diff --git a/.gitignore b/.gitignore index 89bd6d2..6a058df 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,9 @@ doc_source/notebooks/Matisse/outlines/* benchmarks/results/* +src/ect/embed_graph.py +src/ect/embed_cw.py + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/docs/doctrees/nbsphinx/notebooks/tutorial_cw.ipynb b/Extra_Notebooks/tutorial_cw.ipynb similarity index 98% rename from docs/doctrees/nbsphinx/notebooks/tutorial_cw.ipynb rename to Extra_Notebooks/tutorial_cw.ipynb index 575387a..100de40 100644 --- a/docs/doctrees/nbsphinx/notebooks/tutorial_cw.ipynb +++ b/Extra_Notebooks/tutorial_cw.ipynb @@ -3,13 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial: ECT for CW complexes\n", - "\n", - "\n", - "\n", - " This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform" - ] + "source": "# Tutorial: ECT for CW complexes\n\nThis tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform.\\n\\n**Note**: This tutorial uses `EmbeddedCW`, which is now an alias for the unified `EmbeddedComplex` class. The new unified class supports arbitrary dimensional cells beyond just 2-cells (faces). See `Tutorial-EmbeddedComplex.ipynb` for comprehensive coverage of the new capabilities." }, { "cell_type": "code", @@ -25,9 +19,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - " The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!" - ] + "source": "The CW complex class extends the `EmbeddedGraph` functionality with the ability to add faces (2-cells). With the new unified `EmbeddedComplex` class, you can now add cells of any dimension!\\n\\n**Face/Cell Addition Methods:**\\n- `add_face(vertices)` - Add 2-cells (backward compatible)\\n- `add_cell(vertices, dim=k)` - Add k-cells of any dimension (new!)\\n\\nFaces are added by passing in a list of vertices. We generally assume that these vertices form a cycle bounding an empty region, and that all boundary edges are already included in the complex." }, { "cell_type": "code", @@ -348,6 +340,28 @@ "result = ect.calculate(K)\n", "result.plot()\n" ] + }, + { + "cell_type": "code", + "source": "# Example: Adding 3-cells to create a tetrahedron\\nK_tetra = EmbeddedCW() # Still works - now creates EmbeddedComplex\\n\\n# Add tetrahedron vertices\\ntetra_vertices = {\\n 'A': [0, 0, 0],\\n 'B': [1, 0, 0],\\n 'C': [0.5, 0.866, 0],\\n 'D': [0.5, 0.289, 0.816]\\n}\\n\\nfor name, coord in tetra_vertices.items():\\n K_tetra.add_node(name, coord)\\n\\n# Add all edges\\nfrom itertools import combinations\\nfor edge in combinations(['A', 'B', 'C', 'D'], 2):\\n K_tetra.add_edge(*edge)\\n\\n# Add all triangular faces (2-cells)\\nfor face in combinations(['A', 'B', 'C', 'D'], 3):\\n K_tetra.add_face(list(face)) # Using the familiar add_face method\\n\\n# NEW: Add the 3-cell (volume)\\nK_tetra.add_cell(['A', 'B', 'C', 'D'], dim=3)\\n\\nprint(f\\\"Tetrahedron complex:\\\")\\nprint(f\\\" 0-cells (vertices): {len(K_tetra.nodes())}\\\")\\nprint(f\\\" 1-cells (edges): {len(K_tetra.edges())}\\\")\\nprint(f\\\" 2-cells (faces): {len(K_tetra.faces)}\\\")\\nprint(f\\\" 3-cells (volumes): {len(K_tetra.cells[3])}\\\")\\n\\n# Plot the tetrahedron\\nfig = plt.figure(figsize=(8, 6))\\nax = fig.add_subplot(111, projection='3d')\\nK_tetra.plot(ax=ax, face_alpha=0.3, node_size=100)\\nax.set_title('Tetrahedron with 3-cell')\\nplt.show()\\n\\n# Compute ECT (now includes the 3-cell in the calculation!)\\nect_tetra = ECT(num_dirs=20, num_thresh=30)\\nresult_tetra = ect_tetra.calculate(K_tetra)\\nresult_tetra.plot()\\nplt.title('ECT of Tetrahedron (includes 3-cell contribution)')\\nplt.show()\"", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": "## New Capabilities: Beyond 2-Cells\\n\\nWith the unified `EmbeddedComplex` class, you can now add cells of arbitrary dimension. Here's a quick example showing 3-cells:\"", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "## Enhanced ECT Computation\\n\\nThe ECT now properly computes the Euler characteristic using the alternating sum over **all** cell dimensions:\\n\\n**χ(threshold) = Σ(-1)^k × |{k-cells with projection ≤ threshold}|**\\n\\nThis means:\\n- 0-cells (vertices) contribute **+1** each\\n- 1-cells (edges) contribute **-1** each \\n- 2-cells (faces) contribute **+1** each\\n- 3-cells (volumes) contribute **-1** each\\n- And so on...\\n\\nCompare this to the traditional graph-only ECT which only considered vertices and edges!\"", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "## Summary and Next Steps\\n\\nThis tutorial showed CW complex functionality using `EmbeddedCW`. Key takeaways:\\n\\n- **Backward Compatibility**: All existing `EmbeddedCW` code continues to work\\n- **Enhanced Capabilities**: The unified `EmbeddedComplex` now supports arbitrary dimensional cells\\n- **Improved ECT**: Properly includes all cell dimensions in Euler characteristic computation\\n\\n### For More Advanced Features:\\n- See `Tutorial-EmbeddedComplex.ipynb` for comprehensive examples of high-dimensional cells\\n- The unified interface supports complexes with cells of any dimension\\n- Enhanced visualization and analysis capabilities\\n\\n### Migration Guide:\\n```python\\n# Old way (still works):\\nfrom ect import EmbeddedCW\\nK = EmbeddedCW()\\n\\n# New way (same result, more features):\\nfrom ect import EmbeddedComplex \\nK = EmbeddedComplex()\\n\\n# Both create the same object with identical functionality!\\n```\"", + "metadata": {} } ], "metadata": { @@ -371,4 +385,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/docs/doctrees/nbsphinx/notebooks/tutorial_graph.ipynb b/Extra_Notebooks/tutorial_graph.ipynb similarity index 99% rename from docs/doctrees/nbsphinx/notebooks/tutorial_graph.ipynb rename to Extra_Notebooks/tutorial_graph.ipynb index 7988362..d6f6008 100644 --- a/docs/doctrees/nbsphinx/notebooks/tutorial_graph.ipynb +++ b/Extra_Notebooks/tutorial_graph.ipynb @@ -3,13 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial: ECT for Embedded Graphs\n", - "\n", - "\n", - "\n", - "This tutorial demonstrates how to use the `ect` package..." - ] + "source": "# Tutorial: ECT for Embedded Graphs\n\nThis tutorial demonstrates how to use the `ect` package to compute the Euler Characteristic Transform (ECT) for embedded graphs.\n\n**Note**: This tutorial uses `EmbeddedGraph`, which is now an alias for the unified `EmbeddedComplex` class. All functionality shown here works identically, but `EmbeddedComplex` offers additional features like arbitrary dimensional cells. See the `Tutorial-EmbeddedComplex.ipynb` for the full capabilities." }, { "cell_type": "code", @@ -25,14 +19,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Basic Usage\n", - "\n", - "\n", - "\n", - "First, let's create a simple graph\"\"\"\n", - "\n" - ] + "source": "## Basic Usage\n\nFirst, let's create a simple graph. The `EmbeddedGraph` class (now unified with `EmbeddedComplex`) allows you to create graphs with vertices embedded in Euclidean space." }, { "cell_type": "code", @@ -87,11 +74,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - " The embedded graph class inherits from the networkx graph class with the additional attributes `coord_matrix` and `coord_dict`.\n", - "\n", - " The coordinates of all vertices can be accessed using the `coord_matrix` attribute." - ] + "source": "The embedded graph class inherits from the networkx graph class with additional attributes for spatial embedding. The unified `EmbeddedComplex` class provides:\\n\\n- `coord_matrix`: N×D matrix of vertex coordinates\\n- `node_list`: Ordered list of vertex identifiers \\n- `cells`: Dictionary storing k-cells by dimension (new feature)\\n\\nThe coordinates of all vertices can be accessed using the `coord_matrix` attribute." }, { "cell_type": "code", @@ -770,6 +753,11 @@ "result_3d = ect_3d.calculate(graph_3d)\n", "result_3d.plot()\n" ] + }, + { + "cell_type": "markdown", + "source": "## What's New: Unified EmbeddedComplex\\n\\nThis tutorial showed the graph functionality using `EmbeddedGraph`. The new unified `EmbeddedComplex` class provides all this functionality plus:\\n\\n- **Arbitrary dimensional cells**: Add 2-cells (faces), 3-cells (volumes), and higher\\n- **Enhanced ECT computation**: Properly includes all cell dimensions in Euler characteristic\\n- **Backward compatibility**: `EmbeddedGraph` is now an alias to `EmbeddedComplex`\\n\\n### Quick example with the same graph:\\n\\n```python\\n# This graph can now also have faces!\\nG.add_face(['A', 'B', 'C']) # Add a 2-cell if vertices form a triangle\\n\\n# Or even higher dimensional cells\\nG.add_cell(['A', 'B', 'C', 'D'], dim=3) # 3-cell if you have 4 vertices\\n```\\n\\nSee `Tutorial-EmbeddedComplex.ipynb` for comprehensive examples of these new capabilities!\"", + "metadata": {} } ], "metadata": { @@ -788,4 +776,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/doc_source/directions.md b/doc_source/directions.md index 35f204e..4ce7076 100644 --- a/doc_source/directions.md +++ b/doc_source/directions.md @@ -1,5 +1,8 @@ # Directions +The `Directions` class provides helper functions for selecting directions to compute the ECT along. + + ```{eval-rst} .. automodule:: ect.directions :members: diff --git a/doc_source/ect_on_graphs.md b/doc_source/ect_on_graphs.md index ecda18f..f717327 100644 --- a/doc_source/ect_on_graphs.md +++ b/doc_source/ect_on_graphs.md @@ -1,7 +1,7 @@ # ECT on Graphs ```{eval-rst} -.. automodule:: ect.ect_graph +.. automodule:: ect.ect :members: ``` @@ -9,3 +9,8 @@ .. automodule:: ect.sect :members: ``` + +```{eval-rst} +.. automodule:: ect.dect + :members: +``` diff --git a/doc_source/embed_complex.md b/doc_source/embed_complex.md new file mode 100644 index 0000000..e9b0001 --- /dev/null +++ b/doc_source/embed_complex.md @@ -0,0 +1,46 @@ +# Embedded Complex + +The `EmbeddedComplex` class is a unified representation for embedded cell complexes supporting arbitrary dimensional cells. + +## Overview + +`EmbeddedComplex` combines and extends the functionality of the previous `EmbeddedGraph` and `EmbeddedCW` classes into a single interface. It supports: + +- **0-cells (vertices)**: Points embedded in Euclidean space +- **1-cells (edges)**: Line segments between vertices +- **k-cells for k ≥ 2**: Higher dimensional cells (faces, volumes, etc.) + + +## Basic Usage + +```python +from ect import EmbeddedComplex + +# Create a complex +K = EmbeddedComplex() + +# Add vertices +K.add_node("A", [0, 0]) +K.add_node("B", [1, 0]) +K.add_node("C", [0.5, 1]) + +# Add edges +K.add_edge("A", "B") +K.add_edge("B", "C") +K.add_edge("C", "A") + +# Add a 2-cell (face) +K.add_face(["A", "B", "C"]) + +# Or use the general method for any dimension +K.add_cell(["A", "B", "C"], dim=2) +``` + +## API Reference + +```{eval-rst} +.. automodule:: ect.embed_complex + :members: + :show-inheritance: + :undoc-members: +``` \ No newline at end of file diff --git a/doc_source/embed_cw.md b/doc_source/embed_cw.md deleted file mode 100644 index 4efb095..0000000 --- a/doc_source/embed_cw.md +++ /dev/null @@ -1,6 +0,0 @@ -# Embedded CW complex - -```{eval-rst} -.. automodule:: ect.embed_cw - :members: -``` diff --git a/doc_source/embed_graph.md b/doc_source/embed_graph.md deleted file mode 100644 index a9128b3..0000000 --- a/doc_source/embed_graph.md +++ /dev/null @@ -1,6 +0,0 @@ -# Embedded graphs - -```{eval-rst} -.. automodule:: ect.embed_graph - :members: -``` diff --git a/doc_source/index.rst b/doc_source/index.rst index d35502e..ea6c229 100644 --- a/doc_source/index.rst +++ b/doc_source/index.rst @@ -1,7 +1,7 @@ ect: Euler Characteristic Transform in Python ============================================= -Python computation tools for computing the Euler Characteristic Transform of embedded graphs. +Python computation tools for computing the Euler Characteristic Transform of embedded cell complexes with arbitrary dimensional cells. Table of Contents ----------------- @@ -13,7 +13,7 @@ Table of Contents Getting Started Modules - Tutorials + Tutorials Contributing License Citing @@ -21,7 +21,7 @@ Table of Contents Description ----------- -Right now, the content includes stuff for doing ECT on graphs embedded in 2D. Eventually the goal is to get voxel versions, higher dimensional simplicial complexes, etc in here. +This package provides tools for computing the Euler Characteristic Transform (ECT) of embedded cell complexes efficienctly and provides many useful utilities for visualizing and comparing different ECTs. For more information on the ECT, see: @@ -34,7 +34,7 @@ Documentation and tutorials ^^^^^^^^^^^^^^^^^^^^^^^^^^^ * The documentation is available at: `munchlab.github.io/ect `_ -* A tutorial jupyter notebook can be found `here `_ +* A comprehensive tutorial for the unified `EmbeddedComplex` class can be found `here `_ * The source code can be found at: `github.com/MunchLab/ect `_ Dependencies diff --git a/doc_source/modules.rst b/doc_source/modules.rst index 13c0753..9dbfbfa 100644 --- a/doc_source/modules.rst +++ b/doc_source/modules.rst @@ -5,7 +5,7 @@ Table of Contents :maxdepth: 2 - Embedded graphs - Embedded CW complex + Embedded Complex + Validation System ECT on graphs Directions \ No newline at end of file diff --git a/doc_source/notebooks/Matisse/example_matisse.ipynb b/doc_source/notebooks/Matisse/example_matisse.ipynb index 6f0c4f8..9921e17 100644 --- a/doc_source/notebooks/Matisse/example_matisse.ipynb +++ b/doc_source/notebooks/Matisse/example_matisse.ipynb @@ -18,51 +18,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 150 files in the directory\n" - ] - } - ], - "source": [ - "# -----------------\n", - "# Standard imports\n", - "# -----------------\n", - "import numpy as np # for arrays\n", - "import matplotlib.pyplot as plt # for plotting\n", - "from sklearn.decomposition import PCA # for PCA for normalization\n", - "from scipy.spatial import distance_matrix\n", - "\n", - "from os import listdir # for retrieving files from directory\n", - "from os.path import isfile, join # for retrieving files from directory\n", - "from sklearn.manifold import MDS # for MDS\n", - "import pandas as pd # for loading in colors csv\n", - "import os\n", - "import zipfile\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "# ---------------------------\n", - "# The ECT packages we'll use\n", - "# ---------------------------\n", - "from ect import ECT, EmbeddedGraph # for calculating ECTs\n", - "\n", - "# Simple data paths\n", - "data_dir = \"outlines/\"\n", - "colors_path = \"colors.csv\"\n", - "\n", - "file_names = [\n", - " f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n", - "]\n", - "file_names.sort()\n", - "print(f\"There are {len(file_names)} files in the directory\")\n" - ] + "outputs": [], + "source": "# -----------------\n# Standard imports\n# -----------------\nimport numpy as np # for arrays\nimport matplotlib.pyplot as plt # for plotting\nfrom sklearn.decomposition import PCA # for PCA for normalization\nfrom scipy.spatial import distance_matrix\n\nfrom os import listdir # for retrieving files from directory\nfrom os.path import isfile, join # for retrieving files from directory\nfrom sklearn.manifold import MDS # for MDS\nimport pandas as pd # for loading in colors csv\nimport os\nimport zipfile\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# ---------------------------\n# The ECT packages we'll use\n# ---------------------------\nfrom ect import ECT, EmbeddedComplex # for calculating ECTs\n# Note: EmbeddedGraph is now unified into EmbeddedComplex\n# For backward compatibility, you can still use:\n# from ect import EmbeddedGraph\n\n# Simple data paths\ndata_dir = \"outlines/\"\ncolors_path = \"colors.csv\"\n\nfile_names = [\n f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n]\nfile_names.sort()\nprint(f\"There are {len(file_names)} files in the directory\")" }, { "cell_type": "markdown", @@ -77,38 +36,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "i = 3\n", - "shape = np.loadtxt(data_dir + file_names[i])\n", - "# shape = normalize(shape)\n", - "G = EmbeddedGraph()\n", - "G.add_cycle(shape)\n", - "G.plot(with_labels=False, node_size=10)\n" - ] + "outputs": [], + "source": "i = 3\nshape = np.loadtxt(data_dir + file_names[i])\n# shape = normalize(shape)\nG = EmbeddedComplex() # Using the unified EmbeddedComplex class\nG.add_cycle(shape)\nG.plot(with_labels=False, node_size=10)" }, { "cell_type": "markdown", @@ -199,20 +130,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "def matisse_ect(filename, ect):\n", - " shape = np.loadtxt(data_dir + filename)\n", - " G = EmbeddedGraph()\n", - " G.add_cycle(shape)\n", - " G.transform_coordinates(projection_type=\"pca\")\n", - " G.scale_coordinates(1)\n", - " result = ect.calculate(G)\n", - " return result\n", - "\n" - ] + "source": "def matisse_ect(filename, ect):\n shape = np.loadtxt(data_dir + filename)\n G = EmbeddedComplex() # Using the unified EmbeddedComplex class \n G.add_cycle(shape)\n G.transform_coordinates(projection_type=\"pca\")\n G.scale_coordinates(1)\n result = ect.calculate(G)\n return result" }, { "cell_type": "markdown", @@ -386,4 +307,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/doc_source/notebooks/Tutorial-EmbeddedComplex.ipynb b/doc_source/notebooks/Tutorial-EmbeddedComplex.ipynb new file mode 100644 index 0000000..18945fd --- /dev/null +++ b/doc_source/notebooks/Tutorial-EmbeddedComplex.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: ECT for Embedded Cell Complexes\n", + "\n", + "This tutorial will walk you through using the `ECT` package. Particularly we will show the features of `EmbeddedComplex` class for computing the Euler Characteristic Transform on complexes with arbitrary dimensional cells.\n", + "\n", + "The `EmbeddedComplex` class combines and extends the functionality of the previous `EmbeddedGraph` and `EmbeddedCW` classes, supporting **k-cells** for k ≥ 2.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ect import EmbeddedComplex, ECT, Directions\n", + "from ect.utils.examples import create_example_graph, create_example_cw, create_example_3d_complex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Usage: Creating Simple Complexes\n", + "\n", + "### Example 1: Graph (1-skeleton)\n", + "\n", + "Let's start with a simple triangle graph (for legacy users this can be equivalently done using `EmbeddedGraph`). " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of nodes: 3\n", + "Number of edges: 3\n", + "Embedding dimension: 2\n" + ] + } + ], + "source": [ + "K = EmbeddedComplex()\n", + "\n", + "K.add_node('A', [0, 0])\n", + "K.add_node('B', [1, 0])\n", + "K.add_node('C', [0.5, 0.866])\n", + "\n", + "K.add_edge('A', 'B')\n", + "K.add_edge('B', 'C')\n", + "K.add_edge('C', 'A')\n", + "\n", + "#using built-in plotting function along with matplotlib\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400)\n", + "ax.set_title('Simple Triangle Graph\\n(0-cells: vertices, 1-cells: edges)')\n", + "plt.show()\n", + "\n", + "#print some information about the complex\n", + "print(f\"Number of nodes: {len(K.nodes())}\")\n", + "print(f\"Number of edges: {len(K.edges())}\")\n", + "print(f\"Embedding dimension: {K.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's add a 2-cell (face) to fill in the triangle:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of 2-cells (faces): 1\n", + "Faces: [('A', 'B', 'C')]\n", + "Internal cells dictionary: {2: [(0, 1, 2)]}\n" + ] + } + ], + "source": [ + "K.add_face(['A', 'B', 'C'])\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400, face_alpha=0.3, face_color='lightblue')\n", + "ax.set_title('Triangle with Face\\n(0-cells: vertices, 1-cells: edges, 2-cells: faces)')\n", + "plt.show()\n", + "\n", + "print(f\"Number of 2-cells (faces): {len(K.faces)}\")\n", + "print(f\"Faces: {K.faces}\")\n", + "print(f\"Internal cells dictionary: {dict(K.cells)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Cells of Arbitrary Dimension\n", + "\n", + "The key new feature is the ability to add cells of any dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimension 2: 4 cells\n", + "Dimension 3: 1 cells\n", + "\n", + "Total embedding dimension: 3\n" + ] + } + ], + "source": [ + "# Create a 3D tetrahedron with 0, 1, 2, and 3-cells\n", + "K_tetra = EmbeddedComplex()\n", + "\n", + "# Add vertices (0-cells)\n", + "vertices = {\n", + " 'A': [0, 0, 0],\n", + " 'B': [1, 0, 0],\n", + " 'C': [0.5, 0.866, 0],\n", + " 'D': [0.5, 0.289, 0.816] \n", + "}\n", + "\n", + "for name, coord in vertices.items():\n", + " K_tetra.add_node(name, coord)\n", + "\n", + "# Add edges (1-cells) - all pairs\n", + "edges = [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]\n", + "K_tetra.add_edges_from(edges)\n", + "\n", + "# Add faces (2-cells) - all triangular faces\n", + "faces = [['A', 'B', 'C'], ['A', 'B', 'D'], ['A', 'C', 'D'], ['B', 'C', 'D']]\n", + "for face in faces:\n", + " K_tetra.add_cell(face, dim=2) # Explicitly specify dimension\n", + "\n", + "# Add volume (3-cell) - the entire tetrahedron\n", + "K_tetra.add_cell(['A', 'B', 'C', 'D'], dim=3)\n", + "\n", + "# Plot the tetrahedron\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "K_tetra.plot(ax=ax, face_alpha=0.3, face_color='cyan', node_size=100)\n", + "ax.set_title('Tetrahedron with All Cell Types\\n0-cells: 4, 1-cells: 6, 2-cells: 4, 3-cells: 1')\n", + "plt.show()\n", + "\n", + "# Display cell counts\n", + "for dim in sorted(K_tetra.cells.keys()):\n", + " print(f\"Dimension {dim}: {len(K_tetra.cells[dim])} cells\")\n", + " \n", + "print(f\"\\nTotal embedding dimension: {K_tetra.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ECT Computation with Higher-Dimensional Cells\n", + "\n", + "The ECT computation now properly includes all cell dimensions in the Euler characteristic calculation:\n", + "\n", + "**χ = Σ(-1)^k × |k-cells below threshold|**\n", + "\n", + "Let's see how this works:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ECT result shape: (8, 20)\n", + "Directions: 8 directions in 3D\n", + "Thresholds: 20 threshold values\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute ECT for the tetrahedron\n", + "ect = ECT(num_dirs=8, num_thresh=20)\n", + "result = ect.calculate(K_tetra)\n", + "\n", + "print(f\"ECT result shape: {result.shape}\")\n", + "print(f\"Directions: {len(result.directions)} directions in {K_tetra.dim}D\")\n", + "print(f\"Thresholds: {len(result.thresholds)} threshold values\")\n", + "\n", + "# Plot the ECT matrix\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "result.plot()\n", + "plt.title('ECT of Tetrahedron (includes 3-cells in computation)')\n", + "plt.show()\n", + "\n", + "single_direction = ECT(num_thresh=20, directions=Directions.from_vectors([[1, 0, 0]])).calculate(K_tetra)\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "ax.plot(single_direction.thresholds, single_direction[0], 'b-', marker='o', linewidth=2)\n", + "ax.set_xlabel('Threshold')\n", + "ax.set_ylabel('Euler Characteristic')\n", + "ax.set_title('ECT Curve for Single Direction (v=[1, 0, 0])')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Validation System\n", + "Sometimes we need our `EmbeddedComplex` to satisfy certain constraints such as no-self intersections. You can enforce these constraints using " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Edge Interior Validation: Vertex 4 lies on edge interior\n", + "adding intersecting edge failed\n", + "4D Simplex Cell Counts:\n", + " 1-cells: 1\n", + " 2-cells: 4\n", + " 3-cells: 1\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "K_tetra.enable_embedding_validation()\n", + "#add intersecting edge\n", + "K_tetra.add_node(\"E\", [0.5,0,0])\n", + "try:\n", + " K_tetra.add_cell([\"A\", \"B\"], dim=1, check=True)\n", + "except Exception as e:\n", + " print(e)\n", + " print(\"adding intersecting edge failed\") \n", + "\n", + "K_tetra.disable_embedding_validation()\n", + "K_tetra.add_cell([\"A\", \"B\"], dim=1)\n", + "\n", + "print(\"4D Simplex Cell Counts:\")\n", + "for dim in sorted(K_tetra.cells.keys()):\n", + " print(f\" {dim}-cells: {len(K_tetra.cells[dim])}\")\n", + "\n", + "# Plot (showing 3D projection)\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "K_tetra.plot(ax=ax, face_alpha=0.1, node_size=80)\n", + "ax.set_title(\"4D Simplex (5 vertices, cells up to dimension 4)\")\n", + "plt.show()\n", + "\n", + "# Compute ECT\n", + "ect_4d = ECT(num_dirs=6, num_thresh=15)\n", + "result_4d = ect_4d.calculate(K_tetra)\n", + "\n", + "result_4d.plot()\n", + "plt.title(\"ECT of 4D Simplex\")\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dataexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc_source/notebooks/Tutorial-ExactECT.ipynb b/doc_source/notebooks/Tutorial-ExactECT.ipynb index 19a4e29..1870585 100644 --- a/doc_source/notebooks/Tutorial-ExactECT.ipynb +++ b/doc_source/notebooks/Tutorial-ExactECT.ipynb @@ -13,56 +13,22 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "from ect import ECT, EmbeddedGraph, EmbeddedCW,create_example_graph\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.patches import Circle\n", - "import numpy as np\n", - "import networkx as nx" - ] + "source": "from ect import ECT, EmbeddedComplex, create_example_graph\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Circle\nimport numpy as np\nimport networkx as nx\n\n# Note: EmbeddedGraph and EmbeddedCW are now unified into EmbeddedComplex\n# For backward compatibility, you can still use:\n# from ect import EmbeddedGraph, EmbeddedCW" }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "We can use the `EmbeddedGraph` class to find the angle normal to any pair of vertices in the graph, whether or not there is a connecting edge. Setting `angle_labels_circle=True` in the plotting command will try to draw these on the circle. Note that this doesn't tend to do well for large inputs, but can be helpful for small examples. " - ] + "source": "We can use the `EmbeddedComplex` class (which unifies the old `EmbeddedGraph` and `EmbeddedCW` classes) to find the angle normal to any pair of vertices in the graph, whether or not there is a connecting edge. Setting `angle_labels_circle=True` in the plotting command will try to draw these on the circle. Note that this doesn't tend to do well for large inputs, but can be helpful for small examples." }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Super simple graph \n", - "G = EmbeddedGraph()\n", - "G.add_node('A', 0,0)\n", - "G.add_node('B', 1,0)\n", - "G.add_node('C', 2,1)\n", - "G.add_node('D', 1,2)\n", - "G.add_edge('A', 'B')\n", - "G.add_edge('B', 'D')\n", - "G.add_edge('D', 'C')\n", - "\n", - "fig, ax = plt.subplots()\n", - "G.plot(ax = ax)\n", - "G.plot_angle_circle(ax = ax)\n" - ] + "outputs": [], + "source": "# Super simple graph using the unified EmbeddedComplex class\nG = EmbeddedComplex()\nG.add_node('A', 0,0)\nG.add_node('B', 1,0)\nG.add_node('C', 2,1)\nG.add_node('D', 1,2)\nG.add_edge('A', 'B')\nG.add_edge('B', 'D')\nG.add_edge('D', 'C')\n\nfig, ax = plt.subplots()\nG.plot(ax = ax)\nG.plot_angle_circle(ax = ax)" }, { "cell_type": "code", @@ -99,52 +65,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ nan 5.49778714 5.60844436 5.8195377 5.03413953 5.49778714]\n", - " [2.35619449 nan 5.6951827 0. 3.92699082 5.49778714]\n", - " [2.46685171 2.55359005 nan 2.03444394 2.89661399 2.67794504]\n", - " [2.67794504 3.14159265 5.17603659 nan 3.46334321 3.92699082]\n", - " [1.89254688 0.78539816 6.03820664 0.32175055 nan 0. ]\n", - " [2.35619449 2.35619449 5.8195377 0.78539816 3.14159265 nan]]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# If return type is `matrix`, the function returns the matrix of angles and the labels of the angles in the order of the rows/columns in the matrix \n", - "M,Labels = G.get_all_normals_matrix()\n", - "print(M)\n", - "\n", - "plt.matshow(M)\n", - "plt.xticks(range(len(Labels)), Labels)\n", - "plt.yticks(range(len(Labels)), Labels)\n", - "plt.colorbar()" - ] + "outputs": [], + "source": "# If return type is `matrix`, the function returns the matrix of angles and the labels of the angles in the order of the rows/columns in the matrix \nM,Labels = G.get_normal_angle_matrix() # Updated method name\nprint(M)\n\nplt.matshow(M)\nplt.xticks(range(len(Labels)), Labels)\nplt.yticks(range(len(Labels)), Labels)\nplt.colorbar()" }, { "cell_type": "markdown", @@ -252,4 +176,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/doc_source/notebooks/tutorial_cw.ipynb b/doc_source/notebooks/tutorial_cw.ipynb deleted file mode 100644 index 575387a..0000000 --- a/doc_source/notebooks/tutorial_cw.ipynb +++ /dev/null @@ -1,374 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: ECT for CW complexes\n", - "\n", - "\n", - "\n", - " This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from ect import ECT, EmbeddedCW\n", - "from ect.utils.examples import create_example_cw\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "K = EmbeddedCW()\n", - "\n", - "# Add vertices with coordinates\n", - "K.add_node(\"A\", [0, 0])\n", - "K.add_node(\"B\", [1, 0])\n", - "K.add_node(\"C\", [1, 1])\n", - "K.add_node(\"D\", [0, 1])\n", - "\n", - "# Add edges to form a square\n", - "K.add_edges_from([(\"A\", \"B\"), (\"B\", \"C\"), (\"C\", \"D\"), (\"D\", \"A\")])\n", - "\n", - "# Add the square face\n", - "K.add_face([\"A\", \"B\", \"C\", \"D\"])\n", - "\n", - "K.center_coordinates()\n", - "K.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Just to have something a bit more interesting, let's make a more complicated example that's built into the class." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "K = create_example_cw()\n", - "K.plot(bounding_circle=True)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "ect = ECT(num_dirs=100, num_thresh=80)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/lizliz/Library/CloudStorage/Dropbox/Math/Code/ect/src/ect/ect_graph.py:211: NumbaPerformanceWarning: \u001b[1m\u001b[1m\n", - "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", - "\n", - "To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.\n", - "\u001b[1m\n", - "File \"src/ect/ect_graph.py\", line 216:\u001b[0m\n", - "\u001b[1m @njit(parallel=True, fastmath=True)\n", - "\u001b[1m def shape_descriptor(simplex_counts_list):\n", - "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - " result[i, j] = shape_descriptor(simplex_counts_list)\n", - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "override_bound_radius = 1.2 * K.get_bounding_radius()\n", - "result = ect.calculate(K, theta=0, override_bound_radius=override_bound_radius)\n", - "result.plot();\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result = ect.calculate(K, override_bound_radius=override_bound_radius)\n", - "result.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also look at the Smooth ECT:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate SECT and plot\n", - "smooth = result.smooth()\n", - "smooth.plot()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also compute the ECT in 3D." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "vertices = [\n", - " (letter, coordinates) for letter, coordinates in zip(\"abcde\", np.random.randn(5, 3))\n", - "]\n", - "edges = [(\"a\", \"b\"), (\"a\", \"c\"), (\"a\", \"d\"), (\"b\", \"c\"), (\"b\", \"d\"), (\"c\", \"d\")]\n", - "faces = [\n", - " (\"a\", \"b\", \"c\"),\n", - " (\"a\", \"b\", \"d\"),\n", - " (\"a\", \"c\", \"d\"),\n", - " (\"b\", \"c\", \"d\"),\n", - " (\"a\", \"b\", \"c\", \"d\"),\n", - "]\n", - "K = EmbeddedCW()\n", - "K.add_nodes_from(vertices)\n", - "K.add_edges_from(edges)\n", - "\n", - "K.add_faces_from(faces)\n", - "K.plot(bounding_circle=True)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ect = ECT(num_dirs=100, num_thresh=80)\n", - "result = ect.calculate(K)\n", - "result.plot()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/doc_source/notebooks/tutorial_graph.ipynb b/doc_source/notebooks/tutorial_graph.ipynb deleted file mode 100644 index 7988362..0000000 --- a/doc_source/notebooks/tutorial_graph.ipynb +++ /dev/null @@ -1,791 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: ECT for Embedded Graphs\n", - "\n", - "\n", - "\n", - "This tutorial demonstrates how to use the `ect` package..." - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "from ect import ECT, EmbeddedGraph\n", - "\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic Usage\n", - "\n", - "\n", - "\n", - "First, let's create a simple graph\"\"\"\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Construct an example graph\n", - "# Note that this is the same graph that is returned by:\n", - "# G = create_example_graph()\n", - "\n", - "G = EmbeddedGraph()\n", - "\n", - "G.add_node(\"A\", [1, 2])\n", - "G.add_node(\"B\", [3, 4])\n", - "G.add_node(\"C\", [5, 7])\n", - "G.add_node(\"D\", [3, 6])\n", - "G.add_node(\"E\", [4, 3])\n", - "G.add_node(\"F\", [4, 5])\n", - "\n", - "G.add_edge(\"A\", \"B\")\n", - "G.add_edge(\"B\", \"C\")\n", - "G.add_edge(\"B\", \"D\")\n", - "G.add_edge(\"B\", \"E\")\n", - "G.add_edge(\"C\", \"D\")\n", - "G.add_edge(\"E\", \"F\")\n", - "\n", - "G.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " The embedded graph class inherits from the networkx graph class with the additional attributes `coord_matrix` and `coord_dict`.\n", - "\n", - " The coordinates of all vertices can be accessed using the `coord_matrix` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1., 2.],\n", - " [3., 4.],\n", - " [5., 7.],\n", - " [3., 6.],\n", - " [4., 3.],\n", - " [4., 5.]])" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G.coord_matrix\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " It's often useful to center the graph, so you can use the `center_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points." - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-2.33333333 -2.5 ]\n", - " [-0.33333333 -0.5 ]\n", - " [ 1.66666667 2.5 ]\n", - " [-0.33333333 1.5 ]\n", - " [ 0.66666667 -1.5 ]\n", - " [ 0.66666667 0.5 ]]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G.center_coordinates(center_type=\"mean\")\n", - "print(G.coord_matrix)\n", - "G.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " To get a bounding radius we can use the `get_bounding_radius` method." - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The radius of bounding circle centered at the origin is 3.2015621187164243\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# This is actually getting the radius\n", - "r = G.get_bounding_radius()\n", - "print(f\"The radius of bounding circle centered at the origin is {r}\")\n", - "\n", - "# plotting the graph with it's bounding circle of radius r.\n", - "G.plot(bounding_circle=True)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also rescale our graph to have unit radius using `scale_coordinates`" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The radius of bounding circle centered at the origin is 0.9362075413977773\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G.scale_coordinates(radius=1)\n", - "G.plot(bounding_circle=True)\n", - "\n", - "r = G.get_bounding_radius()\n", - "print(f\"The radius of bounding circle centered at the origin is {r}\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of directions: 16\n", - "Number of thresholds: 20\n" - ] - } - ], - "source": [ - "myect = ECT(num_dirs=16, num_thresh=20)\n", - "\n", - "# The ECT object will automatically create directions when needed\n", - "print(f\"Number of directions: {myect.num_dirs}\")\n", - "print(f\"Number of thresholds: {myect.num_thresh}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can override the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures." - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thresholds chosen are: [-1.12344905 -1.00519125 -0.88693346 -0.76867567 -0.65041787 -0.53216008\n", - " -0.41390228 -0.29564449 -0.17738669 -0.0591289 0.0591289 0.17738669\n", - " 0.29564449 0.41390228 0.53216008 0.65041787 0.76867567 0.88693346\n", - " 1.00519125 1.12344905]\n" - ] - } - ], - "source": [ - "custom_bound_radius = 1.2 * G.get_bounding_radius()\n", - "result = myect.calculate(G, override_bound_radius=custom_bound_radius)\n", - "\n", - "print(f\"Thresholds chosen are: {myect.thresholds}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " If we want the Euler characteristic curve for a fixed direction, we use the `calculate` function with a specific angle. This returns an ECTResult object containing the computed values." - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ECT values for direction pi/2: [0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0]\n" - ] - } - ], - "source": [ - "result = myect.calculate(G, theta=np.pi / 2)\n", - "print(f\"ECT values for direction pi/2: {result[0]}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " To calculate the full ECT, we call the `calculate` method without specifying theta. The result returns the ECT matrix and associated metadata." - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ECT matrix shape: (16, 20)\n", - "Number of directions: 16\n", - "Number of thresholds: 20\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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dO6bq1aurXbt2+vzzzx36GDhwoE6ePKmJEycqOztb8fHxWrlyZYmFwtdjMwyjEm1N5Ru5ubkKCwtTd/VXVVs1r/d/cEZnr/eJ62s2dpO/QwBQCV0xLmuNVuj8+fM+W5dy7Xep/6qHVK1GoNv9XM4v1Ipe83wa643CCA0AABblzvYFP21fWbAoGAAAWB4jNAAAWJSndyr54y4nXyGhAQDAokhoillyysmVbcbT09NLbFseHBx8A6MFAAC+ZrmExp1txkNDQx22LT9y5MgNjBgAAN+w4oP1fMVyCc2Ptxlv3bq10tLSVL16dc2bN6/MNjabTZGRkWa53r3tBQUFJXYeBQCgoiGhKWaphMbdbcbz8vLUuHFjxcTEqH///tqzZ0+550lNTXXYdTQmJsZrnwEAAHifpRIad7YZb9GihebNm6cVK1bovffek91uV5cuXfT999+XeZ4JEybo/PnzZsnMzPTq5wAAwBsMFT+Lxp1SmZ6sW+nvckpMTHTY4KpLly5q1aqV/vznP+vll18utU1QUJDXtk4HAMBXuMupmKVGaLyxzXi1atXUvn17h821AACAtVkqofHGNuNFRUX65ptvzK3LAQCwKhYFF7PclNP1thkfMmSIGjZsqNTUVEnSlClT1LlzZzVv3lznzp3T66+/riNHjmjkyJEun/vQa3cqgGfYVBq+3BSUjS/hCjaoLZ0v/z3y5TW3X7okPbvCZ/07nIspJ5PlEprrbTN+9OhRBQQUDzydPXtWo0aNUnZ2turUqaMOHTpow4YNat26tb8+AgAAXkFCU8xyCY0kjRkzRmPGjCn1vTVr1ji8nj59uqZPn34DogIAAP5iyYQGAABIhmGT4cEoiydtKxoSGgAALOra82Q8aV9ZWOouJwAAgNIwQgMAgEWxKLgYCQ0AABbFGppiTDkBAADLY4QGAACLYsqpGAkNAAAWxZRTMaacAACA5TFCAwCARRkeTjlVphEaEhoAACzKkGQYnrWvLEhoAACwKLtssvGkYEmsoQEAAJUAIzQAAFgUdzkVI6EBAMCi7IZNNp5DI4mEBvCJgzM6+6zvZmM3+axvlM2Xf6coHdccriChAQDAogzDw7ucKtFtTiQ0AABYFGtoinGXEwAAsDxGaAAAsChGaIqR0AAAYFHc5VSMKScAACzq2qJgT4or1q1bp379+ik6Olo2m03Lly8vt/4HH3ygn//856pfv75CQ0OVmJiozz77zKHOSy+9JJvN5lBatmzp4pUgoQEAAE7Kz89XXFycZs2a5VT9devW6ec//7k+/fRTbdu2TT169FC/fv20Y8cOh3q33367srKyzLJ+/XqXY2PKCQAAi7o6yuLJGpqr/8zNzXU4HhQUpKCgoBL1+/Tpoz59+jjd/4wZMxxe/+EPf9CKFSv097//Xe3btzePV61aVZGRkc4HXgpGaAAAsKhri4I9KZIUExOjsLAws6SmpvokXrvdrgsXLig8PNzh+P79+xUdHa2mTZvqN7/5jY4ePepy34zQAABwk8vMzFRoaKj5urTRGW944403lJeXpwceeMA8lpCQoPT0dLVo0UJZWVmaPHmy7rrrLu3evVu1atVyum8SGgAALMr4T/GkvSSFhoY6JDS+sHDhQk2ePFkrVqxQgwYNzOM/nsJq166dEhIS1LhxYy1ZskQjRoxwun8SGgAALMoqz6FZtGiRRo4cqaVLlyopKancurVr19Ztt92mAwcOuHQO1tAAAACf+dvf/qbhw4frb3/7m/r27Xvd+nl5eTp48KCioqJcOg8jNAAAWJW35pyclJeX5zBycujQIe3cuVPh4eFq1KiRJkyYoGPHjmnBggWSrk4zDR06VDNnzlRCQoKys7MlSSEhIQoLC5MkjRs3Tv369VPjxo11/PhxTZo0SVWqVNHgwYNdio0RGgAArMrTO5xcnHLaunWr2rdvb95ynZKSovbt22vixImSpKysLIc7lGbPnq0rV67oscceU1RUlFmefPJJs87333+vwYMHq0WLFnrggQdUt25dbdq0SfXr13cpNkZoAACAU7p37y6jnMcLp6enO7xes2bNdftctGiRh1Fd5VZCU1RUpHfffVf79u3TLbfcori4OMXHx6tu3bpeCQoAAFyfO9sX/LR9ZeFWQvP4449r2bJlSkpK0jvvvCObzaYrV66oYcOGio+P10cffeTtOAEAwE9Y5S6nG8GthOaDDz7QggULlJycrI8++kgbNmzQ2rVrNWXKFDVu3NjbMQL4kYMzOvu0/2ZjN/m0fwBe5MY6mBLtKwm3Epq8vDy1bt1aklStWjVVrVpVY8aM0eXLl3X8+HGvBggAAHA9bt3l1LRpUzNxadiwoY4dOyZJ6tevn9577z3vRQcAAMp0bQ2NJ6WycCuhue+++/SPf/xDknTPPfdo3rx5kqRvv/1WP/zwg/eiAwAAZTO8UCoJt6acXnrpJfPP48eP15133qn69esrNzfXpX0XAAAAvMHj59A0atRIe/bs0d///nfVrVtX/fr180ZcAADgOrjLqZhXHqxXr149DR8+3BtdAQAAV1SiaSNPsPUBAACwPLY+AADAophyKkZCAwCAVd3g3bYrMqacAACA5TFCAwCAZdn+UzxpXzk4ndCkpKQ43em0adPcCgYAALiAKSeT0wnNjh07HF5v375dV65cUYsWLSRJ//73v1WlShV16NDBuxECAIDSkdCYnE5ovvzyS/PP06ZNU61atTR//nzVqVNHknT27FkNHz5cd911l/ejBAAAKIdbi4LffPNNpaammsmMJNWpU0evvPKK3nzzTa8FBwAAymHYPC+VhFuLgnNzc3Xy5MkSx0+ePKkLFy54HBQAALg+T3fMvul32/7lL3+p4cOH64MPPtD333+v77//XsuWLdOIESN03333eTtGAACAcrk1QpOWlqZx48bp17/+tS5fvizDMFStWjWNGDFCr7/+urdjBHADfXb8a3+HUEFxXUrTfMkj/g7h5saiYJNbCU316tX1xz/+Ua+//roOHjwoSWrWrJlq1Kjh1eAAAEA5PF0HczOuoeE5NAAAoKJy+zk0ZbHZKk+2BwBARWYzrhZP2lcWbj2HBgAAVACsoTGxOSUAALA8tzenPHfunObOnau9e/dKklq3bq0RI0YoLCzMa8EBAIBysCjY5NYIzdatW9WsWTNNnz5dZ86c0ZkzZzR9+nQ1a9ZM27dv93aMAACgNIYXSiXh1gjNU089pf/6r//SnDlzVLXq1S6uXLmikSNHauzYsVq3bp1XgwQAAKVgDY3JrYRm69atDsmMJFWtWlXjx49Xx44dvRYcAACAM9yacgoNDdXRo0dLHM/MzFStWrU8DgoAADiBKSeTWwnNwIEDNWLECC1evFiZmZnKzMzUokWLNHLkSA0ePNjbMQIAgNKw27bJrSmnN954QzabTUOGDNGVK1ckSdWqVdPo0aP16quvejVAAACA63EroQkMDNTMmTOVmprqsJdT9erVvRocAAAoG08KLub2c2ikq5tUtm3b1luxAAAAV3CXk8ntJwVnZGTo+eef18iRI/XQQw85FF+bNWuWYmNjFRwcrISEBG3ZsqXc+kuXLlXLli0VHBystm3b6tNPP/V5jAAAVDbr1q1Tv379FB0dLZvNpuXLl1+3zZo1a3THHXcoKChIzZs3V3p6eok6rv6ul8athGby5Mnq1auXMjIydOrUKZ09e9ah+NLixYuVkpKiSZMmafv27YqLi1NycrJOnDhRav0NGzZo8ODBGjFihHbs2KEBAwZowIAB2r17t0/jBACgssnPz1dcXJxmzZrlVP1Dhw6pb9++6tGjh3bu3KmxY8dq5MiR+uyzz8w6rv6ul8VmGIbLA05RUVGaOnWqHnzwQVebeiwhIUF33nmn3nnnHUmS3W5XTEyMHn/8cT333HMl6g8cOFD5+fn6+OOPzWOdO3dWfHy80tLSnDpnbm6uwsLC1Pi1VxQQHOydDwJUUAce+LO/Q4CFNF/yiL9DqHDsly7pyLMv6Pz58woNDfXJObz1u+RJrDabTR9++KEGDBhQZp1nn31Wn3zyicMgwqBBg3Tu3DmtXLlSkuu/62Vxa4SmsLBQXbp0caepRwoLC7Vt2zYlJSWZxwICApSUlKSNGzeW2mbjxo0O9SUpOTm5zPqSVFBQoNzcXIcCAEBl9dPfvIKCAq/0e73fYHd+18vi1qLgkSNHauHChXrxxRfdae62U6dOqaioSBEREQ7HIyIi9N1335XaJjs7u9T62dnZZZ4nNTVVkydP9jxgwAcYQcHNwpff9UozsuSlzSljYmIcDk+aNEkvvfSSB4FdVdZvcG5urn744QedPXvW5d/1sjid0KSkpJh/ttvtmj17tj7//HO1a9dO1apVc6g7bdo0l4KoaCZMmODweXNzc0v8ZQMA4HdeusspMzPTYcopKCjIo7D8wemEZseOHQ6v4+PjJanE4lqbzXdPHaxXr56qVKminJwch+M5OTmKjIwstU1kZKRL9aWrf5FW/MsEANxkvJTQhIaG+mS9T1m/waGhoQoJCVGVKlVc/l0vi9MJzZdffulSx74QGBioDh06KCMjw1yEZLfblZGRoTFjxpTaJjExURkZGRo7dqx5bPXq1UpMTLwBEQMAcPNKTEws8aiUH/8Gu/O7Xha31tD88MMPMgzDfDLwkSNH9OGHH6p169bq1auXO106LSUlRUOHDlXHjh3VqVMnzZgxQ/n5+Ro+fLgkaciQIWrYsKFSU1MlSU8++aTuuecevfnmm+rbt68WLVqkrVu3avbs2T6NEwAAX7vRTwrOy8vTgQMHzNeHDh3Szp07FR4erkaNGmnChAk6duyYFixYIEl69NFH9c4772j8+PF66KGH9MUXX2jJkiX65JNPzD6u97vuLLcSmv79++u+++7To48+qnPnzqlTp04KDAzUqVOnNG3aNI0ePdqdbp0ycOBAnTx5UhMnTlR2drbi4+O1cuVKc0HR0aNHFRBQfPNWly5dtHDhQr3wwgt6/vnndeutt2r58uVq06aNz2IEAOCGuMFPCt66dat69Ohhvr623nTo0KFKT09XVlaWjh49ar7fpEkTffLJJ3rqqac0c+ZM3XLLLXr33XeVnJxs1rne77qz3HoOTb169bR27Vrdfvvtevfdd/X2229rx44dWrZsmSZOnKi9e/e62mWFxnNoUJFwlxMqEl/eLWTVu5xu5HNoYl/5vcfPoTn8wu98GuuN4tYIzcWLF1WrVi1J0qpVq3TfffcpICBAnTt31pEjR7waIAAAKAN7OZncerBe8+bNtXz5cmVmZuqzzz4z182cOHHC8hkeAABWcW0NjSelsnAroZk4caLGjRun2NhYJSQkmKuVV61apfbt23s1QAAAgOtxa8rpv//7v9WtWzdlZWUpLi7OPN6zZ0/98pe/9FpwAACgHF56UnBl4FZCI119WM5PH3rTqVMnjwMCAABOYg2Nya0pJ0n65z//qd/+9rdKTEzUsWPHJEl//etftX79eq8FBwAA4Ay3Epply5YpOTlZISEh2rFjh7kr5/nz5/WHP/zBqwECAIDSsSi4mFsJzSuvvKK0tDTNmTPHYWPKrl27avv27V4LDgAAlMPwQqkk3FpDs2/fPt19990ljoeFhencuXOexgQAAJzh6ShLJUpo3BqhiYyMdNjL4Zr169eradOmHgcFAADgCrcSmlGjRunJJ5/U5s2bZbPZdPz4cb3//vsaN26cT/dxAgAAP8KUk8mtKafnnntOdrtdPXv21MWLF3X33XcrKChI48aN0+OPP+7tGFGBsa9Q6Xy5T4wv+5b4O/UHX/+dohLjtm2TywnN5cuX1bt3b6WlpemZZ57RgQMHlJeXp9atW6tmzZq+iBEAAKBcLic01apV065duyRJgYGBat26tdeDAgAA1+fprdc3/W3bv/3tbzV37lxvxwIAAOAWt9bQXLlyRfPmzdPnn3+uDh06qEaNGg7vT5s2zSvBAQAAOMOthGb37t264447JEn//ve/Hd6z2SrPRlcAAFRoLAo2uZXQfPnll96OAwAAuIg1NMXc3pwSAACgonBrhEaSMjIylJGRoRMnTshutzu8N2/ePI8DAwAATqhEoyyecCuhmTx5sqZMmaKOHTsqKiqKdTMAAPgDa2hMbiU0aWlpSk9P14MPPujteAAAgJNYQ1PMrTU0hYWF6tKli7djAQAAcItbCc3IkSO1cOFCb8cCAABcweaUJqennFJSUsw/2+12zZ49W59//rnatWunatWqOdTlwXoAAPgeU07FnE5oduzY4fA6Pj5e0tWH7P0YC4QBAMCN5nRC8+WXX2rKlCl6+umnS2x1AAAA/IC7nEwuraGZPHmy8vPzfRULAABwBWtoTC4lNIZRiT45AACoNFx+Dg1rZAAAqBhYFFzM5YTmtttuu25Sc+bMGbcDAuBfzZc84u8QAEl8F53CGhqTywnN5MmTFRYW5otYAAAA3OJyQjNo0CA1aNDAF7EAAABXMEJjcimhYf0MAAAVB2toirmU0HCXEwAAFQgjNCaXbtu22+1MNwEAcJObNWuWYmNjFRwcrISEBG3ZsqXMut27d5fNZitR+vbta9YZNmxYifd79+7tUkwur6EBAAAVgz+mnBYvXqyUlBSlpaUpISFBM2bMUHJysvbt21fqoMcHH3ygwsJC8/Xp06cVFxen+++/36Fe79699Ze//MV8HRQU5FJcbu22DQAAKgA/PCl42rRpGjVqlIYPH67WrVsrLS1N1atX17x580qtHx4ersjISLOsXr1a1atXL5HQBAUFOdSrU6eOS3GR0AAAcJPLzc11KAUFBaXWKyws1LZt25SUlGQeCwgIUFJSkjZu3OjUuebOnatBgwaV2BdyzZo1atCggVq0aKHRo0fr9OnTLn0GEhoAAKzKSyM0MTExCgsLM0tqamqppzt16pSKiooUERHhcDwiIkLZ2dnXDXfLli3avXu3Ro4c6XC8d+/eWrBggTIyMvTaa69p7dq16tOnj4qKipy7DmINDQAAlmX7T/GkvSRlZmYqNDTUPO7q+hVnzZ07V23btlWnTp0cjg8aNMj8c9u2bdWuXTs1a9ZMa9asUc+ePZ3qmxEaAABucqGhoQ6lrISmXr16qlKlinJychyO5+TkKDIystxz5Ofna9GiRRoxYsR142natKnq1aunAwcOOP0ZSGgAALCqG7woODAwUB06dFBGRoZ5zG63KyMjQ4mJieW2Xbp0qQoKCvTb3/72uuf5/vvvdfr0aUVFRTkdGwkNAAAWde22bU+Kq1JSUjRnzhzNnz9fe/fu1ejRo5Wfn6/hw4dLkoYMGaIJEyaUaDd37lwNGDBAdevWdTiel5enZ555Rps2bdLhw4eVkZGh/v37q3nz5kpOTnY6LtbQAAAApw0cOFAnT57UxIkTlZ2drfj4eK1cudJcKHz06FEFBDiOl+zbt0/r16/XqlWrSvRXpUoV7dq1S/Pnz9e5c+cUHR2tXr166eWXX3ZpLQ8JDQAAVuWnrQ/GjBmjMWPGlPremjVrShxr0aJFmdsnhYSE6LPPPnMvkB8hoQEAwMoq0X5MniChAQDAothtuxgJDTzSfMkjPuv7wAN/9lnfVtZs7Caf9n9wRmef9o/KxdffR1/he175kNAAAGBVflpDUxGR0AAAYFFMORXjOTQAAMDyGKEBAMCqmHIykdAAAGBRTDkVY8oJAABYHiM0AABYFVNOJhIaAACsioTGxJQTAACwPEZoAACwKBYFFyOhAQDAqphyMpHQAABgUTbDkM1wPyvxpG1FwxoaAABgeYzQAABgVUw5mUhoAACwKBYFF2PKCQAAWB4jNAAAWBVTTiYSGgAALIopp2KWSmjOnDmjxx9/XH//+98VEBCgX/3qV5o5c6Zq1qxZZpvu3btr7dq1DsceeeQRpaWl+TpceKj5kkd82n+zsZt817d817ev+fK6HJzR2Wd9o3S+/PsEKhJLJTS/+c1vlJWVpdWrV+vy5csaPny4Hn74YS1cuLDcdqNGjdKUKVPM19WrV/d1qAAA+B5TTibLJDR79+7VypUr9a9//UsdO3aUJL399tu699579cYbbyg6OrrMttWrV1dkZOSNChUAgBuCKadilrnLaePGjapdu7aZzEhSUlKSAgICtHnz5nLbvv/++6pXr57atGmjCRMm6OLFi+XWLygoUG5urkMBAAAVl2VGaLKzs9WgQQOHY1WrVlV4eLiys7PLbPfrX/9ajRs3VnR0tHbt2qVnn31W+/bt0wcffFBmm9TUVE2ePNlrsQMA4BNMOZn8ntA899xzeu2118qts3fvXrf7f/jhh80/t23bVlFRUerZs6cOHjyoZs2aldpmwoQJSklJMV/n5uYqJibG7RgAAPCVyjRt5Am/JzRPP/20hg0bVm6dpk2bKjIyUidOnHA4fuXKFZ05c8al9TEJCQmSpAMHDpSZ0AQFBSkoKMjpPgEA8AvDuFo8aV9J+D2hqV+/vurXr3/deomJiTp37py2bdumDh06SJK++OIL2e12M0lxxs6dOyVJUVFRbsULAAAqHsssCm7VqpV69+6tUaNGacuWLfrqq680ZswYDRo0yLzD6dixY2rZsqW2bNkiSTp48KBefvllbdu2TYcPH9ZHH32kIUOG6O6771a7du38+XEAAPDYtbucPCmVhd9HaFzx/vvva8yYMerZs6f5YL233nrLfP/y5cvat2+feRdTYGCgPv/8c82YMUP5+fmKiYnRr371K73wwgv++ggAAHgPi4JNlkpowsPDy32IXmxsrIwfzQfGxMSUeEowAACofCyV0AAAgGI2+9XiSfvKgoQGAACrYsrJZJlFwQAAAGUhoQEAwKL8dZfTrFmzFBsbq+DgYCUkJJh3F5cmPT1dNpvNoQQHBzvUMQxDEydOVFRUlEJCQpSUlKT9+/e7FBMJDQAAVnXtwXqeFBctXrxYKSkpmjRpkrZv3664uDglJyeXePjtj4WGhiorK8ssR44ccXh/6tSpeuutt5SWlqbNmzerRo0aSk5O1qVLl5yOi4QGAAA4bdq0aRo1apSGDx+u1q1bKy0tTdWrV9e8efPKbGOz2RQZGWmWiIgI8z3DMDRjxgy98MIL6t+/v9q1a6cFCxbo+PHjWr58udNxkdAAAGBR3ppyys3NdSgFBQWlnq+wsFDbtm1TUlKSeSwgIEBJSUnauHFjmXHm5eWpcePGiomJUf/+/bVnzx7zvUOHDik7O9uhz7CwMCUkJJTb50+R0AAAYFWGF4quPrctLCzMLKmpqaWe7tSpUyoqKnIYYZGkiIgIZWdnl9qmRYsWmjdvnlasWKH33ntPdrtdXbp00ffffy9JZjtX+iwNt20DAGBRnm5fcK1tZmamQkNDzePe3KA5MTFRiYmJ5usuXbqoVatW+vOf/6yXX37Za+dhhAYAgJtcaGioQykroalXr56qVKminJwch+M5OTmKjIx06lzVqlVT+/btdeDAAUky23nSp0RCAwCAdd3gu5wCAwPVoUMHZWRkmMfsdrsyMjIcRmHKU1RUpG+++UZRUVGSpCZNmigyMtKhz9zcXG3evNnpPiWmnAAAsCxvTTm5IiUlRUOHDlXHjh3VqVMncwPo4cOHS5KGDBmihg0bmutwpkyZos6dO6t58+Y6d+6cXn/9dR05ckQjR468GoPNprFjx+qVV17RrbfeqiZNmujFF19UdHS0BgwY4HRcJDQAAMBpAwcO1MmTJzVx4kRlZ2crPj5eK1euNBf1Hj16VAEBxRNAZ8+e1ahRo5Sdna06deqoQ4cO2rBhg1q3bm3WGT9+vPLz8/Xwww/r3Llz6tatm1auXFniAXzlIaEBAMCq/LSX05gxYzRmzJhS31uzZo3D6+nTp2v69Onl9mez2TRlyhRNmTLFvYBEQgMAgGX5Y8qpomJRMAAAsDxGaAAAsCq7cbV40r6SIKEBAMCq/LSGpiJiygkAAFgeIzQAAFiUTR4uCvZaJP5HQgMAgFW58bTfEu0rCRIaAAAsitu2i7GGBgAAWB4jNAAAWBV3OZlIaAAAsCibYcjmwToYT9pWNCQ0Lmjy7L9U1VbN6/0enNHZ631WBs3GbvJ3CPAyX/6dWvnfI77rN54vr/kV47KO+Kx3lIWEBgAAq7L/p3jSvpIgoQEAwKKYcirGXU4AAMDyGKEBAMCquMvJREIDAIBV8aRgE1NOAADA8hihAQDAotj6oBgJDQAAVsWUk4mEBgAAi7LZrxZP2lcWrKEBAACWxwgNAABWxZSTiYQGAACr4jk0JqacAACA5TFCAwCARbGXUzESGgAArIo1NCamnAAAgOUxQgMAgFUZkjx5lkzlGaAhoQEAwKpYQ1OMhAZApdBs7CZ/hwDAj0hoAACwKkMeLgr2WiR+R0IDAIBVcZeTiYQGAACrskuyedi+kuC2bQAAYHkkNAAAWNS1u5w8Ke6YNWuWYmNjFRwcrISEBG3ZsqXMunPmzNFdd92lOnXqqE6dOkpKSipRf9iwYbLZbA6ld+/eLsVEQgMAgFVdW0PjSXHR4sWLlZKSokmTJmn79u2Ki4tTcnKyTpw4UWr9NWvWaPDgwfryyy+1ceNGxcTEqFevXjp27JhDvd69eysrK8ssf/vb31yKi4QGAAA4bdq0aRo1apSGDx+u1q1bKy0tTdWrV9e8efNKrf/+++/rf/7nfxQfH6+WLVvq3Xffld1uV0ZGhkO9oKAgRUZGmqVOnTouxUVCAwCAVXlphCY3N9ehFBQUlHq6wsJCbdu2TUlJSeaxgIAAJSUlaePGjU6FfPHiRV2+fFnh4eEOx9esWaMGDRqoRYsWGj16tE6fPu3SpSChAQDAqryU0MTExCgsLMwsqamppZ7u1KlTKioqUkREhMPxiIgIZWdnOxXys88+q+joaIekqHfv3lqwYIEyMjL02muvae3aterTp4+KioqcvhTctg0AwE0uMzNToaGh5uugoCCfnOfVV1/VokWLtGbNGgUHB5vHBw0aZP65bdu2ateunZo1a6Y1a9aoZ8+eTvXNCA0AAFZl90KRFBoa6lDKSmjq1aunKlWqKCcnx+F4Tk6OIiMjyw31jTfe0KuvvqpVq1apXbt25dZt2rSp6tWrpwMHDpRb78dIaAAAsKgbfdt2YGCgOnTo4LCg99oC38TExDLbTZ06VS+//LJWrlypjh07Xvc833//vU6fPq2oqCinYyOhAQAATktJSdGcOXM0f/587d27V6NHj1Z+fr6GDx8uSRoyZIgmTJhg1n/ttdf04osvat68eYqNjVV2drays7OVl5cnScrLy9MzzzyjTZs26fDhw8rIyFD//v3VvHlzJScnOx0Xa2gAALAqP+zlNHDgQJ08eVITJ05Udna24uPjtXLlSnOh8NGjRxUQUDxe8qc//UmFhYX67//+b4d+Jk2apJdeeklVqlTRrl27NH/+fJ07d07R0dHq1auXXn75ZZfW8pDQAABgVXZDsnmQ0NjdaztmzBiNGTOm1PfWrFnj8Prw4cPl9hUSEqLPPvvMrTh+jIQGAACrYrdtE2toAACA5TFCUwE0G7vJ3yEAACzJwxEaVZ4RGhIaAACsiiknE1NOAADA8hihAQDAquyGPJo2cvMup4qIhAYAAKsy7FeLJ+0rCUtNOf3+979Xly5dVL16ddWuXdupNoZhaOLEiYqKilJISIiSkpK0f/9+3wYKAABuKEslNIWFhbr//vs1evRop9tMnTpVb731ltLS0rR582bVqFFDycnJunTpkg8jBQDgBri2KNiTUklYaspp8uTJkqT09HSn6huGoRkzZuiFF15Q//79JUkLFixQRESEli9f7rBdOQAAlsMaGpOlRmhcdejQIWVnZyspKck8FhYWpoSEBG3cuLHMdgUFBcrNzXUoAACg4qrUCU12drYkmRtmXRMREWG+V5rU1FSFhYWZJSYmxqdxAgDgFqacTH5PaJ577jnZbLZyy3fffXdDY5owYYLOnz9vlszMzBt6fgAAnGLIw4TG3x/Ae/y+hubpp5/WsGHDyq3TtGlTt/qOjIyUJOXk5CgqKso8npOTo/j4+DLbBQUFubRlOQAAfsGTgk1+T2jq16+v+vXr+6TvJk2aKDIyUhkZGWYCk5ubq82bN7t0pxQAAKjY/D7l5IqjR49q586dOnr0qIqKirRz507t3LlTeXl5Zp2WLVvqww8/lCTZbDaNHTtWr7zyij766CN98803GjJkiKKjozVgwAA/fQoAALzEbve8VBJ+H6FxxcSJEzV//nzzdfv27SVJX375pbp37y5J2rdvn86fP2/WGT9+vPLz8/Xwww/r3Llz6tatm1auXKng4OAbGjsAAF7HlJPJZhiV6NP4SG5ursLCwtRd/VXVVs3f4QAAKrArxmWt0QqdP39eoaGhPjnHtd+lpPojVDUg0O1+rtgL9fnJuT6N9Uax1AgNAAD4EUZoTCQ0AABYFU8KNllqUTAAAEBpGKEBAMCiDMMuw3D/TiVP2lY0JDQAAFiVYXg2bVSJ1tAw5QQAACyPERoAAKzK8HBRcCUaoSGhAQDAqux2yebBOhjW0AAAAL9jhMbEGhoAAGB5jNAAAGBRht0uw4MpJ27bBgAA/seUk4kpJwAAYHmM0AAAYFV2Q7IxQiOR0AAAYF2GIcmT27YrT0LDlBMAALA8RmgAALAow27I8GDKyahEIzQkNAAAWJVhl2dTTpXntm2mnAAAsCjDbnhc3DFr1izFxsYqODhYCQkJ2rJlS7n1ly5dqpYtWyo4OFht27bVp59+6vg5DEMTJ05UVFSUQkJClJSUpP3797sUEwkNAABw2uLFi5WSkqJJkyZp+/btiouLU3Jysk6cOFFq/Q0bNmjw4MEaMWKEduzYoQEDBmjAgAHavXu3WWfq1Kl66623lJaWps2bN6tGjRpKTk7WpUuXnI7LZlSmCTQfOX/+vGrXrq1uuldVVc3f4QAAKrAruqz1+lTnzp1TWFiYT86Rm5ursLAwj3+XrsWamZmp0NBQ83hQUJCCgoJKbZOQkKA777xT77zzjiTJbrcrJiZGjz/+uJ577rkS9QcOHKj8/Hx9/PHH5rHOnTsrPj5eaWlpMgxD0dHRevrppzVu3DhJV393IyIilJ6erkGDBjn3YQxcV2Zm5rVHMVIoFAqF4lTJzMz02e/SDz/8YERGRnolzpo1a5Y4NmnSpFLPW1BQYFSpUsX48MMPHY4PGTLE+K//+q9S28TExBjTp093ODZx4kSjXbt2hmEYxsGDBw1Jxo4dOxzq3H333cYTTzzh9DVhUbAToqOjlZmZqVq1aslms123fm5urmJiYkpkvBWdVeOWiN0frBq3ROz+YNW4JddjNwxDFy5cUHR0tM9iCg4O1qFDh1RYWOhxX4ZhlPhtK2t05tSpUyoqKlJERITD8YiICH333XeltsnOzi61fnZ2tvn+tWNl1XEGCY0TAgICdMstt7jcLjQ01HL/4krWjVsidn+watwSsfuDVeOWXIvdV1NNPxYcHKzg4GCfn8cqWBQMAACcUq9ePVWpUkU5OTkOx3NychQZGVlqm8jIyHLrX/unK32WhoQGAAA4JTAwUB06dFBGRoZ5zG63KyMjQ4mJiaW2SUxMdKgvSatXrzbrN2nSRJGRkQ51cnNztXnz5jL7LA1TTj4QFBSkSZMmlTkHWVFZNW6J2P3BqnFLxO4PVo1bsnbsvpCSkqKhQ4eqY8eO6tSpk2bMmKH8/HwNHz5ckjRkyBA1bNhQqampkqQnn3xS99xzj95880317dtXixYt0tatWzV79mxJks1m09ixY/XKK6/o1ltvVZMmTfTiiy8qOjpaAwYMcDoubtsGAAAueeedd/T6668rOztb8fHxeuutt5SQkCBJ6t69u2JjY5Wenm7WX7p0qV544QUdPnxYt956q6ZOnap7773XfN8wDE2aNEmzZ8/WuXPn1K1bN/3xj3/Ubbfd5nRMJDQAAMDyWEMDAAAsj4QGAABYHgkNAACwPBIaAABgeSQ0Xubqluq4uZw7d04dO3ZUfHy82rRpozlz5vg7pJtCbGys2rVrp/j4ePXo0cPf4TjNqt+Xffv2KT4+3iwhISFavny5v8MqV2pqqu68807VqlVLDRo00IABA7Rv3z5/hwUXcJeTFy1evFhDhgxRWlqaEhISNGPGDC1dulT79u1TgwYN/B1emb777jt17dpVtWvXVq1atXTgwAHFx8dr/fr1/g7tuqwWe1FRkQoKClS9enXl5+erTZs22rp1q+rWrevv0Cq12NhY7d69WzVr1vR3KC6pDN+XvLw8xcbG6siRI6pRo4a/wylT7969NWjQIN155526cuWKnn/+ee3evVvffvtthY4bP+L0Npa4rk6dOhmPPfaY+bqoqMiIjo42UlNT/RiVc/r06WPs2rXLMAzDuO2224y8vDw/R+Q8q8Z++vRpo3HjxsbJkyeNvXv3GuHh4UbTpk2NuLg4o0aNGkbXrl39HWKpWrZsWeauvW+//ba/wytV48aNjQsXLjgcs9I1Nwzrfl/ef/9944EHHjAMw1rX/MSJE4YkY+3atYZhWPN7f7MhofESd7ZUr0iaNm1qFBQUGPn5+UazZs38HY5LrBb72bNnjXbt2hkhISHGO++8Yx63SmK2Z88eQ5KRkZFhZGVlGYcPHzYCAgKMpUuXGpcuXfJ3eKWKjY017rjjDqNjx47Ge++9Zx63wjW3+velf//+xrJly8zXVol7//79hiTjm2++MQzDmt/7mw1raLykvC3VXdn+3B8uXLigoKAgBQYGas+ePWrVqpW/Q3KaFWOvXbu2vv76ax06dEgLFy40N2Tbt2+fWrRooYsXL6qoqKjCDnPn5OSoatWq6tq1qyIjI3Xq1CnZ7XbdddddFfbR8OvXr9e2bdv00Ucf6Q9/+IN27dolyRrX3Mrfl9zcXG3YsMHhibBWiNtut2vs2LHq2rWr2rRpI8ma3/ubDQkN9O2336p169aSpL1797r0qGl/s3LsERERiouL0z//+U9LJWbffPONbrvtNvM/4l9//bUaNGhQIpmvSBo2bChJioqK0r333qvt27db6ppL1vy+rFixQr169VJwcLAk6/wPyGOPPabdu3dr0aJF5jErfu9vNiQ0XuLOluoVxZ49e3T77bdLkmrUqKFVq1bp9OnTfo7KOVaLPScnRxcuXJAknT9/XuvWrVOLFi0slZjt2rVLbdu2NV9//fXXDq8rmvz8fPOa5+Xl6YsvvtDtt99uiWtu9e/LkiVLNHDgQPO1FeIeM2aMPv74Y3355Ze65ZZbzONW+97fjEhovMSdLdUrioceekiTJ0+WJP3qV7/SN998Y5m7KKwW+5EjR3TXXXcpLi5Od911lx5//HG1bdvWUonZrl271K5dO/P1119/7fC6osnJyVG3bt0UFxenzp07a8iQIbrzzjstcc2t/H05f/68tmzZouTkZPNYRY7bMAyNGTNGH374ob744gs1adLE4X2rfe9vRty27UWLFy/W0KFD9ec//9ncUn3JkiX67rvvGJZEpWC321WrVi0tXrxYv/jFLyRJMTExeuaZZ/TEE0/4OTrAff/zP/+jhQsXasWKFWrRooV5PCwsTEFBQXzvLYCExsvK21K9orLZbOW+X5G/IlaO3Yr279+v2267TUeOHFGjRo0kSffee682btyojz/+WF27dvVzhIB7yvpvyV/+8hd17dqV770FkNDAdPHiRbVq1Ur333+/3njjDX+H45TMzEw9+OCDOnHihKpWraoXX3xR999/v7/DKtP1ErCy8K+p+6x8za0au1XjhrWxhgam3//+9+rcubO/w3BJ1apVNWPGDH377bdatWqVxo4dq/z8fH+HVSbj6rOfHEp+fr4aNWqkp59+utT3+Y+8Z6x8zX8cz9GjR3XPPfeoVatWatu2rZYsWVJhY7fyNYd1kdBA0tWphO+++059+vTxdyguiYqKUnx8vCQpMjJS9erV05kzZ/wblIusmEhanRWvudWS95+y4jWHtZDQQJI0btw4paam+jsMj2zbtk1FRUWKiYnxdyhOs2oiaWVWveZWTt6tes1hLSQ00IoVK3TbbbdVyGdCOOvMmTMaMmSIZs+e7e9QXFIZEkmrqQzX3GrJe2W45qj4SGigTZs2adGiRYqNjdW4ceM0Z84cTZkyxd9hOa2goEADBgzQc889py5duvg7HKdVhkTSairDNbda8l4Zrjmsoaq/A4D/paammv/3lJ6ert27d2vixIl+jso5hmFo2LBh+tnPfqYHH3zQ3+G45FoiuXTpUuXl5eny5csKDQ21zLW3Iqtfcysm71a/5rAObtuGg2sJjVVu216/fr3uvvtuhyd2/vWvf7XcI8mtdt0rA6tdc8Mw9Otf/1otWrTQSy+95O9w3GK1aw5rYYQGDoYNG+bvEFzSrVs32e12f4cB+NxXX32lxYsXq127dlq+fLkkaybvgK8wQgMAACyPRcEAAMDySGgAAIDlkdAAAADLI6EBAACWR0IDAAAsj4QGAABYHgkNAACwPBIaAABgeSQ0AADA8khoAACA5ZHQAAAAyyOhAbyke/fuGjt27E13bledPn1aDRo00OHDh33S/4+vhT+uy6BBg/Tmm2/e0HMCYHNK4LqGDRum+fPnS5KqVq2q8PBwtWvXToMHD9awYcMUEHD1/wvOnDmjatWqqVatWj6Np3v37oqPj9eMGTPMYzfq3N6QkpKiCxcuaM6cOT7p/8fXxx/XZffu3br77rt16NAhhYWF3bDzAjc7RmgAJ/Tu3VtZWVk6fPiw/vGPf6hHjx568skn9Ytf/EJXrlyRJIWHh5f7w1lYWOiz+K537ori4sWLmjt3rkaMGFFmHW9eJ39clzZt2qhZs2Z67733buh5gZsdCQ3ghKCgIEVGRqphw4a644479Pzzz2vFihX6xz/+ofT0dEklpze6d++uMWPGaOzYsapXr56Sk5MlSXa7XampqWrSpIlCQkIUFxen//3f/zXb2e12TZ06Vc2bN1dQUJAaNWqk3//+95KujhatXbtWM2fOlM1mk81m0+HDh0ucu6CgQE888YQaNGig4OBgdevWTf/6178cPlP37t31xBNPaPz48QoPD1dkZKReeuml616L0aNHq1u3bqW+d8stt+jVV18ts+2nn36qoKAgde7c+brXaeXKlerWrZtq166tunXr6he/+IUOHjzo0F9+fr6GDBmimjVrKioqqsRUz0+vizN9OnNd/vd//1dt27ZVSEiI6tatq6SkJOXn55vv9+vXT4sWLSrzOgDwPhIawE0/+9nPFBcXpw8++KDMOvPnz1dgYKC++uorpaWlSZJSU1O1YMECpaWlac+ePXrqqaf029/+VmvXrpUkTZgwQa+++qpefPFFffvtt1q4cKEiIiIkSTNnzlRiYqJGjRqlrKwsZWVlKSYmpsR5x48fr2XLlmn+/Pnavn27mjdvruTkZJ05c6ZEfDVq1NDmzZs1depUTZkyRatXry7z8+zZs0ezZ8/W1KlTS32/VatW2rlzZ5nt//nPf6pDhw5OXaf8/HylpKRo69atysjIUEBAgH75y1/Kbreb7Z555hmtXbtWK1as0KpVq7RmzRpt3769zPM70+f1rktWVpYGDx6shx56SHv37tWaNWt033336cez9506ddKWLVtUUFBQZiwAvMwAUK6hQ4ca/fv3L/W9gQMHGq1atTIMwzDuuece48knnzTfu+eee4z27ds71L906ZJRvXp1Y8OGDQ7HR4wYYQwePNjIzc01goKCjDlz5pQZz0/P89NjeXl5RrVq1Yz333/ffL+wsNCIjo42pk6d6tCmW7duDv3ceeedxrPPPlvmuYcOHWokJCSU+f4DDzxg3HPPPWW+379/f+Ohhx4qEftPr1NpTp48aUgyvvnmG8MwDOPChQtGYGCgsWTJErPO6dOnjZCQEPNalHatyuvzWpvyrsu2bdsMScbhw4fL7Pfrr7++bh0A3sUIDeABwzBks9nKfP+noxEHDhzQxYsX9fOf/1w1a9Y0y4IFC3Tw4EHt3btXBQUF6tmzp9sxHTx4UJcvX1bXrl3NY9WqVVOnTp20d+9eh7rt2rVzeB0VFaUTJ06U2u+VK1f0wQcf6Fe/+pV57JFHHtHcuXPN1xcuXFBISEiZsf3www8KDg4ucby0UZv9+/dr8ODBatq0qUJDQxUbGytJOnr0qPk5CwsLlZCQYLYJDw9XixYtyjz/9fq8przrEhcXp549e6pt27a6//77NWfOHJ09e9ah/rVrcPHixTJjAeBdVf0dAGBle/fuVZMmTcp8v0aNGg6v8/LyJEmffPKJGjZs6PBeUFCQzp075/UYy1OtWjWH1zabrcT0yzUHDx7UhQsX1LZtW0lX1/osXbrUIfnatWuXBg4cWOb56tWrV+LHXyp5naSr61AaN26sOXPmKDo6Wna7XW3atPFo0bCzfZZ3XapUqaLVq1drw4YNWrVqld5++2397ne/0+bNm83vwrWpvfr167sdKwDXMEIDuOmLL77QN9984zBicT2tW7dWUFCQjh49qubNmzuUmJgY3XrrrQoJCVFGRkaZfQQGBqqoqKjM95s1a2auR7nm8uXL+te//qXWrVs7HetPXUu2atasKUn67LPPdPbsWXPEZdOmTTp27Jh++ctfltlH+/bt9e233173XKdPn9a+ffv0wgsvqGfPnmrVqlWJRKhZs2aqVq2aNm/ebB47e/as/v3vf7vdp7NsNpu6du2qyZMna8eOHQoMDNSHH35ovr97927dcsstqlevnlv9A3AdIzSAEwoKCpSdna2ioiLl5ORo5cqVSk1N1S9+8QsNGTLE6X5q1aqlcePG6amnnpLdble3bt10/vx5ffXVVwoNDdXQoUP17LPPavz48QoMDFTXrl118uRJ7dmzx7zVOTY2Vps3b9bhw4dVs2ZNhYeHO5yjRo0aGj16tJ555hmFh4erUaNGmjp1qi5evFju7dLX07hxY9lsNv3tb39TjRo1NG7cOPXt21crVqxQTEyMHn30USUlJZV5B5QkJScna8KECTp79qzq1KlTZr06deqobt26mj17tqKionT06FE999xzDnVq1qypESNG6JlnnlHdunXVoEED/e53vzOfC+ROn87YvHmzMjIy1KtXLzVo0ECbN2/WyZMn1apVK7POP//5T/Xq1cvlvgG4j4QGcMLKlSsVFRWlqlWrqk6dOoqLi9Nbb72loUOHlvkDWpaXX35Z9evXV2pqqv7v//5PtWvXNm8Fl6QXX3xRVatW1cSJE3X8+HFFRUXp0UcfNduPGzdOQ4cOVevWrfXDDz/o0KFDJc7x6quvym6368EHH9SFCxfUsWNHffbZZ+UmEdcTGRmp3//+93r11Ve1bNky/eEPf1CHDh3Uv39/LV68WP369dMf//jHcvto27at7rjjDi1ZskSPPPJImfUCAgK0aNEiPfHEE2rTpo1atGiht956S927d3eo9/rrrysvL0/9+vVTrVq19PTTT+v8+fMe9Xk9oaGhWrdunWbMmKHc3Fw1btxYb775pvr06SNJunTpkpYvX66VK1e61C8Az/CkYAA31CeffKJnnnlGu3fvdjkZtII//elP+vDDD7Vq1Sp/hwLcVBihAXBD9e3bV/v379exY8dKfYaO1VWrVk1vv/22v8MAbjqM0AAAAMurfOO9AADgpkNCAwAALI+EBgAAWB4JDQAAsDwSGgAAYHkkNAAAwPJIaAAAgOWR0AAAAMsjoQEAAJb3/4mfwNiBsiSTAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result = myect.calculate(G)\n", - "\n", - "print(f\"ECT matrix shape: {result.shape}\")\n", - "print(f\"Number of directions: {myect.num_dirs}\")\n", - "print(f\"Number of thresholds: {myect.num_thresh}\")\n", - "\n", - "# We can plot the result matrix\n", - "result.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " ## SECT\n", - "\n", - "\n", - "\n", - " The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n", - "\n", - " $$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n", - "\n", - " Then the SECT is defined by\n", - "\n", - " $$\n", - "\n", - " \\begin{matrix}\n", - "\n", - " \\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n", - "\n", - " & \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n", - "\n", - " \\end{matrix}\n", - "\n", - " $$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " The SECT can be computed from the ECT result:" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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AAByKoR8AAGBbDP0AAACEER0VAAAcygpy6McJHRUKFQAAHMqSZAXxnFKzjzgNDQoVAAAcyiuXXNyZFgAAIDzoqAAA4FBc9QMAAGzrxH1UgllqIzs7WxdffLESEhLUvHlzjRgxQtu3bzf01R1HR6UWvi9tpCi5Qx63pCwm5DFP5q2INBa7vNLcKVRSEfpjfbLdx5oYi90o7qix2KalxhcZi90pPs9Y7BbRR4zFlqSL3fuMxX6mpKOx2LtKGhuLLUmHfmhoLHbn8wuMxTZ5XKpKy43FDrfVq1drwoQJuvjii1VVVaXf/e53uuKKK/Ttt98qPj7eyD4pVAAAcCjLCvKqn1puu3z58mqv58+fr+bNm2vdunW67LLLAk/kDChUAABwqFDNUSkqqt5JdbvdcrvP3tUuLCyUJDVubK5DxRwVAADqubS0NCUlJfmW7Ozss27j9Xo1adIkDRgwQN26dTOWGx0VAAAcKlQdldzcXCUmJvrW+9NNmTBhgjZv3qxPPvkk4P37g0IFAACH8louuULw9OTExMRqhcrZTJw4Uf/4xz/00UcfqVWrVgHv3x8UKgAAOFRdT6a1LEt33nmnlixZolWrVqldu3aB79xPFCoAAMAvEyZM0MKFC7Vs2TIlJCQoL+/4LQeSkpIUFxdnZJ9MpgUAwKGOd1RcQSy1299zzz2nwsJCDR48WKmpqb7l9ddfN/MFio4KAACOVde30LeCGWcKEB0VAABgW3RUAABwKOs/SzDb2x2FCgAADsXTkwEAAMKIjgoAAE5VD8Z+KFQAAHCqIId+5IChn4AKFY/Ho7/+9a/avn27WrVqpfT0dGVkZKhJkyahzg8AANRjARUqd955pxYtWqTMzEw988wzcrlcqqqqUsuWLZWRkaG333471HkCAIAfqetb6IdDQJNpFy9erJdfflmvvvqq3G63vvrqKz355JMqKytTmzZtQp0jAAA4jeDuShvksFEdCaijUlJSoq5du0qSoqOjFRUVpYkTJ6qyslL79u0LaYJ2UlCSoEjv2R99XVvlZTEhj3kyV5m5i7uqPOZil5abPS57S5KMxd5f6P9TSGsrNanIWGxJahlXaCx2Z/d+Y7HbRh8yFluScqvijcXee8zcuZhfnGAstiRFHDL3c7rzSFNjscsqzU3R9Bw1+7urGssV3DwTBxQqAf0v0759e19B0rJlS+3du1eSNHz4cL3yyiuhyw4AANRrARUq119/vd59911J0qBBg/TSSy9Jkr799lsdO3YsdNkBAIAanZijEsxidwH1vh588EHfv++9915dfPHFatasmYqKijRu3LhQ5QYAAM6E+6icXevWrbVlyxb9v//3/9SkSRMNHz48FHkBAACE5oZvTZs21dixY0MRCgAA+Kk+POuHO9MCAOBkDhi+CQYPJQQAALZFRwUAAIdi6AcAANhXPbjqh6EfAABgW353VLKysvwOOnv27ICSAQAAteH6zxLM9vbmd6GyYcOGaq/Xr1+vqqoqde7cWZL0z3/+U5GRkerVq1doMwQAAKdXD4Z+/C5UPvzwQ9+/Z8+erYSEBC1YsECNGjWSJB0+fFhjx47VpZdeGvosAQDAqepBoRLQHJXHH39c2dnZviJFkho1aqQ//vGPevzxx0OWHAAAqN8CuuqnqKhIBw4cOGX9gQMHVFxcHHRSAADAD5br+BLM9jYXUEfluuuu09ixY7V48WL9+9//1r///W8tWrRI48aN0/XXXx/qHAEAwGnw9OQazJ07V5MnT9YvfvELVVZWyrIsRUdHa9y4cXr00UdDnSMAAKinAipUGjRooL/85S969NFHtXPnTklShw4dFB8fH9Lk7KakKFYRlbEhj2tVRoY85skiys3dLqfKYO6W12xL8oC3obHY5T/EGYu912P29kc/b7neWOwLY04dMg6VtlEJxmJL0mvljc7+oQDtL000Fru40Ny5KEmxP5j7Of3hiLmfUW+Vuby9x+pwOKUeTKblPioAADhVPZijEvB9VGrictn/iwYAAM4Q0H1UAABA+Lms40sw29sdDyUEAMCpmKNSsyNHjujFF1/U1q1bJUldu3bVuHHjlJSUFLLkAABA/RbQ5QNfffWVOnTooCeeeEKHDh3SoUOH9MQTT6hDhw5av97cFQMAAOAkJybTBrPUwkcffaThw4erRYsWcrlcWrp0qZmv6yQBFSp33323fvazn2n37t1avHixFi9erF27dumaa67RpEmTQpwiAAA4LSsESy2UlpYqPT1dzz77bGjy90NAQz9fffWV5s2bp6io/24eFRWle++9V7179w5ZcgAA4AzqeI7KsGHDNGzYsCB2WHsBdVQSExO1Z8+eU9bn5uYqIcHsTZcAAED9EVBH5cYbb9S4ceP02GOPqX///pKkTz/9VFOmTNHNN98c0gQBAEANQtRRKSoqqrba7XbL7XYHETh0AipUHnvsMblcLo0aNUpVVVWSpOjoaN1xxx16+OGHQ5ogAACoQYjuTJuWllZt9YwZM/Tggw8GkVjoBFSoxMTE6Mknn1R2dna1Z/00aNAgpMkBAADzcnNzlZj432dO2aWbIgV5w7cGDRqoe/fuocoFAADUQqjuTJuYmFitULGTgAuVnJwc5eTkqKCgQF6vt9p7L730UtCJncmzzz6rRx99VHl5eUpPT9fTTz+tPn361Pj5N998Uw888IB2796t888/X3/+85911VVXGc0RAADj6viqn5KSEu3YscP3eteuXdq4caMaN26s1q1bB5FIzQK66mfmzJm64oorlJOTo4MHD+rw4cPVFpNef/11ZWVlacaMGVq/fr3S09M1dOhQFRQUnPbzn332mW6++WaNGzdOGzZs0IgRIzRixAht3rzZaJ4AAJxrvvrqK/Xs2VM9e/aUJGVlZalnz56aPn26sX0G1FGZO3eu5s+fr1tvvTXU+ZzV7NmzNX78eI0dO9aXyzvvvKOXXnpJU6dOPeXzTz75pK688kpNmTJFkvSHP/xBK1eu1DPPPKO5c+fWae4AADjZ4MGDZVl1+4CggDoqFRUVvsuS61JFRYXWrVunzMxM37qIiAhlZmZqzZo1p91mzZo11T4vSUOHDq3x8wAAOIVL/52nEtAS7i/ADwEVKrfddpsWLlwY6lzO6uDBg/J4PEpOTq62Pjk5WXl5eafdJi8vr1afl6Ty8nIVFRVVWwAAQN3ze+gnKyvL92+v16sXXnhB77//vnr06KHo6Ohqn509e3boMgyD7OxszZw585T1VlG0rMro02xhb5Fl5mrmqsqAal2/eA3GlqTKsqAuejuj6IPmYldWmv0b6IXvBhqL/U1q2tk/FKBO8TX/8REK7+V3NRZ79/YUY7HjcyONxZakmEJzsY8dijEX3CDrmKcOdxaa+6jYmd+/TTds2FDtdUZGhiSdMinV5TL3RTdt2lSRkZHKz8+vtj4/P18pKaf/QU9JSanV5yVp2rRp1QqzoqKiU26GAwBA2NXxVT/h4Heh8uGHH5rMwy8xMTHq1auXcnJyNGLECEnHuzs5OTmaOHHiabfp16+fcnJyqj3VeeXKlerXr1+N+7HTrYMBAKgRhcrpHTt2TJZl+e5E+/3332vJkiXq2rWrrrjiipAm+GNZWVkaPXq0evfurT59+mjOnDkqLS31XQU0atQotWzZUtnZ2ZKku+66S4MGDdLjjz+uq6++Wq+99pq++uorvfDCC0bzBAAAwQuoULn22mt1/fXX6/bbb9eRI0fUp08fxcTE6ODBg5o9e7buuOOOUOfpc+ONN+rAgQOaPn268vLylJGRoeXLl/smzO7Zs0cREf+d29C/f38tXLhQ999/v373u9/p/PPP19KlS9WtWzdjOQIAUBdCdWdaOwuoUFm/fr2eeOIJSdJbb72llJQUbdiwQYsWLdL06dONFiqSNHHixBqHelatWnXKupEjR2rkyJFGcwIAoM7Vg6GfgC6rOHr0qBISEiRJK1as0PXXX6+IiAhdcskl+v7770OaIAAAqL8CKlQ6duyopUuXKjc3V++9955vXkpBQYFtH2oEAMA5xwrBYnMBFSrTp0/X5MmT1bZtW/Xt29d3Bc2KFSt89/8HAABmBXVX2iDnt9SVgOao/PznP9fAgQO1f/9+paen+9YPGTJE1113XciSAwAA9VvAt89MSUk55aZpffr0CTohAADgp3pwZ9qA71H+8ccf65e//KX69eunvXv3SpL+/ve/65NPPglZcgAA4AyYo3J6ixYt0tChQxUXF6cNGzaovLxcklRYWKg//elPIU0QAADUXwEVKn/84x81d+5czZs3r9oDCQcMGKD169eHLDkAAFAzJtPWYPv27brssstOWZ+UlKQjR44EmxMAAPAHN3w7vZSUFO3YseOU9Z988onat28fdFIAAMAPwXZTztVCZfz48brrrru0du1auVwu7du3T6+++qomT55s/Pb5AACg/gho6Gfq1Knyer0aMmSIjh49qssuu0xut1uTJ0/WnXfeGeocAQDA6dSDoZ9aFyqVlZW68sorNXfuXE2ZMkU7duxQSUmJunbtqoYNG5rI0TaiiyIVUREZ8rhet9kzJbLCXOxKT8BXuJ+V5TF7fb+r3FzusT8YC62knebylqSiQ42MxV5ZGGcs9heNWhuLLUlH9pl7PMh5W0P/e8UXe0elsdiSVNnQXO6ur83FLm5rLLS8ZebyPgWFyqmio6P1zTffSJJiYmLUtWvXkCcFAAAgBThH5Ze//KVefPHFUOcCAABqgcuTa1BVVaWXXnpJ77//vnr16qX4+Phq78+ePTskyQEAgPotoEJl8+bNuuiiiyRJ//znP6u953LZ/7kBAADAGQIqVD788MNQ5wEAAGqLybQAAMCugp1ncs7OUZGknJwc5eTkqKCgQF6vt9p7L730UtCJAQAABFSozJw5U7NmzVLv3r2VmprKvBQAAMLFAV2RYARUqMydO1fz58/XrbfeGup8AACAv5ijcnoVFRXq379/qHMBAAC1UB/mqAR0w7fbbrtNCxcuDHUuAAAA1fjdUcnKyvL92+v16oUXXtD777+vHj16KDo6utpnueEbAAB1gKGf/9qwYUO11xkZGZKO3/ztZEysBQCgbtSHoR+/C5UPP/xQs2bN0j333HPKLfMBAED98eyzz+rRRx9VXl6e0tPT9fTTT6tPnz5G9lWrOSozZ85UaWmpkUQAAEAtWSFYaun1119XVlaWZsyYofXr1ys9PV1Dhw5VQUFB8F/PadSqULEsB/SIAACoL8JQqMyePVvjx4/X2LFj1bVrV82dO1cNGjQwdrPXWl/1wxwUAADqp4qKCq1bt06ZmZm+dREREcrMzNSaNWuM7LPW91Hp1KnTWYuVQ4cOBZwQAADwT6gm0xYVFVVb73a75Xa7T/n8wYMH5fF4lJycXG19cnKytm3bFngiZ1DrQmXmzJlKSkoykYvtRRdLkRWhj1sZb7ZLFVlmLnbsv2KMxTZxrE8WXWIytrlh0gYHPcZiS5L7iLnz8UhprLHYJY1P/aUaSgl55o6LyfMl5rDBXwCSoo6ae7Ztwnfmci9v3MhYbE+5sdCnCtHlyWlpadVWz5gxQw8++GAQgUOn1mfYTTfdpObNm5vIBQAAhEFubq4SExN9r0/XTZGkpk2bKjIyUvn5+dXW5+fnKyUlxUhutZqjwvwUAABsJESTaRMTE6stNRUqMTEx6tWrl3JycnzrvF6vcnJy1K9fPxNfYe06Klz1AwCAfYTjhm9ZWVkaPXq0evfurT59+mjOnDkqLS3V2LFjA0/kDGpVqHi9XiNJAACAAIThFvo33nijDhw4oOnTpysvL08ZGRlavnz5KRNsQ8XcLCgAAHBOmjhxoiZOnFgn+6JQAQDAoXjWDwAAsK968PTkWt+ZFgAAoK7QUQEAwKnqQUeFQgUAAIdy/WcJZnu7Y+gHAADYFh0VAACciqEfAABgV/Xh8mSGfgAAgG3RUQEAwKkY+gEAALbmgGIjGBQqAAA4VH2Yo0KhUgsxRVJkTOjjWpGhj3myyHJzsRNyzZ3lUUfNPq07rsDcgalIijYWOzb/qLHYkuSJN3CS/8fR5nHGYnujzd4RItrgYY8pNneuRx4sNhZbkiIjDE51LDKXe0xhI2OxPRXGQtdLFCoAADgVc1QAAIBd1YehHy5PBgAAtkVHBQAAp2LoBwAA2BVDPwAAAGFERwUAAKdi6AcAANhWPShUGPoBAAC2RUcFAACHqg+TaSlUAABwqnow9EOhAgCAQ7ksSy4r8GojmG3rCnNUAACAbdFRAQDAqRj6AQAAdlUfJtMy9AMAAGyLjgoAAE5VD4Z+HNVROXTokG655RYlJibqvPPO07hx41RSUnLGbQYPHiyXy1Vtuf322+soYwAAzDkx9BPMYneO6qjccsst2r9/v1auXKnKykqNHTtWv/71r7Vw4cIzbjd+/HjNmjXL97pBgwYB7T+myKuoaG9A255JVZzZejGyzFzsiEpzZ3nCN/nGYktS1b92G4sde9GFxmJ74mOMxZak6LwiY7HdrdzGYnvcZn+OokvNnesxRR5jsa0DPxiLLUnW+a2NxY5wuYzFdheG/nf5CVWV5mLXR44pVLZu3arly5fryy+/VO/evSVJTz/9tK666io99thjatGiRY3bNmjQQCkpKXWVKgAAdYOhH/tYs2aNzjvvPF+RIkmZmZmKiIjQ2rVrz7jtq6++qqZNm6pbt26aNm2ajh49ajpdAACMY+jHRvLy8tS8efNq66KiotS4cWPl5eXVuN0vfvELtWnTRi1atNA333yj++67T9u3b9fixYtr3Ka8vFzl5eW+10VF5lrhAACcix566CG988472rhxo2JiYnTkyJGA4oS9ozJ16tRTJrv+eNm2bVvA8X/9619r6NCh6t69u2655Ra9/PLLWrJkiXbu3FnjNtnZ2UpKSvItaWlpAe8fAABjrBAshlRUVGjkyJG64447gooT9o7KPffcozFjxpzxM+3bt1dKSooKCgqqra+qqtKhQ4dqNf+kb9++kqQdO3aoQ4cOp/3MtGnTlJWV5XtdVFREsQIAsCW7Dt/MnDlTkjR//vyg4oS9UGnWrJmaNWt21s/169dPR44c0bp169SrVy9J0gcffCCv1+srPvyxceNGSVJqamqNn3G73XK7zV2dAABASFjW8SWY7XXqFAc7/T8Y9qEff3Xp0kVXXnmlxo8fry+++EKffvqpJk6cqJtuusl3xc/evXt1wQUX6IsvvpAk7dy5U3/4wx+0bt067d69W2+//bZGjRqlyy67TD169AjnlwMAgG2kpaVVm/KQnZ0d7pR8wt5RqY1XX31VEydO1JAhQxQREaEbbrhBTz31lO/9yspKbd++3XdVT0xMjN5//33NmTNHpaWlSktL0w033KD7778/XF8CAAAhE6pn/eTm5ioxMdG3vqZuytSpU/XnP//5jDG3bt2qCy64IPCkfsRRhUrjxo3PeHO3tm3byjqpBZaWlqbVq1fXRWoAANS9EN1HJTExsVqhUhN/55WGkqMKFQAAED7+zisNJQoVAAAcyuU9vgSzvSl79uzRoUOHtGfPHnk8Ht/FLB07dlTDhg39jkOhAgCAU9n4FvrTp0/XggULfK979uwpSfrwww81ePBgv+M45qofAADgHPPnz5dlWacstSlSJDoqAAA4Vqiu+rEzChUAAJwqRDd8szOGfgAAgG3RUQEAwKEY+kE1sYc9ioryhDxueZLZxlZkubkzMaLS4Fl+rMxcbElRqf4/zLK2KhLMPSPDExtpLLYkRTRqYCx27KHQ//ycUBXrMhZbkqJLzF3HGV1Ubiy2q1kTY7ElqdLguR4ZZe53o9Fzscpc7FPY+KqfUKFQAQDAoepDR4U5KgAAwLboqAAA4FT14KofChUAAByKoR8AAIAwoqMCAIBTcdUPAACwK4Z+AAAAwoiOCgAATuW1ji/BbG9zFCoAADhVPZijwtAPAACwLToqAAA4lEtBTqYNWSbmUKgAAOBU3JkWAADYFZcnAwAAhBEdFQAAnKoeXPVDoQIAgEO5LEuuIOaZBLNtXaFQqYWYw2WKMnDEolLMfhsiK8ydiJGV5mJbJaXGYkuS2rUyFrqqobnvaZXb7Dx9l8dtLHbM4TJjsd3xDYzFlqToEo+x2JGHjxqL7WmaYCy2ZPZc90abm51g8lyMqDIXuz6iUAEAwKm8/1mC2d7mKFQAAHCo+jD0w1U/AADAtuioAADgVFz1AwAAbKse3JmWoR8AAGBbdFQAAHCo+nALfQoVAACcqh4M/VCoAADgUC7v8SWY7e2OOSoAAMC26KgAAOBU9WDoh44KAABOZYVgMWD37t0aN26c2rVrp7i4OHXo0EEzZsxQRUVFrWPRUQEAACG1bds2eb1ePf/88+rYsaM2b96s8ePHq7S0VI899litYlGoAADgUHZ91s+VV16pK6+80ve6ffv22r59u5577jkKFQAA6o0QzVEpKiqqttrtdsvtdgeT2SkKCwvVuHHjWm/HHBUAAOq5tLQ0JSUl+Zbs7OyQxt+xY4eefvpp/eY3v6n1tnRUAABwKktSMPdC+U8zJjc3V4mJib7VNXVTpk6dqj//+c9nDLl161ZdcMEFvtd79+7VlVdeqZEjR2r8+PG1TpFCBQAAhwrVHJXExMRqhUpN7rnnHo0ZM+aMn2nfvr3v3/v27dPll1+u/v3764UXXggoRwqVWqhoFCtvVGzI40YfNXsde0SlufiuSoO3NWzTwlxsSRXNGpiL3dDcqKo32mUstmlWZOh/fk5wF1YZiy1JUSWVxmJXppz9P4hAVTUw+2ve5Lke4TZ3rps8F6vMnoph1axZMzVr1syvz+7du1eXX365evXqpb/97W+KiAjsXKFQAQDAqSwFOZk2ZJlUs3fvXg0ePFht2rTRY489pgMHDvjeS0lJqVUsChUAAJzKpnemXblypXbs2KEdO3aoVatWP9pl7fbJVT8AADiVNwSLAWPGjJFlWaddaotCBQAA2BZDPwAAOJRd70wbShQqAAA4lU3nqIQSQz8AAMC26KgAAOBU9aCjQqECAIBT1YNChaEfAABgW3RUAABwKq+kYJ40YPApKKFCoQIAgEPVh8uTGfoBAAC2RUcFAACnqgeTaSlUAABwKq8luYIoNrwUKgAAwJR60FFhjgoAALAtOiq1UNYoUlHRkSGP6y4ye32Yq8pcxRxZYS73spaJxmJLUkVi6L+XJ1TGB3O94Jl5I83FPh7f3N8vHre53COqzH0/JSmyoblfl16DqZs85pJUFWcuvstjLrbJ41JVafZcrC7Ijors31GhUAEAwKkY+gEAAAgfOioAADiV11JQwzdc9QMAAIyxvMeXYLa3OUcN/Tz00EPq37+/GjRooPPOO8+vbSzL0vTp05Wamqq4uDhlZmbqu+++M5soAAAICUcVKhUVFRo5cqTuuOMOv7d55JFH9NRTT2nu3Llau3at4uPjNXToUJWVlRnMFACAOnBiMm0wi805auhn5syZkqT58+f79XnLsjRnzhzdf//9uvbaayVJL7/8spKTk7V06VLddNNNplIFAMC8ejBHxVEdldratWuX8vLylJmZ6VuXlJSkvn37as2aNTVuV15erqKiomoLAACoe+d0oZKXlydJSk5OrrY+OTnZ997pZGdnKykpybekpaUZzRMAgIDUg6GfsBcqU6dOlcvlOuOybdu2Os1p2rRpKiws9C25ubl1un8AAPxiKchCJdxfwNmFfY7KPffcozFjxpzxM+3btw8odkpKiiQpPz9fqampvvX5+fnKyMiocTu32y232x3QPgEAqDP14M60YS9UmjVrpmbNmhmJ3a5dO6WkpCgnJ8dXmBQVFWnt2rW1unIIAACER9iHfmpjz5492rhxo/bs2SOPx6ONGzdq48aNKikp8X3mggsu0JIlSyRJLpdLkyZN0h//+Ee9/fbb2rRpk0aNGqUWLVpoxIgRYfoqAAAIEa83+MXmwt5RqY3p06drwYIFvtc9e/aUJH344YcaPHiwJGn79u0qLCz0febee+9VaWmpfv3rX+vIkSMaOHCgli9frtjY2DrNHQCAkGPox17mz59/1nuoWD866C6XS7NmzdKsWbMMZgYAAExwVKECAABOQkcFAADYFnemBQAACB86KrVQkRghT0zoa7uqOFfIY1ZjsGCO8Jg7hbyRxkIfjx9j7rh7YoyFlmX4zwuTx6XKYy62y/Afhi6PudiWwV8Blumfo2hzsV0GL0gxeS56KuquB2BZXllW4AcqmG3rCh0VAACcyrKOD98Euhico/Kzn/1MrVu3VmxsrFJTU3Xrrbdq3759tY5DoQIAAELu8ssv1xtvvKHt27dr0aJF2rlzp37+85/XOg5DPwAAOJUV5GRagx2Vu+++2/fvNm3aaOrUqRoxYoQqKysVHe3/mCGFCgAATuX1BjeZ5z9zVIqKiqqtDvUz7w4dOqRXX31V/fv3r1WRIjH0AwCAcwX15OT/zlFJS0tTUlKSb8nOzg5Jevfdd5/i4+PVpEkT7dmzR8uWLat1DAoVAADqudzcXBUWFvqWadOmnfZzU6dOlcvlOuOybds23+enTJmiDRs2aMWKFYqMjNSoUaNOuYP82TD0AwCAQ1ler6wghn5OXJ6cmJioxMTEs37+nnvu0ZgxY874mfbt2/v+3bRpUzVt2lSdOnVSly5dlJaWps8//1z9+vXzO0cKFQAAnKqOJ9M2a9ZMzZo1C2hX3v88qbm8vLxW21GoAACAkFq7dq2+/PJLDRw4UI0aNdLOnTv1wAMPqEOHDrXqpkjMUQEAwLmCudnbicWABg0aaPHixRoyZIg6d+6scePGqUePHlq9enWtryaiowIAgFNZlqRgLk82U6h0795dH3zwQUhi0VEBAAC2RUcFAACHsryWrCCeyFnbS4XDgUIFAACnsrwKbujH/k9PplABAMCh6kNHhTkqAADAtuio+OFExempKDOzgwrDFa3B8JbHXGxvpLnYkuSVy1hsj8ljbvjPC5PfU5fJ2IZ/jEzmbpk7FWWZ/jkyOHJg8ntq8vt54v+KuuhWVFnlQQ3fVKkyhNmYQaHih+LiYknSt3+fFeZMAABOUVxcrKSkJCOxY2JilJKSok/y/i/oWCkpKYqJiQlBVma4LCcMUIWZ1+vVvn37lJCQIJfr7H/6FBUVKS0tTbm5uX49O8EunJq3RO7h4NS8JXIPB6fmLdU+d8uyVFxcrBYtWigiwlwLtKysTBUVFUHHiYmJUWxsbAgyMoOOih8iIiLUqlWrWm/n70Oe7MapeUvkHg5OzVsi93Bwat5S7XI31Uk5WWxsrK0LjFBhMi0AALAtChUAAGBbFCoGuN1uzZgxo9YPXgo3p+YtkXs4ODVvidzDwal5S87O/VzAZFoAAGBbdFQAAIBtUagAAADbolABAAC2RaECAABsi0IlxJ599lm1bdtWsbGx6tu3r7744otwpwQAOIvs7GxdfPHFSkhIUPPmzTVixAht37493GlBFCoh9frrrysrK0szZszQ+vXrlZ6erqFDh6qgoCDcqZ3Rtm3b1KRJE3Xo0EEZGRlq2LChBg4cGO60/OK03I8cOaLevXsrIyND3bp107x588KdUr3Qtm1b9ejRQxkZGbr88svDnY7fnHq+bN++XRkZGb4lLi5OS5cuDXdaZ7R69WpNmDBBn3/+uVauXKnKykpdccUVKi0tDXdqsBAyffr0sSZMmOB77fF4rBYtWljZ2dlhzMo/w4YNs7755hvLsiyrU6dOVklJSZgz8p+Tcq+qqrJKS0sty7KskpISq23bttbBgwfDnFXtXHDBBZaOP5P7lOXpp58Od3qn1aZNG6u4uDjcadTauXC+FBcXW02aNLH1z+XpFBQUWJKs1atXW5blzPP+XEFHJUQqKiq0bt06ZWZm+tZFREQoMzNTa9asCWNm/tm+fbs6d+6so0ePyuPxKD4+Ptwp+c1JuUdGRqpBgwaSpPLyclmWJcuyHNUZWrRokSQpJydH+/fv1+7duxUREaE333xT48ePD3N2/nPCMT8Xzpe3335bQ4YMUXx8vKPyLiwslCQ1btxY0rlz3jsRhUqIHDx4UB6PR8nJydXWJycnKy8vL0xZ+ae4uFhut1sxMTHasmWLunTpEu6U/ObE3I8cOaL09HS1atVKU6ZMUdOmTXXBBReob9++Wrp0qTZu3KiWLVvqvffeC3eqp5Wfn6+oqCgNGDBAKSkpOnjwoLxery699FLb3rnT5XJp0KBBuvjii/Xqq69KkmOOudPPlzfeeEM33nijJOccc6/Xq0mTJmnAgAHq1q2bJGee9+cKChXo22+/VdeuXSVJW7duVadOncKckf+cmPt5552nr7/+Wrt27dLChQuVn58vyTmdoU2bNqlTp06+X85ff/21mjdvfkqRbieffPKJ1q1bp7ffflt/+tOf9M0330hyxjF38vlSVFSkzz77TFdddZVvnRPynjBhgjZv3qzXXnvNt86J5/25gkIlRJo2barIyEjfL5ET8vPzlZKSEqas/LNlyxZdeOGFkqT4+HitWLFCP/zwQ5iz8o+Tc09OTlZ6ero+/vhjR3WGvvnmG3Xv3t33+uuvv6722o5atmwpSUpNTdVVV12l9evXO+qYS848X5YtW6YrrrhCsbGxkpzRAZ04caL+8Y9/6MMPP1SrVq1865143p8rKFRCJCYmRr169VJOTo5vndfrVU5Ojvr16xfGzM7uV7/6lWbOnClJuuGGG7Rp0yY1adIkzFn5x2m55+fnq7i4WNLxMfCPPvpInTt3dlRn6JtvvlGPHj18r7/++utqr+2mtLTUd8xLSkr0wQcf6MILL3TEMXf6+XLysI9k7w6oZVmaOHGilixZog8++EDt2rWr9r7TzvtzCYVKCGVlZWnevHlasGCBtm7dqjvuuEOlpaUaO3ZsuFODTXz//fe69NJLlZ6erksvvVR33nmnunfv7pjOkNfr1ZYtW6r9gt65c6fatm0bvqTOIj8/XwMHDlR6erouueQSjRo1ShdffLEjjrmTz5fCwkJ98cUXGjp0qG+dnfOeMGGCXnnlFS1cuFAJCQnKy8tTXl6ejh075sjz/lzC05ND7JlnntGjjz6qvLw8ZWRk6KmnnlLfvn3DndYZuVyuM75v51PEybk70XfffadOnTrp+++/V+vWrSVJV111ldasWaN//OMfGjBgQJgzBAJT0++Sv/3tbxowYADnfRhRqMDn6NGj6tKli0aOHKnHHnss3On4JTc3V7feeqsKCgoUFRWlBx54QCNHjgx3WgCAEIkKdwKwj4ceekiXXHJJuNOolaioKM2ZM0cZGRnKy8tTr169dNVVV9nySgLp7B2gmvD3ROCcfMydmrtT84Y9MUcFko639Ldt26Zhw4aFO5VaSU1NVUZGhiQpJSVFTZs21aFDh8Kb1BmcuGHXyUtpaalat26te+6557Tv88s7OE4+5ifns2fPHg0aNEhdunRR9+7d9cYbb9g2dycfc9gPhQokSZMnT1Z2dna40wjKunXr5PF4lJaWFu5UasWJnSync+IxP9E9/Pbbb7VixQpNmjTJUc+hceIxhz1QqEDLli1Tp06dbHWpYG0dOnRIo0aN0gsvvBDuVGrFqZ0sJ3PqMXda9/BkTj3msAcKFejzzz/Xa6+9prZt22ry5MmaN2+eZs2aFe60/FZeXq4RI0Zo6tSp6t+/f7jTqZVzoZPlNOfCMXda9/BcOOYIHwoVKDs7W7m5udq9e7cee+wxjR8/XtOnTw93Wn6xLEtjxozRT37yE916663hTqdWzoVOltOcC8fcad3Dc+GYI7y46geO9umnn+r1119Xjx49tHTpUknS3//+d0fc2vpEJ+vNN99USUmJKisrlZiY6Jgi0Ymcfsyd2D10+jFH+HEfFcAG5s+fr82bNzvm/jXnAqcdc8uy9Itf/EKdO3fWgw8+GO50AuK0Yw57YOgHABzgRPdw6dKlysjIUEZGhjZt2hTutADj6KgAAADboqMCAABsi0IFAADYFoUKAACwLQoVAABgWxQqAADAtihUAACAbVGoAAAA26JQAQAAtkWhAgAAbItCBQAA2BaFChAigwcP1qRJk+rdvmvrhx9+UPPmzbV7924j8U8+FuE4LjfddJMef/zxOt0ncC7jWT/AWYwZM0YLFiyQJEVFRalx48bq0aOHbr75Zo0ZM0YREcfr/UOHDik6OloJCQlG8xk8eLAyMjI0Z84c37q62ncoZGVlqbi4WPPmzTMS/+TjE47jsnnzZl122WXatWuXkpKS6my/wLmKjgrghyuvvFL79+/X7t279e677+ryyy/XXXfdpWuuuUZVVVWSpMaNG5/xP8SKigpj+Z1t33Zx9OhRvfjiixo3blyNnwnlcQrHcenWrZs6dOigV155pU73C5yrKFQAP7jdbqWkpKhly5a66KKL9Lvf/U7Lli3Tu+++q/nz50s6dZhh8ODBmjhxoiZNmqSmTZtq6NChkiSv16vs7Gy1a9dOcXFxSk9P11tvveXbzuv16pFHHlHHjh3ldrvVunVrPfTQQ5KOd3dWr16tJ598Ui6XSy6XS7t37z5l3+Xl5frtb3+r5s2bKzY2VgMHDtSXX35Z7WsaPHiwfvvb3+ree+9V48aNlZKSogcffPCsx+KOO+7QwIEDT/teq1at9PDDD9e47f/93//J7XbrkksuOetxWr58uQYOHKjzzjtPTZo00TXXXKOdO3dWi1daWqpRo0apYcOGSk1NPWXI5cfHxZ+Y/hyXt956S927d1dcXJyaNGmizMxMlZaW+t4fPny4XnvttRqPAwD/UagAAfrJT36i9PR0LV68uMbPLFiwQDExMfr00081d+5cSVJ2drZefvllzZ07V1u2bNHdd9+tX/7yl1q9erUkadq0aXr44Yf1wAMP6Ntvv9XChQuVnJwsSXryySfVr18/jR8/Xvv379f+/fuVlpZ2yn7vvfdeLVq0SAsWLND69evVsWNHDR06VIcOHTolv/j4eK1du1aPPPKIZs2apZUrV9b49WzZskUvvPCCHnnkkdO+36VLF23cuLHG7T/++GP16tXLr+NUWlqqrKwsffXVV8rJyVFERISuu+46eb1e33ZTpkzR6tWrtWzZMq1YsUKrVq3S+vXra9y/PzHPdlz279+vm2++Wb/61a+0detWrVq1Stdff71OHkXv06ePvvjiC5WXl9eYCwA/WQDOaPTo0da111572vduvPFGq0uXLpZlWdagQYOsu+66y/feoEGDrJ49e1b7fFlZmdWgQQPrs88+q7Z+3Lhx1s0332wVFRVZbrfbmjdvXo35/Hg/P15XUlJiRUdHW6+++qrv/YqKCqtFixbWI488Um2bgQMHVotz8cUXW/fdd1+N+x49erTVt2/fGt//n//5H2vQoEE1vn/ttddav/rVr07J/cfH6XQOHDhgSbI2bdpkWZZlFRcXWzExMdYbb7zh+8wPP/xgxcXF+Y7F6Y7VmWKe2OZMx2XdunWWJGv37t01xv3666/P+hkA/qGjAgTBsiy5XK4a3/9x92DHjh06evSofvrTn6phw4a+5eWXX9bOnTu1detWlZeXa8iQIQHntHPnTlVWVmrAgAG+ddHR0erTp4+2bt1a7bM9evSo9jo1NVUFBQWnjVtVVaXFixfrhhtu8K37zW9+oxdffNH3uri4WHFxcTXmduzYMcXGxp6y/nRdlu+++04333yz2rdvr8TERLVt21aStGfPHt/XWVFRob59+/q2ady4sTp37lzj/s8W84QzHZf09HQNGTJE3bt318iRIzVv3jwdPny42udPHIOjR4/WmAsA/0SFOwHAybZu3ap27drV+H58fHy11yUlJZKkd955Ry1btqz2ntvt1pEjR0Ke45lER0dXe+1yuU4ZBjlh586dKi4uVvfu3SUdn0vz5ptvViuqvvnmG91444017q9p06an/KcunXqcpOPzPNq0aaN58+apRYsW8nq96tatW1CTbf2NeabjEhkZqZUrV+qzzz7TihUr9PTTT+v3v/+91q5d6zsXTgyxNWvWLOBcARxHRwUI0AcffKBNmzZV6zCcTdeuXeV2u7Vnzx517Nix2pKWlqbzzz9fcXFxysnJqTFGTEyMPB5Pje936NDBN9/jhMrKSn355Zfq2rWr37n+2IkiqmHDhpKk9957T4cPH/Z1SD7//HPt3btX1113XY0xevbsqW+//fas+/rhhx+0fft23X///RoyZIi6dOlySoHToUMHRUdHa+3atb51hw8f1j//+c+AY/rL5XJpwIABmjlzpjZs2KCYmBgtWbLE9/7mzZvVqlUrNW3aNKD4AP6Ljgrgh/LycuXl5cnj8Sg/P1/Lly9Xdna2rrnmGo0aNcrvOAkJCZo8ebLuvvtueb1eDRw4UIWFhfr000+VmJio0aNH67777tO9996rmJgYDRgwQAcOHNCWLVt8l/S2bdtWa9eu1e7du9WwYUM1bty42j7i4+N1xx13aMqUKWrcuLFat26tRx55REePHj3jZcFn06ZNG7lcLv3v//6v4uPjNXnyZF199dVatmyZ0tLSdPvttyszM7PGK4IkaejQoZo2bZoOHz6sRo0a1fi5Ro0aqUmTJnrhhReUmpqqPXv2aOrUqdU+07BhQ40bN05TpkxRkyZN1Lx5c/3+97/33dcmkJj+WLt2rXJycnTFFVeoefPmWrt2rQ4cOKAuXbr4PvPxxx/riiuuqHVsAKeiUAH8sHz5cqWmpioqKkqNGjVSenq6nnrqKY0ePbrG/xhr8oc//EHNmjVTdna2/vWvf+m8887zXfIsSQ888ICioqI0ffp07du3T6mpqbr99tt920+ePFmjR49W165ddezYMe3ateuUfTz88MPyer269dZbVVxcrN69e+u99947Y3FwNikpKXrooYf08MMPa9GiRfrTn/6kXr166dprr9Xrr7+u4cOH6y9/+csZY3Tv3l0XXXSR3njjDf3mN7+p8XMRERF67bXX9Nvf/lbdunVT586d9dRTT2nw4MHVPvfoo4+qpKREw4cPV0JCgu655x4VFhYGFfNsEhMT9dFHH2nOnDkqKipSmzZt9Pjjj2vYsGGSpLKyMi1dulTLly+vVVwAp8edaQHUqXfeeUdTpkzR5s2ba13kOcFzzz2nJUuWaMWKFeFOBTgn0FEBUKeuvvpqfffdd9q7d+9p7wHjdNHR0Xr66afDnQZwzqCjAgAAbOvc67sCAIBzBoUKAACwLQoVAABgWxQqAADAtihUAACAbVGoAAAA26JQAQAAtkWhAgAAbItCBQAA2BaFCgAAsK3/H0McM5GNhrw/AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sect = result.smooth()\n", - "\n", - "sect.plot()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " ## ECT for higher dimensions\n", - "\n", - "\n", - "\n", - " The `ECT` class can also be used for higher dimensional graphs." - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# generate a 3d graph\n", - "list_3d = [\n", - " (\"A\", [0, 0, 0]),\n", - " (\"B\", [1, 0, 0]),\n", - " (\"C\", [0, 1, 0]),\n", - " (\"D\", [1, 1, 0]),\n", - " (\"E\", [0, 0, 1]),\n", - " (\"F\", [1, 0, 1]),\n", - " (\"G\", [0, 1, 1]),\n", - " (\"H\", [1, 1, 1]),\n", - "]\n", - "graph_3d = EmbeddedGraph()\n", - "graph_3d.add_nodes_from(list_3d)\n", - "\n", - "# add edges\n", - "graph_3d.add_edge(\"A\", \"B\")\n", - "graph_3d.add_edge(\"B\", \"C\")\n", - "graph_3d.add_edge(\"C\", \"D\")\n", - "graph_3d.add_edge(\"D\", \"E\")\n", - "graph_3d.add_edge(\"E\", \"F\")\n", - "graph_3d.add_edge(\"F\", \"G\")\n", - "graph_3d.add_edge(\"G\", \"H\")\n", - "graph_3d.add_edge(\"H\", \"A\")\n", - "\n", - "# plot the graph\n", - "graph_3d.plot()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " lets center the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "graph_3d.center_coordinates(center_type=\"mean\")\n", - "graph_3d.