From 51fb5b5e8aa8b3aef7377dca45555af2790a6f3f Mon Sep 17 00:00:00 2001 From: Tim Sherratt Date: Sun, 24 Aug 2025 15:59:02 +1000 Subject: [PATCH 1/5] add metadata to notebooks, update python packages --- .pre-commit-config.yaml | 2 +- agencies-gannt-timeline.ipynb | 57 ++--- dev-requirements.in | 10 + govt-agencies-network.ipynb | 165 ++++++++------- pyproject.toml | 2 +- requirements.in | 3 +- requirements.txt | 384 ++++++++++++++++++---------------- single-agency-network.ipynb | 366 ++------------------------------ 8 files changed, 349 insertions(+), 640 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ab0fa20..b2fffd3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/nbQA-dev/nbQA - rev: 1.2.3 + rev: 1.9.1 hooks: - id: nbqa-isort - id: nbqa-black diff --git a/agencies-gannt-timeline.ipynb b/agencies-gannt-timeline.ipynb index b89bc3d..3e27ece 100644 --- a/agencies-gannt-timeline.ipynb +++ b/agencies-gannt-timeline.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "id": "9b657cfd-13ec-4436-9011-b10fb7da5b0f", "metadata": {}, "outputs": [], @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "1b9ac56f-dc6e-4fef-9571-16f77b2a6971", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "df1f92d9-a13d-4b90-95ed-0f19bcd83313", "metadata": {}, "outputs": [], @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "015d67c9-8f81-45a3-98e8-429db105036f", "metadata": {}, "outputs": [], @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "ff5f5424-90e8-46ab-88be-421a38a2db59", "metadata": { "tags": [ @@ -237,7 +237,7 @@ "4 Postmaster-General's Department " ] }, - "execution_count": 12, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "id": "aa2d16b2-f637-4d6e-ac2b-ccfc6089fa94", "metadata": {}, "outputs": [], @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "57b07bcd-02f3-4bf0-992f-11f93f6ba95d", "metadata": {}, "outputs": [ @@ -270,13 +270,13 @@ "data": { "text/html": [ "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -361,7 +361,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.12 64-bit ('3.8.12')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -375,19 +375,24 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.11" }, - "vscode": { - "interpreter": { - "hash": "f54aba2de7a75230217f549a064c6555500d2132634fbcab9606dbfda34a2a1b" - } - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } + "rocrate": { + "author": [ + { + "mainEntityOfPage": "https://timsherratt.au", + "name": "Sherratt, Tim", + "orcid": "https://orcid.org/0000-0001-7956-4498" + } + ], + "description": "This notebook creates a Gannt-style chart showing the creation and dissolution dates of Australian government departments.", + "name": "Create a Gannt chart of Australian government departments", + "workExample": [ + { + "name": "View the interactive chart", + "url": "https://glam-workbench.net/wikidata/examples/agencies-gannt-timeline.html" + } + ] } }, "nbformat": 4, diff --git a/dev-requirements.in b/dev-requirements.in index dce7d2c..96df9aa 100644 --- a/dev-requirements.in +++ b/dev-requirements.in @@ -6,5 +6,15 @@ isort flake8 nbqa pre-commit +rdflib +rocrate +giturlparse +requests +python-dotenv jupyterlab-code-formatter +PyGithub +GitPython +beautifulsoup4 +lxml +arrow diff --git a/govt-agencies-network.ipynb b/govt-agencies-network.ipynb index 30ca5b6..9d0f31f 100644 --- a/govt-agencies-network.ipynb +++ b/govt-agencies-network.