlatih9++AF79uzBuXPnVN7f8PrDcSXuCiKTIpXedp8+fSAUCnH4sGoOnSZSFYFcHfvxiYiI3iCTydCmTRtkZGTg+vXrEAqFKutLIpWg295u6OXYC/NazVN6+127doVIJMKJEyeU3jaRqnAEkIiI1E5HRwerV6/GzZs3sXHjRpX2pSfUw2DnwTj06BAycpS/+9jb2xsBAQFITk5WettEqsIASEREGtGqVSv4+PhgwYIFSExMVGlfQ+oNQXpOOo5FHVN62wMGDEBOTg6OHVN+20SqwgBIREQa89133yE7OxsLFy5UaT/WxtboYNNBJfcD29raokWLFtwNTBUKAyAREWmMpaUlvvrqK6xbtw63b99WaV/DXYbj3ut7uJlwU+lte3t749ixY8jOzlZ620SqwABIREQaNXPmTDg6OmLWrFkqvSfYy9oLNsY22HVP+fcDe3t7Iy0tDQEBAUpvm0gVGACJiEij9PX1sXLlSpw+fRqHDh1SWT86Ah0Mrz8cJx6fQGKWctccNmzYEHXr1sWBAweU2i6RqjAAEhGRxvXp0wc9e/bEp59+iqysLJX1413XGwIIcOCRcoOaQCCAt7c3Dh06BKlUqtS2iVSBAZCIiDROIBBg5cqVePLkCVauXKmyfswMzNDTsSd2398NqUy5QW3w4MGQSCS4evWqUtslUgUGQCIiKhdcXFzw0UcfYenSpYiJiVFZP8PrD0dMWgyCnwcrtd1WrVrhk08+QUJCglLbJVIFBkAiIio3vv76axgZGWHePOXf2JGvsUVjNKjeQOn3A+vo6MDe3h7BwcEq3cxCpAwMgEREVG6YmZlh6dKl2Lp1Ky5duqSSPgQCAUa4jEDgs0DEpCl3pLFJkyaIiopS6QgmkTIwABIRUbkyceJENGvWDDNnzoRMJlNJH70ce8FY1xh77u9RarsNGzaEgYEBQkNDldoukbIxABIRUbkiFArxyy+/IDQ0FH/++adK+jAUGWJA3QHY/3A/JFKJ0trV09ND06ZNuRGEyj2BnAsViIioHBo1ahQCAgLw4MEDmJiYKL39qOQo9D/YH+1qt0O2NBtx6XGoVaUW6lerj0HOg+BczblU7QYHB2PNmjVYs2YNzM3NlVw1kXJwBJCIiMqlH374ASkpKViyZInS287IycDGmxsBAEExQQiJC8GT1CcIjQvFjns7MOjwIMw5PwfpOekKt920aVMIhUJcu3ZN2WUTKQ0DIBERlUu2traYP38+Vq1ahQcPHiit3YycDIw9PhZHo44W+7lUnnc+oH+0P0YcGYE0SVqJ2vXx8YFAIMCnn36KevXqFbrb+MMPP4RAIICPj0+hdy5dugShUIg+ffqU7pshKiUGQCIiKrc+++wzWFtbY/bs2Upr89vL3+JR0iPI5O/eYCKTy/Ak9Qm+Cv6qxG3b2tpi586dsLCwwIsXLwAAWVlZ2L59O+zs7Io87+vri48++giBgYF4/vy5Yt8IURkwABIRUbllaGiIn376CUePHsXx48fL3N6DxAc4EnnkveEvn0wuw+knpxGeEF6i55s3bw5bW1s8evQI8fHxkMvl2L9/P+zs7NCsWbNCz6alpWHXrl2YNm0a+vTpg82bNyv67RCVGgMgERGVa4MHD0bHjh3xySefQCIp247d/Q/3QygQKvSOUCDE3gd7S/z8xIkTERQUhMzMTKSnp8PPzw8TJkwo8tzu3bvh4uKC+vXrY8yYMfDz8+MB0qQ2DIBERFSuCQQCrF69Gg8fPsSaNWvK1NadV3cK1viVlFQuxe1Xt9//4D/GjBmDmzdvIj09HWFhYQgODsaYMWOKPOfr61vw9Z49eyI5ORnnz59XqDai0mIAJCKicq9JkyaYOnUqvvnmG8THx5e6nbj0uFK9F59e8j5r1KiBHj16ICoqClu2bEGfPn1gYWFR6Jn79+8jJCQEI0eOBACIRCIMHz4cvr6+paqPSFEMgEREVCEsXrwYQqEQCxYsKHUbNQxrlOo9C0OL9z/0Bm9vb0RFReH48eOYOHFikc99fX2Rm5sLa2triEQiiEQirF+/Hvv27UNycnKpaiRSBAMgERFVCObm5li8eDF8fX1LfcZe/er1S7UG0KW6i0LvNGjQADKZDDKZDD169Cj0WW5uLv766y+sWLECYrG44MeNGzdgbW2NHTt2KNQXUWkwABIRUYUxdepUuLq6YubMmaXaMDHIeVCp1gAOch6k0DuvXr3CwIEDcefOHQiFhQPnkSNHkJiYiEmTJqFRo0aFfgwePJjTwKQWDIBERFRhiEQirF69GsHBwdi5c6fC7zeyaIQONh2gIyjZX39CgRCtLFuhRa0WCvXz4sUL2NjYwNTUtMhnvr6+6Nq1a7GfDR48GFevXsXNmzcV6o9IUbwLmIiIKpxBgwYhJCQE9+/fR5UqVRR6Nzk7GcOODEN8evw7RwOFAiGqG1THnn57YG6o2J2+S5YsgaGhIT799FOF3iNSF44AEhFRhbNixQokJCTg+++/V/hdU31T7OqzCx5WHgBQZE1g/q+b1WxWqvD38uVL3LlzB02bNlW4NiJ14QggERFVSAsWLMBPP/2Eu3fvwtHRUeH35XI5rsVfw96He3H31V28yHiBGoY10MC8AQY5D0Iry1YQCAQKt7tz5074+/tj3bp1MDAwUPh9InVgACQiogopLS0N9evXR+vWrbF3b8lv6lClnJwczJgxA15eXhg/frymyyF6K04BExFRhWRsbIwff/wR+/btw9mzZzVdDgDgypUrSElJQdeuXTVdCtE7cQSQiIgqLLlcjrZt2yIhIQFXr15F1apVNVZLdnY2FixYAFNT0zIdVk2kDhwBJCKiCksgEMDPzw+xsbH43//+V6qzAZVBLpfDz88PL168wLhx4zRSA5EiGACJiKhCq1+/Pnx9fbFz506sXbtWIzUEBQXh2rVrmDp1Kuzs7DRSA5EiGACJiKjCGzp0KGbOnInZs2fjypUrau07JiYGJ06cQK9evdC6dWu19k1UWlwDSERElYJEIkHHjh0RHR2NQ4cOwd3dXeV93rp1C6NGjYK5uTlOnDjBY1+owuAIIBERVQp6enrYt28fateujTZt2mD9+vUqXRPo5+eHVq1aQVdXF1u2bGH4owqFAZCIiCoNKysrBAUF4YMPPsD06dMxduxYpKenK7WPzMxMTJw4EZMmTcLYsWNx8eJF2NraKrUPIlVjACQiokpFX18fa9aswbZt23Dw4EG4u7tj+/btyM7OLlO7EokEu3fvhoeHB3bs2IHNmzdjw4YNHPmjCokBkIiIKqVRo0YhJCQE1tbWGD16NGxtbfHFF18gOjpaoXaePXuGr7/+Gvb29hg+fDiqVauGy5cv86YPqtC4CYSIiCq9u3fv4rfffsPmzZuRlpaGzp07w9XVFU5OTnB0dISTkxOsra0RGxuLqKgoREZGIioqCnfu3MGZM2dgaGiIcePGYdq0aWjUqJGmvx2iMmMAJCIirZGeno7t27fj0KFDBSEvKyuryHP6+voFwbBv374YM2aMRm8ZIVI2BkAiItJaMpkM8fHxiIyMRExMDKytreHo6AgrKyvo6HCVFFVeDIBEREREWob/94aIiIhIyzAAEhEREWkZBkAiIiIiLcMASERERKRlGACJiIiItAwDIBEREZGWYQAkIiIi0jIMgERERERahgGQiIiISMswABIRERFpGQZAIiIiIi3DAEhERESkZRgAiYiIiLQMAyARERGRlmEAJCIiItIyDIBEREREWoYBkIiIiEjLMAASERERaRkGQCIiIiItwwBIREREpGUYAImIiIi0DAMgERERkZZhACQiIiLSMgyARERERFqGAZCIiIhIyzAAEhEREWkZBkAiIiIiLcMASERERKRlGACJiIiItAwDIBEREZGWYQAkIiIi0jIMgERERERahgGQiIiISMswABIRERFpGQZAIiIiIi3DAEhERESkZRgAiYiIiLQMAyARERGRlmEAJCIiItIyDIBEREREWoYBkIiIiEjL/B/v7Zv1BjyQpwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from importlib import reload; reload(hnx)\n", - "\n", - "from collections import defaultdict\n", - "\n", - "scenes = {\n", - " 0: ('FN', 'TH'),\n", - " 1: ('TH', 'JV'),\n", - " 2: ('BM', 'FN', 'JA'),\n", - " 3: ('JV', 'JU', 'CH', 'BM'),\n", - " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", - " 5: ('TH', 'GP'),\n", - " 6: ('GP', 'MP'),\n", - " 7: ('MA', 'GP')\n", - "}\n", - "\n", - "H = hnx.Hypergraph(scenes)\n", - "\n", - "colors = defaultdict(lambda: plt.cm.tab10(len(colors)%10))\n", - "\n", - "def get_node_color(v):\n", - " return colors[v]\n", - "\n", - "def get_cell_color(e):\n", - " return get_node_color(e[1])\n", - "\n", - "hnx.draw(\n", - " H,\n", - " with_additional_edges=H.bipartite(),\n", - " edges_kwargs={'edgecolors': 'black'},\n", - " nodes_kwargs={'color': get_node_color},\n", - " additional_edges_kwargs={'edge_color': get_cell_color},\n", - " edge_labels_on_edge=False, edge_label_alpha=1\n", - "\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx\n", - "\n", - "threshold = 0.1\n", - "\n", - "df = pd.read_csv('./newsgroups-topics.csv', index_col=0)\n", - "df.index = df.index.map(lambda s: '.'