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Now we can compute the ECT of the 3d graph." - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ect_3d = ECT(num_dirs=16, num_thresh=20)\n", - "result_3d = ect_3d.calculate(graph_3d)\n", - "result_3d.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Note that the each of the directions are appended in a list for the ECT result, so we won't see the same periodic behavior as in the 2d case.\n", - "\n", - " ECT results inherit from ndarrays but they store the associated directions and thresholds." - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.48609512, -0.71244342, -0.50609872],\n", - " [ 0.00364975, -0.82190818, 0.56960831],\n", - " [-0.77710663, -0.23260984, 0.5848059 ],\n", - " [ 0.75362655, -0.61431121, -0.23381352],\n", - " [-0.18133219, -0.98270096, -0.03764916],\n", - " [-0.92728701, 0.26549447, 0.26391569],\n", - " [-0.81397529, 0.14802792, 0.56172232],\n", - " [ 0.54073592, -0.29337918, 0.78837385],\n", - " [ 0.48363696, 0.74722911, -0.45578937],\n", - " [ 0.99631892, 0.01735462, 0.08394891],\n", - " [ 0.74179171, -0.62822234, -0.23469503],\n", - " [ 0.09906268, -0.01998319, -0.99488052],\n", - " [ 0.57301878, 0.71110718, -0.40740159],\n", - " [ 0.93645869, -0.11437729, -0.33160663],\n", - " [-0.19170715, 0.53609831, -0.82209913],\n", - " [ 0.69801901, 0.38101835, -0.6062957 ]])" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_3d.directions.vectors\n" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.8660254 , -0.77486483, -0.68370427, -0.5925437 , -0.50138313,\n", - " -0.41022256, -0.31906199, -0.22790142, -0.13674085, -0.04558028,\n", - " 0.04558028, 0.13674085, 0.22790142, 0.31906199, 0.41022256,\n", - " 0.50138313, 0.5925437 , 0.68370427, 0.77486483, 0.8660254 ])" - ] - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_3d.thresholds\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also define custom directions and thresholds for the ECT in case we need finer control. We use random sampling from the unit sphere for the directions and cosine sampling for the thresholds." - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from ect import Directions\n", - "\n", - "directions = Directions.random(16, dim=3)\n", - "thresholds = np.cos(np.linspace(0, np.pi, 20))\n", - "ect_3d = ECT(directions=directions, thresholds=thresholds)\n", - "result_3d = ect_3d.calculate(graph_3d)\n", - "result_3d.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of direction vectors: 10000\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 137, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# another example sampling from the half sphere\n", - "sample_size = 100\n", - "theta = np.linspace(0, np.pi / 2, sample_size) # Only go up to pi/2 for half sphere\n", - "phi = np.linspace(0, 2 * np.pi, sample_size)\n", - "theta, phi = np.meshgrid(theta, phi)\n", - "\n", - "# Flatten the meshgrid arrays and create vectors\n", - "half_sphere_vectors = np.column_stack(\n", - " [\n", - " np.sin(theta.flatten()) * np.cos(phi.flatten()),\n", - " np.sin(theta.flatten()) * np.sin(phi.flatten()),\n", - " np.cos(theta.flatten()),\n", - " ]\n", - ")\n", - "\n", - "# Normalize the vectors\n", - "half_sphere_vectors = half_sphere_vectors / np.linalg.norm(\n", - " half_sphere_vectors, axis=1, keepdims=True\n", - ")\n", - "\n", - "directions = Directions.from_vectors(half_sphere_vectors)\n", - "print(f\"Number of direction vectors: {len(directions)}\")\n", - "ect_3d = ECT(directions=directions, num_thresh=20) # Reduced number of thresholds\n", - "result_3d = ect_3d.calculate(graph_3d)\n", - "result_3d.plot()\n" - ] - } - ], - "metadata": { - "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 - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/doc_source/tutorials.rst b/doc_source/tutorials.rst index 5a5d1b1..849d783 100644 --- a/doc_source/tutorials.rst +++ b/doc_source/tutorials.rst @@ -5,8 +5,7 @@ Tutorials :maxdepth: 2 :caption: Contents: - notebooks/tutorial_graph - notebooks/tutorial_cw + notebooks/Tutorial-EmbeddedComplex + notebooks/Tutorial-ExactECT notebooks/Matisse/example_matisse - .. notebooks/Tutorial-ExactECT diff --git a/doc_source/validation.md b/doc_source/validation.md new file mode 100644 index 0000000..8cafcca --- /dev/null +++ b/doc_source/validation.md @@ -0,0 +1,114 @@ +# Validation System + +We often require our cell complexes to satisfy geometric constraints. The validation system provides modular, extensible validation for embedded cell complexes to ensure they represent proper embeddings in Euclidean space. + +## Overview + +The validation system distinguishes between two types of rules: + +- **Structural Rules** (always checked): Basic requirements like vertex counts and dimension validity +- **Geometric Rules** (optional): Embedding properties like non-intersecting edges and faces + +## Architecture + +The validation system consists of several components: + +1. **Base Classes**: Abstract interfaces for validation rules and results +2. **Validation Rules**: Concrete implementations for specific validation checks +3. **Validator**: Main orchestrator that manages and applies rules + +## Validation Rules + +### Structural Rules (Always Enforced) + +- **DimensionValidityRule**: Ensures cell dimensions are non-negative +- **VertexCountRule**: Validates correct vertex counts for cell dimensions + - 0-cells must have exactly 1 vertex + - 1-cells must have exactly 2 vertices + - k-cells (k ≥ 2) must have at least 3 vertices + +### Geometric Rules (Optional) + +- **EdgeInteriorRule**: Ensures no vertices lie on edge interiors +- **FaceInteriorRule**: Ensures no vertices lie inside face interiors +- **SelfIntersectionRule**: Validates that face edges don't self-intersect +- **BoundaryEdgeRule**: Ensures required boundary edges exist for faces + +## Usage + +```python +from ect import EmbeddedComplex + +# Enable validation during construction +K = EmbeddedComplex(validate_embedding=True) + +# Or enable/disable later +K.enable_embedding_validation(tol=1e-10) +K.disable_embedding_validation() + +# Override per operation +K.add_cell(vertices, dim=2, check=True) +``` + +## Custom Validation Rules + +You can create custom validation rules by inheriting from `ValidationRule`: + +```python +from ect.validation import ValidationRule, ValidationResult + +class MyCustomRule(ValidationRule): + @property + def name(self) -> str: + return "My Custom Rule" + + @property + def is_structural(self) -> bool: + return False # Geometric rule + + def applies_to_dimension(self, dim: int) -> bool: + return dim == 2 # Only for 2-cells + + def validate(self, cell_coords, all_coords, cell_indices, all_indices, dim): + # Your validation logic here + if some_condition: + return ValidationResult.valid() + else: + return ValidationResult.invalid("Validation failed") + +# Add to validator +K.get_validator().add_rule(MyCustomRule()) +``` + +## API Reference + +### Main Module + +```{eval-rst} +.. automodule:: ect.validation + :members: +``` + +### Base Classes + +```{eval-rst} +.. automodule:: ect.validation.base + :members: + :show-inheritance: +``` + +### Validation Rules + +```{eval-rst} +.. automodule:: ect.validation.rules + :members: + :show-inheritance: +``` + +### Validator + +```{eval-rst} +.. automodule:: ect.validation.validator + :members: + :show-inheritance: +``` \ No newline at end of file diff --git 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a/docs/doctrees/modules.doctree and b/docs/.doctrees/modules.doctree differ diff --git a/docs/doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb b/docs/.doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb similarity index 83% rename from docs/doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb rename to docs/.doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb index 6f0c4f8..ccef47b 100644 --- a/docs/doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb +++ b/docs/.doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb @@ -18,17 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 150 files in the directory\n" - ] - } - ], + "outputs": [], "source": [ "# -----------------\n", "# Standard imports\n", @@ -51,7 +43,10 @@ "# ---------------------------\n", "# The ECT packages we'll use\n", "# ---------------------------\n", - "from ect import ECT, EmbeddedGraph # for calculating ECTs\n", + "from ect import ECT, EmbeddedComplex # for calculating ECTs\n", + "# Note: EmbeddedGraph is now unified into EmbeddedComplex\n", + "# For backward compatibility, you can still use:\n", + "# from ect import EmbeddedGraph\n", "\n", "# Simple data paths\n", "data_dir = \"outlines/\"\n", @@ -61,7 +56,7 @@ " f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n", "]\n", "file_names.sort()\n", - "print(f\"There are {len(file_names)} files in the directory\")\n" + "print(f\"There are {len(file_names)} files in the directory\")" ] }, { @@ -77,37 +72,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "i = 3\n", "shape = np.loadtxt(data_dir + file_names[i])\n", "# shape = normalize(shape)\n", - "G = EmbeddedGraph()\n", + "G = EmbeddedComplex() # Using the unified EmbeddedComplex class\n", "G.add_cycle(shape)\n", - "G.plot(with_labels=False, node_size=10)\n" + "G.plot(with_labels=False, node_size=10)" ] }, { @@ -199,19 +173,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def matisse_ect(filename, ect):\n", " shape = np.loadtxt(data_dir + filename)\n", - " G = EmbeddedGraph()\n", + " G = EmbeddedComplex() # Using the unified EmbeddedComplex class \n", " G.add_cycle(shape)\n", " G.transform_coordinates(projection_type=\"pca\")\n", " G.scale_coordinates(1)\n", " result = ect.calculate(G)\n", - " return result\n", - "\n" + " return result" ] }, { diff --git a/docs/.doctrees/nbsphinx/notebooks/Tutorial-EmbeddedComplex.ipynb b/docs/.doctrees/nbsphinx/notebooks/Tutorial-EmbeddedComplex.ipynb new file mode 100644 index 0000000..1738074 --- /dev/null +++ b/docs/.doctrees/nbsphinx/notebooks/Tutorial-EmbeddedComplex.ipynb @@ -0,0 +1,624 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: ECT for Embedded Cell Complexes\n", + "\n", + "This tutorial will walk you through using the `ECT` package. Particularly we will show the features of `EmbeddedComplex` class for computing the Euler Characteristic Transform on complexes with arbitrary dimensional cells.\n", + "\n", + "The `EmbeddedComplex` class combines and extends the functionality of the previous `EmbeddedGraph` and `EmbeddedCW` classes, supporting:\n", + "- **0-cells** (vertices) with embedded coordinates\n", + "- **1-cells** (edges)\n", + "- **k-cells** for k ≥ 2 (faces, volumes, and higher-dimensional cells).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ect import EmbeddedComplex, ECT, Directions\n", + "from ect.utils.examples import create_example_graph, create_example_cw, create_example_3d_complex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Usage: Creating Simple Complexes\n", + "\n", + "### Example 1: Graph (1-skeleton)\n", + "\n", + "Let's start with a simple triangle graph (for legacy users this can be equivalently done using `EmbeddedGraph`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of nodes: 3\n", + "Number of edges: 3\n", + "Embedding dimension: 2\n" + ] + } + ], + "source": [ + "K = EmbeddedComplex()\n", + "\n", + "K.add_node('A', [0, 0])\n", + "K.add_node('B', [1, 0])\n", + "K.add_node('C', [0.5, 0.866])\n", + "\n", + "K.add_edge('A', 'B')\n", + "K.add_edge('B', 'C')\n", + "K.add_edge('C', 'A')\n", + "\n", + "#using built-in plotting function along with matplotlib\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400)\n", + "ax.set_title('Simple Triangle Graph\\n(0-cells: vertices, 1-cells: edges)')\n", + "plt.show()\n", + "\n", + "#print some information about the complex\n", + "print(f\"Number of nodes: {len(K.nodes())}\")\n", + "print(f\"Number of edges: {len(K.edges())}\")\n", + "print(f\"Embedding dimension: {K.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's add a 2-cell (face) to fill in the triangle:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of 2-cells (faces): 1\n", + "Faces: [('A', 'B', 'C')]\n", + "Internal cells dictionary: {2: [(0, 1, 2)]}\n" + ] + } + ], + "source": [ + "K.add_face(['A', 'B', 'C'])\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400, face_alpha=0.3, face_color='lightblue')\n", + "ax.set_title('Triangle with Face\\n(0-cells: vertices, 1-cells: edges, 2-cells: faces)')\n", + "plt.show()\n", + "\n", + "print(f\"Number of 2-cells (faces): {len(K.faces)}\")\n", + "print(f\"Faces: {K.faces}\")\n", + "print(f\"Internal cells dictionary: {dict(K.cells)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Cells of Arbitrary Dimension\n", + "\n", + "The key new feature is the ability to add cells of any dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimension 2: 4 cells\n", + "Dimension 3: 1 cells\n", + "\n", + "Total embedding dimension: 3\n" + ] + } + ], + "source": [ + "# Create a 3D tetrahedron with 0, 1, 2, and 3-cells\n", + "K_tetra = EmbeddedComplex()\n", + "\n", + "# Add vertices (0-cells)\n", + "vertices = {\n", + " 'A': [0, 0, 0],\n", + " 'B': [1, 0, 0],\n", + " 'C': [0.5, 0.866, 0],\n", + " 'D': [0.5, 0.289, 0.816] \n", + "}\n", + "\n", + "for name, coord in vertices.items():\n", + " K_tetra.add_node(name, coord)\n", + "\n", + "# Add edges (1-cells) - all pairs\n", + "edges = [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]\n", + "K_tetra.add_edges_from(edges)\n", + "\n", + "# Add faces (2-cells) - all triangular faces\n", + "faces = [['A', 'B', 'C'], ['A', 'B', 'D'], ['A', 'C', 'D'], ['B', 'C', 'D']]\n", + "for face in faces:\n", + " K_tetra.add_cell(face, dim=2) # Explicitly specify dimension\n", + "\n", + "# Add volume (3-cell) - the entire tetrahedron\n", + "K_tetra.add_cell(['A', 'B', 'C', 'D'], dim=3)\n", + "\n", + "# Plot the tetrahedron\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "K_tetra.plot(ax=ax, face_alpha=0.3, face_color='cyan', node_size=100)\n", + "ax.set_title('Tetrahedron with All Cell Types\\n0-cells: 4, 1-cells: 6, 2-cells: 4, 3-cells: 1')\n", + "plt.show()\n", + "\n", + "# Display cell counts\n", + "for dim in sorted(K_tetra.cells.keys()):\n", + " print(f\"Dimension {dim}: {len(K_tetra.cells[dim])} cells\")\n", + " \n", + "print(f\"\\nTotal embedding dimension: {K_tetra.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ECT Computation with Higher-Dimensional Cells\n", + "\n", + "The ECT computation now properly includes all cell dimensions in the Euler characteristic calculation:\n", + "\n", + "**χ = Σ(-1)^k × |k-cells below threshold|**\n", + "\n", + "Let's see how this works:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ECT result shape: (8, 20)\n", + "Directions: 8 directions in 3D\n", + "Thresholds: 20 threshold values\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute ECT for the tetrahedron\n", + "ect = ECT(num_dirs=8, num_thresh=20)\n", + "result = ect.calculate(K_tetra)\n", + "\n", + "print(f\"ECT result shape: {result.shape}\")\n", + "print(f\"Directions: {len(result.directions)} directions in {K_tetra.dim}D\")\n", + "print(f\"Thresholds: {len(result.thresholds)} threshold values\")\n", + "\n", + "# Plot the ECT matrix\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "result.plot()\n", + "plt.title('ECT of Tetrahedron (includes 3-cells in computation)')\n", + "plt.show()\n", + "\n", + "# Show a single direction\n", + "\n", + "single_direction = ECT(num_thresh=20, directions=Directions.from_vectors([[1, 0, 0]])).calculate(K_tetra)\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "ax.plot(single_direction.thresholds, single_direction[0], 'b-', marker='o', linewidth=2)\n", + "ax.set_xlabel('Threshold')\n", + "ax.set_ylabel('Euler Characteristic')\n", + "ax.set_title('ECT Curve for Single Direction (v=[1, 0, 0])')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also restrict self-intersections by using the 'validate_embeddings' argument. Currently without checks we can add a node inside of our tetrahedron." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unexpected exception formatting exception. Falling back to standard exception\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3548, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/81/3x5xj5kx4ys30p1c2z55bhbw0000gn/T/ipykernel_80902/4266954845.py\", line 1, in \n", + " K_valid = K_tetra.copy()\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/networkx/classes/graph.py\", line 1642, in copy\n", + " G.add_nodes_from((n, d.copy()) for n, d in self._node.items())\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 177, in add_nodes_from\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 120, in wrapper\n", + " )\n", + " \n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 142, in wrapper\n", + " def wrapper(self, *args, **kwargs):\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 157, in add_node\n", + " return wrapper\n", + " ^^^^^^^^^^^\n", + "TypeError: float() argument must be a string or a real number, not 'dict'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 2142, in showtraceback\n", + " stb = self.InteractiveTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1435, in structured_traceback\n", + " return FormattedTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1326, in structured_traceback\n", + " return VerboseTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1173, in structured_traceback\n", + " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1088, in format_exception_as_a_whole\n", + " frames.append(self.format_record(record))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 970, in format_record\n", + " frame_info.lines, Colors, self.has_colors, lvals\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 792, in lines\n", + " return self._sd.lines\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 734, in lines\n", + " pieces = self.included_pieces\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 681, in included_pieces\n", + " pos = scope_pieces.index(self.executing_piece)\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 660, in executing_piece\n", + " return only(\n", + " ^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/executing/executing.py\", line 116, in only\n", + " raise NotOneValueFound('Expected one value, found 0')\n", + "executing.executing.NotOneValueFound: Expected one value, found 0\n" + ] + } + ], + "source": [ + "K_valid = K_tetra.copy()\n", + "\n", + "K_valid.add_node('E', [0.5, 0.289, 0.204])\n", + "K_valid.add_cell(['E', 'B'], dim=1)\n", + "\n", + "\n", + "\n", + "# Display cell counts\n", + "print(\"4D Simplex Cell Counts:\")\n", + "for dim in sorted(K_valid.cells.keys()):\n", + " print(f\" {dim}-cells: {len(K_valid.cells[dim])}\")\n", + "\n", + "# Plot (showing 3D projection)\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "K_valid.plot(ax=ax, face_alpha=0.1, node_size=80)\n", + "ax.set_title('4D Simplex (5 vertices, cells up to dimension 4)')\n", + "plt.show()\n", + "\n", + "# Compute ECT\n", + "ect_4d = ECT(num_dirs=6, num_thresh=15)\n", + "result_4d = ect_4d.calculate(K_valid)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "result_4d.plot()\n", + "plt.title('ECT of 4D Simplex\\n(alternating sum includes all dimensions 0-4)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding ECT with Projection Visualization\n", + "\n", + "Let's visualize how the ECT computation works by showing projection values:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dk6arj4FZs2Zp1LNs2TIBQAAQYWFh+dom6vL9+vXTWPbZs2eFmZmZcHJyEsnJydL0OnXqCBsbG43jSwgh/v33XwFAjB07Nl/LVW/XBw8eaEy/cOGCsLCwEDKZTNy8eVNjfStVqiRu3bqlUV5f+3TXrl0CgAgMDNRYN5VKJT788EMBQISEhEjTt2zZIgCIunXriri4OI260tPTxcOHD6XX6vjV6/MyNzc34ebmpjFt5syZAoB49913NfbL6dOnhZmZmbC3txcJCQnS9KCgIAFA2NnZicuXL0vTk5OTRY0aNYRcLtc4hnIze/ZsAUBMnTpVa1737t0FAPHvv/9K027cuKFV7v79+6JChQqievXqGtNv3rwpAIiBAwfmuQ3UdO2vn3/+WQAQgwcPFmlpadL01NRU0blzZwFAhIeHCyGyrmcymUw0aNBAZGRkaNSTkZEhnjx5onO5L1Of89mp962Hh4e4f/++ND02NlbY29sLGxsbjWtEXvXXrFkz1zJ79uwRAESLFi2kaTltU/V1Z+XKlVr15DavX79+AoD45ZdfNKZHR0eLypUrCycnJ43rrnr/eHt7i0ePHmm8Jy0tTbi7uwsbGxtx+vRpjXkHDx4UJiYmolOnThrT1dekkSNHarR9y5cvFwDE8OHDNcrrOj7yI6drUHYpKSnC1NRUyOVykZ6eLk3Xdbyqz0ErKytx7tw5rboKul179uwpAIjPPvtMq66HDx9qxKPr2FTT1W4X5rrp5uYmAIiuXbtqHNO7d++Wrp1q586dEwBEt27dtOJJSUnJVztPRERUmjAhSkRGrVatWgKARjIiL6mpqcLc3Fw4OjqKpKQkrflt2rQRAMSBAwekaeov4KtWrdIqX6VKFQFA7N+/X2N6RkaGUCgUomXLlhrT1V8I16xZo1XX6tWrBQAxatQoaZquL1anT5/W+eVJbfz48QKAOH/+vMb0sWPHCgBi4sSJolatWkKhUEgJjLw8fPhQABDly5fPV3m1AwcOCACiffv2WvMSExOFg4ODMDc31/iyp+vLdkBAgAAgjh8/rlXPV199JQCIL774QpqmTj50795dq3xmZqYoU6aMqFq1qkbiS23z5s0CgFiyZIkQ4sUx4+zsrJUMzszMFNWrVy9wQtTExERERUVpzRsyZIhWInDRokUCgFi+fLlG2W7dugkA4uLFi/larnq7TpgwQQQFBYmpU6eKd999V1hYWAgAYvTo0VJZ9TG/aNEirXr0tU+7dOkiAGglXIV4kVDr2bOnNK19+/YCgNi7d2+e61qYhGiVKlWEQqEQd+7c0Sr/wQcfCADit99+k6apkzHTp0/XKq+et3nz5jxjFUKIBw8eCFNTU1G5cmWNhFRMTIxQKBSiQoUKWolFXT7++GMBQOPY0ldCtF69esLKykojWa+mTsZMmDBBCCFEfHy8ACB8fX11nmP5lVtCdMWKFVrl1fN0Jcdyqj+vhGhkZKQAIGrXri1N02dCNDY2VpiYmIiAgACdy1+8eLEAILZs2SJNU++fTZs2aZX/+++/ta6H2fXo0UPI5XIRHx8vTVMnFV9OmqWnpwtTU1Ph4+OjMd2QCVEhhChXrpwAIKKjo6VpuSVEx40bp1VHQbfrgwcPhEwmE1WrVtVI+OekoAnRwlw31QlRXT+AuLm5CQcHB+m1+hzs27dvnrETERG9DthlnoiogC5fvoyUlBT4+/vrfHqxv78/QkNDERERgRYtWmjM09UdzsXFBTdu3NCaZ2JiAmdnZ+lBGi97ue7s086cOZPrOhw7dgwAEB0drXOMssuXL0v/q7s5AsDs2bOxb98+qVvrnDlz0KBBg1yX9arU66LrifPW1tZo2LAhdu3ahStXrqBu3bq51mNpaamzy6q6y3pERITWPF3lr1y5gidPnqBChQpaY8sCWUMqAC+245UrV6Tu1Obm5hpl5XI5fH19cfXq1Rxj18XV1VVn98UWLVrg119/1RhO4b333sPkyZPxyy+/SONNRkdH499//8Wbb76JOnXqFGjZ6iEZZDIZbG1t0bBhQwwZMkQanzI7XdtPX/v02LFjsLKyynHsTAsLC2kfAFlP6VYqlWjVqlWu61cYCQkJuHHjBmrXro1KlSppzff398cvv/yCiIgIrQcf6TqH1HU8ffo0X8svX748OnbsiE2bNiE0NBSBgYEAssbtTU9Px8CBA6VuwABw48YNfPPNN9i7dy/u3buH1NRUjfru37+v1+6xycnJOH/+PCpUqIA5c+ZozU9PTwfw4pyxtbVFhw4dsG3bNvj4+KB3797w8/NDo0aNoFAo9BKTPrZ7SXDy5ElkZmYiNTVV5/VcfW25fPkyOnXqpDFP1/mpbh+uXLmis76HDx9CpVLhv//+Q8OGDaXpNWrUgLW1tUZZU1NTlCtXrsRvT13boaDbNTw8HEII+Pv76+0Yza6w183sQyFkV6lSJRw9elR6Xbt2bdSrVw/r1q3D3bt30a1bN/j5+cHb2zvPIW6IiIhKIyZEiciolS9fHpcvX8a9e/dQs2bNfL1HPYZkTmOOuri4aJTLTtdDhExNTXOdp04UvEzX8tXT8npC++PHjwEAW7duxdatW3Msl5SUpPFaqVSiffv2iIiIgLm5OYYOHZrrcrJzdHSEQqHAo0ePkJqaqjFGaG5eZXu/XE/lypULXIeu5aq338WLF3Hx4sUcl6nefur9oR6/MT/LyEtO79F1DNjb26NPnz5YtWoVLly4AE9PTwQHByMjIwMffPBBgZf94MEDrSc8FyROfe3Tx48fIyMjQ2dSWi37MRwfH4+KFSsa5Mu9oa4LOT0YSpchQ4Zg06ZNWLFihZQQXblyJQDg/fffl8pdu3YNjRs3RkJCAvz9/dG5c2fY2tpCLpdj37592L9/v1aC9FU9efIEQgjcu3cv3/vrr7/+wtdff43ff/8dn3/+OYCsbTV48GB8/fXXOn+QKgh9bfe8qH/UcnJy0lud2amvR4cPH9b58C61l6/nQO7Xt7zGr325Pl3bE8japvrcnnlJTU3Fo0ePYGJiAgcHh3y9J7ftkN/tqr7mVqxYsaAh50thrzF2dnY6y5uammo8QMzU1BR79+7FjBkzsGHDBkyYMAFA1nE7atQofP755xo/qhAREZV2/LmPiIyar68vAGDPnj35fo/6S190dLTO+Q8fPtQoZyi6lq+eltMXIDV1bOqn0uf0T/1UY7Xjx4/ju+++g6OjI1JSUjBixIh8x2tqaorGjRsjPT29QE9W19f2trW1RUxMTIHr0PWkbHW5nj175rr91Mko9f7Iafk5rVtucnpPTsfAhx9+CAD45ZdfAGQ94MrW1hZ9+vQp8LILIrftp4996ujomOs+uHnzplTe3t5eurtN30rCdaFDhw5wcXHBpk2b8PjxY5w8eRIXLlxAq1atUK1aNancggUL8OTJEwQHByM0NBQLFy7EF198gRkzZqBWrVr5Xp5cLkdGRobOeS//KKNe7wYNGuS6v7I/5d7S0hKzZs3CjRs3cOPGDfz666+oWbMmFi1apPMBaCWV+inrjRo1Mkj96m07YcKEXLdt9gcYqeV2fm7ZsiXX+gxxp7U+HD58GBkZGfD29pYS3HnJbTvkd7uqH7527949/axIDvEY8hrj6OiIJUuW4N69e7h06RK+//57ODg4ICgoCN9++22h6yUiIiqJmBAlIqM2aNAgmJiY4Oeff5a6OedEfcdUrVq1YG5ujpMnTyI5OVmrnPrLrz6emJubgwcP5jitfv36ub63SZMmAKDRXS4viYmJ6NevH0xNTbFv3z707NkTf/75Z47dlXVRd9f++uuvIYTItax6e6vXRb1ds0tKSkJ4eDgsLCzyvMO3fv36SE5OxokTJ7TmFXSf1a5dG7a2tggPD8/xDt7satSoAXNzc4SHhyMlJUVjnkqlwpEjR/K13Oxu376NW7duaU3P6Rho2rQp6tWrhzVr1mDXrl24evUq3n333Ve+y64w9LVPmzRpgkePHuV7uIHGjRsjNTUV+/fvz7Os+k6o/N7ZZmtriypVquDatWs6EyJFcV0wMTHBwIEDkZqaijVr1kjnpvq8U7t+/ToAaD1JXgiR651wLytTpgxiYmK0kqJJSUla+8TGxga1a9dGZGRkobpPe3h44P3338f+/fthbW2NzZs3F7iO4hAbG4tly5YBAN555x2DLKNRo0aQyWQFup7npjDtQ0EV9PzKL5VKha+++goA0Ldv31eqq6DbtWHDhpDL5QgLC8tXuyCXywu0/vq6buaHTCZD7dq18dFHHyE0NBQASs05R0RElF9MiBKRUatWrRomTZqEuLg4tG/fXuNuMrWUlBTMnz9fGkPMzMwMffv2RVxcHL755huNsjt27MDOnTtRrVo16e5TQ/nyyy817sKKj4/HrFmzIJPJtO7sfFnjxo3RpEkTrFu3DuvXr9ear1KptJJGI0eOxI0bNzB37lx4enril19+QeXKlTF69Gj8999/+Yp5wIABaNGiBfbt24fBgwcjMTFRq0x0dDQ++OAD7NixA0DWXbxVq1bF9u3bsXv3bo2ys2bNwqNHj9C3b1+YmZnlumz1NpkyZYrGl9U7d+5g/vz5MDU1xbvvvpuv9TA1NcWIESNw69YtTJw4UeeX3wsXLkh3hCqVSvTp0wcxMTHS+Jtqy5cvz/f2yy4zMxOfffaZRmL53LlzWL16NZycnNChQwet9wwfPhyPHz/G4MGDAaBQ3eX1QV/7dPTo0QCyuoM/evRIa/7Dhw8RGRkpvf7oo48AAGPGjJG6w6plZGRo3Hml7mp7586dfK/XwIEDkZ6ejilTpmjtl+DgYNjZ2aFbt275rq8w1F3jf/75Z/zxxx+ws7NDr169NMqoxwY9dOiQxvTZs2fjwoUL+V5Wo0aNkJ6ertG1WgiBKVOm6OyePXr0aCQnJ+ODDz7QOf/mzZuIiooCkJVI1BXLkydPkJqaqjUWb0l08eJFtG3bFjExMRg4cKDGeJv6VL58efTp0wdHjhzBd999p/PHpuPHj+v8AU+Xrl27wtXVFfPnz9d5N396errWsVNQhTm/8hIXF4f+/ftj7969qFOnToF6MOhS0O1arlw59OzZE9evX9c5LMTLPx44ODjg7t27+Y5HX9fNnERFRUnnX3bq62JpOOeIiIgKgmOIEpHRmzVrFlJSUrBgwQLUrFkTAQEB8PT0hEKhwM2bN7F79248evQIs2bNkt4zZ84c7N+/H7NmzcKRI0fQpEkTREVF4a+//oKlpSVWrlxp8IcQ1KhRA56entKDczZs2IC7d+9i/Pjx+frivW7dOvj7++Odd97BwoUL4ePjAwsLC9y+fRtHjx5FbGysdDfjmjVrsGbNGnTu3FlKKpUpUwZr1qyBv78/+vXrh6NHj+b5IAlTU1Ns3LgRvXv3xqpVq7B582a0bdsWHh4eSEtLw6VLl7Bv3z6kp6ejf//+ALLuogkODkZgYCA6dOiA3r17w83NDUePHsW+fftQtWpVzJ49O8/1HTBgAP7++29s2rQJ9erVQ6dOnZCUlIT169fj8ePHmDdvHqpUqZJnPWozZ87E6dOnsXjxYmzduhUtW7aEs7Mz7t27h/Pnz+Ps2bM4evSoNG7o7NmzsWfPHkydOhWHDh1C/fr1ERkZiW3btqFt27bYtWtXvpcNAPXq1cOhQ4fQqFEjtG7dGrGxsVi/fj0yMjLw888/w8LCQus9/fv3x6RJk3D//n00aNAgzzuJDUVf+7Rdu3aYNm0avvzyS1SrVg3t2rWDm5sbHj16hGvXruHgwYOYNWsWateuDSCrS/nEiRMxd+5cVK9eHd27d5f22Z49ezBx4kSMHTsWABAQEIC5c+di2LBh6NmzJ6ysrODm5qb1QKTsJk2ahK1bt2L16tWIjIzEW2+9hZiYGGm//PLLL7CxsdHLNsxJ9erV0bJlSymR9eGHH2odCx9++CFWrlyJnj17ok+fPnB0dMSxY8dw+vRpdOzYMddxhbMbNWoUVq5ciaFDhyI0NBROTk44ePAgnj59Ci8vL5w9e1aj/PDhw3Hs2DGsWrUKhw8fRuvWrVGhQgVER0fj8uXLOH78OH7//Xe4u7vj3r17qF+/Pry8vFCvXj1UrFgRjx49wqZNm5Ceno6JEyfqZ4PpQVxcnPSDWUZGBh49eoTTp09Ld6MPHToUP/zwg0FjWLp0Ka5cuYJJkyZh9erVaNasGezt7XHnzh2Eh4fj6tWrePDgQb7uCFcqlQgJCUH79u3RqlUrBAQEoG7dupDJZLh16xYOHjwIR0dHjQeWFVRAQABCQkLQs2dPtG/fHubm5vDy8kLnzp3z9f65c+fC2toaKpUKCQkJuHTpEg4ePIiUlBT4+vpi3bp1ern7vaDbdenSpbhw4QK++uorbNu2DQEBARBC4L///sOuXbsQHR0tda0PCAjAn3/+iW7duqF+/fowMTFBly5dUK9ePZ2x6Ou6mZOIiAj06NEDjRs3Rp06dVC+fHncu3cPGzduhFwuL1XDVBAREeWLnp5WT0RU6p08eVK8//77olq1asLCwkIolUrh7u4u+vXrJ0JDQ7XKx8bGitGjRws3NzehUChE2bJlRa9evcT58+e1yg4cOFAAEDdv3tSa16pVK5HT5djNzU24ubnpLP/8+XMxadIkUblyZWFmZiZq1qwpFi9eLFQqlUb5sLAwAUAEBQVp1f/48WMxdepU4enpKSwsLIS1tbWoXr266Nevn/j777+FEELcuHFD2NraChcXFxEbG6tVx+effy4AiIkTJ+pcB11UKpUICQkR3bp1ExUqVBBmZmbC0tJSeHp6itGjR4tLly5pvefcuXOiV69eomzZskKhUAg3NzcxZswYnTHltE3T09PF3LlzRd26dYVSqRQ2NjaiVatWYtOmTVplV65cKQCIlStX5rgeGRkZYtmyZcLX11fY2toKpVIpXF1dRbt27cSPP/4onj17plH+1q1b4u233xb29vbC0tJStGjRQuzfv18EBQUJACIsLCzvjSeEACBatWol7ty5I95++23h4OAgzM3NRbNmzcSuXbtyfW///v0FAPHTTz/la1nZqbfrgwcP8iyb2zGvpo99KoQQoaGhonPnzsLJyUkoFApRvnx50axZM/Hll1+K27dva5XfsGGD8Pf3F3Z2dtJ5PmDAAHHhwgWNct9++62oXr26UCgU0jZX03VuCiHEs2fPxLRp00SNGjWEmZmZsLe3F+3btxcHDx7UKpvbfs/P8ZeTVatWCQACgDhx4oTOMmFhYcLX11fY2NgIe3t70aFDB3Hq1CmdMd28eVMAEAMHDtSqZ+/evaJJkyZCqVQKR0dHMWDAABEdHZ3r/lq/fr1o3bq1KFOmjFAoFKJixYrCz89PzJs3T9r3T548ETNmzBAtW7YULi4uwszMTFSoUEG0a9dObN++Pd/b4uX9JkTux2ZhzsXs/5RKpXB2dha+vr5i4sSJ4uzZszrfl9M2zW2/53VMJCcni2+//VY0aNBAWFlZCQsLC+Hh4SG6desmfvvtN5Geni6VzW3/qN29e1eMGTNGVK9eXSiVSmFraytq164thg4dKvbs2aO1HV7ezmq6zpX09HQxadIk4erqKkxNTXM8vl6mjlv9z9TUVJQpU0Z4eXmJ999/X+zYsUNkZmbmO4787O+CbFchhIiPjxfTpk0TtWrVEkqlUtjZ2Qlvb28xffp0kZaWJpV78OCB6NOnjyhbtqyQy+Ua+za3drsg182crlPZt6XanTt3xOTJk0XTpk2Fs7OzMDMzE66urqJHjx7i6NGjOW4fIiKi0komRB6DuBERUYni5+eH/fv35zkGJ5EudevWxc2bN3H//n2DP/hLn5o2bYozZ87o/ennRERERERkfDiGKBERkZHYvn07Lly4gHfffbdUJUMzMzNx8+ZNVKpUqbhDISIiIiKi1wDHECUiInrN/fjjj7hz5w6WL18Oc3NzTJ48ubhDyrfZs2cjLCwMMTExeO+994o7HCIiIiIieg2wyzwRUSnDLvNUUO7u7rh79y5q1qyJOXPmoFOnTsUdUr45ODjA2toaPXv2xNdff63zYVFEREREREQFwYQoERERERERERERGQ2OIUpERERERERERERGgwlRIiIiIiIiIiIiMhpMiBJRqTZjxgzIZDLs27evuEMxGlFRUZDJZBg0aFBxh1LqBAcHQyaTITg4uLhDAVC6zh9dsZa2Y7E0be+HDx9i4MCBqFy5MkxMTCCTyfD06dPiDouIXlFJuw4NGjQIMpkMUVFRxR0KEREZGSZEieiVqBMS2f8pFApUrFgRffr0QXh4eHGHSGS09u3bB5lMhhkzZhR3KEbhddregwYNwurVq9GyZUtMnToVQUFBMDc3L+6wiArEz88PMpmsuMMwaiXth0AiIiI10+IOgIheD1WrVkX//v0BAElJSTh16hT++usvbNy4Ebt370bLli2LOUKi4te9e3c0bdoULi4uxR0KAGDUqFF455134OrqWtyhFErFihURGRkJOzu74g4lX0rL9k5LS0NoaChat26NtWvXFnc4RKRHJe069M0332Dy5MmoWLFicYdCRERGhglRItKLatWqad0VNXv2bEyZMgXTpk3D/v37iycwohLEzs6uRCXvypYti7JlyxZ3GIWmUChQq1at4g4j30rL9n748CFUKhUqVKhQ3KEQkZ6VtOuQi4tLifmRkIiIjAu7zBORwQwZMgQAcOrUKa15aWlpmD9/Pnx8fGBlZQUbGxu0aNECmzdv1lnXnTt30LdvXzg4OMDa2hqtWrXCgQMHChzTP//8g759+6JatWqwtLSEnZ0dWrRogQ0bNmiVzT4+YWRkJLp37w5HR0dprKvs3cC2bNkCX19f2NjYwN3dXaojLi4OY8eOhYeHB5RKJZydndGnTx9cuHBBY1mLFi2CTCZDSEiIxvSxY8dCJpOhefPmOmMbPHiwNC0+Ph7Tp09HnTp1YG1tDVtbW1SrVg0DBw7ErVu38rV9NmzYgFatWsHZ2Rnm5uaoUKECWrdurXP7AMC1a9fQvXt3lClTBlZWVmjdujXOnj2rs+yFCxfQp08fODs7Q6lUwsPDA2PHjsWjR480ynXv3h1yuRyxsbEa0729vSGTyTB16lSN6er9sGrVqjzXTz1W2Y0bN/Dtt9+ievXqMDc3h4eHB7744gukp6drlM/eBfrIkSNo27Yt7O3tNbpgJiUlISgoCLVq1YK5uTkcHBzQsWNHHD58WGv5uXUdvHnzJoYOHQpXV1colUq4uLhg0KBBOe67GzduYNiwYRrHlp+fn1T3jBkz4O/vDwCYOXOmxrAW6rHachtLbsuWLfD394ednR0sLCzg5eWF+fPnIyMjQ6Nc9vOkIMdDTgpyruc0hqi6m2xKSgqmTp2KqlWrQqFQaPxow+2tm5+fH9zc3AAAq1atktZBvY3v37+PoKAgNG3aVDqX3d3dMXLkSMTExOisMy0tDQsWLECjRo1gY2MDa2tr1KlTB+PHj8eTJ080ysbExGDcuHGoVq0alEolypYti549e2pdMwHg6tWrGDx4sLRPHBwc4OXlhbFjx0IIkee65jZ2YU77qiDXyHPnzuGdd96Bi4sLzMzM4Obmho8//ljrmpcbd3d3uLu7Iz4+HiNGjICLiwusrKzQsmVLnD59GkDWPunfvz+cnZ1hYWGBtm3b4urVq1p1FaT9+/DDDyGTyTB79uwc582ZMyfX2FevXg2ZTIYvvvhC5/zTp09DJpPh3Xff1ZhekGNAvX2ePn2KUaNGoXLlyjA1NZWuteofY7Ofjy9fL/Kznx49eoRKlSrBxsYG165d03h/bvN0yX59mjx5MlxdXWFubo7atWtjyZIlWseuPj9rALlfhwp6zJ49exbvvvsuKlWqJF1H27Vrhy1btgDIOsfUn1MGDx6ssR/UcjsPV65ciSZNmsDa2hrW1tZo0qSJzvYze1sdHh6ONm3awMbGBnZ2dujevTvHJyUiIp14hygRGZypqealJjU1Fe3atcO+ffvg7e2NIUOGID09HVu3bkXXrl2xZMkSjBo1Sir/4MEDNGvWDPfu3UNgYCB8fHwQGRmJNm3aSAmI/JoyZQrMzMzQvHlzuLi4IDY2Fps3b0avXr2wePFifPzxx1rvuXbtGpo2bYq6deti0KBBePToEczMzKT5f/31F3bt2oVOnTph5MiRSEhIAADExsaiWbNmuH79Ovz8/PDOO+/g5s2bCAkJwdatW7Fz504p0alej7CwMPTq1UuqOywsDABw4sQJJCUlwcrKSmO6+n1CCAQGBuL48ePw9fVFu3btIJfLcevWLWzevBkDBgyQkhw5+fHHHzFy5Ei4uLhIyd+HDx/ixIkT+Oeff9CzZ0+N8lFRUWjatCneeOMNvP/++7h+/To2bdoEf39/REZGoly5clLZQ4cOITAwEGlpaejVqxfc3d1x9OhRLFq0CP/++y+OHTsm3bHi7++PjRs3Yt++fejduzeArC+c586d01j3l7dRQY6FsWPH4vDhw+jTpw+sra2xZcsWBAUF4dy5c1pJaQA4cuQIvv76a/j7+2PYsGG4ffs2ACAlJQUBAQE4ceIEfHx8MHbsWERHR2P9+vXYuXMn1q1bJ61Dbo4fP47AwEAkJSWhU6dOqF69OqKiorB27Vps374dR48eRZUqVTS2Z8eOHZGYmIjAwEC88847ePLkCc6cOYNFixZh0KBB8PPzQ1RUFFatWoVWrVrBz89Per+9vX2u8cyfPx8TJkyAg4MD+vXrBysrK2zevBkTJkzAwYMH8ffff2uNy1eQ4yEn+jzXAaBnz544e/Ys2rVrB3t7e3h4eADg9s7NoEGD4O3tjUWLFsHLywvdunUDkPWDBAAcOHAA8+bNw1tvvYUmTZpAoVDgzJkz+PHHH7Fz506cPn1a4y7o58+fo02bNjh8+DCqV6+OwYMHQ6lU4urVq1i2bBnee+89lClTBgCka+Xdu3fRtm1bdOvWDTExMdiwYQN27tyJPXv2oEmTJgCykoCNGzdGUlISOnbsiLfffhtJSUm4evUqli5dirlz52q1Pa+qINfIzZs3o0+fPpDL5ejatSsqV66MS5cu4fvvv8fOnTtx/Phxab3zkpaWhjZt2iAlJQVvv/02oqOj8eeff6J169Y4cuQIAgMD4eLigv79++PatWvYsmULOnbsiMjISJiYmEj1FKT9W7BgAQ4cOIDp06fjrbfeQqNGjQBkJVWXLVuGgIAAfPLJJ7nG3aNHD4wYMQJr167F9OnTteavXr0aADBgwABpWkGOAbXU1FQEBATg2bNn6NKlC0xNTVGuXDkEBQUhODgYt27dQlBQkFRefSwD+d9Pjo6O+O2339CmTRv069cPhw8fhkKhAJD14++9e/cQHByMatWq5bU7JX369MGZM2ek42bDhg0YPXo0oqKiMG/ePK3y+viskZuCHrMbNmxAv379IIRA586dUbNmTcTExOD48eP49ddf0blzZ3Tr1g1Pnz7Fpk2b0LVrV41tn5fRo0djyZIlqFixovQD+4YNGzB48GDp2vuykydP4ttvv4W/vz+GDx+OM2fOYOPGjTh//jwuXLjAcZCJiEiTICJ6BTdv3hQARGBgoNa8r7/+WgAQHTt21Jj+2WefCQBi2rRpQqVSSdMTEhJEw4YNhZmZmbh37540feDAgQKAmDVrlkY9y5YtEwAEABEWFpaveK9fv641LTExUdStW1fY2dmJpKQkrXUDIKZPn671vpUrVwoAQi6Xi9DQUK35gwcPFgDElClTNKZv3bpVABDVqlUTmZmZQgghVCqVcHR0FLVr15bKxcXFCZlMJt566y0BQOzcuVOaN2DAAAFA3L59WwghxLlz5wQA0a1bN604UlJSRGJiYl6bRvj4+AgzMzMRHR2tNS8uLk76O/t2mT17tka5qVOnCgDim2++kaZlZmaKqlWrCgBix44dGuU/+eQTAUC8//770jT1uowYMUKatmHDBgFAvPXWW0KhUIhnz55J8ypXriyqVKmS5/oJ8eJYcnJyEnfu3JGmp6amipYtWwoAIiQkRJoeFhYmreuKFSu06ps5c6YAIN59912NY/n06dPCzMxM2Nvbi4SEBGm6+phZuXKlNC0tLU24u7sLGxsbcfr0aY36Dx48KExMTESnTp2kaSkpKaJixYpCLpeL7du3a8WUfb3U8QcFBencHkFBQVrnz7Vr14SpqalwdnaWji/1cps3by4AiN9++02aXtDjITcFPdfVyx44cKBG+VatWgkAwtvbWzx69EhjHrd33nLarkIIER0drfN6smrVKp37bsKECQKAGDBggMjIyNCY9/TpU4263nzzTWFiYqJ1nbhy5YqwsbERdevWlaYtXrxYABALFy7UiuXlfZ4T9fF28+ZNrXm69lV+r5FxcXHC1tZWVKxYUURFRWmUW7dunQAgRo0ala8Y3dzcBADRu3dvkZ6eLk2fM2eOACDs7e3FuHHjNK4/I0aMEADEhg0bNOoqSPsnhBARERFCqVSKqlWrisTERHHnzh3h4OAgHB0dNdro3PTv318AEMePH9eYnpGRIcqVKyfKly+vcVwU5BjIvn0CAwNFcnKy1vLV1wJdCrOfJk+eLACISZMmCSGE+OGHHwQA0bdv3zy2hHZMNWvWFE+fPpWmP336VNSsWVPIZDJx8uRJabo+P2sIofvYLui2ePjwobCyshJWVlZa11EhNK+Lutq97HSdh/v37xcARO3atTW20ePHj0WNGjUEAHHgwAFpeva2+o8//tCoX/15ad26dTqXT0RExosJUSJ6JeovzlWrVhVBQUEiKChITJw4Ufj7+wsAoly5cuLSpUtS+czMTFGmTBlRtWpVjS9waps3bxYAxJIlS4QQWYkqc3Nz4ezsLJ4/f65RNjMzU1SvXr1ACdGczJs3TwAQ+/bt01q38uXLi9TUVK33qD/kd+/eXWueOm5HR0etL5lCCNGmTRutD/Q9e/YUAMSDBw+EEEKEhIRI66ZUKsWnn34qla1UqZJGElCdRCzIl7KX+fj4CCsrK/H48eNcy6m3i4eHh8aXrOzzevToIU07cOCAACDat2+vVVdiYqJwcHAQ5ubm0jZWqVSibNmyombNmlK5UaNGCWtra7Fz504BQEpMXbt2TSuhmpucEm5CZCXDAGgkw9Rfsnx8fHTWV6VKFaFQKDS+/Kl98MEHWsksXV8M//77bwFAfPHFFzqX0aNHDyGXy0V8fLwQQoj169cLAOK9997Lc30Lk6D74osvBAAxZ84crfKHDx8WAERAQIA0raDHQ04Kc67nlRDdtGmT1nK4vfOWW0I0JyqVStja2go/Pz9pWnp6urCxsRF2dnZ5XldOnz6d67k8fvx4AUCcP39eCPEiIbps2bJ8x/iywiRE83ONnD9/vta5n52Pj48oW7ZsvmJUJ/xu3bqlMf327dsCgLC2ttZqY9TXXF0/5Omiq/1TW7hwoQAg+vfvL/z8/HI8r3KivmZ//PHHGtO3bdsmAIixY8dK0wp6DAjxYvucPXtW53tyS4gWZj+lpaWJRo0aCblcLhYvXiwsLCyEu7u7dL3ID3VMa9as0Zq3evVqreSjvj9r6Dq2C7ot1An5/BxjhUmIvv/++wKAWL9+vVb5tWvXah0n6mtvy5Yttcqr540fPz7PWImIyLiwyzwR6cX169cxc+ZMjWnly5fHwYMHNbqQXblyBU+ePEGFChW0ygOQxo28fPmyVF7dLfnlrk5yuRy+vr46x0rLSUxMDGbPno3t27fj1q1beP78ucb8+/fva73Hy8tLo4v8yxo3bqw17fLly0hJSYG/vz8sLS215vv7+yM0NBQRERFo0aKFNG3Dhg0ICwtD3759ERYWBhsbGzRv3hxNmzaVuoZfu3YNd+/elbqQAUDt2rVRr149rFu3Dnfv3kW3bt3g5+cHb29vyOX5Gy76nXfewaRJk+Dp6Yl+/frB398fzZs3h62trc7yuuquVKkSAODp06fStDNnzgCARhdiNWtrazRs2BC7du3ClStXULduXchkMvj5+SEkJAQPHjyAi4sLwsLC0KJFC7Rs2RJKpRJhYWFo165dobrLA5C2eXbNmjWDqampFG926u6i2SUkJODGjRuoXbu2tN7Z+fv745dffkFERIRGl9CXHTt2DEDWsf7yg8mAFw+4+e+//9CwYUOcOHECANC2bdsc63wVue2vZs2awdzcHBEREVrz8ns85ETf5zqg+9zk9n51f//9N5YtW4bTp0/jyZMnyMzMlOZlv4ZevnwZiYmJaN26dZ7dw9X7JTo6Wud+UbcJly9fhqenJzp37owpU6bgo48+wp49e9CuXTu0atVKY6gDfcvvNVK9LsePH8f169e16klJSUFcXBzi4uLy9XCbMmXKaD0RXP0QmurVq2u1Mep5L7dnhWn/Ro8ejZ07d2LNmjUAgBEjRqBLly55xqz21ltvwcXFBX/88Qfmz58vDWOgri/7tbGgx4Caubk56tatm++YXl5eQfaTQqHAunXr4O3tjdGjR8PExARr167NsZ3Mja52SD1NVzukr88auhR0WxTndVHd3uu6LjZo0EBrWlFdF4mIqPRhQpSI9CIwMBA7duwAkJXUXLVqFT799FN06dIFJ06cgLW1NQDg8ePHAICLFy/i4sWLOdaXlJQEIOtBQQDg7Oyss1xBxsl7/PgxGjVqhNu3b8PX1xetW7eGvb09TExMEBERgU2bNiE1NbXAy9A1Xz22V07vVX9hVZcDNMcRVSdEW7ZsCVNTU/j7++PLL79EQkKCziSgqakp9u7dixkzZmDDhg2YMGECAMDJyQmjRo3C559/rjGWnC4TJ06Eo6MjfvzxR8ybN08ag69jx45YsGCBNP6imq4vgOovu9kTJIXdFiEhIQgLC0ObNm1w8eJFDBo0CObm5mjWrJm0DQqbENUVi4mJCRwdHaVjLq/yhVkvXdTnxNq1a3Mt9/I5UbFixVzLF1Zu6yWTyVCuXDncu3dPa15+j4ec6PNcz+093N6vZt68eZg4cSKcnJzQtm1bVKpUCRYWFgCAhQsXalxDC7Lt1Ptl69at2Lp1a47l1PvF3d0dx44dw4wZM7Bt2zb8+eefAIBatWrhiy++yNfYvQWV32ukel1++OGHXOtLSkrKV0I0t32d27zsD4krbPsnk8nQrVs3bN++HQB0jrOdGxMTE/Tr1w/z5s3Dzp070bFjRzx79gwbN25EnTp14OPjoxEjkP9jQM3Z2VlrjN38KOx+qlKlCry8vHD48GE0aNAAb775ZoGXDeg+59XTiqsdyu+2KIrrolwuh5OTk9a8cuXKQSaT6Vyn4rouEhFR6cSnzBOR3jk5OWHixIn47LPPEBkZqfFUcPWH1Z49e0JkDduh89/KlSsBQHo4R05PL46Ojs53XL/++itu376NL7/8EocOHcKSJUvw5ZdfYsaMGWjatGmO78vri5au+er1zCm+hw8fapQDgDp16qBcuXIICwtDTEwMLl26JCX6/P39kZmZiYMHD0pPhn05Cejo6IglS5bg3r170oMQHBwcEBQUhG+//TbXdVCvx/vvv4+TJ08iNjYW//zzD3r06IFNmzahU6dOhf4yUZhtkT05/PL6+vv74/Tp04iPj8e+fftQvXr1An8p0xVLZmYmHj16pPFAGDV97WNd1PO3bNmS6znRqlUrAC8e0KMrSaYPua2XEALR0dGFuhsqL/o819Vy22/Gvr0LIyMjA19++SVcXFxw4cIFrF27FnPmzMGMGTMQFBSEtLQ0jfIF2XbqdVQ/ZTunfwMHDpTe4+npiZCQEDx+/BhHjx7F9OnT8fDhQ7z99ts4fPhwnstU32GbkZGhNU9XQiq/10j1upw/fz7XdcnrQXf6VNj27+bNm/jkk0/g4OAAmUyGoUOHFrgtUN8Fqr4rdMOGDUhOTta6c74wxwCQdxudk8Lup/nz5+Pw4cNwdHTEiRMnsHTp0kItX9c5r55WXO1QfrdFUVwXVSqV1Gsou5iYGAghSsx1kYiISi8mRInIYD777DNUqFABS5cuRVRUFICsrt22trYIDw/XuHslJzVq1IC5uTnCw8ORkpKiMU+lUuHIkSP5jkfdDaxr165a8w4ePJjvevKjVq1aMDc3x8mTJ5GcnKw1X53ke/mJq35+frh27Zr09N2AgAAAQNOmTWFhYYG9e/ciLCwM1atXR4UKFXQuWyaToXbt2vjoo48QGhoKIOvpsQXh6OiIbt26Yf369QgICMClS5dw7dq1AtWhVr9+fQAv1jm7pKQkhIeHw8LCAjVr1pSm165dG+XLl5fWt0yZMlI9AQEByMzMxPLly3H//n2dXeryomt/Hz16FBkZGdJy8mJra4sqVarg2rVrOr8U5rSPX6Z+YvLRo0fztVx1t8ldu3blWVZ9V3BBEhi57a/jx48jJSWlQE8Kzi99nuu54fYuvLi4OMTHx6NZs2Zad/KGh4drdcGuWbMmbG1tcfLkSTx58iTXugu6X7JTKBRo2rQpZs6cicWLF0MIgX///TfP96m78es6f3V1Wc4ut2vkq6yLoRSm/cvIyMC7776LxMRErF+/HuPHj8eRI0d0DneTGy8vL9StWxebNm1CYmIi1qxZA5lMhnfffVejnCG2W27nZGGWd+bMGXz22WeoWbMmzp8/Dw8PD0ycODHXHi850bXd1dPy2w4V9rPGy0rTdTG/60RERJQXJkSJyGAsLCzw6aefIj09HV9++SWArK5LI0aMwK1btzBx4kSdSdELFy5Id4kplUr06dMHMTExmDdvnka55cuX47///st3POo7Gw4dOqQx/ffff8e2bdsKtG55MTMzQ9++fREXF4dvvvlGY96OHTuwc+dOVKtWDb6+vhrz1HdBzpkzBw4ODvDy8pLq8/X1xerVq/HgwQOtu0OjoqKkpHN26rtGXh6TUZd9+/ZBCKExLT09XepKl586dPH19UXVqlWxfft27N69W2PerFmz8OjRI/Tt21drnFY/Pz/cuHEDISEhaNWqlXQ3V+PGjWFpaYk5c+YAKHh3eQBYtGgR7t69K71OS0vD559/DgAYNGhQvusZOHAg0tPTMWXKFI1td+7cOQQHB8POzg7dunXLtY6uXbvC1dUV8+fPx4EDB7Tmp6enaxyzXbp0QaVKlbBmzRrs3LlTq3z25I6DgwMA4M6dO/lep379+sHU1BTz58/XGFMwLS0Nn376KYCCbaP80ue5nhtj3N7BwcGQyWSvXI+zszMsLCxw+vRpjeTLkydPdHalNjU1xfDhwxEfH48xY8ZoJUTi4+Px7NkzAFnndZMmTbBu3TqsX79eqy6VSoX9+/dLr0+dOqWzy2xBrnnqsYGDg4M1poeEhGgsSy2/18jBgwfDxsYGn3/+uc5EWXJysjRmY1EpTPs3c+ZMHD16FBMmTEDr1q3x9ddfw8fHB19//XWBf0QcMGAAnj9/jsWLF2Pv3r1o1aoVKleurFGmoMdAfuR2ThZ0PyUlJaFv374AgHXr1sHFxQW///470tPT0bdvX60fcvLy5ZdfatyJHB8fj1mzZkEmk2ndBZuTwn7WeFlBt8XAgQNhbW2NefPm6RzL81Wvi+r1nzlzpsZ5Hh8fLyXk87uNiIiIcsIxRInIoIYNG4Y5c+bgt99+w2effYaqVati5syZOH36NBYvXoytW7eiZcuWcHZ2xr1793D+/HmcPXsWR48ele5Amj17Nvbs2YOpU6fi0KFDqF+/PiIjI7Ft2za0bds2X3coAFlfyObMmYOPP/4YYWFhcHNzw9mzZ7Fnzx706NEDf//9t17Xfc6cOdi/fz9mzZqFI0eOoEmTJoiKisJff/0FS0tLrFy5UuuhKOrkXmxsLLp3764x39/fX0oovpwEjIiIQI8ePdC4cWPUqVMH5cuXx71797Bx40bI5XKMGzcuz3i7desGW1tbNG3aFG5ubkhPT0doaCguXbqEXr16Fbp7p1wuR3BwMAIDA9GhQwf07t0bbm5uOHr0KPbt24eqVati9uzZWu/z9/fHH3/8gdjYWI31VSeH1Xe/FuYO0aZNm8LLywtvv/02rKyssGXLFly5cgU9evRAz549813PpEmTsHXrVqxevRqRkZF46623EBMTg/Xr1yMjIwO//PILbGxscq1DqVQiJCQE7du3R6tWrRAQECA9XOrWrVs4ePAgHB0dpQeKKJVK/Pnnn2jXrh3at2+Pdu3awcvLCwkJCYiIiEBycrJ0d1utWrVQoUIF/PHHH1AqlahUqRJkMhk+/vhjnV0yAaBq1aqYM2cOJkyYgHr16qFPnz4a26hr167o379/vrdRQejrXM+NMW5vlUoF4MVYeoUll8sxcuRIzJs3D15eXujcuTMSEhKwfft2uLm56bxr/YsvvsCxY8ewevVqHDt2DO3bt4dSqcSNGzewY8cOHDp0SLrTa926dfD398c777yDhQsXwsfHBxYWFrh9+zaOHj2K2NhYKem0evVqLFu2DC1btkTVqlVha2uLS5cuYdu2bXBwcMDgwYPzXJ+uXbuiatWqCA4Oxp07d6Tjbe/evejQoYNWojC/10gnJyesW7cOvXv3hpeXF9q1a4datWohNTUVUVFR2L9/P958801p3O2iUND278CBA1IC9KuvvgKQde39/fff0aBBA/Tv3x9nz56Vuk7npV+/fpg8eTJmzpwJlUqV44PmCnIM5EdAQABCQkLQs2dPtG/fHubm5tKxW9D9NGbMGFy5cgVz586V7mBs2rQpgoKCMG3aNHzyySdYsmRJvmOrUaMGPD09pTZnw4YNuHv3LsaPH4+GDRvmu57CfNZ4WUG3hbOzM3777Te88847aNy4Mbp06YKaNWsiLi4Ox48fh7u7OzZu3Agg6+FwFhYWWLhwIZ48eSKNC5p9OKWXtWzZEh9//DGWLFkibSMhhLSNRo8ejZYtW+Z7GxEREen06g+qJyJjdvPmTQFABAYG5lhmyZIlAoAYMGCANC0jI0MsW7ZM+Pr6CltbW6FUKoWrq6to166d+PHHH8WzZ8806rh165Z4++23hb29vbC0tBQtWrQQ+/fvF0FBQQKACAsLy1e8ERERom3btqJMmTLCxsZGtGrVSuzevVusXLlSABArV67UWreBAwfqrEvXe14WGxsrRo8eLdzc3IRCoRBly5YVvXr1EufPn8/xPRUrVhQAxJIlSzSmHzlyRAAQAMSDBw805t25c0dMnjxZNG3aVDg7OwszMzPh6uoqevToIY4ePZrndhFCiKVLl4ouXboINzc3YW5uLhwdHUXjxo3Fjz/+KNLS0qRyeW0XAKJVq1Za08+dOyd69eolypYtKxQKhXBzcxNjxowRsbGxOuv577//pPV9eXt9/fXXAoCoWbNmvtZNbeDAgQKAuH79upg9e7aoVq2aMDMzE25ubmLGjBkiNTVVo3xYWJgAIIKCgnKs89mzZ2LatGmiRo0awszMTNjb24v27duLgwcPapXN7Zi5e/euGDNmjKhevbpQKpXC1tZW1K5dWwwdOlTs2bNHq/y1a9fEkCFDRKVKlYRCoRDOzs7Cz89P/Pbbbxrljh07Jlq1aiVsbGyk7Xnz5k0hhMj1/Nm0aZP0PqVSKerWrSvmzZsn0tPTNcoV9njISUHO9ZyW3apVK5HXRxxj2t7jxo0TAERoaGiu2yQ/y0hLSxNfffWVtN1cXV3FhAkTRGJionBzcxNubm5a70lJSRFz584V3t7ewsLCQlhbW4s6deqICRMmiCdPnmiUffz4sZg6darw9PSUylavXl3069dP/P3331K5Y8eOieHDhwtPT09hb28vLCwsRPXq1cWoUaPErVu38rWe6nXt1q2bsLGxEVZWVuKtt94SJ0+e1Lmv8nuNVLt8+bIYMmSIcHNzE2ZmZqJMmTKibt26YvTo0eLEiRP5ii+nbSpEzudWTvsvv+3f48ePReXKlYWVlZW4cuWKVv2//PKLACB69eqVr3VQa926tQAgzM3NRXx8fI7l8nsMCJH79hFCiPT0dDFp0iTh6uoqTE1NdW6X/OynkJAQAUC0adNGqFQqjfdnZmaKli1bCgBiy5YteW4H9fXp+fPnYtKkSaJy5crCzMxM1KxZUyxevFirfn1/1sjtOlTQY/bMmTOiT58+oly5ckKhUAgXFxfRvn178e+//2qU27p1q2jUqJGwsLCQrotq6nZZfZ3MbsWKFaJRo0bC0tJSWFpaikaNGokVK1Zolcutrc7rmklERMZLJsRLfX+IiIheU4MGDcKqVatw8+ZNuLu7F/nyf/rpJ4wYMQK///671PWSyNAaNmwIExMTHD9+vLhDITJ6fn5+2L9/v9bwC0Vl8uTJmDNnDo4cOYJmzZoVSwxEREQlAbvMExERFRH1Q1cqVapUzJGQsUhMTERERARCQkKKOxQiKgHYDhEREWVhQpSIiMjAQkNDsXHjRqxcuRIVKlRA06ZNizskMhI2NjbIyMgo7jCIqJj9+eef2L59OzZu3IiGDRtqPdSKiIjI2PAp80RERAa2detWrF27Fs2aNcOuXbugUCiKOyQiIjIiv//+O7Zs2YJOnTrp/SGSREREpRHHECUiIiIiIiIiIiKjwTtEiYiIiIiIiIiIyGgwIUpERERERERERERGgwlRIiIiIiIiIiIiMhpMiBIREREREREREZHRYEKUiIiIiIiIiIiIjAYTokRERERERERERGQ0mBAlIiIiIiIiIiIio8GEKBERERERERERERkNJkSJiIiIiIiIiIjIaDAhSkREREREREREREaDCVEiIiIiIiIiIiIyGkyIEhERERERERERkdFgQpSIiIiIiIiIiIiMBhOiREREREREREREZDSYECUiIiIiIiIiIiKjwYQoERERERERERERGQ0mRImIiIiIiIiIiMhoMCFKRERERERERERERoMJUSIiIiIiIiIiIjIaTIgSERERERERERGR0WBClIiIiIiIiIiIiIwGE6JERERERERERERkNJgQJSIiIiIiIiIiIqPBhCgREREREREREREZDSZEiYiIiIiIiIiIyGgwIUpERERERERERERGgwlRIiIiIiIiIiIiMhpMiBIREREREREREZHRYEKUiIiIiIiIiIiIjAYTokRERERERERERGQ0mBAlIiIiIiIiIiIio8GEKBERERERERERERkNJkSJiIiIiIiIiIjIaDAhSkREREREREREREaDCVEiIiIiIiIiIiIyGkyIEhERERERERERkdFgQpSIiIiIiIiIiIiMBhOiREREREREREREZDSYECUiIiIiIiIiIiKjwYQoERERERERERERGQ0mRIlykZiYiGHDhsHR0RH29vYYMGAAHj9+XNxh5UkIgbS0tOIOg4iIsimtbUpBpKamFncIRERGwRjalIJg+0NEBcWEKFEOhBDo0aMH/vnnH0yaNAnTpk3D7t270a5dO6Snpxd5PEeOHEHz5s1haWmJ8uXLY/To0Xj27JlWuTVr1qBs2bKwsbHB4MGDmRglIioBSlqbkpaWhq+//hq1atWCubk5ypUrh44dO+Lu3bsa5VJTU/Hpp5+iQoUKsLCwQJMmTRAaGqpV36NHjxAYGAhLS0t4eHhg3759RbQmRETGpyS1Kbt27cKQIUPg6ekJExMTuLu76yx3+fJlTJo0Cd7e3rCxsYGLiws6duyI8PBwneXv3buHPn36wN7eHra2tujatStu3LihVe706dOoVasWLCws0KJFC9y5c0efq0dErzGZEEIUdxBEJdE///yDHj164OjRo2jatCkA4OzZs/Dx8cGyZcswdOjQIoslIiICzZo1Q+3atTFs2DDcvXsXc+fOhb+/P7Zv3y6Vi4qKQt26dTFjxgy4ublh5syZGDBgACZNmlRksRIRkbaS1Kakp6ejQ4cOOHLkCD744APUq1cPT548wfHjxxEUFIQ33nhDKtu3b1+EhIRg7NixqF69OoKDg3Hy5EmEhYWhefPmUrkPPvgA//33H8aNG4fw8HD8/PPPuHnzJqysrIpsvYiIjEVJalMGDRqE9evXw8fHB7dv34aJiQmioqK0yk2cOBG//vorevbsicaNGyM+Ph7Lli1DVFQUduzYgdatW0tlnz17Bh8fH8THx2PChAlQKBRYsGABhBCIiIiAo6OjVLZGjRoICAhAhw4dsGrVKjx//hzbtm0rilUnolKOCVGiHHTv3h137tzR+tXS398fKpUK+/fvL7JYOnTogIiICFy+fBm2trYAgOXLl+ODDz7Azp070bZtWwBASEgI1q5di3/++QcAsGnTJixfvhxbtmwpsliJiEhbSWpTvv32W0ydOhWHDh1C48aNcyx34sQJNGnSBN999x0mTpwIAEhJSYGnpyecnZ1x5MgRqaynpyfWrl0LLy8vAED9+vWxfPlyNGjQwLArQ0RkhEpSm3L//n04OTlBoVCgU6dOuHDhgs6E6KlTp1CzZk1YW1tL0x49eoTatWujRo0aOHTokDT922+/xaeffooTJ06gUaNGALLuMPX09MSkSZPw9ddfAwDi4uJQo0YNaaiA+Ph4VKpUCYmJiQZcYyJ6XbDLPFEOjhw5gmbNmmlNb9KkCY4dO4bMzMwiiSMhIQGhoaHo37+/lAwFgPfeew/W1tb4888/pWlVqlTBgQMHEBoaiitXruDnn39G9erViyROIiLKWUlpU1QqFRYtWoTu3bujcePGyMjIQHJyss6yISEhMDExwbBhw6Rp5ubmGDJkCI4eParRLbFKlSpYvHgxrl+/jr/++gvXrl2Dm5ubwdeHiMgYlZQ2BQAqVKgAhUKRZ7kGDRpoJEMBwNHRES1atEBkZKTG9JCQEDRq1EhKhgJArVq18NZbb2l893FwcAAALF26FDdv3sR3333H7z5ElG+mxR0AUUmUmJiImJgYODk5IS4uTmOes7Mz0tLScPfu3Vy/7D158iRfH0YsLS1haWmZ4/zz588jIyMDDRs21JhuZmYGb29vnDlzRprm4+ODd999V7pjtF69eggODs4zBiIiMpyS1KZcunQJ9+/fR7169TBs2DCsWrUKaWlpqFu3LhYtWgR/f3+p7JkzZ1CjRg2NH+MASHeVRkREoHLlygCAr7/+GoGBgVixYgVMTU2xcOFClC1bNs94iYioYEpSm6IPDx8+1GgvVCoVzp07h/fff1+rbOPGjbFr1y4kJibCxsYGcrkcP/zwAwYPHoyPPvoIjo6O2Lx5s0HjJaLXBxOiRDokJCQAAIKCghAUFKSzTHx8fK511K9fH7du3cpzWUFBQZgxY0aO8x88eAAAcHFx0Zrn4uKCgwcPakxbvHgxxo4di6dPn6JevXowNeVpTkRUnEpSm3L16lUAwIIFC+Dg4IBly5YByEpotmvXDidPnkS9evUAZLU/ObU9QFY3STVPT09cvXoVFy5cgKurK8qXL59nrEREVHAlqU15VQcPHsTRo0cxdepUadrjx4+RmpqaZ/tTs2ZNAFljXbdp0wbXr19HnTp1YGNjY7B4iej1wkwJkQ4ymQwAMHToULz99tsa83bs2IF58+ZJZXKydu1aPH/+PM9lValSJdf56jqUSqXWPHNzc53LyKtOIiIqOiWpTXn27BmArDuMzpw5I93hGRAQgGrVquHbb7/FmjVrAGS1Pzm1Per52VlaWuY6JikREb26ktSmvIqYmBj069cPHh4eGg+Azeu7T/YyamXLlmWvBCIqMCZEiXRQdw+sXLmyxhMPgawBvbOXyYmvr69eYrGwsAAApKamas1LSUmR5hMRUclUEtsUX19fKRkKAK6urmjevLnGg5IsLCxybHuy10VEREWnJLUphZWUlIROnTohMTERhw4d0hhbNK/vPtnLEBG9CiZEiXSwtraGs7Oz1F09uzt37sDMzAyVKlXKtY7Y2Nh8jc1jbW2tNcB4duquIbpiefDgASpUqJDnMoiIqPiUpDZF3WaUK1dOa56zs7PGuNQuLi64d++eVjn1erD9ISIqeiWpTSmMtLQ09OjRA+fOncPOnTvh6empMd/BwQFKpTLH7z4A2x8i0g8mRIly4Ovri/DwcK3pR48eRdOmTWFiYpLr+xs1aqSXsXk8PT1hamqK8PBw9OnTR5qelpaGiIgIjWlERFQylZQ2pW7dulAoFDoTnffv34eTk5P02tvbG2FhYUhISNC42+j48ePSfCIiKnolpU0pKJVKhffeew979uzBn3/+iVatWmmVkcvlqFu3rs71O378OKpUqcJxQolIL5gQJcrBgAED0KNHDxw8eBAtWrQAAISHh+PQoUP4+eef83y/vsbmsbOzQ+vWrbFmzRpMmzZN+gCwevVqPHv2DL17987H2hARUXEqKW2KjY0NOnTogH///ReXL19GrVq1AACRkZE4cuQIhg8fLpXt1asX5s6di59//hkTJ04EkNWFceXKlWjSpIlGl3siIio6JaVNKaiPP/4Y69evx7Jly9CjR48cy/Xq1QuTJ09GeHg4GjZsCAC4cuUK9u7dK7VHRESvSiaEEMUdBFFJJIRAmzZtcObMGXzyyScwNTXFd999Bzc3Nxw+fBgKhaLIYjl9+jTefPNN1KlTB8OGDcPdu3cxb948tGzZEjt37iyyOIiIqHBKUpty6dIlNGnSBDY2Nhg9ejQAYPHixcjIyMCZM2dQsWJFqWyfPn3wzz//YNy4cahWrRpWrVqFEydOYM+ePWjZsmWRxUxERC+UpDbl3Llz2Lx5MwBgzZo1iI6OxoQJEwAAXl5e6Ny5MwBg4cKFGDduHJo1a4aRI0dq1dO9e3dYWVkByHrwX/369ZGYmIiJEydCoVBg/vz5yMzMREREhEZvBiKiwmJClCgXiYmJmDBhAkJCQqBSqdC5c2csWrQIDg4ORR7LoUOH8Omnn+L06dOwsbFBnz598M0337DLCBFRKVGS2pTTp0/j008/xdGjRyGXyxEQEIDvvvsO1atX1yiXkpKCadOmYc2aNXjy5Anq1auHL7/8EoGBgUUeMxERvVBS2pTg4GAMHjxY57yBAwciODgYADBo0CCsWrUqx3pu3rwJd3d36fXdu3cxbtw47Nq1CyqVCn5+fliwYAGqVaumz/CJyIgxIUpERERERERERERGQ17cARAREREREREREREVFSZEiYiIiIiIiIiIyGgwIUpERERERERERERGw6AJ0W+++QaNGjWCjY0NnJ2d0a1bN1y5ciXP9/3111+oVasWzM3NUbduXWzbts2QYRIRUSnFdoaIiAyJ7QwREdHryaAJ0f379+Ojjz7CsWPHEBoaivT0dLRt2xZJSUk5vufIkSPo27cvhgwZgjNnzqBbt27o1q0bLly4YMhQiYioFGI7Q0REhsR2hoiI6PVUpE+Zj42NhbOzM/bv34+WLVvqLPP2228jKSkJ//77rzStadOm8Pb2xk8//aRVPjU1FampqdJrlUqFx48fw9HRETKZTP8rQURkZIQQSExMRIUKFSCXl+yRVgzRzgBsa4iIDK20tDVsZ4iISqfS0s5Q0TEtyoXFx8cDABwcHHIsc/ToUYwfP15jWmBgIDZu3Kiz/DfffIOZM2fqLUYiItLtzp07qFSpUnGHkStDtDMA2xoioqJS0tsatjNERKVbSW9nqOgUWUJUpVJh7Nix8PX1haenZ47lHj58iHLlymlMK1euHB4+fKiz/JQpUzQ+cMTHx8PV1RU3b96Evb29XmI3FJVKhbi4OJQtW7bE/0LBWPWvtMQJMFZDKS2xPn36FB4eHrCxsSnuUHJlqHYGKL1tTWk5xgDGaiilJdbSEifAWA2lNLQ1bGd0Ky3HWWmJE2CshlJaYi0tcQKlK9bS0M5Q0SqyhOhHH32ECxcu4NChQ3qtV6lUQqlUak23t7cvFR8e0tLSYG9vX+IvHoxV/0pLnABjNZTSFCuAEt9lz1DtDFB625rSdIwxVsMoLbGWljgBxmpoJbmtYTujW2k5zkpLnABjNZTSEmtpiRMoXbGqleR2hopWkSRER40ahX///RcHDhzI89bk8uXLIzo6WmNadHQ0ypcvb8gQiYioFGM7Q0REhsR2hoiI6PVi0BS+EAKjRo3CP//8g71798LDwyPP9zRr1gx79uzRmBYaGopmzZoZKkwiIiql2M4QEZEhsZ0hIiJ6PRn0DtGPPvoIv//+OzZt2gQbGxtp3Bw7OztYWFgAAN577z1UrFgR33zzDQBgzJgxaNWqFebNm4eOHTvijz/+QHh4OH7++WdDhkpERKUQ2xkiIjIktjNERESvJ4PeIfrjjz8iPj4efn5+cHFxkf6tX79eKnP79m08ePBAev3mm2/i999/x88//wwvLy+EhIRg48aNuQ5cTkRExontDBERGRLbGSIioteTQe8QFULkWWbfvn1a03r37o3evXsbICIiInqdsJ0hIiJDYjtDRET0eiodjwEjIiIiIiIiIiIi0gMmRImIiIiIiIiIiMhoMCFKRERERERERERERoMJUSIiIiIiIiIiIjIaTIgSERERERERERGR0WBClIiIiIiIiIiIiIwGE6JERERERERERERkNJgQJSIiIiIiIiIiIqPBhCgREREREREREREZDSZEiYiIiIiIiIiIyGgwIUpERERERERERERGgwlRIiIiIiIiIiIiMhpMiBIREREREREREZHRYEKUiIiIiIiIiIiIjAYTokRERERERERERGQ0mBAlIiIiIiIiIiIio8GEKBERERERERERERkN0+IOgIiIiIiIiIiI6HWUkpKCtLS0Il+umZkZzM3Ni3y5pQUTokRERERERERERHqWkpICdw9rRD/MLPJlly9fHjdv3mRSNAdMiBIREREREREREelZWloaoh9m4uL1yrCxLbpRKxMTVHij6h2kpaUxIZoDJkSJiIiIiIiIiIgMxMZWDtsiTIhS3pgQJSIiIiIiIiIiMhSVCaAqwoSoSlZ0yyqlmJ4mIiIiIiIiIiIio8GEKBERERERERERERkNdpknIiIiIiIiIiIyEJkKkBVhN3aZqsgWVWrxDlEiIiIiIiIiIiIyGkyIEhERERERERERkdFgl3kiIiIiIiIiIiJDEbKsf0W5PMoV7xAlIiIiIiIiIiIio8E7RImIiIiIiIiIiAxEppIV8UOVeIdoXniHKBERERERERERERkNgyZEDxw4gM6dO6NChQqQyWTYuHFjruX37dsHmUym9e/hw4eGDJOIiEoptjNERGRobGuIiIhePwZNiCYlJcHLyws//PBDgd535coVPHjwQPrn7OxsoAiJiKg0YztDRESGxraGiIhelUxV9P8odwYdQ7R9+/Zo3759gd/n7OwMe3t7/QdERESvFbYzRERkaGxriIiIXj8l8qFK3t7eSE1NhaenJ2bMmAFfX98cy6ampiI1NVV6nZCQAABQqVRQqUp2SlylUkEIUeLjBBirIZSWOAHGaiilJdaSHl9hFKSdAUpvW1NajjGAsRpKaYm1tMQJMFZDKQ0xFhS/05QspSVOgLEaSmmJtbTECZS+WImyK1EJURcXF/z0009o2LAhUlNTsXz5cvj5+eH48ePw8fHR+Z5vvvkGM2fO1JoeGxuLtLQ0Q4f8SlQqFeLj4yGEgFxesp9vxVj1r7TECTBWQyktscbHxxd3CHpTmHYGKL1tTWk5xgDGaiilJdbSEifAWA3F2Nua0trOAKXnOCstcQKM1VBKS6ylJU6gdMVa7O2M6v//inJ5lCuZEEIUyYJkMvzzzz/o1q1bgd7XqlUruLq6YvXq1Trn6/o1tXLlynj06FGJ76KiUqkQGxsLJyenEn/xYKz6V1riBBiroZSWWJ8+fQpHR0fEx8fD1ta2uMPJkaHaGaD0tjWl5RgDGKuhlJZYS0ucAGM1FGNva0prOwOUnuOstMQJMFZDKS2xlpY4gdIVa3G1MwkJCbCzs8O9qGqwtTUpwuVmoqL7tRLfrhanEnWHqC6NGzfGoUOHcpyvVCqhVCq1psvl8hJ/QgJZH6oYq/6VllhLS5wAYzWU0hBrSY5NH/JqZ4DS3daUhmNMjbEaRmmJtbTECTBWQyjp8b0qfqcpGUpLnABjNZTSEmtpiRMoPbEWd3wykfWvKJdHuSvZRyyAiIgIuLi4FHcYRET0mmI7Q0REhsa2hoiIqGQx6B2iz549w7Vr16TXN2/eREREBBwcHODq6oopU6bg3r17+O233wAACxcuhIeHB9544w2kpKRg+fLl2Lt3L3bt2mXIMImIqJRiO0NERIbGtoaIiOj1Y9CEaHh4OPz9/aXX48ePBwAMHDgQwcHBePDgAW7fvi3NT0tLw4QJE3Dv3j1YWlqiXr162L17t0YdREREamxniIjI0NjWEBHRq5IJQFaEDzpil/m8GTQh6ufnh9ye2RQcHKzxetKkSZg0aZIhQyIiotcI2xkiIjI0tjVERESvnxI/higRERERERERERGRvpT4p8wTERERERERERGVWiqR9a8ol0e54h2iREREREREREREZDSYECUiIiIiIiIiIiKjwS7zREREREREREREBiITRfvkdz5lPm+8Q5SIiIiIiIiIiIiMBu8QJSIiIiIiIiIiMhTV//8V5fIoV7xDlIiIiIiIiIiIiIwGE6JERERERERERERkNNhlnoiIiIiIiIiIyEBkKgGZquiedFSUyyqteIcoERERERERERERGQ0mRImIiIiIiIiIiMhosMs8ERERERERERGRofAp8yUO7xAlIiIiIiIiIiIio8GEKBERERERERERERkNdpknIiIiIiIiIiIyEJnI+leUy6Pc8Q5RIiIiIiIiIiIiMhq8Q5SIiIiIiIiIiMhQ+FClEocJ0dfAw4cP8d9//yE5Odlgy1CpVHj69Cns7e0hlxvmxmJHR0e88cYbsLS0NEj9VPzu37+Pq1ev4vnz5wZbRlEcq05OTnjjjTdgbm5ukPqJSprnz5/j4sWLiIuLM9gyiuLctbGxQe3ateHg4GCQ+qlkuX37Nq5du4a0tDS91muoY9XU1BSurq6oUaOG3uokKi2EEIiMjMS9e/eQmZlpkGUURTtjZmYGDw8PeHh4GKR+KlmePHmCyMhIJCQk6LVeQx2rMpkMTk5O8PT0hJmZmd7qJSqtmBAt5U6fPo0xY8YYNMGkplKpDPbhQe2NN97ADz/8AGtra4Muh4resWPHMH78eL1/MdWlKI5VHx8fLFy4kAl8eu09e/YMH3/8Mc6fP2/wZRXFuevg4ICffvoJVapUMehyqHiFhobi888/h0plmNsjDHmsjho1CoMGDTJI3UQlkRACS5YswW+//WbwZRVFOyOTyTB9+nR07tzZoMuh4nX37l0MGzYMMTExBqnfkMdqkyZNMH/+fCiVSoPUT1RaMCFaisXFxWHMmDF444038MUXX8DW1tZgyxJCICMjA6amppDJZHqvX6VSITIyEhMnTsS0adOwYMECvS+Dis/9+/cxfvx4NGrUCFOnToWNjY3BllUUx+q5c+cwadIkzJo1C19//bXel0FUkkyfPh1RUVFYtmwZateubbAP54Y+d4UQiI2NxaRJk/Dhhx9i27ZtMDXlx6DX0ZUrV/D555+jbdu2GD9+vN5/uDLUsZqWloY1a9bg+++/h6urKwICAvRWN1FJ9s8//+C3337DmDFj0LVrV4PduWbodgbI6lHx448/4osvvoCrqyu8vLwMshwqXkIIjBgxAubm5vjzzz/h4uKi12PKUMeqSqXCqVOnMHnyZMyZMwfTp0/XW92UN5kq619RLo9yx28CpdiNGzfw/PlzfPbZZ3B2djbosoriA0SDBg3Qr18//Pnnnwapn4rP1atXkZaWhqlTp8LJycmgyyqKY7Vp06bo1asXdu/ebZD6iUqSCxcuoG/fvmjQoIFBl1MU566rqyvGjx+PkSNH4v79+3B1dTXIcqh4RUZGQgiB6dOnGySxYqhj1dzcHCNHjsSOHTtw4cIFJkTJaFy4cAF16tTBgAEDDLqcomhnzM3NMXnyZGzduhUXL15kQvQ19fjxYzx48ABz5841SI8TQx6rLVq0QNeuXREeHq7XeolKIyZESzF1N3lD3G0nhMCNGzcQHh6OCxcuID4+HnK5HOXLl0fDhg3RoEEDlClTRu/Ltba2LpLu/1S0DH2sXrt2TTpWExMTIZfLUaFCBTRs2BA+Pj6wt7fX+3J5rJKxSElJMdgwJnFxcTh16hROnz6NmJgYZGZmws7ODl5eXmjYsCHc3Nz0/kVAfR1KSUnRa71Ucjx//hxKpVKvydCYmBiEh4fjzJkziI2NhUqlQpkyZeDt7Y2GDRuicuXKelsW2xcyNoZsZ54/f46zZ88iPDwcUVFRSE1NhYWFBWrWrImGDRvijTfe0HtvAblcDisrK57HrzFDfLdJTk5GREQEwsPDcevWLaSlpcHS0hK1a9dGgwYNUKdOHZiYmOhlWTY2Njw+icCEKL3k6dOnWLVqFX788UdcuXIlx3IymQwdOnTARx99hMDAQIOPxUP0ssePH2PlypX48ccfcf369RzLyeVydOnSBSNHjkTr1q0NdkcAEeVPZmYmtmzZgqVLlyI0NDTXsp6enhg5ciT69+9v0KE2iHRJT0/Hpk2bsHTpUoSFheVatn79+hg5ciT69evHsaWJSoCIiAgsXboUa9euzfXBs2XLlsWQIUPw4Ycfwt3dvegCJPq/kydPYunSpfjjjz9y/bG2XLlyGDp0KIYPH67XH+GoCAkAQhTt8ihXzGIRgKy77NauXYuqVati7NixuSZD1eW3bt2KDh06wNfXF5cvXy6iSMnYCSHw66+/wsPDAxMnTsw1GQpkjZWzceNGtG3bFn5+fnmWJyLDOXv2LBo2bIju3bvnmQwFsrpRjhw5EtWqVcPff/9dBBESZTl58iTq16+P3r1755kMBYAzZ87ggw8+QPXq1bFt27YiiJCIdHny5Anee+891K9fH7/88kuuyVAgq6fCnDlzUK1aNUydOhWpqalFFCkZu9jYWLz99tto3LgxgoOD8+y5Eh0dja+++gpVq1bFrFmzkJ6eXkSREr2+eIcoITk5Ge+99x42bNigMd1E5gETkyowkVWETGYFARWEeIxM1V1kZF6CQDyArKeHe3t7Y8mSJfjggw+KYxXISCQmJqJv377YunWrxnQTeVWYyD3+f6xaZh2rqkfIFOpjNQEAcODAAdSrVw/Lli1D//79i2MViIzWggULMGnSJGRkZEjTZDIHmMprw0ReEXKZAwAZVCIRKtV9ZKiuQiXuAMjqrtyzZ08MGDAAv/zyC5+KSgYjhMA333yD6dOnIzMzU5pe1d4GbapURD1nB1SysYJMBkQnpeB8zGPsu/UAZ2MeA8h6iGDHjh0xbNgwfP/991AoFMW1KkRG58iRI+jVqxcePHggTbOQmaORpTeqKt1RUeECM7kZnqtScDvtLv5LvYHTyeeQiUxkZmbiq6++wubNm7Fp0yZ4eHgU45rQ6y4sLAxvv/02YmNjpWl29nJ07mYJnwZK1KytgFIpQ0K8ChfOpeHokVSE7khGRkZW74Vp06Zh48aN2LhxIypVqlSMa0JUujEhauSSk5PRvn17HDhwQJpmKq8HM9MAmMjL6XiHOxQmPhCmnZChuozUjG0Q4hFSU1MxbNgwJCYmYvz48UW3AmQ0EhIS0KZNG5w4cUKaZiqvD6WpP+RyHQ9qkrtDgQb/P1YvITVjO4R4guTkZAwYMABJSUkYPnx4Ea4BkfGaMWMGZs6cKb2Wy8pBadoeJvLqkMk0O6uYAIDJG1CiDTJVD5CaEYpMVSQAYPXq1YiLi8PGjRsN9hRiMl5CCEyaNAlz586VptV1LoOpvvXRonI5nUOudKxWGZPf9EJE9CPMOXoO+25lJWJ+/vlnxMXFYf369Xofn5CItO3fvx8dOnSQ7gi1lFmgd5kuaGXdDOZyc63ydS1qoyOA+MwE7EwIw5b4nciECufPn0fz5s1x4MABVK1atYjXgozBzp070aVLF6SlpQEAyjjIMf2LMnj7XWtYWmp34A1oY4nRE4AH9zOwdHECflgUD5UKOHXqFFq0aIEDBw6wC30pIRNF/JR5dpnPE7vMGzEhBAYMGJAtGaqEueJdWJj1zSEZ+oJMJofCpA6szEZDYdJUmj5hwgSEhIQYMGoyRkII9OnTJ1sy1AIWioGwMOujOxmajUxmAoVJXViZjYHCpKE0fcSIEVp3mhKR/q1YsUIjGWpm0hKWZqNgalJTKxn6MhO5CywUA2Cu6A0g60677du348MPPzRkyGSkvv/+e41k6IQmntjaJxAtXcvnOf60dzlH/N7VD9+91RhmJlnH9d9//41x48YZNGYiAq5evYrOnTtLydA65jUwp+J0BNr660yGZmdnYos+ZbpiVoXPUEFRHkDWnd5t27ZFQkKCwWMn43LhwgV0795dSoa2bmuBY2cqYvAHtjqTodm5VDDFl7MdsPtgBbh7ZP3QFhUVhXbt2uU5NAQR6caEqBFbu3ZttjHZlLA0+wAKE88C1SGTmcFc0RVmpq2laR9++CGio6P1GCkZu2XLlmHnzp3/f2UBS7NhMDWpVaA6ZDIllKY9YGbSEkBWkvWDDz7AkydP9BwtEandunULY8aMkV4rTTtCqWgPmSz/d8zJZDIoTHxgYTYY6qToypUrsWXLFn2HS0bsv//+w6RJk6TX373VGBOb1oPCJP8flWUyGfp7VsOqzq2kpOj333+PPXv26D1eIsqSmZmJwYMHIzExEQBQ36IuPi33MRxNyxSoHjezSphefiIqKSoAAG7cuKFxTSB6Venp6Rg4cKD0dPeuPSyx7u9yKFe+YL0IfBoqsSPMBVWrZb3v0qVLmDZtmt7jJQNQFcM/yhUTokbqyZMnGD16tPTaXNEbJvKKha7PzCQApvK6AIBHjx6x2zzpTXR0ND755BPptYXiHZjIyxeqLplMBjPTdjCR1wQAPHjwAJ9++qle4iQibR999BGePXsGAFCYNISZafNC12Uq94C5oqv0evjw4bwjgvRm+PDh0gMthnjXQH/PaoWuy8/NBTNb+Eivhw4dKt0NRET69csvv+Dw4cMAgHKmTvjYaSgUssKN3WtrYo2JziOhlGWNU71s2TIcOnRIb7GScVu0aBFOnz4NAKhVW4FlK52gUOTe+yAn5V1MsW5DOSiVWe9fsGABTp06pbdYiYwFE6JGauXKldKdcabyelCYvPFK9clkMigVXSCDJQBg/fr1uHv37ivHSfTzzz9nS6g0gqlJjVeqTyaTwVzRHUDWh91Vq1YhLi7uVcMkopdERkZKw1LIYAulacdXrtNU7qPxg8a6deteuU6iU6dOYd++fQAANztrfPam9yvX+V696nizkjOArC6N//zzzyvXSUSaVCoV5s+fL70eVvY9mMtf7aF7zoqy6Fumu/Q6e/1EhZWRkYGFCxcCAGQyYOlyJ5ibv1oqpkYtM3w23R5AVs83df1ElH9MiBohIQR+/PFH6bUyW3f3VyGXWUNh0gxAVveVX375RS/1kvHKyMjAsmXL/v9KBjPTAL3UK5fZQWHSGACQlpaGFStW6KVeInrhp59+kv42M20BmSz3cdzyQyaTQWn6lvR66dKlr1wnUfbPRB81qANLxas/BEkuk2FCk7rSax6rRPq3Z88eXL16FQDwhnkt1Davrpd6A2xawN7EDgCwadMm3uRBr2zLli24d+8eAKBdR0v4NHy1xL3asI9sUcYhK6Xz559/ajy1nkoemSj6f5Q7gyZEDxw4gM6dO6NChQqQyWTYuHFjnu/Zt28ffHx8oFQqUa1aNQQHBxsyRKP033//4dq1awAAE3mVPB9KUxAK00YAsm7d//fff/VWLxmnc+fOSR8eTOQ1IZfZ661us/8nRAEeq6UZ25mS68VDy0ygMPHJtWxBmMgrQy7LGuPt9OnTePjwod7qJuOkPlatFKboUdNNb/U2q+iMqmVsAWRdq+Lj4/VWNxUttjUlU/bPb2/ZtNBbvaYyE/hb+wLIugt1x44dequbjFP2B7kOHmqjt3otLOToN8AaQNZNHhyzmqhgDJoQTUpKgpeXF3744Yd8lb958yY6duwIf39/REREYOzYsRg6dGi2h6mQPmQfX8REXlWvdctldpDLshKs58+fR2pqql7rJ+MSHh4u/W2q72NVXhYy2AMAzpw5A5WKo06XRmxnSqanT5/i+vXrAAC5rAJkMku91p+97eKYWfQq7t+/LyXVfco7wsqscGMP6iKTydC8Ujnp9ZkzZ/RWNxUttjUlU/br/xvmNfVat6fFi4d3sp2hV6U+huRyoHmrV+8xk11LPwut5RBR/rx6n6BctG/fHu3bt893+Z9++gkeHh6YN28eAKB27do4dOgQFixYgMDAQEOFaXTOnz8v/W3y/7ts9EkuqwCViEF6ejquXLmCevXq6X0ZZByyH6vyV3joV05M5BWRoXqKZ8+eISoqClWqVNH7Msiw2M6UTBcuXJD+fpUH9uXERF4R6ZlZf587dw4dO776+KRknLK3M3WdHfRef71sdZ47dw5+fn56XwYZHtuakkl9/pY1cYCNibVe63Yzq6y1HKLCUKlU0ueiGjUVsLTU7z1p3j5m0t/nzp3Ta92kZ0X95Hfe75MngyZEC+ro0aNo3VpzPMvAwECMHTs2x/ekpqZq3IWYkJAAIOvCU9Lv+FKpVBBCFDpO9fuEEBAi/wNEZO+yJZPp98PDy3U+jI2De3JKvt/7PDUNz56nYk/ElUIvXwgVnifEw+LBE8hkJXeY3NISJ/DqsZ65fhdp6RlISE5BWgEO98dPnkp/y2BV4OXmRSZ7UWd8fHyBziP1efcq15lXvQYUlZIeX0EUpp0BSm9bo69j7NXaGQOcu3jRzsQ9foKEArQzz56nIiUtHUcjb+BOcuEGVzKm63dR0WecF6LuS21OXh7GvnioXlkL/d61AwBlLV+ME5eQkJDv80hfbUNpaWcAtjWltZ0BXv04U7cxBf0cpt5Gtib664KsZim3gEKmQLpIx9On8QVqZwAgNT0DkbcfFvo7TWm5dgPGGWts9EM8T0vHs5TUPI+N5ORkZGRkAADKOpsUepk5ccpWZ2JiYr7OI318j1FjO0OlWYlKiD58+BDlypXTmFauXDkkJCTg+fPnsLCw0HrPN998g5kzZ2pNj42NRVpamsFi1QeVSiUlYeTygl+Qnzx5ApVKhYyMDOkimx8ymSzbK0OMtPviQvMsIQGPYmPy/c7EhAQ8fhSHVT9+X+ilCwAiPQ0yhRlkeZYuPqUlTuDVY7135xaSk5PxODYGSvP8f+FMS8s+5IIhjtUXdQohCnQeqc+9mJj8H9+66niVa0BReZ3GvStMOwOU3rZGH8dYeno6MjMzC3R+aCjAF9z8e9HOpKWmFKidefrkEZ49S8Q/v6+BrX2ZQi3dmK7fRUWfcd689h+Sk5PzdVwkPUuU/lYZ4FjNzFanTCbL13mUmZmJZ8+evVL7olZa2hmAbU1pbWeAVz/OkpKSpM9VBWFiYoLMzEwIA3xGzJ6gFUJVoHYGAJ4nJeHwvr2IKeQ416Xl2g0YZ6xJz57hWUIC4p88yfPYyP5DhyHycZmZL/6Wy+X5Oo9UKhXS09PZzhQ13iFa4pSohGhhTJkyBePHj5deJyQkoHLlynBycoK9vX3xBZYPKpUKMpkMTk5Ohbp4lClTBnK5HKampjA1zf+uLF++/IsYxCOYoHIupQtOJR5Lf1etUROOTs75fq+NrS0cHMti4IejCr186Zc/W7sS/StlaYkTePVYTxw+iHW/3IaDkzPMC5AQrVTZNVsMjwGUy7lwIajEI+nvChUqFOg8Up97zs75P761lv+K14CiYmZmlneh11xpbWv0cYwpFAqYmJgU6PxwcXF5EUO2NkFfstfp6uZeoHYm7tFjWFvboHvf/qjk5lGo5RvT9buo6DPOvTv+RejGv/J1XFSt9uKp1FHxz15pubrcylZn+fLl83UemZiYwNra+pXaF7XS0s4AbGtKazsDvPpxZmVlhfT09AK1MwDg7OyMBw8eICYjDkKIl276eDWPM58iA1mJpfIuFQrUzgCAhZUVGrUMQPtuvQq1/NJy7QaMM9bY6Ie4fP407MqUyfPYEELA1tYWCQkJiLqRXuhl5uTm9Rd1litXLl/nkVwuh0KhYDtDRq9EJUTLly+P6OhojWnR0dGwtbXN8a4dpVIJpVKpNV0ul5f4ExLIulugsLGq3yOTyTQ+AGzfvh0dOnTAxx9/jMWLF2u9r0GDBtLfmar7UJh4FzzwHGTdLp/1VHB7e3tUq1Zd68NJ7x7dsHPHdhwLP406dd7QmCc3MYWpqQLO5Qs/tqkQKiSbm8PS3qFEN8qlJU7g1WO1d3CA3MQEpqYKmJq+eGDFzh3b0aNrZ4wYOQpzFyzUel+Dho2kvzNVd2FqUrtQ8esihAqZqvsAgIoVK2r8UKDWpUsXbNu2DWfPnsUbb2geq+rz7lWvM69yDSgqJTm2gipMOwOU7rZGH8dYQdsZT09PmJmZIS0tDSpxr9DLzUn2On0aNNS4rggh0LRRA9y+FYWzFy9rfdg3MTGF3MQUDmWdC93WGNP1u6joM05buzJSm5Odrjanvk9DyGQyCCFwLkb/yftz0S/qbNCgAWQyGZ4/fw53d3dYWFjg8uXLOn8o1Ef78nJdJf1aVdLjKwh+pyn4e19uZ4C82xofHx9s3boVSapkxGTEoZzCqdDxv+xm2q0Xy2nQQOt6kldbI5ebwMbWnu1MCaOvWFUqkfVj8f+/u6rl9N3Gu359HNi/H/fvZSL6YQbKlddfGibizIs7yH18fPLVzujre0z2+krDtaqkx0dFr0QdEc2aNcOePXs0poWGhqJZs2bFFFHpI4TAxIkTIZfLMXHiRJ1lGjZsKP2doboIIfR3L7VK3IFAVvez+Ph41KjqjmmfT0F6+otfrsaOn4DMzExM+2yK3pZLpY8QAp9P/hRyuRxjxo3XWaZBg+zH6qUCjS2Vl0zVDQBZY/7cv38flSpVwuTJkzWO1U8++QSZmZn49NNP9bZcKl5sZ15dftoZMzMzVKpUCQCgErHIVEXrLFe45WcgIzNSet3avxWslKaoXSPryfMymQxjxo1HQkICZn81S2/LpdJNV5uTkJCAKZM+kb4gnY95jDsJ+rtLNDk9A3tv3Zdejx49GgBgYWGBUaNG4datW/j++8IPEUQlF9uaV5eftqZx48bS3yeTz+h1+SeTIqS/5347B1ZKU7Y1lCtd7cwPSxajSUMfHDxwQCr376ZkvS53y8Yk6e/169cDYDtTUskEIBOyIvxX3Gtc8hk0Ifrs2TNEREQgIiICAHDz5k1ERETg9u3bALK6hrz33ntS+Q8//BA3btzApEmTcPnyZSxduhR//vknxo0bZ8gwXyuhoaG4dOkSWrRoAVdXV51lnJ2d4e7uDiCrG3Km6qrelp+WcVT6u0nTZlCYKjB/7neY9vmL5Oebvs3h6uaGnTu24/q1a3pbNpUue3aHIjLyEnybN0flHI5VN3d3uFTI+mVdJR4iU0Tpbfnpmcekv1u0aAGFQoE5c+Zg8uTJ0vTmzZvDzc0N27ZtwzUeqyUS25mil592BgA8PF50R89+vr2qDNVFCGQlrapUrQo3N3cAQLVsXZ+7dusOS0tLrFm9SnroBhk3XW3O0MEDseLXX+Dg6Agga2y5Vef095lo45VbiE998SNbrVq1pL/79+8PAPj+++/5kIdSgG1N0ctPW/POO+9If+9OPACVnm7ySMh8hmNJ4QCy7tz9YPgItjWUJ13tzOnTp2Bvb4fy5V8MJfTrsvw/YC8v9+5mYNuWFwnWRo1e9K5jO0OUN4MmRMPDw1G/fn3Ur18fADB+/HjUr18f06dPBwA8ePBA+iABZH152rp1K0JDQ+Hl5YV58+Zh+fLlCAwMNGSYr5V//vkHAPDWW2/lWm7+/PnS36kZ2yFEIR+WkU2m6jYyVGcBABYWlvh3+06s+ysEAPDzTz8iLi7rSa4ymQyt/PwhhMC/Wza/8nKpdNqyaRMAwM8/INdyX371jfR3avpWCJGZS+n8yci8jgzVRQCAra0tdu/eLZ07P/zwg8axGhAQACEENv0/XipZ2M4Uvfy2M3/99ZfUNTQ98yQyVYV7sER2QqQiNWOn9Pqrb+Yg9v8PMxg99kWiwcrKCg0bN0ZSUhL27tn9ysul0u/lNuf8+XPY+u8WKBQK7Ny9FwpFVpfHX8/+hxtPXj2x8TQlDd8dO6cxLXsyzMPDAx4eHrh16xbOnNHvnW2kf2xril5+2poyZcpI3exjMuKwI2GvXpb9+5MNSP//+KHDPhyBaUEz2NZQnnR9t/l15Srs3B0G3+bNpWmXLqZj1a+JWu8vjM8nPdZ4qFL2u6nZzhDlzaAJUT8/P+kJfdn/BQcHAwCCg4Oxb98+rfecOXMGqampuH79OgYNGmTIEF87p0+fBgDUqVMn13JdunSBj48PAEAlopGaseuVlitEClLSQ6B+avd7AwfBwsICXl7eUCqVSE1NxeXIS1L52rWz4jt9KvyVlkulV0RE1rFaq3bux2rPXr1Rq1bW2KEqcQ9pGWGvtFwhkpGSsUF6PWzYMCgUCnh7vzhWL116cayqxw49efLkKy2XDIPtTNHLbztTpkyZbAmgTKSk/wUhCv8wASEEUjO2QognAIDmLVrg0sULSE5OhmfdemjTVjPRUKd21rl7KpznLmm3ORH//3JYpUpV1KxZCx8MHwEASMnIxNjQY0jNKPyPb0IIfL4vHA+TnkvTOnXqhNq1NcfBZvtSerCtKXr5aWsuXryocafd+qebcDftfo7l8yM8OQIHnmX1eLO1tcXHo8di+c8/sa2hPOX3uw0ATP30MW5ce7UHLP31xzNs3PCiu3zbtm3ZzpR0qmL4R7kqUQ9Volf35EnWF0U7OzsAwI4dO7Bjxw5pfr9+/dC4cWOYmJhg5cqVqF+/PlQqFdIzD0Ius4aZacsCL1OIFDxP+w0qEStNGzRkiPS3tbU1UlNT8fDhi7uDbG1tAQCPH+v/AQZUOjx98hTAi2Nh184dCN31IjH/9jvvoGGjxjAzM8Oy5b/Cr4UvhBBIy9wDmcwKZqYFH4dLiOdITlspJVQAaHRxUx+rDx48kKbxWCXSlN92BgCmT5+OZcuW4dGjR1CJ+3ievgYWinchkxXsKZ9CCKRlhCI9M+sDvaWlJRYtWYr2ga0BQOc4xDa2NlnxPn6iNY+Mz8ttTnR01mcSK2trAMCML77Ehr/WIzo6GicfxGHEjsP4sZ0vlKYmBVqOEAIzDp7G31eiNKZPmjRJqyzbF6Kc5aetUX+3MDc3R0pKCtJFOmZHL8Zn5ceigkL7YZl5ufj8Mr6P/VV6PfvbuSjr5ISfflwKgG0N5e7ldkaXqtWq4fq1a3j2TKBbh4fYvLM83D0UOZbPyc5tyfjog1iNaVOnTtUqx3aGKHdMiL5m7O3tAUAax+bYsWNYtGiRNN/b21v6olqvXj0EBARg9+6sLh6pGduRqXoAc0VnyGSW+Vpepuo2UtJDNJKhAJCakiL9/exZ1lhv2Z/irY6vTJkyBVk9eo3Y2Wd9wE1MzOoycvLEcSz9/sUTROt5eaFho6xjtWGjxnjTtzkOHzoIAEjN2AyVeAClaQfIZNpP59UlQ3UTKekhEELzA0Fy8otxd9THqovLi3F+eKwSaSpIO6NUKtG5c2fpLqpM1X9ITvsJ5opeMJHn78m7KpGI1PRN0jAXALBg0WIcO3oEMdHRqFipEnr3eVvrfYkJWdcW+zL2BV1Feg293OaUK5f1mSTp/9d9KysrfDB8BGZ9MQMAsP36XXQLCcXCNs1Q09EuX8t4+CwZn+w5gd1RmneoNWnSBC1atNAqz/aFKGf5aWtePBNBwMfHB6dPn8bjzKeY/uBbDHTog+ZWTbSeXK9LpsjElvhd2PD0X2Qi6+7w7j174r1Bg7Fq5Qq2NZQvL7czujRo0BAKhQKXIyNx+1YG3mp+H/MWl0W3nlb5WkZamsB33zzF/DlPNbrKN27cmO0MUSGUqKfM06tTd4NXd/mdMWOGRteel7vrNGjQQON1hioCSakLkZqxDyqR85NWM1V38DztLySn/SQlQ8uUKQN3jyoAgPD/35YfEZHVVUipVGp0H7h8OesJwV7/H4uJjI+3d9axqh5K4fNpQUhKzZD+DXhvoFRWpVLBs249jfenZ55EUuoCpGUcgEokQRchBDJVt/A87Q88T/tFSoY6OzujZs2aAIATJ04AgNStTalUanTPUp9L6nOLyNgVtJ0pV64cAMDEJOtOO5V4gOS0H5CS/g8yVQ+QE5WIR2r6biSnLtRIhk6b8QXatu+IxYsWAAA+GjVaGv8xu8j/X1u86/PcJe02x8vbGwBw/fo16c6Z+/ezEpnqp85HRD9G23Xb8VnYSVx59DTHuu8mJGH2kbPwW7NVSobKZDJYWWV9wf3kk090vo/tC1HO8tPWvPHGGzAzM0Nqairmz58P7/+f18mqZPwYF4yvoxfiVPLZHB+2lKZKw/7EI/j8/tf48+kmKRnauk1bzPrmW6RnpGPJooUA2NZQ3l5uZ3SRm8jx66rfUK16DQDAozgVBvWLQa/ODxG6Ixkqle6HLSUlqbDq1wS0aHQP3339IhlqZpbV40ZXLwSA7UyJwy7zJQ7vEH3NdOvWDcuWLcOePXt03javtnHjRmzcuBGnTp0CkHU7/fPnz5Geng6BRKRl7ERaRijksnKQyytADmsIqCDEI2Sq7kEgXqM+nwYNsHzFKpw6FY4P3h+EoGlTcebMaRw6cAAAMHTYcJQtW1Yqvz8saxzITp266HsTUCnRqUsX/Lr8Z+wL24tPp3yeY7lNG//BhpC/cP5c1gO7bGxs8Pz5c2RkZEAgAakZ25GasRNyWXmYyCtABqv/H6tx/z9WNR+O8eabb2L16tU4c+YMevXqhc8++wynTp3C/v37AQAjRozQOFb37s0aoL9r16763gREpVJh2xk7OzsIIf7fDVKF9MwTSM88AZmsDExkFSGTlYEMcqhEIlTiHlQiBupxqQHAsWxZLP5+Kdp16IgNIX/hyuXLsLOzw/tDP9BadnJyMsJPnoCFhQXeat1G35uASqGX25x69bzQvkNHbN+2FYGtA1CzVi1s2bQRcrkcy5avwHezv8F//11BWqYKK89dxcpzV+FmZ416zg6obGsFGYDopOc4F/MEVx/HI/tX2HLlyqF79+746aefUK1aNXTv3l0rnqioKNy4cQMVK1ZEw4YNi2w7EJUW+WlrnJycMGzYMHz//fcYNGgQmjVrhvPnzyPz/9miiylXcDHlCqzklvAwc0UlRQWYyRV4rkrBrbS7uJV2B6kiTaPOcRMmYmrQDMTHJyDkz79w+XIk2xrKF13fbYJX/IojRw7j5P9vwDhy+DAyMjLRs1dvRF66iM2bNgIAdu96jt27nsPBUQ7v+krUrK2AubkMCfEqnD+XhvNn0/D8+YuWxtTUFG3atMH27dvZzhC9AiZEXzOBgYGoVasWDhw4gNu3b8PV1VVnuYiICKxatUp6rb6d3tLSEs+fP///AOUqqMQDqDJzvoNHoVBg9NhxmD7jC5iamqJmrVqIj4/H0u+X4M8/1qGskxPGjp+AoJlfSu85cvgQbt2KQus2bVGzVi39rDiVOm3aBqJmzVo4dPAg7ty+jco6jlWVSoXjx45hw19/StPU3VAsLCzw/Ln6gRUqqMR9qDJzHkjfzMwMU6ZMwbRp02BiYoIqVapg+fLlmDt3Ln7//Xc4OTnhk08+waxZs6T3HDp0CFFRUdJ5RUSFb2fUd+HZ2dkhMzNTGqJCiCfIEDmPvSaTydC5S1cs+n4pnJ2dAQC/Ba8AALzT711YW2t3M9u08R8kJSVhyNBh7CZGAHS3OctXrsLnUz7F5k0bcfXqf6hdpw6CZnyB9h07oXuPnpj8yQQs/+VnqY5b8c9wKz7n3jOmpqYYMGAAvv32WzRrljXO9bhx4/7H3n3HN1H/YQB/LmmbtnTRQcvesmSUIUNBlCkqKA7ghzJERBAHICqKIDhAhqgoIhsEARkiqICI4gJBRpllFwqU7pE2bbPufn+Ehpbu9DKued6vV162yeXuU7zcJ3nyvftaR5zmt3btWgDA+PHjraOniei2svaaefPmwdvbG+vWrcPmzZvRuHFj9OjRAz///DNiYmIAADoxG6dyz+JU7tlitxcWVg1ffb0UDz38MADAM9QTK5ZZXv/DRz4Hf3//Qs9hr6H8iuoz+/f/g3XfrLEuE3v1KmKvXkXXbt2w85e9+OrLLzB50gTr46kpIn77NQe//ZpT1CYAAJ07d8bChQsxePBgAOwziiIBKHoQsP22RyXiKfOVjCAImD9/PkRRxLx584pd7s7TTvJuOp0Oly5dwptvvom77767yIOrv78/unXvjkWLlyAuMQUzP/gIHh63s/Wx417C3/8ewpnzl3D0xGl8OOtj63B+AFgwfx7UajU+nP2xvH88KYogCPjo4zkQRRGfLfik0OOiKCI5JQUvv/Ya0rKyC5xOr9ObkJyeieOnovHKaxPQrFnzIvfVwMBA9OzZE6tXr0ZGRgbee++9Am8IRo0ahejoaBgMBty4cQNz5swpsK/OmTMHarUac+fOtc8/ApECVbTPpKenIy4uDosWLULXrl2tpxXn5+HhgVat2+Ctt9/BuYsxWP/dZmsYCgC79/yGFK0Or785BckpqZDynQ4pSRI+/WQ+/P398c606fL+8aRYRfWcoKAgfDDrYxw8fAzXbibi3/+O4qGHHwFg+dLtsy8WIS4xBfM++RSdOneGj49PofV6enqibdu2mDFjBmJjY7FixQqEhobiwoULkCQJ48aNK/ScnJwcLFy4EHXr1sWrr75q3z+cSKHK2ms0Gg3mzp2LuLg4GAwGREdH44svvsDFixfx888/Y+DAgdZLt9ypXr36eGbYcOz76x/EXLthDUMBQK1SY/fe33D5WhxemTAJRlPBGcHZa+hORfWZJctWIDkjExeuxCL2ZgKyco3Q6U3Ytec3CIKAceNfRoYuF99t/h6PPNofoWFhRa67YcOGeP7553H48GHs378f7dq1Y58hkgFHiFYCltGct/Xr16/QfeVRv359zJ49G7Nnz0Z2djaio6MRn5SCrEwtGt/VBHc1aVpk+JRf4K0Z7bJujeYLyPet6qat24p9XkXqJtd35//fvg/1g05vKrRcXhhqNpsQEhICL8+iZ6Ru1LgxZn08F7M+ngudTodzZ6ORkpoGY04WmjVrhmbNmpW6r5Zk+/btNj+XqDKraJ/x9/fH2LFjMXbsWJjNZpw/fx7XbtxAcnIKatepixZ3t4S3d8kTpnlrNAgODkZqaiqSU1IRGhIMQVBBEAQcPHy02OdJ/LrcLRS1f97Zc9LS05GTnQ3/gAD435pt/k6BgYEY+9J4jH1pPEwmEy5cOI+bN+ORpU1Dw/r10apVK2g0mnLV5uPjg4SEhPL9QUSVXFGv2Yr0GpVKhYceeggPPfQQJElCXFwczp0/j5vxCQiPqI4mTZshJCSkxHWoVWqEhoYgOTkFyckpCA0NgaeH5TqipfUacg+lfbbJyc1BWloaNBoNgqsGFznJl4eHBx5+9FE8/OijkCQJN65fx+WYy0hNSkStWjXR8u67yz0CubQ+w8/cRBYMRBUs78AYExOD4OBgu2zD19cX7dq1gzY7FylJiQgJq1bmgKmkULQ4V6/EIODWrJJUeQQEBkGSgCtXYtAs3+RaRSlrGHqnKlWqoG279sjVG5CVloyIiPAKhaGluXz5Mk+PIrcQFBSEy5cv2239arUazZo1Q5169ZGQmIjQsGrw8Cg8cUVRigtFS3I1JgaAhIAgvn4rq8CqVaHX63EzLg7Va9QocpmyhKF38vDwQLNmzVG/YSNoU5NQIyKiwFkFcsjOzkZ8fDy6d+8u63qJXFlQUBBOnToFg8Eg+2sKsISXNWvWREhoKOLiExASGgbPMr6/LCkULU5qaioyMtIRyPeJlVZAkOWzTUxMDNq2K/r6nGUJQ+8kCAJq1a6NahHhSEtOQo3qEdDI/JqQJAkxMTH8HOMEgihAEEvfD+TcHpWMgaiCNW7cGHfddRfefPNNvPDCCwi4FUDaQ3auAdqMdAQEBkFVzmuQZGdnIzc3F94+PvAt4nQzwBKCnTt7Ft9/vw2P/W94kcuQcjW8qykiatXBm6+/jhHPPQe/Yj58SpIErVYLs2hGQEAAPNTlP0QZjSbkZGlRtWpQkbOBVpQoijhx4gR27dqFV155Rfb1E7maRx99FF9//TW8vb3RvHnRl6eQQ67egLT0dATa0GeMRiMyMzOh9vBAYIA/gMJvACVJQnJyMpYtXYLmrduhanDJI4NIuZq3jERQSBhefXk8hj77bKFT3bN0Ohj0evj4+sKnlJHIRTGaTMjOzEBw1aqy9hm9Xo9t27bBbDYzECW30rt3b2zbtg2vvfYa+vbta5dQFAAMRiNSUtMQEBgIdTnfY4qSCG1GJkRJRGBAQLHXZMzNzcV3GzfA1z8Qrdp2kKNsckG+vlXQrnNXfP7pp8jKykJERESBwNNgNCArMwuenp5FXn+2NCazCVkZGQgJlrfPiKKII0eO4Pfffy92Znoid8JAVMF8fHzw1Vdf4fXXX8f8+fOtMyraQ7b+diBa3jcQAKDLzkZuTg58fH2LDUV9fH3xyNND8cTQERWsllxNFT9/vPvxp5g3423MnTsXoigWWkaURGi1mRDNt8JQD9sOTwajCTlZlg+qtq6jNH5+fhg3bhyGDRtml/UTuZLnn38eZrMZ69evx4YNG+y2Hb3BiLS0NAQE2dZnDEYjsm6FogH+/kWOxPD08kLLth3wylvTyzRSg5SpakgI3p3zOebPmILZs2cX6DlZOh30ubnwrVLFpjAUsHzxpsu0fFCVu89Ur14dX375Je666y5Z10vkytq0aYNPP/0U06dPx8yZM+22HYPRiNTUNMvnGRteu6IoIkOrhSRJxYaigiAgomZtvDvnM4SFR8hRNrmoV6ZMx6J5H2HpsmUwGgzW+/UGA7KyLGFogL8fivqStjQmkwmZ2gyE2qHPBAQEYMKECXj66adlXS+REjEQVbjAwEAsXboUoijCkO9ALLffos5j3bKv8MzzYxEWXt2mdfz86284tP8f3NvtfvTq3q3Q414aDT+gVmKBVYPx/qeLYTabYTIWvDB9pk6H1WvXQZuegWeeGYo6NYs+xbEszlyOxf6tqzB54muoWbNmRcsuRBAEeHl5cV8ltyEIAl588UWMGTMGer3ebts5du4yPv98IYaNtr3PRF+8jC3ffYfwGjXxzP+eho9XwWs7enp52fVSGuQ6qkVUx8dfrYLJZILZZIIkSfj+p504cfQIHuzVG906d7R53ediYrH3u5V4Z/IEWfuMWq22y5kNREpwzz33YOfOnTAYDEV+cS6H8zFXMXvepxgy6kVUr27be830zEysWvMN9Dm5GPbsM6geXq3A42oPD7t9IU+uxdPTE69OmQ5RFK2B6PEz0fjh++9Rt34D/O+pJ2w+pl+9fgPbVi/Bu29ORJ3atWSrWRCEcl/3mmTEWeZdDo/WlYRKpSp1AoqK8NJooPbwgJdGA42N23n8kX7w9PTCv//8BU8vL/R5sLu8RZIiqNXqAt+oZ2bpsG7Dd9Bl6fDcqOdQt1bFPlx6aTTw8PCAt7e3XV8TRO5GEAS7vqY0MvSZNnc3h5fmWXy3fj02bPoew58ZAh++8XdrHh4eUKvV2Lz9J5w+cRx9Hn4E3e/tXKF1ss8Q2Y+9TpcHAI3Gu8J9JtzbG2NGj8aylauwbv0GjBwxHDUiip7FntyDSqWCxtsbUafO4McdO9DwriZ4dvDTFfqCK++zN/sMkX1xmAQ51CN9eqLTvV3x177fsfu3fc4uh5wsM0uHFavXICM9HcOHD6twGEpE1LxxQzw9ZAjib1zH6rXrkWPHUa3k+iRJwubtP+H4kf/Qs0/fCoehRERBAf54fuQIaHx8sHLVasTFFz+bN7mHqFNnsHXLZtRr0LDCYSgROQ4DUXI4hqIEMAwlIvthKEoAw1Aish+GopSHYSiVmSQAogNvEi/xVhoGouQUDEXdG8NQIrI3hqLujWEoEdkbQ1FiGEqkbAxEyWkYironhqFE5CgMRd0Tw1AichSGou6LYSiVm+iEG5WIgSg5FUNR98IwlIgcjaGoe2EYSkSOxlDU/TAMJaocGIiS0zEUdQ8MQ4nIWRiKugeGoUTkLAxF3QfDUKLKg4EouQSGopUbw1AicjaGopUbw1AicjaGopUfw1CqEMkJNyoRA1FyGQxFKyeGoUTkKhiKVk4MQ4nIVTAUrbwYhhJVPgxEyaUwFK1cGIYSkathKFq5MAwlIlfDULTyYRhKVDkxECWXw1C0cmAYSkSuiqFo5cAwlIhcFUPRyoNhKMlGFBx/oxIxECWXxFBU2RiGEpGrYyiqbAxDicjVMRRVPoahRJUbA1FyWQxFlYlhKBEpBUNRZWIYSkRKwVBUuRiGkrv5888/8eijj6JGjRoQBAHbtm0r9Tn79u1D27ZtodFo0KhRI6xatcrudcqJgSi5NIaiysIwlIiUhqGosjAMJSKlYSiqPAxDyS4kwfG3ctDpdGjdujW+/PLLMi0fExODhx9+GA888ACioqLw2muv4fnnn8fu3btt+ddxCgai5PIYiioDw1AiUiqGosrAMJSIlIqhqHIwDCV39dBDD+GDDz7A448/XqblFy9ejPr162P+/Plo1qwZxo8fjyeffBILFiywc6XyYSBKisBQ1LUxDCUipWMo6toYhhKR0jEUdX0MQ8meBNHxNwDQarUFbnqZ3uMeOHAAPXv2LHBfnz59cODAAVnW7wgMREkxGIq6JoahRFRZMBR1TQxDiaiyYCjquhiGUmVVu3ZtBAYGWm+zZs2SZb3x8fEIDw8vcF94eDi0Wi1ycnJk2Ya9MRAlRWEo6loYhhJRZcNQ1LUwDCWiyoahqOthGEqV2bVr15CRkWG9TZkyxdkluQwGoqQ4DEVdA8NQIqqsGIq6BoahRFRZMRR1HQxDyWFEwfE3AAEBAQVuGo1Glj8nIiICCQkFj10JCQkICAiAj4+PLNuwNwaipEgMRZ2LYSgRVXYMRZ2LYSgRVXYMRZ2PYSiR7Tp37oy9e/cWuG/Pnj3o3Fk579kYiJJiMRR1DoahROQuGIo6B8NQInIXDEWdh2EoUUFZWVmIiopCVFQUACAmJgZRUVGIjY0FAEyZMgXDhg2zLv/iiy/i8uXLeOONN3D27FksWrQI3333HSZMmOCM8m3ikED0yy+/RL169eDt7Y2OHTvi0KFDxS67atUqCIJQ4Obt7e2