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "id": "6dfdf0a6-eb86-47a9-b595-0640faa16fba", "metadata": {}, "outputs": [], @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "14fe492f-5528-4c63-b9c5-61ae0eefa985", "metadata": { "tags": [ @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "600960d8-d8e1-400b-8bc8-6c68ac8c5d3e", "metadata": {}, "outputs": [], @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "4bfd91b9-c1a7-45e9-a356-7f57ffbf2eb6", "metadata": { "tags": [ @@ -129,87 +129,87 @@ " \n", " 0\n", " uri\n", - " http://www.wikidata.org/entity/Q16956105\n", + " http://www.wikidata.org/entity/Q16956444\n", " literal\n", - " CA 15\n", + " CA 1954\n", " literal\n", - " CA 8\n", + " CA 1869\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1901-01-01T00:00:00Z\n", + " 1975-04-21T00:00:00Z\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1916-11-14T00:00:00Z\n", + " 1975-12-22T00:00:00Z\n", " literal\n", - " Department of Home Affairs (1901-1916)\n", + " Department of the Environment (1975-1975)\n", " \n", " \n", " 1\n", " uri\n", - " http://www.wikidata.org/entity/Q16956105\n", + " http://www.wikidata.org/entity/Q16956444\n", " literal\n", - " CA 14\n", + " CA 1957\n", " literal\n", - " CA 8\n", + " CA 1869\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1901-01-01T00:00:00Z\n", + " 1975-04-21T00:00:00Z\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1916-11-14T00:00:00Z\n", + " 1975-12-22T00:00:00Z\n", " literal\n", - " Department of Home Affairs (1901-1916)\n", + " Department of the Environment (1975-1975)\n", " \n", " \n", " 2\n", " uri\n", - " http://www.wikidata.org/entity/Q16956110\n", + " http://www.wikidata.org/entity/Q16956449\n", " literal\n", - " CA 27\n", + " CA 1476\n", " literal\n", - " CA 24\n", + " CA 1407\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1928-12-10T00:00:00Z\n", + " 1971-05-31T00:00:00Z\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1932-04-12T00:00:00Z\n", + " 1972-12-19T00:00:00Z\n", " literal\n", - " Department of Home Affairs (1928-1932)\n", + " Department of the Environment, Aborigines and ...\n", " \n", " \n", " 3\n", " uri\n", - " http://www.wikidata.org/entity/Q16956114\n", + " http://www.wikidata.org/entity/Q16956449\n", " literal\n", - " CA 3068\n", + " CA 1479\n", " literal\n", - " CA 2474\n", + " CA 1407\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1977-12-20T00:00:00Z\n", + " 1971-05-31T00:00:00Z\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1980-11-03T00:00:00Z\n", + " 1972-12-19T00:00:00Z\n", " literal\n", - " Department of Home Affairs (1977-1980)\n", + " Department of the Environment, Aborigines and ...\n", " \n", " \n", " 4\n", " uri\n", - " http://www.wikidata.org/entity/Q16956119\n", + " http://www.wikidata.org/entity/Q16956449\n", " literal\n", - " CA 4131\n", + " CA 1486\n", " literal\n", - " CA 3068\n", + " CA 1407\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1980-11-03T00:00:00Z\n", + " 1971-05-31T00:00:00Z\n", " http://www.w3.org/2001/XMLSchema#dateTime\n", " literal\n", - " 1984-12-13T00:00:00Z\n", + " 1972-12-19T00:00:00Z\n", " literal\n", - " Department of Home Affairs and Environment (19...\n", + " Department of the Environment, Aborigines and ...\n", " \n", " \n", "\n", @@ -217,25 +217,25 @@ ], "text/plain": [ " item_type item_value after_id_type \\\n", - "0 uri http://www.wikidata.org/entity/Q16956105 literal \n", - "1 uri http://www.wikidata.org/entity/Q16956105 literal \n", - "2 uri http://www.wikidata.org/entity/Q16956110 literal \n", - "3 uri http://www.wikidata.org/entity/Q16956114 literal \n", - "4 uri http://www.wikidata.org/entity/Q16956119 literal \n", + "0 uri http://www.wikidata.org/entity/Q16956444 literal \n", + "1 uri http://www.wikidata.org/entity/Q16956444 literal \n", + "2 uri http://www.wikidata.org/entity/Q16956449 literal \n", + "3 uri http://www.wikidata.org/entity/Q16956449 literal \n", + "4 uri http://www.wikidata.org/entity/Q16956449 literal \n", "\n", " after_id_value id_type id_value start_date_datatype \\\n", - "0 CA 15 literal CA 8 http://www.w3.org/2001/XMLSchema#dateTime \n", - "1 CA 14 literal CA 8 http://www.w3.org/2001/XMLSchema#dateTime \n", - "2 CA 27 literal CA 24 http://www.w3.org/2001/XMLSchema#dateTime \n", - "3 