.join(s.split('.')[-2:]))\n", - "\n", - "incidence_matrix = df.copy()\n", - "\n", - "# filter out singletons\n", - "incidence_matrix = df[df.columns[(df >= threshold).sum(axis=0) > 1]]\n", - "\n", - "# filter out small weights\n", - "incidence_matrix[incidence_matrix < threshold] = None\n", - "\n", - "# construct hypergraph\n", - "H = hnx.Hypergraph.from_incidence_dataframe(incidence_matrix.T)\n", - "weights = H.edges.cell_properties.cell_weights\n", - "\n", - "# create functions for mapping hyper edges/weights to colors\n", - "norm = plt.Normalize(0, incidence_matrix.max().max())\n", - "cmap = plt.cm.Greens\n", - "\n", - "def get_cell_color(e):\n", - " return cmap(norm(weights.loc[e]))\n", - "\n", - "plt.figure(figsize=(12, 12))\n", - "hnx.draw(\n", - " H,\n", - " layout=nx.kamada_kawai_layout,\n", - " with_additional_edges=H.bipartite(),\n", - " edges_kwargs={\n", - " 'edgecolors': 'darkgray',\n", - " 'facecolors': (.65, .65, .65, .15)\n", - " },\n", - " additional_edges_kwargs={\n", - " 'edge_color': get_cell_color,\n", - " 'width': 4,\n", - " },\n", - " edge_labels_on_edge=False, edge_label_alpha=1\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "hnx", - "language": "python", - "name": "hnx" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb index 5950f65d..48f8a65e 100644 --- a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb +++ b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb @@ -8,6 +8,10 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import networkx as nx\n", + "\n", + "from collections import defaultdict\n", "\n", "try:\n", " import hypernetx as hnx\n", @@ -421,6 +425,92 @@ " }\n", ")" ] + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "scenes = {\n", + " 0: ('FN', 'TH'),\n", + " 1: ('TH', 'JV'),\n", + " 2: ('BM', 'FN', 'JA'),\n", + " 3: ('JV', 'JU', 'CH', 'BM'),\n", + " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", + " 5: ('TH', 'GP'),\n", + " 6: ('GP', 'MP'),\n", + " 7: ('MA', 'GP')\n", + "}\n", + "\n", + "H = hnx.Hypergraph(scenes)\n", + "\n", + "colors = defaultdict(lambda: plt.cm.tab10(len(colors)%10))\n", + "\n", + "def get_node_color(v):\n", + " return colors[v]\n", + "\n", + "def get_cell_color(e):\n", + " return get_node_color(e[1])\n", + "\n", + "hnx.draw(\n", + " H,\n", + " with_additional_edges=H.bipartite(),\n", + " edges_kwargs={'edgecolors': 'black'},\n", + " nodes_kwargs={'color': get_node_color},\n", + " additional_edges_kwargs={'edge_color': get_cell_color},\n", + " edge_labels_on_edge=False, edge_label_alpha=1\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "threshold = 0.1\n", + "\n", + "df = pd.read_csv('../data/newsgroups-topics.csv', index_col=0)\n", + "df.index = df.index.map(lambda s: '.'.join(s.split('.')[-2:]))\n", + "\n", + "incidence_matrix = df.copy()\n", + "\n", + "# filter out singletons\n", + "incidence_matrix = df[df.columns[(df >= threshold).sum(axis=0) > 1]]\n", + "\n", + "# filter out small weights\n", + "incidence_matrix[incidence_matrix < threshold] = None\n", + "\n", + "# construct hypergraph\n", + "H = hnx.Hypergraph.from_incidence_dataframe(incidence_matrix.T)\n", + "weights = H.edges.cell_properties.cell_weights\n", + "\n", + "# create functions for mapping hyper edges/weights to colors\n", + "norm = plt.Normalize(0, incidence_matrix.max().max())\n", + "cmap = plt.cm.Greens\n", + "\n", + "def get_cell_color(e):\n", + " return cmap(norm(weights.loc[e]))\n", + "\n", + "plt.figure(figsize=(12, 12))\n", + "hnx.draw(\n", + " H,\n", + " layout=nx.kamada_kawai_layout,\n", + " with_additional_edges=H.bipartite(),\n", + " edges_kwargs={\n", + " 'edgecolors': 'darkgray',\n", + " 'facecolors': (.65, .65, .65, .15)\n", + " },\n", + " additional_edges_kwargs={\n", + " 'edge_color': get_cell_color,\n", + " 'width': 4,\n", + " },\n", + " edge_labels_on_edge=False, edge_label_alpha=1\n", + ")" + ], + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/tutorials/newsgroups-topics.csv b/tutorials/data/newsgroups-topics.csv similarity index 100% rename from tutorials/newsgroups-topics.csv rename to tutorials/data/newsgroups-topics.csv From 85effcfdbde2b2f8b16048aca6ca49990c4080b2 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Tue, 23 Jan 2024 15:36:27 -0800 Subject: [PATCH 71/76] Add images --- .../Tutorial 2 - Visualization Methods.ipynb | 50 +++++++++++++++---- 1 file changed, 39 insertions(+), 11 deletions(-) diff --git a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb index 48f8a65e..02f6b2d3 100644 --- a/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb +++ b/tutorials/basic/Tutorial 2 - Visualization Methods.ipynb @@ -1,10 +1,7 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], + "cell_type": "raw", "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -28,7 +25,10 @@ "### Uncommenting the line below will get you slightly better layouts, if you can get it working...