IMkmBGIo6VqaOYSi5JvYZsheGoo7FMJRcFfsM2QtDUceLOnWWYSg5nuSEWzkcPnwYkZGRiIyMBABMnDgRkZGRmDZtGgDg5s2b1nAUAOrXr4+ffvoJe/bsQevWrTF//nwsW7YMffr0Kd+GncjD3hvYuHEjJk6ciMWLF6Njx4749NNP0adPH5w7dw7VqlUr8jkBAQE4d+6c9XdBEOxdJinYI316AgD+2vc7AKD3A92cWU6llZ2djW+//w4ZGRkMQ8mlsM+QveWFot+tX4/Va9dj2NBBzi6pUpIkCVu2/4zjRw8zDCWXwj5D9pYXii5buQorV63GiOHDEORt94/qbin6/Hls37kH9Ro2YhhKlE/37t0hScWnqKtWrSryOceOHbNjVfZl9xGin3zyCUaPHo2RI0eiefPmWLx4MXx9fbFixYpinyMIAiIiIqy38PBwe5dJCldwpOifzi6n0snU6bD+u00MQ8klsc+QI+QfKbpm7QboDRwpKidJkrBzz68cGUouiX2GHCH/SNFVq1cjISnJ2SVVOlGnzuKnH3/kyFByDidNqkTFs+vXTgaDAUeOHMGUKVOs96lUKvTs2RMHDhwo9nlZWVmoW7cuRFFE27Zt8dFHH6FFixZFLqvX66HPd/qaVqsFAIiiCFEUZfpL7EMURUiS5PJ1AoAkiZCkvP+6Zr0P934QkiTh732/QczRom+/fs4uqUSWf0vJZf8982TqdFi58htkaTPw7MjRqFOzukvXLEmWswN4DJCPK9fniD4DKLfXKGUfAyyBGG4dE131GNOsUX08NXgwNq1fj81bvsczI5+Dj4ufBquEXiNJErb88BNORh3Dg/0G4P4uHV28XgCQXP71DyjrGOCqNbLPlE4p+5mYd1xx4WNioH8VjBoxDMtXrMJ3G7/DM6NeQM3qrh2mK6HPAJYw9Pstm1CvTl0MHfQkPDzULluzJEmQ2Gdkp4QaybHsGogmJyfDbDYX+kY0PDwcZ8+eLfI5TZo0wYoVK9CqVStkZGRg3rx56NKlC06fPo1atWoVWn7WrFmYMWNGofuTkpJgMBjk+UPsRBRFZGRkQJIkqFSuPb9VbmY6JJMBuZnpyHbhD38PdoyElJuFQ39ZTp+//94uTq6oJCIMuiwIggRXnd8sOzsb67/bhCxtBh5/pB+q+WuQnZ7q7LJKJGanw6jPRXJysst/66uUY0BGRoazSyiWI/oMoNxeo5R9DAAy09IU0WfqhQXh8Uf7YseWTVi7ciWefOIxaLw0zi6rBK7da/JGhp6MOoZuXTqh492NXb7PmHXpMBn07DMyc9Vewz5TOqXsZ+mpKRCNBuRmpiE73XX7jBeAoU/0x8Z132Dt8iV4etDTCA8Lc3ZZJXDtPgNYTpP/6ccfUa9OPTzS636YsrUwZbtmrQBgzEyHaDQgJSUF3hovZ5dTIqW8/gHX7TPkPC53YZLOnTujc+fbp0l16dIFzZo1w9dff43333+/0PJTpkzBxIkTrb9rtVrUrl0bYWFhCAoKckTJNhNFEYIgICwszOUPHt5xaRA8vODtHwTfoGBnl1OiR/o/CkgS9h88DJVPAPo86JrXFLV8myrAJ7AqBMH1/v9n6nSWa4ZmG/DsyNEI8/N22VrzU6Xo4KnxRmhoaLHX9XIVSjkGeHm59hux8ipvnwGU22uUso8BwI20LMX0mZZtgiAB2LZjF77b8QuGPTMYPhrXDEVduddYrxl6KhoP9huAe1rc5ZJ13kmdqoOHl4Z9RmaVqde4U58BlLOfpelyoPL0grd/VZfvMz6BQXj6f8/i263bsX7rdowYPhw1IlzzeOPKfQawjAzdvnMP6jVujqGDnoRRl+mytebxzLTsqyEhIewzMnJ6n5EEy82R26MS2TUQDQ0NhVqtRkJCwZnyEhISEBERUaZ1eHp6IjIyEhcvXizycY1GA00RH0JUKpXLvyABy/WFlFCrIKggCHn/de1aAaBH924QfPzx9x+/QxCAPg92d3ZJRbLMPOp6/6aZWTqsXL3Wes3QOjWrIzs91SVrvZMgAAJ4DJCTK9fmiD4DKLvXKGEfA25NOOKix8SiNK5fH08NGYJNGzZgzbqNGP7MEJcNRV2x10iShC07bk2g1Pch3N+lo6L6DKCM1xWgnGOAq9bHPlM2StjPVHnHllvHRFcXGOCPUc+NwPJVa7Bq9RqMHDEcNSJc8/R5V+wzABB16gy+37rZOoGSh4capmzXrDU/QRAgsM/IztXrI8ez6x7h5eWFdu3aYe/evdb7RFHE3r17C3xrWhKz2YyTJ0+ievXq9iqTKqlH+vTIN9HSPmeXoxiZWTqsWL0GGenpnECJXB77DDlT88YNrBMtrV67Hjl6TrRUFpIkYfP2nziBEikC+ww5U/6JllauWo24+ITSn0QALGHo1i2bOYESERXL7hH5xIkTsXTpUqxevRrR0dEYO3YsdDodRo4cCQAYNmxYgYuUz5w5E7/88gsuX76Mo0eP4plnnsHVq1fx/PPP27tUqoQKzj6/z9nluDyGoaRE7DPkTPlnn2coWjqGoaRE7DPkTAxFy49hKLkk0Qk3KpHdryE6aNAgJCUlYdq0aYiPj0ebNm2wa9cu64XJY2NjCwxdTktLw+jRoxEfH4+qVauiXbt22L9/P5o3b27vUqmSeqRPTwDAX/ssEy256unzzsYwlJSKfYacLS8U/W79eqxeu96lT593JoahpFTsM+RseaHospWrsHLVapc+fd7ZGIYSUVk5ZFKl8ePHY/z48UU+tm/fvgK/L1iwAAsWLHBAVeROGIqWjGEoKR37DDkbQ9GSMQwlpWOfIWdjKFo62cNQSYIqWweVVgu1NgNqrRaqzAyoMzJgCg2Drmt3WeomIudwuVnmieyFoWjRGIYSEcmDoWjRGIYSEcmDoWjxbA1DA7/fBP8/9kKVkQF1phbqW+GnKtMSgApmc6HnSCoVLv24t4i1EZWAs8y7HE6zRW6F1xQtiGEoEZG8eE3RghiGEhHJi9cULawiI0Nzm7VA0JaNCNz9E/z2/wWfU8fhFXsFHmlpRYahAJA09lXktGknV/lE5CQMRMntMBS1YBhKRGQfDEUtGIYSEdkHQ9HbKnqavL5pc6QMG1Xm5XMb3YXESVNKX5DoDpIkQBIdeOMI0VIxECW35O6hKMNQIiL7cvdQlGEoEZF9MRSV75qhia+/DVPVqqUuJwkCbnzyJSRvb5u2Q0SuhYEouS13DUUZhhIROYa7hqIMQ4mIHMOdQ1E5J1AyVw1G8ouvlrpc8gsvIbt9R5u3Q0SuhYEouTV3C0UZhhIROZa7haIMQ4mIHMsdQ1FZZ5M3m1F32NOImPVeiYvp6zdAwuSptm+HKG9SJUfeqEQMRMntuUsoyjCUiMg53CUUZRhKROQc7hSKyhmGBq9ehpa1qyLg110lLicJAq5/sgiSr6/N2yIi18NAlAiVPxRlGEpE5FyVPRRlGEpE5FzuEIrKFYZ6nziOljUCUHPKxEKP6evVL3RfysgXkN2xi03bIiLXxUCU6JbKGooyDCUicg2VNRRlGEpE5BoqcygqRxiq0mpR+6Xn0OjhBwo9lvzcGJy8kYEr67ZC9PKy3m+oUw/xb79XkdKJLEQn3KhEDESJ8qlsoSjDUCIi11LZQlGGoURErqUyhqIVDkNFETUnjEPzFvWg1ukKPGSoXRenLt7EzQ/mAoIAQ/2GSB79kvXx6/O/gORbRY4/g4hcDANRojtUllCUYSgRkWuqLKEow1AiItdUmULRioahgds2o2WtIARvXFvosfP7DuHcwZOFAs+kV1+HMawaUoaNgu7ebhWqn4hcFwNRoiIoPRRlGEpE5NqUHooyDCUicm2VIRStSBjqdekCWtYIQJ1xzxV67PonX+JknBb6u5oW+VzRzx835i1E/NSZNtdOVAhnmXc5DESJiqHUUJRhKBGRMig1FGUYSkSkDEoORW0NQ4WcHDTu2g5NurYr9FjGgCdwZeV6pD81pNT1ZPZ6CKKff7nrJiLlYCBKVAKlhaIMQ4mIlEVpoSjDUCIiZVFiKGpTGCpJiHj/XdzdMBzely4UeMjsWwWnz1zFtS+WAgIjEHISUXD8jUrEowFRKZQSijIMJSJSJqWEogxDiYiUSUmhqC1hqN/ve9CyZiDCvvqs0GMXf/oNZy7ehBhU1R7lEpGCMRAlKgNXD0UZhhIRKZurh6IMQ4mIlE0JoWh5w1CPuBtoWSMA9Yc+UeixuBmzcDJOi5zI9vYql4gUjoEoURm5aijKMJSIqHJw1VCUYSgRUeXgyqFoucJQoxENHuuDZu2bFXoos9sDOBmbipTRL9mxWiIbcFIll8NAlKgcXC0UZRhKRFS5uFooyjCUiKhyccVQtDxhaOiXn6Jl3RBUOXSg0GPRx87jyoYfAA8Pe5ZLRJUEjxREJTCbzfhmyZc4ezIKOdk66/3JqWk4/sdOrP2sKoKrBtm+fpMRao+yzZhYuDYRcfE3YTKaUL16BD6ZGmV9LCgkFP2fHop2HbvYXBsRETnGn7/uwq8//YCMtFTrfbqcHBzduwO/blyB6hERUKls/5a/Ir0mMTkFWZlaVA0OwfexZ/H90k8BAL5+/oi8pzOeevY5CAJHIBARubJrVy5jzdcLkZwQD1EUAQAmsxlxcTdxaPf3qF69OjRetvUJoGJ9JkunQ2JiEnx8fJB++RSO790OAPD09EKteg3w/CuT4OcfAN9D/6LhY72LXMflDT9A1+0Bm+snIvfEQJSoGKIo4os5H+DYgT/Rv39/BAYGFXg8JzcXBr0eGm8NvDXeNmxBgiRKEFQCgPJ9mJQkCVk6HUTRjCpVqsBDffulLIoiTp48gc8+eBevTn2foSgRkQv7fffPWP75HNzbuQu6de5YIFw0mkzIztZBrVKjil8VCOXsFRa295rsnBwYDQZovL3hrdEUeCwxMQHbN3yDnOxsDH/xZRvqIiIiR7h+NQYfvjUB1UKD0bdXT3jkGz0pSiKysnSQJAl+flWgVqlt2ILtfcZoNCI7JxseHh7w9fUt0Odyc3Oxc+fPmDVpPNb+9AMCJKnQ8xNfm4yEyVMBfjFHSuDomd85y3ypGIgSFSPueiwO/fUb3p06DQ8/+miRy2RotdBlZcHP3x8B/v7l3IIE0WyGSq1Ged48iKKI5JQUmM0mhISEwMvTq9AyJpMJr778EnZs+paBKBGRC9u2fjV6PPgg3v9wVpEjLXP1eqSmpsLT0xOhIcEQhPJe7ci2XpOWno6c7Gz4BwTA38+vyGVq16mLRYsW4alhz8HXt0o56yIiIkfY89N2+Gq8sGjxEgQGBhZ63CyakZycAlEUERoaAs9yj/S0rc/k5OYgLS0NGo0GwVWDi+iBEvq3aY1hI4ZjvyShb/7nNm+JSz/uheRty6AUIiILXkOUqBhpKckQICCybdtilwkMCEAVPz9kZWZCm5lZ5nWbzWakpKQgMSEBWVlZZX5eWcJQAPDw8EDLlq2QlpxU5nUTEZHjpaelonWbyGJPO/fWaBAcHAyj0YjklFRIklim9UqSBK1Wi4SEBKSlpUEqYmRNccoShgJAm8hICAKQmpRY5nUTEZFjpaUko2HDhkWGoQCgVqkRGhoClUqF5OQUGE3GMq87JycHiYmJSEpKgtFYjueVEoZ6pKbA90QUmgdXRRU/P+TvMuf2R+Hir/8wDCWiCuMIUaJSCKqSvzcIDAgAAGTdCkSLGyl66tRJrPtmDf47dBAnjh+HTnf7mqQ1a9VC27bt8MCDPTD4f0OLfMNS1jA0j6qUuomIyPkkSSr1eJ0XiqampiI5JbXYkaJJSUlY980a/PnHPhw7dhSJCbcnyggMDETrNm3QqXMXPDtsBBo0bFjktsoahgLsM0RESlCWPpMXiiYnpyA5OaXYkaIGgwE/bPseP//0I44dOYILF85bH/P09ETzFnejXbv2ePLpp9Ht/u5FftlXUhiqys2B9/mzBWtTW07jv7r0G2gfHlCuv53IpUi3bo7cHpWIgSiRDEoKRQ/s/wfT330H//z9d7HPv3H9Om5cv44d23/Au+9MwbPDRmDq9PdQtWpVAOUPQ4mIqHIpKRS9efMmpr/7DjZt3ACDwVDk8zMyMvDnH3/gzz/+wJzZs9Crdx+8/9EstGzZyrpMecJQIiKqXEoKRQ0GAz779BN8ufBzJCUWfVaA0WjE8ahjOB51DCuWL0XTps3w5tvv4KmnB1lDz+LCUEE0Q3M2GqoiRqeKPj649ulihqFEJDt+tU8kkztPn8/JycEbr09Erwe7FwpDa/gEonNIfXQNa4Q2QbVQRX074NTpdFj81Zfo0LY1dv38E8NQIiICUPTp8+u/XYf2bVpi3TdrCoShAR7eaFu1NrqGNULHkHqopil49sKeX3aja+eOmP3RBzCZTAxDiYioyNPnT5w4jm73dsZ7704tEIZ6qdRoHhCBrmENcV9YQzSoEgpVvmuInj0bjZHDnsGQp59EQkJCsWGo141r8Dl1olAYKnp6IufuVjD7B5Z3riYilySJgsNvVDKOECWSUd5I0bgb1/Hi6FH479Ah62P1qoTgqdpt0ad6MwR5+RZ4nihJuJCZiG3Xj+PHuJPIMRtxMy4OTzw+AO++NwPDRz7HMJSIiKyhaEpKCl6fNBGLv/zC+liAhzcG1GqFATVbo26VwtdkS8rNxM83T2Nz7FHczNXCaDTi/Rnv4d8DB7Dg84UIDavGMJSIyM3lHym6ZfNmjH/xBeTk5FgeEwQ8UK0JnqjdBpFVa8Pjjlnps00G/J10CZuuHcGxtOsAgB3bf8CxY0excs1aNGna1BqGqjPSobkaU2QNuXc1hejtY98/lIjcHgNRIpl5enhgzPOjcPg/SxiqUXlgbOOuGFK3A9TFzA6sEgQ0CQjHm817Y0SDTvjw9C7sT74MAHj/vekIDAzE2HHjHfY3EBGR6/LWaPD1oi8LhKF9qzfH6017Icir+A+QYd7+GF6/E4bUbY+ll/7Bmph/YZYk7PllNya99iq+2/K9I8onIiIXp1apEX3mFMa+8DwMej0AoJFfGN5r+TCaBkQU+zxfDy/0rt4Mvas3w974s5gd/QvSDNm4fu0ahg0dgt//+Bthfn7wPnumyOcbatWBKTjELn8TEdGdeMo8kcymvzvVGoYGefpgecdn8Ey9jsWGoXcK9w7AZ22fwuiG91rve2vy6zh+PMoe5RIRkcL8uucXzJ/7sfX3t5r3wQet+pcYhubnpfLAS43vx5ftBsNHbbk+3O5dO/H5pwvsUi8RESlLSkoKRg571hqG9ghvgjWdh5cYht6pR0RTrO/yHBr5hQEAbsbFYcyQp+F15lShZU2BQchu1YZhKFVukuD4G5WIgSiRjP75+y989eVCAJaRoV+0H1SuNw55BEHAmEZdMbRuBwCAyWTCmOdHFTtZBhERuQetVouXxo6x/v5akwfwZO1Im9bVPqQu5kU+Yb002wcz38PZ6OiKF0lERIo2edIEJCYkAAA6hdTHh636w0tV/pNLQzV+WNR+MKp7Wy4r9sfhw1i0ebP1cUkQkNOiJQx164MXCiUiR2MgSiSjaVPfhiRJAIBxjbvZFIbmN/6u7tZvVU+eOI6NG9ZXuEYiIlKurxcvwvVr1wAA9wTXxdC691RofR1D6mFoPcs69Ho9Ppj5XgUrJCIiJTtx4jg2rv8WAODvocH0u/sVulZoeQRrqmDa3f2sv09fsgQ5ubnIbXQXclq2gaTmVfyIyDkYiBLJ5PjxKPx74AAAoIFfKAbXbV/hdXqq1HireW/r78uWLK7wOomISJnMZjOWL10CwDKOZkrzvoUmTrLFi426IsSrCgDL5Bc34+IqvE4iIlKmZV9/bf15TKOuCPP2r/A6O4TUQ9/qzQEAqRkZ+ObcBYi+VSq8XiJFEQXH36hEDglEv/zyS9SrVw/e3t7o2LEjDuWbebsomzZtQtOmTeHt7Y2WLVvi559/dkSZRBWy7ps11p+frt22zNcMLU3roFq4y78aAODwf/8hOrroi5ATuTP2GXIHf+z7HddiYwEA94Y1RO0qVWVZr7faE4/Vag3AcomWDbdGBhHRbewz5A4MBgM2brD0AF+1Fx6p2VK2dQ+q08768zerV8q2XiIiW9k9EN24cSMmTpyI6dOn4+jRo2jdujX69OmDxMTEIpffv38/hgwZglGjRuHYsWN47LHH8Nhjj+HUqcIXXyZyJYcO/mv9uVdEM9nWKwgC+lZvkW87B2VbN1FlwD5D7iJ/n+ktY58BgD63Ru4AwH+H2GeI8mOfIXdx5sxpZGVlAQDuC2sIPw+NbOu+O7AGavgEAgCOHD4Mk8kk27qJiGxh90D0k08+wejRozFy5Eg0b94cixcvhq+vL1asWFHk8p999hn69u2LyZMno1mzZnj//ffRtm1bfPHFF/YulchmJpMJJ0+cAADU9q2KwDLO9FtWzfNdizTq6FFZ102kdOwz5C6ijh2z/twisIas665bJRi+ai8AwLFj7DNE+bHPkLvI/zmjeWB1WdctCAKaB1jWmZOTg3Pnzsq6fiKXJ8HBs8w7+w92fXa9grHBYMCRI0cwZcoU630qlQo9e/bEgVvXWrzTgQMHMHHixAL39enTB9u2bStyeb1eD71eb/1dq9UCAERRhCiKFfwL7EsURUiS5PJ1AoAkiZCkvP+6dr2WGqUK12mZHCn/rXgpKcnIzc0FANSrElyh7Ralnl+I9efr16+VWo+FZRk5/n/J9W/qCJJk+ct5DJCPK9fniD4DKLfXKGUfA24dc28dZ1z9WCPrMVEqW58B8o7/gAoCavvKc7p8HrWgQp0qVXFWm4Dr165BksQyXJ80r8/I0XOV1WcAyeVf/4CyjgGuWiP7TOmUsp+JeccXBRxr5D0mlr/PAEC9KiElLGmb/J+Trl+7hhYtWpSwdD4y/VsopddIkgSJfUZ2SqiRHMuugWhycjLMZjPCw8ML3B8eHo6zZ4v+Rig+Pr7I5ePj44tcftasWZgxY0ah+5OSkmAwGGys3DFEUURGRgYkSYJK5drzW+VmpkMyGZCbmY5sb29nl1MKEQZdFgRBQkUGQedmaiGJIiSzGaLZXOKyxnxvYD0E22dhLI5nvnUaDYZS6wEASZRgNhmRnZ4qQwXy/Js6gpidDqM+F8nJyfD09HR2OSVSyjEgIyPD2SUUyxF9BlBur1HKPgYAmWlpbtdnAEAUzZBEsUzHdZPRcnqhh0oFlQyTKd0pr9eIogizyVTqPiOZRUiiiBxtugy9Rjl9xqxLh8mgZ5+Rmav2GvaZ0illP0tPTYFoNCA3Mw3Z6e7TZ4z6XEjenmXqM8Z8+5pnBWaWL46n6nb8UObPNJIIfbbOrT7TGDPTIRoNSElJgbfGy9nllEgpr3/ABfqM5OCJjiROqlQauwaijjBlypQC38BqtVrUrl0bYWFhCAoKcl5hZSCKltEXYWFhLn/w8I5Lg+DhBW//IPgGyT8CUk6Wb/4E+ARWhVCBiY28/QMgqFQQ1Gqo1CW/IfD187P+nGXSl7CkbTJNudafq/j5lVoPAAgqAWoPT1n+f8n1b+oIqhQdPDXeCA0NRbVq1ZxdTomUcgzw8nLtN2KOoNReo5R9DABupGW5XZ8BAJVKDUGlKtNx3cfXcjkWg2hGrtkIb7W8YVzmrf7l7e0NjzIEfYJaBUGlgk9Axf+fKanPqFN18PDSsM/IzN17jVL7DKCc/SxNlwOVpxe8/au6VZ/x1HhDEMQy9Zkq+T7TZBpzS1jSNjZ9phFU0PhWcavPNJ6Zln01JCSEfUZG7t5nqDC7BqKhoaFQq9VISEgocH9CQgIiIiKKfE5ERES5ltdoNNBoCl/sWaVSufwLErBcS0UJtQqCCoKQ91/XrhWw/LtWtFbLqYL5bxa7d+3EwAGPYuy48Zi34FMAQNWqwQirVg1JiYk4p02AJEllONWw7M5pb78mmjRtCkCAJEno1KEdYq9ewfHTZ4tolsKtv0Oe/19y/Js6giBY/nIlvK4AZRwDXLk2R/QZQNm9Rgn7GHDrmKuQ4wwg4zFRKFufAYAmTZtZJ9Y7n5mIVkE1K7btfHJMBsTqUq3bKV+fEWT5f6akPgMo43UFKOcY4Kr1sc+UjRL2M1XesUWmY5a9yXdMLNxngKJ7jeX4b3E+MxE9I5pWcNsF5f9M07TMvQay/j9TQq8RBAEC+4zsXL0+cjy77hFeXl5o164d9u7da71PFEXs3bsXnTt3LvI5nTt3LrA8AOzZs6fY5YkcSZIkvPPWm1CpVHh1guVbfK1Wi1deGof09HTL76ZcxOhSZN1uVPp1689t27YHYGk8r06YCK1Wi9kffiDr9oiUgn2GKpvi+szL48Zi65bN1uWi0q4XtwqbnMiIg3jr+nKRbdsCYJ8hAthnqHIqrtds27LFukxU2rXinm4TvdmEM1rLZSP8/f1RvYZlckD2GnIXty6X79AblczuEfnEiROxdOlSrF69GtHR0Rg7dix0Oh1GjhwJABg2bFiBi5S/+uqr2LVrF+bPn4+zZ8/ivffew+HDhzF+/Hh7l0pUqr2/7kF09Bnce999qF2nDgDg+ZHDsWL5UgRXvX0ax/fXo2TbZq7ZiJ/jTll/Pxt9xvrzgMceh6+vL9Z+s9p68X0id8M+Q5VJSX0mIuL2jL/brkdBlPGd7rZ8fctoNFp/Zp8hYp+hyqe4XvPddxuspxUfTbuGKzIO8tgTHw3drUuzZGZm4qsvv7A+xl5DRM5g90B00KBBmDdvHqZNm4Y2bdogKioKu3btsl5oPDY2Fjdv3rQu36VLF3z77bdYsmQJWrdujc2bN2Pbtm24++677V0qUal2/PADAKD7Aw8CAE6ePIGfftwBT09P7PltH7xvTQSy/cZJJOuzZNnmtuvHkZHvGj5ffPE5zLcuQF6lShW0v+ce6HQ6/Lb3V1m2R6Q07DNUmZTUZ/b99Q+6de8OAIjNTsPvCedk2eYVXQp+Szhv/f2XXTvZZ4jyYZ+hyqakXvPGlLety62+/K8s2zOJZqy9cqjAfR/P/oi9hoicyiEXURg/fjyuXr0KvV6PgwcPomPHjtbH9u3bh1WrVhVY/qmnnsK5c+eg1+tx6tQp9OvXzxFlEpUqKuooAKBps+aW348dAwA0aNAQDRs1wuD/DQUA6Ex6zD6zG1IFR+/cyE7Hlxf+KHBfUmIi4m7csP7evFkLAMCRw/9VaFtESsY+Q5VFSX0mODgYL730inXZOdF7kG7IqdD2zJKImad+hlkSrfclJSUh8VYdAPsMEcA+Q5VLSb1mzIvj4HdrcqUdcSfxb3JMhbe3KuZfXMxKAgDc3bIVAH6mITckCY6/UYl4VVmickhPSwcABAQEAAASEizXwcmbkfG9mR8gICAQALAv8QJWxxy0eVtZxly8efx75Jgtpy4OGzHS+lh8/O1RCP4B/gCAtNQ0m7dFRESuobQ+8/Cjj6Jde8u1pFMMOrx94gfozSabtiVJEj49+xtOpFs+kDaseXuSpuRzZ+F74hjUWZnsM0RElUxJvSY4OBgfzvrYuuy0kzusk+7Z4u+kS1h66R8AgFqtxvwFn1kf42caInImBqJE5RAYZAk7MzMzAQDh4ZbZQnVZltPjw8LC8Ozw4dblv7iwD19d+BMmUUR5xOWk48XD63H21kyMderWxYRJk62P57+OXKbWUktQ1aBy/jVERORqSuszgiBg6LO3+8yhlCuYcHQTUvW6cm0n12zErDO7sT72MADLzKuzZ7xvfbx6aCgAQHP5InJiLgNgnyEiqixK6zWjRr+A5i0sl3hINWTjhf++xan0uHJtQ5Ik/Bx3CpOPbbWehTBp8hto2KiRdRl+piG3IgqOv1GJGIgSlUObNpaZd/MmNmrdpg0A4NKli0hNtXxzKpoLhp/LL+/HcwfX4HTGTZRGbzbhu9gjGPzPCmsY6ufnh2/Wb7RuMzQsDDXyjeKJvnV/m8i2FfjLiIjIFZSlz1w4b7nep0qtBgAcSr2Kp/9Zhp/jTpX6BZwkSfgv5QqGHliJrfkmUpo9bz6M/pZRqGHBwagZFmZ97MxlSyDaoUYNAJyylIhI6UrrNYIgoH2HDtblk/VZeO7gN/j83O/Q5pvboDg3stPxRtT3mHbyRxgly3VC23e4B2++/Q7+O2Q5g46faYjI2RiIEpXDI/37AwD2/f4bAKBVq9Z4qN/DMJlM6NPzQTw3/FksXbIYKpUK48a/DJXK8hI7o43H8H9XY8S/a/Dtlf8QlXYd6YYc6Ex63MzJwL6E81hwdi8e/uNLzIneg2yzwbpND09PfPbJfIwf9yIA4I03p0B960NwdnY2Dv93CD4+PujRs5cj/ymIiMgOSu0zI4Zh6ddfQaVSYcGnnyP01kjOdGMOpp38Ef3//AqLLvyJv5MuIjE3EzqTHql6HQ6nXMXqmH8xeP8KjD28AVfznf7o7++Pf/fvx8vjxgIAJr89FYa7WwMAsnNzcfD0afhoNOjbpjV8T0TBIznRkf8kREQks9J6zfBnh+Lbtd9AEATcfXdLAIAICWuuHMRD+77AjFM/YVfcaVzRpSDLpEemMRdntfH4/loUXju6CY/9tRi/J54vsM3Lly/hhVHPWXsNP9MQkbN5OLsAIiXp1bsPmjRpir//+gvXYmNRu04dLFu5Gu9MeRM7tv+AixfO464mTfHue++hf//H8PSgwXhu+DBcvnwJAHAqIw6nMsp2usmI50aheo0a+Pabb7B92/eIiKiOqdPew7jxL1uX+WHb99DpdBj1/AuoWrWqXf5mIiJynJL6zI/bf8D3Wyx9Zso772LgE0+g/2OPY8zzI/HL7t0AgER9JlZc3l+mbbXv0AEdO3fBjz/8YO0zb70zFmNfegmSSo3sVpHYuHwZdDk5GDNwIKreutacV9wNeMXdgCrXUMoWiIjIFZX4meaHbbhw4TyaNm2G6TPfR+8+fbHgk3n4cOYMGI1G6EUTdtw4iR03Tpa6ndCwMCz4bCEuX76EFUuX4IfvtyIiojpmvP8BP9OQ25EkAZIDJzpy5LaUiiNEicpBEAR89PEciKKIzxZ8AgAICgrCwi+/wuGokzhz4RL+OXgI/fs/BgDocE9H/HfsOD7/YpF1RsWSeHp64qlBg/HbH3/hy6++xtR3p+PM+YvI0OXi0LHjGD7yOWTeuraPJEn49JP58Pf3xzvTptvtbyYiIscprs98+dXXiDpzDqfPXcTvf/6DgU88AQCoVq0atv7wI7b/tBMPP/Ko9cyEknTs1AnLV63B3n1/Yc7c+dY+c/zMWYwaPQYpaWmQJBGSJOGTrxbB398fU99+p9B6NNeuwuv6NagyMuT9RyAiIrsqrtfMW/Ap/j1yDJeuXsO/h4+i38OPwMPDA5PfeAuHj53A2HHjrRMxlaRW7dqYPmMmjh4/hYFPPInXJ7+JM+cvITVThz8PHMTQYSNgujUhID/TEJGzcIQoUTn1fagfdPrbM/qKoojklBSYzSaEhITAy9OrwPLe3t4YNfoFPPf8aBw/HoXDhw4h6r9DuHnhPIwAqnh6onHHTmjbrj263HsfqlWrVuR2A2+9+ci6dfHzAH9/HDx81D5/JBEROc2dfQYA0jIykKPTISAgEH5+VQo8JggCevTshR49e+HG9ev455+/cezoEVw4fx65ubnw9PBA3Xr1ENm2HTp26oymzZoVuV1vjQbBwcFITU1FcmoqQoODC/SZHEmE5sI5qHLzXT9OktC4bzcE9x+Ia4tWAGUIZImIyPnu7DU5uTlIS0uDRqNBcNVgCELB0WWNGjfGvAWf4r33P8Dff/2JqGNHcerkSWRkZEAlCAgJC0WbNm3Rrn17dOzUGR4ehaMGtUqN0NAQJCenIDk5BaGhIfD08ORnGiJyCgaiRMVQqy0vD31u8RcOLy0MzU8QBLRpE4k2bSKhGjYMmvNnYapSBR46HXJaRZappqJC0eLk5ubCw9OzTOslIiLn8PDwQG4JfQYoOQy9U81atfD0oMF4etDgW/dIEM3mWxMwlX7qVFGhqCBYQk5JUCH3rmZQGfTwPnsGuQYDIEnwBBC0fSuCtm/FtQVfIX3Q0DL85URE5Agenp7I0aWXuExpYWh+fn5+6PtQP/R9qN+te8rXZ4oLRYsiSRL0+lx4FPM4kaI4euZ3zjJfKn6NT1SM6jVrA4KA1atWQCxi1t7yhKFyCgwIQBU/P2RlZkJ7Kxi907XYWOzZ8wuq16rrkJqIiMg2NWrXxY4ffkBycnKRj5cnDJVLXihqNBiRnJoKSSrYA0UvDdKbtsDKP/6Ed3Y2qud7rPaEsWhZIwCac9EOqZWIiEpWs05dnDx5CocO/lvk4+UJQ+WSF4qqVCokJ6fAaDIWudy369YiV69HzTr8TENE8uMIUaJiVA0Jwfg3p+GLj2fi77//LnC9HEmSkJ2dA1EU4evrC7W6fN8tCCYTPFKSYfb0hNpohDE8otz15er1MBgM0HhpoNHcDmPNZhHJyUmoVqM2Rr8yqdzrJSIixxn/5jS8/8arGPhYf4SEhEKluv1BNCdXD6PRAI3GGxov20fHSKIIwYZT2U0mM3JycqBSq+Dr62sd9yNJEjLSM2AwmTF5605o165C8LerCzz3rgc6wlCjFi78cQhiFT+baycioooZ8NT/cPlcNF595RWEhYXB0/N2BGA0mZCTkwMPDw/4+vjYvA1b+4wkSdBlZ0OSJMtnqnzryMnOQWp6GgYMHoZWbTvYXBsRUXEYiBKVoFO3BxAcGoazp08gW6cDAOj1Buz/9yByJRM6du6I4KCgcq/XI+Emqn0+H5l16sA/NhZxH86zqb5T0WcRc+kSGtaqjeZNGgOwnJofHBqGe7p0RWDVYJvWS0REjhFRoybem/8Fjh7cj7TUFMudkoRjJ08jNf0qmjZvjcYN6tm+AUmCIUcHL58qgA2jfuITk3Hk8GEIal907NAenreuCefn748WrduhfqPGuNGmHW5O/xBNurSBR8rtka5ecdfRonENJI98ATc/mGvT9omIqGI8vbwwYer7+GffXiQnxsNkslw39HrcTURFHUNIaA3c064t1Gq1bRuoYJ/Jzc3FPwf+RUquEZ07dUBggOWSYF5eGtSp3wDtOt3rkFGrRHYnCZabI7dHJWIgSlSKu5rfjbua3w0AyMzSYcXqNajZqBmGDx+GurVq2rROzdkzaLTwEyRWq4Zq167h9IjRNtf34+5f8e8/fyGoTmP0fbC7zeshIiLnCAuPQJ/+AwFYRsts2fEzPP1u4IUJ/8P993as0LolSUR2eip8g25fC7S8oi9cwob166FT+WD4kCHw0WgKLSP6ByD65GV4nzqBxr3vK/BY6MolCF25BFdWrEdm34dtqoGIiGzn6eWF7r0fsv4edeoMzmzZjAcffRLPDn4anhWYd0COPvP4/0Zg2cpVSM7OwSNPD0DNiKInmSUikhOvIUpURnlhaEZ6eoXCULk90qcnOt3bFX/v+x27ftvn7HKIiMhGeWFo1OFD6NWnX4XDULk0a9wQg4cMwc3r17F63Xrk6PXFLpt7dyucjNPixsefFnqs3nND0LJGADxjr9ivWCIiKlHUqTPYumUz6jVoWOEwVC6BAf54fuQIePv4YNWqVbgRn+jskohkJ0mCw29UMgaiRGXgqmFoHoaiRETK5qphaJ7yhKIAkPrsczh5LQ0ZfQqPCG3aqRUa9ekGoZR1EBGRvFwxDM3DUJSIHI2BKFEpXD0MzcNQlIhImVw9DM1T3lAUajViV67HmZOXCz3kczIKd9cPQ7V5HwGSZKeKiYgojyuHoXkYihKRIzEQJSqBUsLQPAxFiYiURSlhaJ5yh6IAzCGhOBmnxaXvdxV6LPyT2WhZMxBV9v9lj3KJiAjKCEPzMBSlSkt0wo1KxECUqBhKC0PzMBQlIlIGpYWheWwJRQEgu2MXnIzTIv6taYUeqz9oAOqNGASPxDJ+8BX5Lp+IqCyUFIbmYShKRI7AQJSoCEoNQ/MwFCUicm1KDUPz2BqKAkDSK6/j1JVk6NrdU+ixpu2aot7/HgdMpmKfL+j1qDtyCITcXJtqJyJyF0oMQ/MwFCUie2MgSnQHpYeheRiKEhG5JqWHoXkqEopKXl64vONXnD10utBj/vv2omWdYIQsX1zkc0NWfI2APTtRbf4sm2snIqrslByG5mEoSpWKJDj+RiViIEqUT2UJQ/MwFCUici2VJQzNU5FQFACMtWrjZJwWV1dtKPRYjXffQMsaAfA5ftR6n0dSIqot+BgAEPbVZ/CJOlKxP4CIqBKqDGFoHoaiRGQvDESJbqlsYWgehqJERK6hsoWheSoaigJAZo/euLJqA5JfeKnQY40e6o7mTWpBlZGO8Nkzoc7KBAAIoohaE8ZBsGF7RESVVWUKQ/MwFCUie2AgSoTKG4bmYShKRORclTUMzSNHKAoIiH/3fZy6GA99/QYFHlFnatGiWR0Er19T4H7vc9Go9tmcClRORFR5VMYwNA9DUVI6SRQcfqOSMRAlt1fZw9A8DEWJiJyjsoeheeQJRQHJ1xfn/4nC+T8Ol2n5sIWfwPvkcZu2RURUWVTmMDQPQ1EikhMDUXJr7hKG5mEoSkTkWO4ShuaRKxQFAH3ju3AyTotrXywtcTnBbLacOm8w2LwtIiIlc4cwNA9DUVIsTqrkchiIkttytzA0D0NRIiLHcLcwNI+coSgAZPR9FIYatUpcxufMSYR98UmFtkNEpETuFIbmYShKRHJgIEpuyV3D0DwMRYmI7Mtdw9A8coaiYV99Bq+466Uv99lcaKJP27wdIiKlcccwNA9DUSKqKAai5HbcPQzNw1CUiMg+3D0MzSNHKOp54zrCvvy0TMuqjEbUmjAOMJnKvR0iIqVx5zA0D0NRUhJJEhx+o5IxECW3wjC0IIaiRETyYhhaUEVD0YgPp0GVm1Pm5X1PHEPYV5+Vt0wiIkVhGHobQ1EispWHswsgchSGoUV7pE9PAMDf+34HAPR9sLsTqyEiUi6GoUXLC0U3rF+P1evWY/jQIfDRaEp/oiQh5bkxSHtyMNRaLdSZWqgyMqDO1EKtTYdaq4UqUwv1rfvyHqu24GNo+zwM/V1N7f/HERE5GMPQwvJC0WUrV2HVqlUYMWIEakZUc3ZZROTiGIiSW2AYWjKGokREFcMwtGQ2haKCgOz2Nvw7ms0QeNo8EVVCDEOLx1CUXJ4kAKIDT2PnKfOl4inzVOkxDC0bnj5PRGQbhqFlI/fs88VSqyGVZQQqEZGCMAwtHU+fJ6LyYCBKlRrD0PJhKEpEVD4MQ8vHYaEoEVElwjC07BiKElFZ2TUQTU1NxdChQxEQEICgoCCMGjUKWVlZJT6ne/fuEAShwO3FF1+0Z5lUSWXqGIbagqEoKQn7DDkTw1DbMBQlJWGfIWdjGFp+DEXJJUmC429UIrsGokOHDsXp06exZ88e/Pjjj/jzzz/xwgsvlPq80aNH4+bNm9bbnDlz7FkmVULZ2dlYufobhqE2YihKSsE+Q84iSRK27tjJMNRGDEVJKdhnyJmiTp1lGGojhqJEVBq7TaoUHR2NXbt24b///kP79u0BAAsXLkS/fv0wb9481KhRo9jn+vr6IiIiwl6lUSWXqdNh/XebkJFtYBhaAfknWpIkoFvbu51cEVFB7DPkLJIkYdevexF18gx69X2YYaiN8k+0tGbdRjz9SC/4OrsoonzYZ8iZos+fx/ade1CvYSOGoTa6c6Kl4cOHo6q32tllkZuSJMvNkdujktktED1w4ACCgoKsbx4AoGfPnlCpVDh48CAef/zxYp+7bt06rF27FhEREXj00Ufx7rvvwte36LfIer0e+nyjCrRaLQBAFEWIoijTX2MfoihCkiSXrxMAJEm89QIWIUmuW2+mToeVq75BljYDz44cjTo1q7tkvZIkQVSpIAmC5b8uWCMAPNz7QUCS8M++3yDlaNG3Xz9nl1QqSQIk8BggJ1etz1F9BlBur1HKPgZYjouQJJfvM5IkYev2n3Hi2FH0ePhxdOvSwcXrtewDrlpj00b1MWjwYGxcvx6bt27DMyNGwsfb29lllcjyAUNy+dc/oKxjgCvWyD5TNkrZz8S846ALHxPzHDsZjZ927EC9xi3wzKAn4eGhdtmaXb3PBPhXwagRw7B85WqsXrUKQwY+inqBQc4uq0SSJEFin5GdEmokx7JbIBofH49q1aoV3JiHB4KDgxEfH1/s8/73v/+hbt26qFGjBk6cOIE333wT586dw9atW4tcftasWZgxY0ah+5OSkmAwGCr2R9iZKIrIyMiAJElQqVx7fqvczHRIJgNyM9OR7aIfVLKzs7H+u03I0mbg8Uf6oZq/Btnpqc4uq0hGkx6Jbdsio1EjSIDL1gkAD3SMhJSbhUN//Q4AuP/eLk6uqGRidjqM+lwkJye7/DfpSjkGZGRkOLuEIjmqzwDK7TVK2ccAIDMtzeX7TN7I0BPHjuL+e7vgnhYNXfr4bSHCoMuCIEhw1bk064YFYeCjfbFjyyasXbUSTw58DBov150l3qxLh8mgZ5+RmSv2GvaZslHKfpaemgLRaEBuZhqy012zzwCWkaE/7diBOjWr4/GHHoRRlwmjs4sqkev3GU8AQwf2x/qNG7Fp/To8NeQZhFcLc3ZZxTJmpkM0GpCSkgJvjZezyymRUl7/gGv2GXKucgeib731Fj7++OMSl4mOjra5oPzX5GnZsiWqV6+OHj164NKlS2jYsGGh5adMmYKJEydaf9dqtahduzbCwsIQFBRkcx2OIIoiBEFAWFiYyx88vOPSIHh4wds/CL5Bwc4up5BMnQ7fbvsOGdkGPDtyNML8vOETWBWC4Jr/rpr4BFQ7ehQCgLCjR5Hsgv+m+T3c/1FIkoT9Bw9D8AlA3we7ObukYqlSdPDUeCM0NLTQhxhXo5RjgJeXY9+IuVqfAZTba5SyjwHAjbQsl+4z1muGnjyDHg8/jg7NG7l0n8ljGbkjuHytLdsEAQC27tiF737cg2FDB8FH45qhqDpVBw8vDfuMzBzZa9hn5KWU/SxNlwOVpxe8/au6ZJ8BLNcM3b5zD+o1boHHH3oQAaHVXPrYDSinz/gGBWPE6DFYtfRrrN+6HcNHjEDNCNcMRT0zLftqSEgI+4yMHP2ZphBRsNwcuT0qUbkD0UmTJmHEiBElLtOgQQNEREQgMbHghYtNJhNSU1PLdT2djh0t1+W6ePFikW8gNBoNNEW8YVapVC7/ggQAQRAUUasgqCAIef91rVozs3RYuXotMtIzMHz4MNSpWR3Z6akuWWseQRCgEkUIkmT5r4vWmV+P7t2g8vHHP3/8DkEA+j7Y3dklFUkQAAE8BsjJ0bW5Wp8BlN1rlLCPAZY6IQgueeyWJAlbf9yJqCP/oVffh9GtSweX7zP5CS7673qnRvXrY9CQIdi4YQPWfLsRw4cOcclQVBAAQBmvK0A5xwBH1sc+Iz8l7GeqvGPgrWOiq4k6dQbfb92Meg0b4ZlBT8Koy1TEsRtQTp8JCgzAkEGDsG7rdqxevRojRoxAzQjXCxwFQYDAPiM7V6+PHK/cgWhYWBjCwkr/JqVz585IT0/HkSNH0K5dOwDAb7/9BlEUrW8KyiIqKgoAUL169fKWSm4gM0uHFavXFJhN3lWvX1MZPNynByAI+Huf5fR5Vw1FSdnYZ8iVSJKELTt+LjCbPPuM/TRr3MA60dLqdetdNhQlZWOfIVcTdepMgdnkPTzULn6avHL5+/th1MjhWL5qDVatWuWyoSgR2Z/dIvJmzZqhb9++GD16NA4dOoR//vkH48ePx+DBg60zMt64cQNNmzbFoUOHAACXLl3C+++/jyNHjuDKlSvYvn07hg0bhm7duqFVq1b2KpUUqqgwlOzvkT490enervh73+/Y9ds+Z5dDbox9huytqDCU7C9v9vmb169j9br1yMk30QyRI7HPkCPcGYa6+rWJK4O82ee9fXywatUq3IhPLP1JRBUkSYLDb1Qyu44ZXrduHZo2bYoePXqgX79+uO+++7BkyRLr40ajEefOnUN2djYAyzUdfv31V/Tu3RtNmzbFpEmT8MQTT2DHjh32LJMUiGGoczEUJVfBPkP2wjDUuRiKkqtgnyF7YhjqPAxFichus8wDQHBwML799ttiH69Xrx4kSbL+Xrt2bfzxxx/2LIkqAYahruGRPj0BgKfPk1Oxz5A9MAx1DXmhKE+fJ2dinyF7YRjqfHmh6LKVq3j6PNmfJFhujtwelYhXlSVFYRjqWjhSlIgqG4ahroUjRYmoMmIY6jo4UpTIfTEQJcVgGOqaGIoSUWXBMNQ1MRQlosqEYajrYShK5J4YiJIiMAx1bQxFiUjpGIa6NoaiRFQZMAx1XQxFyd4kUXD4jUrGQJRcHsNQZWAoSkRKxTBUGRiKEpGSMQx1fQxFidwLA1FyaQxDlYWhKBEpDcNQZWEoSkRKxDBUORiKErkPBqLkshiGKhNDUSJSCoahysRQlIiUhGGo8jAUJbuQcHumeYfcnP0Huz4GouSSGIYqG0NRInJ1DEOVjaEoESkBw1DlYihKVPkxECWXwzC0cmAoSkSuimFo5cBQlIhcGcNQ5WMoSlS5MRAll1IZw1AhOxu+Bw8UvEUdLbDMnY97nznlpGrlxVCUiFwNw9DKhaEoEbkihqGVB0NRkoskCQ6/Uck8nF0AUZ7KGIYCgKTRoObr4+F96UKB+0XV7e8jGj7ep8BjN6e+j9zmdzukPnt7pE9PAMDf+34HAPR9sLsTqyEid8YwtHLKC0U3rF+P1evWY/jQIfDRaJxdFhG5KYahlU9eKLps5SqsWrUKI0aMQM2Ias4ui4gqiCNEySVU1jAUAKBWI3HCm2Ve3BQcgpQRz9uxIMfjSFEicjaGoZUbR4oSkStgGFp5caQoVZgoOP5GJWIgSk5XqcPQWzIGPIHcho3LtGzSuNcg+Vaxc0WOx1CUiJyFYah7YChKRM7EMLTyYyhKVLkwECWncocwFECZR4lWxtGh+TEUJSJHYxjqXhiKEpEzMAx1HwxFiSoPBqLkNG4Tht5SllGilXV0aH4MRYnIURiGuieGokTkSAxD3Q9DUbKFJDn+RiVjIEpO4W5hKIBSR4lW9tGh+TEUJSJ7Yxjq3hiKEpEjMAx1XwxFiZSPgSg5nFuGobeUNErUHUaH5sdQlIjshWEoAQxFici+GIYSQ1EiZWMgSg7lzmEogGJHibrT6ND8GIoSkdwYhlJ+DEWJyB4YhlIehqJUVpIkOPxGJWMgSg7j9mHoLUWNEnW30aH5MRQlIrkwDKWiMBQlIjkxDKU7MRQlUiYPZxdA8jGbzTh8+DDi4+MhiqKs6z5++QZiYy7h79/2ILBqcLmfn5OTi72/70N2tg4PdO+OiyeP4uLJo/DSaFCvQSPUrtdA1npd2q1RorVeeQGAe44ONRqNOHP8KFKTkyFKInwA+Ht7YtOqpTj13wFEtrrb5nVfi0/C5UsXsXPnToSEhMhWs4+PD5o0aYL69evLtk4ipcjKysKxY8eQnJxst21cuh6PaxXoM5Ik4d//jiLm0gW0btMWJm0i9u7cUWCZKlX80Khpc4RWC5erbHJhVy5dwLUrl2EwGAAADWqG4++/fsPlM8fxQLeu8LIxxLgRn4SYy/L3GQ8PD9SpUwetWrWCIHBUB7kXSZJw9OhR3LhxA2az2S7buBGfiNiYS/jn9z0IDrbttRtz9Rr+/fcAqoVHoGaQL/78dVeBxz09PVGrbn00aNxEjpLJxaUkJ+Hi2TPIytQCAO6qFY49v/2GD959Cw888CBCqgbatN7E5BRcjbmEXbt2Iiw0VLZ6BUFAWFgY2rZtCx8fH9nWS6RUDEQrCbPZjKlTp2LPnj12Wb/BZEJuTg62fLMMKpW6XM+VJAk5ObmQJBHe3t74acOVgo9DwLjJU9Glew8ZK3ZtGQOeQOhncwEAyS++4lajQ41GIz79cBqOHzqAOz/v6fUG/HP1HP77dQe8vLxsWr/JbIYxNweLFi2CSiXvIHgPDw/MmTMH3bp1k3W9RK5Mq9Vi7NixOHfunF23YzSZkZ2dbVOfAQC9Xg+j0QgvjQb/7b2B//YWXkYC4OPrh7dnLUD9RkVfz5kqhz/27MSSBR9DdUefMZnMSImJxuWj++Ht7W1T8Ggym2GwU58BgOeffx5jxoxhKEpuQ5IkzJkzB5s2bbLrdkxmM7J12dj2zXKo1OXvMyaTCbl6PdRqNXITr2Htqf+KXE4UJQwf9xp6P/p4RUsmF3Y99go+ePM1ZGnTkf9oLYkScnJzsOHCSfj4+NjUJ8xmM/S5Ofjqq6+gtkOfiYyMxGeffQZfX1/Z100lEAXLzZHboxIxEK0kli5dit9++w2zZs3Cgw8+CLUNTb4k2uxcpCQlIiSsGjw8yj6iQhRFJKekwGw2ISQkBF6eBUOu7OxszJ/7MRbN/QC169V3n5GiajWSXp0Mjy3fInX4KGdX41AbVn6Ns1FHMGfuXHS5975C+2qGVgtdVhb8/P0R4O9f7vXn6g3ISktGRES4zaFqUbKysjBz5ky88cYb2Lx5M2rVqiXbuolc2dSpU5GQkIA1a9agadOmdgmAAECXk4uExESElrPPAEBaRgZydDoEBATCz6/4L5iSk5Px1uRJ+GjKBCz69nue5lhJXTp/FksWfIyBjz+Gl1+dUGgUTK5ej9TUVHh6eSI0OBiCUL59OtdggDY1CTUiImTtMwaDAd9++y2++OILNGjQAL1795Zt3USubNOmTdi0aROmTJmC/v372+3YnKvXIy4+ASGhYfD0LN9rNyc3B2lpadBoNAiuGlzsFxa5ublYsvgrrF70KWrVrY/mrdrIUDm5GkmS8NGUSYioFoq5a9YgPDy8wD5hFs1ITk6BKIoIDQ2BZznf1xiMBqQlJ6FG9QhoZOwzoiji2LFjmDBhAmbPno2ZM2fKtm4iJeI1RCuJ6OhodOnSBb169ZI9DLVVaWEoAPj6+mLS5DchQMKVyxedUKXzZPQfiJSRL0Bys2/mLl84j/u734+u3e4vcl8NDAhAFT8/ZGVmQpuZ6YQKi+bn54c333wTJpMJFy+6175K7u3s2bMYNGgQmjdvbrcwtCLKGoYCQGhoKMaNfxnZukwkJ8Q7qEJytCuXLsBDrcKESZOLPCXQW6NBcHAwjAYjklNTIUnyXmbIVl5eXhgxYgRq1aqF6OhoZ5dD5DBnz55FixYt8MQTT7jkF1VlDUMBwNvbGy+/+hp8fX0Qc9G+Z1aQ86SnpiIjNRljxryIiIiIQvuEWqVGaGgIVCoVkpNTYDQZnVRpQSqVCu3atUP//v3ZZ5yAkyq5Ho4QrSRyc3MRFhYm2/rMZjNOnTqFI0eO4OTJk0hJS4fJYEDtunXRtm17tGvfHjVqFj8pUlnC0Dx5Q/UN7jbJgVqF7Hb3wL3iUMCoz0WVKiWHFoEBAQCArFuBaEkjRU0mE06dOomoo0dx+vRpaLUZEE1GNGjQAB06dECHDh1QvXp1WWrPqzs3N1eW9REpQW5u6a9ZW8XGxuK///7DsWPHcCPuJrJzslEtrBpatm6DyLZt0axZySFsecLQPL6+VQAI0Ov5Oq6s9Lm58PLyKjFYyQtFU1NTkZyaWupI0SsxMTh69AiORx1DYmIi9Lk5qB4RgbZt26J9+/Zo2rSpbKe4+/r6ss+QW7Fnn0lPT8fhw4dx5MgRXLp0CekZGQgKqoq7mjRFZNu2aBNZ8rUUyxOG5hEEAT4+vtDnutlnGzei1+cCAkrcb/NC0eTkFCQnp5Q6UjQtLQ1HDv+HY8eO4kpMDHRZmQgNCUHz5s3Rrl07REZGwtvbW5b6q1Spwj5DBAaidIfExEQsXboUS5YsQWxsbInLdrv/foweMxb9BzwGD4/bu1J5wlCi4pQWit68eRMrly/FiuXLcDMursR19erVC2PHjkX//v1dZgQ1kbsyGAz47rvv8NVXX2H//v0lLtugQUM89/xoDBsxstDkNbaEoUT5lRaK5ubmYtN3G7H0669w5PDhEtfVpEkTvPjiixgxYgSCgoLsXDkRlUSSJPzzzz9YtGgRNm/eDKOx+NF5/v7++N/QZzH6xRfRrFnzAo/ZEoYS5VdaKCpJEv78Yx+Wfr0YO7b/AJPJVOy6goKCMGLECIwdOxZ33XWXI8onN/Tll19i7ty5iI+PR+vWrbFw4ULcc889RS67atUqjBw5ssB9Go1GUWG76537Rk4hSRKWLFmCRo0aYerUqaWGoQDw5x9/4Nn/DcZ9nTvi+PEoAAxDSV5FnT4viiIWfbEQrZo3wYfvzyw1DAWAPXv2YODAgejcuTPOnDlj77KJqBgHDx5E69at8eyzz5YahgLA5cuXMPXtt9C6RVN8u24tJEkCwDCU5FPc6fP//P0XOkS2xoujR5UahgLAuXPnMGHCBDRu3NjuE8MQUfESEhLw5JNPomvXrli/fn2JYSgAZGZm4uvFi9AhsjXeeH0isrOzATAMJfkUd/r8zbg4PPn4APTr0wvfb91SYhgKWEY7f/rpp2jWrBmmTJkCvbudXVkpCIDkwBvKd9zauHEjJk6ciOnTp+Po0aNo3bo1+vTpg8TExGKfExAQgJs3b1pvV69ereC/kWNxhChBq9Vi0KBB2LVrl/U+QQB69vZBt+4+aBXphbBqaphMEmIumxB1RI8d27Jx8YLlYH7yxHF069IJH3w0G4P+N5RhKMkq/0jR9PR0jHl+JP7ct8/6uEoF9H7IF/d180brSC+EhFr21csXTTh2RI/t3+sQc9nyBuO///5DZGQkFi5ciBdeeMEZfw6RW5IkCR9++CGmT58OUbx9vcbanjXQzrc16nvVRTXPUAgQkGHOQIzhGk7lRONU7lkAltPIRj83Aj/t2I65n34OiCLDUJJN/pGiSSkp+PKzTzF/7hxrAA8ALVt74aFHfNEm0gt16nlCEID4OBOORxmwd08O/vnTMhoiOTkZTz/9NIYOHYrly5dDo9E4688icju//fYbnn76aaSkpFjvE1AFHuqWUKtqQSWEw/LxVw+zGA+zeBUm8RQAIyRJwpcLP8funTvxzYYNCA0NYxhKsrlzpOjRw//hhVEjkZ6ebl0mzNcb/RvXQevwENwVHAiNWgWtwYgzSWk4FJeEnZeuI9dshiiKmD17Nnbs2IEdO3agfv36zvvDqFL55JNPMHr0aOuoz8WLF+Onn37CihUr8NZbbxX5HEEQEBER4cgyZcVA1M1ptVr06tULhw4dst73zAg/vP5WEOrVL3yNk1atNRjweBVMe78q/vgtF2+/kYIzp4wwmUx4643XkZAQj2kzZjIMJVkFBgQgLS0NTwx4FGejb4/wHPWCPya8EYRatQsfylq11uCxJ6pg+gdVsfeXHLzzRirOnzPCYDBgzJgx0Ol0mDBhgiP/DCK3JEkS3nzzTcydO9d6XwOvungm+Ek00TQq4oNmTbT0aY7+gX1w05iAzek7cEBnGaG37futiI+Px4ZNWxmGkqy8NRpUrVoVr44fh3XfrLHe36mLBjNnBaNDR02hfbXF3V7o0dsXE98IwtloAz6YnoYff7CMLlu3bh1SU1Oxbds2WWeiJ6Ki7d69G/3794fBYABgCUI1Hn3hoW4NQSj8mUatqgugIyTpURjMB2Ew7QVgwsWLF/Bwn97YtHUbOnbsxDCUZJMXin67di3Gjx1jHRFazdcb73aNRP/GdeBVxKW97qkRhhGt70Jqjh4rjp/D5/+dgVEUcfr0adx33334888/0bBhQ0f/OaQgWq22wO8ajabQF7YGgwFHjhzBlClTrPepVCr07NkTBw4cKHbdWVlZqFu3LkRRRNu2bfHRRx+hRYsW8v4BdsRT5t2YJEkYPHiwNQytGqzClh3h+OLrsCLD0PwEQUD3Hj7Yd6AmXns90Hr/gvnzsIWnipHMRFHEi8+PtIah1cLV2L47AvMXhhYZhuanUgno1dcXfxysgTEvBVjvnzhxIrZu3WrXuokIWLRoUYEw9MmgRzGj+hto6t241A+a1T3D8XLY85gQNgY+gmUigX8P7MfrE1+1a83knr76YqE1DBUE4P1Zwfjp1+q4p5N3qftq02Ze+GZjNSxbEwYfH8uyO3fuxLhx4+xeN5G7O3PmDB5//HFrGKpWNYGv5jV4erQvMgzNTxB8oPHojiper0AlWCbhTE1JwfCh/ysweo9IDieOH8crL421hqEPNayFfc8+jCeb1i8yDM0v2EeD1zu1wu4hfdGoquUzTVxcHPr27YusrCy7104V56xZ5mvXro3AwEDrbdasWYVqS05OhtlsRnh4eIH7w8PDER8fX+Tf06RJE6xYsQI//PAD1q5dC1EU0aVLF1y/fl3+fzw7YSDqxpYvX46dO3cCsIShP+6pjh69yzfnuZeXgPc+DMb7s4Ot90167RXE3bgha63k3hZ9sRB//fknAEsY+vOv1dGte/EzghbFx0eFjz8JwVvvBlnve/HFF5GUlCRnqUSUz8WLFzF58mTr76NChmJg0MNQC+Wb3KxDlUhMiXgV3oLl2+zN323E1i2bZa2V3Nvp06cw871pACxh6Ncrw/DyxECo1WUfHSYIAp4c5IfNO8Ktoejy5cvx008/2aVmIgJMJhOGDx+OnJwcAICH6m74eD4LleBXrvWoVGHw9RoNlVADAHD9+jW8OXmS7PWS+9Lr9Xhh1HPW4P6JpvWwtN99qOpdvkurNAsNwraneqJJiGVQ0sWLF/Hmm2/KXi9VHteuXUNGRob1ln8UaEV07twZw4YNQ5s2bXD//fdj69atCAsLw9dffy3L+h2BgaibSk5OxqRJt5v81yvC0OJu20/penlCIJ7+n+X0xYyMDLzx+sQK10gEAHE3buC9aVOtvy//JgyN7ir52/6SvPlOEB7ubwn+k5KSCoQ1RCSvcePGWT+k9vK/Hz38u9q8rkaa+ng+5Bnr7xNeGc8RESQLSZLwyktjrZOvvDIxEE8PKV+Ykt+9XX3w8YIQ6+9jxoxR1IyrREqyaNEiHL418ZlKCIO359MQyvmlWx5B8IGP1zAAloBq3TdrsO/33+Qqldzc559+gjOnTwEA7g6rigU9O0Gtsi2OCfHxxqpHusHHw7KvL1q0CP/++69stVLlEhAQUOBW1PXNQ0NDoVarkZCQUOD+hISEMl8j1NPTE5GRkbh48aIsdTsCA1E3tWLFCuu1JIY864feD5VvZGhRPp4fgmrhloPytu+34uqVKxVeJ9GK5UutgcrzY/zR9f7yjQy9kyAIWPBFKIKqWg5/3377baEDPxFV3KlTp7Bnzx4AQKg6GEOqPl7hdXau0h7tfVsDsHyxt3H9txVeJ9GRw//h31vXx2rU2BNTpgVVeJ3PjvDDgz0t/erGjRvYvJkjmonkJooiPv30U+vv3p5PlnqKfGlUQiA0Hg9bf/9y4ecVWh8RABiNRny16EsAgFoQ8GmvTvBUVyyKqRfkjyldWlt//+yzzyq0PnIAUXD8rYy8vLzQrl077N2793a5ooi9e/eic+fOZVqH2WzGyZMnUb169XL/0zgLA1E3JIoiFi9ebP198ltBsqy3arAao8f6A7CMtlixfKks6yX3ZTQasWL5MgCAWg1MfDNIlvVWC1dj5PP+1m0sX75clvUS0W1fffWV9eeHA3vBW+Vd4XUKgoDHA29/UF3y9eISliYqm/z70auvB8Lbu+JvjwVBwOS3g6y/L1q0qMLrJKKCdu/ejZiYGACAWtUYalUdWdbrqW4LAZbTkXf+/BNir16VZb3kvnZs/wEJt67D2KdBLbQIqyrLeoe1bIxgH8tovy1btnCQB1XIxIkTsXTpUqxevRrR0dEYO3YsdDqdddb5YcOGFTjdfubMmfjll19w+fJlHD16FM888wyuXr2K559/3ll/QrnZLRD98MMP0aVLF/j6+iIoKKhMz5EkCdOmTUP16tXh4+ODnj174sKFC/Yq0W2dO3fO+ubhgR7eaNCoYt+k5jdspD/yRv7/snu3bOsl93TyxHHrm4e+D/uiRs2SJ1AqjxG3AlEA+Pnnn2VbLzkWe43ryrtGtafgia5+nWRbb31NHTTwqgsAOHXyBG7Gxcm2bnI/kiThl927AAABAQKeeLqKbOvu1EWDZs0t77EOHDiAtLQ02dZNjsM+47ryv3/zVHeUbb2CoIanRwcAlv+Xv+75RbZ1k3vK6zMA8GzLRrKtV+OhxuDmDQBYBnnkH91HrkeSHH8rj0GDBmHevHmYNm0a2rRpg6ioKOzatcs60VJsbCxu3rxpXT4tLQ2jR49Gs2bN0K9fP2i1Wuzfvx/NmzeX85/NruwWiBoMBjz11FMYO3ZsmZ8zZ84cfP7551i8eDEOHjyIKlWqoE+fPrzuksyOHDli/blrOSemKU14hAeaNLW8+T9z+hT/31GFHM2/r95f8dFl+dWt54k6dS0B67Fjx2A2m2VdPzkGe41rSk1NtX7xVt+rNnxV8vaaFt5NrD8fO3qkhCWJShZ34waSEhMBAO3v8Yavr3xvjQVBwH3dbveuY8eOybZuchz2GdeV/zONh6qBrOtW51vfsaNHZV03uZ+8fUgtCOhYM0zWdXepdXtW8PyvCSJbjB8/HlevXoVer8fBgwfRsePtL5v27duHVatWWX9fsGCBddn4+Hj89NNPiIyMdELVtrNbIDpjxgxMmDABLVu2LNPykiTh008/xdSpUzFgwAC0atUKa9asQVxcHLZt22avMt3SqVOnrD+3am37RErFaRVpGbZvMplw/vw52ddP7uPM6dPWn1tHlm8GxrJoHWnZ/7Ozs3GF17xVJPYa13Q632u3npc8pzDmV09ze52nT58qYUmikuXff1q1sd97IgA4efKk7Osn+2OfcV15n2kEoSoEQd4v3tS3ZpsHgDNn2GfIdqIoIvqM5X1R4+AA+HjId8YbALTKd/o9+wxR+cj7aqyAmJgYxMfHo2fPntb7AgMD0bFjRxw4cACDBw8u8nl6vR56vd76e95EQaIoQhRF+xZdQaIoQpIkWeqUJMl6K01GRob159Aw22ZhLElo6O2cPS0tHbl6Q6nPMZnNuBafhNOXKn6NHkkCxOw0qFJ0EMp+HWGHU0qdgLy1ZmRlw2gyl2m/SM+/r4bK//1NSMjt/V+r1Zb6+sl7XK7ji5zHAHty9frKw916jVz7WFn7S568fx8ACFD7l7CkbQJUt2cAT8/IKNPxJI/eaITRZMLF2DjoYFsA5q7Hb3uSs87ricll7jOpqbdPYw8Jtcd7otvrzMzMLPPrSK7eoJQ+A1SeXuNufQao+H5Wns8x+Z+TmZkJABAg36Uu8giCBpaPyiZotZnl6jMAYDKbEJeUYvNnG6UcuwH3rDUlMR5Gowl6g7HUfSM7O9t6JlqIj7xnvAFAiO/tdWZlZZXpdZT3emOfcSxJEiBJjnuROHJbSuUygWj8resE5l2fIE94eLj1saLMmjULM2bMKHR/UlISDIbyNS5HE0URGRkZkCQJKlXFwp6cnByIogiTyVTqsvm3ZTKV88ISZZD/zGO9LhNZacmlPseYm4Po/b9Cn3ClwtuXJAlGgx6eXhoILtyVlVInIG+tydcuw3B3kzLtF6LJaP3ZZIcz2s3i7f1fkqRSXz8mkwmiKCI9PR2Jt06xrAg5jwH2lP9LFKVzt14jxz5mNBphNpvL1F+KYpbkf/GKuP2G1mwwlOl4kic7Iw16XRaO7NqMoKq2TWrgrsdve5KzzvPnzsKYm12m/cKQo7P+bDbb4z3R7XUKglCm15HZbEZWVpZb9Rmg8vQad+szQMX3M51OV+bPMfmp1epbQZP8IYclVJLyfilXnwEAQ04OLhz+C545tl07WCnHbsA9a83KzESuLhM5meml7hv5v+gw2SGQy/95RqVSlel1JIoijEYj+wy5vXIFom+99RY+/vjjEpeJjo5G06ZNK1RUeUyZMgUTJ060/q7ValG7dm2EhYWV+cLnziKKIgRBQFhYWIUPHj4+PlCpVPC4Ywj+zp078fDDD2P8+PH4/PPPAQA1atw+BeTyJRPa31OhTRdy+eLtEKtZs6aIiAiHJEno0KEDrl69itOnT6NatWoFnlOlShUMfGwAHnrooQpvXxRFpKSkICQkxKUPykqpE5C31oS46/D390NERMEPCrt27cKAAQMwbtw4LFiwAABQv3496+OXLxrRrLm8pzNevnT7DUPNmjXh4eEBSZIQGRmJK1eu4Pz58wX2VQ8PD6hUKgQFBRXah20h5zHAnry85D+NtCTsNfKRYx/z9PSEWq0u0F+K6i351axZ0/pzginJpu2WJN54+w18gwb1CxxPSus3aWmpCAwMwJjRo9CggW3XnHPX47c9yVnntm3bsHbt2jL1mWbNbh9HLl8yQm753xNVr14dHh4eyMnJQf369eHj44Po6Gh4exccMaRWq+Hn5+dWfQZwbK9hn5FXRfezKlWqwGg0lulzTH4RERG4ceMGRCkVkiTJGsZJyABg+UKvVq2ahY4npfUaP78qeKhvHwwZMsSm7Svl2A24Z61xcXGIOnoYISEhBfaNovqMJEkICgpCeno6rmRkVfhvuNPl9EzrzxEREWXqMyqVCp6enuwz5PbKFYhOmjQJI0aMKHEZWz9cREREAAASEhJQvXp16/0JCQlo06ZNsc/TaDTQaApfW1ClUrn8CxKwjBaQo1ZBEKy3PJIkYfLkyVCpVJg8eTIEQcBnn32G5cuXW5eJOqrH00P8ilqlTSRJQtQxy7fYPj4+uOuuu6w1TZ48GcOGDcPs2bPxxRdfFHieSqVCSEhIgQ/RthJF0XqAd+V9QCl1AvLW6uPjA7VaXaAhSZKEKVOmQKVS4c0334SXlxc+++wzbNy40brMsaMGPNxfvlOiRFHCiWOWb2yDgoKsIzkEQcDrr7+OYcOG4f333y+wr+bty3IeX+Q6BtiTo2tjr5GXHPtY/v5SVG+5U4sWLaDRaKDX63FZX/FLodwpxhBr/XnChAmYMGEC6tata70WcEn9xsvLC2q1GuHh4Tb3HHc9ftuTnHUGBweXuc8sW7bMukzUUflH4eW9JwKARYsW4bnnnoOvry/Gjx+PadOm4csvv8Trr79e6Hl5r1s5KKHPAI7tNewz8qvIflbWzzF3atu2LW7cuAEgF5KUAkEIrcifUIBZvGH9+ZdffrH+vylrr1Gr1ahatSr7jIuRq1ZJkuDh4QFPT09rrymqz2i1WkyePBk6neVshARdDuKzshHh5yvL3wMAJxJTrT8fPXoUgiCU2mfyXm/sMw4mCZabI7dHJSrXHhEWFoamTZuWeLM1da9fvz4iIiKwd+9e631arRYHDx5E586dbVqnu9uzZw/OnDmDrl27ok4dywQUhw8ftr5RA4AdP2TLeorYv/v1SEywfJuak5NT4M3BwIED4evri1WrVhW4vhxRcftq/tPNdnyvK9e1pUrz+685yMy0rC89PR0LFy60PsZ91bnYa1xbUa/XO3l6eqLqrdPR402JiDVcl237RsmII9knAFje2ObVcNddd1mX4WuY7lRcnwkODoanpycA4ESUocCIzorKyhLx6+5s6+/5w7BnnnkGAPDFF184/Zpm7oh9xvWVpdfcc8/t09yMorwTH5nMtyeneeSRR1CvXj0A7DVUvKL22WeffRZLlixBYGCgdbmfLl6Tdbs/Xbz9JXGzZs2sP7PPEJXObhF5bGwsoqKiEBsbC7PZjKioKERFRSEr6/Yw8aZNm+L7778HYPlW4bXXXsMHH3yA7du34+TJkxg2bBhq1KiBxx57zF5lVmp5/7Y9evSw3vfNN99g//791m+sr101YffP2UU+3xbLFhd8M/DBBx9YLyJdpUoVdOzYETqdDnv27JFtm6R8xe2rBw8eRFhYGADg3Fkj/tyXK9s2ly7OLPA791VlYq9xvKJer3dKSkpCcvLta2r9ov1Dtu0f1B1Fpmj5/ztgwADrdiZNmmRdhq9hulNxfeaPP/4o8AFy2dfyhRqb1mdBq739RV7+fbR+/fqoX78+rl69imPHjsm2TZIf+4xzlKXX/O9//7OOHDWaDkKS6ZrVopQJ062ANTQ0FIsXL7Zea5G9hopz5z574sQJbN++HZ6enti2bZt1uVUnLkCUaZDHNW0W9sTEWX+fPXu29Wf2GRckCpAceIPIEaKlsVsgOm3aNERGRmL69OnIyspCZGQkIiMjcfjwYesy586dK3Bh2zfeeAMvv/wyXnjhBXTo0AFZWVnYtWtXoWteUNkcPXoUANC8efNCjzVq1Mj687tvpSInp+LfGv39Zw62fGc5HSA4OBgAkJiYeOtUFosWLVoAAP77778Kb48qj7Luq29PToXBUPE3EHt2ZWPXT5YvAvKuqct9VZnYaxyvpNdrntOnTxe4qP++rH8Qo48tdvmyyhZzsCFtm/X34OBgZGdno1WrVujTp0+BZfkapvxK2m/znxq9bLEWZ6Mrfup8SrIZH828PZlK165dCwSvAPdRpWCfcY6y9JoGDRpY5x+QkA6D+S9Ztq03/oS864eOGjUKK1euZK+hUt25z+b93rBhQ9x7773o1q0bAOBimhYrj5+v8PYkScLUfUes4WqTJk3QsmXLAstw/yQqmd0C0VWrVkGSpEK37t27W5eRJKnA9XsEQcDMmTMRHx+P3Nxc/PrrrwVOS6DySUuzvBHPP0Q/T/5rGl26aMK0KbbNgJgnPc2Ml8fcHg305ptvWn++efOm9eeAgAAAQGrq7WudEJW0r9aqVcv68+mTBnw0o2L7alKiGa+Ou72vTpkyxfoz91XlYa9xvDtfr7t27cJrr71mvR06dMg6k7Kvr+UaWSJELE5ehVzR9lHekiRhVcoGpJot2+/Rowd27NgBAEVeg5GvYcqvpD6T/7qNBgMwdlRShb4oFkUJE8YnIynx9jo+/PDDQstxH1UG9hnnKEuvAYDp06dbrwtoMP0KsxhX9ArLyGg+AZN4HABQtWpVjB071noJMPYaKsmd+2zeeyE/P8t8HTNnzrQu++E/UTiXUrEZzzecuYxfYm4P5pg/f36hZbh/EpXMta96SxWSNyNlUde0yT85DAAs/UqL2R+k2XSNxrRUM554NAExly2jge677z4MHTrU+nj+8DWvlrxryxEBJe+refto3j776bwMfD4/3abtJCWa8fjD8Yi7YfnWv3fv3hg4cKD1ce6rRKW78/X677//4rPPPrPezpw5Y71Wtdlstl438ZoxDvMSFyHHhlBUkiSsTduMv3UHAQD+/v7o1asXEhMTUatWLQwePLjQc/gapvxK6jN58j7EHjtiwLNPJyI7u/yhqChKmPRyCrZ/f/tyRJGRkejatWuhZbmPEhWvLL0GsFxH9HZQaUaOYSXM4s0i1lg6k/ksco3fWX9fuHAh9uzZg4SEBPYaKtWd+2zee6G8y2vcf//96NWrFwAgx2TGkG2/4UKqbaHojxdiMXnvIevvjRs3xsMPP1xoOe6frkWSBIffqGQMRCuxtm3bAoD1DUNROnbsaP159vvpeHZQonVSpLL4c18OuneOw5H/bs/WvXr1auu3tmFhYQVmV8yrJa82IqBs+2r79u2tP097Ow2jnk1EakrZ99W9v2Sje+cbOHXCcipkaGgoli9fjoMHLQEL91Wisrnz9free+8VGDU1YsQItGjRAl5eXtDr9Zg+fbr1Q8KZ3POYGjcL53Mvl3l7KaZUzEn8Aju1lglKBEHAkiVLsHr1agDAa6+9Zp0UJz++him/svSZrl27Wkfy/PpLDnp2jUPUMX2Zt3ElxogBD8Vj5bKC16h+5513ilye+yhR8crSa/LMmDHDegkkCVnINiyGwXSwzAM9JMkEvXE3cozfIO9U+eHDh2PIkCH45JNPALDXUOnu3GcjIyMBABcvXrSO0Mx/KbCbWTl4ZOMv2Hjmcpn31WyjCe/9eRQv/Pw3zPmeM2vWrCKX5/5JVDIGopVY3oXb889yuWzZMowYMcIaAiUmJqJDhw7Wx3/8IRv3tL6O96el4lqsCUWRJAl//ZGD4UMS0L9PPK5eub2cSqXC+++/jzFjxgCwfAhQq9UAgOzsbBw8eBA+Pj7o3bu3rH8rKVtZ9tXU1FTrGwsA2PKdDh1aXcdHM9MQd6PofVUUJfz+aw6GPJGAJx5NwI3r5nyPiXj33Xe5rxKVU1Gv1zuFhYXhhRdeAABMmDABXbp0sT5205SAGfFz8UXScpzPvVTsh4AEYxLWpW7BGzdm4njOaQCWMHTp0qXw9/dHdHQ0AgMDrdvJj69hulNZ+kx0dDTuu+8+6yn0Z04Z0ePeOIx9PglHD+uL3VcvnDPgrUkpuLfdDfx1a/K/vLMbGjVqhMcff7zQc65cuYLLly+jZs2aBb7wIyKLsvQaANi2bRtefPHFOy6HYYDetA3Zhq9gNEdBkor7TJMLg2k/dIZPYTDvA2AZFT5o0CAsW7YMO3fuZK+hMrtzn23dujUeeeQRmEwm3H///XjmmWewdOlSCIKAJk2aAAC0BiNe2/MvHtv8K3ZciIXRXPSZCem5Bnx99CweWPsTvj52Fvm7UcOGDdlniGzk4ewCyH769OmDpk2b4s8//0RsbCzq1KmDv//+2zqqBgAuXbqES5cuoXnz5khKSkJSUhLS00TM/zgD8z/OQP0GHmgdqUFYmApmM3D5khHHowxISy14sO7atSs6deqETZs2Ye3atahVqxZef/11vPLKK9Zltm7dCp1OhzFjxnDYPhVQ1n0VsMzkmpCQgLS0NKQki5jzYTrmfJiORo090aqNF0JC1TAZJVy6aNlXM9IL7qs9e/ZE+/btsWHDBu6rRDYo6vValHnz5sHb2xvr1q3Dnj17UL9+fQBATEwMJEjYr/sP+3X/IUDlj3petRHmGQoVBGSYtYgxxCLJlFJgfTVq1MCyZcvw0EMP4cEHHwQAvPDCC/D39y+0bb6G6U7leU/Url0762ziZjOw/pssrP8mC9XC1WgT6YXadTwgCEB8vBlRx/S4HlvwbIU6derAZDIhLi4OEyZMsIaj+a1duxYAMH78eOuXcUR0W1l7TVRUVIHXcX6idA25xo0AtkIlRECtCgfgCUnKhSjFQ5QSkBeCAoCnpyemTZuGKVOmQK1WW6/JyF5DZVHUPvvNN99g8uTJ2LZtG86fP4+WLVti5syZ6Nq1K15++WV88803AIBDcUk4FJcEX08PtAitisbBAfD2UEOrN+B0UjrOp2YUGBGq0Wjg5+eHlJQUTJw4kX1GKSTBcnPk9qhEHCFaiQmCgPnz50MURcybNw9A8ReGP336NE6fPo1hw4bBw+N2Th5z2YRtW3RYujgTK5ZmYt9vuQXC0PDwcHz++efYt28f5syZg5iYGBiNRsTExOCNN96wXvdRkiTMnTsX/v7+mDFjhmP/IcjllWdfjY6OxunTpzF48OACzf3iBSO2btJh6VdarFyWiT/35RYIQ2vUqIElS5bgl19+waxZs7ivEtmoqNdrUTQaDebOnYu4uDgYDAZcvnwZ58+fx5w5cxAcHGxdTitm4kTuGezN/BN7Mv/AoexjBcJQLy8vvPDCCzh16pR1NuHffvsNkiRhzpw5hbbL1zAVpTx9D5N3PAABAABJREFU5vDhwzh06BDef/996+UeACAxwYxfduVg+ZJMLPs6Ez/+kF0gDPX29sb48eNx6tQp3LhxA5IkYdy4cYVqycnJwcKFC1G3bl28+uqrdv/biZSorL3mzlPpJUnC7t27rbNrWxghStdgNB+G0XwAJvEYROkm8oeh999/Pw4fPoypU6da31+y11B5FLXPBgUFYenSpUhKSoJer8fRo0fxyCOPIDAwEGvWrMH27dsLTLiWbTThv5tJ+Pb0Jaw4fh6bz15BdEp6gTC0Z8+eiIqKQnJyMvsMUQVxhGgl169fvzJfkyQsLAyrV6/G7NmzsWzZMvz888+IiopCbm7BCTDCw8PRoUMHDB06FAMHDoSXl1ep6xYEAcePH7fpbyD3UJ59tXr16li/fj3mzp2LpUuXYteuXTh+/Dj0en2h5e655x48++yz6N+/f5HXfroT91Wi0pXn9Zqfh4cHJk+ejPHjx2PTpk1Yv349Dh8+jOTk5ALL+fr6IjIyEo8++iiee+45hIWFlXkbfA1Tccqz33p6emLq1KmYOHEiNmzYgI0bN+Lw4cOFZur18/NDZGQkBgwYgJEjRxYI+4vj4+ODhIQEm/4GIndia6/p3bs3Tp48ib/++gvLli3D/v37rWca5VGpVGjevDnuv/9+jBkzBi1btizXNthrqCjl3WcfffRRPPLII/jtt9+wYsUKHDhwADExMQWWUavVaNGiBR544AGMGTMGzZo1K3W97DNEZcNAtBIRxfLPhlqU6tWr491338W7774Lk8mES5cuISPDMgNejRo1ULNmTetoOjnY8kaHlE2ufbVWrVqYMWMGZsyYAaPRiIsXL0Kr1UIQBNSqVct6gX25yFU3kdLIfZz28fHBsGHDMGzYMEiShOvXryMxMREGgwHBwcFo1KiR3U7vYs9xD3Idr319ffHcc8/hueeegyRJiI2NRVJSEkwmE0JCQtCwYcMiT1WsCO6j5I7k3u8FQUC3bt3QrVs3AEBaWhquXLmCnJwc+Pn5oVGjRvD19ZV1m/nxdewe5Og1giCgR48e6NGjBwDLvAlXr15FTk4O/P390ahRI/j4+FR4O/lx/3QOR8/8zlnmS8dAtJIICQlBTEwMTCZTgVPeK8rDwwNNmjSBJEnWdcsZhgLAhQsXAFj+Bqr8QkJCcOnSJYiiKOuHSE9PTzRr1qzAvio37qvkjoKDg3H+/Hm7rV8QBNSuXRu1atWyW5/JL+9v4eu48goJCYHBYCjxuoO2EAQBdevWtV4j1B77qlarRXx8PPdPcivBwcE4ceIEcnJyZA9+8lStWhVBQUEO6TOJiYlIT0/n67gSyzsb4MKFCwUmKJZr3VWrVrXbvipJEi5cuMD9kwgMRCuNRx99FBMmTMBrr72G+++/X/YwSJIkmM1mqNVqWQ/KOp0Oa9euRYMGDdCmTRvZ1kuua8CAAXjjjTcwadIk3HvvvbKPArPXvpqZmYk1a9agSZMmd1yXiqhyGzhwID7//HN4eHigRYsWdvsQaa/Xbv71JycnY9WqVejWrVuZTm0mZWrfvj1q166NcePGYciQIbKPArPXvqrX67F9+3YIgmCdOIzIHfTt2xc//PADXnrpJfTt27dMlziyhb37DGC5duOmTZsQHh6OLl262GUb5Hy+vr7o27cvPvvsM6SnpyMiIkL2Myjtsa+KooijR4/ir7/+wrvvvivbeqlsJNFyc+T2qGQMRCuJzp07Y+7cudabPU7ttceoO29vbzRv3hyzZ89GQECArOsm19S9e3d89NFH+Pzzz/HPP/8oZl/18fFBy5YtMXv2bLueYkXkavJOa9+wYQN++OEHu27LXqO78wQEBKBHjx6YNm2aXUcHkXMFBwfj66+/xltvvYVFixYVur60HOyxr3p4eKBOnTpYvHgxGjRoIOu6iVzZ3XffjYULF2LmzJmYP38+zGZz6U+ykb37jJeXFxo1aoRZs2ahWrVqdtsOOd+MGTPg7e2NLVu2WC8vJyd77KuCIKBatWp4++23MWDAAFnXTaREDEQrka5du6Jr1652WbcoikhMTES1atVkv1YWuZ+ePXuiZ8+edlk391Ui+Q0fPhzDhw+36zb42iU5VatWDStWrLDLurmvEsmvTZs22Lp1q123wdcuyUmtVmPq1KmYOnWq7OvmvkrkGAxEiYiIiIiIiIiI7EUSLDdHbo9KxK8biIiIiIiIiIiIyG0wECUiIiIiIiIiIiK3wVPmiYiIiIiIiIiI7ESSBEgOPI3dkdtSKo4QJSIiIiIiIiIiIrfBQJSIiIiIiIiIiIjcBk+ZJyIiIiIiIiIishOeMu96OEKUiIiIiIiIiIiI3AZHiBIREREREREREdmLJFhujtwelYgjRImIiIiIiIiIiMhtMBAlIiIiIiIiIiIit8FT5omIiIiIiIiIiOxEkgBJdOSkSg7blGJxhCgRERERERERERG5DQaiRERERERERERE5DZ4yjwREREREREREZGdSJIAyYEzvztyW0rFEaJERERERERERETkNhiIEhERERERERERkdvgKfNERERERERERET2It26OXJ7VCKOECUiIiIiIiIiIiK3wRGiREREREREREREdsJJlVwPR4gSERERERERERGR27BbIPrhhx+iS5cu8PX1RVBQUJmeM2LECAiCUODWt29fe5VIREQKx15DRET2xD5DRERUOdntlHmDwYCnnnoKnTt3xvLly8v8vL59+2LlypXW3zUajT3KIyKiSoC9hoiI7Il9hoiI5MBT5l2P3QLRGTNmAABWrVpVrudpNBpERETYoSIiIqps2GuIiMie2GeIiIgqJ5ebVGnfvn2oVq0aqlatigcffBAffPABQkJCil1er9dDr9dbf9dqtQAAURQhiqLd660IURQhSZLL1wmwVntQSp0Aa7UXpdTq6vXZwl16jVL2MYC12otSalVKnQBrtRcl1Fge7tJnAOXsZ0qpE2Ct9qKUWpVSJ6C8Wkm5cnJyIEkSfH19AQBXr17F999/j+bNm6N37942rdOlAtG+ffti4MCBqF+/Pi5duoS3334bDz30EA4cOAC1Wl3kc2bNmmX95ja/pKQkGAwGe5dcIaIoIiMjA5IkQaVy7fmtWKv8lFInwFrtRSm1ZmRkOLsEWblTr1HKPgawVntRSq1KqRNgrfZSmXqNO/UZQDn7mVLqBFirvSilVqXUCSirVmf3GUkUIIkOPGXegdtyhAEDBmDgwIF48cUXkZ6ejo4dO8LT0xPJycn45JNPMHbs2HKvs1yB6FtvvYWPP/64xGWio6PRtGnTchcCAIMHD7b+3LJlS7Rq1QoNGzbEvn370KNHjyKfM2XKFEycONH6u1arRe3atREWFlbmC587iyiKEAQBYWFhLn/wYK3yU0qdAGu1F6XU6uXl5dDtsdfIRyn7GMBa7UUptSqlToC12osjew37jLyUsp8ppU6AtdqLUmpVSp2Asmp19GcaktfRo0exYMECAMDmzZsRHh6OY8eOYcuWLZg2bZr9A9FJkyZhxIgRJS7ToEGDchdR0rpCQ0Nx8eLFYt88aDSaIi9SrlKpXP4FCQCCILBWO1BKrUqpE2Ct9qKEWh1dG3uNvJSwj+VhrfahlFqVUifAWu3BkfWxz8hPKfuZUuoEWKu9KKVWpdQJKKdWp9cnCZabI7dXiWRnZ8Pf3x8A8Msvv2DgwIFQqVTo1KkTrl69atM6yxWIhoWFISwszKYN2eL69etISUlB9erVHbZNIiJyLvYaIiKyJ/YZIiIiZWnUqBG2bduGxx9/HLt378aECRMAAImJiQgICLBpnXaLyGNjYxEVFYXY2FiYzWZERUUhKioKWVlZ1mWaNm2K77//HgCQlZWFyZMn499//8WVK1ewd+9eDBgwAI0aNUKfPn3sVSYRESkYew0REdkT+wwREZHzTZs2Da+//jrq1auHe+65B507dwZgGS0aGRlp0zrtNqnStGnTsHr1auvveQX+/vvv6N69OwDg3Llz1gvbqtVqnDhxAqtXr0Z6ejpq1KiB3r174/333y/y9BEiIiL2GiIisif2GSIikoMkCZAceBq7I7flCE8++STuu+8+3Lx5E61bt7be36NHDzz++OM2rdNugeiqVauwatWqEpeRJMn6s4+PD3bv3m2vcoiIqBJiryEiIntinyEiInINERERyMrKwp49e9CtWzf4+PigQ4cOEATbwl/XvuotERERERERERERua2UlBT06NEDd911F/r164ebN28CAEaNGoVJkybZtE4GokRERERERERERHaSd8q8I2+VyYQJE+Dp6YnY2Fj4+vpa7x80aBB27dpl0zrtdso8ERERERERERERUUX88ssv2L17N2rVqlXg/saNG+Pq1as2rZMjRImIiIiIiIiIiMgl6XS6AiND86Smpto8aSEDUSIiIiIiIiIiIjuRJMffKpOuXbtizZo11t8FQYAoipgzZw4eeOABm9bJU+aJiIiIiIiIiIjIJc2ZMwc9evTA4cOHYTAY8MYbb+D06dNITU3FP//8Y9M6OUKUiIiIiIiIiIjITjipUsXcfffdOH/+PO677z4MGDAAOp0OAwcOxLFjx9CwYUOb1skRokREREREREREROSyAgMD8c4778i2PgaiRERERERERERE5JL+/PPPEh/v1q1budfJQJSIiIiIiIiIiMheRMFyc+T2KpHu3bsXuk8Qbv+NZrO53OvkNUSJiIiIiIiIiIjIJaWlpRW4JSYmYteuXejQoQN++eUXm9bJEaJERERERERERETkkgIDAwvd16tXL3h5eWHixIk4cuRIudfJQJSIiIiIiIiIiMhOHD3ze2WbZb444eHhOHfunE3PZSBKRERERERERERELunEiRMFfpckCTdv3sTs2bPRpk0bm9bJQJSIiIiIiIiIiIhcUps2bSAIAiRJKnB/p06dsGLFCpvWyUCUiIiIiIiIiIjITnjKfMXExMQU+F2lUiEsLAze3t42r5OBKBEREREREREREbmkunXryr5OBqJERERERERERER2whGi5ff555+XedlXXnml3OtnIEpEREREREREREQuY8GCBWVaThAEBqJERERERERERESkbHdeN1RuDESJiIiIiIiIiIjsxrGnzAPKP2Xe3hiIEhERERERERERkcu6fv06tm/fjtjYWBgMhgKPffLJJ+VeHwNRIiIiIiIiIiIickl79+5F//790aBBA5w9exZ33303rly5AkmS0LZtW5vWqZK5RiIiIiIiIiIiIsojCY6/VSJTpkzB66+/jpMnT8Lb2xtbtmzBtWvXcP/99+Opp56yaZ0MRImIiIiIiIiIiMglRUdHY9iwYQAADw8P5OTkwM/PDzNnzsTHH39s0zoZiBIREREREREREZFLqlKlivW6odWrV8elS5esjyUnJ9u0Tl5DlIiIiIiIiIiIyE4k0XJz5PYqk06dOuHvv/9Gs2bN0K9fP0yaNAknT57E1q1b0alTJ5vWyUCUiIiIiIiIiIiIXEpqaiqCg4PxySefICsrCwAwY8YMZGVlYePGjWjcuLFNM8wDDESJiIiIiIiIiIjsRpIESA6c6MiR27KnGjVq4LHHHsOoUaPQq1cvAJbT5xcvXlzhdfMaokRERERERERERORSli5diqSkJPTt2xf16tXDe++9hytXrsiybgaiRERERERERERE5FKeffZZ7N27FxcvXsTw4cOxevVqNGrUCL169cLGjRutEy3Zwm6B6JUrVzBq1CjUr18fPj4+aNiwIaZPn15qsbm5uXjppZcQEhICPz8/PPHEE0hISLBXmUREpFDsM0REZE/sM0REJJe8U+YdeatM6tevjxkzZiAmJga7du1CtWrV8Nxzz6F69ep45ZVXbFqn3QLRs2fPQhRFfP311zh9+jQWLFiAxYsX4+233y7xeRMmTMCOHTuwadMm/PHHH4iLi8PAgQPtVSYRESkU+wwREdkT+wwREZHr6dmzJ9atW4c1a9YAAL788kub1mO3SZX69u2Lvn37Wn9v0KABzp07h6+++grz5s0r8jkZGRlYvnw5vv32Wzz44IMAgJUrV6JZs2b4999/0alTJ3uVS0RECsM+Q0RE9sQ+Q0RE5FquXr2KlStXYvXq1bh27RoeeOABjBo1yqZ1OXSW+YyMDAQHBxf7+JEjR2A0GtGzZ0/rfU2bNkWdOnVw4MCBIt9A6PV66PX6AtsAgPT0dPkKtxNRFKHVauHl5QWVyrUv58pa5aeUOgHWai9KqTXveCpJknMLKQN79BlAub1GKfsYwFrtRSm1KqVOgLXai1J6DftMYUrZz5RSJ8Ba7UUptSqlTkBZtTq7z3CWedvp9Xps2bIFK1aswL59+1CzZk2MGDECI0eORL169Wxer8MC0YsXL2LhwoXFfpsKAPHx8fDy8kJQUFCB+8PDwxEfH1/kc2bNmoUZM2YUur9+/foVqpeIiApKSUlBYGCgs8solr36DMBeQ0TkKK7ca9hniIiUz5X7DBU2btw4bNiwAdnZ2RgwYAB+/vln9OrVC4JQ8cC33IHoW2+9hY8//rjEZaKjo9G0aVPr7zdu3EDfvn3x1FNPYfTo0eWvsgRTpkzBxIkTrb+np6ejbt26iI2NdfmdXKvVonbt2rh27RoCAgKcXU6JWKv8lFInwFrtRSm1ZmRkoE6dOiWOiJGTq/UZQLm9Rin7GMBa7UUptSqlToC12osjew37jLyUsp8ppU6AtdqLUmpVSp2Asmp19Gcaksfff/+N6dOn45lnnkFISIis6y53IDpp0iSMGDGixGUaNGhg/TkuLg4PPPAAunTpgiVLlpT4vIiICBgMBqSnpxf4VjUhIQERERFFPkej0UCj0RS6PzAw0OVfkHkCAgJYqx0opVal1AmwVntRSq2OOg3G1foMoPxeo5R9DGCt9qKUWpVSJ8Ba7cURvYZ9xj6Usp8ppU6AtdqLUmpVSp2Asmp11qn9kuTY09hd/Ao0ZXbixAm7rbvcgWhYWBjCwsLKtOyNGzfwwAMPoF27dli5cmWpO167du3g6emJvXv34oknngAAnDt3DrGxsejcuXN5SyUiIgVinyEiIntinyEiIiK7ReM3btxA9+7dUadOHcybNw9JSUmIj48vcO2cGzduoGnTpjh06BAAyzego0aNwsSJE/H777/jyJEjGDlyJDp37swZGYmIqAD2GSIisif2GSIiko0kOP5GJbLbpEp79uzBxYsXcfHiRdSqVavAY3mzehmNRpw7dw7Z2dnWxxYsWACVSoUnnngCer0effr0waJFi8q8XY1Gg+nTpxd5yomrYa32oZRalVInwFrtRSm1umqdzuozgOv+m9xJKXUCrNVelFKrUuoEWKu9uGKt7DNlo5RalVInwFrtRSm1KqVOgLWSsgmSVFmuLEBEREREREREROQatFotAgMDcbD/i/DzdFwY+3/27ju+ifKPA/jn0l26KF2MAmXvPWTIkLKHOBCVrWyQKQg/maIiyFD2kj1lyxDEypAhUGiZZW/o3jvj7vdHaGjoTpM0aT9vX3mZXO6e+zZc7pt889zzJChS0fSPVYiNjTWb8V2NzWA9RImIiIiIiIiIiIo6SRKMPKlS4btkPiYmBpcuXUJYWBhEUdR6rn///nlujwVRIiIiIiIiIiIiMkmHDh1Cnz59kJCQACcnJwjCm4KvIAg6FUQNNqkSERERERERERERUX5MnDgRX3zxBRISEhATE4Po6GjNLSoqSqc22UOUiIiIiIiIiIjIQHjJfP68fPkSY8aMgb29vd7aZA9RIiIiIiIiIiIiMkkdO3aEv7+/Xts0+4LokydP8OWXX8LHxwd2dnaoWLEiZs6cCblcnu12KSkpGDVqFEqUKAEHBwd89NFHCA0NNXi8P/zwA5o3bw57e3u4uLjkapuBAwdCEAStW6dOnUwuTkmSMGPGDJQsWRJ2dnbw9fXF/fv3DRonAERFRaFPnz5wcnKCi4sLvvzySyQkJGS7TZs2bTK8psOHD9d7bMuXL0f58uVha2uLpk2b4tKlS9muv3v3blSrVg22traoXbs2jh49qveYspKXWDdu3Jjh9bO1tTV4jGfOnEH37t1RqlQpCIKAAwcO5LjNqVOn0KBBA9jY2KBSpUrYuHGjweME8h7rqVOnMrymgiAgJCTEoHHOnTsXjRs3hqOjIzw8PNCzZ0/cvXs3x+0K8lg1NuYZw2Gu0Q9zyTXmkGcA88k15pJnAOaa3DCnXMM8YxjMM/phDrnGXPIMYD65xhzyjCQa/1aYdO3aFZMmTcKsWbOwd+9e/PHHH1o3XZh9QfTOnTsQRRGrV6/GrVu3sHjxYqxatQr/+9//st1u/PjxOHToEHbv3o3Tp0/j1atX+PDDDw0er1wuR69evTBixIg8bdepUycEBwdrbjt27DBQhGq6xDl//nwsWbIEq1atwsWLF1GsWDF07NgRKSkpBowU6NOnD27duoUTJ07g8OHDOHPmDIYOHZrjdkOGDNF6TefPn6/XuHbt2oUJEyZg5syZuHr1KurWrYuOHTsiLCws0/XPnz+Pzz77DF9++SUCAgLQs2dP9OzZEzdv3tRrXPqIFQCcnJy0Xr+nT58aPM7ExETUrVsXy5cvz9X6jx8/RteuXdG2bVsEBgZi3LhxGDx4MI4fP27gSPMea5q7d+9qva4eHh4GilDt9OnTGDVqFP777z+cOHECCoUCHTp0QGJiYpbbFOSxWhCYZwyHuSb/zCXXmEueAcwn15hLngGYa3LDnHIN84xhMM8YP1aA32lyYi65hnmm8BsyZAieP3+O7777Dr169dL8e/Xs2RMffPCBbo1KhdD8+fMlHx+fLJ+PiYmRrKyspN27d2uWBQUFSQCkCxcuGCNEacOGDZKzs3Ou1h0wYID0/vvvGzSerOQ2TlEUJS8vL+nnn3/WLIuJiZFsbGykHTt2GCy+27dvSwCky5cva5b9+eefkiAI0suXL7PcrnXr1tLYsWMNFpckSVKTJk2kUaNGaR6rVCqpVKlS0ty5czNd/5NPPpG6du2qtaxp06bSsGHDDBqnJOU91rwcv4YCQNq/f3+260yePFmqWbOm1rLevXtLHTt2NGBkGeUm1pMnT0oApOjoaKPElJWwsDAJgHT69Oks1ynIY9VUMM/oF3ON7swl15hjnpEk88k15pRnJIm5JrdMPdcwz+gP84x+mGOuMZc8I0nmlWtMKc/ExsZKAKTzXUZK198fb7Tb+S4jJQBSbGysXv+ewsTse4hmJjY2Fq6urlk+f+XKFSgUCvj6+mqWVatWDWXLlsWFCxeMEWKenTp1Ch4eHqhatSpGjBiByMjIgg5Jy+PHjxESEqL1mjo7O6Np06YGfU0vXLgAFxcXNGrUSLPM19cXMpkMFy9ezHbbbdu2wc3NDbVq1cLUqVORlJSkt7jkcjmuXLmi9XrIZDL4+vpm+XpcuHBBa31APU6GoY9JXWIFgISEBJQrVw7e3t54//33cevWLYPGqYuCek3zo169eihZsiTat2+Pc+fOGX3/sbGxAJDtOdQcX1d9Y54pGMw12swl1xTmPAOY3zmxoPMMwFyTW4Ut1zDPZI15pmBiBcwj15jj+bCgc40p5pm0SZWMeaPsFbqC6IMHD7B06VIMGzYsy3VCQkJgbW2dYRwZT09Po4yjlFedOnXC5s2b4efnh3nz5uH06dPo3LkzVCpVQYemkfa6eXp6ai039GsaEhKSofu9paUlXF1ds93v559/jq1bt+LkyZOYOnUqtmzZgr59++otroiICKhUqjy9HiEhIUZ//QDdYq1atSrWr1+PgwcPYuvWrRBFEc2bN8eLFy8MGmteZfWaxsXFITk5uYCiylzJkiWxatUq7N27F3v37oW3tzfatGmDq1evGi0GURQxbtw4tGjRArVq1cpyvYI6Vk0F80zBYa7RZi65pjDnGcB8co0p5BmAuSa3CluuYZ7Jeb/MM/lTmHONueQZwDRyDfNM4XX69Gl0794dlSpVQqVKldCjRw/8+++/OrdnsgXRKVOmZDoYb/rbnTt3tLZ5+fIlOnXqhF69emHIkCEmHWtefPrpp+jRowdq166Nnj174vDhw7h8+TJOnTplUnHqk6FjHTp0KDp27IjatWujT58+2Lx5M/bv34+HDx/q8a8ovJo1a4b+/fujXr16aN26Nfbt2wd3d3esXr26oEMzW1WrVsWwYcPQsGFDNG/eHOvXr0fz5s2xePFio8UwatQo3Lx5Ezt37jTaPgsS88wb+sozxohVn5hrTBfzjP6ZQp4BmGtMNdcwzxgG84xpY67RP1PINUUtzxQVW7duha+vL+zt7TFmzBiMGTMGdnZ2aNeuHbZv365Tm5Z6jlFvJk6ciIEDB2a7ToUKFTT3X716hbZt26J58+ZYs2ZNttt5eXlBLpcjJiZG6xfV0NBQeHl5GTzW/KpQoQLc3Nzw4MEDtGvXLtfbGTLOtNctNDQUJUuW1CwPDQ1FvXr18txebmP18vLKMEi2UqlEVFRUnv4tmzZtCkD9a3zFihXzHO/b3NzcYGFhkWGWz+yOMS8vrzytry+6xPo2Kysr1K9fHw8ePDBEiDrL6jV1cnKCnZ1dAUWVe02aNMHZs2eNsq/Ro0drBvAvU6ZMtusW1LGqb8wz2belS54BmGuyU1RzTWHOM4B55xpj5hmAuSYrppBrmGfUmGe0mUueAQp3rjHnPAPwO00aY1/GXtgumf/hhx8wf/58jB8/XrNszJgxWLRoEebMmYPPP/88z22abEHU3d0d7u7uuVr35cuXaNu2LRo2bIgNGzZAJsu+42vDhg1hZWUFPz8/fPTRRwDUs6A9e/YMzZo1M2is+vDixQtERkZqJencMGScPj4+8PLygp+fn+bDQlxcHC5evJjnGSiB3MfarFkzxMTE4MqVK2jYsCEA4J9//oEoipoPBLkRGBgIAHl+TbNibW2Nhg0bws/PDz179gSg7rrv5+eH0aNHZ7pNs2bN4Ofnh3HjxmmWnThxQqdj0tCxvk2lUuHGjRvo0qWLASPNu2bNmuHo0aNay4zxmupLYGCg3o7JrEiShK+++gr79+/HqVOn4OPjk+M2BXWs6hvzTNZ0zTMAc012imquKcx5BjDvXGOMPAMw15hDrmGeUWOe0WYueUbXWN9mqrnGnPMMwO80pB+PHj1C9+7dMyzv0aMH/ve//+nWaAFP6pRvL168kCpVqiS1a9dOevHihRQcHKy5pV+natWq0sWLFzXLhg8fLpUtW1b6559/JH9/f6lZs2ZSs2bNDB7v06dPpYCAAGn27NmSg4ODFBAQIAUEBEjx8fGadapWrSrt27dPkiRJio+Pl77++mvpwoUL0uPHj6W///5batCggVS5cmUpJSXFZOKUJEn66aefJBcXF+ngwYPS9evXpffff1/y8fGRkpOTDRanJElSp06dpPr160sXL16Uzp49K1WuXFn67LPPNM+//e//4MED6bvvvpP8/f2lx48fSwcPHpQqVKggtWrVSq9x7dy5U7KxsZE2btwo3b59Wxo6dKjk4uIihYSESJIkSf369ZOmTJmiWf/cuXOSpaWltGDBAikoKEiaOXOmZGVlJd24cUOvcekj1tmzZ0vHjx+XHj58KF25ckX69NNPJVtbW+nWrVsGjTM+Pl5zLAKQFi1aJAUEBEhPnz6VJEmSpkyZIvXr10+z/qNHjyR7e3tp0qRJUlBQkLR8+XLJwsJCOnbsmEHj1CXWxYsXSwcOHJDu378v3bhxQxo7dqwkk8mkv//+26BxjhgxQnJ2dpZOnTqldf5MSkrSrGNKx2pBYJ4xnVglibnmbeaSa8wlz0iS+eQac8kzksRckxvmlGuYZwyDecb4sfI7jf5j5XeajNJmmT/babQU2H2i0W5nO40uVLPMV6xYUVq1alWG5StXrpQqVaqkU5tmXxDdsGGDBCDTW5rHjx9LAKSTJ09qliUnJ0sjR46UihcvLtnb20sffPCB1gcOQxkwYECmsaaPDYC0YcMGSZIkKSkpSerQoYPk7u4uWVlZSeXKlZOGDBmiOambSpySJEmiKErTp0+XPD09JRsbG6ldu3bS3bt3DRqnJElSZGSk9Nlnn0kODg6Sk5OTNGjQIK0POW//+z979kxq1aqV5OrqKtnY2EiVKlWSJk2aZJATxdKlS6WyZctK1tbWUpMmTaT//vtP81zr1q2lAQMGaK3/+++/S1WqVJGsra2lmjVrSkeOHNF7TPqIddy4cZp1PT09pS5dukhXr141eIwnT57M9LhMi23AgAFS69atM2xTr149ydraWqpQoYLWMWtKsc6bN0+qWLGiZGtrK7m6ukpt2rSR/vnnH4PHmdX5M/3rZGrHqrExz5hOrJLEXJMZc8k15pBnJMl8co255BlJYq7JDXPKNcwzhsE8Y/xY+Z1G/7HyO01GaQXRfzt+JQV0+9pot387flWoCqIrVqyQrK2tpeHDh0ubN2+WNm/eLA0bNkyysbHJtFCaG4IkSRKIiIiIiIiIiIhIb+Li4uDs7Ix/O34FBysbo+03QZGKd48vRWxsLJycnIy2X0Pav38/Fi5ciKCgIABA9erVMWnSJLz//vs6tWeyY4gSERERERERERGZO06qlH8ffPABPvjgA721l/1I3URERERERERERESFCHuIEhERERERERERkclwdXXFvXv34ObmhuLFi0MQsu71GhUVlef2WRAlIiIiIiIiIiIyEF4yn3eLFy+Go6Oj5n52BVFdsCBKREREREREREREJmPAgAGa+wMHDtR7+xxDlIiIiIiIiIiIiEyShYUFwsLCMiyPjIyEhYWFTm2yhygREREREREREZGB8JL5/JEkKdPlqampsLa21qlNFkSJiIiIiIiIiIjIpCxZsgQAIAgC1q1bBwcHB81zKpUKZ86cQbVq1XRqmwVRIiIiIiIiIiKiImz58uX4+eefERISgrp162Lp0qVo0qRJluvv3r0b06dPx5MnT1C5cmXMmzcPXbp00WtMixcvBqDuIbpq1Sqty+Otra1Rvnx5rFq1Sqe2WRAlIiIiIiIiIiIyFEkARCNexp7HS+Z37dqFCRMmYNWqVWjatCl++eUXdOzYEXfv3oWHh0eG9c+fP4/PPvsMc+fORbdu3bB9+3b07NkTV69eRa1atfT1V+Dx48cAgLZt22Lfvn0oXry43toWpKwuxCciIiIiIiIiIiKdxMXFwdnZGafajYeDpY3R9pugTEUbv8V4/vw5nJycNMttbGxgY5MxjqZNm6Jx48ZYtmwZAEAURXh7e+Orr77ClClTMqzfu3dvJCYm4vDhw5pl77zzDurVq6dzj01j4yzzREREREREREREBpI2qZIxbwDg7e0NZ2dnzW3u3LkZYpPL5bhy5Qp8fX01y2QyGXx9fXHhwoVM/54LFy5orQ8AHTt2zHL9/Proo48wb968DMvnz5+PXr166dQmL5knIiIiIiIiIiIqZDLrIfq2iIgIqFQqeHp6ai339PTEnTt3Mm03JCQk0/VDQkL0EHVGZ86cwaxZszIs79y5MxYuXKhTmyyIEhERERERERERFTJOTk5aBVFzlZCQAGtr6wzLraysEBcXp1ObvGSeiIiIiIiIiIjIQArqkvnccHNzg4WFBUJDQ7WWh4aGwsvLK9NtvLy88rR+ftWuXRu7du3KsHznzp2oUaOGTm2yhygREREREREREVERZG1tjYYNG8LPzw89e/YEoJ5Uyc/PD6NHj850m2bNmsHPzw/jxo3TLDtx4gSaNWtmkBinT5+ODz/8EA8fPsR7770HAPDz88OOHTuwe/dundpkQZSIiIiIiIiIiKiImjBhAgYMGIBGjRqhSZMm+OWXX5CYmIhBgwYBAPr374/SpUtrJmUaO3YsWrdujYULF6Jr167YuXMn/P39sWbNGoPE1717dxw4cAA//vgj9uzZAzs7O9SpUwd///03WrdurVObLIgSEREREREREREZiCSpb8bcX1707t0b4eHhmDFjBkJCQlCvXj0cO3ZMM3HSs2fPIJO9GXWzefPm2L59O6ZNm4b//e9/qFy5Mg4cOIBatWrp88/Q0rVrV3Tt2lVv7QmSZMx/EiIiIiIiIiIiosIvLi4Ozs7O8GszEQ6WGWd4N5QEZSranVqI2NjYQjGpEgDExMRgz549ePToEb7++mu4urri6tWr8PT0ROnSpfPcHnuIEhERERERERERkUm6fv06fH194ezsjCdPnmDw4MFwdXXFvn378OzZM2zevDnPbXKWeSIiIiIiIiIiIkMx9gzzeZhl3hxMmDABAwcOxP3792Fra6tZ3qVLF5w5c0anNlkQJSIiIiIiIiIiIpN0+fJlDBs2LMPy0qVLIyQkRKc2eck8ERERERERERGRgWh6bhpxf4WJjY0N4uLiMiy/d+8e3N3ddWqTPUSJiIiIiIiIiIjIJPXo0QPfffcdFAoFAEAQBDx79gzffPMNPvroI53aZEGUiIiIiIiIiIiITNLChQuRkJAADw8PJCcno3Xr1qhUqRIcHR3xww8/6NQmL5knIiIiIiIiIiIyEF4ynz/Ozs44ceIEzp49i+vXryMhIQENGjSAr6+vzm2yIEpEREREREREREQmrWXLlmjZsqVe2mJBlIiIiIiIiIiIiEzGkiVLMHToUNja2mLJkiXZruvg4ICaNWuiadOmuW6fBVEiIiIiIiIiIiID4SXzebd48WL06dMHtra2WLx4cbbrpqamIiwsDOPHj8fPP/+cq/ZZECUiIiIiIiIiIiKT8fjx40zvZ+XEiRP4/PPPWRAlIiIiIiIiIiIqaJIoQBKN2EPUiPsyFS1btsS0adNyvb7MgLEQERERERERERER5cvp06fRvXt3VKpUCZUqVUKPHj3w77//ap63s7PD2LFjc90eC6JERERERERERERkkrZu3QpfX1/Y29tjzJgxGDNmDOzs7NCuXTts375dpzYFSZIkPcdJRERERERERERUpMXFxcHZ2RnHmk1BMUtbo+03UZmCThd+QmxsLJycnIy2X0OpXr06hg4divHjx2stX7RoEdauXYugoKA8t8keolTkxMfHY+jQoShRogRcXFzQr18/REVFFXRYBSY1NbWgQyAiKlSYZ7QxzxAR6R9zjTbmGqLC7dGjR+jevXuG5T169MjVhEuZYUGUihRJkvDhhx9i//79mDx5MqZPn46///4bnTp1gkKhMGosf/31F7788kvUqlULFhYWKF++fJbrBgcHY+jQofDx8YGdnR0qVqyICRMmIDIyMsO6QUFB6NSpExwcHODq6op+/fohPDw80/2XKVMGxYoVQ48ePRATE6PHv46IqGgylTyTlJSE5cuXo0OHDihZsiQcHR1Rv359rFy5EiqVKtttt23bBkEQ4ODgkOnzzDNERAXLVHINAPz4449455134O7uDltbW1SuXBnjxo3LNC+Iooj58+fDx8cHtra2qFOnDnbs2JFpu8w1RJSet7c3/Pz8Miz/+++/4e3trVObvGSeipT9+/fjww8/xIULF/DOO+8AAK5du4YGDRpg9erVGDx4sNFiGThwIHbt2oUGDRrg2bNnsLCwwJMnTzKsl5CQgFq1aiExMREjR46Et7c3rl27htWrV6NmzZq4cuUKZDL1bxsvXrxA/fr14ezsjDFjxiAhIQELFixA2bJlcenSJVhbW2vaLFeuHEaOHImGDRti8eLFqFmzJlasWGG0v5+IqDAylTxz8+ZN1KlTB+3atUOHDh3g5OSE48ePY//+/ejfvz82bdqU6XYJCQmoWrUqYmNjNY/TY54hIip4ppJrAOCjjz6Cu7s7qlWrBkdHRwQFBWHt2rXw8PBAYGAgihUrpll36tSp+OmnnzBkyBA0btwYBw8exJEjR7Bjxw58+umnmvWYa6gwSbtk/s93phr9kvnO/80tNJfMr1y5EuPGjcMXX3yB5s2bAwDOnTuHjRs34tdff8WwYcPy3CYLolSkfPDBB3j+/Dn8/f21lrdt2xaiKOL06dNGi+XVq1dwd3eHlZUVunXrhps3b2ZaEN2+fTv69OmDw4cPo2vXrprlM2fOxHfffYerV6+ifv36AICRI0di48aNuHPnDsqWLQtA/YtJ+/btsXr1agwdOhQA4O/vjyFDhiAgIACA+gNU3759cePGDQP/1UREhZup5JmIiAiEhoaiZs2aWsu/+OILbNiwAffv30elSpUybDdlyhQcOHAAjRo1woEDBzIURJlniIgKnqnkmqzs3bsXH3/8sVah8+XLl/Dx8cHQoUOxbNkyAOqerq1bt8bjx4/x5MkTWFhYAGCuocKFBVH92b9/PxYuXKgZL7R69eqYNGkS3n//fZ3a4yXzVKScP38ezZo1y7C8adOm+O+//3K8jFCfSpUqBSsrqxzXi4uLAwB4enpqLS9ZsiQAwM7OTrNs79696Natm+aDAwD4+vqiSpUq+P333zXLypYti/v372P37t14+PAhlixZgsqVK+fr7yEiItPJM25ubhmKoYD6SzSATAeev3//PhYvXoxFixbB0tIy03aZZ4iICp6p5JqspA0Flv7y9YMHD0KhUGDkyJGaZYIgYMSIEXjx4gUuXLigWc5cQ0TpKZVKfPfdd2jcuDHOnj2LyMhIREZG4uzZszoXQwEg80+7RIVQfHw8wsLC4O7ujoiICK3nPDw8IJfL8eLFC5QrVy7LNqKjo3P1AcPe3h729vb5jhkAWrVqBZlMhrFjx2LhwoUoU6YMrl+/jh9++AE9e/ZEtWrVAKh/dQ0LC0OjRo0ytNGkSRMcPXpU89jDwwOzZs3Cp59+ClEUUa5cOfz11196iZeIqKgyhzwTEhICQF0wfdu4cePQtm1bdOnSResLZxrmGSKigmeKuUaSJERGRkKpVOL+/fuYMmUKLCws0KZNG806AQEBKFasGKpXr661bZMmTTTPt2zZkrmGCi1JEiBJglH3V1hYWlpi/vz56N+/v37b1WtrRCYsraflzJkzMXPmzEzXSRszLSv169fH06dPc9zXzJkzMWvWrDzHmJkaNWpgzZo1+Prrr7V+CR4wYADWrVuneRwcHAzgTc/R9EqWLImoqCikpqbCxsYGAPD111/js88+w6tXr1C7dm3Y2hqv+z4RUWFk6nlGLpfjl19+gY+PDxo3bqz13JEjR/DXX3/h2rVrWW7PPENEVPBMMdeEhoZq5YYyZcpg+/btmo4bgDqHeHp6QhC0izRp27169UqzXvrlb6/LXENUNLVr1w6nT5/OdjLqvGJBlIqMtOQ7ePBg9O7dW+u5Y8eOYeHChRkS9Nu2bduG5OTkHPdVoUIF3QPNROnSpdGkSRN06dIF5cqVw7///oslS5bAzc0NCxYsAABNXGkfDtJL+2CQnJys9Xzp0qVRunRpvcZKRFRUmXqeGT16NG7fvo0jR45oXRIvl8sxfvx4DB8+HDVq1Mhye+YZIqKCZ4q5xtXVFSdOnEBKSgoCAgKwb9++DGNQv50f0qTPH+n/z1xDhQ17iOZP586dMWXKFNy4cQMNGzbUmrANAHr06JHnNlkQpSIjbSBhb29v+Pr6aj13584drXWy0qJFC8MEl41z586hW7du+O+//zSXjvTs2RNOTk6YPXs2vvjiC9SoUUMzlmhqamqGNlJSUgBojzdKRET6Zcp55ueff8batWsxZ84cdOnSReu5xYsXIyIiArNnz862DeYZIqKCZ4q5xtraWhNLt27d0K5dO7Ro0QIeHh7o1q0bAHV+yE3+YK4hosykjT+8aNGiDM8JgqDT2MksiFKR4eDgAA8PD81lGOk9f/4c1tbWKFOmTLZthIeH5+qN5uDgAAcHB51jTW/16tXw9PTMMI5Ojx49MGvWLJw/fx41atTQXFaS2d8XHBwMV1fXTH9pJSIi/TDVPLNx40Z88803GD58OKZNm6b1XGxsLL7//nuMHDkScXFxmksxExISIEkSnjx5Ant7e3h4eDDPEBGZAFPNNek1b94cJUuWxLZt2zQF0ZIlS+LkyZOQJEmrB2va31GqVCnNeumXp8dcQ1R0iaKo9zZZEKUipUWLFvD398+w/MKFC3jnnXdgYWGR7faNGzc2+hiioaGhmX5gUSgUANQzrgHqS0Xc3d0z/fsuXbqEevXq6SUeIiLKmqnlmYMHD2Lw4MH48MMPsXz58gzPR0dHIyEhAfPnz8f8+fMzPO/j44P3338fBw4cYJ4hIjIRppZrMpOSkqI1lmm9evWwbt06BAUFaQ3PcvHiRc3zAL/TUOHFS+Z19+TJE5w4cQIKhQKtW7dGzZo19dIuC6JUpPTr1w8ffvgh/v33X7z77rsAAH9/f5w9exZr1qzJcfuCGEO0SpUq+Ouvv3Dq1CmtmRp37NgBQD0oepqPPvoImzZtwvPnz+Ht7Q0A8PPzw7179zB+/Hi9xURERJkzpTxz5swZfPrpp2jVqhW2bdsGmUyWYR0PDw/s378/w/IlS5bgwoUL2LFjh9bEFswzREQFz1RyTWJiIgRByDAT/d69exEdHa11hdv777+P8ePHY8WKFVi2bBkA9ez0q1atQunSpdG8eXPNusw1RJTm5MmT6Natm+acZWlpifXr16Nv3775bluQJEnKdytEZkKSJLRv3x4BAQGYNGkSLC0t8fPPP6NcuXI4d+4crKysjBbL9evX8ccffwAAtm7ditDQUEycOBEAULduXXTv3h0AcPfuXTRs2BCCIOCrr75CuXLlcPr0aezYsQPt27fHX3/9pWnz+fPnqF+/PlxcXDB27FgkJCTg559/RpkyZXD58mVeXkJEZGCmkmeePn2KunXrQi6XY8GCBRnGk6tTpw7q1KmT5fYDBw7Enj17MkyKwTxDRFTwTCXXBAYGwtfXF71790a1atUgk8ng7++PrVu3okyZMvD390eJEiU060+ePBk///wzhg4disaNG+PAgQM4cuQItm3bhs8//1yzHnMNFSZxcXFwdnbG4cbTUMzS1mj7TVSmoNvl7xEbG5vjuMKmrGXLlnBzc8PKlStha2uLadOmYf/+/Xj16lW+22ZBlIqc+Ph4TJw4EXv27IEoiujevTt+/fVXuLq6GjWOjRs3YtCgQZk+N2DAAGzcuFHz+O7du5g2bRouXryIkJAQlCpVCr169cLs2bMz/CJ769YtTJgwAWfPnoW1tTW6du2KhQsXwtPT05B/DhERvWYKeebUqVNo27Ztls/ndBlkVgVRgHmGiMgUmEKuiYiIwLfffoszZ87g+fPnUCgUKFeuHLp27Ypvv/0Wbm5uWuuLooh58+Zh9erVCA4ORuXKlTF16lT06dMnQ9vMNVRYpBVEDzWabvSCaHf/OWZfEHVxcdHMmwIASUlJcHJyQmhoqNYPLrpgQZSIiIiIiIiIiEjPWBDNH5lMhpCQEHh4eGiWOTo64tq1a/keqpBjiBIREREREREREZHJOX78OJydnTWPRVGEn58fbt68qVnWo0ePPLfLgigREREREREREZGBcJZ53Q0YMCDDsmHDhmnuC4IAlUqV53ZZECUiIiIiIiIiIiKTIoqiwdqWGaxlAHPnzkXjxo3h6OgIDw8P9OzZE3fv3s1xu927d6NatWqwtbVF7dq1cfToUUOGSUREZop5hoiIDIl5hoiI9EGSBEiiEW+FqIeooRi0IHr69GmMGjUK//33H06cOAGFQoEOHTogMTExy23Onz+Pzz77DF9++SUCAgLQs2dP9OzZU2tsACIiIoB5hoiIDIt5hoiIqHAy6izz4eHh8PDwwOnTp9GqVatM1+nduzcSExNx+PBhzbJ33nkH9erVw6pVqzKsn5qaitTUVM1jURQRFRWFEiVKQBBYESciyi9JkhAfH49SpUpBJjPo72j5Zog8AzDXEBEZmrnkGuYZIiLzVFB5Jm2W+YMNZqKYhRFnmVel4P2rs81+lnlDMuoYorGxsQAAV1fXLNe5cOECJkyYoLWsY8eOOHDgQKbrz507F7Nnz9ZbjERElLnnz5+jTJkyBR1GtgyRZwDmGiIiYzH1XMM8Q0Rk3goqz3BSJdNjtIKoKIoYN24cWrRogVq1amW5XkhICDw9PbWWeXp6IiQkJNP1p06dqvWBIzY2FmXLlsXjx4/h4uKil9gNRRRFREREwM3NzaR/CQcYqyGYS5wAYzUUc4k1JiYGPj4+cHR0LOhQsmWoPAOYb64xl2MMYKyGYi6xmkucAGM1FHPINcwzmTOX48xc4gQYq6GYS6zmEidgXrGaQ54h4zJaQXTUqFG4efMmzp49q9d2bWxsYGNjk2G5i4uLWXx4kMvlcHFxMfmTB2PVP3OJE2CshmJOsQIw+Uv2DJVnAPPNNeZ0jDFWwzCXWM0lToCxGpop5xrmmcyZy3FmLnECjNVQzCVWc4kTMK9Y05hynqHsxcTEYM+ePXj48CEmTZoEV1dXXL16FZ6enihdunSe2zNKQXT06NE4fPgwzpw5k2PXZC8vL4SGhmotCw0NhZeXlyFDJCIiM8Y8Q0REhsQ8Q0RE+SFJ6psx91eYXL9+Hb6+vnB2dsaTJ08wZMgQuLq6Yt++fXj27Bk2b96c5zYNWsKXJAmjR4/G/v378c8//8DHxyfHbZo1awY/Pz+tZSdOnECzZs0MFSYREZkp5hkiIjIk5hkiIqKCN2HCBAwcOBD379+Hre2byam6dOmCM2fO6NSmQXuIjho1Ctu3b8fBgwfh6OioGTfH2dkZdnZ2AID+/fujdOnSmDt3LgBg7NixaN26NRYuXIiuXbti586d8Pf3x5o1awwZKhERmSHmGSIiMiTmGSIiooJ3+fJlrF69OsPy0qVLZztGd3YM2kN05cqViI2NRZs2bVCyZEnNbdeuXZp1nj17huDgYM3j5s2bY/v27VizZg3q1q2LPXv24MCBA9kOXE5EREUT8wwRERkS8wwREemDKAlGvxUmNjY2iIuLy7D83r17cHd316lNg/YQlXIxaMGpU6cyLOvVqxd69eplgIiIiKgwYZ4hIiJDYp4hIiIqeD169MB3332H33//HYB6cqxnz57hm2++wUcffaRTm+YxDRgREREREREREZEZkiTB6LfCZOHChUhISICHhweSk5PRunVrVKpUCY6Ojvjhhx90atMos8wTERERERERERER5ZWzszNOnDiBs2fP4vr160hISECDBg3g6+urc5ssiBIREREREREREZFJa9myJVq2bKmXtlgQJSIiIiIiIiIiMhRjX8ZeCC6ZX7JkSa7XHTNmTJ7bZ0GUiIiIiIiIiIiITMbixYu1HoeHhyMpKQkuLi4AgJiYGNjb28PDw0OngignVSIiIiIiIiIiIiKT8fjxY83thx9+QL169RAUFISoqChERUUhKCgIDRo0wJw5c3RqnwVRIiIiIiIiIiIiA+Es8/kzffp0LF26FFWrVtUsq1q1KhYvXoxp06bp1CYLokRERERERERERGSSgoODoVQqMyxXqVQIDQ3VqU0WRImIiIiIiIiIiAyEPUTzp127dhg2bBiuXr2qWXblyhWMGDECvr6+OrXJgigRERERERERERGZpPXr18PLywuNGjWCjY0NbGxs0KRJE3h6emLdunU6tclZ5omIiIiIiIiIiMgkubu74+jRo7h37x6CgoIgCAKqVauGKlWq6NwmC6JEREREREREREQGIokCJMF4l7FLYuG6ZD5NlSpVULlyZQCAkM/Xk5fMExERERERERERkcnavHkzateuDTs7O9jZ2aFOnTrYsmWLzu2xhygRERERERERERGZpEWLFmH69OkYPXo0WrRoAQA4e/Yshg8fjoiICIwfPz7PbbIgSkREREREREREZCDGnvm9sM0yv3TpUqxcuRL9+/fXLOvRowdq1qyJWbNm6VQQ5SXzREREREREREREZJKCg4PRvHnzDMubN2+O4OBgndpkQZSIiIiIiIiIiIhMUqVKlfD7779nWL5r1y7NJEt5xUvmiYiIiIiIiIiIDISXzOfP7Nmz0bt3b5w5c0Yzhui5c+fg5+eXaaE0N9hDlIiIiIiIiIiIiEzSRx99hIsXL8LNzQ0HDhzAgQMH4ObmhkuXLuGDDz7QqU32ECUiIiIiIiIiIjIQ9hDNv4YNG2Lr1q16a489RImIiIiIiIiIiKjIYA9RIiIiIiIiIiIiMikymQyCkH1vV0EQoFQq89w2C6JEREREREREREQGIkqAaMTL2EXJaLsyqP3792f53IULF7BkyRKIoqhT2yyIEhERERERERERkUl5//33Myy7e/cupkyZgkOHDqFPnz747rvvdGqbY4gSERERERERERGRyXr16hWGDBmC2rVrQ6lUIjAwEJs2bUK5cuV0ao89RImIiIiIiIiIiAyEs8zrLjY2Fj/++COWLl2KevXqwc/PD++++26+22VBlIiIiIiIiIiIiEzK/PnzMW/ePHh5eWHHjh2ZXkKvKxZEiYiIiIiIiIiIyKRMmTIFdnZ2qFSpEjZt2oRNmzZlut6+ffvy3DYLokRERERERERERAbCS+Z1079/fwiCYf4WFkSJiIiIiIiIiIjIpGzcuNFgbbMgSkREREREREREZCCSBEiicfdH2ZMZsvEzZ86ge/fuKFWqFARBwIEDB7Jd/9SpUxAEIcMtJCTEkGESEZGZYp4hIiJDY64hIiIqfAxaEE1MTETdunWxfPnyPG139+5dBAcHa24eHh4GipCIiMwZ8wwRERkacw0REVHhY9BL5jt37ozOnTvneTsPDw+4uLjoPyAiIipUmGeIiMjQmGuIiCi/OKmS6THJMUTr1auH1NRU1KpVC7NmzUKLFi2yXDc1NRWpqamax3FxcQAAURQhikYcoEEHoihCkiSTjxNgrIZgLnECjNVQzCVWU49PF3nJM4D55hpzOcYAxmoo5hKrucQJMFZDMYcY84rfaUyLucQJMFZDMZdYzSVOwPxiJUrPpAqiJUuWxKpVq9CoUSOkpqZi3bp1aNOmDS5evIgGDRpkus3cuXMxe/bsDMvDw8Mhl8sNHXK+iKKI2NhYSJIEmcygoxfkG2PVP3OJE2CshmIuscbGxhZ0CHqjS54BzDfXmMsxBjBWQzGXWM0lToCxGkpRzzXmmmcA8znOzCVOgLEairnEai5xAuYVa2HKM6QfJlUQrVq1KqpWrap53Lx5czx8+BCLFy/Gli1bMt1m6tSpmDBhguZxXFwcvL294e7ubvKXqIiiCEEQ4O7ubvInD8aqf+YSJ8BYDcVcYrW2ti7oEPRGlzwDmG+uMZdjDGCshmIusZpLnABjNZSinmvMNc8A5nOcmUucAGM1FHOJ1VziBMwr1oLOM6IkQDTiZezG3Je5MqmCaGaaNGmCs2fPZvm8jY0NbGxsMiyXyWQm/4YEAEEQGKsBmEus5hInwFgNxRxiNeXY9CGnPAOYd64xh2MsDWM1DHOJ1VziBBirIZh6fPnF7zSmwVziBBiroZhLrOYSJ2A+sZp6fGR8Jn9EBAYGomTJkgUdBhERFVLMM0REZGjMNURERKbFoD1EExIS8ODBA83jx48fIzAwEK6urihbtiymTp2Kly9fYvPmzQCAX375BT4+PqhZsyZSUlKwbt06/PPPP/jrr78MGSYREZkp5hkiIjI05hoiIsovzjJvegxaEPX390fbtm01j9PGxRkwYAA2btyI4OBgPHv2TPO8XC7HxIkT8fLlS9jb26NOnTr4+++/tdogIiJKwzxDRESGxlxDRERU+Bi0INqmTRtIkpTl8xs3btR6PHnyZEyePNmQIRERUSHCPENERIbGXENERPnFHqKmx+THECUiIiIiIiIiIiLSFxZEiYiIiIiIiIiIqMgw6CXzRERERERERERERRkvmTc97CFKRERERERERERERQYLokRERERERERERFRk8JJ5IiIiIiIiIiIiA5EkASIvmTcp7CFKRERERERERERERQZ7iBIRERERERERERmIJKlvxtwfZY89RImIiIiIiIiIiKjIYEGUiIiIiIiIiIiIigxeMk9ERERERERERGQgkihAghEnVRI5qVJO2EOUiIiIiIiIiIiIigwWRImIiIiIiIiIiKjI4CXzREREREREREREBiJJAiTJiJfMG3Ff5oo9RImIiIiIiIiIiKjIYEGUiIiIiIiIiIiIigxeMk9ERERERERERGQgoiRANOJl7Mbcl7liD1EiIiIiIiIiIiIqMthDtBARRRE3b95ESEgIRFHUe9sxMTFwcXGBTKa/OrqtrS0qVqwIb29vvbVJpk+lUuHGjRsICwszm2PVzs4OlStXRqlSpfTWJpE5evHiBR48eICUlBS9tmuo966FhQXKlCmDatWqQRD4S3lhl5iYiFu3biEqKspg+zDUsZqek5MTqlevjuLFixukfSJTFhcXh1u3biE2Nlav7Rryvevu7o6aNWvC1tZWr+2S6ZEkCTdv3kRwcLDev8ekMUaesba2RqVKlVC2bFmDtE8ZSZL6Zsz9UfZYEC0kRFHEnDlzcOjQIYPuwxAnZEtLS8yfPx+tWrXSe9tkelQqFaZNm4YTJ04YbB+GOlatra2xaNEivPPOO3pvm8gc/Pvvv5g0aRKUSqVB2jfUexcABg8ejGHDhrEoWojFxsZixIgRuHfvnsH3ZchjNY2HhwdWr17NH42pSAkJCcGwYcPw8uVLg7RvyPdu/fr18euvv8Le3t4g7VPBkyQJ8+fPx+7duw2+L2PkGZlMhu+//x4dOnQw6H6ITBULooXEhg0bcOTIEcyYMQPvvfceLC31+08rSRKUSiUsLS31+mUyMTER8+bNw+TJk7Fjxw74+PjorW0yTStWrMA///yDOXPmoFWrVrCwsNBr+4Y6VuPj4/H9999jwoQJ2LNnD3uKUpHz5MkTTJo0Ce+++y6mTJmCYsWK6bV9Q7135XI59uzZgxUrVsDb2xtdu3bVW9tkWqZOnYqwsDD89ttvqFy5ssG+SBrqWE3ffkhICCZOnIjhw4fj8OHDLORTkfHVV19BkiTs2LEDZcqU0euxb6j3riiKuHHjBiZNmoQ5c+Zg7ty5emubTMuOHTuwe/duTJ48GZ07d4aVlZVB9mPoPAMASUlJ+OWXXzBt2jSULVsW1apVM8h+iEwZC6KFxI0bN9CsWTP06NHDIO0b6qRsa2uLmTNnonXr1rh79y4LokXAzZs30bZtW3Tu3Nkg7RvyWJ02bRo6d+6M+/fvsyBKRc69e/egVCoxY8YMODo66r19Q753v/jiCxw/fhw3b95kQbQQu3HjBoYMGYK6desadD/G+KLq4+ODcePGYcKECYiIiIC7u7tB9kNkShITE/H48WPMmTMHlStX1nv7hnzvNm3aFL169cLx48f12i6Zlps3b6JBgwb45JNPDLofY+SZtO82x44dQ1BQEAuiRiBJAiQjTnRkzH2ZKxZEC4nk5GS9fliWJAmPHj2Cv78/bt68idjYWMhkMnh5eaFRo0Zo2LCh3sa1SutllJycrJf2yLQlJyfrdawaSZLw4MEDzbEaHx8PmUyGUqVKoVGjRmjQoAFcXFz0sq+0IhCPVSqK0o57fRZDIyIicOXKFVy9ehVhYWFQqVRwdnZG3bp10ahRI5QrV05vXwQcHR353i3kkpOTDVKsB4CwsDD4+/sjICAA4eHhEEURxYsXR7169dCoUSODXNbu4OAAgDmHio60sanTjn19SE5OxrVr1+Dv748nT54gNTUVdnZ2qFq1Kho1aoSaNWvq7co65pnCz5B5JikpCYGBgfD398fTp08hl8thb2+P6tWro2HDhqhRo4ber6yztraGra0tj1sqslgQJS0xMTHYtGkTVq5cibt372a5niAI6NKlC0aNGoWOHTsafHwTordFRUVhw4YNWLlyJR4+fJjlejKZDD169MDIkSPh6+vLyw6JCphKpcKhQ4ewYsWKHMcSrlWrFkaOHIm+ffsa7AsIUVYUCgUOHjyIFStW4OTJk9muW79+fYwcORKff/45xw8kMgGBgYFYsWIFtm3bhqSkpCzXc3Nzw5dffonhw4ejfPnyxguQ6LXLly9jxYoV2LlzZ7YTVnp6emrGQ+fY0kT6wSoWAVD3stu2bRsqVqyIcePGZVsMTVv/yJEj6NKlC1q0aIE7d+4YKVIq6iRJwm+//QYfHx98/fXX2RZDAfW4TgcOHECHDh3Qpk2bHNcnIsO5du0aGjVqhA8++CBXE6vdvHkTI0eORKVKlbBv3z4jREikdvnyZdSvXx+9evXKsRgKAAEBARgyZAgqV66Mo0ePGiFCIspMdHQ0+vfvj/r162Pt2rXZFkMB9ZUK8+bNQ6VKlTBt2jSkpqYaKVIq6sLDw9G7d280adIEGzduzLYYCgChoaH44YcfULFiRXz//fdQKBRGipT0RZQEo98oe+whSkhKSkL//v2xd+9ereXN37VFqza2qFvPBm7uMqhUwONHCgRckePooUS8eK4CAPz333+oV68eli5diiFDhhTEn0BFRHx8PD777DMcOXJEa3mrNrZo2Vp9rLqWUB+rDx8oEHglFYf/SELwK/WxeubMGdSpUwerV69G3759C+JPICqyFi9ejMmTJ2vNUF/exxKdu9mjfkMblCtvCZkMCA1R4VqgHKf8knH5ovqLaVhYGD766CP069cPa9euhY2NTUH9GVTISZKEuXPnYsaMGVCpVJrlJS090cC+Nnysy8HN0hWAgBhVLJ7In+F68m08kj8FALx69Qpdu3bF0KFDsWzZMoNNuEFEGZ0/fx4ff/wxgoODNcscrC3RpaI36nuWQJUSzrC1tECCXInbEdG4/CoCJx6/hEIUoVKp8MMPP+CPP/7AwYMHOa8BGdTJkyfRu3dvhIeHp1tqCyuLWpAJZSCTeUCABSQpBaIUApX4BErxLgARCoUC06dPx4EDB3DgwAGUKVOmoP4MIrPHgmgRl5SUhM6dO+PMmTOaZR/2KobJ37qgWnXrDOu/09wWn/UF5i5wxbEjSZgxNQoPHyiRmpqKoUOHIj4+HhMmTDDmn0BFRFxcHNq3b49Lly5pln3a1wFff+OCSlUyfuF8p7kt+vR3xI8LJBw9lITpU6Lw7KkSSUlJ6NevHxITEzFs2DBj/glERdasWbMwe/ZszeMaNa0w+0dXtOtgB5ks46/X3d4vhm9nFseN66n4YVYMjh1R9/DZsmULIiIicODAAVhbZ8xRRPkhSRImT56MBQsWaJaVty6Lz4p/gFq21TIdcqVJsfr4pPj7eJT6FL9HH8T1lNsAgDVr1iAiIgK7du3S2/iERJS106dPo0uXLpoeoU7WVpjcrA4+rVEBxawzfk5sVdYLwxsA4YnJ+O3aPSy/chtKUcKNGzfQsmVLnDlzBhUrVjT2n0FFwPHjx9GjRw/I5fLXS+xgY9kRVhb1IQiZfbapAqAVRCkOcuVZKFRnAUi4cuUK3n33XZw5c4aX0BPpiJfMF2GSJKFfv36aYqiTk4DNuzywfqtHpsXQ9CwsBHTtUQz/Xi6NIcPfjOs2ceJE7Nmzx6BxU9EjSRI++eQTTTG0uKsMu/Z7YtVv7pkWQ9OzshLw/ofFcP5qafQd+GaQ/hEjRmToaUpE+rd+/XqtYujYic44eaE02neyz7QYml7tOjbYsdcDq9a7wc5Ove6ff/6J4cOHGzRmKpqWLVumVQz90Lkrviv5DWrbVc9x/OkKNuXwjedXGFyiDyxf9zfYt28fxo8fb9CYiQi4f/8+unfvrimGNi/jgZN9u+LLelUzLYam517MDlOa18WxTzuhUnEnAOqe3h06dEBcXJzBY6ei5ebNm/jggw80xVALWRUUsxkPa8umWRRD35AJTrC16gJ76xEQBFcAwJMnT9CpU6cch4Yg0yBJxr9R9lgQLcK2bdumGZPNyUnAH8dLokfPYnlqw95ehp9/dcPUGS6aZcOHD0doaKg+Q6UibvXq1Th+/DgAdTH08ImS6Nglb5NWODjIsHSVG8ZOdAagLrIOGTIE0dHReo+XiNSePn2KsWPHah7/+LMrZv/oChub3I9pJAgCPu3jiD2HPDVF0Q0bNuDQoUN6j5eKrnv37mHy5Mmax4NL9MHHxbvDUsj9jL6CIOA9x3fxtedITVF02bJl8PPz03u8RKSmUqkwaNAgxMfHAwB8y5fC9vfbopRj3j4n1nQvjgO9fFG1hPpz4qNHj7TOCUT5pVAoMGDAAM2M7payWrCz6g+ZkLdJIy1k3rC3HgZBKAEAuH37NqZPn673eImKAhZEi6jo6GiMGTNG83j5OnfUa6D7mGyT/+eCnh+pi6mRkZG8bJ70JjQ0FJMmTdI8/m2zO2rW0u1SWUEQMOuH4ujQyQ4AEBwcjG+++UYvcRJRRqNGjUJCQgIAoN8gB4wc46xzWy3etcPCpSU0j4cNG8YeEaQ3w4YN00xo0dGxLd5zfFfnturY1UA/1481jwcPHpzu0kgi0qe1a9fi3LlzAIDyzg5Y3aUlbCxz/0NGeiXsbLG5e2vYW6l/0Fi9ejXOnj2rt1ipaPv1119x9epVAIBM8ICt1ScQ8vCjW3oywQl2Vv2RNgLi4sWLceXKFX2FSgYiSYLRb5Q9FkSLqA0bNmh6xn30STF0fz9vPUPfJggCFvxaAq4l1IfUrl278OLFi3zHSbRmzRpNQaX/Fw54r33efvF/myAI+GWFG5yc1Ali06ZNiIiIyHecRKQtKChIMyxFqdIW+H5eiRy2yNlnfR20ftDYsWNHvtskunLlCk6dOgUA8LB0Q+/iPfPdZjvHVqhhWwWA+pLG/fv357tNItImiiIWLVqkebyo/TuaYqauyjo7YFqLem/aTNc+ka6USiV++eWX148E2Fp9DEHI36R7FjIPWFv6AlBf+famfSLKLRZEiyBJkrBy5UrN4ynTXPTSrpu7BYaMUI+9o1KpsHbtWr20S0WXUqnE6tWrAQAyGTBpanG9tFuqtCUGfKk+VuVyOdavX6+XdonojVWrVmnujxrrDGfn/H/kEAQBU6a/OQ+sWLEi320Spf9M1N25I2xlul8xk0YmyPChc1fNYx6rRPrn5+eH+/fvAwDe9fZEs9Ieemm3b61K8Cym/vHt4MGD7ORB+Xbo0CG8fPkSAGApqwYLmX4mQbK2aA5Afaz+/vvvb81aT2Q4UVFR6NOnD5ycnODi4oIvv/xS04kpK23atIEgCFq3gp4XwKAF0TNnzqB79+4oVaoUBEHAgQMHctzm1KlTaNCgAWxsbFCpUiVs3LjRkCEWSffu3cODBw8AAO+2sUXlqvqbqXfAF45Im3fg8OHDemuXiqbr169rPjy072QH77L6m6l30OA34/XwWDVfzDOmK613qLU18Hk/hxzWzr0GjWxQt746b129ehUhISF6a5uKprRj1VawQYtijfXWbnXbKihp6QlAfa6KjY3VW9tkXMw1pin957d+tSvrrV0rCxk+r6meYV4URRw7dkxvbVPRlH4iVyuLJnprVxCsYGXREIC6kwfHrDZtoiQY/WYoffr0wa1bt3DixAkcPnwYZ86cwdChQ3PcbsiQIQgODtbc5s+fb7AYc8OgBdHExETUrVsXy5cvz9X6jx8/RteuXdG2bVsEBgZi3LhxGDx4sGYyFdKP9OOLtGpjq9e2S5W2RJWq6u7/N27cQGpqql7bp6LF399fc791Wzu9tl2hkhXKlFWP2xMQEABRFPXaPhkH84xpiomJwcOHDwEAdevboLirbmNkZaVVmzfnA46ZRfnx6tUrTVG9oo0PbGX6+1wkCAJq2lXVPA4ICNBb22RczDWmKf35v0UZT7223dL7TXvMM5Rfb44hARayCnpt21JWMZP9EL0RFxendctvjSYoKAjHjh3DunXr0LRpU7Rs2RJLly7Fzp078erVq2y3tbe3h5eXl+bm5OSUr1jyS3/drTLRuXNndO7cOdfrr1q1Cj4+Pli4cCEAoHr16jh79iwWL16Mjh07GirMIufGjRua+3Xr5f+ysLfVbWCNu3cUUCgUuHv3LurUqaP3fVDRoHWs1tdfT+Y09erb4MWzJCQkJODJkyeoUEG/H1DI8JhnTNPNmzc19+vWM8B7t8GbNq9fv46uXbtmszZR1tLnGR9r/VzCmJ6PdTnN/evXr6NNmzZ63wcZHnONaUp7/5ZxLAZXO/1+p6nt7pphP0S6EEVR87lIJrhDEPT7uUgmK625f/36db22TYWDt7f255uZM2di1qxZOrd34cIFuLi4oFGjRpplvr6+kMlkuHjxIj744IMst922bRu2bt0KLy8vdO/eHdOnT4e9ff7mCMkPgxZE8+rChQvw9fXVWtaxY0eMGzcuy21SU1O1KtxxcXEA1CceU+/xJYoiJEnSS5ySJGluOUl/yZa7h3577QCAu/ubNkPCI1A+KSXHbeRKJa4/fgmnwLv53r8kiUiOi4VdcDQEwXSHyTWXOAH9xhoaHYdyCiXicnFcREXHaO6nP670xc3tTZuxsbE5vn/SntfX+UWf5wBDMvX48kKXPAOYb67R5zGW1kZe84ybAfKMW7rzQURUdK7OJ6kKJR6FRMCvCOUZwHxi1UecSalyJKXKc3U8pAkJfzOpnpOFYzZr6iZ9m3Fxcbl6/6TRR84xlzwDMNeYa54B9Hecpc8zuflMlvYaudnrv4OHo40VbC0skKJSISYmNlfnleRUBeKSUphnTFh+Y30WHo1Srs55yjNJSUlQKpUAAEHQ3xBCaQS8mRw5Pj4+T3kmDfOMcUgSIBkxhLRD4fnz51o9MW1s8nfODAkJgYeH9pjNlpaWcHV1zXYoq88//xzlypVDqVKlcP36dXzzzTe4e/cu9u3bl6948sOkCqIhISHw9NS+3MHT0xNxcXFITk6GnV3GS2bnzp2L2bNnZ1geHh4OuVxusFj1QRRFTRFGJstf8khOToYoipqTbXYE4c1YEoY4J6hUb+4nxMUhMjwsx21SkpJx8thRPLyrhw8QACSFHIKVNQw3akb+mUucgH5jffLwAWpXq5yr40Iuf/PFQMx7bs+RmO4DgyRJOb5/lEolRFFETEwMwsJyjj/H/evxHGBIhWncO13yDGC+uUafx1hsbGyu84x2DPnabeZtpssz8tSUXJ1PkpMScf/mbWySluV7/0X1/G1I+ogzNiYa8bGxuToe0iQmxKeLQf+JRkz3zUcQhDy9f1QqFVQqFSIiImBrq9ul/OaSZwDmGnPNM4D+jrOoqCjNcZ+b94qFhQVUKpVBPiNKkgTx9TlBksRcnVcS4uMQHhqCTSuZZ0xVfmO9fS0Qxd9pnKc8o3V5sg7Fypy9aVMmk+nwOU1EXFyczt9tmGdMn5OTU64uTZ8yZQrmzZuX7TpBQUE6x5F+jNHatWujZMmSaNeuHR4+fIiKFStms6XhmFRBVBdTp07FhAkTNI/j4uLg7e0Nd3d3uLi4FFxguSCKIgRBgLu7e75PHnZ2dpDJZLC0zPmf1MvLS3P/8SMFGjbW76+qjx8pNPcrVqmKEu45z/hoa2+Hth27oOV77fO9f80vf07OJv0rpbnECeg31tBXz2FXzCFXx0UZ77Ka+48fKlGtun4vMXn86M0HhlKlSuX4/rG0tIRMJoOLi0uGX8V0oc9zgCFZW+v/kmdzY665Rp/HmLOzc67zTMmSJTX3n6TLCfqSPs+ULVc+V+cTO/tiqNuoCQYMH53v/RfV87ch6SNO/3On4ejsnKvjIU3FSm8mYglVRGSzpm7ClG9m/PXy8srV+yeNhYUFLCws4ObmpnPOMZc8AzDXmGueAfR3nKUd8xYWFrl6r3h4eCA4OBjPYhMgSZJWp4/8Ck5Ihlyl/kHDq2SpXJ1XHByd4O7hxTxjwvIba2JCPOzs7POUZyRJgpOTE+Li4iBKkXneZ07St+np6ZmnPAOoi6hOTk7MM4SJEydi4MCB2a5ToUIFeHl5ZSigK5VKREVFadWactK0aVMAwIMHD1gQBdQfFENDQ7WWhYaGwsnJKcteOzY2Npl2+ZXJZCb/hgTUvQX0EasgCJpben/++Se6dOmCr776CkuWLAEANGzYUPN84NVUfNxbf133JUnCtQD1r9guLi6oVKkyBEGAJEl4p3FDPHv6BNdu3clwwpXJLOBc3BUeXqX0EIOIJFtb2Lu4mnRSNpc4Af3GamtrBwuZDJaWVlrLjx/7Ex++3x0jRo7GgsW/AAAaNnoz22/A1VR07qa/8UVE8c2xWrp0ac3JW5Ik1KtXD0+ePMH9+/e1jtW095c+zy/6OgcYkinHlle65BnAvHONvo6xtO1zk2dq1aoFa2tryOVyBFzRf8+mwIA3vS0aNGwES0srJCcno3qVirCzs0PA9VsZetTJZDLYOzgWqTwDmE+s+ojTwtISFhaWWvkls9ySXv0GjTSfUx7Ln+oafpYey59p7jds2FDr/ZNdvgH0l3PMIc8AzDXmnGcA/Rxn6fNM+vdKZnkGABo0aIAjR44gJlWOp7EJKO+iv2EvrodFvdlPw4a5yzMWFrC0tmKeMWH5jdW+WDEIufwek169+vVx5vRpSIiDKMVDJujvWBWll5r7DRo0yPA5LadcAzDPGIskCZCM2I9ayuMs8+7u7nB3d89xvWbNmiEmJgZXrlzR1Jb++ecfiKKoKXLmRmBgIADtjhTGZlJHbLNmzeDn56e17MSJE2jWrFkBRWTeJEnC119/DZlMhq+//hoA8Ouvv2LSpEmadY78kQRRj9eZ+F9KRUiw+lrGEm5umhOyIAgYO34C4uLi8NMP3+ttf1Q4SJKEb6d8A5lMhrHj1b0jli9dgjmzZ2nWOXwwUacxcbLy76kUxMWqf/lP3/NCEAR8/fXXiIuLw3fffae3/ZFpYJ7Rr6zyTOPGjTWXeN6/p8CdIP0VReVyCceOJGsez5j2PwDqKyWGjxiJZ0+fYtWK3M0ETYVbZrnlbQ4ODqhatRoA4In8OcL12Es0VZQjMPmW5nH9+vUhCALKly8PgPmmMGOu0Z/M8kxcXByGDRuGkydPatb78+ELve736MPnmvtHDh8CwDxDmctNrmnQ8M3kM0rVrUzX0VX69qZNm6b5QYG5hgylevXq6NSpE4YMGYJLly7h3LlzGD16ND799FOUKqX+Qejly5eoVq0aLl26BAB4+PAh5syZgytXruDJkyf4448/0L9/f7Rq1apAJ+E2aEE0ISEBgYGBmsrv48ePERgYiGfP1L+WT506Ff3799esP3z4cDx69AiTJ0/GnTt3sGLFCvz+++8YP368IcMstE6cOIHbt2/j3XffRdmy6kuP/f394ebmpvlF8/EjJfz+Ss6umTxZuypOc//hgwdYufzNGDrv9/wA9vb22Lplk2YQdCIA8Pv7BIKCbqNFy5bwfn2sXr16BR4e7preErdvKnDhXGp2zeRJ+mP11q1bWLp0qebxhx9+CHt7e2zcuJHHqoljnilYWeUZFxcXuLq+maH3t1X6ex8dOpCIsNA3g4hWrVpdc//Tz/oAAFavWlHgA+dTwcsst2TGydkZgHoM0b/jz+ht/+cTLyNJTAKg/vKQ9uW0SpUqmnWYb8wDc03BySzP9OvXD2vWrEHp0m9m19504z5UejrvRyan4I97b3qMN2rURHOfeYbelptc88mnn2nuK1T/6a2ThyjFQimqx3S0t7fH6NGjmWtMmCgJRr8ZyrZt21CtWjW0a9cOXbp0QcuWLbFmzRrN8wqFAnfv3kVSkvpzkLW1Nf7++2906NAB1apVw8SJE/HRRx/h0KFDBosxNwxaEPX390f9+vVRv359AMCECRNQv359zJgxAwDUY748e3MpkY+PD44cOYITJ06gbt26WLhwIdatW4eOHTsaMsxCa//+/QCAdu3aaZZt2bIFp0+f1rpsfsbUKKSm5v+kfPliCvbsTAQAFCumvgx/3k8/QvV6lqVixYqhUZMmSExMxD9+f+d7f1R4HDp4EADQpu17mmW/bdiE43+fRJ269TTL/jcpEgpF/o/V0yeTcfig+uRcvHhxAMD333+vdaw2bdoUiYmJOHHiRL73R4bDPFOwssszrVu31izb+Fs8bt3Mfy/RhAQRs6dFay0bPWas5n55Hx+UL++DZ0+f4lpgQL73R+Yts9zytvDwcAQGXNU8Ph5/EsGK0CzXz60EVSL2xPyhefzrr79qxtuaOHGiZjnzjXlgrik4b+eZ69ev448//oCVlRX+++8/tG+vnn/gaWwC1gXe08s+5/wbiFTVm2LnuAlv3rPMM/S23OSaunXroek77wAARCkUCtVlvew7VXEEgPpYnThxImbNmsVcQ0bh6uqK7du3Iz4+HrGxsVi/fj0cHN4MxVi+fHlIkoQ2bdoAALy9vXH69GlERkYiJSUF9+/fx/z583M12ZMhGbQg2qZNG0iSlOG2ceNGAMDGjRtx6tSpDNsEBAQgNTUVDx8+zHFQV8ra1avqD/g1atTI8FxaV2YACLqtwPczozOskxdxcSJGDo7QzCY8aswYAEB4WBhevXwzrkmN6jUBAFf89ZMEqHAIDFQfq9WqZzxWvb3LvFnvqhwLforJ176io1T4atibSyLThpAICwvDy3THas2a6mP18mUeq6aMeaZgZZdn0g9cr1AAIweHIzlZ9940kiTh20lRePb0zWRonbt0RbXq1bXWq/46liv+/jrviwqH7HJLmqDbtyCXy2FhYQEAkEsKrI7YBIWk+2RgkiRhU9QuRKvUs9l27twZ//33H5KSklCnTp0MRTHmG9PHXFNw3s4zaY8rVqwIV1dXzJgxQzNE10/nr+FuZEy+9nfs4QvsCnqkedz2vXbMM5St3OQaAPh2+kzN/VTlEYhi/oZoUagCoRRvAABKlCiB0aNHY+XKlcw1RHlgUmOIkn5FR6uLnM6vLwVLL21A4bT/L10ci6WLYnXaT1yciE8/DMX9e+ovD40aN8bYcW/GTwkJCdbcd3RSDyAdHZW/AiwVLjHRMQCQ6S9EMpn6S2rah91538dgnY6X38ZEq/Bxj1BNQaV169YYNWqU5vng4DfHalosUVFRIKLMZZdn0qQ9dy1Ajn6fhCEpKe9FUUmS8MPsGGxaH6+1fPzErzOs6/j6vRsdzfduUfd2bvnr+DFMmjhBc/O/fEkz8Y2Liwt8fCoAAO6lPsKy8N90KopKkoSt0XtwLvGSZt9Lly7F8uXq8QbTxkBMj/mGKGtv55mQkBAA0PREatmyJbp16wYASFGp8NmBk3gQrdvnxLPPQzDi2DmtZVO/nZZhPeYZSi83uQYA2vm2x4BBX7zeSo4kxXqIom7HkFJ1BymKPZrHy5Ytg7OzM5YtUw9Xx1xjoiRAMuIN+pt+o9BiQbQQS5soJrtxQho1ejPA8/SpURg6KAzRUaos13+b/6UU+LZ8hfP/pgAAnJ1dsPa3jUhOfjMuqZfXm1nD4uPUX2Zdirvkeh9U+Dm7qD/kxsfHZ7lOnbp1Nfe/HhuJMSPCEReX+8LK+bMpaNv8Fa5cVo9D6u7ujg0bNiAxMVGzTvoZ7tLeN2mX1BNRRrnJM61atYKjo/rHsL//SkaH1sG4fi334wGHharQ/9MwLJgbo7W8cZMmaNHy3Qzrx7+OxcWF792i7u3ccvnSRaxYtkRzCwoKgqenJwAgMTER6zdthu3rGcAvJwVidvACvJC/yvX+opUxWBC2An/GqSfTEQQB69atw8mTJxEaGooyZcrg008/zbAd8w1R1t7OM15eXgDU47qm+fDDDzX3gxOS0W3XcewOepzrcRqVoohfL93C5wdOIUX55ntQo8aNmWcoR7nJNWl+mr8AriVKAAAkKRpJ8hVQqG7kel+SpESq4gSSFVsAqI/VQYMGoXfv3tiyZQtzDVEesSBaiDVo0AAAcPv27SzXqV69Or7//s2s779vT8Q79V9i0fwYhIdlXRi96p+KEYPD0aF1MO7dfdODYuTor+BVqhQuX7oIAHBzd0epdAOeBwWpY6lXv4FufxQVSvXqqY+HO0FZH6sVK1XG8HS9OTevT8A79V5gycIYREZkfqxKkoSLF1IwZEAYuvoG4/GjN5faTp8+HT4+Pvjvv/8AqAuk6QfnT3vfpL2PiCij3OQZV1dXHDlyRFMUvXldjveav8K4kRG4cT3rwuirl0rMnRONd+q/wKEDSZrldq8LVlnN5Hrn9RePeq/H+qOi6+3c8u30mUhMVWpu/foPQPUaNWFtbY2UlBQoVSJWrl4LKysrAMAj+VP879WP2BC5I9vCaIQyCr9HH8SkV98hIFn9xVYQBKxduxYff/wxFi1aBAAYN26cpu30mG+IsvZ2nkkbx/XBgweanm4BAeqxPNN6kcamKjDmrwv4ZN8/OP7oRZaTLSUrldh1+xE67jiGny5cg+L1epaW6vfpV+MynwSLeYbSy02uSWNlbYUuXbpqHktIRIpiO5LkG6BU3YUkZX6sSpIccuUlJMmXQq76B2njhvbu3VszkQ1zDVHeWRZ0AGQ4PXv2xOrVq+Hn54dp09SXe6xbtw5nz57FxYvqguXZs2cBAL169cJff/2F2NhYhIao8N30aPwwKxrVa1qjbn1ruLtbQKUCHj1U4FpAKl481y5AlS5TBi9fvMDaNatw9+4dnD51EgAw+ZupmnG5kpKS4H/5Euzs7NDOt72xXgYyA9169MBv69bg1Ml/8M3UbwEAG9f/hvPnz+HyJfVlJv6XL6FFy3fR/f2eOOn3NxISEvDqpQoz/heN72ZEo0Yt9bHq5mYBhULCwwdKXAtIxauX2sdq+fLl8eTJE3z//fe4evUqjhw5AgD49ttvtY7Vixcvws7ODh06dDDiK0FkXvKSZwYNGoSTJ0/ixo0bUCrVEy1t/C0eZctZol4Da5QrbwULCyA0VIVrV1NxJ0iB9N9h3dzc0LV7D2zasB7lypdH85atIEkiBOHNb7tPnzzB48ePUKp0aTRo+OYKCCqaMsstb3N3d8cXXw7BqpXLMahfHzRv2RIWFhZQKNQ/9iqhxIn40zgRfxoelm7wsS4Ld8sSECAgWhWLx/JneKUIgZTuujRPT0/89ttv6Nq1K44cOYKgoCA4Oztj6NChGfbPfEOUvbfzTN26ddGtWzccPnwYrVu3Rt26dbFr1y7IZDIcPHgQq1evxo4dOwAAZ1+E4uyLULjYWKOOhyuqlHCGraUFEuQK3AqPwc3wKCSn6xEqk8nQpu17+Mfvb5Qr74NmzVtCoVTAyvJNcYl5ht6Wm1wDAHv3/I79+/bh9q2bAAAbGxukpqp/GFaJ95As3oMAe8hkpSETPCDAEhJSoBKDIUrBALSHcZkxYwZmzJgBCwsL5hozIUqACMPN/J7Z/ih7LIgWYh07dkS1atVw5swZPHv2DGXLlsXZs2exadMmzToPHz7Ew4cP0bp1a9y6dQuDBw/GsWPHAAAqlbonz83rWc8M7OLignETv8a48RPx6y+LsH7tWvxxYD+8vEpi2oxZGDn6K826Bw/sR2JiIr4cPJRd9UlL+w4dUbVqNZz99188f/YM3mXL4vz5c9i2ZbNmnWdPn+LZ06d4t1UrXLoSiFHDh+LkyX8AAEolcD1QjuuBWR+rrq6umDZtGr766issWLAAq1evxtatW1GmTBl8/fXXGPN6IjAA2LdvHxITEzFs2DAeq0TZyGueuXz5MiZPnowlS5Zonn/2VKk1UdLbZDIZPv6kN36a9zPav9cGADDqq7FQqVSIiIyCWwlXTVF0545tAIBhI0ZqfuCgoiuz3JKZSVP/B4VSicN/HMT+vXtQoUJFjPxqDJ4+eYxVK5YjKUndQzlMGYEwZdaTYMhkMvTq1QsrVqyAq6srAGDhwoUAgKFDh2p6SafHfEOUvczyzJYtWzBp0iQcOHAA9+7dQ+3atfHdd9+hdevWaN26NVq1aoURI0Zo2ohJlePM8xCceR6S5X4aNGyEBYt+wZAvBgIAxowbB0tLS0RERMLNrYSmKMo8Q2/LTa5JTknG5UuXsH/vm3E/04qhFhYWUKnUhXkJSVCJ96HC/Sz35+XlhU2bNmkVNplriHTDS+YLMUEQsHDhQoiiiAULFgBQz4KZ2SyZp06dQunSpfHnn3/i0aNH+Oabb1CrVi3NpEvpOTo6olWbNlixag3uP36GSZOnwMrKCl9P+ga37z1AbGIKLgVcw4BBXyD+9fg+kiThl0UL4ejoiG9nzMzQJhVtgiDgx3nzIYoifl2svtxj1Zp1ePwiGA+ePkd0QpLmspNjJ/5BufLlcfjYX7h2Mwhjxo1H9eo1Mj1WnZ2d4evri02bNuHly5cYP348LC0tMWXKFDx+/BgKhQKPHz/G5MmTNZM2SZKEn3/+GY6Ojpg9e7ZRXwcic5PXPGNjY4Nff/0VcXFxWLFiBd59910UK1YsQ7uWlpaoU7cepvzvW9y5/wgbNm2Bp5cXrt++g8RUJUaN/gqurq5QKBSIiIyCJIlITk7GyhXLUbZcOYwaPSZDm1T0ZJZb3hYdEwNRpcIPP83Do2cvEJOQjKvXb2LwkKGY88NcPHjyHAsW/YJ3mjXTDNeQnpWVFRo0aIDZs2fjxYsX2Llzp6YYCgD//PMPJEnC/PnzM2zLfEOUs8zyjIuLC9auXYvw8HCkpqbi6tWrmomVAGD48OFQqVQ4evQoPvzwQ81YwW8rX94HffsPwKl/z+HMuQto+s47mjwzfMQouLmVgEwmQ0REJBRKBfMMZSqnXJOckozo6GhMmjIVCSkKrcvpE1OViIpLxO979qNb9x5wc3fPdB8VK1bE4MGD4e/vj+Dg4Ay9PJlriHTDHqKFSGYDh3fp0iXXA4qn8fHxwU8//YSffvoJSUlJCAoKQkh4JBLi41C5SlVUqVot0+JTes6vZ7FLeD24tJOjIy76X8113EWGKvcTWBUmmf2bd+rcBYmp6l5ioigiIjISKpUSJUqUgLWVdabtVKpcGXPn/Yy5835GYmIi7t4JQmRUNBTJCahevTqqV6+e47H6NkEQcO3atVzHTVTUSJKk+QEB0C3PODo6YsSIERgxYgRUKhXu3buH5y9fIiIiEt5ly6FmrdqwtbXNtg1bGxu4uroiKipK01P0yfOsx3nk+7doePvfOX1ueVt0TAySk5Lg6OQEx9czVr/N2dkZI0aNxohRo6FUKnH//j0EB4cgIS4aFX18UKdOHdjY2OgUa3b5JrO/haioePvY1yXPyGQydO7cGZ07d4YkSXj16hXu3ruH4JBQeHqVRNVq1VHi9eQ2WbGQWcDNrQQiIiI1PUWZZyin7zHppRVDbWxs4FrcVevzUxpLS0t07d4dXbt3hyRJePniBR49foSo8DCUKVMatWvVylevTuYa0yFJxp34nf+0OWNBtJBwdXXF06dPIYpingtA2bG3t0fDhg0Rl5SCyPAwlHD3yHX7mRVFM/Pk8WNIAJyL2EyNFpGRcN26ASljJxd0KEbl4OyS7bGa22Lo24oVK4YGDRshJVWOhOgIeHl56vW9AACPHz8GAK3eP0RFRdqH8cePH6NChQp6a9fCwgLVq1dH2fI+CA0Lg5u7h2ZCi5xkVhRNP6ZompSUFAS/CkbDitX1FjeZHmeX4njy+jydk9wUQ99maWmJ6tVrwKdiJcRFhaOUlxesrXOXo3Tx6NEjAMw5VHQ4OTlBJpPhyZMnaNWqld7aFQQBpUuXRgk3N7wKCUUJN3dY5fLzZWZFUassctTTJ4/h7ML3a2Hm5OyCF4/uQqlUwtIy+1JKboqhbxMEAWW8veHh5YnoiHCUKukFGwPmmeDgYCQnJzPPUJHFgmgh0blzZ3z99deYNGkS2rZtm+MJOq+SUuSIi42Bk7MLZHkcLycpKQkpKSmwtbOD/VuXmyUlJWHzpo1w8yyJKjVq6TNkk+e2ZhnES+cgdv0QispVCjoco2nVriNWzJ+DWTOmoXnzFlrHkyRJiIuLg0pUwcnJCZYWeT+OFQolkhPiULy4S6YzLOoqPj4ev/32G3x8fFCjRg29tUtkLurUqQNvb2+MGTMGgwYNyvRS9/xISZUjOiYGzjrkGYVCgfj4eFhYWsLZyRFIN2C9Qq7A0aOHER0bhyYtW+s1ZjItLdt1xM6dO2FpaYmKlSpl+aNYQmIi5KmpsLO3h10OPZEzo1AqkRQfC9fixfWaZ9JIkoTg4GCsXbsWrVu3hkMuC7ZE5s7KygqdOnXCsmXLkJKSAm9v71wVkXJLrlAgMioaTs7OsMjjZ0xREhEXGw9REuHs5KQ1fqgoirh16yaOHv0TnwzKOKENFR7N27TDzydPYMqkr/Feu3awzCIHyBVyJMQnwMrKKtMxPXOiVCmREBuLEq6GyTOA+nv4li1b4OXlhYYNGxpkH6RNkgRIRpxUSZKMty9zxYJoIdGqVSv88MMP+Pnnn3H69Gm9t5+U+qYgmtcPEACQmJSElORk2NnbaxVFZTIZvH0qYsqsuXBydtFjxKbNIjICrhvXIbJ6NXgsXYCXS9YUdEhG07xNOygUcmxftxLH/zqhWS5KIuLi4iGqXhdDdSzqyxVKJCeov6jq84cBmUyGGjVqYNGiRfxySkWSi4sLVq9ejYkTJ2LevHkQ008BrwepcgWio6Ph5KJbnpErFEh4XRR1cnTU+hJdvIQ7Jn03D5Wr8ceMwuzzL4dDlET8vmcPUpKTM10nITERqSkpsC9WTKdiKKD+4S0xXv1FVd8/QKextrZG27ZtOd4bFTkzZ86EhYUFNm/ejJSUFL22LVcoEBUVrf4+o8N7VxRFxMbFQZKkDEVR+2LF8GHfL9Cj1+f6DJlMTJ0GjTFu2hys+/Vn/HvuXKbrpMrlSEhQF0OdHB0AHQpgSqUS8XGxcDNgnpHJZKhQoQKWLVsG9yzGLiUq7FgQLUTat2+P9u3bQy6X6/2L6j+B97Bt3Ur0HTwC7p4ldWrj6N//4NL5c2jRqjXat1FfBmNpZVUkZ2h0W7UUFkmJAADnA3sQPmYS5JUqF3BUxtO6fWe0bt8ZitfHanxiIjZt3Ya4mFj07dsHZUuX0rnt24+e4fy+jZg0YRxKly6tt5itiuixSpSeh4cHtmzZApVKBYVCode2A+4+wpIlS9F/iO55JujBI+z9/Xd4liqNvp9/AjtrG8hkMlgZ8HIzMh2CIKDfkFHoO3gk5K9n700jSRL2H/kT169ewXvtO6BVs6Y67+fu42fw+30Dvp00Xq95Jj1ra2u9D/tCZA4sLCwwc+ZMzJgxQzMLt77ce/wUPy34BZ99ORwlS+r2WTMmPh4bN29BanIK+vfri5KeHhAEAVbW1nrtzUqmq2HT5mi4fb/me0x6124H4eD+/SjnUwGf9/pI596dT1+8xIFNazD9mwko611GH2FnYGlpabBiK5G54DugEDLEeFbWNjawsLSEtY0NbHTsUfFBty6wsrLGf+f+hZW1NTq+10a/QZoJi8gIlNjwpkeoIIrw+HU+XixdW4BRFQwra2vEJyRi287fkZiQiC++/ALlyuTvy6W1jQ0sLS1ha2ub46QsRKQbCwsLvf9AYKOHPFOvVg1Y2/TD7zt2YOfu/RjQ9zODjr1FpkkQBK1jSJIk7PnjCG5dv4aOXbuhTYtm+WqfeYbI8ARB0Pv7y8bGNt95xtPWFsOGDMG6DRuxbcdODBo4AKW8Mp/Fngq3t39sDbx5G4cPHULFKlXR79NP8nWpe9p3b+aZwkWUAP12W8t5f5Q9/vRMRtWtoy/eafEu/j11Esf/OVXQ4RSI9L1D07js3w3rB/cLKKKCE5+QiPWbNiM2JgYDBvTPdzGUiKhG5Yr45LPPEPLyBTZt3YFkPfcwIvOSVgy9duUyfDt2yncxlIjIxckRgwcNhI2dHTZs3IRXIaEFHRIVsMCbt7Fv7x6Ur1Ax38VQIjIeFkTJ6IpyUfTt3qFp0nqJFiUshhKRobAoSgCLoURkOCyKUhoWQ4nMFwuiVCCKalE0s96haYpSL1EWQ4nI0FgULdpYDCUiQ2NRlFgMpbyQJOPfKHssiFKBKWpF0ax6h6YpKr1EWQwlImNhUbRoYjGUiIyFRdGii8VQIvPHgigVqKJUFM2ud2iawt5LlMVQIjI2FkWLFhZDicjYWBQtelgMJV2IkmD0G2WPBVEqcEWhKJpT79A0hbmXKIuhRFRQWBQtGlgMJaKCwqJo0cFiKFHhwYIomYTCXhTNTe/QNIWxlyiLoURU0FgULdxYDCWigsaiaOHHYihR4cKCKJmMwloUlSXEw+nvY5CXKau5qZyctdZJ/5yiVBkU37W1gKLVPxZDichUsChaOLEYSkSmgkXRwovFUMovTqpkeiwLOgCi9Lp19AUA/HvqJACg43ttCjAa/RAdHHH/5EWtZTZ3bqOSb3PN47uXbho7LKNgMZSITE1aUfT3HTuwaesODOj7GexsbAo6LNIRi6FEZGrSiqLrNmzEho2bMGjgAJTy8izosCgfWAwlKpzYQ5RMTmHtKVrUsBhKRKaKPUULBxZDichUsado4cFiKFHhxYIomSQWRc0bi6FEZOpYFDVvLIYSkaljUdT8sRhK+sRL5k0PC6JkslgUNU8shhKRuWBR1DyxGEpE5oJFUfPFYihR4ceCKJk0FkXNC4uhRGRuWBQ1LyyGEpG5YVHU/LAYSlQ0sCBKJo9FUfPAYigRmSsWRc0Di6FEZK5YFDUfLIaSoYiSYPQbZY8FUTILLIqaNhZDicjcsShq2lgMJSJzx6Ko6WMxlKhoYUGUzAaLoqaJxVAiKixYFDVNLIYSUWHBoqjpYjGUDE0qgBtljwVRMissipoWFkOJqLBhUdS0sBhKRIUNi6Kmh8VQoqKJBVEyOyyKmgYWQ4mosGJR1DSwGEpEhRWLoqaDxVCioosFUTJLLIoWLBZDiaiwY1G0YLEYSkSFHYuiBY/FUDImUTL+jbLHgiiZLRZFCwaLoURUVLAoWjBYDCWiooJF0YLDYigRGaUgunz5cpQvXx62trZo2rQpLl26lOW6GzduhCAIWjdbW1tjhElmiEVR44pPZDGUTBPzDBkKi6LGxWIomSrmGTIUFkWNL/DmHRZDicjwBdFdu3ZhwoQJmDlzJq5evYq6deuiY8eOCAsLy3IbJycnBAcHa25Pnz41dJhkxlgUNY6kpCRs2LiFxVAyOcwzZGgsihqHJEnY+8dRFkPJ5DDPkKFlLIpmfWxR/gTdu4f9e3ezGEpGJ0Ew+o2yZ/CC6KJFizBkyBAMGjQINWrUwKpVq2Bvb4/169dnuY0gCPDy8tLcPD09DR0mmTntouiZgg6n0IlPTMSO33cjNjaWxVAyOcwzZAzpi6Kbt+5EqpxFUX2SJAl/nvibxVAyScwzZAzpi6IbN21CaHh4QYdU6ATevIMjhw+zGEpEAABLQzYul8tx5coVTJ06VbNMJpPB19cXFy5cyHK7hIQElCtXDqIookGDBvjxxx9Rs2bNTNdNTU1FarqeGnFxcQAAURQhiqKe/hLDEEURkiSZfJwAIEkiJCnt/6YZb9cO70GSJJw99Q/E5Dh06tKloEPKkiRJEGUySIKg/r+JvqaAuhi6YcMWJMTFot+gIShbuqRJxytJgASeA/TJlOMzRp4BzDfXmMsxBqjPi5Akk84z1Sv5oNenn2L3jh3Ys3c/+g76AnYmfhms+vWUTPY1BV73DD14BDcCA/Bel/fRunlTE48XACSTf/8D5nUOMNUYmWdyZi7HmZh2XjHhc6KzYzF8ObA/flu/Eb/v+h19vxyK0iVNu5huDnkGUBdD9+/djfJly6FP749haWlhsjFLkgSJeUbvzCFGMi6DFkQjIiKgUqky/CLq6emJO3fuZLpN1apVsX79etSpUwexsbFYsGABmjdvjlu3bqFMmTIZ1p87dy5mz56dYXl4eDjkcrl+/hADEUURsbGxkCQJMplpz2+VEh8DSSlHSnwMkkz4y997TetDSknApX9PAgBat2hewBFlTqFMRViDBoitVAkSgKSYqIIOKVNJSUnY8ftuJMTF4oNuXeDhaGOysaYRk2KgSE1BRESEyf/qay7ngNjY2IIOIUvGyDOA+eYacznGACA+Otos8kx5dxd80L0TDu3dja0bNuDjj3rCxtqmoMPKhgh5YgIEQYIpzqWZ1jP0RmAAWjV/B01rVTb5PKNKjIFSnso8o2emmmuYZ3JmLsdZTFQkRIUcKfHRSIox3TxjDaDPRz2wa9sWbP1tDT7p/Qk83d0LOqxsmHaeAdSXyR85fBjly5ZHt/atoUyKgzLJNGMFAEV8DESFHJGRkbC1sS7ocLJlLu9/oODzjCQBxizJSpxlPkcGLYjqolmzZmjW7M1lUs2bN0f16tWxevVqzJkzJ8P6U6dOxYQJEzSP4+Li4O3tDXd3d7i4uBgjZJ2JoghBEODu7m7yJw/bV9EQLK1h6+gCexfXgg4nW916dAckCecv+kNm54SO77Uq6JAysAkJhcfVqxAAuF+9iggTfE3jExOxff/viE2So9+gIXB3sIWdc3EIgmkfq7LIRFjZ2MLNzQ0eHh4FHU62zOUcYG1t2h/E8iqveQYw31xjLscYALyMTjCbPFO7ngskAAcOHcPvh/5C/76fws7GNIui6p47gkmevzVjht4Mwntd3keTmlVMMs63WUQlwtLahnlGzwpTrilKeQYwn+MsOjEZMitr2DoWN/k8Y+fsgk8+74ft+/7Ajn1/YOCAASjlZZrnG1POM4C6Z+gff55A+co10Kf3x1AkxptsrGms4tXHaokSJZhn9Kgw5RnSD4MWRN3c3GBhYYHQUO2Z8kJDQ+Hl5ZWrNqysrFC/fn08ePAg0+dtbGxgk8mXEJlMZvJvSEA9vpA5xCoIMghC2v9NO1YAaNemFQQ7R5w9fRKCAHR8r01Bh6RFEATIRBGCJKn/b2KvaXxCIjZs2qoZM7Rs6ZJIiokyi39/QQAE8BygT6YcmzHyDGDeucYcjjFAHScEwSzOMwBQ2ccHvT77DLt37sTmbbswoO9nJlsUFUzwdZUkCXsPHcW1q/7w7dQZrZs3Nas8A5jH+wown3OAqcbHPJM75nCcydLOLa/PiabO2ckRX34xEL9t3IyNmzZj0MABKOVlmpfPm2KeAYDAm7exf98elK9YCf0+/QSWlhZQJplmrOkJggCBeUbvCjo+6fXNmPuj7Bn0iLC2tkbDhg3h5+enWSaKIvz8/LR+Nc2OSqXCjRs3ULJkSUOFSYVUt47tOPu8DuITErF+02bOJk9mgXmGClKNyhU4+7wOJEnCnj+OcAIlMgvMM1SQMs4+H5rzRgRAXQzdt3cPJ1AioiwZvEQ+YcIErF27Fps2bUJQUBBGjBiBxMREDBo0CADQv39/rUHKv/vuO/z111949OgRrl69ir59++Lp06cYPHiwoUOlQkh79vlTBR2OyWMxlMwR8wwVpPSzz7MomjMWQ8kcMc9QQWJRNO9YDCWi3DD4GKK9e/dGeHg4ZsyYgZCQENSrVw/Hjh3TDEz+7Nkzra7L0dHRGDJkCEJCQlC8eHE0bNgQ58+fR40aNQwdKhVS3Tr6AgD+PaWeaMnULp83FSyGkrlinqGCllYU/X3HDmzausOkL58vSCyGkrlinqGCllYUXbdhIzZs3GTSl88XNBZDyVSJRp5USeQ18zkyyqRKo0ePxujRozN97tSpU1qPFy9ejMWLFxshKipKWBTNHouhZO6YZygnFlGRcDxxDKKTE1SOTlA5OavvOzlD5eQMWObvIxGLotljMZTMHfMMFTQWRXPGYigR5YXJzTJPZCgsimaOxVAiKgpUxV3hfHg/nPz+yvR50c4eKmdn7WKpoxNULsUR8u1siI5OOe6DRdHMsRhKRKQfLIpmjcVQIsor054GjEjPOKaoNhZDiajIEAS8nPcrVFkUNmXJSbAKCYbt/bsoduUSHE/+DZc/9kGWmJCrYmgajimqjcVQIiL94piiGbEYSuZAKoAbZY8FUSpyWBRVYzGUiIoaZanSCJ75Y67XF+3sEfK/2XneD4uiaiyGEhEZBouib7AYSkS6YkGUiqSiXhRlMZSIiqroz/ohvvV7uVo3bMxEKEuW0mk/Rb0oymIoEZFhsSjKYigR5Q8LolRkFdWiKIuhRFSkCQJe/rwUqmIOOa4aMTTzCVRyq6gWRVkMJSIyjqJcFGUxlMyNKBn/RtljQZSKtKJWFGUxlIgIUJTxRsj0OTmuV6uiJ+wCr+RrX0WtKMpiKBGRcRXFoiiLoUSkDyyIUpFXVIqiLIYSEb0R1XcQElq0ynG9Sl3aokblUpDFROu8r6JSFGUxlIioYBSloiiLoWSuOKmS6WFBlAiFvyjKYigR0VtkMrxYsBSinX2Gp1TOLlqPLRITULNGOXiP+hIQRZ12V9iLoiyGEhEVrKJQFGUxlIj0iQVRotcKa1GUxVAioswpyvkg5H+ztJZF9vsCt4Oe4e6ZjJfKu+zfjdplXOCyd5dO+yusRVEWQ4mITENhLoqyGEpE+saCKFE6ha0oymIoEVH2IgcNRWLT5gAAlZMzQidPAwDIK1XGjVdxeLZsXYZtyowbgfIDe8Pm/r0876+wFUVZDCUiMi2FsSjKYigVBmIB3Ch7LIgSvaWwFEVZDCUiygWZDC8WLoNoa4vQCd9AVcJN6+nYDz/BjRcxiO71WYZNK7/3Dqo2rQ0hKTFPuywsRVEWQ4mITFNhKoqyGEpEhsKCKFEmzL0oymIoEVHuyStUwvMlaxA1cGjmK8hkePHratwKegali4vWU9bPn6JWpZIoOeMbQMr98PXmXhRlMZSIyLQVhqIoi6FEZEgsiBJlwVyLoiyGEhHlXVy3npCsrbNdR3R2QdDtZ3hw5J8Mz7mtW4napZ3h+Nefud6nuRZFWQwlIjIP5lwUZTGUChvOMm96WBAlyoa5FUVZDCUiMryUOvXwZOMuvJozP8Nz5Qf2Ru1STrB68SxXbZlbUZTFUCIi82KORVEWQ4nIGFgQJcqBuRRFWQwlIjKuqIGDceN5NOLadcjwXLUmtVCxa1sIcnmO7ZhLUZTFUCIi82RORVEWQ6mw4qRKpocFUaJcMPWiKIuhREQFxMICT7fsQdC1Bxmesg+4glrl3eD+S8aepG8z9aIoi6FERObNHIqiLIYSkTGxIEqUS6ZaFGUxlIio4CndPXDjVRwe7T2a4Tmv+d+jdikn2P93Lts2TLUoymIoEVHhYMpFURZDicjYWBAlygNTK4qyGEpEZFoSm7XEjVdxCJ30bYbnKn7YGbVLOcEiIjzL7U2tKMpiKBFR4WKKRVEWQ6kokABIkhFvBf0HmwHLgg6AyJSpVCpsWbMcd24EIjkpUbM8Iioa107/ia2/FodrcZc8tytLSoSdnR0Uz5/Dys4OiQN76xCbiFchwVAqlChZ0guLpgVqnnMp4YYen/RBw6bN89wuERHlX9j4bxA+chwqfNAR9oFXtZ6rUaci4nw74emGHYCFBc78fQx/HzmI2OgozTqJycm46ncIf+9aj5JeXpDJBJ1jUSkVsLDU7ctlWEQkEuLjUNy1BPY/u4P9a38BANg7OKJ+k2bo1e8LCILusRERkeE9f/IIm1cvRURoCERRPbKgUqXCq1fBuHR8P0qWLAkba92LkPnJMwmJiQgLC4ednR1iHt3ENb8/AABWVtYoU74CBo+ZCAdHJ51jIyLKCguiRFkQRRHL5n+PgAtn0KNHDzg7u2g9n5ySAnlqKmxsbWBrY5untgWFApbN34FoYwNZaioUpb3ztL0kSUhITIQoqlCsWDFYWrx5K4uiiBs3ruPX76dj7LQ5LIoSERUQycYGD4+egtXzp6jWtLbWc05/H0Nt7+LY0O8LrLkZiBbNmqNVs6ZaxUWFUomkpERYyCxQzKEYBOhSeJQgiRIEmQDkcfuk5GQo5HLY2NrC1sZG67mwsFD8sXMLkpOSMGD4VzrERURExvDi6WP8MGU8PNxc0am9Lywt031vkEQkJCRCkiQ4OBSDhcxChz3onmcUCgWSkpNgaWkJe3t7rTyXkpKCP/88ih+mTsC0nxajmIOjDrEREWWNBVGiLLx68QyX/v0H06fNQNfu3TNdJzYuDokJCXBwdISTY+6TtCwlGTb37kBZrBgsExORXKd+rrcVRRERkZFQqZQoUaIErK2sM6yjVCox9qtROLR7OwuiREQFTOFdDjdexcHx+FGUH/Sp1nP7d29Hp169MOuHHyDZF8uwbUpqKqKiomBlZQW3Eq4QhLyOdiRBVKkgs7BAXr6oRsfEIDkpCY5OTnB0cMh0He+y5bBixQr06v8F7DOJnYiICt6JI3/A3sYaK1atgbOzc4bnVaIKERGREEURbm4lYJXnnp665ZnklGRER0fDxsYGrsVdM73aoHOXrujfvy8CLv+Hlm3b5zEuItNi7JnfOct8zjiGKFEWoiMjIEBA/QYNslzH2ckJxRwckBAfj7j4+Fy3rVKpEBkTg9CICCQkJeV6u9wUQwHA0tIStWvXQXQ249QREZFxxXfsghsvYxHx5XDNsghLS9SvVg12D+7B7tZ1CKJKaxtbGxu4urpCoVAgIjIKkpS7j7eSJCEuLg6hoaGIjo6GJOV+JKncFEMBoF79+hAEICo8LNdtExGRcUVHRqBixYqZFkMBwEJmATe3EpDJZIiIiIRCqch128nJyQgLC0N4eDgUijxsl4tiKABUrlIFDsWKIYrfaYjIANhDlCgHgiz73w2cndRj2iS8Lohm1VP05s0b2LZlMy5fuojr164hMfHNmKSly5RBgwYN0fa9dvj08z6ZfmDJbTE0jSyHuImIqAAIAoLnzEfI1Jmo0vYdSNGRkL3+IiioVLC7eR3K4q6Qe5dFWk+btKJoVFQUIiKjsuwpGh4ejm1bNuPM6VMICLiKsNA3E2U4Ozujbr16eKdZc/TrPxAVKlbMNLzcFkMB5hkiInMgSVKO5+u0omhERCQiIiKz7Ckql8tx8MB+HD1yGAFXruD+/Xua56ysrFCjZi00bNgIH3/yCVq1bpNpoTO3xdA0Mp0u4yciyhk/yRLpQXY9RS+cP4cO7dqgacP6WPLLYlw4f16rGAoAL1+8wKE/DmLCuDGo7FMWE8eNRXR0tOb5vBZDiYjItEn2xXD34g2k+mQsTFpGR8H+eiAsYt7kgex6igYHB2Po4C9QpUI5fDv1Gxw/9qdWMRQAYmNjceb0acz/aS5q16iKnt274saN61rr5KUYSkREhUt2PUXlcjl+nv8TqlQsj4H9+uD3nTu0iqGAejzQa4EBWP/bWnTp2B6N6tXB77t2al2hkNdiKFFhIhXAjbLHgiiRnrxdFE1OTsbkryeg/XttcO7sWa11S9k5o1kJH7zrXgn1XMqgmMWbAmdiYiJWrVyOxg3q4tjRIyyGEhEVYpKNDVLL+UDuXS7DczbPnsD+egBkqSkAMi+K7ti+DY3q1ca2LZshl8s12zpZ2qJBcW+8614JTUuUh4eN9tULJ/46jnebNcVPP34PpVLJYigREWVaFL1+/RpatWiGWdOnITzszRAp1jIL1HDywrvuFdHSvSIqFHODLN0YonfuBGFQ/7747JOPERoaymIoEZkcXjJPpEdpl8+/evkCw4d8icuXLmmeK1+sBHp5N0DHktXhYm2vtZ0oSbgfH4YDL67h8KsbSFYpEPzqFT764H1MnzUbAwZ9wWIoEVEhpizuCmXx4rB+9hSW6XqGAoDt3SCINjZIrVJNUxSNjIzE1xMnYNXyZZr1nCxt8X6ZOni/dF2UK5bxy2Z4SjyOBt/CnmdXEZwSB4VCgTmzZ+G/CxeweMlSuLl7sBhKRFTEpb98fu+ePRg9fCiSk5PVzwkC2npUxUfe9VC/uDcs37qcPUkpx9nwh9j9/AoCol8AAA79cRABAVexYfNWVK1WjcVQKrIkGHeiI/YQzRl7iBLpmZWlJYYNflMMtZFZYlzVttjV4kv0LtcwQzEUAGSCgKpOnvimRgfsaTkEzd0qaJ6bM2smDuzby2IoEVGhJ0BetjySa9aG9NZ4b7LUVNjduAar4JewtbHB6hXLtYqhnUrWwL53h2Fs1fdQ3qFEpl823W0dMcDnHex9dygGVWgGi9frnPjrOCaOGws7W1vD/nlERGQWLGQWCLp9EyOGDtYUQys5uGPTOwPwU72eaFyifIZiKADYW1qjQ8nqWNukL+bV7Ynir7/3vHj+HP37fIakhEQWQ4nIZLAgSqRnM6dPg/9ldTHUxcoOvzXti77lm8IikwkwMuNp64RfG/TCkIotNMumTPoa164FGiJcIiIyMZKFJZJr1UVK5SoZnrMKD8PZNSux8Od5mmVTanTE93V6wMXaLlftW8ssMapyayxv+CnsLNSTZhw/9ieW/LJYP38AERGZtcjISAzq3w/y1FQAQDvPqtjcbACqOXnluo12XtWwo/kXqOTgDgAIfvUKQ74cCFE0Zh85IqKssSBKpEfnzv6LlcuXAlD3DF3WqHeePjikEQQBwyq9iz7lGgMAlEolhg3+Umt8OCIiKtxEu2JIqlMf8lKlNcviEhIw+PvvNY/HVW2Lj73r69R+oxLlsKD+R5oR377/bhbuBAXlI2IiIioMJk0cr5mc750SPvihTg9Yy/I+2p6bjQNWNPoUJW3Vw4r9e+YM1qxaqddYicyFWAA3yh4LokR6NGPa/zQzKY6s3EqnYmh6o6u00fyqeuP6NezauSPfMRIRkXlRunkgqU49qBwcsXz3bjx//SW1iWs59CnXJF9tNy1RHn3Kq9tITU3F99/Nyme0RERkzq5fv4ZdO7YDABwtbTCzVpdML4/PLVebYphRq4vm8Q9zZmsuwyciKkgsiBLpybVrgfjvwgUAQAUHN3xarlG+27SSWWBKjQ6ax+vWrMp3m0REZI4EJJXzwcpDh14/AqbW6KSXsdiGV3oXJayLAVBPfhH86lW+2yQiIvO0bvVqzf1hld6Fu61jvttsXKI8OpWsAQCIiorC3j27890mEVF+GaUgunz5cpQvXx62trZo2rQpLqWbeTszu3fvRrVq1WBra4vatWvj6NGjxgiTKF+2bdmsuf+Jd4Ncjxmak7ouZVDF0QMA4H/5MoKCbuulXaLChHmGioLTp07i+fPnAIAW7hXhXay4Xtq1tbBCzzJ1AaiHaNn5umcQEb3BPENFgVwux66d6hxgb2GNbqVr663t3mUbau5v2bRBb+0SmQupAG6UPYMXRHft2oUJEyZg5syZuHr1KurWrYuOHTsiLCws0/XPnz+Pzz77DF9++SUCAgLQs2dP9OzZEzdv3jR0qET5cunif5r77b2q661dQRDQqWTNdPu5qLe2iQoD5hkqKtLnmQ56zDMA0PF1zx0AuHyJeYYoPeYZKipu376FhIQEAEBL94pwsLTRW9u1nEuhlJ0zAOCKvz+USqXe2iYi0oXBC6KLFi3CkCFDMGjQINSoUQOrVq2Cvb091q9fn+n6v/76Kzp16oRJkyahevXqmDNnDho0aIBly5YZOlQinSmVSty4fh0A4G1fHM65nOk3t2qkG4s08OpVvbZNZO6YZ6ioCAwI0Nyv6VxKr22XK+YKewtrAEBAAPMMUXrMM1RUpP+eUcO5pF7bFgQBNZzUbSYnJ+Pu3Tt6bZ+IKK/yPlVcHsjlcly5cgVTp07VLJPJZPD19cWF12Mtvu3ChQuYMGGC1rKOHTviwIEDma6fmpqK1NRUzeO4uDgAgCiKEEXTnldLFEVIkmTycQKAJImQpLT/m3a86hilfMepnhwpdx3OIyMjkJKSAgAoX8w1X/vNTHmHEpr7L148zzEeNfU6+vj30tdragySpP7LeQ7QH1OOzxh5BjDfXGMuxxjw+pz7+jxj6ucavZ4Tpdxf2KQ+/wMyCPC218/l8mksBBnKFiuOO3GhePH8OSRJzMX4pGl5Rh8517zyDCCZ/PsfMK9zgKnGyDyTM3M5zsS084sZnGv0e07Me54BgPLFSmSzpm7Sf0968fw5atasmc3a6ejptTCXXCNJEiTmGb0r6BiNPfO76f+LFDyDFkQjIiKgUqng6emptdzT0xN37mT+i1BISEim64eEhGS6/ty5czF79uwMy8PDwyGXy3WM3DhEUURsbCwkSYJMZtrzW6XEx0BSypESH4MkW9uCDicHIuSJCRAECfnpBJ0SHwdJFCGpVBBVqmzXVaT7AGsp6D4LY1as0rWpkMtzjAcAJFGCSqlAUkyUHiLQz2tqDGJSDBSpKYiIiICVlVVBh5MtczkHxMbGFnQIWTJGngHMN9eYyzEGAPHR0UUuzwCAKKogiWKuzutKhfryQkuZDDI9TKb0trRcI4oiVEpljseMpBIhiSKS42L0kGvMJ8+oEmOglKcyz+iZqeYa5pmcmctxFhMVCVEhR0p8NJJiik6eUaSmQLK1ylWeUaQ71qzyMbN8Vqxkb8oPuf5OI4lITUosUt9pFPExEBVyREZGwtbGuqDDyZa5vP8B080zVHAMWhA1hqlTp2r9AhsXFwdvb2+4u7vDxcWl4ALLBVFU975wd3c3+ZOH7atoCJbWsHV0gb2L/ntA6pP6lz8Bds7FIeRjYiNbRycIMhkECwvILLL/QGDv4KC5n6BMzWZN3cQrUzT3izk45BgPAAgyARaWVnr599LXa2oMsshEWNnYws3NDR4eHgUdTrbM5RxgbW3aH8SMwVxzjbkcYwDwMjqhyOUZAJDJLCDIZLk6r9vZq4djkYsqpKgUsLXQbzEu/nX+srW1hWUuCn2ChQyCTAY7p/z/m5lTnrGISoSltQ3zjJ4V9VxjrnkGMJ/jLDoxGTIra9g6Fi9SecbKxhaCIOYqzxRL950mXpGSzZq60ek7jSCDjX2xIvWdxipefayWKFGCeUaPCjrPSK//M+b+KHsGLYi6ubnBwsICoaGhWstDQ0Ph5eWV6TZeXl55Wt/GxgY2NhkHe5bJZCb/hgTUY6mYQ6yCIIMgpP3ftGMF1K9rfmNVXyqY/qZ2/Nif+PD97hgxcjQWLP4FAFC8uCvcPTwQHhaGu3GhkCQpF5ca5t7duDfviarVqgEQIEkS3mncEM+ePsG1W3cySZbC679DP/9e+nhNjUEQ1H+5ObyvAPM4B5hybMbIM4B55xpzOMaA1+dcMznPAHo8Jwq5yzMAULVadc3Eevfiw1DHpXT+9p1OslKOZ4lRmv3kLc8Ievk3M6c8A5jH+wown3OAqcbHPJM75nCcydLOLXo6Zxma/s6JGfMMkHmuUZ//1e7Fh8HXq1o+960t/XeaarnONdDrv5k55BpBECAwz+idqcdHxmfQI8La2hoNGzaEn5+fZpkoivDz80OzZs0y3aZZs2Za6wPAiRMnslyfyJgkScK3U76BTCbD2PHqX/Hj4uIwZtRIxMTEqB8rU/A4MVKv+w2MeaG536BBIwDqxDN2/ATExcXhpx++1+v+iMwF8wwVNlnlma9GjsC+vXs06wVGv8iqCZ1cj30F8XVPgvoNGgBgniECmGeocMoq1xzYu1ezTmD086w210mqSonbcephIxwdHVGylHpyQOYaIiooBi+RT5gwAWvXrsWmTZsQFBSEESNGIDExEYMGDQIA9O/fX2uQ8rFjx+LYsWNYuHAh7ty5g1mzZsHf3x+jR482dKhEOfL7+wSCgm6jRcuW8C5bFgAweNAArP9tLVyLv7mMY/+LQL3tM0WlwNFXNzWP7wTd1tx/v+cHsLe3x9YtmzSD7xMVNcwzVJhkl2e8vN7M+HvgRSBESX+XQh1Il7cUCoXmPvMMEfMMFT5Z5Zrff9+puaz4avRzPNFjJ48TIUFIfD00S3x8PFYuX6Z5jrmGigKxAG6UPYMXRHv37o0FCxZgxowZqFevHgIDA3Hs2DHNQOPPnj1DcHCwZv3mzZtj+/btWLNmDerWrYs9e/bgwIEDqFWrlqFDJcrRoYMHAQBt2r4HALhx4zqOHD4EKysrnPjnFGxfTwTyx8sbiEhN0Ms+D7y4hth0Y/gsW7YEqtcDkBcrVgyNmjRBYmIi/vH7Wy/7IzI3zDNUmGSXZ079ew6t2rQBADxLisbJ0Lt62eeTxEj8E3pP8/j4sT+ZZ4jSYZ6hwia7XDNl/JuxbDc9+k8v+1OKKmx9cklr2byffmSuIaICZZRBFEaPHo2nT58iNTUVFy9eRNOmTTXPnTp1Chs3btRav1evXrh79y5SU1Nx8+ZNdOnSxRhhEuUoMPAqAKBa9RrqxwEBAIAKFSqiYqVK+PTzPgCARGUqfrp9HFI+e++8TIrB8vuntZaFh4Xh1cuXmsc1qtcEAFzxv5yvfRGZM+YZKiyyyzOurq4YNWqMZt35QScQI0/O1/5Ukojvbh6FSnrTjyAiPJx5hugtzDNUmGSVayqWLo2x7X3hYG8PADj06gb+i3ic7/1tfPwfHiSEAwBq1a4DgN9piKjgcVRZojyIiY4BADg5OQEAQkPV4+Ckzcg467vv4eTkDAA4FXYfmx5f1HlfCYoUfHNtP5JV6ksX+w8cpHkuJORNLwRHJ0cAQHRUtM77IiIi05BTnunavTsaNlKPJR0pT8T/rh9Eqkqp074kScIvd/7B9Rj1F1KfChU0zzHPEBEVXulzjSCJiLx1AwDgYGcHV2dn/DzmzY9vM24c0ky6p4uz4Q+x9uE5AICFhQUWLv5V8xxzDRUlUgHcKHssiBLlgbOLutgZHx8PAPD0VM8Wmpigvjze3d0d/QYM0Ky/7P4prLx/BkoxbyN4vEqOwXD/HbjzeibGsuXKYfzESZrn048jFx+njsWluEse/xoiIjI1OeUZQRDQp9+bPHMp8gnGX92NqNTEPO0nRaXA3NvHseOZPwD1zKtz5/2seZ55hoio8ErLNUnPn8HuxjWUev0jXEKy+qqDYR9+iJo11T02o+RJGHp5O27GvMrTPiRJwtFXNzEpYJ/mKoSJkyajYqVKmnWYa4ioILEgSpQH9eqpZ95Nm9iobr16AICHDx8gKkr9y6mo0i5+/vboPL64uBm3YoORk1SVEr8/u4JPz63XFEMdHBywZccuzT7d3N1RqnRpzTZBr5fXq98gH38ZERGZgtzkmfv31ON9yiwsAACXop7ik3PrcPTVzRx/gJMkCZcjn6DPhQ3Yl24ipZ8WLIQoqsdyY54hIirc6r8ez/ZegPrS+fpVqwIAHjx/jleubkiu2wANGzfRrB+RmoAvLm7BkrsnEZduboOsvEyKweTA/Zhx4zAUkjq3NGrcBN/871tcvqS+go65hogKGguiRHnQrUcPAMCpk/8AAOrUqYvOXbpCqVSio+97+GJAP6xdswoymQwjR38FmUz9FrsdF4IB/23CwP82Y/uTywiMfoEYeTISlakITo7FqdB7WHzHD11PL8f8oBNIUsk1+7S0ssKvixZi9MjhAIDJ30yFxesvwUlJSfC/fAl2dnZo59vemC8FEREZQI55ZmB/rF29EjKZDIt/WQI3NzcAQIwiGTNuHEaPMyux4v4ZnA1/gLCUeCQqUxGVmgj/yKfY9Pg/fHp+PUb478TTdJc/Ojo64r/z5/HVyBEAmGeIiAorQamA/fUAfFS/PgDA77J6vM66Vaqgi68vlCoV2vfojgH9+mD71i0QBAG1atUGAIiQsPnJRXQ+tQyzbx7BsVe38CQxEgnKVMQrUnAnLgT7nwdi3NXd6PnvKpwMu6e170ePHv6/vfuOb6re3wD+nKRp2lLaUlo6WJZZREaZgsoW6mS4UGQoFxDFK4IgcBUE9KcgiooislERvSggKOMiywXIKltGBQotBbp30uR8f3+UhJbOtDlJTvO8ffVlk5zxafiefJInZ2D0yOfZa8ht8SrzrsfD2QUQqcn9ffuhefNI/P7bb7gcF4f6DRpg6YpV+M/U17Fp4484f+4smjWPxJtvvYVHHx2AJ58ajOeHD8M//8QCAE6kJ+BEesUONxnx/EiEhYfjm6++wsYN6xEaGoY3pr+FF8e9bJ3mxw3rkZ2djZH/Go1atWop8jcTEZHjlNVnftr4I9b/UNBnpv7nTQx67DE8OmAgxvzrOfxv2zYAwHVDJpb/82eF1tWhY0d07tIVP/34o7XPTPnPWIx96SXrNOwzRETVgYD+Qiy0mRkAgH5duiDyjjvw65EjuJCWjtD7umPJ6m8LPtP8uAHnzp1FZGQLzJg1G337RWP+h/PwzqyZyM/Ph0E2YVP8cWyKP17uWoOCgzH/4wX4559YLF+yGD+uX4fQ0DDMnP02P9MQkdNxD1EiG0iShP+bMxeyLOPj+R8CAAICArDgs89xMOY4Tp2LxR/7/8Kjjw4AAHTs1BkHjhzFJ58utF5RsSw6nQ5PPDUYO/f8hs8+/wJvvDkDp86eR3p2Hv46chTDn3semTfPIyeEwEcffoCaNWviP9NnKPY3ExGR45TWZz77/AvEnDqDk2fOY9evf2DQY48BAOrUqYN1P/6EjT9vwUMPP2I9MqEsne++G8tWfokdu3/D3Pc/sPaZo6f+xshRY5CcmgohZPYZIqJqwPvEUXgk3bCGoUBBr/lg/HjIsoz312+AkCQEBARg3vyPsO/QEcReuox9Bw/jwYcehoeHByZNnoKDR45h7IvjrBf9K0u9+vUxY+YsHD56AoMeexyvTXodp87GIiUzG7/u3Y8hw0bAdPOCgOw15C4EBITkwB9eVqlc3EOUyEbRDzyIbMOtK/rKsoyk5GSYzSbUrl0bnjrPItN7eXlh5KjReP5fo3D0aAwO/vUXjhw5jPgrV2Ay5cPHpwYiW7RAu/Yd0PWee1GnTp0S1+tvOdn5zQtt+NWsif0HDyv0VxIRkbPc3mcAIDU9HbnZ2fDz84evb40ij0mShN597kfvPvcj/soV/PHH7zhy+BDOnT2LvLw86Dw80PCOOxDVrj06390FkS1alLheL70egYGBSElJQVJKCoICA9lniIhUyvvoYTR5oAd8ARiefLLIY4aIRujROgrZY8dZ78vNy0Vqair0ej0CawVCkqQi8zRp2hTz5n+Et2a/jd9/+xUxRw7jxPHjSE9Ph0aSUDs4CG3btkP7Dh3Q+e4u8PAoHjVoNVoEBdVGUlIykpKSERRUGzoPHXsNETkFA1GiUmi1BZuHIa/0E4eXF4YWJkkS2raNQtu2UTfvEZDN5psXxZBKna+wkkLR0uTl5cFDp6vQcomIyDk8PDyQV0afAcoOQ29Xt149PPnUYDz51OCb99jWa0oKRSWp5L1OLf3Rw4O9hojIVWjS0xDZ6S7rHqE6AOk5OQCA/OA6yA+rW2ye8sLQwnx9fRH9wIOIfuDBm/fY1mdKC0VLIoSAwZDHPkNEiuAh80SlCKtbH5AkrFq5HHIJV+21JQy1J38/P9Tw9UVWZiYybgajt7scF4ft2/+HsHoNHVITERFVTnj9htj0449ISkoq8XFbwlB7sYSi+cZ8JKWkQIjiPdBoNGL1119D56lHUEioQ+oiIqIyyDLq/Xs0WrZoUOTw+AgAx06exJ7svCqHofZiCUU1Gg2SkpKRb8ovcbpvVn+NPIMBdRvwMw2pHy+q5Hq4hyhRKWrVro1xr0/Hp3Nm4ffffy9yvhwhBHJyciHLMnx8fKDVVu67BSHLkCpwvreS5BkMMBqN0HvqodffCmPNZhlJSTdQJ7w+Rv17YqWWTUREjjHu9emYPfkVDBrwKGrXDoJGc+uDaG6eAfn5Ruj1XtB7Vn7vmMr2GpPJjNzcXGi0Gvj4+Fj3+xFCID0tHUaTGZNmvVfiYZFEROQ4Aeu+Q/1xo0p8rMsve/Hrt1/i3+NfQXBwMHS6W6/Z+SYTcnNz4eHhAR9v70qvv7J9RgiB7JwcCCEKPlMVWkZuTi5S0lLRf/AwtG7XsdK1ERGVhu9gicpwd7eeCAwKxt8njyEnOxsAYDAY8ee+/cgTJnTu0hmBAQGVW7gQMOZmw9O7BlDJb2JPnP4bF2Jj0bhefdzZvCmAgkPzA4OC0anrffCvFVi52oiIyCFCw+virQ8+xeH9fyI1JbngTiFw5PhJpKRdQuSdbdC00R2VX0EVe03i9SQcOngQktYHnTt2gO5m+OlbsyZatmmPiCZNK18bERFVif7cWTTr3qHExy5/ugRpg54CALz6xmz8sXsHkq4nwmQqOEf1lYSriIk5gtpB4ejUvh20Wm3liqhin8nLy8Mfe/chOS8fXe7uCH+/glOCeXrq0SCiEdrffY9D9lolIvfDQJSoHM3uvAvN7rwLAJCZlY3lq75E3SYtMHz4MDSsV/ywk4oSQkZOWgp8Ako/P1tF/LTtF+z74zcENGiK6F49Kr0cIiJyjuCQUPR7dBCAgr1lfti0GTrfeIx+9Rl0v6dzlZZtj15z+lwsvl2zBtkabwx/+ml46/VVqomIiKpGyslB0z5doL94odhjqU88jSvzPwcK7W2p8/REj74PWG/HnDiFUz98j16PPI6hg5+ErgrXHbBHnxn4zAgsXbESSTm5ePjJ/qgbWvJFZonUzNGHsfOQ+fLxHKJEFWQJQ9PT0qochtrTw/364O577sPvu3dh687dzi6HiIgqyRKGxhz8C/f3e7DKYai9tGjaGIOffhpXr1zBqtVrkGswOLskIiL3JATC3pqKu5qEFgtDzX7+OHk6Dlc+/qJIGHq7mBOnsO6H73FHo8ZVDkPtxd+vJv713Ah4eXtj5cqViE+87uySiMgNMBAlqgBXDUMtGIoSEambq4ahFgxFiYicq+b2LWhV1x9Biz8r9ti5rXtw6u/LkP0DylyGK4ahFgxFqfoTDv0PEM7+g10eA1Gicrh6GGrBUJSISJ1cPQy1YChKROR4uiuX0SrcD3cMf6rYYwlvv4/jCRnIax1V7nJcOQy1YChKRI7EQJSoDGoJQy0YihIRqYtawlALhqJERI4hmfLRqH9fRHZqWeyxzF7343hcCpKfH1OhZakhDLVgKEpEjsJAlKgUagtDLRiKEhGpg9rCUAuGokRElSAEIFfsMifBCz5Ew389C5/DB4s9dvroeVz8+gfAo2LXR1ZTGGrBUJSqI9kJP1Q2BqJEJVBrGGrBUJSIyLWpNQy1YChKRGSbWmu+QsC678qcxmf/n2gV7oeQuW8Xe+yf73/G8YQMmIIrfgV2NYahFgxFiUhpDESJbqP2MNSCoSgRkWtSexhqwVCUiKhiPBLiETZzGkL/7y1osrOKPa5NuoFW4X5oPDC62GPXJk7F8fh0ZHe9z6Z1qjkMtWAoSkRKYiBKVEh1CUMtGIoSEbmW6hKGWjAUJSIqhxCo+/or0GZmQJd4FcGffnjrMbMZDYc/hTtbNy42m+GORjh5PhHXJ04FJMmmVVaHMNSCoShVF469xrzlSvNUFgaiRDdVtzDUgqEoEZFrqG5hqAVDUSKi0gV8vwZ+O/5nvR20aAF0cRcR+NVytKpfC37btxSb58yfMbj61rsQek+b11edwlALhqJEpAQGokSovmGoBUNRIiLnqq5hqAVDUSKi4jyuJSJ8+pQi92kMBkTe3Rp1Xx9fbPqLy9fgeEIG8us3qNT6qmMYasFQlIjsjYEoub3qHoZaMBQlInKO6h6GWjAUJSIqRAiET3kV2vS0cidNem40jsenIzP6oUqvrjqHoRYMRUnNeJV518NAlNyau4ShFgxFiYgcy13CUAuGokREBfw3fA//bT+XOY0xrC5OnkvA1Xfm2Xye0MLcIQy1YChKRPbCQJTclruFoRYMRYmIHMPdwlALhqJE5O48blxH+BuTyp3uxvhJkGv4Vmld7hSGWjAUJTUSkuN/qGwMRMktuWsYasFQlIhIWe4ahlowFCUidxb+n9fgkZpS7nQh782CJi210utxxzDUgqEoEVUVA1FyO+4ehlowFCUiUoa7h6EWDEWJyB35/bQB/j9tqNC0HqkpCJk/p1Lrcecw1IKhKBFVBQNRcisMQ4tiKEpEZF8MQ4tiKEpE7kSbnIy6UyfYNE/tFYuhP3fWpnkYht7CUJTUouBCR8KBP1QeD2cXQOQoDENL9nC/PgCA33fvAgBE9+rhxGqIiNSLYWjJLKHot2vWYNXqNRg+5Gl46/XOLouIyO7C35wETUY6TLWDYPbzg9nPH3JNf5j9/WGu6Qf55n1mPz+Ya/pDtv5es8LrYBhanCUUXbpiJVauXIkRI0agbmgdZ5dFRC6OgSi5BYahZWMoSkRUNQxDy8ZQlIiqPSFw5YPPID5bVqUrxpeFYWjpGIoSka14yDxVewxDK4aHzxMRVQ7D0Irh4fNEVK1JEoS3N8NQJ+Lh8+TKZCf8UNkYiFK1xjDUNgxFiYhswzDUNgxFiYhsxzC04hiKEinvnXfeQdeuXeHj44OAgIAKzSOEwPTp0xEWFgZvb2/06dMH586dU7bQcigaiKakpGDIkCHw8/NDQEAARo4ciaysrDLn6dGjByRJKvLzwgsvKFkmVVOZ2QxDK4OhKKkJ+ww5E8PQymEoSmrCPkPOxjDUdgxFiZRlNBrxxBNPYOzYsRWeZ+7cufjkk0+waNEi7N+/HzVq1EC/fv2Ql5enYKVlUzQQHTJkCE6ePInt27fjp59+wq+//orRo0eXO9+oUaNw9epV68/cuXOVLJOqoZycHKxY9RXD0EpiKEpqwT5DziKEwLpNWxiGVhJDUVIL9hlyppgTfzMMrSSGouRqhBP+U8rMmTPx6quvolWrVhX724XARx99hDfeeAP9+/dH69at8eWXXyIhIQEbNmxQrM7yKBaInj59Glu3bsXSpUvRuXNn3HvvvViwYAG+/fZbJCQklDmvj48PQkNDrT9+fn5KlUnVUGZ2Ntb8dy3S09IZhlZB0VD0V2eXQ1QM+ww5ixACW3/ZwTC0igqHol+u/g4GI0NRci3sM+RMp8+exfof1jIMrYLioegNZ5dE5HAZGRlFfgxO+BL6woULSExMRJ8+faz3+fv7o3Pnzti7d6/D67FQ7Crze/fuRUBAADp06GC9r0+fPtBoNNi/fz8GDhxY6ryrV6/G119/jdDQUDzyyCN488034ePjU+K0BoOhyD9oRkYGAECWZciya59GVpZlCCFcvk4AEEKGEJb/u269mdnZWLHyK2RlpGPoc6PQoG6Yy9Zb8FwKl60PAB7q2wsQAn/s3gmRm4HoBx90dknlEgIQ4GuAPblqfY7qM4B6e41axhhQEDLi5muiK78uCiGwbuNmHDtyGL0fGohuXTu6eL2u3Wsim0TgqcGD8d2aNfh+3QY8O+I5eHt5ObusMgkBAMLlt39AXa8Brlgj+0zFqGWcyZbXQRd+TbQ4cvw0ft60CXc0bYlnn3ocHh5al63Z1fuMX80aGDliGJatWIVVK1fi6UGP4A7/AGeXVSYhCvbu4/ZvX86u0dEXOrKsq379+kXunzFjBt566y0HVgIkJiYCAEJCQorcHxISYn3MGRQLRBMTE1GnTp2iK/PwQGBgYJl/8DPPPIOGDRsiPDwcx44dw+uvv44zZ85g3bp1JU7/7rvvYubMmcXuv3HjBoxGY9X+CIXJsoz09HQIIaDRuPb1rfIy0yBMRuRlpiHHRT+o5OTkYM1/1yIrIx0DH34QdWrqkZOW4uyyyiDDmJ0FSRJw5eub9ewcBZGXhb9+2wUA6H5PVydXVDY5Jw35hjwkJSW5/DfpankNSE9Pd3YJJXJUnwHU22vUMsYAIDM11eX7jGXP0GNHDqP7PV3RqWVjF+8zgBp6TcPgAAx6JBqbfliLr1euwOODBkDvqXd2WaUyZ6fBZDSwz9iZK/Ya9pmKUcs4S0tJhpxvRF5mKnLSXLPPAAV7hv68aRMa1A3DwAd6IT87E/nOLqpMrt9ndACGDHoUa777DmvXrMYTTz+LkDrBzi6rVPmZaZDzjUhOToaX3tPZ5ZRJLds/4Jp9xhEuX75c5CgFvb7k91hTpkzBnDlzylzW6dOnERkZadf6nMnmQLSiT1JlFT4nT6tWrRAWFobevXsjNjYWjRs3Ljb91KlTMWHCBOvtjIwM1K9fH8HBwRW+2pWzyLIMSZIQHBzs8i8eXgmpkDw84VUzAD4Bgc4up5jM7Gx8s+G/SM8xYuhzoxDs6wVv/1qQJNd9Xgu+TZVcvk4AeOjRRyCEwJ/7D0Ly9kN0r27OLqlUmuRs6PReCAoKKvYhxtWo5TXA09Oxb8Rcrc8A6u01ahljABCfmuXSfcZ6ztDjp9D7oYHoeGcTVbx+q6XXtGobAABYt2kr/vvTdgwb8hS8S3nD7mzalGx4eOrZZ+zMkb2Gfca+1DLOUrNzodF5wqtmLZfsM0DBOUM3btmOO5q2xMAHesEvqI5Lv3YD6ukzPgGBGDFqDFYu+QJr1m3E8BEjUDfUNUNRXWbBWK1duzb7jB05+jONq/Dz86vQaVsmTpyIESNGlDlNo0aNKlVDaGgoAODatWsICwuz3n/t2jW0bdu2Usu0B5sD0Yo+SaGhobh+veiJi00mE1JSUqxPRkV07lxwXq7z58+X+AZCr9eXmHBrNBqX3yABQJIkVdQqSRpIkuX/rlVrZlY2Vqz62nrO0AZ1w5CTluKStd6u4Mqjrl8nAPTu0Q0a75r4Y88uSBIQ3auHs0sqkSQBEvgaYE+Ors3V+gyg7l6jhjEGFNQJF31NFEJg3U9bEHPoAO6PfgjdunZUTZ8B1NNrmkRE4Kmnn8Z3336LL7/5DsOHPO2SoagkAYA6titAPa8BjqyPfcb+1DDONJbXwJuvia4m5sQprF/3Pe5o3ATPPvU48rMzVfHaDainzwT4++Hpp57C6nUbsWrVKowYMQJ1Q10vcJQkCRL7jN05uz4ZArKCFzoqaX22CA4ORnCwMl8SREREIDQ0FDt27LAGoBkZGdi/f79NV6q3N5sD0Yo+SV26dEFaWhoOHTqE9u3bAwB27twJWZatbwoqIiYmBgCKpMhEFplZ2Vi+6ssiV5N31fPXVAcP9esNSBJ+311w+LyrhqKkbuwz5EqEEPhh0+YiF1Bin1FOi6aNMPjpp/HtmjVYtXqNy4aipG7sM+RqYk6cKnI1eQ8PrYsfJq9eNWv6YuRzw7Fs5ZdYuXKly4aiRK4sLi4OKSkpiIuLg9lstva5Jk2awNfXFwAQGRmJd999FwMHDoQkSRg/fjzefvttNG3aFBEREXjzzTcRHh6OAQMGOO3vUCwib9GiBaKjozFq1Cj89ddf+OOPPzBu3DgMHjwY4eHhAID4+HhERkbir7/+AgDExsZi9uzZOHToEC5evIiNGzdi2LBh6NatG1q3bq1UqaRSJYWhpLyiV5/f7exyyI2xz5DSSgpDSXmFrz6/avUa5DrhaqhEAPsMOcbtYairn5u4Oih+9fnr5c9ERFbTp09HVFQUZsyYgaysLERFRSEqKgoHDx60TnPmzJki522dPHkyXn75ZYwePRodO3ZEVlYWtm7dCi8nXjtA0X2GV69ejcjISPTu3RsPPvgg7r33XixevNj6eH5+Ps6cOYOcnBwABed0+OWXX9C3b19ERkZi4sSJeOyxx7Bp0yYlyyQVYhjqXAxFyVWwz5BSGIY6F0NRchXsM6QkhqHOw1CUHE1Ijv9RysqVKyGEKPbTo0ePW3+vEEVOTyNJEmbNmoXExETk5eXhl19+QbNmzZQrsgIUu8o8AAQGBuKbb74p9fE77rgDQtw6r0H9+vWxZ88eJUuiaoBhqGt4uF8fAODh8+RU7DOkBIahrsESivLweXIm9hlSCsNQ57OEoktXrOTh80RuyLXPekt0G4ahroV7ihJRdcMw1LVwT1Eiqo4YhroO7ilK5L4YiJJqMAx1TQxFiai6YBjqmhiKElF1wjDU9TAUJUewXGXekT9UNgaipAoMQ10bQ1EiUjuGoa6NoSgRVQcMQ10XQ1Ei98NAlFwew1B1YChKRGrFMFQdGIoSkZoxDHV9DEVJWcKh/4F7iJaLgSi5NIah6sJQlIjUhmGoujAUJSI1YhiqHgxFidwHA1FyWQxD1YmhKBGpBcNQdWIoSkRqwjBUfRiKErkHBqLkkhiGqhtDUSJydQxD1Y2hKBGpAcNQ9WIoSvYmO+GHysZAlFwOw9DqgaEoEbkqhqHVA0NRInJlDEPVj6EoUfXGQJRcCsPQ6oWhKBG5Goah1QtDUSJyRQxDqw+GokTVFwNRchkMQ6snhqJE5CoYhlZPDEWJyJUwDK1+GIqSPcgQDv+hsjEQJZfAMLR6YyhKRM7GMLR6YyhKRK6AYWj1xVCUqPphIEpOxzDUPTAUJSJnYRjqHhiKEpEzMQyt/hiKUlUIJ/xQ2RiIklMxDHUvDEWJyNEYhroXhqJE5AwMQ90HQ1Gi6oOBKDkNw1D3xFCUiByFYah7YihKRI7EMNT9MBQlqh4YiJJTMAx1bwxFiUhpDEPdG0NRInIEhqHui6Eo2UqWhMN/qGwMRMnhGIYSwFCUiJTDMJQAhqJEpCyGocRQlEjdGIiSQzEMpcIYihKRvTEMpcIYihKREhiGkgVDUSL1YiBKDsMwlErCUJSI7IVhKJWEoSgR2RPDULodQ1GqCBnC4T9UNg9nF0BVFxcXh9OnTyMnJ0exdRz9Jx5xF2Lx+87t8K8VaPP8ubl52LFrN3JystGzRw+cP34Y548fLjKNf61AtLirNWr41rRX2eRirsRdxMXYczDk5RW53xtATS8d1q5cghMH9iKq9V2VXsflxBv4J/Y8tmzZgtq1a1ex4pIFBwejXbt28PHxUWT5RK4sNjYWZ8+eRd5t23GVl3slEZer0GeEENh34DAuxJ5Dm7btYMq4jh1bNkGj0SIkLBwtWrWBJEl2rZlcT1ZmBv4+eRzpqSnFHmtUNwS//7YT/5w6ip7d7oNnJUOM+MQbuPCPsn3G398fd911F+rUqaPI8olcWXJyMo4ePYr09HS7Ljc+8TriLsTij13bERhYuW33wqXL2LdvL+qEhKJugA9+/WUrAKBWYBBatGoDb743rPZkWcbJo4eRdP06ZNlc5LFm9UKwfedOvP3mFPTs2Qu1a/lXah3Xk5Jx6UIstm7dguCgIHuUXYxer0eTJk3QrFkzRZZPpAYMRFVu3759mDBhAoxGo6LrMZpMyMvNxQ9fLYVGo7VpXiEEcnPzIIQMLy8v/PztxVKmA+pFNMJ/3p2Pmn6Vax7kug7u+wMfvzMduO2NQ2EGgxF/XDqDA79sgqenZ6XWYzKbkZ+Xi4ULF0KjUW4n+JYtW+Kzzz6Dr6+vYusgcjW//PILpk2bBlmW7b7sfJMZOTk5leozAGAwGJCfnw9PvR4HdsTjwI5bj8lCIHrAE3h21EsMRaux9NQUzH59PBKvxKG0f2aTyYzkC6fxz+E/4eXlVanxYDKbYXRAnwkMDMSiRYvQqFEjxdZB5GquXLmC0aNH4/p1++9hZzKbkZOdgw1fLYNGa3ufMZlMyDMYoNVqkXf9Mr4+ccD6mBDAHU2bY+o787hzRzUmyzIWfzQXv/2yFZpS2oeQBXLzcvHtuePw9vauVJ8wm80w5OXi888/h1bBPiNJEqZPn45HHnlEsXUQuTIGoip2/fp1TJgwAR07dsTs2bPh5+en2LoycvKQfOM6agfXgYdHxfeokGUZScnJMJtNqF27Njx1JYdcsizjzJm/8dqEV/HpnNmY+s48e5VOLiAx/go+fmc6enbvhmlvTEeNGjVKnTY9IwPZWVnwrVkTfjVtf0OZZzAiKzUJoaEhlQ5VyyLLMk6cOIFXXnkFs2bNwty5c+2+DiJX9M8//2DatGno168fpk6davc9pLNz83Dt+nUE2dhnACA1PR252dnw8/OHr2/R15f8/HxsWL8OH37wAeo1iEDP6IfsWTa5kI/emQFzXg6+/OprNGnatNSwM89gQEpKCnSeOgQFBkKSbPuwmWc0IiPlBsJDQxXpM0IIXLt2DePHj8fYsWOxZcsWRYNXIlfy8ssvw8vLCxs2bEDdunXt+iVWnsGAhMRrqB0UDF0pn0lKk5uXi9TUVOj1egTWCixSlyzLOHnyBCZNmIAvPpyDCdPftlvN5Fp++uFb/Lnzf5gxfTru7xcND4+S4xSzbEZSUjJkWUZQUG3obHxfY8w3IjXpBsLDQqFXoM8AQG5uLubPn49Zs2ahcePGuPPOOxVZD90ibv44cn1UNr67UrELFy7AaDRi0qRJioahlVXRMBQANBoNWrS4E08++RQunj/rwCrJES5fugAhmzHhtcllhqEA4O/nhxq+vsjKzERGZqaDKqw4jUaD1q1bY9CgQTh9+rSzyyFymHPnzkGWZUyZMsWlThdRVhgKADqdDk88+RSaNm2Kf86dcUKF5CgXz5/F00OGoGmzZmWGKF56PQIDA5FvzEdSSgqEsP8ez1UhSRJCQ0Px0ksvITk5GUlJSc4uicghsrKycPnyZYwZMwb16tVzmT36ywpDgYL3hq1atcbAQYNw4fzfTqqSHOHCubNo1y4KDzz0cKlhKABoNVoEBdWGRqNBUlIy8k35DqyyYry9vTF58mRIkoS//+a4JffEPURVzHL+tvICpsowm804ceIEDh06hOPHjyM5NQ0moxH1GzZEu3Yd0L5DB4TXLf2iSLaEoYV5+/jAyIsdVDsGQx4kVHys+t8M+LNuBqJl7SlqMplw4sRxxBw+jJMnTyIjIx2yKR+NGjVCx44d0bFjR4SFhVX5b7hdjRo17H4ORSJXpkTPiYuLw4EDB3DkyBHEJ1xFTm4O6gTXQas2bRHVrh1atLizzD3jygtDC6tRwwcGA7fZ6sxgyKvw+LSEoikpKUhKSSl3T9GLFy7g8OFDOBpzBNevX4chLxdhoaFo164dOnTogMjISLuHN5a/hb2G3IXh5mcAe37plpaWhoMHD+LQoUOIjY1FWno6AgJqoVnzSES1a4e2Ue3g7e1d6vzlhaGF+dSoYf0bqHoyGPJQq4J9xhKKJiUlIykpudw9RVNTU3Ho4AEcOXIYFy9cQHZWJoJq18add96J9u3bIyoqCl5eXvb6UwAAHh4e8PLyYp9xEEdf6IgXVSofA1Eq4vr161iyZAkWL16MuLi4Mqft1r07Ro0Zi0f7DyjyDVllw1CiwsoLRa9evYoVy5Zg+bKluJqQUOay7r//fowdOxaPPvootJU4ZxQR2Y/RaMR///tffP755/jzzz/LnLZRo8Z4/l+jMGzEc8UuXmNLGEpUkvJC0by8PKz973dY8sXnOHTwYJnLat68OV544QWMGDECAQEBCldORGURQuCPP/7AwoUL8f333yM/v/S982rWrIlnhgzFqBdeQIsWRQ8ZtiUMJSpJeaGoEAK/7tmNJV8swqaNP8JkMpW6rICAAIwYMQJjx47lhZCI7ISHzBOAghfjxYsXo0mTJnjjjTfKDUMB4Nc9ezD0mcG4t0tnHD0aA4BhKNlXSYfPy7KMhZ8uQOs7m+Od2bPKDUMBYPv27Rg0aBC6dOmCU6dOKV02EZVi//79aNOmDYYOHVpuGAoA//wTizemTUGblpH4ZvXXEKLgm26GoWQvpR0+/8fvv6FjVBu8MGpkuWEoAJw5cwavvvoqmjZtirVr1ypdNhGV4tq1a3j88cdx3333Yc2aNWWGoQCQmZmJLxYtRMeoNpj82gTk5OQAYBhK9lPa4fNXExLw+MD+eLDf/Vi/7ocyw1CgYG/njz76CC1atMDUqVO5NzKRHXAPUUJGRgaeeuopbN26tdC9ErSapvDQNIZGqgtJqgFAhixSIMtXkC+fhBAF57Q6fuwounW9G2//33t46pkhDEPJrgrvKZqWloYx/3oOv+7eXWgKCVpNJDw0ETfHqg8KxmryzbF6AkKkAAAOHDiAqKgoLFiwAKNHj3b430LkroQQeOeddzBjxowiV6iPrO2Pfo3qoU1IIBr4+UIjSbienYvjN1LxW1wifr2cCKDgMLJRz4/Az5s24v2PPgFkmWEo2U3hPUVvJCfjs48/wgfvz7UG8ACgkcLgoWkBraYuJKkWAAlCZMAsEmA2n4VZXAAAJCUl4cknn8SQIUOwbNky6PV6J/1VRO5n586dePLJJ5GcnGy9r3aQBgMfq4F2HfVocacn9F4SsjJlnDhmxP59Bmxan42cHAEhBD5b8Am2bdmCr779FkFBwQxDyW5u31P08MEDGD3yOaSlpVmn8df44e4a7dFI3xB1dWHQSR7IkXMRZ7yCM4ZYHMiJQb7IhyzLeO+997Bp0yZs2rQJERERzvvDyCY8ZN71MBB1cxkZGbj//vvx119/We/TaTvAU9sTGk1gsem1CAe0d8FT9INZjoXB9DNkkQiTyYQpk1/DtWuJmD5zFsNQsit/Pz+kpqbisf6P4O/Tt/bw1Gk7w9OjBzRSQLF5CsZqq5tj9dzNsXoDRqMRY8aMQXZ2Nl599VUH/hVE7kkIgddffx3vv/++9b42dQLxVrd26BweXOyDZougAHRvGIZxHe7EP6kZeH/fcWw4ewkAsGH9OiQmJuLbtesYhpJdeen1qFWrFl4Z9yJWf/Wl9X6t1BB63QPQSA1KCEVC4YFmgEcPmOVrMJq2wySfBACsXr0aKSkp2LBhgyJXoieiorZt24ZHH30URqMRQEEQ+tY7gXhicA14eRU/KLLT3V54fjQw58NALF+cibnvpCEvT+D8+XN4qF9frF23AZ07380wlOzGEop+8/XXGDd2jHWP0ACtH56p9RjurtEeHlLxeKa5VxPcjx7INGdhW8Yu/Ji+FWaYcfLkSdx777349ddf0bhxY0f/OUTVAg+Zd2NCCAwePLhQGOoNb91z8NI9VmIYWpgkSfDQNoGP50vw1Ha33j//g3n4gYeKkZ3JsowX/vWcNQyV4Atv3b/gpRtQYhhamCRp4KFtDh/Pl6HTdrXeP2HCBKxbt07JsokIwMKFC4uEoZPuboWfnuqLu+vWKfeDZqNafvj8gXuw7KH74OtZ8CFh394/8dqEVxStmdzT558uKBSGStB7PABvz9HQahqWO1a1mhB46YbAS/cUgILzw23ZsgUvvviiskUTEU6dOoWBAwdaw9C+0d7Yd6Qeho6oWWIYWlhAgBYTJgfgt7/CcVfrgi8vUpKTMXzIM0X23iOyh2NHj+LfL421hqEdfdpibvgM3OvbucQwtLCaWl88XusRvBM+DeG6UABAQkICoqOjkZWVpXjtRNURA1E3tmzZMmzZsuXmLW/4eI6Gh9a2EzRLkgf0umjoPR603jdx/L+REB9vx0rJ3S38dAF++/VXAAVhaMFYte2bUEnSwUv3CDw9elvve+GFF3Djxg271kpEt5w/fx6TJk2y3p7bqxMmdG4FjzKuHF+SB5vUx38H9kINXcGHhe//+x3W/fC9XWsl93by5AnMemv6zVsSvHRPwNOjW5lXnr+dJEnQadvC2/M5WELRZcuW4eeff7Z/wUQEADCZTBg+fDhyc3MBAI8O9MHq70MQXMe2i2g2be6Jn7aHok1UQSh65cplvD5pot3rJfdlMBgweuTz1uD+3hqd8UrwaPhqbTvipYFnXUwPfQ31dOEACt5rvf7663avl+xPdsIPlY2BqJtKSkrCxIm3mry37kloNaGVXp6nx33w0LQFAKSnp2PyaxOqWiIRACAhPh5vTX/DettL9zQ0muBKL89T2xsemoKriN64caNIWENE9vXiiy9aP6Q+17ophrZqUullRYUGYV7vTtbbr/57HPeIILsQQuDfL421XnzFU3sfdNqoSi/PQxMBvccj1ttjxoxBXl5eleskouIWLlyIgzcvfNasuQ5frAiGTle5w9wDArT45vsQ+PkVzL/6qy+xe9dOu9VK7u2Tjz7EqZMnAAANPetjdNAwaGz40q0wP60vJtYZC71UEOAvXLgQ+/bts1utRO6CgaibWr58OTIyMgAAHpp28NBGVnmZXrpHIMEXQMF53i5dvFjlZRItX7bEGqjotHfDQ9uoSsuTJAl63QAA3gCAb775BteuXatilUR0uxMnTmD79u0AgHo1a+A/97St8jL7N2uI6Mb1ABR8sffdmm+qvEyiQwcPYN/evQAASQqCp0efKi9Tp+0AraYpACA+Ph7ff889monsTZZlfPTRR9bbny0Jgrd31T7e1q3ngbfn1L61zAWfVGl5RACQn5+Pzxd+BgDQQIMXgobDQ7JtL+bbheiC8WSt/tbbH3/8cZWWR+SOGIi6IVmWsWjRIuttvUdPuyxXknyg8+gCoGBvi+XLlthlueS+8vPzsXzZ0pu3NPD06GGX5WqkmvDUdrKuY9myZXZZLhHd8vnnn1t/f6FdJGp46qq8TEmS8Gqnu6y3F3+xqIypiSqm8DjSa7tDkuwzVj09ellvL1y4sMrLJKKitm3bhgsXLgAAet/vjY6dveyy3GeG+aJe/YKwasvmnxF36ZJdlkvua9PGH3EtMREA0N6nDRp61rPLcvvU7IaamoIdkn744Qfu5OHihBP+o7IpFoi+88476Nq1K3x8fBAQEFCheYQQmD59OsLCwuDt7Y0+ffrg3LlzSpXots6cOWN986DVNIFGE2S3Zeu0HQEUHGbyv23b7LZcck/Hjx21vnnw0ERCI/nbbdk6bWfr75s3b7bbcsmx2Gtcl+Uc1V5aLZ5oEWG35bauE4i2IQUX/jtx/BiuJiTYbdnkfoQQ+N+2rTdv6eGhbW23ZWulhtBIIQCAvXv3IjU11W7LJsdhn3Fdhd+/jRhV027L9fCQMPS5guUJIfDL9v/Zbdnknm71GaB3zfvstlydpEN334IdkvLz87Fjxw67LZvIHSgWiBqNRjzxxBMYO3ZsheeZO3cuPvnkEyxatAj79+9HjRo10K9fP553yc4OHTpk/V2rse3CNOXRSDWhkeoAAE6dPMF/O6qSw0XGatUOlb+dRlMLklQLAHDkyBGYzWa7Lp8cg73GNaWkpFi/eGtVpxb89J52Xf499UKsvx85fKiMKYnKlhAfjxvXrwMAtJoGkCT7jVVJkqDV3Poy4MiRI3ZbNjkO+4zrKvyZ5r5u9tk71Lq87t7W348cPmzXZZP7sYwhDTSI1Ff+fOoludOrufX3wtsEuR4BAdmBP9xDtHyKBaIzZ87Eq6++ilatWlVoeiEEPvroI7zxxhvo378/WrdujS+//BIJCQnYsGGDUmW6pRMnTlh/10phdl++Riq44p3JZMLZs2fsvnxyH6dOnrT+rtHUtfvytTfHak5ODi7ynLeqxF7jmk4W2nZb1Qm0+/ILL/PkyRNlTElUtsLjx9IT7Ekr3epdx48ft/vySXnsM67L8pmm4R0eCKhVtfMx3q5121tfjpw6xT5DlSfLMk6fKnhfVFcXCk+Nfb8kjtA3sP7OPkNkGw9nF2Bx4cIFJCYmok+fWyey9/f3R+fOnbF3714MHjy4xPkMBgMMBoP1tuVCQbIsQ5ZlZYuuIlmWIYSodJ2W+YQQEKLi6X96err1d0nyrdS6yyJJNay/p6amIc9grPC8+fkm5BmNOBlb+XP1CAHIOanQJGdDqtxFJh1CLXUCVa/10tXrMJnMBWPBhqspphUaqxrUKGPKyik8VjMyMmzajizbXVVeZ6r6GuAorl6fLdyt19hzjBXuOeWxPD8AUNtbX+V1367wMtPS0yvUZ0xmM26kplepv1i40+u3o9ijTmN+Poz5+Ta970hJuXUYe+GeYC+Fl5mZmWlznwGq9jqjlj4DVJ9e4259BrDfOLPls40QApmZmQCAoGD7hqEA4OurgZeXhLw8gYyMzAq9ruSbTMjNM7DPuLCq1pqclgE/faBNfSYnJ8d6JFpNrf1O7WBhOYcoAGRlZdnUZyzYZ8hduUwgmnjzPIEhISFF7g8JCbE+VpJ3330XM2fOLHb/jRs3YDRW/IXKGWRZRnp6OoQQ0Ghs31k3NTUVsizDZDLBZDJVeL6i61LiMOFbLzSG7ExkpSZVeM687Exkp6Xg97WVv8iNEAL5RgN0nnpILtyV1VInUPVaL128CGNeDrJSk2DyqvghTbIp/1YNUKKB3VqmEMKm7ciy7V2/eahlpdZexdcARyn8JYrauVuvsecYS09Pt457W5hk+x+uYy60TLPRWKE+Y8zNQVzcmSr1Fwt3ev12FHvUmZOZjrws2953GHOzb9WgQJ8pvExJkmzafsxmM8xmM5KSkuBlQ+8sTC19Bqg+vcbd+gxgv3GWkpJiHfcV2Va0Wu3Nae3fZ4QQMJuF5UaFXlcM2VnITLnBPuPCqlprwrmTqOvfwaY+U/iLDlnY/7O3XOiwaI1GY/P7NFmWkZGRUenPNOwzFSdLApLkuMPYZR4yXy6bAtEpU6Zgzpw5ZU5z+vRpREZGVqkoW0ydOhUTJkyw3s7IyED9+vURHBxc4ROfO4ssy5AkCcHBwZV68ahVqxY0Gg08PDzg4XHrn3LLli146KGHMG7cOHzyySfF5gsPv3VImCySoUWDYtNUhSySrb+3aBGJ0NCibwgHDRqErVu34uDBg7jzzjuLPBYQ4I86dYIxdfJrlV+/LCM5ORm1a9d26RdltdQJVL3WXbt24cMPLyE0NKTIh7qtW7eif//+ePHFFzF//vxi80VE3HGrBpEMLUKKTVMVsnxrrNatW7fIdgQA/fv3x+bNmxETE4OWLVsWecyy7dWpU6cK66/aa4CjeHra99Ce8rDX2I89x5i/v7913BdWUs+pW/fWYcIX0zKrtN6SXCi0zEaNIhAaGoLc3Fw0a9YM3t7eOHbsWLEAydfXF/fe0xUTJ06s8vrd6fXbUexR5+97dqFWrYAi7zvK6zMtWtx6HRGF3r/YS+FlhoWFFdl+hBCIiorCxYsXcfbs2WL9RKvVQqvVIigoqNK9Ri19BnBsr2GfsS97jTPLmNdqtRX6bBMaGor4+Hhc+McEIYRdw7grl83Iv/m9fL16dSvUZ/z9/RAeFlalzzEWanntBtyr1rdyslCjhk+xz7dl9RohBAICApCWloZE040q1V+SxPxbV5YPDQ0t9j6tvF6j0Wjg5+fHPkNuyaZAdOLEiRgxYkSZ0zRqVLkLn4SGhgIArl27hrCwW+e1vHbtGtq2bVvqfHq9Hnp98cPxNBqNy2+QQMHeApWt1TKPJEnWNwBCCEyaNAkajQaTJk0q8Y1B+/btrb+b5XjotFGVrL64gt3l4623e/XqhaFDh2L27NnQ6XQAgNdffx0///wz3njjDfz0009F5vfw8IBOpyvyYdpWsixDp9OhTp06Lj0G1FInUPVag4KCoNFo4OnpaW1EQghMnToVGo0Gr7/+eokNqlOnToVquAJo7yw2TWUJIcMsbl2dun379nj22WeLjNVJkyZh06ZNmDJlSrGxatnuqvpvV5XXAEdxdG3sNfZlrzFWuOdYlNRzMjIy8Nlnn1mnOXo9pUrrLcmxQstctWoVxo8fD09PT4wbNw7Tp0/H4sWL8dprRT+QarVa+Pn5Vam/WLjT67ej2KNOyxfEtvSZDh06QJKkgr3BCr1/sZfCyxw5ciRGjhyJhg0b4uLFi5AkCa+99hqGDRuG2bNn49NPPy0yr2Vbq+r2q4Y+Azi217DP2J89xllFP9t8/PHHWL58OeLjC7avjHQZ/5w3oXFTXdX/kJtijtzaq+/vv/+2voctq89YXn/YZ1xXVWv19fW1fqaxqEivadeuHXbu3Ik0czpSTWmo5RFQlT+jiAvGOOvvP/zwg/XvqmivAdhnyH3ZNCKCg4MRGRlZ5k9lU/eIiAiEhoZix44d1vsyMjKwf/9+dOnSpVLLdEfbt2/HqVOncN9996FBg5L3/OzQoYP1d5P5JISw3yFiZnEJAlkACvZE9fT0xJw5czBlyhTrNPfeey8aNmyIzZs34/z583ZbN6lLRcZq4UDUJJ+s1DlxSmOWzwMoeLPboEED6HQ6jlUXwV6jHiVtx0OHDsXSpUvh7V1whd5/0jJx6kZqWYuxicFkxrYLt0KmqKhbX+o9++yzAIBPP/2U54miCvWZGjVqICgoCAAgiwTIcsUPgyyPEAaY5IKLS+p0OjRs2BAA0KxZM+s0gwYNgo+PD1auXFnk3LukLPYZ9ShpOz548CACAgKK7Dm7cUN2KUuonB9/uLW85s1vXcWbfYZuZ+tnmr9yjth1/X9lH7b+PnDgQNxxxx0A2GtckeyEHyqbYhF5XFwcYmJiEBcXB7PZjJiYGMTExCArK8s6TWRkJNavXw+g4FuF8ePH4+2338bGjRtx/PhxDBs2DOHh4RgwYIBSZVY7luezd+/epU4TFBSEBx54AAAgkAaz/Lfd1p9v2mv9fc6cOdZ6PvvsMyQlFXzIkCQJvXr1ghACP/74o93WTepSkbFav359dOvWDQAgi+swy//Ybf1G8z7r7x9//DHHqkqx1zjX7dvxsWPHsHHjRuh0Orz11lvW6VYcO2e3dW46H4eU3Ft77kyaNMn6e0REBCIiInDp0iUcOWLfDxykPhXpMwCK7OVVuDdUVb45BpYv3p588kncuFFwqGTh0zbUqFEDnTt3RnZ2NrZv3263dZP9sM84V0nb8VdffYU9e/bgnnvusd63fEmm3c4lei3RhB/X3QpE586da/2dfYZuV5FeY/nsDQDbM/dAttMOSTfyk3A4t+DK8nXr1sXnn39uPRcoew1R+RQLRKdPn46oqCjMmDEDWVlZiIqKQlRUFA4ePGid5syZM0VObDt58mS8/PLLGD16NDp27IisrCxs3bq10ieSd0eHDxd8Q3T7uTlv9+KLL1p/zzNthhD5ZUxdMSb5H5jkYwCAgIAAPP7442jbti30ej0MBgNOnTplndZyPsYDBw5Ueb2kTpUZqwbTTxDCthOFl8RkPgOzfBoAUKdOHTz88MMcqyrFXuNct2/HltuNGzfGCy+8gBo1Cq6wveZkbJHD3Csrw2DEu38ctd7u2rUrWrRoUWQabrNkUdE+8/zzz1sPVc4374NZvlbm9BUhi2wYTb9Yb/v5+SEnJwetW7dGv379ikzLMeva2Gecq6zt2Nf31tW1L18yYcF8+1ww5T+TU6znD23SpEmxUx1wm6XCKtJrCu9NnJCfiO2Ze6q8XiEEVqX8F+LmhXPGjBmDJUuWsNcQ2UCxq8yvXLkSK1euLHOa2w9/lSQJs2bNwqxZs5Qqq9pLTS04LNHf3x9AwQmet27dan38mWeeQadOnfDggw/i7rvvxr59+yBEMgymLfDSPVrp9QqRi7z8H6y3x4wZY33T5+vrC4PBgKtXr1of9/PzA1BwNUlyTxUdq4MGDUKbNm1w9OhRyCIRRtMv0OuiK71eWWQhL3+d9faLL75oPfk4x6r6sNc41+3bseUKyr6+vvDz88PkyZMxY8YMmIXAK//bi5+e7IsanpU7x5sQAv/ZfRAJWTnW+959991i03GbJYuK9pmgoCC88sorN/cCMyMvfy18PMdAkio7VmUY8jdYTyH06KOPYt26gr5z+zkHAY5ZV8c+41y3b8clsZwH+N1Zqeh1vzfatC1+LtaKWrc2C99/d2vv0Pfff7/YNNxmqbCK9BrL+yOLb1PXo6VXc9TzDEdl7cn6E4dzC3ZGCgkJwb/+9S/raYTYa1yTDAHJgVd+51Xmy6dYIErOYTmXjuXcIPv27cPHH39sfbxt27bo1KkTNBoNVqxYgbvuugtmsxn55r2QJB94anvbfIVGIXKQY1wBIW69uPbv39/6u+WQosInlrfUV6tWLdv+QKo2KjpWdTodVq5cifbt20OWZRjNewrGqkc3m9cpiyzkGpdD4Na5cwp/e8qxSmSb27djy8VELNvS1KlTsXz5cly6dAl/J6dj2KY9WPVId/jaGIoKIfDWb4fx/d8Xrfe1bdvWekqNwrjNkkVF+wwAzJw5E0uXLkVKSgpkEY/c/K/hrRsCSbLtPJJCyDCYfoRJPgEACAwMRLdu3bBx40bUq1cPgwcPLjYPxyxR6W7fjkvSsmVLnDhxAkYj8PgjiVi/OQx3tbL9HLDbNufghedvXQW8cePGJZ7mgNssFVaRXmM5r6dWq4XZbIZBGPHetU8wNWQ86nqG2rzO/dmHsDR5tfX2okWL8PPPP+PatWvsNUQ24GW2qpl27doBgPWQ37feegtCCOtP4StqRkZG4v7777feNpp2IC9/NWSRWeH1mcyxyDZ+CllcAQDrnnaWw4iOHDkCg8EAvV5f5DACS32Wesn92DJW27Zti549e1pvG0xbkGtcAyEqfgJ9k/kscgyfQhYFe39ariRvOWyEY5XIdrdvx5Y9E86fP4+UlBTodDrce++91un/vHId0d9uw8GrN4ovrBTxmdkY8uNuLD5ypsj9b7zxRonTc5slC1v6jJeXFx599NaRMmb5LHKMn9t05XlZTkFu/jLkm/8CUHA12+XLl2PZsmUAgPHjx1t7T2Ecs0Slu307Lknbtm2tF6y6cV1GdI8ELF+cUeGLcRoMArOnp+CZx6/BaLx1/3vvvVfi9NxmqbCK9JqWLVvC09MTZrPZeqqfFHMaZlydgz2Zf1Z8rMpGfJ3yPT65sRTyzUvmvPjii+jfvz8+/PBDAOw1rkw44T8qGwPRasbyLWbhK1uWZMOGDRgxYgSuXLlS5H6TfBLZhvkw5G+DLNJKnFcIAZP5H+QaVyM3fymEKDhMICQkBHPmzAEATJs2DSNGjMCgQYMAAGPHjrVexRUAdu7cCaDonqTkXmwdq9euFT2nm0k+dnOsbocsSj5nlBAyTOZzyDV+idz8FRAomK5evXqYN28eAI5Voqq4fTtu06YNHn74YZhMJnTv3h3PPvssvvvuO0iSZD1UKzY1A/3X/oIXt/6BAwk3Sv0QcDEtE7N+O4IeX/+MXZcKvsiwHMHQpEkTDBw4sPg8Fy/in3/+Qd26ddGhQwd7/7mkMrb2GcuXuZZxJotE5BgXIte4Fmb5cqljVZZvIC9/E7KNH1sv/qfVavHVV1/Bw8MDp0+fhr+/P0aPHl1s3pycHOzfvx/e3t7o27dvZf9UomqrpO146dKlGDFiBPbv3w8A2Lt3Lxo2bIj69esDALKyBCa8nIz7u13F2m+zYDCUvO1mZMhYvDADXdtdwQdz0mE233qscePG1veGhbHP0O0q0muCg4OtPSArKwuBgYEAgByRiy+Sv8SsxA+wL/sQTMJc4vxZ5mxsTv8FkxNmYXPGL9ag67nnnsMnn3yCzZs3s9cQVQIPma9m+vXrh8jISPz666+Ii4tDgwYNSpwuJiYGq1atKmUpuTCad8No3g1JCoRWqgtJqgFAQBbJN/eWyC0yx3333YdVq1YhIiIC/v7+mDdvHr755hsEBwdj0qRJePvtt63T/v7777h48aK1VnJP9hirAtkwmnfCaN4JSQqCVgq/OVbNhcZqXpF5+vTpgxUrVqBevXqoUaMGxypRFZS0HX/11VeYNGkSNmzYgLNnz6JVq1aYNWsWmjZtiqFDh+LAgQOQhcD6M5ew/swl1PbWo1WdQDTwqwGtJOF6Th6OXU/B5Yyie4CHh4dDCIGrV6/i1VdfhUZT/Dvdr7/+GgAwbtw4aLVahzwH5Loq22eKBp8yTPJhmIyHIcEXGk1daKQAABKEyIRZjodAWpHlNWjQACtWrECvXr3Qq1cvAMDo0aNRs2bNYutet24dsrOzMWbMGB7GSFSCkrbj33//vcg2Gxsbi9jYWNx7773o168fli5dCgA4+JcBB/+6gX+/kIS7Wnsi8k4dvLw0yMyUceKYEX+fMsJU6FqdOp0ONWvWREpKCiZMmMA+QxVS0V4zb948eHl5YfXq1cjIyICfn5/1MPYzhvM4c+M89JIeDT3roa4uFDpJhxw5F5eMVxCff9W6RygA6PV6zJ49GxMnToRGo8EHH3wAgL2GyFbcQ7SakSQJH3zwAWRZtu4BV5Lbd+UXQuD69esYNmyY9bB3ABAiBSb5OPLN+5Bv3g+zfB6Fw9CQkBB88skn2L17NyIiIgAAI0eOxOnTp2E0GhEfH4+5c+fC0/PWeXzmzp0LrVZb4knKyX1UZawmJCRg8ODBRd6ICpEEk3wM+ea9yDf/BbMci8JhaHh4OBYvXoz//e9/qFevHgCOVaKqKmk7DggIwJIlS3Djxg0YDAYcPnwYDz/8MJo3b44///wTc+fOte4ZAQDJuQbsvnQVXx4/jxXHzuHn85eLhKGenp4YPXo0Tpw4gYSEBAgh8OKLLxarJTc3FwsWLEDDhg3xyiuvKP/Hk8urSp8xGo2YPXu29dxwACCQBbN8Bvnm/cg374NJPlkkDPXy8sK4ceNw4sQJaxC6c+dOCCFuXrCpKCEE3n//fdSsWRMzZ860299NVJ2UtB2vXLmy2DYrhMBvv/2GJUuWYNu2bdYragNAbq7Agf0GfLUiC0s+z8C3X2fhxLGiYWj37t1x8OBBJCcns8+QTSraa/R6Pd5//30kJCQgPz8f6enp2LhxI5o1a2adxiAMOGuIxa6sP/C/zN34PXs/LufHFwlD+/Tpg5iYGEyaNMka2rPXqIMM4fAfKhv3EK2GHnzwwQqfh6Sw4OBgrFq1Cu+99x6WLl2KzZs3IyYmBnl5RfewCwkJQceOHTFkyBAMGjSoSIBUERs3brS5NqqeKjtWw8LCsGbNGrz//vtYsmQJtm7diqNHj8JgMBSbrlOnThg6dCgeffTREs+nUxaOVaLy2bIde3h4YNKkSRg3bhzWrl2LNWvW4ODBg0hKSioynY+PD6KiovDII4/g+eefR3BwcLnL9vb2LnZqDaLK9hmdToc33ngDEyZMwLfffovvvvsOBw8eLHZ1Xl9fX0RFRaF///547rnnioT95ZEkCUePHrW5NiJ3Y+t23LdvXxw/fhy//fYbli5dij///BOxsbFFptFoNLjzzjvRvXt3jBkzBq1atSp3uewzVJrK9ppHHnkEDz/8MHbu3Inly5dj7969uHDhQpFptFotWrZsiZ49e2LMmDHWc5BWFHsNUekYiFYDlXnxLUtYWBjefPNNvPnmmzCZTIiNjUV6esG5F8PDw1G3bl2br0RfUfb+W8i1yLJc/kQ2qFevHmbOnImZM2ciPz8f58+fR0ZGBiRJQr169RAeHm7X9RXGsUruSpblEg8jtIW3tzeGDRuGYcOGQQiBK1eu4Pr16zAajQgMDESTJk3sfiiivV9/yDXZ+7XZx8cHzz//PJ5//nkIIRAXF4cbN27AZDKhdu3aaNy4cZW3h9JwzJK7ssd2LEkSunXrhm7dugEAUlNTcfHiReTm5sLX1xdNmjSBj49PlddTGLdZ92DvPiNJEnr37o3evXsDAFJSUnDp0iXk5uaiZs2aaNKkCby9ve26zsI4bsmdMRBVMcteCGfPnsXdd9+tyDo8PDzQvHnzggspmUzw8PBQLAwFgHPnzqF27dqKLZ+cwzJWz507hzZt2iiyDp1OhxYtWhQZq0oRQuDs2bMcq+RWCm/HzZs3t9tyJUlC/fr1Ua9ePcX6TE5ODuLj4xEVFWXX5ZJrCQwMxNmzZxVbviRJaNiwIRo0aOCw90QA2GvIbfj5+UGj0eDcuXPWINNeatWqhYCAAMW2XSEEP8e4gcDAQBw5cgRGo9HmoyRtWUetWrUc0mcuX76MvLy8IheUJeXIEJAceBg7D5kvHwNRFWvWrBlatWqFqVOnYsSIEdYr+CpBCAGz2QytVqvIi7IQAqdOncKmTZvw8ssv23355FwtW7ZEs2bNMHHiRAwbNqzEk33bi9JjVZZlHDt2DDt27MDkyZPtvnwiV9WmTRs0atQI//73vzF06FDUqFHDrstXats1Go3YsmUL8vLyeGXVau7xxx/H4sWLARS8R1LyaBal3xMlJiZi1apV6Nu3r923NSJXpdPp0L9/f3zxxRfIzs5GvXr17LqNKbXtCiFw7NgxbN++HZMmTbLbcsn1PPTQQ9iyZQv+/e9/o1evXjafjquilO4zQMGXxWvWrEH9+vXRoUMHRdZB5OoYiKqYXq/HggULMG3aNCxbtgw5OTmKrk/pve6Cg4Mxbtw4DB8+XLF1kHPUqFEDn3/+OaZOnYolS5YgNze3/JmqQMmxKkkSgoODMXHiRDz55JOKrIPIFfn5+WHRokWYMmUKFi1aVOz80vagxLbr4eGBevXqYeHChXbds5Vcz6hRowAAP/zwA77//ntF16X0eyJ/f388/PDDmDZtmmLrIHJFU6dOhU6nw6ZNm5Cammr35Sux7VreG06YMAFPPfWUXZdNrqVdu3b46KOP8H//93+YN2+eooebK91n9Ho9mjdvjvfee8+m818TVScMRFXO19cXn3zyieLrkWUZ169fR506dRQ7VxZVb/7+/li4cKHi6+FYJVJOYGCgdQ88e+O2S1UlSRJGjx6N0aNHK7oejlUi5Wg0GkyePFmRo3C47ZI9dOrUCRs2bFB0HRyr1RMPmXc93LqIiIiIiIiIiIjIbXAPUSIiIiIiIiIiIoXIgIP3EKXycA9RIiIiIiIiIiIichsMRImIiIiIiIiIiMht8JB5IiIiIiIiIiIihQgJkCUHrs9xq1It7iFKREREREREREREboOBKBEREREREREREbkNHjJPRERERERERESkEBkCjjyQXeZB8+XiHqJERERERERERETkNriHKBERERERERERkUK4h6jr4R6iRERERERERERE5DYYiBIREREREREREZHb4CHzRERERERERERECjFDQPCQeZfCPUSJiIiIiIiIiIjIbTAQJSIiIiIiIiIiIrfBQ+aJiIiIiIiIiIgUwqvMux7uIUpERERERERERERug4EoERERERERERERuQ0eMk9ERERERERERKQQHjLveriHKBEREREREREREbkN7iFKRERERERERESkELMkQ0iyw9Ynw3HrUivF9hB955130LVrV/j4+CAgIKBC84wYMQKSJBX5iY6OVqpEIiJSOfYaIiJSEvsMERFR9aTYHqJGoxFPPPEEunTpgmXLllV4vujoaKxYscJ6W6/XK1EeERFVA+w1RESkJPYZIiKi6kmxQHTmzJkAgJUrV9o0n16vR2hoqAIVERFRdcNeQ0RESmKfISIiezBDQPCiSi7F5c4hunv3btSpUwe1atVCr1698Pbbb6N27dqlTm8wGGAwGKy3MzIyAACyLEOWXfucCbIsQwjh8nUCrFUJaqkTYK1KUUutrl5fZbhLr1HLGANYq1LUUqta6gRYq1LUUKMt3KXPAOoZZ2qpE2CtSlFLrWqpE1BfrUSFuVQgGh0djUGDBiEiIgKxsbGYNm0aHnjgAezduxdarbbEed59913rN7eF3bhxA0ajUemSq0SWZaSnp0MIAY1GsdO52gVrtT+11AmwVqWopdb09HRnl2BX7tRr1DLGANaqFLXUqpY6AdaqlOrUa9ypzwDqGWdqqRNgrUpRS61qqRNQV63Vqc+QfdgUiE6ZMgVz5swpc5rTp08jMjKyUsUMHjzY+nurVq3QunVrNG7cGLt370bv3r1LnGfq1KmYMGGC9XZGRgbq16+P4ODgCp/43FlkWYYkSQgODnb5Fw/Wan9qqRNgrUpRS62enp4OXR97jf2oZYwBrFUpaqlVLXUCrFUpjuw17DP2pZZxppY6AdaqFLXUqpY6AXXV6ujPNLeTHXzIvCPXpVY2BaITJ07EiBEjypymUaNGVamn2LKCgoJw/vz5Ut886PX6Ek9SrtFoXH6DBABJklirAtRSq1rqBFirUtRQq6NrY6+xLzWMMQvWqgy11KqWOgHWqgRH1sc+Y39qGWdqqRNgrUpRS61qqRNQT62uXh85nk2BaHBwMIKDg5WqpZgrV64gOTkZYWFhDlsnERE5F3sNEREpiX2GiIiIFIvI4+LiEBMTg7i4OJjNZsTExCAmJgZZWVnWaSIjI7F+/XoAQFZWFiZNmoR9+/bh4sWL2LFjB/r3748mTZqgX79+SpVJREQqxl5DRERKYp8hIiJ7MEvC4T9UNsUuqjR9+nSsWrXKejsqKgoAsGvXLvTo0QMAcObMGeuJbbVaLY4dO4ZVq1YhLS0N4eHh6Nu3L2bPnl3i4SNERETsNUREpCT2GSIisgcBAxx5Wk8Bg+NWplKKBaIrV67EypUry5xGiFujwdvbG9u2bVOqHCIiqobYa4iISEnsM0REVBWenp4IDQ1FYuJ7Dl93aGio0y8m5coUC0SJiIiIiIiIiIjclZeXFy5cuACj0ejwdXt6esLLy8vh61ULBqJEREREREREREQK8PLyYjDpghS7qBIRERERERERERGRq2EgSkRERERERERERG6DgSgRERERERERERG5DQaiRERERERERERE5DYYiBIREREREREREZHbYCBKREREREREREREboOBKBEREREREREREbkNBqJERERERERERETkNhiIEhERERERERERkdtgIEpERERERERERERug4EoERERERERERERuQ0GokREREREREREROQ2GIgSERERERERERGR22AgSkRERERERERERG6DgSgRERERERERERG5DQaiRERERERERERE5DYYiBIREREREREREZHbYCBKREREREREREREboOBKBEREREREREREbkNBqJERERERERERETkNhiIEhERERERERERkdtgIEpERERERERERERug4EoERERERERERERuQ0GokREREREREREROQ2GIgSERERERERERGR22AgSkRERERERERERG6DgSgRERERERERERG5DQaiRERERERERERE5DYYiBIREREREREREZHbUCwQvXjxIkaOHImIiAh4e3ujcePGmDFjBoxGY5nz5eXl4aWXXkLt2rXh6+uLxx57DNeuXVOqTCIiUin2GSIiUhL7DBERUfWlWCD6999/Q5ZlfPHFFzh58iTmz5+PRYsWYdq0aWXO9+qrr2LTpk1Yu3Yt9uzZg4SEBAwaNEipMomISKXYZ4iISEnsM0RERNWXh1ILjo6ORnR0tPV2o0aNcObMGXz++eeYN29eifOkp6dj2bJl+Oabb9CrVy8AwIoVK9CiRQvs27cPd999t1LlEhGRyrDPEBGRkthniIiIqi/FAtGSpKenIzAwsNTHDx06hPz8fPTp08d6X2RkJBo0aIC9e/eW+AbCYDDAYDAUWQcApKWl2a9whciyjIyMDHh6ekKjce3TubJW+1NLnQBrVYpaarW8ngohnFtIBSjRZwD19hq1jDGAtSpFLbWqpU6AtSpFLb2GfaY4tYwztdQJsFalqKVWtdQJqKtWtfQZchyHBaLnz5/HggULSv02FQASExPh6emJgICAIveHhIQgMTGxxHneffddzJw5s9j9ERERVaqXiIiKSk5Ohr+/v7PLKJVSfQZgryEichRX7jXsM0RE6ufKfYYcy+ZAdMqUKZgzZ06Z05w+fRqRkZHW2/Hx8YiOjsYTTzyBUaNG2V5lGaZOnYoJEyZYb6elpaFhw4aIi4tz+UGekZGB+vXr4/Lly/Dz83N2OWVirfanljoB1qoUtdSanp6OBg0alLlHjD25Wp8B1Ntr1DLGANaqFLXUqpY6AdaqFEf2GvYZ+1LLOFNLnQBrVYpaalVLnYC6anX0ZxpyfTYHohMnTsSIESPKnKZRo0bW3xMSEtCzZ0907doVixcvLnO+0NBQGI1GpKWlFflW9dq1awgNDS1xHr1eD71eX+x+f39/l98gLfz8/FirAtRSq1rqBFirUtRSq6MOg3G1PgOov9eoZYwBrFUpaqlVLXUCrFUpjug17DPKUMs4U0udAGtVilpqVUudgLpqdfVD+8lxbA5Eg4ODERwcXKFp4+Pj0bNnT7Rv3x4rVqwod+C1b98eOp0OO3bswGOPPQYAOHPmDOLi4tClSxdbSyUiIhVinyEiIiWxzxAREZFi0Xh8fDx69OiBBg0aYN68ebhx4wYSExOLnDsnPj4ekZGR+OuvvwAUfAM6cuRITJgwAbt27cKhQ4fw3HPPoUuXLrwiIxERFcE+Q0RESmKfISIiqr4Uu6jS9u3bcf78eZw/fx716tUr8pjlql75+fk4c+YMcnJyrI/Nnz8fGo0Gjz32GAwGA/r164eFCxdWeL16vR4zZswo8ZATV8NalaGWWtVSJ8BalaKWWl21Tmf1GcB1n5PbqaVOgLUqRS21qqVOgLUqxRVrZZ+pGLXUqpY6AdaqFLXUqpY6AdZK6iYJSzcnIiIiIiIiIiIiquZ4NlkiIiIiIiIiIiJyGwxEiYiIiIiIiIiIyG0wECUiIiIiIiIiIiK3wUCUiIiIiIiIiIiI3AYDUSIiIiIiIiIiInIbqg9EL168iJEjRyIiIgLe3t5o3LgxZsyYAaPRWOZ8eXl5eOmll1C7dm34+vrisccew7Vr1xSv95133kHXrl3h4+ODgICACs0zYsQISJJU5Cc6Otrl6hRCYPr06QgLC4O3tzf69OmDc+fOKVonAKSkpGDIkCHw8/NDQEAARo4ciaysrDLn6dGjR7Hn9IUXXrB7bZ999hnuuOMOeHl5oXPnzvjrr7/KnH7t2rWIjIyEl5cXWrVqhc2bN9u9ptLYUuvKlSuLPX9eXl6K1/jrr7/ikUceQXh4OCRJwoYNG8qdZ/fu3WjXrh30ej2aNGmClStXKl4nYHutu3fvLvacSpKExMRERet899130bFjR9SsWRN16tTBgAEDcObMmXLnc+ZYdTT2GeWw19iHWnqNGvoMoJ5eo5Y+A7DXVISaeg37jDLYZ+xDDb1GLX0GUE+vYZ+hylB9IPr3339DlmV88cUXOHnyJObPn49FixZh2rRpZc736quvYtOmTVi7di327NmDhIQEDBo0SPF6jUYjnnjiCYwdO9am+aKjo3H16lXrz5o1axSqsEBl6pw7dy4++eQTLFq0CPv370eNGjXQr18/5OXlKVgpMGTIEJw8eRLbt2/HTz/9hF9//RWjR48ud75Ro0YVeU7nzp1r17q+++47TJgwATNmzMDhw4fRpk0b9OvXD9evXy9x+j///BNPP/00Ro4ciSNHjmDAgAEYMGAATpw4Yde67FErAPj5+RV5/i5duqR4ndnZ2WjTpg0+++yzCk1/4cIFPPTQQ+jZsydiYmIwfvx4/Otf/8K2bdsUrtT2Wi3OnDlT5HmtU6eOQhUW2LNnD1566SXs27cP27dvR35+Pvr27Yvs7OxS53HmWHUG9hnlsNdUnVp6jVr6DKCeXqOWPgOw11SEmnoN+4wy2GccXyvAzzTlUUuvYZ+hShHV0Ny5c0VERESpj6elpQmdTifWrl1rve/06dMCgNi7d68jShQrVqwQ/v7+FZp2+PDhon///orWU5qK1inLsggNDRXvv/++9b60tDSh1+vFmjVrFKvv1KlTAoA4cOCA9b4tW7YISZJEfHx8qfN1795dvPLKK4rVJYQQnTp1Ei+99JL1ttlsFuHh4eLdd98tcfonn3xSPPTQQ0Xu69y5sxgzZoyidQphe622jF+lABDr168vc5rJkyeLli1bFrnvqaeeEv369VOwsuIqUuuuXbsEAJGamuqQmkpz/fp1AUDs2bOn1GmcOVZdBfuMfbHXVJ5aeo0a+4wQ6uk1auozQrDXVJSr9xr2Gfthn7EPNfYatfQZIdTVa9hnqCJUv4doSdLT0xEYGFjq44cOHUJ+fj769OljvS8yMhINGjTA3r17HVGizXbv3o06deqgefPmGDt2LJKTk51dUhEXLlxAYmJikefU398fnTt3VvQ53bt3LwICAtChQwfrfX369IFGo8H+/fvLnHf16tUICgrCXXfdhalTpyInJ8dudRmNRhw6dKjI86HRaNCnT59Sn4+9e/cWmR4A+vXrp/iYrEytAJCVlYWGDRuifv366N+/P06ePKlonZXhrOe0Ktq2bYuwsDDcf//9+OOPPxy+/vT0dAAo8zVUjc+rvbHPOAd7TVFq6TXVuc8A6ntNdHafAdhrKqq69Rr2mdKxzzinVkAdvUaNr4fO7jXsM1QRHs4uwN7Onz+PBQsWYN68eaVOk5iYCE9Pz2LnkQkJCXHIeZRsFR0djUGDBiEiIgKxsbGYNm0aHnjgAezduxdardbZ5QGA9XkLCQkpcr/Sz2liYmKx3e89PDwQGBhY5nqfeeYZNGzYEOHh4Th27Bhef/11nDlzBuvWrbNLXUlJSTCbzSU+H3///XeJ8yQmJjr8+QMqV2vz5s2xfPlytG7dGunp6Zg3bx66du2KkydPol69eorWa4vSntOMjAzk5ubC29vbSZUVFxYWhkWLFqFDhw4wGAxYunQpevTogf3796Ndu3YOqUGWZYwfPx733HMP7rrrrlKnc9ZYdRXsM87DXlOUWnpNde4zgHp6jSv0GYC9pqKqW69hnyl/vewzVVOde41a+gzgGr2GfYYqymX3EJ0yZUqJJ+Mt/HP7C1t8fDyio6PxxBNPYNSoUS5dqy0GDx6MRx99FK1atcKAAQPw008/4cCBA9i9e7dL1WlPStc6evRo9OvXD61atcKQIUPw5ZdfYv369YiNjbXjX1F9denSBcOGDUPbtm3RvXt3rFu3DsHBwfjiiy+cXZpqNW/eHGPGjEH79u3RtWtXLF++HF27dsX8+fMdVsNLL72EEydO4Ntvv3XYOp2JfeYWe/UZR9RqT+w1rot9xv5coc8A7DWu2mvYZ5TBPuPa2GvszxV6jbv1Gao8l91DdOLEiRgxYkSZ0zRq1Mj6e0JCAnr27ImuXbti8eLFZc4XGhoKo9GItLS0It+oXrt2DaGhoYrXWlWNGjVCUFAQzp8/j969e1d4PiXrtDxv165dQ1hYmPX+a9euoW3btjYvr6K1hoaGFjtJtslkQkpKik3/lp07dwZQ8G1848aNba73dkFBQdBqtcWu8lnWGAsNDbVpenupTK230+l0iIqKwvnz55UosdJKe079/Pxc6pvU0nTq1Am///67Q9Y1btw46wn8y/tG3Flj1d7YZ8peVmX6DMBeUxZ37TXVuc8A6u41juwzAHtNaVyh17DPFGCfKUotfQao3r1GzX0G4Gcacl0uG4gGBwcjODi4QtPGx8ejZ8+eaN++PVasWAGNpuwdX9u3bw+dTocdO3bgscceA1BwFbS4uDh06dJF0Vrt4cqVK0hOTi7SpCtCyTojIiIQGhqKHTt2WN8sZGRkYP/+/TZfgRKoeK1dunRBWloaDh06hPbt2wMAdu7cCVmWrW8IKiImJgYAbH5OS+Pp6Yn27dtjx44dGDBgAICCXfd37NiBcePGlThPly5dsGPHDowfP9563/bt2ys1JpWu9XZmsxnHjx/Hgw8+qGCltuvSpQs2b95c5D5HPKf2EhMTY7cxWRohBF5++WWsX78eu3fvRkRERLnzOGus2hv7TOkq22cA9pqyuGuvqc59BlB3r3FEnwHYa9TQa9hnCrDPFKWWPlPZWm/nqr1GzX0G4GcacmFOvqhTlV25ckU0adJE9O7dW1y5ckVcvXrV+lN4mubNm4v9+/db73vhhRdEgwYNxM6dO8XBgwdFly5dRJcuXRSv99KlS+LIkSNi5syZwtfXVxw5ckQcOXJEZGZmWqdp3ry5WLdunRBCiMzMTPHaa6+JvXv3igsXLohffvlFtGvXTjRt2lTk5eW5TJ1CCPHee++JgIAA8eOPP4pjx46J/v37i4iICJGbm6tYnUIIER0dLaKiosT+/fvF77//Lpo2bSqefvpp6+O3//ufP39ezJo1Sxw8eFBcuHBB/Pjjj6JRo0aiW7dudq3r22+/FXq9XqxcuVKcOnVKjB49WgQEBIjExEQhhBBDhw4VU6ZMsU7/xx9/CA8PDzFv3jxx+vRpMWPGDKHT6cTx48ftWpc9ap05c6bYtm2biI2NFYcOHRKDBw8WXl5e4uTJk4rWmZmZaR2LAMSHH34ojhw5Ii5duiSEEGLKlCli6NCh1un/+ecf4ePjIyZNmiROnz4tPvvsM6HVasXWrVsVrbMytc6fP19s2LBBnDt3Thw/fly88sorQqPRiF9++UXROseOHSv8/f3F7t27i7x+5uTkWKdxpbHqDOwzrlOrEOw1t1NLr1FLnxFCPb1GLX1GCPaailBTr2GfUQb7jONr5Wca+9fKzzSkJqoPRFesWCEAlPhjcVCfDcQAAAIPSURBVOHCBQFA7Nq1y3pfbm6uePHFF0WtWrWEj4+PGDhwYJE3HEoZPnx4ibUWrg2AWLFihRBCiJycHNG3b18RHBwsdDqdaNiwoRg1apT1Rd1V6hRCCFmWxZtvvilCQkKEXq8XvXv3FmfOnFG0TiGESE5OFk8//bTw9fUVfn5+4rnnnivyJuf2f/+4uDjRrVs3ERgYKPR6vWjSpImYNGmSSE9Pt3ttCxYsEA0aNBCenp6iU6dOYt++fdbHunfvLoYPH15k+v/+97+iWbNmwtPTU7Rs2VL8/PPPdq/JHrWOHz/eOm1ISIh48MEHxeHDhxWvcdeuXSWOS0ttw4cPF927dy82T9u2bYWnp6do1KhRkTHrSrXOmTNHNG7cWHh5eYnAwEDRo0cPsXPnTsXrLO31s/Dz5Gpj1dHYZ1ynViHYa0qill6jhj4jhHp6jVr6jBDsNRWhpl7DPqMM9hnH18rPNPavlZ9pSE0kIYS4fa9RIiIiIiIiIiIiourIZa8yT0RERERERERERGRvDESJiIiIiIiIiIjIbTAQJSIiIiIiIiIiIrfBQJSIiIiIiIiIiIjcBgNRIiIiIiIiIiIichsMRImIiIiIiIiIiMhtMBAlIiIiIiIiIiIit8FAlIiIiIiIiIiIiNwGA1EiIiIiIiIiIiJyGwxEiYiIiIiIiIiIyG0wECUiIiIiIiIiIiK38f93SKp12DsMqgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a simple 2D example for visualization\n", + "K_viz = EmbeddedComplex()\n", + "\n", + "# Square with center point\n", + "K_viz.add_node('A', [-1, -1])\n", + "K_viz.add_node('B', [1, -1])\n", + "K_viz.add_node('C', [1, 1])\n", + "K_viz.add_node('D', [-1, 1])\n", + "K_viz.add_node('E', [0, 0]) # center\n", + "\n", + "# Add edges\n", + "edges = [('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A'), # boundary\n", + " ('A', 'E'), ('B', 'E'), ('C', 'E'), ('D', 'E')] # to center\n", + "K_viz.add_edges_from(edges)\n", + "\n", + "# Add triangular faces\n", + "faces = [['A', 'B', 'E'], ['B', 'C', 'E'], ['C', 'D', 'E'], ['D', 'A', 'E']]\n", + "for face in faces:\n", + " K_viz.add_face(face)\n", + "\n", + "# Visualization function\n", + "def plot_with_projections(K, theta, ax):\n", + " \"\"\"Plot complex with nodes colored by projection values\"\"\"\n", + " direction = np.array([np.sin(theta), -np.cos(theta)])\n", + " node_projections = np.dot(K.coord_matrix, direction)\n", + " \n", + " # Plot edges\n", + " for u, v in K.edges():\n", + " u_idx = K.node_to_index[u]\n", + " v_idx = K.node_to_index[v]\n", + " x = [K.coord_matrix[u_idx, 0], K.coord_matrix[v_idx, 0]]\n", + " y = [K.coord_matrix[u_idx, 1], K.coord_matrix[v_idx, 1]]\n", + " ax.plot(x, y, 'k-', alpha=0.5, linewidth=1)\n", + " \n", + " # Plot faces with transparency\n", + " for face_indices in K.cells.get(2, []):\n", + " face_coords = K.coord_matrix[list(face_indices)]\n", + " face_projection = np.max(node_projections[list(face_indices)])\n", + " ax.fill(face_coords[:, 0], face_coords[:, 1], \n", + " alpha=0.3, color=plt.cm.Blues(0.5))\n", + " \n", + " # Plot nodes colored by projection\n", + " scatter = ax.scatter(K.coord_matrix[:, 0], K.coord_matrix[:, 1], \n", + " c=node_projections, cmap='plasma', s=300, \n", + " edgecolors='black', linewidth=2, zorder=10)\n", + " \n", + " # Add node labels with projection values\n", + " for i, node in enumerate(K.node_list):\n", + " ax.annotate(f'{node}\\n({node_projections[i]:.2f})', \n", + " (K.coord_matrix[i, 0], K.coord_matrix[i, 1]),\n", + " ha='center', va='center', fontsize=9, fontweight='bold',\n", + " bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.8))\n", + " \n", + " # Direction arrow\n", + " ax.arrow(0, 0, direction[0]*0.7, direction[1]*0.7,\n", + " head_width=0.1, head_length=0.1, fc='red', ec='red', linewidth=2)\n", + " \n", + " ax.set_xlim(-2, 2)\n", + " ax.set_ylim(-2, 2)\n", + " ax.set_aspect('equal')\n", + " ax.grid(True, alpha=0.3)\n", + " ax.set_title(f'θ = {theta*180/np.pi:.0f}°')\n", + " \n", + " return scatter\n", + "\n", + "# Show projections in multiple directions\n", + "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", + "axes = axes.flatten()\n", + "\n", + "thetas = np.linspace(0, 2*np.pi, 6, endpoint=False)\n", + "for ax, theta in zip(axes, thetas):\n", + " scatter = plot_with_projections(K_viz, theta, ax)\n", + "\n", + "# Add shared colorbar\n", + "fig.subplots_adjust(right=0.9)\n", + "cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])\n", + "fig.colorbar(scatter, cax=cbar_ax, label='Node Projection Values')\n", + "\n", + "plt.suptitle('Complex Colored by Projection Values in Different Directions\\n' + \n", + " 'Red arrows show projection direction, faces use max vertex projection', \n", + " fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Graph vs. Complex ECT\n", + "\n", + "Let's compare ECT results for the same geometry with and without 2-cells:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4/PhxPfDAA3r77bdr5KTq3yJHjx5VXl5eteXyarN7925t2bKl3jkHgNNhAhqAUT169JB08sYlTW3mGnPszZs36+DBg2rXrp3r+W7duqlbt26SVO1qkJ/66RUGVW655Ra9+uqruvXWW5Wamqr4+Hg5HA5dfvnlTbpZx6nurH26q2B69uypjz76SFu2bNGYMWNq3abqpiq9evVyyzHT09OVlJSkN954Q2PGjNEbb7yh1q1bV1tfGgAAIJCZ7m83bdqk77//XsnJyW7d96n6w8b2jfXldDrVt29fPfXUU7V+/+fvc9OmTa4J0q+//lpXXHFFvY4zatQoLVy4UMXFxdqwYYPuu+8+9enTR82aNdPy5cu1fft2xcTEaODAgU16P57K16WXXqpVq1bp9ttv14ABAxQTEyOn06kpU6Y0+N8iTqdTkyZN0h133FHr96v+rQQA9cUSHACMOu+88yRJb7zxhtePXXWVc9XH1prqvffe07Rp0/Tkk0/qkksu0aRJkzRq1CidOHGi1u13795d7euCggIdPnxYHTt2dEs8Ve/vtddeq/X7lZWVeuutt5SQkKCRI0e65ZjBwcG68sor9d577yknJ0cfffSRrrjiilM22gAAAIHGZH/bkGN36NBBkrRz584a39u5c6fr++7UmP63S5cuOn78uCZMmKCJEyfWePz0Cu7CwkJNnz5dvXr10vXXX6/HHntM69atq1dso0eP1oEDB/T222+rsrJSI0aMUFBQkEaNGqXly5dr+fLlGjFixGn72lPdTNGTcnJytHDhQt1111164IEHdNFFF2nSpEnq3Llzte0SExMVFxenrVu31rm/Ll26qKCgoNZ8T5w4sdZPaAJAXZiABmBUamqqpkyZopdfftl1B+ufKisr0//7f/+vQfs8cOCAduzYcdrtLr30UvXq1UsPPfSQ1qxZU+s2DbkSITg4uMb2f//731VZWVnr9i+++KLKy8tdXz///POqqKjQWWedVe9j1mXEiBGaOHGiXn31Vf33v/+t8f177rlHu3bt0h133FHrFd2NddVVVyknJ0c33HCDCgoKTrmMCQAAQCAy2d9ecskl6tu3rx5++GGtXr26xvfz8/N1zz33SJKGDBmiVq1a6YUXXlBpaalrmy+++ELbt2/XOeec06AY66Mx/e+ll16qgwcP6qWXXqrxveLiYhUWFrq+vvPOO3XgwAH9+9//1lNPPaWOHTtq2rRp1d7fqVRdrf7oo4+qX79+io+Pdz2/cOFCrV+/vl5XtEdHR5/yAhRPqZoU//m/RZ555plqXwcFBenCCy/Up59+qvXr19fYT9XrL730Uq1evVoZGRk1tjlx4oQqKircFDkAu2AJDgAe9cUXX9TaLI8YMcL1F/nXXntNkydP1sUXX6zzzjtPEyZMUHR0tHbv3q23335bhw8f1hNPPFHvY06dOlVLly497eRxaGioPvzwQ6Wnp2vUqFG6+OKLNXr0aEVHR+vgwYP65JNPdODAgXo33+eee65ef/11xcfHq1evXlq9erUWLFigFi1a1Lp9WVmZJkyYoEsvvVQ7d+7Uc889p1GjRun888+v93s9nddee00TJkzQBRdcoCuvvFKjR49WaWmpPvjgAy1ZskSXXXaZbr/9drcdT5IGDhyoPn36uG4YM2jQILfuHwAAwCRf728/+OADTZw4UWPGjNGll16qkSNHKjQ0VNu2bXN9+u3hhx9WaGioHn30UU2fPl1paWm64oorlJWVpb/+9a/q2LGjbrvttoYlph4a0/9eddVVeuedd/Tb3/5Wixcv1siRI1VZWakdO3bonXfeUUZGhoYMGaJFixbpueee08yZM13956uvvqqxY8fqT3/6kx577LE6Y+vatatat26tnTt36pZbbnE9P2bMGN15552SVK8J6MGDB+v555/Xn//8Z3Xt2lWtWrVy3TDRU+Li4jRmzBg99thjKi8vV7t27TRv3jzt3bu3xraPPPKI5s2bp7S0NF1//fXq2bOnDh8+rHfffVcrVqxQs2bNdPvtt+uTTz7Rueeeq6uvvlqDBw9WYWGhvv76a7333nvat29fretQA8CpMAENwKPuu+++Wp9/9dVXXQ16YmKiVq1apeeee05z5szRPffco7KyMnXo0EHnn3++/vCHP3gsvm7dumnz5s3629/+pg8//FBffPGFysrKlJSUpJSUFM2cOdO1lMXp/PWvf1VwcLDefPNNlZSUaOTIkVqwYIHS09Nr3f4f//iH3nzzTd13330qLy/XFVdcob/97W9u/dhemzZttHbtWj355JN699139f777yskJET9+vXT7NmzNXXqVI98THDq1Km64447uPkgAAAIOL7e33bt2lWbN2/W008/rQ8//FAfffSRnE6nunbtqmuvvVa///3vXdteffXVioqK0l/+8hfdeeedio6O1kUXXaRHH33UIzdQbEz/GxQUpI8++khPP/20XnvtNX344YeKiopS586d9Yc//EHdunVTfn6+fvOb32jgwIGuK7ylkxPGf/jDH/Tkk0/q4osv1vDhw+uMb/To0Xr33Xc1atQo13ODBw9WVFSUKioqlJKSctr3eN9992n//v167LHHlJ+fr7S0NI9PQEvSW2+9pVtuuUXPPvusLMvS5MmT9cUXX6ht27bVtmvXrp2+/PJL/elPf9Kbb76pvLw8tWvXTmeddZaioqIkSVFRUVq6dKkeeeQRvfvuu3rttdcUFxenbt266YEHHnBdHQ4A9eWw3HVnAABAvcyePVvTp0/XunXr6ryDuj/761//qttuu0379u1jjTgAAACbs0P/CwA4NdaABgC4lWVZ+te//qW0tDQmnwEAAAAAsDmW4AAAuEVhYaE++eQTLV68WF9//bU+/vhj0yEBAAAAAADDmIAGALjF0aNHdeWVV6pZs2b6v//7P7feTBEAAAAAAPgn1oAGAAAAAAAAAHgEa0ADAAAAAAAAADyCCWgAAAAAAAAAgEcwAQ1Ajz32mHr06CGn02k6FJ90//33y+FwmA7DqCVLlsjhcGjJkiVG4/jmm28UEhKirVu3Go0DAAD4JvrautHX+k5f6y2PP/64OnfurODgYA0YMMB0OHW6/PLLdemll5oOA4AHMAEN2FxeXp4effRR3XnnnQoKsu+QUFRUpPvvv982jai/6tWrl8455xzdd999pkMBAAA+hr72JPpaVJk3b57uuOMOjRw5Uq+++qoeeeQR0yHV6c4779T777+vr776ynQoANzMvmdlAJKkV155RRUVFbriiitMh2JUUVGRHnjggVob9XvvvVfFxcXeDwq1+u1vf6sPP/xQ3333nelQAACAD6GvPYm+FlUWLVqkoKAg/etf/9LUqVN19tlnmw6pTgMHDtSQIUP05JNPmg4FgJsxAQ3Y3Kuvvqrzzz9fERERpkMxwul0qqSkpM5tQkJCbJsfXzRx4kQlJCTo3//+t+lQAACAD6Gvpa8NRFVLhuzbt6/Brz1y5IgiIyMVFhbm/sA85NJLL9UHH3yggoIC06EAcCMmoAEb27t3r7Zs2aKJEye6ntu3b59CQ0PVq1cvffLJJ9W2r1ozbvHixR6Prby8XM2bN9f06dNrfC8vL08RERH6f//v/7meKy0t1cyZM9W1a1eFh4crOTlZd9xxh0pLS6u91uFw6Oabb9abb76p3r17Kzw8XC+88IISExMlSQ888IAcDoccDofuv/9+SadeK++NN97QsGHDFBUVpYSEBI0ZM0bz5s2rts0XX3yh0aNHKzo6WrGxsTrnnHO0bdu2attkZmZq+vTpat++vcLDw9WmTRtdcMEFjWoy63Lw4EH95je/UVJSksLDw9W7d2+98sorNbb74YcfdOGFFyo6OlqtWrXSbbfdViOPVZ599ll17txZkZGRGjZsmJYvX66xY8dq7Nix1barb33mz5+vUaNGqVmzZoqJiVH37t31f//3f9W2CQ0N1dixY/Xxxx83LSEAACBg0NfS1/pjX+tJDodDr776qgoLC10/B7Nnz5Z08o8148ePV6tWrRQeHq5evXrp+eefr3U/X3zxhdLS0hQbG6u4uDgNHTpUb731VrVtvvzyS02ZMkXx8fGKiopSWlqaVq5cWW2b/Px83XrrrerYsaPCw8PVqlUrTZo0SRs3bqy23aRJk1RYWKj58+e7LxkAjAsxHQAAc1atWiVJGjRokOu5xMREPffcc3ruuef0q1/9SgcOHFBCQoJyc3P117/+VaNGjdK4cePq3G92dna9jh8bG6vw8PBavxcaGqqLLrpIH3zwgf75z39W+6v9Rx99pNLSUl1++eWSTl7tcf7552vFihW6/vrr1bNnT3399dd6+umntWvXLn300UfV9r1o0SK98847uvnmm9WyZUv1799fzz//vH73u9/poosu0sUXXyxJ6tev3yljf+CBB3T//fdrxIgRevDBBxUWFqYvv/xSixYt0uTJkyVJr7/+uqZNm6b09HQ9+uijKioq0vPPP69Ro0Zp06ZN6tixoyTpF7/4hbZt26ZbbrlFHTt21JEjRzR//nwdOHDAtU1TZWVlafjw4a5/qCQmJuqLL77QNddco7y8PN16662SpOLiYk2YMEEHDhzQ73//e7Vt21avv/66Fi1aVGOfzz//vG6++WaNHj1at912m/bt26cLL7xQCQkJat++vWu7+tZn27ZtOvfcc9WvXz89+OCDCg8P17ffflujeZWkwYMH6+OPP1ZeXp7i4uLckiMAAOC/6Gvpa/21r/WU119/XS+++KLWrl2rl19+WZI0YsQI1/vt3bu3zj//fIWEhOjTTz/VjTfeKKfTqZtuusm1j9mzZ+s3v/mNevfurbvvvlvNmjXTpk2bNHfuXF155ZWSTv4MnnXWWRo8eLBmzpypoKAg1wT38uXLNWzYMEknl9F77733dPPNN6tXr146duyYVqxYoe3bt1f7ve3Vq5ciIyO1cuVKXXTRRd5KFwBPswDY1r333mtJsvLz82t8b//+/VZQUJD18ssvW5ZlWQ888IAlyZo3b95p9yupXo9XX321zv1kZGRYkqxPP/202vNnn3221blzZ9fXr7/+uhUUFGQtX7682nYvvPCCJclauXJltdiCgoKsbdu2Vdv26NGjliRr5syZNeKYOXOm9dPhcvfu3VZQUJB10UUXWZWVldW2dTqdlmVZVn5+vtWsWTPruuuuq/b9zMxMKz4+3vV8Tk6OJcl6/PHH68xFU11zzTVWmzZtrOzs7GrPX3755VZ8fLxVVFRkWZZlPfPMM5Yk65133nFtU1hYaHXt2tWSZC1evNiyLMsqLS21WrRoYQ0dOtQqLy93bTt79mxLkpWWluZ6rr71efrppy1J1tGjR0/7ft566y1LkvXll182KA8AACAw0df+iL7Wv/ra01m8eLElydq7d2+DXztt2jQrOjq6xvNVOfqp9PT0aj+LJ06csGJjY62UlBSruLi42rZVPxtOp9M688wzrfT0dNdzVfvv1KmTNWnSJNdz8fHx1k033VSvuLt162adddZZ9doWgH9gCQ7Axo4dO6aQkBDFxMTU+N4ZZ5yhtLQ0zZkzR3l5eXrmmWc0fPhwTZo06bT7nT9/fr0e6enpde5n/PjxatmypebMmeN6LicnR/Pnz9dll13meu7dd99Vz5491aNHD2VnZ7se48ePl6QaH61MS0tTr169Tvs+TuWjjz6S0+nUfffdV+MO61UfaZw/f75OnDihK664olpMwcHBSklJccVUtSbbkiVLlJOT0+iY6mJZlt5//32dd955siyrWjzp6enKzc11ffTt888/V5s2bXTJJZe4Xh8VFaXrr7++2j7Xr1+vY8eO6brrrlNIyI8fpvnVr36lhISEatvWtz7NmjWTJH388cdyOp11vqeqY9T3qiQAABDY6Gsbh77WfF/7c7m5udX2nZubK+nkz8tPn2/KGsmRkZE1jpeWlqY9e/a4jjd//nzl5+frrrvuqrFueNXPxubNm7V7925deeWVOnbsmCu2wsJCTZgwQcuWLXO9/2bNmunLL7/UoUOHThtfQkICfT4QYFiCA8ApXXbZZbrpppt07733KicnR/fdd1+9XvfTtfeaIiQkRL/4xS/01ltvqbS0VOHh4frggw9UXl5erVHfvXu3tm/f7lrv7ueOHDlS7etOnTo1Ka7vvvtOQUFBdTb7u3fvliRXM/pzVctGhIeH69FHH9Uf//hHJSUlafjw4Tr33HM1depUtW7d+pT7Ly4udjWHVU61/dGjR3XixAm9+OKLevHFF2vdpipH+/fvV9euXWusDdi9e/dqX+/fv1+S1LVr12rPh4SE1Ph4ZX3rc9lll+nll1/Wtddeq7vuuksTJkzQxRdfrEsuuaTGP4gsy5KkWtcwBAAA+Dn62trR15rva3/uggsu0NKlS2s8/9NlKiRp2rRprjWdG2rlypWaOXOmVq9eraKiomrfy83NVXx8vL777jtJUp8+fU65n6qfjWnTpp1ym9zcXCUkJOixxx7TtGnTlJycrMGDB+vss8/W1KlT1blz5xqvsSyLPh8IMExAAzbWokULVVRUKD8/X7GxsTW+/4tf/EI333yz/v73v2vIkCE666yz6rXfzMzMem0XHx9f7a/vtbn88sv1z3/+U1988YUuvPBCvfPOO+rRo4f69+/v2sbpdKpv37566qmnat1HcnJyta9Pd0x3qPpL/+uvv15rA/3TqytuvfVWnXfeefroo4+UkZGhP/3pT5o1a5YWLVqkgQMH1rr/OXPm1LiRTdWk7Kli+fWvf33K5rCudQGbqr71iYyM1LJly7R48WJ99tlnmjt3rubMmaPx48dr3rx5Cg4Odr2m6qqali1beixuAADgP+hrPYe+tvrx3d3X/tyTTz5Z7Qryr776Sv/v//0/vfHGG0pKSnI937Zt20a9h++++04TJkxQjx499NRTTyk5OVlhYWH6/PPP9fTTTzfoiu2qbR9//HENGDCg1m2qPpVw6aWXavTo0frwww81b948Pf7443r00Uf1wQcf1Ph9zMnJ0Zlnntmo9wfANzEBDdhYjx49JJ28a3htjVrLli2VlpamhQsX6k9/+lO999umTZt6bffqq6/q6quvrnObMWPGqE2bNpozZ45GjRqlRYsW6Z577qm2TZcuXfTVV19pwoQJjf5LeUNe16VLFzmdTn3zzTenbLS6dOkiSWrVqlW9rpzp0qWL/vjHP+qPf/yjdu/erQEDBujJJ5/UG2+8Uev26enp9b4zdGJiomJjY1VZWXnaWDp06KCtW7fWuOpg586dNbaTpG+//bbazXsqKiq0b9++aj9PDalPUFCQJkyYoAkTJuipp57SI488onvuuUeLFy+uFvvevXsVFBSkbt26nT4BAAAg4NHX/oi+9iR/6Wt/bvDgwdW+rprgHzlypFtu5Pjpp5+qtLRUn3zyic444wzX8z9f3qWq7lu3bq1xdfjPt4mLi6vXz0abNm1044036sYbb9SRI0c0aNAgPfzww9UmoCsqKvT999/r/PPPb/B7A+C7WAMasLHU1FRJJ9c9q01xcbG2bdsmqeZHvurirrXypJON2yWXexhNxwAAWhBJREFUXKJPP/1Ur7/+uioqKqp9TFE6+df0gwcP6qWXXqr1PRQWFp72OFFRUZKkEydOnHbbCy+8UEFBQXrwwQdrXCFQdbVGenq64uLi9Mgjj6i8vLzGPo4ePSpJKioqUklJSbXvdenSRbGxsSotLT1lDG3atNHEiROrPU4lODhYv/jFL/T+++9r69atp4xFks4++2wdOnRI7733nuu5oqKiGh9xHDJkiFq0aKGXXnpJFRUVrufffPPNGmv+1bc+x48fr/H9qn8I/TwXGzZsUO/evRUfH3+qtw0AAGyEvvZH9LUn+Utf621VV1//9Crz3Nxcvfrqq9W2mzx5smJjYzVr1qwada167eDBg9WlSxc98cQTta5JXVWPysrKGsustGrVSm3btq2Rj2+++UYlJSUaMWJEI98hAF/EFdCAjXXu3Fl9+vTRggUL9Jvf/KbG91966SXXxw7fe+893XrrrfXar7vWyqty2WWX6e9//7tmzpypvn37qmfPntW+f9VVV+mdd97Rb3/7Wy1evFgjR45UZWWlduzYoXfeeUcZGRkaMmRInceIjIxUr169NGfOHHXr1k3NmzdXnz59al3zrGvXrrrnnnv00EMPafTo0br44osVHh6udevWqW3btpo1a5bi4uL0/PPP66qrrtKgQYN0+eWXKzExUQcOHNBnn32mkSNH6h//+Id27dqlCRMm6NJLL1WvXr0UEhKiDz/8UFlZWbr88svdlsO//OUvWrx4sVJSUnTdddepV69eOn78uDZu3KgFCxa4muTrrrtO//jHPzR16lRt2LBBbdq00euvv+76h0yVsLAw3X///brllls0fvx4XXrppdq3b59mz56tLl26VLsipL71efDBB7Vs2TKdc8456tChg44cOaLnnntO7du316hRo1z7Ky8v19KlS3XjjTe6LT8AAMC/0df+iL7Wf/paEyZPnqywsDCdd955uuGGG1RQUKCXXnpJrVq10uHDh13bxcXF6emnn9a1116roUOH6sorr1RCQoK++uorFRUV6d///reCgoL08ssv66yzzlLv3r01ffp0tWvXTgcPHtTixYsVFxenTz/9VPn5+Wrfvr0uueQS9e/fXzExMVqwYIHWrVunJ598slp88+fPV1RUVL1uEgrAj1gAbO2pp56yYmJirKKiomrPl5SUWO3atbO6d+9unXvuudbw4cMNRWhZTqfTSk5OtiRZf/7zn2vdpqyszHr00Uet3r17W+Hh4VZCQoI1ePBg64EHHrByc3Nd20mybrrpplr3sWrVKmvw4MFWWFiYJcmaOXOmZVmWNXPmTKu24fKVV16xBg4c6DpeWlqaNX/+/GrbLF682EpPT7fi4+OtiIgIq0uXLtbVV19trV+/3rIsy8rOzrZuuukmq0ePHlZ0dLQVHx9vpaSkWO+8805jUlWnrKws66abbrKSk5Ot0NBQq3Xr1taECROsF198sdp2+/fvt84//3wrKirKatmypfWHP/zBmjt3riXJWrx4cbVt//a3v1kdOnSwwsPDrWHDhlkrV660Bg8ebE2ZMqXadvWpz8KFC60LLrjAatu2rRUWFma1bdvWuuKKK6xdu3ZV29cXX3xhSbJ2797t9hwBAAD/RV/7I/rak3y9r62PxYsXW5KsvXv3Nvi106ZNs6Kjo2s8/8knn1j9+vWzIiIirI4dO1qPPvqo9corr9R6nE8++cQaMWKEFRkZacXFxVnDhg2z/vOf/1TbZtOmTdbFF19stWjRwgoPD7c6dOhgXXrppdbChQsty7Ks0tJS6/bbb7f69+9vxcbGWtHR0Vb//v2t5557rkZsKSkp1q9//esGv1cAvs1hWadY3R+ALeTm5qpz58567LHHdM0117ief/7553XjjTfqtddeU0hIiK688krt27fPtUYaUBun06nExERdfPHFtX400R0uvPBCORwOffjhhx7ZPwAA8E/0tXAnb/S1qG7z5s0aNGiQNm7ceMo1yQH4J9aABmwuPj5ed9xxhx5//HHXum/l5eX6y1/+oq5du+rKK6/U+eefr6ioKL3++uuGo4UvKSkpqXGH8tdee03Hjx/X2LFjPXLM7du367///a8eeughj+wfAAD4L/paNJaJvhY1/eUvf9Ell1zC5DMQgLgCGkANL7/8sq677jq98sormj59uiRp6tSpeu+993THHXfowgsvpCmAlixZottuu02//OUv1aJFC23cuFH/+te/1LNnT23YsEFhYWGmQwQAADZHX4v6oK8FAM9iAhpANRUVFerevbssy9KuXbsUEnLyXqVZWVm65pprtHjxYt1+++26//77zQYK4/bt26ff//73Wrt2rY4fP67mzZvr7LPP1l/+8he1atXKdHgAAMDm6GtRX/S1AOBZTEADAADAJy1btkyPP/64NmzYoMOHD+vDDz/UhRdeWOdrlixZohk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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ECT value ranges:\n", + "Graph only: [0, 1]\n", + "With face: [0, 1]\n", + "\n", + "The face contributes +1 to the Euler characteristic when included.\n" + ] + } + ], + "source": [ + "# Create two versions: graph only vs. complex with faces\n", + "K_graph = EmbeddedComplex()\n", + "K_complex = EmbeddedComplex()\n", + "\n", + "# Same vertices and edges for both\n", + "vertices = {'A': [0, 0], 'B': [2, 0], 'C': [1, 1.732]}\n", + "edges = [('A', 'B'), ('B', 'C'), ('C', 'A')]\n", + "\n", + "for K in [K_graph, K_complex]:\n", + " for name, coord in vertices.items():\n", + " K.add_node(name, coord)\n", + " K.add_edges_from(edges)\n", + "\n", + "# Add face only to the complex version\n", + "K_complex.add_face(['A', 'B', 'C'])\n", + "\n", + "# Compute ECT for both\n", + "ect = ECT(num_dirs=20, num_thresh=30)\n", + "result_graph = ect.calculate(K_graph)\n", + "result_complex = ect.calculate(K_complex)\n", + "\n", + "# Plot comparison\n", + "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n", + "\n", + "# Visualizations\n", + "K_graph.plot(ax=axes[0,0], with_labels=True, node_size=400)\n", + "axes[0,0].set_title('Graph Only (no 2-cells)')\n", + "\n", + "K_complex.plot(ax=axes[0,1], with_labels=True, node_size=400, \n", + " face_alpha=0.3, face_color='lightblue')\n", + "axes[0,1].set_title('Complex with 2-cell (face)')\n", + "\n", + "# ECT comparisons\n", + "result_graph.plot(ax=axes[1,0])\n", + "axes[1,0].set_title('ECT: Graph Only\\n(χ = vertices - edges)')\n", + "\n", + "result_complex.plot(ax=axes[1,1])\n", + "axes[1,1].set_title('ECT: Complex with Face\\n(χ = vertices - edges + faces)')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Show numerical difference\n", + "print(\"ECT value ranges:\")\n", + "print(f\"Graph only: [{result_graph.min()}, {result_graph.max()}]\")\n", + "print(f\"With face: [{result_complex.min()}, {result_complex.max()}]\")\n", + "print(f\"\\nThe face contributes +1 to the Euler characteristic when included.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dataexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/doctrees/nbsphinx/notebooks/Tutorial-ExactECT.ipynb b/docs/.doctrees/nbsphinx/notebooks/Tutorial-ExactECT.ipynb similarity index 55% rename from docs/doctrees/nbsphinx/notebooks/Tutorial-ExactECT.ipynb rename to docs/.doctrees/nbsphinx/notebooks/Tutorial-ExactECT.ipynb index 19a4e29..7ec6520 100644 --- a/docs/doctrees/nbsphinx/notebooks/Tutorial-ExactECT.ipynb +++ b/docs/.doctrees/nbsphinx/notebooks/Tutorial-ExactECT.ipynb @@ -13,44 +13,37 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from ect import ECT, EmbeddedGraph, EmbeddedCW,create_example_graph\n", + "from ect import ECT, EmbeddedComplex, create_example_graph\n", "\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Circle\n", "import numpy as np\n", - "import networkx as nx" + "import networkx as nx\n", + "\n", + "# Note: EmbeddedGraph and EmbeddedCW are now unified into EmbeddedComplex\n", + "# For backward compatibility, you can still use:\n", + "# from ect import EmbeddedGraph, EmbeddedCW" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can use the `EmbeddedGraph` class to find the angle normal to any pair of vertices in the graph, whether or not there is a connecting edge. Setting `angle_labels_circle=True` in the plotting command will try to draw these on the circle. Note that this doesn't tend to do well for large inputs, but can be helpful for small examples. " + "We can use the `EmbeddedComplex` class (which unifies the old `EmbeddedGraph` and `EmbeddedCW` classes) to find the angle normal to any pair of vertices in the graph, whether or not there is a connecting edge. Setting `angle_labels_circle=True` in the plotting command will try to draw these on the circle. Note that this doesn't tend to do well for large inputs, but can be helpful for small examples." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Super simple graph \n", - "G = EmbeddedGraph()\n", + "# Super simple graph using the unified EmbeddedComplex class\n", + "G = EmbeddedComplex()\n", "G.add_node('A', 0,0)\n", "G.add_node('B', 1,0)\n", "G.add_node('C', 2,1)\n", @@ -61,7 +54,7 @@ "\n", "fig, ax = plt.subplots()\n", "G.plot(ax = ax)\n", - "G.plot_angle_circle(ax = ax)\n" + "G.plot_angle_circle(ax = ax)" ] }, { @@ -99,45 +92,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ nan 5.49778714 5.60844436 5.8195377 5.03413953 5.49778714]\n", - " [2.35619449 nan 5.6951827 0. 3.92699082 5.49778714]\n", - " [2.46685171 2.55359005 nan 2.03444394 2.89661399 2.67794504]\n", - " [2.67794504 3.14159265 5.17603659 nan 3.46334321 3.92699082]\n", - " [1.89254688 0.78539816 6.03820664 0.32175055 nan 0. ]\n", - " [2.35619449 2.35619449 5.8195377 0.78539816 3.14159265 nan]]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# If return type is `matrix`, the function returns the matrix of angles and the labels of the angles in the order of the rows/columns in the matrix \n", - "M,Labels = G.get_all_normals_matrix()\n", + "M,Labels = G.get_normal_angle_matrix() # Updated method name\n", "print(M)\n", "\n", "plt.matshow(M)\n", diff --git a/docs/doctrees/nbsphinx/notebooks_Matisse_example_matisse_13_1.png b/docs/.doctrees/nbsphinx/notebooks_Matisse_example_matisse_13_1.png similarity index 100% rename from docs/doctrees/nbsphinx/notebooks_Matisse_example_matisse_13_1.png rename to docs/.doctrees/nbsphinx/notebooks_Matisse_example_matisse_13_1.png diff --git a/docs/doctrees/nbsphinx/notebooks_Matisse_example_matisse_16_1.png b/docs/.doctrees/nbsphinx/notebooks_Matisse_example_matisse_16_1.png similarity index 100% rename from docs/doctrees/nbsphinx/notebooks_Matisse_example_matisse_16_1.png rename to 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+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for ect.directions