CA 3068 literal CA 2474 http://www.w3.org/2001/XMLSchema#dateTime \n", - "4 CA 4131 literal CA 3068 http://www.w3.org/2001/XMLSchema#dateTime \n", + "0 CA 1954 literal CA 1869 http://www.w3.org/2001/XMLSchema#dateTime \n", + "1 CA 1957 literal CA 1869 http://www.w3.org/2001/XMLSchema#dateTime \n", + "2 CA 1476 literal CA 1407 http://www.w3.org/2001/XMLSchema#dateTime \n", + "3 CA 1479 literal CA 1407 http://www.w3.org/2001/XMLSchema#dateTime \n", + "4 CA 1486 literal CA 1407 http://www.w3.org/2001/XMLSchema#dateTime \n", "\n", " start_date_type start_date_value \\\n", - "0 literal 1901-01-01T00:00:00Z \n", - "1 literal 1901-01-01T00:00:00Z \n", - "2 literal 1928-12-10T00:00:00Z \n", - "3 literal 1977-12-20T00:00:00Z \n", - "4 literal 1980-11-03T00:00:00Z \n", + "0 literal 1975-04-21T00:00:00Z \n", + "1 literal 1975-04-21T00:00:00Z \n", + "2 literal 1971-05-31T00:00:00Z \n", + "3 literal 1971-05-31T00:00:00Z \n", + "4 literal 1971-05-31T00:00:00Z \n", "\n", " end_date_datatype end_date_type \\\n", "0 http://www.w3.org/2001/XMLSchema#dateTime literal \n", @@ -245,21 +245,21 @@ "4 http://www.w3.org/2001/XMLSchema#dateTime literal \n", "\n", " end_date_value label_type \\\n", - "0 1916-11-14T00:00:00Z literal \n", - "1 1916-11-14T00:00:00Z literal \n", - "2 1932-04-12T00:00:00Z literal \n", - "3 1980-11-03T00:00:00Z literal \n", - "4 1984-12-13T00:00:00Z literal \n", + "0 1975-12-22T00:00:00Z literal \n", + "1 1975-12-22T00:00:00Z literal \n", + "2 1972-12-19T00:00:00Z literal \n", + "3 1972-12-19T00:00:00Z literal \n", + "4 1972-12-19T00:00:00Z literal \n", "\n", " label_value \n", - "0 Department of Home Affairs (1901-1916) \n", - "1 Department of Home Affairs (1901-1916) \n", - "2 Department of Home Affairs (1928-1932) \n", - "3 Department of Home Affairs (1977-1980) \n", - "4 Department of Home Affairs and Environment (19... " + "0 Department of the Environment (1975-1975) \n", + "1 Department of the Environment (1975-1975) \n", + "2 Department of the Environment, Aborigines and ... \n", + "3 Department of the Environment, Aborigines and ... \n", + "4 Department of the Environment, Aborigines and ... " ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "59fc0ebf-dc52-4790-94c7-2b51c7d416cd", "metadata": { "tags": [ @@ -361,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 6, "id": "4ab5f05f-be42-4f3e-a280-225f1aa95232", "metadata": { "tags": [ @@ -375,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 7, "id": "88286772-5533-42e1-9d5a-3f8d0bffe593", "metadata": { "tags": [ @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 8, "id": "be969744-128e-4ad6-9faf-1710b8cda1e0", "metadata": {}, "outputs": [], @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 9, "id": "3a4de068-76fe-45b3-99f9-0def739955b3", "metadata": { "tags": [ @@ -455,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 10, "id": "37545bc4-f1c1-4c00-9437-9562ebfabb95", "metadata": {}, "outputs": [ @@ -474,7 +474,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -503,7 +503,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.12 64-bit ('3.8.12')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -517,19 +517,24 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.11" }, - "vscode": { - "interpreter": { - "hash": "f54aba2de7a75230217f549a064c6555500d2132634fbcab9606dbfda34a2a1b" - } - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } + "rocrate": { + "author": [ + { + "mainEntityOfPage": "https://timsherratt.au", + "name": "Sherratt, Tim", + "orcid": "https://orcid.org/0000-0001-7956-4498" + } + ], + "description": "This notebook visualises changes in Australian government departments over time, using data from Wikidata. It creates a hierarchically-ordered network graph where each agency is represented as a node whose position and colour is determined by the