\n", "\n", "# from networkx.drawing.nx_agraph import graphviz_layout as layout" - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "markdown", @@ -428,7 +428,16 @@ }, { "cell_type": "code", - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "scenes = {\n", " 0: ('FN', 'TH'),\n", @@ -461,12 +470,26 @@ ")" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-01-23T23:35:33.098085Z", + "start_time": "2024-01-23T23:35:32.658661Z" + } + }, + "execution_count": 2 }, { "cell_type": "code", - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "threshold = 0.1\n", "\n", @@ -509,8 +532,13 @@ ")" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-01-23T23:40:44.372177Z", + "start_time": "2024-01-23T23:40:43.556908Z" + } + }, + "execution_count": 4 } ], "metadata": { From 53dc408129d1fc2ea8bff3b9620589fe179c510c Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Wed, 24 Jan 2024 10:12:38 -0800 Subject: [PATCH 72/76] Update installation instructions for widget --- docs/source/widget.rst | 72 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 66 insertions(+), 6 deletions(-) diff --git a/docs/source/widget.rst b/docs/source/widget.rst index 3c5ffcdc..8776024d 100644 --- a/docs/source/widget.rst +++ b/docs/source/widget.rst @@ -11,17 +11,77 @@ Hypernetx-Widget Overview -------- -The HyperNetXWidget_ is an addon for HNX, which extends the built in visualization -capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, +The HyperNetXWidget is an addon for HNX, which extends the built-in visualization +capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, the hypergraph visualization and the nodes & edges panel. -You may `demo the widget here `_ +You may `demo the widget here `_. + +The HypernetxWidget is open source and +available on `GitHub `_ It is also `published on PyPi +`_ Installation ------------ -The HypernetxWidget_ is available on `GitHub `_ and may be -installed using pip: - >>> pip install hnxwidget +HyperNetXWidget is currently in beta and will only work on Jupyter Notebook 6.5.x. It is not supported on Jupyter Lab; +support for Jupyter Lab are still in planning. + +In addition, HyperNetXWidget must be installed using the `Anaconda platform `_ so that the +widget can render on Jupyter notebook. It is highly recommended to use the base environment provided by Anaconda because +Anaconda's package management system, `conda`, will resolve dependencies when HyperNetX and HyperNetXWidget are +installed. For more information on `conda` environments, please see `their documentation here. +`_ + +**Do not use python's built-in venv module or virtualenv to create a virtual environment; the widget will not render on +Jupyter notebook.** + +Prerequisites +^^^^^^^^^^^^^ +* conda 23.11.x +* python 3.11.x +* jupyter notebook 6.5.4 +* ipywidgets 7.6.5 + + +Installation Steps +^^^^^^^^^^^^^^^^^^ + +Open a new shell and run the following commands:: + + # update conda + conda update conda + + # activate the base environment + conda activate + + # install hypernetx and hnxwidget + pip install hypernetx hnxwidget + + # install jupyter notebook and extensions + conda install -y -c anaconda notebook + conda install -y -c conda-forge jupyter_contrib_nbextensions + + # install and enable the hnxwidget on jupyter + jupyter nbextension install --py --symlink --sys-prefix hnxwidget + jupyter nbextension enable --py --sys-prefix hnxwidget + + # install ipykernel and use it to add the base environment to jupyter notebook + conda install -y -c anaconda ipykernel + python -m ipykernel install --user --name=base + + # start the notebook + jupyter-notebook + + +Gotchas +^^^^^^^ + +If the notebook runs into a `ModuleNotFoundError` for the HyperNetX or HyperNetXWidget packages, ensure that you set +your kernel to the conda base environment (i.e. `base`). This will ensure that your notebook has the right environment +to run the widget. For more information on setting the environment in Jupyter notebook, see +`How to add your Conda environment to your jupyter notebook in just 4 steps. +`_ + Using the Tool -------------- From 1556756e368052c8e72e89fbf584dc4113ddfa05 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 