+from typing import Optional, Sequence
+from enum import Enum
+
+import numpy as np
+
+
+

+[docs]
+class Sampling(Enum):
+    UNIFORM = "uniform"
+    RANDOM = "random"
+    CUSTOM = "custom"

+
+
+
+

+[docs]
+class Directions:
+    """
+    Manages direction vectors for ECT calculations.
+    Supports uniform, random, or custom sampling of directions.
+
+    Examples:
+        # Uniform sampling in 2D (default)
+        dirs = Directions.uniform(num_dirs=8)
+
+        # Uniform sampling in 3D
+        dirs = Directions.uniform(num_dirs=10, dim=3)
+
+        # Random sampling in 2D
+        dirs = Directions.random(num_dirs=10, seed=42)
+
+        # Custom angles (2D only)
+        dirs = Directions.from_angles([0, np.pi/4, np.pi/2])
+
+        # Custom vectors in any dimension
+        dirs = Directions.from_vectors([(1,0,0), (0,1,0), (0,0,1)])
+    """
+
+

+[docs]
+    def __init__(
+        self,
+        num_dirs: int = 360,
+        sampling: Sampling = Sampling.UNIFORM,
+        dim: int = 2,
+        endpoint: bool = False,
+        seed: Optional[int] = None,
+    ):
+        self.num_dirs = num_dirs
+        self.sampling = sampling
+        self.dim = dim
+        self.endpoint = endpoint
+
+        self._rng = np.random.RandomState(seed)
+        self._thetas = None
+        self._vectors = None
+        self._initialize_directions()

+
+
+    def _initialize_directions(self):
+        """
+        Initialize direction vectors using the chosen sampling strategy.
+        For 2D, the angles may be stored; for n-dim (n>2) the vectors are generated from
+        random normal samples and normalized to lie on the unit sphere.
+        """
+        if self.sampling == Sampling.UNIFORM:
+            if self.dim == 2:
+                self._thetas = np.linspace(
+                    0, 2 * np.pi, self.num_dirs, endpoint=self.endpoint
+                )
+            else:
+                # generate random normal samples and normalize to lie on the unit sphere
+                self._vectors = self._rng.randn(self.num_dirs, self.dim)
+                self._vectors /= np.linalg.norm(self._vectors,
+                                                axis=1, keepdims=True)
+        elif self.sampling == Sampling.RANDOM:
+            if self.dim == 2:
+                self._thetas = self._rng.uniform(0, 2 * np.pi, self.num_dirs)
+                self._thetas.sort()
+            else:
+                self._vectors = self._rng.randn(self.num_dirs, self.dim)
+                self._vectors /= np.linalg.norm(self._vectors,
+                                                axis=1, keepdims=True)
+
+

+[docs]
+    @classmethod
+    def uniform(
+        cls,
+        num_dirs: int = 360,
+        dim: int = 2,
+        endpoint: bool = False,
+        seed: Optional[int] = None,
+    ) -> "Directions":
+        """
+        Factory method for uniform sampling.
+
+        Parameters:
+            num_dirs: Number of direction vectors.
+            dim: Dimension of the space (default 2).
+            endpoint: Whether to include the endpoint (for 2D angles).
+            seed: Optional random seed.
+        """
+        return cls(num_dirs, Sampling.UNIFORM, dim, endpoint, seed)

+
+
+

+[docs]
+    @classmethod
+    def random(
+        cls, num_dirs: int = 360, dim: int = 2, seed: Optional[int] = None
+    ) -> "Directions":
+        """
+        Factory method for random sampling.
+
+        Parameters:
+            num_dirs: Number of direction vectors.
+            dim: Dimension of the space.
+            seed: optional random seed.
+        """
+        return cls(num_dirs, Sampling.RANDOM, dim, seed=seed)

+
+
+

+[docs]
+    @classmethod
+    def from_angles(cls, angles: Sequence[float]) -> "Directions":
+        """
+        Create an instance for custom angles (2D only).
+        """
+        instance = cls(len(angles), Sampling.CUSTOM, dim=2)
+        instance._thetas = np.array(angles)
+        return instance

+
+
+

+[docs]
+    @classmethod
+    def from_vectors(cls, vectors: Sequence[tuple]) -> "Directions":
+        """
+        Create an instance from custom direction vectors.
+        Works in any number of dimensions.
+        """
+        vectors = np.array(vectors, dtype=float)
+        norms = np.linalg.norm(vectors, axis=1, keepdims=True)
+        if np.any(norms == 0):
+            raise ValueError("Zero-magnitude vectors are not allowed")
+        normalized = vectors / norms
+        instance = cls(len(vectors), Sampling.CUSTOM, dim=vectors.shape[1])
+        instance._vectors = normalized
+        if instance.dim == 2:
+            instance._thetas = np.arctan2(normalized[:, 1], normalized[:, 0])
+        return instance

+
+
+    @property
+    def thetas(self) -> np.ndarray:
+        """Get the angles for 2D directions. Raises an error if dim > 2."""
+        if self.dim != 2:
+            raise ValueError(
+                "Angle representation is only available for 2D directions."
+            )
+        if self._thetas is None:
+            # Compute the angles from the vectors.
+            self._thetas = np.arctan2(self.vectors[:, 1], self.vectors[:, 0])
+        return self._thetas
+
+    @property
+    def vectors(self) -> np.ndarray:
+        """Get unit direction vectors.
+        For 2D, they are computed from thetas if not already created.
+        For n-dim (n>2), they should be available.
+        """
+        if self._vectors is None:
+            if self.dim == 2:
+                self._vectors = np.column_stack(
+                    (np.cos(self._thetas), np.sin(self._thetas))
+                )
+            else:
+                raise ValueError(
+                    "Direction vectors for dimensions >2 should be generated during initialization."
+                )
+        return self._vectors
+
+    def __len__(self) -> int:
+        return self.num_dirs
+
+    def __getitem__(self, idx) -> np.ndarray:
+        """Return the direction vector at index idx."""
+        return self.vectors[idx]

+
+
+ +
+
+ +
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/docs/_modules/ect/ect_graph.html b/docs/_modules/ect/ect.html similarity index 76% rename from docs/_modules/ect/ect_graph.html rename to docs/_modules/ect/ect.html index 3c21c6d..1adced2 100644 --- a/docs/_modules/ect/ect_graph.html +++ b/docs/_modules/ect/ect.html @@ -3,7 +3,7 @@ - ect.ect_graph — ect 0.1.5 documentation + ect.ect — ect 0.1.5 documentation @@ -54,26 +54,44 @@
  • 2. Modules
      -
    • 2.1. Embedded graphs
        -
      • EmbeddedGraph
        • +
      • 2.1. Embedded Complex
        • -
      • 2.2. Embedded CW complex
      • 3. Tutorials
          -
        • 3.1. Tutorial: ECT for Embedded Graphs
            -
          • 3.1.1. Basic Usage
            • +
          • 3.1. Tutorial: ECT for Embedded Cell Complexes
            • -
          • 3.2. Tutorial: ECT for CW complexes
            • +
          • 3.2. Tutorial for exact ECT computation
          • 3.3. ECT on Matisse’s “The Parakeet and the Mermaid” @@ -111,7 +129,7 @@ @@ -120,22 +138,22 @@
            -

            Source code for ect.ect_graph

            +  
            Source code for ect.ect
             import numpy as np
 from numba import prange, njit
-from typing import Optional, Union
+from numba.typed import List
+from typing import Optional
 
-from .embed_cw import EmbeddedCW
-from .embed_graph import EmbeddedGraph
+from .embed_complex import EmbeddedComplex
 from .directions import Directions
 from .results import ECTResult
 
 
 
            
-[docs]
+[docs]
 class ECT:
     """
-    A class to calculate the Euler Characteristic Transform (ECT) from an input :any:`EmbeddedGraph` or :any:`EmbeddedCW`.
+    A class to calculate the Euler Characteristic Transform (ECT) from an input :any:`EmbeddedComplex`.
 
     The result is a matrix where entry ``M[i,j]`` is :math:`\chi(K_{a_i})` for the direction :math:`\omega_j` where :math:`a_i` is the ith entry in ``self.thresholds``, and :math:`\omega_j` is the ith entry in ``self.thetas``.
 
@@ -151,7 +169,7 @@ 
            Source code for ect.ect_graph
                 """
 
 
            
-[docs]
+[docs]
     def __init__(
         self,
         directions: Optional[Directions] = None,
@@ -183,8 +201,7 @@ 
            Source code for ect.ect_graph
                     """Ensures directions is a valid Directions object of correct dimension"""
         if self.directions is None:
             if self.num_dirs is None:
-                raise ValueError(
-                    "Either 'directions' or 'num_dirs' must be provided.")
+                raise ValueError("Either 'directions' or 'num_dirs' must be provided.")
             self.directions = Directions.uniform(self.num_dirs, dim=graph_dim)
         elif isinstance(self.directions, list):
             # if list of vectors, convert to Directions object
@@ -235,18 +252,18 @@ 
            Source code for ect.ect_graph
                         self.thresholds = self.thresholds.astype(float)
 
 
            
-[docs]
+[docs]
     def calculate(
         self,
-        graph: Union[EmbeddedGraph, EmbeddedCW],
+        graph: EmbeddedComplex,
         theta: Optional[float] = None,
         override_bound_radius: Optional[float] = None,
     ):
         """Calculate Euler Characteristic Transform (ECT) for a given graph and direction theta
 
         Args:
-            graph (EmbeddedGraph/EmbeddedCW):
-                The input graph to calculate the ECT for.
+            graph (EmbeddedComplex):
+                The input complex to calculate the ECT for.
             theta (float):
                 The angle in :math:`[0,2\pi]` for the direction to calculate the ECT.
             override_bound_radius (float):
@@ -257,12 +274,10 @@ 
            Source code for ect.ect_graph
             
         # override with theta if provided
         directions = (
-            self.directions if theta is None else Directions.from_angles([
-                                                                         theta])
+            self.directions if theta is None else Directions.from_angles([theta])
         )
 
-        simplex_projections = self._compute_simplex_projections(
-            graph, directions)
+        simplex_projections = self._compute_simplex_projections(graph, directions)
 
         ect_matrix = self._compute_directional_transform(
             simplex_projections, self.thresholds, self.shape_descriptor, self.dtype
@@ -275,34 +290,46 @@ 
            Source code for ect.ect_graph
                     """Compute inner products of coordinates with directions"""
         return np.matmul(coords, directions.vectors.T)
 
-    def _compute_simplex_projections(
-        self, graph: Union[EmbeddedGraph, EmbeddedCW], directions
-    ):
-        """Compute projections of each simplex (vertices, edges, faces)"""
-        simplex_projections = []
+    def _compute_simplex_projections(self, graph: EmbeddedComplex, directions):
+        """Compute projections of each k-cell for all dimensions"""
+        simplex_projections = List()
         node_projections = self._compute_node_projections(
             graph.coord_matrix, directions
         )
-        edge_maxes = np.maximum(
-            node_projections[graph.edge_indices[:, 0]],
-            node_projections[graph.edge_indices[:, 1]],
-        )
+        num_dirs = node_projections.shape[1]
 
         simplex_projections.append(node_projections)
-        simplex_projections.append(edge_maxes)
-
-        if isinstance(graph, EmbeddedCW) and len(graph.faces) > 0:
-            node_to_index = {n: i for i, n in enumerate(graph.node_list)}
-            face_indices = [[node_to_index[v] for v in face]
-                            for face in graph.faces]
-            face_maxes = np.array(
-                [np.max(node_projections[face, :], axis=0)
-                 for face in face_indices]
+
+        all_cells = {0: [(i,) for i in range(len(graph.node_list))]}
+
+        if graph.edge_indices.shape[0] > 0:
+            all_cells[1] = [tuple(edge) for edge in graph.edge_indices]
+        else:
+            all_cells[1] = []
+
+        all_cells.update(graph.cells)
+
+        max_dim = max(all_cells.keys()) if all_cells else 0
+        for dim in range(1, max_dim + 1):
+            cell_projections = self._compute_cell_projections(
+                all_cells.get(dim, []), node_projections, num_dirs
             )
-            simplex_projections.append(face_maxes)
+            simplex_projections.append(cell_projections)
 
         return simplex_projections
 
+    def _compute_cell_projections(self, cells, node_projections, num_dirs):
+        """Compute projections for k-cells of any dimension k >= 1"""
+        if len(cells) > 0:
+            return np.array(
+                [
+                    np.max(node_projections[list(cell_indices), :], axis=0)
+                    for cell_indices in cells
+                ]
+            )
+        else:
+            return np.empty((0, num_dirs))
+
     @staticmethod
     @njit(parallel=True, fastmath=True)
     def _compute_directional_transform(
@@ -323,7 +350,7 @@ 
            Source code for ect.ect_graph
                     num_thresh = thresholds.shape[0]
         result = np.empty((num_dir, num_thresh), dtype=dtype)
 
-        sorted_projections = []
+        sorted_projections = List()
         for proj in simplex_projections_list:
             sorted_proj = np.empty_like(proj)
             for i in prange(num_dir):
@@ -333,7 +360,7 @@ 
            Source code for ect.ect_graph
                     for j in prange(num_thresh):
             thresh = thresholds[j]
             for i in range(num_dir):
-                simplex_counts_list = []
+                simplex_counts_list = List()
                 for k in range(len(sorted_projections)):
                     projs = sorted_projections[k][:, i]
                     simplex_counts_list.append(
@@ -343,9 +370,9 @@ 
            Source code for ect.ect_graph
                     return result
 
 
            
-[docs]
+[docs]
     @staticmethod
-    @njit(parallel=True, fastmath=True)
+    @njit(fastmath=True)
     def shape_descriptor(simplex_counts_list):
         """Calculate shape descriptor from simplex counts (Euler characteristic)"""
         chi = 0
diff --git a/docs/_modules/ect/embed_graph.html b/docs/_modules/ect/embed_complex.html
similarity index 65%
rename from docs/_modules/ect/embed_graph.html
rename to docs/_modules/ect/embed_complex.html
index 55ae7c5..d35dcb6 100644
--- a/docs/_modules/ect/embed_graph.html
+++ b/docs/_modules/ect/embed_complex.html
@@ -3,7 +3,7 @@
 
   
   
-  ect.embed_graph — ect 0.1.5 documentation
+  ect.embed_complex — ect 0.1.5 documentation
       
       
       
@@ -54,26 +54,44 @@
        • 2. Modules
            -
          • 2.1. Embedded graphs
              -
            • EmbeddedGraph
              • +
            • 2.1. Embedded Complex
              • -
            • 2.2. Embedded CW complex
            • 3. Tutorials
                -
              • 3.1. Tutorial: ECT for Embedded Graphs
                  -
                • 3.1.1. Basic Usage
                  • +
                • 3.1. Tutorial: ECT for Embedded Cell Complexes
                  • -
                • 3.2. Tutorial: ECT for CW complexes
                  • +
                • 3.2. Tutorial for exact ECT computation
                • 3.3. ECT on Matisse’s “The Parakeet and the Mermaid” @@ -111,7 +129,7 @@ @@ -120,49 +138,84 @@
                  -

                  Source code for ect.embed_graph

                  +  
                  Source code for ect.embed_complex
                   from collections import defaultdict
 from typing import Dict, List, Tuple, Optional, Union
 
 import networkx as nx
 import numpy as np
 import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
 from sklearn.decomposition import PCA
 
 from .utils.naming import next_vert_name
+from .utils.face_check import (
+    point_in_polygon,
+    validate_face_embedding,
+    validate_edge_embedding,
+)
+from .validation import EmbeddingValidator, ValidationRule
 
 
 CENTER_TYPES = ["mean", "bounding_box", "origin"]
 TRANSFORM_TYPES = ["pca"]
 
 
-
                  
-[docs]
-class EmbeddedGraph(nx.Graph):
+
                  
+[docs]
+class EmbeddedComplex(nx.Graph):
     """
-    A class to represent a graph with embedded coordinates for each vertex with simple geometric graph operations.
+    A unified class to represent an embedded cell complex with cells of arbitrary dimension.
+
+    This combines the functionality of EmbeddedGraph and EmbeddedCW, supporting:
+    - 0-cells (vertices) with embedded coordinates
+    - 1-cells (edges)
+    - k-cells for k >= 2 (faces, volumes, etc.)
+
+    Args:
+        validate_embedding (bool): If True, automatically validate embedding properties
+            when adding cells. Default: False
+        embedding_tol (float): Tolerance for geometric validation. Default: 1e-10
 
-    Attributes
-        graph : nx.Graph
-            a NetworkX graph object
+    Attributes:
         coord_matrix : np.ndarray
-            a matrix of embedded coordinates for each vertex
+            A matrix of embedded coordinates for each vertex
         node_list : list
-            a list of node names
+            A list of node names
         node_to_index : dict
-            a dictionary mapping node ids to their index in the coord_matrix
+            A dictionary mapping node ids to their index in the coord_matrix
         dim : int
-            the dimension of the embedded coordinates
-
+            The dimension of the embedded coordinates
+        cells : dict
+            Dictionary mapping dimension k to list of k-cells, where each k-cell
+            is represented as a tuple of vertex indices
+        validate_embedding : bool
+            Whether to automatically validate embedding properties
+        embedding_tol : float
+            Tolerance for geometric validation
     """
 
-
                  
-[docs]
-    def __init__(self):
+
                  
+[docs]
+    def __init__(self, validate_embedding=False, embedding_tol=1e-10):
         super().__init__()
         self._node_list = []
         self._node_to_index = {}
-        self._coord_matrix = None
                  
+        self._coord_matrix = None
+        self.cells = defaultdict(list)
+
+        self.validate_embedding = validate_embedding
+        self.embedding_tol = embedding_tol
+
+        def edge_checker(v1_idx: int, v2_idx: int) -> bool:
+            # closure to check if edge exists by converting indices back to node names
+            if v1_idx >= len(self._node_list) or v2_idx >= len(self._node_list):
+                return False
+            v1_name = self._node_list[v1_idx]
+            v2_name = self._node_list[v2_idx]
+            return self.has_edge(v1_name, v2_name)
+
+        self._validator = EmbeddingValidator(embedding_tol, edge_checker)
                  
 
 
     @property
@@ -198,99 +251,66 @@ 
                  Source code for ect.embed_graph
                       def edge_indices(self):
         """Return edges as array of index pairs"""
         edges = np.array(
-            [(self._node_to_index[u], self._node_to_index[v])
-             for u, v in self.edges()],
+            [(self._node_to_index[u], self._node_to_index[v]) for u, v in self.edges()],
             dtype=int,
         )
         return edges if len(edges) > 0 else np.empty((0, 2), dtype=int)
 
-    # ======================================
-    # Node Management
-    # ======================================
-    @staticmethod
-    def _validate_coords(func):
-        """Validates if coordinates are nonempty and have valid dimension"""
-
-        def wrapper(self, *args, **kwargs):
-            coords = next(
-                (arg for arg in args if isinstance(arg, (list, np.ndarray))), None
-            )
-            if coords is not None:
-                coords = np.asarray(coords, dtype=float)
-                if coords.ndim != 1:
-                    raise ValueError("Coordinates must be a 1D array")
-
-                # Skip dimension check for first node
-                if len(self._node_list) > 0:
-                    if coords.size != self._coord_matrix.shape[1]:
-                        raise ValueError(
-                            f"Coordinates must have dimension {self._coord_matrix.shape[1]}"
-                        )
-
-            return func(self, *args, **kwargs)
-
-        return wrapper
-
-    @staticmethod
-    def _validate_node(exists=True):
-        """Validates if nodes exist or not already"""
-
-        def decorator(func):
-            def wrapper(self, *args, **kwargs):
-                # Handle both positional and keyword arguments
-                if args:
-                    nodes = args[0] if isinstance(
-                        args[0], (list, tuple)) else [args[0]]
-                else:
-                    node_id = kwargs.get("node_id") or kwargs.get("node_id1")
-                    nodes = [node_id] if node_id else []
-
-                for node_id in nodes:
-                    node_exists = node_id in self._node_to_index
-                    if exists and not node_exists:
-                        raise ValueError(f"Node {node_id} does not exist")
-                    if not exists and node_exists:
-                        raise ValueError(f"Node {node_id} already exists")
-                return func(self, *args, **kwargs)
-
-            return wrapper
-
-        return decorator
-
-
                  
-[docs]
-    @_validate_coords
-    @_validate_node(exists=False)
+    @property
+    def faces(self):
+        """Return list of 2-cells (faces) for backward compatibility"""
+        return [
+            tuple(self._node_list[i] for i in cell) for cell in self.cells.get(2, [])
+        ]
+
+
                  
+[docs]
     def add_node(self, node_id, coord):
-        """Add a vertex to the graph.
+        """Add a vertex to the complex.
 
         Args:
             node_id: Identifier for the node
             coord: Array-like coordinates for the node
         """
+        # validate coordinates using validator
+        expected_dim = (
+            self._coord_matrix.shape[1] if self._coord_matrix is not None else None
+        )
+        coord_result = self._validator.validate_coordinates(coord, expected_dim)
+        if not coord_result.is_valid:
+            raise ValueError(coord_result.message)
+
+        # validate node doesn't already exist
+        node_result = self._validator.validate_nodes(
+            [node_id], lambda n: n in self._node_to_index, expect_exists=False
+        )
+        if not node_result.is_valid:
+            raise ValueError(node_result.message)
+
         coord = np.asarray(coord, dtype=float)
 
         if len(self._node_list) == 0:
+            # initialize coordinate matrix with first node
             self._coord_matrix = coord.reshape(1, -1)
         else:
+            # append new coordinate as row
             coord_reshaped = coord.reshape(1, -1)
-            self._coord_matrix = np.vstack(
-                [self._coord_matrix, coord_reshaped])
+            self._coord_matrix = np.vstack([self._coord_matrix, coord_reshaped])
 
         self._node_list.append(node_id)
         self._node_to_index[node_id] = len(self._node_list) - 1
         super().add_node(node_id)
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def add_nodes_from_dict(self, nodes_with_coords: Dict[Union[str, int], np.ndarray]):
         for node_id, coordinates in nodes_with_coords.items():
             self.add_node(node_id, coordinates)
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def add_nodes_from(
         self, nodes_with_coords: List[Tuple[Union[str, int], np.ndarray]]
     ):
@@ -298,54 +318,207 @@ 
                  Source code for ect.embed_graph
                               self.add_node(node_id, coordinates)
                  
 
 
-    # ======================================
-    # Coordinate Access
-    # ======================================
+
                  
+[docs]
+    def add_edge(self, node_id1, node_id2):
+        """Add an edge (1-cell) between two nodes"""
+        # validate nodes exist
+        node_result = self._validator.validate_nodes(
+            [node_id1, node_id2], lambda n: n in self._node_to_index, expect_exists=True
+        )
+        if not node_result.is_valid:
+            raise ValueError(node_result.message)
+
+        super().add_edge(node_id1, node_id2)
                  
+
+
+
                  
+[docs]
+    def add_cell(
+        self,
+        cell_vertices: List,
+        dim: Optional[int] = None,
+        check: Optional[bool] = None,
+        embedding_tol: Optional[float] = None,
+    ):
+        """
+        Add a k-cell to the complex.
+
+        Args:
+            cell_vertices: List of vertex identifiers that form the cell
+            dim: Dimension of the cell. If None, inferred as len(cell_vertices) - 1
+            check: Whether to validate the cell embedding. If None, uses self.validate_embedding
+            embedding_tol: Tolerance for geometric validation. If None, uses self.embedding_tol
+        """
+        if check is None:
+            check = self.validate_embedding
+        if embedding_tol is None:
+            embedding_tol = self.embedding_tol
+        if dim is None:
+            dim = len(cell_vertices) - 1
+
+        # check vertex existence before validation (can't validate non-existent vertices)
+        missing_vertices = [v for v in cell_vertices if v not in self._node_to_index]
+        if missing_vertices:
+            raise ValueError(f"Vertices do not exist: {missing_vertices}")
+
+        # convert vertex names to indices for storage
+        cell_indices = tuple(self._node_to_index[v] for v in cell_vertices)
+
+        # always validate structural requirements
+        cell_coords = (
+            self._coord_matrix[list(cell_indices)]
+            if self._coord_matrix is not None
+            else None
+        )
+        all_coords = self._coord_matrix
+        all_indices = list(range(len(self._node_list)))
+
+        # Always check structural rules (vertex count, dimension validity)
+        structural_result = self._validator.validate_cell(
+            cell_coords, all_coords, list(cell_indices), all_indices, dim, check_geometric=False
+        )
+        if not structural_result.is_valid:
+            raise ValueError(structural_result.message)
+
+        # Optionally check geometric rules (embedding properties)
+        if check:
+            geometric_result = self._validator.validate_cell(
+                cell_coords, all_coords, list(cell_indices), all_indices, dim, check_geometric=True
+            )
+            if not geometric_result.is_valid:
+                raise ValueError(geometric_result.message)
+
+        # update graph structure and store cell
+        if dim == 1:
+            # also add to networkx graph structure
+            self.add_edge(cell_vertices[0], cell_vertices[1])
+
+        self.cells[dim].append(cell_indices)
                  
+
+
+
                  
+[docs]
+    def enable_embedding_validation(self, tol: float = 1e-10):
+        """
+        Enable automatic embedding validation for all subsequent cell additions.
+
+        Args:
+            tol: Tolerance for geometric validation
+        """
+        self.validate_embedding = True
+        self.embedding_tol = tol
+        self._validator.set_tolerance(tol)
                  
+
+
+
                  
+[docs]
+    def disable_embedding_validation(self):
+        """Disable automatic embedding validation for all subsequent cell additions."""
+        self.validate_embedding = False
                  
+
+
+
                  
+[docs]
+    def get_validator(self) -> "EmbeddingValidator":
+        """
+        Get the embedding validator instance for advanced configuration.
+
+        Returns:
+            The EmbeddingValidator instance used by this complex
+        """
+        return self._validator
                  
+
+
+
                  
+[docs]
+    def set_validation_rules(self, rules: List["ValidationRule"]) -> "EmbeddedComplex":
+        """
+        Set custom validation rules.
+
+        Args:
+            rules: List of ValidationRule instances
+
+        Returns:
+            Self for method chaining
+        """
+        # replace validation rules
+        self._validator.rules = rules
+        return self
                  
+
+
+
                  
+[docs]
+    def add_face(self, face: List, check: Optional[bool] = None):
+        """Add a 2-cell (face) to the complex. Provided for backward compatibility."""
+        self.add_cell(face, dim=2, check=check)
                  
+
 
-
                  
-[docs]
-    @_validate_node(exists=True)
+
                  
+[docs]
+    def add_faces_from(self, faces: List[List]):
+        """Add multiple 2-cells (faces) to the complex."""
+        for face in faces:
+            self.add_face(face)
                  
+
+
+
                  
+[docs]
     def get_coord(self, node_id):
         """Return the coordinates of a node"""
+        # validate node exists
+        node_result = self._validator.validate_nodes(
+            [node_id], lambda n: n in self._node_to_index, expect_exists=True
+        )
+        if not node_result.is_valid:
+            raise ValueError(node_result.message)
+
         return self._coord_matrix[self._node_to_index[node_id]].copy()
                  
 
 
-
                  
-[docs]
-    @_validate_coords
-    @_validate_node(exists=True)
+
                  
+[docs]
     def set_coord(self, node_id, new_coords):
         """Set the coordinates of a node"""
+        # validate coordinates
+        expected_dim = (
+            self._coord_matrix.shape[1] if self._coord_matrix is not None else None
+        )
+        coord_result = self._validator.validate_coordinates(new_coords, expected_dim)
+        if not coord_result.is_valid:
+            raise ValueError(coord_result.message)
+
+        # validate node exists
+        node_result = self._validator.validate_nodes(
+            [node_id], lambda n: n in self._node_to_index, expect_exists=True
+        )
+        if not node_result.is_valid:
+            raise ValueError(node_result.message)
+
         idx = self._node_to_index[node_id]
         self._coord_matrix[idx] = new_coords
                  
 
 
-    # ======================================
-    # Graph Operations
-    # ======================================
-
-
                  
-[docs]
+
                  
+[docs]
     def add_cycle(self, coord_matrix):
         """Add nodes in a cyclic pattern from coordinate matrix"""
+        # generate sequential node names and add cyclic edges
         n = coord_matrix.shape[0]
-        new_names = next_vert_name(
-            self._node_list[-1] if self._node_list else 0, n)
+        new_names = next_vert_name(self._node_list[-1] if self._node_list else 0, n)
         self.add_nodes_from(zip(new_names, coord_matrix))
-        self.add_edges_from(
-            [(new_names[i], new_names[(i + 1) % n]) for i in range(n)])
                  
-
+        self.add_edges_from([(new_names[i], new_names[(i + 1) % n]) for i in range(n)])
                  
 
-    # ======================================
-    # Geometric Calculations
-    # ======================================
 
-
                  
-[docs]
+
                  
+[docs]
     def get_center(self, method: str = "bounding_box") -> np.ndarray:
         """Calculate center of coordinates"""
 
         coords = self._coord_matrix
+        if coords is None or coords.size == 0:
+            return np.zeros(0)
+
         if method == "mean":
             return np.mean(coords, axis=0)
         elif method == "bounding_box":
@@ -355,24 +528,27 @@ 
                  Source code for ect.embed_graph
                           raise ValueError(f"Unknown center method: {method}")
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def get_bounding_box(self):
         """Get (min, max) for each dimension"""
         return [(dim.min(), dim.max()) for dim in self._coord_matrix.T]
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def get_bounding_radius(self, center_type: str = "bounding_box") -> float:
         """Get radius of minimal bounding sphere"""
-        center = self.get_center(center_type)
         coords = self._coord_matrix
+        if coords is None or coords.size == 0:
+            return 0.0
+
+        center = self.get_center(center_type)
         return np.max(np.linalg.norm(coords - center, axis=1))
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def get_normal_angle_matrix(
         self, edges_only: bool = False, decimals: Optional[int] = None
     ) -> Tuple[np.ndarray, List[str]]:
@@ -394,6 +570,7 @@ 
                  Source code for ect.embed_graph
                           angle_matrix = np.full((n, n), np.nan, dtype=np.float64)
 
         if edges_only:
+            # compute angles only for connected vertex pairs
             edges = np.array(list(self.edges()))
             if edges.size == 0:
                 return angle_matrix, vertices
@@ -404,6 +581,7 @@ 
                  Source code for ect.embed_graph
                               dx = coords[v_indices, 0] - coords[u_indices, 0]
             dy = coords[v_indices, 1] - coords[u_indices, 1]
 
+            # angles from u to v and reverse direction
             angles = np.arctan2(dx, -dy) % (2 * np.pi)
             rev_angles = (angles + np.pi) % (2 * np.pi)
 
@@ -415,28 +593,27 @@ 
                  Source code for ect.embed_graph
                               angle_matrix[v_indices, u_indices] = rev_angles
 
         else:
+            # compute angles between all vertex pairs using broadcasting
             x = coords[:, 0]
             y = coords[:, 1]
 
-            # compute all pairwise differences
             dx = x[:, None] - x[None, :]
             dy = y[:, None] - y[None, :]
 
-            # Compute angles and mask invalid pairs
             angle_matrix = np.arctan2(dx, -dy) % (2 * np.pi)
-            angle_matrix[np.isclose(dx**2 + dy**2, 0)] = np.nan  # Zero vectors
+            # nan for coincident points
+            angle_matrix[np.isclose(dx**2 + dy**2, 0)] = np.nan
 
             if decimals is not None:
                 angle_matrix = np.round(angle_matrix, decimals)
 
-            # mask diagonal since we don't want
             np.fill_diagonal(angle_matrix, np.nan)
 
         return angle_matrix, vertices
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def get_normal_angles(
         self, edges_only: bool = False, decimals: int = 6
     ) -> Dict[float, List[Tuple[str, str]]]:
@@ -450,10 +627,10 @@ 
                  Source code for ect.embed_graph
                           Returns:
             Dictionary mapping rounded angles to vertex pairs
         """
-        angle_matrix, vertices = self.get_angle_matrix(edges_only, decimals)
+        angle_matrix, vertices = self.get_normal_angle_matrix(edges_only, decimals)
         n = len(vertices)
 
-        # Extract upper triangle indices
+        # extract upper triangle to avoid duplicate pairs
         rows, cols = np.triu_indices(n, k=1)
         angles = angle_matrix[rows, cols]
         valid_mask = ~np.isnan(angles)
@@ -461,12 +638,11 @@ 
                  Source code for ect.embed_graph
                           if not valid_mask.any():
             return defaultdict(list)
 
-        # Filter valid pairs
         valid_rows = rows[valid_mask]
         valid_cols = cols[valid_mask]
         valid_angles = angles[valid_mask]
 
-        # Group pairs by rounded angle
+        # group vertex pairs by their angle
         angle_dict = defaultdict(list)
         unique_angles, inverse = np.unique(valid_angles, return_inverse=True)
 
@@ -481,12 +657,8 @@ 
                  Source code for ect.embed_graph
                           return angle_dict
                  
 
 
-    # ============================
-    # Coordinate transformations
-    # ============================
-
-
                  
-[docs]
+
                  
+[docs]
     def transform_coordinates(self, center_type="bounding_box", projection_type="pca"):
         """Transform coordinates center and orientation"""
         if projection_type not in TRANSFORM_TYPES:
@@ -497,8 +669,8 @@ 
                  Source code for ect.embed_graph
                           self.center_coordinates(center_type)
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def center_coordinates(self, center_type="mean"):
         if center_type not in CENTER_TYPES:
             raise ValueError(f"Unknown center method: {center_type}")
@@ -507,17 +679,18 @@ 
                  Source code for ect.embed_graph
                           self._coord_matrix -= center
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def scale_coordinates(self, radius=1):
         """Scale coordinates to fit within given radius"""
+        # scale so largest distance from origin equals target radius
         current_max = np.linalg.norm(self._coord_matrix, axis=1).max()
         if current_max > 0:
             self._coord_matrix *= radius / current_max
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def project_coordinates(self, projection_type="pca"):
         """Project coordinates using a function"""
         if projection_type == "pca":
@@ -526,41 +699,27 @@ 
                  Source code for ect.embed_graph
                               raise ValueError(f"Unknown projection type: {projection_type}")
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
     def pca_projection(self, target_dim=2):
         """Dimensionality reduction using PCA"""
+        # only reduce dimension
         if self.dim <= target_dim:
             return
 
         pca = PCA(n_components=target_dim)
-        self._coord_matrix = pca.fit_transform(self._coord_matrix)
-        self.dim = target_dim
                  
-
+        self._coord_matrix = pca.fit_transform(self._coord_matrix)
                  
 
-
                  
-[docs]
-    @_validate_node(exists=True)
-    def add_edge(self, node_id1, node_id2):
-        """Add an edge between two nodes"""
-        super().add_edge(node_id1, node_id2)
                  
 
-
-    # ===================
-    # Visualization
-    # ===================
-
                  
-[docs]
+
                  
+[docs]
     def validate_plot_parameters(func):
-        """Decorator to validate plot method parameters"""
-
+        # decorator to check plotting requirements
         def wrapper(self, *args, **kwargs):
-            bounding_center_type = kwargs.get(
-                "bounding_center_type", "bounding_box")
+            bounding_center_type = kwargs.get("bounding_center_type", "bounding_box")
 
             if self.dim not in [2, 3]:
-                raise ValueError(
-                    "At least 2D or 3D coordinates required for plotting")
+                raise ValueError("At least 2D or 3D coordinates required for plotting")
 
             if bounding_center_type not in CENTER_TYPES:
                 raise ValueError(
@@ -573,8 +732,42 @@ 
                  Source code for ect.embed_graph
                           return wrapper
                  
 
 
-
                  
-[docs]
+
                  
+[docs]
+    def plot_faces(self, ax=None, **kwargs):
+        """
+        Plots the 2-cells (faces) of the complex.
+
+        Parameters:
+            ax (matplotlib.axes.Axes):
+                The axes to plot the graph on. If None, a new figure is created.
+            **kwargs:
+                Additional keyword arguments to pass to the ax.fill function.
+
+        Returns:
+            matplotlib.axes.Axes
+                The axes object with the plot.
+        """
+        if ax is None:
+            _, ax = plt.subplots()
+
+        # render each 2-cell as filled polygon
+        for cell_indices in self.cells.get(2, []):
+            face_coords = self._coord_matrix[list(cell_indices)]
+
+            if self.dim == 2:
+                ax.fill(face_coords[:, 0], face_coords[:, 1], **kwargs)
+            else:
+                # 3d faces need polygon collection
+                verts = [face_coords]
+                collection = Poly3DCollection(verts, **kwargs)
+                ax.add_collection3d(collection)
+
+        return ax
                  
+
+
+
                  
+[docs]
     @validate_plot_parameters
     def plot(
         self,
@@ -587,31 +780,32 @@ 
                  Source code for ect.embed_graph
                           edge_color: str = "gray",
         elev: float = 25,
         azim: float = -60,
+        face_color: str = "lightblue",
+        face_alpha: float = 0.3,
         **kwargs,
     ) -> plt.Axes:
         """
-        Visualize the embedded graph in 2D or 3D
+        Visualize the embedded complex in 2D or 3D
         """
         ax = self._create_axes(ax, self.dim)
 
-        pos = {node: self._coord_matrix[i]
-               for i, node in enumerate(self._node_list)}
+        if 2 in self.cells and len(self.cells[2]) > 0:
+            self.plot_faces(ax=ax, facecolor=face_color, alpha=face_alpha)
+
+        pos = {node: self._coord_matrix[i] for i, node in enumerate(self._node_list)}
 
         if self.dim == 2:
-            self._draw_2d(ax, pos, with_labels,
-                          node_size, edge_color, **kwargs)
+            self._draw_2d(ax, pos, with_labels, node_size, edge_color, **kwargs)
         else:
             self._draw_3d(ax, pos, node_size, edge_color, elev, azim, **kwargs)
 
         if color_nodes_theta is not None:
-            # calculate directional projection values
+            # color nodes by projection in specified direction
             direction = np.array(
                 [np.sin(color_nodes_theta), -np.cos(color_nodes_theta)]
             )
             node_colors = np.dot(self._coord_matrix, direction)
-
-            self._add_node_coloring(
-                ax, pos, node_colors, node_size, self.dim, **kwargs)
+            self._add_node_coloring(ax, pos, node_colors, node_size, self.dim, **kwargs)
 
         if bounding_circle:
             self._add_bounding_shape(ax, bounding_center_type, self.dim)
@@ -622,18 +816,7 @@ 
                  Source code for ect.embed_graph
                   
 
     def _create_axes(self, ax, dim=None):
-        """Create appropriate axes if not provided
-
-        Parameters:
-            ax (matplotlib.axes.Axes, optional):
-                The axes to use. If None, creates new axes
-            dim (int, optional):
-                Dimension of the plot. If None, uses self.dim
-
-        Returns:
-            matplotlib.axes.Axes:
-                The configured axes object
-        """
+        """Create appropriate axes if not provided"""
         if dim is None:
             dim = self.dim
 
@@ -686,27 +869,13 @@ 
                  Source code for ect.embed_graph
                               ax.plot3D(x, y, z, color=edge_color, linewidth=1.5)
 
     def _add_node_coloring(self, ax, pos, node_colors, node_size, dim=None, **kwargs):
-        """Add node coloring based on provided values
-
-        Parameters:
-            ax (matplotlib.axes.Axes):
-                The axes to add coloring to
-            pos (dict):
-                Dictionary of node positions
-            node_colors (array-like):
-                Values to use for coloring nodes
-            node_size (int):
-                Size of nodes in visualization
-            dim (int, optional):
-                Dimension of the plot. If None, uses self.dim
-            **kwargs:
-                Additional keyword arguments for plotting
-        """
+        """Add node coloring based on provided values"""
         if dim is None:
             dim = self.dim
 
         if dim == 2:
-            nodes = nx.draw_networkx_nodes(
+            # 2d colored nodes using networkx
+            nx.draw_networkx_nodes(
                 self,
                 pos=pos,
                 ax=ax,
@@ -718,8 +887,9 @@ 
                  Source code for ect.embed_graph
                                   **kwargs,
             )
         else:
+            # 3d colored scatter plot
             coords = np.array(list(pos.values()))
-            nodes = ax.scatter3D(
+            ax.scatter3D(
                 coords[:, 0],
                 coords[:, 1],
                 coords[:, 2],
@@ -738,16 +908,7 @@ 
                  Source code for ect.embed_graph
                           cbar.set_label("Node Values")
 
     def _add_bounding_shape(self, ax, center_type="bounding_box", dim=None):
-        """Add bounding circle/sphere visualization
-
-        Parameters:
-            ax (matplotlib.axes.Axes):
-                The axes to add the bounding shape to
-            center_type (str, optional):
-                Method to compute center ("mean", "bounding_box", or "origin")
-            dim (int, optional):
-                Dimension of the plot. If None, uses self.dim
-        """
+        """Add bounding circle/sphere visualization"""
         if dim is None:
             dim = self.dim
 
@@ -755,6 +916,7 @@ 
                  Source code for ect.embed_graph
                           radius = self.get_bounding_radius(center_type)
 
         if dim == 2:
+            # draw bounding circle
             circle = plt.Circle(
                 center[:2],
                 radius,
@@ -766,12 +928,10 @@ 
                  Source code for ect.embed_graph
                               )
             ax.add_patch(circle)
             padding = radius * 0.1
-            ax.set_xlim(center[0] - radius - padding,
-                        center[0] + radius + padding)
-            ax.set_ylim(center[1] - radius - padding,
-                        center[1] + radius + padding)
+            ax.set_xlim(center[0] - radius - padding, center[0] + radius + padding)
+            ax.set_ylim(center[1] - radius - padding, center[1] + radius + padding)
         else:
-            # sphere wireframe
+            # draw bounding sphere as wireframe
             u = np.linspace(0, 2 * np.pi, 30)
             v = np.linspace(0, np.pi, 30)
             x = radius * np.outer(np.cos(u), np.sin(v)) + center[0]
@@ -782,17 +942,13 @@ 
                  Source code for ect.embed_graph
                                   x, y, z, color="darkred", linewidth=0.5, alpha=0.3, rstride=2, cstride=2
             )
             padding = radius * 0.1
-            ax.set_xlim3d(center[0] - radius - padding,
-                          center[0] + radius + padding)
-            ax.set_ylim3d(center[1] - radius - padding,
-                          center[1] + radius + padding)
-            ax.set_zlim3d(center[2] - radius - padding,
-                          center[2] + radius + padding)
+            ax.set_xlim3d(center[0] - radius - padding, center[0] + radius + padding)
+            ax.set_ylim3d(center[1] - radius - padding, center[1] + radius + padding)
+            ax.set_zlim3d(center[2] - radius - padding, center[2] + radius + padding)
 
     def _configure_axes(self, ax):
         """Finalize plot appearance"""
         if hasattr(ax, "zaxis"):
-            # 3D plot configuration
             ax.grid(True, linestyle=":", linewidth=0.5, alpha=0.7)
             ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
             ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
@@ -801,7 +957,6 @@ 
                  Source code for ect.embed_graph
                               ax.set_ylabel("Y")
             ax.set_zlabel("Z")
         else:
-            # 2D plot configuration
             ax.set_aspect("equal")
             ax.grid(True, linestyle=":", linewidth=0.5, alpha=0.7)
 
@@ -827,24 +982,14 @@ 
                  Source code for ect.embed_graph
                           )
 
     def _get_nice_interval(self, range_size):
-        """Calculate a nice interval for tick spacing
-
-        Args:
-            range_size: Size of the axis range
-
-        Returns:
-            float: Nice interval value for tick spacing
-        """
-        # Calculate rough interval size (aim for ~5 major ticks)
+        # calculate visually appealing tick spacing
         rough_interval = range_size / 5
 
-        # Get magnitude
         magnitude = 10 ** np.floor(np.log10(rough_interval))
 
-        # Normalize rough interval to between 1 and 10
         normalized = rough_interval / magnitude
 
-        # Choose nice interval
+        # choose from standard intervals: 1, 2, 5, 10
         if normalized < 1.5:
             nice_interval = 1
         elif normalized < 3:
@@ -856,6 +1001,10 @@ 
                  Source code for ect.embed_graph
                   
         return nice_interval * magnitude
                  
 
+
+
+EmbeddedGraph = EmbeddedComplex
+EmbeddedCW = EmbeddedComplex
 
                  
 
            
                  
diff --git a/docs/_modules/ect/embed_cw.html b/docs/_modules/ect/embed_cw.html
deleted file mode 100644
index f901a23..0000000
--- a/docs/_modules/ect/embed_cw.html
+++ /dev/null
@@ -1,333 +0,0 @@
-
-
-
-  
-  
-  ect.embed_cw — ect 0.1.5 documentation
-      
-      
-      
-
-  
-    
-  
-  
-        
-        
-        
-        
-        
-        
-    
-    
-     
-
-
- 
-  
                  
-    
-
-    
                  
-
-      
                  
-        
                  
-          
                  
-  
-  
                  
-
                  
-          
                  
-           
                  
-             
-  
                  Source code for ect.embed_cw
                  -import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d.art3d import Poly3DCollection
-from .embed_graph import EmbeddedGraph
-from .utils.face_check import point_in_polygon
-from typing import Optional
-
-
-
                  
-[docs]
-class EmbeddedCW(EmbeddedGraph):
-    """
-    A class to represent a straight-line-embedded CW complex. We assume that the coordinates for the embedding of the vertices are given, the 1-skeleton is in fact a graph (so not as general as a full CW complex) with straight line embeddings, and 2-Cells are the interior of the shape outlined by its boundary edges.
-
-    Faces should be passed in as a list of vertices, where the vertices are in order around the face. However, the ECT function will likely still work if the ordering is different. The drawing functions however might look strange. Note the class does not (yet?) check to make sure the face is valid, i.e. is a cycle in the graph, and bounds a region in the plane.
-
-    """
-
-
                  
-[docs]
-    def __init__(self):
-        """
-        Initializes an empty EmbeddedCW object.
-        """
-
-        super().__init__()
-        self.faces = []
                  
-
-
-
                  
-[docs]
-    def add_from_embedded_graph(self, G):
-        """
-        Adds the edges and coordinates from an EmbeddedGraph object to the EmbeddedCW object.
-
-        Parameters:
-            G (EmbeddedGraph):
-                The EmbeddedGraph object to add from.
-        """
-        nodes_with_coords = [
-            (node, G.coord_matrix[G.node_to_index[node]]) for node in G.nodes()
-        ]
-        self.add_nodes_from(nodes_with_coords)
-        self.add_edges_from(G.edges())
                  
-
-
-    @EmbeddedGraph._validate_node(exists=True)
-    def add_face(self, face, check=False):
-        """
-        Adds a face to the list of faces.
-
-        Parameters:
-            face (list):
-                A list of vertices that make up the face.
-            check (bool):
-                Whether to check that the face is a valid addition to the cw complex.
-        """
-        if len(face) < 3:
-            raise ValueError("Face must have at least 3 vertices")
-
-        if check:
-            edges = list(zip(face, face[1:] + [face[0]]))
-            for u, v in edges:
-                if not self.has_edge(u, v):
-                    raise ValueError(f"Edge ({u},{v}) missing")
-
-            polygon = np.array(
-                [self.coord_matrix[self._node_to_index[v]] for v in face]
-            )
-            for node in self.nodes:
-                if node in face:
-                    continue
-                if point_in_polygon(
-                    self.coord_matrix[self._node_to_index[node]], polygon
-                ):
-                    raise ValueError(f"Node {node} inside face {face}")
-
-        self.faces.append(tuple(face))
-
-
                  
-[docs]
-    def add_faces_from(self, faces):
-        """
-        Adds a list of faces to the graph.
-
-        Parameters:
-            faces (list):
-                A list of faces to add.
-        """
-        for face in faces:
-            self.add_face(face)
                  
-
-
-
                  
-[docs]
-    def plot_faces(self, theta=None, ax=None, **kwargs):
-        """
-        Plots the faces of the graph in the direction of theta.
-
-        Parameters:
-            theta (float):
-                The angle in :math:`[0,2\pi]` for the direction to sort the edges.
-            ax (matplotlib.axes.Axes):
-                The axes to plot the graph on. If None, a new figure is created.
-            **kwargs:
-                Additional keyword arguments to pass to the ax.fill function.
-
-        Returns:
-            matplotlib.axes.Axes
-                The axes object with the plot.
-        """
-        if ax is None:
-            fig, ax = plt.subplots()
-        else:
-            fig = ax.get_figure()
-
-        for face in self.faces:
-            face_coords = np.array(
-                [self.coord_matrix[self.node_to_index[v]] for v in face]
-            )
-
-            if self.dim == 2:
-                ax.fill(face_coords[:, 0], face_coords[:, 1], **kwargs)
-            else:  # 3D case
-                verts = [face_coords]
-                collection = Poly3DCollection(verts, **kwargs)
-                ax.add_collection3d(collection)
-
-        return ax
                  
-
-
-
                  
-[docs]
-    def plot(
-        self,
-        bounding_circle: bool = False,
-        bounding_center_type: str = "bounding_box",
-        color_nodes_theta: Optional[float] = None,
-        ax: Optional[plt.Axes] = None,
-        with_labels: bool = True,
-        node_size: int = 300,
-        edge_color: str = "gray",
-        elev: float = 25,
-        azim: float = -60,
-        face_color: str = "lightblue",
-        face_alpha: float = 0.3,
-        **kwargs,
-    ) -> plt.Axes:
-        """
-        Visualize the embedded CW complex in 2D or 3D, including faces
-        """
-        # plot faces then use parent class
-        ax = self._create_axes(ax, self.dim)
-        self.plot_faces(ax=ax, facecolor=face_color, alpha=face_alpha)
-
-        return super().plot(
-            bounding_circle=bounding_circle,
-            bounding_center_type=bounding_center_type,
-            color_nodes_theta=color_nodes_theta,
-            ax=ax,
-            with_labels=with_labels,
-            node_size=node_size,
-            edge_color=edge_color,
-            elev=elev,
-            azim=azim,
-            **kwargs,
-        )
                  
-
                  
-
-
                  
-
-           
                  
-          
                  
-          
-        
                  
-      
                  
-    
                  
-  
                  
-  
-    
-    
-     
-
-
-
\ No newline at end of file
diff --git a/docs/_modules/ect/sect.html b/docs/_modules/ect/sect.html
new file mode 100644
index 0000000..7aed625
--- /dev/null
+++ b/docs/_modules/ect/sect.html
@@ -0,0 +1,249 @@
+
+
+
+  
+  
+  ect.sect — ect 0.1.5 documentation
+      
+      
+      
+
+  
+    
+  
+  
+        
+        
+        
+        
+        
+        
+    
+    
+     
+
+
+ 
+  
                  
+    
+
+    
                  
+
+      
                  
+        
                  
+          
                  
+  
+  
                  
+
                  
+          
                  
+           
                  
+             
+  
                  Source code for ect.sect
                  +from ect import ECT
+from .embed_complex import EmbeddedGraph, EmbeddedCW
+from .directions import Directions
+from .results import ECTResult
+from typing import Optional, Union
+import numpy as np
+
+
+
                  
+[docs]
+class SECT(ECT):
+    """
+    A class to calculate the Smooth Euler Characteristic Transform (SECT).
+    Inherits from ECT and applies smoothing to the final result.
+    """
+
+
                  
+[docs]
+    def __init__(
+        self,
+        directions: Optional[Directions] = None,
+        num_dirs: Optional[int] = None,
+        num_thresh: Optional[int] = None,
+        bound_radius: Optional[float] = None,
+        thresholds: Optional[np.ndarray] = None,
+        dtype=np.float32,
+    ):
+        """Initialize SECT calculator with smoothing parameter
+
+        Args:
+            directions: Optional pre-configured Directions object
+            num_dirs: Number of directions to sample (ignored if directions provided)
+            num_thresh: Number of threshold values (required if directions not provided)
+            bound_radius: Optional radius for bounding circle
+            thresholds: Optional array of thresholds
+            dtype: Data type for output array
+        """
+        super().__init__(
+            directions, num_dirs, num_thresh, bound_radius, thresholds, dtype
+        )
                  
+
+
+
                  
+[docs]
+    def calculate(
+        self,
+        graph: Union[EmbeddedGraph, EmbeddedCW],
+        theta: Optional[float] = None,
+        override_bound_radius: Optional[float] = None,
+    ) -> ECTResult:
+        """Calculate Smooth Euler Characteristic Transform (SECT)
+
+        Args:
+            graph: The input graph to calculate the SECT for
+            theta: The angle in [0,2π] for the direction to calculate the SECT
+            override_bound_radius: Optional override for bounding radius
+
+        Returns:
+            ECTResult: The smoothed transform result containing the matrix,
+                      directions, and thresholds
+        """
+        ect_result = super().calculate(graph, theta, override_bound_radius)
+        return ECTResult(
+            ect_result, ect_result.directions, ect_result.thresholds
+        ).smooth()
                  
+
                  
+
+
                  
+
+           
                  
+          
                  
+          
+        
                  
+      
                  
+    
                  
+  
                  
+  
+    
+    
+     
+
+
+
\ No newline at end of file
diff --git a/docs/_modules/ect/validation/base.html b/docs/_modules/ect/validation/base.html
new file mode 100644
index 0000000..368ca74
--- /dev/null
+++ b/docs/_modules/ect/validation/base.html
@@ -0,0 +1,318 @@
+
+
+
+  
+  
+  ect.validation.base — ect 0.1.5 documentation
+      
+      
+      
+
+  
+    
+  
+  
+        
+        
+        
+        
+        
+        
+    
+    
+     
+
+
+ 
+  
                  
+    
+
+    
                  
+
+      
                  
+        
                  
+          
                  
+  
+  
                  
+
                  
+          
                  
+           
                  
+             
+  
                  Source code for ect.validation.base
                  +"""
+Base classes for the embedding validation system.
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import List, Optional
+import numpy as np
+
+
+
                  
+[docs]
+@dataclass
+class ValidationResult:
+    """Result of a validation check."""
+
+    is_valid: bool
+    message: str = ""
+    violating_indices: Optional[List[int]] = None
+
+
                  
+[docs]
+    @classmethod
+    def valid(cls) -> "ValidationResult":
+        """Create a valid result."""
+        return cls(is_valid=True)
                  
+
+
+
                  
+[docs]
+    @classmethod
+    def invalid(
+        cls, message: str, violating_indices: Optional[List[int]] = None
+    ) -> "ValidationResult":
+        """Create an invalid result with error message."""
+        return cls(is_valid=False, message=message, violating_indices=violating_indices)
                  
+
                  
+
+
+
+
                  
+[docs]
+class ValidationRule(ABC):
+    """Abstract base class for embedding validation rules."""
+
+
                  
+[docs]
+    def __init__(self, tolerance: float = 1e-10):
+        """
+        Initialize validation rule.
+
+        Args:
+            tolerance: Numerical tolerance for geometric checks
+        """
+        self.tolerance = tolerance
                  
+
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Human-readable name of this validation rule."""
+        pass
+
+    @property
+    def is_structural(self) -> bool:
+        """
+        Check if this is a structural rule that should always be validated.
+        
+        Structural rules check basic requirements like vertex counts and dimension validity.
+        They should always be checked regardless of validation settings.
+        Geometric rules check embedding properties and are optional.
+        
+        Returns:
+            True if this rule should always be checked, False if optional
+        """
+        return False
+
+
                  
+[docs]
+    @abstractmethod
+    def applies_to_dimension(self, dim: int) -> bool:
+        """
+        Check if this rule applies to cells of the given dimension.
+
+        Args:
+            dim: Cell dimension
+
+        Returns:
+            True if this rule should validate cells of this dimension
+        """
+        pass
                  
+
+
+
                  
+[docs]
+    @abstractmethod
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """
+        Validate a cell against this rule.
+
+        Args:
+            cell_coords: Coordinates of vertices forming the cell
+            all_coords: Coordinates of all vertices in the complex
+            cell_indices: Indices of vertices forming the cell
+            all_indices: Indices of all vertices in the complex
+            dim: Dimension of the cell being validated
+
+        Returns:
+            ValidationResult indicating if the cell is valid
+        """
+        pass
                  
+
+
+
                  
+[docs]
+    def set_tolerance(self, tolerance: float) -> "ValidationRule":
+        """
+        Set the tolerance for this rule.
+
+        Args:
+            tolerance: New tolerance value
+
+        Returns:
+            Self for method chaining
+        """
+        self.tolerance = tolerance
+        return self
                  
+
                  
+
+
                  
+
+           
                  
+          
                  
+          
+        
                  
+      
                  
+    
                  
+  
                  
+  
+    
+    
+     
+
+
+
\ No newline at end of file
diff --git a/docs/_modules/ect/validation/rules.html b/docs/_modules/ect/validation/rules.html
new file mode 100644
index 0000000..bb368ec
--- /dev/null
+++ b/docs/_modules/ect/validation/rules.html
@@ -0,0 +1,595 @@
+
+
+
+  
+  
+  ect.validation.rules — ect 0.1.5 documentation
+      
+      
+      
+
+  
+    
+  
+  
+        
+        
+        
+        
+        
+        
+    
+    
+     
+
+
+ 
+  
                  
+    
+
+    
                  
+
+      
                  
+        
                  
+          
                  
+  
+  
                  
+
                  
+          
                  
+           
                  
+             
+  
                  Source code for ect.validation.rules
                  +"""
+Unified validation rules for embedding validation system.
+
+This module contains all validation rules including both structural rules
+(which are always checked) and geometric rules (which are optional).
+"""
+
+from typing import List, Callable, Optional
+import numpy as np
+
+from .base import ValidationRule, ValidationResult
+from ..utils.face_check import (
+    validate_edge_embedding,
+    validate_face_embedding,
+    segments_intersect,
+)
+
+
+# ================================
+# STRUCTURAL RULES (Always checked)
+# ================================
+
+
                  
+[docs]
+class DimensionValidityRule(ValidationRule):
+    """Validates that cell dimension is non-negative."""
+
+    @property
+    def name(self) -> str:
+        return "Dimension Validity"
+
+    @property
+    def is_structural(self) -> bool:
+        return True
+
+
                  
+[docs]
+    def applies_to_dimension(self, dim: int) -> bool:
+        return True
                  
+
+
+
                  
+[docs]
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """Validate that dimension is non-negative."""
+        if dim is not None and dim < 0:
+            return ValidationResult.invalid("Cell dimension must be non-negative")
+        return ValidationResult.valid()
                  
+
                  
+
+
+
+
                  
+[docs]
+class VertexCountRule(ValidationRule):
+    """Validates that cells have the correct number of vertices for their dimension."""
+
+    @property
+    def name(self) -> str:
+        return "Vertex Count Validation"
+
+    @property
+    def is_structural(self) -> bool:
+        return True
+
+
                  
+[docs]
+    def applies_to_dimension(self, dim: int) -> bool:
+        return True
                  
+
+
+
                  
+[docs]
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """Validate vertex count matches cell dimension requirements."""
+        if dim is None:
+            return ValidationResult.valid()
+
+        vertex_count = len(cell_indices)
+
+        if dim == 0 and vertex_count != 1:
+            return ValidationResult.invalid("0-cells must contain exactly one vertex")
+        elif dim == 1 and vertex_count != 2:
+            return ValidationResult.invalid("1-cells must contain exactly two vertices")
+        elif dim >= 2 and vertex_count < 3:
+            return ValidationResult.invalid(
+                f"{dim}-cells must contain at least 3 vertices"
+            )
+
+        return ValidationResult.valid()
                  
+
                  
+
+
+
+
                  
+[docs]
+class CoordinateDimensionRule(ValidationRule):
+    """Validates that coordinates have consistent dimensions."""
+
+
                  
+[docs]
+    def __init__(
+        self, tolerance: float = 1e-10, dimension_checker: Optional[Callable] = None
+    ):
+        super().__init__(tolerance)
+        self.dimension_checker = dimension_checker  # function to get expected dimension
                  
+
+
+    @property
+    def name(self) -> str:
+        return "Coordinate Dimension Validation"
+
+    @property
+    def is_structural(self) -> bool:
+        return True
+
+
                  
+[docs]
+    def applies_to_dimension(self, dim: int) -> bool:
+        return True  # applies to all operations involving coordinates
                  
+
+
+
                  
+[docs]
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """Validate coordinate dimensions are consistent."""
+        if self.dimension_checker is None or cell_coords is None:
+            return ValidationResult.valid()
+
+        expected_dim = self.dimension_checker()
+        if expected_dim is None or expected_dim == 0:
+            return ValidationResult.valid()
+
+        # check that all coordinates have the expected dimension
+        if cell_coords.ndim != 2:
+            return ValidationResult.invalid("Coordinates must be a 2D array")
+
+        if cell_coords.shape[1] != expected_dim:
+            return ValidationResult.invalid(
+                f"Coordinates must have dimension {expected_dim}, got {cell_coords.shape[1]}"
+            )
+
+        return ValidationResult.valid()
                  
+
                  
+
+
+
+# ================================
+# GEOMETRIC RULES (Optional)
+# ================================
+
+
                  
+[docs]
+class EdgeInteriorRule(ValidationRule):
+    """Validates that no vertices lie on edge interiors."""
+
+    @property
+    def name(self) -> str:
+        return "Edge Interior Validation"
+
+
                  
+[docs]
+    def applies_to_dimension(self, dim: int) -> bool:
+        return dim == 1
                  
+
+
+
                  
+[docs]
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """Validate that no other vertices lie on this edge's interior."""
+        is_valid, error_msg = validate_edge_embedding(
+            cell_coords, all_coords, cell_indices, all_indices, self.tolerance
+        )
+
+        if is_valid:
+            return ValidationResult.valid()
+        else:
+            return ValidationResult.invalid(error_msg)
                  
+
                  
+
+
+
+
                  
+[docs]
+class FaceInteriorRule(ValidationRule):
+    """Validates that no vertices lie inside face interiors."""
+
+    @property
+    def name(self) -> str:
+        return "Face Interior Validation"
+
+
                  
+[docs]
+    def applies_to_dimension(self, dim: int) -> bool:
+        return dim == 2
                  
+
+
+
                  
+[docs]
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """Validate that no other vertices lie inside this face."""
+        is_valid, error_msg = validate_face_embedding(
+            cell_coords, all_coords, cell_indices, all_indices, self.tolerance
+        )
+
+        if is_valid:
+            return ValidationResult.valid()
+        else:
+            return ValidationResult.invalid(error_msg)
                  
+
                  
+
+
+
+
                  
+[docs]
+class SelfIntersectionRule(ValidationRule):
+    """Validates that face edges don't self-intersect."""
+
+    @property
+    def name(self) -> str:
+        return "Self-Intersection Validation"
+
+
                  
+[docs]
+    def applies_to_dimension(self, dim: int) -> bool:
+        return dim == 2
                  
+
+
+
                  
+[docs]
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """Validate that face edges don't intersect each other"""
+
+        if cell_coords.shape[1] > 2:
+            coords_2d = cell_coords[:, :2]
+        else:
+            coords_2d = cell_coords
+
+        n = len(coords_2d)
+        if n < 3:
+            return ValidationResult.invalid("Face must have at least 3 vertices")
+
+        for i in range(n):
+            edge1_start = coords_2d[i]
+            edge1_end = coords_2d[(i + 1) % n]
+
+            for j in range(i + 2, n):
+                if j == n - 1 and i == 0:
+                    continue
+
+                edge2_start = coords_2d[j]
+                edge2_end = coords_2d[(j + 1) % n]
+
+                if segments_intersect(
+                    edge1_start, edge1_end, edge2_start, edge2_end, self.tolerance
+                ):
+                    return ValidationResult.invalid(
+                        f"Face edges intersect: edge {i}-{(i + 1) % n} intersects edge {j}-{(j + 1) % n}"
+                    )
+
+        return ValidationResult.valid()
                  
+
                  
+
+
+
+
                  
+[docs]
+class BoundaryEdgeRule(ValidationRule):
+    """Validates that required boundary edges exist for faces."""
+
+
                  
+[docs]
+    def __init__(self, tolerance: float = 1e-10, edge_checker=None):
+        """
+        Initialize boundary edge rule.
+
+        Args:
+            tolerance: Numerical tolerance
+            edge_checker: Function to check if an edge exists (injected dependency)
+        """
+        super().__init__(tolerance)
+        self.edge_checker = edge_checker
                  
+
+
+    @property
+    def name(self) -> str:
+        return "Boundary Edge Validation"
+
+
                  
+[docs]
+    def applies_to_dimension(self, dim: int) -> bool:
+        return dim == 2
                  
+
+
+
                  
+[docs]
+    def validate(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int = None,
+    ) -> ValidationResult:
+        """Validate that all boundary edges exist for this face."""
+        if self.edge_checker is None:
+            return ValidationResult.valid()
+
+        n = len(cell_indices)
+        if n < 3:
+            return ValidationResult.invalid("Face must have at least 3 vertices")
+
+        for i in range(n):
+            v1_idx = cell_indices[i]
+            v2_idx = cell_indices[(i + 1) % n]
+
+            if not self.edge_checker(v1_idx, v2_idx):
+                return ValidationResult.invalid(
+                    f"Boundary edge between vertices {v1_idx} and {v2_idx} missing for face"
+                )
+
+        return ValidationResult.valid()
                  
+
                  
+
+
+
+# ================================
+# STANDALONE UTILITY FUNCTIONS
+# ================================
+
+
                  
+[docs]
+def validate_coordinate_array(
+    coords, expected_dim: Optional[int] = None
+) -> ValidationResult:
+    """Standalone function to validate coordinate array format."""
+    if coords is None:
+        return ValidationResult.valid()
+
+    coords = np.asarray(coords, dtype=float)
+
+    if coords.ndim != 1:
+        return ValidationResult.invalid("Coordinates must be a 1D array")
+
+    if expected_dim is not None and coords.size != expected_dim:
+        return ValidationResult.invalid(
+            f"Coordinates must have dimension {expected_dim}, got {coords.size}"
+        )
+
+    return ValidationResult.valid()
                  
+
+
+
+
                  
+[docs]
+def validate_node_existence(
+    nodes: List, node_checker: Callable, expect_exists: bool = True
+) -> ValidationResult:
+    """Standalone function to validate node existence."""
+    if node_checker is None:
+        return ValidationResult.valid()
+
+    for node_id in nodes:
+        node_exists = node_checker(node_id)
+        if expect_exists and not node_exists:
+            return ValidationResult.invalid(f"Node {node_id} does not exist")
+        if not expect_exists and node_exists:
+            return ValidationResult.invalid(f"Node {node_id} already exists")
+
+    return ValidationResult.valid()
                  
+
+
                  
+
+           
                  
+          
                  
+          
+        
                  
+      
                  
+    
                  
+  
                  
+  
+    
+    
+     
+
+
+
\ No newline at end of file
diff --git a/docs/_modules/ect/validation/validator.html b/docs/_modules/ect/validation/validator.html
new file mode 100644
index 0000000..c31fc15
--- /dev/null
+++ b/docs/_modules/ect/validation/validator.html
@@ -0,0 +1,498 @@
+
+
+
+  
+  
+  ect.validation.validator — ect 0.1.5 documentation
+      
+      
+      
+
+  
+    
+  
+  
+        
+        
+        
+        
+        
+        
+    
+    
+     
+
+
+ 
+  
                  
+    
+
+    
                  
+
+      
                  
+        
                  
+          
                  
+  
+  
                  
+
                  
+          
                  
+           
                  
+             
+  
                  Source code for ect.validation.validator
                  +"""
+Main embedding validator class that orchestrates validation rules.
+"""
+
+from typing import List, Callable, Optional
+import numpy as np
+
+from .base import ValidationRule, ValidationResult
+from .rules import (
+    EdgeInteriorRule,
+    FaceInteriorRule,
+    SelfIntersectionRule,
+    BoundaryEdgeRule,
+    DimensionValidityRule,
+    VertexCountRule,
+    validate_coordinate_array,
+    validate_node_existence,
+)
+
+
+
                  
+[docs]
+class EmbeddingValidator:
+    """
+    Main validator that orchestrates multiple validation rules.
+
+    This class manages a collection of validation rules and applies them
+    to cells based on their dimension and the configured rule set.
+    """
+
+
                  
+[docs]
+    def __init__(
+        self, tolerance: float = 1e-10, edge_checker: Optional[Callable] = None
+    ):
+        """
+        Initialize the embedding validator.
+
+        Args:
+            tolerance: Default tolerance for geometric validation
+            edge_checker: Function to check if an edge exists between two vertex indices
+        """
+        self.tolerance = tolerance
+        self.edge_checker = edge_checker
+
+        self.rules: List[ValidationRule] = [
+            DimensionValidityRule(tolerance),
+            VertexCountRule(tolerance),
+            EdgeInteriorRule(tolerance),
+            FaceInteriorRule(tolerance),
+            SelfIntersectionRule(tolerance),
+            BoundaryEdgeRule(tolerance, edge_checker),
+        ]
                  
+
+
+
                  
+[docs]
+    def add_rule(self, rule: ValidationRule) -> "EmbeddingValidator":
+        """
+        Add a custom validation rule.
+
+        Args:
+            rule: ValidationRule to add
+
+        Returns:
+            Self for method chaining
+        """
+        self.rules.append(rule)
+        return self
                  
+
+
+
                  
+[docs]
+    def remove_rule(self, rule_class: type) -> "EmbeddingValidator":
+        """
+        Remove all rules of a specific type.
+
+        Args:
+            rule_class: Class of rules to remove
+
+        Returns:
+            Self for method chaining
+        """
+        self.rules = [rule for rule in self.rules if not isinstance(rule, rule_class)]
+        return self
                  
+
+
+
                  
+[docs]
+    def set_tolerance(self, tolerance: float) -> "EmbeddingValidator":
+        """
+        Set tolerance for all rules.
+
+        Args:
+            tolerance: New tolerance value
+
+        Returns:
+            Self for method chaining
+        """
+        self.tolerance = tolerance
+        for rule in self.rules:
+            rule.set_tolerance(tolerance)
+        return self
                  
+
+
+
                  
+[docs]
+    def set_edge_checker(self, edge_checker: Callable) -> "EmbeddingValidator":
+        """
+        Set the edge checker function for boundary validation.
+
+        Args:
+            edge_checker: Function that takes two vertex indices and returns bool
+
+        Returns:
+            Self for method chaining
+        """
+        self.edge_checker = edge_checker
+
+        for rule in self.rules:
+            if isinstance(rule, BoundaryEdgeRule):
+                rule.edge_checker = edge_checker
+
+        return self
                  
+
+
+
                  
+[docs]
+    def validate_cell(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int,
+        check_geometric: bool = True,
+    ) -> ValidationResult:
+        """
+        Validate a cell using applicable rules.
+
+        Args:
+            cell_coords: Coordinates of vertices forming the cell
+            all_coords: Coordinates of all vertices in the complex
+            cell_indices: Indices of vertices forming the cell
+            all_indices: Indices of all vertices in the complex
+            dim: Dimension of the cell
+            check_geometric: Whether to check geometric rules (structural rules always checked)
+
+        Returns:
+            ValidationResult for the cell
+
+        Raises:
+            ValueError: If validation fails
+        """
+        # check all rules that apply to this dimension
+        for rule in self.rules:
+            if not rule.applies_to_dimension(dim):
+                continue
+            
+            # Always check structural rules, only check geometric rules if requested
+            if rule.is_structural or check_geometric:
+                result = rule.validate(
+                    cell_coords, all_coords, cell_indices, all_indices, dim
+                )
+
+                if not result.is_valid:
+                    return ValidationResult.invalid(
+                        f"{rule.name}: {result.message}", result.violating_indices
+                    )
+
+        return ValidationResult.valid()
                  
+
+
+
                  
+[docs]
+    def validate_cell_safe(
+        self,
+        cell_coords: np.ndarray,
+        all_coords: np.ndarray,
+        cell_indices: List[int],
+        all_indices: List[int],
+        dim: int,
+        check_geometric: bool = True,
+    ) -> ValidationResult:
+        """
+        Validate a cell, catching and wrapping any exceptions.
+
+        Same as validate_cell but returns ValidationResult instead of raising.
+
+        Args:
+            cell_coords: Coordinates of vertices forming the cell
+            all_coords: Coordinates of all vertices in the complex
+            cell_indices: Indices of vertices forming the cell
+            all_indices: Indices of all vertices in the complex
+            dim: Dimension of the cell
+            check_geometric: Whether to check geometric rules (structural rules always checked)
+
+        Returns:
+            ValidationResult for the cell
+        """
+        try:
+            return self.validate_cell(
+                cell_coords, all_coords, cell_indices, all_indices, dim, check_geometric
+            )
+        except Exception as e:
+            return ValidationResult.invalid(f"Validation error: {str(e)}")
                  
+
+
+
                  
+[docs]
+    def get_rules_for_dimension(self, dim: int) -> List[ValidationRule]:
+        """
+        Get all rules that apply to a specific dimension.
+
+        Args:
+            dim: Cell dimension
+
+        Returns:
+            List of applicable ValidationRules
+        """
+        return [rule for rule in self.rules if rule.applies_to_dimension(dim)]
                  
+
+
+
                  
+[docs]
+    def get_rule_names(self) -> List[str]:
+        """
+        Get names of all registered rules.
+
+        Returns:
+            List of rule names
+        """
+        return [rule.name for rule in self.rules]
                  
+
+
+
                  
+[docs]
+    def disable_rule(self, rule_class: type) -> "EmbeddingValidator":
+        """
+        Temporarily disable a rule type.
+
+        Args:
+            rule_class: Class of rule to disable
+
+        Returns:
+            Self for method chaining
+        """
+        return self.remove_rule(rule_class)
                  
+
+
+
                  
+[docs]
+    def enable_strict_validation(self) -> "EmbeddingValidator":
+        """
+        Enable strict validation with tight tolerance.
+
+        Returns:
+            Self for method chaining
+        """
+        return self.set_tolerance(1e-12)
                  
+
+
+
                  
+[docs]
+    def enable_permissive_validation(self) -> "EmbeddingValidator":
+        """
+        Enable permissive validation with loose tolerance.
+
+        Returns:
+            Self for method chaining
+        """
+        return self.set_tolerance(1e-6)
                  
+
+
+
                  
+[docs]
+    def validate_coordinates(
+        self, coords, expected_dim: Optional[int] = None
+    ) -> ValidationResult:
+        """
+        Validate coordinate array format and dimension consistency.
+
+        Args:
+            coords: Coordinate array to validate
+            expected_dim: Expected dimension, if known
+
+        Returns:
+            ValidationResult for the coordinates
+        """
+        return validate_coordinate_array(coords, expected_dim)
                  
+
+
+
                  
+[docs]
+    def validate_nodes(
+        self, nodes: List, node_checker: Callable, expect_exists: bool = True
+    ) -> ValidationResult:
+        """
+        Validate node existence/non-existence.
+
+        Args:
+            nodes: List of node identifiers to check
+            node_checker: Function that takes node_id and returns bool for existence
+            expect_exists: Whether nodes should exist (True) or not exist (False)
+
+        Returns:
+            ValidationResult for the nodes
+        """
+        return validate_node_existence(nodes, node_checker, expect_exists)
                  
+
+
+    def __repr__(self) -> str:
+        """String representation of the validator."""
+        rule_names = [rule.name for rule in self.rules]
+        return f"EmbeddingValidator(tolerance={self.tolerance}, rules={rule_names})"
                  