decade in which the agency was created.", + "name": "Create a network graph visualisation of Australian government departments", + "workExample": [ + { + "name": "View the interactive graph", + "url": "https://glam-workbench.net/wikidata/examples/govt-agencies-network.html" + } + ] } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 909c16f..c7a7f2d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.nbqa.addopts] flake8 = [ - "--ignore=E501,W503" + "--ignore=E501,W503,E402" ] [tool.pytest.ini_options] addopts = "--ignore-glob=Untitled* --ignore=snippets.ipynb --ignore-glob=draft*" \ No newline at end of file diff --git a/requirements.in b/requirements.in index 0f535ac..f818c77 100644 --- a/requirements.in +++ b/requirements.in @@ -12,4 +12,5 @@ python-slugify requests-cache tqdm ipywidgets -python-dotenv \ No newline at end of file +python-dotenv +upsetplot \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 16d354a..ae69eea 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,381 +1,399 @@ # -# This file is autogenerated by pip-compile with python 3.8 -# To update, run: +# This file is autogenerated by pip-compile with Python 3.12 +# by the following command: # # pip-compile requirements.in # -altair==4.2.0 +altair==5.5.0 # via -r requirements.in -anyio==3.6.1 - # via jupyter-server -appdirs==1.4.4 - # via requests-cache -argon2-cffi==21.3.0 +anyio==4.10.0 # via + # httpx # jupyter-server - # nbclassic - # notebook -argon2-cffi-bindings==21.2.0 +argon2-cffi==25.1.0 + # via jupyter-server +argon2-cffi-bindings==25.1.0 # via argon2-cffi -arrow==1.2.3 - # via -r requirements.in -asttokens==2.0.8 +arrow==1.3.0 + # via + # -r requirements.in + # isoduration +asttokens==3.0.0 # via stack-data -attrs==22.1.0 +async-lru==2.0.5 + # via jupyterlab +attrs==25.3.0 # via # cattrs # jsonschema + # referencing # requests-cache -babel==2.10.3 +babel==2.17.0 # via jupyterlab-server -backcall==0.2.0 - # via ipython -beautifulsoup4==4.11.1 +beautifulsoup4==4.13.4 # via nbconvert -bleach==5.0.1 +bleach[css]==6.2.0 # via nbconvert -cattrs==22.2.0 +cattrs==25.1.1 # via requests-cache -certifi==2022.6.15 - # via requests -cffi==1.15.1 +certifi==2025.8.3 + # via + # httpcore + # httpx + # requests +cffi==1.17.1 # via argon2-cffi-bindings -charset-normalizer==2.1.1 +charset-normalizer==3.4.3 # via requests -contourpy==1.0.5 +comm==0.2.3 + # via + # ipykernel + # ipywidgets +contourpy==1.3.3 # via matplotlib -cycler==0.11.0 +cycler==0.12.1 # via matplotlib -debugpy==1.6.3 +debugpy==1.8.16 # via ipykernel -decorator==5.1.1 +decorator==5.2.1 # via ipython defusedxml==0.7.1 # via nbconvert -entrypoints==0.4 - # via - # altair - # jupyter-client - # nbconvert -exceptiongroup==1.0.0rc9 - # via cattrs -executing==0.10.0 +executing==2.2.0 # via stack-data -fastjsonschema==2.16.1 +fastjsonschema==2.21.1 # via nbformat -fonttools==4.37.3 +fonttools==4.59.0 # via matplotlib -idna==3.3 +fqdn==1.5.1 + # via jsonschema +h11==0.16.0 + # via httpcore +httpcore==1.0.9 + # via httpx +httpx==0.28.1 + # via jupyterlab +idna==3.10 # via # anyio + # httpx + # jsonschema # requests -importlib-metadata==4.12.0 - # via jupyterlab-server -importlib-resources==5.9.0 - # via jsonschema -ipykernel==6.15.1 - # via - # ipywidgets - # nbclassic - # notebook -ipython==8.4.0 + # url-normalize +ipykernel==6.30.1 + # via jupyterlab +ipython==9.4.0 # via # ipykernel # ipywidgets - # jupyterlab # pyvis -ipython-genutils==0.2.0 - # via - # nbclassic - # notebook -ipywidgets==8.0.2 +ipython-pygments-lexers==1.1.1 + # via ipython +ipywidgets==8.1.7 # via -r requirements.in -isodate==0.6.1 - # via rdflib -jedi==0.18.1 +isoduration==20.11.0 + # via jsonschema +jedi==0.19.2 # via ipython -jinja2==3.1.2 +jinja2==3.1.6 # via # altair # jupyter-server # jupyterlab # jupyterlab-server - # nbclassic # nbconvert - # notebook # pyvis -json5==0.9.10 +json5==0.12.0 # via jupyterlab-server -jsonpickle==2.2.0 +jsonpickle==4.1.1 # via pyvis -jsonschema==4.14.0 +jsonpointer==3.0.0 + # via jsonschema +jsonschema[format-nongpl]==4.25.0 # via # altair + # jupyter-events # jupyterlab-server # nbformat -jupyter-client==7.3.4 +jsonschema-specifications==2025.4.1 + # via jsonschema +jupyter-client==8.6.3 # via # ipykernel # jupyter-server - # nbclassic # nbclient - # notebook # voila -jupyter-core==4.11.1 +jupyter-core==5.8.1 # via + # ipykernel # jupyter-client # jupyter-server # jupyterlab - # nbclassic + # nbclient # nbconvert # nbformat - # notebook # voila -jupyter-server==1.18.1 +jupyter-events==0.12.0 + # via jupyter-server +jupyter-lsp==2.2.6 + # via jupyterlab +jupyter-server==2.16.0 # via + # jupyter-lsp # jupyterlab # jupyterlab-server - # nbclassic # notebook-shim # voila -jupyterlab==3.4.5 +jupyter-server-terminals==0.5.3 + # via jupyter-server +jupyterlab==4.4.5 # via -r requirements.in -jupyterlab-pygments==0.2.2 +jupyterlab-pygments==0.3.0 # via nbconvert -jupyterlab-server==2.15.0 +jupyterlab-server==2.27.3 # via # jupyterlab # voila -jupyterlab-widgets==3.0.3 +jupyterlab-widgets==3.0.15 # via ipywidgets -kiwisolver==1.4.4 +kiwisolver==1.4.9 # via matplotlib -lxml==4.9.1 - # via nbconvert -markupsafe==2.1.1 +lark==1.2.2 + # via rfc3987-syntax +markupsafe==3.0.2 # via # jinja2 # nbconvert -matplotlib==3.6.0 - # via wordcloud -matplotlib-inline==0.1.6 +matplotlib==3.10.5 + # via + # upsetplot + # wordcloud +matplotlib-inline==0.1.7 # via # ipykernel # ipython -mistune==0.8.4 +mistune==3.1.3 # via nbconvert -nbclassic==0.4.3 - # via jupyterlab -nbclient==0.5.13 +narwhals==2.0.1 + # via altair +nbclient==0.10.2 # via # nbconvert # voila -nbconvert==6.5.3 +nbconvert==7.16.6 # via # jupyter-server - # nbclassic - # notebook # voila -nbformat==5.4.0 +nbformat==5.10.4 # via # jupyter-server - # nbclassic # nbclient # nbconvert - # notebook -nest-asyncio==1.5.5 - # via - # ipykernel - # jupyter-client - # nbclassic - # nbclient - # notebook -networkx==2.8.6 +nest-asyncio==1.6.0 + # via ipykernel +networkx==3.5 # via pyvis -notebook==6.4.12 +notebook-shim==0.2.4 # via jupyterlab -notebook-shim==0.1.0 - # via nbclassic -numpy==1.23.2 +numpy==2.3.2 # via - # altair # contourpy # matplotlib # pandas # wordcloud -packaging==21.3 +overrides==7.7.0 + # via jupyter-server +packaging==25.0 # via + # altair # ipykernel + # jupyter-events # jupyter-server # jupyterlab # jupyterlab-server # matplotlib # nbconvert -pandas==1.4.3 +pandas==2.3.1 # via # -r requirements.in - # altair -pandocfilters==1.5.0 + # upsetplot +pandocfilters==1.5.1 # via nbconvert -parso==0.8.3 +parso==0.8.4 # via jedi -pexpect==4.8.0 - # via ipython -pickleshare==0.7.5 +pexpect==4.9.0 # via ipython -pillow==9.2.0 +pillow==11.3.0 # via # matplotlib # wordcloud -pkgutil-resolve-name==1.3.10 - # via jsonschema -prometheus-client==0.14.1 +platformdirs==4.3.8 # via - # jupyter-server - # nbclassic - # notebook -prompt-toolkit==3.0.30 + # jupyter-core + # requests-cache +prometheus-client==0.22.1 + # via jupyter-server +prompt-toolkit==3.0.51 # via ipython -psutil==5.9.1 +psutil==7.0.0 # via ipykernel ptyprocess==0.7.0 # via # pexpect # terminado -pure-eval==0.2.2 +pure-eval==0.2.3 # via stack-data -pycparser==2.21 +pycparser==2.22 # via cffi -pygments==2.13.0 +pygments==2.19.2 # via # ipython + # ipython-pygments-lexers # nbconvert -pyparsing==3.0.9 +pyparsing==3.2.3 # via # matplotlib - # packaging # rdflib -pyrsistent==0.18.1 - # via jsonschema -python-dateutil==2.8.2 +python-dateutil==2.9.0.post0 # via # arrow # jupyter-client # matplotlib # pandas -python-dotenv==0.21.0 +python-dotenv==1.1.1 # via -r requirements.in -python-slugify==6.1.2 +python-json-logger==3.3.0 + # via jupyter-events +python-slugify==8.0.4 # via -r requirements.in -pytz==2022.2.1 - # via - # babel - # pandas -pyvis==0.3.0 +pytz==2025.2 + # via pandas +pyvis==0.3.2 # via -r requirements.in -pyzmq==23.2.1 +pyyaml==6.0.2 + # via jupyter-events +pyzmq==27.0.1 # via # ipykernel # jupyter-client # jupyter-server - # nbclassic - # notebook -rdflib==6.2.0 +rdflib==7.1.4 # via sparqlwrapper -requests==2.28.1 +referencing==0.36.2 + # via + # jsonschema + # jsonschema-specifications + # jupyter-events +requests==2.32.4 # via # -r requirements.in # jupyterlab-server # requests-cache -requests-cache==0.9.6 +requests-cache==1.2.1 # via -r requirements.in -send2trash==1.8.0 +rfc3339-validator==0.1.4 # via - # jupyter-server - # nbclassic - # notebook -six==1.16.0 + # jsonschema + # jupyter-events +rfc3986-validator==0.1.1 + # via + # jsonschema + # jupyter-events +rfc3987-syntax==1.1.0 + # via jsonschema +rpds-py==0.27.0 + # via + # jsonschema + # referencing +send2trash==1.8.3 + # via jupyter-server +six==1.17.0 # via - # asttokens - # bleach - # isodate # python-dateutil - # url-normalize -sniffio==1.2.0 + # rfc3339-validator +sniffio==1.3.1 # via anyio -soupsieve==2.3.2.post1 +soupsieve==2.7 # via beautifulsoup4 sparqlwrapper==2.0.0 # via -r requirements.in -stack-data==0.4.0 +stack-data==0.6.3 # via ipython -terminado==0.15.0 +terminado==0.18.1 # via # jupyter-server - 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# via - # importlib-metadata - # importlib-resources # The following packages are considered to be unsafe in a requirements file: # setuptools diff --git a/single-agency-network.ipynb b/single-agency-network.ipynb index 99755fd..b29458f 100644 --- a/single-agency-network.ipynb +++ b/single-agency-network.ipynb @@ -3,7 +3,13 @@ { "cell_type": "markdown", "id": "b73c156b-9c40-45b9-b921-dec23346a4d9", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# Visualise the connections of a single Australian government agency\n", "\n", @@ -394,354 +400,18 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.11" }, - "vscode": { - "interpreter": { - "hash": "f54aba2de7a75230217f549a064c6555500d2132634fbcab9606dbfda34a2a1b" - } - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "051b2b2afb004b7bba03ed6742c0a581": { - "model_module": "@jupyter-widgets/base", - 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"Department of Agriculture and Water Resources (2015-2019)", - "Department of Agriculture, Fisheries and Forestry (1998-2013)", - "Department of Agriculture, Fisheries and Forestry (2022-)", - "Department of Agriculture, Water and the Environment (2020-2022)", - "Department of Air (1939-1973)", - "Department of Aircraft Production (1941-1946)", - "Department of Arts, Heritage and Environment (1984-1987)", - "Department of Aviation (1982-1987)", - "Department of Broadband, Communications and the Digital Economy (2007-2013)", - "Department of Business and Consumer Affairs (1975-1982)", - "Department of Civil Aviation (1938-1973)", - "Department of Climate Change (2007-2010)", - "Department of Climate Change and Energy Efficiency (2010-2013)", - "Department of Climate Change, Energy, the Environment and Water (2022-)", - "Department of Commerce (1932-1942)", - "Department of Commerce and Agriculture (1942-1956)", - "Department of Communications (1980-1987)", - "Department of Communications (1993-1994)", - "Department of Communications (2013-2015)", - "Department of Communications and the Arts (1994-1998)", - "Department of Communications and the Arts (2015-2020)", - "Department of Communications, Information Technology and the Arts (1998-2007)", - "Department of Communications, the Information Economy and the Arts (1997-1998)", - "Department of Community Services (1984-1987)", - "Department of Community Services and Health (1987-1991)", - "Department of Construction (1975-1978)", - "Department