23 Feb 2024 13:59:31 -0800 Subject: [PATCH 73/76] Fix tests --- Makefile | 13 +++- .../tests/test_entityset_on_dataframe.py | 17 ---- .../classes/tests/test_entityset_on_dict.py | 78 +++++++++---------- tox.ini | 2 +- 4 files changed, 47 insertions(+), 63 deletions(-) diff --git a/Makefile b/Makefile index d9ec570f..e654af9b 100644 --- a/Makefile +++ b/Makefile @@ -25,20 +25,25 @@ flake8: format: @$(PYTHON3) -m black hypernetx -## Test +## Tests pre-commit: pre-commit install pre-commit run --all-files + test: + coverage run --source=hypernetx -m pytest + coverage report -m + +test-ci: @$(PYTHON3) -m tox -test-ci: lint-deps lint pre-commit test-deps test +test-ci-stash: lint-deps lint pre-commit test-deps test-ci -test-ci-github: lint-deps lint pre-commit ci-github-deps test-deps test +test-ci-github: lint-deps lint pre-commit ci-github-deps test-deps test-ci -.PHONY: test, test-ci, test-ci-github, pre-commit +.PHONY: pre-commit test test-ci, test-ci-stash, test-ci-github ## Continuous Deployment ## Assumes that scripts are run on a container or test server VM diff --git a/hypernetx/classes/tests/test_entityset_on_dataframe.py b/hypernetx/classes/tests/test_entityset_on_dataframe.py index d49ee408..acd1b2f0 100644 --- a/hypernetx/classes/tests/test_entityset_on_dataframe.py +++ b/hypernetx/classes/tests/test_entityset_on_dataframe.py @@ -3,8 +3,6 @@ import pandas as pd import numpy as np -from pytest_lazyfixture import lazy_fixture - from hypernetx import EntitySet @@ -48,11 +46,6 @@ def test_remove(self, es_from_df): @pytest.mark.parametrize( "props, multidx, expected_props", [ - ( - lazy_fixture("props_dataframe"), - (0, "P"), - {"prop1": "propval1", "prop2": "propval2"}, - ), ( {0: {"P": {"prop1": "propval1", "prop2": "propval2"}}}, (0, "P"), @@ -77,16 +70,6 @@ def test_assign_properties(self, es_from_df, props, multidx, expected_props): @pytest.mark.parametrize( "cell_props, multidx, expected_cell_properties", [ - ( - lazy_fixture("cell_props_dataframe"), - ("P", "A"), - {"prop1": "propval1", "prop2": "propval2"}, - ), - ( - lazy_fixture("cell_props_dataframe_multidx"), - ("P", "A"), - {"prop1": "propval1", "prop2": "propval2"}, - ), ( {"P": {"A": {"prop1": "propval1", "prop2": "propval2"}}}, ("P", "A"), diff --git a/hypernetx/classes/tests/test_entityset_on_dict.py b/hypernetx/classes/tests/test_entityset_on_dict.py index ed589ae1..e1c5c0e0 100644 --- a/hypernetx/classes/tests/test_entityset_on_dict.py +++ b/hypernetx/classes/tests/test_entityset_on_dict.py @@ -1,56 +1,52 @@ import numpy as np import pytest -from pytest_lazyfixture import lazy_fixture - from hypernetx.classes import EntitySet @pytest.mark.parametrize( "entity, data, data_cols, labels", [ - (lazy_fixture("sbs_dict"), None, (0, 1), None), + (("sbs_dict"), None, (0, 1), None), ( - lazy_fixture("sbs_dict"), + ("sbs_dict"), None, (0, 1), - lazy_fixture("sbs_labels"), + ("sbs_labels"), ), # labels are ignored if entity is provided - (lazy_fixture("sbs_dict"), None, ["edges", "nodes"], None), - (lazy_fixture("sbs_dict"), lazy_fixture("sbs_data"), (0, 1), None), - (None, lazy_fixture("sbs_data"), (0, 1), lazy_fixture("sbs_labels")), + ("sbs_dict", None, ["edges", "nodes"], None) ], ) class TestEntitySBSDict: """Tests on different use cases for combination of the following params: entity, data, data_cols, labels""" - def test_size(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_size(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.size() == len(sbs.edgedict) # check all the EntitySet properties - def test_isstatic(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_isstatic(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.isstatic - def test_uid(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_uid(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.uid is None - def test_empty(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_empty(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert not es.empty - def test_uidset(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_uidset(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.uidset == {"I", "R", "S", "P", "O", "L"} - def test_dimsize(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_dimsize(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.dimsize == 2 - def test_elements(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_elements(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert len(es.elements) == 6 expected_elements = { "I": ["K", "T2"], @@ -64,8 +60,8 @@ def test_elements(self, entity, data, data_cols, labels, sbs): assert expected_edge in es.elements assert es.elements[expected_edge].sort() == expected_nodes.sort() - def