diff --git a/docs/_static/images/centrography.png b/docs/_static/images/centrography.png new file mode 100644 index 0000000..dce912c Binary files /dev/null and b/docs/_static/images/centrography.png differ diff --git a/docs/_static/images/quadrat.png b/docs/_static/images/quadrat.png index bb6eb26..e23b581 100644 Binary files a/docs/_static/images/quadrat.png and b/docs/_static/images/quadrat.png differ diff --git a/docs/_static/images/ripleyg.png b/docs/_static/images/ripleyg.png new file mode 100644 index 0000000..f876719 Binary files /dev/null and b/docs/_static/images/ripleyg.png differ diff --git a/docs/conf.py b/docs/conf.py index 29d4865..2745be0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -20,7 +20,7 @@ import sphinx_bootstrap_theme -sys.path.insert(0, os.path.abspath('../')) +sys.path.insert(0, os.path.abspath("../")) import pointpats @@ -33,17 +33,18 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = [#'sphinx_gallery.gen_gallery', - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.viewcode', - 'sphinxcontrib.bibtex', - 'sphinx.ext.mathjax', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'numpydoc', - 'matplotlib.sphinxext.plot_directive'] - +extensions = [ #'sphinx_gallery.gen_gallery', + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.viewcode", + "sphinxcontrib.bibtex", + "sphinx.ext.mathjax", + "sphinx.ext.doctest", + "sphinx.ext.intersphinx", + "numpydoc", + "matplotlib.sphinxext.plot_directive", + "nbsphinx", +] # sphinx_gallery_conf = { @@ -56,21 +57,21 @@ # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] -source_suffix = '.rst' +source_suffix = ".rst" # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = 'pointpats' -copyright = '2018-, pysal developers' -author = 'pysal developers' +project = "pointpats" +copyright = "2018-, pysal developers" +author = "pysal developers" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -90,15 +91,15 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'tests/*'] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "tests/*"] # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False -bibtex_bibfiles = ['_static/references.bib'] +bibtex_bibfiles = ["_static/references.bib"] # -- Options for HTML output ---------------------------------------------- @@ -106,15 +107,15 @@ # a list of builtin themes. # # html_theme = 'alabaster' -html_theme = 'bootstrap' +html_theme = "bootstrap" html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() html_title = "%s v%s Manual" % (project, version) # (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24). # Path should be relative to the ``_static`` files directory. -#html_logo = "_static/images/CGS_logo.jpg" -#html_logo = "_static/images/CGS_logo_green.png" -#html_logo = "_static/images/pysal_logo_small.jpg" +# html_logo = "_static/images/CGS_logo.jpg" +# html_logo = "_static/images/CGS_logo_green.png" +# html_logo = "_static/images/pysal_logo_small.jpg" html_favicon = "_static/images/pysal_favicon.ico" @@ -123,27 +124,20 @@ # documentation. # html_theme_options = { - # Navigation bar title. (Default: ``project`` value) - 'navbar_title': "pointpats", - + "navbar_title": "pointpats", # Render the next and previous page links in navbar. (Default: true) - 'navbar_sidebarrel': False, - + "navbar_sidebarrel": False, # Render the current pages TOC in the navbar. (Default: true) #'navbar_pagenav': True, #'navbar_pagenav': False, - # No sidebar - 'nosidebar': True, - + "nosidebar": True, # Tab name for the current pages TOC. (Default: "Page") #'navbar_pagenav_name': "Page", - # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. - 'globaltoc_depth': 2, - + "globaltoc_depth": 2, # Include hidden TOCs in Site navbar? # # Note: If this is "false", you cannot have mixed ``:hidden:`` and @@ -151,52 +145,45 @@ # will break. # # Values: "true" (default) or "false" - 'globaltoc_includehidden': "true", - + "globaltoc_includehidden": "true", # HTML navbar class (Default: "navbar") to attach to
element. # For black navbar, do "navbar navbar-inverse" #'navbar_class': "navbar navbar-inverse", - # Fix navigation bar to top of page? # Values: "true" (default) or "false" - 'navbar_fixed_top': "true", - - + "navbar_fixed_top": "true", # Location of link to source. # Options are "nav" (default), "footer" or anything else to exclude. - 'source_link_position': 'footer', - + "source_link_position": "footer", # Bootswatch (http://bootswatch.com/) theme. # # Options are nothing (default) or the name of a valid theme # such as "amelia" or "cosmo", "yeti", "flatly". - 'bootswatch_theme': "yeti", - + "bootswatch_theme": "yeti", # Choose Bootstrap version. # Values: "3" (default) or "2" (in quotes) - 'bootstrap_version': "3", - - 'navbar_links': [ - ("Installation", "installation"), - ("API", "api"), - ("References", "references"), - ], - + "bootstrap_version": "3", + "navbar_links": [ + ("Installation", "installation"), + ("User Guide", "user-guide/intro"), + ("API", "api"), + ("References", "references"), + ], } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] # Custom sidebar templates, maps document names to template names. -#html_sidebars = {} +# html_sidebars = {} # html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. -htmlhelp_basename = 'pointpatsdoc' +htmlhelp_basename = "pointpatsdoc" # -- Options for LaTeX output --------------------------------------------- @@ -205,15 +192,12 @@ # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. # # 'preamble': '', - # Latex figure (float) alignment # # 'figure_align': 'htbp', @@ -223,8 +207,13 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'pointpats.tex', u'pointpats Documentation', - u'pysal developers', 'manual'), + ( + master_doc, + "pointpats.tex", + "pointpats Documentation", + "pysal developers", + "manual", + ), ] @@ -232,10 +221,7 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'pointpats', u'pointpats Documentation', - [author], 1) -] +man_pages = [(master_doc, "pointpats", "pointpats Documentation", [author], 1)] # -- Options for Texinfo output ------------------------------------------- @@ -244,9 +230,15 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'pointpats', u'pointpats Documentation', - author, 'pointpats', 'One line description of project.', - 'Miscellaneous'), + ( + master_doc, + "pointpats", + "pointpats Documentation", + author, + "pointpats", + "One line description of project.", + "Miscellaneous", + ), ] # ----------------------------------------------------------------------------- @@ -270,16 +262,18 @@ # automatically document class members autodoc_default_options = { - 'members': True, - 'undoc-members': True, - 'inherited-members': True + "members": True, + "undoc-members": True, + "inherited-members": True, } # display the source code for Plot directive plot_include_source = True + def setup(app): app.add_css_file("pysal-styles.css") + # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {"python": ('https://docs.python.org/3', None), 'numpy': ('https://numpy.org/doc/stable', None), @@ -291,4 +285,36 @@ def setup(app): 'statsmodels':("https://www.statsmodels.org/stable/", None), } +# List of arguments to be passed to the kernel that executes the notebooks: +nbsphinx_execute_arguments = [ + "--InlineBackend.figure_formats={'svg', 'pdf'}", + "--InlineBackend.rc={'figure.dpi': 96}", +] + +mathjax3_config = { + "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}}, +} + +# This is processed by Jinja2 and inserted after each notebook +nbsphinx_epilog = r""" +.. raw:: latex + + \nbsphinxstopnotebook{\scriptsize\noindent\strut + \textcolor{gray}{\dotfill\ \sphinxcode{\sphinxupquote{\strut + {{ env.doc2path(env.docname, base='doc') | escape_latex }}}} ends here.}} +""" +# List of arguments to be passed to the kernel that executes the notebooks: +nbsphinx_execute_arguments = [ + "--InlineBackend.figure_formats={'svg', 'pdf'}", + "--InlineBackend.rc={'figure.dpi': 96}", +] + +# --- nbsphinx execution policy --------------------------------------------- +nbsphinx_execute = "always" +nbsphinx_timeout = 600 +# nbsphinx_allow_errors = True # optional + +mathjax3_config = { + "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}}, +} diff --git a/docs/index.rst b/docs/index.rst index b502e24..52b3c27 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,62 +1,156 @@ -.. pointpats documentation master file, created by - sphinx-quickstart on Mon Nov 12 16:37:22 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. +.. pointpats documentation master file. Point Pattern Analysis (pointpats) -======================================== +================================== -pointpats is an open-source python library for the statistical analysis of planar point patterns. -It is a subpackage of `PySAL`_ (Python Spatial Analysis Library) and is under active development -for the inclusion of many newly proposed analytics for point patterns. +Statistical analysis of planar point patterns in Python. +`pointpats` is an open-source library for the analysis of planar point patterns +and a subpackage of the Python Spatial Analysis Library, `PySAL`_. + + +Key workflows (notebooks) +------------------------- + +Explore the main workflows through executable notebooks: .. raw:: html
-
-
+
+ + - - +Quickstart +---------- + +Install the latest release: + +.. code-block:: bash + + pip install pointpats + +or using conda-forge: + +.. code-block:: bash + + conda install -c conda-forge pointpats + +A minimal example: + +.. code-block:: python + + import numpy as np + from pointpats import PointPattern + + # toy coordinates + coords = np.random.random((100, 2)) + pp = PointPattern(coords) + + print("n:", pp.n) + print("mean center:", pp.mean_center) + print("average nearest-neighbor distance:", pp.nnd.mean()) + +What you can do with ``pointpats`` +---------------------------------- + +- Build and summarize **point pattern objects** from coordinate data. +- Compute **centrographic measures** (mean center, standard distance, ellipses). +- Perform **quadrat statistics** for tests of complete spatial randomness. +- Use **distance-based functions** (G, K, L) and simulation envelopes. +- Work with **marked patterns** and **simulated point processes**. + +User Guide +---------- + +The full user guide is organized around executable notebooks: .. toctree:: + :maxdepth: 1 + :caption: User Guide + + user-guide/centrography + user-guide/sd_ellipse + user-guide/Quadrat_statistics + user-guide/ripley + user-guide/random + user-guide/marks + +.. toctree:: + :maxdepth: 2 + :caption: Reference :hidden: - :maxdepth: 3 - :caption: Contents: Installation API References +Part of the PySAL ecosystem +--------------------------- + +``pointpats`` is part of the `PySAL`_ family of spatial analysis libraries, +alongside components for spatial weights, regression, clustering, and more. + +- Source code: https://github.com/pysal/pointpats +- Bug reports and feature requests: https://github.com/pysal/pointpats/issues + +Citation +-------- + +If you use ``pointpats`` in your work, please cite the Zenodo record: + +.. code-block:: bibtex + + @software{wei_kang_2023_7706219, + author = {Wei Kang and Levi John Wolf and Sergio Rey and Hu Shao + and Mridul Seth and Martin Fleischmann and Sugam Srivastava + and James Gaboardi and Giovanni Palla and Dani Arribas-Bel + and Qiusheng Wu}, + title = {pysal/pointpats: pointpats 2.3.0}, + year = {2023}, + publisher = {Zenodo}, + version = {v2.3.0}, + doi = {10.5281/zenodo.7706219}, + url = {https://doi.org/10.5281/zenodo.7706219} + } .. _PySAL: https://github.com/pysal/pysal diff --git a/docs/user-guide/Quadrat_statistics.ipynb b/docs/user-guide/Quadrat_statistics.ipynb new file mode 100644 index 0000000..11ea698 --- /dev/null +++ b/docs/user-guide/Quadrat_statistics.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quadrat-based statistics in `pointpats`\n", + "\n", + "This notebook provides an introduction to **quadrat-based methods** for planar\n", + "point patterns, focusing on:\n", + "\n", + "- The intuition behind quadrat counts for testing **complete spatial randomness** (CSR)\n", + "- The `QStatistic` class in `pointpats.quadrat_statistics`\n", + "- Analytical and empirical (simulation-based) chi-squared tests under CSR\n", + "- Rectangular and hexagonal tessellations for the study region\n", + "\n", + "We will:\n", + "\n", + "1. Review the quadrat-based approach to testing a point pattern for CSR.\n", + "2. Work through a juvenile point-pattern example using `QStatistic`, comparing\n", + " analytical and simulation-based inference.\n", + "3. Explore how changing the tessellation (rectangles vs. hexagons) affects the\n", + " quadrat test and its interpretation.\n", + "\n", + "This notebook is designed to live in the `docs/user-guide/` folder and be\n", + "executed automatically as part of the `pointpats` documentation build.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Quadrat statistics and CSR\n", + "\n", + "In the previous notebooks, we introduced the concept of Complete Spatial Randomness (CSR) process which serves as the benchmark process. Utilizing CSR properties, we can discriminate those that are not from a CSR process. Quadrat statistic is one such method. Since a CSR process has two major characteristics:\n", + "1. Uniform: each location has equal probability of getting a point (where an event happens).\n", + "2. Independent: location of event points are independent.\n", + "\n", + "We can imagine that for any point pattern, if the underlying process is a CSR process, the expected point counts inside any cell of area $|A|$ should be $\\lambda |A|$ ($\\lambda$ is the intensity which is uniform across the study area for a CSR). Thus, if we impose a $m \\times k$ rectangular tessellation over the study area (window), we can easily calculate the expected number of points inside each cell under the null of CSR. By comparing the observed point counts against the expected counts and calculate a $\\chi^2$ test statistic, we can decide whether to reject the null based on the position of the $\\chi^2$ test statistic in the sampling distribution. \n", + "\n", + "$$\\chi^2 = \\sum^m_{i=1} \\sum^k_{j=1} \\frac{[x_{i,j}-E(x_{i,j})]^2}{\\lambda |A_{i,j}|}$$\n", + "\n", + "There are two ways to construct the sampling distribution and acquire a p-value:\n", + "1. Analytical sampling distribution: a $\\chi^2$ distribution of $m \\times k -1$ degree of freedom. We can refer to the $\\chi^2$ distribution table to acquire the p-value. If it is smaller than $0.05$, we will reject the null at the $95\\%$ confidence level.\n", + "2. Empirical sampling distribution: a distribution constructed from a large number of $\\chi^2$ test statistics for simulations under the null of CSR. If the $\\chi^2$ test statistic for the observed point pattern is among the largest $5%$ test statistics, we would say that it is very unlikely that it is the outcome of a CSR process at the $95\\%$ confidence level. Then, the null is rejected. A pseudo p-value can be calculated based on which we can use the same rule as p-value to make the decision:\n", + "$$p(\\chi^2) = \\frac{1+\\sum^{nsim}_{i=1}\\phi_i}{nsim+1}$$\n", + "where \n", + "$$ \n", + "\\phi_i =\n", + " \\begin{cases}\n", + " 1 & \\quad \\text{if } \\psi_i^2 \\geq \\chi^2 \\\\\n", + " 0 & \\quad \\text{otherwise } \\\\\n", + " \\end{cases}\n", + "$$\n", + "\n", + "$nsim$ is the number of simulations, $\\psi_i^2$ is the $\\chi^2$ test statistic calculated for each simulated point pattern, $\\chi^2$ is the $\\chi^2$ test statistic calculated for the observed point pattern, $\\phi_i$ is an indicator variable.\n", + "\n", + "We are going to introduce how to use the **quadrat_statistics.py** module to perform quadrat based method using either of the above two approaches to constructing the sampling distribution and acquire a p-value.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Juvenile example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import libpysal as ps\n", + "import numpy as np\n", + "from pointpats import PointPattern, as_window\n", + "from pointpats import PoissonPointProcess as csr\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the quadrat_statistics module to conduct quadrat-based method. \n", + "\n", + "Among the three major classes in the module, **RectangleM, HexagonM, QStatistic**, the first two are aimed at imposing a tessellation (rectangular or hexagonal shape) over the minimum bounding rectangle of the point pattern and calculate the number of points falling in each cell; **QStatistic** is the main class with which we can calculate a p-value, as well as a pseudo p-value to help us make the decision of rejecting the null or not." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pointpats.quadrat_statistics as qs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir(qs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Open the point shapefile \"juvenile.shp\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "juv = ps.io.open(ps.examples.get_path(\"juvenile.shp\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(juv) # 168 point events in total" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "juv_points = np.array([event for event in juv]) # get x,y coordinates for all the points\n", + "juv_points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construct a point pattern from numpy array **juv_points**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp_juv = PointPattern(juv_points)\n", + "pp_juv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp_juv.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp_juv.plot(window= True, title= \"Point pattern\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Rectangle quadrats & analytical sampling distribution\n", + "\n", + "We can impose rectangle tessellation over mbb of the point pattern by specifying **shape** as \"rectangle\". We can also specify the number of rectangles in each row and column. For the current analysis, we use the $3 \\times 3$ rectangle grids." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r = qs.QStatistic(pp_juv,shape= \"rectangle\",nx = 3, ny = 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the plot method to plot the quadrats as well as the number of points falling in each quadrat." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r.mr.rectangle_width * q_r.mr.rectangle_height #calculate the area of each grid cell " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r.chi2 #chi-squared test statistic for the observed point pattern" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r.df #degree of freedom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r.chi2_pvalue # analytical pvalue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the p-value based on the analytical $\\chi^2$ distribution (degree of freedom = 8) is 0.0000589, much smaller than 0.05. We might determine that the underlying process is not CSR. We can also turn to empirical sampling distribution to ascertain our decision." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Rectangle quadrats & empirical sampling distribution\n", + "\n", + "To construct a empirical sampling distribution, we need to simulate CSR within the window of the observed point pattern a lot of times. Here, we generate 999 point patterns under the null of CSR." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "csr_process = csr(pp_juv.window, pp_juv.n, 999, asPP=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We specify parameter **realizations** as the point process instance which contains 999 CSR realizations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r_e = qs.QStatistic(pp_juv,shape= \"rectangle\",nx = 3, ny = 3, realizations = csr_process)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_r_e.chi2_r_pvalue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pseudo p-value is 0.002, which is smaller than 0.05. Thus, we reject the null at the $95\\%$ confidence level." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Hexagon quadrats & analytical sampling distribution\n", + "\n", + "We can also impose hexagon tessellation over mbb of the point pattern by specifying **shape** as \"hexagon\". We can also specify the length of the hexagon edge. For the current analysis, we specify it as 15." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_h = qs.QStatistic(pp_juv,shape= \"hexagon\",lh = 15)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_h.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_h.chi2 #chi-squared test statistic for the observed point pattern" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_h.df #degree of freedom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_h.chi2_pvalue # analytical pvalue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to the inference of rectangle tessellation, since the analytical p-value is much smaller than 0.05, we reject the null of CSR. The point pattern is not random." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Hexagon quadrats & empirical sampling distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_h_e = qs.QStatistic(pp_juv,shape= \"hexagon\",lh = 15, realizations = csr_process)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q_h_e.chi2_r_pvalue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because 0.001 is smaller than 0.05, we reject the null." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Recap\n", + "\n", + "In this notebook, we:\n", + "\n", + "- Introduced the use of **quadrat counts** to test a point pattern for\n", + " complete spatial randomness (CSR).\n", + "- Used the `QStatistic` class in `pointpats` to compute the quadrat test\n", + " statistic and its **analytical chi-squared p-value** under CSR.\n", + "- Compared analytical inference with **simulation-based (Monte Carlo)**\n", + " inference using repeated CSR realizations.\n", + "- Explored how the **choice of tessellation** (rectangles vs. hexagons) can\n", + " affect the test outcome and its interpretation.\n", + "\n", + "Together with the distance-based statistics notebook (F, G, K, L), this\n", + "provides a complementary, intensity-based view of spatial point pattern\n", + "structure in `pointpats`.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/user-guide/centrography.ipynb b/docs/user-guide/centrography.ipynb new file mode 100644 index 0000000..425fbdd --- /dev/null +++ b/docs/user-guide/centrography.ipynb @@ -0,0 +1,758 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Centrography in `pointpats`\n", + "\n", + "This notebook introduces **centrographic statistics** for planar point\n", + "patterns, focusing on:\n", + "\n", + "- Measures of **central tendency** (mean center, weighted mean center,\n", + " Manhattan and Euclidean medians)\n", + "- Measures of **dispersion and orientation** (standard distance and\n", + " the standard deviational ellipse)\n", + "- Basic **shape descriptors** (convex hull and minimum bounding rectangle)\n", + "\n", + "We will:\n", + "\n", + "1. Construct a simple example point pattern and compute its centrographic\n", + " summaries using functions from `pointpats.centrography`.\n", + "2. Visualize central tendency, dispersion, and shape diagnostics to build\n", + " intuition about how these measures behave.\n", + "3. Explore an additional example based on simulated point patterns within the\n", + " Virginia state border under complete spatial randomness (CSR).\n", + "\n", + "This notebook is designed to live in the `docs/user-guide/` folder and be\n", + "executed automatically as part of the `pointpats` documentation build.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The python file **centrography.py** contains several functions with which we can conduct centrography analysis.\n", + "* Central Tendency\n", + " 1. mean_center: calculate the mean center of the unmarked point pattern.\n", + " 2. weighted_mean_center: calculate the weighted mean center of the marked point pattern.\n", + " 3. manhattan_median: calculate the manhattan median\n", + " 4. euclidean_median: calculate the Euclidean median\n", + "* Dispersion and Orientation\n", + " 1. std_distance: calculate the standard distance\n", + "* Shape Analysis\n", + " 1. hull: calculate the convex hull of the point pattern\n", + " 2. mbr: calculate the minimum bounding box (rectangle)\n", + " \n", + "All of the above functions operate on a series of coordinate pairs. That is, the data type of the first argument should be $(n,2)$ array_like. In case that you have a point pattern (PointPattern instance), you need to pass its attribute \"points\" instead of itself to these functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pointpats import PointPattern\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "points = [[66.22, 32.54], [22.52, 22.39], [31.01, 81.21],\n", + " [9.47, 31.02], [30.78, 60.10], [75.21, 58.93],\n", + " [79.26, 7.68], [8.23, 39.93], [98.73, 77.17],\n", + " [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]]\n", + "pp = PointPattern(points) #create a point pattern \"pp\" from list\n", + "pp.points " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "type(pp.points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use PointPattern class method **plot** to visualize **pp**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#import centragraphy analysis functions \n", + "from pointpats.centrography import hull, mean_center, minimum_bounding_rectangle, weighted_mean_center, manhattan_median, std_distance,euclidean_median,ellipse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Central tendency\n", + "\n", + "Central Tendency concerns about the center point of the two-dimensional distribution. It is similar to the first moment of a one-dimensional distribution. There are several ways to measure central tendency, each having pros and cons. We need to carefully select the appropriate measure according to our objective and data status.\n", + "\n", + "### Mean Center $(x_{mc},y_{mc})$\n", + "\n", + "$$x_{mc}=\\frac{1}{n} \\sum^n_{i=1}x_i$$\n", + "$$y_{mc}=\\frac{1}{n} \\sum^n_{i=1}y_i$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mc = mean_center(pp.points)\n", + "mc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot()\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Weighted mean center $(x_{wmc}, y_{wmc})$\n", + "\n", + "$$x_{wmc}=\\sum^n_{i=1} \\frac{w_i x_i}{\\sum^n_{i=1}w_i}$$\n", + "$$y_{wmc}=\\sum^n_{i=1} \\frac{w_i y_i}{\\sum^n_{i=1}w_i}$$\n", + "\n", + "Weighted mean center is meant for marked point patterns. Aside from the first argument which is a seris of $(x,y)$ coordinates in **weighted_mean_center** function, we need to specify its second argument which is the weight for each event point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.arange(12)\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wmc = weighted_mean_center(pp.points, weights)\n", + "wmc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot() #use class method \"plot\" to visualize point pattern\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center') \n", + "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Manhattan median $(x_{mm}, y_{mm})$\n", + "\n", + "$$min f(x_{mm},y_{mm})= \\sum^n_{i=1}(|x_i-x_{mm}|+|y_i-y_{mm}|)$$\n", + "\n", + "The Manhattan median is the location which minimizes the absolute distance to all the event points. It is an extension of the median measure in one-dimensional space to two-dimensional space. Since in one-dimensional space, a median is the number separating the higher half of a dataset from the lower half, we define the Manhattan median as a tuple whose first element is the median of $x$ coordinates and second element is the median of $y$ coordinates.\n", + "\n", + "Though Manhattan median can be found very quickly, it is not unique if you have even number of points. In this case, pysal handles the Manhattan median the same way as numpy.median: return the average of the two middle values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#get the number of points in point pattern \"pp\"\n", + "pp.n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Manhattan Median is not unique for \"pp\"\n", + "mm = manhattan_median(pp.points)\n", + "mm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot()\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", + "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Euclidean median $(x_{em}, y_{em})$\n", + "\n", + "$$min f(x_{em},y_{em})= \\sum^n_{i=1} \\sqrt{(x_i-x_{em})^2+(y_i-y_{em})^2}$$\n", + "\n", + "The Euclidean Median is the location from which the sum of the Euclidean distances to all points in a distribution is a minimum. It is an optimization problem and very important for more general location allocation problems. There is no closed form solution. We can use first iterative algorithm (Kuhn and Kuenne, 1962) to approximate Euclidean Median. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, we define a function named median_center with the first argument **points** a series of $(x,y)$ coordinates and the second argument **crit** the convergence criterion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def median_center(points, crit=0.0001):\n", + " points = np.asarray(points)\n", + " x0, y0 = points.mean(axis=0)\n", + " dx = np.inf\n", + " dy = np.inf\n", + " iteration = 0\n", + " while np.abs(dx) > crit or np.abs(dy) > crit:\n", + " xd = points[:, 0] - x0\n", + " yd = points[:, 1] - y0\n", + " d = np.sqrt(xd*xd + yd*yd)\n", + " w = 1./d\n", + " w = w / w.sum()\n", + " x1 = w * points[:, 0]\n", + " x1 = x1.sum()\n", + " y1 = w * points[:, 1]\n", + " y1 = y1.sum()\n", + " dx = x1 - x0\n", + " dy = y1 - y0\n", + " iteration +=1 \n", + " print(x0, x1, dx, dy, d.sum(), iteration)\n", + " x0 = x1\n", + " y0 = y1\n", + " \n", + " return x1, y1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "median_center(pp.points, crit=.0001)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After 18 iterations, the convergence criterion is reached. The Euclidean Median is $(54.167594287646125,44.424308658832047)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also call the function **euclidean_median** in pysal to calculate the Euclidean Median." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "em = euclidean_median(pp.points)\n", + "em" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two results we get from **euclidean_median** function in pysal and the **median_center** function we define here are very much the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot()\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", + "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", + "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Dispersion and orientation\n", + "\n", + "### Standard Distance & Standard Distance Circle\n", + "\n", + "$$SD = \\displaystyle \\sqrt{\\frac{\\sum^n_{i=1}(x_i-x_{m})^2}{n} + \\frac{\\sum^n_{i=1}(y_i-y_{m})^2}{n}}$$\n", + "\n", + "The Standard distance is closely related to the usual definition of the standard deviation of a data set, and it provides a measure of how dispersed the events are around their mean center $(x_m,y_m)$. Taken together, these measurements can be used to plot a summary circle (standard distance circle) for the point pattern, centered at $(x_m,y_m)$ with radius $SD$, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stdd = std_distance(pp.points)\n", + "stdd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot mean center as well as the standard distance circle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r')\n", + "ax = pp.plot(get_ax=True, title='Standard Distance Circle')\n", + "ax.add_artist(circle1)\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "ax.set_aspect('equal')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the above figure, we can observe that there are five points outside the standard distance circle which are potential outliers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Standard deviational ellipse\n", + "\n", + "Compared with standard distance circle which measures dispersion using a single parameter $SD$, standard deviational ellipse measures dispersion and trend in two dimensions through angle of rotation $\\theta$, dispersion along major axis $s_x$ and dispersion along minor axis $s_y$:\n", + "\n", + "* Major axis defines the direction of maximum spread in the distribution. $s_x$ is the semi-major axis (half the length of the major axis):\n", + "\n", + "$$ s_x = \\displaystyle \\sqrt{\\frac{2(\\sum_{i=1}^n (x_i-\\bar{x})\\cos(\\theta) - \\sum_{i=1}^n (y_i-\\bar{y})\\sin(\\theta))^2}{n-2}}$$\n", + "\n", + "* Minor axis defines the direction of minimum spread and is orthogonal to major axis. $s_y$ is the semi-minor axis (half the length of the minor axis):\n", + "\n", + "$$ s_y = \\displaystyle \\sqrt{\\frac{2(\\sum_{i=1}^n (x_i-\\bar{x})\\sin(\\theta) - \\sum_{i=1}^n (y_i-\\bar{y})\\cos(\\theta))^2}{n-2}}$$\n", + "\n", + "* The ellipse is rotated clockwise through an angle $\\theta$:\n", + "\n", + "$$\\theta = \\displaystyle \\arctan{\\{ (\\sum_i(x_i-\\bar{x})^2-\\sum_i(y_i-\\bar{y})^2) + \\frac{[(\\sum_i(x_i-\\bar{x})^2-\\sum_i(y_i-\\bar{y})^2)^2 + 4(\\sum_i(x-\\bar{x})(y_i-\\bar{y}))^2]^\\frac{1}{2}}{2\\sum_i(x-\\bar{x})(y_i-\\bar{y})}\\}}$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sx, sy, theta = ellipse(pp.points)\n", + "sx, sy, theta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "theta_degree = np.degrees(theta) #need degree of rotation to plot the ellipse\n", + "theta_degree" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Standard Deviational Ellipse for the point pattern is rotated clockwise by $63.25^{\\circ}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.patches import Ellipse\n", + "from pylab import figure, show,rand\n", + "fig = figure()\n", + "#ax = fig.add_subplot(111, aspect='equal')\n", + "e = Ellipse(xy=mean_center(pp.points), width=sx*2, height=sy*2, angle=-theta_degree) #angle is rotation in degrees (anti-clockwise)\n", + "ax = pp.plot(get_ax=True, title='Standard Deviational Ellipse')\n", + "ax.add_artist(e)\n", + "e.set_clip_box(ax.bbox)\n", + "e.set_facecolor([0.8,0,0])\n", + "e.set_edgecolor([1,0,0])\n", + "ax.set_xlim(0,100)\n", + "ax.set_ylim(0,100)\n", + "ax.set_aspect('equal')\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "plt.legend(numpoints=1)\n", + "show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Shape analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Convex hull\n", + "\n", + "[Convex hull](https://en.wikipedia.org/wiki/Convex_hull)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hull(pp.points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By specifying \"hull\" argument **True** in PointPattern class method **plot**, we can easily plot convex hull of the point pattern." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot(title='Centers', hull=True ) #plot point pattern \"pp\" as well as its convex hull\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", + "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", + "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Minimum bounding rectangle\n", + "\n", + "[Minimum bounding rectangle](https://en.wikipedia.org/wiki/Minimum_bounding_rectangle)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "minimum_bounding_rectangle(pp.points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, four vertices of the minimum bounding rectangle is $(8.23,7.68),(98.73,7.68),(98.73,92.08),(8.23,92.08)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot(title='Centers', window=True ) #plot point pattern \"pp\" as well as its Minimum Bounding Rectangle\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", + "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", + "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "plt.plot(wmc[0], wmc[1], 'gd', label='Weighted Mean Center')\n", + "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", + "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Standard Distance Circle and Convex Hull." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r',alpha=0.2)\n", + "ax = pp.plot(get_ax=True, title='Standard Distance Circle', hull=True)\n", + "ax.add_artist(circle1)\n", + "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", + "ax.set_aspect('equal')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Additional example: simulated patterns in Virginia\n", + "\n", + "We apply the centrography statistics and visualization to 2 simulated random datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#from pysal.contrib import shapely_ext\n", + "from libpysal.cg import shapely_ext\n", + "from pointpats import PoissonPointProcess as csr\n", + "import libpysal as ps\n", + "from pointpats import as_window\n", + "#import pysal_examples\n", + "\n", + "# open \"vautm17n\" polygon shapefile\n", + "va = ps.io.open(ps.examples.get_path(\"vautm17n.shp\"))\n", + "\n", + "# Create the exterior polygons for VA from the union of the county shapes\n", + "polys = [shp for shp in va]\n", + "state = shapely_ext.cascaded_union(polys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 Simulate a 100-point dataset within the Virginia state border under CSR (complete spatial randomness)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp = csr(as_window(state), 100, 1, asPP=True).realizations[0]\n", + "pp.plot(window=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot(window=True, hull=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mc = mean_center(pp.points)\n", + "mm = manhattan_median(pp.points)\n", + "em = euclidean_median(pp.points)\n", + "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", + "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", + "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", + "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 Simulate a 500-point dataset within the Virginia state border under CSR (complete spatial randomness)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp = csr(as_window(state), 500, 1, asPP=True).realizations[0]\n", + "pp.plot(window=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.plot(window=True, hull=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mc = mean_center(pp.points)\n", + "mm = manhattan_median(pp.points)\n", + "em = euclidean_median(pp.points)\n", + "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", + "plt.plot(mc[0], mc[1], 'c^', label='Mean Center')\n", + "plt.plot(mm[0], mm[1], 'rv', label='Manhattan Median')\n", + "plt.plot(em[0], em[1], 'm+', label='Euclidean Median')\n", + "plt.legend(numpoints=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we calculate the Euclidean distances between every event point and Mean Center (Euclidean Median), and sum them up, we can see that Euclidean Median is the optimal point in iterms of minimizing the Euclidean distances to all the event points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pointpats import dtot\n", + "\n", + "print(dtot(mc.tolist(), pp.points) > dtot(em.tolist(), pp.points))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Recap\n", + "\n", + "In this notebook, we:\n", + "\n", + "- Reviewed a range of **centrographic statistics** for planar point patterns,\n", + " including measures of central tendency, dispersion, and shape.\n", + "- Used functions in `pointpats.centrography` to compute and visualize\n", + " the mean center, weighted mean center, Manhattan and Euclidean medians.\n", + "- Explored measures of spread and orientation via the **standard distance**\n", + " and **standard deviational ellipse**.\n", + "- Illustrated basic **shape descriptors**, such as the convex hull and\n", + " minimum bounding rectangle, and examined how these relate to dispersion.\n", + "- Worked through an additional example based on simulated point patterns\n", + " within the Virginia state border under CSR.\n", + "\n", + "Together with the quadrat- and distance-based statistics notebooks, these\n", + "tools provide a complementary set of summaries for exploring spatial point\n", + "pattern structure in `pointpats`.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/user-guide/distance_statistics.ipynb b/docs/user-guide/distance_statistics.ipynb new file mode 100644 index 0000000..4f66f94 --- /dev/null +++ b/docs/user-guide/distance_statistics.ipynb @@ -0,0 +1,560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cda4ae4c-9703-4ba5-bdeb-819d44cd0bc9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Distance Based Tests Point Patterns\n", + "\n", + "**Goal:** Apply Ripley family of distance statistics to an empirical point pattern.\n", + "\n", + "You will be analyzing a point pattern of fires from a [CAL Fire](https://www.fire.ca.gov/incidents) database." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b5594f6e-8433-4f22-9b9d-f4505aa3dc6b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import numpy\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0b2c4baa-832a-44e2-985a-9a48bb5848d9", + "metadata": {}, + "outputs": [], + "source": [ + "fires = gpd.read_parquet(\"sd_fires.parquet\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4486f6bb-3569-4e08-81d2-17c749c99944", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fires.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e2c06e7a-6d8d-4e7a-9460-309dcd7cd3e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete. Libraries imported.\n", + "\n", + "--- Generating Gabriel Graph (GG) ---\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ==============================================================================\n", + "# Part 0: Setup\n", + "# ==============================================================================\n", + "# Core spatial analysis libraries from PySAL\n", + "import libpysal.weights as weights\n", + "from libpysal import examples\n", + "from libpysal import graph\n", + "from pointpats import PointPattern\n", + "from pointpats.process import PoissonPointProcess\n", + "\n", + "# For generating point patterns\n", + "import numpy as np\n", + "\n", + "# For plotting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(\"Setup complete. Libraries imported.\")\n", + "\n", + "\n", + "\n", + "\n", + "# --- Proximity Graphs ---\n", + "\n", + "\n", + "# --- Gabriel Graph (GG) ---\n", + "print(\"\\n--- Generating Gabriel Graph (GG) ---\")\n", + "# Build the graph using the libpysal.graph module\n", + "gg = graph.Graph.build_triangulation(\n", + " fires, method='gabriel', coplanar='clique'\n", + ")\n", + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "gg.plot(fires, ax=ax, \n", + " edge_kws=dict(color='k', linestyle='-'), \n", + " node_kws=dict(color='orange', edgecolor='k', s=150))\n", + "ax.set_title(\"Gabriel Graph (GG) of Fire Locations\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "988352e0-9758-4bfa-8a72-de3f43c83ce7", + "metadata": {}, + "outputs": [], + "source": [ + "import pointpats\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fd7310f1-e73b-431d-9408-133643bc76e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m\n", + "\u001b[0mpointpats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msupport\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdistances\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'euclidean'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mhull\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0medge_correction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeep_simulations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mn_simulations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9999\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Ripley's G function\n", + "\n", + "The G function is computed from the cumulative density function of the nearest neighbor\n", + "distances between points in the pattern.\n", + "\n", + "When the G function is below the simulated values, it suggests dispersion.\n", + "\n", + "Parameters\n", + "----------\n", + "coordinates : geopandas object | numpy.ndarray, (n,2)\n", + " input coordinates to function\n", + "support : tuple of length 1, 2, or 3, int, or numpy.ndarray\n", + " tuple, encoding (stop,), (start, stop), or (start, stop, num)\n", + " int, encoding number of equally-spaced intervals\n", + " numpy.ndarray, used directly within numpy.histogram\n", + "distances: numpy.ndarray, (n, p) or (p,)\n", + " distances from every point in a random point set of size p\n", + " to some point in `coordinates`\n", + "metric: str or callable\n", + " distance metric to use when building search tree\n", + "hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon\n", + " the hull used to construct a random sample pattern, if distances is None\n", + "edge_correction: bool or str\n", + " whether or not to conduct edge correction. Not yet implemented.\n", + "keep_simulations: bool\n", + " whether or not to keep the simulation envelopes. If so,\n", + " will be returned as the result's simulations attribute\n", + "n_simulations: int\n", + " how many simulations to conduct, assuming that the reference pattern\n", + " has complete spatial randomness.\n", + "\n", + "Returns\n", + "-------\n", + "a named tuple with properties\n", + "- support, the exact distance values used to evalute the statistic\n", + "- statistic, the values of the statistic at each distance\n", + "- pvalue, the percent of simulations that were as extreme as the observed value\n", + "- simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points))\n", + " or None if keep_simulations=False (which is the default)\n", + "\u001b[0;31mFile:\u001b[0m ~/projects/dev-pysal/pointpats/pointpats/distance_statistics.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pointpats.g_test?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b113afb5-cb62-488a-80f6-c8e322b8ada6", + "metadata": {}, + "outputs": [], + "source": [ + "coordinates = fires.get_coordinates()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "672e568c-f859-4283-81ed-f7f106683167", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(246, 2)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coordinates.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0bcd6450-781b-4c99-bc26-d01f27422bfc", + "metadata": {}, + "outputs": [], + "source": [ + "g_test = pointpats.g_test(coordinates, keep_simulations=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "214350e6-7536-4867-bb80-9ef81c23c12d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__add__',\n", + " '__class__',\n", + " '__class_getitem__',\n", + " '__contains__',\n", + " '__delattr__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getitem__',\n", + " '__getnewargs__',\n", + " '__getstate__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__iter__',\n", + " '__le__',\n", + " '__len__',\n", + " '__lt__',\n", + " '__match_args__',\n", + " '__module__',\n", + " '__mul__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__rmul__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__slots__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '_asdict',\n", + " '_field_defaults',\n", + " '_fields',\n", + " '_make',\n", + " '_replace',\n", + " 'count',\n", + " 'index',\n", + " 'pvalue',\n", + " 'simulations',\n", + " 'statistic',\n", + " 'support']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(g_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1662f2bb-3f80-4509-a087-da77f1849fd7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.000e+00, 1.000e-04, 1.000e-04, 1.000e-04, 1.000e-04, 1.000e-04,\n", + " 1.000e-04, 1.000e-04, 1.000e-04, 1.000e-04, 1.000e-04, 1.000e-04,\n", + " 3.000e-04, 2.680e-02, 2.678e-01, 4.733e-01, 1.381e-01, 2.154e-01,\n", + " 4.600e-02, 0.000e+00])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g_test.pvalue" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c465b91c-3344-4703-9c9b-32e8fcdd0c1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(g_test.pvalue)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f48158c3-bb1a-4488-b3c3-2b363efcb7a0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 522.43529507, 1044.87059013, 1567.3058852 ,\n", + " 2089.74118027, 2612.17647534, 3134.6117704 , 3657.04706547,\n", + " 4179.48236054, 4701.9176556 , 5224.35295067, 5746.78824574,\n", + " 6269.22354081, 6791.65883587, 7314.09413094, 7836.52942601,\n", + " 8358.96472107, 8881.40001614, 9403.83531121, 9926.27060627])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g_test.support" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c24433d7-482c-431d-9006-1865725a6b2b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5cca68c5-9a91-459d-9d34-5cd1213c6da3", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_ripley(test, gdf, name='G'):\n", + " f, ax = plt.subplots(\n", + " 1, 2, figsize=(9, 3), gridspec_kw=dict(width_ratios=(6, 3))\n", + " )\n", + " # plot all the simulations with very fine lines\n", + " ax[0].plot(\n", + " test.support, test.simulations.T, color=\"k\", alpha=0.01\n", + " )\n", + " # and show the average of simulations\n", + " ax[0].plot(\n", + " test.support,\n", + " numpy.median(test.simulations, axis=0),\n", + " color=\"cyan\",\n", + " label=\"median simulation\",\n", + " )\n", + " \n", + " \n", + " # and the observed pattern's G function\n", + " ax[0].plot(\n", + " test.support, test.statistic, label=\"observed\", color=\"red\"\n", + " )\n", + " \n", + " # clean up labels and axes\n", + " ax[0].set_xlabel(\"distance\")\n", + " ax[0].set_ylabel(\"% of nearest neighbor\\ndistances shorter\")\n", + " ax[0].legend()\n", + " ax[0].set_xlim(0, test.support.max())\n", + " ax[0].set_title(f\"Ripley's {name} function\")\n", + " \n", + " # plot the pattern itself on the next frame\n", + " gdf.plot(ax=ax[1])\n", + " \n", + " # and clean up labels and axes there, too\n", + " ax[1].set_xticks([])\n", + " ax[1].set_yticks([])\n", + " ax[1].set_xticklabels([])\n", + " ax[1].set_yticklabels([])\n", + " ax[1].set_title(\"Pattern\")\n", + " f.tight_layout()\n", + " plt.show()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "15f3713d-1eb4-49d2-a977-34916779dcee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_ripley(k_test, fires, name='k')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user-guide/intro.rst b/docs/user-guide/intro.rst new file mode 100644 index 0000000..8625816 --- /dev/null +++ b/docs/user-guide/intro.rst @@ -0,0 +1,26 @@ +========== +User Guide +========== + +This user guide covers essential features of pointpats, mostly in the form of interactive Jupyter notebooks. Reading this guide, you will learn: + +- descriptive statistics for point patterns +- quadrat analysis of point patterns +- distance-based tests for clustering in point patterns + +Notebooks cover just a small selection of functions as an illustration of +principles. For a full overview of pointpats's capabilities, head to the `API <../api.rst>`_. + + +.. toctree:: + :maxdepth: 1 + + + centrography + sd_ellipse + Quadrat_statistics + ripley + random + marks + + diff --git a/docs/user-guide/marks.ipynb b/docs/user-guide/marks.ipynb new file mode 100644 index 0000000..0fe886a --- /dev/null +++ b/docs/user-guide/marks.ipynb @@ -0,0 +1,903 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b046916b", + "metadata": {}, + "source": [ + "# Working with marks in `pointpats`\n", + "\n", + "In addition to the *locations* of events, a point pattern can have extra attributes\n", + "attached to each point: these are called **marks**.\n", + "\n", + "Examples of marks:\n", + "\n", + "- species of a tree (categorical)\n", + "- crime type (categorical)\n", + "- tree diameter (continuous)\n", + "- number of injuries at an accident (count)\n", + "\n", + "In `pointpats`, a **marked point pattern** is represented by a\n", + "`PointPattern` whose underlying data frame (`pp.df`) has extra columns\n", + "beyond the coordinates, or by adding marks using the `add_marks` method.\n", + "You can also split a marked pattern into separate unmarked patterns using\n", + "`explode`. \n", + "\n", + "In this notebook we will:\n", + "\n", + "1. Create an unmarked point pattern.\n", + "2. Attach categorical and numeric marks with `add_marks`.\n", + "3. Explore marks in the underlying pandas DataFrame.\n", + "4. Visualize the pattern by mark category.\n", + "5. Use `explode` to split the pattern into one pattern per mark level.\n", + "6. Compute a **weighted mean center** using a numeric mark as weights. \n" + ] + }, + { + "cell_type": "markdown", + "id": "f133c33f", + "metadata": {}, + "source": [ + "## Setup and imports\n", + "\n", + "We will simulate points in the unit square `[0, 1] × [0, 1]` using the\n", + "random distributions in `pointpats.random` and then work with their marks.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4dcf9af3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 1., 1.])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from pointpats import PointPattern\n", + "from pointpats import random as ppr\n", + "from pointpats import weighted_mean_center, mean_center # centrography functions\n", + "\n", + "# Make results reproducible\n", + "np.random.seed(42)\n", + "\n", + "# Define a simple square hull as a bounding box: [xmin, ymin, xmax, ymax]\n", + "hull = np.array([0.0, 0.0, 1.0, 1.0])\n", + "hull" + ] + }, + { + "cell_type": "markdown", + "id": "67e5f4cb", + "metadata": {}, + "source": [ + "## 1. Create an unmarked point pattern\n", + "\n", + "First, we simulate 200 locations from a homogeneous Poisson point process\n", + "in the unit square and construct a `PointPattern` from the coordinates. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ee524a72", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200, 2)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Simulate 200 points in the unit square (unmarked pattern)\n", + "coords = ppr.poisson(hull, size=200)\n", + "coords.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ab0d2505", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Point Pattern\n", + "200 points\n", + "Bounding rectangle [(0.005061583846218687,0.009197051616629648), (0.9905051420006733,0.9900538501042633)]\n", + "Area of window: 0.9665790135416406\n", + "Intensity estimate for window: 206.91531390401323\n", + " x y\n", + "0 0.374540 0.950714\n", + "1 0.731994 0.598658\n", + "2 0.156019 0.155995\n", + "3 0.058084 0.866176\n", + "4 0.601115 0.708073\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x y\n", + "0 0.374540 0.950714\n", + "1 0.731994 0.598658\n", + "2 0.156019 0.155995\n", + "3 0.058084 0.866176\n", + "4 0.601115 0.708073" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Build an unmarked PointPattern\n", + "pp = PointPattern(coords)\n", + "\n", + "pp.summary() # quick textual summary\n", + "pp.df.head() # underlying DataFrame with coordinates only (x, y)" + ] + }, + { + "cell_type": "markdown", + "id": "688e56e9", + "metadata": {}, + "source": [ + "At this stage, `pp` has only coordinates (`x`, `y`) and **no marks**.\n", + "\n", + "Internally, `PointPattern` stores the data in a pandas DataFrame (`pp.df`).\n", + "Any extra columns we add will be treated as marks (attributes) of the\n", + "points. \n" + ] + }, + { + "cell_type": "markdown", + "id": "11f0062c", + "metadata": {}, + "source": [ + "## 2. Add categorical and numeric marks with `add_marks`\n", + "\n", + "We will assign two marks to each point:\n", + "\n", + "- `type`: a **categorical** mark taking values `'A'` or `'B'`.\n", + "- `value`: a **continuous** mark drawn from a normal distribution.\n", + "\n", + "We use the `add_marks(marks, mark_names=None)` method of `PointPattern`\n", + "to attach these attributes. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "58797dbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x y type value\n", + "0 0.374540 0.950714 A 1.071554\n", + "1 0.731994 0.598658 B 0.838322\n", + "2 0.156019 0.155995 A 1.532274\n", + "3 0.058084 0.866176 B 0.655587\n", + "4 0.601115 0.708073 A 5.707410" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = pp.n\n", + "\n", + "# Categorical mark: two types, A and B\n", + "types = np.random.choice([\"A\", \"B\"], size=n, p=[0.6, 0.4])\n", + "\n", + "# Numeric mark: e.g. \"intensity\" or \"size\" for each event\n", + "values = np.random.gamma(shape=2.0, scale=1.0, size=n)\n", + "\n", + "# Attach both marks to the point pattern\n", + "pp.add_marks([types, values], mark_names=[\"type\", \"value\"])\n", + "\n", + "pp.df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "72f763b1", + "metadata": {}, + "source": [ + "Now `pp` is a **marked point pattern**. The DataFrame has four columns:\n", + "`x`, `y` (coordinates) and `type`, `value` (marks).\n", + "\n", + "We can work with marks using standard pandas tools, for example to\n", + "compute simple summaries by type:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1b8e99e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type\n", + "A 114\n", + "B 86\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Counts by categorical mark\n", + "pp.df[\"type\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4a80a2a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type\n", + "A 2.000269\n", + "B 1.736881\n", + "Name: value, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Mean of the numeric mark by type\n", + "pp.df.groupby(\"type\")[\"value\"].mean()" + ] + }, + { + "cell_type": "markdown", + "id": "51984ed0", + "metadata": {}, + "source": [ + "## 3. Visualizing a marked point pattern\n", + "\n", + "Let’s write a helper to plot points colored by a categorical mark, here\n", + "`type`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "24547460", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_marked_pattern(pp, mark_column=\"type\", ax=None, title=None):\n", + " \"\"\"Plot a marked point pattern, coloring by a categorical mark.\n", + "\n", + " Parameters\n", + " ----------\n", + " pp : pointpats.PointPattern\n", + " Marked point pattern.\n", + " mark_column : str\n", + " Column in `pp.df` containing categorical marks.\n", + " ax : matplotlib.axes.Axes, optional\n", + " Axis to plot on.\n", + " title : str, optional\n", + " Plot title.\n", + " \"\"\"\n", + " if ax is None:\n", + " fig, ax = plt.subplots(figsize=(5, 5))\n", + "\n", + " df = pp.df\n", + " categories = df[mark_column].astype(\"category\")\n", + " cats = categories.cat.categories\n", + "\n", + " # Basic color cycle\n", + " colors = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n", + "\n", + " for i, cat in enumerate(cats):\n", + " sub = df[categories == cat]\n", + " ax.scatter(sub[pp.coord_names[0]], sub[pp.coord_names[1]],\n", + " s=15, label=f\"{mark_column} = {cat}\",\n", + " color=colors[i % len(colors)], alpha=0.8)\n", + "\n", + " # Use the window of the pattern to set plot limits\n", + " xmin, ymin, xmax, ymax = pp.window.bbox\n", + " ax.set_xlim(xmin, xmax)\n", + " ax.set_ylim(ymin, ymax)\n", + " ax.set_aspect(\"equal\", adjustable=\"box\")\n", + " ax.set_xlabel(pp.coord_names[0])\n", + " ax.set_ylabel(pp.coord_names[1])\n", + " if title:\n", + " ax.set_title(title)\n", + " ax.legend(loc=\"best\")\n", + "\n", + " return ax\n", + "\n", + "\n", + "plot_marked_pattern(pp, mark_column=\"type\",\n", + " title=\"Marked point pattern colored by type\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "efef5a3e", + "metadata": {}, + "source": [ + "The colors distinguish the mark categories (`type = 'A'` vs `type = 'B'`).\n", + "You can adapt the plotting function to map numeric marks to size or\n", + "color gradients if desired." + ] + }, + { + "cell_type": "markdown", + "id": "ae87e7b0", + "metadata": {}, + "source": [ + "## 4. Splitting a marked pattern with `explode`\n", + "\n", + "To analyze each mark category as its own point pattern, we can use the\n", + "`explode(mark)` method of `PointPattern`. It returns a list of\n", + "`PointPattern` objects, one per unique value of the mark. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b6421f39", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['A', 'B'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Unique categories in the \"type\" mark\n", + "categories = pp.df[\"type\"].astype(\"category\").cat.categories\n", + "categories" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b367b558", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Explode into a list of unmarked patterns, one per category\n", + "pps_by_type = pp.explode(\"type\")\n", + "\n", + "len(pps_by_type) # should match the number of unique categories" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e7870bdb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Category 'A' -> 114 points\n", + "Category 'B' -> 86 points\n" + ] + } + ], + "source": [ + "for cat, sub_pp in zip(categories, pps_by_type):\n", + " print(f\"Category {cat!r} -> {sub_pp.n} points\")" + ] + }, + { + "cell_type": "markdown", + "id": "f4d3b414", + "metadata": {}, + "source": [ + "We can visualize each category in its own panel to compare their\n", + "spatial distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e2a5098e-f418-41b6-8857-7313589a58e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, len(pps_by_type), figsize=(5 * len(pps_by_type), 4))\n", + "if len(pps_by_type) == 1:\n", + " axes = [axes]\n", + "\n", + "for ax, cat, sub_pp in zip(axes, categories, pps_by_type):\n", + " df = sub_pp.df\n", + " xname, yname = sub_pp.coord_names\n", + "\n", + " ax.scatter(df[xname], df[yname], s=15, alpha=0.8)\n", + " xmin, ymin, xmax, ymax = sub_pp.window.bbox\n", + " ax.set_xlim(xmin, xmax)\n", + " ax.set_ylim(ymin, ymax)\n", + " ax.set_aspect(\"equal\", adjustable=\"box\")\n", + " ax.set_title(f\"type = {cat}\")\n", + " ax.set_xlabel(xname)\n", + " ax.set_ylabel(yname)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "5711efc6", + "metadata": {}, + "source": [ + "Each panel now shows the points belonging to a single mark category.\n", + "\n", + "This is useful when you want to treat each type as its **own** point\n", + "pattern (e.g., trees of different species, crimes of different types)\n", + "and compare their intensities, clustering, or nearest-neighbor behavior." + ] + }, + { + "cell_type": "markdown", + "id": "80cda364", + "metadata": {}, + "source": [ + "## 5. Using numeric marks as weights: weighted mean center\n", + "\n", + "Numeric marks are often used as **weights** when summarizing the pattern.\n", + "For example, you might weight accidents by the number of injuries or\n", + "trees by their biomass.\n", + "\n", + "The function `weighted_mean_center(points, weights)` computes a\n", + "weighted mean center, where each point contributes proportionally to its\n", + "weight. \n", + "\n", + "Here we compare the (unweighted) mean center of the pattern to the\n", + "weighted mean center using the `value` mark as weights." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b11cb2d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0.49831839, 0.49006298]), array([0.50793006, 0.47675179]))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Unweighted mean center\n", + "mc = mean_center(pp.points)\n", + "\n", + "# Weighted mean center using the numeric mark \"value\" as weights\n", + "wmc = weighted_mean_center(pp.points, pp.df[\"value\"].values)\n", + "\n", + "mc, wmc" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e09e6e5c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "\n", + "# Plot marked pattern with small gray points\n", + "ax.scatter(pp.df[pp.coord_names[0]], pp.df[pp.coord_names[1]],\n", + " s=10, alpha=0.5, label=\"events\")\n", + "\n", + "xmin, ymin, xmax, ymax = pp.window.bbox\n", + "ax.set_xlim(xmin, xmax)\n", + "ax.set_ylim(ymin, ymax)\n", + "ax.set_aspect(\"equal\", adjustable=\"box\")\n", + "\n", + "# Add unweighted and weighted mean centers\n", + "ax.plot(mc[0], mc[1], \"b^\", markersize=10, label=\"mean center\")\n", + "ax.plot(wmc[0], wmc[1], \"ro\", markersize=10, label=\"weighted mean center\")\n", + "\n", + "ax.set_xlabel(pp.coord_names[0])\n", + "ax.set_ylabel(pp.coord_names[1])\n", + "ax.set_title(\"Unweighted vs weighted mean center\")\n", + "ax.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5a67d5d9", + "metadata": {}, + "source": [ + "The weighted mean center is pulled toward points with larger `value`.\n", + "This is a simple example of how numeric marks can affect spatial\n", + "summaries.