of Customs and Excise (1956-1975)", - "Department of Defence (1901-1921)", - "Department of Defence (1921-1939)", - "Department of Defence (1942-)", - "Department of Defence Co-ordination (1939-1942)", - "Department of Defence Production (1951-1958)", - "Department of Defence Support (1982-1984)", - "Department of Education (1972-1983)", - "Department of Education (1984-1987)", - "Department of Education (2013-2014)", - "Department of Education (2019-2020)", - "Department of Education (2022-)", - "Department of Education and Science (1966-1972)", - "Department of Education and Training (2014-2019)", - "Department of Education and Youth Affairs (1983-1984)", - "Department of Education, Employment and Workplace Relations (2007-2013)", - "Department of Education, Science and Training (2001-2007)", - "Department of Education, Skills and Employment (2020-2022)", - "Department of Education, Training and Youth Affairs (1998-2001)", - "Department of Employment (2013-2017)", - "Department of Employment and Industrial Relations (1975-1978)", - "Department of Employment and Industrial Relations (1982-1987)", - "Department of Employment and Workplace Relations (2001-2007)", - "Department of Employment and Workplace Relations (2022-)", - "Department of Employment and Youth Affairs (1978-1982)", - "Department of Employment, Education and Training (1987-1996)", - "Department of Employment, Education, Training and Youth Affairs (1996-1998)", - "Department of Employment, Skills, Small and Family Business (2019-2020)", - "Department of Employment, Workplace Relations and Small Business (1998-2001)", - "Department of Environment and Conservation (1972-1975)", - "Department of Environment, Housing and Community Development (1975-1978)", - "Department of External Affairs (1901-1916)", - "Department of External Affairs (1921-1970)", - "Department of External Territories (1941-1951)", - "Department of External Territories (1968-1973)", - "Department of Families, Community Services and Indigenous Affairs (2006-2007)", - "Department of Families, Housing, Community Services and Indigenous Affairs (2007-2013)", - "Department of Family and Community Services (1998-2006)", - "Department of Finance (1976-1997)", - "Department of Finance (2013-)", - "Department of Finance and Administration (1997-2007)", - "Department of Finance and Deregulation (2007-2013)", - "Department of Foreign Affairs (1970-1987)", - "Department of Foreign Affairs and Trade (1987-)", - "Department of Fuel, Shipping and Transport (1950-1951)", - "Department of Health (1921-1987)", - "Department of Health (2013-2022)", - "Department of Health and Aged Care (1998-2001)", - "Department of Health and Aged Care (2022-)", - "Department of Health and Ageing (2001-2013)", - "Department of Health and Family Services (1996-1998)", - "Department of Health, Housing and Community Services (1991-1993)", - "Department of Health, Housing, Local Government and Community Services (1993-1993)", - "Department of Home Affairs (1901-1916)", - "Department of Home Affairs (1928-1932)", - "Department of Home Affairs (1977-1980)", - "Department of Home Affairs (2017-)", - "Department of Home Affairs and Environment (1980-1984)", - "Department of Home Security (1941-1946)", - "Department of Home and Territories (1916-1928)", - "Department of Housing (1963-1973)", - "Department of Housing and Construction (1973-1975)", - "Department of Housing and Construction (1978-1982)", - "Department of Housing and Construction (1983-1987)", - "Department of Housing and Regional Development (1994-1996)", - "Department of Human Services (2004-2019)", - "Department of Human Services and Health (1993-1996)", - "Department of Immigration (1945-1974)", - "Department of Immigration and Border Protection (2013-2017)", - "Department of Immigration and