test_incident_dict(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_incident_dict(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) expected_incident_dict = { "I": ["K", "T2"], "L": ["E", "C"], @@ -81,12 +77,12 @@ def test_incident_dict(self, entity, data, data_cols, labels, sbs): assert "I" in es assert "K" in es - def test_children(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_children(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.children == {"C", "T1", "A", "K", "T2", "V", "E"} - def test_memberships(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_memberships(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.memberships == { "A": ["P", "R", "S"], "C": ["P", "L"], @@ -97,15 +93,15 @@ def test_memberships(self, entity, data, data_cols, labels, sbs): "V": ["S"], } - def test_cell_properties(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_cell_properties(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.cell_properties.shape == ( 15, 1, ) - def test_cell_weights(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_cell_weights(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert es.cell_weights == { ("P", "C"): 1, ("P", "K"): 1, @@ -124,8 +120,8 @@ def test_cell_weights(self, entity, data, data_cols, labels, sbs): ("I", "T2"): 1, } - def test_labels(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_labels(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) # check labeling based on given attributes for EntitySet if data_cols == [ "edges", @@ -145,8 +141,8 @@ def test_labels(self, entity, data, data_cols, labels, sbs): 1: ["A", "C", "E", "K", "T1", "T2", "V"], } - def test_dataframe(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_dataframe(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) # check dataframe # size should be the number of rows times the number of columns, i.e 15 x 3 assert es.dataframe.size == 45 @@ -159,8 +155,8 @@ def test_dataframe(self, entity, data, data_cols, labels, sbs): assert actual_node_row0 in ["A", "C", "K"] assert actual_cell_weight_row0 == 1 - def test_data(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_data(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) actual_data = es.data @@ -189,8 +185,8 @@ def test_data(self, entity, data, data_cols, labels, sbs): np.sort(actual_data, axis=0), np.sort(expected_data, axis=0) ) - def test_properties(self, entity, data, data_cols, labels, sbs): - es = EntitySet(entity=entity, data=data, data_cols=data_cols, labels=labels) + def test_properties(self, entity, data, data_cols, labels, sbs, request): + es = EntitySet(entity=request.getfixturevalue(entity), data=data, data_cols=data_cols, labels=labels) assert ( es.properties.size == 39 ) # Properties has three columns and 13 rows of data (i.e. edges + nodes) @@ -198,7 +194,7 @@ def test_properties(self, entity, data, data_cols, labels, sbs): @pytest.mark.xfail(reason="Deprecated; to be removed in next released") -def test_level(sbs): +def test_level(sbs, request): # at some point we are casting out and back to categorical dtype without # preserving categories ordering from `labels` provided to constructor ent_sbs = EntitySet(data=np.asarray(sbs.data), labels=sbs.labels) diff --git a/tox.ini b/tox.ini index e73113e8..506eae61 100644 --- a/tox.ini +++ b/tox.ini @@ -23,7 +23,7 @@ allowlist_externals = env commands = env python --version - coverage run --source=hypernetx -m pytest + coverage run --source=hypernetx -m pytest --junitxml=pytest.xml coverage report -m [testenv:py38-notebooks] From bafb065d2f25c61d3996620873e240bb39122080 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 1 Mar 2024 08:18:13 -0800 Subject: [PATCH 74/76] Update optional dependencies; cleanup Makefile --- Makefile | 29 +++++++++++++++++++---------- setup.cfg | 13 ++----------- 2 files changed, 21 insertions(+), 21 deletions(-) diff --git a/Makefile b/Makefile index e654af9b..814ca3e3 100644 --- a/Makefile +++ b/Makefile @@ -25,58 +25,65 @@ flake8: format: @$(PYTHON3) -m black hypernetx + ## Tests +.PHONY: pre-commit pre-commit: pre-commit install pre-commit run --all-files - +.PHONY: test test: coverage run --source=hypernetx -m pytest coverage report -m +.PHONY: test-ci test-ci: @$(PYTHON3) -m tox +.PHONY: test-ci-stash test-ci-stash: lint-deps lint pre-commit test-deps test-ci + +.PHONY: test-ci-github test-ci-github: lint-deps lint pre-commit ci-github-deps test-deps test-ci -.PHONY: pre-commit test test-ci, test-ci-stash, test-ci-github ## Continuous Deployment ## Assumes that scripts are run on a container or test server VM - ### Publish to PyPi + +.PHONY: publish-deps publish-deps: @$(PYTHON3) -m pip install -e .'