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ff56dbf8", + "metadata": {}, + "source": [ + "## 6. Creating a marked pattern directly from an array\n", + "\n", + "Instead of starting with an unmarked pattern and calling `add_marks`,\n", + "you can also pass a full `(n, p)` array (coordinates + attributes) into\n", + "`PointPattern` and specify `names` for the columns. \n", + "\n", + "In this example we build a second point pattern whose coordinates and\n", + "marks are all stored in a single NumPy array:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "16c80159", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x y type_code value\n", + "0 0.374540 0.950714 0.0 1.071554\n", + "1 0.731994 0.598658 1.0 0.838322\n", + "2 0.156019 0.155995 0.0 1.532274\n", + "3 0.058084 0.866176 1.0 0.655587\n", + "4 0.601115 0.708073 0.0 5.707410" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Build a new (n, 4) array: x, y, type_code, value\n", + "type_code = (types == \"B\").astype(float) # 0 for A, 1 for B\n", + "points_with_marks = np.column_stack([pp.points, type_code, values])\n", + "\n", + "names = [\"x\", \"y\", \"type_code\", \"value\"]\n", + "pp2 = PointPattern(points_with_marks, names=names)\n", + "\n", + "pp2.df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "e5d08eb8", + "metadata": {}, + "source": [ + "Here, `pp2` is also a marked point pattern: `x`, `y` are coordinates,\n", + "and `type_code`, `value` are marks.\n", + "\n", + "This pattern (constructing a full `(n, p)` table and passing it to\n", + "`PointPattern`) is convenient when your data already live in a\n", + "DataFrame or when loading from a CSV or GIS table." + ] + }, + { + "cell_type": "markdown", + "id": "09d0bb9f", + "metadata": {}, + "source": [ + "## Recap\n", + "\n", + "In this notebook, we have:\n", + "\n", + "- Built an **unmarked** `PointPattern` from simulated coordinates.\n", + "- Attached **categorical** and **numeric** marks with `add_marks`.\n", + "- Used `pp.df` (a pandas DataFrame) to summarize marks by category.\n", + "- Visualized a marked point pattern with color by categorical mark.\n", + "- Applied `explode` to split a marked pattern into separate patterns,\n", + " one per mark category.\n", + "- Computed a **weighted mean center** using a numeric mark as weights.\n", + "- Created a marked pattern directly from a full `(n, p)` array with\n", + " coordinate and mark columns.\n", + "\n", + "These tools form the core of working with **marked point patterns** in\n", + "`pointpats`. You can combine them with quadrat statistics, distance-based\n", + "functions, and space–time tests for more advanced analyses.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user-guide/random.ipynb b/docs/user-guide/random.ipynb new file mode 100644 index 0000000..ab7dc57 --- /dev/null +++ b/docs/user-guide/random.ipynb @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5212af16", + "metadata": {}, + "source": [ + "# Simulating random point patterns with `pointpats.random`\n", + "\n", + "This notebook illustrates how to use the `pointpats.random` module to simulate\n", + "random point patterns inside a study region (the *hull*).\n", + "\n", + "We will look at several families of random patterns:\n", + "\n", + "1. **Homogeneous Poisson** (`random.poisson`)\n", + "2. **Normal cluster** (`random.normal`)\n", + "3. **Poisson cluster (Neyman–Scott)** (`random.cluster_poisson`)\n", + "4. **Normal cluster with random seeds** (`random.cluster_normal`)\n", + "\n", + "All examples use a simple square window as the hull:\n", + "\n", + "$$[0, 1] \\times [0, 1].$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6003346e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 1., 1.])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pointpats import random as ppr # pointpats.random\n", + "\n", + "# Make results reproducible\n", + "np.random.seed(12345)\n", + "\n", + "# Define a simple square hull as a bounding box:\n", + "# [xmin, ymin, xmax, ymax]\n", + "hull = np.array([0.0, 0.0, 1.0, 1.0])\n", + "\n", + "hull" + ] + }, + { + "cell_type": "markdown", + "id": "11dc1585", + "metadata": {}, + "source": [ + "## Helper function for plotting point patterns\n", + "\n", + "We'll use a small helper to visualize realizations of the random point processes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fd2889cf", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_pattern(points, hull, ax=None, title=None):\n", + " \"\"\"Plot a 2D point pattern inside a rectangular hull.\n", + "\n", + " Parameters\n", + " ----------\n", + " points : array-like, shape (n_points, 2)\n", + " Coordinates of the points.\n", + " hull : array-like, shape (4,)\n", + " Bounding box [xmin, ymin, xmax, ymax].\n", + " ax : matplotlib.axes.Axes, optional\n", + " Axis to plot on.\n", + " title : str, optional\n", + " Title for the plot.\n", + " \"\"\"\n", + " points = np.asarray(points)\n", + "\n", + " if ax is None:\n", + " fig, ax = plt.subplots(figsize=(4, 4))\n", + "\n", + " xmin, ymin, xmax, ymax = hull\n", + " ax.scatter(points[:, 0], points[:, 1], s=10, alpha=0.7)\n", + " ax.set_xlim(xmin, xmax)\n", + " ax.set_ylim(ymin, ymax)\n", + " ax.set_aspect(\"equal\", adjustable=\"box\")\n", + " ax.set_xlabel(\"x\")\n", + " ax.set_ylabel(\"y\")\n", + " if title:\n", + " ax.set_title(title)\n", + "\n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "id": "f05bca84", + "metadata": {}, + "source": [ + "## 1. Homogeneous Poisson process (`random.poisson`)\n", + "\n", + "The **homogeneous Poisson point process** is the canonical model of\n", + "*complete spatial randomness* (CSR):\n", + "\n", + "- Each point is independently and uniformly distributed in the hull.\n", + "- The expected number of points is controlled by the **intensity**\n", + " (points per unit area).\n", + "\n", + "A typical interface (check the installed `pointpats` version for details) allows usage patterns like:\n", + "\n", + "- `poisson(hull, size=n)` — simulate `n` points once.\n", + "- `poisson(hull, intensity=lambda_, size=r)` — draw `r` *replications* from a Poisson\n", + " process with intensity `lambda_` points per unit area.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "870559d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 2)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A single realization with exactly 100 points\n", + "points_poisson_fixed = ppr.poisson(hull, size=100)\n", + "\n", + "points_poisson_fixed.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bb7f6f94", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "plot_pattern(points_poisson_fixed, hull, ax=ax,\n", + " title=\"Homogeneous Poisson process (n = 100)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0ac04ea4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 200, 2)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Simulate 4 replications with intensity λ = 200 points per unit area\n", + "lambda_ = 200 # expected number of points in unit square\n", + "n_replications = 4\n", + "\n", + "points_poisson_intensity = ppr.poisson(hull, intensity=lambda_, size=n_replications)\n", + "\n", + "points_poisson_intensity.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b0e48b36", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(6, 6))\n", + "\n", + "for i, ax in enumerate(axes.ravel()):\n", + " plot_pattern(points_poisson_intensity[i], hull, ax=ax,\n", + " title=f\"Poisson, λ={lambda_}, rep {i+1}\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ff89ebf4", + "metadata": {}, + "source": [ + "## 2. Normal cluster (`random.normal`)\n", + "\n", + "`random.normal` simulates points from a **bivariate normal distribution**\n", + "truncated to the hull.\n", + "\n", + "A typical interface looks like:\n", + "\n", + "```python\n", + "ppr.normal(hull, center=None, cov=None, size=None)\n", + "```\n", + "\n", + "Key arguments:\n", + "\n", + "- `center`: 2D location of the cluster center. If `None`, often the centroid of the hull.\n", + "- `cov`: covariance structure (scalar = iid variance, or 2×2 matrix).\n", + "- `size`: number of points (and optionally replications).\n", + "\n", + "We'll start with a single tight cluster in the center of the window." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c34bfe45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200, 2)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 200 points in a single bivariate normal cluster\n", + "points_normal = ppr.normal(hull, size=200)\n", + "\n", + "points_normal.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7fbf7901", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "plot_pattern(points_normal, hull, ax=ax,\n", + " title=\"Normal cluster (default center & spread)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a58f6a59", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Three different covariance structures\n", + "covariances = [\n", + " 0.05, # scalar: isotropic variance\n", + " np.array([[0.05, 0.0], [0.0, 0.01]]), # elongated along x\n", + " np.array([[0.01, 0.0], [0.0, 0.05]]), # elongated along y\n", + "]\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", + "\n", + "for cov, ax in zip(covariances, axes):\n", + " pts = ppr.normal(hull, size=300, cov=cov)\n", + " plot_pattern(pts, hull, ax=ax, title=f\"cov =\\n{cov}\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f74ce910", + "metadata": {}, + "source": [ + "## 3. Poisson cluster process (`random.cluster_poisson`)\n", + "\n", + "`cluster_poisson` implements a simple **Neyman–Scott**–type cluster process:\n", + "\n", + "1. \"Seed\" points are drawn from a Poisson process.\n", + "2. Around each seed, a small cloud of points is placed within a radius.\n", + "\n", + "A typical use looks like:\n", + "\n", + "```python\n", + "ppr.cluster_poisson(\n", + " hull,\n", + " intensity=None,\n", + " size=None,\n", + " n_seeds=2,\n", + " cluster_radius=None,\n", + ")\n", + "```\n", + "\n", + "Key arguments:\n", + "\n", + "- `n_seeds`: number of cluster centers.\n", + "- `cluster_radius`: radius of each cluster. If `None`, it may default to a value\n", + " based on distances between seeds.\n", + "- `size`: controls how many points you simulate in total and how many replications.\n", + "\n", + "Here we simulate 300 points organized into a few clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f1137841", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(300, 2)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_points = 300\n", + "n_replications = 1\n", + "n_seeds = 4\n", + "\n", + "points_cluster_poisson = ppr.cluster_poisson(\n", + " hull,\n", + " size=(n_points, n_replications),\n", + " n_seeds=n_seeds,\n", + " # cluster_radius can be None (default) or a scalar / array\n", + ")\n", + "\n", + "points_cluster_poisson.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3601664c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "plot_pattern(points_cluster_poisson, hull, ax=ax,\n", + " title=f\"Poisson cluster process (n={n_points}, seeds={n_seeds})\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b43e4bc9", + "metadata": {}, + "source": [ + "## 4. Normal clusters with random seeds (`random.cluster_normal`)\n", + "\n", + "`cluster_normal` builds clusters whose centers are themselves drawn from a\n", + "Poisson process, but the points within clusters are drawn from a **normal**\n", + "distribution around each seed:\n", + "\n", + "```python\n", + "ppr.cluster_normal(hull, cov=None, size=None, n_seeds=2)\n", + "```\n", + "\n", + "- `cov` controls the within-cluster spread.\n", + "- `n_seeds` controls how many clusters you get.\n", + "- `size` controls total number of points and replications.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7baf7061", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(300, 2)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_points = 300\n", + "n_replications = 1\n", + "n_seeds = 5\n", + "\n", + "points_cluster_normal = ppr.cluster_normal(\n", + " hull,\n", + " size=(n_points, n_replications),\n", + " n_seeds=n_seeds,\n", + " # cov=None uses a default based on distances between seeds\n", + ")\n", + "\n", + "points_cluster_normal.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2d02eb84", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "plot_pattern(points_cluster_normal, hull, ax=ax,\n", + " title=f\"Normal cluster process (n={n_points}, seeds={n_seeds})\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f21340aa", + "metadata": {}, + "source": [ + "## Recap\n", + "\n", + "In this notebook we have:\n", + "\n", + "- Used `pointpats.random.poisson` to simulate homogeneous Poisson\n", + " point patterns under complete spatial randomness.\n", + "- Used `pointpats.random.normal` to generate truncated bivariate normal clusters.\n", + "- Used `pointpats.random.cluster_poisson` for Poisson cluster processes\n", + " (Neyman–Scott–like).\n", + "- Used `pointpats.random.cluster_normal` for clustered patterns with\n", + " normally distributed clusters.\n", + "\n", + "These simulated patterns are useful as:\n", + "\n", + "- **Null models** for hypothesis testing (e.g., against observed patterns).\n", + "- **Toy data** for teaching, examples, and algorithm development.\n", + "\n", + "You can adapt the hull, intensities, and covariance structures to match\n", + "your own study regions and applications." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92eec114-d372-4458-b4f3-514bd34a2b7f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user-guide/ripley.ipynb b/docs/user-guide/ripley.ipynb new file mode 100644 index 0000000..cd22979 --- /dev/null +++ b/docs/user-guide/ripley.ipynb @@ -0,0 +1,597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8aaf7749", + "metadata": {}, + "source": [ + "# Distance-based statistics in `pointpats`\n", + "\n", + "This notebook provides an introduction to the\n", + "**distance-based statistics** available in `pointpats`, focusing on:\n", + "\n", + "- Ripley’s **G** (nearest-neighbor) function\n", + "- Ripley’s **F** (empty-space) function\n", + "- Ripley’s **K** and **L** functions (inter-event distances)\n", + "- Simulation-based **envelopes** using `g_test` and `k_test`\n", + "\n", + "We will:\n", + "\n", + "1. Construct two toy point patterns in the unit square: one **CSR** (completely\n", + " spatially random) and one **clustered**.\n", + "2. Compare their distance-based statistics using the functional API:\n", + " `f`, `g`, `k`, and `l`.\n", + "3. Use `g_test` and `k_test` to generate Monte Carlo simulation envelopes\n", + " under CSR and visualize departures from complete spatial randomness.\n", + "\n", + "This notebook is designed to live in the `docs/user-guide/` folder and be\n", + "executed automatically as part of the `pointpats` documentation build." + ] + }, + { + "cell_type": "markdown", + "id": "c485e8fa", + "metadata": {}, + "source": [ + "## 1. Setup and imports\n", + "\n", + "We work in the unit square `[0, 1] × [0, 1]` and simulate patterns using\n", + "the random distributions in `pointpats.random`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5747bcbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 1., 1.])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pointpats import PointPattern, random\n", + "from pointpats import f, g, k, l, g_test, k_test\n", + "\n", + "# Make results reproducible\n", + "np.random.seed(12345)\n", + "\n", + "# Unit square hull encoded as [xmin, ymin, xmax, ymax]\n", + "hull = np.array([0.0, 0.0, 1.0, 1.0])\n", + "hull" + ] + }, + { + "cell_type": "markdown", + "id": "f74d9e8f", + "metadata": {}, + "source": [ + "## 2. Constructing example point patterns\n", + "\n", + "We construct two patterns within the same hull:\n", + "\n", + "- a **CSR** pattern from a homogeneous Poisson process; and\n", + "- a **clustered** pattern from a Poisson cluster process.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d12eeab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200, 200)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_points = 200\n", + "\n", + "# CSR pattern: homogeneous Poisson process in the hull\n", + "coords_csr = random.poisson(hull, size=n_points)\n", + "pp_csr = PointPattern(coords_csr)\n", + "\n", + "# Clustered pattern: Poisson cluster process\n", + "coords_cluster = random.cluster_poisson(\n", + " hull,\n", + " size=n_points,\n", + " n_seeds=5,\n", + ")\n", + "pp_cluster = PointPattern(coords_cluster)\n", + "\n", + "pp_csr.n, pp_cluster.n" + ] + }, + { + "cell_type": "markdown", + "id": "c4241430", + "metadata": {}, + "source": [ + "### Helper: plotting point patterns\n", + "\n", + "We will reuse a simple helper to visualize point patterns in their hull." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a77ea131", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_pattern(points, hull, ax=None, title=None):\n", + " \"\"\"Plot a 2D point pattern inside a rectangular hull.\n", + "\n", + " Parameters\n", + " ----------\n", + " points : array-like of shape (n, 2)\n", + " Point coordinates.\n", + " hull : array-like of length 4\n", + " Bounding box [xmin, ymin, xmax, ymax].\n", + " ax : matplotlib.axes.Axes, optional\n", + " Axis to plot on.\n", + " title : str, optional\n", + " Plot title.\n", + " \"\"\"\n", + " points = np.asarray(points)\n", + "\n", + " if ax is None:\n", + " fig, ax = plt.subplots(figsize=(4, 4))\n", + "\n", + " xmin, ymin, xmax, ymax = hull\n", + " ax.scatter(points[:, 0], points[:, 1], s=10, alpha=0.7)\n", + " ax.set_xlim(xmin, xmax)\n", + " ax.set_ylim(ymin, ymax)\n", + " ax.set_aspect(\"equal\", adjustable=\"box\")\n", + " ax.set_xlabel(\"x\")\n", + " ax.set_ylabel(\"y\")\n", + " if title:\n", + " ax.set_title(title)\n", + "\n", + " return ax\n", + "\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", + "plot_pattern(coords_csr, hull, ax=axes[0], title=\"CSR (Poisson)\")\n", + "plot_pattern(coords_cluster, hull, ax=axes[1], title=\"Clustered\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b4dc3fc8", + "metadata": {}, + "source": [ + "## 3. Distance-based summary functions: F, G, K, L\n", + "\n", + "`pointpats` provides a functional API for distance-based statistics.\n", + "Each function takes `coordinates` and returns a pair `(support, values)`:\n", + "\n", + "```python\n", + "support, G_vals = g(coordinates)\n", + "support, F_vals = f(coordinates, hull=hull)\n", + "support, K_vals = k(coordinates)\n", + "support, L_vals = l(coordinates, linearized=True)\n", + "```\n", + "\n", + "- **G(d)**: nearest-neighbor distribution from events to events.\n", + "- **F(d)**: empty-space function from random locations to events\n", + " (requires a hull when distances are not precomputed).\n", + "- **K(d)**: cumulative inter-event distance function.\n", + "- **L(d)**: scaled and shifted version of K; for CSR, linearized L\n", + " should be close to zero across distances." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f494e7bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((20,), (20,))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# G: nearest-neighbor distribution\n", + "support_g_csr, G_csr = g(coords_csr)\n", + "support_g_cluster, G_cluster = g(coords_cluster)\n", + "\n", + "# F: empty-space function (needs hull when distances=None)\n", + "support_f_csr, F_csr = f(coords_csr, hull=hull)\n", + "support_f_cluster, F_cluster = f(coords_cluster, hull=hull)\n", + "\n", + "# K: inter-event distance function\n", + "support_k_csr, K_csr = k(coords_csr)\n", + "support_k_cluster, K_cluster = k(coords_cluster)\n", + "\n", + "# L: linearized version of K\n", + "support_l_csr, L_csr = l(coords_csr, linearized=True)\n", + "support_l_cluster, L_cluster = l(coords_cluster, linearized=True)\n", + "\n", + "(support_g_csr.shape, support_k_csr.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "c7ba6b75", + "metadata": {}, + "source": [ + "### 3.1 Comparing G and F\n", + "\n", + "We first compare the G and F functions for the CSR and clustered patterns.\n", + "Clustering tends to increase the probability of short distances, which is\n", + "reflected differently in G and F." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bfdad630", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "# G function\n", + "ax = axes[0]\n", + "ax.plot(support_g_csr, G_csr, label=\"CSR\", lw=2)\n", + "ax.plot(support_g_cluster, G_cluster, label=\"Clustered\", lw=2)\n", + "ax.set_xlabel(\"distance\")\n", + "ax.set_ylabel(\"G(d)\")\n", + "ax.set_title(\"Ripley's G function\")\n", + "ax.legend()\n", + "\n", + "# F function\n", + "ax = axes[1]\n", + "ax.plot(support_f_csr, F_csr, label=\"CSR\", lw=2)\n", + "ax.plot(support_f_cluster, F_cluster, label=\"Clustered\", lw=2)\n", + "ax.set_xlabel(\"distance\")\n", + "ax.set_ylabel(\"F(d)\")\n", + "ax.set_title(\"Ripley's F function\")\n", + "ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a4e52fb0", + "metadata": {}, + "source": [ + "### 3.2 Comparing K and L\n", + "\n", + "Under complete spatial randomness (CSR) with intensity `lambda`, we expect:\n", + "\n", + "- `K(d)` to grow like `pi * d^2`.\n", + "- `L(d)` (linearized) to be close to zero.\n", + "\n", + "Departures above zero in L suggest clustering, while departures below zero\n", + "suggest inhibition (regularity)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fdea0ce1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "# K function\n", + "ax = axes[0]\n", + "ax.plot(support_k_csr, K_csr, label=\"CSR\", lw=2)\n", + "ax.plot(support_k_cluster, K_cluster, label=\"Clustered\", lw=2)\n", + "ax.set_xlabel(\"distance\")\n", + "ax.set_ylabel(\"K(d)\")\n", + "ax.set_title(\"Ripley's K function\")\n", + "ax.legend()\n", + "\n", + "# L function\n", + "ax = axes[1]\n", + "ax.axhline(0.0, color=\"k\", ls=\"--\", lw=1, label=\"CSR expectation\")\n", + "ax.plot(support_l_csr, L_csr, label=\"CSR\", lw=2)\n", + "ax.plot(support_l_cluster, L_cluster, label=\"Clustered\", lw=2)\n", + "ax.set_xlabel(\"distance\")\n", + "ax.set_ylabel(\"L(d)\")\n", + "ax.set_title(\"Ripley's L function (linearized)\")\n", + "ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5c171ed7", + "metadata": {}, + "source": [ + "## 4. Simulation envelopes with `g_test` and `k_test`\n", + "\n", + "The functional API (`f`, `g`, `k`, `l`) gives a single curve for a given\n", + "pattern. To assess whether this curve is consistent with CSR, `pointpats`\n", + "provides test functions like `g_test` and `k_test` that:\n", + "\n", + "1. Simulate many CSR patterns in the same hull.\n", + "2. Compute the statistic for each simulation.\n", + "3. Return the observed statistic, the simulated statistics, and Monte Carlo\n", + " p-values at each distance.\n", + "\n", + "We will apply `g_test` and `k_test` to the **clustered** pattern and visualize\n", + "the simulation envelopes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d74a1211", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((20,), (199, 20), (199, 20))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_sims = 199\n", + "\n", + "g_cluster = g_test(\n", + " coords_cluster,\n", + " hull=hull,\n", + " keep_simulations=True,\n", + " n_simulations=n_sims,\n", + ")\n", + "\n", + "k_cluster = k_test(\n", + " coords_cluster,\n", + " hull=hull,\n", + " keep_simulations=True,\n", + " n_simulations=n_sims,\n", + ")\n", + "\n", + "g_cluster.support.shape, g_cluster.simulations.shape, k_cluster.simulations.shape" + ] + }, + { + "cell_type": "markdown", + "id": "6c4128d8", + "metadata": {}, + "source": [ + "### Helper: plotting simulation envelopes\n", + "\n", + "We define a small helper that takes a test result and produces a two-panel\n", + "plot showing:\n", + "\n", + "- the observed function and a simulation envelope; and\n", + "- the point pattern used in the test." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fcc769bd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_envelope(test_result, coordinates, name=\"G\"):\n", + " \"\"\"Plot simulation envelopes and the observed distance function.\n", + "\n", + " Parameters\n", + " ----------\n", + " test_result : namedtuple\n", + " Result from g_test, k_test, etc. Must have attributes\n", + " `support`, `statistic`, and `simulations`.\n", + " coordinates : array-like of shape (n, 2)\n", + " The point pattern being tested.\n", + " name : str, default \"G\"\n", + " Name of the statistic, used for labels and titles.\n", + " \"\"\"\n", + " support = test_result.support\n", + " observed = test_result.statistic\n", + " sims = test_result.simulations\n", + "\n", + " fig, axes = plt.subplots(\n", + " 1,\n", + " 2,\n", + " figsize=(10, 3),\n", + " gridspec_kw=dict(width_ratios=(2, 1)),\n", + " )\n", + "\n", + " # Left: simulation envelope + observed\n", + " ax = axes[0]\n", + "\n", + " # All simulations as faint lines\n", + " ax.plot(support, sims.T, color=\"0.8\", alpha=0.1)\n", + "\n", + " # 95% envelope\n", + " lower = np.percentile(sims, 2.5, axis=0)\n", + " upper = np.percentile(sims, 97.5, axis=0)\n", + " ax.fill_between(\n", + " support,\n", + " lower,\n", + " upper,\n", + " color=\"0.9\",\n", + " alpha=0.7,\n", + " label=\"95% envelope\",\n", + " )\n", + "\n", + " # Observed\n", + " ax.plot(support, observed, lw=2, label=\"observed\")\n", + "\n", + " ax.set_xlabel(\"distance\")\n", + " ax.set_ylabel(f\"{name}(d)\")\n", + " ax.set_title(f\"Ripley's {name} with CSR envelope\")\n", + " ax.legend()\n", + "\n", + " # Right: the pattern itself\n", + " ax = axes[1]\n", + " coords = np.asarray(coordinates)\n", + " ax.scatter(coords[:, 0], coords[:, 1], s=10, alpha=0.7)\n", + " xmin, ymin, xmax, ymax = hull\n", + " ax.set_xlim(xmin, xmax)\n", + " ax.set_ylim(ymin, ymax)\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.set_aspect(\"equal\", adjustable=\"box\")\n", + " ax.set_title(\"Pattern\")\n", + "\n", + " fig.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_envelope(g_cluster, coords_cluster, name=\"G\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0caf89eb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_envelope(k_cluster, coords_cluster, name=\"K\")" + ] + }, + { + "cell_type": "markdown", + "id": "8039d778", + "metadata": {}, + "source": [ + "In these plots, departures of the observed curve outside the simulation\n", + "envelope indicate scales (distances) where the clustered pattern is unlikely\n", + "to have arisen under CSR.\n", + "\n", + "For example, if the observed G curve lies above the envelope at short\n", + "distances, that suggests an excess of close neighbors, consistent with\n", + "clustering. If it lies below, that suggests inhibition or regular spacing.\n" + ] + }, + { + "cell_type": "markdown", + "id": "36cefc61", + "metadata": {}, + "source": [ + "## 5. Recap\n", + "\n", + "In this notebook, we have:\n", + "\n", + "- Constructed **CSR** and **clustered** point patterns in a simple\n", + " rectangular window.\n", + "- Used the functional API (`f`, `g`, `k`, `l`) to compute distance-based\n", + " statistics directly from coordinate arrays.\n", + "- Compared how **G**, **F**, **K**, and **L** respond to CSR vs clustering.\n", + "- Used `g_test` and `k_test` to generate **simulation envelopes** under CSR\n", + " and visualize departures from complete spatial randomness.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user-guide/sd_ellipse.ipynb b/docs/user-guide/sd_ellipse.ipynb new file mode 100644 index 0000000..357eb15 --- /dev/null +++ b/docs/user-guide/sd_ellipse.ipynb @@ -0,0 +1,602 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9c9ec342-b3cc-4628-9b8b-2100134c77b6", + "metadata": {}, + "source": [ + "# Standard deviational ellipse in `pointpats`\n", + "\n", + "This notebook demonstrates how to compute and visualize the\n", + "**standard deviational ellipse** (SDE) for planar point patterns using\n", + "`pointpats.centrography`.\n", + "\n", + "The SDE provides a compact summary of:\n", + "\n", + "- **Central location** of the point pattern\n", + "- **Dispersion** along two principal axes\n", + "- **Orientation** of the pattern in the plane\n", + "- Optional **weighting** of points (e.g., counts, magnitudes, intensities)\n", + "\n", + "We will:\n", + "\n", + "1. Construct a simple example point pattern and compute its standard\n", + " deviational ellipse.\n", + "2. Show how the SDE computation **dispatches on input type**, including\n", + " NumPy arrays and GeoPandas GeoDataFrames.\n", + "3. Compare **unweighted** and **weighted** ellipses to see how weights\n", + " affect the ellipse size and orientation.\n", + "4. Explore additional options in the ellipse construction, including\n", + " corrections that align or differ from CrimeStat-style ellipses.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b6fbd94a-9b3a-47a5-a04a-f16b9146f9ed", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import geopandas as gpd\n", + "from pointpats import ellipse" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8c22f909-f5d5-4c26-a8a0-2f1118eef336", + "metadata": {}, + "outputs": [], + "source": [ + "import pointpats\n" + ] + }, + { + "cell_type": "markdown", + "id": "5fa8c9eb-b023-4d3e-8914-2fd690cbcf0b", + "metadata": {}, + "source": [ + "## 1. Example point pattern\n", + "\n", + "- 100 points\n", + "- randomly distributed in [(0,0), (10, 10)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a567c58f-dec2-4ca6-b7d5-c8fedbce2a46", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "seed = 65647437836358831880808032086803839626\n", + "rng = np.random.default_rng(seed)\n", + "points = rng.integers(0, 100, (50, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "90686986-ba5c-4a56-9694-5ba521946be0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "points_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(points[:,0], points[:,1]))\n", + "\n", + "points_gdf.