+
+
                  
+
+           
                  
+          
                  
+          
+        
                  
+      
                  
+    
                  
+  
                  
+  
+    
+    
+     
+
+
+
\ No newline at end of file
diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index 5720205..e2e0f34 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -54,26 +54,44 @@
              • 2. Modules
                  -
                • 2.1. Embedded graphs
                    -
                  • EmbeddedGraph
                    • +
                  • 2.1. Embedded Complex
                    • -
                  • 2.2. Embedded CW complex
                  • 3. Tutorials
                      -
                    • 3.1. Tutorial: ECT for Embedded Graphs
                        -
                      • 3.1.1. Basic Usage
                        • +
                      • 3.1. Tutorial: ECT for Embedded Cell Complexes
                        • -
                      • 3.2. Tutorial: ECT for CW complexes
                        • +
                      • 3.2. Tutorial for exact ECT computation
                      • 3.3. ECT on Matisse’s “The Parakeet and the Mermaid” @@ -120,9 +138,13 @@ diff --git a/docs/_sources/directions.md.txt b/docs/_sources/directions.md.txt new file mode 100644 index 0000000..35f204e --- /dev/null +++ b/docs/_sources/directions.md.txt @@ -0,0 +1,6 @@ +# Directions + +```{eval-rst} +.. automodule:: ect.directions + :members: +``` \ No newline at end of file diff --git a/docs/_sources/ect_on_graphs.md.txt b/docs/_sources/ect_on_graphs.md.txt index 7333ad5..eb95da6 100644 --- a/docs/_sources/ect_on_graphs.md.txt +++ b/docs/_sources/ect_on_graphs.md.txt @@ -1,6 +1,11 @@ # ECT on Graphs ```{eval-rst} -.. automodule:: ect.ect_graph +.. automodule:: ect.ect + :members: +``` + +```{eval-rst} +.. automodule:: ect.sect :members: ``` diff --git a/docs/_sources/embed_complex.md.txt b/docs/_sources/embed_complex.md.txt new file mode 100644 index 0000000..a65adcb --- /dev/null +++ b/docs/_sources/embed_complex.md.txt @@ -0,0 +1,52 @@ +# Embedded Complex + +The `EmbeddedComplex` class is a unified representation for embedded cell complexes supporting arbitrary dimensional cells. + +## Overview + +`EmbeddedComplex` combines and extends the functionality of the previous `EmbeddedGraph` and `EmbeddedCW` classes into a single, more powerful interface. It supports: + +- **0-cells (vertices)**: Points embedded in Euclidean space +- **1-cells (edges)**: Line segments between vertices +- **k-cells for k ≥ 2**: Higher dimensional cells (faces, volumes, etc.) + +## Key Features + +- **Unified Interface**: Single class for all cell complex operations +- **Arbitrary Dimensions**: Support for k-cells of any dimension k ≥ 0 +- **Modular Validation**: Pluggable validation system for embedding properties +- **Backward Compatible**: `EmbeddedGraph` and `EmbeddedCW` remain as aliases + +## Basic Usage + +```python +from ect import EmbeddedComplex + +# Create a complex +K = EmbeddedComplex() + +# Add vertices +K.add_node("A", [0, 0]) +K.add_node("B", [1, 0]) +K.add_node("C", [0.5, 1]) + +# Add edges +K.add_edge("A", "B") +K.add_edge("B", "C") +K.add_edge("C", "A") + +# Add a 2-cell (face) +K.add_face(["A", "B", "C"]) + +# Or use the general method for any dimension +K.add_cell(["A", "B", "C"], dim=2) +``` + +## API Reference + +```{eval-rst} +.. automodule:: ect.embed_complex + :members: + :show-inheritance: + :undoc-members: +``` \ No newline at end of file diff --git a/docs/_sources/embed_cw.md.txt b/docs/_sources/embed_cw.md.txt deleted file mode 100644 index 4efb095..0000000 --- a/docs/_sources/embed_cw.md.txt +++ /dev/null @@ -1,6 +0,0 @@ -# Embedded CW complex - -```{eval-rst} -.. automodule:: ect.embed_cw - :members: -``` diff --git a/docs/_sources/embed_graph.md.txt b/docs/_sources/embed_graph.md.txt deleted file mode 100644 index a9128b3..0000000 --- a/docs/_sources/embed_graph.md.txt +++ /dev/null @@ -1,6 +0,0 @@ -# Embedded graphs - -```{eval-rst} -.. automodule:: ect.embed_graph - :members: -``` diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt index d35502e..89a6b83 100644 --- a/docs/_sources/index.rst.txt +++ b/docs/_sources/index.rst.txt @@ -1,7 +1,7 @@ ect: Euler Characteristic Transform in Python ============================================= -Python computation tools for computing the Euler Characteristic Transform of embedded graphs. +Python computation tools for computing the Euler Characteristic Transform of embedded cell complexes with arbitrary dimensional cells. Table of Contents ----------------- @@ -13,7 +13,8 @@ Table of Contents Getting Started Modules - Tutorials + Tutorials + Migration Guide Contributing License Citing @@ -21,7 +22,9 @@ Table of Contents Description ----------- -Right now, the content includes stuff for doing ECT on graphs embedded in 2D. Eventually the goal is to get voxel versions, higher dimensional simplicial complexes, etc in here. +The package now supports ECT computation on embedded cell complexes with arbitrary dimensional cells through the unified `EmbeddedComplex` class. This includes graphs (1-dimensional), CW complexes with faces (2-dimensional), and higher dimensional cell complexes. + +**Note**: The previous `EmbeddedGraph` and `EmbeddedCW` classes are now aliases for the unified `EmbeddedComplex` class. All existing code will continue to work, but new code should use `EmbeddedComplex` directly. For more information on the ECT, see: @@ -34,7 +37,7 @@ Documentation and tutorials ^^^^^^^^^^^^^^^^^^^^^^^^^^^ * The documentation is available at: `munchlab.github.io/ect `_ -* A tutorial jupyter notebook can be found `here `_ +* A comprehensive tutorial for the unified `EmbeddedComplex` class can be found `here `_ * The source code can be found at: `github.com/MunchLab/ect `_ Dependencies diff --git a/docs/_sources/modules.rst.txt b/docs/_sources/modules.rst.txt index c47c1e7..9dbfbfa 100644 --- a/docs/_sources/modules.rst.txt +++ b/docs/_sources/modules.rst.txt @@ -5,6 +5,7 @@ Table of Contents :maxdepth: 2 - Embedded graphs - Embedded CW complex - ECT on graphs \ No newline at end of file + Embedded Complex + Validation System + ECT on graphs + Directions \ No newline at end of file diff --git a/docs/_sources/notebooks/Matisse/example_matisse.ipynb.txt b/docs/_sources/notebooks/Matisse/example_matisse.ipynb.txt index 6f0c4f8..9921e17 100644 --- a/docs/_sources/notebooks/Matisse/example_matisse.ipynb.txt +++ b/docs/_sources/notebooks/Matisse/example_matisse.ipynb.txt @@ -18,51 +18,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 150 files in the directory\n" - ] - } - ], - "source": [ - "# -----------------\n", - "# Standard imports\n", - "# -----------------\n", - "import numpy as np # for arrays\n", - "import matplotlib.pyplot as plt # for plotting\n", - "from sklearn.decomposition import PCA # for PCA for normalization\n", - "from scipy.spatial import distance_matrix\n", - "\n", - "from os import listdir # for retrieving files from directory\n", - "from os.path import isfile, join # for retrieving files from directory\n", - "from sklearn.manifold import MDS # for MDS\n", - "import pandas as pd # for loading in colors csv\n", - "import os\n", - "import zipfile\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "# ---------------------------\n", - "# The ECT packages we'll use\n", - "# ---------------------------\n", - "from ect import ECT, EmbeddedGraph # for calculating ECTs\n", - "\n", - "# Simple data paths\n", - "data_dir = \"outlines/\"\n", - "colors_path = \"colors.csv\"\n", - "\n", - "file_names = [\n", - " f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n", - "]\n", - "file_names.sort()\n", - "print(f\"There are {len(file_names)} files in the directory\")\n" - ] + "outputs": [], + "source": "# -----------------\n# Standard imports\n# -----------------\nimport numpy as np # for arrays\nimport matplotlib.pyplot as plt # for plotting\nfrom sklearn.decomposition import PCA # for PCA for normalization\nfrom scipy.spatial import distance_matrix\n\nfrom os import listdir # for retrieving files from directory\nfrom os.path import isfile, join # for retrieving files from directory\nfrom sklearn.manifold import MDS # for MDS\nimport pandas as pd # for loading in colors csv\nimport os\nimport zipfile\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# ---------------------------\n# The ECT packages we'll use\n# ---------------------------\nfrom ect import ECT, EmbeddedComplex # for calculating ECTs\n# Note: EmbeddedGraph is now unified into EmbeddedComplex\n# For backward compatibility, you can still use:\n# from ect import EmbeddedGraph\n\n# Simple data paths\ndata_dir = \"outlines/\"\ncolors_path = \"colors.csv\"\n\nfile_names = [\n f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n]\nfile_names.sort()\nprint(f\"There are {len(file_names)} files in the directory\")" }, { "cell_type": "markdown", @@ -77,38 +36,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "i = 3\n", - "shape = np.loadtxt(data_dir + file_names[i])\n", - "# shape = normalize(shape)\n", - "G = EmbeddedGraph()\n", - "G.add_cycle(shape)\n", - "G.plot(with_labels=False, node_size=10)\n" - ] + "outputs": [], + "source": "i = 3\nshape = np.loadtxt(data_dir + file_names[i])\n# shape = normalize(shape)\nG = EmbeddedComplex() # Using the unified EmbeddedComplex class\nG.add_cycle(shape)\nG.plot(with_labels=False, node_size=10)" }, { "cell_type": "markdown", @@ -199,20 +130,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "def matisse_ect(filename, ect):\n", - " shape = np.loadtxt(data_dir + filename)\n", - " G = EmbeddedGraph()\n", - " G.add_cycle(shape)\n", - " G.transform_coordinates(projection_type=\"pca\")\n", - " G.scale_coordinates(1)\n", - " result = ect.calculate(G)\n", - " return result\n", - "\n" - ] + "source": "def matisse_ect(filename, ect):\n shape = np.loadtxt(data_dir + filename)\n G = EmbeddedComplex() # Using the unified EmbeddedComplex class \n G.add_cycle(shape)\n G.transform_coordinates(projection_type=\"pca\")\n G.scale_coordinates(1)\n result = ect.calculate(G)\n return result" }, { "cell_type": "markdown", @@ -386,4 +307,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/docs/_sources/notebooks/Tutorial-EmbeddedComplex.ipynb.txt b/docs/_sources/notebooks/Tutorial-EmbeddedComplex.ipynb.txt new file mode 100644 index 0000000..1738074 --- /dev/null +++ b/docs/_sources/notebooks/Tutorial-EmbeddedComplex.ipynb.txt @@ -0,0 +1,624 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: ECT for Embedded Cell Complexes\n", + "\n", + "This tutorial will walk you through using the `ECT` package. Particularly we will show the features of `EmbeddedComplex` class for computing the Euler Characteristic Transform on complexes with arbitrary dimensional cells.\n", + "\n", + "The `EmbeddedComplex` class combines and extends the functionality of the previous `EmbeddedGraph` and `EmbeddedCW` classes, supporting:\n", + "- **0-cells** (vertices) with embedded coordinates\n", + "- **1-cells** (edges)\n", + "- **k-cells** for k ≥ 2 (faces, volumes, and higher-dimensional cells).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ect import EmbeddedComplex, ECT, Directions\n", + "from ect.utils.examples import create_example_graph, create_example_cw, create_example_3d_complex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Usage: Creating Simple Complexes\n", + "\n", + "### Example 1: Graph (1-skeleton)\n", + "\n", + "Let's start with a simple triangle graph (for legacy users this can be equivalently done using `EmbeddedGraph`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ceeedCA4OhqenJyZNmoSff/65R69n06ZNiI+Ph5+fH/z9/TFu3LgO7V24cKHbte0PHToEhUKBL7/8ssNz6enpUCgU+O9//2t9rKSkBA8++CDCw8OhVqsxZswYfPbZZzb7td8HsWnTJjz33HMYMGAAvL298b//+7/4f//v/wEAZs+eDYVCYXO/hL17CFpaWvDiiy9i+PDh8PT0RGRkJG6//Xab12WxWPDBBx9gzJgx8PT0RHh4OB555BHU1dV1eK0ajQYhISHw8vLC4MGDe3zyV6vViIiI6NG+1/KPf/wDkydPhre3N4KCgjBz5swOV0l+/fVXzJgxAz4+PvDz88PNN9/c7boJ9jAajXjppZcwbNgweHp6ol+/fpg+fbrNdMtGoxFnzpxxeHltR/v0qVOnMGfOHHh5eWHgwIF49dVX7d7HY7FY8OKLL6J///7w9vbG7Nmzcfr0abv3Q9TX1+NPf/oToqKioFarMXToULz55psdjuvIeyUsLAzjx4/Hv//9b4deNyEu6AoBwcS+ffsAABMnTrR5/OjRowCASZMm2Tzev39/DBw40Pp8V2RmZuKWW25BZGQkVq5ciYiICBQUFOC///0vVq5cCaDthDht2jQMGDAATz/9NHx8fPCvf/0LCxYswPfff4/bbrvN4deSmZmJe+65BykpKdZFcQoKCrB3715rewCQkpICAF1+Xz9p0iTExsbiX//6V4eFcTZv3oygoCBoNBoAQEVFBaZMmQKFQoHHHnsMoaGh+PXXX7F06VJotVr86U9/stn/lVdegUqlwpNPPgmDwYC0tDT88Y9/xP/+7//imWeewahRowDA+t/rMZvNuOWWW5CdnY27774bK1euhE6nQ2ZmJk6ePIkhQ4YAaLvc+8UXX2DJkiX44x//iKKiInz00Uc4evQo9u7dCw8PD1RWViItLQ2hoaF4+umnERgYiOLiYvzwww8Oe+ebl156CS+++CKmTp2Kl19+GSqVCgcOHMD27duRlpYGAPj666/xwAMPQKPR4M0330RzczM+/vhjTJ8+HUePHmW62fTFF1/EunXr8NBDD2Hy5MnQarU4dOgQjhw5grlz5wJoK/hGjRqFBx54oNt7Ihzt0+Xl5Zg9ezZMJpN1uw0bNsDLy6vDMdesWYO33noL8+fPh0ajwbFjx6DRaNDS0mKzXXNzM5KTk1FSUoJHHnkEgwYNwr59+7BmzRqUlZXhgw8+AOD4ewUA4uPj8dNPPznskxARHEEw8Nxzz3EAOJ1OZ/P422+/zQHgLl++3GGfhIQEbsqUKV0e12QycYMHD+aio6O5uro6m+csFov1/1NSUrhx48ZxLS0tNs9PnTqVGzZsmPWxnJwcDgCXk5NjfeyBBx7goqOjrb+vXLmS8/f350wmU5fZoqOjbfbrjDVr1nAeHh5cbW2t9TGDwcAFBgZyDz74oPWxpUuXcpGRkVx1dbXN/nfffTcXEBDANTc327yG2NhY62PtfPfddx1eXzvJyclccnKy9ffPPvuMA8C99957HbZtd7t7924OAPfNN9/YPL9t2zabx3/88UcOAJeXl9etD1by8vI4ANznn3/u8D6FhYWcm5sbd9ttt3Fms9nmufbXptPpuMDAQG7ZsmU2z5eXl3MBAQE2j69du5a7/rQYHR3NPfDAA9bfJ0yYwN18881d5ioqKuIA2OzXGY726T/96U8cAO7AgQPWxyorK7mAgAAOAFdUVGR9Xe7u7tyCBQts2nnxxRc7ZHrllVc4Hx8f7ty5czbbPv3005xSqbS+nx19r3Acx73++uscAK6ioqLbbQlxQV8ZEEzU1NTA3d0dvr6+No/r9XoAbZeAr8fT09P6fGccPXoURUVF+NOf/tRhSeL2YWC1tbXYvn077rrrLuh0OlRXV6O6uho1NTXQaDQoLCxESUmJw68lMDAQTU1N3a6sV1xc7NDd/AsXLoTRaLT5azkjIwP19fVYuHAhAIDjOHz//feYP38+OI6zvobq6mpoNBo0NDTgyJEjNsd94IEH7P4V6Cjff/89QkJC7N7c2e72u+++Q0BAAObOnWuTKT4+Hr6+vsjJyQEA67/Nf//7XxiNxh5n4ouffvoJFosFL7zwQocRL+2vLTMzE/X19bjnnntsXptSqURiYqL1tTlKYGAgTp06hcLCwk63iYmJAcdx3V4dYOnTW7duxZQpUzB58mTr/qGhobjvvvtsjpmdnQ2TyYTly5fbPG7v3/+7777DjBkzEBQUZOMmNTUVZrMZu3btsr5mR94rQNu01EDbDaOEtKCvDAheaP/AMhgMHZ5raWmxPq/X69HQ0GDzfEREhPW77LFjx3baxvnz58FxHJ5//nk8//zzdreprKzEgAEDHMq8fPly/Otf/8KNN96IAQMGIC0tDXfddRfmzZvn0P7XM2HCBIwcORKbN2/G0qVLAbR9XRASEoI5c+YAAKqqqlBfX48NGzZgw4YNnb6Gaxk8eHCP8rRz4cIFjBgxwubejuspLCxEQ0MDwsLCusyUnJyMO+64Ay+99BLef/99zJo1CwsWLMC9995rtxjki4aGBpuiUqVSITg4GBcuXICbmxtGjx7d6b7tH9zt/wbX4+/vz5Tl5Zdfxu9+9zsMHz4cY8eOxbx583D//fdj/PjxTMcB2Pr0pUuXkJiY2OH5ESNG2Px+6dIlAMDQoUNtHg8ODrZ+WLdTWFiI48ePIzQ0tNO2Abb3Cvd/s+FfP58DIX6oICCY6NevH0wmE3Q6Hfz8/KyPR0ZGAgDKysoQFRVls09ZWZn1r5rNmzdjyZIlNs9zDi6n0X6T05NPPmn9Pv56rj8JdkVYWBjy8/ORnp6OX3/9Fb/++is+//xzLFq0yO7NgY6wcOFCvPbaa6iuroafnx9+/vln3HPPPdYP4/bX8Pvf/77DvQbtXP/B0purA45isVgQFhaGb775xu7z7R8YCoUCW7Zswf79+/Gf//wH6enpePDBB/Huu+9i//79Ha4c8cXKlStt/k2Sk5NtJp3qinbnX3/9td0bGLsqlOwxc+ZMXLhwAf/+97+RkZGBv//973j//ffxySef4KGHHmI6Ft99mhWLxYK5c+di9erVdp8fPnw4ALb3SvtNqCEhIU7LTTgHKggIJtqHDhYVFdl8cMXFxQFouwP92kuapaWluHr1Kh5++GEAgEajsXvZsf3GtpMnT3YYwdBObGwsAMDDw6PTbVhRqVSYP38+5s+fD4vFguXLl+Nvf/sbnn/++R6diBcuXIiXXnoJ33//PcLDw6HVanH33Xdbnw8NDYWfnx/MZnOvXgPLX19DhgzBgQMHYDQa4eHh0ek2WVlZmDZtmkMFyJQpUzBlyhS89tpr+Pbbb3Hfffdh06ZNzB+IjrJ69Wr8/ve/t/7e/pfukCFDYLFYcPr0aWsfvJ72vhUWFsZbvwkODsaSJUuwZMkSNDY2YubMmXjxxReZXz9Ln46Ojrb7NcXZs2c7bAe0XX249upSTU1NhxEjQ4YMQWNjo0NeHH2vFBUVISQkpNOrDoR4oXsICCaSkpIAtH3wX8uYMWMwcuRIbNiwAWaz2fr4xx9/DIVCgTvvvBNA25WE1NRUmx+gbdTC4MGD8cEHH6C+vt7m2O1XEMLCwjBr1iz87W9/szuci3XK1JqaGpvf3dzcrEXOtV99ODLssJ1Ro0Zh3Lhx2Lx5MzZv3ozIyEjMnDnT+rxSqcQdd9yB77//HidPnuzxa/Dx8QGADq7scccdd6C6uhofffRRh+fa3d51110wm8145ZVXOmxjMpms7dTV1XW4otP+QWzv6yK+GD16tE2fiY+PBwAsWLAAbm5uePnllzsMk2vPqdForPNh2Lvvobf9xtfXF0OHDrV5/Y4OO2Tp0zfddBP279+PgwcP2jx//VWdlJQUuLu74+OPP7Z53N6//1133YXc3Fykp6d3eK6+vt46y6ij7xUAOHz4sPU8QUgLukJAMBEbG4uxY8ciKyurw9jzt99+G7feeivS0tJw99134+TJk/joo4/w0EMPdTokrh03Nzd8/PHHmD9/PuLi4rBkyRJERkbizJkzOHXqlPWEtX79ekyfPh3jxo3DsmXLEBsbi4qKCuTm5uLq1as4duyYw6/loYceQm1tLebMmYOBAwfi0qVL+PDDDxEXF2eT15Fhh9eycOFCvPDCC/D09MTSpUs73Oz2xhtvICcnB4mJiVi2bBlGjx6N2tpaHDlyBFlZWaitre22jbi4OCiVSrz55ptoaGiAWq3GnDlz7N4DsGjRInz11VdYtWoVDh48iBkzZqCpqQlZWVlYvnw5fve73yE5ORmPPPII1q1bh/z8fKSlpcHDwwOFhYX47rvv8Je//AV33nknvvzyS/z1r3/FbbfdhiFDhkCn02Hjxo3w9/fHTTfdZG1z8eLF+PLLL1FUVNTtkL6PPvoI9fX1KC0tBQD85z//wdWrVwG03QgXEBDQ6b5Dhw7Fs88+i1deeQUzZszA7bffDrVajby8PPTv3x/r1q2Dv78/Pv74Y9x///2YOHEi7r77boSGhuLy5cv45ZdfMG3aNLsflp0xevRozJo1C/Hx8QgODsahQ4ewZcsWPPbYY9ZtWIYdOtqnV69eja+//hrz5s3DypUrrcMOo6Ojcfz4cevxwsPDsXLlSrz77ru49dZbMW/ePBw7dgy//vorQkJCbK4uPfXUU/j5559xyy23YPHixYiPj0dTUxNOnDiBLVu2oLi4GCEhIQ6/VyorK3H8+HGsWLHCYZ+EiBBodAMhYd577z3O19e3w1A4jmsblhYXF8ep1Wpu4MCB3HPPPce1trY6fOw9e/Zwc+fO5fz8/DgfHx9u/Pjx3IcffmizzYULF7hFixZxERERnIeHBzdgwADulltu4bZs2WLdxpFhh1u2bOHS0tK4sLAwTqVScYMGDeIeeeQRrqyszKY9R4cdtlNYWMgB4ABwe/bssbtNRUUFt2LFCi4qKorz8PDgIiIiuJSUFG7Dhg0dXsN3331n9xgbN27kYmNjOaVSafNarx92yHEc19zczD377LPc4MGDre3deeed3IULF2y227BhAxcfH895eXlxfn5+3Lhx47jVq1dzpaWlHMdx3JEjR7h77rmHGzRoEKdWq7mwsDDulltu4Q4dOmRznDvuuIPz8vLqMITUHtHR0VZf1/+0D6Xrjs8++4y74YYbOLVazQUFBXHJyclcZmamzTY5OTmcRqPhAgICOE9PT27IkCHc4sWLbbI7Muzw1Vdf5SZPnswFBgZyXl5e3MiRI7nXXnvNpp+zDDvkOMf6NMdx3PHjx7nk5GTO09OTGzBgAPfKK69wn376aQdXJpOJe/7557mIiAjOy8uLmzNnDldQUMD169eP+8Mf/mBzTJ1Ox61Zs4YbOnQop1KpuJCQEG7q1KncO++8Y31Njr5XPv74Y87b25vTarUOvW5CXCg4zsE7ugji/2hoaEBsbCzeeust6930BHEt4eHhWLRoEd5++22hoxD/R319PYKCgvDqq6/i2WefdUobN9xwA2bNmoX333/fKccnnAvdQ0AwExAQgNWrV+Ptt98W7fLHhHCcOnUKer0e//M//yN0lD6LvXk/2mcddNbS2Nu2bUNhYSHWrFnjlOMTzoeuEBAEQciML774Al988QVuuukm+Pr6Ys+ePfjnP/+JtLQ0uzcQEgRANxUSBEHIjvHjx8Pd3R1vvfUWtFqt9UbDV199VehohIihKwQEQRAEQdA9BARBEARBUEFAEARBEAQkcg+BxWJBaWkp/Pz8aMEMgiAIgmCA4zjodDr079+/w0Rp1yKJgqC0tLTDgjkEQRAEQTjOlStXMHDgwE6fl0RB0L6q3pUrV5iXKnUV16/+R3QPOWOHnLFDztghZ2yI3ZdWq0VUVFS3GSVRELR/TeDv7y/agsBgMIg2m1ghZ+yQM3bIGTvkjA2p+OruK3e6qZAnpNAZxAY5Y4ecsUPO2CFnbMjFFxUEPFFdXS10BMlBztghZ+yQM3bIGRty8SWJiYm0Wi0CAgLQ0NAgm0qMIAiCIFyBo5+hdIWAJ0pKSoSOIDnIGTvkjB1yxg45Y0MuvugKAU9wHEdzJDBCztghZ+yQM3bIGRti90VXCFxMaWmp0BEkBzljh5yxQ87YIWdsyMUXFQQ8ERISInQEyUHO2CFn7JAzdsgZG3LxRQUBT2i1WqEjSA5yxg45Y4ecsUPO2JCLLyoIeEKtVgsdQXKQM3bIGTvkjB1yxoZcfFFBQBAEQRAEFQR80draKnQEyUHO2CFn7JAzdsgZG3LxRQUBT/j4+AgdQXKQM3bIGTvkjB1yxoZcfFFBwBN1dXVCR5Ac5IwdcsYOOWOHnLEhF180MRFPWCwWuLlRfcUCOWOHnLFDztghZ2yI3RdNTORiysrKhI4gOcgZO+SMHXLGDjljQy6+6AoBQRAEQcgYukLgYuSyuIUrIWfskDN2yBk75IwNufiigoAnQkNDhY4gOcgZO+SMHXLGDjljQy6+3IUO0BXr16/H+vXrYTabhY7SLfX19QgLCxM6hqQgZ+yIxVlBQQFyc/cjNy8PxZcugeMAhQKIiY5GUkICkpKmYNSoUULHBCAeZ1KCnLEhF190DwFPNDY2wtfXV+gYkoKcsSOkM71ej2++/RZffbsJIYMGI3ZCPGLHjEd4VDQUCgU4jkPFlUu4eOo4Lh47jOrLRVh0792479574eXlJUhmgPpZTyBnbIjdl6OfoaK+QiAlTCaT0BEkBzljRyhnu3fvxlPPPIfEm2/Ho+/9DWrPjh/wCoUCEYNiEDEoBlNvvBWGFj1yt/6MTzU34q3XXsGMGTMESE79rCeQMzbk4osKAp6wWCxCR5Ac5IwdVzuzWCx46uk1OHu1DCve3wAfP8ev0Kk9vTDr9oVImHsj3nj3Vfz0n//i7TfWuXy8NvUzdsgZG3LxRTcV8oSnp6fQESQHOWPHlc4sFguWLHsYrQGheHDtm0zFwLX4+Plj6YtvoTUgFEuWPezykyf1M3bIGRty8UUFAU/IZT1sV0LO2HGls6eeXgP/2JGYfed9vBxv9p33wT92JFaveYaX4zkK9TN2yBkbcvFFBQFPhISECB1BcpAzdlzlbNeuXThzpZS3YqCd2Xfeh4JLV7F7925ej9sV1M/YIWdsyMUXFQQ8UVFRIXQEyUHO2HGFM71ej9XPPo+7//ycU45/95+fw1PPPAe9Xu+U418P9TN2yBkbcvFFBQFPDBgwQOgIkoOcseMKZ998+y0Sb769x/cMdIePfwAm33wbvv3nP51y/OuhfsYOOWNDLr6oIOAJuUxd6UrIGTuucPbVt5sw5cb5Dm1rNpnwr4/exeM3zsCf5s/Gnxek4uPnn0KTtqHL/ZJuvBVffuOagoD6GTvkjA25+KJhhzwRHh4udATJQc7YcbazgoIChAwabHeeAXv89dk/Q9dQh3Wb/gPfgEBwHIfc9P9C11APH/+ATvdTe3ohZFAMCgoKnD6jIfUzdsgZG3LxRVcIeKK6ulroCJKDnLHjbGe5ufsROyHeoW3LLhVhX/p/8Ni69+EbEAigbXKiqfPmIyIqutv9B4+Px/79B3oT1yGon7FDztiQiy8qCHhCrFMqixlyxo6zneXm5SF2zHiHtr14+gQio2PhH9SvR23FjhmP3Ly8Hu3LAvUzdsgZG3LxRQUBT7S0tAgdQXKQM3ac7az40iWEO/DXPR9EDIpBUXGx09uhfsYOOWNDLr6oIOAJhUIhdATJQc7YcbaztlULHWsjdvQ4lF26CF1dbY/aalsQqUe7MrdDsEHO2JCLLyoIeMLDw0PoCJKDnLHjbGcKBeDoAqiR0YMxJe1mrH/uz9ZRBW03Ff6C8iuXut2f4zi44jxK/YwdcsaGXHxRQcATTU1NQkeQHOSMHWc7i4mORoUDH+btrHjtPcSMGI2n77oZK2+ZhZU3J+PY3p3w+7+bDLui/HIxBsfE9Dysg1A/Y4ecsSEXXzTskCeCg4OFjiA5yBk7znaWlJCA06eOI2JQjEPbu3t44O4/PoW7//gUc1sXTx1HUkIC836sUD9jh5yxIRdfdIWAJyorK4WOIDnIGTvOdpaUNAUXjx12ahvtFB0/jClTEp3eDvUzdsgZG3LxRQUBT8hl6kpXQs7YcbazUaNGofpyEQwtzl1nwNCiR/XlYqdPSgRQP+sJ5IwNufiigoAn5DJ1pSshZ+y4wtmie+/G/l//49Q2cn/9GQ/cd49T22iH+hk75IwNufiigoAnIiMjhY4gOcgZO65wdt+99+LALz+gSeecNd6btA04+MuPuPce1xQE1M/YIWdsyMUXFQQ8UV5eLnQEyUHO2HGFMy8vL7z9+qvY9O6rTjn+pndfxTvrXoOXl2PrJfQW6mfskDM25OKLCgKeCAoKEjqC5CBn7LjK2YwZMzAyqj9ytnzD63FztnyDUdEDMX36dF6P2xXUz9ghZ2zIxRcVBDzR3NwsdATJQc7YcaWzt99YB+3FM7wVBTlbvoH24hm8te51Xo7nKNTP2CFnbMjFFxUEPOHuTlM6sELO2HGlMzc3N3y+cQM86ivx0f883uN7Cpq0Dfjbs6ug1lbj840b4Obm2tMO9TN2yBkbcvFFBQFPuPokJwfIGTuudubm5obbf3crYvoF4PUHF2LHD5sdHpJoaNFj+5Zv8eqS/4cR/cOw7tVXBPk3p37GDjljQy6+5FHWiICWlhb4+fkJHUNSkDN2XO1Mp9Nh165diB40CH945BGcOHkSH696BCGDYjB4fDxix4xHxKCY/1uoiEP55WJcPHUcRccPo+bKJdy38P8h6v7fo6WlBfv27UNycrLLsrdD/YwdcsaGXHwpOEdXMhEQrVaLgIAANDQ0iHbdaYPBALVaLXQMSUHO2HG1s59++gnHjh3DwIED8eCDD1pXdSsoKMD+/QeQm5eHouLi/1slERgcE4OkhARMmZJonXTo5MmT+P777+Hu7o7HHnsMAQEBLssPUD/rCeSMDbH7cvQzlK4Q8ER1dbVsZqtyFeSMHVc6KykpwbFjxwAA8+bNs1niddSoURg1ahSWLFnc7XHGjBmDvLw8XL58GdnZ2bj99tudFdku1M/YIWdsyMWXPL74EAH9+/cXOoLkIGfsuMoZx3HYtm0bAGDChAm9OtkpFApoNBoAwIkTJ3DlyhVeMjoK9TN2yBkbcvFFBQFPlJaWCh1BcpAzdlzl7MSJE7h69So8PDyQkpLS6+P1798fN9xwAwBg27ZtcOU3ldTP2CFnbMjFFxUEPCGXCtGVkDN2XOGstbUVWVlZANomKOLrZqk5c+ZApVKhtLTU+lWEK6B+xg45Y0Muvqgg4Am5VIiuhJyx4wpne/bsgU6nQ2BgIJKSkng7rq+vr3WUQVZWFgwGA2/H7grqZ+yQMzbk4osKAp4ICQkROoLkIGfsONtZfX099u3bBwBIS0vjfcKVxMREBAcHo6mpCbt37+b12J1B/YwdcsaGXHz1qCBYv349YmJi4OnpicTERBw8eLDL7T/44AOMGDECXl5eiIqKwhNPPIGWlpYeBRYrWq1zVoaTM+SMHWc7y8zMhNlsRkxMDEaOHMn78ZVKJdLS0gAA+/fvR21tLe9tXA/1M3bIGRty8cVcEGzevBmrVq3C2rVrceTIEUyYMAEajQaVlZV2t//222/x9NNPY+3atSgoKMCnn36KzZs345lnnul1eDHh6ekpdATJQc7Ycaaz4uJinD59GgqFosMwQz4ZPnw4hgwZArPZjMzMTKe0cS3Uz9ghZ2zIxRdzQfDee+9h2bJlWLJkCUaPHo1PPvkE3t7e+Oyzz+xuv2/fPkybNg333nsvYmJikJaWhnvuuafbqwpSw2KxCB1BcpAzdpzlzGKxWIcZxsfHIzw83CntAL8NQ1QoFDhz5gwuXrzotLYA6mc9gZyxIRdfTAVBa2srDh8+jNTU1N8O4OaG1NRU5Obm2t1n6tSpOHz4sLUAuHjxIrZu3Yqbbrqp03YMBgO0Wq3Nj9gxmUxCR5Ac5IwdZzk7evQoKioq4OnpidmzZzuljWsJDQ1FQkICgLZhiM48oVI/Y4ecsSEXX0wFQXV1Ncxmc4e/HsLDw1FeXm53n3vvvRcvv/wypk+fDg8PDwwZMgSzZs3q8iuDdevWISAgwPoTFRUFoG2+6NLSUlgsFpSUlABom02ttbUVlZWVaGxsRH19PWpra6HX61FeXg6TyWSzrdFoRHl5OZqbm1FbW4u6ujo0NTWhoqICRqPRZluz2YzS0lLo9XrU1NSgoaEBOp0OVVVVMBgMNtt6eXmhpKQEBoMBVVVV1kKmuroaer3e4dzNzc12c5tMpg65Gxsb7ea2WCzd5uY4zua/7bl1Oh0aGhpQU1PTaW6j0YiKigo0Njairq6OKXd9fT0aGxtRWVkJDw+PTnNXV1dbHdrL3b5PV7nNZrPd3E1NTR1yX++wPbder++Qu7W11W7ulpYWu7lLS0vt5q6srOyQu6ysrMvcRqOx2z5bVlZmk1un03WZu6GhAbm5ufD09MS0adPQ3Nzc7XtNp9PZvNe6y11XV9ch98iRI+Hl5QWj0Yi8vDxr7mv7bGVlZYf3GsdxKC0ttftes5dbq9WK4hxx/XtNzOcIrVYrinNEZ31WbOeI1tZW0Zwj7L3XHB0FwbSWQWlpKQYMGIB9+/bZDEdavXo1du7ciQMHDnTYZ8eOHbj77rvx6quvIjExEefPn8fKlSuxbNkyPP/883bbMRgMNkOStFotoqKiRL2WQWlpqWzGoroKcsaOM5ylp6dj//79CAkJwR/+8AcolUpej98VeXl52Lp1K7y8vPD444/Dy8uL9zaon7FDztgQuy+nrGUQEhICpVKJiooKm8crKioQERFhd5/nn38e999/Px566CEAwLhx49DU1ISHH34Yzz77rN1lI9VqtagXirBHZ6+f6Bxyxg7fzqqrq61f52k0GpcWA0Db/QqHDh1CZWUlduzYgRtvvJH3NqifsUPO2JCLL6avDFQqFeLj45GdnW19zGKxIDs7u9MJTJqbmzt86LefdCSw0KLDlJWVCR1BcpAzdvh2lpGRAYvFguHDh2Po0KG8HtsR3NzcrOsc5OXldTpaqTdQP2OHnLEhF1/MowxWrVqFjRs34ssvv0RBQQEeffRRNDU1YcmSJQCARYsWYc2aNdbt58+fj48//hibNm1CUVERMjMz8fzzz2P+/Pku/2vEmchhpStXQ87Y4dNZYWEhCgsL4ebmZp0bQAhiY2MxcuRIcByH9PR03v9QoH7GDjljQy6+mKchW7hwIaqqqvDCCy+gvLwccXFx2LZtm/VGw8uXL9tcEXjuueegUCjw3HPPoaSkBKGhoZg/fz5ee+01/l6FCCgpKZFNp3AV5IwdvpyZzWakp6cDaJs9sF+/fr0+Zm9IS0tDYWEhLl68iHPnzmHEiBG8HZv6GTvkjA25+GK6qVAoHL0hQkiMRiM8PDyEjiEpyBk7fDnLzc1FRkYGvL298fjjj4tiYpWsrCzs3bsXQUFBWL58OW/TJlM/Y4ecsSF2X45+htJaBjzhiilY5QY5Y4cPZ01NTdi5cycAICUlRRTFANC2sqKvry/q6ursjljqKdTP2CFnbMjFFxUEPOHj4yN0BMlBztjhw1lOTg4MBgMiIiIQFxfX+1A8oVarkZKSAgDYtWsXGhsbeTku9TN2yBkbcvFFBQFPGI1GoSNIDnLGTm+dlZeX4/DhwwCAefPm2R32KyQTJkxA//790draajOaqTdQP2OHnLEhF1/iOhtIGAnciiE6yBk7vXHGcZx1vYIxY8YgOjqar1i80b6wEgDk5+fzss489TN2yBkbcvFFBQFPiOV7WClBztjpjbOCggJcunQJ7u7uNuuRiI2oqCiMGzcOQNs6B7092VI/Y4ecsSEXX1QQ8IQUFmASG+SMnZ46MxqN1qWGp06disDAQB5T8U9qaio8PDxw5coVnDp1qlfHon7GDjljQy6+qCDgiZCQEKEjSA5yxk5PneXm5qK+vh7+/v6YNm0az6n4x9/fH9OnTwcAZGZm9uo7Wupn7JAzNuTiiwoCnrh+fQeie8gZOz1xptVqsWfPHgBtf3mrVCq+YzmFpKQkBAQEQKvVYu/evT0+DvUzdsgZG3LxRQUBT8hhlipXQ87Y6Ymz7OxsGI1GREVFYezYsU5I5Rw8PDwwd+5cAMDevXvR0NDQo+NQP2OHnLEhF19UEPBE+9rUhOOQM3ZYnV29ehXHjx8H0DbMUKFQOCOW0xg9ejSio6NhMpms90CwQv2MHXLGhlx8UUHAE+1rORCOQ87YYXF27TDDuLg4Ua/X3hnXDkM8deoULl26xHwM6mfskDM25OKLCgKeqK6uFjqC5CBn7LA4O378OEpKSqBSqawzAEqRiIgITJw4EUDbMESLxcK0P/UzdsgZG3LxRQUBT4h10SUxQ87YcdSZwWBAVlYWAGDmzJnw9fV1ZiynM2fOHKjVapSXlyM/P59pX+pn7JAzNuTiiwoCnmhpaRE6guQgZ+w46mzPnj1obGxEUFAQEhMTnZzK+fj4+CA5ORkAsH37dqa+Q/2MHXLGhlx8UUHAE2KbE14KkDN2HHFWV1eH3NxcAEBaWhpvywgLzeTJk9GvXz80NTVh165dDu9H/YwdcsaGXHzJ41WIALmcdF0JOWPHEWeZmZkwm82IjY3FiBEjXJDKNSiVSmg0GgDAgQMHUFNT49B+1M/YIWdsyMUXFQQ80dzcLHQEyUHO2OnOWVFREQoKCqBQKKDRaCQ3zLA7hg0bhqFDh8JisSAjI8OhfaifsUPO2JCLLyoIeELsc8OLEXLGTlfOLBaLdZjhpEmTEBYW5qJUrkWj0cDNzQ3nzp3D+fPnu92e+hk75IwNufiigoAnqqqqhI4gOcgZO105O3z4MCorK+Hl5YXZs2e7MJVrCQkJweTJkwEA6enpMJvNXW5P/YwdcsaGXHxRQcATcpm60pWQM3Y6c6bX65GTkwMAmDVrFry8vFwZy+UkJyfD29sb1dXVOHToUJfbUj9jh5yxIRdfVBDwhFymrnQl5Iydzpzt3LkTer0eoaGhmDRpkotTuR5PT0/rVZAdO3Z0+R0u9TN2yBkbcvFFBQFPREZGCh1BcpAzduw5q6qqwsGDBwG0rVcglyFQ3TFx4kSEh4ejpaXFenXEHtTP2CFnbMjFV984c7iA8vJyoSNIDnLGzvXOOI5Deno6OI7DiBEjEBsbK1Ay1+Pm5mZd5+Dw4cOdLkFL/YwdcsaGXHxRQcATQUFBQkeQHOSMneudFRYW4sKFC1AqlUhLSxMolXDExMRg9OjR1oWcOI7rsA31M3bIGRty8UUFAU80NTUJHUFykDN2rnVmNpuRnp4OAEhMTERwcLBQsQRl7ty5UCqVKC4uxpkzZzo8T/2MHXLGhlx8UUHAEyqVSugIkoOcsXOtswMHDqC2thY+Pj6YOXOmgKmEJTAwEFOnTgUAZGRkwGQy2TxP/YwdcsaGXHxRQUAQEqSxsdE6n39KSgrUarXAiYRl+vTp8PPzQ319Pfbv3y90HIKQJFQQ8ITBYBA6guQgZ+y0O9u+fTsMBgMiIyMRFxcnbCgRoFKpkJqaCgDYtWsXdDqd9TnqZ+yQMzbk4osKAp6Qy3rYroScsePv74+ysjIcPXoUAHDjjTfKbr2CnjJu3DgMHDgQRqMR2dnZ1sepn7FDztiQiy8qCHiiurpa6AiSg5yxU11dbV2vYOzYsYiKihI4kXhQKBTWYYjHjh2zThZD/YwdcsaGXHxRQcAT/fv3FzqC5CBn7NTW1uLy5ctwd3e3XiInfmPAgAGYMGECAFiHIVI/Y4ecsSEXX6IuCNavX4/Ro0cjISFB6CjdUlpaKnQEyUHO2DAajdYb5qZPn46AgACBE4mTlJQUeHh44OrVqzhx4gT1sx5AztiQiy8FZ28mD5Gh1WoREBCAhoYG2XxXQxCs7Ny5Ezt27EBAQABWrFgBDw8PoSOJlt27d2P79u3w8/PDY489JpthYQTRExz9DBX1FQIpIZfFLVwJOXOchoYG7NmzB4GBgZg7dy4VA92QlJSEwMBA6HS6Ltc5IOxD70025OKLCgKeCAkJETqC5CBnjpOVlQWTyYSAgACMHj1a6Diix93d3TqV85EjR1BfXy9sIIlB70025OKLCgKe0Gq1QkeQHOTMMS5fvoyTJ08CAKZMmULDDB1k5MiRiImJgUqlQmZmptBxJAW9N9mQiy8qCHjC09NT6AiSg5x1T/uiPQBwww03YMCAAQInkg7twxBNJhNOnz6N4uJioSNJBnpvsiEXX1QQ8ITFYhE6guQgZ92Tn5+PsrIyqNVqzJkzh5wxEh4ejjFjxgBoG4ZI/hyDPLEhF19UEPDE9QuqEN1DzrrGYDBYZ9ybOXMmfH19yVkPuOGGG+Dp6YmKigrrDI9E11A/Y0Muvqgg4Alvb2+hI0gOctY1u3btQlNTE4KDg5GYmAiAnPWE4OBgJCcnA2hbA6KlpUXgROKH+hkbcvFFBQFP1NXVCR1BcpCzzqmtrbVOQqTRaKBUKgGQs55QV1eHhIQEhISEoLm5GTt37hQ6kuihfsaGXHxRQcAT4eHhQkeQHOSsczIyMmCxWDBkyBAMGzbM+jg5Yyc8PBxKpRIajQYAcPDgQdnMPe8sqJ+xIRdfVBDwRHl5udARJAc5s8+FCxdw9uxZKBQKaDQam2GG5IyddmdDhw7F8OHDYbFYkJGRIXAqcUP9jA25+KKCgCdoOBg75KwjFosF6enpAIDJkycjNDTU5nlyxs61ztLS0uDm5obCwkIUFhYKmErcUD9jQy6+qCDgCblMXelKyFlHDh06hKqqKnh5eVlvhLsWcsbOtc769etnvUEzPT0dZrNZqFiihvoZG3LxRQUBT4SFhQkdQXKQM1uam5ut8+7Pnj0bXl5eHbYhZ+xc72zmzJnw9vZGTU0NDh48KFAqcUP9jA25+KKCgCdqa2uFjiA5yJktO3bsQEtLC8LCwhAfH293G3LGzvXOPD09kZKSAqBtBcmmpiYhYoka6mdsyMUXFQQ84evrK3QEyUHOfqOyshKHDh0CAMybNw9ubvbfmuSMHXvO4uLiEBERAYPBQKsh2oH6GRty8UUFAU+0trYKHUFykLM22tcr4DgOo0aNwuDBgzvdlpyxY8+Zm5sb5s2bBwA4fPiwbO4S5wvqZ2zIxRcVBDzBcZzQESQHOWvj7NmzKCoqglKpxNy5c7vclpyx05mz6Ohom3UOyO1vkAs25OKLCgKekMtqV66EnLXNgd4+Jj4pKQlBQUFdbk/O2OnK2dy5c+Hu7o5Lly6hoKDAhanEDfUzNuTiiwoCnpDLetiuhJwBBw4cQF1dHXx9fTF9+vRutydn7HTlLCAgAFOnTgUAZGZmwmg0uiqWqKF+xoZcfFFBwBP9+vUTOoLk6OvOGhsbsWvXLgBAamoq1Gp1t/v0dWc9oTtn06ZNg7+/P+rr65Gbm+uiVOKG+hkbcvFFBQFPVFZWCh1BcvR1Z9nZ2WhtbcWAAQMwfvx4h/bp6856QnfOVCoVUlNTAQB79uyRzV97vYH6GRty8UUFAU/IZepKV9KXnZWUlCA/Px9A2zDDa9cr6Iq+7KynOOJs7NixiIqKgtFoRHZ2tgtSiRvqZ2zIxRcVBDwhl6krXUlfddY+zBAAxo8fj4EDBzq8b1911hsccaZQKKzDEI8fP44rV644O5aooX7Ghlx8UUHAE3JZ/tKV9FVnJ0+exNWrV+Hh4WGdMc9R+qqz3uCos/79+yMuLg4ADUOkfsaGXHxRQcATtL46O33RWWtrK7KysgAA06dPh7+/P9P+fdFZb2FxlpKSApVKhdLSUhw/ftyJqcQN9TM25OKLCgKeCAgIEDqC5OiLzvbu3QutVovAwEAkJSUx798XnfUWFme+vr6YOXMmACArKwsGg8FZsUQN9TM25OKLCgKe0Ov1QkeQHH3NWX19Pfbt2wegbUIcDw8P5mP0NWd8wOosMTERQUFBaGxsxJ49e5yUStxQP2NDLr6oIOCJzhajITqnrznLysqCyWRCdHQ0Ro0a1aNj9DVnfMDqzN3dHWlpaQCA3Nxc1NXVOSOWqKF+xoZcfMnjVYgAd3d3oSNIjr7k7NKlSzh16pT1bnZHhxleT19yxhc9cTZixAjExsbCbDZbp5buS1A/Y0Muvqgg4Inm5mahI0iOvuLMYrFYhxlOnDgRERERPT5WX3HGJz1xplAooNFooFAocObMGRQVFTkhmXihfsaGXHxRQcATgYGBQkeQHH3FWX5+PsrLy6FWqzF79uxeHauvOOOTnjoLCwvDpEmTALQNQ7RYLDymEjfUz9iQiy8qCHiiqqpK6AiSoy84a2lpsc58N2vWLPj4+PTqeH3BGd/0xtns2bPh5eWFyspKHD58mMdU4ob6GRty8UUFAU/IZepKV9IXnO3atQvNzc3o168fEhISen28vuCMb3rjzMvLC7NmzQIA5OTkyOZu8u6gfsaGXHz1qCBYv349YmJi4OnpicTERBw8eLDL7evr67FixQpERkZCrVZj+PDh2Lp1a48CixW5TF3pSuTurKamBgcOHAAAaDQaKJXKXh9T7s6cQW+dTZo0CaGhodDr9dixYwc/oUQO9TM25OKLuSDYvHkzVq1ahbVr1+LIkSOYMGECNBpNp6s9tba2Yu7cuSguLsaWLVtw9uxZbNy4UTYVVTuRkZFCR5AccneWkZEBi8WCYcOGYdiwYbwcU+7OnEFvnbm5uVnXOcjLy5PN5eGuoH7Ghlx8MRcE7733HpYtW4YlS5Zg9OjR+OSTT+Dt7Y3PPvvM7vafffYZamtr8dNPP2HatGmIiYlBcnIyJkyY0OvwYqK8vFzoCJJDzs7Onz+Pc+fOwc3NzTqmnQ/k7MxZ8OEsNjYWI0aMAMdxSE9Pl/06B9TP2JCLL6aCoLW1FYcPH7auHQ60Vc+pqanIzc21u8/PP/+MpKQkrFixAuHh4Rg7dixef/11mM3mTtsxGAzQarU2P2InODhY6AiSQ67OzGYz0tPTAQCTJ09GSEgIb8eWqzNnwpeztLQ0KJVKXLhwAYWFhbwcU6xQP2NDLr6YCoLq6mqYzeYOKzuFh4d3WiFdvHgRW7ZsgdlsxtatW/H888/j3XffxauvvtppO+vWrUNAQID1JyoqCkDbHdulpaWwWCzW72xKSkrQ2tqKyspKNDY2or6+HrW1tdDr9SgvL4fJZLLZ1mg0ory8HM3NzaitrUVdXR2amppQUVEBo9Fos63ZbEZpaSn0ej1qamrQ0NAAnU6HqqoqGAwGm211Oh1KSkpgMBhQVVVlLWSqq6uh1+sdzt3c3Gw3t8lk6pC7sbHRbm6LxdJtbo7jbP7bnlun06GhoQE1NTWd5jYajaioqEBjYyPq6uqYctfX16OxsRGVlZWor6/vNHd1dbXVob3c7ft0ldtsNtvN3dTU1CH39Q7bc+v1+g65W1tb7eZuaWlBdXU19u3bh8bGRvTr1w9TpkxBaWmp3dyVlZUdcpeVlXWZu6ysrNs+W1ZWZpNbp9M5lPvaPtvde02n09m817rLXVdX53Dua/tsZWVlh/cax3EoLS21+16zl7uoqIiXc4TRaMTkyZMRGBiI9PR0XLlyhekccf17TczniOLiYlGcIzrrs2I7R5SWljKdI67Pzec5wt57rbS09PqPWbsoOIZrX6WlpRgwYAD27dtnszDL6tWrsXPnTusNVNcyfPhwtLS0oKioyHpT1XvvvYe3334bZWVldtsxGAw2i4potVpERUWhoaGBeXU4V6HVakWbTazI0VlzczM+/PBDtLS04JZbbkF8fDyvx5ejM2fDpzODwYAPP/wQTU1NmDt3LqZOncrLccUG9TM2xO5Lq9UiICCg289QpisEISEhUCqVqKiosHm8oqKi09nXIiMjMXz4cJs7rEeNGoXy8nK0trba3UetVsPf39/mhyCkQE5ODlpaWhAeHo4bbrhB6DgEz6jVaqSkpABoG1La2NgocCKC4A+mgkClUiE+Pt460QrQNi1rdnZ2p0u5Tps2DefPn7eZ5evcuXOIjIyESqXqYWzx0VeXSe0NcnNWUVFhnbxm3rx5TlnwRG7OXAHfzuLi4hAZGQmDwYDt27fzemyxQP2MDbn4Yj5jrVq1Chs3bsSXX36JgoICPProo2hqasKSJUsAAIsWLcKaNWus2z/66KOora3FypUrce7cOfzyyy94/fXXsWLFCv5ehQigqxjsyMkZx3HYtm0bOI7D6NGjERMT45R25OTMVfDtTKFQ4MYbbwQAHD16tNOvPqUM9TM25OKLuSBYuHAh3nnnHbzwwguIi4tDfn4+tm3bZr3R8PLlyzZvkKioKKSnpyMvLw/jx4/HH//4R6xcuRJPP/00f69CBNTU1AgdQXLIydmZM2dQXFwMpVKJuXPnOq0dOTlzFc5wFhUVhbFjxwKAtRCUE9TP2JCLL6abCoXC0RsihITjuB4vadtXkYszk8mE9evXo76+HjNmzMCcOXOc1pZcnLkSZzlraGjARx99BJPJhDvuuMNaIMgB6mdsiN2XU24qJDrH0WEdxG/IxVlubi7q6+vh5+eH6dOnO7UtuThzJc5yFhAQYP33zsrKgtFodEo7QkD9jA25+KKCgCfkNhWzK5CDM51Oh927dwMAUlNTnX6jrBycuRpnOps6dar1L699+/Y5rR1XQ/2MDbn4ooKAJ+SyuIUrkYOz7OxsGI1GDBw4EOPGjXN6e3Jw5mqc6czDw8N6z8iePXvQ0NDgtLZcCfUzNuTiiwoCnuBzetq+gtSdXb16FceOHQPQNszQFd8hSt2ZEDjb2ejRozFo0CCYTCZkZWU5tS1XQf2MDbn4ooKAJ+Tyl4ErkbKz9mGGADBhwgSXXTKUsjOhcLYzhUJhXQ3x5MmTuHz5slPbcwXUz9iQiy8qCHjCy8tL6AiSQ8rOTpw4gZKSEqhUKuvMda5Ays6EwhXOIiMjrTNTymEYIvUzNuTiiwoCnrh2JkbCMaTqrLW11XppeMaMGfDz83NZ21J1JiSucjZnzhyo1WqUlZUhPz/fJW06C+pnbMjFFxUEPGEymYSOIDmk6mzPnj3Q6XQICgrClClTXNq2VJ0Jiauc+fr6YubMmQDabjaV8nS21M/YkIsvKgh4wtvbW+gIkkOKzurq6qzDy9LS0uDu7u7S9qXoTGhc6SwxMRHBwcFoamrCrl27XNYu31A/Y0Muvqgg4In6+nqhI0gOKTrLzMyE2WzG4MGDMWLECJe3L0VnQuNKZ0qlEhqNBgCwf/9+yU5pS/2MDbn4ooKAJ8LCwoSOIDmk5qy4uBgFBQVQKBTQaDSCTFUqNWdiwNXOhg0bhiFDhsBisSAzM9OlbfMF9TM25OKLCgKeKC8vFzqC5JCSM4vFYh1mGB8fb13My9VIyZlYcLWzawvGs2fP4sKFCy5tnw+on7EhF19UEPCEXKaudCVScnbkyBFUVFTA09MTs2fPFiyHlJyJBSGchYaGYvLkyQCA9PR0yd2FTv2MDbn4ooKAJ+QydaUrkYozvV6P7du3AwBmzZol6A1EUnEmJoRylpycDC8vL1RVVeHQoUOCZOgp1M/YkIsvKgh4Qi7fIbkSqTjbuXMn9Ho9QkJCMGnSJEGzSMWZmBDKmZeXl/VqUk5ODpqbmwXJ0ROon7EhF19UEPBEbW2t0BEkhxScVVdXIy8vDwCg0WigVCoFzSMFZ2JDSGfx8fEICwtDS0sLduzYIVgOVqifsSEXX1QQ8ISvr6/QESSHFJy1f/87fPhwDB06VOg4knAmNoR05ubmZl3n4NChQ6isrBQsCwvUz9iQiy8qCHiitbVV6AiSQ+zOCgsLcf78ebi5uSEtLU3oOADE70yMCO1s8ODBGDVqlHVBLCmscyC0M6khF19UEBCEHcxmM9LT0wEAU6ZMQb9+/QROREiZuXPnQqlUoqioCGfPnhU6DkHYhQoCnlCpVEJHkBxidnbw4EHU1NTAx8fHOj+9GBCzM7EiBmdBQUFISkoCAGRkZIh+7nsxOJMScvFFBQFPNDY2Ch1BcojVWVNTE3bu3AngtxXsxIJYnYkZsTibPn06fH19UVdXh/379wsdp0vE4kwqyMUXFQQ8ERwcLHQEySFWZ9u3b4fBYEBkZCTi4uKEjmODWJ2JGbE4U6vVSE1NBQDs3r1b1B8iYnEmFeTiiwoCnpDK3cNiQozOysvLceTIEQDAvHnz4OYmrreIGJ2JHTE5Gz9+PAYMGIDW1lZkZ2cLHadTxORMCsjFl7jOdhJGLlNXuhKxOWu/CxwAxo4di0GDBgmcqCNicyYFxORMoVBYhyHm5+eLdoY7MTmTAnLxRQUBT4j1jS1mxObs9OnTuHTpEtzd3a2XdsWG2JxJAbE5GzhwIMaPHw8Aoh2GKDZnYkcuvqgg4ImIiAihI0gOMTkzGo3WpWqnTZuGgIAAgRPZR0zOpIIYnaWkpMDDwwNXr17FyZMnhY7TATE6EzNy8UUFAU/I5TskVyImZ7m5uWhoaIC/vz+mTZsmdJxOEZMzqSBGZ/7+/pg+fToAICsrS3QT24jRmZiRiy8qCHgiMDBQ6AiSQyzOtFot9uzZA6BtAhkPDw+BE3WOWJxJCbE6S0pKQmBgILRaLfbu3St0HBvE6kysyMUXFQQ8odfrhY4gOcTiLCsrC0ajEVFRURgzZozQcbpELM6khFideXh4YO7cuQCAffv2ob6+XthA1yBWZ2JFLr6oIOAJsQ1PkwJicHblyhWcOHECQNswQ4VCIXCirhGDM6khZmejRo1CdHQ0TCYTsrKyhI5jRczOxIhcfMnjVYgAoZfFlSJCO7t2mGFcXBz69+8vaB5HENqZFBGzs/ZhiAqFAqdOncKlS5eEjgRA3M7EiFx8ibogWL9+PUaPHo2EhASho3SLXC4ZuRKhnR07dgylpaVQqVRISUkRNIujCO1MiojdWUREBCZOnAigbRiixWIROJH4nYkNufgSdUGwYsUKnD59Gnl5eUJH6Ra53FTiSoR0ZjAYrDPFzZw5UzLrmVM/Y0cKzmbPng21Wo3y8nLk5+cLHUcSzsSEXHyJuiCQElVVVUJHkBxCOmufSz44OBiJiYmC5WCF+hk7UnDm4+ODWbNmAQCys7PR0tIiaB4pOBMTcvFFBQFPyGXqSlcilLPa2lrranNpaWlwd3cXJEdPoH7GjlScJSQkICQkBM3Nzdi1a5egWaTiTCzIxRcVBDwhl6krXYlQzjIzM2E2mxEbG4vhw4cLkqGnUD9jRyrOlEol0tLSAAAHDhxATU2NYFmk4kwsyMUXFQQ8ERkZKXQEySGEs4sXL+LMmTNQKBTQaDSiH2Z4PdTP2JGSs2HDhmHYsGGwWCzIyMgQLIeUnIkBufiigoAnysvLhY4gOVztzGKxID09HUDb5dmwsDCXts8H1M/YkZqztLQ0uLm54dy5czh//rwgGaTmTGjk4osKAp4IDg4WOoLkcLWzw4cPo7KyEl5eXtYbuKQG9TN2pOYsJCQEkydPBgCkp6fDbDa7PIPUnAmNXHxRQcATjY2NQkeQHK50ptfrkZOTA6BtiJeXl5fL2uYT6mfsSNFZcnIyvL29UV1dLciwayk6ExK5+KKCgCdUKpXQESSHK53t2LEDer0eYWFhiI+Pd1m7fEP9jB0pOvP09MScOXMAADt37kRzc7NL25eiMyGRiy8qCAjZU1VVZf0rS6PRyGbecULe3HDDDQgPD0dLS4v16hZBOBM6M/KE2NYzlwKucNa+XgHHcRg5ciRiY2Od3qYzoX7GjlSdubm5Yd68eQDa7n+pqKhwWdtSdSYUcvFFBQFPSGXqWzHhCmfnzp3DxYsXoVQqrUvNShnqZ+xI2VlMTAxGjx5tU9i6Aik7EwK5+KKCgCdqa2uFjiA5nO3MZDJZx3JPmTJFFncCUz9jR+rO5s6dC3d3dxQXF+PMmTMuaVPqzlyNXHxRQcATERERQkeQHM52duDAAdTW1sLX1xczZsxwaluugvoZO1J3FhgYiKSkJABARkYGTCaT09uUujNXIxdfVBDwRFlZmdARJIcznTU2Nlrng09JSYFarXZaW66E+hk7cnA2ffp0+Pn5ob6+Hrm5uU5vTw7OXIlcfFFBwBNyWdzClTjT2fbt29Ha2or+/ftjwoQJTmvH1VA/Y0cOzlQqFVJTUwG0rdSp0+mc2p4cnLkSufiigoAn5LK4hStxlrPS0lIcPXoUADBv3jzJrVfQFdTP2JGLs3HjxmHgwIEwGo3Izs52altyceYq5OKLCgKeCA0NFTqC5HCGs/a7sYG2E2hUVBTvbQgJ9TN25OJMoVBYhyEeO3YMV69edVpbcnHmKuTiiwoCnqivrxc6guRwhrNTp07hypUr8PDwsF5ilRPUz9iRk7MBAwZYvwJz5jBEOTlzBXLxRQUBT0h1bnwh4duZ0WhEZmYmAGDatGnw9/fn9fhigPoZO3JzlpKSApVKhZKSEhw/ftwpbcjNmbORiy8qCHhCiBXJpA7fzvbu3QutVouAgABMnTqV12OLBepn7MjNmZ+fn3UYbXZ2tlNmyZObM2cjF19UEPCExWIROoLk4NNZQ0MD9u7dC6BtIhcPDw/eji0mqJ+xI0dnU6ZMQVBQEHQ6Hfbs2cP78eXozJnIxRcVBDwhl0tGroRPZ1lZWTCZTIiOjsbo0aN5O67YoH7Gjhydubu7Iy0tDQCwb98+1NXV8Xp8OTpzJnLxRQUBT8jlphJXwpezS5cu4eTJkwDaVjOU0zDD66F+xo5cnY0YMQKDBw+G2Wy23jvDF3J15izk4osKAp4ICwsTOoLk4MMZx3FIT08HAEycOBGRkZG9PqaYoX7GjlydKRQKawFcUFCA4uJi3o4tV2fOQi6+qCDgifLycqEjSA4+nOXn56OsrAxqtRpz5szhIZW4oX7GjpydhYeHIz4+HkDbMES+vsuWszNnIBdfVBDwhFymrnQlvXVmMBisM7YlJyfDx8eHj1iihvoZO3J3Nnv2bHh6eqKiogJHjhzh5Zhyd8Y3cvFFBQFPyGXqSlfSW2e7du1CU1MT+vXrh8mTJ/OUStxQP2NH7s68vb0xa9YsAG1reOj1+l4fU+7O+EYuvqgg4Am5fIfkSnrjrKamBvv37wcApKWlQalU8hVL1FA/Y6cvOJs0aRJCQkKg1+uxc+fOXh+vLzjjE7n4ooKAJ2pra4WOIDl64ywjIwMWiwVDhw7FsGHDeEwlbqifsdMXnCmVSmg0GgBAXl4eqqure3W8vuCMT+TiiwoCnvD19RU6guToqbMLFy7g3LlzcHNzQ1pamqyHGV4P9TN2+oqzoUOHYvjw4bBYLNaRNz2lrzjjC7n4ooKAJ5wxfajc6Ykzs9lsPdklJCTIZpUxR6F+xk5fcpaWlgY3NzecP38ehYWFPT5OX3LGB3LxRQUBISkOHTqEqqoqeHt7Izk5Weg4BCEq+vXrhylTpgAA0tPTZTPHPuEaqCDgCZVKJXQEycHqrLm5GTt27ADQNtRKLtOFskD9jJ2+5mzmzJnw8fFBTU0NDh482KNj9DVnvUUuvnpUEKxfvx4xMTHw9PREYmKiw51u06ZNUCgUWLBgQU+aFTWNjY1CR5AcrM5ycnLQ0tKC8PBwTJw40UmpxA31M3b6mrNrJ+nauXMnmpqamI/R15z1Frn4Yi4INm/ejFWrVmHt2rU4cuQIJkyYAI1Gg8rKyi73Ky4uxpNPPmldtlNuBAcHCx1BcrA4q6iowOHDhwG0rVfg5tY3L25RP2OnLzqLi4tDZGQkDAYDtm/fzrx/X3TWG+Tii/ms+t5772HZsmVYsmQJRo8ejU8++QTe3t747LPPOt3HbDbjvvvuw0svvYTY2NheBRYr3RVEREccdda+XgHHcRg1ahQGDx7s5GTihfoZO33RmZubG+bNmwcAOHLkCPPUun3RWW+Qiy+mgqC1tRWHDx9Gamrqbwdwc0Nqaipyc3M73e/ll19GWFgYli5d6lA7BoMBWq3W5kfsyGXqSlfiqLOzZ8+iqKgISqUSc+fOdXIqcUP9jJ2+6mzQoEEYO3YsgLZ1DjiOc3jfvuqsp8jFF1NBUF1dDbPZjPDwcJvHw8PDO61A9+zZg08//RQbN250uJ1169YhICDA+hMVFQUAaGlpQWlpKSwWi3WqyJKSErS2tqKyshKNjY2or69HbW0t9Ho9ysvLYTKZbLY1Go0oLy9Hc3MzamtrUVdXh6amJlRUVMBoNNpsazabUVpaCr1ej5qaGjQ0NECn06GqqgoGg8Fm26tXr6KkpAQGgwFVVVXWQqa6uhp6vd7h3M3NzXZzm0ymDrkbGxvt5rZYLN3m5jjO5r/tuXU6HRoaGlBTU9NpbqPRiIqKCjQ2NqKuro4pd319PRobG1FZWYlLly51mru6uhparRZ1dXXYuXMn3N3dkZCQgMDAQJt9usptNpvt5m5qauqQ+3qH7bn1en2H3K2trXZzt7S0WHNrtVqr79LSUqvna3NXVlZ2yF1WVtZl7nPnznXbZ8vKymxy63Q6ptzV1dXdvtd0Op3Ne6273HV1dQ7nvrbPVlZWdnivcRyH0tJSu+81e7lPnDghinPE9e81V5wjpk6disDAQFy9ehUHDhxw+Bxx4sQJUZwjOuuz154jrn2vXZvXleeIs2fPiuYcYe+9VlpaCkdQcAxlY2lpKQYMGIB9+/YhKSnJ+vjq1auxc+dOa4drR6fTYfz48fjrX/+KG2+8EQCwePFi1NfX46effuq0HYPBAIPBYP1dq9UiKioKDQ0N8Pf3dzSuSzGbzX1m+ly+cMTZnj17kJ2dDV9fXzz++OOyuZu3p1A/Y6evO9uxYwd27tyJgIAArFixAh4eHt3u09edsSJ2X1qtFgEBAd1+hjJdIQgJCYFSqURFRYXN4xUVFYiIiOiw/YULF1BcXIz58+fD3d0d7u7u+Oqrr/Dzzz/D3d0dFy5csNuOWq2Gv7+/zY/Ykct3SK6kO2c6nQ67d+8GAKSmpvb5YgCgftYT+rqzadOmwd/fHw0NDV1+tXstfd0ZK3LxxVQQqFQqxMfHW5ecBQCLxYLs7GybKwbtjBw5EidOnEB+fr7159Zbb8Xs2bORn59v/SpADgQGBgodQXJ052z79u1obW3FgAEDMH78eNeEEjnUz9jp6848PDys997s2bPHoXuy+rozVuTii3mUwapVq7Bx40Z8+eWXKCgowKOPPoqmpiYsWbIEALBo0SKsWbMGAODp6YmxY8fa/AQGBsLPzw9jx46V1V98zc3NQkeQHF05KykpQX5+PgBg3rx5fWq9gq6gfsYOOQPGjBmDqKgoGI1GZGVldbs9OWNDLr6YC4KFCxfinXfewQsvvIC4uDjk5+dj27Zt1hsNL1++jLKyMt6Dih13d3ehI0iOzpxxHIdt27YBACZMmICBAwe6MpaooX7GDjkDFAqFdRjiiRMncOXKlS63J2dsyMUX002FQuHoDRFCotPp4OfnJ3QMSdGZs+PHj+PHH3+Eh4cHHn/8cfJ6DdTP2CFnv/Hvf/8b+fn56N+/Px566KFOr7yRMzbE7sspNxUSnaPX64WOIDnsOWttbbVe0pwxY4ao32RCQP2MHXL2GykpKVCpVCgtLcWxY8c63Y6csSEXX1QQ8ERAQIDQESSHPWd79+6FTqdDYGCg3RtV+zrUz9ghZ7/h6+uLmTNnAgCys7NthndfCzljQy6+qCDgierqaqEjSI7rndXX12Pfvn0A2tZ1l8v3cnxC/YwdcmZLYmIigoOD0djYaB3Wez3kjA25+KKCgCfkMnWlK7neWWZmJkwmE2JiYjBy5EiBUokb6mfskDNb3N3dkZaWBgDYv38/amtrO2xDztiQiy8qCHiifRpJwnGudVZcXIzTp09b74amYYb2oX7GDjnryPDhwxEbGwuz2YzMzMwOz5MzNuTiiwoCnujfv7/QESRHuzOLxWIdZjhx4sQOa2UQv0H9jB1y1hGFQgGNRgOFQoEzZ87g4sWLNs+TMzbk4osKAp7oi3Mv9JZ2Z0ePHkVFRQU8PT0xe/ZsgVOJG+pn7JAz+4SFhSEhIQEAkJ6eDovFYn2OnLEhF19UEPBEv379hI4gOfr164eWlhZs374dAJCcnAwfHx+BU4kb6mfskLPOmTVrFry8vFBZWYnDhw9bHydnbMjFFxUEPOHI/OCELVqtFjt37kRzczNCQkKsf60QnUP9jB1y1jleXl7Wq3I5OTnW8fTkjA25+KKCgCfUarXQESSHXq/HwYMHAQAajUbUy4eKBepn7JCzromPj0dYWBj0ej127NgBgJyxIhdfVBAQgrF3715YLBYMGzYMQ4cOFToOQfRJ3NzcoNFoAAB5eXmyWcqXYIcKAp5obW0VOoKkKCwsRElJic3JiOge6mfskLPuiY2NxciRI8FxHNLT08kZI3LxRQUBT/j6+godQTKYzWakp6fDYDAgMTFRNjfkuALqZ+yQM8eYO3culEolLl68iIqKCqHjSAq59DEqCHjC3mxfhH3y8vJQU1OD4OBg67zqhGNQP2OHnDlGcHAwpkyZAgDIzc2FyWQSOJF0kEsfo4KAJyIiIoSOIAmampqsNy4lJCTA09NT2EASg/oZO+TMcWbMmAFfX1+UlJTgwIEDQseRDHLpY1QQ8IRcJqZwNjk5OTAYDIiIiKAZCXsA9TN2yJnjqNVqpKSkIDAwELt27UJjY6PQkSSBXPoYFQQ8IZfFLZxJeXk5jhw5AgCYN28eoqKiBE4kPaifsUPO2JgwYQK8vb3R2tpqnTSM6Bq59DEqCHhCLotbOIv2u5c5jsOYMWMQHR1NznoAOWOHnLGhUCgwefJkAG3TipeWlgqcSPzIpY9RQcAToaGhQkcQNQUFBSguLoa7uztSU1MBkLOeQM7YIWfsjBo1CuPGjQMAbNu2DRzHCZxI3Milj1FBwBP19fVCRxAtRqPRusTq1KlTERgYCICc9QRyxg45Y6e+vh6pqanw8PDAlStXcOrUKaEjiRq59DEqCHjC29tb6AiiJTc3F/X19fDz88O0adOsj5MzdsgZO+SMHW9vb/j7+1vfr5mZmTAajQKnEi9y6WNUEPAEjdm1j1arxZ49ewC0TXyiUqmsz5EzdsgZO+SMnXZnU6dORUBAALRaLfbu3StwKvEilz5GBQFPXLuWOPEb2dnZMBqNiIqKwtixY22eI2fskDN2yBk77c48PDwwd+5cAG1rjzQ0NAgZS7TIpY9RQcATXl5eQkcQHVevXsXx48cBtA0zVCgUNs+TM3bIGTvkjJ1rnY0ePRrR0dEwmUzIysoSMJV4kUsfo4KAJ6hytoXjOGzbtg0AEBcXh/79+3fYhpyxQ87YIWfsXOtMoVBg3rx5AICTJ0/i0qVLQsUSLXLpY1QQ8ERISIjQEUTF8ePHUVJSApVKhZSUFLvbkDN2yBk75Iyd651FRERg4sSJAGCdT4T4Dbn0MSoIeIJWB/uN1tZW66XF9rnR7UHO2CFn7JAzduw5mzNnDtRqNcrKypCfn+/6UCJGLn2MCgKekMvUlXywe/duNDY2IigoyLp6mj3IGTvkjB1yxo49Zz4+PkhOTgbQdrOwwWBwdSzRIpc+RgUBT8hl6sreUldXh9zcXABAWloa3N3dO92WnLFDztghZ+x05mzy5Mno168fmpqasGvXLhenEi9y6WNUEPBEWFiY0BFEQWZmJsxmM2JjYzFixIgutyVn7JAzdsgZO505UyqV0Gg0AID9+/ejpqbGlbFEi1z6GBUEPEFvDKCoqAgFBQVQKBTQaDQdhhleDzljh5yxQ87Y6crZsGHDMHToUFgsFmRkZLgwlXiRSx8TdUGwfv16jB49GgkJCUJH6RZ/f3+hIwiKxWKxDjOcNGmSQxVzX3fWE8gZO+SMne6cpaWlwc3NDefOncOFCxdclEq8yKWPibogWLFiBU6fPo28vDyho3RLS0uL0BEE5ciRI6isrISnpydmzZrl0D593VlPIGfskDN2unMWGhpq/UMtPT0dZrPZFbFEi1z6mKgLAinR3eVxOaPX67F9+3YAwOzZsx1e6KMvO+sp5IwdcsaOI86Sk5Ph7e2NqqoqHDp0yAWpxItc+hgVBDxx7aI9fY2dO3dCr9cjNDQUkyZNcni/vuysp5AzdsgZO4448/LywuzZswEAO3bsQHNzs7NjiRa59DEqCHiisbFR6AiCUFVVhYMHDwIANBoN3Nwc71J91VlvIGfskDN2HHU2ceJEhIeHo6WlBTk5OU5OJV7k0seoIOCJ4OBgoSO4HI7jrNOYjhgxAkOGDGHavy866y3kjB1yxo6jztzc3KzDEA8fPiybGftYkUsfo4KAJyorK4WO4HIKCwtx4cIFuLm5IS0tjXn/vuist5AzdsgZOyzOBg8ejFGjRtn8gdDXkEsfo4KAJ+QydaWjmM1mpKenAwCmTJnSowq5rznjA3LGDjljh9XZ3LlzoVQqUVRUhLNnzzoplXiRSx+jgoAn5DJ1paMcOHAAtbW18PHxwcyZM3t0jL7mjA/IGTvkjB1WZ0FBQZg6dSoAICMjAyaTyRmxRItc+hgVBDwREREhdASX0djYaJ3HPCUlBWq1ukfH6UvO+IKcsUPO2OmJs+nTp8PX1xd1dXXYv3+/E1KJF7n0MSoIeKIv3Uyzfft2GAwGREZGIi4ursfH6UvO+IKcsUPO2OmJM5VKhdTUVADArl27oNPp+I4lWuTSx6gg4ImgoCChI7iEsrIyHD16FAAwb968Xk3I0Vec8Qk5Y4ecsdNTZ+PHj8eAAQNgNBqtk5X1BeTSx6gg4Im+MCkHx3HW9QrGjh2LQYMG9ep4fcEZ35AzdsgZOz11plAoMG/ePABAfn6+bL5b7w659DEqCHjC3d1d6AhO5/Tp07h8+TLc3d2tlwZ7Q19wxjfkjB1yxk5vnA0cOBATJkwAAGzbtq1PDEOUSx+jgoAnWGbokyJGoxGZmZkAgGnTpiEgIKDXx5S7M2dAztghZ+z01llKSgo8PDxw9epVnDhxgqdU4kUufUwer0IEyGW1q87Yt28fGhoa4O/vj2nTpvFyTLk7cwbkjB1yxk5vnfn5+WHGjBkAgKysLLS2tvIRS7TIpY9RQcATclkP2x4NDQ3Ys2cPgLYJSDw8PHg5rpydOQtyxg45Y4cPZ0lJSQgMDIROp8PevXt5SCVe5NLHqCDgierqaqEjOI3s7GyYTCYMGjQIY8aM4e24cnbmLMgZO+SMHT6cubu7W6c037dvH+rr63t9TLEilz5GBQFPyGXqyuu5fPmy9TvA3g4zvB65OnMm5IwdcsYOX85GjhyJmJgYmEwm6z1IckQufYwKAp6Q4/Caa4cZ3nDDDYiMjOT1+HJ05mzIGTvkjB2+nLUPQ1QoFDh9+jSKi4t5Oa7YkEsfo4KAJ/r37y90BN7Jz89HWVkZVCoV5syZw/vx5ejM2ZAzdsgZO3w6Cw8Px8SJEwG0DUO0WCy8HVssyKWPUUHAE6WlpUJH4BWDwYDs7GwAQHJyMnx9fXlvQ27OXAE5Y4ecscO3s9mzZ8PT0xMVFRXWmU7lhFz6GBUEPBESEiJ0BF7ZvXs3mpqaEBwcjMTERKe0ITdnroCcsUPO2OHbmY+PD5KTkwG0rYUil2F67cilj1FBwBNarVboCLxRW1trXa1Mo9FAqVQ6pR05OXMV5IwdcsaOM5wlJCQgJCQEzc3N2LlzJ+/HFxK59DEqCHiip0sAi5GMjAyYzWYMGTIEw4YNc1o7cnLmKsgZO+SMHWc4UyqV0Gg0AICDBw/KZqgeIJ8+RgUBYcPFixdx9uxZKBQKaDQaXocZEgTRtxk6dCiGDRsGi8WCjIwMoeMQ10EFAU/IYWpOi8ViHWaYkJCA0NBQp7YnB2euhpyxQ87YcaYzjUYDNzc3FBYWorCw0GntuBK59DEqCHjCx8dH6Ai95tChQ6iqqoKXlxdmzZrl9Pbk4MzVkDN2yBk7znTWr18/643K6enpMJvNTmvLVcilj1FBwBN1dXVCR+gVzc3NyMnJAdA2RMjLy8vpbUrdmRCQM3bIGTvOdjZz5kx4e3ujpqYGeXl5Tm3LFcilj1FBwBMRERFCR+gVO3bsQEtLC8LCwhAfH++SNqXuTAjIGTvkjB1nO/P09ERKSgqAtnNPU1OTU9tzNnLpY1QQ8ERZWZnQEXpMZWUlDh06BKBtvQJXre0tZWdCQc7YIWfsuMJZXFwcIiIiYDAYrFcnpYpc+hgVBDwh1cUtOI5Deno6OI7DyJEjMXjwYJe1LVVnQkLO2CFn7LjCmZubG+bNmwcAOHLkCMrLy53eprOQSx+jgoAnpLq4xblz53Dx4kUolUrrUqWuQqrOhIScsUPO2HGVs+joaIwZM8bmDxMpIpc+RgUBTzh7iJ4zMJlMSE9PBwAkJSUhKCjIpe1L0ZnQkDN2yBk7rnSWmpoKd3d3FBcXo6CgwGXt8olc+hgVBDxRX18vdARmDhw4gLq6Ovj6+mL69Okub1+KzoSGnLFDzthxpbPAwEBMnToVAJCZmQmj0eiytvlCLn2sRwXB+vXrERMTA09PTyQmJuLgwYOdbrtx40bMmDEDQUFBCAoKQmpqapfbSxVvb2+hIzDR2NiIXbt2AQBSUlIEmXpTas7EADljh5yx42pn06ZNg5+fH+rr65Gbm+vStvlALn2MuSDYvHkzVq1ahbVr1+LIkSOYMGECNBoNKisr7W6/Y8cO3HPPPcjJyUFubi6ioqKQlpYmm+9c2jGZTEJHYCI7Oxutra3o378/JkyYIEgGqTkTA+SMHXLGjqudqVQqzJ07FwCwZ88eyS0WJJc+xlwQvPfee1i2bBmWLFmC0aNH45NPPoG3tzc+++wzu9t/8803WL58OeLi4jBy5Ej8/e9/h8ViQXZ2dqdtGAwGaLVamx+xY7FYhI7gMKWlpcjPzwfQNsxQqPUKpORMLJAzdsgZO0I4Gzt2LKKiomA0Grv8fBAjculjTAVBa2srDh8+jNTU1N8O4OaG1NRUhy/zNDc3w2g0Ijg4uNNt1q1bh4CAAOtPVFQUAKClpQWlpaWwWCzWKwwlJSVobW1FZWUlGhsbUV9fj9raWuj1epSXl8NkMtlsazQaUV5ejubmZtTW1qKurg5NTU2oqKiA0Wi02dZsNqO0tBR6vR41NTVoaGiATqdDVVUVDAaDzbZqtRolJSUwGAyoqqqyFjLV1dXQ6/UO525ubrab22Qydcjd2NhoN7fFYuk0d0tLC7Zv3w4AGD9+PAYOHGiTW6fToaGhATU1NZ3mNhqNqKioQGNjI+rq6phy19fXo7GxEZWVlVAqlZ3mrq6utjq81jfHcTb7dJXbbDbbzd3U1NQh9/UO23Pr9foOuVtbW+3mbmlpsZu7tLTUbu7KysoOucvKyrrMbTAYuu2zZWVlNrl1Oh1T7urq6m7fazqdzua91l3uuro6h3Nf22crKys7vNc4jkNpaand95q93PX19aI4R7T3AbGfIwwGA+rr6615r8/trHOEXq/H9OnT4eXlhTNnzuDUqVOd9lmxnSNaWlpEc46w914rLS2FIyg4hnEepaWlGDBgAPbt24ekpCTr46tXr8bOnTtx4MCBbo+xfPlypKen49SpU/D09LS7jcFggMFgsP6u1WoRFRWFhoYG+Pv7OxrXpZSXl0titqoTJ07ghx9+gIeHBx577DFBfUrFmZggZ+yQM3aEdPbvf/8b+fn5GDBgAJYuXSqJFVfF3se0Wi0CAgK6/Qx16SiDN954A5s2bcKPP/7YaTEAtK0t7e/vb/MjdkJCQoSO0C2tra3IysoCAEyfPl1wr1JwJjbIGTvkjB0hnaWkpEClUqGkpATHjx8XLAcLculjTAVBSEgIlEolKioqbB6vqKjotjp655138MYbbyAjIwPjx49nTypyrnciRvbu3WutFK+9wiMUUnAmNsgZO+SMHSGd+fr6YsaMGQCArKwsSSwtLJc+xlQQqFQqxMfH29zw0X6DYFcfMG+99RZeeeUVbNu2DZMmTep5WhEj9qkrGxoasG/fPgBAWloaPDw8BE4kfmdihJyxQ87YEdrZlClTEBQUhMbGRuzevVvQLI4gtC++YP7KYNWqVdi4cSO+/PJLFBQU4NFHH0VTUxOWLFkCAFi0aBHWrFlj3f7NN9/E888/j88++wwxMTEoLy9HeXk5Ghsb+XsVIkDswygzMzNhMpkQHR2NUaNGCR0HgPidiRFyxg45Y0doZ+7u7tap1HNzc0W/vLDQvviCuSBYuHAh3nnnHbzwwguIi4tDfn4+tm3bhvDwcADA5cuXbVZ++vjjj9Ha2oo777wTkZGR1p933nmHv1chAtpfvxi5dOkSTp06BYVCIegww+sRszOxQs7YIWfsiMHZiBEjEBsbC7PZjMzMTKHjdIkYfPEB0ygDoXD0DkkhEetdphaLBRs3bkR5eTkmTpyI+fPnCx3JilidiRlyxg45Y0csziorK/HJJ5+A4zgsWrTIpauxsiAWX50hylEGckashUp+fj7Ky8uhVqsxZ84coePYIFZnYoacsUPO2BGLs7CwMOt9Z9u2bRPtBEBi8dVbqCDgiZaWFqEjdODaSYiSk5Ph4+MjcCJbxOhM7JAzdsgZO2JyNmvWLHh6eqKyshJHjhwROo5dxOSrN1BBwBNi+V7+Wnbt2oWmpib069cPkydPFjpOB8ToTOyQM3bIGTticubt7Y3Zs2cDALZv3w69Xi9woo6IyVdvoIKAJ8QwjO9aampqrDNHajQaKJVKgRN1RGzOpAA5Y4ecsSM2Z5MmTUJoaCj0ej127twpdJwOiM1XT6GCgCeampqEjmBDRkYGLBYLhg4dimHDhgkdxy5icyYFyBk75IwdsTlzc3ODRqMBABw8eBBVVVUCJ7JFbL56ChUEPNHVYk2u5vz58zh37pzNm0iMiMmZVCBn7JAzdsTobMiQIRgxYgQ4jkN6ejrENEBOjL56AhUEPFFZWSl0BACA2WxGeno6AGDy5MminmNbLM6kBDljh5yxI1ZnaWlpcHNzw4ULF1BYWCh0HCti9cUKFQQ8IZapKw8dOoTq6mp4e3sjOTlZ6DhdIhZnUoKcsUPO2BGrs+DgYEyZMgUAkJ6eDrPZLHCiNsTqixUqCHhCDFNXNjc3Y8eOHQCAOXPmdLmipBgQgzOpQc7YIWfsiNnZzJkz4ePjg9raWuuN00IjZl8sUEHAE5GRkUJHQE5ODlpaWhAeHo4bbrhB6DjdIgZnUoOcsUPO2BGzM7VajZSUFABtQ6vFsC6OmH2xQAUBT5SXlwvafkVFBQ4fPgwAmDdvHtzcxP9PK7QzKULO2CFn7IjdWVxcHCIjI2EwGKyTrwmJ2H05ivg/NSRCUFCQYG1zHIdt27aB4ziMHj0aMTExgmVhQUhnUoWcsUPO2BG7s/aF2gDg6NGjNgvqCYHYfTkKFQQ80dzcLFjbZ86cQXFxMZRKJebOnStYDlaEdCZVyBk75IwdKTgbNGgQxo4dCwDWP4iEQgq+HIEKAp5wd3cXpF2TyYSMjAwAwNSpUxEYGChIjp4glDMpQ87YIWfsSMVZamoq3N3dcfnyZZw+fVqwHFLx1R1UEPCEUN/Z5+bmor6+Hn5+fpg+fbogGXqKFO5zEBvkjB1yxo5UnAUEBFjPe5mZmTAajYLkkIqv7pDHqxABQqx2pdPpsHv3bgBtlbJKpXJ5ht4glxXCXAk5Y4ecsSMlZ1OnToW/vz8aGhqwb98+QTJIyVdXUEHAE0Ksh52dnQ2j0YiBAwdi3LhxLm+/t8hlDXFXQs7YIWfsSMmZh4eH9d6pPXv2oKGhweUZpOSrK6gg4Inq6mqXtldSUoJjx44BaBtmKMXlN13tTA6QM3bIGTtSczZmzBgMGjQIJpMJ2dnZLm9far46gwoCnujfv7/L2uI4Dr/++isAYMKECZKdNtOVzuQCOWOHnLEjNWfXDkM8ceIELl++7NL2pearM6gg4InS0lKXtXXixAmUlJRApVJZZ+ySIq50JhfIGTvkjB0pOouMjLTO0OrqYYhS9GUPKgh4wlUVYmtrK7KysgAA06dPh5+fn0vadQZyqapdCTljh5yxI1Vnc+bMgUqlQllZGfLz813WrlR9XQ8VBDzhqgpxz5490Ol0CAwMRFJSkkvadBZyqapdCTljh5yxI1Vnvr6+1lVes7OzYTAYXNKuVH1dDxUEPBESEuL0Nurr663DatLS0iQ/GYYrnMkNcsYOOWNHys4SExMRHByMpqYm67BsZyNlX9dCBQFPaLVap7eRmZkJs9mMwYMHY+TIkU5vz9m4wpncIGfskDN2pOxMqVRCo9EAAPbv34/a2lqntyllX9dCBQFPeHp6OvX4xcXFOH36NBQKBTQajSSHGV6Ps53JEXLGDjljR+rOhg0bhiFDhsBsNlundncmUvfVDhUEPGGxWJx67G3btgEA4uPjER4e7rS2XIkznckVcsYOOWNH6s6u/cPp7NmzuHDhglPbk7qvdqgg4AmTyeS0Yx85cgQVFRXw9PTE7NmzndaOq3GmM7lCztghZ+zIwVloaCgSEhIAAOnp6U790JaDL4AKAt7w9vZ2ynFbWlqQk5MDAJg1a5bT2hECOb0WV0HO2CFn7MjF2axZs+Dl5YWqqiocOnTIae3IxRcVBDxRV1fnlOPu3LkTzc3NCAkJwaRJk5zShlA4y5mcIWfskDN25OLMy8vLelU1JycHzc3NTmlHLr6oIOCJiIgI3o9ZXV2NgwcPAgA0Gg2USiXvbQiJM5zJHXLGDjljR07O4uPjERYWhpaWFuzYscMpbcjFFxUEPFFWVsb7Mdu/9xo+fDiGDh3K+/GFxhnO5A45Y4ecsSMnZ25ubtZ1Dg4dOoTKykre25CLL1EXBOvXr8fo0aOtN4aIGb4XGCosLMT58+fh5uaGtLQ0Xo8tFqS6KJOQkDN2yBk7cnPWPncLx3FIT0/nfZ0DufgSdUGwYsUKnD59Gnl5eUJH6ZaSkhLejmU2m5Geng6gbdatfv368XZsMcGns74COWOHnLEjR2dpaWlQKpW4ePEizp07x+ux5eJL1AWBlAgLC+PtWAcPHkRNTQ18fHwwc+ZM3o4rNvh01lcgZ+yQM3bk6CwoKMi6/kt6ejqvQwXl4osKAp7ga3rMpqYm7Ny5E0Dbyl1ymQHLHq6YUlRukDN2yBk7cnU2ffp0+Pr6oq6uDgcOHODtuHLxRQUBT/j4+PBynO3bt8NgMCAiIgJxcXG8HFOs8OWsL0HO2CFn7MjVmVqtRkpKCgBg165daGxs5OW4cvFFBQFPGI3GXh+jvLwcR44cAQDMmzcPbm7y/ufhw1lfg5yxQ87YkbOzCRMmoH///mhtbUV2djYvx5SLL3l/4riQ3t61ynGcdb2CMWPGIDo6mo9YoobvO337AuSMHXLGjpydKRQK6zDE/Px8lJaW9vqYcvFFBQFP9Pa7/oKCAly6dAnu7u6YO3cuT6nEjZzvj3AW5IwdcsaO3J1FRUVh/PjxAIBt27b1+gNdLr6oIOCJ3qyHbTQarUt0Tps2DQEBAXzFEjVyWUPclZAzdsgZO33BWUpKCjw8PHDlyhWcPHmyV8eSiy8qCHgiJCSkx/vm5uaioaEB/v7+mDZtGo+pxE1vnPVVyBk75IydvuDM398f06dPBwBkZWWhtbW1x8eSiy8qCHiioqKiR/tptVrs2bMHAJCamgoPDw8+Y4manjrry5AzdsgZO33FWVJSEgICAqDVarF3794eH0cuvqgg4ImeTl2ZnZ0No9GIqKgojB07ludU4kYu0326EnLGDjljp6848/DwsE4Nv2/fPjQ0NPToOHLxRQUBT/Rk6sorV67g+PHjANqGGSoUCr5jiRq5TPfpSsgZO+SMnb7kbNSoUYiOjobJZEJmZmaPjiEXX1QQ8ER4eDjT9tcOM4yLi0P//v2dEUvUsDojyFlPIGfs9CVn7cMQFQoFTp06hUuXLjEfQy6+qCDgierqaqbtjx07htLSUqhUKuvMWX0NVmcEOesJ5IydvuYsIiICEydOBNA2DNFisTDtLxdfVBDwhL+/v8PbGgwG6wxZM2fOhK+vr7NiiRoWZ0Qb5IwdcsZOX3Q2e/ZsqNVqlJeXIz8/n2lfufiigoAnWlpaHN52z549aGxsRFBQEBITE52YStywOCPaIGfskDN2+qIzHx8fJCcnA2hbU4bFgVx8UUHAE46uO1BXV4fc3FwAgEajgbu7uzNjiRq5r9XgDMgZO+SMnb7qbPLkyejXrx+ampqwa9cuh/eTiy95vAoR4OgHe0ZGBsxmM2JjYzF8+HAnpxI3fbkY6inkjB1yxk5fdaZUKqHRaAAABw4cQE1NjUP7ycUXFQQ80dzc3O02Fy9exJkzZ6BQKKDRaPrcMMPrccQZYQs5Y4ecsdOXnQ0bNgzDhg2DxWKxTinfHXLxRQUBTwQGBnb5vMViQXp6OgBg0qRJCAsLc0EqcdOdM6Ij5IwdcsZOX3eWlpYGNzc3nDt3DufPn+92e7n4ooKAJ6qqqrp8/vDhw6isrISXlxdmz57tolTipjtnREfIGTvkjJ2+7iwkJASTJ08GAKSnp8NsNne5vVx8UUHAE11NXanX65GTkwMAmDVrFry8vFwVS9TIZbpPV0LO2CFn7JAzIDk5Gd7e3qiursahQ4e63FYuvqgg4Imupq7csWMH9Ho9QkNDMWnSJBemEjdyme7TlZAzdsgZO+QM8PT0xJw5cwC0ncO7uk9ALr7kcWukABQUFCA3dz9y8/JQfOkSOA5QKICY6GgkJSQgKWkKRo0ahaqqKuTl5QFoW69ALsNT+CAyMlLoCJKDnLFDztghZ23ccMMNyMvLQ0VFBXJycnDzzTcDcPz8LzUUHMdxQofoDq1Wi4CAADQ0NAg6I5Rer8c3336Lr77dhJBBgxE7IR6xY8YjPCoaCoUCHMeh4solXDx1HBePHUb15SKMGBKL8LBQjB07Fnfffbdg2cVIaWlpn1zDoTeQM3bIGTvk7DeKi4vx5ZdfwmQyIahfP/z0n18cPv8vuvdu3HfvvYJ/TezoZyhdIXCQ3bt346lnnkPizbfj0ff+BrVnx39ghUKBiEExiBgUg6k33gpDix67f/4eW/71DyQlJQmQWtwEBQUJHUFykDN2yBk75Ow3YmJi4Obmhn/9+G/MvWcx0/k/d+vP+FRzI9567RXMmDFDgPRs0BWCbrBYLHjq6TU4e7UMC1c9Cx8/9vabdFpsevdVjIzqj7ffWEdfG/wf1dXVCAkJETqGpCBn7JAzdshZG+3n/4LLJbjnyecle/539DOUPpm6wGKxYMmyh9EaEIoH177Zo84AAD5+/lj64ltoDQjFkmUPM6+kJVdUKpXQESQHOWOHnLFDzmzP/w+99HafOP9TQdAFTz29Bv6xIzH7zvt4Od7sO++Df+xIrF7zDC/HIwiCIJxDXzz/U0HQCbt27cKZK6W8dYZ2Zt95HwouXcXu3bt5Pa4UMRgMQkeQHOSMHXLGTl931lfP/3QPgR30ej1mp83Divc39PgyUVc0aRuwftUjyMnYJvjdp0JiMBigVquFjiEpyBk75IydvuxMjud/uoegF3zz7bdIvPl2p3QGAPDxD8Dkm2/Dt//8p1OOLxWqq6uFjiA5yBk75IydvuysL5//qSCww1ffbsKUG+c7tY2kG2/Fl9+Ir0O4EhrnzA45Y4ecsdOXnfXl83+PCoL169cjJiYGnp6eSExMxMGDB7vc/rvvvsPIkSPh6emJcePGYevWrT0K6woKCgoQMmiw3XGm9tA3NuK+iUOx/tlVTO2oPb0QMigGBQUFPYkpC0pLS4WOIDnIGTvkjJ2+6oz1/P+HOZPx+Lzp+POCVPzxppl4/8/L0eLAUshiPf8zFwSbN2/GqlWrsHbtWhw5cgQTJkyARqNBZWWl3e337duHe+65B0uXLsXRo0exYMECLFiwACdPnux1eGeQm7sfsRPiHd5+76//Ruzo8TiQ+Sv0TU1MbQ0eH4/9+w+wRpQNclkQxJWQM3bIGTt91Rnr+R8AVr3/Cd79KQsf/HcHmht1yPlxs0P7ifH8zzxT4XvvvYdly5ZhyZIlAIBPPvkEv/zyCz777DM8/fTTHbb/y1/+gnnz5uGpp54CALzyyivIzMzERx99hE8++aSX8fknNy8PN9x+v8PbZ2/ZhDuX/wmZm7/G3l//jdQ773V439gx47H3h69x332O7yMnysrKaM50RsgZO+SMnb7qbO+BA4i/84Ee7WsytsKg18PXP8Ch7WPHjEfuT99gyZLFPWrPGTAVBK2trTh8+DDWrFljfczNzQ2pqanIzc21u09ubi5WrbK9nK7RaPDTTz912o7BYLAZ9qLValli9oriS5cwLyraoW2vnD+H6vJSxE2fBYvZhB83fMRUEEQMisH+Awewbt26nsaVNEqlstt1xglbyBk75IydvurswMGDuOmJF5j2ee+JP0Dl6YnKkqsYMmY8pt54q0P7RQyKwU/FxT1I6TyYvjKorq6G2WxGeHi4zePh4eEoLy+3u095eTnT9gCwbt06BAQEWH+ioqIAAC0tLSgtLYXFYrEuN1lSUoLW1lZUVlaisbER9fX1qK2thV6vR3l5OUwmk822RqMR5eXlaG5uRm1tLerq6tDU1ISKigoYjUa0thqhUCgc8pG95Z+Y9bs7oVQqMXFmCipKruDqhUKH9gXa5r5WKpVQqVTw8vKCt7c3PDw84O/vDzc3NwQGBgIAAgMD4ebmBn9/f6hUKnh7e8PLywsqlQp+fn5QKpU22yoUCgQEBMDd3R0+Pj7w9PSEWq2Gr69vh23b/6tUKuHr6wu1Wg1PT0/4+PjA3d0dAQEBUCgUHbb18/Ozya1SqbrM7eHh0SG3j4+Pw7nbsziS28PDw25uNzc3h3MrFAq7uTtzeH1uT09Ph3N7eXlZc/v7+3eZOzAw0Jrbz8/Podxd/du35/b19e1R7mv7bFe5r/+37yq3j49Pj3Jf22ev3TY0NNSa2957zV7uzt5r9nIHBARYc3f3Xrs2d0/fa644R4SGhoriHMGSm49zhELh5vD5v532rwy+yD2J0AED8fU7rzm0n0KhgNFogsFgQGlpKTiOs/msMhgMqKyshE6nQ0NDA2pqaqDX61FWVgaz2dzhc62iogJNTU2oq6vr8Lnm6D0holzcaM2aNTZXFbRaLaKiouDp6WkdQ9n+HVf7f8PCwjocp32M5/XbRkREAAC8vb2t2/r4+AAAVCoPcBzXbacwGY3Y+fMWuLt7YPd/fwQAtOr1yN7yLR74n7UOvU6O4xAaGoo///nPDm0vNxobG+Hr6yt0DElBztghZ+z0VWf/2Zbu0PnfHkp3d0xJuxlfvf0KFqP7zwCO4+Dh4Q61Wm0d1dGbz7X2P7zbP8uu/X9HR40wFQQhISFQKpWoqKiwebyiosL6IXs9ERERTNsDgFqtFmxSjJjoaFRcuYSIQTFdbpe3PQPhUdF4Y/N/rY9dvVCIFxbdgftWPQN3D49u2yq/XIzYwYP77Lzh7VdHCMchZ+yQM3b6qrPBMTEOnf874+T+PRgweIhD25ZfLsbgmJ614yyYvjJQqVSIj49Hdna29TGLxYLs7OxOl/dNSkqy2R4AMjMzRbsccFJCAi6eOt7tdtnf/xMzb7nN5rGBQ4YhODwCh3IyHWrr4qnjSEpI6FFOOWAymYSOIDnIGTvkjJ2+6szR8/+1vPfEH/DnBan40/zZuHrxPB585mWH9hPj+Z/5K4NVq1bhgQcewKRJkzB58mR88MEHaGpqso46WLRoEQYMGGC9UW7lypVITk7Gu+++i5tvvhmbNm3CoUOHsGHDBn5fCU8kJU3Bf996v9sbQ57b8A+7j7/zQ4bDbRUdP4yHVrPNXyAnrv3KhnAMcsYOOWOnrzpz9Pzfzifbu56DpyvEeP5nLggWLlyIqqoqvPDCCygvL0dcXBy2bdtm/f7i8uXLNus9T506Fd9++y2ee+45PPPMMxg2bBh++uknjB07lr9XwSOjRo1C9eUiGFr0Dk9O0RMMLXpUXy7GqFGjnNaG2Kmrq+vTazn0BHLGDjljp6866+vnf1rcyA5///RTnKhqRPJtdzmtjR0/bsaEMH8sffBBp7UhdsxmM5RKpdAxJAU5Y4ecsdOXncnx/E+LG/WC++69Fwd++QFNOufMf9CkbcDBX37Evffc45TjS4Wuhp4S9iFn7JAzdvqys758/qeCwA5eXl54+/VXsendV51y/E3vvop31r3WJy/JXUtfnR61N5AzdsgZO33ZWV8+/1NB0AkzZszAyKj+yNnyDa/HzdnyDUZFD8T06dN5Pa4UaZ9Yg3AccsYOOWOnrzvrq+d/uoegCywWC5Ysexj+sSMx+877en28nC3fQHvxDD7fuMHmxsu+itFohIcD8zUQv0HO2CFn7JAzeZ3/6R4CHnBzc8PnGzdAra3Gpy+u7vF3Sk3aBny69imotdVUDFxDbW2t0BEkBzljh5yxQ8765vmfrhA4yO7du/HUM89h8s23IenGWx0akmJo0SP3159x8Jcf8fbrr2LGjBkuSCodmpqabKbZJLqHnLFDztghZ7ZI/fzv6GcoFQQM6PV6fPvPf+LLb/6JkEExGDw+HrFjxiNiUAwUCgU4jkP55WJcPHUcRccPo+bKJSy6927ce889oryBRGjq6uoQFBQkdAxJQc7YIWfskLOOSPn8TwWBkykoKMD+/QeQm5eHouJimExmuLsrMTgmBkkJCZgyJVF0k06IjdraWgQHBwsdQ1KQM3bIGTvkrGukdv6ngsDFNDc399npPnsKOWOHnLFDztghZ2yI3RfdVOhitFrnTGIhZ8gZO+SMHXLGDjljQy6+qCDgiX79+gkdQXKQM3bIGTvkjB1yxoZcfFFBwBOVlZVCR5Ac5IwdcsYOOWOHnLEhF190DwFBEARByBi6h8DF9PWpPnsCOWOHnLFDztghZ2zIxRcVBDwRHh4udATJQc7YIWfskDN2yBkbcvFFBQFPVFdXCx1BcpAzdsgZO+SMHXLGhlx8UUHAEwEBAUJHkBzkjB1yxg45Y4ecsSEXX1QQ8IRerxc6guQgZ+yQM3bIGTvkjA25+KKCgCfEvIKVWCFn7JAzdsgZO+SMDbn4kserEAHu7u5CR5Ac5IwdcsYOOWOHnLEhF19UEPBEc3Oz0BEkBzljh5yxQ87YIWdsyMUXFQQ8ERgYKHQEyUHO2CFn7JAzdsgZG3LxRQUBT1RVVQkdQXKQM3bIGTvkjB1yxoZcfNHUxQRBEAQhY2jqYhcjl6krXQk5Y4ecsUPO2CFnbMjFF10h4AmLxSKboSeugpyxQ87YIWfskDM2xO6LrhC4mPLycqEjSA5yxg45Y4ecsUPO2JCLL0kMnmy/iKHVagVO0jnu7u6izidGyBk75IwdcsYOOWND7L7as3X3hYAkCgKdTgcAiIqKEjgJQRAEQUgTnU7X5boLkriHwGKxoLS0FH5+fpg8eTLy8vKEjmSDVqtFVFQUrly5Isp7HBISEsgZA2L0BZCznkDO2CFnbIjZF9Dm7ODBg9DpdOjfv3+X9zpI4gqBm5sbBg4cCABQKpWilA4A/v7+osxGztgQsy+AnPUEcsYOOWNDjL6ANmcBAQEOrcgouZsKV6xYIXQEyUHO2CBf7JAzdsgZO+SMHRZnkvjKQOxIYVik2CBn7JAzdsgZO+SMDTn5ktwVAjGiVquxdu1aqNVqoaNIBnLGDjljh5yxQ87YkJMvukJAEARBEARdISAIgiAIggoCgiAIgiBABQFBEARBEKCCgCAIgiAIUEFAEARBEASoIHCY9evXIyYmBp6enkhMTMTBgwe73P67777DyJEj4enpiXHjxmHr1q0uSioeWJxt3LgRM2bMQFBQEIKCgpCamtqtYznC2s/a2bRpExQKBRYsWODcgCKE1Vl9fT1WrFiByMhIqNVqDB8+vE+9P1l9ffDBBxgxYgS8vLwQFRWFJ554Ai0tLS5KKzy7du3C/Pnz0b9/fygUCvz000/d7rNjxw5MnDgRarUaQ4cOxRdffOH0nLzAEd2yadMmTqVScZ999hl36tQpbtmyZVxgYCBXUVFhd/u9e/dySqWSe+utt7jTp09zzz33HOfh4cGdOHHCxcmFg9XZvffey61fv547evQoV1BQwC1evJgLCAjgrl696uLkwsHqrJ2ioiJuwIAB3IwZM7jf/e53rgkrElidGQwGbtKkSdxNN93E7dmzhysqKuJ27NjB5efnuzi5MLD6+uabbzi1Ws198803XFFREZeens5FRkZyTzzxhIuTC8fWrVu5Z599lvvhhx84ANyPP/7Y5fYXL17kvL29uVWrVnGnT5/mPvzwQ06pVHLbtm1zTeBeQAWBA0yePJlbsWKF9Xez2cz179+fW7dund3t77rrLu7mm2+2eSwxMZF75JFHnJpTTLA6ux6TycT5+flxX375pbMiio6eODOZTNzUqVO5v//979wDDzzQ5woCVmcff/wxFxsby7W2troqoqhg9bVixQpuzpw5No+tWrWKmzZtmlNzihVHCoLVq1dzY8aMsXls4cKFnEajcWIyfqCvDLqhtbUVhw8fRmpqqvUxNzc3pKamIjc31+4+ubm5NtsDgEaj6XR7udETZ9fT3NwMo9GI4OBgZ8UUFT119vLLLyMsLAxLly51RUxR0RNnP//8M5KSkrBixQqEh4dj7NixeP3112E2m10VWzB64mvq1Kk4fPiw9WuFixcvYuvWrbjppptcklmKSPn8L4nVDoWkuroaZrMZ4eHhNo+Hh4fjzJkzdvcpLy+3u315ebnTcoqJnji7nv/5n/9B//79O7yx5EpPnO3Zsweffvop8vPzXZBQfPTE2cWLF7F9+3bcd9992Lp1K86fP4/ly5fDaDRi7dq1rogtGD3xde+996K6uhrTp08Hx3EwmUz4wx/+gGeeecYVkSVJZ+d/rVYLvV4PLy8vgZJ1D10hIETHG2+8gU2bNuHHH3+Ep6en0HFEiU6nw/3334+NGzciJCRE6DiSwWKxICwsDBs2bEB8fDwWLlyIZ599Fp988onQ0UTJjh078Prrr+Ovf/0rjhw5gh9++AG//PILXnnlFaGjEU6ArhB0Q0hICJRKJSoqKmwer6ioQEREhN19IiIimLaXGz1x1s4777yDN954A1lZWRg/frwzY4oKVmcXLlxAcXEx5s+fb33MYrEAANzd3XH27FkMGTLEuaEFpif9LDIyEh4eHlAqldbHRo0ahfLycrS2tkKlUjk1s5D0xNfzzz+P+++/Hw899BAAYNy4cWhqasLDDz+MZ599Fm5u9Dfl9XR2/vf39xf11QGArhB0i0qlQnx8PLKzs62PWSwWZGdnIykpye4+SUlJNtsDQGZmZqfby42eOAOAt956C6+88gq2bduGSZMmuSKqaGB1NnLkSJw4cQL5+fnWn1tvvRWzZ89Gfn4+oqKiXBlfEHrSz6ZNm4bz589biycAOHfuHCIjI2VdDAA989Xc3NzhQ7+9mOJoXTy7SPr8L/RdjVJg06ZNnFqt5r744gvu9OnT3MMPP8wFBgZy5eXlHMdx3P333889/fTT1u337t3Lubu7c++88w5XUFDArV27tk8OO2Rx9sYbb3AqlYrbsmULV1ZWZv3R6XRCvQSXw+rsevriKANWZ5cvX+b8/Py4xx57jDt79iz33//+lwsLC+NeffVVoV6CS2H1tXbtWs7Pz4/75z//yV28eJHLyMjghgwZwt11111CvQSXo9PpuKNHj3JHjx7lAHDvvfced/ToUe7SpUscx3Hc008/zd1///3W7duHHT711FNcQUEBt379ehp2KDc+/PBDbtCgQZxKpeImT57M7d+/3/pccnIy98ADD9hs/69//YsbPnw4p1KpuDFjxnC//PKLixMLD4uz6OhoDkCHn7Vr17o+uICw9rNr6YsFAcexO9u3bx+XmJjIqdVqLjY2lnvttdc4k8nk4tTCweLLaDRyL774IjdkyBDO09OTi4qK4pYvX87V1dW5PrhA5OTk2D03tXt64IEHuOTk5A77xMXFcSqViouNjeU+//xzl+fuCQqOo+s+BEEQBNHXoXsICIIgCIKggoAgCIIgCCoICIIgCIIAFQQEQRAEQYAKAoIgCIIgQAUBQRAEQRCggoAgCIIgCFBBQBAEQRAEqCAgCIIgCAJUEBAEQRAEASoICIIgCIIA8P8BQONqZ0Du41wAAAAASUVORK5CYII=", + "text/plain": [ + "
                        " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of nodes: 3\n", + "Number of edges: 3\n", + "Embedding dimension: 2\n" + ] + } + ], + "source": [ + "K = EmbeddedComplex()\n", + "\n", + "K.add_node('A', [0, 0])\n", + "K.add_node('B', [1, 0])\n", + "K.add_node('C', [0.5, 0.866])\n", + "\n", + "K.add_edge('A', 'B')\n", + "K.add_edge('B', 'C')\n", + "K.add_edge('C', 'A')\n", + "\n", + "#using built-in plotting function along with matplotlib\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400)\n", + "ax.set_title('Simple Triangle Graph\\n(0-cells: vertices, 1-cells: edges)')\n", + "plt.show()\n", + "\n", + "#print some information about the complex\n", + "print(f\"Number of nodes: {len(K.nodes())}\")\n", + "print(f\"Number of edges: {len(K.edges())}\")\n", + "print(f\"Embedding dimension: {K.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's add a 2-cell (face) to fill in the triangle:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
                        " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of 2-cells (faces): 1\n", + "Faces: [('A', 'B', 'C')]\n", + "Internal cells dictionary: {2: [(0, 1, 2)]}\n" + ] + } + ], + "source": [ + "K.add_face(['A', 'B', 'C'])\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400, face_alpha=0.3, face_color='lightblue')\n", + "ax.set_title('Triangle with Face\\n(0-cells: vertices, 1-cells: edges, 2-cells: faces)')\n", + "plt.show()\n", + "\n", + "print(f\"Number of 2-cells (faces): {len(K.faces)}\")\n", + "print(f\"Faces: {K.faces}\")\n", + "print(f\"Internal cells dictionary: {dict(K.cells)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Cells of Arbitrary Dimension\n", + "\n", + "The key new feature is the ability to add cells of any dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
                        " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimension 2: 4 cells\n", + "Dimension 3: 1 cells\n", + "\n", + "Total embedding dimension: 3\n" + ] + } + ], + "source": [ + "# Create a 3D tetrahedron with 0, 1, 2, and 3-cells\n", + "K_tetra = EmbeddedComplex()\n", + "\n", + "# Add vertices (0-cells)\n", + "vertices = {\n", + " 'A': [0, 0, 0],\n", + " 'B': [1, 0, 0],\n", + " 'C': [0.5, 0.866, 0],\n", + " 'D': [0.5, 0.289, 0.816] \n", + "}\n", + "\n", + "for name, coord in vertices.items():\n", + " K_tetra.add_node(name, coord)\n", + "\n", + "# Add edges (1-cells) - all pairs\n", + "edges = [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]\n", + "K_tetra.add_edges_from(edges)\n", + "\n", + "# Add faces (2-cells) - all triangular faces\n", + "faces = [['A', 'B', 'C'], ['A', 'B', 'D'], ['A', 'C', 'D'], ['B', 'C', 'D']]\n", + "for face in faces:\n", + " K_tetra.add_cell(face, dim=2) # Explicitly specify dimension\n", + "\n", + "# Add volume (3-cell) - the entire tetrahedron\n", + "K_tetra.add_cell(['A', 'B', 'C', 'D'], dim=3)\n", + "\n", + "# Plot the tetrahedron\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "K_tetra.plot(ax=ax, face_alpha=0.3, face_color='cyan', node_size=100)\n", + "ax.set_title('Tetrahedron with All Cell Types\\n0-cells: 4, 1-cells: 6, 2-cells: 4, 3-cells: 1')\n", + "plt.show()\n", + "\n", + "# Display cell counts\n", + "for dim in sorted(K_tetra.cells.keys()):\n", + " print(f\"Dimension {dim}: {len(K_tetra.cells[dim])} cells\")\n", + " \n", + "print(f\"\\nTotal embedding dimension: {K_tetra.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ECT Computation with Higher-Dimensional Cells\n", + "\n", + "The ECT computation now properly includes all cell dimensions in the Euler characteristic calculation:\n", + "\n", + "**χ = Σ(-1)^k × |k-cells below threshold|**\n", + "\n", + "Let's see how this works:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ECT result shape: (8, 20)\n", + "Directions: 8 directions in 3D\n", + "Thresholds: 20 threshold values\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
                        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute ECT for the tetrahedron\n", + "ect = ECT(num_dirs=8, num_thresh=20)\n", + "result = ect.calculate(K_tetra)\n", + "\n", + "print(f\"ECT result shape: {result.shape}\")\n", + "print(f\"Directions: {len(result.directions)} directions in {K_tetra.dim}D\")\n", + "print(f\"Thresholds: {len(result.thresholds)} threshold values\")\n", + "\n", + "# Plot the ECT matrix\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "result.plot()\n", + "plt.title('ECT of Tetrahedron (includes 3-cells in computation)')\n", + "plt.show()\n", + "\n", + "# Show a single direction\n", + "\n", + "single_direction = ECT(num_thresh=20, directions=Directions.from_vectors([[1, 0, 0]])).calculate(K_tetra)\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "ax.plot(single_direction.thresholds, single_direction[0], 'b-', marker='o', linewidth=2)\n", + "ax.set_xlabel('Threshold')\n", + "ax.set_ylabel('Euler Characteristic')\n", + "ax.set_title('ECT Curve for Single Direction (v=[1, 0, 0])')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also restrict self-intersections by using the 'validate_embeddings' argument. Currently without checks we can add a node inside of our tetrahedron." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unexpected exception formatting exception. Falling back to standard exception\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3548, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/81/3x5xj5kx4ys30p1c2z55bhbw0000gn/T/ipykernel_80902/4266954845.py\", line 1, in \n", + " K_valid = K_tetra.copy()\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/networkx/classes/graph.py\", line 1642, in copy\n", + " G.add_nodes_from((n, d.copy()) for n, d in self._node.items())\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 177, in add_nodes_from\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 120, in wrapper\n", + " )\n", + " \n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 142, in wrapper\n", + " def wrapper(self, *args, **kwargs):\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 157, in add_node\n", + " return wrapper\n", + " ^^^^^^^^^^^\n", + "TypeError: float() argument must be a string or a real number, not 'dict'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 2142, in showtraceback\n", + " stb = self.InteractiveTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1435, in structured_traceback\n", + " return FormattedTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1326, in structured_traceback\n", + " return VerboseTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1173, in structured_traceback\n", + " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1088, in format_exception_as_a_whole\n", + " frames.append(self.format_record(record))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 970, in format_record\n", + " frame_info.lines, Colors, self.has_colors, lvals\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 792, in lines\n", + " return self._sd.lines\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 734, in lines\n", + " pieces = self.included_pieces\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 681, in included_pieces\n", + " pos = scope_pieces.index(self.executing_piece)\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 660, in executing_piece\n", + " return only(\n", + " ^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/executing/executing.py\", line 116, in only\n", + " raise NotOneValueFound('Expected one value, found 0')\n", + "executing.executing.NotOneValueFound: Expected one value, found 0\n" + ] + } + ], + "source": [ + "K_valid = K_tetra.copy()\n", + "\n", + "K_valid.add_node('E', [0.5, 0.289, 0.204])\n", + "K_valid.add_cell(['E', 'B'], dim=1)\n", + "\n", + "\n", + "\n", + "# Display cell counts\n", + "print(\"4D Simplex Cell Counts:\")\n", + "for dim in sorted(K_valid.cells.keys()):\n", + " print(f\" {dim}-cells: {len(K_valid.cells[dim])}\")\n", + "\n", + "# Plot (showing 3D projection)\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "K_valid.plot(ax=ax, face_alpha=0.1, node_size=80)\n", + "ax.set_title('4D Simplex (5 vertices, cells up to dimension 4)')\n", + "plt.show()\n", + "\n", + "# Compute ECT\n", + "ect_4d = ECT(num_dirs=6, num_thresh=15)\n", + "result_4d = ect_4d.calculate(K_valid)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "result_4d.plot()\n", + "plt.title('ECT of 4D Simplex\\n(alternating sum includes all dimensions 0-4)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding ECT with Projection Visualization\n", + "\n", + "Let's visualize how the ECT computation works by showing projection values:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
                        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a simple 2D example for visualization\n", + "K_viz = EmbeddedComplex()\n", + "\n", + "# Square with center point\n", + "K_viz.add_node('A', [-1, -1])\n", + "K_viz.add_node('B', [1, -1])\n", + "K_viz.add_node('C', [1, 1])\n", + "K_viz.add_node('D', [-1, 1])\n", + "K_viz.add_node('E', [0, 0]) # center\n", + "\n", + "# Add edges\n", + "edges = [('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A'), # boundary\n", + " ('A', 'E'), ('B', 'E'), ('C', 'E'), ('D', 'E')] # to center\n", + "K_viz.add_edges_from(edges)\n", + "\n", + "# Add triangular faces\n", + "faces = [['A', 'B', 'E'], ['B', 'C', 'E'], ['C', 'D', 'E'], ['D', 'A', 'E']]\n", + "for face in faces:\n", + " K_viz.add_face(face)\n", + "\n", + "# Visualization function\n", + "def plot_with_projections(K, theta, ax):\n", + " \"\"\"Plot complex with nodes colored by projection values\"\"\"\n", + " direction = np.array([np.sin(theta), -np.cos(theta)])\n", + " node_projections = np.dot(K.coord_matrix, direction)\n", + " \n", + " # Plot edges\n", + " for u, v in K.edges():\n", + " u_idx = K.node_to_index[u]\n", + " v_idx = K.node_to_index[v]\n", + " x = [K.coord_matrix[u_idx, 0], K.coord_matrix[v_idx, 0]]\n", + " y = [K.coord_matrix[u_idx, 1], K.coord_matrix[v_idx, 1]]\n", + " ax.plot(x, y, 'k-', alpha=0.5, linewidth=1)\n", + " \n", + " # Plot faces with transparency\n", + " for face_indices in K.cells.get(2, []):\n", + " face_coords = K.coord_matrix[list(face_indices)]\n", + " face_projection = np.max(node_projections[list(face_indices)])\n", + " ax.fill(face_coords[:, 0], face_coords[:, 1], \n", + " alpha=0.3, color=plt.cm.Blues(0.5))\n", + " \n", + " # Plot nodes colored by projection\n", + " scatter = ax.scatter(K.coord_matrix[:, 0], K.coord_matrix[:, 1], \n", + " c=node_projections, cmap='plasma', s=300, \n", + " edgecolors='black', linewidth=2, zorder=10)\n", + " \n", + " # Add node labels with projection values\n", + " for i, node in enumerate(K.node_list):\n", + " ax.annotate(f'{node}\\n({node_projections[i]:.2f})', \n", + " (K.coord_matrix[i, 0], K.coord_matrix[i, 1]),\n", + " ha='center', va='center', fontsize=9, fontweight='bold',\n", + " bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.8))\n", + " \n", + " # Direction arrow\n", + " ax.arrow(0, 0, direction[0]*0.7, direction[1]*0.7,\n", + " head_width=0.1, head_length=0.1, fc='red', ec='red', linewidth=2)\n", + " \n", + " ax.set_xlim(-2, 2)\n", + " ax.set_ylim(-2, 2)\n", + " ax.set_aspect('equal')\n", + " ax.grid(True, alpha=0.3)\n", + " ax.set_title(f'θ = {theta*180/np.pi:.0f}°')\n", + " \n", + " return scatter\n", + "\n", + "# Show projections in multiple directions\n", + "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", + "axes = axes.flatten()\n", + "\n", + "thetas = np.linspace(0, 2*np.pi, 6, endpoint=False)\n", + "for ax, theta in zip(axes, thetas):\n", + " scatter = plot_with_projections(K_viz, theta, ax)\n", + "\n", + "# Add shared colorbar\n", + "fig.subplots_adjust(right=0.9)\n", + "cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])\n", + "fig.colorbar(scatter, cax=cbar_ax, label='Node Projection Values')\n", + "\n", + "plt.suptitle('Complex Colored by Projection Values in Different Directions\\n' + \n", + " 'Red arrows show projection direction, faces use max vertex projection', \n", + " fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Graph vs. Complex ECT\n", + "\n", + "Let's compare ECT results for the same geometry with and without 2-cells:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
                        " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ECT value ranges:\n", + "Graph only: [0, 1]\n", + "With face: [0, 1]\n", + "\n", + "The face contributes +1 to the Euler characteristic when included.\n" + ] + } + ], + "source": [ + "# Create two versions: graph only vs. complex with faces\n", + "K_graph = EmbeddedComplex()\n", + "K_complex = EmbeddedComplex()\n", + "\n", + "# Same vertices and edges for both\n", + "vertices = {'A': [0, 0], 'B': [2, 0], 'C': [1, 1.732]}\n", + "edges = [('A', 'B'), ('B', 'C'), ('C', 'A')]\n", + "\n", + "for K in [K_graph, K_complex]:\n", + " for name, coord in vertices.items():\n", + " K.add_node(name, coord)\n", + " K.add_edges_from(edges)\n", + "\n", + "# Add face only to the complex version\n", + "K_complex.add_face(['A', 'B', 'C'])\n", + "\n", + "# Compute ECT for both\n", + "ect = ECT(num_dirs=20, num_thresh=30)\n", + "result_graph = ect.calculate(K_graph)\n", + "result_complex = ect.calculate(K_complex)\n", + "\n", + "# Plot comparison\n", + "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n", + "\n", + "# Visualizations\n", + "K_graph.plot(ax=axes[0,0], with_labels=True, node_size=400)\n", + "axes[0,0].set_title('Graph Only (no 2-cells)')\n", + "\n", + "K_complex.plot(ax=axes[0,1], with_labels=True, node_size=400, \n", + " face_alpha=0.3, face_color='lightblue')\n", + "axes[0,1].set_title('Complex with 2-cell (face)')\n", + "\n", + "# ECT comparisons\n", + "result_graph.plot(ax=axes[1,0])\n", + "axes[1,0].set_title('ECT: Graph Only\\n(χ = vertices - edges)')\n", + "\n", + "result_complex.plot(ax=axes[1,1])\n", + "axes[1,1].set_title('ECT: Complex with Face\\n(χ = vertices - edges + faces)')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Show numerical difference\n", + "print(\"ECT value ranges:\")\n", + "print(f\"Graph only: [{result_graph.min()}, {result_graph.max()}]\")\n", + "print(f\"With face: [{result_complex.min()}, {result_complex.max()}]\")\n", + "print(f\"\\nThe face contributes +1 to the Euler characteristic when included.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dataexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/_sources/notebooks/Tutorial-ExactECT.ipynb.txt b/docs/_sources/notebooks/Tutorial-ExactECT.ipynb.txt index 19a4e29..1870585 100644 --- a/docs/_sources/notebooks/Tutorial-ExactECT.ipynb.txt +++ b/docs/_sources/notebooks/Tutorial-ExactECT.ipynb.txt @@ -13,56 +13,22 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "from ect import ECT, EmbeddedGraph, EmbeddedCW,create_example_graph\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.patches import Circle\n", - "import numpy as np\n", - "import networkx as nx" - ] + "source": "from ect import ECT, EmbeddedComplex, create_example_graph\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Circle\nimport numpy as np\nimport networkx as nx\n\n# Note: EmbeddedGraph and EmbeddedCW are now unified into EmbeddedComplex\n# For backward compatibility, you can still use:\n# from ect import EmbeddedGraph, EmbeddedCW" }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "We can use the `EmbeddedGraph` class to find the angle normal to any pair of vertices in the graph, whether or not there is a connecting edge. Setting `angle_labels_circle=True` in the plotting command will try to draw these on the circle. Note that this doesn't tend to do well for large inputs, but can be helpful for small examples. " - ] + "source": "We can use the `EmbeddedComplex` class (which unifies the old `EmbeddedGraph` and `EmbeddedCW` classes) to find the angle normal to any pair of vertices in the graph, whether or not there is a connecting edge. Setting `angle_labels_circle=True` in the plotting command will try to draw these on the circle. Note that this doesn't tend to do well for large inputs, but can be helpful for small examples." }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Super simple graph \n", - "G = EmbeddedGraph()\n", - "G.add_node('A', 0,0)\n", - "G.add_node('B', 1,0)\n", - "G.add_node('C', 2,1)\n", - "G.add_node('D', 1,2)\n", - "G.add_edge('A', 'B')\n", - "G.add_edge('B', 'D')\n", - "G.add_edge('D', 'C')\n", - "\n", - "fig, ax = plt.subplots()\n", - "G.plot(ax = ax)\n", - "G.plot_angle_circle(ax = ax)\n" - ] + "outputs": [], + "source": "# Super simple graph using the unified EmbeddedComplex class\nG = EmbeddedComplex()\nG.add_node('A', 0,0)\nG.add_node('B', 1,0)\nG.add_node('C', 2,1)\nG.add_node('D', 1,2)\nG.add_edge('A', 'B')\nG.add_edge('B', 'D')\nG.add_edge('D', 'C')\n\nfig, ax = plt.subplots()\nG.plot(ax = ax)\nG.plot_angle_circle(ax = ax)" }, { "cell_type": "code", @@ -99,52 +65,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ nan 5.49778714 5.60844436 5.8195377 5.03413953 5.49778714]\n", - " [2.35619449 nan 5.6951827 0. 3.92699082 5.49778714]\n", - " [2.46685171 2.55359005 nan 2.03444394 2.89661399 2.67794504]\n", - " [2.67794504 3.14159265 5.17603659 nan 3.46334321 3.92699082]\n", - " [1.89254688 0.78539816 6.03820664 0.32175055 nan 0. ]\n", - " [2.35619449 2.35619449 5.8195377 0.78539816 3.14159265 nan]]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# If return type is `matrix`, the function returns the matrix of angles and the labels of the angles in the order of the rows/columns in the matrix \n", - "M,Labels = G.get_all_normals_matrix()\n", - "print(M)\n", - "\n", - "plt.matshow(M)\n", - "plt.xticks(range(len(Labels)), Labels)\n", - "plt.yticks(range(len(Labels)), Labels)\n", - "plt.colorbar()" - ] + "outputs": [], + "source": "# If return type is `matrix`, the function returns the matrix of angles and the labels of the angles in the order of the rows/columns in the matrix \nM,Labels = G.get_normal_angle_matrix() # Updated method name\nprint(M)\n\nplt.matshow(M)\nplt.xticks(range(len(Labels)), Labels)\nplt.yticks(range(len(Labels)), Labels)\nplt.colorbar()" }, { "cell_type": "markdown", @@ -252,4 +176,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/docs/_sources/notebooks/tutorial_cw.ipynb.txt b/docs/_sources/notebooks/tutorial_cw.ipynb.txt deleted file mode 100644 index 575387a..0000000 --- a/docs/_sources/notebooks/tutorial_cw.ipynb.txt +++ /dev/null @@ -1,374 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: ECT for CW complexes\n", - "\n", - "\n", - "\n", - " This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from ect import ECT, EmbeddedCW\n", - "from ect.utils.examples import create_example_cw\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "K = EmbeddedCW()\n", - "\n", - "# Add vertices with coordinates\n", - "K.add_node(\"A\", [0, 0])\n", - "K.add_node(\"B\", [1, 0])\n", - "K.add_node(\"C\", [1, 1])\n", - "K.add_node(\"D\", [0, 1])\n", - "\n", - "# Add edges to form a square\n", - "K.add_edges_from([(\"A\", \"B\"), (\"B\", \"C\"), (\"C\", \"D\"), (\"D\", \"A\")])\n", - "\n", - "# Add the square face\n", - "K.add_face([\"A\", \"B\", \"C\", \"D\"])\n", - "\n", - "K.center_coordinates()\n", - "K.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Just to have something a bit more interesting, let's make a more complicated example that's built into the class." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "K = create_example_cw()\n", - "K.plot(bounding_circle=True)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "ect = ECT(num_dirs=100, num_thresh=80)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/lizliz/Library/CloudStorage/Dropbox/Math/Code/ect/src/ect/ect_graph.py:211: NumbaPerformanceWarning: \u001b[1m\u001b[1m\n", - "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", - "\n", - "To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.\n", - "\u001b[1m\n", - "File \"src/ect/ect_graph.py\", line 216:\u001b[0m\n", - "\u001b[1m @njit(parallel=True, fastmath=True)\n", - "\u001b[1m def shape_descriptor(simplex_counts_list):\n", - "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - " result[i, j] = shape_descriptor(simplex_counts_list)\n", - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "override_bound_radius = 1.2 * K.get_bounding_radius()\n", - "result = ect.calculate(K, theta=0, override_bound_radius=override_bound_radius)\n", - "result.plot();\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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qwx2lGaVYu3btVdt69+6t3r17u3Uub5CoAABgMjOm0A8UAVVMCwAAQgs9KggaTABnDTzk0BoKfx/cVVs/FP96CTUs0h6Xrxx4sZkHEQU3M4Z+AgWJCgAAJiNRcY2hHwAAYFn0qAAAYDJ6VFwjUQEAwGQkKq6RqABBzuyJtyiu9Q1vi2OtpPGTrgttJf8X21phgjeUHokKAAAmM+T9PCjB+pAZEhUAAEzG0I9r3PUDAAAsix4VAABMRo+KayQqCFpXFh8yS6118PTl0jG7eParb5o7rd/avviZan3J7GJbM5CouEaiAgCAyUhUXKNGBQAAWBY9KgAAmMwwbDK87BHx9nirIlFBSAilJyubPcGbt0Jpgjiz61DcYWbNSmHu1rAEwgRvdtm8nkfF2+OtiqEfAABgWfSoAABgMoppXSNRAQDAZNSouMbQDwAAsCx6VBCSgqlgM9CLZ0ty5fUF8uckBVbxbCD78o3ZTutN3n/YpEhKj6Ef10hUAAAwGUM/rjH0AwAALIseFQAATGb4YOgnWHtUSFQAADCZIckwvG8jGJGoICQVnqmytoKnuDaYBVoRdDAXz1ppptqS7P/zm07rViyutcsmGzPTFokaFQAAYFn0qAAAYDLu+nGNRAUAAJPZDZtszKNSJBIVoAglTaJmdm1EsE/yFqiCuSalJGbWrLxT7yu39g+EmhX8hkQFAACTGYYP7voJ0tt+SFQAADAZNSqucdcPAACwrIBLVL788kv16NFDtWvXls1m09KlS80OCQAAr1zuUfF2CUYBN/STl5enhIQEPfTQQ7r33nvNDueaK1ygVriADZ65agK4dcUP9lq92NYbgRy7v4VysWwws0JxLXf9uBZwPSrdu3fXc889p3vuucfsUAAA8InLxbTeLu5wd4Ri7dq1stlsVy2ZmZmeX3gpBFyPirvy8/OVn5/vWM/JyTExGgAArMHTEYp9+/YpKirKsV6zZk1/hOcQ9IlKamqqJk2aZHYYAAC4dKlHxNu7fi79t/D/kEdGRioyMvKq/bt3767u3bu7fZ6aNWuqcuXKnoTokaBPVMaNG6eRI0c61nNychQXF6ekNnsVUTFCkrXrPKhJCUy+fnieNxO8UXPiOWpSPHfl7ypfT/7m7gRvhQ1Mv7XY16+M93zueR326myl48vbk+Pi4py2T5gwQRMnTvSq7SslJiYqPz9fLVq00MSJE9WhQweftV2UoE9UXGWSAAAEo4yMDKehGV/9DYyNjdWsWbPUpk0b5efn66233lJKSoo2bdqkVq1a+eQcRQn6RAUAAKszfl28bUOSoqKinBIVX2natKmaNm3qWE9OTtaBAwc0bdo0/fOf//T5+S4LuEQlNzdX+/fvd6wfPHhQO3bsUNWqVVWvXj0TIwMAwDOBOjNt27ZttX79er+eI+ASlS1btqhz586O9cv1J/3799e8efNMigoAgNCzY8cOxcbG+vUcAZeopKSkyPDxk5esVLB6LZ84CtfcnQCuJL4urvVXW6GO4tngUFKxrCX5cuynlEoaoRg3bpyOHDmid999V5I0ffp0NWzYUDfddJPOnTunt956S6tXr9bKlSu9DLx4AZeoAAAQdHwxBb6bx5c0QnH06FGlp6c7Xj9//rxGjRqlI0eOqHz58oqPj9cXX3zh1IY/kKgAABCCShqhKFxO8cQTT+iJJ57wc1RX8yhRKSgo0FtvvaV9+/apbt26SkhIUGJioqpVq+br+AAACHqeTIFfVBvByKNE5dFHH9WHH36oLl266PXXX5fNZtPFixdVp04dJSYm6uOPP/Z1nAAABK1AvevnWvAoUfnoo4/07rvvqmvXrvr444+1YcMGrVu3TpMnT1b9+vV9HeM1dy2La0sqnmUm2uBUUnGtP4tv8RuKZ6+Nwr/H3L1poPBMtAFZLFsSw+Z2jUmRbQQhjxKV3NxcNW9+6QcvPDxcZcqU0fDhw3XhwgX99NNPPg0QAACErjBPDmrUqJEjIalTp46OHDkiSerRo4f+9a9/+S46AABCwOUaFW+XYORRonLvvffq888/lyR16tRJc+fOlST98MMP+uWXX3wXHQAAocDw0RKEPBr6ufIpjE888YRuueUW1ahRQzk5ORo0aJCvYrOMK8dT3a0ZcXcslpoUa/L1BHCFefN0ZCDQlFSzUvj1gX6PCFbm9Twq9erV0+7du/Xvf/9b1apVU48ePXwRFwAAIYO7flzzyYRv1atX18CB5LwAAHgsSIduvOVRjQoAAMC1wBT6AACYjKEf10hU3FRS0RdPP4Y/MAGcbzDBmzXxe1SmPD05UDD0AwAALIseFQAATGf7dfG2jeBT6kRl5MiRpW506tSpHgUDAEBIYujHpVInKtu3b3da37Ztmy5evKimTZtKkv7zn//ouuuuU+vWrX0bocXt//ObTuvuPiyLCd4Ck78ngCvJlTUr1Ku45m5NSkjWRrjgy99Nbn8/QrGUiETFpVInKmvWrHH8e+rUqapUqZLmz5+vKlWqSJJOnTqlgQMH6tZbg/CplgAAwBQeFdNOmTJFqampjiRFkqpUqaLnnntOU6ZM8VlwAACEBMPmmyUIeVRMm5OTo+PHj1+1/fjx4zpz5ozXQQEAEEp88fRjnp58hXvuuUcDBw7URx99pP/+97/673//qw8//FCDBg3Svffe6+sYAQBAiPKoR2XWrFkaPXq07r//fl24cEGGYSg8PFyDBg3Syy+/7OsYLeWdel+59bq7xbWAu0J5MrjCxbKFi2Fri+JYT3lbWOxOMW7h4vTGXp05QFFM65JHiUr58uX1j3/8Qy+//LIOHDggSWrcuLEqVKjg0+AAAAgJvqgxCfUaFeZRAQAA15rH86i4YrMFZ0YHAIC/2IxLi7dtBCOP5lEBAAA+RI2KSzzrpxRKKqB159gm7z/sbTiwILNnqr1SoBfXujObLDPJmsebYll32+ZzDm0eJyqnT5/W22+/rT179kiSmjdvrkGDBik6OtpnwQEAEBIopnXJo3lUtmzZosaNG2vatGk6efKkTp48qWnTpqlx48batm2br2MEACC4GT5agpBHPSqPP/64/vCHP2jOnDkqU+ZSExcvXtTgwYM1YsQIffnllz4NEgCAoEaNikseJSpbtmxxSlIkqUyZMnriiSfUpk0bnwVnFm9qUgorPOFb4bFWnp4Mf7vWNSvuPrHYHdQqmMfM31XUrIQ2j4Z+oqKilJ6eftX2jIwMVapUyeugAAAIKQz9uORRotKnTx8NGjRIixYtUkZGhjIyMrRw4UINHjxYffv29XWMAAAEN56e7JJHQz+vvPKKbDab+vXrp4sXL0qSwsPDNXToUL3wwgs+DRAAAIQujxKViIgIzZgxQ6mpqU7P+ilfvrxPgwMAIBQwM61rXk34Vr58ebVs2dJXsZjGl8WzgGStCeAKc7e41p/FsaVB4aQ5KPS/xrjrxyWPalQkKS0tTU899ZQGDx6shx56yGnxtzfeeEMNGjRQ2bJl1a5dO23evNnv5wQAIJh8+eWX6tGjh2rXri2bzaalS5eWeMzatWvVqlUrRUZGqkmTJpo3b57f4/QoUZk0aZLuuOMOpaWl6cSJEzp16pTT4k+LFi3SyJEjNWHCBG3btk0JCQnq2rWrjh075tfzAgAQTPLy8pSQkKA33nijVPsfPHhQd911lzp37qwdO3ZoxIgRGjx4sFasWOHXOD0a+pk1a5bmzZunBx980NfxlGjq1KkaMmSIBg4c6Ijl008/1dy5czV27NhrHg8AAN6yyQc1Km7u3717d3Xv3r3U+8+aNUsNGzbUlClTJEnNmjXT+vXrNW3aNHXt2tXNs5eeRz0q58+fV3Jysq9jKdV5t27dqi5duji2hYWFqUuXLtq4cWORx+Tn5ysnJ8dpAQAgWBX+m5efn++Tdjdu3Oj091eSunbt6vLvr6941KMyePBgLViwQM8884yv4ynWiRMnVFBQoFq1ajltr1Wrlvbu3VvkMampqZo0adJV22fGbVBUpet8HmPhmWhLwky1uNauepJtJ3OLZQujePbaCOTfNf6eqfbKGyxyzhRooU9bd8GHDyWMi4tz2jxhwgRNnDjRu7YlZWZmFvn3NycnR7/88ovKlSvn9TmKUupEZeTIkY5/2+12zZ49W1988YXi4+MVHh7utO/UqVN9F6GXxo0b5xR7Tk7OVR8iAACm8uFdPxkZGYqKinJsjoyM9LJhc5U6Udm+fbvTemJioiRp165dTtttNv/NjFe9enVdd911ysrKctqelZWlmJiYIo+JjIwM+A8JABDkfJioREVFOSUqvhITE1Pk39+oqCi/9aZIbiQqa9as8VsQpRUREaHWrVsrLS1NvXr1knSpdyctLU3Dhw83NzgAAIJYUlKSPvvsM6dtq1atUlJSkl/P61GNyi+//CLDMBwz0R4+fFhLlixR8+bNdccdd/g0wMJGjhyp/v37q02bNmrbtq2mT5+uvLw8x11AgY6aFbjrqpoToAj8LnHNCpN+mjEzbW5urvbv3+9YP3jwoHbs2KGqVauqXr16GjdunI4cOaJ3331XkvTII4/o9ddf1xNPPKGHHnpIq1ev1vvvv69PP/3Uu8BL4FGi0rNnT91777165JFHdPr0abVt21YRERE6ceKEpk6dqqFDh/o6Toc+ffro+PHjGj9+vDIzM5WYmKjly5dfVeADAEDAMGFm2i1btqhz586O9cv1nP3799e8efN09OhRpaenO15v2LChPv30Uz3++OOaMWOG6tatq7feesuvtyZLHiYq27Zt07Rp0yRJH3zwgWJiYrR9+3Z9+OGHGj9+vF8TFUkaPnw4Qz0AAHghJSVFhuE6uylq1tmUlJSralb9zaNE5ezZs6pUqZIkaeXKlbr33nsVFham9u3b6/Dhwz4NEACAoMezflzyaMK3Jk2aaOnSpcrIyNCKFSscdSnHjh3zS6UxAADB7HKNirdLMPKoR2X8+PG6//779fjjj+v22293VPyuXLlSN998s08DtDp3J3hzF8W1waGkpymHckEsE7z5Rij/bnB3AjgrFM+i9DxKVP70pz+pY8eOOnr0qBISEhzbb7/9dt1zzz0+Cw4AgJDgw5lpg41HiYp0aeKXwpOstW3b1uuAAAAIOdSouORRjYokffXVV3rggQeUlJSkI0eOSJL++c9/av369T4LDgAAhDaPelQ+/PBDPfjgg/rLX/6i7du3O57MmJ2drb///e9XzVwH36FmJThQkwJv8d0vvUCoSTFjwrdA4VGPynPPPadZs2Zpzpw5Tg8k7NChg7Zt2+az4AAACAmGj5Yg5FGPyr59+3TbbbddtT06OlqnT5/2NiYAAEKLL24vDtJExaMelZiYGKfnA1y2fv16NWrUyOugAAAAJA8TlSFDhuixxx7Tpk2bZLPZ9NNPP+m9997T6NGj/T59PgAAQYehH5c8GvoZO3as7Ha7br/9dp09e1a33XabIiMjNXr0aD366KO+jtFS/D3Bm7u8LUykIO8S3kf/oXjWM/xMhRhuT3bJ7UTlwoUL6tatm2bNmqUxY8Zo//79ys3NVfPmzVWxYkV/xAgAAEKU24lKeHi4vv/+e0lSRESEmjcn6wcAwBvcnuyaRzUqDzzwgN5++21fxwIAAODEoxqVixcvau7cufriiy/UunVrVahQwen1qVOn+iQ4AAAQ2jxKVHbt2qVWrVpJkv7zn/84vWazBd+Mm1YroPUlCh09Q6Gja/xMeYafKc+V9DNX+He4JWeqpZjWJY8SlTVr1vg6DgAAQhY1Kq55/FBCAAAAf/OoR0WS0tLSlJaWpmPHjslutzu9NnfuXK8DAwAgpARpj4i3PEpUJk2apMmTJ6tNmzaKjY0NyroUAKVDTUrpUYfiG0H5M0eNikseJSqzZs3SvHnz9OCDD/o6HgAAQg41Kq55VKNy/vx5JScn+zoWAAAAJx4lKoMHD9aCBQt8HQsAAKGJhxK6VOqhn5EjRzr+bbfbNXv2bH3xxReKj49XeHi4075M+AYAQOkx9ONaqROV7du3O60nJiZKujT525WCobA2mCd4g2cogvxNUBYy+gk/N54p/DNWeII2b39HB8QEcHAodaKyZs0aTZ48WaNGjbpqynwAAOAF7vpxya0alUmTJikvL89fsQAAEJqoUXHJrUTFMIL0XQAAAJbk9jwqwVCDAgCAlVBM65rbicoNN9xQYrJy8uRJjwMCYC0Uz7pGsaxv+PrpxwF5QwQ1Ki65nahMmjRJ0dHR/ogFAADAiduJyn333aeaNWv6IxYAAEITPSouuZWoUJ8CAIDvUaPimluJSrDe9ROQ45nwq1CuPQjlmpRQ/tyvJX//jLlbw2KJCeDoUXHJrduT7XY7wz4AAASJN954Qw0aNFDZsmXVrl07bd682eW+8+bNk81mc1rKli3r9xg9eiihWZ5//nklJyerfPnyqly5stnhAADgE5eHfrxd3LFo0SKNHDlSEyZM0LZt25SQkKCuXbvq2LFjLo+JiorS0aNHHcvhw4e9vPKSBVSicv78efXu3VtDhw41OxQAAHzHhJlpp06dqiFDhmjgwIFq3ry5Zs2apfLly2vu3Lkuj7HZbIqJiXEstWrVcu+kHgioRGXSpEl6/PHH1bJlS7NDAQDAknJycpyW/Pz8q/Y5f/68tm7dqi5duji2hYWFqUuXLtq4caPLtnNzc1W/fn3FxcWpZ8+e2r17t1+u4Upu354caPLz850+pJycHEnS0IxkRVSMMCssACagWNYcvi6e9bb41Z1i2/O55yX9n1vte8SHxbRxcXFOmydMmKCJEyc6bTtx4oQKCgqu6hGpVauW9u7dW2TzTZs21dy5cxUfH6/s7Gy98sorSk5O1u7du1W3bl0vg3ct6BOV1NRUTZo0yewwAABwyfbr4m0bkpSRkaGoqCjH9sjISC9bviQpKUlJSUmO9eTkZDVr1kxvvvmmnn32WZ+coyimD/2MHTv2qiriwour7K40xo0bp+zsbMeSkZHhw+gBALCWqKgop6WoRKV69eq67rrrlJWV5bQ9KytLMTExpTpPeHi4br75Zu3fv98ncbtieo/KqFGjNGDAgGL3adSokcftR0ZG+iybBADAL67xPCoRERFq3bq10tLS1KtXL0mXpiBJS0vT8OHDS9VGQUGBdu7cqTvvvNODYEvP9ESlRo0aqlGjhqkxmDK5j4eYnM4/qF34De8FPBHKEwX6ghkz044cOVL9+/dXmzZt1LZtW02fPl15eXkaOHCgJKlfv36qU6eOUlNTJUmTJ09W+/bt1aRJE50+fVovv/yyDh8+rMGDB3sXeAlMT1TckZ6erpMnTyo9PV0FBQXasWOHJKlJkyaqWLGiucEBABBA+vTpo+PHj2v8+PHKzMxUYmKili9f7iiwTU9PV1jYbxUip06d0pAhQ5SZmakqVaqodevW2rBhg5o39+//3ARUojJ+/HjNnz/fsX7zzTdLktasWaOUlBSTogIAwEsmTaE/fPhwl0M9a9eudVqfNm2apk2b5kFg3jG9mNYd8+bNk2EYVy0kKQCAgHcNJ3sLJAHVowIAQDDi6cmukajIIk/OBAALs3KxrLe/w7lJwdpIVAAAMJtJNSqBgEQFAACTMfTjWkAV0wIAgNBCjwoAAGZj6MclEpUiWKm4liIv/2D2VaB4Vi6e9ZYVf68y9OMaQz8AAMCy6FEBAMBsDP24RKICAIDZSFRcIlGxGCuOnQJAILFSnSG8R6ICAIDJKKZ1jUQFAACzMfTjEokKAAAmsxmGbIZ3mYa3x1sVtycDAADLokelFK4szPJ1URbFs9cGE7wB7in8nQnkCeCavP+w07olr4WhH5dIVAAAMBnFtK4x9AMAACyLHhUAAMzG0I9LJCoAAJiMoR/XSFTc5O6MhxTLmocCWsB3Aqm4lu9+cCFRAQDAbAz9uESiAgCAyRj6cY27fgAAgGXRo+Jj1KQACAWBVLNSmCVjZ+jHJRIVAAAsIFiHbrxFogIAgNkM49LibRtBiBoVAABgWfSoAABgMu76cY1ExUsB8VTOEOHOJE+11zl/o3/qZHNr/8JKOh4IdmYWqAbFBG8U07rE0A8AALAselQAADCZzX5p8baNYESiAgCA2Rj6cYlEpRTcGf+05ERCKLHGpKTXvW2fGhaEmuJ+b3r7e9HXNSn83rY2alQAADDZ5bt+vF3c9cYbb6hBgwYqW7as2rVrp82bNxe7/+LFi3XjjTeqbNmyatmypT777DMPr7j0SFQAADDb5QnfvF3csGjRIo0cOVITJkzQtm3blJCQoK5du+rYsWNF7r9hwwb17dtXgwYN0vbt29WrVy/16tVLu3bt8sU74BKJCgAAIWjq1KkaMmSIBg4cqObNm2vWrFkqX7685s6dW+T+M2bMULdu3TRmzBg1a9ZMzz77rFq1aqXXX3/dr3EGTKJy6NAhDRo0SA0bNlS5cuXUuHFjTZgwQefPnzc7NAAAvOLLoZ+cnBynJT8//6rznT9/Xlu3blWXLl0c28LCwtSlSxdt3LixyBg3btzotL8kde3a1eX+vhIwxbR79+6V3W7Xm2++qSZNmmjXrl0aMmSI8vLy9Morr3jVtj8nC6JIy39K+ty8LZD1JXdjofgWwSwoJmjzNR/e9RMXF+e0ecKECZo4caLTthMnTqigoEC1atVy2l6rVi3t3bu3yOYzMzOL3D8zM9O7uEsQMIlKt27d1K1bN8d6o0aNtG/fPs2cOdPrRAUAADP5cgr9jIwMRUVFObZHRkZ617DJAiZRKUp2draqVq1a7D75+flO3V45OTn+DgsAANNERUU5JSpFqV69uq677jplZWU5bc/KylJMTEyRx8TExLi1v68ETI1KYfv379drr72mhx9+uNj9UlNTFR0d7VgKd4kBAGC6a3zXT0REhFq3bq20tDTHNrvdrrS0NCUlJRV5TFJSktP+krRq1SqX+/uK6YnK2LFjZbPZil0Kj5cdOXJE3bp1U+/evTVkyJBi2x83bpyys7MdS0ZGhj8vBwAAt5kxj8rIkSM1Z84czZ8/X3v27NHQoUOVl5engQMHSpL69euncePGOfZ/7LHHtHz5ck2ZMkV79+7VxIkTtWXLFg0fPtyXb8VVTB/6GTVqlAYMGFDsPo0aNXL8+6efflLnzp2VnJys2bNnl9h+ZGRkkeNzB6c3VZnwspdWOrkVslcorvVcIBXPesvMa6GQF2Yz+7t8YF0zx78vXjhnYiT+1adPHx0/flzjx49XZmamEhMTtXz5ckfBbHp6usLCfuvPSE5O1oIFC/T000/rqaee0vXXX6+lS5eqRYsWfo3T9ESlRo0aqlGjRqn2PXLkiDp37qzWrVvrnXfecXoDAQAIWCY962f48OEue0TWrl171bbevXurd+/e7p/IC6YnKqV15MgRpaSkqH79+nrllVd0/Phxx2v+LuQBAMCffHnXT7AJmERl1apV2r9/v/bv36+6des6vWa4OW0wAAAIDAGTqAwYMKDEWhZPFR4PvZZj9NSseM7scexgZeb3AaGJ77Iku3Fp8baNIBQwiQoAAEHLpBqVQEA1KgAAsCx6VAAAMJlNPiim9Ukk1kOiAgCA2dycWdZlG0GIRMViKK79TeH3goI7IDjwXb4atye7Ro0KAACwLHpUAAAwG3f9uESiAgCAyWyGIZuXNSbeHm9VJCpFsNKEV6FUs0JNijVZ6fuAwMR3Gd4gUQEAwGz2Xxdv2whCJCoAAJiMoR/XuOsHAABYFj0qAACYjbt+XCJRKYUrC8HMLiQM5uJaCu4CA8W1KA2+z25iZlqXGPoBAACWRY8KAAAmYwp910hUAAAwG0M/LpGoAABgMpv90uJtG8GIRMVNViskLFxcW5iVim2ZeTY4We07EahK+j5Y7X3l+4trhUQFAACzMfTjEokKAABmYx4Vl7g9GQAAWBY9Kl6y+vh8STUsV3K3nsWdtiXGtEOF1b8TVuLOd8Ld74+77zvfT3PxrB/XSFQAADAbNSouMfQDAAAsix4VAADMZkjydh6U4OxQIVEBAMBs1Ki4RqICB3eLY0tCcR7g7Fp+J/j+IViQqAAAYDZDPiim9UkklkOiAgCA2bjrxyUSFQAAzGaX5O2UQ0H6UEJuTwYAAJZFouJjtdcZTksoCeVrh2uh/HMRytcO91y+68fbxV9Onjypv/zlL4qKilLlypU1aNAg5ebmFntMSkqKbDab0/LII4+4fW6GfgAAMJvFa1T+8pe/6OjRo1q1apUuXLiggQMH6q9//asWLFhQ7HFDhgzR5MmTHevly5d3+9wkKgAAwKU9e/Zo+fLl+vbbb9WmTRtJ0muvvaY777xTr7zyimrXru3y2PLlyysmJsar8zP0AwCA2S73qHi7SMrJyXFa8vPzvQpt48aNqly5siNJkaQuXbooLCxMmzZtKvbY9957T9WrV1eLFi00btw4nT171u3zB1SPyh/+8Aft2LFDx44dU5UqVdSlSxe9+OKLxWZzZitpXDqQnyzLmDs8wXcCKIIPh37i4uKcNk+YMEETJ070uNnMzEzVrFnTaVuZMmVUtWpVZWZmujzu/vvvV/369VW7dm19//33evLJJ7Vv3z599NFHbp0/oBKVzp0766mnnlJsbKyOHDmi0aNH609/+pM2bNhgdmgAAFhCRkaGoqKiHOuRkZFF7jd27Fi9+OKLxba1Z88ej+P461//6vh3y5YtFRsbq9tvv10HDhxQ48aNS91OQCUqjz/+uOPf9evX19ixY9WrVy9duHBB4eHhJkYGAIAXfDiPSlRUlFOi4sqoUaM0YMCAYvdp1KiRYmJidOzYMaftFy9e1MmTJ92qP2nXrp0kaf/+/cGbqFzp5MmTeu+995ScnFxskpKfn+80PpeTk3MtwgMAoNTMeChhjRo1VKNGjRL3S0pK0unTp7V161a1bt1akrR69WrZ7XZH8lEaO3bskCTFxsa6FWfAFdM++eSTqlChgqpVq6b09HQtW7as2P1TU1MVHR3tWAqP3QEAANeaNWumbt26aciQIdq8ebO+/vprDR8+XPfdd5+jRvTIkSO68cYbtXnzZknSgQMH9Oyzz2rr1q06dOiQPv74Y/Xr10+33Xab4uPj3Tq/zTDMfThAacfIbrzxRknSiRMndPLkSR0+fFiTJk1SdHS0PvnkE9lsRfeZFdWjEhcXp3Y9nlWZ8LK+uxAfsVIhIYWBsAK+EzDTxQvntOnfzyg7O7tUwynuysnJUXR0tLpc/7jKXFd0LUlpXSzI1xc/TvNLrCdPntTw4cP173//W2FhYfrjH/+oV199VRUrVpQkHTp0SA0bNtSaNWuUkpKijIwMPfDAA9q1a5fy8vIUFxene+65R08//bTbsZk+9FPaMbLLqlevrurVq+uGG25Qs2bNFBcXp2+++UZJSUlFHhsZGemykAgAAEuwG5LNy0TY7r9EumrVqsVO7tagQQNd2e8RFxendevW+eTcpicqpR0jK4rdfqlyyNt7xAEAMJXFZ6Y1k+mJSmlt2rRJ3377rTp27KgqVarowIEDeuaZZ9S4cWOXvSkAACCwBUyiUr58eX300UeaMGGC8vLyFBsbq27duunpp58OqqGdaz0ZFmPusLrifkb5PiB4+KBHRcH58xswiUrLli21evVqs8MAAMD3GPpxKeBuTwYAAKEjYHpUAAAIWnZDXg/d+PGuHzORqAAAYDbDfmnxto0gRKISYCj2A37D9wEIfiQqAACYjWJal0hUAAAwGzUqLnHXDwAAsCx6VAAAMBtDPy6RqAAAYDZDPkhUfBKJ5ZCoAABgNnpUXKJGBQAAWBY9KgAAmM1ul+TlhG12JnwDAAD+wNCPSwz9AAAAy6JHBQAAs9Gj4hKJCgAAZmNmWpcY+gEAAJZFjwoAACYzDLsMw7u7drw93qpIVAAAMJtheD90E6Q1Kgz9AAAAy6JHBQAAsxk+KKYN0h4VEhUAAMxmt0s2L2tMqFEBAAB+QY+KS9SoAAAAy6JHBQAAkxl2uwwvh364PRkAAPgHQz8uMfQDAAAsix4VAADMZjckGz0qRSFRAQDAbIYhydvbk4MzUWHoBwAAWBY9KgAAmMywGzK8HPoxgrRHhUQFAACzGXZ5P/QTnLcnM/QDAIDJDLvhk8Vfnn/+eSUnJ6t8+fKqXLly6a7JMDR+/HjFxsaqXLly6tKli3788Ue3z02iAgAAinX+/Hn17t1bQ4cOLfUxL730kl599VXNmjVLmzZtUoUKFdS1a1edO3fOrXOH3NDP5TG8ixfce6MAAKHn8t8Kf9d/XDTyvR66uagLkqScnByn7ZGRkYqMjPSq7UmTJkmS5s2bV6r9DcPQ9OnT9fTTT6tnz56SpHfffVe1atXS0qVLdd9995X63CGXqJw5c0aStHX58yZHAgAIFGfOnFF0dLTP242IiFBMTIzWZ37mk/YqVqyouLg4p20TJkzQxIkTfdJ+aR08eFCZmZnq0qWLY1t0dLTatWunjRs3kqgUp3bt2srIyJBhGKpXr54yMjIUFRVldlgeycnJUVxcHNdgMq7BGrgG6wiG67h8Denp6bLZbKpdu7ZfzlO2bFkdPHhQ58+f90l7hmHIZrM5bfO2N8UTmZmZkqRatWo5ba9Vq5bjtdIKuUQlLCxMdevWdXSNRUVFBewX6TKuwRq4BmvgGqwjGK4jOjra79dQtmxZlS1b1q/nKMrYsWP14osvFrvPnj17dOONN16jiIoWcokKAACQRo0apQEDBhS7T6NGjTxqOyYmRpKUlZWl2NhYx/asrCwlJia61RaJCgAAIahGjRqqUaOGX9pu2LChYmJilJaW5khMcnJytGnTJrfuHJJC+PbkyMhITZgwwZSxO1/hGqyBa7AGrsE6guE6guEafCk9PV07duxQenq6CgoKtGPHDu3YsUO5ubmOfW688UYtWbJEkmSz2TRixAg999xz+vjjj7Vz507169dPtWvXVq9evdw6t80I1jl3AQCATwwYMEDz58+/avuaNWuUkpIi6VJy8s477ziGkwzD0IQJEzR79mydPn1aHTt21D/+8Q/dcMMNbp2bRAUAAFhWyA79AAAA6yNRAQAAlkWiAgAALItEBQAAWFZIJipvvPGGGjRooLJly6pdu3bavHmz2SEhQJw+fVpt2rRRYmKiWrRooTlz5pgdUshq0KCB4uPjlZiYqM6dO5sdjtuC4Wdp3759SkxMdCzlypXT0qVLzQ6r1FJTU3XLLbeoUqVKqlmzpnr16qV9+/aZHRYKCbm7fhYtWqR+/fpp1qxZateunaZPn67Fixdr3759qlmzptnhlcrevXvVoUMHVa5cWZUqVdL+/fuVmJio9evXmx1aqQXqNRQUFCg/P1/ly5dXXl6eWrRooS1btqhatWpmhxZyGjRooF27dqlixYpmh+KRYPtZys3NVYMGDXT48GFVqFDB7HBKpVu3brrvvvt0yy236OLFi3rqqae0a9cu/fDDDwFzDSHBCDFt27Y1hg0b5lgvKCgwateubaSmppoYlfu6d+9ufP/994ZhGMYNN9xg5ObmmhyR+wL9Gn7++Wejfv36xvHjx409e/YYVatWNRo1amQkJCQYFSpUMDp06GB2iCW68cYbDUlFLq+99prZ4RWrfv36xpkzZ5y2BernEAw/S++9957x5z//2TCMwP0cjh07Zkgy1q1bZxhGYH8/gklIJSr5+fnGddddZyxZssRpe79+/Yw//OEP5gTloUaNGhn5+flGXl6e0bhxY7PD8UigXsOpU6eM+Ph4o1y5csbrr7/u2B6Iidfu3bsNSUZaWppx9OhR49ChQ0ZYWJixePFi49y5c2aHV6wGDRoYrVq1Mtq0aWP861//cmwPpM8hmH6WevbsaXz44YeO9UC8hh9//NGQZOzcudMwjMD+fgSTkKpROXHihAoKCnzy2GkznTlzRpGRkYqIiNDu3bvVrFkzs0NyWyBfQ+XKlfXdd9/p4MGDWrBggbKysiRdGq9v2rSpzp49q4KCgoDoOs7KylKZMmXUoUMHxcTE6MSJE7Lb7br11lstP3X4+vXrtXXrVn388cf6+9//ru+//15SYH0OwfKzlJOTow0bNujOO+90bAu0a7Db7RoxYoQ6dOigFi1aSArs70cwCalEJVj88MMPat68uaRLj+B2dzpiKwiGa6hVq5YSEhL01VdfBWzitXPnTt1www2OX7rfffedataseVUyb0V16tSRJMXGxurOO+/Utm3bAvZzCPSfpWXLlumOO+5Q2bJlJQXm/4gMGzZMu3bt0sKFCx3bAvn7EUxCKlGpXr26rrvuOsf/tVyWlZXleCR1INi9e7duuukmSVKFChW0cuVK/fzzzyZH5Z5AvYasrCydOXNGkpSdna0vv/xSTZs2DdjE6/vvv1fLli0d6999953TulXl5eU5Pofc3FytXr1aN910U0B9DsH0s/T++++rT58+jvVAu4bhw4frk08+0Zo1a1S3bl3H9kD9fgSbkEpUIiIi1Lp1a6WlpTm22e12paWlKSkpycTI3PPQQw9p0qRJkqQ//vGP2rlzZ8DdKRCo13D48GHdeuutSkhI0K233qpHH31ULVu2DNjE6/vvv1d8fLxj/bvvvnNat6qsrCx17NhRCQkJat++vfr166dbbrkloD6HYPlZys7O1ubNm9W1a1fHtkC5BsMwNHz4cC1ZskSrV69Ww4YNnV4P1O9HsAnJ25P79++vN998U23bttX06dP1/vvva+/evXTnIaTY7XZVqlRJixYt0t133y1JiouL05gxY/S3v/3N5OgA//t//+//acGCBVq2bJmaNm3q2B4dHa3IyEi+HxYRcomKJL3++ut6+eWXlZmZqcTERL366qtq166d2WGVms1mK/b1QPhIg+EaAt2PP/6oG264QYcPH1a9evUkSXfeeac2btyoTz75RB06dDA5QsC/XP0eeuedd9ShQwe+HxYRkolKsDh79qyaNWum3r1765VXXjE7HLdkZGTowQcf1LFjx1SmTBk988wz6t27t9lhlaikBMsVvma+FQyfA9cAlE5I1agEm+eff17t27c3OwyPlClTRtOnT9cPP/yglStXasSIEcrLyzM7rBIZl+Yeclry8vJUr149jRo1qsjX+aXse8HwOVwZV3p6ujp16qRmzZqpZcuWev/99wPuGgL1c4D1kagEqB9//FF79+5V9+7dzQ7FI7GxsUpMTJQkxcTEqHr16jp58qS5QXkokBPGYBLIn0OgJu5FCeTPAdZEohKgRo8erdTUVLPD8ImtW7eqoKBAcXFxZofitkBPGINFoH8OwZK4B/rnAGsiUQlAy5Yt0w033GD5uQlK4+TJk+rXr59mz55tdigeCaaEMZAF0+cQyIl7MH0OsA4SlQD0zTffaOHChWrQoIFGjx6tOXPmaPLkyWaH5bb8/Hz16tVLY8eOVXJystnhuC2YEsZAFkyfQyAn7sH0OcBaypgdANyXmprq+L+WefPmadeuXRo/frzJUbnHMAwNGDBAv/vd7/Tggw+aHY5HLieMixcvVm5uri5cuKCoqKiA+ywCXbB8DoGeuAfL5wDr4fbkAHc5UQm025PXr1+v2267zWmWx3/+858BOz11oH4OwSZQPwfDMHT//feradOmmjhxotnheC1QPwdYEz0qAW7AgAFmh+CRjh07ym63mx0GYAlff/21Fi1apPj4eC1dulRSYCfugC/RowIAACyLYloAAGBZJCoAAMCySFQAAIBlkagAAADLIlEBAACWRaICAAAsi0QFAABYFokKAACwLBIVAABgWSQqAADAskhUAACAZZGoAEVISUnRiBEjQu7c7vr5559Vs2ZNHTp0yC/tX/lemPG+3HfffZoyZco1PScAZzyUECFlwIABmj9/viSpTJkyqlq1quLj49W3b18NGDBAYWGXcveTJ08qPDxclSpV8ms8KSkpSkxM1PTp0x3brtW5fWHkyJE6c+aM5syZ45f2r3x/zHhfdu3apdtuu00HDx5UdHT0NTsvgN/Qo4KQ061bNx09elSHDh3S559/rs6dO+uxxx7T3XffrYsXL0qSqlatWuwfxPPnz/stvpLObRVnz57V22+/rUGDBrncx5fvkxnvS4sWLdS4cWP961//uqbnBfAbEhWEnMjISMXExKhOnTpq1aqVnnrqKS1btkyff/655s2bJ+nqYYaUlBQNHz5cI0aMUPXq1dW1a1dJkt1uV2pqqho2bKhy5copISFBH3zwgeM4u92ul156SU2aNFFkZKTq1aun559/XtKl3p1169ZpxowZstlsstlsOnTo0FXnzs/P19/+9jfVrFlTZcuWVceOHfXtt986XVNKSor+9re/6YknnlDVqlUVExOjiRMnlvheDB06VB07dizytbp16+qFF15weexnn32myMhItW/fvsT3afny5erYsaMqV66satWq6e6779aBAwec2svLy1O/fv1UsWJFxcbGXjXkUvh9KU2bpXlfPvjgA7Vs2VLlypVTtWrV1KVLF+Xl5Tle79GjhxYuXOjyfQDgXyQqgKTf/e53SkhI0EcffeRyn/nz5ysiIkJff/21Zs2aJUlKTU3Vu+++q1mzZmn37t16/PHH9cADD2jdunWSpHHjxumFF17QM888ox9++EELFixQrVq1JEkzZsxQUlKShgwZoqNHj+ro0aOKi4u76rxPPPGEPvzwQ82fP1/btm1TkyZN1LVrV508efKq+CpUqKBNmzbppZde0uTJk7Vq1SqX17N7927Nnj1bL730UpGvN2vWTDt27HB5/FdffaXWrVuX6n3Ky8vTyJEjtWXLFqWlpSksLEz33HOP7Ha747gxY8Zo3bp1WrZsmVauXKm1a9dq27ZtLs9fmjZLel+OHj2qvn376qGHHtKePXu0du1a3XvvvbpyRLxt27bavHmz8vPzXcYCwI8MIIT079/f6NmzZ5Gv9enTx2jWrJlhGIbRqVMn47HHHnO81qlTJ+Pmm2922v/cuXNG+fLljQ0bNjhtHzRokNG3b18jJyfHiIyMNObMmeMynsLnKbwtNzfXCA8PN9577z3H6+fPnzdq165tvPTSS07HdOzY0amdW265xXjyySddnrt///5Gu3btXL7+5z//2ejUqZPL13v27Gk89NBDV8Ve+H0qyvHjxw1Jxs6dOw3DMIwzZ84YERERxvvvv+/Y5+effzbKlSvneC+Keq+Ka/PyMcW9L1u3bjUkGYcOHXLZ7nfffVfiPgD8hx4V4FeGYchms7l8vXDvwf79+3X27Fn9/ve/V8WKFR3Lu+++qwMHDmjPnj3Kz8/X7bff7nFMBw4c0IULF9ShQwfHtvDwcLVt21Z79uxx2jc+Pt5pPTY2VseOHSuy3YsXL+qjjz7SH//4R8e2hx9+WG+//bZj/cyZMypXrpzL2H755ReVLVv2qu1F9bL8+OOP6tu3rxo1aqSoqCg1aNBAkpSenu64zvPnz6tdu3aOY6pWraqmTZu6PH9JbV5W3PuSkJCg22+/XS1btlTv3r01Z84cnTp1ymn/y+/B2bNnXcYCwH/KmB0AYBV79uxRw4YNXb5eoUIFp/Xc3FxJ0qeffqo6deo4vRYZGanTp0/7PMbihIeHO63bbLarhkEuO3DggM6cOaOWLVtKulRLs3jxYqek6vvvv1efPn1cnq969epX/VGXrn6fpEt1HvXr19ecOXNUu3Zt2e12tWjRwqti29K2Wdz7ct1112nVqlXasGGDVq5cqddee03/8z//o02bNjl+Fi4PsdWoUcPjWAF4jh4VQNLq1au1c+dOpx6GkjRv3lyRkZFKT09XkyZNnJa4uDhdf/31KleunNLS0ly2ERERoYKCApevN27c2FHvcdmFCxf07bffqnnz5qWOtbDLSVTFihUlSStWrNCpU6ccPSTffPONjhw5onvuucdlGzfffLN++OGHEs/1888/a9++fXr66ad1++23q1mzZlclOI0bN1Z4eLg2bdrk2Hbq1Cn95z//8bjN0rLZbOrQoYMmTZqk7du3KyIiQkuWLHG8vmvXLtWtW1fVq1f3qH0A3qFHBSEnPz9fmZmZKigoUFZWlpYvX67U1FTdfffd6tevX6nbqVSpkkaPHq3HH39cdrtdHTt2VHZ2tr7++mtFRUWpf//+evLJJ/XEE08oIiJCHTp00PHjx7V7927HLb0NGjTQpk2bdOjQIVWsWFFVq1Z1OkeFChU0dOhQjRkzRlWrVlW9evX00ksv6ezZs8XeFlyS+vXry2az6X//939VoUIFjR49WnfddZeWLVumuLg4PfLII+rSpYvLO4IkqWvXrho3bpxOnTqlKlWquNyvSpUqqlatmmbPnq3Y2Filp6dr7NixTvtUrFhRgwYN0pgxY1StWjXVrFlT//M//+OY18aTNktj06ZNSktL0x133KGaNWtq06ZNOn78uJo1a+bY56uvvtIdd9zhdtsAfINEBSFn+fLlio2NVZkyZVSlShUlJCTo1VdfVf/+/V3+YXTl2WefVY0aNZSamqr/+7//U+XKlR23PEvSM888ozJlymj8+PH66aefFBsbq0ceecRx/OjRo9W/f381b95cv/zyiw4ePHjVOV544QXZ7XY9+OCDOnPmjNq0aaMVK1YUmxyUJCYmRs8//7xeeOEFffjhh/r73/+u1q1bq2fPnlq0aJF69Oihf/zjH8W20bJlS7Vq1Urvv/++Hn74YZf7hYWFaeHChfrb3/6mFi1aqGnTpnr11VeVkpLitN/LL7+s3Nxc9ejRQ5UqVdKoUaOUnZ3tVZsliYqK0pdffqnp06crJydH9evX15QpU9S9e3dJ0rlz57R06VItX77crXYB+A4z0wLw2KeffqoxY8Zo165dbid5gWDmzJlasmSJVq5caXYoQMiiRwWAx+666y79+OOPOnLkSJFzwAS68PBwvfbaa2aHAYQ0elQAAIBlBV9fLQAACBokKgAAwLJIVAAAgGWRqAAAAMsiUQEAAJZFogIAACyLRAUAAFgWiQoAALAsEhUAAGBZ/x+QaE6LTKsk2QAAAABJRU5ErkJggg==", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result = ect.calculate(K, override_bound_radius=override_bound_radius)\n", - "result.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also look at the Smooth ECT:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate SECT and plot\n", - "smooth = result.smooth()\n", - "smooth.plot()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also compute the ECT in 3D." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "vertices = [\n", - " (letter, coordinates) for letter, coordinates in zip(\"abcde\", np.random.randn(5, 3))\n", - "]\n", - "edges = [(\"a\", \"b\"), (\"a\", \"c\"), (\"a\", \"d\"), (\"b\", \"c\"), (\"b\", \"d\"), (\"c\", \"d\")]\n", - "faces = [\n", - " (\"a\", \"b\", \"c\"),\n", - " (\"a\", \"b\", \"d\"),\n", - " (\"a\", \"c\", \"d\"),\n", - " (\"b\", \"c\", \"d\"),\n", - " (\"a\", \"b\", \"c\", \"d\"),\n", - "]\n", - "K = EmbeddedCW()\n", - "K.add_nodes_from(vertices)\n", - "K.add_edges_from(edges)\n", - "\n", - "K.add_faces_from(faces)\n", - "K.plot(bounding_circle=True)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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pUlJSdOzYMb388suSpIceekjLli3T1KlTNXr0aG3ZskVvvPGG3n33XX+9BQAATCklJUX9+/dX06ZNde7cOa1du1ZZWVm26UCGDx+uxo0bKzU1VZI0ceJE9ezZUwsXLtSAAQO0bt067dq1SytWrHDqvKZLVnbt2qXevXvb1q/UlowYMULp6ek6fvy4cnNzba/HxcXp3Xff1aOPPqqlS5eqSZMmWrlypdu3LTOWDyCY8W9gYPD1DLYnT57U8OHDdfz4cUVGRqpDhw7atGmT7rjjDklSbm6uQkL+c8yEhAStXbtWjz/+uKZPn66bb75ZGRkZateunVPnNV2y0qtXLxlG+U9eKmt22l69emnv3r1ejAoAAN+7eijH1fbOeOmllyp8PSsrq9S2IUOGaMiQIU6d51qVvmYFAACYm+l6VgAAwGWuPN/n2vZmQLICAIBJ+XoYyF9IVkwqkCYoCvZCO2+//0bb7Gu0vutpjn9cHHHte9N/+SeOQBHsf5euFujf+0D6NzgYkKwAAGBS9KwAAICAFizJCncDAQCAgEbPCjzuemPN13vd3bFgxv3L5+86gGs/m0YyKnzd03UAvv5ulKrJuYa719/fn6eZVNb6qGDpWSFZAQDApAy5d/txxel04CBZAQDApIKlZ4WaFQAAENDoWQEAwKSCpWeFZMVLKPKEGVGwGTz4N6pyCJZkhWEgAAAQ0OhZAQDApIKlZ4VkBQAAkzIMiww3Eg532voSw0AAACCg0bMCeBmFjOYVzAXHlXXG18rGKotbk8K509aXSFYAADCpYKlZYRgIAAAENHpWAAAwqWApsCVZQSnUWPjX9Z7UG8i1BMFc4+EKrhfcFSzDQCQrAACYVLD0rFCzAgAAAho9KwAAmJTh5jCQWXpWSFYAADApQ5JRcZnbddubAcmKSVyv6JWiWADexL8x8CeSFQAATMoqiyzMYAsAAAIVdwMBAAAEAHpWTILJozyHsXfA+679e3bov17wUySVm9WwyMKkcAAAIFAZhpt3A5nkdiCGgQAAQECjZwUAAJMKlgJbkhUAAEyKZCWAPffcc1qwYIHy8/PVsWNHPfvss+revXuZ+6anp2vUqFF228LCwnT+/HlfhArAiyg8Nw8K270jWApsTVez8vrrrys5OVkzZ87Unj171LFjRyUmJurkyZPltomIiNDx48dty9GjR30YMQAAlUNqaqpuueUW1apVSw0bNlRSUpIOHjxYYZv09HRZLBa7JTw83Knzmi5ZWbRokcaNG6dRo0apTZs2SktLU40aNbRq1apy21gsFkVHR9uWqKgoH0YMAIB3XLkbyJ3FGdu2bdP48eP1ySefaPPmzbp48aLuvPNOFRUVVdjO3U4DUw0DXbhwQbt371ZKSoptW0hIiPr27avs7Oxy2xUWFqpZs2ayWq3q0qWLnnrqKbVt27bc/YuLi1VcXGxbLygo8MwbAADAgy4nHO7UrDi3/8aNG+3W09PT1bBhQ+3evVu33357ue2udBq4ylQ9K6dOnVJJSUmpnpGoqCjl5+eX2aZly5ZatWqVNmzYoFdffVVWq1UJCQn69ttvyz1PamqqIiMjbUtsbKxH3wcAAIGkoKDAbrn6P+wVOXv2rCSpbt26Fe53pdMgNjZWgwYN0hdffOFUfKbqWXFFfHy84uPjbesJCQlq3bq1XnjhBc2dO7fMNikpKUpOTratFxQU+DxhubZwEPAHfxewjsr9jVeP7+/3B9d5+7thFp66G+ja37iZM2dq1qxZFba1Wq2aNGmSbrvtNrVr167c/a50GnTo0EFnz57V008/rYSEBH3xxRdq0qSJQ3GaKlmpX7++qlSpohMnTthtP3HihMPdS9WqVVPnzp116NChcvcJCwtTWFiYW7ECAOBtxi+LO+0lKS8vTxEREbbtjvwGjh8/Xvv379f27dsr3M+VToNrmWoYKDQ0VF27dlVmZqZtm9VqVWZmpt2FqEhJSYn27dunmJgYb4UJAICpRERE2C3XS1YmTJigd955R1u3bnW4d+QKRzoNrmWqZEWSkpOT9eKLL2rNmjU6cOCAHn74YRUVFdnmUhk+fLhdAe6cOXP0wQcf6JtvvtGePXt0//336+jRoxo7dqy/3gIAAB5xZRjIncW58xmaMGGC1q9fry1btiguLs7pmF3pNDDVMJAkDR06VN9//71mzJih/Px8derUSRs3brQV3ebm5iok5D852I8//qhx48YpPz9fderUUdeuXfXxxx+rTZs2/noLpnO9cX1v19cwmRQAXzNNPZOnxoEcNH78eK1du1YbNmxQrVq1bDe3REZGqnr16pIudxo0btxYqampki53Gtx6661q0aKFzpw5owULFjjdaWC6ZEW63P00YcKEMl/LysqyW1+8eLEWL17sg6gAAPAxNwts5WTb5cuXS5J69eplt3316tUaOXKkJO90GpgyWQEAAL5nODAxizc6DVxKVkpKSrRy5UodPHhQTZo0UceOHdWpUyfVq1fPrWAAAIDjXJmF9tr2ZuBSsvLII4/orbfeUt++fbVs2TJZLBZdunRJjRs3VqdOnfT3v//d03ECAIBrBMtTl126G+jtt9/Wyy+/rNdee01hYWHatWuXli5dqvPnz6tZs2aejhEIKI22GXYLAMC7XOpZKSwstBXGVKtWTVWrVtWECRN08eJFfffddx4NEAAAlMOwOF0kW6q9CbjUs3LTTTfZkpLGjRvr2LFjkqSBAwfq1Vdf9Vx0AACgXL5+6rK/uJSsDB48WO+//74kqWfPnlq1apUk6V//+pd+/vlnz0UHAACCnkvDQFc/3Gjq1Km65ZZb1KBBAxUUFGjMmDGeig0AAFTEx5PC+Yvb86w0bdpUX3zxhf7xj3+oXr16GjhwoCfiAvzm9vEP2m/o6Z84AOB6guVuII9MCle/fn3bs3kAAAA8iRlsAQAwM5MM5biDZAUAAJNiGAiAS643UVzAPr01AB3+c2v7DdQPwUGjcn/j1uvOuvrp8Nbz5yU97tHjlytICmxdunUZAADAV+hZAQDAtCy/LO60D3wOJyvJyckOH3TRokUuBQMAAJwQJMNADicre/futVvfs2ePLl26pJYtW0qSvvrqK1WpUkVdu3b1bIQAACCoOZysbN261fbnRYsWqVatWlqzZo3q1KkjSfrxxx81atQo/eY3ni1agmOuLeqkiNNxvn5yMp8V4D6Kr38RJD0rLhXYLly4UKmpqbZERZLq1KmjJ598UgsXLvRYcAAAoAJXnrrszmICLiUrBQUF+v7770tt//7773Xu3Dm3gwIAALjCpWTl7rvv1qhRo/T222/r22+/1bfffqu33npLY8aM0eDBgz0dIwAAKINhuL+YgUu3Lqelpemxxx7TH//4R128eFGGYahatWoaM2aMFixY4OkYTYm6BO9h0jUA+EWQ1Ky4lKzUqFFDzz//vBYsWKDDhw9Lkpo3b64bbrjBo8EBAAAwzwoAAGblbpGsSQpsXZ5npTwWizneOAAAZmcxLi/utDcDl+ZZAQAAAYCaFQQrX0+SBvdc/bTXQMd3C4ArXE5Wzpw5o5deekkHDhyQJLVp00ZjxoxRZGSkx4IDAAAVCJKaFZfmWdm1a5eaN2+uxYsX6/Tp0zp9+rQWL16s5s2ba8+ePZ6OEQAAlMXwwGICLvWsPProo/r973+vF198UVWrXj7EpUuXNHbsWE2aNEkfffSRR4MEAADBy6VkZdeuXXaJiiRVrVpVU6dOVbdu3TwWHAAAqAAFtuWLiIhQbm6uWrVqZbc9Ly9PtWrV8khggajT+tEKCQ93aN/q63fYb+h5qxciCg7eLsq89rP6+e4eXj2ft119vZjNF6jkgiRZcalmZejQoRozZoxef/115eXlKS8vT+vWrdPYsWN13333eTpGAAAQxFzqWXn66adlsVg0fPhwXbp0SZJUrVo1Pfzww5o/f75HAwQAAOUIkruBXEpWQkNDtXTpUqWmpto9G6hGjRoeDQ4AAJSPGWwdUKNGDbVv395TsSBAVLYaDngOTxOHq6733WHCQFTEpZoVScrMzNT06dM1duxYjR492m7xtueee0433nijwsPD1aNHD3366acV7v/mm2+qVatWCg8PV/v27fXee+95PUYAALzOx/OspKam6pZbblGtWrXUsGFDJSUl6eDBg9dt5+7vsEvJyuzZs3XnnXcqMzNTp06d0o8//mi3eNPrr7+u5ORkzZw5U3v27FHHjh2VmJiokydPlrn/xx9/rPvuu09jxozR3r17lZSUpKSkJO3fv9+rcQIAUNls27ZN48eP1yeffKLNmzfr4sWLuvPOO1VUVFRuG0/8Drs0DJSWlqb09HQ98MADrjR3y6JFizRu3DiNGjXKFsu7776rVatWadq0aaX2X7p0qfr166cpU6ZIkubOnavNmzdr2bJlSktL82nsAAB4kkVu1qw4uf/GjRvt1tPT09WwYUPt3r1bt99+e5ltPPE77FLPyoULF5SQkOBKU7dcuHBBu3fvVt++fW3bQkJC1LdvX2VnZ5fZJjs7225/SUpMTCx3f0kqLi5WQUGB3QIAQGV17W9ecXGxQ+3Onj0rSapbt265+7jyO3wtl3pWxo4dq7Vr1+qJJ55wpbnLTp06pZKSEkVFRdltj4qK0pdffllmm/z8/DL3z8/PL/c8qampmj17dqnt0dsNVa1WdgpLoSEAd1HA7DmH/9zao8dzpkD40kVDRz169gp46Nbl2NhYu80zZ87UrFmzKmxqtVo1adIk3XbbbWrXrl25+7nyO3wth5OV5ORkuwBXrFihDz/8UB06dFC1atXs9l20aJHDAQSilJQUu/dbUFBQ6oMEAMDvPDSDbV5eniIiImybw8LCrtt0/Pjx2r9/v7Zv3+5GAI5xOFnZu3ev3XqnTp0kqVSBjMXivf8J1K9fX1WqVNGJEyfstp84cULR0dFltomOjnZqf+nyh+TIBwUAQGUQERFhl6xcz4QJE/TOO+/oo48+UpMmTSrc15Xf4Ws5nKxs3brV4YN6S2hoqLp27arMzEwlJSVJutzLk5mZqQkTJpTZJj4+XpmZmZo0aZJt2+bNmxUfH++DiAEA8CIfPxvIMAw98sgjWr9+vbKyshQXF3fdNp74HXapZuXnn3+WYRi2GWuPHj2q9evXq02bNrrzzjtdOaTDkpOTNWLECHXr1k3du3fXkiVLVFRUZLs7aPjw4WrcuLFSU1MlSRMnTlTPnj21cOFCDRgwQOvWrdOuXbu0YsUKr8YJAIC3+XoG2/Hjx2vt2rXasGGDatWqZas7iYyMVPXq1SV553fYpWRl0KBBGjx4sB566CGdOXNG3bt3V2hoqE6dOqVFixbp4YcfduWwDhk6dKi+//57zZgxQ/n5+erUqZM2btxoK97Jzc1VSMh/bnJKSEjQ2rVr9fjjj2v69Om6+eablZGRUWExEPyLmSwrDz5L3+KJ274TrN/t5cuXS5J69eplt3316tUaOXKkJO/8DruUrOzZs0eLFy+WJP3tb39TdHS09u7dq7feekszZszwarIiXR4rK2/YJysrq9S2IUOGaMiQIV6NCQAAn/PDMND1eON32KVk5aefflKtWrUkSR988IEGDx6skJAQ3XrrrTp61Gc3bAEAENx8nKz4i0uTwrVo0UIZGRnKy8vTpk2bbHUqJ0+edKqaGAAA4Hpc6lmZMWOG/vjHP+rRRx9Vnz59bBW9H3zwgTp37uzRAIFAF6xj167gid6Vx/W+956umeHvWdl8XWDrLy4lK3/4wx/061//WsePH1fHjh1t2/v06aO7777bY8EBAIAKeGgG20DnUrIiXZ7k5doJXbp37+52QAAAwEHUrFTs//7v/3T//fcrPj5ex44dkyS98sorPpl2FwAABA+XkpW33npLiYmJql69uvbu3Wt7OuPZs2f11FNPeTRAAABQtis1K+4sZuDSMNCTTz6ptLQ0DR8+XOvWrbNtv+222/Tkk096LDgAQOXk74LZUk9l7umfONzGMFD5Dh48qNtvv73U9sjISJ05c8bdmAAAAGxcSlaio6N16NChUtu3b9+um266ye2gAACAA9wdAqrMPSvjxo3TxIkTtWPHDlksFn333Xd67bXX9Nhjj3l9qn0AAPALwwOLCbhUszJt2jRZrVb16dNHP/30k26//XaFhYXpscce0yOPPOLpGFHJ+XvsujLj2qI81343mMQNgczpZOXixYvq16+f0tLSNGXKFB06dEiFhYVq06aNatas6Y0YAQBAWYKkwNbpZKVatWr6/PPPJUmhoaFq06aNx4MCAADXFyzT7btUs3L//ffrpZde8nQsAAAApbhUs3Lp0iWtWrVKH374obp27aobbrjB7vVFixZ5JDgAAACXkpX9+/erS5cukqSvvvrK7jWLxRwPRQo2FRW7ebqwDvau96RhChFxxbXfFfW81T+BwDyoWSnf1q1bPR0HAABwEjUrAAAAAcClnhVJyszMVGZmpk6ePCmr1Wr32qpVq9wODAAAOMAkvSPucClZmT17tubMmaNu3bopJiaGOhUAAPyBmpXypaWlKT09XQ888ICn4wEQQK5XHAzv8vYss4BZuJSsXLhwQQkJCZ6OBQAAOIEC2wqMHTtWa9eu9XQsAADAGTzI0F5ycrLtz1arVStWrNCHH36oDh06qFq1anb7MikcAADwFIeTlb1799qtd+rUSdLlCeKuRrGtYxiL/g/qIgDPsPu7FOATyvH33jOCZRjI4WRl69atmjNnjiZPnlxqen0AAOAHQXI3kFM1K7Nnz1ZRUZG3YgEAACjFqbuBDMMkKRgAAMEgSHpWnL51mZoUAAACAzUr5fjVr3513YTl9OnTLgcEwDk8tRkIYvSslG327NmKjIz0RiwAAAClOJ2s3HvvvWrYsKE3YgEAAM6gZ6U06lUAAAgcwVKz4tSty9wNBAAAfM2pZMVqtfp1COj06dMaNmyYIiIiVLt2bY0ZM0aFhYUVtunVq5csFovd8tBDD/koYgSiRtsMuwXBq/r6HXYLYDo+fjbQRx99pIEDB6pRo0ayWCzKyMiocP+srKxSv8EWi0X5+flOndelpy77y7Bhw3T8+HFt3rxZFy9e1KhRo/Tggw9e96GK48aN05w5c2zrNWrU8HaoAAB4na+HgYqKitSxY0eNHj1agwcPdrjdwYMHFRERYVt3tuPDNMnKgQMHtHHjRu3cuVPdunWTJD377LO666679PTTT6tRo0bltq1Ro4aio6N9FSoAAJVS//791b9/f6fbNWzYULVr13b5vE4NA/lTdna2ateubUtUJKlv374KCQnRjh0Vd9++9tprql+/vtq1a6eUlBT99NNPFe5fXFysgoICuwUAgIDjoWGga3/ziouLPRpmp06dFBMTozvuuEP//Oc/nW5vmp6V/Pz8Ut1GVatWVd26dSsc+/rjH/+oZs2aqVGjRvr888/1pz/9SQcPHtTbb79dbpvU1FTNnj3bY7FLpSfuKjU+HuBPSAWCQWWuYarM7y2oeejW5djYWLvNM2fO1KxZs9w48GUxMTFKS0tTt27dVFxcrJUrV6pXr17asWOHunTp4vBx/J6sTJs2TX/+858r3OfAgQMuH//BBx+0/bl9+/aKiYlRnz59dPjwYTVv3rzMNikpKUpOTratFxQUlPogAQCoLPLy8uxqSsLCwjxy3JYtW6ply5a29YSEBB0+fFiLFy/WK6+84vBx/J6sTJ48WSNHjqxwn5tuuknR0dE6efKk3fZLly7p9OnTTtWj9OjRQ5J06NChcpOVsLAwj31QAAB4i+WXxZ32khQREWGXrHhT9+7dtX37dqfa+D1ZadCggRo0aHDd/eLj43XmzBnt3r1bXbt2lSRt2bJFVqvVloA4IicnR9LlrikAAEzNhDPY5uTkOP0b7PdkxVGtW7dWv379NG7cOKWlpenixYuaMGGC7r33XtudQMeOHVOfPn308ssvq3v37jp8+LDWrl2ru+66S/Xq1dPnn3+uRx99VLfffrs6dOjg53cEAIB7fH3rcmFhoQ4dOmRbP3LkiHJyclS3bl01bdpUKSkpOnbsmF5++WVJ0pIlSxQXF6e2bdvq/PnzWrlypbZs2aIPPvjAqfOaJlmRLt/VM2HCBPXp00chISG655579Mwzz9hev3jxog4ePGi72yc0NFQffvihlixZoqKiIsXGxuqee+7R448/7q+34DO+LKa7tlj457sd7+kyw/kAfwmkQnwKdCFJu3btUu/evW3rV+o7R4wYofT0dB0/fly5ubm21y9cuKDJkyfr2LFjqlGjhjp06KAPP/zQ7hiOMFWyUrdu3QongLvxxhvtHgkQGxurbdu2+SI0AAB8z8fDQL169arw0Tvp6el261OnTtXUqVNdCMyeqZIVAABwjSDo9DLNpHAAACA40bOCSufasfXverpzYx986Xp1EYFWN8F3Df7m6wJbfyFZAQDArEx467IrGAYCAAABjZ4VAABMimEgAAAQ2IJkGIhkBUDA8PaEf4FWoAvP4bOt3EhWAAAwKYaBAABAYGMYCAAABLQgSVa4dRkAAAQ0elacUP0fu1TVUk1S6cI/Xxd3XXs+dwoTA70wrdSTZ53cv5F4KrO3BPp3B54T6E87D9anv1OzAgAAAhvDQAAAAP5HzwoAACZlMQxZDNe7R9xp60skKy7y9HglY/+A/wVKHQLgMIaBAAAA/I+eFQAATIq7gQAAQGBjGAgAAMD/6FmppLxZKEgxMALVtd/N73pavHp8Z18PZN6O3dnJHeEYhoEAAEBgC5JhIJIVAABMKlh6VqhZAQAAAY2eFXidmcfxJfPH70lmmzStsn12le39BBJ3r+3VfzcuGRfdDcdxDAMBAIBAZ5ahHHcwDAQAAAIaPSsAAJiVYVxe3GlvAiQrAACYVLDcDUSyEiSuLv4K9KJITzNbUagvmb1gk8+2fGb7bPksURGSFQAAzIq7gQAAQCCzWC8v7rQ3A+4GAgAAAY2eFQAAzCpIhoHoWQEqmerrd9gWAJXblbuB3Fmc8dFHH2ngwIFq1KiRLBaLMjIyrtsmKytLXbp0UVhYmFq0aKH09HSn36epkpV58+YpISFBNWrUUO3atR1qYxiGZsyYoZiYGFWvXl19+/bV119/7d1AAQDwhSvzrLizOKGoqEgdO3bUc88959D+R44c0YABA9S7d2/l5ORo0qRJGjt2rDZt2uTUeU01DHThwgUNGTJE8fHxeumllxxq85e//EXPPPOM1qxZo7i4OD3xxBNKTEzUv/71L4WHh3s5YgAAKo/+/furf//+Du+flpamuLg4LVy4UJLUunVrbd++XYsXL1ZiYqLDxzFVsjJ79mxJcrgLyTAMLVmyRI8//rgGDRokSXr55ZcVFRWljIwM3Xvvvd4KFQAAr/PUpHAFBQV228PCwhQWFuZGZJdlZ2erb9++dtsSExM1adIkp45jqmTFWUeOHFF+fr7dhYqMjFSPHj2UnZ1dbrJSXFys4uJi2/q1H6I/MGFS5VWZP1vqZgAv81CBbWxsrN3mmTNnatasWW4c+LL8/HxFRUXZbYuKilJBQYF+/vlnVa9e3aHjVOpkJT8/X5LKvFBXXitLamqqrRcHAIDKLi8vTxEREbZ1T/SqeJLfC2ynTZsmi8VS4fLll1/6NKaUlBSdPXvWtuTl5fn0/AAAOMJTdwNFRETYLZ5KVqKjo3XixAm7bSdOnFBERITDvSpSAPSsTJ48WSNHjqxwn5tuusmlY0dHR0u6fGFiYmJs20+cOKFOnTqV285TY3UAAHhVgD91OT4+Xu+9957dts2bNys+Pt6p4/g9WWnQoIEaNGjglWPHxcUpOjpamZmZtuSkoKBAO3bs0MMPP+yVcwIAUFkVFhbq0KFDtvUjR44oJydHdevWVdOmTZWSkqJjx47p5ZdfliQ99NBDWrZsmaZOnarRo0dry5YteuONN/Tuu+86dV6/JyvOyM3N1enTp5Wbm6uSkhLl5ORIklq0aKGaNWtKklq1aqXU1FTdfffdslgsmjRpkp588kndfPPNtluXGzVqpKSkJI/GRiEhAMDXPHU3kKN27dql3r1729aTk5MlSSNGjFB6erqOHz+u3Nxc2+txcXF699139eijj2rp0qVq0qSJVq5c6dRty5LJkpUZM2ZozZo1tvXOnTtLkrZu3apevXpJkg4ePKizZ8/a9pk6daqKior04IMP6syZM/r1r3+tjRs3MscKAMD8fDzdfq9evWRUMHRU1tQivXr10t69e50MzJ6pkpX09PTrzrFy7UW0WCyaM2eO5syZ48XIAACAt5gqWQEAAP/h62EgfyFZAQDArKzG5cWd9iZAsgLAZZV59l04x2zfBbPFWy4f16z4i98nhQMAAKgIPSsAAJiURW7WrHgsEu8iWQEAwKwCfAZbTyFZgdOYAA+Ar1WaGhO4hGQFAACT4tZlAAAQ2LgbCAAAwP/oWQEAwKQshiGLG0Wy7rT1JZIVAHAQRZ6O83chftB8VtZfFnfamwDDQAAAIKDRswIAgEkxDAQAAAJbkNwNRLJiUv4eDwYABIAgmcGWmhUAABDQ6FkBAMCkmMEWAAAENoaBAAAA/I+eFcDkKiq2DpqJsRB0uMngMov18uJOezMgWQEAwKwYBgIAAPA/elYAADArJoUDAACBjOn2AQ8xW5Gn2eL1JIoWg0cwf89hPiQrAACYVZAU2JKsAABgVoYkd24/NkeuQrICAIBZUbMCuIi6B3gKdRXwF757gYVkBQAAszLkZs2KxyLxKpIVAADMKkgKbJnBFgAABDR6VgAAMCurJIub7U2AZCUIUTjmHK4XAkUgfRcppA8M/rob6LnnntOCBQuUn5+vjh076tlnn1X37t3L3Dc9PV2jRo2y2xYWFqbz5887fD5TDQPNmzdPCQkJqlGjhmrXru1Qm5EjR8pisdgt/fr1826gAABUUq+//rqSk5M1c+ZM7dmzRx07dlRiYqJOnjxZbpuIiAgdP37cthw9etSpc5oqWblw4YKGDBmihx9+2Kl2/fr1s7tIf/3rX70UIQAAPnSlwNadxUmLFi3SuHHjNGrUKLVp00ZpaWmqUaOGVq1aVW4bi8Wi6Oho2xIVFeXUOU01DDR79mxJl7uUnBEWFqbo6GgvRAQAgB/5+G6gCxcuaPfu3UpJSbFtCwkJUd++fZWdnV1uu8LCQjVr1kxWq1VdunTRU089pbZt2zp8XlP1rLgqKytLDRs2VMuWLfXwww/rhx9+qHD/4uJiFRQU2C0AAFRW1/7mFRcXl7nfqVOnVFJSUqpnJCoqSvn5+WW2admypVatWqUNGzbo1VdfldVqVUJCgr799luH4zNVz4or+vXrp8GDBysuLk6HDx/W9OnT1b9/f2VnZ6tKlSpltklNTbX14oBCuusJpKJHAEHGQz0rsbGxdptnzpypWbNmuRHYf8THxys+Pt62npCQoNatW+uFF17Q3LlzHTqG35OVadOm6c9//nOF+xw4cECtWrVy6fj33nuv7c/t27dXhw4d1Lx5c2VlZalPnz5ltklJSVFycrJtvaCgoNQHCQCA33no1uW8vDxFRETYNoeFhZW5e/369VWlShWdOHHCbvuJEyccLreoVq2aOnfurEOHDjkcpt+TlcmTJ2vkyJEV7nPTTTd57Hw33XST6tevr0OHDpWbrISFhZX7QQEAECg8detyRESEXbJSntDQUHXt2lWZmZlKSkqSJFmtVmVmZmrChAkOnbOkpET79u3TXXfd5XCcfk9WGjRooAYNGvjsfN9++61++OEHxcTE+OycAABUFsnJyRoxYoS6deum7t27a8mSJSoqKrLNpTJ8+HA1btxYqampkqQ5c+bo1ltvVYsWLXTmzBktWLBAR48e1dixYx0+p9+TFWfk5ubq9OnTys3NVUlJiXJyciRJLVq0UM2aNSVJrVq1Umpqqu6++24VFhZq9uzZuueeexQdHa3Dhw9r6tSpatGihRITE/34TlARamQAx3jy7wq1VxUL2H+X/PBsoKFDh+r777/XjBkzlJ+fr06dOmnjxo22otvc3FyFhPzn/p0ff/xR48aNU35+vurUqaOuXbvq448/Vps2bRw+p6mSlRkzZmjNmjW29c6dO0uStm7dql69ekmSDh48qLNnz0qSqlSpos8//1xr1qzRmTNn1KhRI915552aO3cuwzwAAPOzGpLFjWTF6lrbCRMmlDvsk5WVZbe+ePFiLV682KXzXGGqZCU9Pf26c6wYV2WJ1atX16ZNm7wcFQAA8CZTJSsAAOAqfhgG8geSFQAATMvNZEUkK0BACthCOQBAmUhWAAAwK4aBAABAQLMacmsox8W7gXwtKB5kCAAAzIueFQDwEiZaCxyVtlbNsF5e3GlvAiQrAACYFTUrAAAgoFGzAgAA4H/0rAAAYFYMA8GfKm0xGOAGs/+98GfBrdmvHcphyM1kxWOReBXDQAAAIKDRswIAgFkxDAQAAAKa1SrJjblSrOaYZ4VhIAAAENDoWYHpVfbCQWZB9Z/K/t1CJcAwEAAACGhBkqwwDAQAAAIaPSsAAJhVkEy3T7LiI9cb+/bn2Livzx3sdQCB/F0AYC6GYZXhxpOT3WnrSyQrAACYlWG41ztCzQoAAID76FkBAMCsDDdrVkzSs0KyAgCAWVmtksWNuhNqVoDKyd8FsoFcgOtsbMF8rQA4jmQFAACzYhgIAAAEMsNqleHGMJBZbl3mbiAAABDQ6FkBgggPRfSuQKvZcYe/Y/P3+U2DYSAAABDQrIZkqfzJCsNAAAAgoNGzAgCAWRmGJHfmWTFHzwrJCgAAJmVYDRluDAMZJCsA4Fs8QRxBx7DKvZ4Vbl0GAACV0HPPPacbb7xR4eHh6tGjhz799NMK93/zzTfVqlUrhYeHq3379nrvvfecOh/JCgAAJmVYDbcXZ73++utKTk7WzJkztWfPHnXs2FGJiYk6efJkmft//PHHuu+++zRmzBjt3btXSUlJSkpK0v79+x0+J8kKAABmZVjdX5y0aNEijRs3TqNGjVKbNm2UlpamGjVqaNWqVWXuv3TpUvXr109TpkxR69atNXfuXHXp0kXLli1z+JzUrDjgSgHSJV10a+4dINBcunjeft246KdI4Gl8ts7x5PW6pMttfVG86u7v0pVYCwoK7LaHhYUpLCys1P4XLlzQ7t27lZKSYtsWEhKivn37Kjs7u8xzZGdnKzk52W5bYmKiMjIyHI6TZMUB586dkyRtl3NjbEDA+8cGf0cAb+GzdY4Xrte5c+cUGRnp8eNKUmhoqKKjo7U93/3fpZo1ayo2NtZu28yZMzVr1qxS+546dUolJSWKioqy2x4VFaUvv/yyzOPn5+eXuX9+fr7DMZKsOKBRo0bKy8uTYRhq2rSp8vLyFBER4e+wTKWgoECxsbFcOxdx/VzHtXMd1841hmHo3LlzatSokdfOER4eriNHjujChQtuH8swDFksFrttZfWq+BPJigNCQkLUpEkTWzdZREQEf3FdxLVzD9fPdVw713HtnOetHpWrhYeHKzw83OvnuVr9+vVVpUoVnThxwm77iRMnFB0dXWab6Ohop/YvCwW2AADAIaGhoeratasyMzNt26xWqzIzMxUfH19mm/j4eLv9JWnz5s3l7l8WelYAAIDDkpOTNWLECHXr1k3du3fXkiVLVFRUpFGjRkmShg8frsaNGys1NVWSNHHiRPXs2VMLFy7UgAEDtG7dOu3atUsrVqxw+JwkK04ICwvTzJkzA24szwy4du7h+rmOa+c6rh3KMnToUH3//feaMWOG8vPz1alTJ23cuNFWRJubm6uQkP8M3CQkJGjt2rV6/PHHNX36dN18883KyMhQu3btHD6nxTDLgwEAAEBQomYFAAAENJIVAAAQ0EhWAABAQCNZAQAAAY1kxUHOPg47GKWmpuqWW25RrVq11LBhQyUlJengwYN2+5w/f17jx49XvXr1VLNmTd1zzz2lJgvCZfPnz5fFYtGkSZNs27h+5Tt27Jjuv/9+1atXT9WrV1f79u21a9cu2+uGYWjGjBmKiYlR9erV1bdvX3399dd+jDhwlJSU6IknnlBcXJyqV6+u5s2ba+7cuXbPtuH6wa8MXNe6deuM0NBQY9WqVcYXX3xhjBs3zqhdu7Zx4sQJf4cWUBITE43Vq1cb+/fvN3Jycoy77rrLaNq0qVFYWGjb56GHHjJiY2ONzMxMY9euXcatt95qJCQk+DHqwPTpp58aN954o9GhQwdj4sSJtu1cv7KdPn3aaNasmTFy5Ehjx44dxjfffGNs2rTJOHTokG2f+fPnG5GRkUZGRobx2WefGb///e+NuLg44+eff/Zj5IFh3rx5Rr169Yx33nnHOHLkiPHmm28aNWvWNJYuXWrbh+sHfyJZcUD37t2N8ePH29ZLSkqMRo0aGampqX6MKvCdPHnSkGRs27bNMAzDOHPmjFGtWjXjzTfftO1z4MABQ5KRnZ3trzADzrlz54ybb77Z2Lx5s9GzZ09bssL1K9+f/vQn49e//nW5r1utViM6OtpYsGCBbduZM2eMsLAw469//asvQgxoAwYMMEaPHm23bfDgwcawYcMMw+D6wf8YBrqOK4/D7tu3r23b9R6HjcvOnj0rSapbt64kaffu3bp48aLdtWzVqpWaNm3KtbzK+PHjNWDAALvrJHH9KvL3v/9d3bp105AhQ9SwYUN17txZL774ou31I0eOKD8/3+7aRUZGqkePHkF/7aTLk3ZlZmbqq6++kiR99tln2r59u/r37y+J6wf/Ywbb63Dlcdi4/KyISZMm6bbbbrPNUpifn6/Q0FDVrl3bbl9nHxVema1bt0579uzRzp07S73G9SvfN998o+XLlys5OVnTp0/Xzp079T//8z8KDQ3ViBEjbNfH3cfUV1bTpk1TQUGBWrVqpSpVqqikpETz5s3TsGHDJInrB78jWYFXjB8/Xvv379f27dv9HYpp5OXlaeLEidq8ebPPn6RqdlarVd26ddNTTz0lSercubP279+vtLQ0jRgxws/RBb433nhDr732mtauXau2bdsqJydHkyZNUqNGjbh+CAgMA12HK4/DDnYTJkzQO++8o61bt6pJkya27dHR0bpw4YLOnDljtz/X8rLdu3fr5MmT6tKli6pWraqqVatq27ZteuaZZ1S1alVFRUVx/coRExOjNm3a2G1r3bq1cnNzJcl2ffh7XLYpU6Zo2rRpuvfee9W+fXs98MADevTRR20PouP6wd9IVq7DlcdhByvDMDRhwgStX79eW7ZsUVxcnN3rXbt2VbVq1eyu5cGDB5Wbm8u1lNSnTx/t27dPOTk5tqVbt24aNmyY7c9cv7LddtttpW6T/+qrr9SsWTNJUlxcnKKjo+2uXUFBgXbs2BH0106SfvrpJ7sHz0lSlSpVZLVaJXH9EAD8XeFrBuvWrTPCwsKM9PR041//+pfx4IMPGrVr1zby8/P9HVpAefjhh43IyEgjKyvLOH78uG356aefbPs89NBDRtOmTY0tW7YYu3btMuLj4434+Hg/Rh3Yrr4byDC4fuX59NNPjapVqxrz5s0zvv76a+O1114zatSoYbz66qu2febPn2/Url3b2LBhg/H5558bgwYN4tbbX4wYMcJo3Lix7dblt99+26hfv74xdepU2z5cP/gTyYqDnn32WaNp06ZGaGio0b17d+OTTz7xd0gBR1KZy+rVq237/Pzzz8Z///d/G3Xq1DFq1Khh3H333cbx48f9F3SAuzZZ4fqV7x//+IfRrl07IywszGjVqpWxYsUKu9etVqvxxBNPGFFRUUZYWJjRp08f4+DBg36KNrAUFBQYEydONJo2bWqEh4cbN910k/G///u/RnFxsW0frh/8yWIYV01RCAAAEGCoWQEAAAGNZAUAAAQ0khUAABDQSFYAAEBAI1kBAAABjWQFAAAENJIVAAAQ0EhWgABmsViUkZERdOd2Vq9evTRp0iR/hwHAS0hWAB8bOXKkLBaLLBaLqlWrpqioKN1xxx1atWqV7VksVxw/flz9+/f3ajyzZs1Sp06dSm33xbnT09NVu3Ztr54DgPmRrAB+0K9fPx0/flz//ve/9f7776t3796aOHGifve73+nSpUu2/aKjoxUWFlbucS5evOi1GK93bgDwFZIVwA/CwsIUHR2txo0bq0uXLpo+fbo2bNig999/X+np6bb9rh6K+fe//y2LxaLXX39dPXv2VHh4uF577TVJ0sqVK9W6dWuFh4erVatWev755+3O9+233+q+++5T3bp1dcMNN6hbt27asWOH0tPTNXv2bH322We23p4r5792GGjfvn367W9/q+rVq6tevXp68MEHVVhYaHt95MiRSkpK0tNPP62YmBjVq1dP48ePdyqhutLL88orr+jGG29UZGSk7r33Xp07d862T1FRkYYPH66aNWsqJiZGCxcuLHWc4uJiPfbYY2rcuLFuuOEG9ejRQ1lZWZKk8+fPq23btnrwwQdt+x8+fFi1atXSqlWrHI4VgO+QrAAB4re//a06duyot99+u8L9pk2bpokTJ+rAgQNKTEzUa6+9phkzZmjevHk6cOCAnnrqKT3xxBNas2aNJKmwsFA9e/bUsWPH9Pe//12fffaZpk6dKqvVqqFDh2ry5Mlq27atjh8/ruPHj2vo0KGlzllUVKTExETVqVNHO3fu1JtvvqkPP/xQEyZMsNtv69atOnz4sLZu3ao1a9YoPT3dLvlyxOHDh5WRkaF33nlH77zzjrZt26b58+fbXp8yZYq2bdumDRs26IMPPlBWVpb27Nljd4wJEyYoOztb69at0+eff64hQ4aoX79++vrrr21J3po1a7RhwwaVlJTo/vvv1x133KHRo0c7FSsAH/H3kxSBYDNixAhj0KBBZb42dOhQo3Xr1rZ1Scb69esNwzCMI0eOGJKMJUuW2LVp3ry5sXbtWrttc+fONeLj4w3DMIwXXnjBqFWrlvHDDz+Uec6ZM2caHTt2LLX96nOvWLHCqFOnjlFYWGh7/d133zVCQkKM/Px82/tq1qyZcenSJds+Q4YMMYYOHVrmeQ3DMFavXm1ERkbaxVKjRg2joKDAtm3KlClGjx49DMMwjHPnzhmhoaHGG2+8YXv9hx9+MKpXr257OvXRo0eNKlWqGMeOHbM7V58+fYyUlBTb+l/+8hejfv36xoQJE4yYmBjj1KlT5cYJwL+q+jlXAnAVwzBksVgq3Kdbt262PxcVFenw4cMaM2aMxo0bZ9t+6dIlRUZGSpJycnLUuXNn1a1b1+W4Dhw4oI4dO+qGG26wbbvttttktVp18OBBRUVFSZLatm2rKlWq2PaJiYnRvn37nDrXjTfeqFq1atkd4+TJk5Iu97pcuHBBPXr0sL1et25dtWzZ0ra+b98+lZSU6Fe/+pXdcYuLi1WvXj3b+uTJk5WRkaFly5bp/ffft3sNQGAhWQECyIEDBxQXF1fhPlcnDFdqRl588UW7H3BJtqShevXqHo6yfNWqVbNbt1gspe5w8vYxCgsLVaVKFe3evdsucZKkmjVr2v588uRJffXVV6pSpYq+/vpr9evXz6k4AfgONStAgNiyZYv27dune+65x+E2UVFRatSokb755hu1aNHCbrmS9HTo0EE5OTk6ffp0mccIDQ1VSUlJhedp3bq1PvvsMxUVFdm2/fOf/1RISIhdr4a3NW/eXNWqVdOOHTts23788Ud99dVXtvXOnTurpKREJ0+eLHVNoqOjbfuNHj1a7du315o1a/SnP/1JBw4c8Nn7AOAckhXAD4qLi5Wfn69jx45pz549euqppzRo0CD97ne/0/Dhw5061uzZs5WamqpnnnlGX331lfbt26fVq1dr0aJFkqT77rtP0dHRSkpK0j//+U998803euutt5SdnS3p8rDLkSNHlJOTo1OnTqm4uLjUOYYNG6bw8HCNGDFC+/fv19atW/XII4/ogQcesA0B+ULNmjU1ZswYTZkyRVu2bNH+/fs1cuRIhYT855+yX/3qVxo2bJiGDx+ut99+W0eOHNGnn36q1NRUvfvuu5Kk5557TtnZ2VqzZo2GDRumpKQkDRs2TBcuXPDZewHgOJIVwA82btyomJgY3XjjjerXr5+2bt2qZ555Rhs2bCg1dHE9Y8eO1cqVK7V69Wq1b99ePXv2VHp6uq1nJTQ0VB988IEaNmyou+66S+3bt9f8+fNt57nnnnvUr18/9e7dWw0aNNBf//rXUueoUaOGNm3apNOnT+uWW27RH/7wB/Xp00fLli1z/2I4acGCBfrNb36jgQMHqm/fvvr1r3+trl272u2zevVqDR8+XJMnT1bLli2VlJSknTt3qmnTpvryyy81ZcoUPf/884qNjZUkPf/88zp16pSeeOIJn78fANdnMQzD8HcQAAAA5aFnBQAABDSSFQAAENBIVgAAQEAjWQEAAAGNZAUAAAQ0khUAABDQSFYAAEBAI1kBAAABjWQFAAAENJIVAAAQ0EhWAABAQCNZAQAAAe3/A8mre6+0q+FTAAAAAElFTkSuQmCC", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ect = ECT(num_dirs=100, num_thresh=80)\n", - "result = ect.calculate(K)\n", - "result.plot()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/_sources/notebooks/tutorial_graph.ipynb.txt b/docs/_sources/notebooks/tutorial_graph.ipynb.txt deleted file mode 100644 index 7988362..0000000 --- a/docs/_sources/notebooks/tutorial_graph.ipynb.txt +++ /dev/null @@ -1,791 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: ECT for Embedded Graphs\n", - "\n", - "\n", - "\n", - "This tutorial demonstrates how to use the `ect` package..." - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "from ect import ECT, EmbeddedGraph\n", - "\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic Usage\n", - "\n", - "\n", - "\n", - "First, let's create a simple graph\"\"\"\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Construct an example graph\n", - "# Note that this is the same graph that is returned by:\n", - "# G = create_example_graph()\n", - "\n", - "G = EmbeddedGraph()\n", - "\n", - "G.add_node(\"A\", [1, 2])\n", - "G.add_node(\"B\", [3, 4])\n", - "G.add_node(\"C\", [5, 7])\n", - "G.add_node(\"D\", [3, 6])\n", - "G.add_node(\"E\", [4, 3])\n", - "G.add_node(\"F\", [4, 5])\n", - "\n", - "G.add_edge(\"A\", \"B\")\n", - "G.add_edge(\"B\", \"C\")\n", - "G.add_edge(\"B\", \"D\")\n", - "G.add_edge(\"B\", \"E\")\n", - "G.add_edge(\"C\", \"D\")\n", - "G.add_edge(\"E\", \"F\")\n", - "\n", - "G.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " The embedded graph class inherits from the networkx graph class with the additional attributes `coord_matrix` and `coord_dict`.\n", - "\n", - " The coordinates of all vertices can be accessed using the `coord_matrix` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1., 2.],\n", - " [3., 4.],\n", - " [5., 7.],\n", - " [3., 6.],\n", - " [4., 3.],\n", - " [4., 5.]])" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G.coord_matrix\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " It's often useful to center the graph, so you can use the `center_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points." - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-2.33333333 -2.5 ]\n", - " [-0.33333333 -0.5 ]\n", - " [ 1.66666667 2.5 ]\n", - " [-0.33333333 1.5 ]\n", - " [ 0.66666667 -1.5 ]\n", - " [ 0.66666667 0.5 ]]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G.center_coordinates(center_type=\"mean\")\n", - "print(G.coord_matrix)\n", - "G.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " To get a bounding radius we can use the `get_bounding_radius` method." - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The radius of bounding circle centered at the origin is 3.2015621187164243\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# This is actually getting the radius\n", - "r = G.get_bounding_radius()\n", - "print(f\"The radius of bounding circle centered at the origin is {r}\")\n", - "\n", - "# plotting the graph with it's bounding circle of radius r.\n", - "G.plot(bounding_circle=True)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also rescale our graph to have unit radius using `scale_coordinates`" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The radius of bounding circle centered at the origin is 0.9362075413977773\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G.scale_coordinates(radius=1)\n", - "G.plot(bounding_circle=True)\n", - "\n", - "r = G.get_bounding_radius()\n", - "print(f\"The radius of bounding circle centered at the origin is {r}\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of directions: 16\n", - "Number of thresholds: 20\n" - ] - } - ], - "source": [ - "myect = ECT(num_dirs=16, num_thresh=20)\n", - "\n", - "# The ECT object will automatically create directions when needed\n", - "print(f\"Number of directions: {myect.num_dirs}\")\n", - "print(f\"Number of thresholds: {myect.num_thresh}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can override the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures." - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thresholds chosen are: [-1.12344905 -1.00519125 -0.88693346 -0.76867567 -0.65041787 -0.53216008\n", - " -0.41390228 -0.29564449 -0.17738669 -0.0591289 0.0591289 0.17738669\n", - " 0.29564449 0.41390228 0.53216008 0.65041787 0.76867567 0.88693346\n", - " 1.00519125 1.12344905]\n" - ] - } - ], - "source": [ - "custom_bound_radius = 1.2 * G.get_bounding_radius()\n", - "result = myect.calculate(G, override_bound_radius=custom_bound_radius)\n", - "\n", - "print(f\"Thresholds chosen are: {myect.thresholds}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " If we want the Euler characteristic curve for a fixed direction, we use the `calculate` function with a specific angle. This returns an ECTResult object containing the computed values." - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ECT values for direction pi/2: [0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0]\n" - ] - } - ], - "source": [ - "result = myect.calculate(G, theta=np.pi / 2)\n", - "print(f\"ECT values for direction pi/2: {result[0]}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " To calculate the full ECT, we call the `calculate` method without specifying theta. The result returns the ECT matrix and associated metadata." - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ECT matrix shape: (16, 20)\n", - "Number of directions: 16\n", - "Number of thresholds: 20\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result = myect.calculate(G)\n", - "\n", - "print(f\"ECT matrix shape: {result.shape}\")\n", - "print(f\"Number of directions: {myect.num_dirs}\")\n", - "print(f\"Number of thresholds: {myect.num_thresh}\")\n", - "\n", - "# We can plot the result matrix\n", - "result.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " ## SECT\n", - "\n", - "\n", - "\n", - " The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n", - "\n", - " $$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n", - "\n", - " Then the SECT is defined by\n", - "\n", - " $$\n", - "\n", - " \\begin{matrix}\n", - "\n", - " \\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n", - "\n", - " & \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n", - "\n", - " \\end{matrix}\n", - "\n", - " $$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " The SECT can be computed from the ECT result:" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sect = result.smooth()\n", - "\n", - "sect.plot()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " ## ECT for higher dimensions\n", - "\n", - "\n", - "\n", - " The `ECT` class can also be used for higher dimensional graphs." - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# generate a 3d graph\n", - "list_3d = [\n", - " (\"A\", [0, 0, 0]),\n", - " (\"B\", [1, 0, 0]),\n", - " (\"C\", [0, 1, 0]),\n", - " (\"D\", [1, 1, 0]),\n", - " (\"E\", [0, 0, 1]),\n", - " (\"F\", [1, 0, 1]),\n", - " (\"G\", [0, 1, 1]),\n", - " (\"H\", [1, 1, 1]),\n", - "]\n", - "graph_3d = EmbeddedGraph()\n", - "graph_3d.add_nodes_from(list_3d)\n", - "\n", - "# add edges\n", - "graph_3d.add_edge(\"A\", \"B\")\n", - "graph_3d.add_edge(\"B\", \"C\")\n", - "graph_3d.add_edge(\"C\", \"D\")\n", - "graph_3d.add_edge(\"D\", \"E\")\n", - "graph_3d.add_edge(\"E\", \"F\")\n", - "graph_3d.add_edge(\"F\", \"G\")\n", - "graph_3d.add_edge(\"G\", \"H\")\n", - "graph_3d.add_edge(\"H\", \"A\")\n", - "\n", - "# plot the graph\n", - "graph_3d.plot()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " lets center the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "graph_3d.center_coordinates(center_type=\"mean\")\n", - "graph_3d.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Now we can compute the ECT of the 3d graph." - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ect_3d = ECT(num_dirs=16, num_thresh=20)\n", - "result_3d = ect_3d.calculate(graph_3d)\n", - "result_3d.plot()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Note that the each of the directions are appended in a list for the ECT result, so we won't see the same periodic behavior as in the 2d case.\n", - "\n", - " ECT results inherit from ndarrays but they store the associated directions and thresholds." - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.48609512, -0.71244342, -0.50609872],\n", - " [ 0.00364975, -0.82190818, 0.56960831],\n", - " [-0.77710663, -0.23260984, 0.5848059 ],\n", - " [ 0.75362655, -0.61431121, -0.23381352],\n", - " [-0.18133219, -0.98270096, -0.03764916],\n", - " [-0.92728701, 0.26549447, 0.26391569],\n", - " [-0.81397529, 0.14802792, 0.56172232],\n", - " [ 0.54073592, -0.29337918, 0.78837385],\n", - " [ 0.48363696, 0.74722911, -0.45578937],\n", - " [ 0.99631892, 0.01735462, 0.08394891],\n", - " [ 0.74179171, -0.62822234, -0.23469503],\n", - " [ 0.09906268, -0.01998319, -0.99488052],\n", - " [ 0.57301878, 0.71110718, -0.40740159],\n", - " [ 0.93645869, -0.11437729, -0.33160663],\n", - " [-0.19170715, 0.53609831, -0.82209913],\n", - " [ 0.69801901, 0.38101835, -0.6062957 ]])" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_3d.directions.vectors\n" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.8660254 , -0.77486483, -0.68370427, -0.5925437 , -0.50138313,\n", - " -0.41022256, -0.31906199, -0.22790142, -0.13674085, -0.04558028,\n", - " 0.04558028, 0.13674085, 0.22790142, 0.31906199, 0.41022256,\n", - " 0.50138313, 0.5925437 , 0.68370427, 0.77486483, 0.8660254 ])" - ] - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_3d.thresholds\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can also define custom directions and thresholds for the ECT in case we need finer control. We use random sampling from the unit sphere for the directions and cosine sampling for the thresholds." - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from ect import Directions\n", - "\n", - "directions = Directions.random(16, dim=3)\n", - "thresholds = np.cos(np.linspace(0, np.pi, 20))\n", - "ect_3d = ECT(directions=directions, thresholds=thresholds)\n", - "result_3d = ect_3d.calculate(graph_3d)\n", - "result_3d.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of direction vectors: 10000\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 137, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
                        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# another example sampling from the half sphere\n", - "sample_size = 100\n", - "theta = np.linspace(0, np.pi / 2, sample_size) # Only go up to pi/2 for half sphere\n", - "phi = np.linspace(0, 2 * np.pi, sample_size)\n", - "theta, phi = np.meshgrid(theta, phi)\n", - "\n", - "# Flatten the meshgrid arrays and create vectors\n", - "half_sphere_vectors = np.column_stack(\n", - " [\n", - " np.sin(theta.flatten()) * np.cos(phi.flatten()),\n", - " np.sin(theta.flatten()) * np.sin(phi.flatten()),\n", - " np.cos(theta.flatten()),\n", - " ]\n", - ")\n", - "\n", - "# Normalize the vectors\n", - "half_sphere_vectors = half_sphere_vectors / np.linalg.norm(\n", - " half_sphere_vectors, axis=1, keepdims=True\n", - ")\n", - "\n", - "directions = Directions.from_vectors(half_sphere_vectors)\n", - "print(f\"Number of direction vectors: {len(directions)}\")\n", - "ect_3d = ECT(directions=directions, num_thresh=20) # Reduced number of thresholds\n", - "result_3d = ect_3d.calculate(graph_3d)\n", - "result_3d.plot()\n" - ] - } - ], - "metadata": { - "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 - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/_sources/tutorials.rst.txt b/docs/_sources/tutorials.rst.txt index 5a5d1b1..849d783 100644 --- a/docs/_sources/tutorials.rst.txt +++ b/docs/_sources/tutorials.rst.txt @@ -5,8 +5,7 @@ Tutorials :maxdepth: 2 :caption: Contents: - notebooks/tutorial_graph - notebooks/tutorial_cw + notebooks/Tutorial-EmbeddedComplex + notebooks/Tutorial-ExactECT notebooks/Matisse/example_matisse - .. notebooks/Tutorial-ExactECT diff --git a/docs/_sources/validation.md.txt b/docs/_sources/validation.md.txt new file mode 100644 index 0000000..7d295ce --- /dev/null +++ b/docs/_sources/validation.md.txt @@ -0,0 +1,114 @@ +# Validation System + +The validation system provides modular, extensible validation for embedded cell complexes to ensure they represent proper embeddings in Euclidean space. + +## Overview + +The validation system distinguishes between two types of rules: + +- **Structural Rules** (always checked): Basic requirements like vertex counts and dimension validity +- **Geometric Rules** (optional): Embedding properties like non-intersecting edges and faces + +## Architecture + +The validation system consists of several components: + +1. **Base Classes**: Abstract interfaces for validation rules and results +2. **Validation Rules**: Concrete implementations for specific validation checks +3. **Validator**: Main orchestrator that manages and applies rules + +## Validation Rules + +### Structural Rules (Always Enforced) + +- **DimensionValidityRule**: Ensures cell dimensions are non-negative +- **VertexCountRule**: Validates correct vertex counts for cell dimensions + - 0-cells must have exactly 1 vertex + - 1-cells must have exactly 2 vertices + - k-cells (k ≥ 2) must have at least 3 vertices + +### Geometric Rules (Optional) + +- **EdgeInteriorRule**: Ensures no vertices lie on edge interiors +- **FaceInteriorRule**: Ensures no vertices lie inside face interiors +- **SelfIntersectionRule**: Validates that face edges don't self-intersect +- **BoundaryEdgeRule**: Ensures required boundary edges exist for faces + +## Usage + +```python +from ect import EmbeddedComplex + +# Enable validation during construction +K = EmbeddedComplex(validate_embedding=True) + +# Or enable/disable later +K.enable_embedding_validation(tol=1e-10) +K.disable_embedding_validation() + +# Override per operation +K.add_cell(vertices, dim=2, check=True) +``` + +## Custom Validation Rules + +You can create custom validation rules by inheriting from `ValidationRule`: + +```python +from ect.validation import ValidationRule, ValidationResult + +class MyCustomRule(ValidationRule): + @property + def name(self) -> str: + return "My Custom Rule" + + @property + def is_structural(self) -> bool: + return False # Geometric rule + + def applies_to_dimension(self, dim: int) -> bool: + return dim == 2 # Only for 2-cells + + def validate(self, cell_coords, all_coords, cell_indices, all_indices, dim): + # Your validation logic here + if some_condition: + return ValidationResult.valid() + else: + return ValidationResult.invalid("Validation failed") + +# Add to validator +K.get_validator().add_rule(MyCustomRule()) +``` + +## API Reference + +### Main Module + +```{eval-rst} +.. automodule:: ect.validation + :members: +``` + +### Base Classes + +```{eval-rst} +.. automodule:: ect.validation.base + :members: + :show-inheritance: +``` + +### Validation Rules + +```{eval-rst} +.. automodule:: ect.validation.rules + :members: + :show-inheritance: +``` + +### Validator + +```{eval-rst} +.. automodule:: ect.validation.validator + :members: + :show-inheritance: +``` \ No newline at end of file diff --git a/docs/citing.html b/docs/citing.html index 20c8317..019fd5a 100644 --- a/docs/citing.html +++ b/docs/citing.html @@ -56,26 +56,44 @@
                    • 2. Modules
                        -
                      • 2.1. Embedded graphs
                          -
                        • EmbeddedGraph
                          • +
                        • 2.1. Embedded Complex
                          • -
                        • 2.2. Embedded CW complex
                        • 3. Tutorials
                            -
                          • 3.1. Tutorial: ECT for Embedded Graphs
                          • 2. Modules
                              -
                            • 2.1. Embedded graphs
                                -
                              • EmbeddedGraph
                                • +
                              • 2.1. Embedded Complex
                                • -
                              • 2.2. Embedded CW complex
                              • 3. Tutorials
                                  -
                                • 3.1. Tutorial: ECT for Embedded Graphs
                                    -
                                  • 3.1.1. Basic Usage
                                    • +
                                  • 3.1. Tutorial: ECT for Embedded Cell Complexes
                                    • -
                                  • 3.2. Tutorial: ECT for CW complexes
                                    • +
                                  • 3.2. Tutorial for exact ECT computation
                                  • 3.3. ECT on Matisse’s “The Parakeet and the Mermaid” diff --git a/docs/directions.html b/docs/directions.html new file mode 100644 index 0000000..c6a28da --- /dev/null +++ b/docs/directions.html @@ -0,0 +1,292 @@ + + + + + + + 2.4. Directions — ect 0.1.5 documentation + + + + + + + + + + + + + + + + + + + + + + +
                                    + + +
                                    + +
                                    +
                                    +
                                    + +
                                    +
                                    +
                                    +
                                    + +
                                    +