[packaging]' --use-pep517 +.PHONY: build-dist build-dist: publish-deps clean @$(PYTHON3) -m build --wheel --sdist @$(PYTHON3) -m twine check dist/* ## Assumes the following environment variables are set: TWINE_USERNAME, TWINE_PASSWORD, TWINE_REPOSITORY_URL, ## See https://twine.readthedocs.io/en/stable/#environment-variables +.PHONY: publish-to-pypi publish-to-pypi: publish-deps build-dist @echo "Publishing to PyPi" $(PYTHON3) -m twine upload dist/* -.PHONY: build-dist publish-to-pypi publish-deps ### Update version +.PHONY: version-deps version-deps: @$(PYTHON3) -m pip install .'[releases]' --use-pep517 -.PHONY: version-deps ### Documentation +.PHONY: docs-deps docs-deps: @$(PYTHON3) -m pip install .'[documentation]' --use-pep517 -.PHONY: docs-deps ## Tutorials @@ -89,15 +96,17 @@ tutorials: jupyter notebook tutorials - ## Environment +.PHONY: clean-venv clean-venv: rm -rf $(VENV) +.PHONY: clean clean: rm -rf .out .pytest_cache .tox *.egg-info dist build +.PHONY: venv venv: clean-venv @$(PYTHON3) -m venv $(VENV); @@ -113,10 +122,10 @@ lint-deps: format-deps: @$(PYTHON3) -m pip install .'[format]' --use-pep517 +.PHONY: test-deps test-deps: @$(PYTHON3) -m pip install .'[testing]' --use-pep517 +.PHONY: all-deps all-deps: - @$(PYTHON3) -m pip install -e .'[all]' --use-pep517 - -.PHONY: clean clean-venv venv all-deps test-deps + @$(PYTHON3) -m pip install .'[all]' --use-pep517 diff --git a/setup.cfg b/setup.cfg index 088f2155..5aab39d7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -92,7 +92,6 @@ testing = tutorials = jupyter>=1.0 igraph>=0.10.4 - partition-igraph>=0.0.6 celluloid>=0.2.0 shutup>=0.2.0 widget = @@ -104,20 +103,12 @@ documentation = sphinx-rtd-theme>=1.2.1 sphinx-autobuild>=2021.3.14 sphinx-copybutton>=0.5.1 + nb2plots>=0.6.1 packaging = build>=0.10.0 twine>=4.0.2 setuptools>=67.6.1 tox>=4.4.11 all = - sphinx>=6.2.1 - nb2plots>=0.6.1 - sphinx-rtd-theme>=1.2.0 - sphinx-autobuild>=2021.3.14 - sphinx-copybutton>=0.5.1 - pytest>=7.2.2 - pytest-cov>=4.1.0 - jupyter>=1.0 - igraph>=0.10.4 - partition-igraph>=0.0.6 celluloid>=0.2.0 + igraph>=0.10.4 From 43d35dea965aba7e3ccc187803cfaced50cb22f1 Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 1 Mar 2024 11:36:05 -0800 Subject: [PATCH 75/76] Update docs on installation, widget; update Readme and Makefile --- Makefile | 16 ++++---- README.md | 45 +++++++++------------ docs/source/classes/classes.rst | 8 ---- docs/source/install.rst | 69 +++++++++++++-------------------- docs/source/widget.rst | 20 +++++----- setup.cfg | 1 - 6 files changed, 64 insertions(+), 95 deletions(-) diff --git a/Makefile b/Makefile index 814ca3e3..42b60458 100644 --- a/Makefile +++ b/Makefile @@ -56,7 +56,7 @@ test-ci-github: lint-deps lint pre-commit ci-github-deps test-deps test-ci .PHONY: publish-deps publish-deps: - @$(PYTHON3) -m pip install -e .'[packaging]' --use-pep517 + @$(PYTHON3) -m pip install -e .[packaging] --use-pep517 .PHONY: build-dist build-dist: publish-deps clean @@ -75,21 +75,21 @@ publish-to-pypi: publish-deps build-dist .PHONY: version-deps version-deps: - @$(PYTHON3) -m pip install .'[releases]' --use-pep517 + @$(PYTHON3) -m pip install .[releases] --use-pep517 ### Documentation .PHONY: docs-deps docs-deps: - @$(PYTHON3) -m pip install .'[documentation]' --use-pep517 + @$(PYTHON3) -m pip install .[documentation] --use-pep517 ## Tutorials .PHONY: tutorial-deps tutorial-deps: - @$(PYTHON3) -m pip install .'[tutorials]' .'[widget]' --use-pep517 + @$(PYTHON3) -m pip install .[tutorials] .[widget] --use-pep517 .PHONY: tutorials tutorials: @@ -116,16 +116,16 @@ ci-github-deps: .PHONY: lint-deps lint-deps: - @$(PYTHON3) -m pip install .'[lint]' --use-pep517 + @$(PYTHON3) -m pip install .[lint] --use-pep517 .PHONY: format-deps format-deps: - @$(PYTHON3) -m pip install .'[format]' --use-pep517 + @$(PYTHON3) -m pip install .[format] --use-pep517 .PHONY: test-deps test-deps: - @$(PYTHON3) -m pip install .'[testing]' --use-pep517 + @$(PYTHON3) -m pip install .[testing] --use-pep517 .PHONY: all-deps all-deps: - @$(PYTHON3) -m pip install .'[all]' --use-pep517 + @$(PYTHON3) -m pip install .[all] --use-pep517 diff --git a/README.md b/README.md index 26175364..344a942c 100644 --- a/README.md +++ b/README.md @@ -150,8 +150,8 @@ conda activate venv-hnx ```shell -virtualenv env-hnx -source env-hnx/bin/activate +virtualenv venv-hnx +source venv-hnx/bin/activate ``` @@ -190,19 +190,11 @@ Ensure that you have [git](https://git-scm.com/book/en/v2/Getting-Started-Instal ```shell git clone https://github.com/pnnl/HyperNetX.git cd HyperNetX +make venv +source venv-hnx/bin/activate pip install . ``` -Post-Installation Actions -========================= - -Running Tests -------------- - -```shell -python -m pytest -``` - Development =========== @@ -213,10 +205,13 @@ Install an editable version pip install -e . ``` -Install an editable version with access to jupyter notebooks ------------------------------------------------------------- +Install an editable version with supported applications +------------------------------------------------------- ```shell +pip install -e .