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "ade6fe62-313e-4e4f-bced-a16fd9f84f55", + "metadata": {}, + "source": [ + "## 2. Dispatching on input type\n", + "\n", + "`ellipse` can take the following inputs\n", + "\n", + "- points as a 2-D numpy array\n", + "- a geodataframe with a point GeoSeries\n", + "\n", + "It is important to note that the return types differ between these two cases." + ] + }, + { + "cell_type": "markdown", + "id": "c08e18cb-59ae-4952-80c6-9ea7203364f0", + "metadata": {}, + "source": [ + "### 2.1 Passing an array" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f381e968-3868-4a44-bc4f-f8fb620bba81", + "metadata": {}, + "outputs": [], + "source": [ + "ellipse_ = ellipse(points)" + ] + }, + { + "cell_type": "markdown", + "id": "20caa5b8-fd65-4c94-b18f-f9069ca4f2ba", + "metadata": {}, + "source": [ + "This will return a tuple with\n", + "- length of the major axis\n", + "- length of the minor axis\n", + "- angle of rotation (in radians)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "706c3026-a75c-4a46-a848-f22d58694118", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(43.85494662229593, 36.28453973005919, -0.9362557045365753)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ellipse_" + ] + }, + { + "cell_type": "markdown", + "id": "68de8ebb-0354-4c7a-beff-bd07db2a221d", + "metadata": {}, + "source": [ + "### 2.2 Passing a GeoDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6b1272a6-5180-4211-936f-9fb70044b82d", + "metadata": {}, + "outputs": [], + "source": [ + "ellipse_poly = ellipse(points_gdf)" + ] + }, + { + "cell_type": "markdown", + "id": "459548fb-2bc5-4e11-95fe-9f989006aeb0", + "metadata": {}, + "source": [ + "This will return a `shapely` `Polygon`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3420d0d1-5c2a-491c-abd9-9ea2b7d64a0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "shapely.geometry.polygon.Polygon" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(ellipse_poly)" + ] + }, + { + "cell_type": "markdown", + "id": "c4dcaefb-cd34-4272-a8c1-c7dd45079bbb", + "metadata": {}, + "source": [ + "## 3. Unweighted and weighted ellipses\n", + "\n", + "The default is to treat the points as an unmarked point pattern and construct the ellipse accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "23e497c8-3a62-4d37-8a74-6c59fd94f03c", + "metadata": {}, + "outputs": [], + "source": [ + "ellipse_gdf = gpd.GeoDataFrame(geometry=[ellipse_poly])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c3dd02c1-e139-4413-8044-5e15bef12149", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "base = ellipse_gdf.plot()\n", + "points_gdf.plot(ax=base, color='k')" + ] + }, + { + "cell_type": "markdown", + "id": "73b29b5a-96be-428d-ba68-3b7bc41ca7a1", + "metadata": {}, + "source": [ + "### 3.1 Weighted points\n", + "\n", + "If marks are available to attach to each point, the weighted standard deviational ellipse can be constructed. Here we use use the $y$ coordinate as the weight for demonstration:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "143ddf77-89b6-4ef4-8405-0ce17b2a54ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Marked Point Pattern')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PWyKRSCSSfkZK3hKJRCKR9DNS8pZIJBKJpJ+RkrdEIpFIJP2MlLwlEolEIulnpOQtkUgkEkk/0y8XaTGbzRQXF+Pi4iKtESyRSCSSAUEURRoaGggMDLzsAkn9MnkXFxcTEhJi6zAkEolEIrG4goKC9i1gL6ZfJu/zu/cUFBT0y12PJBLJlU0URT744EO27s1g4jW/w8vv0l/U9qa8OI/0jR8wa/IIFi1caOtwBoz6+npCQkK6tENdv0ze55vKXV1dpeQtkUj6nX379rHn4GnGXXUvwZFDbB1Ot4VFD8douIudu1aRMjGbxMREW4c0oHSlO1gasCaRSCR9qL6+nhWffIZHcCLh0Qm2DqfHIgePxslvKB+uWEVjY6Otw7niSMlbIpFI+ogoinzy6aeU1QmMnTbf1uH0iiAITJhxG4WVej5bs8bW4VxxpOQtkUgkfWT//v2k7TrCyMm34KBxtnU4vaZxcmXExJvYnLZP2re8j0nJWyKRSPqATqdj5adr+n1z+W+dbz7/aOVqDAaDrcO5YkjJWyKRSPrAvn37KKpoYvSkebYOxaIEQSBp0vUUlNRy6NAhW4dzxZCSt0QikViZKIps2boNz6ChOLl42Doci3P18MXVL4YtqWm2DuWKISVviUQisbKcnBwyzuQTm5Bi61CsJnb4JI6fzkGr1do6lCuClLwlEonEylK3bgW1N4GhsbYOxWqCI4diUriybds2W4dyRZCSt0QikVhRfX09u9IPERk/cUDvxSCTyYgYnMy2nftpbm62dTgDnpS8JRKJxIpOnDhBXaOJ6CFjbB2K1UXHj6O6XsfJkydtHcqA1+3kvWPHDubOnUtgYCCCIPD99993eF4URZYuXUpgYCAajYYpU6Zw6tSpDsfodDqWLFmCt7c3Tk5OXHfddRQWFvbqQiQSicQe5efn4+Dqi1rjZOtQrM7R2Q2Vo4fU790Hup28m5qaSEhI4K233ur0+VdeeYXXX3+dt956iwMHDuDv78/MmTNpaGhoP+bRRx/lu+++4/PPP2fXrl00NjYyZ84cTCZTz69EIpFI7NC57Fxcva6cXRCdPUPIzZOSt7V1e2OSa665hmuuuabT50RRZPny5fztb3/jxhtvBGDlypX4+fnx2Wef8eCDD1JXV8eHH37Ip59+yowZMwBYtWoVISEhbNmyhVmzZvXiciQSicR+mEwmsnML8R8y3Nah9BkvvxDOZqchiuKA7uO3NYv2eefm5lJaWspVV13V/pharWby5Mns2bMHgEOHDmEwGDocExgYSHx8fPsxv6XT6aivr+/wI5FIJPaupKSEphY93v6hfVqu2WQi4+gO0lO/JOPoDsx92Krp7RdCXWML5eXlfVbmlciiW4KWlpYC4Ofn1+FxPz+/9j6Q0tJSVCoVHh4eFxxz/vW/9dJLL/Hcc89ZMlSJRCKxOq1Wi84o4OnTd/t1H9ixltVvPUl1RVH7Y54+QSxY/ApJfbC6m7dfKDq9SH5+/gW5QGI5Vhlt/tumkq40n1zqmKeffpq6urr2n4KCAovFKpFIJNZSUVGBytEdpUrdJ+Ud2LGWN5+9s0PiBqiuKObNZ+/kwI61Vo9BrXFCrnamoqLC6mVdySyavP39/QEuqEGXl5e334H5+/uj1+upqam56DG/pVarcXV17fAjkUgk9s5gMCBXqPqkLLPJxOq3ngTETp5te2z1W3/pkyZ0uUKJXq+3ejlXMos2m0dERODv78/mzZtJTEwEQK/Xs337dpYtWwbAqFGjUCqVbN68mVtuuQVo6xc6efIkr7zyiiXDkdA2YKakpISCggKam5vR6/UYjUbkcjlKpRKVSoW/vz+hoaFoNBpbhyuRDCh6vR6ZXNknZWWd2H1BjbsjkeqKQrJO7GbwiElWjUUmV0o7jFlZt5N3Y2Mj586da/89NzeXo0eP4unpSWhoKI8++igvvvgi0dHRREdH8+KLL+Lo6Mgdd9wBgJubG/fddx9//vOf8fLywtPTk8cff5xhw4a1jz6X9FxVVRVZWVlotVqyc/LIzi2ksUWHzgAyuQpBrkAuV2I2mzCbDJiMBpRyMw5KgeBAP6KjwggLCyMqKorIyEhptKhE0gtms7nPPkO1VZ2PGerpcb0hCHJp6q+VdTt5Hzx4kKlTp7b//thjjwGwcOFCPv74Y5588klaWlp46KGHqKmpYezYsWzatAkXF5f217zxxhsoFApuueUWWlpamD59Oh9//DFyudwCl3TlMZvNnD59mq1paew9cILGVhEHFx9cvULwGzyMIX4hePoGo1I5dPrauuoyqsoLqCzNJ+1wAU2ph1HKDAwKD2Dm9CmMHz8eR0dHG1yZRNK/KZVKRHPfJDF3L3+LHtcbZpMBlapvuguuVIIoip11kNi1+vp63NzcqKuru6L7vxsbG9m9ezdbUreTU1COwjmImOEphMcmdpqou8psNlNenEPWsV1U5B/D00XJlJQxTJ06ldDQvp3yIpH0Z9988w1f/nyQOXc9Y/WyzCYTj90+hOqKYjrv9xbw9Ani9TWnkFm5orR2xbMsnD+ZuXPnWrWcgaY7uc2ifd6SviGKImlpaaz+/Fsq6434ho0g+boFePuHWaSJTiaT4R88CP/gQbQ01XPmRDprU3fz0+ZdTE0exZ13LujQkiKxHlEUMZlMGAyG9j5EpVKJUqlEoZA+vvZOo9Ggb23skwVLZHI5Cxa/wpvP3gkIdEzgbWUvWLzM6onbbDZj1DdJY2isTPr09zPl5eWs+Hgl6YezCIxOZs4Ns3HQOFutPI2TKwnjZjFszExyMw+xcde3nDz9LPcsvIPRo0dbrdwrkSiK1NTUoNVqyc/PJzcvj7PZWmpqGxBFON9GJsjavoo9PVyJiQojPDycsLAwQkNDcXd3l8Yp2JHg4GAwtdBYX42Lm5fVy0uaNI8lz626yDzvZX0yz7u+phwZhrZrl1iNlLz7ifO17U/XfEO9wYkJ1y4mICS6z8qXyWREDUkiMDyOvalf8vLr7zI9ZbRUC7eAkpIStm/fzq49+6msbqDVICJTueDiGYSn3zgGxXojVyhR/G/KkdGox2TQU19bycFz+Ww7sBWzoREHhYCPlxspyWOYPHmytECGHQgLC0OtFKgqK+iT5A1tCXxU8hyyTuymtqoUdy9/YoclW73GfV5lWT4OSkHqYrMyKXn3A83NzfznnXfZdeA0QTETSZl4XZ8t+vBbGkcXps69j5ysw2za8TUnTz/Low//gejovruRGAhMJhNHjx4ldWsah49locOJkOgkhg2Pwss/BEcnty7XoEVRpLmxlsqyAsoKz/HZ2l18u24zSSOHMG3qFBISEpDJpN1/bcHNzQ1fbw+qygsIjxnRZ+XK5HKrTwe7mKqyAoICfKVBrlYmJW87V19fz+tv/IujWeWMn/0QgaGxtg4JgMjYkQSERLNz/UpeXLacPz/6B+Lj420dlt0zm81s3bqVdT9tpKCkBifPSAZPvIuw6ATk8p59HAVBwMnFAycXD8IGDWfkxLlozxzl0Imd7N7/NqGBnsybew1TpkyRmtRtICYqlP1n8m0dRp+pqShg+IgwW4cx4EnJ247V1dXx8rJXySpoZNqNj+DhHWDrkDrQOLowbd6D7Pj5Y15+9U3+/Mjv2xfnkVyotLSUDz9awYHjOfhHjmXKTZPx9Am0eDkKhZKoIUlEDUmiqryQ04e38a93V7P/wEHuWbQQX19fi5cpubjw8HBS0zddEbtsmc1mmmqKCAuTxsNYm9SWZqeampp49bU3yCpsYvpN9pe4z1MolEyZcx8OPsN5483/cvr0aVuHZHfMZjObNm3iqb89x5Fz9Uyc+zDJV91ulcT9W16+waRcfScT5ixm/+kK/vLX50hNTaUfzhDtt6KiopCZm6ksHfh7XJcVnkMh6ImKirJ1KAOelLztkMFgYPm/3uREdjVT5/0RV3cfW4d0STKZjJSr70JwjebV5f8hLy/P1iHZjfLycl56eRn/+egrXEImcO0df8EvKLLP4wgIiebaO57CMTCJN99fwyv/fJWqqqo+j+NKFBsbS2SIL5nHdtk6FKvLPL6LmIhAKXn3ASl526Eff/yR/cdzmTT3932yGpIlyORypsy5j2bBh3ff+1Ba1xgoKCjgHy8s48iZWibOfZgxU25Eoeibda47o1SpGTftZiZcu5j9p8r5xwvLKCkpsVk8VwqZTMaM6ZMpyzuCrqXJ1uFYTVNjLVUFJ5g5Y+qA7x6wB1LytjN5eXl89f16ohOvxse/fw36UCiUJM+6i4ycCr7//ntbh2NT2dnZPP/Sq1S0ujDr1sdsUtu+mICQaGbd8hglDQ7844VlaLUDvznX1pKTk3F3FDhzcq+tQ7GaMyfS8XJTMW7cOFuHckWQkrcdMRgMvP/hCswOgQxL6p+btLh7+hE7ajbf/rCZ7OxsW4djE1qtlpf/+S8a8WPmTUusuohOT2mcXJl50xJqTV689MrrFBcX2zqkAc3Z2ZnJE5PIPrVrQI43MJtM5GXsYdqkcdLKan1ESt525Mcff+TU2TImzLyzX8/LHTpqKoJTCO9/+PEV13xeWlrKK6/+i0bRi2nXP9irNeatTe3gyLR5v6dG78ZLr7xORUWFrUMa0KZMmYLMUE1+9glbh2JxuWeOoDA3MHnyZFuHcsXovxligCkoKOCr79czKHGW3Y4s7yqZTMb4mQvIyKnghx9+sHU4fcZgMPDvN/9DebOG6df/wa4T93lqB0em3/AQJXUK3nr7HYxGo61DGrAiIiKYkDSMIzu/Rq9rsXU4FqNraeLY7u+YNH4EISEhtg7niiElbzuxefNm9IJ7v20u/y13Tz8i4qewftM2dDqdrcPpEz/88AOns8tJueYe1BonW4fTZRpHFyZecw9HMwvZsGGDrcMZsARBYOHdd+GpMbB/23e2Dsdi9m79Cj83kbvuvNPWoVxRpEVa7EBjYyPbdx8gcug1/bq5/LfiEiay/kQq+/btY9Ik2yzV2FdycnL4+vsNDEq8pl+2nHj5BhM5bAaff/0DCQkJUg3KSjw9PVl45y28/tbHFOYmEBwxtNfn1BsMNDY00NjYSENjAy0tOsxmMyaTGQC5XIZMJkOjccDF2RlnF2ecnV1QKXs/8yHv7DFqCo/w5KMP4Obm1uvzSbpOSt52YPfu3dQ2iUyMH2vrUCzKycUDz6ChbNm6jZSUlAE7fcRgMPDBhx8jaoL6dctJwrhZrM87yQcfruD/PfNXactRK0lOTmb/gYPsSvsc38C/olJ3b4CXKIpUVVVRVl5OXV0Dza06TGYRBAVKtRNKlSMymQxB3lYRMIhmzGYz9TWtFJbkI2BCLhPQaNS4u7rg5+eHp6dntz+fupYmDm//kukTExkzZky3XivpPenTaWNms5ktqdvxDRthl6OSeys2IYVDG98hJydnwC7ccL65fNr8x/t1y4lcrmD8zAWkffsa69evZ+7cubYOaUASBIFFC+8mI2spe7Z8weTZC7uUOPV6PaWlpRQWFdPYrEOucsbB0QsPd2ccNE4ouzjGwqBvobWlidbmRoor6ygsOYmLkwNBQQH4+wd0qUZuNpvZvXlNe3P5QL0xt2f995tmgDh9+jQ5BeUMHpFi61CsIjA0FtTebE1Ls3UoVtHQ0MC6nzYTmTCjXzaX/5aXbzDhQ6fy7boNNDc32zqcAcvT05MH77+b5rJj7N/27SWnjzU1N3E6I4Nde/aScVaLSeFOQEQCIVHD8QkIw8XNq8uJG0Cp0uDi5o1PQDihgxLwDx+OQXAl44yW3Xv2kpmZSXPLxf/2oiiSnvoluqpT/OF390rN5TYiJW8bO3ToEAonf7z72YIsXSUIAuGDx5G+7whms9nW4Vjc+S6PITbaftEaho6cQnW9gfT0dFuHMqCNHj2a39+3gPJzOzmy5+cLErgoiuQX5LP/wGGKyupx9gwnLGY0fkFRqB0sNyDSQeOMf8ggwmJG4+gRSn5JLfv2H6awsLDTmA7uWEuNdi+LH1xEQkKCxeKQdI+UvG0sO0eLh1/EgG528gkIp6FZR2lpqa1Dsaj2Lo/wkf1qdPnlaJxc8Q4dzpbUbQNyQRF7MnnyZO67ez6FpzdxYMf37e93U3MTh48cISMrD5VzAKHRI3D3DkDWw21ju0ImV+DhHUhYdCIqZ19OZeVw9Nix9lq4KIrs3foVpWe38eC9tzNhwgSrxSK5PKnP24YMBgO5+UWEjhhYA9V+y8svBL1BIC8vj8BA6++k1VdOnjxJTmEFE+fdZetQLC4uIYV9P79JVlYWcXFxtg5nQJs1axZKpZL/frSG3a3NBMWMIy+/GCNqAsLjcXB06dN4BJkMn4AInN28KC88R+3+w0SEBZJ/eieNpUdZ8uDdpKQMzG6+/kSqedtQQUEBLToT3n6htg7FqlQqB9TO3uTn59s6FIvamrYNlWtwv1uDviv8gqKQO/qzdevAHKtgb6ZNm8bDf1jEqfSv+erDF9DpzYQOSujzxP1rGkdXQgeNoKVVxxfvP8eZg+v40+L7pcRtJ6TkbUP5+fkYTHI8+mBf5/PMJhMZR3eQnvolGUd3YDaZ+qRcV68QsnPy+qSsvlBfX8/+wyeJjp9o61CsQhAEIodOIH3/UWngWh8wGAwcOnwYJzdffL3dyT74NVkHf8ZktN3ywiajnowD68g9/D3+Pl44uHhz8OAhaRU+OyE1m9uQVqtF4+aP3Ir9WL92YMdaVr/1JNUVRe2PefoEsWDxKyRNmmfVsr38QzmXuR6z2dyvp1Odl5eXR7POTEBojK1DsZrAsDiy9n2LVqtl8ODBtg5nwDKZTLzz7ruk7j7FlOsfITA0lhMHtrB/189UFJxkWModuPv0betcdVkuJ3d+BoZaJs24nqGjplKYe5pNm1cgCB/w4IO/GxCf4/5MevdtqLqmBo2zV5+UdWDHWt589s4OiRuguqKYN5+9kwM71lq1fBc3L1pa9TQ1DYz9jLVaLTKFE86unrYOxWrcPHwxCaoB191hT0RR5KMVK0jdfZJxs+4jOGIIMrmchHGzmL/oKbzd1Bz4+Q0ObPwvZdoTiFacsSGazZTkHmP/+v9weMO/8fVyYf49TzMsaToymYzQqHiSZixiw/bDfPLJJ9JgRhuTat42pNMbkCusvzCL2WRi9VtPAp192ERAYPVbf2FU8hxkcrlVYpDLFYgiA6bJTavV4uwZPKBnCQiCgItHMHl5ebYOZcDatGkTP2/Zx6ipCwmOGNLhOQ/vAObe8WeyMw+ScXQnp7avINPBnaCY8YTEjEXt6GqRGFqb6yjITKfo7F7M+nqCQiIYP28hEXGjLqhdhw4ahkF/B+s2rCIsLIypU6daJAZJ90nJ24b0egMKpcrq5WSd2H1BjbsjkeqKQrJO7GawleYryxVKRLFtlaiBIOtsLp6+SX1WntlkIuvEbmqrSnH38id2WLLVbrR+zcM3hLPZGVYv50pUUlLCZ19+T/DgyUTEJnZ6jEwuJ3roWKKHjqWyNJ+MYzs5c2ozecc34uo7CDfvkPYfByf3y95MiqJIS2MNdZUF1FUWUF9VQF3ZOTQOCobGjyEuYSJevsGXPEfUkCTKS3L5dM03xMfH4+Pj0+P3QNJzUvK+AtRWdW1+dVeP642BUFOtra2lorqeofF90w9p07EKviGc2buD5uZmHB0drVrWlcRsNvPhRx/TJLozJXlOl17j7R9Kiv8Cxky6nnOn91OkzaK8YD/FGakYzSIKlRPOnm1JXCZXIFe0VQxMRj0mo4HWphoaqwsxG5qRywScXd3w8w9hZOJNDBoypltrrI9OmcdPq0/z0YqPefKJxwfE57q/kZK3DalUSoz11q+Junv5W/S4njAZDQgCKC2wk5GtlZeXozeKVn2/zjs/VuG3XR7nxyoseW6VVRO4u3cAeqNIeXk54eHhVivnSrN582YOnshh4tyHUSi695lQa5wYOmoqQ0e1NVk3NdZSVVZIVVk+lWUFNDeWYDAa0BkMCIKAXKFAqVDi5uHK0MFT8fYLwdsvFI1Tz5vdlSo1Y6ffQfrPb7Nt2zap+dwGpORtQw5qFSaj9ZN37LBkPH2CqK4opvN+bwFPnyBihyVbLQaTyYggMCB2qjIYDIgiKK3c5WEPYxWUShVm88Dp7rAHpaWlfPbl94QMnoxfUGSvz+fk7I6TszuhUfEWiK7rAkJjCIhO5tM13zBs2DC8vb37tPwrnTTa3IY8PTxoaayyejkyuZwFi1/532+/bd5q+33B4mVW7UOtr6nASaPGyan/LyOq1+sRRVAorJu8uzNWwVrkCiUibTcsEsv46aefaDQ6M7KLzeX2bHTKPGpblGzYsMHWoVxxpORtQ+Hh4TTXlmLsg4UYkibNY8lzq/D8zYIwnj5BVm96Bagsy2dQVOiAmBsqiiKiCFi5n88exioIggxRbJuLLOm9hoYGduw+SFR8Sreby+2RUqUmfEgyW3fspaWlxdbhXFH6fxtmPxYaGopKYaamsrhPlthMmjSPUclzbDJqubG6kKgxI61eTl9QKBQIAphN1p32ZhdjFUwGZAKoVNafFXEl2LNnD7XNIinx42wdisXEDBvP+qOb2Lt3r9T33Yek5G1DQUFBaNRyqkrz+2x9bJlcbrXpYBej17XQ2lhJaOjAWMNdpVIhE8Bo5fEKdjFWwWgcMAMNbc1sNrN5yzb8whMH1C50Ts7ueIUMY/OWNKZMmSKNPO8j/b8Nsx9TKpVEhgdTWVZg61CsqqqsAAelQFjYwNjAw9nZGbkcmhvrrFqOPYxVaGqsRS4XcHGx3QYZA8WpU6fIKawgbsTA29gjLiGFM7nFnDlzxtahXDGk5G1jgyLDqS7LGdBLDZYV5eDq7ICfn5+tQ7GIgIAAnBxUVJZaf9lQW49VqCorwM1ZIy3EYQF79+5F6Rw4IHcR9A8ehODgw759+2wdyhVDaja3sdGjR7N2/XbKi3PwC4qydTgWZzabycvcy5wpIwfEYDUAuVxOVEQw+eWFfVKeLccqVJbmMzgqVGoKtYCz2Vq8A6IH5HspCAJeAYM4l51n61CuGAPj27Qfi4uLIyrMn8yjO20dilUU5Z1GbqwdcANZoiLDqa/qu+6O82MVxk+/hcEjJvVJ4gZorCkkInxgdHfYUktLC4XFZXj7D7xa93levsHk5hdJ0wr7iJS8bUwQBGZOn0JFwXFamuptHY7FZR3bSXxc+IDp7z4vLCyMlvpyDHqdrUOxGl1LE/qm6gH3t7OF/Px8Wg3igGwyP8/bL5QWnYmCgoE9hsdeSMnbDkyYMAFPZwVnTqTbOhSLaqirpK40i+nTBt4I1NDQUNQKkcqygbtdZkWpFrVq4Aw0tCWtVosZFa4evn1SntlkIuPoDtJTvyTj6A7MfTBP38MnEINJLm0h20ekPm874OjoyJSUMaxN3U180nTk8oHxZ8k4uhN/b0eSkvpu962+EhQUREigN9mnDxAQEm3rcKwi+/R+IkL88PXtm4QzkGm1WhzdA/tk3IetNrKRyxU4ugeg1WqtVobkF1LN207MnDkTJ1kjR9MHxjKD1RVF5Gfs5NqrZwzIBT5kMhkzp0+mNPcQutZmW4djcS1N9VQUHGfm9IHXamILFZXVOLlaf+3v8xvZ/HZZ3fMb2RzYsdaq5WucvaisqrZqGZI2UvK2E4GBgdw6fy65J1P7ZAqSNZlNJvZsWsWwmACuueYaW4djNcnJybg5wtlTA296zJmTe/F0VjBhwgRbhzIg6PUGq6+Ff/mNbGD1W3+xahO6XKFEr5cGrPUFKXnbkWuuuYbEwSGkb16NycpLb1rT8f2bUejLeOC+ewbELmIX4+rqysTxo8g5uWtAzdM3m83knNrFlJQx0h7eFqLTG5Bb+bNgLxvZSMm7b0jJ247I5XLuv+8eVKaKftt8Xl1RxLljm7jlxtlXxECnaVOngq6SorwMq5fVtiGK9W8SCnNOoTDVM2XKlC6/ZiDdvFhDX3Q92MNGNsCFiwFKrGLgVov6qeDgYG6bP5f3V60jKDwO/+BBtg6py/T6VnZv+pRhMQFce+21tg6nT0RFRTF8SCRHdq0lIDSm14MNzSYTNVUlVJUVUFlWQEWpltqaSkxGAyZjW2uMXKFAoVDh7umNj38o3n6hePmF4O4V0OsBUUajgSO7vycpIabDzZcoipSXl6PVatFqteTmadHmF9OqN2DQ6xFFEYVCiUqlwM/Hi6jIMMLC2n6Cg4Ov+LXR1SolJt0VsJGN0YB6AI5xsUdS8rZD11xzDaczMtn103tMveFhvHyDbR3SZRmNBtLWvY+7vI7f/+7JAd1c/muCIHDPort5+pl/cDR9A6Mmdn+PZpPJiPbsMTKP7aa4MAeDwYhZFNC4+OLsFYJP1FDkChWy/90YmE1GTEYdjbXlnM7IpHX/zv/t/KUgMCSKwQkTCY0a1qOFXA7v+gFnWR0L734UURTJzMwkLW0bR45nUNfQgs4gonT0wMUzBI/ACTirHNq3tjSbjBiNBsprKzmzO4fmn/egkpvQqOXERIUxedJExo4di1qt7nZc/Z1SqcDYNPA3sjEa9KhUV8Zn39akd9kOyeVyHl6ymNZ/vsb2de8w9frFeHgH2DqsizKZjGz/6SOUrQU88ZdHCQ62/5sNSwoODubW+XP5YNU6wgYN7/IqWo311WQd30PGsd3U1Tfg6hNFyLA5uHmH4OoVhELZtSRn1LdSX11EbWUB5doT5H7zAW5ubgwZkUzM8Ak4Obt36TxlRTkUZO7g3jvmcurUKZb/+23O5ZUgc/QnNHoqUf6hePuGdHlHLJPJSE1FMZVl+WTnnebg25/i+9nXTJ8ygcmTJxMQYL//05YW4O/LCa11B6Ke38jmzWfvpK3t+tcJvG82smmuL8fXJ85q55f8QhD7YWdVfX09bm5u1NXV4erqautwrKaxsZF/vvo6x85WMmnu7/ts29DuMOh1pP3wAfLmXB7/0x+Jj4+3dUg2YTKZ+MfzL3I6X8fsO564ZPN5c2Md+9K+4WzGUUwo8Y9KIjQuGRcPyzRn1lcVoc3cTXnuIeSCidihIxkz5UY0jhffGcxoNPDjqhdRG4pRObhS02TCJ2Q4cSNS8A2MtEif7fmbFW1mOmqaGJ04mDsX3IG/v/Wace1Famoq/3rvK2763StWX9q283newSxYvMyq87yNBj3fvf8kTyy5m5SUgbdzWl/oTm6Tkreda25uZvm//s2+4/mMmbGQ0Cj7SY5NDTXs+HkFjuZynvzzEmJiYmwdkk0VFhby9DP/wCNiEkmTr7/geVEUyck4yK4tX9NqlBGZMIugQaO7XMPuLqO+lcKzB8g5vhEntUDyzFuIjB3Z6bGpP67k6LbV+AVFMXTcdcQNT0bjZJ3P1vlughP71+Ms1HLHrTcwY8aMAbNxTWfOnTvHX/7fy6Tc8ESfdIOZTaY+38imvDiXvT8u57WX/05o6MBdBtaapOQ9wOh0Ot7/4APSdh3BK2w0Y6bchNrBdlN4RFHkzMl0TqZ/T7i/E48+/BDh4eE2i8eebNq0if+u+JLo0dczdNQvm7E0N9axZ8sXnMk8jnfoSIaMuxGVQ9ean3tL19LAqfSvqSk8TuyQRMbPuKW9Fm4ymdj842r2b/6EsKgh3LDomT5bwtNoNHB4948UZGxnTEIU996zaMDWwnU6Hff9bgkRo28hZth4W4djFaePbKfk5A988N83r5gxL5YmJe8BSBRF0tPTWfnpF1Q0yRk95VZCIvu+Ft5YX036ljW0Vp3l2qsmcvPNN0tzgX9FFEW+/fZbVn21nmETbyc6fhyFuadI/eETWo0yBo+7Gf/wYTaJrTjnCJn7vsFJLTBt7iLcvUPYuO4zju/6msSx07juridtsppaWVEOe1M/w1mo5b5FdwzYJte/PvMsNYQzYcYttg7FKnZuWEWwUyV//39/s3Uo/VZ3cpt0e9RPCILAhAkTGDJkCB+v/IQdm98nN3Q0oybOwcnFw+rlG40Gzp3cy6n9PxDu78T9Tz9yxfZvX4ogCNx44420tLTw9Q9ryM8+QfaZkzj7xjIq5Y4+q213JjAyEa+AQRzf8Rnr1ryFoHShPP8kU665jcnXLrLZMqh+QZFce8df2J/2LW+++wmNjY1cffXVA25Z1tjoSH7YloEoigPu2sxmM1UlZ5l69Shbh3LFGLidTAOUu7s7jzy8hMeX3Iu8MYP1q55j67r3KdJmWmWhjIa6Sg7sWMvaFX8n+9A3XH9VEi8+v1RK3JcgCAILFixgVHwYOzeuRukaxqjp99k0cZ+n1rgwbNKdGBTenDmyhRFJKTZN3OcpFErGz7iF4PhZfPDJN6xda901uG1h/PjxoKukpOCMrUOxuKK808iNdW3XKOkTUs27HzpfCx85ciT79+9nc2oahza+w0G1N1HxE4mMHYmjs1uPz280GijWZnHm+E7qSrPw93JkwfUpTJo0CT8/PwteycB1+PBhTp8tYOSkm3H0jacw9xR+wYNQqTU2jaupsZ7cs8fxCU/E1dWd0pJcCnJO2cVASEEQSBx/DUqlmlVfrkWj0TBr1ixbh2Ux0dHRxEYGkXVsF4GhsbYOx6Kyju0kPi5cGvvSh6Tk3Y85ODgwadIkUlJSyM7OZmtaGrvSfyRr//coHNxw9gzG0zcEb78QvP1CcXB06VDDEkURo0FPTWXbXNyqskLqqwpori/DQSEyNDaMGbcsIikpaUDuDGYtZ8+eZfnb7+PkP4JJ19xNfUM9mZlnKMo5hrt3KB4+gTaJq6I0nyJtJoIoMnh4MhrHqzmS9jGbvv+AeXc8ik9AuE3i+q340dMw6Fv58JOvcXNzY9y4cbYOySIEQWDmjKm88c5nNDXWdnn+vb1rqKukrjSLGbfeY+tQrijSgLUBpqGhgbNnz5KXl0dunpaz2XnU1DWjM4iYzCDI5MjlCsxmE2aTEUEAlQKcHBREhgcTFRlOaGgokZGRBAUF2bw5tb/R6XT87f8tpaTRhatuWtw+PcdkNpGXl0eetggUzvgFRaHqoxkDutYmyoqyaaguBpmS2OEpKJRtN2Nmk5G9P72Jk1LHDQufal8tzdZEUWTnhlVQd5plLy7F09PT1iFZRGtrK4sfeQLXsEkkTpht63As4sD279GX7+ffb/xTusnvJWnA2hXMxcWFkSNHMnJk23xeURSpqqqioKCA5uZmDAYDBoMBuVyOUqlEpVLh7+9PUFCQNL3DAr755hvOFdQy89YHO8yrlcvkREVG4e3t3VYLzz2K0sENd09/nF09wcI3SaIo0lhXRV11KQZdPSq5iEqtITBieHviBpDJFQxLuZ19P77Gkd0/dTo/3RYEQWDs1Jv4efUZVny8ksf+9OiAuJF0cHBg6qRxfL1+N0NGTrHplE9LaG1pJD9rLwtumCQl7j4mfVsPcIIg4O3tjbe3t61DGfCysrJY93Mag8fcgKu7T6fHuLm6kTR6FJVVlRQXFVNZcobKUiUu7n64efn1es9no0FHbVUZjXVlyEQj3l5u+EQO4ty5XFw8/TqdmeDi4U9EwtUc3vczYdEJ+AZG9CoGS1E7OJI07TZ2b3yPsbt3M3HiRFuHZBGzZ89m+8597N/2HSlXL7B1OL2yd+tXBHjKB9TYhP5CSt4SiQXodDre//Bj5K4RDB4x6ZLHymQyfH188fXxpbGpkZKSEopLSiioKkCmUKNUO+OgccLB0RkHjXP7hiS/ZTIaaG1pRNfSRGtLIwZdE2ajDo1aQWSoHwEBATg6OnLi+Al0Jjmhl0jKkfFTKcs7zvb1q+yq+Tw4Yii54WNYuepLhgwZMiCazz08PFh416289tYKCnNHEBwx1NYh9UjemaPUFR3lyUd/J3Vf2oCUvCUSC0hNTeVcQQ0zb32wW827zk7ORA+KJiI8guqaahobGqlvaKC+vpiGahNmswiCHEEmQxDaZnaKohnRbAbRhFwmoFIp8HB1xsXfFxdnFzw9PZH/r8m+qrqK0ooafIIHI5NdfHlMQSZraz7/4VUyj+4kfvS03r0hFjRmyg38/Fkm33//Pffee6+tw7GICRMmsP/AQXamfY5PwNP9rvm8taWRwzu+YkbySJKSkmwdzhVJSt4SSS+ZzWY2p27HP2L0RZvLL0ehULTXxgFERFpbWmlobECv02M2mzGbzUBbzV0mk6FWq3F2ccbBwQGBzm8YioqKkaucu7SQj4uHPz5hIzh1ZCdDR021mz5mtYMjUfGT2b57A7fccgvOzs62DqnXBEFg0cK7ych8tl82n+/d+hX+7nDXXXfazf/JlUZK3jbW3NxMSUkJra2t6PV6RFFEoVCgVCrx8vLCx8dH+nDYuePHj6MtqiLlBstNlREQ0Gg0aDQ9nxfe0tpCeWUN7r5RXX5NSFwyhzceoigvg+CIIT0u29Jiho3jzOH17Nmzh6uuusrW4VjE+ebzN95ewekjwQxJnGzrkLrkxIFU6ouP8sQjUnO5LUnJuw8ZjUays7PRarXk5eVxLiefwuIydAYRswji/34EAWQCKBQCbs4aoqNCiYwIJywsjEGDBuHhYf3lUCVdl7o1DQePMLz9QmwdSgclJSWYRDku7l0frOjhG47GNZDMY7vsKnk7aJzxDRvB5i3bmDlz5oC5oZ0wYQIlJSWs+vpbVGoNg4aMsXVIl5R1fDfnDq1j4e1zGTPGvmMd6KTk3Qeqq6vZsWMHW7bupKSiDhMqnDyC8PCJIXrcdDx9glE7aJArlAiCDLPJiNGop762ksrSfDJKCjhwah/65g04aWSMGz2MaVOnMmTIkAG9jWJ/UFZWxoEjp4kdf4etQ+nAbDZTVFyKs5tve195VwiCQEhcMtkHv2FsXRUubl5WjLJ74hImsueH5Zw6dWrALM8rCAI33XQTTU1NfLd+DYDdJvCs47s5tecrbpk3neuuu87W4VzxpORtJaIokpGRwda0NPbsP0azQUnQoCRSJo7HwzuwS0nXxc2boLC49t91LU1ozx1n9/GdbNuznKgQX2ZMn8zEiRNxcrL9utlXov3796MzOxAek2jrUDqorq6mudVAcFD3l7MNjBrF2YNryc06zPAxM60QXc/4BISjcA4kPT19wCRvaEvgd911F4Ig8N3Pq9G3NjNk5BRbh9XBiQOpnDu0jlvmTee2224bMC0f/ZmUvK2gurqaj1d+wp4DJ5E7BTJo5E1EDU5CqVL36rxqjRMxw8YTHT+OylItGUd38p8V3/Hdup+55+47GDNmjPSh6mM5ubm4eofZzdSq8xoaGpDJ1ChV3e8zVyjVOHuFUlGitUJkPScIAj6BgzhzLsvWoVicTCbjrrvuwsnJiTXffEdFqZZxU+ej1tj2pryluYG9qV/SUHqcRXdcx9y5c6XvGDshJW8LEkWR3bt3s3LVl1Q1Kxg1/QFCIoda/J9dEAR8AsLxCQinpfl69qd9wyvL32fqxIPcfddd0iCSPiKKImfO5eHpZ39rbzc0NKDoxfQjV68QykuPWzAiy/DyC+Hs3p00NzcPuH3kz28nGxwczIpPPuOnz15i1ORbCBs03Cbx5GQd5uiOrwn0lPHQn37P6NGjbRKHpHNS8raQmpoaVny8kl37TuITMZbZ11/fJ3M3NY4uTL52EXlnE0nd/iUnT/8/qRbeR2pra6mqbmDY8FBbh9KBiEhdfSMOTv49PoebVzClWdtobWnEQWM/U7O8/UI5YRDJz88nLi7u8i/oZwRBYOzYscTFxfHJp5+yLfVD8s6M7NNa+Pnadn3Jca5KGc2ddy7AxcWlT8qWdJ2UvC2goKCAV15dTlGNSNKs39lkxaTw6AQCggexd+tX/PNf73P7TYXceOONUgK3Iq1WS6tBxMvf+qPMzSYTWSd2U1tViruXP7HDkjusnf5rulYdOr0RZ5+eJ1037xBMZpHK0vxLjjrvTlyW4OrhixkVWq12QCbv89zc3Fj8xz+SNHofH3+6hh8//QchsWOJG56Mq4evVcqsqy4j89guCs/uJ8hLyUOPSbVteyYl717Kzs7mldf+TZ3Rg2tu/4NNaylqjROTr13E6SMRrPrqW5qbm1mwYIE0It1K8vPzkatdcHTq+d7pXXFgx1pWv/Uk1RVF7Y95+gSxYPErJE2ad8HxjY2NmMxir/4XHV29EeRqqsoLL5q8uxuXJchkMpw8gtBq7as/3hoEQWDcuHEMHjyYLVu2sCVtJ5s/34arXyyxw1MIjhza68+22WwmP/sEZ47voqH8DEG+Ltw9fxozZ86Uatt2TkrevaDVann5n/+iWebPzPkPolI52DokAIYkTkal1vDNj58hCAILFiyQauBW0NDQgFrjbtX39sCOtbz57J1Ax517qyuKefPZO1ny3KoLEqXeoEdEQN6LQXSCIKB2ckfX0mixuCzFwcmd+obO4xqI3NzcuOmmm7juuus4ePAgW1LTOJb2IYd3uuLuG4m3XyhefiF4+YVc9jtI19pMVXkBVWWFVJblU1uWg0JsIGFIFDPufIBRo0ZJuwv2E9JfqYdKSkpY9s83aBJ8mHH973s9ktzSBg0Zg2g2882Pa3BycuKGG26wdUgDjsFgQLjEeuG9ZTaZWP3Wk/w2QbYRAYHVb/2FUclzOjRVm83mbs3tvhhBpsBoMFgsLkuRy5XodA0WP6+9UyqVjB8/nvHjx6PVatm3bx/nsnM5m7WB3CN6Wg0iDi4+ODh7I5cr22/eTEYDRqMeXWMlrY2VOCgFXJzUDIoMIXrseMaNG0dIiH0tMCS5PIsnb6PRyNKlS1m9ejWlpaUEBASwaNEinnnmmfYmHlEUee6553jvvfeoqalh7NixvP322wwd2j921zEajbz9zn+pbHHmqpvtL3GfFx0/Dp2uhTVfryUqKorhw20zanWgMhgMyKw4RSzrxO4OTdIXEqmuKCTrxO4OO5mZzWaL7A8ukysxmYwWi8tS5HIFev2FNxVXkrCwMMLCwoC2v3dpaSlarRatVkt1dTU6vR6dvq11Qq1SolY54uWV2P46Pz8/qTutn7N48l62bBnvvvsuK1euZOjQoRw8eJB77rkHNzc3HnnkEQBeeeUVXn/9dT7++GNiYmJ4/vnnmTlzJllZWf2in+XHH3/kxJkSpt30uN3vBjR05BRKC7J4/6NPePmF56TFXCxILpcjmkxWO39tVWmPjpMJsrZ1dntJNJs6rTn3NC5LMZtNKJRSo+F5MpmMwMBAAgMDGT9+vK3DkfQRi996paenM2/ePK699lrCw8OZP38+V111FQcPHgTaat3Lly/nb3/7GzfeeCPx8fGsXLmS5uZmPvvsM0uHY3FarZavvltP9IhZePoE2jqcyxIEgfEzbqOgXMfnX3xh63AGFJVKhdlsvRqgu1fXpnr99jiZTIZogeRtNhmQd7KXeE/jshST0YBaZV+L4kgkfc3iyXvixImkpqZy5swZAI4dO8auXbuYPXs2ALm5uZSWlnbYGUitVjN58mT27Nlj6XAsymg08v6HKzCp/RmWNMPW4XSZk7M7w5NvYEPqHo4ft7+FN/orBwcHjPpmq50/dlgynj5BcJHtPkHA0yeY2GHJHR6VK+TAL1uI9pRR34yykwFQPY3LUvS6Jhw19jE4VCKxFYsn77/85S/cfvvtxMXFoVQqSUxM5NFHH+X2228HoLS0rSnNz6/jmst+fn7tz/2WTqejvr6+w48tbN68mRNnShg/806rzmW1hkFDxuDoM5gPV3yKoZNBSJLuCw4OprWxCr2uxSrnl8nlLFj8yv9++22ibPt9weJlF/wvOjk6IRdA19rzEdm65noMrfX/S9KWicsSRFGkqaZYGmAlueJZPHl/8cUXrFq1is8++4zDhw+zcuVKXn31VVauXNnhuN9OrxFF8aJTbl566SXc3Nzaf2zxwTWZTKzftJXAqLH9orn8twRBIGnSDWiLazh06JCtwxkQQkNDcVAKVJUXWq2MpEnzWPLcqgv+5zx9gi46HcvJyQmFQkZrc8+Td11VIXKZgPdFFqDpSVyW0NxUh0nfQGiofa1qJ5H0NYuP+njiiSd46qmnuO222wAYNmwYWq2Wl156iYULF+Lv39YPdn4k+nnl5eUX1MbPe/rpp3nsscfaf6+vr+/zBH7s2DEKSmqYclNKn5ZrSW6efrj4xpC6dRvjxtnfetz9jb+/P85OaqrKCggIibZaOUmT5jEqeU6XVzITBAE3F2caW5p6XGZdZQEajSPOrhffErS7cVlCVWkBDkqhfaS1RHKlsnjybm5uvmAKglwub+9/i4iIwN/fn82bN5OY2LaNol6vZ/v27SxbtqzTc6rVatRq207HSt2ahpNnZL+sdf9abEIKx7Z9RH5+vlR76SWZTMagiBCyy/KtX5Zc3q1pVy6uzlQX1fS4vPqqAnz9gy+7AE134+qtqvICPD2c8fDw6LMyJRJ7ZPFm87lz5/LCCy/w008/kZeXx3fffcfrr7/evkiIIAg8+uijvPjii3z33XecPHmSRYsW4ejoyB133GHpcCyipKSEQ0cziR4+0dah9FpIZDwmuSvbtm2zdSgDQlRkOHUVeRYZ3W1JLs4uGA2tmDuZp305oijSUFWAt7/93dxVlOYSMyhcWjFQcsWzePJ+8803mT9/Pg899BCDBw/m8ccf58EHH+Qf//hH+zFPPvkkjz76KA899BCjR4+mqKiITZs22e0c7z179qDDibDoBFuH0msymYzwuAls27lPGrhmAQkJCaCvobTwnK1D6cDT0xOVAuprK7r92sqiTEy6ekKjhlkhsp5rrK+mvuwsiSNG2DoUicTmLJ68XVxcWL58OVqtlpaWFrKzs3n++edRqVTtxwiCwNKlSykpKaG1tZXt27cTHx9v6VAsJuvsOTwDojud89ofBUUMpr5JR2Gh9QZaXSliY2OJDg8g6/guW4fSgUqlwt/Pm7rq7i+Uos3YhX9ACL6BEVaIrOeyju/B10PD2LFjbR2KRGJz0vp4l2E2mzmXU4CX78CZmuLhHYjeKLsidmayNkEQmDljKpX5x2lurLN1OB0EBgaCsaVbcTU3VFNTnMGQxIl21TRtMhnRZqYzbfJ4HBykOd4SiZS8L6O0tJTGJh3efgMneSsUShzdA6TkbSHjxo3Dy1XFmRPptg6lAzc3N9xcnbq1TGlBVjpOjg5ExtnXPs7as8dQ0cSUKVNsHYpEYhek5H0Z+fn5tBpEvKycvM0mExlHd5Ce+iUZR3dgtuKa2QDu3iFk50jJ2xIcHR2ZOmksuRm7Meh1tg6nnYBAcFAguuZqDPrWyx5v0LdQfHYvccPG2tVmO6IoknEkjVEJsR2ml0okV7KB0YlrRfn5+Tg4e6NSa6xWxoEda1n91pMddmry9AliweJXrLbYhZdfCLlHD2AwGFAqpXWie+vqq69m6/a9HNq1jnHTbrZ1OO38/PzILyikrPAcwZGXHleSsW8taoWJ+NHT+ii6rsk4ugOxqZDr5z1h61AkErsh1bwvo76+HrWT9eaUHtixljefvfOCLRarK4p589k7ObBjrVXKdXb1wGAw09TU84U8JL/w8fHhzttvpChrFyUFZ20dTju5XE5cbAxmfQO1VSUXPa6iMIPynH2Mn3o9zq6efRjhpdXXVpCx/wfmzZ5KTEyMrcORSOyGlLwvQ6/Xg2CdFaPMJhOr33oS6GyOcNtjq9/6i1Wa0GVyBWbaNluRWMa0adMYPyqWfamf2VXzubu7O+GhgdSUazttPjfoWzi150siBsURM2yCDSLsnCiK7Nn0GdGhHtx00022DkcisStS8r4Mg8GAQmGdZuWsE7svqHF3JFJdUUjWid0WL1suVyKKojTX24IEQeCeRQtxVTRxaNc6W4fTQUREBG7Oaso6mY+esW8tKkFHyqw77GqEecbRHRjrc3ngvkU2X2FRIrE3UvK+DLlcbrXBY10dBdyd0cJdZTabEBCQ97Pd0eydr68vd91xEyVnd5F1wn62uD3ffC4aGigvzml/PPfUDipy9zF+2g121VxepM0kY986rr92mtRcLpF0QhqwdhlqtRqz2Tp7Nrt7+Vv0uO4wGfUIAtJgNSuYOnUqpaWlfLnuS5QqByJjR9o6JKCt+XxIXDQnM85SJVfSUl/KuYPfkTRhOjHx420dXrvy4lzS13/A1PFDuPlm+xn8J5HYEyl5X4ajoyMGXYFVzh07LBlPnyCqK4rpvN9bwNMniNhhyRYvu7WlCbkMNBrrjaK3Fy0tLRQWFtLU1ITBYMBkMqFQKFCpVHh6ehIYGHjBZjq9IQgCt99+O80tLfyw+VNEk4moIUkWO39vBAQEYDQZ2bHlRwoyd5I8ZTZJk663m+bykoKz7Fn/PhNGRvLQH36PQiF9RUkknZE+GZcRHBxMc+12jEbL933L5HIWLH6FN5+9ExDomMDbvkwXLF5mlS0Wq8oKCArwHXCrVYmiSG5uLufOnSMvL49zOfkUFpehM4iYzGD+31ssQFvLgwKcNWqiIoKJigwnNDSUoUOH4u7u3qs4BEHg3nvuQSGXs3bDanS6ZgaPmGTzJCmKIrUlZ2gp3oOLvB6ZXIloNiPYQfdJ/rkTHNjyMSljYln8x4c6LKkskUg6kpL3ZYSFhaFSmKmpLMbH3/J7CCdNmseS51ZdZJ73MqvN866pKGD4iIGzJ3Jrayt79+5lc2oaZ7KL0JuVOHkE4eETTfS46Xj5hqBxckEuVyLIZJhNRkxGA3U15VSVFZBXVsCJLSdobdiKi6OMCWNGMG3aVGJjY3uccGUyGQsXLsTJyYkvv/+WkvxMxk2/FSdnd8tefBc1NdSwZ/MaWqvP8KeH7sbLy4v/friK9V9oGT9zAZ4+QTaJS69v5cD27ynPTueqSaP43e8ekGrcEsllCKK97WXYBfX19bi5uVFXV4erq6tVyzIYDNz7u8WEJtxA3IgUq5VjNpnIOrGb2qpS3L38iR2WbJUaN7St1/7d+3/hD4vmMWvWLKuU0VfKy8vZsmULW7enU1Grwys4ntiEifgHR/eoKVyvayEn4yBnT+7C3FzKoPAAZk6fwqRJk3o1PuDYsWN8sOJTCsp1JEy8kajBSX1WCxdFkTMn0zmZ/j3h/k7cf+/d7RsBabVa3v9wBSfOlDAo4SqGj5lptf+7zhTlZbB/6xo8NXruXnAzKSkpNm+dkEhspTu5TUreXfD3Z/+PMn0AE2fZ537j3VVbVUra1y/x4rOPExcXZ+twesRsNpOamsrqL76jTqcmYvAEYoZPsFitVhRFyoqyyTy2k8qC4wyN8ueB++8hIqLnO201NTWx5vPP2ZiajpPfEJIm34iru49F4r2Yuuoy9m/7mtaqs8yemcwtt9yCo6Njh2OMRiM//vgjX323HqPKj7HTbsUnINyqcbU01XN4z8+UZ6czIWkI9yxaiJeXl1XLlEjsnZS8LezTTz9l3dZTzL37mQFRK8g8tou8w9/w/jvLL/gi7w/Kysr4aMXH7Dt6juDYFEZNnItCab3+0ZrKEvZsWoWstZj511/N3LlzLVMLL6nDzT+WmOEpBEcMsdigObPZTEHOSbKO7aSh/AzhQZ4datsXc74WfvpMIWq3EKKHpxARO9JiYz3O3xBlHdtFRcFxfNxUUm1bIvkVKXlb2JkzZ/jr0lcYc/UfCQjt33NORVHk58/+SXKCD0sWL7Z1ON0iiiLbtm1j5eqvaDS5Mm7GHfgFRfVJ2WazmRMHtnDuyEaGRPny0B9+R3BwcI/Pp9frOXjwIFtS0ziZmYdJ4U7k0GTCBg3H1cO328nMbDZTX1OO9uwxcjN2IzfWkzA0iunTpjBq1Kgu32yYzWZOnjxJ6tY09h06SatZQ2jMWCLiRuHpHdjtJnVRFGmsr6Yo9zTnTu7G1FxCVJg/V82Yyvjx4/vlzaNEYi1S8rYwURR55v8tpaTFi6lz77N6edZUXpzLnh+W89zfHr1sTcyeiKLIt99+y5pv1uM3KIXRKddZtbZ9MTWVJeze+Alu8lqefPxhBg0a1Otz5uXlsW3bNrbt2k99ox6zTI2zexAeviF4+YXi5umHUqlCJm8bxGUyGTHqddRVl1FZlk9NRQFNNUXI0OPmpGbqpLFMmTKF0NDQXsVVXl7Ojh072JK2k4rqJoyiAkf3ANy9QvDyD8HTJwilUo1coUQQBExGAyaTkfqaCqrKC6guz6ehqhBMzTiqZSSPTWTq1CnExcVJNW2JpBNS8raCtLQ0lr+7hmvuetZmo4UtYfvPn+AhFPDKyy/0my9QURT57LPP+PqHNOLG3MCQkVNsGo9e30rauvdRthbwl8eXWGzcQEtLC3l5eeTn55OXl8fZbC1FpRUYjCJmEc5/UgUBZAKoFAJBAb5ER4URHt42zS08PNzi0/8MBgP5+flotVq0Wi3ncrRo84tpNZgR/xeX+Ku4FHIBX28PYqLa4gkLa4vPxcXFonFJJAONlLytoKWlhcWPPolb+GQSx1/TJ2VaWmtLIz+ufJY/3nsjV111la3D6ZLzNe5VX60nPvk2YoZZfiWwlqZ6Ksvyqa+pwGg0YDYZQRBQKFSo1A54+gbj4R3Yoe/XaDSQtu59VLp8/vbUY70ayHYpTU1NVFZWYjAY2tehVyqVKJVKfHx8bNbsrNPpKC8vR6/XYzAYMJvN7XF5eHhgNBrJz8+nqqoKg8GA0WhELpejUChwdnYmNDSUgIAAaXleieRXupPbpMmUXaTRaJg2aRxf/byDuISJaBz7Xy3i2N6NeLnImTDBfnaOupxt27ax5pv1xI25wWKJu7aqlLwzR6ksy6esJJ/G+jpMZhFkyrZ54HIliGbMZiMmQysyQUQhl+PlG4Cvfyh+QZGExyQyde79bPnuPyx7dTkv/N/frTJa2snJCScnJ4uft7fUajUhISHALwvjHD9+nJzcPM5m51Fd24TOICIKSuSKtiZ/UTRjMuoxG/WoFSLOjr8sjhMTE0NCQoI0v1si6SKp5t0NDQ0NPPXXZ2lRhTNlzr39ptkZoKwom10/vMkf77uFmTNn2jqcLikrK+Opv/0fjoFJjJvWuzWuzSYT+dknOH1kB/l5Z0HugLNnCK5eIbh5h+DmHYzG2fOCv6nJaKChpoS6ygLqKgtoqC6kubYYJ42auOFjiYgbTfrGjxk/PJA/P/anfvU/0Vs6nY4DBw6wJTWNU2e0mGQuuHi19dN7+Ybg7ReCo7PbBa/T61upLi+kqqyAyrIC6qsKMDSVE+zvzoypKUyaNAkPDw8bXJFEYltSs7kVHTx4kJdff5dhkxfazYYTl2M06Pnps2WMjPXgqSefsOg63tZiNptZ9so/OZRZzbULnurx4DSTycjJg1s5eXgHdbW1OHtHEhqXjH/48PYBYN3V0lhDfmY6xWfTEY1NuLm5oa8v4P/9ZQmTJ0/u0Tn7k+bmZn766Se2bN1FWXUz7oGDiR0+kaDwIT2+eampLCHz2E6Ksw/iqDSQPGYE8+Zd16sR/RJJfyMlbyt755132bjrNLMX/LVfNJ/v3/YtDYXpLHvhWfz8/GwdTpds2bKFtz/4gglzFuMf3LMR3RWlWrb/vIry8nL8IpMIGzwRVy/LLQFqNhkpzTtO3ukdFJ9Nx89VZPWnKwgKss0yo33hxIkTfLjiU/JKmwiNSyYuIRkXN2+LnV+vbyUn4yBZx7ahEWu4bf5crr76aqlvXHJFkJK3lZ1vPm+SBzP1ugfsuiZbpM1k7/p3+1VzeUVFBU8+vRSnoLGMnXpTt19vMhk5sudnDqVvQeUSyLCU23H1DLRCpG1EUSTv9A4ObHiXQHcz/3lrOYMHD7ZaebbQ3NzMF198wfote3DwimHCzNtxcrFe07bJZORo+gbyTqYyYnAI9993j1QLlwx4UvLuAydOnODlV9/COXA0yVfdbpd9nRUleexY9zaTx8byyMNL7Pom49fWrFnDlz/v57q7/97t5vKGuko2ffseZWVlhA+/isjh05HJ+qbWVlF8joMb/kOAq447b72OW265xS7/L7orNzeXf735DrklTSQk38igoWP77LoqS/NJ37watbmSe+66hWnTpvVJuRKJLXQnt/WPb3M7NGzYMB5+6F7qCvazL+1r7O0eqLI0nx0/vMu4EeH88aE/9JvE3draSuq2PYTHje924q6pLGHdZ29Q06BnzJzHGDTiqj5L3AA+gYOIm3AHrmEprPluCx98+CEmk6nPyreGzMxMXnj5dcpbXLnmjqeJjh/Xpzck3v6hzL7jCdzDkvnPB5+xdu1au/usSSS2IM3L6IWxY8fyR72e/7z/Kbs26km+6na7SJKlhefY/fN7jB4azMNLFverfZH37dtHRZ2O0cO7N52tprKEH9YsxyR3Y+zsB1E72qZFxs0rgBa5kajoOH7csgaD3sDvf/+gXfxfdFdGRgbLXv03ZqdIZs693yYr2gHI5QrGTLmR446urFzzA0ajkRtvvHFAtGpIJD3V/75R7ExKSgqP/vE+mksPseXb/9BYX22zWERR5PThbez+6R0mjoriiccf61drR4uiyObUNLwCh3arP7WhrpKfv3oLk9yNpKsfslniBnDz8EVvBI1rAGNm3svGHUf45JNP+l1tMScnh1ffeBvROYpp835ns8T9a8PHzCAm6Xo++3o9GzZssHU4EolNSTVvCxg7diyurq6898HHbPz8ZeLHzSNm2IQ+rRnU15SzZ/MaTPW53HrdNObPn9+rna9sIS8vj6xzhYyaNbfLrzGZjGz69j1aDErGzn4QlYNtFzSRK5Q4OHtRVFzC2DFJGPQLWLdxFYGBgf1mVbv6+npee+MtWhWBzJhzH/IeTqmzhqGjpqLXt7Ji9bcEBgaSkJBg65AkEpuQat4WMnjwYF58fik3zBrDmX1fsbmPauHna9ubvlhGoEsj//f3J7j99tv7XeIGOHfuHHqzgoCQru/cdjR9PWVlZYyYdo9Na9y/5uzqQWNTCwaDgajBowmKncRnX3xHaWmprUO7LFEUWbVqNcXVJibPudcuaty/NWLc1Wi84/hgxac0NTXZOhyJxCak5G1BGo2GhQsX8uxfH8VbVcXGNS+ye/PnVFcUWbwsvb6VzKM7+XH1y+Qe/p5br5vE8//3LDEx/XfLUq1Wi5NHUJf7hytKtRzcs5nw4VdZdSpYdzlonDCZRRoaGwAYOXEujaI7H634GLPZbOPoLu3QoUNs3XWIxEk32+0aBoIgMH7GbRSU61jz+ee2DkcisQn7aQ8bQIYOHcqLzy8lNTWVzVt3sO2bdDQe4cQMTyE8ZkSvmiHPr0RVdO4ATiojU8YmMmvWPURF9c2+1tZ0NluLh0/XrsNkMrL951WoXAKJHD69S68xm03kZ6bTWFuKs7s/oXHjrTIaXanSICKnsbERTw9PFAol46bfwa4f/s2WLVvstvm8vr6ejz5ehWtgAhGxibYO55KcnN1JmHgjG7euJmn0aKn5XHLFkZK3lWg0GubMmcM111zD8ePHSd2axsHdqzi680ucPUPw9AnBy69t/WcXd59O+8f1upb29Z+rygtoqCrA0FxJsL87991+NSkpKQNmDejW1lYKikoZNHZql44/eXAr5eXljJnzWJcScOaBH9j4yVM0VBe3P+biGcisu18mLqnrfexdpXRwprGhsf13v6BIQuIm8dkX35OUlGSXf7evv/mGkhqRaxb0bh35vhI1OIm8M4f56ONVvPbPodKmJpIrivTfbmVyuZzExEQSExMpLS3lyJEjbXs155wk89w2dAYRs6BCodK0rbUtyMBsxGg0YNQ14qAUcHFSExUZQtToRKKjoxk+fPiA+6IqLCxEZxDx8g257LFmk4lTh3fgF5nUpebyzAM/8PXyhbTtOv2LhuoSvl6+kPmPrrR4Alc7OFFXX9PhsRETZrNuRTo7d+7kuuuus2h5vdXQ0MC2nfuISbzWbpvLf0sQBJIm3cCWL17k0KFDjB071tYhSSR9ZmBlADvn7+/PNdf8shd4Y2Mj+fn5bYlLp8NgMGAymVCpVCiVSjw9PQkLC8PPz69fzhPujqamJkxm0DhdPnHk55yktraWpInJlz3WbDax8ZOn+G3ibiMCAps+eZqYUbMt2oQuVyjRtxg7PKZSORA0KInNW3cwe/Zsu7oB27VrF3XNMGlo/0qAbp5+uPhGk5q2TUrekiuK/Xx7XIGcnZ0ZMmQIQ4YMsXUoNmcwGBDFtqR3ORlHduDsFYGb1+XXus7PTO/QVH4hkfrqIvIz0wkfMrEbEV+aTCbDZLpwcFpcQgppX+/m6NGjjB492mLl9YbZbGZz6nb8wkei1tjf3uGXEzM8haPbV1BQUNC+x7hEMtAN7OqcpN8wmUyIgEy49L9kbXUZ+blnCInr2gpsjbVdm57V1eO6TkAUL0zeHt4BOHlGsjVtm4XL67mTJ0+SV1TJ4BEptg6lR0KjhmGUubB9+3ZbhyKR9BkpeUvsglKpRKBtFPmlaM8cxSyoCIgY0aXzOrv7W/S4rhJF80W7OqLix3PkeBYNDQ0WLbOnDh06hNI5EG//UFuH0iMymYzQmLHsSj/Q71ayk0h6SkreErugUqkQZGA06i95XEWpFhev0LbBfV0QGjceF89A4GKr3Qm4egYRGje+ewFfhmg2I79I8vYLiqTVIKLVai1aZk+dzc7Dyz/C1mH0il9QBDV1zVRVVdk6FImkT0jJW2IXPD09UckF6msqLnlceWkBrt5d79eUyeTMuvvl//322wTe9vtVd79k8fneel0rjo4OnT7n7OoFcg35+fkWLbMndDod2oKSLo3yt2defqHo7OiGSCKxNil5S+yCv78/zk5qqsoKLnpMS3MDDXW1uHZhoNqvxSXNZf6jK3HxDOjwuKtn4AXTxMxmE3mnd3Fyz9fknd6F2dyzLT31ukZcXTofOS8IAs4eweTm5fXo3JZUUFBAq95s1SZzs8lExtEdpKd+ScbRHZitsE2qxtEFhYO7lLwlVwxptLnELshkMgZFhJBddvHaaFVZPiaziFs3at7nxSXNJWbU7EuusGaphVxE0YxR34Kzy8VvMjx8Qzh77li3r8PStFotBrMCd0/L9vmfd2DHWla/9WSHJYI9fYJYsPgVkibNs2hZzp5B5OZJyVtyZZBq3hK7ERkRRkN14UWfr6+tBEGBo4tXj84vk8kJHzKR+AnzCR8y8YLE/fXyhRdMKzu/kEvmgR+6XI6upQm5IOLs7HzRY9y9/CmvrMZovPQAPWsrLy/H0dUHmdzyy8Qe2LGWN5+984K1/asrinnz2Ts5sGOtRctz9wqgsKjMoueUSOyVlLwldiMsLIzWhgp0LZ3vFGU0GJAplBbfavXyC7nApk+e7nITemtzIwqFDCfHi8+ZVirViCLo9ZceoGdter0eucLyO4eZTSZWv/Ukl3pPV7/1F4s2oSsUKnQ2fj8lkr4iJW+J3RgyZAiujnKyMw92+rzJZEAmt/xWp91ZyKUr6uvK8fb0uOSqeDK5AlFsW5zGlgwGA4LM8r1nWSd2X2Y3PZHqikKyTuy2WJlypQq9wbYtGRJJX5GSt8RuuLm5MXH8SM6d3NXpfF2ZIOt04ZPesuRCLq0tjZj1TQQGBVz6QFEEAZsve9tWvuXnRtdWde097epxXWE2mZDLLNsqI5HYKyl5S+zKtKlTEVvKKS04e8FzcoUSs8nyNVVLLuRSW1mKq5MGTw/PSx5nNBmQCW3z221JpVJZ5T119+rae9rV47rCZDKgUlm+ZUYisUdS8pbYlejoaGIjg8g8tvOC51RqDWajHpPRssnGUgu5mE1GWhorCQoOuGy/fGtzA0qFHKXStslGo9FgaG28/IHdFDssGU+fIC71nnr6BBM77PKby3SVrqURZ6f+tza7RNITUvKW2BVBEJgxfQrVRSeprujYD+3pE4RMaOt/tiRLLeRSXVGESgF+fpevTVaVFRIRHmzzZvOQkBD0zTUXHSTYUzK5nAWLX/nfb52/pwsWL7PoKPfaikKiIvr3YjMSSVdJyVtid1JSUoiPCSR986oOo5E9fAJRKuTUVV58OllPdWchl860tjTSUFNMVEQYqi7UpuurCxkUGd6bkC0iLCwMtVKgsvzii+P0VNKkeSx5bhWePh33XPf0CWLJc6sumOet0+uoqq6ipKSEoqIiCgsLKS4uprS0lNra2ktOqzObzTTVFhEWFmbx65BI7JG0SIvE7igUCh647x7++vcXOXFgCwnjZgEglyvw8gmkrtLyiQa6tpBLZ0RRpKzwHN7uzl3aktJoNNBcV0po6AxLhd5jvr6+uLloqCorJCgszuLnT5o0j1HJc8g6sZvaqlLcvfyJHZaMTC6nqamJyspKGhoaqKtvoKXVgEkUEc0CgkygrYYuIprNyGQglwk4OWpwc3PG1cUVHx+f9jEDddVlyDBIyVtyxZCSt8QuhYWFcfMN17Di8/WERMX/r/8UfAPDyDqbbbVyzy/k0h1VZfnIaSUubmSX5qBXlxeikpvtItEIgkB0VCgZJdZbmUwmlzN4xCSgrYZcWVVJcXEJlVV1mJCjUjuj1vjg6emMg8YZhVJ9wTn0rc20tjbR2txIcUUj+UWVKM9m4+/nQ1BgIJVlWhyUgrSft+SKISVvid2aM2cOBw8fZffGT5l18yOo1Bp8AyM4enA3zQ1VPV5pzZKaG+toqClmcHQ4Tl0cLFWQcwoPV0eCgoKsHF3XxEQPIv1oKkajAYXCOgPoRFGksLAQbX4hTa16lGpXPAJicHH1hC7c8KgcHFE5OOLq7gO0DQ6sqymnqLyMouKjFGakEuWlQKPRWCV+icTeSH3eErulUCj4w4MP4KmuZ+va9zAaDYRHj8DJUdPlBVOsqbWlkbKCTAJ8Pbpc4zOZjORn7WXa5PEoFPZx75ycnIxa1kpu5iGrnL+xqZHDh49w+kwuosqToMhEgiPjcXHz6lLi7oxMrsDDO5Cw6ERcvIIpzjvNmXNa/vveezQ2Wn70vERib6TkLbFrQUFBPPnnR3AwFrN17XsADB4+luJzezGbbLeaVmtLIyV5p/H1cmbokCFdXrJVe+44SrGRyZMnWznCrvPx8WHsqHjOntxl0fOKokh+fj77DxyhutFEQPgw/IIiUaktWzuuKjxNQGAoKfP+xPrtJ3nqr89y+PBhi5YhkdgbKXlL7F5kZCR/efxh1PoCtnz7NpGxIxGMzZTkHrVJPM2NdZTkncLXy4lh8fHIuzHd6eyxnYxKiCUwMPDyB/eh6dOmoqstoKLUMn3fRqORY8eOkXE2DwfXQEIHJeCgufhGLT1lNpsoOruX2GFjiB0+gdl3PE2TIoSXXv0Pa9as6XSlPolkIJCSt6RfiImJ4Zmn/4y7oopdG1bg5uaG9vSOPv1yFkWRylItZfmnCfB1Y/iwYd1q+q4szaepOodpU6dYLcaeio+PJzzYm1OH0np9Lr3BwNFjxyirbsI/LB5v/1CLbyZzXmnuMcz6euIS2gYZapxcmTr3fmLGzOeLtVv58KOPMJstv6SuRGJrUvKW9Bvh4eE8/39/J3lEMPqGQorP7iX31PY+Kbu1pZH8c0fRNZQwOCa82zVus8nE3tTPiY8JJjEx0YqR9oxMJuPG6+dQU3CEwtzTPT6P0Wjk+PHjVNXpCAqPR+PoYsEoO9K3NpF14HsGxQ7Dy/eXvdMFQSBuRAqJU+/mh837WLlypVQDlww49jFiRiLpIk9PTx7706OMSRrNs8+9wIEN7+LsHohvcIxVyjOZDFSXF9NYW4y3uzNxcUO7PKr8147t34TSUMr99/21W0m/LyUnJ7Nv/wF2p32Ob+DT3e6bNpvNnDhxkqq6VoLC41E5OFop0jan936Lg8LMhBm3dvp8ZOxIEEXWbvwUZ2dnbr75ZqvGc55Op6OwsJD8/Hzq6uowGAyYzWYUCgUqlQp/f3/CwsLw8fGxWouELdXW1qLVaikqKqK1tbV9cR2VSoVGoyEkJISwsLAefY4kv5CSt6TfEQSBlJQUVn+6gjvvvoeDm95l8IQ7cPP0x83TD7kFpju1NjdQW1VKS2MVKoXA4OhwQkJCevRlW11RRPbRTdx7x7V2Mbf7YgRB4J5FC8n861L2b/uOibPu6NbrtflayqvrCQgfZvXEXZp3gsr8w8y6biGOzm4XPS4ybhQtTfV8tXYtQ4YMYejQoRaPxWw2c+rUKQ4ePMjZ7DzyC0poNZgxmBWoNC7I5EoEQYbZZMRk1GFsbUCtEnBz1hAzKIyY6EEkJyfj7e1t8dj6QlNTE3v37uX06dOcOZdHRXU9rQYRQa5BodK0b+NrNhkwtDYiE/WoVQIBft7ERIUxfPhwkpKSbL7Of38jiP2wPam+vh43Nzfq6upwdXW1dTgSG8rKyuKJv/4Dl5BkHDwi0RnNODh54eTqgYPGucu1R9Fs/t8iIA001FZgNjTh6qwhKCgAPz//Li152hmjQc+GL5cTGyjj7//vb3YzPexSdu7cyRtvr2TUzAcIjYrv0msaGhrYf/AITh4heP6qCdsadM317Fn3KlGREUyf98Blb6hEUWTzt//BW1XFi88vtdhc8IaGBvbs2cPmLdvIKaxA6RSAp18EXv4hePuF4u7lj1x+4d+7taWRytJ8qsoLqSrLp6b0LI4KPePHDGfqlCkMHTq0X9TItVotaWlpbNu1n5pGE26+g/D0DcXLLwRvvxCcXDwuuA5RFKmvKaeqvJDKsnyqyrQ0V+Xh7+3I9CkTmTx5Mr6+vja6ItvrTm6Tkrek3/vmm2/49KsNDEu5HUf3IIqLS2lobMZkFhGRo1Q7odY4IVeoEAQBQRAQxbZlN/W6FvStTRgNLcgFEYVCho+XB4GBgXh4XPjl0x0mk5G0de+jbNXy7DNP9pvVv0RR5O3//Ictu04ycc5D+AVFXvJ4s9nMwUOHqW+F0KgEq8Zm0DWz7+e3cJC1cP1dT1yy1v1rjfXVbPz8ZW6YNYaFCxf2KgadTse3337Lhs07qG0W8QtPJG5ECj7+PWtVMeh15GYd5szxnRgbi4gM9eWuBbcxfPjwXsVpLYWFhaz8ZBXHTmVjUrgRMSSZmGHjezy+ob62gqxju8k/sw8HWQvJY0dw54I78PDwsHDk9k9K3pIriiiKfLRiBes27iVp5j2EDRqOwWigsbGRxoZGGhoaqG9owmAwYDKb29bKlsuRy2RoNGpcXV1wdnbGxdkFR0dHi+z0ZTIZ2fHzSkw1GTz1xMMMHjzYAlfadwwGA6+/sZy9x/JJmfuHSyamvLw8srILCIoY3uvmctFsxqBvwWwyYjabkMsVyBRKFEoHjPoWDm5+H0FXwXV3/Knbe4FnHd/Nmf1f8dzfHuvx3yMrK4sPPlzJmfwaohOvIm54MmqNZfpuRVGkoiSPY3vX01KVxdXTJ3D7bbfh6GjdLoiuMplMbNiwgc+//gGdzJuE8XMIjhxqsZ3xjEYDuZmHOLn/J7wcjSy661YmTJjQL1ohLEVK3pIrjtls5v33P2B92kESJt3OoCFjbBaL0aAn7YcPEBpzeOyR35OQYN3aqLW0trby+hvL2Xc8n+TZvyMgJPqCY0wmE7v37EXh5Id3D2qejXXl1JZrqa8qpLYin7qqYowGHQAiv2wmqlCqqavIw9FBzsx597fV9Jy699kXRZENX7zBiCgNTz7xeLdeq9Pp+Oabb1j7cxpKt0gmXHUHLm7W6aMWRZFzp/dzfPe3hPlpuO+eu2xeCy8sLOSDD1dwJKOAiPjpjBh/daddApaga21m/7bvqMzbT8q4YSxaePcVUwuXkrfkimQ2m/n0009Zu34nATEpjJo4F6Xqwk0urKmqvJD0zatxkdXw50cf6nc17t/S6XS89fZ/2Lk/i+iR1xA/elqHmlZxSTEnTp8jZNDITjcU6YzJqKc45wh5p3dRU1GAKILGxRsnj2BcvUJwcPZs7+Iwm020NFRRpj1Oa30parkZ0axDKZcRGRNPXMJEgsIHd7l2lpN5iIxdn/LGP/+PgICAy7+Atr7t115fztHMYoaMvY64hJQ+qQ02NdaSvnkNrVVZ3Hf3zVx11VVWL7MzJ0+e5LXl79CMJ+NmLsDbr2+6fwpzT3Eg7XOCPAT+8sSfCA627lgKeyAlb8kVSxRFUlNTWf35dzQYnRk74w78gwdZvVyTycjx/ZvJPrqJhLggHrj/3n7Tx305RqORtWvX8s3ajYiaICZcdSfuXv6IiBw8eIgmg5rALmwn2tpUR87JNPKz9qHXteLhH0tAzATcfSNRqi9sGhbNJmrK82ipL8fF1Z2AkEHIFSp0zfWU5Z+kMGsPLfXFeHn5MHTkJOISJl52YxWTycjaFc9y65xx3HbbbZeNuba2lmWvvEZmfgNT5j3UvrtdXxFFkSN7fqbg1CbuunUu8+bNu/yLLOjw4cO8/u//ovCIY/K191ht45qLaW1pJPX7d3BX1PCXxx8lIiKiT8vva1LyllzxysrK+GjFx+w/eo7AmImMTL7W4mtqn1dZms/e1DUoDWXccuO1zJ49u1+MKu+unJwc3v9gBadzKogZeQ3BUSM4dPQUPiFDcHR2v+jrRFGk6NwBTqZ/h8ks4B81hqCYCWhcLt7s3NJYQ01ZLjKM+AWGd9q/LYoiNeV5FGTupkJ7FF9fHyZdvQDfwEt/wR/a9SNNhbt561//RK2+eGtBU1MTL778ChnaRqbfsBhXD9uNgj5xIJXsw+t4YOF8rr766j4p8+TJk7z86ps4+Awn5eq7kNlofQK9vpWt37+Li1DOM08/MaBr4FLylkhoa0bfunUrqz//jpomkYDI0cSNmGiR2pPJZER79hhnju+kuTqXYbEh3H/fPYSGhlogcvtlMBhYt24d36zbSH5JAwqPWEZNu+ui27O2NtVxfNcXlGpP4xMxiujR16NUdz7ASzSbaKqrpLG2FJOhGRdXd/yDB3Wp66OhppQTO9egqy9k5NhpJCZfe9FaYmN9NRtXP8czT/ye0aNHd3qMyWTi5WX/5ODpUqbf+DBunn6XjcHajqSvp/DkRh5bch/jxo2zalkFBQX8/bmXwTWaKXPus9igtJ7S61rY/M2b+Dg28cL//X3Afu9LyVsi+ZXa2lp27drF5q07yC+uRuMRzqCh4/ELisTFveurXBkNeqrKCynMPYU2cy9qmhg1Io7p06aSkJBgtyunWUNZWRkPP/wop3NrcPIIxCMgjqDosbj7huHg6IYgCFQUZnBo6yeYURA77ha8gy9cIMVsNmFoaaSpsZqW+nIERFzdPfHw8r9kbb4zotlMzsk0co9twNfHh1k3PXjRQWXrPl7KbXPHcdNNN3X6/M8//8x7n3zPpHmP4BMQ3q04rEUURXZuWIVQd5plLz1ntUFcRqOR//vHC2QVm7n61j/1eVP5xbQ0N7D+s5eZPj6aPz700IAchS4lb4mkEyaTiWPHjrE1bRuHj2XQqhcxy9Q4ewTj4RuCu6c/CpUahVyJWTRjMhrQtTRSVV5IXWUBLfVlqBUi7q6OTJ8ygcmTJ3d50NNAYzQauf/BJfjGXY3awYlTR3ZQXlKI0SyiUDljEqGiOBufsFHEjJmPysEJkba59SajAb2uEaOuCaO+FUEApVKFh7cf7p5+yOQK8jPTaawtxdndn9C48chkXb8xaqgp5Ujqh2iUBmbfvBgP7wv/RlvXfcCQQCNPPP7nC54rKiri6f/3DzwjpzBq4txevU+Wpmtt5ufPXiJlZBiPPvKwVRLY2rVr+eizn5g2//E+7+O/nLyzxziW9hFPPvo7xoyx3YwSa+lObht4HXMSyUXI5XJGjhzJyJEjaWxsJD8/H61WS15eHmdzTpGTvR0zcP52ViaASqkgMjyYycMGExo6i7CwMAIDAwdkn3Z3FBcX09RiwDcwEr+gSGKGjaepsZaq0gJOH9nOgd0b8AxKJHBQEk21hTT96rUymQwHjTMuHp44ODp3WAkv88APbPzkKRqqi9uPd/EMZNbdLxOX1LVE6uLhz9jZSziw8V1++uLfzL390Quavb38QjlzLg1RFDskQJPJxAcfrqBF8GLE+Gt6/gZZidrBkaSpt7Fz03uM2bOH5ORki54/Pz+fr777magRV9ld4gYIj05AeyaRFZ98Rlxc3BVdebuyv4EkVyxnZ2eGDBnCkCFD2h8zmUwYDAYMBgMymQylUolSqRyQzXO9pdVq0RmFDrt5OTm7U6sspUB7ltgxN5Aw6U4QBESxbWEcQRAQZDIEofP+08wDP/D18oW0zfD+RUN1CV8vX8j8R1d2OYGrHV0Zc81D7Pv5LX768i3mLXgMJ5dfmpm9/ULIP9FCRUVFh+U4t23bxpFTWibd8CerzWPureCIoeSGjWHlqi9ITEy02CIuoijy8cpPMSj9SRhjm2lpXTF22nx+Xv0SX371Ffffd5+tw7EZaUtQieR/5HI5Dg4OuLi44OTkhEqlkhL3RVRXV6PSuKFQqtofa21pJO3HlTh6DmL4pAX/S9QCMpkcuUKJTK64aOI2m01s/OQpfpu427Q9tumTpzGbTV2OUeXgTNKsP9DYKrJjw+oO24K6evhgNIpUV1f/UooosmFTKt5hI3u81GlfGZ0yl7IaHenp6RY7Z05ODicychkxYY7NRpZ3hYPGmdjEGWzfuZ+6ujpbh2MzUvKWSCTdZjQaL9i9LT31KxqbjQyfdEe3+qgB8jPTOzSVX0ikvrqI/MzuJSsHJzeGTriV3HOZZB3f3f64XK7ELLaNnj/v9OnT5OSXM3hESrfKsAWNkys+IcPZtCXNYnuVb01LA7U3QeH2v7DQoKFjadDJ2LVrl61DsRmrJO+ioiLuvPNOvLy8cHR0ZMSIERw6dKj9eVEUWbp0KYGBgWg0GqZMmcKpU6esEYpEIrECo9GI8KsEnXf2GJknDxE79kbUjt3vh2ysLbXocb/mExyH36BxpG/9joa6KoD2JvHze01DW/JSOgfZzejyy4lNmEi2tpTMzMxen6uhoYGdew4SGT+xX7Q2qR0c8Y8czebU7ZjNZluHYxMWT941NTUkJyejVCpZv349p0+f5rXXXsPd3b39mFdeeYXXX3+dt956iwMHDuDv78/MmTNpaGiwdDgSicQKFAoFZlNb4tO1NrNr0+e4Bw4lMGpUj87n7N61TUa6etxvDU6ah0HQsGvTGqBtnj7QPvCwurqa9APHGTQsuV8kLwC/oChkjv6kpW3r9bl2795NXTNE23BPgO6KS5hIfnE1x44ds3UoNmHx5L1s2TJCQkJYsWIFY8aMITw8nOnTpxMVFQW01bqXL1/O3/72N2688Ubi4+NZuXIlzc3NfPbZZ5YORyKRWIFSqWxP3mdOpFPf2MLQCTf3OPGFxo3HxTOQX7Yi+S0BV88gQuPG9+j8CpUDcWNuIC87i4pSLSaTAZnQdh0AmZmZNLaIRMT17ObDFgRBIDR6FEeOZ/S66fzEyVN4BMRZbIe0vuDlG4zCyZeMjAxbh2ITFk/e69atY/To0dx88834+vqSmJjI+++/3/58bm4upaWlHRbZV6vVTJ48mT179lg6HIlEYgWenp7oW+rQ61o5fWQnPqEjcHDs2t7anZHJ5My6++X//fbbBN72+1V3v9TtvvRf8wuNR6HxIPPoTuqqy1EoBDw9PYG20fMaFx9UKocen98WvP1DqWtsoby8vMfnEEWRs9n5PdoVztZcvULIzsmzdRg2YfHknZOTwzvvvEN0dDQbN27k97//PQ8//DCffPIJAKWlbX1Wfn4d5136+fm1P/dbOp2O+vr6Dj8SicR2wsLCUCtEMo5sp6q6ktDBvZ9vHJc0l/mPrsTFs+OiKq6egd2aJnYxgkxGcMx4zpw+RGnhOdxdHPHx8QEgOycPFy/LbyRjNpnIOLqD9NQvyTi6A7Op66Plu8LbNwSdXiQ/P7/H56ioqKC2obnDtD9Lsfr1+4WQnVuIycLn7Q8sPpHRbDYzevRoXnzxRQASExM5deoU77zzDnfffXf7cb9tXvvtYgm/9tJLL/Hcc89ZOlSJRNJDAQEBOGmUHN+/GY1rEO4+lqm1xSXNJWbU7F6tsHYpwTFjyT2+kcyjO5k5YdD/th01k51biO/gYRYp47wDO9ay+q0nqa4oan/M0yeIBYtfIWmSZXYHU2ucUDl6oNVqSUpK6tE5tFotOr2It79l1+Xvi+v39g8l+5CekpKSAb1hSWcsXvMOCAjosPAFwODBg9vvDP392wac/LaWXV5efkFt/Lynn36aurq69p+CggJLhy2RdGAwGMjLy+PUqVMcPXqUI0eOcPLkSXJyctDpdLYOz+YUCgVhIQEU5J0lKGacRQd5yWRywodMJH7CfMKHTLRY4gZQa1zwChpKkfY0EeFtNxxlZWU0tei7VfM0Go3U1dVRXV1NZWUlVVVVVNdU09jUiCiKHNixljefvbND4gKorijmzWfv5MCOtRa7JmfPYLT5Pf9OLCoqQqlxx0HjbLGY+ur6PX2C0RmhsLDQIufrTyxe805OTiYrK6vDY2fOnCEsrO2DEhERgb+/P5s3byYxMREAvV7P9u3bWbZsWafnVKvVl9y6TyLprfr6eo4cOUJeXh7ZOVpy84to0ZkwmX9ZLlUQQC4DB6WM0JAAoqPCCQsLY8SIEe19p1cSdzcXDAY9nn79a49lZ89gWpoa2vdbb21txWQWUTtcfLBWU3MT1dXVNDQ0UF/fSFNz22tE+GVdGaGtNiSTwTf/epSLLzgjsPqtvzAq2TKLoagdnGhq7nmfd0tLC4pO9lPvKbPJxOq3nqQvrl+pUoOgoKWlpVfn6Y8snrz/9Kc/MWHCBF588UVuueUW9u/fz3vvvcd7770HtDWXP/roo7z44otER0cTHR3Niy++iKOjI3fccYelw5FILkoURc6dO0fatm3s3HOIumYzjm4BuHuHEDpiLF5+ITg6uSJXKBEQMBr1tLY0UVVWQGVZAZv25dO8cR9O6s+ZMCaBaVOnMnjw4H4z1ai3vL29kckVyJSWHeRlNpus1mwOIFOoUakc8PJq28bUYDAgilywXr3ZbKaqqoqi4mIqq+owiTKUaifUGnfc/JxxcHBCplAgE2SIoogomjEYdGQf20pjXcUlIhCprigk68RuBo+Y1OvrkSsU6PWGyx94EQaDAbnccjuHZZ3YfUGNuyMLX79c0WGxnSuFxZN3UlIS3333HU8//TT/93//R0REBMuXL2fBggXtxzz55JO0tLTw0EMPUVNTw9ixY9m0aRMuLi6WDkci6dSJEyf44qtvyDxXCGpvBsXPYfLQsagdLl4DUeOEk4sHXr7BxAxrm7Jk0OvIzjjAziO7SNv1OoPC/Zl/4zxGjx494JO4wWDAJzCSuppynF0738+7uyyxMcmliKKIiAx3Tx9KS0vbp7D+9pii4iK02kIaW3Qo1a54BETj7Op12b+pQqkG0XjJY86rrer+gjMXY0//a129Lkte/5XIKivvz5kzhzlz5lz0eUEQWLp0KUuXLrVG8RLJRTU3N7Pm88/ZkLoHB88YRs/6A4GhsT3+8lOq1MQlTCR2eDLlxbmcPJTKy6//l2kTD3DXXXcO6F2PzuVoCY0agr6pFqNB15a4esGSG5NcTENdJSqFDP+QaPLz89sXlJLJ2vqxm5ubyczKoqK6AUdXX4L8A1Bd4oauM11dSMbVo/MxPt1lMhpROfb8q1ypVGIyWa7m6u7Vtevv6nGXYzIZ2+frX0nsc9scicQKTpw4wYcrPiWvtJnhE25n0NCxFquxCIKAX1Db9ph5Z4+xZfuXnMz4O/fcfQdJSUl2VTOylNq6BgICh9NglFNTWdKrZUUvvzGJwKZPniZm1OxeNaHXVZXg4+2OucanfUVHjUaDTIC83HPUtQgYUeMfFo/GsWctgecXnGmoLrnI9Qg4unrRYHCgoaGh1y2Ora2NOHprevx6jUaDUdd0+QO7KHZYMp4+QVRXFHOx6/f0CSJ2WO+nF+r1rSAa0Wh6fv39lbQxieSKsHHjRp5f9m9qTL5cfftTRMdbdoT0r4VHJzB7wdMYNNH8c/l7fPvttxbbPMKe6PUGlCo1YaHBNNaUoGvteQKw1sYkv1ZbVYpoaCIsNBSZXIlerwfaFpwpLsrn+LHDqJz9CB2U0OPEDV1bcGbmnS/RqJNz8PBRKisre1wWQGN1EeFhPZ/mFRISgrG1jpZmyyxPLZPLWbD4lf/91vn1L1i8zCKD9arKClAraR98eCWRkrdkwFu7di3vr/yKgLgZTL/+9x32dbYWB40zk69dxKDRN7Dqq/WsXr16wCVwk9mMQNsXp5e7E2WF534Zmt9N1tyYBNrGJtSUawkLCcDd3R2ZTNa+f/tbb/+H6tomZIIcn4CIi25b2h2XW3BmWPJ8QqOGI3Pw5NiJ05SVl/WonJbmBgwtNe2zeXoiLCwMtVKgqqznC738VtKkeSx5bhWePoEdHvf0CWLJc6ssNs+7qrwQZ0c1AQEBlz94gJGazSUD2oYNG/j0ix+ITLyOYUnT+7z8ISOnoFQ58M2Pn6NWq5k/f/6AaUJXKRWYTCZkMhmxcTHsP3CEqvJCvPy6Xwuy9sYkZUXncHVSEhkZCYDJZECpdOTd//6X7fsyGTFhLkVlVT0698VcdsEZQSAgJIayomxOnspCLpPj7e3drTKqygpwUAqEhva85u3l5YWHqyOVpQUERwzt8Xl+K2nSPEYlzyHrxG5qq0px9/IndliyRfcKryrNZ1BECDLZlVcPlZK3ZMA6ePAgH636htD4WTZJ3OdFx4/DaNTz+bff4OPjw5QpU2wWiyU5ODhg0LfNr3VxdiEqIpSsc/k4urh3u9m5K/3Erp6BPdqYpKaiGLOunriRw5D/L3EY9a0cO3aW0joYN+t+WpobyD73KQZdM0oLznk+v+DMpfgFRVGSb+LEqQxGjxqBi3PX37vK0nzcfrXMa08IgsCgqDBOFGh7fI6LkcnlFpkO1hlRFKmrzCdq1AirnN/eXXm3K5IrQn19PR99vApnv2GMGH+NrcNh8IhJ+A1K5pPPvqai4lJzgPuP8NAAait/6acODQ3Fz8eNUm1Gt/u/rbUxSX1tBbWVeURFhODh3tZdIooiVSU5ZJzRMmTsPIIjhhAYFotKKaco+3C3zm8p/iHRmAQNmZlZXd6fWhRFtGcPMipxaK9bc4YPi6e29IzF+r37QmVZPqbWygtW9LxSSMlbMuCIosiqVaspqREZN/0Wu2mmHp0yjzq9Ix+t+HhA9H9HhIfTUP3LspQymYz4oUPxctdQnHe62wnc0huT1NdWUFVyjoiQAMIjwtsfr6kqpaggBzf/we21Qidnd6JihlGYtdsmfxtBEPALHkR1XXOXNxkpKTgDrRVMtUBLTnJyMh5OAmdP7uv1ufpK5tGdRAb7EB8fb+tQbEJK3pIB5+DBg2zddYjESTf3atSwpSlVasZOv4P0w1mkpaXZOpxeCwsLw6Srp7mxrv0xhUJBwvBheLs7UJx7kpam7u0AGJc0lyX/Os6df/uB6//4Pnf+7QcW/+tYtxN3bWUJVSVniQjxJzo6GuFXtfkTR/bR3NzMtOt/1+HGbnBiCq0NpVSXZnerLEtROzjh6hlMdm4+DY2XrwFnHd1JbGQQ0dHRvS7bycmJyRPHkHNqV5dr/raka2miLO8IM6ZPviL7u0FK3pIBxmw2s3rNV7gGDCciNtHW4VwgICSagEHJfP7l97S2tto6nF4JDQ1FrRSoLOu4KYZSqWREQgJ+3i6U5p+ioiQPUex6QujNxiRGo56ivAzqKvOIjgghOia6Q4Jubmnm3JnT+AVF4e3XcZBXQEgMPj5+5Gfs6nJ5lublG4wodyQ7O+eSxzU11FBVdIqZM6ZarGVp6tSpyIw1FOaetsj5rOnMyb24OwokJ/d+rnh/JSVvyYBy7Ngx8ourGT52lq1DuaiEsTMpq2lh//79tg6lVzw9PQkN9EF79ugFz8nlchKGD2dIbAT6xlLyzx2j1cr9qfU15RScPYKKZkaOGEpkZGSHGjdAcVERlcVnGDrywkFUgiAwbPRUqgqO26z2jSDg4RNEZVUdTc0X73Y4sON7gnwcGTdunMWKDgsLIzE+mqN71mE02u9a4S1N9Zw5spmpk8Ze0UtqS8lbMqCkbk3DwSO8W9s79jUnFw88g4ayOTWtX/d9C4LAjOmTKcs7gq7lwkQjCAIhwSGMTRqFl4uSkryTlBVm92oxl840NdRQmHuKmrJzhAV7M2bMKLw8L1xr3WQycfLofkRjM0MSOx8BHTNsAqFhEZzc9Tkmo96icXaVs6sXZkFBSXFJp8/nnTlKXdFR7lm4AAcHy20KIwgCixbehaNYzdH09RY7ryWJosieLZ8T4qNi/vz5tg7HpqTkLRkwSktLOXgkg5hhl56aYw9iE1LIPFtAdraNangWMmHCBNwdBc6c3HvRYxwdHUlMHMHQwZHITbWU5B6jIPsE9bUVPb55MZkMVFcUkXfmEJVFGbg6mBk5Ip642DiUis7Xua6oqCD/7GEiBg3B27/zRU1kMhkpVy9AMNaRdfCnHsXWW4Ig4OzmR3FJGSaTqcNzrS2NHN7xFdMmjmL06NEWLzswMJDbbr4O7cmtVJRafupYb+VkHKSp7BT3LroTZ2fL7T/eH0nJWzJg7Nu3D52oIdwO+7p/KzA0FlHtxd69F096/YGLiwuTkkeTfWrXJROxIAgEBwUzftxYRiYMxdtNQW3pOfIy91OY29Yv3lBX2T5v/NdE0YyutYm66jLKirIpyD5O/pmDtNQWEBrgztikREaNSuy0tv1rudlZ1FfmkThu1iX7id08/Rg7+TqKs3ZSVXKu62+GBbl7+dGiM1JV9cvCMaIokp76Jf7ucOedC6w2i2LWrFmMjA8nffNqDHqdVcroiaaGmv/P3n2GR1WmDRz/n6npvfeEVCAFQmihV6WIKKgIiwV9XdvKWtd1bVt01dXVtay6NhBExQICIjUQQg0thJJASO+9ZzLtvB8iKJKQSTKThHB+18UHZs55zjMwM/ecp9w3x1O+ZfrkUQwb1v8/45YmJWmRDBhZ57Nx9AhGLu/7t7XRaKS2qpSq8gI0zY0YDXpEUUSuUGJlbYurpz8unoM4e+7KC5OuBlOmTGFr0gHOpu8jIubKC4gEQcDNzQ03Nzeam5uprKykobGRuvpqaktLMBhFjEZAEBAE4ecfBCJyAeQyATs7Gzw87LG398bd3d3kalKiKHLi4Fbs7WwIiYzv9PjBwyaSn5VOWtInjJj5IA6uviZdx1wUSjUypTX1DfV4eHggiiKHk9fTVHqChx6936LV6mQyGfcsu4vnXnyZXRs/Ysq8+/r8M9Xa0sSOdf8lxMeG2xctMkuboihSW1tLXl4elZWV6HQ6DAYDSqUStVqNj48PAQEBZp2aMKe+/5aTSMxAFEXOZeXi6m/ebE5Gg8Hk9I7VFUVknTpIaVEOFWWFaLV6DEYRuUKNXKECwKDXYjRokQmgaapBbCklMiKU8ePHX7XFFYKDg5k9Yzzfbl6HT2Ak9o6m1fa2sbG5JK2nVqejsbEBrVaL0WhENIrIZDJkchnWVtbY2tpezJDWVZknD1KafZDrFjyAQqnq9HiZTMa0G+/lx6/f5sjW9xlx3QPYO1+eP9toNHSc/rSHVGpbGuobEUWRY/t+pCQziQfuXczw4cPN0v6V+Pj48MSjD/Hyq2+ya+MnTJpzd58F8NaWJrZ//x6ethqeeuLJHg2XNzU1sW/fPk6fOcPZrFyqqhvQ6ESQKZHJVQgyOUaDDoNei1JmQK0U8PPxJDw0iOHDhxMbG9vt96C5CeJVuGKmvr4eR0dH6urqBnS9ZInpKisreXD508RNvdds+ZlTk9ez+p0nqa4ouviYi7svix969WJhBYNBT+7Z45w5lkxhQQ6C0g4nrzAc3fxxdPXD0dUPherSX+56rYb66iIqis5SePYgztY6bFVaYgcPYuqUScTHx1919Yk1Gg1PP/M8lVpXpt/0QL9JjANtX/5fvv805YWZ/OGldajVppePbGlu4Ke171JWXsWw6ffh7PHLXHlG6ga2rPzTJdXQ7F18mLn0nz2uOw5taV2ba/OxNpZTkbWHZUsXMHNm7+6iOHnyJP968z0M1kFMnnsPSlXParZ3VVNjLTsuBu7l3fqBK4oiubm5JCUlkbw3ldomEQePQbh4BODmFYCbpz82dk6XvGeNRiN11WVUluVTVZZPVVkurXWF+Hs7M33KBMaPH4+Tk5MZX2mbrsQ2KXhLBoSjR4/y11feY9bSv2Ft2/P3RGryet5+fgmX59lu+4A//OIqPHyCSdn2FbW1dTh6huEfkYhn4FCT77xE0UjOmYMMjQxB31LF2RMpNJSfxd/bibuW3t4rd1jmdOrUKV58+U3CEhYQGds/Fg2KokjyjysoOLkFG7dB3LTs711uo1XTzLbv3ie/IJ+g6BkMiplK5pFNfPPmHXT0/uhORrjfqizN5tCP7xDooueRB5cxceLEHrXXXWfPnuW1N96hRmPNqGm34+UX2ivXzc48yvHkbwjysuJPTz6Kp6dnl9uoqKjgsxUrST2WgUHhRMiQcYQNHdWt5E3VFUVkHE+hJPsw9moDN8yexrx581CpOh/JMZUUvCXXnKSkJP79/lcsvP/1HrdlNBh4dNHgS+64LyVga+9I9JgbcPaNJjx+DvbO3at2lZd5mPAQn4slHeuqyziasoH60nSmTxrJ7YsWXVV7WVetWsXajXsYPfMe/IL7Puf0sX0/UnhqK6GB7hQ0ODHj5ge71Y5er+P4/s0c2b8dlZ03qds+orG2ozKebUVUHnorrVtD6KIoknc6hczU76kvTuffr/2V66/v2/z85eXlfPzJpxw6noVP+Djix8212F14S3MDB3Z8TX3JCaaOH8GSJYu7/BkQRZFdu3axas231LZaM3z8zfiFDDFLNjatVkPG8T2cPfoTUcFu3HvPXQwaNKjH7ULXYps05y0ZEHQ6ndnm5DLT914hcAOINDXU4uQTw/Cpd/dsiFgmw2D8ZTuQo4snk+YuI+fsMTbvXkv6yRe47947iY6O7v41etGiRYuoqa1l+5aPSZx1H94B4X3WlxOHtpN/cgv33rGQc+fOUXSu+/u2FQolI8bfQGBoDN9++tIVAjeASH11EfkZ+zutKHbJWaJIZVEm509sp7kqm+iYEZQ7iPj69u5iufZ4eHjw1JNPsHPnTlZ/+T0/fnGaoSOvJyhimNk+d9rWFrJOHyLj8BZ8XGQ88Ojvu7Udrra2lg//9xH7j2TgHZbI7PHzzPpDQ6WyImbkdAJCY9i/bTV/efEVFsybyfz583s1VasUvCUDgiiKYKZ51tqqUpOOs7J1NMPcrnDZyKsgCIREDMfbL5R929bw8r/e5uHf38WYMV0vh9nb5HI5v7/vPrTad0j+8QNGTr+LgEG9WzhCFEWO7t1E4elt/O7WucycOZPMzEyzvD/cvYMYOmIy+7Z90emxjbWmvY90rc0UnDtEYcZedM2VeHr7MX3Rwzi5efPjiqP9JpGPTCZj2rRpxMTEsPqLNRzct5q0vd8TEDmaiJhEkxcq/tavh6Pt1AZmTxnJrbfc0q0Rp8rKSl557Q3Ol2gYM+tBi/54dHLxZObC5Zw6ksTnazdSUVnJPcuWoVD0TliVgrdkQFAoFIhGvVnacnI1bQjczql7Q+WXEI0d/lq3tnVg8g33sn/7V7z17icYjcarIpezUqnkkT88jPWH/2Prto8oK5rMsLGzUHSQPMWcmhprObDjK1oqzvB/d97CjBkzAFCpVIhG86RndfUw7U7Y2s7lssdEUaSlsZq6ygLqKguoryqkviIHpdxIaGQcg4ctxcMnGEEQaKyvRhDod4sXPTw8+OPyRygtLSU5OZkdSXvZenInNi4BOLv74+oZgJtnAI4unpe9tw0GPTUVxW0LwcoLqSnPQ9tQTICPC/csnsW4ceO6vRCsqqqKv7/0KoU1MqbdvLzbPya6QiaTEZ0wFQdnd37avgK9/gPu//3ve2VFuhS8JQOCtbU1Br0OvV7X4yAREZ2Ii7sv1RXFXL4gCS7MaQZE9vxO2GjQI1d0/EGXyWSMnX4b+7fLePeDFT9nK+v/CSoUCgW///19hIRsZc3X69mcd4rR027H3TvIItcTRZHzZ1JJS/kOfw81f3ziIWJjYy8+b21tjU57paFu03X+/gCVlS3nj/5A3sntyH/egmQwaDHoW8Gga9uz7uCIl3cAsdFzCB2ccNlCS21rC3IZ/XafsZeXF7fccgvz5s3j8OHDnD59mqzs82Qd3MspnYheVKBQWiHI2sKMwaDFoNWgUhixVssJDvBlzLhBREff2OMtWPX19bzy2hsUVgtMX/AHbOwczfUyTRIYGoNcfjfbfvoIK6vPWHZ3D6fTTCAFb8mA4Ovri1oJNRVFPQ4QMrmcxQ+9+vNqc4FLv6DbPpAzlr7c4/28Oq0GMGBrY3vF4wRBYPTUheze1Mh7H3zKqy8H4ezs3KNr9waZTMZ1111HTEwMH338Kcnr3sQvcjyDh0/q9K5IFEUa66upKi+ktrIEna4Vo0GPTCZHrlDi4OyOq4c/Ds4eVFcUknZgM80VZ5gzdQyLbrsNW9tL/019fX1prt2P0djxSIfJr8uE98fiB/+JX1AULc2NGAw6jAYDCoUSuVKFk4sHrh7+ne6KqCorQKUQ8PHx6VF/LU2tVpOYmHhxVKilpYWCggKKiorQaDRotVpkMhlKpRIbGxv8/Pzw9/c324iCKIqsWLmSc4VNTF/4x14P3Bf4BQ9h+OQl/Lh9JVGRkRYfJZOCt2RA8PX1xcZKQWVZvlnu7hImzOPhFz7n4389eEm9agcXH2Ysfdks+3g1LU0/Zw3rPOlE2x34In784mU+W7GS5Y/8oV/tpb4SHx8f/vLM02zdupXv1v3Ili/24OwTRUTMeHyDoi6+Dr1eR07GEbIzj1JenE9TcxMGo4hCZYtCZY0gkyOKRgw6LdrmWjTNdbQ0VqKS6YgICeDRRx/p8AszICAAhaCnrroMZ7fLk610VcKEeTz84qoO8gC8cjEPQE9UlRfg7+vVb++8O2JtbU14eDjh4b2zWPHgwYPs2nuc+KnLsHd065VrdiQkMp6i3AxWfP4VgwcPtuiPbCl4SwYEhUJBcKAvJaUFnR9sImd3H6LHzsPVPx6VlY3ZM2hpmhuxsVKbvE9UbWVDwuTb2LP1Q0bu23dVzH9fcOEufPLkyRw6dIjtO5I4uu0DjqicsbJzp6mhhvKyIgxGOY6eYTgHjyXIzR9HV3+sbB0xGHRomhtpbWlC09JIU30FrfWlKMQWZMZmNLW5vP/x55w6fZopkyczaNCgS37cXKw9XppnluANbQE8PnGOyRn4uqqmooDhI4LM0tZAVVtby2efr8HZfziBoTF93R0ARk6az49fZFj8R7YUvCUDRmhIEGd2ZZilrVZNM3u3f4t74AjiJv3OLG3+lqalAS/nrqV69AseQk7gSFZ8/hWxsbFXXWUltVrN+PHjGTduHJmZmbz33nukHNyEFjtc/ePw8o/G1sENZDJaW3WUFWUhikZEgw6ZTECtVODsYEeQdwheXmMv3pW2tjRx7vQhtu5LYVvSQcaOjObOO5ZevPOxsrLC39eLipI8woaarwa2TC4nKs68KXmhbWFXU00RgYH9f4dBX/rqq68pq5cxe07/KQ/66x/ZiampjBw50iLXkYK3ZMCIiYnh+027qSzNx80roPMTriDr1EEam1oYNusGM/XuUjqtBr2mAVfXrg8tjphwA5tWHmXfvn0XV1NfbbKysvjks8/JL9cybcFjRMSOQ6vV0dDYgLb159zmYltuc0EmYG1tjb2dPVbWVghcfiejtrZlaPxkhgyfRP75dPYkr+VMxvPc8btbGTt2LIIgkBAfy6rvk9Hrb+qVle89kZt5DGuF4arZ398Xqqur2b3vMINH3Iza+srrRnqbX/AQzrhH8NOWbRYL3lJJUMmAER0dTZCfG2eO7+lRO6Iocvp4Cm4BMVjZWGbxS21VGdZqBe4e7l0+19rGHo/AOLZt39Vv9gCbShRF1q1bx3N/e43COmum3fIk0QlTUanU2NnZ4e3lTWBgIMHBwYSEhBAUFERgQCAe7h5YW1u3G7h/TRAEAkNjmHX70+A0lNff/pT/vP0OGo2GCRMmYCVrISfzaC+92u47m76HhOGDu5US9FqxZ88emnVKBkUl9HVX2hURO4GTGbnk5uZapH0peEsGDJlMxvSpEynLPUprS1O32ykpOEtFRRmBUZbJzy2KRhrryvH19UTezfnzyNhxZBeUc+rUKTP3znKMRiMrV67ksy834jd0FjMXPoKji2WCk9rKhvHXLWbEjHvZuT+D1/71Bra2toyKH8q59J79uLO0qvJCNDV5TJk8qa+70m/p9Xq27UjGNzSh14ulmMoveDAGpRO7du2ySPtS8JYMKImJiTjaQGb6/m63kZm2Fyt7L5w9Q8zYs1801FYiR4+3d/cXTrl7B6Gw8yU5uX8HogtEUeTzzz/n+817iZuwhJiR03sllaR/yFAmz/8DRzPK+dfr/2bsmNG01hVQXpxj8Wt31+mjuwnwcblkn7rkUidOnKCovI7I2PF93ZUOyWQyQgYnsjslFY1GY/72zd6iRNKHHBwcuG7aBM4d20JDXWW32iguzMbNb4hFVoka9DqqyvLw8XLDxtqm2+0IgoBv8FBOZZy7KobO169fz/eb9xA34XYGDe7dYU5XDz8mzXuAYxll7NqVzNBwPw4lfY3BYJ6MfOZUUnCO8pxDzJt7fa/myb7aZGVlobB2MTkbYl/xDxlKfVMr+fn5Zm9bendIBpybbrqJQX4O7Nv6RZcDW0tTPY31dTi6db1ucEeMRgO5p1M4ue8b0lLWYq00EBra87KKrh7+VFU3UFtb2/NOWtC5c+f48ptNhA2f2+uB+wIXd18SZ93D/qNnCQsdhFJXxomDW/ukLx3RaVs5uOMLRg8PZ9KkSX3dnX4tJzcPexczfkYNBs4cT2b/jq85czwZo8HQ+UkmcHTxRC8qyMvLM0t7vyatNpcMOFZWVtxz9x288I83yEjb06WtPFXlBRiMIo5ufmbpS0bqBras/BMN1cUXH3N09cL6D6/3OJGHq5c/Gp1IXl5ev8241trayv8+/gy5QxDRCVP7tC9efqEEDJnM9qQ9TJs0hu83byMgNAZXD/P8X/fUkZQfcFA0cfddj181CXj6giiKnDufj+ugaWZpLzV5fQfJdl7t8WdUJpNh6+xrkeAt3XlLBqSoqCjmXjeR0wd/oKLU9A9OZWkBMqV1u0UluiojdQPfvHnHJYEboK6qjLefX0Jq8voetW9j64hcZW+RLwZzWbduHZm5VYydfnu/CEjDxs6iGWfy8guIjfQhZfNnaFoa+7pb5J49TvHZFJYsugkPD4++7k6/VlZWRn2jBlfPnv/oSk1ez9vPL7msBHB1RbFZPqMAzu7+nDsvBW+JxGQLFy5kZEwgyRvep6ayxKRzmhvrUNu69DjQGI0Gtqz8E+0Xrmh7bPU7T/VoeE4QBKzsXKirq+v84D5QVlbG+k07iEyYg4Nz/whICoWSMdMXc/x0Pgkj4vGy17Lj+/+i1Zp/QZGpinLPcHjnSq6fOoopU6b0WT+uFrW1tegNYo+rhhkNBla/8ySW/IwC2Dm6UVVd26M22iMFb8mApVarWf7IH4gNcyPp+7epLOs8darBoEcuNy1d6ZXkZ+y/7I77UiLVFYVkpu/t0XUEuRKdTtejNixl9+7dtBisiYrrXyuC3b2DcPCM4OixNJ58/BEcFTVs++btPrkDzz9/kv0/fcTUxKG9UolqINDr9RgBeQ8T7WSm773sjvtS5vmMKuQKtFptj9pojxS8JQOajY0NTz7xGMOjPNm9/m2yM49ecRGbKBrBDF+gjbWlJh1XW2XacR0RBAGj0dijNiyhtbWV7Ul7CYgcjVze/5bWRMSO52RmLkajkb88/ThuVg1s+fpNk37gmYPRaOTkkSRSt33MdRPjeOD++3ulBvRAYDQaQaTThD2dMfWz19PPKIKAKIpm3xUiBW/JgGdra8sTjz/KdRNiSN+9gl0bP6GluaHdY+UKJaKxZ1uIjAY9Wp1pQ2093epiNOjMVlrRnFJTUymvbiYipn8WT/ENGoxR4UxSUhL+/v48+8yTDAm0Ztf3b3AkZaNFt5HVVZexZe1b5Kf9wOKbp3Pfff+HQtH/fuD0V0qlEplAj/+PTP3s9fQzatDrUKpUZh9VkYK35JpgZWXF739/H08/dj/W2hw2r36Z86dTL7trVamt0Wq6P3zaWF9NftZxvHwCcHTxgg7vDgRc3P2IiO5ZcNO1NmFj0/394pZy8FAqDp7hPZ6XtBSZTEZw1BhS9h/BaDTi6enJs3/5M/cuuYHKrJ1sXvMvyoqyzXpNvV7HycM72fb1q/g5tvDXZ59g4cKF0n7uLrKxsUEmo8fTHBHRibi4+2Lpz6impQk7G/OXdZV+7kmuKfHx8YSHh7Nq1WqS9q7mxIENhAxOJDx6DNa2Dri4+6Jt2oZW04TKyrRiB0aDnrqacuqrS8GgwcvDhfDwcGyF13n7+SW0fTn8esis7cti8UOv9Kh8pFarobWhEj+//rHV6QJRFDmblYtHwMS+7soVefiGkJf+I6Wlpfj4+KBQKJgzZw6xsbF8/Mln7Nv4FioHP8KjxxMcMRyFsntrIeprysk4sZeCzIPYKFtZfNM0brzxRpNLwUou5ePjg7VaTlVZAe5egd1uRyaXs/ihVy36GYWfS7sO6lmhpPZIwVtyzbG3t+f++3/P7Nn5JCUlsWvPDjYd34Kr7xBs7Z3R65qprcjHwz+q3fNF0XixrnRLUwOapmpUCvD3csfHJwp7B3sEBBImzOPhF1d1sIf0lR7vIa0uL0SlFAkM7P4XmCVUVlZSW99MkBm28lxgNBjMXjfb1dOfVh3k5eXh4+Nz8XF/f3+ee/YZTp06xY6dSRw69BXp+9fhGRSHu1cgbl4BOLl4dXj9Vk0zlWX5VJUVUlqQSUP5Wbzd7fjdzZOYMGEC7u5dL0Yj+YVSqSTI34eK8sIet2Xpz6goijRUFxI0ZXJPu3oZKXhLrlkBAQHccccdLFy4kP3797Nv/0Gyss9QX3aes8d3oNFokClUgIAgCIiiEaNBh17bglwQUShk2NvZEhweiJeXN6p25p4TJswjPnGO2QMPQGVZAXbW6h7lSLeEvLw8NDoRV0/z3G1YKomGSmWFlb07eXl5jBlzad1smUxGdHQ00dHRVFZWsnv3bo4cO0HO4YOc1hrRGRXYOnmjUFkjyOSAiFGvo7W5Bm1TNWqlgIOdFTFhQYy7fRkjRozol2sTrlahg4LI2mee/PSW/Iw2N9Zi1DZa5Ae2FLwl1zwbGxumTp3K1KlTMRgMPPfcC5wu1hLk69K2LcVoREREJlOiUNhiZ+uHnb0dtja2Js1XyuTyLmV5M1VZ4TnCBwX0uznT4uJiFCp7rG3se9zWhSQav92LeyGJxsMvrupRALdz9iW/4Mp3cG5ubtx8883cfPPNtLa2UlBQQF5eHkVFRbS2tqLT6ZDJZCgUChwdQwkICCAoKAgPDw9p65eFBAYG0rx5H60tTWap5W2pz2hpQRZWKkEK3hKJpcnlcq6/fian3l6Bp7tzv11w1dRQQ03xGcbOu72vu3KZ1tZWFOqeL6LrPImGwOp3niI+cU6375JUamtaWkxPcqNWqwkNDTVLbnpJ98XHx+No8yXnTh9iaLz5h6TNJevkPobHRFgkfXH/+snex0RRpKqqiqKioou/rCsrK/vlPlqJ5SQkJODhYkPmiZ4lZ7Cks+n7cXdSM3r06L7uymX0ej0yWc/vC3ojiYZcLkfbT5PcSDpmb2/PhLEjyD6Z0m+r6tVUltBUnW2xuuzX9J23TqcjLS2NnJwcsnNyycouoL6xBaNR/Pl3PQgyAQdbK0KC/RgUHERwcDCxsbGo1f2zALyk59RqNdMmJ7Jm/V6GjZ3V75KMGA0Gck7vZeGsMVhZmX8LSk/J5XJEsedVmXojiYbRYESp6l//vxLTTJ48mS07D1Ccl4FvUPuLS/tSRtoe/LyciIuLs0j71+S7try8nOTkZLYn7aG0sgmljQv2Lv64hkwlxN0HtZUNMrmiLdlGawvVFcWcLy/g+NljaJu24e5sw9RJY5k4cWK/WywkMY+JEyfy/YZtnDm+p98Ny2Wm70UtNDFxYv/ciqVQKDDoe54OsjeSaOj1WlR20kKyq1FISAiDwwM4cfAnvAMi+tXaj4a6SoqzDnH3oustloDnmgredXV1fPHFGnbvO4LGaE1A+CimT0/EwenKWzd+/auusb6azBP7+GrjftZt3MHohGh+t2Qxrq79c25U0j2enp7Mmz2VNes2EhAypN8U1mioq+T0wQ0smD0RX1/fvu5Ou9zd3WltqkGnbUWp6v4I1YUkGtUVxbQ/7y3g4u7boyQajXVleEeafw+uxPIEQWDx7bfx3F9f48yx3QzpJz+yRVFk37Y1DPJzZMaMGRa7Tv/5qWJBoihy4MABnvrzc2zdl0n4qNuYd9dfSZh4Y6eB+7fsHFyIHzeHeXe9SGTiEpKP5PGnZ15kz549/XbuRdI98+fPJyLIlX3bvugX/7e/fCk4sGDBgr7uToeCgoJQK8RO5qs7dyGJRpvfrtrueRINg0FPc20JQUFB3e6jpG+Fh4dz45ypnEndSH1NeV93B2gbLtfVnueeu++w6LTWgA/eGo2Gd997j9fe+giDXSSzb3+asKGjUfSwIo1crmBQ1AhmL34auUsM/353BW/8+02amprM1HNJX1Or1dy77E4M9bmcPLyzr7vDmePJvfKl0FPe3t7YWiupKu95kY8LSTRc3H0uedzF3bfH28Rqq0pRyvQEBEh33lez+fPnEx7owt6tqy2ak94U9TXlnD64gbnXTSQqyrLz8AN62LyxsZE3/v0WR8+UMGLqMgJDY8x+DZXamsQZiyjMiSVl+ypqXnmNxx9djpOTk9mvJel9YWFhLJx/HZ+v3YCtgzMhEcP7pB+559I4c+B7bps/1eJfCj2lUCgICfIjvyQPhvW8PUsl0agszcNKKcPf37/nnZT0GbVazX333s3fXnqdPT99zoTr7+iT+e+mxlp2rnuPqGDXXhkZG7B33s3Nzbz2rzc4llnBpBsftkjg/jW/4MFMufkRTuc28Mqrr9PQ0H7VKsnV56abbmLujDEc3fk5OZnHev36+VnpHNmxkuunJnDLLbf0+vW7Iy42moqCdLRajVnau5BEY8zUW4iKm2CW7Fc5GanERkdIOcYHgNDQUJY//H+0lJ8gZcvqXt/e29RQw47v3yXQXcYTj/+xV0bGBmTwNhqNvP3Ou6SdrWDyjQ/h6tE7hRucXDyZOv8hMgra7vh10v7RAUEQBO66807mzhjF0Z0rOHM8uVfmwEVRJPPEXlK3f8J1k4ex7O67+9WK2isZP348dioD2adT+7or7aquKKK5OoepFsg5Lekbw4YN448P3UtL2TGSNnyEtrWlV65bU1nCtm/eIsDFyJ+efAwXF5deue7V8U3QRVu3bmX/kbOMm30vzm69u5XLwdmDiXN/z9HTBWzatKlXry2xHJlMxrK77+a2+VPJSv2OHevep6mhxmLXa2qsZecPH5J58GsWzJnA/91771VV89nZ2ZnEUXGc66dJNDKOp+Dv7UxsbGxfd0ViRgkJCTz56IMomrPZtPplinLPWOxaRqORtINb2fnNv4gKtOUvf34KD4/e25Uy4IJ3cXExa75eT8CQyXj6DuqTPrh6+BEWN5Ovv/+R3NzcPumDxPwEQeC2227j2T/9AWd5OT+t+SfnTh4wa3ASRZGs04f46Yt/Yi8W85cnH2bJkiVXzR33r02ePAljc6lFv0C7o6WpnpLsw0yfMgG5GYbfJf1LTEwMr7z0Aolxfhz66X32bl1j9rvwmsoSNn/5OsWnN3PHrTN5/tlnLJIC9UoEsT/+LO5EfX09jo6O1NXV4eDgcPFxo9HIP176J+k5TVy/6IkeryjvCaPRyOavXmeQh8iLzz97Vd01STrX3NzMV19/zeZtexGt3AkdksigwSNRW3Uvp7e2tYWs04c4f3IvaMqZMWU0i267DVvbnhdd6CuiKPLav17n4MkyZi9+ukd7vs3Zp6QNH2GrL+CfL72InZ1dX3dJYiGiKLJnzx5Wrl5LZYMR39CRRMaNx8nFs9vtFeWeIfPEHmqLzzAkzId777nLrFsNO4pt7RlQwfvYsWP87ZV3GTv3ETx9Q/qwh22qK4pJ+vZVnvzDXYwdO7avuyOxgKysLHbs3MmefUdobJXhFRyPd2AEbp7+2Du6dVhVShRFGuurqCwtoDg/k9LsI9ipDSSOHsbUKVMIDw/v5VdiGZWVlTz15xdQe8YzZmrfL7bLzjhCevJK/vz4Awwf3jc7ByS9q6amht27d7N95x4Ky+qwcw8lMGwYbp4BOLv7XDH9caummaryAiqKc8nJOIBMW83g8ACmTZ3M6NGjzV7mtSvBe0DdDu7YmYSVc1C/CNwALu4+OHpGsH1HkhS8B6gLFaZuu7WOlJQUdu3eS0bKQTQ6EeTW2Dn7YWXriPznUSCDXoemuZ7G6gIwtKBWCvh4urLs9usZP378gNti6ObmxpJFN/OfD1ZTHBaLT0BEn/WlpameY3u+4fpJI6XAfQ1xdnbmxhtvZM6cORw9epSdSbs4dfw7MlsNaA1ybJ28sXFwR6FQIZPJMRj06LQtNNYU0dpUjdXPtdlnjY9j8uTJBAcH94tSrwMmeJeUlHD42BkiE5f0dVcuER4znrSdH5GbmytlchrAHB0dmT17NrNnz6ahoYG8vDzy8/PJyc2lvr4erbZt54HKXolDgC2BgTMJDAwkMDCw01/YV7tJkyZx5OgxUrZ8xpT5f+j1RaTQNi2xc/37+LuruH3Rol6/vqTvKRQKRo4cyciRI9FqtRQWFpKfn09eXh5l5RW0auvR6/SobJVYW6vxGz2CwMBAAgIC8PT07HfrTgZM8N6zZw+t2BIUHtfXXbmEX/Bgjiqd2L17txS8rxH29vYMHTqUoUOH9nVX+gVBEHjg/t/T8Oq/SFr/LlPmP9ztecfu0Go17Fj/AY6KWp56/Ans7e177dqS/kmlUhESEkJISP8Ype2O/vVTogdOnsrAw39IvyvfKJPJ8AmKIf1UZl93RSLpMzY2Njz26HKGBDuy89u3qCjN65XrtjTVs+3bt7GnnKcefwQ/v97J+SCRWNqACN46nY6c/CLcvMyTo9hoMHDmeDL7d3zNmePJGA09q03s6hlAUWmFlPdcck1zcHDg6aeeIH6wF7u/f5Nj+zf3+LPVEVEUyc48yo9fvIy7dSPP/vmJq/ouSyL5rf51m9pNhYWFtLQacPPsefBOTV7P6neevKQikou7L4sferXbRRDcPP05oRXJz8/v93mpJRJLsrW15U9PPcGPP/7I199t4sfsdMbOWIyLu/nKm7Y0N3Bgx9fUl5zguokJLL79dmmoXDLgDIjgXVBQgFYvw/k3lYe6KjV5PW8/v4Tf1g6urijm7eeXdLuKkYOzB0aZWgreEgltC4duuOEG4uLi+OjjT9n17b9w9h1KROw4vP3Du72St7aqlIy0PRRlpeLrqubBx+4nPj7ezL2XSPqHARG8GxoaUFrZ9mi+22gwsPqdJ/lt4G4jAgKr33mK+MQ5XS6KIAgCamsHGhsbu90/iWSgCQgI4LlnnyElJYVt25NI/ek9BGsPgiNH4+kbgou7Lwplx0VDjEYjtVWlVJbmkZORSmPVefw8HLnr1hlMmTJFutuWDGgDInjrdDpkPVyolpm+95Kh8suJVFcUkpm+l6i4CV1uXyZXotVqu99BiWQAUigUTJo0iYkTJ3Lu3DmSdu1i/8HNZB/ToTXIsHHwxM7ZB5XaCrlCidGgR6/T0VBbSlNNEQpBj1opEDs0nCl33sewYcOkbIaSa8KAeJcLgtD+DXMX1FaVmvW43xJFY7/bJyiR9BeCIBAeHk54eDjL7tZTVFR0cQ9ufkERLZoKtBodSqUCta0Sn1AfAgPHEBAQQEBAQK+UYJRI+pMBEbyVSiUGfc/uap1cvcx63G8ZDTqzp9KTSAYihUJxMYHN+PHju91OfX39Lz8A8vNpbGpGp9OjUMixsbbCz8+PgIAAAgMDcXFx6RdZsyT9l16vp6SkhLy8PPLy8iivqKBVq0M0iqhUStxcXS6+n/z8/CxeJ35ABG9XV1f02iZaNc3dLgwREZ2Ii7sv1RXFtH8bL+Di7ktEdGKX29brdWgaa3B1de1W3yQSiWk0Gg2HDh1i+85dnD1fQOuv0tSqrO2QyxUYDQa02mZ2pe5Br6lDrRTw9XJl2pQJjBs3bsBnvJN0TW5uLrt27SJ5byoNTa206gWsHTzbUqoq29ZVGPQ6WjJzafpxHyq5AWu1nNEjYpkyZTKRkZEW+WE4IIJ3YGAgVkqBqvKCbudOlsnlLH7o1Z9XmwtcGsDb/uEXP/RKlxerAdRUFqNSGAkMDOxW3yQSyZU1NTWxYcMGduzaR3lNC66+Q4kaNxl3rwDsHFw7/PJsaaqnsiyfvHMn+GDlD6xZ+wPjx8Zz47x5vVqbWdL/HD9+nB82bOJkRi4GpRPBkVMZ7B+Gi7tvhxXyDAY9NZUllBedZ/ex/STtfZ3QIG9mXz+DcePGmTWID4iqYqIocu/v/4DzoGnEjJzWo7bb3+ftx+KHXun2Pu+M43vIT/ueTz58Rxo6l0jM7NixY3zy2SoKKrQEDR5HZGwitvZdr63cqmkm69RBzp3YjYOymSWLbmLKlCnScPo1pqGhgS/WrGHbrkNYu4YTGTcRv+DBXV6zJIoi5cXZnDmeTHXBccbER3L3XXfi5ubW4TnXZEnQV159jTOlSqbecG+P2zcaDGSm76W2qhQnVy8iohO7dcd9QfLmz/G2LuevLzzX475JJP2VVqulpKSE1tZWDAYDCoUCa2trvL29kffg89MRjUbDys8/Z1vSQWw9hzBm6q3Y2Dn2uF2dtpUjKT9QlJnCmPgI7ll2tzTldY04ceIEH360gsIqHcPGLyA4YrhZfrwV52dyaMcanNQt3LHkFiZMaH/H0jVZEjR++DD2fbSWluYGrG16tr9TJpd3aztYe7RaDRX5J7jh9uvN0p5E0l9otVoOHz7M2bNnOXc+l7yCYjRaI0bjz5kRBJDLwNZKyaBgf0KCAxk6dCjR0dE93nnR2NjIG/9+i6Nniokbt4SQqBFmu0NWqtSMnrKQkrA4Dm5fTelLr/LUE3/Ey6t7i1UlV4c9e/bw3v8+R+0axazbb8Pa1nxrH3wCIpi9+GlSd6/jP++vpKqqihtvvLFH79kBc+fd1NTEg488gVvYdGJHzejjHv7izPFk8o+v4503XxlwtZol16by8nJ2797Njl0plFY1Y+3oh5ObP66efrh4+KFWWyPI5BgNejQtjVSVFVBZVkBdZT76pnKC/NyYPnUiiYmJ3Voc1tzczCuv/osTWdVMvOF+XD0sV2ykubGOHd+/h6ddC88+85Q0Dz5A7dmzh3c+WIlr8FhGT1lo0amSk0eSOJe6jiW3zOKmm2665Lmu3HlbfOPxyy+/jCAILF++/OJjoijywgsv4OPjg7W1NZMmTeLUqVM9uo6trS2Tx48i5/RejEZjD3ttHqIokpWeQuLoYVLgllz1tFota9euZfkTf2H1uj2oPEcxY9FfmLXoccZOv5WImETcvQJxcPbA3tEVRxdPPH0HMXj4JCZc/zvm/u4Zxt/4GE3KQby/Yj3LH3uaHTt20JX7B6PRyNvvvEvauUom3/iQRQM3gI2dI9MXPExpg5pXX/u3VFxoADpx4gTv/e/zXgncAEPjJxOecCOr1/7Ijh07ut2ORYN3amoqH374ITExMZc8/uqrr/LGG2/wzjvvkJqaipeXF9OnT6ehoaFH15s8eTIKQx3nTu7vUTvmkpN5FFFTxtQpU/q6KxJJj5w/f55nn/8rq7/dgd/QOcy7668kTJiHvWPHi2/a4+YVwLiZtzP3jr9i7Z3A2/9bw6uv/Yvy8nKTzt+yZQv7j5xl3Kx7cXbz7s5L6TIrazumzn+ArKJGvlizpleuKekdjY2NfPjRCtSukb0SuC8YEj8Zn4iJrFz9DSUlJd1qw2LBu7GxkcWLF/O///0PZ+dfVn6Kosibb77JM888w0033cTQoUNZsWIFzc3NfPHFFz26ZkBAALOmj+PkgfU01lf39CX0SEtTPcf2fMPUCQmEh4f3aV8kkp5ISkri2b++QkGtmmm3PEl0wlQUip7tmlBb2zJ6ykLGzn6IQ6cr+POzf+t09K24uJgv1/5AwJDJePr2bnlPW3tn4sbdzNadBzh+/HivXltiOV+sWUNhlY6x0xb1+q6C4ePm0owzH338abdGiy0WvB988EFmz57NtGmXbt3KycmhtLSUGTN+mZdWq9VMnDiRffv29fi6t9xyC8HeduzbtqZLw3HmJIoiB3Z8jZ+rksW3394nfZBIzGHz5s2899FqXEMmMHPhchxdPM3avrd/GLNv/xN6m2D++frbpKWltXucKIp8/MlnNAsuDBs7y6x9MFVI1AhsPYfw8aef09zc3Cd9kJhPWloa25IOEjfuZrMuTjOVQqFk9LTbOZKew/bt27t8vkWC95dffsnRo0d5+eWXL3uutLQtN7in56VfAp6enhef+63W1lbq6+sv+dMRa2tr7rl7Kdqac5w8vLMHr6L7zqbvo6E0nbvvXCxVNpJctXbt2sUnq77Ff8hMEibcaLHc/EqVmslz7kHpPJg33nqfjIyMy445c+YMx09lkzBpYY/v+rtLEARGT72FgrIm9u7d2yd9kJjPDxs2YeUSRkhk35WN9fAJxjt0DD9s3IJer+/SuWb/NBYUFPDII4+watWqKxYL+O0QhSiKHQ5bvPzyyzg6Ol784+/vf8U+DBkyhEULZnPuyA+cTe/d+e/szKOc3LeW+XOmMHz48F69tkRiLrm5ufzv0y/wDJ1A3JjrLT6kKJPLmTDrDkS7YN5+94PL1r/sTEpCYeeDl1+oRfvRGVs7J9wCYtm2Y1efjexJei43N5f0MzlEDpvU50l4Bg+fSGFZHceOHevSeWYP3keOHKG8vJz4+HgUCgUKhYLdu3fzn//8B4VCcfGO+7d32eXl5ZfdjV/w9NNPU1dXd/FPQUFBp/2YN28eN8+ZRHrKl5w5ntzzF2aCc6cOcnTnSuZMH82iRYt65ZoSibnp9Xo++uQzDGpvEib0bC9qV8jlCsZdt5TiaiOrVq2++Hh1dTX7DqUROjSxz79oASJjx3E+r4zTp0/3dVck3bRr1y4MCkf8ggf3dVdwcvXCzj2UHTuTunSe2YP31KlTSU9P5/jx4xf/jBgxgsWLF3P8+HFCQkLw8vJi27ZtF8/RarXs3r2bsWPHttumWq3GwcHhkj+dEQSBxYsXs3jBTM4e+pa9W9eg1WrM9jp/Tadt5cDObzi55wtunj2eu++6q198yUgk3bFx40bSz5YwZvqSHmUW7A5rG3viJixgx57DHD58GGjbtdKsUzAoKqFX+9IRD59g5LZeHDx4sK+7IukGg8HAnn2HCY4a22/KNIdFJ3I8/RzV1aYvtDZ7hjV7e3uGDh16yWO2tra4urpefHz58uW89NJLhIWFERYWxksvvYSNjQ23m3lxlyAILFiwAC8vL1as+ppNq84wcsoifIOizHaN0sIsDm7/AntFIw/eu0jKhSy5qtXW1vLt+s0Mip2Oi7tPn/QhJGI4eZlHWb1mLcOHDycnJwc7Z/8Oi0GYylxpjwVBwN0nlMxz53vUH0nfKC4upr5RQ5R/z6ZgzJlG29svlGM6kby8PJPP6ZP0qE8++SQtLS088MAD1NTUMGrUKLZu3WqRxV2CIDB+/HgGDx7MZytWsven93H0iSZq2ES8/EK7FWjbEs7nkHE8mcr8Y4waFsayux+Tsi9JrnopKSk0aORMGj6pT/sRPWome77/F+np6Zw9n4ezx5Aetdd+wSFfFj/0arcKDrl6BpB1cC8ajeaKa3sk/U9+fj6tegEX9+4n+DH3+8na1gGFlZNJU8IX9Erw3rVr1yV/FwSBF154gRdeeKE3Lg+01fx+9I/LGbN/P5t+3MKhze8gs/Zk0JCx+ARG4uDsccUhFFEUqa8pp6TgHFkn96JvLCY0yIvfPfA7xo8f32+GXySS7jIYDGzfmYx3yAhUaus+7Yubpz9WTgFs2bKVkrJKIgcFdLut1OT1P5f6vXSBWXVFMW8/v4SHX1zV5S9cN88ATulE8vPzpTwOV5m8vDysHTy6PZJjifcTgJ2LL3n5/Sx49xeCIDB27FjGjBnDuXPn2LFzJ/sO/kBm6vcYUWHr7IuTmy9KlTVyuQKDQY9Oq6GuqpjGmkJkxlas1TLGj4xlyuTbiIqKkobIJQPGyZMnyS+uZuLN4/q6KwCExYxn746PMCLg4Ny9US2jwcDqd57kt1+0bURAYPU7TxGfOKdLQ56Ozh5o9VBZWSkF76tMRUUFNg796/0E4ODsSUnpEZOPv6aC9wWCIBAeHk54eDh3LG0mPz+f/Px8cnNzyc3PpblGQ6tOh0KpxMFKTXS0N0FBwwgMDCQgIABbW9u+fgkSidllZWUht3LGxd23222Ycx7QP2Qo+zeDtrUehVLVrTYy0/deMrR5OZHqikIy0/d2qZKgTC4HQYZOp+tWvyR9p1WrQ67o3ne4pd5PAAqlima96e+nazJ4/5qNjQ2RkZFERkb2dVckkj51PjsHO5cr51C4EnPPA6qtbFDZulBb3b3czwC1Ve0nfurucb8mCIK01/sq1JP/M0u+n4AupUmVJmolEgmiKHLufD6unt0L3hfmAX97V3JhHjA1eX232nVwDUDT0oKhC3ckv+bkaloNblOPu8BoNCIaDahU3RsRkPQdtVrV795PAAa9DlUX5uGv6eCt0+koLCwkKyuLs2fPkp2dTVVVlfRrWnLNqa2tpaGxpVvbwzqfB4TV7zyF0WDoctvu3oFotXoa6qq6fC5ARHTiz9MAHa1NaVt1HBGd2KV2G+sqUcq5pOiS5Org4uyMprF/vZ8AGuqqcHdzMvn4a2rY3GAwkJaWxsmTJ8k6n0tOfhGaVgMGERBBEEAhF3B2siUsJJDQQSGMGjUKL6+u/4qSSK4mra2tGEW6tcrckvOANrZ2qFQKqsryCQqL7XLfZHI5ix969efVwQKX/sBo+wJe/NArXZ6XrywrQK0SCAjo/ip4Sd8IDAykaWMKer2uy3nyLfV+Amioyico2vQtkddE8K6rqyM5OZltO5MpKKlBbe+Dk3sAAXGjcHH3RWVlg0wQMBgMNNZXUVlWQHpBASlHdrBm7UZGDItiyuRJxMXFSVvCJAOS0Wj8ub5A19/flpwHlMnk2NraUF2e3+VzL0iYMI+HX1zVwXz8K92aj68sy8fb001avHoVCgwMRKUwUlNZjLtXYJfPt8T7qVXTTGtTdad1O35tQAdvURRJTk7m8y++obLegHfICCbdPP6KQ4PObt74h7RlgjMY9OSePc7RE3vYl/oeI2JDufuuO6U7ccmAo1AoEAQBg6FrlY3AsvOAer0ORwcHGqoKMBj0yOXd+8pKmDCP+MQ5ZlsJX1Waw4TYrn/xS/qer68vtlYKyoqyuxW8wfzvp/LibKxUghS8Aaqqqvj0sxXsTT2N56AxzJl/A2ormy61IZcrGBQ1gkFRIygrOs+BHWs4+8yL3H7rfKZNmybdhUsGDDs7OxRyaG6s6/K5F+YBqyuKaX/eW8DF3bdb84AtjXUEBvpTV99C3rm0HpVvlMnlXR62b091RTEtNXkkJPRNXXFJzyiVSkaPjCMpdT9Dhne/qpi53k/QVkY6MtS/S1k6B2T0yc/P57kX/sH+tGJGXX8/idNv63Lg/i1P30HMvv0p7P3H8t9P1vK/jz7qcv1ViaS/srGxwcfLnaqyrg9PX5gHbPPbL8KezQPWVBYSGx1FfFwkZ0/s6fL5lpCRloKflxNxcXF93RVJN02eNAljSxmlhVl93RUa66upKT7NtCld+yEx4IJ3QUEB//jn61RrHbl+0ZP4Bppv/7ZCoWTkpJsYPvUuNu88yvsffNClfXkSSX8WGhJATUVht869MA/42ykpF3ffbqeLFEWRxupCggIDmTplMs3VOVSVd69/5qJtbaHkfCozpk5E3ssV1yTmExERQViQNxm9VC76SjJP7MXD2ZpRo0Z16bwBNWxeVVXFK6+9Sb3BhWk3P4RKZZmCAUFhsSgUd7Ptp4+wt1/FHUuXWuQ6EklvCgoK4qfk9G7PLZt7HrC+phzB0EJgYCCRkZGEB3txKGktMxc+0mdTVqnJ63FzkDN+/Pg+ub7EPARBYM7s63j97U8pyssw601eV9RVl5F7chdLb5mBWq2mtbXV5HMHzJ23KIp8+tkKSuoEpt54v8UC9wV+wUOIGXcLG39KJi0tzaLXkkh6Q0xMDGqZlrysE91u48I84JiptxAVN6FH9cDPnTqIm7MtoaGhyOVy7rn7DsSmfE4f293tNnuiKPcM5ef3s3TxQpycnPqkDxLzGTt2LGMTBpO6cw1arabXr280Gtm3bTURwW7MmTOny+cPmOC9Z88e9qaeJmHKItTWvbN9I2zoaGw9B/PRp5/T1NTUK9eUSCzFx8eH+NgIzqX1/dyywaAnP+MA0yYnola3ZZ0KCwtj/pxpZKZupLa6rFf706pp5tDONSSOHCLddQ8QgiBw15134GzVSurudb1+/VNHkhCbCvi/e+7qVqa+ARG86+vrWbl6LZ6DxvTq8IcgCIyeeisF5a2s/eabXruuRGIpUyZPoqk6++eV430nN/MYaqGZiRMnXvL4jTfeyOBBHuz64X2aGmt7pS96nZZdP/wPD3s9d96xVKokOIC4urpy5+9upTJnP+mpO3rturlnj3P28AYWzJvJoEGDutXGgAjeycnJVNYbGDH+hl6/tq2dExHxM9mxez/19fW9fn2JxJzi4uIY5O/O4eTv+ixNsFarIf3ARkYnROPp6XnJcyqViscfW06Aq8D2796hqaHGon3RaVvZ+cOHWBlKePKxR3B1dbXo9SS9b9y4cfzu1rlkHf2Bk0eSLH693LPHObxzJbOmjWT+/PndbueqD94Gg4HtO5PxDhnR4+1g3RU2ZBT1zbB3794+ub5EYi4KhYJldy+ltSaLzBN9834+vHs9ztatLFl8e7vPu7i48Oc/PU6QG2xd+2/Kis5bpB911WVs/eY/WOmKeOrxPxASEmKR60j63g033MDSW+dw/sg6Duz8Bn03C5dcidFoJD11B0d2fMbsqSNYdvfdPVp4edUH77S0NPKKq4mMG9dnfVBb2eAVHM/W7bukrWOSq15UVBRzZ07g5IH1NNRV9uq1i/IyKD2/jyWLbsbNza3D49zd3fnLn58iYbAHeze8zcGkb9HrtGbpg9Fo5OSRJLZ//SqBrnqe/fPjhIWFmaVtSf8kCAI33ngjD967mKaiA/z4xSuUFWWbrf3a6jK2rH2TgvSNLL31eu5ZtqzHWw2v+q1ip06dQmXn9XOll64zGgxm2doyKGoEqVsOUlpaio9P1yszSST9ycKFC0lLP03SDx8yY8EfsLK2s/g1aypLOLB1BeMSBl82190eJycnnnryCXbu3MnqL79n0+pTRMVPJyQyHoWy6wuAjEYjhdmnOHV4G2JzAbffNI0bb7xRKvt5DZk8eTJRUVF88ulnpGz4D16DRjN42ESc3by71V5DXRUZaSnknU5mcIg79zz+VLfnuH9LEK/C+pf19fU4OjpSV1fH6/9+i1KNF+Nmtj/EdiWpyes7SC7/apeTSmi1GtZ/9BR/Wn43Y8eO7XJfJJL+pqysjL+/9CrlzXZMnX8/1jb2FrtWdUUxu354j9hQF5584jFsbLo2BVZWVsaXX33NwcPptBjU+IWNJDgyHhd33yvuWTcajdRVl5GfdYLs03tRGOqJjgrmtlsXSnfb1zCj0cjOnTtZv/EnCktrsXUJITQ6ES//UGztnK54bmtLE2VF2Zw7tY/a4jO4O1sza+Zk5syZg1J55Spmv45tDg4OVzz2qg7eVVVVLH/8z/jFzOtyjtnU5PU/l3X77ctvW0nanaxQG1b+nZtmxHLbbbd16TyJpL8qKirin6/9m5I6BeOuvwtXDz+zXyP//ElSd6wiLsKDxx5djr19938kVFZWXqwgWFHdiNYgx8bRC0dXP6xs7JArlBgNerStLdRWFtFUU4Rc0OFoq2bS+JFMmjSJwECp4IikjV6v5/jx4+xM2sXRE5lotCIylT32Ln44uHiiUKgQBAG9TktDXRUN1QXommuwUglEhvozbepkRo4ceXG7Y2eumeB95swZnv3rvxg1+xE8fU1fTGI0GHh00eAr1CBuK6TwxppTXRpCT9r4KZGeGp584nGTz5FI+ruKigreefe/HD9TyKDY6cSMmtHt6l6/1qpp5mDSt1TlH2bC6Bj+7957unzH3RG9Xk9+fj75+fnk5eWRnZNPY1MLWp0WhUKJjbWaoABfAgMDL/6xsrJsYifJ1a26upq8vDzy8vLIyc2jpLSCVq0WURRRq9S4uzkRHPTL+8nDw6PL2wq7Eryv6jlvjUaDUaTLq8wz0/deIXADiFRXFJKZvrdLd/QqtTUtLV2vytRfiKJIXV0dra2tGI1GlEolTk5OKBRX9dtE0kPu7u48+5c/s3nzZr76diObs08wOGEGgaEx3QriWq2G86cPkXF0G+62Bh5/+G7GjBlj1v3TCoWCkJAQaYW4xGxcXFxwcXFh2LBhfd0V4CoP3qIoIkKXP/S1VaVmPe4CmUyG3mDo0jl9LTc3l6NHj5Kdk0vW+Txq65sxiiAiIggCNmoFIUF+hAS35ZeOi4uTgvk1SKFQMHfuXOLi4ljz5Vcc2bOS4yl2BEaMJjhiGE4uXlccpdLrdVSVFXD+dCol2YexUxuYmRjPLbcsxNnZuRdfiUQyMFzV38IKhQKZ0JZKsSucXL3MetwFBr0etX3/X5mq1+s5dOgQ23ckcTIjF73MDge3AFz9J+Dv7ovKyhpBkKHXa6mrLqOwrJBTuzL4duMu/LycmDZ5PBMnTpTyO1+D/P39efKJxykuLmb37t3s3L2X5FPb0IsKbJ18cHT1Q2VljUwmx2DQo2lupK6qgJb6MlRyIz6eziy7/XrGjx8vvX8kkh64qoO3g4MDCplAY31Nl7aKRUQn4uLu+3MKyPam/NvmvCOiE7vUn+aGapz9LLci1xzy8vL438efcjKzEAfPCGKn3INf8OAOkwX8Ot1sTWUJGWl7+PSrrWz4cTtLFy9k/PjxUrrIa5CPjw+LFi3ipptuumRuOSevkOZ6DXqDHrVShZudNROGRhAQMJ3AwED8/PykUpoSiRlc1cHbyckJF2c7qsryCRg01OTzZHI5ix969efV5gKXBvC2QLT4oVe6tFhNFEUaawoJDr7e5HN6k9FoZMOGDXz93Y/oVZ5MXvBkl/fGO7t5M2bqLWjHzSV19zrefG8lqYePsOzuu6S7qGuUWq0mLCxM2lYlkfSyqzrDmiAIhIcGUVWW3+VzEybM4+EXV+HifmlCFRd3325tE2usrwJDCwEBAV3ui6Xp9Xo++OBDPl2zCc/I6cy67fFuJ7WBtoV5iTMWMfK637P3WAF/+8c/KSvr3SpPEolEci27qu+8AUKCg9l9aBt6vQ6F4sob4H8rYcI84hPnmCXDWnH+WazVsn63R9RoNPLhh/9ja3IaCdPuJiA02mxt+wZFMeOWR9nx/Xu8/Mrr/OXPT14xpaVEIpFIzOOqvvMGGDVqFGqZhtyzx7p1vkwuJypuAmOm3kJU3IRuBW5RFMlKT2Hk8KE9SjBhCd9//z1bk4+RMP0uswbuC2ztnZl200MUVgv8+8230enMn9BfIpFIJJe66oO3p6cnCcMGcy49pc/6UFmWj7ahiKlTJvdZH9qTnZ3Nt+u3EDZ8dpfWBHSVjZ0jE+f+H6eyytiwYYPFriORSCSSNld98AaYNnUKmto8SgrO9cn10w9tI8TPnSFDhvTJ9duj0+n430efIlr7MnTEFItfz8nVi7Dh1/HNup/IzjZfNR7JwCKKIhqNhvr6ehoaGtBqzVMJTCK51lz1c94A0dHRJMSGcWjnl8y+/aluVRTqrtyzx2koTeeBP97Xo9qs5paSksKZ7DKmLnyq1/o1dMRUCrKOs/abb3nqySd65ZqS/i8vL4+0tDRyc3M5dz6Pqpp6jD9v8JAJ4OPlTtigQIKDg4mPj8fV1bVvOyyRXAUGRPCWyWTcfdednH3mrxxJ2cCoyTf3ynVbmhs4mryW6ePiGTFiRK9c0xSiKLJtexIuftFdTjTTEzKZjKj4qRzds5Li4mKpNOo1TK/Xk5qayvYdSaSfyUGHNfau/rh4jCQiyhuFou0HdqumieqKIvakFbJ513FsVn/DmIQYpkyezODBg6UcAhJJBwZE8Ia2ue/Ft83nvY++IscnhOAIy+afNRj07Nm8Em8nWLJkcb/6kjl79ixnc4oZef1NvX7twNAYjqfYsXv3bhYtWtTr15f0vfz8fD76+FNOZBS0JQKavAy/kCGdjgDptK1kZxwmJS2F3fv+zcSxcfxuyRIph4BE0o4BE7wBpk6dSm5eHhu2fY5crrDI6mpoC9y7N32KvDmHR55c3mn1l952+PBhBLUbXn6hXT7XaDD0aOucXK7APyyB5L0Hue222/rVjxqJZYmiyMaNG/nq243olF5MuvmJLpUQVarURMQkEh49lvzz6ezc9RWnzjzPsjuXkJCQYMGeSyRXnwEVvAVB4K4770Sn1fHT9k9oaV5AePRYswaQluYG9mxeibw5h8eWP0BERITZ2m6PXq+npKSEvLw8Kisr0ev1yGQybG1t8fPzIzAwEDs7u0vOOZ+di7NnSJdfd2ryela/8+QlFddc3H1Z/NCrXUpa4+U3iLSzSVRXV/d4/rKpqYn8/HwKCgpobGzEYDCgVCpxcXEhMDAQX19fqVBKP2A0Gvnk00/ZuO0AIbEziR05o1vbLqHtcxwYGoOX7yAO7FzLa299yP/dWce0adPM3GuJ5Oo14L715HI59933f9jbf8EPm9dScP4EY6YvwtbOqcdt52Qe41jyWrydBR55crnFArfRaOTUqVPsTEriWNoZmlp0tOoF1DZOyOQKRNGIVtOIzNiKWikQ4OfJ5InjSExMxNramuzcQryHdm3aIDV5/c/pYi/N9V5dUczbzy/pUtY5N88AWnUieXl53QreTU1N7N+/n6Rde8jJL0ajFTEKKlTW9iDIEI16WpvrUMmN2FopiBkawZTJk4iNje1XiwavFaIo8ulnn7Fh60Hip9xhtikrtbUtE2bdwdG9rnzw6ZfI5XImT+5f2zElkr4y4II3tC2cWrJkCTExMXz86ef89MXLhMVNJyJ6DGpr2y61JYoilaV5pKdup6E0nenj4lmyZLFFhspFUeTQoUOs/WYd2YUVqOx9CRw8G3evQFzcfVGq1Jcc21BbQWVZAYU5p/nvZ+tY8/V6hsVEUtfYzFAPf5OvazQYWP3Ok7RfpEUEBFa/8xTxiXNMupuytnVArrKnqKiI4cOHm9wPjUbDd999x9adKdQ0GnD3jyF89GRcvQJwdL60sL1er6OmooiK0jyOnD3C3kPvEuDjws3z5zJu3DhpuL4Xbdu2jU1b9zN8ylKzrzURBIHhiXMwGPR88MkX+Pr6Eh4ebtZrSCRXI0EUxfa+sfu1+vp6HB0dqaur6zSINjc38+2337I9aR+1zSKeQcMYNDgBN88AVGrrds+5EBhLCs6RdXIv2oYiQvzcufWWmxgxYoRFAkNdXR0rP/+cXXuP4+gTw9CEabh5Bph8rdaWJs6ePMCJ/RvIzjzGTXe/wJB40+5SzhxP5uU/zur0uKf//SNRcRNManPjqpe4eUYst9xyi0nHnz59mv99vILs4gbCYqcRHj0Ga1vTfyBVlRdy8vBOqvOPkDhyCHfesVTactQLSktL+dMzL2IfkMjIifMtdh1RFNm69j942zfy978+j1qt7vwkieQq05XYNiDvvH/NxsaG3/3ud9x4443s27ePbdt3cWTLYTQ6ESs7N2ydfFBb2SAIMgwGPc2N1TRWFyIzarBWyxg7IpopkxcyeHDHZTN7Kicnh9def4uSOoH4qcsIDI3pchtqa1uiE6bi6hNK9edvs/PHNTQ11JAwcX6nPwBqq0pNuoapxwEIggyj0djpcRcWOa366gdUzmHMvO1B7B27HnRdPfyYOGspBdnxpCR9Sea5F3ls+YMWX5NwLRNFkY8/+YxG0YmJiXMsei1BEBgzfRFbv3qFdevWceutt1r0ehJJfzfgg/cF9vb2zJw5kxkzZlBaWkpeXh55eXkUFBbR3FKOQW9AZavEOdCRoMAYAgMD210MZm7nz5/n5VffpFnmyezb/6/Lw/q/ZefgTMSIOei0TaTu34xOp2XstFuvGMBN3QvelT3jRoMOpfLKhWJEUeS7775j9drNhAybTczI6V0a1WhqrKUgK53KsgLKS/OpqSrDoNcjGo1kni7g2NK7WbxoIRMmTCAmJkZa2GZmp0+f5mh6FqOuf7DLRYG6w8HZg7BhM9m0ZQuzZs3qd3UEJJLedM19mwmCgLe3N97e3owePbpP+1JWVsarr/+HFrk30278/SVz2t1lpbZCJpfhHRKPlY0jafu/xMbWgWFjOx4Wj4hOxMXdl+qKYtqf9xZwcfclIjrRpD4YDHpam2txcXG54nE7duzgi282EzFyPoOHTzKpbYCSgnOcOZZMVuYJdHoRGydv7F388B0aj0KhQhRF/DRN5J9N5bNvktmy6wgh/m5MmzKBiRMndtoviWmSdu1CYeeDl3/v1fKOjEkk69gWUlJSuP7663vtuhJJf3PNBe/+wmg08vEnn1LVYs31i+4zS+AGUKlUWKtVaJob8Q8fhaaplkMpP+EXPBh376B2z5HJ5Sx+6NWfV5sLXBrA2+6EFz/0islbf2oqS1DK9FesbV5cXMzKL77BN3KyyYG7pbmB/du/JvP0cVR2HgTFzcM3bARKVftrF0Jjp1GQnY7KWAPGOlas3cEPm7az+Lb5TJkyRVqZ3gPV1dXsO5RGWPyCXl0cqLa2xSNoONt37ua6666TFiZKrlnSt1cf2bFjB4fSzjN62mJUKiuztu3oaIempRGA0NjpqB382L15FXp9x+U6EybM4+EXV+HifmlKUxd33y5tEwOoKsvHSiXD37/9Fe8Gg4GPPv6UFsGV4eNMmyvNz0pn7cd/5+y5s0SN+x3j5j9F0JDxHQZuAEEmw9MvlBa9Gu9BCdxw51+x9R3Fux99xSuvvkZVVZXJr0lyqYyMDJpaRIIjTN9NcIHRYODM8WT27/iaM8eTMRoMXTo/JCqeguIqSktNX4MhkQw00p13H2hqamLN1+vwixiPp2+I2dt3cnSiqDQHg0GHXK4kevwiDm74FxnH91yxwljChHnEJ87pUYY1gPysNKLCQ1Cp2i8Qs3//fo6eymXCjX9ELu/8LXg2fT9Jm9fg4BVFYuKtqK1Nn+tUW9li7+rL+Zw8vLy9GDX5ZkrDYjmwbRXlL73Kn558FE9PT5Pbk7TJy8tDbefW4Y6NjpgjEZCbZwAanUh+fj7e3t5dur4pjEYjJ0+e5NixY1RVVaHX61GpVAQHB5OQkNCvc/ZrtVoKCwspLi6mtbUVACsrK3x8fPDz8+t0HYrk6iEF7z6wf/9+qhsMjB41wyLte3p6cu58DnXV5bi4+2Lv7IV7YBynju1hSPzkKw41yuRyk7eDtae+ppz6skymLFrW7vOiKLJ9RxJO3oNx9wrstL3zp1NJ+vEL3AeNYciY7g3Rurj7klddQmlJKYGBgXj5hTJ94XJ2fP8eL7/yOs8+85S0rayLsnPycHA1PZcAmC8RkNrKBrWtC3l5eYwaNapLfeiIXq9n3bp1rPnyKzKz8tHoQBRUyBRqBJkMo16PaGxFZtTi6GDN+DHxLFu2jKFDh5rl+j3R3NzM/v37SU7ZR3ZuIS2tBrR6AdnPP4yNBj0qhYi1Wk5YSADjx41l9OjRWFt37YeXpH+RgncvE0WRrduTcAuIxdrGMqtllUol3l7u5BeX4uLuC0BA1DiO/PQWRbln8AsebJHrAmSkpeDtbtdhlbXs7GxOZeYxfPp9nbZVW1VK0ubVuAYldDtwA8hkcmwc3CgsLiEgoG3vvK2dE9PmP8jWb97k/Q8+5Ok/9V7p1IGgpKwCBy/Tc+ebPRGQg4dZpj2MRiMffPABn6z4guoGA47eUQSPXoardwjuvmFY2Ti29VAUqa8qoqL4HBVFZ9mcksLGLXcRFe7Hc395pk+qCjY3N7Nu3Tq27dxLdaMeN/9oAoeNwtXDH2d3n4ujWnq9jprKYqrKCsjNz+TI+2tw+/I7Zk4dzw033ICVlXmn7SS9QwrevSw/P5+c/DISrl/Y5XO7UjTEz9ePouJyqiuKcHH3xck9ECt7L3Iyj1kseNdUllCQsZe7b5/V4fDckSNHEJVO+AZFXbEto9HI7s2fg8qZIWMWIIpG8s7sp7G2FDsnLwIixyCTmT6c7+jiRWluGXV1dRerVNnYOTJm+u9I2fAftm/fzowZlhkJGYh0Oj3WctOHYDPT914yVH45keqKQjLT95o08iOXK9FqtSZfvz3nz5/nscefIP1sGT6RE5k6ci6e/u2/LwVBwNHND0c3P0JjJmOYfjfn03dz5tA6Ft95P7cvnMPTTz/d4VSRuZ08eZKPPllJbmkToTFTGXOFpEYKhRJ3r0DcvQKJjB1HU2MtZ0/s44vvkziYepR77r6DqKgrfx4l/Y8UvHtZXl4erQZZhyu/O9LVuUI7OzuCg/w4l12AnYMLKrU1ju5BlJfm9/QltMtoNLJv6yqGhHpecQtPVnYOjh7Bnd5Fnzm2m8KCfEZc/wfOHfuJLSv/REN18cXn7V18mLn0n0QmzDWpf1bWtojIaWxsvKTEpKdvCP5RE/niq+8ZNmwY7u7uJrV3rZPJZIhi50l4LjB3IiCj0Yi8Cz8efmvbtm08+uRfMFr5kbjgOQIjujb8LlcoCR82jUHREzmatIrPv/meQ6lHWL1qpUVLmIqiyIYNG1j19QasXMK5btEfsLV37lIbtnZODBs7i9AhI9m3bQ0v/uMN7lh8k7R6/yojjRP2svz8fGwcvbqU1OLCXOFv71wuzBWmJq9v97ygwCCcHawpLTiL0WjE0c2f6opS9Lqe3bG059i+H5Fpirn3nrs6vOs2Go1kZRfg5tnxFrILx6Wl7sQzJIGyvHS+efOOSwI3QEN1Cd+8eQcZqRtM7qNCbUNDQ8Nljw8fO5tajZJdu3aZ3Na1zt7OltafdzSYwtyJgHStjdjY2Jh8/V/bsmULf3jsGaw9RzD73re6HLh/Ta5QkjD9LiYu+jvnS/UsWLiI2trabrd3JReSGn225gcCY2Yzbf79XQ7cv2bv6MaMmx/CZ8hMPlr5LRs3bjRjbyWWJgXvXpZfUISdk+krZDufK4TV7zzV7nYbmUzG4KhIlEIrxXlnsHPyQm8wUF9b0c3et+/kkSQKTm1j6e03ERQU1OFx1dXVNDW34ux25ddfmH2K2toa/CPGsGXln7jSa9+68mmMRtO2GqnUtjQ2NV/2uEKpIiBiNDt2pfR4KPZaERLkR01FocnHX0gEdCFvwOUEXNz9TEoEZDQaaa4t7nAr4pWcPHmSx556Dnu/UUxd9BxqK/NkUPQJimHiLS9QUA133X2PSamBu2rXrl2s+XYzEQnzu5yNsCOCIDBszPWEDJvLyjXrSUlJMUNPJb1BCt69TNOqRdmFfd1dmStsj52dHbExQ5AbGqkozcdgMKDTtnax1+0zGo0c2/cjWYfXsXjhLGbOnHnF47VaLUaj2Onrz0zfh41TAHWVhZfdcV9KpL66iPyM/T/3x0Du6RRO7vuG3NMplwV1mUyOXt9+oI+MTaS0qpljx45dsW+SNkFBQTTWFGJqXaMLiYDa/DbodC0RUF11GTJ0BAZ2vlvh1/R6PY8/8RSCXTCTF/wJhdK8xU08/MIZNWc5J86W8O6775q17bKyMlau/gbP0HFdykZoqpiR03ANGs1nn38l5T+4SkjBu58zx1yhk6MTw+JiUAkaKktyKMw50+N+1VWXsWXtW5RkbGPZ725i/vzOC6CYQhRFSgqzcfOLpLHWtNfeWFtKRuoG3n4khlX/mMu6d+9l1T/m8vYjMZcNq3fURXtHN1S27mRnZ/f0JVwTgoKCkIutVJbmmXyOuRIBFedlYGsl7/Kd9xtvvEFWYT0jr7sflVXPagh0JDBiFAHR1/H+R6s4d+6cWdo0Go188ulnNOjtiB93g1nabE/CpPnUaNR8+tkKk3+USfqOtGCtl9nYWKGtuHzotiPmmit0cHBgSFQ4Z/dac/bw99RWFxM9Yhru3kFdLgaScTyFnJNJRAa7cc+jTxAWZlpua7VajUwmoG1t6fCYxvpqmpubCXbzp6Wx1qR2q0uzSf72n/x2eP3CvPiC5SuITJiL0ahHdYU7OwdXf85n55p0zWtdeHg4wX7uZKTt7dLiy54mAhJFkfOn9zFl1LAu7VNubGzk8zXfERh9Az4hsSaf1x0jp9/NhpyjvPHGG/z3v//tcXvp6emkpp1j9KyHzJZGuT0qlRUjJt3CgW0fkpGRcc2sQNfr9RQVFVFfX4/BYECpVOLu7o67u3u/XsAnBe9e5u/rw7Gzpt/5mrNoSGNtORFhwTx43118+/0P7NvwJko7X4IHj8LDJwRnN+/LMp6JokhjfRWVpQXknj1KVeFJ3J3ULF04nblz53Zpa4yLiwv2tlZUVxR3uF2tpqIYg1HE0dUfd78o7F18aKgu6fC12zt7cyxpRQfPt+0f3rryacLjZ6HVNOPu3vEdl6unP+czTiKKYr/+0PYHMpmMaVMn8v5n62htubFL1fB6kgiotOAcYks5U6fc2aXzPv/8c5r1KoaMMT3Nb3eprGwJjp7G7r0rqKysxM3NrUft7UzahdrRH+9eKADjGzSYYzZeJCXtGtDBu6qqij179nAsLZ2cvCKaNXr0RkBsG51TKgSc7G0IDw1k9KiRJCQk9Lsa8lLw7mUBAQG01O9Cp2016Ve0OYuGVJYVEBzgy+jRoxk1ahSnT59mZ1ISBw+v41yqEZ1RgY2DJ3KlFYJMhkGvpaWhAvTNqJUCoUG+LL3/dkaPHt2txA6CIBA2KIDM8oIOj2ltbUYU274AZTI5M5f+k2/evKPD1z5syh0kf/vyFa76y7y4KCiws/Po8Egra1taW7Xo9XopjaQJEhMT+frbDRzb/yOjp3Q9b0FXGY1Gju/fSGSon8mjPRd88+163AOH4+jqa/I5er2Ww9s+prYsGyfPEEZMX4ZCYdqP1fD4mWQeXMuKFSt47LHHutTXX6uoqODgkZOEj17UpfO6khPi1wRBIGzoOPYe/I5Fi2pwdu7+avb+qKysjLXffMO+Q2k065R4BMTgFzMCN88AbO2df/7e01FXXUZlWQEn8rJIObQCD5e1zJw2gTlz5vSbIC4F714WFBSEWilSXpKDb2CkSedcmCtsf5/3KybNFYqiSHVZNomT2n5NC4LAkCFDGDJkCP/X2kphYSH5+fkUFRXR2tqK0WhEpVLh5jacwMBAAgICcHBoPwlEV4QEB3Ho5AGMRmO7Gc3En1fpCkLbc5EJc1mwfMVl+7wdXHyYsfRl9DrTFt/VlOfi7BmCnX3Hq4svJH2xxErhgcje3p7Ft93E2x+uoSQszuJ3hqeOJCE2FXDX4091aWSkvLyc/JJKhs8aZ/I5O9Y8x4FN716yl33H6r8wevaDTF30107Pt3Nww9U/lgMHD5p8zfacOXOGZq1AUFicyef0NH98cGQ86fu+ITMzs8/LJpuL0Whk586drPryOxr19kTGLyAkckSHN1B2Di4/J5KaQUNdFRlpKaxcu40Dh45w77I7CQ8P790X0A4pePcyPz8/woJ8OJu+z+TgDT2fKywtzELUVDBy5B2XPadWqxk0aBCDBg0yuT/dNXLkSNau20phzmkCBl2eF1qhbLuzMei1KH5elR6ZMJfw+FnkZ1yeYS33tGlbW0RBiZ2NFY4Ojh0eo9O1tg2ZSXfdJpsyZQqHjxzl4I4vuP7Wx7s0fN4V1RVFZB75kd8tmNHl9+mhQ4cwosbd17Qv3B1rnmP/xrcve1wUjRcfNyWAO3sEcf7k4S719bcu5IUwda7bHPnj1VY2WNm5kZeXNyCCt1ar5b3/vs/u/en4Roxn4ri5F79nTGHv6ErChHlERI9h/7Y1PPe311i29BamT59uwV53Tlpt3ssEQWD6tMlUFaTT3FjXpXMvzBWOmXoLUXETulTtK/NECmFB3kRERHS1y2YVGBhIdFQwZ0/safd5eyc35DJorCu/5HGZTE7Q4HEMHbuAoMHjLt4lB0SOwd7FhyvtH3Zw8cXRzQ8/X+8r3rHVVZXi7eku5TjvAkEQWHb3XXjZ69ix7r9XXIzYXXXVZez64b8MH+zHvHldn7NOS0tDYWWPk3vnq9P1ei0HNl15m9eBTe+h13eeD8DVZxANzVry8kxfkf9bWdm5OLj4mXRsd3JCtGhaKC8vJzs7m5OnTnLixAnS09Np0inZtn0HWVlZ6PX6bve/r+l0Ot76z9vsOpDBqOvuY9Tkm7sUuH/NwdmDGQv/gGf4FD789Cu2bNli5t52jfQt1QdGjx6Nh5Oa4/t/6pXrVZUXUlVwghnTp/SLhVjTp02hviyD0sKsy55zdvNBIZdTV9nxvPivXZgXb9P+/uFRc/+IlUqBl9eVV+TXVBQSGnLl7G+Sy7m5ufHUE3/EUVHNtm/fpsnEXQKmqCjNY8d3/2FwkAOP/vGRbo2KlJeXY2XnZtJ7//C2jztN+yqKBg5v+7jTtmwdPRBRUFx8pVwFV1ZVXYudo4tJx5qaE+JMWgrl5eUcO36cvftSOXbiDOfzy6moM1LdLKOqEZr1apIPnOCpZ//J8kefYt26dVfl/u8VK1eScvgcibPv67SegikEQWDE+BvwGzKDjz//hiNHjpihl90jBe8+YG1tzeJFN1N6fh/F+ZkWvZbBoGf/ttXERvoxfvx4i17LVCNHjmRkXBgHd6y5LFWrQqHEzcOH2vIck9u7MC9u73Jp5jYHFx9uuP99PPwGExoSeMWV8QaDnsbqgitmiJN0LCAggL88/QRedi389MU/yTp9qEd7hQ0GPUdSNrJ73ZvER3ny5z89ib1996rw6XQ6ZDLTZghry0zb52/KcXK5EhEu1tXuDp1Ob3IBHlNzQhw9vJ9j6WeoaQJn73ACI0YSGB6Pb1AUPgER+ARG4uETQkjUGMbNewycY/ns6+0sf/zP/PDDD1fNnfixY8fYsmM/cRNuxcvP9Ap4phg2dhZ2ntF88tmqdlMu9wYpePeRiRMnMnZEFId2rKG1pcli1zm270eUujLuWXYXCkX/WOIgk8m4+647sZc3kJq87rLng8NjqSg4ga7V9P3wkQlzefitEyx5ZgM3Pvg/ljyzgQf+fRRHzzDcXezx87vy0GNu5jGs5Dri4uK6+GokF/j5+fGPvz3PnKlxnN67mu3f/5fi/MwuBXGDQU92xhF+/OI1KrN2cu+SG3j6T09ia9v9uXS1Wo3RqDPpWCfPELMdZzDoEKBHdbNVKiVGg2nB0tScEDIrd3yD4/ALHoK9o+vFut+/ZjQakCuUuHkFMGryzdxw519xD5vGx6s38re/v0RJSUmXXkdva25u5pMVq7H1HMKgqASzty8IAqOn3kJxtZHVq78we/umkIJ3HxEEgbvvuhMvB4PF5gpPH9tN4ekd/G7RTd3KA21Jnp6e3LX0VirO7yXtwKVzR+HRY1AKRgqzurbY59fz4oGRYynNP4tapicyMqLTIdOz6SmMGBbV6dC65MpsbW25Z9ky/vLkw/g7NJL603tsWPUSJw5toyT/bLvv8+bGOvLPnyQ1eT3rP32eMymfkxDlzD///ixz5sxB3oW1He3x8PCgub7i4k6GKxkxfdnFnQ4dEQQ5I6Yv67SthuoSBFHfo8+eu6szDXWmDVd3nj8e7Jy8iJ+yGJXVlYu6NDdUYu/gdPHvSpWaYWNnMfnmxzhd0Mrf/vEK+fmWqVBoDikpKRSUNjJ66i0Wmyq0trFn6Oi57Np7mLKyMotc40r6x63YNcrV1ZWnnljOS6+8zrZv32bSDf+HrZ1Tj9sVRZETB7eSffxHbl9wXb+tUz1hwgQaGhr4dPX36HStDE+cg0wmw8bOkdDIWM6d2oV/2MiLq85NZdDrKM7LQEELcbHRnVafKsrLQFOby9QpD/bk5Uh+JTY2lpiYGM6dO8fOpCRSj2wnL60VjU5EZe2EXKkCUUSnbcGobUStFHB1suXWOaOZOHEi3t6mF+/pTFxcHMZVG6guy8XV+8p3zAqFitGzH2x3tfkFo2c/YNJ+76rSHBzsrfDx8en02I4MCgni5M5TJh175ZwQAALX3fmqScPwDVWFhMTGXfa4q4cfMxcuZ8f37/HPV9/g+WefxtPT06T+9RZRFNm2YxfugXFm+T69kpCoEZw8sIHdu3dzyy23WPRavyUF7z52Ya7wtdff4qcv/knsuJsYFJXQ7V+LDXWV7Nv6Bbq6bO5aPI85c+b0i0VqHZk9ezZKpZLPVn3DluIsxkxfjJOLJyMm3EBu1stkHN7A0LGmJwBprKuioiQbWzXExETjYH/lvelarYbUnV8yJj5KGjI3M0EQCA8PJzw8nP8zGiktLSU/P5+ysjK0Wi2CIGBl1RbcAgMDcXFxsch7ddSoUcjEViqLz3YavOGXbWC/3ectCHJGz37ApG1iADWl5wkNNj0pTHsCAwNpqU9Cq9WgMuFHbEc5IeycvbnujleITJjbaRutLQ1om6px7aB0r9rKhqk33s9Pa//NBx9+xF+eebpf7dDIzMwkO7+csXNvN/mc7ia1USiU+EeMYseuvSxYsKBX/x2k4N0P+Pn58dLfX+CLNWvYunM1ORmHGRw/BZ+Azod7L2hqqCHzxD5yTu4i1M+Rex5+jMhI0/eR96UZM9r27n740ads//pVAgdPIDJ2HKMnzyNp89d4BgzF3e/KK0VbmuqprixC31yDn7c7YaGhnaZuFUWRQ0nf4Wyl4a477+jXP3KudjKZDB8fnx7dhXaXi4sLgwK9yD21h4j460w6Z+qivzJx4V+6nWGtvrqE6qKT3DGnZ5nnBg8ejJ21QPaZw0TGmpZk5kJOiJNHdnEkdT/WzkHETVhg8sK3ovNHUKnk+AR2vK1UbW3L2OlLSF73Jlu3buW660z7d+0N2dnZGGXWJufc72lSG/+QIRzK3ElJSQm+vj37sdYVUvDuJ2xtbbn3nntIGDGCb75bx5Et/+WIlTv+YfG4ewXi6umPtc0vq20NBj01FcVUlRdQnHuGquJTuDuquX3+JG644YZupS/tS4MGDeJvLz7Hpk2b+GnrLraeSsLBKwI7WyuObP2AUXOW4+wRdPF4o0GPpqURTXMjDXWVoG/Gwd6G4JiotoICV5j3u+D4gZ+ozj/I8gfu6nH+aUn/dustC/j76x9TXZaLi2eQSecoFCpGX39/t66XeeQnrOUali5d2q3zL3B2diZxZBzJx1KIiEk0+QemTC5H7eiH7+ApBIQOMzlwi6JIYcZeQiPjLvm+aY+7dxABgyfx5dofSExM7PZuAHPLyc3FztnPpH8rcyS1cfHwQ6sTyMvLk4L3tSwuLo7Y2FiysrLYmZTEkWN7KDq1BY1ORKawRq5QIRoN6LUtKOUGrFQywkICWHpD93OO9xcqlYr58+cza9YsUlNT2bV7D/oaHaU5GWxd+RSRo2/ByT0QUTQiGvXIZAJqpRwfd2d8fcJwdHI0KWgbjUaO7PmBkswk7v7dzSQmdl7URXJ1W7RoEW+9+z9O7vuOCfMftei1WppqyD25gxkTRpklpfCUKZNJ2vs6Bdmn2s1K2B6dTkdxaTkOLv7tribvSGluGrrmKqLilph0fPTIaWw8s4d9+/Yxc+ZMk69jSXn5xTi5dZ6MqvOkNgKr33mK+MQ5VxxCV6msUNu59Gg/f3dIwbsfEgSBsLAwwsLCEEWRyspK8vLyqKqqQqfTIZfLsba2JiAgAD8/vy5V9roaqNVqxo0bx7hx49DpdGRnZ/P22+9yInMjViQydMQMbO3tsbOzw9ra2qSAfUFtVSn7tq5C0BRx3923MW3aNAu+Ekl/YWVlxX3Lfse/3vmcvKixBEZaLu3ngc0fojJU8uST75ulvcjISBJHRrNn91d4+oag7mSlOLQV4NDqwcul40I8v6VpruPgxrdwdrKnuqIYD+/gTud9razt8AiMY9v2XcyYMaNfTD01t7Sgcux8e56pSW0y0/d2WgVPobTp0X7+7pCCdz8nCMLF2rLXIqVSSUREBG+//VZbYYE135GWvIrIYVNxjkowOXDX11aQkZZC/pkUBoe4c+89fyIkxLQ9vZKB4b777mPrtu0c3vYhngFRWNl0nOe+u7JOJFGauYtnHr+v09wCphIEgTvvWMrpjOc5mPQt469b0mmQrK6uRmXtiFxuWka6M4d+YNNHf0DT1Jayec9Pn5s87xsSGc+xHUcoLy/vFyvPTf0BYWpSG1OOE9u9e7es/rNEUCK5AkEQmDp1Kq+89DxTRwaTc/RbfvjsWfZu+5JzJw9QXVF8STUwrVZDaWEWJ48kse2799i25u/oKw5z96Lr+euLz0mB+xokk8l44/V/odKVsuPLv9KqaTRr+0XZxzjy07uMHhbKHXdcXgCoJ5ydnbnnrsXUFR7h6N5NV0x8IyJSV9+IlXXHFfR+7cyhH/j2rTsuBu4LLsz7piavv+L57l6BtGrFHuVwNydbGxuTEl+ZmtTGlOP02qYeJePpDunOW3JV8fDw4IEH7mfRohqSk5PZf/Aw51MPckprRGcQEGRyRBEQ9aiVYGetZnB4EBMXLWPEiBFSxbBrXHBwMB+8+2/u+f0f2LbqWSbd8gx2Dj1frJhzei8HN/6bwUH2fPjhBxbZMjRq1CiWLa3lo5VrMei1jJhwY7vXadW00qrVY+feefDW67Vs+nh5B8+aNu+rtrZFZeNMQUEBI0eONP0FWUhIkB/Jx680HN7mQlKb6opi2p/3FnBx9yUi+sprYlpbmtA21ZhtpMVUUvCWXJWcnZ2ZN28e8+bNQ6PRUFBQcHH/sEwmw9raGj8/P7y9vfvVHlRJ3xs1ahSffPgOv39gOZs/eoTYyXcRFje1W/O1rZpGUrd+Qn76FkZEB/DpJx93mhSoJ2bOnIlKpeJ/n63hp5JsxkxfjLPbpQlt9Ho9oigi72RbW31VEcnf/hNNY80VjjJt3ldl7UBTk+XSPHdFYGAgjUnHMRqNV/zsXzmpTdt7YfFDr3Q6719ZXoBaKRAYGNjzzneB2b/VXn75ZRISErC3t8fDw4Mbb7yRzMxLi2+IosgLL7yAj48P1tbWTJo0iVOnTMsiJJH8lpWVFWFhYYwbN44pU6YwadIkRo0aha+vrxS4Je1KSEhg86bvGTnEjSM//outq58jL+OAyXnYW5vrObH3Wzb97xHKz2zij/cvYs0Xqy0auC+YPHky/3jhaYLdDOz85l8c2PkNddW/pOe88Bo6+i3SUFPCyX3fcHDTGyiMpk0ddDrvKwiXTFv1pbCwMJSClpKCs50eeyGpjYv7pfkHXNx9TdomBpCfdQJPd0c8PExfHGgOZr/z3r17Nw8++CAJCQno9XqeeeYZZsyYwenTpy8WF3j11Vd54403+OyzzwgPD+fvf/8706dPJzMzs9/sFZRIJAObh4cHK1es4Ouvv+a/73/Evm8PcdQpEO9BI3D1DsXNOxRbh7bCHQZdKzUV+VQWZ1FZnEXp+QPI9HWMHB7Fc8+uIiwsrFf7HhwczIsvPMtPP/3Elm1JbP9qD3buoXj5hWPj6Ia2tQldawsKhRJdawv11UXUVRZQVXKOxorzODg4MG7SbBRKFYf3rOv0ep3N+xp0rf1m10twcDCRYf6cTUvBN7DzRFUXktp0J8OaVquh+Pxh7rq191faC2JP6vaZoKKiAg8PD3bv3s2ECRMQRREfHx+WL1/OU089BbSVzPP09OSVV17hvvvu67TN+vp6HB0dqaurM8s+SolEcm0zGo2kpKSwcuVKTpzOoqauESNqEOQ/j6iKCKIOhcyAr6crE8aPYdmyZb0+z9kevV7P0aNHSdm7j8xzOVTXNpJ26ixqO3dsbB0RBJDLBKytbfDyDSRs6GgCQ2OQyxUYDQYeXTS403nfN9ac6jCYGQx6vvvwSf74+9uYPHmyRV+rqZKTk3n93c+ZfuvTOLpYbgX8ySNJFJ74gXfefAVnZ+cet9eV2GbxOe+6urYVjC4ubQXlc3JyKC0tvaRYhlqtZuLEiezbt6/d4N3a2nrJHrr6+noL91oikVxLZDIZEyZMYMKEtnnd8vJyDh06RHV1NTqdDisrK0JCQhg2bFi/S4SkUCgYOXIkI0eOvJgX4i/PvUBZkwPDxl6PQqHC2d0bOwfXy+4OzTHvW1NRjEpuICCg/VzofWH06NGEb/qJ/dvXMHPhIxa5K26oqyLz8I8smD3BLIG7qywavEVR5NFHH2XcuHEMHdqWGai0tG3u5Lf7AT09PTvcavDyyy/z4osvWrKrEolEcpGHhwdz5szp62502YW8EJMmjOObnw4TFD6s03UfHRUzadvn/Uqn875FuWewt1H3q7LDKpWKe5fdyXN/fY1TR3cxNN68IwJGo5H929cwyM+BBQsWmLVtU1k0eD/00EOcOHGClJSUy5777S8hURQ7/HX09NNP8+ijv6Q0rK+v71dvFIlEIulPxo4dy/cbd1KYfYqA0OhOj+/uvK/RaCTnzD7mTk7oN3PeF4SHh3PzvBms+mY9dvbOBIXHmaVdURTZt+1LjPXZ3PvgH/tsJMZiwfvhhx/mhx9+IDk5+ZJ5IS+vtoUPpaWll9TsvVJ2HrVajVqttlRXJRKJZEAJDAwkOiqY08eS8B801KRhY5lc3mka0N/KzzqBXF/Xb+a6f+vmm2+mpraWTTtWoNNqCB0yqkdD6Hq9jv3bv6Sh+CiPPHB3n1ZuNPs+GlEUeeihh/juu+/YuXMnwcHBlzwfHByMl5cX27Ztu/iYVqtl9+7djB071tzdkUgkkmvSjfPmoq09T9bpQxZpv1XTzLGUbxmTMLTX9zibSiaTcc+yZdw8axyn961h18ZPaGlu6FZb5cU5bPriFbQVaTz68L2MHm25/PimMPud94MPPsgXX3zB+vXrsbe3vzjH7ejo2FZEQhBYvnw5L7300sXiGy+99BI2NjbcfrvpxdMlVz+dTkdWVhZ5eXkUFhbS1NREbW0tGo0GURSxtrbB2dkJGxsb/P39CQwMJDQ0FIVCyi0kkXQmOjqa66aOZcP27/AJCMfW3nyLqkRR5GDSt7ja6Lnzjp6VPbU0mUzG0qVLGTJkCJ98tpofV79EUFQiETFjsXNwueK5oihSXpxDxvE9VBYcY0R0CMvu/sMlo8Z9xexbxToakvj000+58847gbZ/kBdffJEPPviAmpoaRo0axbvvvntxUVtnpK1iV7eamhp27tzJ9qQ9lFU1ojXIadHoqCwvQdOqA0GBlZ0rVnaOWKvV2ForkBsbUQg6fNwdmTZlPJMmTcLJyamvX4pE0q81NTXx7PN/pbBWyfSbHzY533ln0g5sIS/9Rx59aBljxowxS5u9oaGhgU2bNrFj1z7Ka1pwcA/F2cMfN88A7BycEWRyDHottdVlVJUWUFV6HkNzGYMCPbl+5jQmTpxo0cRPXYltFt/nbQlS8L46iaLI7t27WbXmW6oawS9sJPZO7qQd3E5VTQ3uAbH4h4/B2TMYQSajpame2qoyWpurcbBR4e3uQGn+KYqyUnF3kHPHkltITEzsF2UIJZL+qrS0lH+8/BqljVZMvuE+7B1du92W0Wjk2L5NFJ7ezl2L5zN79mwz9rT3aDQaDh06RHp6OplZeZRX1mAwtNUGEwRQK2QE+HsTNiiIkSNHMnjw4F75npGCt6Tf0Wq1vP/BhyTtPY7XoLHET7iB00eSOLhnM7auIQwddxu2HRSI0La2UFaYBfpGwkOD8XB35dCu76nMPcS0CfHce+89UsERieQKysrKeO31NzlXUMfQ0fOIiOn6j97aqlL2b1sNzYXcuWTBJbk6rnb19fU0NDSg1+tRKpW4urr2ySJpKXhL+hWdTsdb/3mblMNZjJpxJ37BQziSsoGDKVsIirmeQbHTTfoiqSzLp6m6kIiwYAICAsjLOkHq9hVMHjOYhx58QJoLl0iuQKPRsHbtWjZuSUaw8SUibiJB4cNQKK78w7eyNJ+MtBTKco4QFeLOvffcJZXUtRApeEv6lVWrVvHNxhQS59yPl18o2RlH2LLuU4KHzyNk6KQutVVdXkhDdQHDYgfj5upGUV4GBzb/j8U3T2XhwoWWeQESyQCSmZnJj5t/4uCRk7QarXB0D8bFwx8nV2+UKhVGg5GmxhoqSwuoq8xH11hKoK8rM6ZNYtq0adIolwX1q/SokmtbRkYGG37azZDRN+PlF0pLUz0p277GNWBYlwM3gIuHHy0tDWRmZuE40hHfwEgiRszmuw0bGDZsGKGhoeZ/ERLJABIREUFERATl5eUcOHCA89nZnM3aS8mZJsS2Et4oFTJCgvwYPzGCmJiFREdHSxX6+hkpeEssRhRFVqxcjdIxhIjYcQAc2buJFq3A8NE3dbtdT99BFGQdJy8vj9BBoQwePomCrON8vuoLXnj+WWkBm0RiAg8PD2644Qag7bPa3NyMTqdDLpdjbW0tTUP1c9JPKYnFZGRkcC63hNgxsxAEgVZNM5knD+EfNR6V1aVbVoxGA7mnUzi57xtyT6eg12sv+bvRaLh4rEKhwt7Jk+LiMgwGAzKZjOhR13H6bD7Z2dm9/TIlkqueIAjY2tri5OSEvb29FLivAtL/kMRikvfsQWbjhafvIACyMw7TqjXiH35pZqKM1A1sWfknGqqLLz4mCDJE0Xjx7/YuPsxc+k8iE+YCbfWFC6qLqaiowMvLC9+gKI6oXNm9ezeDBg3qhVfXf2g0GvLz8ykrK7u4WtbLywt/f38prbBEMkBJwVtiMadOn8UnaMTFYezSgizsXANR2/yyECMjdQPfvHkHv60l/OvADdBQXcI3b97BguUriEyYi0KpRqG2ob6+Hi8vLwRBwCtwCKczMiz+uvqDCzWcd+xMIv10Fq06EZ1BQJDJEY0GlHIRa5WMuJhIpkyeRGxsLPJOikxIJJKrhxS8JRZRV1dHeVUdQ4b8UuO3oqwQB9dfEvkbjQa2rPwTvw3c7WtbSbN15dOEx89CJpOjsrKjrv6XPMVungGcPbCH5uZmbGxszPdi+plz587x0ScryMwuxd4jjEEJt+LuHYSjiycymQyjwUBtdSkVJbkcPXOIfanvMTjcj3uX3dVvc1BLJJKukYK3xCIqKyvR6UUcnT0uPlZXU0VgkPvFv+dn7L9kqLxzIvXVReRn7Cdo8DhUKmua66suPuvg4oFWL1JVVTUgg7coimzYsIE132xEsA1gysI/4ex2eY5lmVyOi7svLu6+RMQkUlmaz8GdX/Ln517iziULmD59eh/0XiKRmJMUvCUWYTAYEEUuDtWKoogoGpHLf3nLNdaWdqvtC+cJMgGj8Ze79gtt6/X67na73xJFke+++47VazcTEjeL6JHTTd664+YVwPW3PsaRvRv54NOv0Ov1XH/99RbusUQisSQpeEssQqlUIpOBTqcF2lazyuVy9HrtxWPsnLy61faF84xGI/JfBTCdthXh52sPNAcPHmTNt5uJGDmfwcMndfl8mVxOwoR5HFOq+HT1d/j6+hITE2P+jkokkl4hbRWTWISnpycqhUBN5S/D4s5uXpcMkwdEjsHexQcwdV+2gIOLLwGRbVWMWjXN2Nn9MjxeW1WClVqOh4dHRw1clWpra/ns8zU4+8d3K3D/Wtzo67B2i+R/n6ykqanJPB2USCS9TgreEouwsbHBz8eTytL8i495eAXQUF148e8ymZyZS//58986C+Btz89Y+jIyWdtQvFbTiIP9L/vFK0vzCfT3QaVSmeU19BebNm2irE5g1KSbe9yWIAiMmXYbheUtbN261Qy9k0gkfUEK3hKLiY2OoiQ3DaOhLcGKT2AEzbVFNNaWXTwmMmEuC5avwN7l0oVXgnDpW9PBxefiNjGAVk0Tor7lYk1vvV5HWV46MUOjLPiKel9LSws7kw8QPGQcamvbDo8zGgycOZ7M/h1fc+Z48sV/8/bY2jnhGzaK7TuTB+T6AInkWiDNeUssZsKECazfvIv88+kEhccRGBaLo4M9eWf2MmTML+lRIxPmEh4/i/yM/TTWlmLn5IVf+EgKzx66+PeAyDEX77ihrTyhnY0aV9e22sR5Z4+jFpqZOHFir79OS0pLS6OqVkNC9JgOj0lNXs/qd56kuqLo4mMu7r4sfuhVEibMa/eciJhEdq5N5tSpU8TGxpq93xKJxLKkO2+Jxfj7+zMiNoK0AxvR63XI5QqGDBtPSdb+S+6+oW0IPWjwOIaOXUDQ4HEoFKpL/v7rwN2qaaK5rhx/P18EQUCnbSX94CZGJ0Tj6enZ2y/TonJyclDbuWNr59Tu86nJ63n7+SWXBG6A6opi3n5+CanJ69s9z9HFE5nSntzcXDP3WCKR9AYpeEssaunvlmAn1HI0ZQMA0SOn4+7mSnrKl4hGYydnX04URcoKs3BxssHX1xdoC2BO6haWLL7drH3vD3Jy87F38Wv3OaPBwOp3nqT9JDdtj61+56l2h9AFQcDOe0xA7AAAI7xJREFUxZf8/PzLnpNIJP2fFLwlFuXl5cWSRTdTlLmbjLQUFAolE69fgraugBN71nQ5gJcWnEUuthAZGYFMJuPUkSQqsveydPFC3NzcLPQq+k5DYzNWNnbtPpeZvveyO+5LiVRXFJKZvrfdZ9XWDtQ3SCvOJZKrkRS8JRY3depUFt4wldP715J2YAtuXoFMnbuU6oKjHNu1Al1rc6dtGA16ivLOYNBUEz0kCltrG46kbOTs4XXcvmAWEyZM6IVX0r/UVpmW5Kaj40TRKJVPlUiuUlLwllicIAjcdttt3L14HsWnf2LL1//G0cWT6266B03VOfaue5XS3HREsf0c5w11VeSdO4bC0EhczFDQN7P5q9epOLeTe5fezPz58wdsEHJzdaLxVylgf83J1bQkNx0d19JQjauLU3e7JpFI+pC02lzSKwRBYO7cuQwZMoT/ffQpu797DVuXEIaPmUFO5jFOJX/CWRs3fMPH4Oo9CJW1I5qWJuprSsGgwdFawN5a4OC2z9DU5P1caOPPA77QRnBQIPvT9rX7XER0Ii7uvlRXFNP+vLfQlt88OvGyZ0RRpLG2iKCgePN2WCKR9AopeEt6VUhICH//2wscP36cHTuTOJ6+CUOrEVullurSNA5l7sEoCihUVqit7XF0sMfWRkWTWk6NSiA+bjBTJj9IbGysybm9r2ahoaEYW3+kuqIIF3ffS56TyeUsfuhV3n5+CW1JbH4dwNtGIhY/9AqydkqBlhZmoaCVkJAQy3VeIpFYjBS8Jb1OLpcTHx9PfHw8zc3N5OfnU1hYSEtLCwaDgdbWVkRRxMrKCrlcjrW1Nf7+/vj7+w/IamFXMmTIEPx9XMg4nsLY6bde9nzChHk8/OKqDvZ5v9LhPu/MEymEBXkTGhpqsb5LJBLLkYK3pE/Z2NgQGRlJZGRk5wdfg+RyOTOnTeLDlT9QO3xiu/PXCRPmEZ84h8z0vdRWleLk6kVEdGK7d9wAFaV5VBWkseS+2wfsWgGJZKCTgrdE0s9Nnz6dPSn72b9tNTMXLm83KMvkcqLiOl9xr9fr2L9tNcOHBA24bHQSybVk4E8aSiRXOaVSyb333IW8tYiUrasxdiO5DYDBoGf3pk+xk9Vyz7K7rok1AxLJQCV9eiWSq0BISAiPPHgvLWXH2fnDh7Q01Xfp/KaGGrZ/91+oP8tjj9yPj4+PhXoqkUh6gyB2tLm2H6uvr8fR0ZG6ujocHBz6ujsSSa85efIk73/4KYVVOoaMnM2gqASUKnWHx2u1Gs6l7yfjyGaCvGx54Pf3EB4e3os9lkgkpupKbJOCt0RylWloaGDNl1+ya08qzXoVHgExuHsF4OTmjUKhQqdrpaaymMrSfCryT2CnNjB98lgWLlx4za3Wl0iuJlLwlkiuAZWVlezZs4fjaSfJzitEozUgiiATBKzVcgYF+zMsLobx48dfrHsukUj6Lyl4SyTXGJ1OR2VlJXq9HoVCgbu7OwqFtJlEIrmadCW2SZ9uiWQAUCqVeHt793U3JBJJL5FWm0skEolEcpWRgrdEIpFIJFcZKXhLJBKJRHKVkYK3RCKRSCRXGSl4SyQSiURylZGCt0QikUgkVxlpq1g/IIoiRUVF1NbWotfrsbGxISAgACsrq77umkQi+VltbS2lpaW0tLRgZWWFu7s7bm5ufd2tPlNZWUlFRQUajQZra2u8vLykZEC9SArefUij0XDw4EG27Uji7PkitAYRRJDJwNnBmikTRjNx4kT8/Pz6uqsSyTVJFEXOnDnDzqQk9h1KQ9NqxCiCIIBaKTAiLoqpUyYTGxt7TVRpMxqNpKWlsWNnEoePn6FVJ/6c1Q+s1DISR8YxZcpkIiMjpVrxFiZlWOsjGRkZvPX2fymuasHVZwjhMYk4uXkjk8lpbWki5+wxcs/sQ2ls4PppiSxZskTKmCWR9KKGhgbefvc9jqSdQ2HnQ+jQRHyDIlGqrNBpWykvyuZsegotNbkMDvXlj8sfHtB34pWVlbzx7/9w5nwx1s5BhEePw8M3BKVKjU6roSg3g6z0FPRNJcTHhvHwgw9gb2/f192+qkjpUfu5kydP8uob7yDahpA443Zs7Z3bPc5oMHDu1AHS933L1MRoHnrwgWvi171E0tcaGhp46eVXychvYPSMpXj7h3d4J1lZms/eLSvwdtDx7DNP4eHh0cu9tbzy8nL+9o9XKKlXknjdnbh5+rd7nCiKlBSc5cDWlUQG2PPnp5+UAngXdCW2SZGgl5WUlPDm2x8gc4xg2o2/7zBwA8jkciJiEhk98x52pJxg7dq1vdhTieTaJIoib7/zLhn5DUxbsByfgIgrDgG7eQUwY+FyShpUvP7v/9Da2tqLvbW81tZWXv/3fyhpUDFj4fIOAzeAIAj4BEQwbcFyMvLbRi6uwvvDq4IUvHvZli1bqNGomTjrTmRy+cXHjQYDZ44ns3/H15w5nozRYLj4nF/wYMKGX8/GLUnU19f3RbclkmvGqVOnOHIiizEz7sDByf2S5zr6nFrb2DNpzr1kZJdx6NChvui2xRw8eJCM7DImzbkXa5tf7qKv9J3l4OTO6BlLOZJ2jtOnT/dFtwc8aRK1FzU1NbEr5RDBg6ejUKouPp6avJ7V7zxJdUXRxcdc3H1Z/NCrJEyYB0Bk7DjOHdtCSkoKs2bN6vW+SyTXiqRdu1DY+eLlH3bJ4519Th1dPHH2Gcz2HUmMGzduQCzYEkWRbTuScPEdgqOL58XHTfnO8vYPR2nnS9KuXQwZMqTX+z7QSXfevejAgQPUNBoIjx5z8bHU5PW8/fySSz4EANUVxbz9/BJSk9cDoLaywSs4nm07dkvDUBKJhVRXV7M/9QRhMZcGX1M/p5Gx4zl9Np+cnJxe7belZGdnk3GugIjY8RcfM/XfQhAEQqMT2XcojZqaml7t97VACt69qLCwEFsn34tDT0aDgdXvPAm0F4zbHlv9zlMXh6N8g6IoLa+mpaWll3oskVxbiouLaWk14hcUdfGxrnxOfQIjadULFBUVtXPs1aeoqAiNrm0eG7r3ndXSaqS4uLiXenztkIJ3L9JoNMiVvyReyUzfe9mv10uJVFcUkpm+FwCV2gqjiBS8JRIL0Wg0GI2gVHXvcyoIAnKFesB8RltaWlAo1RdHIbr6naVUWWE0St9ZliDNeXdRfX09qamp1NbWAuDo6MiIESNMyiykUqkw6rUX/15bVWrSNS8cp9Np25JDqNVd7rdEIumcSqVCEECna0Wltga69jkVRRG9XjtgPqNqtRq9XosoigiC0KV/i4a6KjLSUigpymHPnj00NjYyYsQI7OzsLNzra4MUvE1UUlLCDxs2kLLvCPUaAbWNEwDa5lrsVq1l3JjhzJ0zB19f3w7b8PDwoKk2Fa1Wg0plhZOrl0nXvnBcRXEOTg422NjY9Pj1SCSSy3l4eKBWClQU52IbHgfQpc9pZVk+KrkRd3f3zk+4Cri7u6OUG6ksy8fdK9Dkf4vzZw5z4kgKzS0a9DiTfLSQXQczcLb9//buNKypa+sD+D8DJIxhkkmUoaigODGoL6AWtWqlilqt4tzaXvU6Iba17bV1aBU7ee2g1Gut723VVxywWnu9igOTlkEsgqIiCggCIoMECCEk2e8HalpkCgIJkfV7Hj5wsjlnnZVwVs6w947AKD8fTJky5bke0EYTqHirITMzE59v/xZlNUL0GTQZowcMh0BYX0BltTXIykjCuYRYXLm6DWtD/g53d/cm1+Pr64sDh0/g3s0rcBvsj34D/WDRoyfKHhWg6XtIHFj06Il+A/0gl9fhfmYi5k0bRQO1ENJJbG1tMdjDFTfT4+H0R/Fuy//ppahDcHboATc3N02G3Wnc3d3h1NMKt1Lj0WOioxq5AARCI9QyY7zgHQjGM0APMwGGDB6MGkkl7lxPxKnoWKT8fh3vrF0FJycnje7P84SqQCsKCgrw+fZvIdVzwKS56zDAKwB6egJV/8a7N5PhNsgfk4LfhUzQG1/u2IW8vLwm12Vubg6/YUOQlR4PpVIJLo+HuSs+++PVp7uV1P8+d8Wn4PJ4uHfzCoTcGowaNarzdpYQgnFjAlBZfAflJYUAoPb/aW2tBMU5V/HSuBefmy/YXC4XL40djeKcq6iRVKqViwF+r8Fv6juwdRoCuawKkvJc/Hb+MHIyf4eHVwAmBa9DeZ05Pv/ya5SUlGh0f54nz8cnrBMdP/4zymuNEDD5TejrC5EcewKhwf0RtmYSwj95A2FrJiE0uD9SE/6LgMlvoqLOFMcijze7vgkTxoNX9xDJMcfBGIPPqCCs3LQfFj3sG7Sz6NETKzfth8+oIJQ9KkDa5eMY7ev1XA69SEhX4unpiT5O1og7vQ+1UgkAtPp/6ukXiNhf98HeSghfX19thN1p/P39YW8lROyv+6BQyJvNhYGRCTz8XsPEhfXFPfHMHpzcuRjfbZrT4FiZlnwOY4KWoLACOHXqlDZ26blAY5u3oLy8HMtD1sHZ81W4DfZX9W9sfLmo/hvnyk37ITK3xt3kw/h6+9Zm7+lER0dj154DsHT2xfCA6eDx+FAqFLidfgmPS4tgZmmLfgP9wOXxUJSfhUun92JI3x54b907NE0oIR2gpKRE9eApj8dDz5494e3trXrQ7OHDh9j0cRhKpSYYPfktmIgsAaDJ/1OZrAYxp36AXm0+1r8fihdeeEGbu9YpsrKy8EnYdsiFvTD6lTcgNDBukAsul4erCRcwYPRiWNr3RcLp3Yg5vKGJNf15rNQXGKDkThR2fvU5jIyM1IpDKpUiOTkZhYWFUCgUMDc3x7Bhw2BhYdGBe9vY7du3ERERgUePHoHP56Nv376YO3duh9cfmpikg5w+fRq7f/wFk1//GHyeHkKD+7fQTaL+vtdnP/6OX37ciMXBEzB58uRm1x0fH4/de39CVZ0BHN1GoN8gPxib1n8AFQo5crPScCctHtWldzHCsx9WrlhOD6oR0k7FxcU4FBGBhOR01Mj1IDS2hFIpR23lI1hbGGLCuFEICgoCn89HYWEhvtz+Ne7cL4VV70HoN8gftg6uqm5TJQ/zcCs1DkXZKehpJcTaNSufy8L9RFZWFrbv+BYPSqSwdfaC25CRqnHOEy4ew5XES/AYtQg1VaU4uesNVFc8amZN9cfKLXsTcHr/JqxdPq/V24F1dXX4+eefcfZ8HIrLayA0sQaHw4W0uhSGenL4DhuM4NmzYWlp2aH7fOPGDWzYuBHXMnLAFVrA0NQGSoUc1eX5EPJleHncSHz88ccddlLVltpGD6y1oKysDAYmPaCvL8TN1Fi1+jfevZUMQ1NrlJWVtbhuf39/uLq6Ijo6GuejL+NM+jmAqw8Ojw+FTAoDfQbPQX0xZtESeHp6gveXcdAJIW2Xn5+PsE+342GlHtx9XoNTnyG4eysZj0uLoO80GAqFHD8diUJO7n2sWrkCdnZ2+HjzR7h8+TLOnY9G0ulvIVPwwdcTQCGvBY8jh6O9JZYsmAJ/f3+dnOGwLVxdXbFt62bEx8cj6nwM4n5OhILxweMLkHXzKoQ9BsCQXwset7yFwg08OVbev5sGfUNRq8dKmUyGf+74Cr9dvQsnjwBMmDgC+TkZeFxaBGNTH/D19HAx6QJu3wnDB+vWws7OrkP2Nzk5GW8tXQWFgSMGTwiFc38/pMYcwOOH9yDq6wuhoSV+Pvtf3L4TjEMHD2j85IqKdwvq+zbWPxbQpj7ZHI5aQ5ja2tpi9uzZmDZtGtLS0lBRUYG6ujoYGhrC1dW1xW5nhBD11c+M9Q1KpMaYOGsF0pLP4d0FQxqNzR04ew3ikm7DJiIC8+bNg4GBAcaOHYsxY8YgMzOzfgS2mhoIhUJYW1ujf//+z83DaeowNTXFpEmTMHHiRGRkZKC4uBhSqRRnznBQILGEzzBvJJxXb2jY+mMqB0qlssV2P+3fj8tX72HUlBXIzUrDh3/za/S+zXxrEx4+zMb2Hd9g6yeboKen157dhFgsxrIVawCRGybO2YhLJ75E5I55YOzPWDkcLgb4zsCtnFKsXbsW4eHh7dpmW1HxboFIJEJNVQkUCrna/RtNzaxRmH0NIpGH2tsRCATw8fF51jAJIa1ISkrCvfwyTJizHmnJ55p8dqXsUQF++uYdBM1fh7MX4hEUFKSai5rD4aBfv37o16+fFqLverhcLjw8/jzGlZeX4/i5NICp3y/eyNQCBTX1l4mbU15ejgsxCeg/fCpys9Kafd92b30Li9/+FndyriIlJQUjRox4pv164scff0S5hIfAeW/j0okv8dupbxq1YUyJ65cOw2XwS7gQdwUFBQWwt7dvYm2do/t8ZXwGPj4+EHCkyMlMVfVvbNw94gkOLHo4wMDIBPqopmJMSBdy/kI0zO37w8jYvNWxuWNP/4iKKiUuXbqk0Rh12bBhw4DaUhTmZap9rOTxeDAWMnh5eTW73vj4eFTJ+HBx8271fTv+7zAYWbni/IXodu4NcOz4SVg7+8BI1AMJv+5ssW122nkouCb44Ycf2r3dtqDi3QJbW1t4D3XHrd8vAkCr/RvnLN+GW6kxGDqwDxwcHDQXKCGkWXK5HJl376OXy0C1xuYuLymARM7FvXv3NBajrnN1dYWbqwNuXDkPDpfb6rEyeNlWZKVfgt/woS0OLZ119y7MbPog+3aKWs8cgcPB7TvZ7Zp5USqVIr+wFD1dvXAlam+DS+VNbpkpIZMrcP369Wfe5rOg4t2KoCmToScrRPzZg/Dyn9xsX88VG3+CTCoBtyYfU4OmaClaQsjT6urqwADw9QVqP7sik0pQV1fXuYE9RzgcDma+Og2yskwkxxyH98gpzR4rl2/4N6orS2HCE+OVVwJbXG+trA56+kK137ea6krIFUoo/pjV7FlIJBKAw6nf7kP1vsDVSSshk2n280L3vFvRp08frFr+Jr7a+T3+e6QEHl7j8MWB67hz4zc8Li2CqbkNjEzMcOv3aKA6FyuXvf7cDI1IyPNAKBRCoK+Hqooyte/H8rgcmkCjjQYPHowli+dg996DOFdWhP5eY/DlwRvIvH65/lhp1gMCQ2Pc/j0aQmUxQlctRa9evVpcp8jEGJLsUljbN39p/a94PD6MBAbg85+9tJmZmYHH5aDq8SOY2bio9TdcLh+mpibPvM1nQcVbDT4+PvjwfVMcORqJaxf34irfFIYiGwDAw/vXwZOL4eHmhJkzQpod15wQoh0cDge+w4fi7OXf8Mrc91sdp9zM0gYiQ16L92JJ0wICAmBhYYGjkT8j5Uw4mL4FDE17gDGGopw06LEqeA7qi5kz3oaLS+uF0dvbG2eid8PG/jU1xpe3h1xaAb9xnu3aBy6XC5+h7ki9cRGBb+7A+QPrW7x0zuFwoa/Hw8svv9yu7bYVDdLSRrm5uUhMTER5eTkYYzAzM8OIESNogH1CUN+9Mjc3F9XV1TA3N9fo07d/pVQqkZOTg5qaGlhZWUEsFuODDZ9iwMj5KHv04I+nloGGhaD+fuyLryzCkL498OXnYd2qG9jTqqqqkJeXBw6HA0dHRxgYGKj9t4wx3L17F0lJSRCLxeBwOLC0tISvr2+bPhNyuRwhoetQI3CBkbEZvtk4/8kW/tKq/n2bsfhDcKQF+HzLejg6Oqq9jaZERUVhWciH8Ap8G3k3Y5t82vwJuxe8YW8pQMKl6Had8QM0whohRAsSEhJwNPIEklPSIJFIYGRsiICR/4PZs2ZqrIsVYwyxsbE4fuJX/H7tBiQSCYyNDTEuwB8cAIlpufAeuwCPCnNw4Nt3G/UX9ho5BZZGcry7ZhmGDh2qkZi7msrKShw5cgQXYxORcz8f0hoJrCzNMHf2q5g5c6bG5ypPSkrCl1/vgZWLH7g8PRzcue6p980B46YvgfRxHqZN/B8sWrSo3dtUKpVYuGgREtIeYFjgamSnnUPCrzuf6ufNg0O/ERBwZdi8fg2Cg4PbvV0q3oQQjTp79iw2fvwZrqWmQFL1WLXcwMgUPj7DsfOb7Q36BXeWEydOIOzzb3A9LQXVlQ3jGDZsOAJeHIm0m7kQmDnCpf8IlD8qQEXZQ9RKJZDXlMNYT4plf1v43E0uoq7q6mps2fopzsddRUZaAqoqSlWvCQ2MMG3aVPzvvh+gr6+v0bhiY2Oxe+9+SGECx34jUCOpQlVFKThcDmprxFBUPcCEMSPw+qJF7T77fUIqleL11xcjKS0bZvYecO4/Gg/uJUNckgclY2BKBVj1A4SuegtLlizpkG1S8SaEaEx5eTmmvjoLsRejmniVA4Ah8JUgnDwR2amXoYuKijBj1jxcij3fbBxTp72KD9d/gIvRMbjyewbqFAwcACITAwSMGoGAgACtXervCo4ePYrPvtqL5PgzaHoCJoaPPvoImzZt0nhs+fn5uHDhAi7GJqJKUgsGQI/PwTBPD4wdE4CBAweqxp3vKEqlEhERETgUcRgZmflgHD4ABgGfYbSfF9588014erbvHvtfUfEmhGjMiRMnMDt4LqQ11c204MDAwBDx8bEdeqB7WkREBBa98Sakkqrm4zA0RMqVZLi7u0MsFqOiogI8Hg+WlpYavxzc1cjlcvx95Rr838GDqBI3P964iYkpysvLtDbfglQqRVlZGRQKBUQikcZqQH5+Ph48eAA9PT24urp2ynZpYhJCiMbExcW1ULgBgKGmphpRUVGdWrzj4uJaKNx/xCGpxpkzZ+Du7g5TU1P68v8XYrEYd7LutVi4AaCyUozo6GiMHTtWQ5E1JBQKtXJ1xMHBoUsNvtV9H6UkhHSIyspKtdpVVFR0ahzV1S19gfjT48ePOzUOXaWnp4famhq12j58+LCToyGtoeJNCGkXdc+mvb29OzUOdZ8O7+w4dJWJiQnc+qk3KEl3fi6gq6B73oSQdpHJZLC0skJVC2fgZmZmKCkp6dT7pBKJBDY2Nqiqav7Subm5OR49eqS1+7VdXWJiIgICxqKmhdsgtra2yM/Ppxx2grbUNjrzJoS0i76+Pnb8858tttm5c2enH+wNDQ3xxRdftNgmPDycik4zFAoFampq8NJLLd/L1sR7SVqn1eK9a9cuODs7QygUwsvLC3FxcdoMhxDyjBYvXoyDBw/CysqqwXJra2scPnwYc+bM0UgcS5Yswf79+xvFYWNjgyNHjmDWrFkaiUPXREZGwsnJCQEBATh58iQANOp2ZW9vj2PHjmH69OnaCJE8RWuXzSMiIjB//nzs2rULfn5+2L17N77//ntkZGSgd+/eLf4tXTYnpGtSKBSIi4tDYWEh7OzsMHLkSK2cpXWVOHRBZGQkZsyY0WgaTQ6HA8YYQkJCEBQURDnUAJ3o5z18+HB4enoiPDxctczd3R1Tp05FWFhYi39LxZsQQtpPoVDAyckJ+fn5Tb7O4XDg4OCA7OxsKtwa0OXvectkMqSkpGD8+PENlo8fPx6XL19u1L62thZisbjBDyGEkPaJi4trtnAD9WPF5+Xl0S3NLkgrxbukpAQKhQI2NjYNltvY2KCoqPGk62FhYRCJRKqf1uaAJYQQ0rrCwsIObUc0R6sPrD39QARjrMmxad9//31UVFSofvLy8jQVIiGEPLfs7Ow6tB3RHK0Mj2plZQUej9foLLu4uLjR2TgACASCbj/uMCGEdLSRI0fCwcEBDx48aPTAGvDnPe+RI0dqITrSEq2ceevr68PLywtRUQ1nIYqKiuq2U/ERQoim8Xg8fPXVVwAaXwl98vuOHTvoYbUuSGuXzUNDQ/H999/jhx9+wM2bN7FmzRrcv38fS5cu1VZIhBDS7UyfPh1Hjx5Fz549Gyx3cHDA0aNHqV93F6W1WcVmzZqF0tJSbN68GYWFhfDw8MB//vMfODo6aiskQgjplqZPn46goCDqG69DaGxzQgghpAvo8v28CSGEEPLsqHgTQgghOoaKNyGEEKJjqHgTQgghOoaKNyGEEKJjqHgTQgghOoaKNyGEEKJjqHgTQgghOoaKNyGEEKJjqHgTQgghOoaKNyGEEKJjqHgTQgghOoaKNyGEEKJjtDYlaHs8mQhNLBZrORJCCCGkYzypaepM9qmTxbuyshIA0KtXLy1HQgghhHSsyspKiESiFtvo5HzeSqUSBQUFMDExAYfDaff6xGIxevXqhby8PJofvINQTjse5bTjUU47HuX02THGUFlZCXt7e3C5Ld/V1skzby6XCwcHhw5fr6mpKX3YOhjltONRTjse5bTjUU6fTWtn3E/QA2uEEEKIjqHiTQghhOgYKt4ABAIBNmzYAIFAoO1QnhuU045HOe14lNOORznVDJ18YI0QQgjpzujMmxBCCNExVLwJIYQQHUPFmxBCCNExVLwJIYQQHUPFG8CuXbvg7OwMoVAILy8vxMXFaTsknRAWFgYfHx+YmJjA2toaU6dOxe3btxu0YYxh48aNsLe3h4GBAV588UXcuHFDSxHrnrCwMHA4HISEhKiWUU7b7sGDB5g3bx4sLS1haGiIIUOGICUlRfU65bRt5HI51q9fD2dnZxgYGMDFxQWbN2+GUqlUtaGcdjLWzR06dIjp6emxPXv2sIyMDLZ69WpmZGTEcnNztR1alzdhwgS2b98+dv36dZaamsoCAwNZ7969WVVVlarNtm3bmImJCTt27BhLT09ns2bNYnZ2dkwsFmsxct2QlJTEnJyc2KBBg9jq1atVyymnbVNWVsYcHR3ZokWLWGJiIsvOzmbnzp1jWVlZqjaU07b55JNPmKWlJTt16hTLzs5mR44cYcbGxmzHjh2qNpTTztXti/ewYcPY0qVLGyxzc3Nj7733npYi0l3FxcUMAIuJiWGMMaZUKpmtrS3btm2bqo1UKmUikYh999132gpTJ1RWVrI+ffqwqKgoNnr0aFXxppy23bp165i/v3+zr1NO2y4wMJC98cYbDZZNnz6dzZs3jzFGOdWEbn3ZXCaTISUlBePHj2+wfPz48bh8+bKWotJdFRUVAAALCwsAQHZ2NoqKihrkVyAQYPTo0ZTfVixfvhyBgYEYN25cg+WU07Y7efIkvL29MXPmTFhbW2Po0KHYs2eP6nXKadv5+/vj/PnzyMzMBABcu3YN8fHxmDRpEgDKqSbo5MQkHaWkpAQKhQI2NjYNltvY2KCoqEhLUekmxhhCQ0Ph7+8PDw8PAFDlsKn85ubmajxGXXHo0CFcvXoVycnJjV6jnLbdvXv3EB4ejtDQUHzwwQdISkrCqlWrIBAIsGDBAsrpM1i3bh0qKirg5uYGHo8HhUKBLVu2IDg4GAB9TjWhWxfvJ56eVpQx1iFTjXYnK1asQFpaGuLj4xu9RvlVX15eHlavXo2zZ89CKBQ2245yqj6lUglvb29s3boVADB06FDcuHED4eHhWLBggaod5VR9ERER2L9/Pw4ePIgBAwYgNTUVISEhsLe3x8KFC1XtKKedp1tfNreysgKPx2t0ll1cXNzoGyNp3sqVK3Hy5ElcvHixwVSttra2AED5bYOUlBQUFxfDy8sLfD4ffD4fMTEx+Prrr8Hn81V5o5yqz87ODv3792+wzN3dHffv3wdAn9Nn8c477+C9997D7NmzMXDgQMyfPx9r1qxBWFgYAMqpJnTr4q2vrw8vLy9ERUU1WB4VFQVfX18tRaU7GGNYsWIFIiMjceHCBTg7Ozd43dnZGba2tg3yK5PJEBMTQ/ltxtixY5Geno7U1FTVj7e3N+bOnYvU1FS4uLhQTtvIz8+vURfGzMxMODo6AqDP6bOQSCTgchuWDx6Pp+oqRjnVAC0+LNclPOkqtnfvXpaRkcFCQkKYkZERy8nJ0XZoXd6yZcuYSCRi0dHRrLCwUPUjkUhUbbZt28ZEIhGLjIxk6enpLDg4mLqLtNFfnzZnjHLaVklJSYzP57MtW7awO3fusAMHDjBDQ0O2f/9+VRvKadssXLiQ9ezZU9VVLDIykllZWbF3331X1YZy2rm6ffFmjLGdO3cyR0dHpq+vzzw9PVVdnUjLADT5s2/fPlUbpVLJNmzYwGxtbZlAIGCjRo1i6enp2gtaBz1dvCmnbffLL78wDw8PJhAImJubG/vXv/7V4HXKaduIxWK2evVq1rt3byYUCpmLiwv7xz/+wWpra1VtKKedi6YEJYQQQnRMt77nTQghhOgiKt6EEEKIjqHiTQghhOgYKt6EEEKIjqHiTQghhOgYKt6EEEKIjqHiTQghhOgYKt6EEEKIjqHiTQghhOgYKt6EEEKIjqHiTQghhOgYKt6EEEKIjvl/27Dsk0hCRfMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Normalize or scale marker size (you can tune this)\n", + "size_scale = 20 # try 10, 20, 50 etc. to get a good size visually\n", + "marker_sizes = points[:,1] * size_scale\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "base = points_gdf.plot(ax=ax, markersize=marker_sizes, alpha=0.6, color='cornflowerblue', edgecolor='k')\n", + "points_gdf.plot(ax=base, color='k')\n", + "ax.set_title(\"Marked Point Pattern\", fontsize=14)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4536e782-5e00-445f-86bc-14691eabf366", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ellipse_w_poly = ellipse(points_gdf, weights=points[:,1])\n", + "ellipse_w_poly" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b54a2dc5-8927-41ae-a732-623c3b039bf2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([51.1 , 36.88])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mc = pointpats.mean_center(points)\n", + "mc" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "66738152-5476-4c4d-8c98-3f9d7002d3c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([47.3302603 , 59.13665944])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wmc = pointpats.weighted_mean_center(points, points[:,1])\n", + "wmc" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bb956941-df8b-4410-922f-f34695889519", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "size_scale = 20 \n", + "marker_sizes = points[:,1] * size_scale\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "\n", + "ellipse_gdf.plot(ax=ax)\n", + "gpd.GeoSeries([ellipse_w_poly]).plot(color='yellow', ax=ax)\n", + "points_gdf.plot(ax=ax, color='k')\n", + "base = points_gdf.plot(ax=ax, markersize=marker_sizes, alpha=0.6, color='cornflowerblue', edgecolor='k')\n", + "points_gdf.plot(ax=ax, color='k')\n", + "ax.plot(*mc, marker='o', color='red', markersize=10, label='mean center')\n", + "ax.plot(*wmc, marker='o', color='green', markersize=10, label='weighted mean center')\n", + "ax.legend()\n", + "ax.set_title(\"Marked Point Pattern\", fontsize=14);" + ] + }, + { + "cell_type": "markdown", + "id": "e068b84a-83b8-4f29-939a-6b4b420abd6f", + "metadata": {}, + "source": [ + "## 4. Other options\n", + "\n", + "The default option constructs the standard deviational ellipse following the method used in CrimeStat." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f5efab64-b13e-42a6-8d05-d68391ad8869", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(43.85494662229593, 36.28453973005919, -0.9362557045365753)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ellipse(points)" + ] + }, + { + "cell_type": "markdown", + "id": "af44e1e8-cbe7-4bcd-b496-864a6d3822f5", + "metadata": {}, + "source": [ + "The default construction can be overridden in one of two (or both) ways.\n", + "\n", + "The first employs the `yuill` method which employs a different estimator for the major and minor axes lengths:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "04800f10-f44b-442e-a0ff-fc833e0f6680", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(31.01013014519952, 25.65704409535755, -0.9362557045365753)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ellipse(points, method='yuill', crimestatCorr=False) " + ] + }, + { + "cell_type": "markdown", + "id": "b98d38be-0b7e-420b-b63c-dc2f821d6a55", + "metadata": {}, + "source": [ + "This results in shorter axes lengths relative to to crimestat.\n", + "\n", + "The second approach drops the degrees of freedom correction used in the default:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "93d51f17-71a9-435a-8d5e-9255c465e579", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(42.96889676864705, 35.551443156155464, -0.9362557045365753)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ellipse(points, method='yuill', degfreedCorr=False) " + ] + }, + { + "cell_type": "markdown", + "id": "987d22d8-639f-4ff4-82b4-1a2bfd1c21c2", + "metadata": {}, + "source": [ + "Again, this shortens the estimated axes.\n", + "\n", + "Finally, both corrections can be toggled off:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "55ee9923-9dcb-4580-a9a1-3388cce22fc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(30.383598285215058, 25.138666536685605, -0.9362557045365753)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ellipse(points, method='yuill', crimestatCorr=False, degfreedCorr=False) " + ] + }, + { + "cell_type": "markdown", + "id": "660bc907", + "metadata": {}, + "source": [ + "## 5. Recap\n", + "\n", + "In this notebook, we:\n", + "\n", + "- Introduced the **standard deviational ellipse** (SDE) as a summary of\n", + " the central location, dispersion, and orientation of a point pattern.\n", + "- Showed how `pointpats.centrography` can construct an SDE from both\n", + " NumPy arrays and GeoPandas GeoDataFrames.\n", + "- Compared **unweighted** and **weighted** ellipses to highlight how\n", + " point weights influence the ellipse axes and rotation.\n", + "- Explored additional construction options, including corrections that\n", + " control consistency with CrimeStat-style ellipses.\n", + "\n", + "The SDE complements other centrographic measures (mean center, standard\n", + "distance) and the quadrat- and distance-based statistics notebooks to\n", + "provide a richer description of spatial point pattern structure in\n", + "`pointpats`.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user-guide/sd_fires.parquet b/docs/user-guide/sd_fires.parquet new file mode 100644 index 0000000..b203b02 Binary files /dev/null and b/docs/user-guide/sd_fires.parquet differ diff --git a/notebooks/centrography.ipynb b/notebooks/centrography.ipynb index 3cdc7e3..984de76 100644 --- a/notebooks/centrography.ipynb +++ b/notebooks/centrography.ipynb @@ -41,120 +41,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " x y\n", - "0 66.22 32.54\n", - "1 22.52 22.39\n", - "2 31.01 81.21\n", - "3 9.47 31.02\n", - "4 30.78 60.10\n", - "5 75.21 58.93\n", - "6 79.26 7.68\n", - "7 8.23 39.93\n", - "8 98.73 77.17\n", - "9 89.78 42.53\n", - "10 65.19 92.08\n", - "11 54.46 8.48" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "from pointpats import PointPattern\n", @@ -170,20 +59,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.core.frame.DataFrame" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "type(pp.points)" ] @@ -197,34 +75,21 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp.plot()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#import centragraphy analysis functions \n", - "from pointpats.centrography import hull, mbr, mean_center, weighted_mean_center, manhattan_median, std_distance,euclidean_median,ellipse" + "from pointpats.centrography import hull, mean_center, minimum_bounding_rectangle, weighted_mean_center, manhattan_median, std_distance,euclidean_median,ellipse" ] }, { @@ -243,20 +108,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([52.57166667, 46.17166667])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mc" @@ -264,32 +118,9 @@ }, { "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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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", @@ -310,20 +141,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "weights = np.arange(12)\n", "weights" @@ -331,20 +151,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([60.51681818, 47.76848485])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "wmc = weighted_mean_center(pp.points, weights)\n", "wmc" @@ -352,32 +161,9 @@ }, { "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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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp.plot() #use class method \"plot\" to visualize point pattern\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center') \n", @@ -400,20 +186,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#get the number of points in point pattern \"pp\"\n", "pp.n" @@ -421,28 +196,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/serge/Dropbox/p/pysal/src/subpackages/pointpats/pointpats/centrography.py:151: UserWarning: Manhattan Median is not unique for even point patterns.\n", - " warnings.warn(s)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([59.825, 41.23 ])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#Manhattan Median is not unique for \"pp\"\n", "mm = manhattan_median(pp.points)\n", @@ -451,32 +207,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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54.04117257066307 0.08769180858472225 -0.04872250646902643 465.8115372002813 5\n", - "54.04117257066307 54.09327726928146 0.05210469861838618 -0.025370793047137852 465.80882301324334 6\n", - "54.09327726928146 54.12405125525861 0.030773985977148755 -0.013552246205456697 465.8079149010591 7\n", - "54.12405125525861 54.14215248769505 0.018101232436443127 -0.00739190209046825 465.8076087750224 8\n", - "54.14215248769505 54.15276956049696 0.010617072801906602 -0.0040992658298719675 465.8075052025632 9\n", - "54.15276956049696 54.15898467957115 0.0062151190741914775 -0.0023026998071102867 465.80747009858044 10\n", - "54.15898467957115 54.16261796248172 0.0036332829105703013 -0.0013061853179365812 465.80745819050844 11\n", - "54.16261796248172 54.16473989468326 0.002121932201539778 -0.0007463404183738476 465.80745414933307 12\n", - "54.16473989468326 54.165978319450346 0.00123842476708802 -0.00042875101595285514 465.80745277762423 13\n", - "54.165978319450346 54.166700756153695 0.0007224367033487056 -0.00024727631074483725 465.80745231197506 14\n", - "54.166700756153695 54.16712204754273 0.0004212913890384584 -0.00014302182778891392 465.8074521538953 15\n", - "54.16712204754273 54.16736766581608 0.00024561827334679265 -8.289363293556562e-05 465.8074521002288 16\n", - "54.16736766581608 54.167510839857464 0.0001431740413835314 -4.8115880247223686e-05 465.80745208200943 17\n", - "54.167510839857464 54.167594287646125 8.344778866131719e-05 -2.7959041396741213e-05 465.807452075824 18\n" - ] - }, - { - "data": { - "text/plain": [ - "(54.167594287646125, 44.42430865883205)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "median_center(pp.points, crit=.0001)" ] @@ -595,20 +293,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([54.16770671, 44.4242589 ])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "em = euclidean_median(pp.points)\n", "em" @@ -623,32 +310,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp.plot()\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", @@ -673,20 +337,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "40.14980648908671" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "stdd = std_distance(pp.points)\n", "stdd" @@ -701,32 +354,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r')\n", "ax = pp.plot(get_ax=True, title='Standard Distance Circle')\n", @@ -766,20 +396,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(39.62386788646298, 42.753818949026815, 1.1039268428650906)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sx, sy, theta = ellipse(pp.points)\n", "sx, sy, theta" @@ -787,20 +406,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "63.250348987371304" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "theta_degree = np.degrees(theta) #need degree of rotation to plot the ellipse\n", "theta_degree" @@ -815,31 +423,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from matplotlib.patches import Ellipse\n", "from pylab import figure, show,rand\n", @@ -877,28 +463,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[31.01, 81.21],\n", - " [ 8.23, 39.93],\n", - " [ 9.47, 31.02],\n", - " [22.52, 22.39],\n", - " [54.46, 8.48],\n", - " [79.26, 7.68],\n", - " [89.78, 42.53],\n", - " [98.73, 77.17],\n", - " [65.19, 92.08]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "hull(pp.points)" ] @@ -912,32 +479,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp.plot(title='Centers', hull=True ) #plot point pattern \"pp\" as well as its convex hull\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", @@ -960,22 +504,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(8.23, 7.68, 98.73, 92.08)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "mbr(pp.points)" + "minimum_bounding_rectangle(pp.points)" ] }, { @@ -987,32 +520,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp.plot(title='Centers', window=True ) #plot point pattern \"pp\" as well as its Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", @@ -1024,32 +534,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp.plot(title='Centers', hull=True , window=True )#plot point pattern \"pp\", convex hull, and Minimum Bounding Rectangle\n", "plt.plot(mc[0], mc[1], 'b^', label='Mean Center')\n", @@ -1068,32 +555,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "circle1=plt.Circle((mc[0], mc[1]),stdd,color='r',alpha=0.2)\n", "ax = pp.plot(get_ax=True, title='Standard Distance Circle', hull=True)\n", @@ -1114,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1142,22 +606,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp = csr(as_window(state), 100, 1, asPP=True).realizations[0]\n", "pp.plot(window=True)" @@ -1165,54 +616,18 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mm = manhattan_median(pp.points)\n", @@ -1233,31 +648,9 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pp = csr(as_window(state), 500, 1, asPP=True).realizations[0]\n", "pp.plot(window=True)" @@ -1312,62 +692,18 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SEpmZmWva2wfMPPVmD+0D5qjvG8u1kfQnIgxZd+FQ4FSuNaTm/mnS0tL02s4dgn4t0GjuMlZWVmhubqawsJBDhw4FPc9gMJCens7S0hIpKSlA4DDoSC2XWK6Npj9PMILNvc6TY58hNSOVwcFB7HY76enpOqJtB6NFR6O5i7BYLDQ1NVFaWkplZWXIc1dXV1lcXKS8vNzb5h/S3Nw3HbFwxHJtNP35BiPU7svCOdHLwsICWVlZXL9+nYWFBRISEvjpn/7puLkNNfFDi45Gc5ewtLREU1MT5eXla4TEn9b+KVr6zezbZeVU6dqoNV8rIlgEWjBiuTba/nzXeZxlObS3t7O6ukp9fT1Go5HLly/T29sb0tLTbA86ek2juQtYWFigubmZqqoqysrKgp7XPmDmka81YXMoTEb49n+spfZA/rpzNhqBFu/otUj7U0px6dIlLBYLtbW12Gw2fvzjH1NbW0tOTk7M47iXiXf0mhYdjeYOZ25ujpaWFo4cORK2Bs5Tb97ky9+7gRMwCnzhQwd5/P6KrRmoD5sRWq2UorOzk7m5Oerr65menqazs5OSkhIOHz4cl3vci8RbdHT0mkZzB2M2m2lpaeH48eMRFV07mp+EyShrMgBsNZ4AgS9//zqfebo5bpFuHYOz/GQ6hQlnKufPnyc7O5v77ruP/v5+HA5HXO6hiR29pqPR3KFMT0/T1tbG6dOnKSgoCHre0tISFouFnJwcsh1mnn7kOJ0Tq3G1MqIh3gEHsD7S7c8/XMT58+c5d+4cGRkZmM1m8vLy4vQEmljQoqPR3IFMTExw8eJFqqurw06mvb293vxqKSkpnKws5aePbtFAA+AfIJCdkshTb/bEJIL+QjZiS+X4XlehuOLiYq5evarLXu8QtOhoNHcYY2NjXL58mZqamrCL5HNzczidToxGIw6HgwMHDgQ8r33AzIsdwwjw0TMlm2oBeUKeX+oYZmJhlS99txu7I7a9PYEi3Q6UZWMymbhx4wYmk4mBgQH279+/CU+kiQYtOhrNHcTIyAjd3d3U19evyyTgj81mo6+vjwMHDpCYmEhSUlLAzATtA2Y+/bUmrA5XUNEL7cM896uxbeyMhBc7hlm1OfGEMsXiaguUSBSgrKwMo9HIG2+8QXp6uhadHYAWHY3mDmFwcJDr169z7ty5iMpJDw0NkZubS0ZGRsjzmvumsTnejWINNfnHyyLyuMN8Y2eNxtgCG4LlfSspKeHIkSNcvXqVAwcORFUZVRN/InZwiohRRC6KyKvuz98QkcsickVEviMiae72JBH5RxHpEZEWEdnn08cX3e3XReTDPu0PuNt6ROQPfNr3u/u46e4zMdw9NJq7kf7+fm7cuEFDQ0NEggMu11okJZ7ry3NJML5rAQWLavNYRM+2DPJMyyCf/vrGI8/qy3Mx+FhdAny8euMiFi7nW1FREUePHuXChQtMTU1t6B6a+BDNqtpvA+/4fP5PSqmTSqkTwCDwm+72zwFmpVQF8BfA/wAQkSPAp4CjwAPAX7uFzAg8BTwIHAE+7T4X97V/oZSqBMzuvoPeQ6O5G7l58yb9/f00NjaSmpq65pjNZgt4jcViwWAwRJSDrLosm+ceO8cjdXv5TN3eoK61YBbRRqguy+bJh49hMggCGA3CsaLA7sJwghJJCHZycjLp6emcPXuW9vZ2xsfHNzRuTexE5F4TkRLgZ4A/Bb4AoJSadx8TYBd4LeWHgS+5f/4O8P+6z3kYeF4ptQr0i0gPUOs+r0cp1efu73ngYRF5B3gf8Ij7nG+5+/2bYPdQ98pOV809w7Vr1xgdHaWhoYHk5OQ1x+bn57l+/TqHDh1aZ/0sLCyQlpYW8X0iKUngsYg8az+x7vN5pG4vAE+83IVTKZ58tZuDhelUl2V7N49mpyTy5KvdWO1OTAbhE2dL17n1IgnBtlgspKSkkJubS21tLa2trRw7doyioqINj1+zMSJd0/kK8HvAmr9sEflb4CPAVeB33M3FwBCAUsouInNArru92efyYXcbnvN92uvc18wqpewBzg92jzV2s4g8BjwGsHfv3ggfVaPZGXR3dzM1NUVjYyOJiYnrjg8PD5OXl0dPTw9HjhxZY9XMzMyELT8dbVYAj0UUzyg387IVp1LrSlx79twYRHA4lasUtkPxbMsgL3YMr4lyiyTnm8Ph8H4/2dnZ1NfX09LSgsPhoLS0NKZn0ERHWNERkYeACaVUu4jc53tMKfUrbvfYXwGfBP4Wl3vWHxWiPZCLL9T5hDnmO76vAV8DVxqcANdoNDsOpRRXrlxhYWGBhoaGgJmS5+bmsNvt7Nu3j7GxMW7evMnhw4cxGo1YrVaWlpZCZpneaBmCeBdpW1OmwGhgZNbCSx3DXssFFEbDu8KjWG/NBItc88fXEZKRkcG5c+dobm7G4XCwb9++uD2TJjSRrOk0Aj8nIreA54H3ici3PQeVUg7gH4GPuZuGgVIAETEBmcCMb7ubEuB2iPYpIMvdh297qHtoNHc0SikuXrzI0tIS9fX1AQXHZrMxODhIcXExIsKePXvom4c//ec22gfMTE9Pk5OTg9FoDHqfQC6p7cAjGJ+q3QtK8fyFQV5oG8JkNGAUV2XQJx8+xqfr9pJoMgRN31Ndls3j91cEFRyDwYC/9z0tLY2GhgZ6e3vp6enZtGfUrCWspaOU+iLwRQC3pfO7wC+KSIVSqse9XvOzwDX3Ja8AnwWagI8DP1RKKRF5BXhWRP43UARUAhdwWS2VIrIfGMEVbPCI+5o33X087+7z5VD3iOmb0Gi2GafTSUdHBw6Hg7q6uqCiMTw8TFpaGrcWDTx/pce17vHaEFabk2cuTfMn7yvgw2erQt4r3mUIYqG6LJvmvmnsTpebzeFUfLK2lOKsXWssl4+dKdlwklARWSc6ACkpKTQ2NtLU1ITdbtelELaAje7TEeBbIpLh/vky8OvuY98A/sEdKDCDS0RQSnWLyD/hWv+xA4+7rSRE5DeB7wFG4JtKqW53X78PPC8ifwJcdPcd9B4azZ2Kw+Ggra0Ng8FATU1NyHQtTqeTgSUDv/HCu+seTqVwAjaH4sqYhY+HCauO1CW1VfiL4McCrBdt1LW3vLyMzWYLWtAtOTnZKzwOh4OjR7cxR9A9gC5toNFsM3a7ndbWVpKSkjh16lTY/GDXr1/nu71W/vongziVy0duMLje5BOMBr76qaPcdyz+gTObUY5gs/u32WxcuXKFpKQk9u3bFzKiz2az0dLSQnp6OidOnAiYveFeRNfT2SBadDQ7kWgnOqfTycWLF3Fk7eWX/q7Naxk88dBRzMvWmCfsYBP/RgMPtpv+/n5EJOJAgWhfAO4F4i06Og2ORrNNWK1WmpubycnJ4ejRoxG9WS8sLLBr1y6OHMiPu3vMX1h8hSzcXhh/sdpsqygSlpaWmJ2d5fjx4xFfYzKZqK2tpb29nfb2dqqrq7XwxBktOhrNNrCyskJzczOFhYVRLV7Pzc15E33GO3zZV1isNqd306ZHgIIFHgQSK8+GzkisongLlNPpxGAwsLCwQE5ODiZTdNOc0Wjk7NmzXLx4kQsXLlBTUxMyElATHVrCNZotxmKxcP78eYqLi6OOllpcXFyTfSBcipho8CzmG8W1RuS7adO8bOWZR+v5wocOrhMRfyvota7RiMOxY60i6vv8DoeD/v5+Ll++DLhcZRsVC4PBwJkzZ0hOTqa5uTlouiFN9GhLR6PZQpaWlmhqaqK8vJzy8vIN9eFx98R7ncU3os2TfsbXsglmWflHnj14bA+tt2YiCseOpYqo//M/9fGDFJosOJ1OpqammJiYoKKiYsPfh4hw8uRJurq6aGpqor6+PmBmCE10aNHRaLaIhYUFmpubqaqqoqysLOb+NqPss6+wHCxMj8jtFSj8OtJro9kv5OuGA/jKD26sef7zPVN87lwJw8PDjIyMcOjQIVJSUjb4TbgQEY4fP84777zjLX8dSRJVTXC06Gg0W8Dc3BwtLS0cOXKEkpKSqK9fXV2lq6sLp9PpbdvsDZ7+lk2otRf/cz0/e1xrwYQn0v1CvlaNySAggs1dj8fgzlJQlQVZWVmICAUFBXFdhzl8+DAmk4m3336bc+fOsWvXrrj1fa+hRUej2WTMZjOtra0cP36cPXv2bKiP6elpsrKyKCgo4PqUlebWHurLc7dsg2cgVx4Q9N7RuP4iCYhYY9U5XFnYPIkbGyvyePy9+0icHyElJSVm6yYYlZWVGI1Gr/D4l5nQRIYWHY1mE5menqatrY3Tp09TUFCw4X5mZ2cpKirixoydz3yjZc1k/vj9G1+3iBR/V96LHcPexJyBRCXerr81iUHdlo7D4bLwPv+BKspSHZhVZMXtYqG8vByTycT58+epr6+PuKCe5l206Gg0m8TExAQXL16kurqavLy8Dfdjs9mwWCxkZGTQfLEv7us4kZCdkuiu9KlIMBkQCDmOeLv+/N1wsNbK6u/v3zIB2Lt3L0ajkaamJurq6rwh7JrI0KKj0WwCY2NjXLlyhZqaGnJycmLqa25ujvT0dAwGQ8STeTz3vrQPmHny1W4cTleZgSceOsrBwnRe7BgOOo5Yc7sFGn+wdSNwBWlEUpo7XhQXF2M0Gmlubo7L7/heQouORhNnRkZG6O7u5vDhw2RlZcXcn9lsJjs78tox8Q6l9rjKFGB3Kn50fYJH6vaGHcdGN69GO36bzYbNZtvyxf3CwkKMRiOtra2cOXMmbNE8jQu9OVSjiSODg4NcvXqV06dPMzo6ytWrV1laWtpwf06nk/n5ebKysrwbIYF1tWN8N0nGu1ZOfXkuBsO7KXq+f3WcZ1sGw9aw2SjRjn9+fp709PRtSdCZn59PTU0NHR0djI2Nbfn970S0paPRxIn+/n56e3tpaGhgbm6OgoIC0tLSuHHjBnl5eRQVFUUdxuvJtXbl9mLQ6DHPRk7fNDTxXk85uieDy8Nz3rbXukZ5pC58JutAbjL//Tb+x6NdD/LP0rDV5OTkUFdXx4ULF3A4HBQXF2/bWO4EtOhoNHHg5s2bDA0N0djYyK5du+jv72fv3r1kZGSQmZnJwMAAXV1dHD16NKpcYLOzs2RlZfHm9bVv/y91DPOiO3rMW0/HL2VNPEOpP1mzl8vDnd7PDx4LH/odLMzaf7+N3bHWjRbtetDCwsK2l5vOyspaU/567974l5a4W9Cio9HEyLVr1xgdHaWhoYHk5GTAlV/Ns48jISGBiooKbty4wdzcHLm5kVsezT3jjDrSKchMXfP2r3g3egylMBgEcUeWhUpZs1E8Vs1rXaM8eGxPRFZOIDfZyKyFVZtrfch3v41/BFyk47fZbKyurm7a3pxoSE9Pp6GhwVuFdKNpju52tOhoNDHQ3d3N1NQUjY2Na/JymUwmHA7HGndadnY2ZrM5YtE5f32UP/rhFHbn5LpSAwAvuaPHjEYD763KpyA9iaNFmWGzAESLrzssErHx4O8my05J5P+8cRNPBS+jwZVHzrPfZiNuwIWFBW9k304gNTV1TRXSysrK7R7SjkOLjkazAZRSdHZ2Mj8/T0NDw5pSyBaLBaXUuoXtrKwsBgcHvan3w/Hv10axO9e6zXw3gj7zaD0vdQzzQtsQb7wzjslo4IW2IexOFbdCa6EiycKFZfu7yZr7prE7XGl8BJfL7qNnSkKu+YQbvyeIYCexa9cuGhoaaG5uxm63c/jw4e0e0o5Ci45GEyVKKS5duoTFYqG+vn7dGs3AwADFxcVrhAhcbrZdu3axsLAQ0YbCyixIMBqwB7EEqsuyXRO5jzABAd1VG6F9wLwuqaanz0jDmv3dZL6Wz0fPlATM7xZNuPT8/DwHDhzY8DNuFsnJyV7hcTgcERfpuxfQoqPRRIHT6aSjowO73U5dXV3AaDS73U5aWlrA67OysjCbzWFFx2q1si9N8cyjtbT0m4O+9XszBSiFyWQApXA4VcxRa57J37P+4kmq6RtxFm1mhEgCBKLp126375j1nEAkJiZy7tw5Lly4wOXLlzl58qQWHrToaDQR43A4aGtrw2AwUFtbG9RFZjKZsNvtAY9lZ2dz7do1ysrKQk5AY2Nj5OXlUVaWy9l9wbMOPPlqN053IMGXfvZoxCUFwuG7IdSTVPPzH4DGyfkAACAASURBVKjacFizh3ABAtH0azKZSExM3PaQ6VAkJCRQX19Pa2srHR0dnD59esesP20XWnQ0mgiw2+20traSlJTEqVOnQk4cCQkJQUVn165dpKSk0NXVRUlJiTfTgC82m43JyUmOHz8ecky+VoGgMC9b4xa15j/5+woOxJ7mJhQfPVOCuP8brt+CggImJyd3rOiAq/x1bW0t7e3ttLW1UV1dfU+Xv9aio9GEwWaz0dLSQnp6OidOnAjrIjGZTOvKG69ZHD94ELPZzA8u9dE5vsL7jpVy37F3o8Ju375NTk5O2CqVm1lPxyMqL3YME+xp4x2W7b+e89Ez4esO5eXlceXKlXWRgoGINIBjMzAYDFRXV3Pp0iUuXLhATU1NVPu17iYi/g2IiFFELorIq+7Pz4jIdRHpEpFvikiCu/0+EZkTkUvuf0/49PGA+5oeEfkDn/b9ItIiIjdF5B9FJNHdnuT+3OM+vs/nmi+626+LyIdj/yo0mvVYrVaamprIysqKSHDAVW3S4XB4P3sm0y9//zqfebqZ9gEzffPwRz+c4NuXZ3nsuS5e/HEHc3NzjI2NMTs7G1GhN48wfOFDBwMuuPumxtkoL3UM89yFQT7zdDPPtgzG3F8oNpK+JyEhgYyMDKang5+7vLzMzZs3uXjx4prfy1ZjMBg4ffo0KSkpNDc3r3sxuVeIRmp/G3gHyHB/fgb4BffPzwKPAn/j/vwTpdRDvheLiBF4CvggMAy0isgrSqmrwP8A/kIp9byIfBX4nLuvzwFmpVSFiHzKfd4nReQI8CngKFAE/EBEqpRS2/cXpbnrWFlZobm5mcLCQg4dOhTRNQ6Hg+npaQ4ePOhtCzaZetocTkXfoolbt26hlOLw4cPeyLdIwpI3K+mn77itNidPvNyFU8UvHNsfj+VmtTsREbJTQlt6HvLy8hgdHV1Xr2h5eZnbt2+zsLBAYWEhDoeD2dnZqDbnxhsR4cSJE3R3d3vLX4ezaO82IrJ0RKQE+BngaU+bUurflBvgAhDu1awW6FFK9SmlrMDzwMPienV8H/Ad93nfAn7e/fPD7s+4j7/fff7DwPNKqVWlVD/Q4+5fo4kLFouF8+fPU1xcHLHggCsAICMjY01ElWcyNfpEgPm3ve9YKSdOnOD48eMkJSUBgS2kSIlH0k9PZJwBMBjWptqJNYloIKrLsnnioaMYRHA4FU++2r3mmQNZbk6nk9HRUTIyMrxtFouFnp4erl+/TkpKCidOnGDPnj3k5eWFtIi2ChHh2LFj7N69m7fffpuVlZXtHtKWEqml8xXg94B1q3Vut9ov4rKEPJwTkcvAbeB3lVLdQDEw5HPOMFAH5AKzSim7T7snY573GqWUXUTm3OcXA81+fekse5q4sLS0RFNTE+Xl5VGnMpmcnKSqqmpNW7BFd/+iZH/9o941x2Opvhnreo9/ZNyj79nP3zXd2pT1I1/My1acan1qnGCWm9VqxWazMT4+ztLSEgaDwWvZ7N+/f11GiIGBAWw227o9VNvBoUOHMJlM3vLXOzX0O96EFR0ReQiYUEq1i8h9AU75a+DflVI/cX/uAMqUUosi8hHgX4BKCLgeqUK0s8FrfMf+GPAYoBPwaSJiYWGB5uZmqqqqKCsri+pah8MRtK5LIDeYpy3YhBpIOCLdrR9rdJl/ZFz6roRNi1bzJZhYvtgx/G7ONh8xSk5O5uTJk9hsNhYXF7HZbJSXlwcMKrg0PM+/9tsxG4f4wKmdkRetoqJiTfnrYPu77iYisXQagZ9zC0gykCEi31ZK/YKI/DGQD/ya52Sl1LzPz/8mIn8tInm4rJFSn35LcFlCU0CWiJjc1o6nHZ9rhkXEBGQCMyH6WoNS6mvA1wDOnj27TpQ0Gl/m5uZoaWnhyJEjES3k+2K325mdnSU5OTnqDYDBXGHNfdPr8q1Fs04TS3RZoMk/UH/xrFDqGbO/uLUPmPlO+7BPzjZZZ2klJCQEDD/3Hafnu3vm4jTPZsc38i4W9u3bt6b8ta+r8G4krOgopb4IfBFckWm43GW/ICKPAh8G3q+UcnrOF5FCYFwppUSkFte60TQwC1SKyH5gBFcgwCPu894EPo5rneezwMvu7l5xf25yH/+h+/xXgGdF5H/jCiSoxLWupNFsCLPZTGtrK8ePH2fPnvBp+/3p6+tjZWUl5MQXjECJMQOJy1Nv9mzY3RYt21Gh1Pfevv3452z7xNnSmCw3m0Px9o3xHSM6AKWlpd7y17W1tXGpOLtTiSVQ/KvAANDkfrN7SSn1JC5x+HURsQMW4FPuYAO7iPwm8D3ACHzTvdYD8PvA8yLyJ8BF4Bvu9m8A/yAiPbgsnE8BKKW6ReSfgKuAHXhcR65pNsr09DRtbW2cPn16XQRUJCwvL7O0tMTJkye5ODTHK2/2RPXm71lA95QNMC9bA4rLZu7LCTauUM8Qy5pTNPg/dyT7d0L1YTIIlTtwTvcU+WtpaeHs2bPbGmW3mYhLD+5+zp49q9ra2rZ7GJodxsTEBBcvXqS6upq8vLwN9dHT00Nqaiq3rclRvfm3D5h5qWOYiYVVfnxj0lvM7ImHjvLkq93eSTaazM5bicfSCTTOzbhXrM/t6aO6NJ2EuWFOnjy5IzdoTk1N0d7evuGXoHgjIu1KqbPx6m/nfeMazRYxNjbG5cuXqampIScnZ8P9zM/Ps3fvXl56ezDiN//2ATOf/rpLoHwJV/kzknWarRKmzUyFE+hesfbv20df3xLj4+M7srR0Xl4etbW1Mbl7dzJadDT3JCMjI3R3d1NfXx9RmYFQJCYmsrCwEJX7q7lv2luKwINAzIv2HjHzjOG5Xw1vfcQiUqHEwNNvdkoi5mWr9787wUorKiri6tWrFBYW7sg8aNnZ2dTV1dHS0oLD4Yg6sGUno0VHc88xODjI9evXOXfuXFwSRebm5jI/P0/1/v0Rv/nXl+eS4N59D2AyvFvULNyivclo4OPVJXwswLkvdQx7+7TanbzUMRzWxbcZwQC+/Tp9PPgGYdMyGkRDcnIyGRkZTExM7FhLIjMzk3Pnznlr8kQbwr9T0aKjuafo7++nt7eXhoYGUlNTY+5vZWWFiYkJ7z6wSN1A1WXZPPerrsqfCgIKiC9rUtLYnTzXMshLHcPrJm//FdpQK7ahirTFiu94fdmKyLtIKSoq4p133mF5eZm8vDwyMjJ2XL2b9PT0NVVId2LBumjRoqO5Z7h58yZDQ0M0NjYG3MAZLTabjXfeeYfi4uINhUpHKlDtA2ZGZi2YjC7XncIlJqs2Jy/6WTIfO1PCd9qGsDkURoPLZdc+YA7oqgtVpC1WfPOouTaYsin3iQVPipzp6WmGhoaw2Wzk5+eTm5sbl7+PeJGamkpDQwNNTU04HI51GS/uNHT0muae4Nq1a4yOjnLu3DmSk5Pj0mdvby+JiYmUlpaGP3mDrHGrGYT7Dhbww2vjeJaDEgOs27QPmHmxY5jvtA97I+L8LaKn3uzhy9+/jlO5i7RV5q2rmROPse/UNZ1ALC8vMzU1xfT0NElJSeTl5ZGTk7NjItxWV1dpbm4mPz+fI0eObNl9dfSaRhMl3d3dTE1N0djYGLeMvrOzsywuLnLs2LG49BcMXzeVw6k4WeraYPL9q+MAOBzrXVXVZdneDZXB3FleS8TmxGAQHjy2J+5CEO96O5tNSkoKe/fupbS0lNnZWaamphgaGiIzM5OR1US6JlapL8/btmdKSkryutquXLnC8ePHd5w7MBLu7bqpmrsapRRXrlzBbDbT0NAQN8FxOBwMDAx405dsJv7ZqLNTEvnR9QnvcaMxsKsqUGZrX7wZnd3Zo/0zOu8k4lEXKBpEhOzsbCorKzl58iS3FoRffbaLL3/vBo98vYnz10e3ZByBSEhI4Ny5cywuLnLp0iXuRE+VtnQ0dyVKKS5duoTFYqG+vj6uLpKRkRHS0tJiDrWOBP+9MM1909jdq/MCfLw6cABCJHtozMtWHE5XRmerbWcs7vuzWdF1kWIymbg+q7A7FU5cKXT+te0mB/MSty1jgMlkoq6ujra2Ntrb2zlz5sy2VUTdCFp0NHcdTqeTjo4O7HY7dXV1cbVGVlZWmJqa4vjx43HrMxz+birfvUAfC5ESJpx7Kzsl0Rvd5nR/3mnEM9XORvcj+e+/euDMAQYHB8nKytq2PT5Go5Gamhra29tpbW3l7NmzO3K/USC06GjuKhwOB21tbRgMBmprazflDdBgMGxbPZZ4ZgEwL1sxiCuM2SCuz8HY6vQ7vkEI8cg31z5g5tNfa8LmUCQYheceOxd2U6vvJl3/77yvb5WRkZFtLZliMBg4e/Ysly5doqWlhdra2qgtervdzs2bNzEajVsWFadFR3PXYLfbaW1tJSkpiVOnTm2K4Nhstk2JZopmUo/XAn2kGRS22sXlfz/f8g4bve+LHcNYHS67zupQ60LNPfcNFvXn/52XlpZy5coV9uzZs60F4USEU6dO0dnZSVNTE/X19UHHo5Rifn4eh8PhTft0+/Ztenp6KCoq2rIxa9HR3BXYbDYuXLhAWloaJ06c2LSonpmZmZjSzgcSl+1at4jUatqqbNLB7mdetvL4/RUx9en/1+D/2X/fEoR+1oSEhB0TOSYinDhxgqtXr3qLwSUlJeFwODCbzczMzDAzM4PZ7ArEyMzMpKGhAXCJDrCl2Q606GjueKxWK83NzeTk5HD06NFNnQxmZ2eprKzc0LXBxMV/kn2xY3jLXFm+1UufClKSYTNKKvjv4fG972bc76NnSnihfThoeQTP78C3ZHGge/u+NDjs9h21jlJVVcXi4iLPP/88JSUlrK6ukpGRQU5ODvv27ePMmTN0dnZ6AyBsNhuTk5MkJydvOMP6RtCio7mjWVlZobm5md27d3P48OEtuedGRS2YxeA7yRoNEnJT52YQztKKdzbpQNkQ/F1Z8c5e7Uk7FKxP/9/BJ86WrsuD5/89/dF7MqndIVFjDoeDzs5OMjIyqKioYGJigve///1rqpDabDYmJia8QTDj4669XludWkeLjuaOxWKx0NTURGlp6Yatj2jw7IlwOp1hzgxMsDd430n29qyF5y4MbmmOskjcZ/Hc6OlvVQS672ZsLA3VZyRC5/89vTPtYHR0lPT0dNLS0uI61mgxm82kpKRw8OBBAAYGBmhpaeHcuXPesY2OjpKXl+fdrzYwMACwqRk1AqFFR3NHsrS0RFNTE+Xl5ZSXl2/JPWdnZzEajRtOFBpqYvN1c73YMRxX11I4troiqW82BCc7Jx9bOKFbU33UKBzONbK8vMzY2BgZGRmUlpbGbQNytMzMzKxxkZWVlWE0Gjl//jzJxYfpnFgldek2H652vZzZ7Xbv+uRWB0Jo0dHccSwsLNDc3ExVVdWWLoBOTExQWFgYUx/hJratLIy2Xff0vd9Oz8fmi++4a/ZmcqYsG5PJhMPhsni6urrYvXv3ttToWVlZWZektKSkhKsTFj73zBUcCowCx44lsod3XWux/j1vBC06mjuK+fl5mpubOXLkyJYXtlpcXNwSqyrerqVIwrE3w50V6r53Wl42D4HGbTQaKSkpIT8/n6GhITo7OyktLd30jAUOh8MbmeZwOAImsr05JzgU3hITLf1mzu7L9UatbUdWBS06mjsGs9m8bSV8rVYrIrJlroh4bcaMZzh2NGPazOJwW2kFRsKaMVVUsLCwwMDAALOzs5SXl29aNOW1a9cwGAwUFBSQlZUVcF9afXkuCUbBaneSYDRSX55LT08PY2NjADGF/28ULTqaO4Lp6Wna2to4ffo0BQUFW35/s9m8JhJoM4nnhB2vPTbRjmlN0Tmbk6/84EbMpRO2Ow9bNGOypBbyndabnBm38LPnNieMPzMzE4fDEdBa8RXC//6hIl5pfof/55MPUF2WzXe/+xYAu3bt2pacbVp0NDueiYkJLl68SHV19ZbuJ/Bleno6ol3b8XgTj+dmzHgFCTT3TXtDnCNJDuofLPB2zxStt2Z2hIDGkxc7hr3fi2dMAL/4zVZXhdfLZkTgofr4C09+fj5dXV2UlJSsWUPyL23+niIjZ0rSqS7LXhN5efTo0biOJ1K06Gh2NGNjY1y+fJmamhpv6o6txmKxsLq6GjardKxv4vHINxZo02U8ggSiTQ7qWXT/yg9u8HbP1I4S0HDY7XaUUhiNxpCWQPuAme+0D3u/F6NBvJnAPeJodyraBuc5VniLffv2xVV4kpKSSE9PZ2Zmhvz8fG+7f2nzHw46eGvEQOOAmeN7UklLS6Ouro6UlJS4jSUatOhodiwjIyN0d3dTX1+/JWUEAqGUYmhoiPz8/LATRixv4v6C9cvn9tE9Oh9VcbVQmy5jTSMTTXJQD9Vl2Tx4bA9NvdOAilkotirKrru7G7vdjsFg4NixY0HX8TyF8sCVweATZ0u9Y/IVx4dqq7BYJhgaGop7gtCCggJGRkbWiI5HnN9N6SPYncr793j//ffHdQzRErFDT0SMInJRRF51f35GRK6LSJeIfFNEEtztIiJ/KSI9InJFRM749PFZEbnp/vdZn/ZqEel0X/OX4v6/W0RyROR19/mvi0h2uHto7g4GBwe5evUq586d2zbBARgaGsLpdEbkWgtXOM1DoKJk/msgT7/Vz9s9U1EVVwu16TJWfJ8tMULxaB8w8+Sr3TicCoMITzx0NCKh8P9+fD9Xl2Xz+P0Vm+pWy83NJTc3l/z8fPr7+4Oe5/udJCW8m1rHI45f+NBBnnm0npr9eRw4cIDp6dh/D/5kZmaysrKC1fruS4Dn/o/U7SXBAAZcmbW3ex+Uh2gsnd8G3gE8q6nPAL/g/vlZ4FHgb4AHgUr3vzp3W52I5AB/DJwFFNAuIq8opczucx4DmoF/Ax4AXgP+AHhDKfXnIvIH7s+/H+we0T68ZmfS399Pb28vDQ0NG96IGQ8mJycxm80cPXo0ogXXSN7Eg7ngfF1HIq5qntFaTPHcdBlJev9w+IqgUioi6+jZlkGeeLkLp1LeDNNPvtodscsy2jW1QOcXFBTQ2dnJiRMnuH79OpOTk2ssCQ+RbPb1kJiYiMPhwOFwxHUPj4h4XYH+YztZnM7R1EVab83ysZ8+se3rXx4iEh0RKQF+BvhT4AsASql/8zl+AfBsmngY+HvlyhnSLCJZIrIHuA94XSk1477mdeABEfkRkKGUanK3/z3w87hE52H3dQDfAn6ES3QC3kMptX11ZDVxoaenh8HBQRobG9dtdttKVldXGRoa4tChQ1GVMgi3/ySYC85/w+STr3ZjtbkEKNLiavHadBlMGKPdWxPtGkz7gJknXu7yVka12p281jUacTLUaNfUgp2fmJhIVlYW09PTlJeXc+3aNdLT0wPug4n0OxERTCYTq6urcV1LWVlZQUQCCpnZbKa6LIdM2wznKrd+E2gwIv2/6SvA7wHp/gfcbrVfxGUJARQDQz6nDLvbQrUPB2gH2O0REqXUqIh4YmWD9bVGdETkMVwW1LYWW9JExrVr1xgdHaWhoSHg/+BbhVKK/v5+CgsL477YGmwiDvTG7Xnjf/LVbg4Wpkc0ucVj02W8osSqy7J54qGjvNY1GtHaVHPfNA7PLkbAIMKDx/bQemsmomSo0Y471Pm7d++mt7eX3bt3U1RURF9fH4cPH95wIMDk5CQmkymuf9dLS0vcuHEjqOt3amrKm+ZmJ2XDDis6IvIQMKGUaheR+wKc8tfAvyulfuK5JMA5agPtIYcVyTVKqa8BXwM4e/ZsuD4120h3dzdTU1M0NjZuW/4qD1NTUzidzk3ZgBrIJRPojdu8bN2Qiy0exCNKrH3AzEsdw7zQNoTdqWi9NRNWOOvLc0lKcLkHDQbhyYeP8UjdXg4WpkeUDDXacYc6Py0tDZPJxOzsLLt372Z2dpbR0dENFTtbWVnxWs3x2hczMzPDrVuuiLhAUZ2rq6tYLBby8/O3LUotGJFYOo3Az4nIR4BkIENEvq2U+gUR+WMgH/g1n/OHAd+0pSXAbXf7fX7tP3K3lwQ4H2Dc4zZzu+gmwtxDc4ehlKKzs5P5+XkaGhq2tQqjh+npaQoLCzdtJ7lnovQs8Ad6497qJJz+44slSiySgmiBLLtg9400GWq04w53/u7duxkfHyc7O5vy8nK6urrIzMyMap1RKUVfXx9FRUVxm/zn5uYYGBjg4MGDXJuy8tzl9XWQpqenyc7OZmVl5c4THaXUF4EvArgtnd91C86jwIeB9yulfHO9vwL8pog8j2txf84tGt8D/swTgQZ8CPiiUmpGRBZEpB5oAX4J+Cufvj4L/Ln7vy+HusfGvgLNdqGU4tKlS1gsFurr6zelDHQ0OJ1OXr/Yy0vto+TkrvLxsxJRupdoJ+dA5Zj9BWY7En/6EoubLlxBtFBrL/739f9+w30n8czplpOTw9DQEMvLy6SkpFBWVkZvby9Hjx6NyF3VPmDmjc5BKtKd/IfDu+MyJoDl5WVycnK4NmUN+j1OTU2xf/9+RkdH7zzRCcFXgQGgyf1G+JJS6klc0WcfAXqAZeBXANzi8t+AVvf1T3qCCoBfB/4O2IUrgOA1d/ufA/8kIp8DBoFPuNsD3kNz5+B0Ouno6MBut1NXV7cjfM6vXbjG51+5hc2poG+ZFztGeO6xc0EnsY1uBg1UjjnUG/6dxpqCaEYDH68u4WM+BdH8Ny8GS5ETKqAhHoT7/RkMBvLz8xkeHqaiooLc3FxmZ2e5desW+/fvD7tx1NN3gkEo27cvbuO22+0kJCTQfD3wmtTi4iJKKdLT0+nt7d22TdXBiEp0lFI/wuUSQykV8Fp3RNnjQY59E/hmgPY24FiA9mng/dHcQ7PzcTgctLW1YTAYqK2t3Zb8T/4opTjfM+mNnAKwOVTItZSNLrgHcp3FYzLdKckww1kk3tBu93f31s3AKXI2O+1NJP3v2bOH3t5e3nnnHSoqKti3bx+9vb1cv36dioqKkBtHvX07Q/8dRYvNZiM5OTmoC3Z6etqbj215eXnLs7GHQ2ck0Gwpdrud1tZWkpKSOHXq1I4QHHCJzuFcEwnuyRAIu6Eu0nWXeOx5CcdOS4YZSkR9U+S8dXNqTd6yWAIDoiWS/o1GI1VVVdy+fZubN29y9OhRKisrGRwcpL+/n6qqqrB9Gw3Edex2ux2TyRTw78jpdDIzM+Mt3b68vLytWw8CoUVHs2XYbDYuXLhAWloaJ06c2LSF+o0wOjpKTXkezx05wksdrnxavi6hQMSyGTQWyyaQRRPPbNJbYS1Vl2Xz+Q9UecOh4xEYsJExRNp/UVERZrOZmZkZcnNzKSkp4dKlS6yurpKUlBSi7ykyVyc5VRK/DOUe0fHcx3fcc3NzJCUlkZycjNPpZHV1VYuO5t7EarXS3NxMTk4OR49uTqr3jbK4uMj4+DjHjh0jMTExqHhEsvPcn3BisJEd9OEyGkS6EdP/vhuxlmIRqa0ODIDAVmek/RcXFzM0NEROTg5Go5Hc3FympqYoLi4OeL6n7ytXLHHbFOpwOLBYLAGFDlwl1T01clZWVkhKStox3gQPWnQ0m87q6ipNTU3s3r3ba/bvJPr7+ykrKwu6PygW11UoMYikX/9JMpKMBqGsrjUZD/zuG621FA+XXrwDAzZidUZKVlYWt2/f9lo7BQUF3s2Znpco/zHMzs5it9vj9pI1PT1Nenp60L/V3Nxcent7yc3N9Ubd7TS06Gg2FYvFQlNTE6WlpVRWVm73cAKyuroasoLiRibjYGG+AE+92bMuBX4wK8h/kgwlYv77f4L1ZRDB4VTr1lKitZai+V42220XiaDEwwVZXFzMrVu3yMnJISUlhYSEBObm5sjKylo3hq9+8ijpq5McPHgwbi6u8fFxysrKgh737CGyWq1adDT3HktLSzQ1NVFeXk55efl2DycgnjLUTqczaNh2NJNxqDWcSPbn+BJoknz8/oqgFk2oide3L1AYDa5Ekb73jXYNJZpAis0OcohEUOIRmJCZmUnPrIPv/9sV3ndsL6X5+UxOTpKVlbVuDFdGl/mpPAOrq6ukpaXF/Izz8/MAISvYjo6Okp6eTnp6OiMjI1p0NPcOCwsLNDc3U1VVFfLNbDtxOp3cuHGDPXv2hMyEEM1kHGryi3R/jodgk2Qwl1Soe/v39cRDRwMmBI3G3RXp97JZoc++1lN2SiIGEVDB6/bEIzChfcDMf359HKvdyTeab/MP/7EGw/w8Nptt3XfcWLWb/ZnC8PBwwJLS0TI2Nsbu3cE3mVosFiYmJjh2zLX7ZHl5eVtKu4dDi44m7szPz9Pc3MyRI0d23B4BX1ZXV7Hb7RHl04p0Mg71Nh3t/px4Wh6bFQkWyfeyGaHPa0oyGwTc5SAMhrV1e2IJHAhEc980VoerdITN7uTCrVk+si+HyclJqsuK1n3H09PTcUnyubq6yuLiIgcOHAh6zsDAAEVFRd71HovFsuMi10CLjibOmM1mWltbOX78+KYkzIwnJpMJh8MR1z5DreFsZOKPp+UR70iwSNkMwVtjPTkUypN0x6duz2a49dbuvxHq9mezu2AX165dY2Zmht1ZWXy2ptC7thKvdZWxsTHy8vKCuoCnp6ex2+1rLCG9pqO565menqatrY3Tp0/vSLPen5WVlYjDSaNZCA+2hhPvNC6h7h0JW5nBIJJxRTMe38lfBNz7eXGCt/5QPN16vmPzCGiRaZEM6zQGw15Onz7N4uIis7Oz9PX14XA4yMrKYmlpKWhIdaQ4HA6mpqa8brNAxwcHB6moqPBGyTmdTqxW67aWCAmGFh1NXJicnKSjo4Pq6mry8vK2ezhhcTgc9PX1RbTeFO6NOdhkudlpXGJhp2UwiHY8vtaTb8kDg+C1dOLl1gs0tsfvr8DhcDA+Pk53dzf5+fkUFRWRnp5OaWkpKysrzM7OYrPZYg4imJqaIiMjI+jenPHxcXbt2kV6+rvlzjyZCHbSfjgPO2vXkOaOZGxsjI6ODmpqobpZSQAAIABJREFUau4IwQG4ffs2qampESVDDCQeHjwT0pe/f53PPN1M+4DZe8wz6RljKBu9WQR7pvYBM0+92bPmObZzPKGoLsvm8fsr+OiZEu/3nBggGu8LHzoYk6gGG5vRaKSoqIjjx4/jcDjo7OxkYmICpRTJyckUFhZSVVUVU7mOyclJBgYGMJuD/z5yc3NZWVlhbGzM27ZTXWugLR1NjIyMjNDd3U19fT2ZmZnbPZyI8WQgiIRQb8zBrBmP9RMsSmy7CfRM8bB+Nuqyi8UqCbVmFA93ZrixJSYmsn//fpaWlhgcHOTH3UMMrSTx3iPFMd97amqKkpISDAYDSql1lsvS0hLT09MkJiYyODhISkoKGRkZOzLnmgctOpoNMzg4yPXr1zl37twa0/5OoHfOyVv/3s9PHSyMaI0m2KQWavJetbkWm598+FhUqXW2gkDP9NSbPTG5A2MRrViDDWIRl3C/h0jHlpqaynJKIf/ljWZsdif/961B/u6XqzlXufFaOgsLCywsLACuaqFHjhxZc3xgYIDU1FR2795NaWmp15VnsVi0paO5u+jv76e3t5eGhoaoKinuBC70TfFHP5zE7pzkqz+5FdHkGGxSCzZ5e6pm2p2KJ17uAlhj8WzVmkqoCdX/mWJdA/GvkxOtaG1HdF2kv4dIx/ZSx7C3gJ3NqXj1wnWKElcoLi7eUJHClJQU0tPTcTgc6ywXu92OxWLh4MGD66LalpeXKSwsjPp+W4EWHU3U9PT0MDg4SGNj44414UPROmDG7nBFOlltTt7sHo5psgs0eRsN4q3N43ALj1Mp78S22UEGntLO32kfxu54d0IFQoqQZ2zZKYkB0+mEYsFiw1OOyKnejSILNcbtrv8Tj9+DzWbj1q1b9M3DC21D3oqpJqOBh+sPMzMzTEpKCvn5+VH1u7q6itVqZe/evQEDAubn50lLSwsYRq3XdDR3DdeuXWN0dJSGhoYdGY4ZCfXleSQm9Lje6I0GCg0LrKysRP08oTJPP/nwMa/QBMp15t1Bz9od9PGYiH3de54J0GZ38mLHsPdNPNRb/e1ZC//nBzewO1XEVlj7gJmn3+r3fhbejSILNcbNtPSebRnkta5RHjy2h0fq9gY8Jx4RbiaTicTERH50tR+7w/WNC/BgVSZFSavMJiRsKCPB/Pw8mZmZQSPQlpeXsdlsLC4urouQ06KjuSvo7u5mamqKxsbGoFlu7wR83+iP5CWQLwskJSVFNeGHmzQfqdvLwcL0NVmdPROb57PD6cqB5tlBH6+J2PP27hEcwRU9JxBRglF/sYrk7b+5bxqnerfqqtEQugDeZlt6z7YM8of/3AnAT25OAQQUnnhsXBURysrK+NBJxYvXurE7IcEg1O6GH3UOMGXIZjV9Luq+rVZr0DBpcNX4WV1d5erVqxw8eNAbyGO323E4HCGv3U606GjCopSis7OT+fl5GhoaYgoB3SlUl2VzKC+RV853cdGeypWloYDp/oPhO2mu2lxWRKg1E48A+WaXVri+W49FEK+J2H/X/CfOlvLRM650RC92DIdNMOovVpG8/Xvu6cliHSx4ItAY4xFO7v/C8FrX6Jrjr3WNBrV2Qq3XRPMicv+JffxdUhI/6BzkdHEqRqORz7/cj905y1ffGoj6JcJqtWKz2VhaWlq3bjo/P8/IyAhWq5X9+/evSQK6kyPXQIuOJgxKKS5duoTFYqG+vn5Di6E7kampKV7v6OFL/27Gap9CBO96RCQTfn15LiaDYHW43GbfaR8OWWnUN0vB7VkLJoPL5eY74cZrIg719h5pglGj0cDHq0vCVk/1v+dLHcNMLKzSfXuO9gFzyO8jXqlxAlmIDx7b47VwAB48Fn1Kpo1Yng0H99Bw0HWvp97swe5UG36JSEhIYGZmhsXFxTWiMzQ0xMzMDEVFReTl5a1zv+3kyDXQoqMJgdPppKOjA7vdTl1dXdC8T3cak5OT3L59m3Eysdqn3RaH65iByN7uq8uy+cTZUp5tGUQBDkd0Rc9MRgOfrC1dM6lvZCKOtqJpqLf6WITAE7jwT21D3nWNF9qHee5Xg0/U8YpWC1YCAgi7phNtv/7jDWUJ+Vp/JmP0LxElJSXMzMys246wuLi4zrrxZSev54AWHU0QHA4HbW1tGAwGamtrd1zJ21iYm5ujpKSEgpUl3NnwAZc7qbEyj89/oCqiyfCjZ0pCuqv88Z3EHA4nxVm7woYxhyLSN3HfidEzjniWhw60FgRbl/onmIX4SN3eDYlNuH49PNsyyBMvd+FwKpISXN//6dJMlFIYjUaviL/ZPcy+FGvU34Pdbsdms61zlTkcjpAvgFp0NDuOxcVFTCZT0Ggtu91Oa2srSUlJnDp16q4SHHAl+mwfmOG/fvem16UmQFKCIWLBgc0rehYpkb6J+1pXKPX/t/fm0XFc953v5/aGRgMEiIUkVmLhApIgRYqkSGqxnhz7WbbDWBPJdmQlEzuxozMZe2LHL8dL4uEkmmRekpm8aOw4ThTbiZ1Ylq0llqLxEsuWbEsiIBJcRIAgNmIlSGIh9m70et8fXVWsbvRSvQENsb7n9OnuW1X33tp+v/vbU/JKS2UeMqrdYhFJ3aZjIVUPvmyq6oz22zE8w4nnOzW3eJ8/fP1rHMtMTEywe/duHA4HhxrKOLh1I52dnczOziasUBuNxcVFXC7XCvWZ3+9PmB3d7XYbSu+0VjBMTYQQViHEWSHEi8r/Twgh+oUQUghRqdvvPiHEnBDinPI5odv2biFEj3Lc53TtTUKIdiFEnxDiO0IIh9JeoPzvV7Y36o75vNLeI4S4P7PLcOvA4/Hw2muv8eabb8bc7vf7aW9vx+Vycfvtt7/lGA5AWVkZP+++ij8QJhgW4J4dlWkR4UMN4fxfqRDHTHOBqTCS2y2aMfmDUgvefPyl3qzkWNPPw2EVHGksw2oRhEKSx17sSmmMRLnsEiGV+5AK4vUb7a2nMth/eG2EgTlJd3c3Xq8XCHu31dTUMD4+ntLYi4uLK1RrMzMz+P1+Ll26pPUfjbeSpPNJoBtQFYmvAS8Cr8TY9xdSyuP6BiGEFfgy8H8DY8ApIcQLUsqLwF8Afy2lfEoI8XfAR4GvKN8zUsrtQoiHlf1+TQixB3gYaAVqgJeEEDullNktjvIWQzAY5I033qC5uZnh4WHm5uYi8qX5fD7a2tooLy+ntbU1LzPUZgNVVVUcaZrh6YuLBBRjfioSTibIZtS9kRV+tHOAKumEJLzaN8WpoRsZM8DoebRdnub08ExEXJLR/jO1o6wWor31PnZPU9j70R+WYr/04Ha4dAlPURUdowscayrHHggwPz+fsNy0HouLixH1cdQg1JaWloRlOd4STEcIUQf8MvBnwKcBpJRnlW1GxzoC9EspLyvHPQU8IIToBn4JeETZ7xvAHxNmOg8ovwGeAf5GhAd8AHhKSukFBoUQ/Ur/J41O5lbEuXPnKCkpobKykoGBgQjXZ6/Xy8mTJ9myZQu7d+9ew1nmFvPz81y+fJnWLRv459++g1MjczGJVz4QNiNIxsSiGQLA4y/18mrfVFpMweg80lUjJlNBrlVJBrfbrZUQKC0tjclofQGlomgwxGu9E9yzcwsf/8cOAqFwgPBn3tGA3X7FENORUrK0tBQR9Dk8PExFRQWlpaVxk+v6/X6AvA5rMCrpPA58BjCa1fFOIcR5YBz4AyllF1ALjOr2GQOOAhXArJQyoGtXqx5px0gpA0KIOWX/WqAtqq8VlZKEEI8CjwJs3Zq+QfGtgL6+Pi1P06lTp9i/f7+2GvJ4PJw8eZL6+np27NixxjPNDvx+f8wXb3R0lLq6OiorK9kGHNm2MjVJvtWayRTRDOFT79zJqaEbWS0fHT1eujaWQw1lnDjeqnmdqW7mK4j7GtQompycpLKykqtXr7J3796EjLal3MKlG0GlqimEpOQvfzLMhrdX0NCQXBJZXl7GZrNpz/D09DRut5vm5uaEx+W7lAMGmI4Q4jgwIaXsEELcZ6DPM0CDlHJRCPFe4HvADsK22mjIBO2keczNBimfAJ4AOHz48IrttwquXbvG0NAQb3vb2+jp6aGpqUkrJb20tERbWxtNTU1JH+j1gOnpaUZHR/H7/Wzfvp2ysjBR8Hq9DAwMIKXU2uIhmrA9d2YsKQGNlozyWVLKheE9+nzTVSN2DM9oQbqnhm4ARATtnjjempIUle59iD7O5XJRWlrKhg0bsNvtDAwM0NLSoml61Gv6au91btvipFIsMC0KI3LwhUKSrgkvDxoIPYhObTM5OUl9fX1SG+tbgukAdwPvUxiIEygRQvyLlPI3Yu0spZzX/f6+EOJvFUeDMaBet2sdYUloCtgohLAp0o7aju6YMSGEDSgFbiToy0QUFhYWOH/+PEePHsXpdFJTU8O5c+doaGjA6/XS1tbGzp07DVXQzGeEQiFGRkaYm5tj+/btBAIBxsbGKCsrQ0rJ5cuXKSkpoba2NqlKONoO8vTp0YQeX9GS0Ynjrfzxv91Me5MoVmWtkE3bUiLJMFWiH83wf9B5NeL/jNtnmGGmK7HGO666upqhoSH27t2r5SCsqanRjjvUUEad08fY2Bj2oiJury7lsQf28l+f7yQUkjjsFnZV2AylkIoOCA2FQoYCs9cD00nqmiSl/LyUsk5K2UjYeP/TeAwHQAhRpdhdEEIcUcaYBk4BOxRPNYfS1wtSSgm8DLxf6eLDwPPK7xeU/yjbf6rs/wLwsOLd1kRYknojhfO+JeD3+zl16hStra2aq6ZaVvf111/n5MmT7N69e90znGAwSG9vLz6fj9bWVoqLi3E6nZpb6fXr15FSGmI4EOll9v5DdSuiyqMRndL/iz/p1VyIfYqk9FZGoiqkqXqiRXvkvWdvNQ6bBYsI24/LXA7DnmrpVCNNdFxJSQlWq5WZmRm2bdvG9evXWVxcjDhWrZzb1NREaWkpjxzdyncePcoj+zbwlQ/uYX/tBkPPYLTnWigUMhScne8pcCCDctVCiN8TQowRljLeFEJ8Vdn0fqBTsel8EXhYhhEAPgH8iLAX3HcVWw/AZ4FPKw4BFcDXlPavARVK+6eBzwEox30XuAj8EPi46bkWCSklHR0dVFVVUVdXF7GtqqqKN998k5qamhXb8g1+v58bN27EjUsIBoP09fVht9vZsWOHthq02WwEAgE6OzuZnJykqakpJW88lbA9pCuFHE+doxJKi5JK59p8pCvr5II37RLQa1U+OhXEc91OtwS13q38kaNbOXG8VcvUnYoLtv6+qAwrk/OBsG14dHQUm81GY2MjAwMDBAIBbbvVamXz5s0RpaMPN1bw6D0NbAzcMJTJPBgM4vV6I5hHsoBQFetB0hFS3hqmjsOHD8vTp0+v9TRWDV1dXSwsLHD06NEIYjs9Pc3p06fZsWMHCwsL7N+/fw1nmRzDw8PMzs4SCATYunVrRE0SPcNpbm5ewVRmZmYQQqQUkBcLRlREHcMzEV5hKmxWgQXSCsjMhUNDsnNRt5e5HCmV2Y7Vrzp/Vc2Y7vy//HI/f/XvPYQkWAV8+l0tWpqbZIiVNcDo+Tx3ZgwJK/LP9fX1UVRURE1NDcPDw/h8vggHHJ/Px4ULF9i/f7+2CAoGg8zOzlJQULCiDEE05ufnGRsbi6gSeubMGfbt25fUK+3ll1/m0KFDht2yjUAI0SGlPJyt/syMBG9BjI6Ocv36dd72trdFEOLJyUnOnDnDoUOHcDqduN3uNZxlcgSDQaanp2ltbWVhYYGZmRk2bdqEz+cjFAoxODhIQUFBXCkmmcOAURixfxxqKIvwClOzO0vgqTdG0vK2yranVjImpt8ekjezNCQi1HpmE80IsuWwkEomh2jmN+P2EZJhDzKfPxwQmygma2FhQWMKzyq1h547MxZxDerr6+nq6mLTpk3U19dz8eJFrl+/rsXUOBwOysrKmJiY0Gw+VqvVcE0dVUrXIxQKGQrUzvdkn2AynbccZmZmuHjxInfffTd2u117CXeWQmhygDvuuIPy8nJmZ2fzPmP0wsICTqeTgoICgsEgw8PDdHV1sby8DEB5eTmNjY0IIVI2WBvdP5V+YxFZdcUci2Am61sfgJiKeigekjEx/XZAi+N5No73nhFJLBsOC0aZV6z5aNfQH46hea0/cUBsd3c3O3bsSHitnE4nmzZtYmxsjKamJrZv387FixfZsGGDRvCrqqro6emhuro65SBrp9OpLawsFgtSSkNMx+v1YrVa8/69zu/ZmUgJy8vLnD59mgMHDlBcXBzxEloFfO3Xb9NyMkkpyXfVanFxMR6PB5/Ph8vl4sCBA3R0dFBdXU19/U3nRSMr+GhGEL0/rEyEmY5XVjSRjUcwjRLsE8dbNfXQYy920VK1IW3PsGQSg57JhWTY4Gu1iBUlr9WxUpXEMnEjN8K84mWb/tbHjvH4S7281j8VkQIonsTj8XiSXquamhouXLjA9PQ0FRUVNDQ00NfXx969e7FarVogaTpZPSwWC06nE4/HQ1FRkeZEkKyv9WDPAZPpvGWgprhpamrSxHz9SyiACxNe7m0N719aWsrQ0FDMUrf5ApvNRnl5OZOTk9TW1mK1Wtm/f/+KioiJiF8s4h4rDufZGGWc4/Wbqq0lFsE0SrD16qFM5qDOI5HEoN+u2nTGZz18O456MFW1V7r2KaPMKt589KpP9ZonSgHk8Xg4tK0m4bWy2Wy0tLTQ19eH2+2mrq6O+fl5hoaGcDqdLC0tZZTZo7CwELfbTVFREcFg0JBqzWQ6JlYV58+fp7i4mO3bb+rVGwt9WEXsCpAWi4WamhpGRkbYuXNn1kTyYDDI0tJS1gyZRUVFzM3NRRGeSKaTiPjFIu7R+6uuzdGENV6/2bC1GCXY6cwhEZE2kjYnWkqMV74hFZtNutcsFWaVaD7qtlgpgNT5qefm8XgMXSuXy8WePXsYGBigp6eH5uZmLl26xOLiInv27Mmo/pTL5dJsrkbtOevBXRpMpvOWQH9/P4uLi9x9991a28DAAM6lq3zjwwc5O75EmcuhvWDqi7R582Y8Hg/d3d20trZmJaP0D0/38mrvdX7l6C7u2lmVUV8zMzOMjY2x5NzM73wzPuFJRGziEe2HDtZpnklAXLuLfr90VvjxYJRgx9sv3hyy7fFmRDrKhmovGirjvDLrSYlZJZpPtLOH3WahzOW4eb2sFr5wdwm7rbGzN8eC3W6npaWFkZERuru7aWxspLCwMOPcZy6Xi7m5OSA1d+l4OdnyCSbTWeeYmJhgcHCQe+65R3sw+/r6GB0d5a677qKwsJACZ2xCJISgsbGRN998k+Xl5YxF81cvXeH3X7hMIATPXTrDk4/emRLBk1Jqeuvr168zPj7Ozp07+capa0kJTzxiE000gYhroTKTWA4A0fvF6zNdom6UYMfaL94ccuHxlo10OalcM/21t1gEAuMVXVOdR0RgbzDEeLCIlpCfixcvYrFYqK6u1gj50tISfr8fl8sVkVVACEFDQwNFRUUMDAzQ1NRkKOtAIqQj6Xg8Hi29VT7DZDrrGIuLi5w9e5Y77rhDE6svXbrEtWvXKKxv5ettVwwlSdywYQMzMzMZM52fdV8lEAoHSPpDkp9dvGKYUPn9fs6dO8emTZuQUjI/P8+ePXsoKCjIWLJQifaT7SM88fMBrcKl/lpEE/Zk1ywbXlmZQs9o1P/ZLBSXC6nJyPH6ax9SSl/bLIITx1uzcs2j5+GwWjRJ5x37GmjdUojf72d5eZnLly+zb98+QqEQPT09uFwulpaWaG5uXuGSX1lZidPppL+/H7fbTW3tihzEhmG32xFC4PP5TJuOifyA3+/njTfeYM+ePZpHWldXF1NTUxTW7eHD3+iImySxzOXgyy/3ayvO6upqurq6CAQCLC0tUV9fv6J4VDJIKdlZqrzAwXCNEd/SLF6vd4XhX93f6/Xi8Xhwu91MTU1RXl7O3NwcGzdupLW1VbMzZUOyeLJ9hD/81wva/1h2Lj2yXeUzF4jHFLKVzFNP/L3+sOv0ajBa9drry19LKZlx+7I+1qGGMr71O8f4/uk+Dtbd9Ay0WCyEQiGKi4sZHR0lGAyyefNm6urqmJmZ4cqVK1yeX+nxWFxcTGtrK2+++SabN2/OSM2mOhMYUa9JKddFjA6YTGddwuPxcO7cOaqqqqivr0dKqZXDLazbw5deuay9sF5/iM7xuQivJH3WXpVQ7dy5Uwu+7O/vp6GhwXDJW3VFeFvtBk4c38SJF7oIhiT/eG6e5rI3+dV7D2gvn1qIam5uDpvNhsvlorCwkIaGhoSZAzKVLH7QeTXif0OFi7/64AFDtpRY9rBcw4haK540lu61ih7zWHMFNovAp6Tnf6ZjbEV0fi6gXvtnz4zxTMcYwWBuGf+hhjL21x7kwoULXLx4EY/HgxACh8NBKBTSJI0tW7bQ29vLzp07eencZb7wVBv+GK7kdrudwsJCPB4Pdrsdr9eLlNJQChw9VKZjt9uTMp3l5WUcDse6qPRrMp11hImJCXp6elhaWqKuro7du3cjpeT8+fMsLS1RULub3/zHUxH16vXE4uNv386XX+6PSag2bNigSTcWi4WJiYmkTMfn8zE1NcW1a9eoqalhy5YtvPTKgObiGwhK+hcs9PX10dLSgsfjob+/n/Lycg4cOJAVjzmjNof37K3mF31T2v9H791myMUYWPXaOkbVWtmQxlTvtKkFL6/0Tq6Ix/nA4XqebB9BAsHg6tWvURnnQwfrVqVEhM1mY/fu3Xi9XlwuF3a7HSklvb29SCnZunUrV65cYXZ2llAoxJDbjj+gFG2LoX51Op1cunSJHTt2MDk5idfrZe/evSnF7aiemxaLJSkz8Xg868JzDUymsy6wvLxMV1cXs7OztLa2snnzZk38P3fuHF6vl2PHjvF3Px+MiMtRGY+eWBghVBs3bmRsbIyhoSG2bt0a94EfHh5GSsmuXbs0sT66//tvb8YZnKG7uxufz8ecvYLXBnwckwsZE5FUbA6PHA0X8VOLg6n/kyFRrE6uiKFRZ4BMVWkdwzN86ImT+IKRQcL6MR88WBfXZXo1kKrUlsl9cTqdEdKIEILm5mY6OztZWlpiYWGBgoIC3G43RxvL+OrrY1q583jXZXBwECklLpeLiYmJiPLTyeByubhy5Qoej0fLXh0P68WeAybTyWtIKRkeHqanp4eGhgYOHDigidmhUIiOjg5CoRBHjhzBarVG1oEJp9ZdoZowQqisViutra0MDAwwMjJCY2Pjin0WFhaYn5/ntttui9Bbx+o/FCplfHycUY+d3/3mmaSZAIwi1ViVR45uNcxsnmwf4QedV2mtLlnBpLNhYE9EHFORYDJRpT3+Uu8KhhNt60r0vORbobpcJEhVk8n29PRQU1ODz+fD4/Gws8LOXx1vYHi5IOb519TUUFFRoTkClJSUcOnSJSoqKgxL+E6nk0AgwKZNm6iqShx+YDIdExljfn6e8+fPY7FYuOuuu1bU1jh16hQWi4U77rhDk0RiuQcbSdUSCzabjW3btnHhwgUqKyspLi7WUucsLi7S39/P9u3bYxpKo/u3WCzU1dXxfJRq79kzYzwXIxNANOIRt1zFquidDn7RN8V/ureZDYV2bfx4KkqjSDa/bDoDJBtfD5tV8GuH63kwym4T63nJx5Le2XYXV1FaWsr27dspKSlhYmKC5eVlQqEQhxrLOR7HRTlaalJtLlevXo1I4ZQIFoslYqGZCG6327ANdq1hMp08xMWLFxkbG2PXrl3U19dH6IHVdDcFBQWEyhv4wvNdCNAIRTSByOSls9lsNDQ08L1fnKdryk9rpZ2WSgdWq1V7CeMhFqOIZhKCyEwAscpCJyJuuYpViXY66Lo6zz9/9Kj2P1NbipH5ZcsZINn4FgH7akvZW1u6gtkkOwfVWcXnXz1bTyLk0uNQJehWq1WrdTM7O8umTZs0B5xo+Hw+JiYmmJmZIRAIsHHjxpQZg9GsBmoqnvUAk+nkEaSUXLhwgfn5ed7+9revkCICgQDt7e3hfEwbt/LIP7Rp6pGnO8bSKoucjEgNLgj+9PX5CKJ/MMkYqq3AH5TYrYJvK0GisSQx1V4Qryx0OvEymRKfaKeD9+ytXnGtMpFEckUc4zHoWF5p+vFP/Epk/IsRxlXmcmg2w5Dyf62RTELMhjrQZrMRDAbZsmUL09PTDA8PMz09jcfjYevWSNXt4uIi169fx+FwsGPHjpzmODTVayZSht4L7dixYyv0vn6/n/b2dkpKSti3bx9/+8oAfp0+PhdqHkhPanj2zJjGDH1BGRHfEc0kVCJxZdYTs+5MOgQ6U/VULKeDWNfKaCGxbM8vHmLdK4jtfZfITmNEbTbj9mmVUi2CnMTQpIN4EqJazC0kUy+mp4fVaiUQCCCEoKmpia6uLoqKipifn2d8fFyrnwNh6aikpISpqSn6+/txOBxUV1dnrc6TilAohNfrTdkle61gMp08gJSSs2fPal5o0SK1z+fj5MmTVFZW0toaThN9rLkCu1VoxD1Xap50iH60U2giJ1GVSMSrO5Mugc40rifa6SDb9oJ488tkNR7rXhmN5Uk119lqBM9my1GhY3iGE893ElAKBfkyuH+qpANh77La2lo8Hg8FBQX4/f6Y+1dVVbFlyxZmZmbo6+vj8OHDWY2nWV5epqCgYF3E6IDJdNYcoVCIs2fP4vf7NS80PbxeLydPnqSqqopdu3ZFbPvA4XomFrxs3lCQkj5ehRHCkQ7Rf/BgHU933GQgDx5MrmtONE4qDCRe2eRMiddqEdlMjPOx1Jfjsx5sFkEwgWuvflyb1RKxf3T2inhj5dLZwUiNpERouzxNMHRTK2ARIu37F13Vs6amhlAoRF9fH+Xl5XHnJYRgw4YNWK3WrDOH9aRaA5PprClUt2cpJUeOHFnxMC4vL/P6669TV1fHzp07tfZseQ4ZJRypSg2HGsr49u+svnTFNULdAAAgAElEQVQSrzjbal6rTJANaUovOeoZya8dqY+bTUA/bjAY4uEjW6nZWBg3e0X0WLlAMnf4VO7pseYKCuzh6qEWi+Bj9zSlnWHCZrPh9/uZn5/XHGnUMiEvtl3kT35+I+68/H5/xolAY8FkOiYMo6+vj1AoFOH2rMLtdnPy5EkaGxvZtm1bxLboFzKW15dR5Ipw5JIgxUM8m0a21GLZPCcj3n2ZSFPRjKR2Y2HcuUePq0rNsVzD1b5zHZuT6FokY87R11a/YEjGSJPBZrPR1NTE4OAgTqeT+vp6XC4XGzZsYGDBknBeXq/XZDqYTGdN4Xa7qa6uXsFwlpaWOHnyJNu2baOpqWnFcRFBoDG8vmB1CEO+IR6hyrfEnepK3esPB/E+9sBeHjm6NavSVKoBpkZq9kTUnslxbE6ia5Ho3OJJQeon0xgrgIqKCsrKypiYmKC7u1vLOP2u/Y18s2OCYGiljVX1dDMao5MK3G43mzdvznq/uYLJdNYQZWVlTE1NRbhaLiws0NbWRktLywoXTBX6FzLa6yte6eVcIl8i0+MRqlTquKzGeehjXAIhyYnnO2mp2hDTwJ8uUmVgscaN7iNXwZepzCnWvPT7JJtjtqRJi8VCSUkJo6OjzM/PY7fbObJtE1/54G5+1n2VB47t5lBDmZbgdnl5mZaWFoqKitIaLxHespKOEMIKnAauSCmPCyE+AXwK2AZsklJOKfsJ4H8D7wXcwEeklGeUbR8GvqB0+adSym8o7YeAfwIKge8Dn5RSSiFEOfAdoBEYAj4opZxJNMZ6gtvtjhC35+fnaWtrY8+ePUkDveJ5fcUrvZwr5FtkejzimWxOq3kex5orsFqE5k0VkjLr9ymbxdf0x+eL1BjvniZjKtmUJsfGxhBCEAgE6O3tZdeuXbxjfzM1Di9lTh8+n4+LFy9SXl7Otm3bcuZdtp6SfUJqks4ngW5ADUN/DXgReCVqv/cAO5TPUeArwFGFgfw34DDhXJQdQogXpJQzyj6PAm2Emc67gR8AnwN+IqX8cyHE55T/n403RgrnkhdYXFzUpJnZ2VneeOMN9u3bl1L1v1geS/FKLydCukRqtVe/2UL0+a7meRxqKOOxB/ZGxI3E8ypL556ky0CTjZdtZwp1vDKXgxm3Lyt9GpljtqTJuro6HA4HNpuN6elpenp62LNnD42NjXR3dzM1NcXmzZsjYneyXYIgGAzi8/nWTYwOGGQ6Qog64JeBPwM+DSClPKtsi979AeCbUkoJtAkhNgohqoH7gB9LKW8ox/0YeLcQ4hWgREp5Umn/JvAfCDOdB5TjAL5BmMF9Nt4YUsrI/CV5jEAgoEk6L18Y5vmTFzl+x860ys1Gv0SxSi8negkzWeVH25euzHroGJ7Ja8YT63yTrZCzrXp75OhWWqo2JIyeT/eepMNAjY6XLYKtt2tJwgGm2ZIwV8uJRa/SqqiowO/309vby969e6mqqmJ5eTmC4Xi9Xjo7O7Hb7dTV1VFeXp5SqYNYUKWcTPtZTRiVdB4HPgMYKSdZC4zq/o8pbYnax2K0A2xRGYmU8qoQQrWWxesrgukIIR4lLEHFtY+sNqSUjIyM0NPTQ2VlJWdGZvnd73YTlPDD0V6+VV6e1ZcuV1kH9GN962PHeO7MGE+fHuWpN0Z47sxYztVsmTCBWOf78bdvNxylf+J4a1ZW5omIYyb3JB27xWpLrOp4WiodA+Pmi+0wHsrKyhgfHweIYDYqxsfHqaqq0mxBV69epbm5GZfLxfLyMlNTU1gsFqxWqxbPU1RUFLPyror1Zs8BA0xHCHEcmJBSdggh7jPQZyyWK9NoT2eMyAYpnwCeADh8+HCyPnOOubk5zpw5g9Pp5OjRo3i9Xr75vQ6C0thLlw5ylXVAD1U9FQjJVSFamdpf4p1vPCagv4a+QCgr6VTSnaOKRAQ4HTXYagS/xhrP5w8XQrOI+Fk1VLtlvntpzs/PU1paGlfqUEsclJSU0NraysjICJOTkxQXFzM8PExFRQWhUEgrh+D1erHb7ezYsSPumG63e13Zc8CYpHM38D4hxHsBJ1AihPgXKeVvxNl/DND7BdYB40r7fVHtryjtdTH2B7iuqs0UFd1EkjHyFnNzc7S3t9Pa2kptbS3Xrl3j/PnzPHDnHn44ejFnL7vRrAMnjrdqucbSeYFTIVqZrljjxeOk4qmVClHWn5sQ4Wh9SerMNZXzTjRHI0w3VRVTsvGyTdz14yWy6USr4SCyLEa02/laYm5ujkAgwNLSkualJqVkbm6OUCiE3W7H57uZo66srIyenh5mZ2djerYtLy/T3d2dcMy3pKQjpfw88HkARdL5gwQMB+AF4BNCiKcIG/fnFKbxI+B/CCHUp+pdwOellDeEEAtCiGNAO/CbwJd0fX0Y+HPl+/lEYxg96dXGwsIC7e3t3HbbbVRVVTE+Pk5nZyfHjh2jtLSUb5WU5GzFZoTAdgzPaAFzp4ZuaO672R5HHUuLlLcIPhCjfksyZCN+JBWiHE0gH3uxKy1HjWzNMVeqsFjj5dKrz8g9iFbDqUXmBMR1O18rqHbaxcVFXC4Xk5OTXL0aJktWq5WKigrOjMwydGmJY80V3F5fSlVVFVVVVTELuzmdTqSU+Hy+uEGlaqzfekLacTpCiN8jbOepAt4UQnxfSvkxwt5n7wX6Cbsz/xaAwlz+O3BK6eIx1akA+F1uukz/QPlAmNl8VwjxUWAE+IDSHnOMfMXk5KT2cI2OjnLp0iWOHTumpdHIteEzWf/ZynCQChEJyXAG6ifbR3g2BRuQuurW21VyQYRjRbWrfSZyAIjXz7jBRJpGsJqqsGxd23SlpWhHlfcfquMhJZffd06N5tTtPNV579q1i4sXLyKlpKenh1AoxLZt2+i65ubf2i/RUGXhf/10CH9IYtOdS12CSqJFRUUsLS3FZToej+etJ+noIaV8BcVFWkr5ReCLMfaRwMfjHP914Osx2k8De2O0TwPvSGWMfITdbicYDDI8PExvby933nlnTmtrpIpkGQ5SNewmSrpZ5nLgsFm0VWoqaqpEq+5sEmEjlT2NMki9VGezWlaUD08H2XZdjob+/mWDwWUiLSU6VyNu55kglXl3DM/wkwvD1FiX2O3xUF1dTXV1NWdGZvnwNzrCed+6FjXVrC8Q4tvtN51uILZ6uLi4mMXFxbjlEN6S6jUTmcNut2vFnu66666cRCVHE/p07Qfx6trEGi9ewbBkSTdPHG+lc3yOZzrGUiLC8Vbd2SbC2VrdR+Q/C0l+7Ug9tRsLsxaPkgvpONb9y/TaZno9451rMrfzTNAxPMPjL/Vqi6Ok78I/qMlV4Z9+8yA1NWGVl3buAFJqNYj0C65EZduLi4s1j7hoBAIBgsFgQu+2fITJdFYBPT09zM3Ncf/99+fE00RPKCwinEX3n04OGV5Z6hkUGAsujUdI4hn51TavP0Tn+Bz/41f38dDBuqx5WGWTCGdLfRXdT7wszyrywSU4njt5qvPJtrSUqP90i+kl6lt9n/TutedHZ2PGoLVdntIYSzAEZ8eXuKslvO1wfQk2JfuEzSL4yL4ilhzlPN0xqpWPiC7brmduRUVFuN1upJQrvOLWo+camEwn5+jp6WFwcJB3vvOdOXtA9IQiJCVP/OIyYDz2IZ2VbTxCEq/dZgkXnJPAMx1jGgHOlodVJkiUlTiTcVLpJ1/SCeVKnZbtTAa5vFb690kl80EJ/37xOq/0Tq4oC7+7wobNCoGoRJ9SSsSNIU7cU0rvHNy1rZJyOcftt+/iwUN1EQu9Z+Ms9Gw2G3a7PabtZj2q1sBkOjlFd3c3IyMjtLS05DQL7LHmCixCEJJho6qUYLUIBPELd6lId2Ubj6DGa7+vZTP/fvE6EE61n67KKttqpWRZiVPpJxZRNdpPvqQTSpfh6s8/1rlk096S62sV7SKvOitA7PH2bC7kxD0bGfE6uf/2Zm1bKBSioKCAXz62g99QbDJutxu73b7iuUh0zVVnApPpmIgLKSVdXV309fVRU1NDU1OToToa6apXDjXocnmFJA678aj5TFa28QhqdHvH8Ayv9Exo/63W/CgxANkhYNlYea92cGYipMNw9ef/kTsbsQgBUiZ1aU/nmc/1tYp2kf/jFzqTloXfWWHnwb27I5iA1Wpl//79EfvFYxKJrrnqTLBp06aI9vXouQYm08kapqammJ2dxefz0dXVhdvt5rbbbsNTVM3TXfMc89hzql5J16iaa08oQMtWAGF1xfsPpV5aO1fIBgGLZlzPpuFyvhr3IVeIcIP3h/jqq4OEpMRiEdriJxZjT/eZX41rFe0i/+yZMQRopdf1JbzVdDW5YgDFxcVMTEysaHe73ZSXl+dkzFzCZDpZQH9/PyMjI2zZsoX+/n6cTif33Xcfw0sWfvtrbxh6qTJZcWdqVM2VJ5SKWAZ1PdbSgB6LgKU6nwiXc4vgmY4xAsHUV/WJ7kM2rlGurnO0OiokpWIPkZq0HYuxZ/LM5/qZjTdWLEa5r3pjTkMgCgsL8Xq9BINBrFar1m6q125R+P1+BgYGuOuuu+jt7WXLli3ccccdWK1WvttpvEphuivubKh2ck30E61M88GAnoyoJGMa+vMbn/Xw7Rgu55mcp9FjE93HXGcWSJSxId79zyeVoopkoQfxGGUmZaiTvX8WiwWXy8XS0pIWUA4m07llMTQ0RGVlJZcuXUJKyZEjR7BYLHQMzzA+68FmEZprpP6livWgPXiwThPhjRKERKtFI8xktYh+vJVpvhjQk83HaMDok+0jEfaMbKzqjRybbH6rlbUBYmdsiHX/V1OlmM67cOJ4q5YeymgJjHTmZeT9KyoqYnFxUWM6fr8fCMcArjeYTCcDBAIB+vv7KSgooKSkhEOHDmkMR4tEt1r4tSP1ETEayR7uBw8mrhqqR7RqZ3zWw5PtI1rwZSw1jx5rTfQzfYlTyYqQyXyMEv7HXuyKsGdkY1Vv5Fg1+WW8QMbVJJapqL5WQ01mVHrV32OvP8R3To2kVAIjHRh5rnw+H9evX2fjxo1a23qVcsBkOhlhYGCA69evc9ttt3H77bdrwVsRkejBELUbCxOuOn/QeTXjejbPnhnjmY4xnmwf0QLa9Fl54/WZCTHKhlouk9WukawIqSYVzUQVpCf8qj0jG+cZrb5SA271hPPp06Pa/Y7lHZhtqWKtFyt6JHsOjUqvJ463YrNatKDQrvG5mKmLsskojTxXy8vLQLi6sAqT6dyC8Pv9/OhHP+LIkSMRDAeSP0jR21urSzg5MA0G4mpiQdU1B4I3s/FGZ+WN12cqxCg6c0G21HLpvsSJsiKoxD+dpKLpqIKMEv5MrhHEvuZGvQNXm1jmEvpcftEqsOhzNCq9zrh9vP9QHd9WFm5Shq9ltlIXxYKR98/r9Ub8LigoMJnOrQa/309bWxtbtmzh4MGDK9JTJHuQYhlegyGJNUolkwrUF0tfFMvoKt8IMYpeFT50sG7NV7rxiEmZyxFR0S+d2jexoB4bLWmobbl2C4/HZJN5B2YCI84Tq+11GJ32SfWWi3ePU5Ve9WmgkqUuygaSvX9er5eSkhLm5+dZXFzUmE4+JQ1OBSbTSRFer5e2tjYqKiooKipCytgFSZM9SOr2L7/cr4nzUkaqZOIhFiGIZmTZKKesRzTBkyTO7JxtW0ssxCMmM26fllgRkkt7RpHINpALwh99reIRyVjXwajhPNE+2cq2nW3on0UU+1my7BupSK/5Fi/l9XqpqKhgfn6eGzduUFFRgdvtzmmWk1zCZDoG4fP5GBwc5JVXXmHLli24XC7Ky8szzvCaqpoiWwbcTOf50MG6uAk7n2wf4cTznQRDkgJ74gzU2WA8idQp1jQLxcVCIjtGtlf+8a5VvDH018HIdTayTz7ZbfSIfhaNZt+IhXjMKB/OU4XP56OgoACHw8HMzAywfpN9gsl0EsLv9/PSuQF+1n2VKss8RZ4JDh48yL59+3A4HFgslozHSJVYZZsQGJU84s0zFjE78Xynpmry+ZNnoM4moVb7ijXXTKWsZAuEbBKrePfZyBhGnhEj+8Q637UI5M1VQtb1AtWOU1tby+DgIKFQaN2mwAGT6cREKBRiaGiIH57u5fHzQQIhsAob/9+v3Mkdd6yoNZcxUiFW2TTgphN0mCzjQdvlaS3xKIDFIrQ5Rs+9zOXgQ//Qhj8QwmYNSyTp6tBjnYt+rtmQslaT2OltdEIIylzGgw+NPCNG9ok+X8ie84hRZCsha66RK2YspcTv92O32ykvL2dwcJDp6WmsVmvMEtfrAetz1jnE0tISbW1tbNiwAW/pVgKhQUV3DO2DM9yzayZuFb9cIJerPP1q1+cP8fhLvXzqnTtX2GCMqGrU+WjEUjHyPvbA3rgqKLVwFYA/KCMqKaZ6XslW7plIiPGCIHOJQw1lnDjeqlXGfOzFLlqqNhga28gzEm0DjOUcof5X21T7Y0iG67+shrptrVR8qTCRbKuNpZSaSs3n82Gz2SK0KgMDA+tWygGT6WgIBoNMTU3R09NDU1MTzc3NWIdn+PtXh8OrQauFI41lnD17FqvVSmNjI7W1tTldbeR6lRft8fZa/xTtl6cjbCDJXvpUa6fo5/7smbGIbZl4maXqpl7mckQkbYyHtUzTM+P2JfXMigcjz4i63ej5lbkcmnNGSJKS9JUu1sI1O9V7nm3G6PF46Ozs5NChQ5pqTUXfTIC2yze4c9vapwxKF7ck05FSMjg4SE9Pj5ZET0rJxo0bqa+vp7GxEYi9YpRSMjU1xdDQEN3d3dTW1tLQ0EBJSUnWRexcr/LU83v8pV5e658Kr2Cj4lqSvfSx5mi00uRDB+t45vQofqW4m0Wk72WWbHUfvbJPFtuR6PxWi+mUuRxYhAh7SArBz3omOD86S+WGgpTVkPGezego/OfOjMXtd8bt04KOLcr/XCPefc2lbSnVe55txqg6CFy9ehWn06kxnY7hGf7k57P4g/DvYzO0tKysYroecMsxnYWFBc6fP4/FYuHee++lsLCQUCisN9dncFURvWIUQrBp0yY2bdrE8vIyw8PDtLe3c2XZwf/bvoQ/KLO2Il6NVd6hhjI+9c6dnBq6oQVU6iWOZGk/MpnjoYYyvv3onVlz8062ule3Pf5Sb8KUMXqsVRCkmlInGAoz5GBI8sbQjLb9mdOjfPvROw1dq2Su3voo/KdPj8b19DvWXEGBffWvRfR9zbX0meo9z1TlrarTfD4fXq8Xr9fL1atXGRwcpLS0VPv/3KUl/MEQEoE/JPPGmzBV3FJMp7e3l8HBQXbt2sXWrVu1oM50vdCcTictLS3s3LmTv/y3c/gDi4QI67t/dPYyuzftjql7zdRjLN3+ko2jptJJJe1Hpi/cahqE9cRKXa0nIypr5SmlrrZjR4GFbWBGiU4yV299FH4wATFbi2sR69leLQ1AKucZ/RwHg0G8Xq/GSJaXl1laWsLtdq/4drvdEX0JIQgGg1gsFrxeLwcOHKCurg6xaZn/M3gOfyi8sF3tLBDZwi3DdBYXF5mZmdGkm2xCCME79zfyj6euhT2xLIJGl49XX30Vi8VCRUUFFRUVVFZW0j3pTckwn4woZ2vVp47z0ME6njszFpfYJTo236EnVhYBd2+v1BwnOoZnIgp1qW1GvfayDb1DRijGzbBbhWGik2zlrt5zIyv7tVok6J/t1dIA6M9T9SLzer0ao9B/PB4PS0tLLC0t4fF48Hq92qJWSokQAqfTicvlwuVyUVxcTE1NDUVFRRQXF+N0OnE4HBQUFGC327Hb7SvsxXeXw5OPFq57V3HDTEcIYQVOA1eklMeFEE3AU0A5cAb4j1JKnxDiI8D/BK4oh/6NlPKrSh8fBr6gtP+plPIbSvsh4J+AQuD7wCellFIIUQ58B2gEhoAPSilnRPhu/m/gvYAb+IiU8kyi+asrh+7ubqxW64qPxWJBCIHFYon4LYTAbrdTUZGeiL24uMj09LTmpPD9oaBmuI8VqwKpuaXmYtX3rOJVlq4nmVFkKqFlUmzNbrNEMJwPPXFSK0n8dMcYf/wrK9Par+ZLHm2D6hyfY2ohnIMrVZuOXpIVCban4q21GoQvUazStz52jOfOjDGx4OU5xSEleqEQPbdQKKR9gsEggUAAj8cT8VleXtaYiMpQlpaWWF5eJhQKe1qqzKGgoEBjIi6Xi9raWoqLiykuLsblcuFwOLDb7TgcDmw224p0WelgvSzwEiEVSeeTQDegVhH6C+CvpZRPCSH+Dvgo8BVl23eklJ/QH6wwkP8GHCZsNugQQrwgpZxRjnsUaCPMdN4N/AD4HPATKeWfCyE+p/z/LPAeYIfyOaocfzTR5IuKiqivrycYDK74eL3eiAdSShnxvbS0RFFREVVVVVqxJvUB0n9vdUHDvo0IEWJ2dhaLxYLVamXz5s1UV1eHVzt91/n+8Jv4gxKLgGvD/XzopR4CIbBbLTx0sCapG7Me6a76bty4wfDwMBaLBZvNpjHe71+YjXCLfen8EI3FIcrLyw2/NNmq45OooBakHjMSj7i2XZ7GH7wpTmSa+TtbyDaBUV3UYyU/NTrWanrzxXq2VYbh8/l4+vSotlD47ulR/p//q56//lnYMcVmhc8ecVFb4NWkEK/XSyAQwO/34/f7kVJGMBCHw4HT6aSoqIiysjLq6+txuVyaNKJKIdkICr+VYYjpCCHqgF8G/gz4tCJp/BLwiLLLN4A/5ibTiYX7gR9LKW8off4YeLcQ4hWgREp5Umn/JvAfCDOdB4D7dGO8QpjpPAB8U4YTn7UJITYKIaqllFfjnqjNRlVVlZHTXQE1WPTGjRvawwqs+FZ/61dT+m/18/HdIfrmYedGC5emvfiDViQCXyDI+fMXsFBKCEEIeLVvivaBKf7wrg3sqnRoDEINDiuyWPjz+2s4f9XDvi1OijzX6e6+hnIttXkJISL+u91u6urqKC4u1phvKBTi7h12/uXsFP6gxG4V7CwTDA8PU1ZWZojpxCJKsDLrQKqu2NE1h9JNOBrLKH1l1oPVKggoBMxus/CevdWcGrqRc6N5OlJDOsdkSyJOpx8ppfZ8BQIB7Z1QP4FAQLN9+Hw+TY3l9Xr5/f1WuqZCNBf7uPzGS/S+Hj7+5esOfMFCUGQ3fzDEU6/34As6kYTvZeeEl92tJdTU1FBcXExhYaHGXFQGYmL1YVTSeRz4DLBB+V8BzEopA8r/MaBWt/9DQoh7gV7g96WUo8r2Ud0+6jG1yu/odoAtKiORUl4VQqgZ7uL1FcF0hBCPEpag2Lp1q8FTXQmLxUJzc3Pax8eClBIpJaeHbvCTr5/CHwxht1r53MNvx+128/evjXJ23IMEAiE4d2WRSkIRL6r6Iksp2Qb4xm10Ttg0CUv9qKpCVZqxWCzY7XYmJyeZmZmJ2L+lwsGXHtzBufEl7ti6kWrHMteuXeP111+noKBAc+FUddCq+kDVQ0cTpWfPjGkr7FT08tH9REseyRKOGkF03Z137dnCpg0Fmk0nVgXMbCIdqSHZMfEYUrbsIPp+bFYLO0rD4Qeqamp5eVkznKuMw+/3EwwGAVZoFCD8LqjPj8PhiPjs2lTAgfpS7blTVVWVk15+9lwv/nC3OKwWHn33HTz2fy4q52jlN951dN2rot6KSMp0hBDHgQkpZYcQ4j61Ocau6nL/34BvSym9Qoj/RFhC+aUExyTqK+60jBwjpXwCeALg8OHDqdjGcw5V8jjSXMm3fmelyqeyspJf/2qbRiT+47sTEyRVwtJLLdFqxOg2vapCv60sGOTeyiChxXEmFWLhdruZmZnRCIvP54tgeupq9nrQhYUtmnfY4OXLeP2KJOcP8a0fv8HiDic2m40v3FVC93SA/dUunIvj9PZOYrfbsVqt7CiV2K0WAsFwYO67dm2KkDwSJRw1Cj1jU12TR2646bm2oElEa2Gz0EMl0OpiIdExHUM3+PWvtWsM6ZsfOcyB+lKklOzeVMBXPriHtoEp9m1xUuKb4uLFq9oCJt5Hvd/6z/0OC9esxTQU+uhvG2RUWYSon8LCQjZu3EhhYaHGLFTDeKyPujBKBVu3wlOVlSucP1qqS9a9of2tDiOSzt3A+4QQ7wWchG06jwMbhRA2RdqpA8YBpJTTumP/gbDtB8LSyH26bXWE1WVjym99+7jy+7qqNhNCVAMTur7q4xyz7hCLuKVq3FXjjGLFGuUCeianZ1gej4c7B6c5PTrPnko7oVCQjp9OEgiCzQItZRaWl5cJBAJsCAS43eknOBXk3LWAJr2p3w9utDK87KDassjYz8/x7oIirtqK2epc5vUX+rHb7TjtdjpHbPRESVzREpjebqWqJrc6vdgt4ZgHBPz44nUAftE3xcLCPL9622aklJoROBETj6dOVZmx+g1o/203/FiVaEuLgMD4Rb73vc6IPlWoxyx47Fgp0lZrNy618fXBnyOl5BfTLrz+DUgEXn+QL33nhxxyTWvH2u12KgoLmbhh44aSWiVaXav/rWZSV+0a+u/Ves4SId57YzKb/IaIVw8m5s5hSecPFO+1p4FndY4Eb0op/1ZvWxFC/CrwWSnlMcWRoAM4qHR3BjgkpbwhhDgF/BegnbAjwZeklN8XQvxPYFrnSFAupfyMEOKXgU8Q9l47CnxRSnkk0dwPHz4sT58+bfhcTWQP2fJ2CoVC+P1+LZBO/ag2APVbtQ+o+0Y7iuiZwojbypDbQe+Sk6s+B2qh78YCD+/fFGZCgUBYi6wy9mhVpd4DMlqlmehjs9m4PC/pmQmxb7OT3ZudEcRfzbmlSsXq3C9OeLhw3cvB2mL21RRr/XVdc/OJ53oJBCU2q4Wv/cZtHG6s0PrKhveUiVsPQogOKeXhbPWXSZzOZ4GnhBB/CpwFvqa0/54Q4n1AALgBfARAYS7/HTil7PeY6lQA/C43XaZ/oHwA/hz4rhDio8AI8AGl/fuEGU4/YZfp38rgPEzkGGxgrHYAAAb0SURBVNlafVosFs3TKNt4sn2EP/zXC8o/waPvPcojR9O3AxrFfWkcs3Nn2NMmGjU18GRFhaleMpHXSEnSWc8wJR0TyfBk+wg/6LzKe/ZWrwrDMWFiPSCfJB0TJt5SeOToVpPZmDCRY5hRTiZMmDBhYtVgMh0TJkyYMLFqMJmOCRMmTJhYNZhMx4QJEyZMrBpMpmPChAkTJlYNJtMxYcKECROrhlsmTkcIMQkMZ9hNJTCVhemsFsz55hbrbb6w/uZszje3MDLfBinlpmwNeMswnWxACHE6m0FSuYY539xivc0X1t+czfnmFmsxX1O9ZsKECRMmVg0m0zFhwoQJE6sGk+mkhifWegIpwpxvbrHe5gvrb87mfHOLVZ+vadMxYcKECROrBlPSMWHChAkTqwaT6ZgwYcKEidWDWt/+rfwhXGb7DeA80AX8idLeRLhaaR/wHcChtBco//uV7Y26vj6vtPcA9+va36209QOf07XHHMPgvK2EC+S9mO/zBYaAC8A54LTSVg78WOnrx0CZ0i6ALypjvwkc1PXzYWX/PuDDuvZDSv/9yrEi0RgG5rsReAa4BHQDd+b5fFuUa6t+5oFP5fmcf5/w+9YJfJvwe5jPz/Anlbl2AZ/Kt2cY+DowAXTq2tZsfonGSHidjRLA9fxRLk6x8tuuPJDHgO8CDyvtfwf8rvL7PwN/p/x+GPiO8nsPYcZVoDzYA4QZg1X53Qw4lH32KMfEHMPgvD8NPMlNppO38yXMdCqj2v4ShRgAnwP+Qvn9XsLVYYVyH9p1D/dl5btM+a0+4G8QZgxCOfY9icYwMN9vAB9TfjsIM6G8nW/U3K3ANaAhX+cM1AKDQKHuufpIvOeLNX6Ggb2EGY6LcJ2xl4Ad+XR9gXuBg0QynTWbX7wxkl7rVB/49f5RHqozwFHCkbg2pf1O4EfK7x8Bdyq/bcp+gvCK6/O6vn6kHKcdq7R/XvmIeGMYmGcd8BPgl4AXE/WVJ/MdYiXT6QGqld/VQI/y+++BD0XvB3wI+Htd+98rbdXAJV27tl+8MZLMtYQwQRTrYb4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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "mc = mean_center(pp.points)\n", "mm = manhattan_median(pp.points)\n", @@ -1381,31 +717,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sx, sy, theta = ellipse(pp.points)\n", "sx, sy, theta\n", @@ -1437,19 +751,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "73007071.24620631\n", - "72780262.40401942\n", - "True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from pointpats import dtot\n", "print(dtot(mc, pp.points))\n", @@ -1467,7 +771,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1481,7 +785,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.2" }, "widgets": { "state": {}, @@ -1489,5 +793,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/pointpats/distance_statistics.py b/pointpats/distance_statistics.py index 62b8d9d..2451368 100644 --- a/pointpats/distance_statistics.py +++ b/pointpats/distance_statistics.py @@ -5,6 +5,7 @@ import numpy import shapely from scipy import interpolate, spatial +from joblib import Parallel, delayed from .geometry import ( TREE_TYPES, @@ -553,6 +554,7 @@ def _ripley_test( edge_correction=None, keep_simulations=False, n_simulations=9999, + n_jobs=-1, **kwargs, ): if isinstance(coordinates, geopandas.GeoDataFrame | geopandas.GeoSeries): @@ -566,12 +568,18 @@ def _ripley_test( ) tree = _build_best_tree(coordinates, metric=metric) hull = _prepare_hull(coordinates, hull) + empty_space_points = None + if calltype in ("F", "J"): # these require simulations core_kwargs["hull"] = hull # amortize to avoid doing this every time empty_space_points = poisson(coordinates, size=(1000, 1)) + if distances is None: + # Note: We now use the original coordinates' tree to calculate the observed distance + # for the first time, as per the original logic flow. empty_space_distances, _ = _k_neighbors(tree, empty_space_points, k=1) + if calltype == "F": distances = empty_space_distances.squeeze() else: # calltype == 'J': @@ -582,38 +590,52 @@ def _ripley_test( core_kwargs.update(**kwargs) observed_support, observed_statistic = stat_function( - tree, distances=distances, **core_kwargs + coordinates, distances=distances, **core_kwargs ) + # The original function passed the tree, but the wrapper functions expect coordinates + # Corrected to pass coordinates, relying on stat_function to manage the tree internally. + core_kwargs["support"] = observed_support n_observations = coordinates.shape[0] - if keep_simulations: - simulations = numpy.empty((len(observed_support), n_simulations)).T - pvalues = numpy.ones_like(observed_support) - for i_replication in range(n_simulations): - random_i = poisson(hull, size=n_observations) - if calltype in ("F", "J"): - random_tree = _build_best_tree(random_i, metric) - empty_distances, _ = random_tree.query(empty_space_points, k=1) - if calltype == "F": - core_kwargs["distances"] = empty_distances.squeeze() - else: # calltype == 'J': - n_distances, _ = _k_neighbors(random_tree, random_i, k=1) - core_kwargs["distances"] = ( - n_distances.squeeze(), - empty_distances.squeeze(), - ) - rep_support, simulations_i = stat_function(random_i, **core_kwargs) - pvalues += simulations_i >= observed_statistic - if keep_simulations: - simulations[i_replication] = simulations_i - pvalues /= n_simulations + 1 - pvalues = numpy.minimum(pvalues, 1 - pvalues) + # --- PARALLEL SIMULATION BLOCK --- + if n_simulations <= 0: + warnings.warn("n_simulations must be positive. No simulations performed.") + simulations_array = numpy.empty((0, len(observed_support))) + else: + simulations_list = Parallel(n_jobs=n_jobs)( + delayed(_run_one_ripley_simulation)( + calltype, + n_observations, + hull, + stat_function, + metric, + empty_space_points, + core_kwargs, + observed_support, + ) + for _ in range(n_simulations) + ) + simulations_array = numpy.array(simulations_list) + # --- END PARALLEL SIMULATION BLOCK --- + + # --- VECTORIZED P-VALUE CALCULATION --- + if simulations_array.shape[0] == 0: + pvalues = numpy.nan * numpy.ones_like(observed_support) + else: + # Calculate how many simulations are as extreme as the observed statistic + pvalues_count = (simulations_array >= observed_statistic).sum(axis=0) + + # Conservative p-value calculation + pvalues = (pvalues_count + 1) / (n_simulations + 1) + pvalues = numpy.minimum(pvalues, 1 - pvalues) + # --- END P-VALUE CALCULATION --- + return result_container( observed_support, observed_statistic, pvalues, - simulations if keep_simulations else None, + simulations_array if keep_simulations else None, ) @@ -626,6 +648,7 @@ def f_test( edge_correction=None, keep_simulations=False, n_simulations=9999, + n_jobs=-1, ): """ Ripley's F function @@ -659,6 +682,16 @@ def f_test( n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. + n_jobs : int (default: -1) + The number of CPU cores to use for running the Monte Carlo simulations. + Simulations are independent and can be run in parallel to significantly + reduce execution time. + + * If ``n_jobs = -1``, all available CPU cores will be used. + * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), + disabling parallel processing. This is often useful for debugging or + testing purposes. + * If ``n_jobs > 1``, that specific number of cores will be used. Returns ------- @@ -680,6 +713,7 @@ def f_test( edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, + n_jobs=n_jobs, ) @@ -692,6 +726,7 @@ def g_test( edge_correction=None, keep_simulations=False, n_simulations=9999, + n_jobs=-1, ): """ Ripley's G function @@ -724,6 +759,17 @@ def g_test( n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. + n_jobs : int (default: -1) + The number of CPU cores to use for running the Monte Carlo simulations. + Simulations are independent and can be run in parallel to significantly + reduce execution time. + + * If ``n_jobs = -1``, all available CPU cores will be used. + * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), + disabling parallel processing. This is often useful for debugging or + testing purposes. + * If ``n_jobs > 1``, that specific number of cores will be used. + Returns ------- @@ -744,6 +790,7 @@ def g_test( edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, + n_jobs=n_jobs, ) @@ -757,6 +804,7 @@ def j_test( truncate=True, keep_simulations=False, n_simulations=9999, + n_jobs=-1, ): """ Ripley's J function @@ -787,6 +835,17 @@ def j_test( n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. + n_jobs : int (default: -1) + The number of CPU cores to use for running the Monte Carlo simulations. + Simulations are independent and can be run in parallel to significantly + reduce execution time. + + * If ``n_jobs = -1``, all available CPU cores will be used. + * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), + disabling parallel processing. This is often useful for debugging or + testing purposes. + * If ``n_jobs > 1``, that specific number of cores will be used. + Returns ------- @@ -807,6 +866,7 @@ def j_test( edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, + n_jobs=n_jobs, truncate=False, ) if truncate: @@ -834,6 +894,7 @@ def k_test( edge_correction=None, keep_simulations=False, n_simulations=9999, + n_jobs=-1, ): """ Ripley's K function @@ -866,6 +927,17 @@ def k_test( n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. + n_jobs : int (default: -1) + The number of CPU cores to use for running the Monte Carlo simulations. + Simulations are independent and can be run in parallel to significantly + reduce execution time. + + * If ``n_jobs = -1``, all available CPU cores will be used. + * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), + disabling parallel processing. This is often useful for debugging or + testing purposes. + * If ``n_jobs > 1``, that specific number of cores will be used. + Returns ------- @@ -886,6 +958,7 @@ def k_test( edge_correction=edge_correction, keep_simulations=keep_simulations, n_simulations=n_simulations, + n_jobs=n_jobs, ) @@ -899,6 +972,7 @@ def l_test( linearized=False, keep_simulations=False, n_simulations=9999, + n_jobs=-1, ): """ Ripley's L function @@ -932,6 +1006,16 @@ def l_test( n_simulations: int how many simulations to conduct, assuming that the reference pattern has complete spatial randomness. + n_jobs : int (default: -1) + The number of CPU cores to use for running the Monte Carlo simulations. + Simulations are independent and can be run in parallel to significantly + reduce execution time. + + * If ``n_jobs = -1``, all available CPU cores will be used. + * If ``n_jobs = 1``, the execution will be forced to run sequentially (serially), + disabling parallel processing. This is often useful for debugging or + testing purposes. + * If ``n_jobs > 1``, that specific number of cores will be used. Returns ------- @@ -950,12 +1034,51 @@ def l_test( metric=metric, hull=hull, edge_correction=edge_correction, - linearized=linearized, keep_simulations=keep_simulations, n_simulations=n_simulations, + n_jobs=n_jobs, + linearized=linearized, ) +def _run_one_ripley_simulation( + calltype, + n_observations, + hull, + stat_function, + metric, + empty_space_points, + core_kwargs, + observed_support, +): + """Run a single Monte Carlo simulation for a Ripley function test.""" + + # 1. Generate a random point pattern (CSR) + random_i = poisson(hull, size=n_observations) + current_kwargs = core_kwargs.copy() + current_kwargs["support"] = observed_support + + # 2. Prepare distances for F/J tests + if calltype in ("F", "J"): + random_tree = _build_best_tree(random_i, metric) + empty_distances, _ = random_tree.query(empty_space_points, k=1) + + if calltype == "F": + current_kwargs["distances"] = empty_distances.squeeze() + else: # calltype == 'J': + n_distances, _ = _k_neighbors(random_tree, random_i, k=1) + current_kwargs["distances"] = ( + n_distances.squeeze(), + empty_distances.squeeze(), + ) + + # 3. Calculate the Ripley statistic for the simulated pattern + # rep_support is ignored as we rely on observed_support + _, simulations_i = stat_function(random_i, **current_kwargs) + + return simulations_i + + def _truncate(support, realizations, *rest): is_invalid = numpy.isinf(realizations) | numpy.isnan(realizations) first_inv = is_invalid.argmax() diff --git a/pointpats/tests/test_distance_statistics.py b/pointpats/tests/test_distance_statistics.py index b4b1fc6..4cd34fd 100644 --- a/pointpats/tests/test_distance_statistics.py +++ b/pointpats/tests/test_distance_statistics.py @@ -211,7 +211,10 @@ def test_simulate(): # cluster poisson # cluster normal -@pytest.mark.parametrize("points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"]) + +@pytest.mark.parametrize( + "points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"] +) def test_f(points): # -------------------------------------------------------------------------# # Check f function has consistent performance @@ -220,7 +223,9 @@ def test_f(points): n_obs_at_dist, histogram_support = numpy.histogram(nn_other, bins=support) manual_f = numpy.asarray([0, *numpy.cumsum(n_obs_at_dist) / n_obs_at_dist.sum()]) numpy.random.seed(2478879) - f_test = ripley.f_test(points, support=support, distances=D_other, n_simulations=99) + f_test = ripley.f_test( + points, support=support, distances=D_other, n_simulations=99, n_jobs=1 + ) numpy.testing.assert_allclose(support, f_test.support) numpy.testing.assert_allclose(manual_f, f_test.statistic) @@ -238,7 +243,10 @@ def test_f(points): ) assert f_test.simulations.shape == (99, 15) -@pytest.mark.parametrize("points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"]) + +@pytest.mark.parametrize( + "points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"] +) def test_g(points): # -------------------------------------------------------------------------# # Check f function works, has statistical results that are consistent @@ -247,7 +255,7 @@ def test_g(points): n_obs_at_dist, histogram_support = numpy.histogram(nn_self, bins=support) numpy.random.seed(2478879) manual_g = numpy.asarray([0, *numpy.cumsum(n_obs_at_dist) / n_obs_at_dist.sum()]) - g_test = ripley.g_test(points, support=support, n_simulations=99) + g_test = ripley.g_test(points, support=support, n_simulations=99, n_jobs=1) numpy.testing.assert_allclose(support, g_test.support) numpy.testing.assert_allclose(manual_g, g_test.statistic) @@ -257,11 +265,14 @@ def test_g(points): assert g_test.simulations is None g_test = ripley.g_test( - points, support=support, n_simulations=99, keep_simulations=True + points, support=support, n_simulations=99, keep_simulations=True, n_jobs=1 ) assert g_test.simulations.shape == (99, 15) -@pytest.mark.parametrize("points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"]) + +@pytest.mark.parametrize( + "points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"] +) def test_j(points): # -------------------------------------------------------------------------# # Check j function works, matches manual, is truncated correctly @@ -286,7 +297,10 @@ def test_j(points): numpy.testing.assert_allclose(j_test.statistic, manual_j[:4], atol=0.1, rtol=0.05) -@pytest.mark.parametrize("points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"]) + +@pytest.mark.parametrize( + "points", [points, points_gs], ids=["numpy.ndarray", "GeoSeries"] +) def test_k(points): # -------------------------------------------------------------------------# # Check K function works, matches a manual, slower explicit computation @@ -301,7 +315,10 @@ def test_k(points): k_test.statistic, manual_unscaled_k * 2 / n / intensity ) -@pytest.mark.parametrize("points", [points, points_gs], ids=["numpy.array", "GeoSeries"]) + +@pytest.mark.parametrize( + "points", [points, points_gs], ids=["numpy.array", "GeoSeries"] +) def test_l(points): # -------------------------------------------------------------------------# # Check L Function works, can be linearized, and has the right value