                                    2.4. Directions

                                    +
                                    +
                                    +class ect.directions.Sampling(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
                                    +
                                    +
                                    +UNIFORM = 'uniform'
                                    +
                                    + +
                                    +
                                    +RANDOM = 'random'
                                    +
                                    + +
                                    +
                                    +CUSTOM = 'custom'
                                    +
                                    + +
                                    + +
                                    +
                                    +class ect.directions.Directions(num_dirs=360, sampling=Sampling.UNIFORM, dim=2, endpoint=False, seed=None)[source]
                                    +

                                    Manages direction vectors for ECT calculations. +Supports uniform, random, or custom sampling of directions.

                                    +

                                    Examples

                                    +

                                    # Uniform sampling in 2D (default) +dirs = Directions.uniform(num_dirs=8)

                                    +

                                    # Uniform sampling in 3D +dirs = Directions.uniform(num_dirs=10, dim=3)

                                    +

                                    # Random sampling in 2D +dirs = Directions.random(num_dirs=10, seed=42)

                                    +

                                    # Custom angles (2D only) +dirs = Directions.from_angles([0, np.pi/4, np.pi/2])

                                    +

                                    # Custom vectors in any dimension +dirs = Directions.from_vectors([(1,0,0), (0,1,0), (0,0,1)])

                                    +
                                    +
                                    +__init__(num_dirs=360, sampling=Sampling.UNIFORM, dim=2, endpoint=False, seed=None)[source]
                                    +
                                    + +
                                    +
                                    +classmethod uniform(num_dirs=360, dim=2, endpoint=False, seed=None)[source]
                                    +

                                    Factory method for uniform sampling.

                                    +
                                    +
                                    Parameters:
                                    +
                                      +

                                    • num_dirs – Number of direction vectors.

                                      • +

                                    • dim – Dimension of the space (default 2).

                                      • +

                                    • endpoint – Whether to include the endpoint (for 2D angles).

                                      • +

                                    • seed – Optional random seed.

                                      • +
                                    +
                                    +
                                    +
                                    + +
                                    +
                                    +classmethod random(num_dirs=360, dim=2, seed=None)[source]
                                    +

                                    Factory method for random sampling.

                                    +
                                    +
                                    Parameters:
                                    +
                                      +

                                    • num_dirs – Number of direction vectors.

                                      • +

                                    • dim – Dimension of the space.

                                      • +

                                    • seed – optional random seed.

                                      • +
                                    +
                                    +
                                    +
                                    + +
                                    +
                                    +classmethod from_angles(angles)[source]
                                    +

                                    Create an instance for custom angles (2D only).

                                    +
                                    + +
                                    +
                                    +classmethod from_vectors(vectors)[source]
                                    +

                                    Create an instance from custom direction vectors. +Works in any number of dimensions.

                                    +
                                    + +
                                    +
                                    +property thetas
                                    +

                                    Get the angles for 2D directions. Raises an error if dim > 2.

                                    +
                                    + +
                                    +
                                    +property vectors
                                    +

                                    Get unit direction vectors. +For 2D, they are computed from thetas if not already created. +For n-dim (n>2), they should be available.

                                    +
                                    + +
                                    + +
                                    + + +
                                    +
                                    + +
                                    +
                                    +
                                    +
                                    + + + + + + + \ No newline at end of file diff --git a/docs/doctrees/ect_on_graphs.doctree b/docs/doctrees/ect_on_graphs.doctree deleted file mode 100644 index 549d2f8..0000000 Binary files a/docs/doctrees/ect_on_graphs.doctree and /dev/null differ diff --git a/docs/doctrees/embed_cw.doctree b/docs/doctrees/embed_cw.doctree deleted file mode 100644 index ac0e0dd..0000000 Binary files a/docs/doctrees/embed_cw.doctree and /dev/null differ diff --git a/docs/doctrees/embed_graph.doctree b/docs/doctrees/embed_graph.doctree deleted file mode 100644 index 5d7d14d..0000000 Binary files a/docs/doctrees/embed_graph.doctree and /dev/null differ diff --git a/docs/doctrees/environment.pickle b/docs/doctrees/environment.pickle deleted file mode 100644 index a7bd0b6..0000000 Binary files a/docs/doctrees/environment.pickle and /dev/null differ diff --git a/docs/doctrees/index.doctree b/docs/doctrees/index.doctree deleted file mode 100644 index faba731..0000000 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files a/docs/doctrees/notebooks/Matisse/example_matisse.doctree and /dev/null differ diff --git a/docs/doctrees/notebooks/Tutorial-ExactECT.doctree b/docs/doctrees/notebooks/Tutorial-ExactECT.doctree deleted file mode 100644 index 54ff9d5..0000000 Binary files a/docs/doctrees/notebooks/Tutorial-ExactECT.doctree and /dev/null differ diff --git a/docs/doctrees/notebooks/tutorial_cw.doctree b/docs/doctrees/notebooks/tutorial_cw.doctree deleted file mode 100644 index 8dcbac9..0000000 Binary files a/docs/doctrees/notebooks/tutorial_cw.doctree and /dev/null differ diff --git a/docs/doctrees/notebooks/tutorial_graph.doctree b/docs/doctrees/notebooks/tutorial_graph.doctree deleted file mode 100644 index 220efec..0000000 Binary files a/docs/doctrees/notebooks/tutorial_graph.doctree and /dev/null differ diff --git a/docs/ect_on_graphs.html b/docs/ect_on_graphs.html index 105116d..dac2781 100644 --- a/docs/ect_on_graphs.html +++ b/docs/ect_on_graphs.html @@ -26,8 +26,8 @@ - - + + @@ -59,26 +59,44 @@
                                • 2. Modules
                                    -
                                  • 2.1. Embedded graphs
                                      -
                                    • EmbeddedGraph
                                      • +
                                    • 2.1. Embedded Complex
                                      • -
                                    • 2.2. Embedded CW complex
                                    • 3. Tutorials
                                        -
                                      • 3.1. Tutorial: ECT for Embedded Graphs
                                          -
                                        • 3.1.1. Basic Usage
                                          • +
                                        • 3.1. Tutorial: ECT for Embedded Cell Complexes
                                          • -
                                        • 3.2. Tutorial: ECT for CW complexes
                                          • +
                                        • 3.2. Tutorial for exact ECT computation
                                        • 3.3. ECT on Matisse’s “The Parakeet and the Mermaid” @@ -126,12 +144,12 @@
                                          -
                                          -

                                          2.3. ECT on Graphs

                                          +
                                          +

                                          2.3. ECT on Graphs

                                          -
                                          -class ect.ect_graph.ECT(directions=None, num_dirs=None, num_thresh=None, bound_radius=None, thresholds=None, dtype=<class 'numpy.int32'>)[source]
                                          -

                                          A class to calculate the Euler Characteristic Transform (ECT) from an input EmbeddedGraph or EmbeddedCW.

                                          +
                                          +class ect.ect.ECT(directions=None, num_dirs=None, num_thresh=None, bound_radius=None, thresholds=None, dtype=<class 'numpy.int32'>)[source]
                                          +

                                          A class to calculate the Euler Characteristic Transform (ECT) from an input EmbeddedComplex.

                                          The result is a matrix where entry M[i,j] is \(\chi(K_{a_i})\) for the direction \(\omega_j\) where \(a_i\) is the ith entry in self.thresholds, and \(\omega_j\) is the ith entry in self.thetas.

                                          Attributes
                                          @@ -147,8 +165,8 @@
                                          -
                                          -__init__(directions=None, num_dirs=None, num_thresh=None, bound_radius=None, thresholds=None, dtype=<class 'numpy.int32'>)[source]
                                          +
                                          +__init__(directions=None, num_dirs=None, num_thresh=None, bound_radius=None, thresholds=None, dtype=<class 'numpy.int32'>)[source]

                                          Initialize ECT calculator with either a Directions object or sampling parameters

                                          Parameters:
                                          @@ -165,13 +183,13 @@
                                          -
                                          -calculate(graph, theta=None, override_bound_radius=None)[source]
                                          +
                                          +calculate(graph, theta=None, override_bound_radius=None)[source]

                                          Calculate Euler Characteristic Transform (ECT) for a given graph and direction theta

                                          Parameters:
                                            -

                                          • graph (EmbeddedGraph/EmbeddedCW) – The input graph to calculate the ECT for.

                                            • +

                                          • graph (EmbeddedComplex) – The input complex to calculate the ECT for.

                                          • theta (float) – The angle in \([0,2\pi]\) for the direction to calculate the ECT.

                                          • override_bound_radius (float) – If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a positive float \(R\) where the ECC will be computed at thresholds in \([-R,R]\). Default is None.

                                          @@ -180,21 +198,71 @@
                                          -
                                          -static shape_descriptor(simplex_counts_list)[source]
                                          +
                                          +static shape_descriptor(simplex_counts_list)[source]

                                          Calculate shape descriptor from simplex counts (Euler characteristic)

                                          +
                                          +
                                          +class ect.sect.SECT(directions=None, num_dirs=None, num_thresh=None, bound_radius=None, thresholds=None, dtype=<class 'numpy.float32'>)[source]
                                          +

                                          A class to calculate the Smooth Euler Characteristic Transform (SECT). +Inherits from ECT and applies smoothing to the final result.

                                          +
                                          +
                                          +__init__(directions=None, num_dirs=None, num_thresh=None, bound_radius=None, thresholds=None, dtype=<class 'numpy.float32'>)[source]
                                          +

                                          Initialize SECT calculator with smoothing parameter

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • directions – Optional pre-configured Directions object

                                            • +

                                          • num_dirs – Number of directions to sample (ignored if directions provided)

                                            • +

                                          • num_thresh – Number of threshold values (required if directions not provided)

                                            • +

                                          • bound_radius – Optional radius for bounding circle

                                            • +

                                          • thresholds – Optional array of thresholds

                                            • +

                                          • dtype – Data type for output array

                                            • +
                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +calculate(graph, theta=None, override_bound_radius=None)[source]
                                          +

                                          Calculate Smooth Euler Characteristic Transform (SECT)

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • graph – The input graph to calculate the SECT for

                                            • +

                                          • theta – The angle in [0,2π] for the direction to calculate the SECT

                                            • +

                                          • override_bound_radius – Optional override for bounding radius

                                            • +
                                          +
                                          +
                                          Returns:
                                          +

                                          +
                                          The smoothed transform result containing the matrix,

                                          directions, and thresholds

                                          +
                                          +
                                          +

                                          +
                                          +
                                          Return type:
                                          +

                                          ECTResult

                                          +
                                          +
                                          +
                                          + +
                                          +
                                          diff --git a/docs/embed_complex.html b/docs/embed_complex.html new file mode 100644 index 0000000..5b7da88 --- /dev/null +++ b/docs/embed_complex.html @@ -0,0 +1,689 @@ + + + + + + + 2.1. Embedded Complex — ect 0.1.5 documentation + + + + + + + + + + + + + + + + + + + + + + +
                                          + + +
                                          + +
                                          +
                                          +
                                          + +
                                          +
                                          +
                                          +
                                          + +
                                          +

                                          2.1. Embedded Complex

                                          +

                                          The EmbeddedComplex class is a unified representation for embedded cell complexes supporting arbitrary dimensional cells.

                                          +
                                          +

                                          2.1.1. Overview

                                          +

                                          EmbeddedComplex combines and extends the functionality of the previous EmbeddedGraph and EmbeddedCW classes into a single, more powerful interface. It supports:

                                          +
                                            +

                                          • 0-cells (vertices): Points embedded in Euclidean space

                                            • +

                                          • 1-cells (edges): Line segments between vertices

                                            • +

                                          • k-cells for k ≥ 2: Higher dimensional cells (faces, volumes, etc.)

                                            • +
                                          +
                                          +
                                          +

                                          2.1.2. Key Features

                                          +
                                            +

                                          • Unified Interface: Single class for all cell complex operations

                                            • +

                                          • Arbitrary Dimensions: Support for k-cells of any dimension k ≥ 0

                                            • +

                                          • Modular Validation: Pluggable validation system for embedding properties

                                            • +

                                          • Backward Compatible: EmbeddedGraph and EmbeddedCW remain as aliases

                                            • +
                                          +
                                          +
                                          +

                                          2.1.3. Basic Usage

                                          +
                                          from ect import EmbeddedComplex
+
+# Create a complex
+K = EmbeddedComplex()
+
+# Add vertices
+K.add_node("A", [0, 0])
+K.add_node("B", [1, 0])
+K.add_node("C", [0.5, 1])
+
+# Add edges
+K.add_edge("A", "B")
+K.add_edge("B", "C")
+K.add_edge("C", "A")
+
+# Add a 2-cell (face)
+K.add_face(["A", "B", "C"])
+
+# Or use the general method for any dimension
+K.add_cell(["A", "B", "C"], dim=2)
+
                                          +
                                          +
                                          +
                                          +

                                          2.1.4. API Reference

                                          +
                                          +
                                          +class ect.embed_complex.EmbeddedComplex(validate_embedding=False, embedding_tol=1e-10)[source]
                                          +

                                          Bases: Graph

                                          +

                                          A unified class to represent an embedded cell complex with cells of arbitrary dimension.

                                          +

                                          This combines the functionality of EmbeddedGraph and EmbeddedCW, supporting: +- 0-cells (vertices) with embedded coordinates +- 1-cells (edges) +- k-cells for k >= 2 (faces, volumes, etc.)

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • validate_embedding (bool) – If True, automatically validate embedding properties +when adding cells. Default: False

                                            • +

                                          • embedding_tol (float) – Tolerance for geometric validation. Default: 1e-10

                                            • +
                                          +
                                          +
                                          +
                                          +
                                          +coord_matrix
                                          +

                                          np.ndarray +A matrix of embedded coordinates for each vertex

                                          +
                                          + +
                                          +
                                          +node_list
                                          +

                                          list +A list of node names

                                          +
                                          + +
                                          +
                                          +node_to_index
                                          +

                                          dict +A dictionary mapping node ids to their index in the coord_matrix

                                          +
                                          + +
                                          +
                                          +dim
                                          +

                                          int +The dimension of the embedded coordinates

                                          +
                                          + +
                                          +
                                          +cells
                                          +

                                          dict +Dictionary mapping dimension k to list of k-cells, where each k-cell +is represented as a tuple of vertex indices

                                          +
                                          + +
                                          +
                                          +validate_embedding
                                          +

                                          bool +Whether to automatically validate embedding properties

                                          +
                                          + +
                                          +
                                          +embedding_tol
                                          +

                                          float +Tolerance for geometric validation

                                          +
                                          + +
                                          +
                                          +__init__(validate_embedding=False, embedding_tol=1e-10)[source]
                                          +

                                          Initialize a graph with edges, name, or graph attributes.

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty +graph is created. The data can be an edge list, or any +NetworkX graph object. If the corresponding optional Python +packages are installed the data can also be a 2D NumPy array, a +SciPy sparse array, or a PyGraphviz graph.

                                            • +

                                          • attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.

                                            • +
                                          +
                                          +
                                          +
                                          +

                                          See also

                                          +

                                          convert

                                          +
                                          +

                                          Examples

                                          +
                                          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+>>> G = nx.Graph(name="my graph")
+>>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
+>>> G = nx.Graph(e)
+
                                          +
                                          +

                                          Arbitrary graph attribute pairs (key=value) may be assigned

                                          +
                                          >>> G = nx.Graph(e, day="Friday")
+>>> G.graph
+{'day': 'Friday'}
+
                                          +
                                          +
                                          + +
                                          +
                                          +property coord_matrix
                                          +

                                          Return the N x D coordinate matrix

                                          +
                                          + +
                                          +
                                          +property dim
                                          +

                                          Return the dimension of the embedded coordinates

                                          +
                                          + +
                                          +
                                          +property node_list
                                          +

                                          Return ordered list of node names.

                                          +
                                          + +
                                          +
                                          +property node_to_index
                                          +

                                          Return a dictionary mapping node ids to their index in the coord_matrix

                                          +
                                          + +
                                          +
                                          +property position_dict
                                          +

                                          Return a dictionary mapping node ids to their coordinates

                                          +
                                          + +
                                          +
                                          +property edge_indices
                                          +

                                          Return edges as array of index pairs

                                          +
                                          + +
                                          +
                                          +property faces
                                          +

                                          Return list of 2-cells (faces) for backward compatibility

                                          +
                                          + +
                                          +
                                          +add_node(node_id, coord)[source]
                                          +

                                          Add a vertex to the complex.

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • node_id – Identifier for the node

                                            • +

                                          • coord – Array-like coordinates for the node

                                            • +
                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +add_nodes_from_dict(nodes_with_coords)[source]
                                          +
                                          + +
                                          +
                                          +add_nodes_from(nodes_with_coords)[source]
                                          +

                                          Add multiple nodes.

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • nodes_for_adding (iterable container) – A container of nodes (list, dict, set, etc.). +OR +A container of (node, attribute dict) tuples. +Node attributes are updated using the attribute dict.

                                            • +

                                          • attr (keyword arguments, optional (default= no attributes)) – Update attributes for all nodes in nodes. +Node attributes specified in nodes as a tuple take +precedence over attributes specified via keyword arguments.

                                            • +
                                          +
                                          +
                                          +
                                          +

                                          See also

                                          +

                                          add_node

                                          +
                                          +

                                          Notes

                                          +

                                          When adding nodes from an iterator over the graph you are changing, +a RuntimeError can be raised with message: +RuntimeError: dictionary changed size during iteration. This +happens when the graph’s underlying dictionary is modified during +iteration. To avoid this error, evaluate the iterator into a separate +object, e.g. by using list(iterator_of_nodes), and pass this +object to G.add_nodes_from.

                                          +

                                          Examples

                                          +
                                          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+>>> G.add_nodes_from("Hello")
+>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+>>> G.add_nodes_from(K3)
+>>> sorted(G.nodes(), key=str)
+[0, 1, 2, 'H', 'e', 'l', 'o']
+
                                          +
                                          +

                                          Use keywords to update specific node attributes for every node.

                                          +
                                          >>> G.add_nodes_from([1, 2], size=10)
+>>> G.add_nodes_from([3, 4], weight=0.4)
+
                                          +
                                          +

                                          Use (node, attrdict) tuples to update attributes for specific nodes.

                                          +
                                          >>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
+>>> G.nodes[1]["size"]
+11
+>>> H = nx.Graph()
+>>> H.add_nodes_from(G.nodes(data=True))
+>>> H.nodes[1]["size"]
+11
+
                                          +
                                          +

                                          Evaluate an iterator over a graph if using it to modify the same graph

                                          +
                                          >>> G = nx.Graph([(0, 1), (1, 2), (3, 4)])
+>>> # wrong way - will raise RuntimeError
+>>> # G.add_nodes_from(n + 1 for n in G.nodes)
+>>> # correct way
+>>> G.add_nodes_from(list(n + 1 for n in G.nodes))
+
                                          +
                                          +
                                          + +
                                          +
                                          +add_edge(node_id1, node_id2)[source]
                                          +

                                          Add an edge (1-cell) between two nodes

                                          +
                                          + +
                                          +
                                          +add_cell(cell_vertices, dim=None, check=None, embedding_tol=None)[source]
                                          +

                                          Add a k-cell to the complex.

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • cell_vertices – List of vertex identifiers that form the cell

                                            • +

                                          • dim – Dimension of the cell. If None, inferred as len(cell_vertices) - 1

                                            • +

                                          • check – Whether to validate the cell embedding. If None, uses self.validate_embedding

                                            • +

                                          • embedding_tol – Tolerance for geometric validation. If None, uses self.embedding_tol

                                            • +
                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +enable_embedding_validation(tol=1e-10)[source]
                                          +

                                          Enable automatic embedding validation for all subsequent cell additions.

                                          +
                                          +
                                          Parameters:
                                          +

                                          tol – Tolerance for geometric validation

                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +disable_embedding_validation()[source]
                                          +

                                          Disable automatic embedding validation for all subsequent cell additions.

                                          +
                                          + +
                                          +
                                          +get_validator()[source]
                                          +

                                          Get the embedding validator instance for advanced configuration.

                                          +
                                          +
                                          Returns:
                                          +

                                          The EmbeddingValidator instance used by this complex

                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +set_validation_rules(rules)[source]
                                          +

                                          Set custom validation rules.

                                          +
                                          +
                                          Parameters:
                                          +

                                          rules – List of ValidationRule instances

                                          +
                                          +
                                          Returns:
                                          +

                                          Self for method chaining

                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +add_face(face, check=None)[source]
                                          +

                                          Add a 2-cell (face) to the complex. Provided for backward compatibility.

                                          +
                                          + +
                                          +
                                          +add_faces_from(faces)[source]
                                          +

                                          Add multiple 2-cells (faces) to the complex.

                                          +
                                          + +
                                          +
                                          +get_coord(node_id)[source]
                                          +

                                          Return the coordinates of a node

                                          +
                                          + +
                                          +
                                          +set_coord(node_id, new_coords)[source]
                                          +

                                          Set the coordinates of a node

                                          +
                                          + +
                                          +
                                          +add_cycle(coord_matrix)[source]
                                          +

                                          Add nodes in a cyclic pattern from coordinate matrix

                                          +
                                          + +
                                          +
                                          +get_center(method='bounding_box')[source]
                                          +

                                          Calculate center of coordinates

                                          +
                                          + +
                                          +
                                          +get_bounding_box()[source]
                                          +

                                          Get (min, max) for each dimension

                                          +
                                          + +
                                          +
                                          +get_bounding_radius(center_type='bounding_box')[source]
                                          +

                                          Get radius of minimal bounding sphere

                                          +
                                          + +
                                          +
                                          +get_normal_angle_matrix(edges_only=False, decimals=None)[source]
                                          +

                                          Optimized angle matrix computation using vectorized operations.

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • edges_only – Only compute angles between connected vertices

                                            • +

                                          • decimals – Round angles to specified decimal places

                                            • +
                                          +
                                          +
                                          Returns:
                                          +

                                          NaN-filled matrix with pair angles +vertex_labels: Ordered node identifiers

                                          +
                                          +
                                          Return type:
                                          +

                                          angle_matrix

                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +get_normal_angles(edges_only=False, decimals=6)[source]
                                          +

                                          Optimized angle dictionary construction using NumPy grouping.

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • edges_only – Only include edge-connected pairs

                                            • +

                                          • decimals – Round angles to specified decimal places

                                            • +
                                          +
                                          +
                                          Returns:
                                          +

                                          Dictionary mapping rounded angles to vertex pairs

                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +transform_coordinates(center_type='bounding_box', projection_type='pca')[source]
                                          +

                                          Transform coordinates center and orientation

                                          +
                                          + +
                                          +
                                          +center_coordinates(center_type='mean')[source]
                                          +
                                          + +
                                          +
                                          +scale_coordinates(radius=1)[source]
                                          +

                                          Scale coordinates to fit within given radius

                                          +
                                          + +
                                          +
                                          +project_coordinates(projection_type='pca')[source]
                                          +

                                          Project coordinates using a function

                                          +
                                          + +
                                          +
                                          +pca_projection(target_dim=2)[source]
                                          +

                                          Dimensionality reduction using PCA

                                          +
                                          + +
                                          +
                                          +validate_plot_parameters()[source]
                                          +
                                          + +
                                          +
                                          +plot_faces(ax=None, **kwargs)[source]
                                          +

                                          Plots the 2-cells (faces) of the complex.

                                          +
                                          +
                                          Parameters:
                                          +
                                            +

                                          • ax (matplotlib.axes.Axes) – The axes to plot the graph on. If None, a new figure is created.

                                            • +

                                          • **kwargs – Additional keyword arguments to pass to the ax.fill function.

                                            • +
                                          +
                                          +
                                          Returns:
                                          +

                                          +
                                          matplotlib.axes.Axes

                                          The axes object with the plot.

                                          +
                                          +
                                          +

                                          +
                                          +
                                          +
                                          + +
                                          +
                                          +plot(*args, **kwargs)[source]
                                          +
                                          + +
                                          + +
                                          +
                                          +ect.embed_complex.EmbeddedGraph
                                          +

                                          alias of EmbeddedComplex

                                          +
                                          + +
                                          +
                                          +ect.embed_complex.EmbeddedCW
                                          +

                                          alias of EmbeddedComplex

                                          +
                                          + +
                                          +
                                          + + +
                                          +
                                          + +
                                          +
                                          +
                                          +
                                          + + + + + + + \ No newline at end of file diff --git a/docs/embed_cw.html b/docs/embed_cw.html deleted file mode 100644 index a80c28d..0000000 --- a/docs/embed_cw.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - 2.2. Embedded CW complex — ect 0.1.5 documentation - - - - - - - - - - - - - - - - - - - - - - - - -
                                          - - -
                                          - -
                                          -
                                          -
                                          - -
                                          -
                                          -
                                          -
                                          - -
                                          -

                                          2.2. Embedded CW complex

                                          -
                                          -
                                          -class ect.embed_cw.EmbeddedCW[source]
                                          -

                                          A class to represent a straight-line-embedded CW complex. We assume that the coordinates for the embedding of the vertices are given, the 1-skeleton is in fact a graph (so not as general as a full CW complex) with straight line embeddings, and 2-Cells are the interior of the shape outlined by its boundary edges.

                                          -

                                          Faces should be passed in as a list of vertices, where the vertices are in order around the face. However, the ECT function will likely still work if the ordering is different. The drawing functions however might look strange. Note the class does not (yet?) check to make sure the face is valid, i.e. is a cycle in the graph, and bounds a region in the plane.

                                          -
                                          -
                                          -__init__()[source]
                                          -

                                          Initializes an empty EmbeddedCW object.

                                          -
                                          - -
                                          -
                                          -add_from_embedded_graph(G)[source]
                                          -

                                          Adds the edges and coordinates from an EmbeddedGraph object to the EmbeddedCW object.

                                          -
                                          -
                                          Parameters:
                                          -

                                          G (EmbeddedGraph) – The EmbeddedGraph object to add from.

                                          -
                                          -
                                          -
                                          - -
                                          -
                                          -add_face(*args, **kwargs)
                                          -
                                          - -
                                          -
                                          -add_faces_from(faces)[source]
                                          -

                                          Adds a list of faces to the graph.

                                          -
                                          -
                                          Parameters:
                                          -

                                          faces (list) – A list of faces to add.

                                          -
                                          -
                                          -
                                          - -
                                          -
                                          -plot_faces(theta=None, ax=None, **kwargs)[source]
                                          -

                                          Plots the faces of the graph in the direction of theta.

                                          -
                                          -
                                          Parameters:
                                          -
                                            -

                                          • theta (float) – The angle in \([0,2\pi]\) for the direction to sort the edges.

                                            • -

                                          • ax (matplotlib.axes.Axes) – The axes to plot the graph on. If None, a new figure is created.

                                            • -

                                          • **kwargs – Additional keyword arguments to pass to the ax.fill function.

                                            • -
                                          -
                                          -
                                          Returns:
                                          -

                                          -
                                          matplotlib.axes.Axes

                                          The axes object with the plot.

                                          -
                                          -
                                          -

                                          -
                                          -
                                          -
                                          - -
                                          -
                                          -plot(bounding_circle=False, bounding_center_type='bounding_box', color_nodes_theta=None, ax=None, with_labels=True, node_size=300, edge_color='gray', elev=25, azim=-60, face_color='lightblue', face_alpha=0.3, **kwargs)[source]
                                          -

                                          Visualize the embedded CW complex in 2D or 3D, including faces

                                          -
                                          - -
                                          - -
                                          - - -
                                          -
                                          - -
                                          -
                                          -
                                          -
                                          - - - - - - - \ No newline at end of file diff --git a/docs/embed_graph.html b/docs/embed_graph.html deleted file mode 100644 index b93f18a..0000000 --- a/docs/embed_graph.html +++ /dev/null @@ -1,538 +0,0 @@ - - - - - - - 2.1. Embedded graphs — ect 0.1.5 documentation - - - - - - - - - - - - - - - - - - - - - - -
                                          - - -
                                          - -
                                          -
                                          -
                                          - -
                                          -
                                          -
                                          -
                                          - -
                                          -

                                          2.1. Embedded graphs

                                          -
                                          -
                                          -class ect.embed_graph.EmbeddedGraph[source]
                                          -

                                          A class to represent a graph with embedded coordinates for each vertex with simple geometric graph operations.

                                          -
                                          -
                                          Attributes
                                          -
                                          graphnx.Graph

                                          a NetworkX graph object

                                          -
                                          -
                                          coord_matrixnp.ndarray

                                          a matrix of embedded coordinates for each vertex

                                          -
                                          -
                                          node_listlist

                                          a list of node names

                                          -
                                          -
                                          node_to_indexdict

                                          a dictionary mapping node ids to their index in the coord_matrix

                                          -
                                          -
                                          dimint

                                          the dimension of the embedded coordinates

                                          -
                                          -
                                          -
                                          -
                                          -
                                          -
                                          -__init__()[source]
                                          -

                                          Initialize a graph with edges, name, or graph attributes.

                                          -
                                          -
                                          Parameters:
                                          -
                                            -

                                          • incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty -graph is created. The data can be an edge list, or any -NetworkX graph object. If the corresponding optional Python -packages are installed the data can also be a 2D NumPy array, a -SciPy sparse array, or a PyGraphviz graph.

                                            • -

                                          • attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.

                                            • -
                                          -
                                          -
                                          -
                                          -

                                          See also

                                          -

                                          convert

                                          -
                                          -

                                          Examples

                                          -
                                          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
->>> G = nx.Graph(name="my graph")
->>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
->>> G = nx.Graph(e)
-
                                          -
                                          -

                                          Arbitrary graph attribute pairs (key=value) may be assigned

                                          -
                                          >>> G = nx.Graph(e, day="Friday")
->>> G.graph
-{'day': 'Friday'}
-
                                          -
                                          -
                                          - -
                                          -
                                          -property coord_matrix
                                          -

                                          Return the N x D coordinate matrix

                                          -
                                          - -
                                          -
                                          -property dim
                                          -

                                          Return the dimension of the embedded coordinates

                                          -
                                          - -
                                          -
                                          -property node_list
                                          -

                                          Return ordered list of node names.

                                          -
                                          - -
                                          -
                                          -property node_to_index
                                          -

                                          Return a dictionary mapping node ids to their index in the coord_matrix

                                          -
                                          - -
                                          -
                                          -property position_dict
                                          -

                                          Return a dictionary mapping node ids to their coordinates

                                          -
                                          - -
                                          -
                                          -property edge_indices
                                          -

                                          Return edges as array of index pairs

                                          -
                                          - -
                                          -
                                          -add_node(*args, **kwargs)[source]
                                          -

                                          Add a single node node_for_adding and update node attributes.

                                          -
                                          -
                                          Parameters:
                                          -
                                            -

                                          • node_for_adding (node) – A node can be any hashable Python object except None.

                                            • -

                                          • attr (keyword arguments, optional) – Set or change node attributes using key=value.

                                            • -
                                          -
                                          -
                                          -
                                          -

                                          See also

                                          -

                                          add_nodes_from

                                          -
                                          -

                                          Examples

                                          -
                                          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
->>> G.add_node(1)
->>> G.add_node("Hello")
->>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
->>> G.add_node(K3)
->>> G.number_of_nodes()
-3
-
                                          -
                                          -

                                          Use keywords set/change node attributes:

                                          -
                                          >>> G.add_node(1, size=10)
->>> G.add_node(3, weight=0.4, UTM=("13S", 382871, 3972649))
-
                                          -
                                          -

                                          Notes

                                          -

                                          A hashable object is one that can be used as a key in a Python -dictionary. This includes strings, numbers, tuples of strings -and numbers, etc.

                                          -

                                          On many platforms hashable items also include mutables such as -NetworkX Graphs, though one should be careful that the hash -doesn’t change on mutables.

                                          -
                                          - -
                                          -
                                          -add_nodes_from_dict(nodes_with_coords)[source]
                                          -
                                          - -
                                          -
                                          -add_nodes_from(nodes_with_coords)[source]
                                          -

                                          Add multiple nodes.

                                          -
                                          -
                                          Parameters:
                                          -
                                            -

                                          • nodes_for_adding (iterable container) – A container of nodes (list, dict, set, etc.). -OR -A container of (node, attribute dict) tuples. -Node attributes are updated using the attribute dict.

                                            • -

                                          • attr (keyword arguments, optional (default= no attributes)) – Update attributes for all nodes in nodes. -Node attributes specified in nodes as a tuple take -precedence over attributes specified via keyword arguments.

                                            • -
                                          -
                                          -
                                          -
                                          -

                                          See also

                                          -

                                          add_node

                                          -
                                          -

                                          Notes

                                          -

                                          When adding nodes from an iterator over the graph you are changing, -a RuntimeError can be raised with message: -RuntimeError: dictionary changed size during iteration. This -happens when the graph’s underlying dictionary is modified during -iteration. To avoid this error, evaluate the iterator into a separate -object, e.g. by using list(iterator_of_nodes), and pass this -object to G.add_nodes_from.

                                          -

                                          Examples

                                          -
                                          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
->>> G.add_nodes_from("Hello")
->>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
->>> G.add_nodes_from(K3)
->>> sorted(G.nodes(), key=str)
-[0, 1, 2, 'H', 'e', 'l', 'o']
-
                                          -
                                          -

                                          Use keywords to update specific node attributes for every node.

                                          -
                                          >>> G.add_nodes_from([1, 2], size=10)
->>> G.add_nodes_from([3, 4], weight=0.4)
-
                                          -
                                          -

                                          Use (node, attrdict) tuples to update attributes for specific nodes.

                                          -
                                          >>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
->>> G.nodes[1]["size"]
-11
->>> H = nx.Graph()
->>> H.add_nodes_from(G.nodes(data=True))
->>> H.nodes[1]["size"]
-11
-
                                          -
                                          -

                                          Evaluate an iterator over a graph if using it to modify the same graph

                                          -
                                          >>> G = nx.Graph([(0, 1), (1, 2), (3, 4)])
->>> # wrong way - will raise RuntimeError
->>> # G.add_nodes_from(n + 1 for n in G.nodes)
->>> # correct way
->>> G.add_nodes_from(list(n + 1 for n in G.nodes))
-
                                          -
                                          -
                                          - -
                                          -
                                          -get_coord(*args, **kwargs)[source]
                                          -
                                          - -
                                          -
                                          -set_coord(*args, **kwargs)[source]
                                          -
                                          - -
                                          -
                                          -add_cycle(coord_matrix)[source]
                                          -

                                          Add nodes in a cyclic pattern from coordinate matrix

                                          -
                                          - -
                                          -
                                          -get_center(method='bounding_box')[source]
                                          -

                                          Calculate center of coordinates

                                          -
                                          - -
                                          -
                                          -get_bounding_box()[source]
                                          -

                                          Get (min, max) for each dimension

                                          -
                                          - -
                                          -
                                          -get_bounding_radius(center_type='bounding_box')[source]
                                          -

                                          Get radius of minimal bounding sphere

                                          -
                                          - -
                                          -
                                          -get_normal_angle_matrix(edges_only=False, decimals=None)[source]
                                          -

                                          Optimized angle matrix computation using vectorized operations.

                                          -
                                          -
                                          Parameters:
                                          -
                                            -

                                          • edges_only – Only compute angles between connected vertices

                                            • -

                                          • decimals – Round angles to specified decimal places

                                            • -
                                          -
                                          -
                                          Returns:
                                          -

                                          NaN-filled matrix with pair angles -vertex_labels: Ordered node identifiers

                                          -
                                          -
                                          Return type:
                                          -

                                          angle_matrix

                                          -
                                          -
                                          -
                                          - -
                                          -
                                          -get_normal_angles(edges_only=False, decimals=6)[source]
                                          -

                                          Optimized angle dictionary construction using NumPy grouping.

                                          -
                                          -
                                          Parameters:
                                          -
                                            -

                                          • edges_only – Only include edge-connected pairs

                                            • -

                                          • decimals – Round angles to specified decimal places

                                            • -
                                          -
                                          -
                                          Returns:
                                          -

                                          Dictionary mapping rounded angles to vertex pairs

                                          -
                                          -
                                          -
                                          - -
                                          -
                                          -transform_coordinates(center_type='bounding_box', projection_type='pca')[source]
                                          -

                                          Transform coordinates center and orientation

                                          -
                                          - -
                                          -
                                          -center_coordinates(center_type='mean')[source]
                                          -
                                          - -
                                          -
                                          -scale_coordinates(radius=1)[source]
                                          -

                                          Scale coordinates to fit within given radius

                                          -
                                          - -
                                          -
                                          -project_coordinates(projection_type='pca')[source]
                                          -

                                          Project coordinates using a function

                                          -
                                          - -
                                          -
                                          -pca_projection(target_dim=2)[source]
                                          -

                                          Dimensionality reduction using PCA

                                          -
                                          - -
                                          -
                                          -add_edge(*args, **kwargs)[source]
                                          -

                                          Add an edge between u and v.

                                          -

                                          The nodes u and v will be automatically added if they are -not already in the graph.

                                          -

                                          Edge attributes can be specified with keywords or by directly -accessing the edge’s attribute dictionary. See examples below.

                                          -
                                          -
                                          Parameters:
                                          -
                                            -

                                          • u_of_edge (nodes) – Nodes can be, for example, strings or numbers. -Nodes must be hashable (and not None) Python objects.

                                            • -

                                          • v_of_edge (nodes) – Nodes can be, for example, strings or numbers. -Nodes must be hashable (and not None) Python objects.

                                            • -

                                          • attr (keyword arguments, optional) – Edge data (or labels or objects) can be assigned using -keyword arguments.

                                            • -
                                          -
                                          -
                                          -
                                          -

                                          See also

                                          -
                                          -
                                          add_edges_from

                                          add a collection of edges

                                          -
                                          -
                                          -
                                          -

                                          Notes

                                          -

                                          Adding an edge that already exists updates the edge data.

                                          -

                                          Many NetworkX algorithms designed for weighted graphs use -an edge attribute (by default weight) to hold a numerical value.

                                          -

                                          Examples

                                          -

                                          The following all add the edge e=(1, 2) to graph G:

                                          -
                                          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
->>> e = (1, 2)
->>> G.add_edge(1, 2)  # explicit two-node form
->>> G.add_edge(*e)  # single edge as tuple of two nodes
->>> G.add_edges_from([(1, 2)])  # add edges from iterable container
-
                                          -
                                          -

                                          Associate data to edges using keywords:

                                          -
                                          >>> G.add_edge(1, 2, weight=3)
->>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
-
                                          -
                                          -

                                          For non-string attribute keys, use subscript notation.

                                          -
                                          >>> G.add_edge(1, 2)
->>> G[1][2].update({0: 5})
->>> G.edges[1, 2].update({0: 5})
-
                                          -
                                          -
                                          - -
                                          -
                                          -validate_plot_parameters()[source]
                                          -

                                          Decorator to validate plot method parameters

                                          -
                                          - -
                                          -
                                          -plot(*args, **kwargs)[source]
                                          -
                                          - -
                                          - -
                                          - - -
                                          -
                                          - -
                                          -
                                          -
                                          -
                                          - - - - - - - \ No newline at end of file diff --git a/docs/genindex.html b/docs/genindex.html index bca3533..7f70e5f 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -54,26 +54,44 @@
                                      • 2. Modules
                                          -
                                        • 2.1. Embedded graphs
                                            -
                                          • EmbeddedGraph
                                            • +
                                          • 2.1. Embedded Complex
                                            • -
                                          • 2.2. Embedded CW complex
                                          • 3. Tutorials
                                              -
                                            • 3.1. Tutorial: ECT for Embedded Graphs
                                            • 2. Modules
                                                -
                                              • 2.1. Embedded graphs
                                                  -
                                                • EmbeddedGraph
                                                  • +
                                                • 2.1. Embedded Complex
                                                  • -
                                                • 2.2. Embedded CW complex
                                                • 3. Tutorials
                                                    -
                                                  • 3.1. Tutorial: ECT for Embedded Graphs
                                                  • 2. Modules
                                                      -
                                                    • 2.1. Embedded graphs
                                                        -
                                                      • EmbeddedGraph
                                                        • +
                                                      • 2.1. Embedded Complex
                                                        • -
                                                      • 2.2. Embedded CW complex
                                                      • 3. Tutorials
                                                          -
                                                        • 3.1. Tutorial: ECT for Embedded Graphs
                                                        • 2. Modules
                                                            -
                                                          • 2.1. Embedded graphs
                                                              -
                                                            • EmbeddedGraph
                                                              • +
                                                            • 2.1. Embedded Complex
                                                              • -
                                                            • 2.2. Embedded CW complex
                                                            • 3. Tutorials
                                                                -
                                                              • 3.1. Tutorial: ECT for Embedded Graphs
                                                              • 2. Modules
                                                                  -
                                                                • 2.1. Embedded graphs
                                                                    -
                                                                  • EmbeddedGraph
                                                                    • +
                                                                  • 2.1. Embedded Complex
                                                                    • -
                                                                  • 2.2. Embedded CW complex
                                                                  • 3. Tutorials
                                                                      -
                                                                    • 3.1. Tutorial: ECT for Embedded Graphs
                                                                    • 2. Modules
                                                                        -
                                                                      • 2.1. Embedded graphs
                                                                          -
                                                                        • EmbeddedGraph
                                                                          • +
                                                                        • 2.1. Embedded Complex
                                                                          • -
                                                                        • 2.2. Embedded CW complex
                                                                        • 3. Tutorials
                                                                            -
                                                                          • 3.1. Tutorial: ECT for Embedded Graphs
                                                                              -
                                                                            • 3.1.1. Basic Usage
                                                                              • +
                                                                            • 3.1. Tutorial: ECT for Embedded Cell Complexes
                                                                              • -
                                                                            • 3.2. Tutorial: ECT for CW complexes
                                                                              • +
                                                                            • 3.2. Tutorial for exact ECT computation
                                                                            • 3.3. ECT on Matisse’s “The Parakeet and the Mermaid” @@ -131,8 +149,8 @@

                                                                              3.3. ECT on Matisse’s “The Parakeet and the Mermaid”

                                                                              Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse’s 1952 “The Parakeet and the Mermaid”.

                                                                              matisse.jpg

                                                                              -
                                                                              -
                                                                              [1]:
+
                                                                              
+
                                                                              [ ]:
 
                                                                              
 
                                                                              
 
                                                                              # -----------------
@@ -156,7 +174,10 @@ 
                                                                              3.3. ECT on Matisse’s “The Parakeet
 # ---------------------------
 # The ECT packages we'll use
 # ---------------------------
-from ect import ECT, EmbeddedGraph  # for calculating ECTs
+from ect import ECT, EmbeddedComplex  # for calculating ECTs
+# Note: EmbeddedGraph is now unified into EmbeddedComplex
+# For backward compatibility, you can still use:
+# from ect import EmbeddedGraph
 
 # Simple data paths
 data_dir = "outlines/"
@@ -167,47 +188,23 @@ 
                                                                              3.3. ECT on Matisse’s “The Parakeet
 ]
 file_names.sort()
 print(f"There are {len(file_names)} files in the directory")
-
                                                                              
-
                                                                              
-
                                                                              
-
                                                                              
-
                                                                              
+
                                                                              -
                                                                              -
                                                                              -There are 150 files in the directory
-

                                                                              We’ve taken care of the preprocessing in advance by extracting out the shapes from the image. You can download these outlines here: outlines.zip.

                                                                              Matisse Numbered

                                                                              -
                                                                              -
                                                                              [2]:
+
                                                                              
+
                                                                              [ ]:
 
                                                                              
 
                                                                              
 
                                                                              i = 3
 shape = np.loadtxt(data_dir + file_names[i])
 # shape = normalize(shape)
-G = EmbeddedGraph()
+G = EmbeddedComplex()  # Using the unified EmbeddedComplex class
 G.add_cycle(shape)
 G.plot(with_labels=False, node_size=10)
-
                                                                              
-
                                                                              
-
                                                                              
-
                                                                              
-
                                                                              [2]:
 
                                                                              
 
                                                                              
-
                                                                              
-
                                                                              -<Axes: >
-
                                                                              
-
                                                                              
-
                                                                              
-
                                                                              
-
                                                                              
-
                                                                              
-../../_images/notebooks_Matisse_example_matisse_3_1.png
-
                                                                              
 
                                                                              
 
                                                                              We’re going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons.
                                                                              
 
                                                                              
@@ -268,18 +265,18 @@ 
                                                                              3.3. ECT on Matisse’s “The Parakeet
 
                                                                              
 
                                                                              Let’s just make a data loader with all of this for ease in a bit.
                                                                              
 
                                                                              
-
                                                                              [5]:
+
                                                                              [ ]:
 
                                                                              
 
                                                                              
 
                                                                              def matisse_ect(filename, ect):
     shape = np.loadtxt(data_dir + filename)
-    G = EmbeddedGraph()
+    G = EmbeddedComplex()  # Using the unified EmbeddedComplex class
     G.add_cycle(shape)
     G.transform_coordinates(projection_type="pca")
     G.scale_coordinates(1)
     result = ect.calculate(G)
     return result
-

                                                                              
+
                                                                              
 
                                                                              
 
                                                                              
 
                                                                              And now we can load in all the outlines, compute their ECT and store it in a 3D array.
                                                                              
@@ -394,7 +391,7 @@ 
                                                                              3.3.1. Acknowledgements
-        
+
                                                                              diff --git a/docs/notebooks/Matisse/example_matisse.ipynb b/docs/notebooks/Matisse/example_matisse.ipynb index 6f0c4f8..ccef47b 100644 --- a/docs/notebooks/Matisse/example_matisse.ipynb +++ b/docs/notebooks/Matisse/example_matisse.ipynb @@ -18,17 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 150 files in the directory\n" - ] - } - ], + "outputs": [], "source": [ "# -----------------\n", "# Standard imports\n", @@ -51,7 +43,10 @@ "# ---------------------------\n", "# The ECT packages we'll use\n", "# ---------------------------\n", - "from ect import ECT, EmbeddedGraph # for calculating ECTs\n", + "from ect import ECT, EmbeddedComplex # for calculating ECTs\n", + "# Note: EmbeddedGraph is now unified into EmbeddedComplex\n", + "# For backward compatibility, you can still use:\n", + "# from ect import EmbeddedGraph\n", "\n", "# Simple data paths\n", "data_dir = \"outlines/\"\n", @@ -61,7 +56,7 @@ " f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n", "]\n", "file_names.sort()\n", - "print(f\"There are {len(file_names)} files in the directory\")\n" + "print(f\"There are {len(file_names)} files in the directory\")" ] }, { @@ -77,37 +72,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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                                                                              3.1. Tutorial: ECT for Embedded Cell Complexes

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                                                                              This tutorial will walk you through using the ECT package. Particularly we will show the features of EmbeddedComplex class for computing the Euler Characteristic Transform on complexes with arbitrary dimensional cells.

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                                                                              The EmbeddedComplex class combines and extends the functionality of the previous EmbeddedGraph and EmbeddedCW classes, supporting: - 0-cells (vertices) with embedded coordinates - 1-cells (edges) - k-cells for k ≥ 2 (faces, volumes, and higher-dimensional cells).

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                                                                              import numpy as np
+import matplotlib.pyplot as plt
+from ect import EmbeddedComplex, ECT, Directions
+from ect.utils.examples import create_example_graph, create_example_cw, create_example_3d_complex
+
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                                                                              +

                                                                              3.1.1. Basic Usage: Creating Simple Complexes

                                                                              +
                                                                              +

                                                                              3.1.1.1. Example 1: Graph (1-skeleton)

                                                                              +

                                                                              Let’s start with a simple triangle graph (for legacy users this can be equivalently done using EmbeddedGraph).

                                                                              +
                                                                              +
                                                                              [ ]:
+
                                                                              +
                                                                              +
                                                                              K = EmbeddedComplex()
+
+K.add_node('A', [0, 0])
+K.add_node('B', [1, 0])
+K.add_node('C', [0.5, 0.866])
+
+K.add_edge('A', 'B')
+K.add_edge('B', 'C')
+K.add_edge('C', 'A')
+
+#using built-in plotting function along with matplotlib
+fig, ax = plt.subplots(figsize=(6, 6))
+K.plot(ax=ax, with_labels=True, node_size=400)
+ax.set_title('Simple Triangle Graph\n(0-cells: vertices, 1-cells: edges)')
+plt.show()
+
+#print some information about the complex
+print(f"Number of nodes: {len(K.nodes())}")
+print(f"Number of edges: {len(K.edges())}")
+print(f"Embedding dimension: {K.dim}")
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +../_images/notebooks_Tutorial-EmbeddedComplex_3_0.png +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +Number of nodes: 3
+Number of edges: 3
+Embedding dimension: 2
+
                                                                              +
                                                                              +

                                                                              Now let’s add a 2-cell (face) to fill in the triangle:

                                                                              +
                                                                              +
                                                                              [ ]:
+
                                                                              +
                                                                              +
                                                                              K.add_face(['A', 'B', 'C'])
+
+fig, ax = plt.subplots(figsize=(6, 6))
+K.plot(ax=ax, with_labels=True, node_size=400, face_alpha=0.3, face_color='lightblue')
+ax.set_title('Triangle with Face\n(0-cells: vertices, 1-cells: edges, 2-cells: faces)')
+plt.show()
+
+print(f"Number of 2-cells (faces): {len(K.faces)}")
+print(f"Faces: {K.faces}")
+print(f"Internal cells dictionary: {dict(K.cells)}")
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +../_images/notebooks_Tutorial-EmbeddedComplex_5_0.png +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +Number of 2-cells (faces): 1
+Faces: [('A', 'B', 'C')]
+Internal cells dictionary: {2: [(0, 1, 2)]}
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +

                                                                              3.1.2. Adding Cells of Arbitrary Dimension

                                                                              +

                                                                              The key new feature is the ability to add cells of any dimension:

                                                                              +
                                                                              +
                                                                              [18]:
+
                                                                              +
                                                                              +
                                                                              # Create a 3D tetrahedron with 0, 1, 2, and 3-cells
+K_tetra = EmbeddedComplex()
+
+# Add vertices (0-cells)
+vertices = {
+    'A': [0, 0, 0],
+    'B': [1, 0, 0],
+    'C': [0.5, 0.866, 0],
+    'D': [0.5, 0.289, 0.816]
+}
+
+for name, coord in vertices.items():
+    K_tetra.add_node(name, coord)
+
+# Add edges (1-cells) - all pairs
+edges = [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]
+K_tetra.add_edges_from(edges)
+
+# Add faces (2-cells) - all triangular faces
+faces = [['A', 'B', 'C'], ['A', 'B', 'D'], ['A', 'C', 'D'], ['B', 'C', 'D']]
+for face in faces:
+    K_tetra.add_cell(face, dim=2)  # Explicitly specify dimension
+
+# Add volume (3-cell) - the entire tetrahedron
+K_tetra.add_cell(['A', 'B', 'C', 'D'], dim=3)
+
+# Plot the tetrahedron
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+K_tetra.plot(ax=ax, face_alpha=0.3, face_color='cyan', node_size=100)
+ax.set_title('Tetrahedron with All Cell Types\n0-cells: 4, 1-cells: 6, 2-cells: 4, 3-cells: 1')
+plt.show()
+
+# Display cell counts
+for dim in sorted(K_tetra.cells.keys()):
+    print(f"Dimension {dim}: {len(K_tetra.cells[dim])} cells")
+
+print(f"\nTotal embedding dimension: {K_tetra.dim}")
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +../_images/notebooks_Tutorial-EmbeddedComplex_7_0.png +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +Dimension 2: 4 cells
+Dimension 3: 1 cells
+
+Total embedding dimension: 3
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +

                                                                              3.1.3. ECT Computation with Higher-Dimensional Cells

                                                                              +

                                                                              The ECT computation now properly includes all cell dimensions in the Euler characteristic calculation:

                                                                              +

                                                                              χ = Σ(-1)^k × |k-cells below threshold|

                                                                              +

                                                                              Let’s see how this works:

                                                                              +
                                                                              +
                                                                              [12]:
+
                                                                              +
                                                                              +
                                                                              # Compute ECT for the tetrahedron
+ect = ECT(num_dirs=8, num_thresh=20)
+result = ect.calculate(K_tetra)
+
+print(f"ECT result shape: {result.shape}")
+print(f"Directions: {len(result.directions)} directions in {K_tetra.dim}D")
+print(f"Thresholds: {len(result.thresholds)} threshold values")
+
+# Plot the ECT matrix
+fig, ax = plt.subplots(figsize=(10, 6))
+result.plot()
+plt.title('ECT of Tetrahedron (includes 3-cells in computation)')
+plt.show()
+
+# Show a single direction
+
+single_direction = ECT(num_thresh=20, directions=Directions.from_vectors([[1, 0, 0]])).calculate(K_tetra)
+fig, ax = plt.subplots(figsize=(8, 5))
+ax.plot(single_direction.thresholds, single_direction[0], 'b-', marker='o', linewidth=2)
+ax.set_xlabel('Threshold')
+ax.set_ylabel('Euler Characteristic')
+ax.set_title('ECT Curve for Single Direction (v=[1, 0, 0])')
+ax.grid(True, alpha=0.3)
+plt.show()
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +ECT result shape: (8, 20)
+Directions: 8 directions in 3D
+Thresholds: 20 threshold values
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +../_images/notebooks_Tutorial-EmbeddedComplex_9_1.png +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +../_images/notebooks_Tutorial-EmbeddedComplex_9_2.png +
                                                                              +
                                                                              +

                                                                              You can also restrict self-intersections by using the ‘validate_embeddings’ argument. Currently without checks we can add a node inside of our tetrahedron.

                                                                              +
                                                                              +
                                                                              [26]:
+
                                                                              +
                                                                              +
                                                                              K_valid = K_tetra.copy()
+
+K_valid.add_node('E', [0.5, 0.289, 0.204])
+K_valid.add_cell(['E', 'B'], dim=1)
+
+
+
+# Display cell counts
+print("4D Simplex Cell Counts:")
+for dim in sorted(K_valid.cells.keys()):
+    print(f"  {dim}-cells: {len(K_valid.cells[dim])}")
+
+# Plot (showing 3D projection)
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+K_valid.plot(ax=ax, face_alpha=0.1, node_size=80)
+ax.set_title('4D Simplex (5 vertices, cells up to dimension 4)')
+plt.show()
+
+# Compute ECT
+ect_4d = ECT(num_dirs=6, num_thresh=15)
+result_4d = ect_4d.calculate(K_valid)
+
+fig, ax = plt.subplots(figsize=(10, 6))
+result_4d.plot()
+plt.title('ECT of 4D Simplex\n(alternating sum includes all dimensions 0-4)')
+plt.show()
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +Unexpected exception formatting exception. Falling back to standard exception
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +Traceback (most recent call last):
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3548, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "/var/folders/81/3x5xj5kx4ys30p1c2z55bhbw0000gn/T/ipykernel_80902/4266954845.py", line 1, in <module>
+    K_valid = K_tetra.copy()
+              ^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/networkx/classes/graph.py", line 1642, in copy
+    G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
+  File "/Users/yayub/Code/ect/src/ect/embed_complex.py", line 177, in add_nodes_from
+  File "/Users/yayub/Code/ect/src/ect/embed_complex.py", line 120, in wrapper
+    )
+
+  File "/Users/yayub/Code/ect/src/ect/embed_complex.py", line 142, in wrapper
+    def wrapper(self, *args, **kwargs):
+               ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/Code/ect/src/ect/embed_complex.py", line 157, in add_node
+    return wrapper
+        ^^^^^^^^^^^
+TypeError: float() argument must be a string or a real number, not 'dict'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 2142, in showtraceback
+    stb = self.InteractiveTB.structured_traceback(
+          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py", line 1435, in structured_traceback
+    return FormattedTB.structured_traceback(
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py", line 1326, in structured_traceback
+    return VerboseTB.structured_traceback(
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py", line 1173, in structured_traceback
+    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py", line 1088, in format_exception_as_a_whole
+    frames.append(self.format_record(record))
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py", line 970, in format_record
+    frame_info.lines, Colors, self.has_colors, lvals
+    ^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py", line 792, in lines
+    return self._sd.lines
+           ^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py", line 144, in cached_property_wrapper
+    value = obj.__dict__[self.func.__name__] = self.func(obj)
+                                               ^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py", line 734, in lines
+    pieces = self.included_pieces
+             ^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py", line 144, in cached_property_wrapper
+    value = obj.__dict__[self.func.__name__] = self.func(obj)
+                                               ^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py", line 681, in included_pieces
+    pos = scope_pieces.index(self.executing_piece)
+                             ^^^^^^^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py", line 144, in cached_property_wrapper
+    value = obj.__dict__[self.func.__name__] = self.func(obj)
+                                               ^^^^^^^^^^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py", line 660, in executing_piece
+    return only(
+           ^^^^^
+  File "/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/executing/executing.py", line 116, in only
+    raise NotOneValueFound('Expected one value, found 0')
+executing.executing.NotOneValueFound: Expected one value, found 0
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +

                                                                              3.1.4. Understanding ECT with Projection Visualization

                                                                              +

                                                                              Let’s visualize how the ECT computation works by showing projection values:

                                                                              +
                                                                              +
                                                                              [7]:
+
                                                                              +
                                                                              +
                                                                              # Create a simple 2D example for visualization
+K_viz = EmbeddedComplex()
+
+# Square with center point
+K_viz.add_node('A', [-1, -1])
+K_viz.add_node('B', [1, -1])
+K_viz.add_node('C', [1, 1])
+K_viz.add_node('D', [-1, 1])
+K_viz.add_node('E', [0, 0])  # center
+
+# Add edges
+edges = [('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A'),  # boundary
+         ('A', 'E'), ('B', 'E'), ('C', 'E'), ('D', 'E')]   # to center
+K_viz.add_edges_from(edges)
+
+# Add triangular faces
+faces = [['A', 'B', 'E'], ['B', 'C', 'E'], ['C', 'D', 'E'], ['D', 'A', 'E']]
+for face in faces:
+    K_viz.add_face(face)
+
+# Visualization function
+def plot_with_projections(K, theta, ax):
+    """Plot complex with nodes colored by projection values"""
+    direction = np.array([np.sin(theta), -np.cos(theta)])
+    node_projections = np.dot(K.coord_matrix, direction)
+
+    # Plot edges
+    for u, v in K.edges():
+        u_idx = K.node_to_index[u]
+        v_idx = K.node_to_index[v]
+        x = [K.coord_matrix[u_idx, 0], K.coord_matrix[v_idx, 0]]
+        y = [K.coord_matrix[u_idx, 1], K.coord_matrix[v_idx, 1]]
+        ax.plot(x, y, 'k-', alpha=0.5, linewidth=1)
+
+    # Plot faces with transparency
+    for face_indices in K.cells.get(2, []):
+        face_coords = K.coord_matrix[list(face_indices)]
+        face_projection = np.max(node_projections[list(face_indices)])
+        ax.fill(face_coords[:, 0], face_coords[:, 1],
+               alpha=0.3, color=plt.cm.Blues(0.5))
+
+    # Plot nodes colored by projection
+    scatter = ax.scatter(K.coord_matrix[:, 0], K.coord_matrix[:, 1],
+                        c=node_projections, cmap='plasma', s=300,
+                        edgecolors='black', linewidth=2, zorder=10)
+
+    # Add node labels with projection values
+    for i, node in enumerate(K.node_list):
+        ax.annotate(f'{node}\n({node_projections[i]:.2f})',
+                   (K.coord_matrix[i, 0], K.coord_matrix[i, 1]),
+                   ha='center', va='center', fontsize=9, fontweight='bold',
+                   bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.8))
+
+    # Direction arrow
+    ax.arrow(0, 0, direction[0]*0.7, direction[1]*0.7,
+             head_width=0.1, head_length=0.1, fc='red', ec='red', linewidth=2)
+
+    ax.set_xlim(-2, 2)
+    ax.set_ylim(-2, 2)
+    ax.set_aspect('equal')
+    ax.grid(True, alpha=0.3)
+    ax.set_title(f'θ = {theta*180/np.pi:.0f}°')
+
+    return scatter
+
+# Show projections in multiple directions
+fig, axes = plt.subplots(2, 3, figsize=(15, 10))
+axes = axes.flatten()
+
+thetas = np.linspace(0, 2*np.pi, 6, endpoint=False)
+for ax, theta in zip(axes, thetas):
+    scatter = plot_with_projections(K_viz, theta, ax)
+
+# Add shared colorbar
+fig.subplots_adjust(right=0.9)
+cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])
+fig.colorbar(scatter, cax=cbar_ax, label='Node Projection Values')
+
+plt.suptitle('Complex Colored by Projection Values in Different Directions\n' +
+            'Red arrows show projection direction, faces use max vertex projection',
+            fontsize=14)
+plt.show()
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +../_images/notebooks_Tutorial-EmbeddedComplex_13_0.png +
                                                                              +
                                                                              +
                                                                              +
                                                                              +

                                                                              3.1.5. Comparing Graph vs. Complex ECT

                                                                              +

                                                                              Let’s compare ECT results for the same geometry with and without 2-cells:

                                                                              +
                                                                              +
                                                                              [8]:
+
                                                                              +
                                                                              +
                                                                              # Create two versions: graph only vs. complex with faces
+K_graph = EmbeddedComplex()
+K_complex = EmbeddedComplex()
+
+# Same vertices and edges for both
+vertices = {'A': [0, 0], 'B': [2, 0], 'C': [1, 1.732]}
+edges = [('A', 'B'), ('B', 'C'), ('C', 'A')]
+
+for K in [K_graph, K_complex]:
+    for name, coord in vertices.items():
+        K.add_node(name, coord)
+    K.add_edges_from(edges)
+
+# Add face only to the complex version
+K_complex.add_face(['A', 'B', 'C'])
+
+# Compute ECT for both
+ect = ECT(num_dirs=20, num_thresh=30)
+result_graph = ect.calculate(K_graph)
+result_complex = ect.calculate(K_complex)
+
+# Plot comparison
+fig, axes = plt.subplots(2, 2, figsize=(15, 10))
+
+# Visualizations
+K_graph.plot(ax=axes[0,0], with_labels=True, node_size=400)
+axes[0,0].set_title('Graph Only (no 2-cells)')
+
+K_complex.plot(ax=axes[0,1], with_labels=True, node_size=400,
+               face_alpha=0.3, face_color='lightblue')
+axes[0,1].set_title('Complex with 2-cell (face)')
+
+# ECT comparisons
+result_graph.plot(ax=axes[1,0])
+axes[1,0].set_title('ECT: Graph Only\n(χ = vertices - edges)')
+
+result_complex.plot(ax=axes[1,1])
+axes[1,1].set_title('ECT: Complex with Face\n(χ = vertices - edges + faces)')
+
+plt.tight_layout()
+plt.show()
+
+# Show numerical difference
+print("ECT value ranges:")
+print(f"Graph only: [{result_graph.min()}, {result_graph.max()}]")
+print(f"With face:  [{result_complex.min()}, {result_complex.max()}]")
+print(f"\nThe face contributes +1 to the Euler characteristic when included.")
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +../_images/notebooks_Tutorial-EmbeddedComplex_15_0.png +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +
                                                                              +ECT value ranges:
+Graph only: [0, 1]
+With face:  [0, 1]
+
+The face contributes +1 to the Euler characteristic when included.
+
                                                                              +
                                                                              +
                                                                              +
                                                                              + + +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +
                                                                              +
                                                                              + + + + + + + \ No newline at end of file diff --git a/docs/notebooks/Tutorial-EmbeddedComplex.ipynb b/docs/notebooks/Tutorial-EmbeddedComplex.ipynb new file mode 100644 index 0000000..1738074 --- /dev/null +++ b/docs/notebooks/Tutorial-EmbeddedComplex.ipynb @@ -0,0 +1,624 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: ECT for Embedded Cell Complexes\n", + "\n", + "This tutorial will walk you through using the `ECT` package. Particularly we will show the features of `EmbeddedComplex` class for computing the Euler Characteristic Transform on complexes with arbitrary dimensional cells.\n", + "\n", + "The `EmbeddedComplex` class combines and extends the functionality of the previous `EmbeddedGraph` and `EmbeddedCW` classes, supporting:\n", + "- **0-cells** (vertices) with embedded coordinates\n", + "- **1-cells** (edges)\n", + "- **k-cells** for k ≥ 2 (faces, volumes, and higher-dimensional cells).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ect import EmbeddedComplex, ECT, Directions\n", + "from ect.utils.examples import create_example_graph, create_example_cw, create_example_3d_complex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Usage: Creating Simple Complexes\n", + "\n", + "### Example 1: Graph (1-skeleton)\n", + "\n", + "Let's start with a simple triangle graph (for legacy users this can be equivalently done using `EmbeddedGraph`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
                                                                              " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of nodes: 3\n", + "Number of edges: 3\n", + "Embedding dimension: 2\n" + ] + } + ], + "source": [ + "K = EmbeddedComplex()\n", + "\n", + "K.add_node('A', [0, 0])\n", + "K.add_node('B', [1, 0])\n", + "K.add_node('C', [0.5, 0.866])\n", + "\n", + "K.add_edge('A', 'B')\n", + "K.add_edge('B', 'C')\n", + "K.add_edge('C', 'A')\n", + "\n", + "#using built-in plotting function along with matplotlib\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400)\n", + "ax.set_title('Simple Triangle Graph\\n(0-cells: vertices, 1-cells: edges)')\n", + "plt.show()\n", + "\n", + "#print some information about the complex\n", + "print(f\"Number of nodes: {len(K.nodes())}\")\n", + "print(f\"Number of edges: {len(K.edges())}\")\n", + "print(f\"Embedding dimension: {K.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's add a 2-cell (face) to fill in the triangle:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
                                                                              " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of 2-cells (faces): 1\n", + "Faces: [('A', 'B', 'C')]\n", + "Internal cells dictionary: {2: [(0, 1, 2)]}\n" + ] + } + ], + "source": [ + "K.add_face(['A', 'B', 'C'])\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "K.plot(ax=ax, with_labels=True, node_size=400, face_alpha=0.3, face_color='lightblue')\n", + "ax.set_title('Triangle with Face\\n(0-cells: vertices, 1-cells: edges, 2-cells: faces)')\n", + "plt.show()\n", + "\n", + "print(f\"Number of 2-cells (faces): {len(K.faces)}\")\n", + "print(f\"Faces: {K.faces}\")\n", + "print(f\"Internal cells dictionary: {dict(K.cells)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Cells of Arbitrary Dimension\n", + "\n", + "The key new feature is the ability to add cells of any dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
                                                                              " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimension 2: 4 cells\n", + "Dimension 3: 1 cells\n", + "\n", + "Total embedding dimension: 3\n" + ] + } + ], + "source": [ + "# Create a 3D tetrahedron with 0, 1, 2, and 3-cells\n", + "K_tetra = EmbeddedComplex()\n", + "\n", + "# Add vertices (0-cells)\n", + "vertices = {\n", + " 'A': [0, 0, 0],\n", + " 'B': [1, 0, 0],\n", + " 'C': [0.5, 0.866, 0],\n", + " 'D': [0.5, 0.289, 0.816] \n", + "}\n", + "\n", + "for name, coord in vertices.items():\n", + " K_tetra.add_node(name, coord)\n", + "\n", + "# Add edges (1-cells) - all pairs\n", + "edges = [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]\n", + "K_tetra.add_edges_from(edges)\n", + "\n", + "# Add faces (2-cells) - all triangular faces\n", + "faces = [['A', 'B', 'C'], ['A', 'B', 'D'], ['A', 'C', 'D'], ['B', 'C', 'D']]\n", + "for face in faces:\n", + " K_tetra.add_cell(face, dim=2) # Explicitly specify dimension\n", + "\n", + "# Add volume (3-cell) - the entire tetrahedron\n", + "K_tetra.add_cell(['A', 'B', 'C', 'D'], dim=3)\n", + "\n", + "# Plot the tetrahedron\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "K_tetra.plot(ax=ax, face_alpha=0.3, face_color='cyan', node_size=100)\n", + "ax.set_title('Tetrahedron with All Cell Types\\n0-cells: 4, 1-cells: 6, 2-cells: 4, 3-cells: 1')\n", + "plt.show()\n", + "\n", + "# Display cell counts\n", + "for dim in sorted(K_tetra.cells.keys()):\n", + " print(f\"Dimension {dim}: {len(K_tetra.cells[dim])} cells\")\n", + " \n", + "print(f\"\\nTotal embedding dimension: {K_tetra.dim}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ECT Computation with Higher-Dimensional Cells\n", + "\n", + "The ECT computation now properly includes all cell dimensions in the Euler characteristic calculation:\n", + "\n", + "**χ = Σ(-1)^k × |k-cells below threshold|**\n", + "\n", + "Let's see how this works:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ECT result shape: (8, 20)\n", + "Directions: 8 directions in 3D\n", + "Thresholds: 20 threshold values\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
                                                                              " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute ECT for the tetrahedron\n", + "ect = ECT(num_dirs=8, num_thresh=20)\n", + "result = ect.calculate(K_tetra)\n", + "\n", + "print(f\"ECT result shape: {result.shape}\")\n", + "print(f\"Directions: {len(result.directions)} directions in {K_tetra.dim}D\")\n", + "print(f\"Thresholds: {len(result.thresholds)} threshold values\")\n", + "\n", + "# Plot the ECT matrix\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "result.plot()\n", + "plt.title('ECT of Tetrahedron (includes 3-cells in computation)')\n", + "plt.show()\n", + "\n", + "# Show a single direction\n", + "\n", + "single_direction = ECT(num_thresh=20, directions=Directions.from_vectors([[1, 0, 0]])).calculate(K_tetra)\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "ax.plot(single_direction.thresholds, single_direction[0], 'b-', marker='o', linewidth=2)\n", + "ax.set_xlabel('Threshold')\n", + "ax.set_ylabel('Euler Characteristic')\n", + "ax.set_title('ECT Curve for Single Direction (v=[1, 0, 0])')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also restrict self-intersections by using the 'validate_embeddings' argument. Currently without checks we can add a node inside of our tetrahedron." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unexpected exception formatting exception. Falling back to standard exception\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3548, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/81/3x5xj5kx4ys30p1c2z55bhbw0000gn/T/ipykernel_80902/4266954845.py\", line 1, in \n", + " K_valid = K_tetra.copy()\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/networkx/classes/graph.py\", line 1642, in copy\n", + " G.add_nodes_from((n, d.copy()) for n, d in self._node.items())\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 177, in add_nodes_from\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 120, in wrapper\n", + " )\n", + " \n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 142, in wrapper\n", + " def wrapper(self, *args, **kwargs):\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/Code/ect/src/ect/embed_complex.py\", line 157, in add_node\n", + " return wrapper\n", + " ^^^^^^^^^^^\n", + "TypeError: float() argument must be a string or a real number, not 'dict'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 2142, in showtraceback\n", + " stb = self.InteractiveTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1435, in structured_traceback\n", + " return FormattedTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1326, in structured_traceback\n", + " return VerboseTB.structured_traceback(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1173, in structured_traceback\n", + " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1088, in format_exception_as_a_whole\n", + " frames.append(self.format_record(record))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 970, in format_record\n", + " frame_info.lines, Colors, self.has_colors, lvals\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 792, in lines\n", + " return self._sd.lines\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 734, in lines\n", + " pieces = self.included_pieces\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 681, in included_pieces\n", + " pos = scope_pieces.index(self.executing_piece)\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/stack_data/core.py\", line 660, in executing_piece\n", + " return only(\n", + " ^^^^^\n", + " File \"/Users/yayub/miniconda3/envs/dataexp/lib/python3.11/site-packages/executing/executing.py\", line 116, in only\n", + " raise NotOneValueFound('Expected one value, found 0')\n", + "executing.executing.NotOneValueFound: Expected one value, found 0\n" + ] + } + ], + "source": [ + "K_valid = K_tetra.copy()\n", + "\n", + "K_valid.add_node('E', [0.5, 0.289, 0.204])\n", + "K_valid.add_cell(['E', 'B'], dim=1)\n", + "\n", + "\n", + "\n", + "# Display cell counts\n", + "print(\"4D Simplex Cell Counts:\")\n", + "for dim in sorted(K_valid.cells.keys()):\n", + " print(f\" {dim}-cells: {len(K_valid.cells[dim])}\")\n", + "\n", + "# Plot (showing 3D projection)\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "K_valid.plot(ax=ax, face_alpha=0.1, node_size=80)\n", + "ax.set_title('4D Simplex (5 vertices, cells up to dimension 4)')\n", + "plt.show()\n", + "\n", + "# Compute ECT\n", + "ect_4d = ECT(num_dirs=6, num_thresh=15)\n", + "result_4d = ect_4d.calculate(K_valid)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "result_4d.plot()\n", + "plt.title('ECT of 4D Simplex\\n(alternating sum includes all dimensions 0-4)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding ECT with Projection Visualization\n", + "\n", + "Let's visualize how the ECT computation works by showing projection values:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
                                                                              " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a simple 2D example for visualization\n", + "K_viz = EmbeddedComplex()\n", + "\n", + "# Square with center point\n", + "K_viz.add_node('A', [-1, -1])\n", + "K_viz.add_node('B', [1, -1])\n", + "K_viz.add_node('C', [1, 1])\n", + "K_viz.add_node('D', [-1, 1])\n", + "K_viz.add_node('E', [0, 0]) # center\n", + "\n", + "# Add edges\n", + "edges = [('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A'), # boundary\n", + " ('A', 'E'), ('B', 'E'), ('C', 'E'), ('D', 'E')] # to center\n", + "K_viz.add_edges_from(edges)\n", + "\n", + "# Add triangular faces\n", + "faces = [['A', 'B', 'E'], ['B', 'C', 'E'], ['C', 'D', 'E'], ['D', 'A', 'E']]\n", + "for face in faces:\n", + " K_viz.add_face(face)\n", + "\n", + "# Visualization function\n", + "def plot_with_projections(K, theta, ax):\n", + " \"\"\"Plot complex with nodes colored by projection values\"\"\"\n", + " direction = np.array([np.sin(theta), -np.cos(theta)])\n", + " node_projections = np.dot(K.coord_matrix, direction)\n", + " \n", + " # Plot edges\n", + " for u, v in K.edges():\n", + " u_idx = K.node_to_index[u]\n", + " v_idx = K.node_to_index[v]\n", + " x = [K.coord_matrix[u_idx, 0], K.coord_matrix[v_idx, 0]]\n", + " y = [K.coord_matrix[u_idx, 1], K.coord_matrix[v_idx, 1]]\n", + " ax.plot(x, y, 'k-', alpha=0.5, linewidth=1)\n", + " \n", + " # Plot faces with transparency\n", + " for face_indices in K.cells.get(2, []):\n", + " face_coords = K.coord_matrix[list(face_indices)]\n", + " face_projection = np.max(node_projections[list(face_indices)])\n", + " ax.fill(face_coords[:, 0], face_coords[:, 1], \n", + " alpha=0.3, color=plt.cm.Blues(0.5))\n", + " \n", + " # Plot nodes colored by projection\n", + " scatter = ax.scatter(K.coord_matrix[:, 0], K.coord_matrix[:, 1], \n", + " c=node_projections, cmap='plasma', s=300, \n", + " edgecolors='black', linewidth=2, zorder=10)\n", + " \n", + " # Add node labels with projection values\n", + " for i, node in enumerate(K.node_list):\n", + " ax.annotate(f'{node}\\n({node_projections[i]:.2f})', \n", + " (K.coord_matrix[i, 0], K.coord_matrix[i, 1]),\n", + " ha='center', va='center', fontsize=9, fontweight='bold',\n", + " bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.8))\n", + " \n", + " # Direction arrow\n", + " ax.arrow(0, 0, direction[0]*0.7, direction[1]*0.7,\n", + " head_width=0.1, head_length=0.1, fc='red', ec='red', linewidth=2)\n", + " \n", + " ax.set_xlim(-2, 2)\n", + " ax.set_ylim(-2, 2)\n", + " ax.set_aspect('equal')\n", + " ax.grid(True, alpha=0.3)\n", + " ax.set_title(f'θ = {theta*180/np.pi:.0f}°')\n", + " \n", + " return scatter\n", + "\n", + "# Show projections in multiple directions\n", + "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", + "axes = axes.flatten()\n", + "\n", + "thetas = np.linspace(0, 2*np.pi, 6, endpoint=False)\n", + "for ax, theta in zip(axes, thetas):\n", + " scatter = plot_with_projections(K_viz, theta, ax)\n", + "\n", + "# Add shared colorbar\n", + "fig.subplots_adjust(right=0.9)\n", + "cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])\n", + "fig.colorbar(scatter, cax=cbar_ax, label='Node Projection Values')\n", + "\n", + "plt.suptitle('Complex Colored by Projection Values in Different Directions\\n' + \n", + " 'Red arrows show projection direction, faces use max vertex projection', \n", + " fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Graph vs. Complex ECT\n", + "\n", + "Let's compare ECT results for the same geometry with and without 2-cells:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
                                                                              " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ECT value ranges:\n", + "Graph only: [0, 1]\n", + "With face: [0, 1]\n", + "\n", + "The face contributes +1 to the Euler characteristic when included.\n" + ] + } + ], + "source": [ + "# Create two versions: graph only vs. complex with faces\n", + "K_graph = EmbeddedComplex()\n", + "K_complex = EmbeddedComplex()\n", + "\n", + "# Same vertices and edges for both\n", + "vertices = {'A': [0, 0], 'B': [2, 0], 'C': [1, 1.732]}\n", + "edges = [('A', 'B'), ('B', 'C'), ('C', 'A')]\n", + "\n", + "for K in [K_graph, K_complex]:\n", + " for name, coord in vertices.items():\n", + " K.add_node(name, coord)\n", + " K.add_edges_from(edges)\n", + "\n", + "# Add face only to the complex version\n", + "K_complex.add_face(['A', 'B', 'C'])\n", + "\n", + "# Compute ECT for both\n", + "ect = ECT(num_dirs=20, num_thresh=30)\n", + "result_graph = ect.calculate(K_graph)\n", + "result_complex = ect.calculate(K_complex)\n", + "\n", + "# Plot comparison\n", + "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n", + "\n", + "# Visualizations\n", + "K_graph.plot(ax=axes[0,0], with_labels=True, node_size=400)\n", + "axes[0,0].set_title('Graph Only (no 2-cells)')\n", + "\n", + "K_complex.plot(ax=axes[0,1], with_labels=True, node_size=400, \n", + " face_alpha=0.3, face_color='lightblue')\n", + "axes[0,1].set_title('Complex with 2-cell (face)')\n", + "\n", + "# ECT comparisons\n", + "result_graph.plot(ax=axes[1,0])\n", + "axes[1,0].set_title('ECT: Graph Only\\n(χ = vertices - edges)')\n", + "\n", + "result_complex.plot(ax=axes[1,1])\n", + "axes[1,1].set_title('ECT: Complex with Face\\n(χ = vertices - edges + faces)')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Show numerical difference\n", + "print(\"ECT value ranges:\")\n", + "print(f\"Graph only: [{result_graph.min()}, {result_graph.max()}]\")\n", + "print(f\"With face: [{result_complex.min()}, {result_complex.max()}]\")\n", + "print(f\"\\nThe face contributes +1 to the Euler characteristic when included.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dataexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/notebooks/Tutorial-ExactECT.html b/docs/notebooks/Tutorial-ExactECT.html index 57bc074..0d1cf12 100644 --- a/docs/notebooks/Tutorial-ExactECT.html +++ b/docs/notebooks/Tutorial-ExactECT.html @@ -4,7 +4,7 @@ - Tutorial for exact ECT computation — ect 0.1.5 documentation + 3.2. Tutorial for exact ECT computation — ect 0.1.5 documentation @@ -26,7 +26,9 @@ - + + + @@ -50,7 +52,7 @@
                                                                              diff --git a/docs/validation.html b/docs/validation.html new file mode 100644 index 0000000..c0a321a --- /dev/null +++ b/docs/validation.html @@ -0,0 +1,1547 @@ + + + + + + + 2.2. Validation System — ect 0.1.5 documentation + + + + + + + + + + + + + + + + + + + + + + +
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                                                                              2.2. Validation System

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                                                                              The validation system provides modular, extensible validation for embedded cell complexes to ensure they represent proper embeddings in Euclidean space.