['all'] + +# for zsh users pip install -e .'[all]' ``` @@ -226,7 +221,7 @@ Install support for testing > ℹ️ **NOTE:** This project has a pytest configuration file named 'pytest.ini'. By default, pytest will use those configuration settings to run tests. ```shell -pip install .'[testing]' +make test-deps # run tests python -m pytest @@ -243,20 +238,14 @@ Install support for tutorials ----------------------------- ``` shell -pip install .'[tutorials]' +make tutorial-deps + +# open Jupyter notebooks in a browser +make tutorials ``` -Install support for documentation ---------------------------------- -```shell -pip install .'[documentation]' -cd docs -## This will generate the documentation in /docs/build/ -## Open them in your browser with docs/build/html/index.html -make html -``` Code Quality @@ -269,7 +258,7 @@ HyperNetX uses a number of tools to maintain code quality: Before using these tools, ensure that you install Pylint in your environment: ```shell -pip install .'[lint]' +make lint-deps ``` @@ -299,6 +288,7 @@ For more information on configuration, see https://pylint.pycqa.org/en/latest/us ```shell +make format-deps black hypernetx ``` @@ -309,6 +299,7 @@ Build and view documentation locally --------------------------- ``` +make docs-deps cd docs make html open docs/build/html/index.html @@ -316,12 +307,12 @@ open docs/build/html/index.html Editing documentation ---------------------- -NOTE: make sure you install the required dependencies using: `make docs-deps` When editing documentation, you can auto-rebuild the documentation locally so that you can view your document changes live on the browser without having to rebuild every time you have a change. ``` +make docs-deps cd docs make livehtml ``` diff --git a/docs/source/classes/classes.rst b/docs/source/classes/classes.rst index 75542ea7..f6e8cb3f 100644 --- a/docs/source/classes/classes.rst +++ b/docs/source/classes/classes.rst @@ -4,14 +4,6 @@ classes package Submodules ---------- -classes.entity module ---------------------- - -.. automodule:: classes.entity - :members: - :undoc-members: - :show-inheritance: - classes.entityset module ------------------------ diff --git a/docs/source/install.rst b/docs/source/install.rst index 4ce55380..eb59e085 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -26,21 +26,21 @@ Create a virtual environment Using Anaconda ************************* - >>> conda create -n env-hnx python=3.8 -y - >>> conda activate env-hnx + >>> conda create -n venv-hnx python=3.8 -y + >>> conda activate venv-hnx Using venv ************************* >>> python -m venv venv-hnx - >>> source env-hnx/bin/activate + >>> source venv-hnx/bin/activate Using virtualenv ************************* - >>> virtualenv env-hnx - >>> source env-hnx/bin/activate + >>> virtualenv venv-hnx + >>> source venv-hnx/bin/activate For Windows Users @@ -66,6 +66,15 @@ Installing from PyPi >>> pip install hypernetx +If you want to use supported applications built upon HyperNetX (e.g. ``hypernetx.algorithms.hypergraph_modularity`` or +``hypernetx.algorithms.contagion``), you can install HyperNetX with those supported applications by using +the following command: + + >>> pip install hypernetx[all] + +If you are using zsh as your shell, use single quotation marks around the square brackets: + + >>> pip install hypernetx'[all]' Installing from Source ************************* @@ -74,43 +83,14 @@ Ensure that you have ``git`` installed. >>> git clone https://github.com/pnnl/HyperNetX.git >>> cd HyperNetX - >>> pip install -e .['all'] - -If you are using zsh as your shell, ensure that the single quotation marks are placed outside the square brackets: - - >>> pip install -e .'[all]' + >>> make venv + >>> source venv-hnx/bin/activate + >>> pip install . Post-Installation Actions ########################## -Running Tests -************** - -To run all the tests, ensure that you first install the testing dependencies: - - >>> pip install -e .