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                                                                              2.2.1. Overview

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                                                                              The validation system distinguishes between two types of rules:

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                                                                              • Structural Rules (always checked): Basic requirements like vertex counts and dimension validity

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                                                                              • Geometric Rules (optional): Embedding properties like non-intersecting edges and faces

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                                                                              2.2.2. Architecture

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                                                                              The validation system consists of several components:

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                                                                              1. Base Classes: Abstract interfaces for validation rules and results

                                                                                2. +

                                                                              2. Validation Rules: Concrete implementations for specific validation checks

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                                                                              3. Validator: Main orchestrator that manages and applies rules

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                                                                              2.2.3. Validation Rules

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                                                                              2.2.3.1. Structural Rules (Always Enforced)

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                                                                              • DimensionValidityRule: Ensures cell dimensions are non-negative

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                                                                              • VertexCountRule: Validates correct vertex counts for cell dimensions

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                                                                                • 0-cells must have exactly 1 vertex

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                                                                                • 1-cells must have exactly 2 vertices

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                                                                                • k-cells (k ≥ 2) must have at least 3 vertices

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                                                                              2.2.3.2. Geometric Rules (Optional)

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                                                                              • EdgeInteriorRule: Ensures no vertices lie on edge interiors

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                                                                              • FaceInteriorRule: Ensures no vertices lie inside face interiors

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                                                                              • SelfIntersectionRule: Validates that face edges don’t self-intersect

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                                                                              • BoundaryEdgeRule: Ensures required boundary edges exist for faces

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                                                                              2.2.4. Usage

                                                                              +
                                                                              from ect import EmbeddedComplex
+
+# Enable validation during construction
+K = EmbeddedComplex(validate_embedding=True)
+
+# Or enable/disable later
+K.enable_embedding_validation(tol=1e-10)
+K.disable_embedding_validation()
+
+# Override per operation
+K.add_cell(vertices, dim=2, check=True)
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +

                                                                              2.2.5. Custom Validation Rules

                                                                              +

                                                                              You can create custom validation rules by inheriting from ValidationRule:

                                                                              +
                                                                              from ect.validation import ValidationRule, ValidationResult
+
+class MyCustomRule(ValidationRule):
+    @property
+    def name(self) -> str:
+        return "My Custom Rule"
+    
+    @property
+    def is_structural(self) -> bool:
+        return False  # Geometric rule
+    
+    def applies_to_dimension(self, dim: int) -> bool:
+        return dim == 2  # Only for 2-cells
+    
+    def validate(self, cell_coords, all_coords, cell_indices, all_indices, dim):
+        # Your validation logic here
+        if some_condition:
+            return ValidationResult.valid()
+        else:
+            return ValidationResult.invalid("Validation failed")
+
+# Add to validator
+K.get_validator().add_rule(MyCustomRule())
+
                                                                              +
                                                                              +
                                                                              +
                                                                              +

                                                                              2.2.6. API Reference

                                                                              +
                                                                              +

                                                                              2.2.6.1. Main Module

                                                                              +

                                                                              Embedding validation system for EmbeddedComplex.

                                                                              +

                                                                              This module provides a flexible, extensible system for validating that +cell complexes represent proper embeddings in Euclidean space.

                                                                              +
                                                                              +
                                                                              +class ect.validation.EmbeddingValidator(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Main validator that orchestrates multiple validation rules.

                                                                              +

                                                                              This class manages a collection of validation rules and applies them +to cells based on their dimension and the configured rule set.

                                                                              +
                                                                              +
                                                                              +__init__(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Initialize the embedding validator.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • tolerance – Default tolerance for geometric validation

                                                                                • +

                                                                              • edge_checker – Function to check if an edge exists between two vertex indices

                                                                                • +
                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +add_rule(rule)[source]
                                                                              +

                                                                              Add a custom validation rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              rule – ValidationRule to add

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +remove_rule(rule_class)[source]
                                                                              +

                                                                              Remove all rules of a specific type.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              rule_class – Class of rules to remove

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +set_tolerance(tolerance)[source]
                                                                              +

                                                                              Set tolerance for all rules.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              tolerance – New tolerance value

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +set_edge_checker(edge_checker)[source]
                                                                              +

                                                                              Set the edge checker function for boundary validation.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              edge_checker – Function that takes two vertex indices and returns bool

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_cell(cell_coords, all_coords, cell_indices, all_indices, dim, check_geometric=True)[source]
                                                                              +

                                                                              Validate a cell using applicable rules.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • cell_coords – Coordinates of vertices forming the cell

                                                                                • +

                                                                              • all_coords – Coordinates of all vertices in the complex

                                                                                • +

                                                                              • cell_indices – Indices of vertices forming the cell

                                                                                • +

                                                                              • all_indices – Indices of all vertices in the complex

                                                                                • +

                                                                              • dim – Dimension of the cell

                                                                                • +

                                                                              • check_geometric – Whether to check geometric rules (structural rules always checked)

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the cell

                                                                              +
                                                                              +
                                                                              Raises:
                                                                              +

                                                                              ValueError – If validation fails

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_cell_safe(cell_coords, all_coords, cell_indices, all_indices, dim, check_geometric=True)[source]
                                                                              +

                                                                              Validate a cell, catching and wrapping any exceptions.

                                                                              +

                                                                              Same as validate_cell but returns ValidationResult instead of raising.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • cell_coords – Coordinates of vertices forming the cell

                                                                                • +

                                                                              • all_coords – Coordinates of all vertices in the complex

                                                                                • +

                                                                              • cell_indices – Indices of vertices forming the cell

                                                                                • +

                                                                              • all_indices – Indices of all vertices in the complex

                                                                                • +

                                                                              • dim – Dimension of the cell

                                                                                • +

                                                                              • check_geometric – Whether to check geometric rules (structural rules always checked)

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the cell

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +get_rules_for_dimension(dim)[source]
                                                                              +

                                                                              Get all rules that apply to a specific dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              List of applicable ValidationRules

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +get_rule_names()[source]
                                                                              +

                                                                              Get names of all registered rules.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              List of rule names

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +disable_rule(rule_class)[source]
                                                                              +

                                                                              Temporarily disable a rule type.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              rule_class – Class of rule to disable

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +enable_strict_validation()[source]
                                                                              +

                                                                              Enable strict validation with tight tolerance.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +enable_permissive_validation()[source]
                                                                              +

                                                                              Enable permissive validation with loose tolerance.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_coordinates(coords, expected_dim=None)[source]
                                                                              +

                                                                              Validate coordinate array format and dimension consistency.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • coords – Coordinate array to validate

                                                                                • +

                                                                              • expected_dim – Expected dimension, if known

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the coordinates

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_nodes(nodes, node_checker, expect_exists=True)[source]
                                                                              +

                                                                              Validate node existence/non-existence.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • nodes – List of node identifiers to check

                                                                                • +

                                                                              • node_checker – Function that takes node_id and returns bool for existence

                                                                                • +

                                                                              • expect_exists – Whether nodes should exist (True) or not exist (False)

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the nodes

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.ValidationRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Abstract base class for embedding validation rules.

                                                                              +
                                                                              +
                                                                              +__init__(tolerance=1e-10)[source]
                                                                              +

                                                                              Initialize validation rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              tolerance – Numerical tolerance for geometric checks

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +abstract property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +property is_structural
                                                                              +

                                                                              Check if this is a structural rule that should always be validated.

                                                                              +

                                                                              Structural rules check basic requirements like vertex counts and dimension validity. +They should always be checked regardless of validation settings. +Geometric rules check embedding properties and are optional.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should always be checked, False if optional

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +abstract applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +abstract validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate a cell against this rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • cell_coords – Coordinates of vertices forming the cell

                                                                                • +

                                                                              • all_coords – Coordinates of all vertices in the complex

                                                                                • +

                                                                              • cell_indices – Indices of vertices forming the cell

                                                                                • +

                                                                              • all_indices – Indices of all vertices in the complex

                                                                                • +

                                                                              • dim – Dimension of the cell being validated

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult indicating if the cell is valid

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +set_tolerance(tolerance)[source]
                                                                              +

                                                                              Set the tolerance for this rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              tolerance – New tolerance value

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.ValidationResult(is_valid, message='', violating_indices=None)[source]
                                                                              +

                                                                              Result of a validation check.

                                                                              +
                                                                              +
                                                                              +is_valid
                                                                              +
                                                                              + +
                                                                              +
                                                                              +message = ''
                                                                              +
                                                                              + +
                                                                              +
                                                                              +violating_indices = None
                                                                              +
                                                                              + +
                                                                              +
                                                                              +classmethod valid()[source]
                                                                              +

                                                                              Create a valid result.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +classmethod invalid(message, violating_indices=None)[source]
                                                                              +

                                                                              Create an invalid result with error message.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +__init__(is_valid, message='', violating_indices=None)
                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.EdgeInteriorRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Validates that no vertices lie on edge interiors.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that no other vertices lie on this edge’s interior.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.FaceInteriorRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Validates that no vertices lie inside face interiors.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that no other vertices lie inside this face.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.SelfIntersectionRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Validates that face edges don’t self-intersect.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that face edges don’t intersect each other

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.BoundaryEdgeRule(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Validates that required boundary edges exist for faces.

                                                                              +
                                                                              +
                                                                              +__init__(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Initialize boundary edge rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • tolerance – Numerical tolerance

                                                                                • +

                                                                              • edge_checker – Function to check if an edge exists (injected dependency)

                                                                                • +
                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that all boundary edges exist for this face.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.DimensionValidityRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Validates that cell dimension is non-negative.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +property is_structural
                                                                              +

                                                                              Check if this is a structural rule that should always be validated.

                                                                              +

                                                                              Structural rules check basic requirements like vertex counts and dimension validity. +They should always be checked regardless of validation settings. +Geometric rules check embedding properties and are optional.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should always be checked, False if optional

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that dimension is non-negative.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.VertexCountRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Validates that cells have the correct number of vertices for their dimension.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +property is_structural
                                                                              +

                                                                              Check if this is a structural rule that should always be validated.

                                                                              +

                                                                              Structural rules check basic requirements like vertex counts and dimension validity. +They should always be checked regardless of validation settings. +Geometric rules check embedding properties and are optional.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should always be checked, False if optional

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate vertex count matches cell dimension requirements.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +ect.validation.validate_coordinate_array(coords, expected_dim=None)[source]
                                                                              +

                                                                              Standalone function to validate coordinate array format.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +ect.validation.validate_node_existence(nodes, node_checker, expect_exists=True)[source]
                                                                              +

                                                                              Standalone function to validate node existence.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +

                                                                              2.2.6.2. Base Classes

                                                                              +

                                                                              Base classes for the embedding validation system.

                                                                              +
                                                                              +
                                                                              +class ect.validation.base.ValidationResult(is_valid, message='', violating_indices=None)[source]
                                                                              +

                                                                              Bases: object

                                                                              +

                                                                              Result of a validation check.

                                                                              +
                                                                              +
                                                                              +is_valid
                                                                              +
                                                                              + +
                                                                              +
                                                                              +message = ''
                                                                              +
                                                                              + +
                                                                              +
                                                                              +violating_indices = None
                                                                              +
                                                                              + +
                                                                              +
                                                                              +classmethod valid()[source]
                                                                              +

                                                                              Create a valid result.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +classmethod invalid(message, violating_indices=None)[source]
                                                                              +

                                                                              Create an invalid result with error message.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +__init__(is_valid, message='', violating_indices=None)
                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.base.ValidationRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Bases: ABC

                                                                              +

                                                                              Abstract base class for embedding validation rules.

                                                                              +
                                                                              +
                                                                              +__init__(tolerance=1e-10)[source]
                                                                              +

                                                                              Initialize validation rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              tolerance – Numerical tolerance for geometric checks

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +abstract property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +property is_structural
                                                                              +

                                                                              Check if this is a structural rule that should always be validated.

                                                                              +

                                                                              Structural rules check basic requirements like vertex counts and dimension validity. +They should always be checked regardless of validation settings. +Geometric rules check embedding properties and are optional.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should always be checked, False if optional

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +abstract applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +abstract validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate a cell against this rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • cell_coords – Coordinates of vertices forming the cell

                                                                                • +

                                                                              • all_coords – Coordinates of all vertices in the complex

                                                                                • +

                                                                              • cell_indices – Indices of vertices forming the cell

                                                                                • +

                                                                              • all_indices – Indices of all vertices in the complex

                                                                                • +

                                                                              • dim – Dimension of the cell being validated

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult indicating if the cell is valid

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +set_tolerance(tolerance)[source]
                                                                              +

                                                                              Set the tolerance for this rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              tolerance – New tolerance value

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +

                                                                              2.2.6.3. Validation Rules

                                                                              +

                                                                              Unified validation rules for embedding validation system.

                                                                              +

                                                                              This module contains all validation rules including both structural rules +(which are always checked) and geometric rules (which are optional).

                                                                              +
                                                                              +
                                                                              +class ect.validation.rules.DimensionValidityRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Bases: ValidationRule

                                                                              +

                                                                              Validates that cell dimension is non-negative.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +property is_structural
                                                                              +

                                                                              Check if this is a structural rule that should always be validated.

                                                                              +

                                                                              Structural rules check basic requirements like vertex counts and dimension validity. +They should always be checked regardless of validation settings. +Geometric rules check embedding properties and are optional.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should always be checked, False if optional

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that dimension is non-negative.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.rules.VertexCountRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Bases: ValidationRule

                                                                              +

                                                                              Validates that cells have the correct number of vertices for their dimension.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +property is_structural
                                                                              +

                                                                              Check if this is a structural rule that should always be validated.

                                                                              +

                                                                              Structural rules check basic requirements like vertex counts and dimension validity. +They should always be checked regardless of validation settings. +Geometric rules check embedding properties and are optional.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should always be checked, False if optional

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate vertex count matches cell dimension requirements.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.rules.CoordinateDimensionRule(tolerance=1e-10, dimension_checker=None)[source]
                                                                              +

                                                                              Bases: ValidationRule

                                                                              +

                                                                              Validates that coordinates have consistent dimensions.

                                                                              +
                                                                              +
                                                                              +__init__(tolerance=1e-10, dimension_checker=None)[source]
                                                                              +

                                                                              Initialize validation rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              tolerance – Numerical tolerance for geometric checks

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +property is_structural
                                                                              +

                                                                              Check if this is a structural rule that should always be validated.

                                                                              +

                                                                              Structural rules check basic requirements like vertex counts and dimension validity. +They should always be checked regardless of validation settings. +Geometric rules check embedding properties and are optional.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should always be checked, False if optional

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate coordinate dimensions are consistent.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.rules.EdgeInteriorRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Bases: ValidationRule

                                                                              +

                                                                              Validates that no vertices lie on edge interiors.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that no other vertices lie on this edge’s interior.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.rules.FaceInteriorRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Bases: ValidationRule

                                                                              +

                                                                              Validates that no vertices lie inside face interiors.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that no other vertices lie inside this face.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.rules.SelfIntersectionRule(tolerance=1e-10)[source]
                                                                              +

                                                                              Bases: ValidationRule

                                                                              +

                                                                              Validates that face edges don’t self-intersect.

                                                                              +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that face edges don’t intersect each other

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +class ect.validation.rules.BoundaryEdgeRule(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Bases: ValidationRule

                                                                              +

                                                                              Validates that required boundary edges exist for faces.

                                                                              +
                                                                              +
                                                                              +__init__(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Initialize boundary edge rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • tolerance – Numerical tolerance

                                                                                • +

                                                                              • edge_checker – Function to check if an edge exists (injected dependency)

                                                                                • +
                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +property name
                                                                              +

                                                                              Human-readable name of this validation rule.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +applies_to_dimension(dim)[source]
                                                                              +

                                                                              Check if this rule applies to cells of the given dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              True if this rule should validate cells of this dimension

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate(cell_coords, all_coords, cell_indices, all_indices, dim=None)[source]
                                                                              +

                                                                              Validate that all boundary edges exist for this face.

                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +ect.validation.rules.validate_coordinate_array(coords, expected_dim=None)[source]
                                                                              +

                                                                              Standalone function to validate coordinate array format.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +ect.validation.rules.validate_node_existence(nodes, node_checker, expect_exists=True)[source]
                                                                              +

                                                                              Standalone function to validate node existence.

                                                                              +
                                                                              + +
                                                                              +
                                                                              +

                                                                              2.2.6.4. Validator

                                                                              +

                                                                              Main embedding validator class that orchestrates validation rules.

                                                                              +
                                                                              +
                                                                              +class ect.validation.validator.EmbeddingValidator(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Bases: object

                                                                              +

                                                                              Main validator that orchestrates multiple validation rules.

                                                                              +

                                                                              This class manages a collection of validation rules and applies them +to cells based on their dimension and the configured rule set.

                                                                              +
                                                                              +
                                                                              +__init__(tolerance=1e-10, edge_checker=None)[source]
                                                                              +

                                                                              Initialize the embedding validator.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • tolerance – Default tolerance for geometric validation

                                                                                • +

                                                                              • edge_checker – Function to check if an edge exists between two vertex indices

                                                                                • +
                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +add_rule(rule)[source]
                                                                              +

                                                                              Add a custom validation rule.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              rule – ValidationRule to add

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +remove_rule(rule_class)[source]
                                                                              +

                                                                              Remove all rules of a specific type.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              rule_class – Class of rules to remove

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +set_tolerance(tolerance)[source]
                                                                              +

                                                                              Set tolerance for all rules.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              tolerance – New tolerance value

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +set_edge_checker(edge_checker)[source]
                                                                              +

                                                                              Set the edge checker function for boundary validation.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              edge_checker – Function that takes two vertex indices and returns bool

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_cell(cell_coords, all_coords, cell_indices, all_indices, dim, check_geometric=True)[source]
                                                                              +

                                                                              Validate a cell using applicable rules.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • cell_coords – Coordinates of vertices forming the cell

                                                                                • +

                                                                              • all_coords – Coordinates of all vertices in the complex

                                                                                • +

                                                                              • cell_indices – Indices of vertices forming the cell

                                                                                • +

                                                                              • all_indices – Indices of all vertices in the complex

                                                                                • +

                                                                              • dim – Dimension of the cell

                                                                                • +

                                                                              • check_geometric – Whether to check geometric rules (structural rules always checked)

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the cell

                                                                              +
                                                                              +
                                                                              Raises:
                                                                              +

                                                                              ValueError – If validation fails

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_cell_safe(cell_coords, all_coords, cell_indices, all_indices, dim, check_geometric=True)[source]
                                                                              +

                                                                              Validate a cell, catching and wrapping any exceptions.

                                                                              +

                                                                              Same as validate_cell but returns ValidationResult instead of raising.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • cell_coords – Coordinates of vertices forming the cell

                                                                                • +

                                                                              • all_coords – Coordinates of all vertices in the complex

                                                                                • +

                                                                              • cell_indices – Indices of vertices forming the cell

                                                                                • +

                                                                              • all_indices – Indices of all vertices in the complex

                                                                                • +

                                                                              • dim – Dimension of the cell

                                                                                • +

                                                                              • check_geometric – Whether to check geometric rules (structural rules always checked)

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the cell

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +get_rules_for_dimension(dim)[source]
                                                                              +

                                                                              Get all rules that apply to a specific dimension.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              dim – Cell dimension

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              List of applicable ValidationRules

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +get_rule_names()[source]
                                                                              +

                                                                              Get names of all registered rules.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              List of rule names

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +disable_rule(rule_class)[source]
                                                                              +

                                                                              Temporarily disable a rule type.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +

                                                                              rule_class – Class of rule to disable

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +enable_strict_validation()[source]
                                                                              +

                                                                              Enable strict validation with tight tolerance.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +enable_permissive_validation()[source]
                                                                              +

                                                                              Enable permissive validation with loose tolerance.

                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              Self for method chaining

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_coordinates(coords, expected_dim=None)[source]
                                                                              +

                                                                              Validate coordinate array format and dimension consistency.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • coords – Coordinate array to validate

                                                                                • +

                                                                              • expected_dim – Expected dimension, if known

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the coordinates

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +validate_nodes(nodes, node_checker, expect_exists=True)[source]
                                                                              +

                                                                              Validate node existence/non-existence.

                                                                              +
                                                                              +
                                                                              Parameters:
                                                                              +
                                                                                +

                                                                              • nodes – List of node identifiers to check

                                                                                • +

                                                                              • node_checker – Function that takes node_id and returns bool for existence

                                                                                • +

                                                                              • expect_exists – Whether nodes should exist (True) or not exist (False)

                                                                                • +
                                                                              +
                                                                              +
                                                                              Returns:
                                                                              +

                                                                              ValidationResult for the nodes

                                                                              +
                                                                              +
                                                                              +
                                                                              + +
                                                                              + +
                                                                              +
                                                                              +
                                                                              + + +
                                                                              +
                                                                              + +
                                                                              +
                                                                              +
                                                                              +
                                                                              + + + + + + + \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 7b0b845..05b497a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ect" -version = "1.0.3" +version = "1.1.1" authors = [ { name="Liz Munch", email="muncheli@msu.edu" }, ] diff --git a/src/ect/__init__.py b/src/ect/__init__.py index 63c0614..5a5f2bb 100644 --- a/src/ect/__init__.py +++ b/src/ect/__init__.py @@ -3,21 +3,25 @@ Main classes: ECT: Calculator for Euler Characteristic Transform - EmbeddedGraph: Graph representation for ECT computation - EmbeddedCW: CW complex representation for ECT computation + EmbeddedComplex: Unified embedded cell complex supporting arbitrary dimensional cells + EmbeddedGraph: Alias for EmbeddedComplex (for backward compatibility) + EmbeddedCW: Alias for EmbeddedComplex (for backward compatibility) Directions: Direction vector management for ECT computation + SECT: Smooth Euler Characteristic Transform calculator """ -from .ect_graph import ECT -from .embed_graph import EmbeddedGraph -from .embed_cw import EmbeddedCW +from .ect import ECT +from .embed_complex import EmbeddedComplex, EmbeddedGraph, EmbeddedCW from .directions import Directions from .sect import SECT +from .dect import DECT from .utils import examples __all__ = [ "ECT", "SECT", + "DECT", + "EmbeddedComplex", "EmbeddedGraph", "EmbeddedCW", "Directions", diff --git a/src/ect/dect.py b/src/ect/dect.py new file mode 100644 index 0000000..2223b6c --- /dev/null +++ b/src/ect/dect.py @@ -0,0 +1,75 @@ +from ect import ECT +from .embed_complex import EmbeddedGraph, EmbeddedCW +from .directions import Directions +from .results import ECTResult +from typing import Optional, Union +import numpy as np +from numba import njit + + +class DECT(ECT): + """ + A class to calculate the Differentiable Euler Characteristic Transform (DECT) + """ + + def __init__( + self, + directions: Optional[Directions] = None, + num_dirs: Optional[int] = None, + num_thresh: Optional[int] = None, + bound_radius: Optional[float] = None, + thresholds: Optional[np.ndarray] = None, + dtype=np.float32, + scale: float = 10.0, + ): + """Initialize DECT calculator""" + super().__init__( + directions, num_dirs, num_thresh, bound_radius, thresholds, dtype + ) + self.scale = scale + + @staticmethod + @njit(fastmath=True) + def _compute_directional_transform( + simplex_projections_list, thresholds, dtype=np.float32, scale=10.0 + ): + """Compute DECT using sigmoid for smooth transitions""" + num_dir = simplex_projections_list[0].shape[1] + num_thresh = len(thresholds) + + output = np.zeros((num_dir, num_thresh), dtype=dtype) + + for i, simplex_heights in enumerate(simplex_projections_list): + for d in range(num_dir): + for t, thresh in enumerate(thresholds): + diff = scale * (simplex_heights[:, d] - thresh) + sigmoid = 1 / (1 + np.exp(-diff)) + sign = -1 if i % 2 == 0 else 1 + output[d, t] += sign * np.sum(sigmoid) + + return output + + def calculate( + self, + graph: Union[EmbeddedGraph, EmbeddedCW], + scale: Optional[float] = None, + theta: Optional[float] = None, + override_bound_radius: Optional[float] = None, + ) -> ECTResult: + """Calculate Differentiable Euler Characteristic Transform (DECT)""" + self._ensure_directions(graph.dim, theta) + self._ensure_thresholds(graph, override_bound_radius) + + directions = ( + self.directions if theta is None else Directions.from_angles([theta]) + ) + + simplex_projections = self._compute_simplex_projections(graph, directions) + + scale = self.scale if scale is None else scale + + ect_matrix = self._compute_directional_transform( + simplex_projections, self.thresholds, self.dtype, scale + ) + + return ECTResult(ect_matrix, directions, self.thresholds) diff --git a/src/ect/ect_graph.py b/src/ect/ect.py similarity index 63% rename from src/ect/ect_graph.py rename to src/ect/ect.py index 3a6c5ba..8343fa1 100644 --- a/src/ect/ect_graph.py +++ b/src/ect/ect.py @@ -1,17 +1,16 @@ import numpy as np from numba import prange, njit from numba.typed import List -from typing import Optional, Union +from typing import Optional -from .embed_cw import EmbeddedCW -from .embed_graph import EmbeddedGraph +from .embed_complex import EmbeddedComplex from .directions import Directions from .results import ECTResult class ECT: """ - A class to calculate the Euler Characteristic Transform (ECT) from an input :any:`EmbeddedGraph` or :any:`EmbeddedCW`. + A class to calculate the Euler Characteristic Transform (ECT) from an input :any:`EmbeddedComplex`. The result is a matrix where entry ``M[i,j]`` is :math:`\chi(K_{a_i})` for the direction :math:`\omega_j` where :math:`a_i` is the ith entry in ``self.thresholds``, and :math:`\omega_j` is the ith entry in ``self.thetas``. @@ -58,14 +57,15 @@ def _ensure_directions(self, graph_dim, theta=None): if self.num_dirs is None: raise ValueError("Either 'directions' or 'num_dirs' must be provided.") self.directions = Directions.uniform(self.num_dirs, dim=graph_dim) - elif isinstance(self.directions, list): - # if list of vectors, convert to Directions object - self.directions = np.array(self.directions) - self.directions = Directions.from_vectors(self.directions) elif not isinstance(self.directions, Directions): - raise TypeError( - "directions must be a Directions object, ndarray, or list of vectors." - ) + # convert any array-like to Directions object + try: + self.directions = Directions.from_vectors(np.asarray(self.directions)) + except ValueError: + raise ValueError( + "Invalid directions provided. " + "Must be a numpy array or a Directions object." + ) if theta is not None and graph_dim != 2: raise ValueError( @@ -80,43 +80,35 @@ def _ensure_directions(self, graph_dim, theta=None): def _ensure_thresholds(self, graph, override_bound_radius=None): """Ensures thresholds is a valid 1-dimensional ndarray.""" - - # determine if we need to generate thresholds if self.thresholds is None or override_bound_radius is not None: if self.num_thresh is None: raise ValueError( "Either 'thresholds' or 'num_thresh' must be provided." ) - # determine the radius based on priority - if override_bound_radius is not None: - radius = override_bound_radius - elif self.bound_radius is not None: - radius = self.bound_radius - else: - radius = graph.get_bounding_radius() - - self.thresholds = np.linspace(-radius, radius, self.num_thresh) + # priority: override > bound_radius > graph radius + radius = ( + override_bound_radius + or self.bound_radius + or graph.get_bounding_radius() + ) + self.thresholds = np.linspace(-radius, radius, self.num_thresh, dtype=float) else: - # validate existing thresholds are valid - if not isinstance(self.thresholds, np.ndarray): - raise TypeError("thresholds must be a numpy ndarray") - + # validate and convert existing thresholds + self.thresholds = np.asarray(self.thresholds, dtype=float) if self.thresholds.ndim != 1: raise ValueError("thresholds must be a 1-dimensional array") - self.thresholds = self.thresholds.astype(float) - def calculate( self, - graph: Union[EmbeddedGraph, EmbeddedCW], + graph: EmbeddedComplex, theta: Optional[float] = None, override_bound_radius: Optional[float] = None, ): """Calculate Euler Characteristic Transform (ECT) for a given graph and direction theta Args: - graph (EmbeddedGraph/EmbeddedCW): - The input graph to calculate the ECT for. + graph (EmbeddedComplex): + The input complex to calculate the ECT for. theta (float): The angle in :math:`[0,2\pi]` for the direction to calculate the ECT. override_bound_radius (float): @@ -133,47 +125,47 @@ def calculate( simplex_projections = self._compute_simplex_projections(graph, directions) ect_matrix = self._compute_directional_transform( - simplex_projections, self.thresholds, self.shape_descriptor, self.dtype + simplex_projections, self.thresholds, self.dtype ) return ECTResult(ect_matrix, directions, self.thresholds) - def _compute_node_projections(self, coords, directions): - """Compute inner products of coordinates with directions""" - return np.matmul(coords, directions.vectors.T) - - def _compute_simplex_projections( - self, graph: Union[EmbeddedGraph, EmbeddedCW], directions - ): - """Compute projections of each simplex (vertices, edges, faces)""" + def _compute_simplex_projections(self, graph: EmbeddedComplex, directions): + """Compute projections of each k-cell for all dimensions""" simplex_projections = List() - node_projections = self._compute_node_projections( - graph.coord_matrix, directions - ) - edge_maxes = np.maximum( - node_projections[graph.edge_indices[:, 0]], - node_projections[graph.edge_indices[:, 1]], - ) + node_projections = np.matmul(graph.coord_matrix, directions.vectors.T) + num_dirs = node_projections.shape[1] simplex_projections.append(node_projections) - simplex_projections.append(edge_maxes) - if isinstance(graph, EmbeddedCW) and len(graph.faces) > 0: - node_to_index = {n: i for i, n in enumerate(graph.node_list)} - face_indices = [[node_to_index[v] for v in face] for face in graph.faces] - face_maxes = np.array( - [np.max(node_projections[face, :], axis=0) for face in face_indices] + all_cells = { + 0: [(i,) for i in range(len(graph.node_list))], + 1: [tuple(edge) for edge in graph.edge_indices] + if graph.edge_indices.size + else [], + **graph.cells, + } + + max_dim = max(all_cells.keys()) if all_cells else 0 + for dim in range(1, max_dim + 1): + cells = all_cells.get(dim, []) + cell_projections = ( + np.array( + [np.max(node_projections[list(cell), :], axis=0) for cell in cells] + ) + if cells + else np.empty((0, num_dirs)) ) - simplex_projections.append(face_maxes) + simplex_projections.append(cell_projections) return simplex_projections @staticmethod @njit(parallel=True, fastmath=True) def _compute_directional_transform( - simplex_projections_list, thresholds, shape_descriptor, dtype=np.int32 + simplex_projections_list, thresholds, dtype=np.int32 ): - """Compute ECT by counting simplices below each threshold + """Compute ECT by counting simplices below each threshold - VECTORIZED VERSION Args: simplex_projections_list: List of arrays containing projections for each simplex type @@ -195,23 +187,14 @@ def _compute_directional_transform( sorted_proj[:, i] = np.sort(proj[:, i]) sorted_projections.append(sorted_proj) - for j in prange(num_thresh): - thresh = thresholds[j] - for i in range(num_dir): - simplex_counts_list = List() - for k in range(len(sorted_projections)): - projs = sorted_projections[k][:, i] - simplex_counts_list.append( - np.searchsorted(projs, thresh, side="right") - ) - result[i, j] = shape_descriptor(simplex_counts_list) - return result + for i in prange(num_dir): + chi = np.zeros(num_thresh, dtype=dtype) + for k in range(len(sorted_projections)): + projs = sorted_projections[k][:, i] - @staticmethod - @njit(fastmath=True) - def shape_descriptor(simplex_counts_list): - """Calculate shape descriptor from simplex counts (Euler characteristic)""" - chi = 0 - for k in range(len(simplex_counts_list)): - chi += (-1) ** k * simplex_counts_list[k] - return chi + count = np.searchsorted(projs, thresholds, side="right") + + sign = -1 if k % 2 else 1 + chi += sign * count + result[i] = chi + return result diff --git a/src/ect/embed_graph.py b/src/ect/embed_complex.py similarity index 57% rename from src/ect/embed_graph.py rename to src/ect/embed_complex.py index 9429d8d..38b7756 100644 --- a/src/ect/embed_graph.py +++ b/src/ect/embed_complex.py @@ -4,38 +4,73 @@ import networkx as nx import numpy as np import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d.art3d import Poly3DCollection from sklearn.decomposition import PCA from .utils.naming import next_vert_name +from .utils.face_check import ( + point_in_polygon, + validate_face_embedding, + validate_edge_embedding, +) +from .validation import EmbeddingValidator, ValidationRule CENTER_TYPES = ["mean", "bounding_box", "origin"] TRANSFORM_TYPES = ["pca"] -class EmbeddedGraph(nx.Graph): +class EmbeddedComplex(nx.Graph): """ - A class to represent a graph with embedded coordinates for each vertex with simple geometric graph operations. + A unified class to represent an embedded cell complex with cells of arbitrary dimension. - Attributes - graph : nx.Graph - a NetworkX graph object + This combines the functionality of EmbeddedGraph and EmbeddedCW, supporting: + - 0-cells (vertices) with embedded coordinates + - 1-cells (edges) + - k-cells for k >= 2 (faces, volumes, etc.) + + Args: + validate_embedding (bool): If True, automatically validate embedding properties + when adding cells. Default: False + embedding_tol (float): Tolerance for geometric validation. Default: 1e-10 + + Attributes: coord_matrix : np.ndarray - a matrix of embedded coordinates for each vertex + A matrix of embedded coordinates for each vertex node_list : list - a list of node names + A list of node names node_to_index : dict - a dictionary mapping node ids to their index in the coord_matrix + A dictionary mapping node ids to their index in the coord_matrix dim : int - the dimension of the embedded coordinates - + The dimension of the embedded coordinates + cells : dict + Dictionary mapping dimension k to list of k-cells, where each k-cell + is represented as a tuple of vertex indices + validate_embedding : bool + Whether to automatically validate embedding properties + embedding_tol : float + Tolerance for geometric validation """ - def __init__(self): + def __init__(self, validate_embedding=False, embedding_tol=1e-10): super().__init__() self._node_list = [] self._node_to_index = {} self._coord_matrix = None + self.cells = defaultdict(list) + + self.validate_embedding = validate_embedding + self.embedding_tol = embedding_tol + + def edge_checker(v1_idx: int, v2_idx: int) -> bool: + # closure to check if edge exists by converting indices back to node names + if v1_idx >= len(self._node_list) or v2_idx >= len(self._node_list): + return False + v1_name = self._node_list[v1_idx] + v2_name = self._node_list[v2_idx] + return self.has_edge(v1_name, v2_name) + + self._validator = EmbeddingValidator(embedding_tol, edge_checker) @property def coord_matrix(self): @@ -70,82 +105,49 @@ def position_dict(self): def edge_indices(self): """Return edges as array of index pairs""" edges = np.array( - [(self._node_to_index[u], self._node_to_index[v]) - for u, v in self.edges()], + [(self._node_to_index[u], self._node_to_index[v]) for u, v in self.edges()], dtype=int, ) return edges if len(edges) > 0 else np.empty((0, 2), dtype=int) - # ====================================== - # Node Management - # ====================================== - @staticmethod - def _validate_coords(func): - """Validates if coordinates are nonempty and have valid dimension""" - - def wrapper(self, *args, **kwargs): - coords = next( - (arg for arg in args if isinstance(arg, (list, np.ndarray))), None - ) - if coords is not None: - coords = np.asarray(coords, dtype=float) - if coords.ndim != 1: - raise ValueError("Coordinates must be a 1D array") - - # Skip dimension check for first node - if len(self._node_list) > 0: - if coords.size != self._coord_matrix.shape[1]: - raise ValueError( - f"Coordinates must have dimension {self._coord_matrix.shape[1]}" - ) - - return func(self, *args, **kwargs) - - return wrapper + @property + def faces(self): + """Return list of 2-cells (faces) for backward compatibility""" + return [ + tuple(self._node_list[i] for i in cell) for cell in self.cells.get(2, []) + ] - @staticmethod - def _validate_node(exists=True): - """Validates if nodes exist or not already""" - - def decorator(func): - def wrapper(self, *args, **kwargs): - # Handle both positional and keyword arguments - if args: - nodes = args[0] if isinstance( - args[0], (list, tuple)) else [args[0]] - else: - node_id = kwargs.get("node_id") or kwargs.get("node_id1") - nodes = [node_id] if node_id else [] - - for node_id in nodes: - node_exists = node_id in self._node_to_index - if exists and not node_exists: - raise ValueError(f"Node {node_id} does not exist") - if not exists and node_exists: - raise ValueError(f"Node {node_id} already exists") - return func(self, *args, **kwargs) - - return wrapper - - return decorator - - @_validate_coords - @_validate_node(exists=False) def add_node(self, node_id, coord): - """Add a vertex to the graph. + """Add a vertex to the complex. Args: node_id: Identifier for the node coord: Array-like coordinates for the node """ + # validate coordinates using validator + expected_dim = ( + self._coord_matrix.shape[1] if self._coord_matrix is not None else None + ) + coord_result = self._validator.validate_coordinates(coord, expected_dim) + if not coord_result.is_valid: + raise ValueError(coord_result.message) + + # validate node doesn't already exist + node_result = self._validator.validate_nodes( + [node_id], lambda n: n in self._node_to_index, expect_exists=False + ) + if not node_result.is_valid: + raise ValueError(node_result.message) + coord = np.asarray(coord, dtype=float) if len(self._node_list) == 0: + # initialize coordinate matrix with first node self._coord_matrix = coord.reshape(1, -1) else: + # append new coordinate as row coord_reshaped = coord.reshape(1, -1) - self._coord_matrix = np.vstack( - [self._coord_matrix, coord_reshaped]) + self._coord_matrix = np.vstack([self._coord_matrix, coord_reshaped]) self._node_list.append(node_id) self._node_to_index[node_id] = len(self._node_list) - 1 @@ -161,43 +163,180 @@ def add_nodes_from( for node_id, coordinates in nodes_with_coords: self.add_node(node_id, coordinates) - # ====================================== - # Coordinate Access - # ====================================== + def add_edge(self, node_id1, node_id2): + """Add an edge (1-cell) between two nodes""" + # validate nodes exist + node_result = self._validator.validate_nodes( + [node_id1, node_id2], lambda n: n in self._node_to_index, expect_exists=True + ) + if not node_result.is_valid: + raise ValueError(node_result.message) + + super().add_edge(node_id1, node_id2) + + def add_cell( + self, + cell_vertices: List, + dim: Optional[int] = None, + check: Optional[bool] = None, + embedding_tol: Optional[float] = None, + ): + """ + Add a k-cell to the complex. + + Args: + cell_vertices: List of vertex identifiers that form the cell + dim: Dimension of the cell. If None, inferred as len(cell_vertices) - 1 + check: Whether to validate the cell embedding. If None, uses self.validate_embedding + embedding_tol: Tolerance for geometric validation. If None, uses self.embedding_tol + """ + if check is None: + check = self.validate_embedding + if embedding_tol is None: + embedding_tol = self.embedding_tol + if dim is None: + dim = len(cell_vertices) - 1 + + # check vertex existence before validation (can't validate non-existent vertices) + missing_vertices = [v for v in cell_vertices if v not in self._node_to_index] + if missing_vertices: + raise ValueError(f"Vertices do not exist: {missing_vertices}") + + # convert vertex names to indices for storage + cell_indices = tuple(self._node_to_index[v] for v in cell_vertices) + + cell_coords = ( + self._coord_matrix[list(cell_indices)] + if self._coord_matrix is not None + else None + ) + all_coords = self._coord_matrix + all_indices = list(range(len(self._node_list))) + + # check structural rules (vertex count, dimension validity) + structural_result = self._validator.validate_cell( + cell_coords, + all_coords, + list(cell_indices), + all_indices, + dim, + check_geometric=False, + ) + if not structural_result.is_valid: + raise ValueError(structural_result.message) + + # check geometric rules (embedding properties) + if check: + geometric_result = self._validator.validate_cell( + cell_coords, + all_coords, + list(cell_indices), + all_indices, + dim, + check_geometric=True, + ) + if not geometric_result.is_valid: + raise ValueError(geometric_result.message) + + # update graph structure + if dim == 1: + self.add_edge(cell_vertices[0], cell_vertices[1]) + + self.cells[dim].append(cell_indices) + + def enable_embedding_validation(self, tol: float = 1e-10): + """ + Enable automatic embedding validation for all subsequent cell additions. + + Args: + tol: Tolerance for geometric validation + """ + self.validate_embedding = True + self.embedding_tol = tol + self._validator.set_tolerance(tol) + + def disable_embedding_validation(self): + """Disable automatic embedding validation for all subsequent cell additions.""" + self.validate_embedding = False + + def get_validator(self) -> "EmbeddingValidator": + """ + Get the embedding validator instance for advanced configuration. + + Returns: + The EmbeddingValidator instance used by this complex + """ + return self._validator + + def set_validation_rules(self, rules: List["ValidationRule"]) -> "EmbeddedComplex": + """ + Set custom validation rules. + + Args: + rules: List of ValidationRule instances + + Returns: + Self for method chaining + """ + # replace validation rules + self._validator.rules = rules + return self + + def add_face(self, face: List, check: Optional[bool] = None): + """Add a 2-cell (face) to the complex. Provided for backward compatibility.""" + self.add_cell(face, dim=2, check=check) + + def add_faces_from(self, faces: List[List]): + """Add multiple 2-cells (faces) to the complex.""" + for face in faces: + self.add_face(face) - @_validate_node(exists=True) def get_coord(self, node_id): """Return the coordinates of a node""" + # validate node exists + node_result = self._validator.validate_nodes( + [node_id], lambda n: n in self._node_to_index, expect_exists=True + ) + if not node_result.is_valid: + raise ValueError(node_result.message) + return self._coord_matrix[self._node_to_index[node_id]].copy() - @_validate_coords - @_validate_node(exists=True) def set_coord(self, node_id, new_coords): """Set the coordinates of a node""" + # validate coordinates + expected_dim = ( + self._coord_matrix.shape[1] if self._coord_matrix is not None else None + ) + coord_result = self._validator.validate_coordinates(new_coords, expected_dim) + if not coord_result.is_valid: + raise ValueError(coord_result.message) + + # validate node exists + node_result = self._validator.validate_nodes( + [node_id], lambda n: n in self._node_to_index, expect_exists=True + ) + if not node_result.is_valid: + raise ValueError(node_result.message) + idx = self._node_to_index[node_id] self._coord_matrix[idx] = new_coords - # ====================================== - # Graph Operations - # ====================================== - def add_cycle(self, coord_matrix): """Add nodes in a cyclic pattern from coordinate matrix""" + # generate sequential node names and add cyclic edges n = coord_matrix.shape[0] - new_names = next_vert_name( - self._node_list[-1] if self._node_list else 0, n) + new_names = next_vert_name(self._node_list[-1] if self._node_list else 0, n) self.add_nodes_from(zip(new_names, coord_matrix)) - self.add_edges_from( - [(new_names[i], new_names[(i + 1) % n]) for i in range(n)]) - - # ====================================== - # Geometric Calculations - # ====================================== + self.add_edges_from([(new_names[i], new_names[(i + 1) % n]) for i in range(n)]) def get_center(self, method: str = "bounding_box") -> np.ndarray: """Calculate center of coordinates""" coords = self._coord_matrix + if coords is None or coords.size == 0: + return np.zeros(0) + if method == "mean": return np.mean(coords, axis=0) elif method == "bounding_box": @@ -212,8 +351,11 @@ def get_bounding_box(self): def get_bounding_radius(self, center_type: str = "bounding_box") -> float: """Get radius of minimal bounding sphere""" - center = self.get_center(center_type) coords = self._coord_matrix + if coords is None or coords.size == 0: + return 0.0 + + center = self.get_center(center_type) return np.max(np.linalg.norm(coords - center, axis=1)) def get_normal_angle_matrix( @@ -237,6 +379,7 @@ def get_normal_angle_matrix( angle_matrix = np.full((n, n), np.nan, dtype=np.float64) if edges_only: + # compute angles only for connected vertex pairs edges = np.array(list(self.edges())) if edges.size == 0: return angle_matrix, vertices @@ -247,6 +390,7 @@ def get_normal_angle_matrix( dx = coords[v_indices, 0] - coords[u_indices, 0] dy = coords[v_indices, 1] - coords[u_indices, 1] + # angles from u to v and reverse direction angles = np.arctan2(dx, -dy) % (2 * np.pi) rev_angles = (angles + np.pi) % (2 * np.pi) @@ -258,21 +402,20 @@ def get_normal_angle_matrix( angle_matrix[v_indices, u_indices] = rev_angles else: + # compute angles between all vertex pairs using broadcasting x = coords[:, 0] y = coords[:, 1] - # compute all pairwise differences dx = x[:, None] - x[None, :] dy = y[:, None] - y[None, :] - # Compute angles and mask invalid pairs angle_matrix = np.arctan2(dx, -dy) % (2 * np.pi) - angle_matrix[np.isclose(dx**2 + dy**2, 0)] = np.nan # Zero vectors + # nan for coincident points + angle_matrix[np.isclose(dx**2 + dy**2, 0)] = np.nan if decimals is not None: angle_matrix = np.round(angle_matrix, decimals) - # mask diagonal since we don't want np.fill_diagonal(angle_matrix, np.nan) return angle_matrix, vertices @@ -290,10 +433,10 @@ def get_normal_angles( Returns: Dictionary mapping rounded angles to vertex pairs """ - angle_matrix, vertices = self.get_angle_matrix(edges_only, decimals) + angle_matrix, vertices = self.get_normal_angle_matrix(edges_only, decimals) n = len(vertices) - # Extract upper triangle indices + # extract upper triangle to avoid duplicate pairs rows, cols = np.triu_indices(n, k=1) angles = angle_matrix[rows, cols] valid_mask = ~np.isnan(angles) @@ -301,12 +444,11 @@ def get_normal_angles( if not valid_mask.any(): return defaultdict(list) - # Filter valid pairs valid_rows = rows[valid_mask] valid_cols = cols[valid_mask] valid_angles = angles[valid_mask] - # Group pairs by rounded angle + # group vertex pairs by their angle angle_dict = defaultdict(list) unique_angles, inverse = np.unique(valid_angles, return_inverse=True) @@ -320,10 +462,6 @@ def get_normal_angles( return angle_dict - # ============================ - # Coordinate transformations - # ============================ - def transform_coordinates(self, center_type="bounding_box", projection_type="pca"): """Transform coordinates center and orientation""" if projection_type not in TRANSFORM_TYPES: @@ -342,6 +480,7 @@ def center_coordinates(self, center_type="mean"): def scale_coordinates(self, radius=1): """Scale coordinates to fit within given radius""" + # scale so largest distance from origin equals target radius current_max = np.linalg.norm(self._coord_matrix, axis=1).max() if current_max > 0: self._coord_matrix *= radius / current_max @@ -355,31 +494,20 @@ def project_coordinates(self, projection_type="pca"): def pca_projection(self, target_dim=2): """Dimensionality reduction using PCA""" + # only reduce dimension if self.dim <= target_dim: return pca = PCA(n_components=target_dim) self._coord_matrix = pca.fit_transform(self._coord_matrix) - self.dim = target_dim - @_validate_node(exists=True) - def add_edge(self, node_id1, node_id2): - """Add an edge between two nodes""" - super().add_edge(node_id1, node_id2) - - # =================== - # Visualization - # =================== def validate_plot_parameters(func): - """Decorator to validate plot method parameters""" - + # decorator to check plotting requirements def wrapper(self, *args, **kwargs): - bounding_center_type = kwargs.get( - "bounding_center_type", "bounding_box") + bounding_center_type = kwargs.get("bounding_center_type", "bounding_box") if self.dim not in [2, 3]: - raise ValueError( - "At least 2D or 3D coordinates required for plotting") + raise ValueError("At least 2D or 3D coordinates required for plotting") if bounding_center_type not in CENTER_TYPES: raise ValueError( @@ -391,6 +519,37 @@ def wrapper(self, *args, **kwargs): return wrapper + def plot_faces(self, ax=None, **kwargs): + """ + Plots the 2-cells (faces) of the complex. + + Parameters: + ax (matplotlib.axes.Axes): + The axes to plot the graph on. If None, a new figure is created. + **kwargs: + Additional keyword arguments to pass to the ax.fill function. + + Returns: + matplotlib.axes.Axes + The axes object with the plot. + """ + if ax is None: + _, ax = plt.subplots() + + # render each 2-cell as filled polygon + for cell_indices in self.cells.get(2, []): + face_coords = self._coord_matrix[list(cell_indices)] + + if self.dim == 2: + ax.fill(face_coords[:, 0], face_coords[:, 1], **kwargs) + else: + # 3d faces need polygon collection + verts = [face_coords] + collection = Poly3DCollection(verts, **kwargs) + ax.add_collection3d(collection) + + return ax + @validate_plot_parameters def plot( self, @@ -403,31 +562,32 @@ def plot( edge_color: str = "gray", elev: float = 25, azim: float = -60, + face_color: str = "lightblue", + face_alpha: float = 0.3, **kwargs, ) -> plt.Axes: """ - Visualize the embedded graph in 2D or 3D + Visualize the embedded complex in 2D or 3D """ ax = self._create_axes(ax, self.dim) - pos = {node: self._coord_matrix[i] - for i, node in enumerate(self._node_list)} + if 2 in self.cells and len(self.cells[2]) > 0: + self.plot_faces(ax=ax, facecolor=face_color, alpha=face_alpha) + + pos = {node: self._coord_matrix[i] for i, node in enumerate(self._node_list)} if self.dim == 2: - self._draw_2d(ax, pos, with_labels, - node_size, edge_color, **kwargs) + self._draw_2d(ax, pos, with_labels, node_size, edge_color, **kwargs) else: self._draw_3d(ax, pos, node_size, edge_color, elev, azim, **kwargs) if color_nodes_theta is not None: - # calculate directional projection values + # color nodes by projection in specified direction direction = np.array( [np.sin(color_nodes_theta), -np.cos(color_nodes_theta)] ) node_colors = np.dot(self._coord_matrix, direction) - - self._add_node_coloring( - ax, pos, node_colors, node_size, self.dim, **kwargs) + self._add_node_coloring(ax, pos, node_colors, node_size, self.dim, **kwargs) if bounding_circle: self._add_bounding_shape(ax, bounding_center_type, self.dim) @@ -437,18 +597,7 @@ def plot( return ax def _create_axes(self, ax, dim=None): - """Create appropriate axes if not provided - - Parameters: - ax (matplotlib.axes.Axes, optional): - The axes to use. If None, creates new axes - dim (int, optional): - Dimension of the plot. If None, uses self.dim - - Returns: - matplotlib.axes.Axes: - The configured axes object - """ + """Create appropriate axes if not provided""" if dim is None: dim = self.dim @@ -501,27 +650,13 @@ def _draw_3d(self, ax, pos, node_size, edge_color, elev, azim, **kwargs): ax.plot3D(x, y, z, color=edge_color, linewidth=1.5) def _add_node_coloring(self, ax, pos, node_colors, node_size, dim=None, **kwargs): - """Add node coloring based on provided values - - Parameters: - ax (matplotlib.axes.Axes): - The axes to add coloring to - pos (dict): - Dictionary of node positions - node_colors (array-like): - Values to use for coloring nodes - node_size (int): - Size of nodes in visualization - dim (int, optional): - Dimension of the plot. If None, uses self.dim - **kwargs: - Additional keyword arguments for plotting - """ + """Add node coloring based on provided values""" if dim is None: dim = self.dim if dim == 2: - nodes = nx.draw_networkx_nodes( + # 2d colored nodes using networkx + nx.draw_networkx_nodes( self, pos=pos, ax=ax, @@ -533,8 +668,9 @@ def _add_node_coloring(self, ax, pos, node_colors, node_size, dim=None, **kwargs **kwargs, ) else: + # 3d colored scatter plot coords = np.array(list(pos.values())) - nodes = ax.scatter3D( + ax.scatter3D( coords[:, 0], coords[:, 1], coords[:, 2], @@ -553,16 +689,7 @@ def _add_node_coloring(self, ax, pos, node_colors, node_size, dim=None, **kwargs cbar.set_label("Node Values") def _add_bounding_shape(self, ax, center_type="bounding_box", dim=None): - """Add bounding circle/sphere visualization - - Parameters: - ax (matplotlib.axes.Axes): - The axes to add the bounding shape to - center_type (str, optional): - Method to compute center ("mean", "bounding_box", or "origin") - dim (int, optional): - Dimension of the plot. If None, uses self.dim - """ + """Add bounding circle/sphere visualization""" if dim is None: dim = self.dim @@ -570,6 +697,7 @@ def _add_bounding_shape(self, ax, center_type="bounding_box", dim=None): radius = self.get_bounding_radius(center_type) if dim == 2: + # draw bounding circle circle = plt.Circle( center[:2], radius, @@ -581,12 +709,10 @@ def _add_bounding_shape(self, ax, center_type="bounding_box", dim=None): ) ax.add_patch(circle) padding = radius * 0.1 - ax.set_xlim(center[0] - radius - padding, - center[0] + radius + padding) - ax.set_ylim(center[1] - radius - padding, - center[1] + radius + padding) + ax.set_xlim(center[0] - radius - padding, center[0] + radius + padding) + ax.set_ylim(center[1] - radius - padding, center[1] + radius + padding) else: - # sphere wireframe + # draw bounding sphere as wireframe u = np.linspace(0, 2 * np.pi, 30) v = np.linspace(0, np.pi, 30) x = radius * np.outer(np.cos(u), np.sin(v)) + center[0] @@ -597,17 +723,13 @@ def _add_bounding_shape(self, ax, center_type="bounding_box", dim=None): x, y, z, color="darkred", linewidth=0.5, alpha=0.3, rstride=2, cstride=2 ) padding = radius * 0.1 - ax.set_xlim3d(center[0] - radius - padding, - center[0] + radius + padding) - ax.set_ylim3d(center[1] - radius - padding, - center[1] + radius + padding) - ax.set_zlim3d(center[2] - radius - padding, - center[2] + radius + padding) + ax.set_xlim3d(center[0] - radius - padding, center[0] + radius + padding) + ax.set_ylim3d(center[1] - radius - padding, center[1] + radius + padding) + ax.set_zlim3d(center[2] - radius - padding, center[2] + radius + padding) def _configure_axes(self, ax): """Finalize plot appearance""" if hasattr(ax, "zaxis"): - # 3D plot configuration ax.grid(True, linestyle=":", linewidth=0.5, alpha=0.7) ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) @@ -616,7 +738,6 @@ def _configure_axes(self, ax): ax.set_ylabel("Y") ax.set_zlabel("Z") else: - # 2D plot configuration ax.set_aspect("equal") ax.grid(True, linestyle=":", linewidth=0.5, alpha=0.7) @@ -642,24 +763,14 @@ def _configure_axes(self, ax): ) def _get_nice_interval(self, range_size): - """Calculate a nice interval for tick spacing - - Args: - range_size: Size of the axis range - - Returns: - float: Nice interval value for tick spacing - """ - # Calculate rough interval size (aim for ~5 major ticks) + # calculate visually appealing tick spacing rough_interval = range_size / 5 - # Get magnitude magnitude = 10 ** np.floor(np.log10(rough_interval)) - # Normalize rough interval to between 1 and 10 normalized = rough_interval / magnitude - # Choose nice interval + # choose from standard intervals: 1, 2, 5, 10 if normalized < 1.5: nice_interval = 1 elif normalized < 3: @@ -670,3 +781,7 @@ def _get_nice_interval(self, range_size): nice_interval = 10 return nice_interval * magnitude + + +EmbeddedGraph = EmbeddedComplex +EmbeddedCW = EmbeddedComplex diff --git a/src/ect/embed_cw.py b/src/ect/embed_cw.py deleted file mode 100644 index 042e625..0000000 --- a/src/ect/embed_cw.py +++ /dev/null @@ -1,151 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d.art3d import Poly3DCollection -from .embed_graph import EmbeddedGraph -from .utils.face_check import point_in_polygon -from typing import Optional - - -class EmbeddedCW(EmbeddedGraph): - """ - A class to represent a straight-line-embedded CW complex. We assume that the coordinates for the embedding of the vertices are given, the 1-skeleton is in fact a graph (so not as general as a full CW complex) with straight line embeddings, and 2-Cells are the interior of the shape outlined by its boundary edges. - - Faces should be passed in as a list of vertices, where the vertices are in order around the face. However, the ECT function will likely still work if the ordering is different. The drawing functions however might look strange. Note the class does not (yet?) check to make sure the face is valid, i.e. is a cycle in the graph, and bounds a region in the plane. - - """ - - def __init__(self): - """ - Initializes an empty EmbeddedCW object. - """ - - super().__init__() - self.faces = [] - - def add_from_embedded_graph(self, G): - """ - Adds the edges and coordinates from an EmbeddedGraph object to the EmbeddedCW object. - - Parameters: - G (EmbeddedGraph): - The EmbeddedGraph object to add from. - """ - nodes_with_coords = [ - (node, G.coord_matrix[G.node_to_index[node]]) for node in G.nodes() - ] - self.add_nodes_from(nodes_with_coords) - self.add_edges_from(G.edges()) - - @EmbeddedGraph._validate_node(exists=True) - def add_face(self, face, check=False): - """ - Adds a face to the list of faces. - - Parameters: - face (list): - A list of vertices that make up the face. - check (bool): - Whether to check that the face is a valid addition to the cw complex. - """ - if len(face) < 3: - raise ValueError("Face must have at least 3 vertices") - - if check: - edges = list(zip(face, face[1:] + [face[0]])) - for u, v in edges: - if not self.has_edge(u, v): - raise ValueError(f"Edge ({u},{v}) missing") - - polygon = np.array( - [self.coord_matrix[self._node_to_index[v]] for v in face] - ) - for node in self.nodes: - if node in face: - continue - if point_in_polygon( - self.coord_matrix[self._node_to_index[node]], polygon - ): - raise ValueError(f"Node {node} inside face {face}") - - self.faces.append(tuple(face)) - - def add_faces_from(self, faces): - """ - Adds a list of faces to the graph. - - Parameters: - faces (list): - A list of faces to add. - """ - for face in faces: - self.add_face(face) - - def plot_faces(self, theta=None, ax=None, **kwargs): - """ - Plots the faces of the graph in the direction of theta. - - Parameters: - theta (float): - The angle in :math:`[0,2\pi]` for the direction to sort the edges. - ax (matplotlib.axes.Axes): - The axes to plot the graph on. If None, a new figure is created. - **kwargs: - Additional keyword arguments to pass to the ax.fill function. - - Returns: - matplotlib.axes.Axes - The axes object with the plot. - """ - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.get_figure() - - for face in self.faces: - face_coords = np.array( - [self.coord_matrix[self.node_to_index[v]] for v in face] - ) - - if self.dim == 2: - ax.fill(face_coords[:, 0], face_coords[:, 1], **kwargs) - else: # 3D case - verts = [face_coords] - collection = Poly3DCollection(verts, **kwargs) - ax.add_collection3d(collection) - - return ax - - def plot( - self, - bounding_circle: bool = False, - bounding_center_type: str = "bounding_box", - color_nodes_theta: Optional[float] = None, - ax: Optional[plt.Axes] = None, - with_labels: bool = True, - node_size: int = 300, - edge_color: str = "gray", - elev: float = 25, - azim: float = -60, - face_color: str = "lightblue", - face_alpha: float = 0.3, - **kwargs, - ) -> plt.Axes: - """ - Visualize the embedded CW complex in 2D or 3D, including faces - """ - # plot faces then use parent class - ax = self._create_axes(ax, self.dim) - self.plot_faces(ax=ax, facecolor=face_color, alpha=face_alpha) - - return super().plot( - bounding_circle=bounding_circle, - bounding_center_type=bounding_center_type, - color_nodes_theta=color_nodes_theta, - ax=ax, - with_labels=with_labels, - node_size=node_size, - edge_color=edge_color, - elev=elev, - azim=azim, - **kwargs, - ) diff --git a/src/ect/sect.py b/src/ect/sect.py index da981ef..9b56c92 100644 --- a/src/ect/sect.py +++ b/src/ect/sect.py @@ -1,6 +1,5 @@ from ect import ECT -from .embed_graph import EmbeddedGraph -from .embed_cw import EmbeddedCW +from .embed_complex import EmbeddedComplex from .directions import Directions from .results import ECTResult from typing import Optional, Union @@ -38,7 +37,7 @@ def __init__( def calculate( self, - graph: Union[EmbeddedGraph, EmbeddedCW], + graph: EmbeddedComplex, theta: Optional[float] = None, override_bound_radius: Optional[float] = None, ) -> ECTResult: diff --git a/src/ect/utils/examples.py b/src/ect/utils/examples.py index 475a5e7..6dae52a 100644 --- a/src/ect/utils/examples.py +++ b/src/ect/utils/examples.py @@ -1,20 +1,17 @@ -from ect.embed_graph import EmbeddedGraph -from ect.embed_cw import EmbeddedCW +from ect.embed_complex import EmbeddedComplex import numpy as np def create_example_cw(centered=True, center_type="bounding_box"): """ - Creates an example EmbeddedCW object with a simple CW complex. If centered is True, the coordinates are centered around the center type, which could be ``mean``, ``bounding_box`` or ``origin``. + Creates an example EmbeddedComplex object with a simple CW complex. If centered is True, the coordinates are centered around the center type, which could be ``mean``, ``bounding_box`` or ``origin``. Returns: - EmbeddedCW - The example EmbeddedCW object. + EmbeddedComplex + The example EmbeddedComplex object. """ - G = create_example_graph(centered=False) - K = EmbeddedCW() - K.add_from_embedded_graph(G) + K = create_example_graph(centered=False) extra_coords = {"G": [2, 4], "H": [1, 5], "I": [5, 4], "J": [2, 2], "K": [2, 7]} @@ -44,13 +41,13 @@ def create_example_cw(centered=True, center_type="bounding_box"): def create_example_graph(centered=True, center_type="mean"): """ - Function to create an example ``EmbeddedGraph`` object. Helpful for testing. If ``centered`` is True, the coordinates are centered using the center type given by ``center_type``, either ``mean``, ``bounding_box`` or ``origin``. + Function to create an example ``EmbeddedComplex`` object with graph structure. Helpful for testing. If ``centered`` is True, the coordinates are centered using the center type given by ``center_type``, either ``mean``, ``bounding_box`` or ``origin``. Returns: - EmbeddedGraph: An example ``EmbeddedGraph`` object. + EmbeddedComplex: An example ``EmbeddedComplex`` object with graph structure. """ - graph = EmbeddedGraph() + graph = EmbeddedComplex() coords = { "A": [1, 2], @@ -81,7 +78,7 @@ def create_random_graph(n_nodes=100, n_edges=200, dim=2): n_edges: Number of random edges to add dim: Dimension of embedding space """ - G = EmbeddedGraph() + G = EmbeddedComplex() coords = np.random.random((n_nodes, dim)) nodes_with_coords = [(i, coords[i]) for i in range(n_nodes)] @@ -96,3 +93,140 @@ def create_random_graph(n_nodes=100, n_edges=200, dim=2): G.add_edges_from(edges) return G + + +def create_example_3d_complex(centered=True, center_type="bounding_box"): + """ + Creates an example 3D EmbeddedComplex with vertices, edges, 2-cells, and 3-cells. + + Args: + centered: Whether to center the coordinates + center_type: Method for centering ("mean", "bounding_box", or "origin") + + Returns: + EmbeddedComplex: A complex with cells up to dimension 3 + """ + + K = EmbeddedComplex() + + # Add vertices forming a cube and tetrahedron + coords = { + # Cube vertices + "A": [0, 0, 0], + "B": [1, 0, 0], + "C": [1, 1, 0], + "D": [0, 1, 0], + "E": [0, 0, 1], + "F": [1, 0, 1], + "G": [1, 1, 1], + "H": [0, 1, 1], + # Additional vertices for tetrahedron + "P": [0.5, 0.5, 0.5], # Center point + "Q": [2, 0, 0], # External point + } + + for node, coord in coords.items(): + K.add_node(node, coord) + + # Add edges (1-cells) + edges = [ + # Cube edges + ("A", "B"), + ("B", "C"), + ("C", "D"), + ("D", "A"), # Bottom face + ("E", "F"), + ("F", "G"), + ("G", "H"), + ("H", "E"), # Top face + ("A", "E"), + ("B", "F"), + ("C", "G"), + ("D", "H"), # Vertical edges + # Additional edges + ("A", "P"), + ("B", "P"), + ("C", "P"), + ("D", "P"), # Center connections + ("P", "Q"), + ("A", "Q"), + ("B", "Q"), # External connections + ] + K.add_edges_from(edges) + + # Add 2-cells (faces) + faces = [ + # Cube faces + ["A", "B", "C", "D"], # Bottom + ["E", "F", "G", "H"], # Top + ["A", "B", "F", "E"], # Front + ["C", "D", "H", "G"], # Back + ["B", "C", "G", "F"], # Right + ["A", "D", "H", "E"], # Left + # Triangle faces + ["A", "B", "P"], + ["B", "C", "P"], + ["C", "D", "P"], + ["D", "A", "P"], + ["A", "B", "Q"], + ["A", "P", "Q"], + ["B", "P", "Q"], + ] + + for face in faces: + K.add_cell(face, dim=2) + + # Add 3-cells (volumes) + volumes = [ + # Tetrahedra + ["A", "B", "C", "P"], + ["A", "C", "D", "P"], + ["A", "B", "P", "Q"], + # Part of cube (pyramid with base ABCD and apex P) + ["A", "B", "C", "D", "P"], # 4-cell using 5 vertices + ] + + for volume in volumes[:-1]: # Add tetrahedra + K.add_cell(volume, dim=3) + + # Add the 4-cell + K.add_cell(volumes[-1], dim=4) + + if centered: + K.center_coordinates(center_type) + + return K + + +def create_sparse_dimensional_complex(): + """ + Creates a complex with gaps in cell dimensions (0-cells and 3-cells only). + + Returns: + EmbeddedComplex: A complex with only 0-cells and 3-cells + """ + + K = EmbeddedComplex() + + # Add vertices + coords = { + "A": [0, 0, 0], + "B": [1, 0, 0], + "C": [0, 1, 0], + "D": [0, 0, 1], + "E": [1, 1, 0], + "F": [1, 0, 1], + } + + for node, coord in coords.items(): + K.add_node(node, coord) + + # Add 0-cells explicitly (usually not needed as nodes are 0-cells) + for node in ["A", "B", "C", "D"]: + K.add_cell([node], dim=0) + + # Skip 1-cells and 2-cells - add only 3-cells + K.add_cell(["A", "B", "C", "D"], dim=3) + K.add_cell(["A", "B", "E", "F"], dim=3) + + return K diff --git a/src/ect/utils/face_check.py b/src/ect/utils/face_check.py index e567951..7d4bbad 100644 --- a/src/ect/utils/face_check.py +++ b/src/ect/utils/face_check.py @@ -1,4 +1,5 @@ import numpy as np +from typing import List, Tuple def point_in_polygon(point: np.ndarray, polygon: np.ndarray) -> bool: @@ -14,3 +15,166 @@ def point_in_polygon(point: np.ndarray, polygon: np.ndarray) -> bool: if x <= xinters: inside = not inside return inside + + +def point_on_polygon_boundary(point: np.ndarray, polygon: np.ndarray, tol: float = 1e-10) -> bool: + """Check if a point lies on the boundary of a polygon""" + x, y = point + n = polygon.shape[0] + + for i in range(n): + p1 = polygon[i] + p2 = polygon[(i+1) % n] + + # Check if point is on the line segment p1-p2 + if point_on_line_segment(point, p1, p2, tol): + return True + return False + + +def point_on_line_segment(point: np.ndarray, p1: np.ndarray, p2: np.ndarray, tol: float = 1e-10) -> bool: + """Check if a point lies on a line segment""" + # Vector from p1 to p2 + v = p2 - p1 + # Vector from p1 to point + u = point - p1 + + # Handle 2D cross product properly for NumPy 2.0+ + if len(u) == 2 and len(v) == 2: + # 2D cross product: u[0]*v[1] - u[1]*v[0] + cross_product = u[0] * v[1] - u[1] * v[0] + else: + # For higher dimensions, use the magnitude of cross product + cross_product = np.linalg.norm(np.cross(u, v)) + + # Check if vectors are collinear (cross product is zero) + if abs(cross_product) > tol: + return False + + # Check if point is within the segment bounds + if np.dot(v, v) < tol: # p1 and p2 are the same point + return np.linalg.norm(point - p1) < tol + + # Project point onto line and check if it's within [0, 1] + t = np.dot(u, v) / np.dot(v, v) + return 0 <= t <= 1 + + +def segments_intersect(p1: np.ndarray, p2: np.ndarray, p3: np.ndarray, p4: np.ndarray, tol: float = 1e-10) -> bool: + """Check if two line segments intersect (including endpoints)""" + def ccw(A, B, C): + return (C[1] - A[1]) * (B[0] - A[0]) > (B[1] - A[1]) * (C[0] - A[0]) + + # Check if segments are degenerate (zero length) + if np.allclose(p1, p2, atol=tol) or np.allclose(p3, p4, atol=tol): + return False + + # Standard intersection test + return ccw(p1, p3, p4) != ccw(p2, p3, p4) and ccw(p1, p2, p3) != ccw(p1, p2, p4) + + +def validate_face_embedding(face_coords: np.ndarray, all_coords: np.ndarray, + face_vertex_indices: List[int], all_vertex_indices: List[int], + tol: float = 1e-10) -> Tuple[bool, str]: + """ + Validate that a face is properly embedded (no vertices in interior, no edge intersections) + + Args: + face_coords: Coordinates of face vertices in order + all_coords: Coordinates of all vertices in the complex + face_vertex_indices: Indices of vertices that form the face + all_vertex_indices: Indices of all vertices in the complex + tol: Numerical tolerance + + Returns: + (is_valid, error_message) + """ + # Only validate for 2D faces (projecting higher-dimensional faces to 2D would be complex) + if face_coords.shape[1] > 2: + # For 3D+ faces, we'd need more sophisticated geometric analysis + # For now, just check basic self-intersection in the first 2 dimensions + face_coords_2d = face_coords[:, :2] + else: + face_coords_2d = face_coords + + # Check 1: No other vertices inside the face (2D projection) + for i, vertex_idx in enumerate(all_vertex_indices): + if vertex_idx in face_vertex_indices: + continue + + if i >= len(all_coords): + continue # Skip if vertex index is out of bounds + + vertex_coord = all_coords[i] + + # Project to 2D if needed + if vertex_coord.shape[0] > 2: + vertex_coord_2d = vertex_coord[:2] + else: + vertex_coord_2d = vertex_coord + + # Check if vertex is strictly inside (not on boundary) + if point_in_polygon(vertex_coord_2d, face_coords_2d): + # Double-check it's not on the boundary + if not point_on_polygon_boundary(vertex_coord_2d, face_coords_2d, tol): + return False, f"Vertex {vertex_idx} is inside face interior" + + # Check 2: Face edges don't self-intersect (for non-convex faces) + n = len(face_coords_2d) + if n < 3: + return False, "Face must have at least 3 vertices" + + for i in range(n): + edge1_start = face_coords_2d[i] + edge1_end = face_coords_2d[(i + 1) % n] + + # Check against non-adjacent edges + for j in range(i + 2, n): + if j == n - 1 and i == 0: # Skip last edge vs first edge + continue + + edge2_start = face_coords_2d[j] + edge2_end = face_coords_2d[(j + 1) % n] + + if segments_intersect(edge1_start, edge1_end, edge2_start, edge2_end, tol): + return False, f"Face edges intersect: edge {i}-{(i+1)%n} intersects edge {j}-{(j+1)%n}" + + return True, "" + + +def validate_edge_embedding(edge_coords: np.ndarray, all_coords: np.ndarray, + edge_vertex_indices: List[int], all_vertex_indices: List[int], + tol: float = 1e-10) -> Tuple[bool, str]: + """ + Validate that an edge is properly embedded (no vertices on interior) + + Args: + edge_coords: Coordinates of edge endpoints + all_coords: Coordinates of all vertices in the complex + edge_vertex_indices: Indices of vertices that form the edge + all_vertex_indices: Indices of all vertices in the complex + tol: Numerical tolerance + + Returns: + (is_valid, error_message) + """ + if len(edge_coords) != 2: + return False, "Edge must have exactly 2 vertices" + + p1, p2 = edge_coords[0], edge_coords[1] + + # Check that no other vertices lie on the edge interior + for i, vertex_idx in enumerate(all_vertex_indices): + if vertex_idx in edge_vertex_indices: + continue + + vertex_coord = all_coords[i] + + # Check if vertex is on the edge (excluding endpoints) + if point_on_line_segment(vertex_coord, p1, p2, tol): + # Make sure it's not just very close to an endpoint + if (np.linalg.norm(vertex_coord - p1) > tol and + np.linalg.norm(vertex_coord - p2) > tol): + return False, f"Vertex {vertex_idx} lies on edge interior" + + return True, "" diff --git a/src/ect/validation/__init__.py b/src/ect/validation/__init__.py new file mode 100644 index 0000000..2b85c92 --- /dev/null +++ b/src/ect/validation/__init__.py @@ -0,0 +1,33 @@ +""" +Embedding validation system for EmbeddedComplex. + +This module provides a flexible, extensible system for validating that +cell complexes represent proper embeddings in Euclidean space. +""" + +from .validator import EmbeddingValidator +from .base import ValidationRule, ValidationResult +from .rules import ( + EdgeInteriorRule, + FaceInteriorRule, + SelfIntersectionRule, + BoundaryEdgeRule, + DimensionValidityRule, + VertexCountRule, + validate_coordinate_array, + validate_node_existence, +) + +__all__ = [ + "EmbeddingValidator", + "ValidationRule", + "ValidationResult", + "EdgeInteriorRule", + "FaceInteriorRule", + "SelfIntersectionRule", + "BoundaryEdgeRule", + "DimensionValidityRule", + "VertexCountRule", + "validate_coordinate_array", + "validate_node_existence", +] diff --git a/src/ect/validation/base.py b/src/ect/validation/base.py new file mode 100644 index 0000000..76f1a7a --- /dev/null +++ b/src/ect/validation/base.py @@ -0,0 +1,112 @@ +""" +Base classes for the embedding validation system. +""" + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import List, Optional +import numpy as np + + +@dataclass +class ValidationResult: + """Result of a validation check.""" + + is_valid: bool + message: str = "" + violating_indices: Optional[List[int]] = None + + @classmethod + def valid(cls) -> "ValidationResult": + """Create a valid result.""" + return cls(is_valid=True) + + @classmethod + def invalid( + cls, message: str, violating_indices: Optional[List[int]] = None + ) -> "ValidationResult": + """Create an invalid result with error message.""" + return cls(is_valid=False, message=message, violating_indices=violating_indices) + + +class ValidationRule(ABC): + """Abstract base class for embedding validation rules.""" + + def __init__(self, tolerance: float = 1e-10): + """ + Initialize validation rule. + + Args: + tolerance: Numerical tolerance for geometric checks + """ + self.tolerance = tolerance + + @property + @abstractmethod + def name(self) -> str: + """Human-readable name of this validation rule.""" + pass + + @property + def is_structural(self) -> bool: + """ + Check if this is a structural rule that should always be validated. + + Structural rules check basic requirements like vertex counts and dimension validity. + They should always be checked regardless of validation settings. + Geometric rules check embedding properties and are optional. + + Returns: + True if this rule should always be checked, False if optional + """ + return False + + @abstractmethod + def applies_to_dimension(self, dim: int) -> bool: + """ + Check if this rule applies to cells of the given dimension. + + Args: + dim: Cell dimension + + Returns: + True if this rule should validate cells of this dimension + """ + pass + + @abstractmethod + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """ + Validate a cell against this rule. + + Args: + cell_coords: Coordinates of vertices forming the cell + all_coords: Coordinates of all vertices in the complex + cell_indices: Indices of vertices forming the cell + all_indices: Indices of all vertices in the complex + dim: Dimension of the cell being validated + + Returns: + ValidationResult indicating if the cell is valid + """ + pass + + def set_tolerance(self, tolerance: float) -> "ValidationRule": + """ + Set the tolerance for this rule. + + Args: + tolerance: New tolerance value + + Returns: + Self for method chaining + """ + self.tolerance = tolerance + return self diff --git a/src/ect/validation/rules.py b/src/ect/validation/rules.py new file mode 100644 index 0000000..2bc802f --- /dev/null +++ b/src/ect/validation/rules.py @@ -0,0 +1,338 @@ +""" +Unified validation rules for embedding validation system. + +This module contains all validation rules including both structural rules +(which are always checked) and geometric rules (which are optional). +""" + +from typing import List, Callable, Optional +import numpy as np + +from .base import ValidationRule, ValidationResult +from ..utils.face_check import ( + validate_edge_embedding, + validate_face_embedding, + segments_intersect, +) + + +# ================================ +# STRUCTURAL RULES (Always checked) +# ================================ + +class DimensionValidityRule(ValidationRule): + """Validates that cell dimension is non-negative.""" + + @property + def name(self) -> str: + return "Dimension Validity" + + @property + def is_structural(self) -> bool: + return True + + def applies_to_dimension(self, dim: int) -> bool: + return True + + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """Validate that dimension is non-negative.""" + if dim is not None and dim < 0: + return ValidationResult.invalid("Cell dimension must be non-negative") + return ValidationResult.valid() + + +class VertexCountRule(ValidationRule): + """Validates that cells have the correct number of vertices for their dimension.""" + + @property + def name(self) -> str: + return "Vertex Count Validation" + + @property + def is_structural(self) -> bool: + return True + + def applies_to_dimension(self, dim: int) -> bool: + return True + + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """Validate vertex count matches cell dimension requirements.""" + if dim is None: + return ValidationResult.valid() + + vertex_count = len(cell_indices) + + if dim == 0 and vertex_count != 1: + return ValidationResult.invalid("0-cells must contain exactly one vertex") + elif dim == 1 and vertex_count != 2: + return ValidationResult.invalid("1-cells must contain exactly two vertices") + elif dim >= 2 and vertex_count < 3: + return ValidationResult.invalid( + f"{dim}-cells must contain at least 3 vertices" + ) + + return ValidationResult.valid() + + +class CoordinateDimensionRule(ValidationRule): + """Validates that coordinates have consistent dimensions.""" + + def __init__( + self, tolerance: float = 1e-10, dimension_checker: Optional[Callable] = None + ): + super().__init__(tolerance) + self.dimension_checker = dimension_checker # function to get expected dimension + + @property + def name(self) -> str: + return "Coordinate Dimension Validation" + + @property + def is_structural(self) -> bool: + return True + + def applies_to_dimension(self, dim: int) -> bool: + return True # applies to all operations involving coordinates + + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """Validate coordinate dimensions are consistent.""" + if self.dimension_checker is None or cell_coords is None: + return ValidationResult.valid() + + expected_dim = self.dimension_checker() + if expected_dim is None or expected_dim == 0: + return ValidationResult.valid() + + # check that all coordinates have the expected dimension + if cell_coords.ndim != 2: + return ValidationResult.invalid("Coordinates must be a 2D array") + + if cell_coords.shape[1] != expected_dim: + return ValidationResult.invalid( + f"Coordinates must have dimension {expected_dim}, got {cell_coords.shape[1]}" + ) + + return ValidationResult.valid() + + +# ================================ +# GEOMETRIC RULES (Optional) +# ================================ + +class EdgeInteriorRule(ValidationRule): + """Validates that no vertices lie on edge interiors.""" + + @property + def name(self) -> str: + return "Edge Interior Validation" + + def applies_to_dimension(self, dim: int) -> bool: + return dim == 1 + + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """Validate that no other vertices lie on this edge's interior.""" + is_valid, error_msg = validate_edge_embedding( + cell_coords, all_coords, cell_indices, all_indices, self.tolerance + ) + + if is_valid: + return ValidationResult.valid() + else: + return ValidationResult.invalid(error_msg) + + +class FaceInteriorRule(ValidationRule): + """Validates that no vertices lie inside face interiors.""" + + @property + def name(self) -> str: + return "Face Interior Validation" + + def applies_to_dimension(self, dim: int) -> bool: + return dim == 2 + + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """Validate that no other vertices lie inside this face.""" + is_valid, error_msg = validate_face_embedding( + cell_coords, all_coords, cell_indices, all_indices, self.tolerance + ) + + if is_valid: + return ValidationResult.valid() + else: + return ValidationResult.invalid(error_msg) + + +class SelfIntersectionRule(ValidationRule): + """Validates that face edges don't self-intersect.""" + + @property + def name(self) -> str: + return "Self-Intersection Validation" + + def applies_to_dimension(self, dim: int) -> bool: + return dim == 2 + + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """Validate that face edges don't intersect each other""" + + if cell_coords.shape[1] > 2: + coords_2d = cell_coords[:, :2] + else: + coords_2d = cell_coords + + n = len(coords_2d) + if n < 3: + return ValidationResult.invalid("Face must have at least 3 vertices") + + for i in range(n): + edge1_start = coords_2d[i] + edge1_end = coords_2d[(i + 1) % n] + + for j in range(i + 2, n): + if j == n - 1 and i == 0: + continue + + edge2_start = coords_2d[j] + edge2_end = coords_2d[(j + 1) % n] + + if segments_intersect( + edge1_start, edge1_end, edge2_start, edge2_end, self.tolerance + ): + return ValidationResult.invalid( + f"Face edges intersect: edge {i}-{(i + 1) % n} intersects edge {j}-{(j + 1) % n}" + ) + + return ValidationResult.valid() + + +class BoundaryEdgeRule(ValidationRule): + """Validates that required boundary edges exist for faces.""" + + def __init__(self, tolerance: float = 1e-10, edge_checker=None): + """ + Initialize boundary edge rule. + + Args: + tolerance: Numerical tolerance + edge_checker: Function to check if an edge exists (injected dependency) + """ + super().__init__(tolerance) + self.edge_checker = edge_checker + + @property + def name(self) -> str: + return "Boundary Edge Validation" + + def applies_to_dimension(self, dim: int) -> bool: + return dim == 2 + + def validate( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int = None, + ) -> ValidationResult: + """Validate that all boundary edges exist for this face.""" + if self.edge_checker is None: + return ValidationResult.valid() + + n = len(cell_indices) + if n < 3: + return ValidationResult.invalid("Face must have at least 3 vertices") + + for i in range(n): + v1_idx = cell_indices[i] + v2_idx = cell_indices[(i + 1) % n] + + if not self.edge_checker(v1_idx, v2_idx): + return ValidationResult.invalid( + f"Boundary edge between vertices {v1_idx} and {v2_idx} missing for face" + ) + + return ValidationResult.valid() + + +# ================================ +# STANDALONE UTILITY FUNCTIONS +# ================================ + +def validate_coordinate_array( + coords, expected_dim: Optional[int] = None +) -> ValidationResult: + """Standalone function to validate coordinate array format.""" + if coords is None: + return ValidationResult.valid() + + coords = np.asarray(coords, dtype=float) + + if coords.ndim != 1: + return ValidationResult.invalid("Coordinates must be a 1D array") + + if expected_dim is not None and coords.size != expected_dim: + return ValidationResult.invalid( + f"Coordinates must have dimension {expected_dim}, got {coords.size}" + ) + + return ValidationResult.valid() + + +def validate_node_existence( + nodes: List, node_checker: Callable, expect_exists: bool = True +) -> ValidationResult: + """Standalone function to validate node existence.""" + if node_checker is None: + return ValidationResult.valid() + + for node_id in nodes: + node_exists = node_checker(node_id) + if expect_exists and not node_exists: + return ValidationResult.invalid(f"Node {node_id} does not exist") + if not expect_exists and node_exists: + return ValidationResult.invalid(f"Node {node_id} already exists") + + return ValidationResult.valid() \ No newline at end of file diff --git a/src/ect/validation/validator.py b/src/ect/validation/validator.py new file mode 100644 index 0000000..7af7701 --- /dev/null +++ b/src/ect/validation/validator.py @@ -0,0 +1,271 @@ +""" +Main embedding validator class that orchestrates validation rules. +""" + +from typing import List, Callable, Optional +import numpy as np + +from .base import ValidationRule, ValidationResult +from .rules import ( + EdgeInteriorRule, + FaceInteriorRule, + SelfIntersectionRule, + BoundaryEdgeRule, + DimensionValidityRule, + VertexCountRule, + validate_coordinate_array, + validate_node_existence, +) + + +class EmbeddingValidator: + """ + Main validator that orchestrates multiple validation rules. + + This class manages a collection of validation rules and applies them + to cells based on their dimension and the configured rule set. + """ + + def __init__( + self, tolerance: float = 1e-10, edge_checker: Optional[Callable] = None + ): + """ + Initialize the embedding validator. + + Args: + tolerance: Default tolerance for geometric validation + edge_checker: Function to check if an edge exists between two vertex indices + """ + self.tolerance = tolerance + self.edge_checker = edge_checker + + self.rules: List[ValidationRule] = [ + DimensionValidityRule(tolerance), + VertexCountRule(tolerance), + EdgeInteriorRule(tolerance), + FaceInteriorRule(tolerance), + SelfIntersectionRule(tolerance), + BoundaryEdgeRule(tolerance, edge_checker), + ] + + def add_rule(self, rule: ValidationRule) -> "EmbeddingValidator": + """ + Add a custom validation rule. + + Args: + rule: ValidationRule to add + + Returns: + Self for method chaining + """ + self.rules.append(rule) + return self + + def remove_rule(self, rule_class: type) -> "EmbeddingValidator": + """ + Remove all rules of a specific type. + + Args: + rule_class: Class of rules to remove + + Returns: + Self for method chaining + """ + self.rules = [rule for rule in self.rules if not isinstance(rule, rule_class)] + return self + + def set_tolerance(self, tolerance: float) -> "EmbeddingValidator": + """ + Set tolerance for all rules. + + Args: + tolerance: New tolerance value + + Returns: + Self for method chaining + """ + self.tolerance = tolerance + for rule in self.rules: + rule.set_tolerance(tolerance) + return self + + def set_edge_checker(self, edge_checker: Callable) -> "EmbeddingValidator": + """ + Set the edge checker function for boundary validation. + + Args: + edge_checker: Function that takes two vertex indices and returns bool + + Returns: + Self for method chaining + """ + self.edge_checker = edge_checker + + for rule in self.rules: + if isinstance(rule, BoundaryEdgeRule): + rule.edge_checker = edge_checker + + return self + + def validate_cell( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int, + check_geometric: bool = True, + ) -> ValidationResult: + """ + Validate a cell using applicable rules. + + Args: + cell_coords: Coordinates of vertices forming the cell + all_coords: Coordinates of all vertices in the complex + cell_indices: Indices of vertices forming the cell + all_indices: Indices of all vertices in the complex + dim: Dimension of the cell + check_geometric: Whether to check geometric rules (structural rules always checked) + + Returns: + ValidationResult for the cell + + Raises: + ValueError: If validation fails + """ + # check all rules that apply to this dimension + for rule in self.rules: + if not rule.applies_to_dimension(dim): + continue + + # Always check structural rules, only check geometric rules if requested + if rule.is_structural or check_geometric: + result = rule.validate( + cell_coords, all_coords, cell_indices, all_indices, dim + ) + + if not result.is_valid: + return ValidationResult.invalid( + f"{rule.name}: {result.message}", result.violating_indices + ) + + return ValidationResult.valid() + + def validate_cell_safe( + self, + cell_coords: np.ndarray, + all_coords: np.ndarray, + cell_indices: List[int], + all_indices: List[int], + dim: int, + check_geometric: bool = True, + ) -> ValidationResult: + """ + Validate a cell, catching and wrapping any exceptions. + + Same as validate_cell but returns ValidationResult instead of raising. + + Args: + cell_coords: Coordinates of vertices forming the cell + all_coords: Coordinates of all vertices in the complex + cell_indices: Indices of vertices forming the cell + all_indices: Indices of all vertices in the complex + dim: Dimension of the cell + check_geometric: Whether to check geometric rules (structural rules always checked) + + Returns: + ValidationResult for the cell + """ + try: + return self.validate_cell( + cell_coords, all_coords, cell_indices, all_indices, dim, check_geometric + ) + except Exception as e: + return ValidationResult.invalid(f"Validation error: {str(e)}") + + def get_rules_for_dimension(self, dim: int) -> List[ValidationRule]: + """ + Get all rules that apply to a specific dimension. + + Args: + dim: Cell dimension + + Returns: + List of applicable ValidationRules + """ + return [rule for rule in self.rules if rule.applies_to_dimension(dim)] + + def get_rule_names(self) -> List[str]: + """ + Get names of all registered rules. + + Returns: + List of rule names + """ + return [rule.name for rule in self.rules] + + def disable_rule(self, rule_class: type) -> "EmbeddingValidator": + """ + Temporarily disable a rule type. + + Args: + rule_class: Class of rule to disable + + Returns: + Self for method chaining + """ + return self.remove_rule(rule_class) + + def enable_strict_validation(self) -> "EmbeddingValidator": + """ + Enable strict validation with tight tolerance. + + Returns: + Self for method chaining + """ + return self.set_tolerance(1e-12) + + def enable_permissive_validation(self) -> "EmbeddingValidator": + """ + Enable permissive validation with loose tolerance. + + Returns: + Self for method chaining + """ + return self.set_tolerance(1e-6) + + def validate_coordinates( + self, coords, expected_dim: Optional[int] = None + ) -> ValidationResult: + """ + Validate coordinate array format and dimension consistency. + + Args: + coords: Coordinate array to validate + expected_dim: Expected dimension, if known + + Returns: + ValidationResult for the coordinates + """ + return validate_coordinate_array(coords, expected_dim) + + def validate_nodes( + self, nodes: List, node_checker: Callable, expect_exists: bool = True + ) -> ValidationResult: + """ + Validate node existence/non-existence. + + Args: + nodes: List of node identifiers to check + node_checker: Function that takes node_id and returns bool for existence + expect_exists: Whether nodes should exist (True) or not exist (False) + + Returns: + ValidationResult for the nodes + """ + return validate_node_existence(nodes, node_checker, expect_exists) + + def __repr__(self) -> str: + """String representation of the validator.""" + rule_names = [rule.name for rule in self.rules] + return f"EmbeddingValidator(tolerance={self.tolerance}, rules={rule_names})" diff --git a/tests/test_dect.py b/tests/test_dect.py new file mode 100644 index 0000000..ee69848 --- /dev/null +++ b/tests/test_dect.py @@ -0,0 +1,182 @@ +import unittest +import numpy as np +from ect.utils.examples import create_example_graph, create_example_cw +from ect import DECT, ECT, Directions, EmbeddedComplex +from ect.results import ECTResult + + +class TestDECTBasicFunctionality(unittest.TestCase): + def setUp(self): + self.graph = create_example_graph() + self.num_dirs = 8 + self.num_thresh = 10 + self.bound_radius = 2.0 + + def test_initialization_with_scale_parameter(self): + """Test that DECT initializes correctly with scale parameter""" + # Test with default scale + dect1 = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + ) + self.assertEqual(dect1.scale, 10.0) # Default scale + + # Test with custom scale + custom_scale = 25.0 + dect2 = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + scale=custom_scale, + ) + self.assertEqual(dect2.scale, custom_scale) + + # Verify parent class attributes are initialized + self.assertEqual(dect2.bound_radius, self.bound_radius) + self.assertEqual(dect2.num_dirs, self.num_dirs) + self.assertEqual(dect2.num_thresh, self.num_thresh) + + def test_calculate_with_default_and_custom_scale(self): + """Test calculate method with both default and custom scale values""" + dect = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + scale=15.0, # Init scale + ) + + # Test with default scale from init + result1 = dect.calculate(self.graph) + self.assertIsNotNone(result1) + + # Test with override scale in calculate + result2 = dect.calculate(self.graph, scale=50.0) + self.assertIsNotNone(result2) + + # Results should be different due to different scales + self.assertFalse(np.allclose(result1, result2)) + + # Test that None scale uses init scale + result3 = dect.calculate(self.graph, scale=None) + self.assertTrue(np.allclose(result1, result3)) + + def test_inheritance_from_ect(self): + """Test that DECT properly inherits from ECT""" + dect = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + ) + + # Check that DECT is instance of ECT + self.assertIsInstance(dect, ECT) + + # Verify inherited methods are available + self.assertTrue(hasattr(dect, '_ensure_directions')) + self.assertTrue(hasattr(dect, '_ensure_thresholds')) + self.assertTrue(hasattr(dect, '_compute_simplex_projections')) + self.assertTrue(hasattr(dect, 'calculate')) + + # Verify only _compute_directional_transform is different + ect = ECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + ) + + # These methods should be the same (inherited) + self.assertEqual( + dect._ensure_directions.__code__.co_code, + ect._ensure_directions.__code__.co_code + ) + self.assertEqual( + dect._ensure_thresholds.__code__.co_code, + ect._ensure_thresholds.__code__.co_code + ) + + # This method should be different (overridden) + self.assertNotEqual( + dect._compute_directional_transform.__code__.co_code, + ect._compute_directional_transform.__code__.co_code + ) + + def test_result_shape_and_type(self): + """Test that DECT returns correct result shape and type""" + dect = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + ) + + result = dect.calculate(self.graph) + + # Check result type + self.assertIsInstance(result, ECTResult) + + # Check shape + self.assertEqual(result.shape, (self.num_dirs, self.num_thresh)) + + # Check that directions and thresholds are included + self.assertIsNotNone(result.directions) + self.assertIsInstance(result.directions, Directions) + self.assertEqual(len(result.directions), self.num_dirs) + + self.assertIsNotNone(result.thresholds) + self.assertEqual(len(result.thresholds), self.num_thresh) + + # Check dtype - ECTResult always converts to float64 for float types + dect_float32 = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + dtype=np.float32 + ) + result_float32 = dect_float32.calculate(self.graph) + # ECTResult converts float types to float64 + self.assertEqual(result_float32.dtype, np.float64) + + # Test with float64 + dect_float64 = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + dtype=np.float64 + ) + result_float64 = dect_float64.calculate(self.graph) + self.assertEqual(result_float64.dtype, np.float64) + + def test_calculate_with_single_direction(self): + """Test DECT calculation with a single direction (theta parameter)""" + dect = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + ) + + # Test with specific theta + result = dect.calculate(self.graph, theta=np.pi/4) + + # Should have single direction + self.assertEqual(result.shape, (1, self.num_thresh)) + self.assertEqual(len(result.directions), 1) + + def test_with_different_graph_types(self): + """Test DECT works with both EmbeddedGraph and EmbeddedCW""" + dect = DECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + ) + + # Test with graph + graph = create_example_graph() + result_graph = dect.calculate(graph) + self.assertEqual(result_graph.shape, (self.num_dirs, self.num_thresh)) + + # Test with CW complex + cw = create_example_cw() + result_cw = dect.calculate(cw) + self.assertEqual(result_cw.shape, (self.num_dirs, self.num_thresh)) + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/tests/test_ect.py b/tests/test_ect.py new file mode 100644 index 0000000..94fb6f2 --- /dev/null +++ b/tests/test_ect.py @@ -0,0 +1,214 @@ +import unittest +import numpy as np +from ect.utils.examples import create_example_graph, create_example_cw, create_example_3d_complex, create_sparse_dimensional_complex +from ect import ECT, Directions, EmbeddedComplex + + +class TestECT(unittest.TestCase): + def setUp(self): + self.graph = create_example_graph() + self.num_dirs = 8 + self.num_thresh = 10 + self.bound_radius = 2.0 + self.ect = ECT( + num_dirs=self.num_dirs, + num_thresh=self.num_thresh, + bound_radius=self.bound_radius, + ) + + def test_initialization(self): + self.assertEqual(self.ect.bound_radius, self.bound_radius) + self.assertEqual(self.ect.num_dirs, self.num_dirs) + self.assertEqual(self.ect.num_thresh, self.num_thresh) + self.assertIsNone(self.ect.directions) + + def test_calculate_basic(self): + result = self.ect.calculate(self.graph) + self.assertEqual(result.shape, (self.num_dirs, self.num_thresh)) + self.assertTrue(isinstance(result.directions, Directions)) + self.assertIsNotNone(result.thresholds) + + def test_calculate_single_direction(self): + result = self.ect.calculate(self.graph, theta=0) + self.assertEqual(result.shape, (1, self.num_thresh)) + self.assertEqual(len(result.directions), 1) + + def test_threshold_priority(self): + graph_radius = self.graph.get_bounding_radius() + override_bound_radius = 3 * graph_radius + + no_radius_ect = ECT(num_dirs=self.num_dirs, num_thresh=self.num_thresh) + # test graph radius is used when no other radius specified + result1 = no_radius_ect.calculate(self.graph) + self.assertAlmostEqual(abs(result1.thresholds).max(), graph_radius) + + # test override radius takes precedence over both + result2 = self.ect.calculate( + self.graph, override_bound_radius=override_bound_radius + ) + self.assertAlmostEqual(abs(result2.thresholds).max(), override_bound_radius) + + def test_different_graph_types(self): + cw = create_example_cw() + result_graph = self.ect.calculate(self.graph) + result_cw = self.ect.calculate(cw) + + self.assertEqual(result_graph.shape, result_cw.shape) + self.assertEqual(len(result_graph.directions), len(result_cw.directions)) + + def test_directions_matching(self): + # test that ect raises error when dimensions don't match + G2d = create_example_graph() + directions_3d = Directions.uniform(self.num_dirs, dim=3) + ect = ECT(directions=directions_3d) + + with self.assertRaises(ValueError): + ect.calculate(G2d) + + def test_result_properties(self): + result = self.ect.calculate(self.graph) + + # test smooth transform + smooth = result.smooth() + self.assertEqual(smooth.shape, result.shape) + self.assertEqual(smooth.directions, result.directions) + self.assertEqual(smooth.thresholds.tolist(), result.thresholds.tolist()) + + # verify result is integer-valued + self.assertTrue(np.issubdtype(result.dtype, np.integer)) + + def test_3d_complex_ect(self): + """Test ECT calculation with 3D complex containing higher-dimensional cells""" + complex_3d = create_example_3d_complex() + + # Create ECT for 3D embedding + ect_3d = ECT(num_dirs=6, num_thresh=8) + result = ect_3d.calculate(complex_3d) + + # Should have proper shape + self.assertEqual(result.shape, (6, 8)) + + # Should handle 3-cells and 4-cells in computation + self.assertTrue(np.issubdtype(result.dtype, np.integer)) + + # Result should be different from a graph-only computation + # (because it includes higher-dimensional cells in Euler characteristic) + self.assertIsInstance(result, np.ndarray) + + def test_sparse_dimensional_complex(self): + """Test ECT with complex having gaps in cell dimensions""" + sparse_complex = create_sparse_dimensional_complex() + + ect = ECT(num_dirs=4, num_thresh=6) + result = ect.calculate(sparse_complex) + + # Should handle missing 1-cells and 2-cells gracefully + self.assertEqual(result.shape, (4, 6)) + self.assertTrue(np.issubdtype(result.dtype, np.integer)) + + def test_high_dimensional_cells_projection(self): + """Test that projections are computed correctly for high-dimensional cells""" + complex_3d = create_example_3d_complex() + + # Test with single direction for easier verification + directions = Directions.uniform(1, dim=3) + ect = ECT(directions=directions, num_thresh=5) + + result = ect.calculate(complex_3d) + + # Should compute without errors + self.assertEqual(result.shape, (1, 5)) + + # Verify the internal projection computation works + node_projections = np.matmul(complex_3d.coord_matrix, directions.vectors.T) + simplex_projections = ect._compute_simplex_projections(complex_3d, directions) + + # Should have projections for all cell dimensions present + # 0-cells (vertices), 1-cells (edges), 2-cells (faces), 3-cells, 4-cells + self.assertEqual(len(simplex_projections), 5) # dims 0-4 + + # Each should have correct number of directions + for proj in simplex_projections: + if proj.shape[0] > 0: # If there are cells of this dimension + self.assertEqual(proj.shape[1], 1) # 1 direction + + def test_empty_higher_dimensional_cells(self): + """Test ECT with complex that has some empty cell dimensions""" + # Create complex with only vertices and edges (no higher cells) + simple_graph = create_example_graph() + + ect = ECT(num_dirs=4, num_thresh=5) + result = ect.calculate(simple_graph) + + # Should handle missing higher-dimensional cells + self.assertEqual(result.shape, (4, 5)) + + # Internal projection computation should handle empty dimensions + directions = Directions.uniform(4, dim=2) + simplex_projections = ect._compute_simplex_projections(simple_graph, directions) + + # Should have at least vertices and edges + self.assertGreaterEqual(len(simplex_projections), 2) + + # Higher dimensions should be empty arrays with correct shape + for i, proj in enumerate(simplex_projections): + self.assertEqual(proj.shape[1], 4) # 4 directions + if i >= 2: # dimensions 2 and higher should be empty for simple graph + self.assertEqual(proj.shape[0], 0) + + def test_cell_projections_correctness(self): + """Test that cell projections correctly compute max over vertices""" + # Create simple complex for manual verification + K = EmbeddedComplex() + K.add_node('A', [0, 0]) + K.add_node('B', [1, 0]) + K.add_node('C', [0, 1]) + K.add_node('D', [1, 1]) + + # Add a 2-cell + K.add_cell(['A', 'B', 'C'], dim=2) + + # Test projection in direction [1, 0] (x-direction) + directions = Directions.from_angles([0]) # theta=0 -> direction [0, -1] + + ect = ECT(directions=directions, num_thresh=3) + + # Test internal projection computation + node_projections = np.matmul(K.coord_matrix, directions.vectors.T) + simplex_projections = ect._compute_simplex_projections(K, directions) + + # Verify 2-cell projection is max of its vertices + face_projection = simplex_projections[2][0, 0] # First 2-cell, first direction + vertex_projections = node_projections[[0, 1, 2], 0] # Vertices A, B, C + expected_max = np.max(vertex_projections) + + self.assertAlmostEqual(face_projection, expected_max, places=10) + + def test_euler_characteristic_with_higher_cells(self): + """Test that Euler characteristic includes higher-dimensional cells""" + complex_3d = create_example_3d_complex() + + # Calculate ECT + ect = ECT(num_dirs=1, num_thresh=10) + result = ect.calculate(complex_3d) + + # The result should reflect the alternating sum over all cell dimensions + # χ = |0-cells| - |1-cells| + |2-cells| - |3-cells| + |4-cells| - ... + + # Verify that the computation includes all dimensions + directions = Directions.uniform(1, dim=3) + simplex_projections = ect._compute_simplex_projections(complex_3d, directions) + + # Should have projections for dimensions 0 through 4 + self.assertEqual(len(simplex_projections), 5) + + # Check that we have cells in expected dimensions + self.assertGreater(simplex_projections[0].shape[0], 0) # vertices + self.assertGreater(simplex_projections[1].shape[0], 0) # edges + self.assertGreater(simplex_projections[2].shape[0], 0) # faces + self.assertGreater(simplex_projections[3].shape[0], 0) # 3-cells + self.assertGreater(simplex_projections[4].shape[0], 0) # 4-cells + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_ect_graph.py b/tests/test_ect_graph.py deleted file mode 100644 index 219f89d..0000000 --- a/tests/test_ect_graph.py +++ /dev/null @@ -1,82 +0,0 @@ -import unittest -import numpy as np -from ect.utils.examples import create_example_graph, create_example_cw -from ect import ECT, Directions - - -class TestECT(unittest.TestCase): - def setUp(self): - self.graph = create_example_graph() - self.num_dirs = 8 - self.num_thresh = 10 - self.bound_radius = 2.0 - self.ect = ECT( - num_dirs=self.num_dirs, - num_thresh=self.num_thresh, - bound_radius=self.bound_radius, - ) - - def test_initialization(self): - self.assertEqual(self.ect.bound_radius, self.bound_radius) - self.assertEqual(self.ect.num_dirs, self.num_dirs) - self.assertEqual(self.ect.num_thresh, self.num_thresh) - self.assertIsNone(self.ect.directions) - - def test_calculate_basic(self): - result = self.ect.calculate(self.graph) - self.assertEqual(result.shape, (self.num_dirs, self.num_thresh)) - self.assertTrue(isinstance(result.directions, Directions)) - self.assertIsNotNone(result.thresholds) - - def test_calculate_single_direction(self): - result = self.ect.calculate(self.graph, theta=0) - self.assertEqual(result.shape, (1, self.num_thresh)) - self.assertEqual(len(result.directions), 1) - - def test_threshold_priority(self): - graph_radius = self.graph.get_bounding_radius() - override_bound_radius = 3 * graph_radius - - no_radius_ect = ECT(num_dirs=self.num_dirs, num_thresh=self.num_thresh) - # test graph radius is used when no other radius specified - result1 = no_radius_ect.calculate(self.graph) - self.assertAlmostEqual(abs(result1.thresholds).max(), graph_radius) - - # test override radius takes precedence over both - result2 = self.ect.calculate( - self.graph, override_bound_radius=override_bound_radius - ) - self.assertAlmostEqual(abs(result2.thresholds).max(), override_bound_radius) - - def test_different_graph_types(self): - cw = create_example_cw() - result_graph = self.ect.calculate(self.graph) - result_cw = self.ect.calculate(cw) - - self.assertEqual(result_graph.shape, result_cw.shape) - self.assertEqual(len(result_graph.directions), len(result_cw.directions)) - - def test_directions_matching(self): - # test that ect raises error when dimensions don't match - G2d = create_example_graph() - directions_3d = Directions.uniform(self.num_dirs, dim=3) - ect = ECT(directions=directions_3d) - - with self.assertRaises(ValueError): - ect.calculate(G2d) - - def test_result_properties(self): - result = self.ect.calculate(self.graph) - - # test smooth transform - smooth = result.smooth() - self.assertEqual(smooth.shape, result.shape) - self.assertEqual(smooth.directions, result.directions) - self.assertEqual(smooth.thresholds.tolist(), result.thresholds.tolist()) - - # verify result is integer-valued - self.assertTrue(np.issubdtype(result.dtype, np.integer)) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/test_embed_complex.py b/tests/test_embed_complex.py new file mode 100644 index 0000000..ff96748 --- /dev/null +++ b/tests/test_embed_complex.py @@ -0,0 +1,158 @@ +import unittest +import numpy as np +from ect import EmbeddedComplex + + +class TestEmbeddedComplex(unittest.TestCase): + def setUp(self): + self.complex = EmbeddedComplex() + coords = { + "A": [0, 0, 0], + "B": [1, 0, 0], + "C": [0, 1, 0], + "D": [0, 0, 1], + "E": [1, 1, 1], + } + for node, coord in coords.items(): + self.complex.add_node(node, coord) + + def test_add_cell_validation(self): + """Test validation for different cell dimensions""" + + self.complex.add_cell(["A"], dim=0) + with self.assertRaises(ValueError): + self.complex.add_cell(["A", "B"], dim=0) + + self.complex.add_cell(["A", "B"], dim=1) + with self.assertRaises(ValueError): + self.complex.add_cell(["A"], dim=1) + with self.assertRaises(ValueError): + self.complex.add_cell(["A", "B", "C"], dim=1) + + self.complex.add_cell(["A", "B", "C"], dim=2) + self.complex.add_cell(["A", "B", "C", "D"], dim=3) + with self.assertRaises(ValueError): + self.complex.add_cell(["A", "B"], dim=2) + with self.assertRaises(ValueError): + self.complex.add_cell(["A"], dim=3) + + def test_add_cell_dimension_inference(self): + """Test that cell dimension is correctly inferred from vertex count""" + + self.complex.add_cell(["A", "B", "C"]) + self.assertIn((0, 1, 2), self.complex.cells[2]) + + self.complex.add_cell(["A", "B", "C", "D"]) + self.assertIn((0, 1, 2, 3), self.complex.cells[3]) + + def test_add_cell_nonexistent_vertex(self): + """Test error when adding cell with nonexistent vertex""" + with self.assertRaises(ValueError): + self.complex.add_cell(["A", "B", "Z"]) + + def test_add_cell_storage(self): + """Test that cells are properly stored by dimension""" + + self.complex.add_cell(["A", "B"], dim=1) + self.complex.add_cell(["A", "B", "C"], dim=2) + self.complex.add_cell(["A", "B", "C", "D"], dim=3) + self.complex.add_cell(["A", "B", "C", "D", "E"], dim=4) + + self.assertEqual(len(self.complex.cells[1]), 1) + self.assertEqual(len(self.complex.cells[2]), 1) + self.assertEqual(len(self.complex.cells[3]), 1) + self.assertEqual(len(self.complex.cells[4]), 1) + + self.assertIn((0, 1), self.complex.cells[1]) + self.assertIn((0, 1, 2), self.complex.cells[2]) + self.assertIn((0, 1, 2, 3), self.complex.cells[3]) + self.assertIn((0, 1, 2, 3, 4), self.complex.cells[4]) + + def test_add_face_backward_compatibility(self): + """Test that add_face method works for backward compatibility""" + self.complex.add_face(["A", "B", "C"]) + self.assertIn(("A", "B", "C"), self.complex.faces) + + with self.assertRaises(ValueError): + self.complex.add_face(["A", "B"]) + + def test_faces_property_backward_compatibility(self): + """Test that faces property returns vertex names for backward compatibility""" + self.complex.add_cell(["A", "B", "C"], dim=2) + self.complex.add_cell(["B", "C", "D"], dim=2) + + faces = self.complex.faces + self.assertEqual(len(faces), 2) + self.assertIn(("A", "B", "C"), faces) + self.assertIn(("B", "C", "D"), faces) + + def test_add_multiple_same_dimension_cells(self): + """Test adding multiple cells of the same dimension""" + self.complex.add_cell(["A", "B", "C"], dim=2) + self.complex.add_cell(["B", "C", "D"], dim=2) + self.complex.add_cell(["A", "C", "D"], dim=2) + + self.assertEqual(len(self.complex.cells[2]), 3) + + self.complex.add_cell(["A", "B", "C", "D"], dim=3) + self.complex.add_cell(["A", "B", "C", "E"], dim=3) + + self.assertEqual(len(self.complex.cells[3]), 2) + + def test_high_dimensional_cells(self): + """Test cells with very high dimensions""" + for i in range(10): + self.complex.add_node(f"V{i}", [i, i, i]) + + vertices = [f"V{i}" for i in range(8)] + self.complex.add_cell(vertices, dim=7) + + self.assertEqual(len(self.complex.cells[7]), 1) + self.assertEqual(len(self.complex.cells[7][0]), 8) + + def test_edge_indices_integration(self): + """Test that edge_indices property works with cell structure""" + self.complex.add_edge("A", "B") + self.complex.add_edge("B", "C") + + edge_indices = self.complex.edge_indices + self.assertEqual(edge_indices.shape[0], 2) + + self.complex.add_cell(["C", "D"], dim=1) + + edge_indices = self.complex.edge_indices + self.assertEqual(edge_indices.shape[0], 3) + + def test_empty_complex(self): + """Test behavior with empty complex""" + empty_complex = EmbeddedComplex() + + self.assertEqual(len(empty_complex.cells), 0) + self.assertEqual(len(empty_complex.faces), 0) + self.assertEqual(empty_complex.edge_indices.shape, (0, 2)) + + def test_complex_with_gaps(self): + """Test complex with gaps in cell dimensions""" + self.complex.add_cell(["A"], dim=0) + self.complex.add_cell(["A", "B", "C", "D"], dim=3) + + self.assertEqual(len(self.complex.cells[0]), 1) + self.assertEqual(len(self.complex.cells.get(1, [])), 0) + self.assertEqual(len(self.complex.cells.get(2, [])), 0) + self.assertEqual(len(self.complex.cells[3]), 1) + + def test_cell_validation_with_check_parameter(self): + """Test cell validation when check=True""" + + self.complex.add_edge("A", "B") + self.complex.add_edge("B", "C") + self.complex.add_edge("C", "A") + + self.complex.add_cell(["A", "B", "C"], dim=2, check=True) + + with self.assertRaises(ValueError): + self.complex.add_cell(["A", "B", "D"], dim=2, check=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_embed_cw.py b/tests/test_embed_cw.py deleted file mode 100644 index 65732ad..0000000 --- a/tests/test_embed_cw.py +++ /dev/null @@ -1,55 +0,0 @@ -import unittest -from ect.utils.examples import create_example_cw -import numpy as np - - -class TestEmbeddedCW(unittest.TestCase): - def test_example_cw(self): - G = create_example_cw() - self.assertEqual(len(G.nodes), 11) - self.assertEqual(len(G.faces), 2) - - def test_get_coordinates(self): - G = create_example_cw(centered=False) - coords = G.get_coord("A") - self.assertTrue(np.array_equal(coords, np.array([1, 2]))) - - def test_mean_centered_coordinates(self): - G = create_example_cw(centered=False) - G.center_coordinates("mean") - x_coords = G.coord_matrix[:, 0] - self.assertAlmostEqual(np.average(x_coords), 0, places=1) - - def test_add_face(self): - G = create_example_cw() - face = ["A", "B", "C"] - G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)]) - G.add_face(face) - self.assertIn(tuple(face), G.faces) - - def test_non_existent_vertex(self): - G = create_example_cw() - face = ["A", "B", "C"] - G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)]) - - # test non-existent vertex - with self.assertRaises(ValueError): - G.add_face(["A", "B", "Z"]) - - def test_face_with_missing_edges(self): - G = create_example_cw() - face = ["A", "B", "C"] - G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)]) - with self.assertRaises(ValueError): - G.add_face(["A", "D", "E"], check=True) - - def test_too_short_face(self): - G = create_example_cw() - face = ["A", "B", "C"] - G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)]) - with self.assertRaises(ValueError): - G.add_face(["A", "B"]) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/test_embed_graph.py b/tests/test_embed_graph.py deleted file mode 100644 index c7b37fd..0000000 --- a/tests/test_embed_graph.py +++ /dev/null @@ -1,101 +0,0 @@ -import unittest -from ect.utils.examples import create_example_graph -import numpy as np - - -class TestEmbeddedGraph(unittest.TestCase): - def test_example_graph(self): - G = create_example_graph() - self.assertEqual(len(G.nodes), 6) - self.assertEqual(G.dim, 2) - - def test_coord_matrix(self): - G = create_example_graph() - self.assertEqual(G.coord_matrix.shape, (6, 2)) - self.assertTrue(isinstance(G.coord_matrix, np.ndarray)) - - def test_node_list(self): - G = create_example_graph() - self.assertEqual(len(G.node_list), 6) - self.assertEqual(set(G.node_list), set(G.nodes)) - - def test_add_node(self): - G = create_example_graph() - G.add_node("G", [1, 2]) - self.assertEqual(len(G.nodes), 7) - self.assertEqual(G.coord_matrix.shape, (7, 2)) - self.assertEqual(G.get_coord("G").tolist(), [1, 2]) - - def test_add_edge(self): - G = create_example_graph() - G.add_edge("A", "B") - self.assertEqual(len(G.edges), 6) - - def test_get_coord(self): - G = create_example_graph(centered=False) - coords = G.get_coord("A") - self.assertTrue(np.array_equal(coords, np.array([1, 2]))) - - def test_invalid_node_operations(self): - G = create_example_graph() - with self.assertRaises(ValueError): - G.add_node("A", [1, 2]) - with self.assertRaises(ValueError): - G.get_coord("Z") - - def test_mean_centered_coordinates(self): - G = create_example_graph(centered=False) - G.center_coordinates("mean") - x_coords = G.coord_matrix[:, 0] - self.assertAlmostEqual(np.average(x_coords), 0, places=1) - - def test_get_center(self): - G = create_example_graph() - center = G.get_center("mean") - self.assertIsInstance(center, np.ndarray) - self.assertEqual(len(center), 2) - - coords = G.coord_matrix - expected_center = np.mean(coords, axis=0) - np.testing.assert_almost_equal(center, expected_center) - - def test_rescale_to_unit_disk(self): - G = create_example_graph() - G.scale_coordinates(1.0) - - self.assertAlmostEqual(G.get_bounding_radius(center_type="mean"), 1.0, places=6) - - coords_before = G.coord_matrix.copy() - G.scale_coordinates(2.0) - coords_after = G.coord_matrix - self.assertTrue(np.allclose(coords_after / 2.0, coords_before)) - - def test_bounding_box_centered_coordinates(self): - G = create_example_graph(centered=False) - G.center_coordinates("bounding_box") - x_coords = G.coord_matrix[:, 0] - y_coords = G.coord_matrix[:, 1] - - self.assertAlmostEqual(np.max(x_coords) + np.min(x_coords), 0, places=1) - self.assertAlmostEqual(np.max(y_coords) + np.min(y_coords), 0, places=1) - - def test_PCA_coords(self): - G = create_example_graph(centered=False) - G.project_coordinates("pca") - self.assertEqual(G.coord_matrix.shape, (6, 2)) - - def test_add_cycle(self): - G = create_example_graph(centered=False) - num_verts = len(G.nodes) - num_edges = len(G.edges) - verts_to_add = 8 - loop_coords = 3 * np.random.rand(verts_to_add, 2) - - G.add_cycle(loop_coords) - G.plot() - self.assertEqual(len(G.nodes), num_verts + verts_to_add) - self.assertEqual(len(G.edges), num_edges + verts_to_add) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/test_embedding_validation.py b/tests/test_embedding_validation.py new file mode 100644 index 0000000..eca4671 --- /dev/null +++ b/tests/test_embedding_validation.py @@ -0,0 +1,174 @@ +import unittest +import numpy as np +from ect import EmbeddedComplex + + +class TestEmbeddingValidation(unittest.TestCase): + def setUp(self): + self.complex = EmbeddedComplex() + + def test_valid_triangle_embedding(self): + """Test that a valid triangle passes embedding validation""" + + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [1, 0]) + self.complex.add_node("C", [0.5, 1]) + + self.complex.add_cell(["A", "B"], dim=1, check=True) + self.complex.add_cell(["B", "C"], dim=1, check=True) + self.complex.add_cell(["C", "A"], dim=1, check=True) + + self.complex.add_cell(["A", "B", "C"], dim=2, check=True) + + self.assertEqual(len(self.complex.cells[2]), 1) + + def test_vertex_inside_face_violation(self): + """Test that a vertex inside a face is properly detected""" + + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [2, 0]) + self.complex.add_node("C", [1, 2]) + self.complex.add_node("D", [1, 0.5]) + + self.complex.add_cell(["A", "B"], dim=1) + self.complex.add_cell(["B", "C"], dim=1) + self.complex.add_cell(["C", "A"], dim=1) + + with self.assertRaises(ValueError) as context: + self.complex.add_cell(["A", "B", "C"], dim=2, check=True) + + self.assertIn("inside face interior", str(context.exception)) + + def test_vertex_on_edge_violation(self): + """Test that a vertex on an edge interior is properly detected""" + + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [2, 0]) + self.complex.add_node("C", [1, 0]) + + with self.assertRaises(ValueError) as context: + self.complex.add_cell(["A", "B"], dim=1, check=True) + + self.assertIn("lies on edge interior", str(context.exception)) + + def test_self_intersecting_face_violation(self): + """Test that self-intersecting faces are detected""" + + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [1, 1]) + self.complex.add_node("C", [1, 0]) + self.complex.add_node("D", [0, 1]) + + edges = [("A", "B"), ("B", "C"), ("C", "D"), ("D", "A")] + for edge in edges: + self.complex.add_cell(list(edge), dim=1) + + with self.assertRaises(ValueError) as context: + self.complex.add_cell(["A", "B", "C", "D"], dim=2, check=True) + + self.assertIn("edges intersect", str(context.exception)) + + def test_valid_edge_embedding(self): + """Test that valid edges pass validation""" + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [1, 0]) + self.complex.add_node("C", [0, 1]) + + self.complex.add_cell(["A", "B"], dim=1, check=True) + + self.assertEqual(len(self.complex.cells[1]), 1) + + def test_boundary_edge_validation(self): + """Test that faces require boundary edges to exist""" + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [1, 0]) + self.complex.add_node("C", [0.5, 1]) + + self.complex.add_cell(["A", "B"], dim=1) + + with self.assertRaises(ValueError) as context: + self.complex.add_cell(["A", "B", "C"], dim=2, check=True) + + self.assertIn("missing for face", str(context.exception)) + + def test_tolerance_sensitivity(self): + """Test that tolerance parameter affects validation""" + + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [2, 0]) + self.complex.add_node("C", [1, 0]) + + with self.assertRaises(ValueError): + self.complex.add_cell(["A", "B"], dim=1, check=True, embedding_tol=1e-6) + + self.complex = EmbeddedComplex() + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [2, 0]) + self.complex.add_node("C", [1, 1e-8]) + + try: + self.complex.add_cell(["A", "B"], dim=1, check=True, embedding_tol=1e-6) + + except ValueError: + pass + + def test_skip_validation_when_check_false(self): + """Test that validation is skipped when check=False""" + + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [2, 0]) + self.complex.add_node("C", [1, 2]) + self.complex.add_node("D", [1, 0.5]) + + self.complex.add_cell(["A", "B"], dim=1) + self.complex.add_cell(["B", "C"], dim=1) + self.complex.add_cell(["C", "A"], dim=1) + self.complex.add_cell(["A", "B", "C"], dim=2, check=False) + + self.assertEqual(len(self.complex.cells[2]), 1) + + def test_empty_complex_validation(self): + """Test that validation works with empty complex""" + empty_complex = EmbeddedComplex() + + empty_complex.add_node("A", [0, 0]) + empty_complex.add_node("B", [1, 0]) + empty_complex.add_cell(["A", "B"], dim=1, check=True) + + self.assertEqual(len(empty_complex.cells[1]), 1) + + def test_3d_embedding_validation(self): + """Test validation works in 3D""" + + self.complex.add_node("A", [0, 0, 0]) + self.complex.add_node("B", [1, 0, 0]) + self.complex.add_node("C", [0.5, 1, 0]) + self.complex.add_node("D", [0.5, 0.5, 0]) + + self.complex.add_cell(["A", "B"], dim=1) + self.complex.add_cell(["B", "C"], dim=1) + self.complex.add_cell(["C", "A"], dim=1) + + with self.assertRaises(ValueError) as context: + self.complex.add_cell(["A", "B", "C"], dim=2, check=True) + + self.assertIn("inside face interior", str(context.exception)) + + def test_vertex_outside_face_allowed(self): + """Test that vertices outside faces are properly allowed""" + + self.complex.add_node("A", [0, 0]) + self.complex.add_node("B", [1, 0]) + self.complex.add_node("C", [0.5, 1]) + self.complex.add_node("D", [2, 2]) + + self.complex.add_cell(["A", "B"], dim=1) + self.complex.add_cell(["B", "C"], dim=1) + self.complex.add_cell(["C", "A"], dim=1) + + self.complex.add_cell(["A", "B", "C"], dim=2, check=True) + self.assertEqual(len(self.complex.cells[2]), 1) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_validation_system.py b/tests/test_validation_system.py new file mode 100644 index 0000000..b5f0eaf --- /dev/null +++ b/tests/test_validation_system.py @@ -0,0 +1,319 @@ +import unittest +import numpy as np +from ect import EmbeddedComplex +from ect.validation import ( + EmbeddingValidator, + ValidationResult, + EdgeInteriorRule, + FaceInteriorRule, + SelfIntersectionRule, + BoundaryEdgeRule, +) + + +class TestValidationResult(unittest.TestCase): + def test_valid_result(self): + """Test creating valid results""" + result = ValidationResult.valid() + self.assertTrue(result.is_valid) + self.assertEqual(result.message, "") + self.assertIsNone(result.violating_indices) + + def test_invalid_result(self): + """Test creating invalid results""" + result = ValidationResult.invalid("Test error", [1, 2]) + self.assertFalse(result.is_valid) + self.assertEqual(result.message, "Test error") + self.assertEqual(result.violating_indices, [1, 2]) + + +class TestEdgeInteriorRule(unittest.TestCase): + def setUp(self): + self.rule = EdgeInteriorRule() + + def test_applies_to_dimension(self): + """Test rule applies only to 1D cells""" + self.assertFalse(self.rule.applies_to_dimension(0)) + self.assertTrue(self.rule.applies_to_dimension(1)) + self.assertFalse(self.rule.applies_to_dimension(2)) + + def test_valid_edge(self): + """Test valid edge passes validation""" + cell_coords = np.array([[0, 0], [1, 0]]) + all_coords = np.array([[0, 0], [1, 0], [0.5, 0.5]]) # Third point not on edge + + result = self.rule.validate(cell_coords, all_coords, [0, 1], [0, 1, 2], dim=1) + self.assertTrue(result.is_valid) + + def test_vertex_on_edge(self): + """Test edge with vertex on interior fails validation""" + cell_coords = np.array([[0, 0], [2, 0]]) + all_coords = np.array([[0, 0], [2, 0], [1, 0]]) # Third point on edge + + result = self.rule.validate(cell_coords, all_coords, [0, 1], [0, 1, 2], dim=1) + self.assertFalse(result.is_valid) + self.assertIn("lies on edge interior", result.message) + + +class TestFaceInteriorRule(unittest.TestCase): + def setUp(self): + self.rule = FaceInteriorRule() + + def test_applies_to_dimension(self): + """Test rule applies only to 2D cells""" + self.assertFalse(self.rule.applies_to_dimension(1)) + self.assertTrue(self.rule.applies_to_dimension(2)) + self.assertFalse(self.rule.applies_to_dimension(3)) + + def test_valid_triangle(self): + """Test valid triangle passes validation""" + triangle_coords = np.array([[0, 0], [1, 0], [0.5, 1]]) + all_coords = np.array([[0, 0], [1, 0], [0.5, 1], [2, 2]]) + + result = self.rule.validate( + triangle_coords, all_coords, [0, 1, 2], [0, 1, 2, 3], dim=2 + ) + self.assertTrue(result.is_valid) + + def test_vertex_inside_triangle(self): + """Test triangle with vertex inside fails validation""" + triangle_coords = np.array([[0, 0], [2, 0], [1, 2]]) + all_coords = np.array([[0, 0], [2, 0], [1, 2], [1, 0.5]]) # fourth point inside + + result = self.rule.validate( + triangle_coords, all_coords, [0, 1, 2], [0, 1, 2, 3], dim=2 + ) + self.assertFalse(result.is_valid) + self.assertIn("inside face interior", result.message) + + +class TestSelfIntersectionRule(unittest.TestCase): + def setUp(self): + self.rule = SelfIntersectionRule() + + def test_applies_to_dimension(self): + """Test rule applies only to 2D cells""" + self.assertTrue(self.rule.applies_to_dimension(2)) + self.assertFalse(self.rule.applies_to_dimension(1)) + + def test_valid_square(self): + """Test valid square passes validation""" + square_coords = np.array([[0, 0], [1, 0], [1, 1], [0, 1]]) + + result = self.rule.validate( + square_coords, None, [0, 1, 2, 3], [0, 1, 2, 3], dim=2 + ) + self.assertTrue(result.is_valid) + + def test_self_intersecting_bowtie(self): + """Test self-intersecting bowtie fails validation""" + bowtie_coords = np.array([[0, 0], [1, 1], [1, 0], [0, 1]]) + + result = self.rule.validate( + bowtie_coords, None, [0, 1, 2, 3], [0, 1, 2, 3], dim=2 + ) + self.assertFalse(result.is_valid) + self.assertIn("edges intersect", result.message) + + +class TestBoundaryEdgeRule(unittest.TestCase): + def setUp(self): + self.existing_edges = {(0, 1), (1, 2), (2, 0)} + + def edge_checker(v1_idx, v2_idx): + return (v1_idx, v2_idx) in self.existing_edges or ( + v2_idx, + v1_idx, + ) in self.existing_edges + + self.rule = BoundaryEdgeRule(edge_checker=edge_checker) + + def test_applies_to_dimension(self): + """Test rule applies only to 2D cells""" + self.assertTrue(self.rule.applies_to_dimension(2)) + self.assertFalse(self.rule.applies_to_dimension(1)) + + def test_all_edges_exist(self): + """Test face with all boundary edges passes validation""" + triangle_coords = np.array([[0, 0], [1, 0], [0.5, 1]]) + + result = self.rule.validate(triangle_coords, None, [0, 1, 2], [0, 1, 2], dim=2) + self.assertTrue(result.is_valid) + + def test_missing_edge(self): + """Test face with missing boundary edge fails validation""" + triangle_coords = np.array([[0, 0], [1, 0], [0.5, 1]]) + + self.existing_edges.remove((1, 2)) + + result = self.rule.validate(triangle_coords, None, [0, 1, 2], [0, 1, 2], dim=2) + self.assertFalse(result.is_valid) + self.assertIn("missing for face", result.message) + + +class TestEmbeddingValidator(unittest.TestCase): + def setUp(self): + def edge_checker(v1_idx, v2_idx): + return True # All edges exist for testing + + self.validator = EmbeddingValidator(tolerance=1e-10, edge_checker=edge_checker) + + def test_initialization(self): + """Test validator initializes with default rules""" + self.assertEqual( + len(self.validator.rules), 6 + ) # 6 default rules (added dimensional rules) + self.assertEqual(self.validator.tolerance, 1e-10) + + def test_add_rule(self): + """Test adding custom rule""" + initial_count = len(self.validator.rules) + custom_rule = EdgeInteriorRule() + + self.validator.add_rule(custom_rule) + self.assertEqual(len(self.validator.rules), initial_count + 1) + + def test_remove_rule(self): + """Test removing rule by type""" + initial_count = len(self.validator.rules) + + self.validator.remove_rule(EdgeInteriorRule) + # Should have one less rule + remaining_rules = [ + r for r in self.validator.rules if not isinstance(r, EdgeInteriorRule) + ] + self.assertEqual(len(remaining_rules), len(self.validator.rules)) + + def test_set_tolerance(self): + """Test setting tolerance updates all rules""" + self.validator.set_tolerance(1e-6) + + for rule in self.validator.rules: + self.assertEqual(rule.tolerance, 1e-6) + + def test_validate_valid_cell(self): + """Test validating a valid cell""" + # Valid triangle + cell_coords = np.array([[0, 0], [1, 0], [0.5, 1]]) + all_coords = np.array([[0, 0], [1, 0], [0.5, 1], [2, 2]]) + + result = self.validator.validate_cell( + cell_coords, all_coords, [0, 1, 2], [0, 1, 2, 3], 2 + ) + self.assertTrue(result.is_valid) + + def test_validate_invalid_cell(self): + """Test validating an invalid cell""" + # Triangle with vertex inside + cell_coords = np.array([[0, 0], [2, 0], [1, 2]]) + all_coords = np.array([[0, 0], [2, 0], [1, 2], [1, 0.5]]) + + result = self.validator.validate_cell( + cell_coords, all_coords, [0, 1, 2], [0, 1, 2, 3], 2 + ) + self.assertFalse(result.is_valid) + self.assertIn("Face Interior Validation", result.message) + + def test_get_rules_for_dimension(self): + """Test getting rules for specific dimension""" + edge_rules = self.validator.get_rules_for_dimension(1) + face_rules = self.validator.get_rules_for_dimension(2) + + # Should have different rules for different dimensions + self.assertTrue(len(edge_rules) > 0) + self.assertTrue(len(face_rules) > 0) + + # Edge rules should include EdgeInteriorRule + rule_types = [type(rule) for rule in edge_rules] + self.assertIn(EdgeInteriorRule, rule_types) + + def test_strict_validation(self): + """Test enabling strict validation""" + self.validator.enable_strict_validation() + self.assertEqual(self.validator.tolerance, 1e-12) + + def test_permissive_validation(self): + """Test enabling permissive validation""" + self.validator.enable_permissive_validation() + self.assertEqual(self.validator.tolerance, 1e-6) + + +class TestIntegrationWithEmbeddedComplex(unittest.TestCase): + def test_validator_integration(self): + """Test that EmbeddedComplex uses the new validation system""" + complex_obj = EmbeddedComplex(validate_embedding=True) + + # Should have validator + validator = complex_obj.get_validator() + self.assertIsInstance(validator, EmbeddingValidator) + + def test_valid_complex_construction(self): + """Test building valid complex with new validation""" + complex_obj = EmbeddedComplex(validate_embedding=True) + + # Add triangle + complex_obj.add_node("A", [0, 0]) + complex_obj.add_node("B", [1, 0]) + complex_obj.add_node("C", [0.5, 1]) + + complex_obj.add_cell(["A", "B"], dim=1) + complex_obj.add_cell(["B", "C"], dim=1) + complex_obj.add_cell(["C", "A"], dim=1) + complex_obj.add_cell(["A", "B", "C"], dim=2) + + # Should succeed + self.assertEqual(len(complex_obj.cells[2]), 1) + + def test_invalid_complex_validation(self): + """Test that invalid complex fails validation""" + complex_obj = EmbeddedComplex(validate_embedding=True) + + # Add triangle with vertex inside + complex_obj.add_node("A", [0, 0]) + complex_obj.add_node("B", [2, 0]) + complex_obj.add_node("C", [1, 2]) + complex_obj.add_node("D", [1, 0.5]) # Inside triangle + + complex_obj.add_cell(["A", "B"], dim=1) + complex_obj.add_cell(["B", "C"], dim=1) + complex_obj.add_cell(["C", "A"], dim=1) + + # Should fail + with self.assertRaises(ValueError) as context: + complex_obj.add_cell(["A", "B", "C"], dim=2) + + self.assertIn("Face Interior Validation", str(context.exception)) + + def test_custom_validation_rules(self): + """Test setting custom validation rules""" + complex_obj = EmbeddedComplex(validate_embedding=True) + + # Get validator and remove strict rules + validator = complex_obj.get_validator() + validator.remove_rule(FaceInteriorRule) + + # Now should be able to add invalid triangle + complex_obj.add_node("A", [0, 0]) + complex_obj.add_node("B", [2, 0]) + complex_obj.add_node("C", [1, 2]) + complex_obj.add_node("D", [1, 0.5]) + + complex_obj.add_cell(["A", "B"], dim=1) + complex_obj.add_cell(["B", "C"], dim=1) + complex_obj.add_cell(["C", "A"], dim=1) + complex_obj.add_cell(["A", "B", "C"], dim=2) # Should work now + + self.assertEqual(len(complex_obj.cells[2]), 1) + + def test_tolerance_updates(self): + """Test that tolerance updates propagate to validator""" + complex_obj = EmbeddedComplex(validate_embedding=True) + + complex_obj.enable_embedding_validation(tol=1e-8) + + validator = complex_obj.get_validator() + self.assertEqual(validator.tolerance, 1e-8) + + +if __name__ == "__main__": + unittest.main()