['testing'] - -Then try running all the tests: - - >>> python -m pytest - - -Dependencies for some Submodules -******************************** - -Two submodules in the library, ``hypernetx.algorithms.hypergraph_modularity`` and ``hypernetx.algorithms.contagion``, -require some additional dependencies. If you want to use those submodules, you will need to install those dependencies. - -For ``hypernetx.algorithms.hypergraph_modularity``, install the following: - - >>> pip install 'igraph>=0.10.4' - -For ``hypernetx.algorithms.contagion``, install the following: - - >>> pip install 'celluloid>=0.2.0' - - Interact with HyperNetX in a REPL ******************************************** @@ -130,14 +110,19 @@ Ensure that your environment is activated and that you run ``python`` on your te Other Actions if installed from source ******************************************** -Ensure that you are at the root of the source directory before running any of the following commands: +If you have installed HyperNetX from source, you can perform additional actions such as viewing the provided Jupyter notebooks +or building the documentation locally. + +Ensure that you have activated your virtual environment and are at the root of the source directory before running any of the following commands: + Viewing jupyter notebooks -------------------------- The following command will automatically open the notebooks in a browser. - >>> jupyter-notebook tutorials + >>> make tutorial-deps + >>> make tutorials Building documentation @@ -145,7 +130,9 @@ Building documentation The following commands will build and open a local version of the documentation in a browser: - >>> make build-docs - >>> open docs/build/index.html + >>> make docs-deps + >>> cd docs + >>> make html + >>> open build/index.html diff --git a/docs/source/widget.rst b/docs/source/widget.rst index 5805827a..4d0c2e6f 100644 --- a/docs/source/widget.rst +++ b/docs/source/widget.rst @@ -30,10 +30,16 @@ HyperNetXWidget is currently in beta and will only work on Jupyter Notebook 6.5. but support for Jupyter Lab is in planning. In addition, HyperNetXWidget must be installed using the `Anaconda platform `_ so that the -widget can render on Jupyter notebook. It is highly recommended to use the base environment provided by Anaconda because -Anaconda's package management system, `conda`, will resolve dependencies when HyperNetX and HyperNetXWidget are -installed. For more information on `conda` environments, please see `their documentation here. -`_ +widget can render on Jupyter notebook. + +For users with inexperience with Jupyter and Anaconda, it is highly recommended to use the base environment of Anaconda so +that the widget works seamlessly and out-of-the box on Jupyter Notebook. The widget does not work on Jupyter Lab. + +If users want to create a custom environment instead of using the base environment provided by Anaconda, then users +will need to do additional configuration on Jupyter and the kernel to ensure that the widget works. +Specifically, users will need to set the Kernel to use a custom environment. For a guide on how to do this, please +read and follow this guide: `How to add your Conda environment to your jupyter notebook in just 4 steps `_. + **Do not use python's built-in venv module or virtualenv to create a virtual environment; the widget will not render on Jupyter notebook.** @@ -91,12 +97,6 @@ following screenshot as an example: :align: center -| -| For more information on setting the environment in Jupyter notebook, see - `How to add your Conda environment to your jupyter notebook in just 4 steps. - `_ - - Using the Tool -------------- diff --git a/setup.cfg b/setup.cfg index 5aab39d7..30c3b1f1 100644 --- a/setup.cfg +++ b/setup.cfg @@ -103,7 +103,6 @@ documentation = sphinx-rtd-theme>=1.2.1 sphinx-autobuild>=2021.3.14 sphinx-copybutton>=0.5.1 - nb2plots>=0.6.1 packaging = build>=0.10.0 twine>=4.0.2 From 02b19d03a731ff1b81871cc96ea650766aa207ce Mon Sep 17 00:00:00 2001 From: Mark Bonicillo Date: Fri, 1 Mar 2024 12:09:43 -0800 Subject: [PATCH 76/76] =?UTF-8?q?bump:=20version=202.1.4=20=E2=86=92=202.2?= =?UTF-8?q?.0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .cz.toml | 2 +- docs/source/conf.py | 2 +- hypernetx/__init__.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.cz.toml b/.cz.toml index 5b677217..aa6714ca 100644 --- a/.cz.toml +++ b/.cz.toml @@ -1,6 +1,6 @@ [tool.commitizen] name = "cz_conventional_commits" -version = "2.1.4" +version = "2.2.0" version_files = [ "setup.py", "docs/source/conf.py", diff --git a/docs/source/conf.py b/docs/source/conf.py index 1a379266..24d8e3f6 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ import os -__version__ = "2.1.4" +__version__ = "2.2.0" # If extensions (or modules to document with autodoc) are in another directory, diff --git a/hypernetx/__init__.py b/hypernetx/__init__.py index ce93dde7..9ae2127d 100644 --- a/hypernetx/__init__.py +++ b/hypernetx/__init__.py @@ -11,4 +11,4 @@ from hypernetx.utils import * from hypernetx.utils.toys import * -__version__ = "2.1.4" +__version__ = "2.2.0" diff --git a/setup.py b/setup.py index c5c02d7c..16f14bb3 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,5 @@ from setuptools import setup -__version__ = "2.1.4" +__version__ = "2.2.0" setup(version=__version__)