Citizenship (2007-2013)", - "Department of Immigration and Ethnic Affairs (1975-1987)", - "Department of Immigration and Ethnic Affairs (1993-1996)", - "Department of Immigration and Multicultural Affairs (1996-2001)", - "Department of Immigration and Multicultural Affairs (2006-2007)", - "Department of Immigration and Multicultural and Indigenous Affairs (2001-2006)", - "Department of Immigration, Local Government and Ethnic Affairs (1987-1993)", - "Department of Industrial Relations (1978-1982)", - "Department of Industrial Relations (1987-1997)", - "Department of Industry (1928-1940)", - "Department of Industry (2013-2014)", - "Department of Industry and Commerce (1975-1982)", - "Department of Industry and Commerce (1982-1984)", - "Department of Industry and Science (2014-2015)", - "Department of Industry, Innovation and Science (2015-2020)", - "Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education (2013-2013)", - "Department of Industry, Innovation, Science, Research and Tertiary Education (2011-2013)", - "Department of Industry, Science and Resources (1998-2001)", - "Department of Industry, Science and Resources (2022-)", - "Department of Industry, Science and Technology (1994-1996)", - "Department of Industry, Science and Tourism (1996-1998)", - "Department of Industry, Science, Energy and Resources (2020-2022)", - "Department of Industry, Technology and Commerce (1984-1993)", - "Department of Industry, Technology and Regional Development (1993-1994)", - "Department of Industry, Tourism and Resources (2001-2007)", - "Department of Information (1939-1950)", - "Department of Infrastructure and Regional Development (2013-2017)", - "Department of Infrastructure and Transport (2010-2013)", - "Department of Infrastructure, Regional Development and Cities (2017-2019)", - "Department of Infrastructure, Transport, Cities and Regional Development (2019-2020)", - "Department of Infrastructure, Transport, Regional Development and Communications (2020-2022)", - "Department of Infrastructure, Transport, Regional Development, Communications and the Arts (2022-)", - "Department of Innovation, Industry, Science and Research (2007-2011)", - "Department of Labor and Immigration (1974-1975)", - "Department of Labour (1972-1974)", - "Department of Labour and National Service (1940-1972)", - "Department of Local Government and Administrative Services (1984-1987)", - "Department of Manufacturing Industry (1974-1975)", - "Department of Markets (1928-1928)", - "Department of Markets (1930-1932)", - "Department of Markets and Migration (1925-1928)", - "Department of Markets and Transport (1928-1930)", - "Department of Minerals and Energy (1972-1975)", - "Department of Munitions (1940-1948)", - "Department of National Development (1950-1972)", - "Department of National Development (1977-1979)", - "Department of National Development and Energy (1979-1983)", - "Department of National Resources (1975-1977)", - "Department of Northern Australia (1975-1975)", - "Department of Northern Development (1972-1975)", - "Department of Overseas Trade (1972-1977)", - "Department of Police and Customs (1975-1975)", - "Department of Post-War Reconstruction (1942-1950)", - "Department of Primary Industries and Energy (1987-1998)", - "Department of Primary Industry (1956-1974)", - "Department of Primary Industry (1975-1987)", - "Department of Productivity (1976-1980)", - "Department of Reconciliation and Aboriginal and Torres Strait Islander Affairs (2001-2001)", - "Department of Regional Australia, Local Government, Arts and Sport (2011-2013)", - "Department of Regional Australia, Regional Development and Local Government (2010-2011)", - "Department of Repatriation (1975-1976)", - "Department of Repatriation and Compensation (1974-1975)", - "Department of Resources and Energy (1983-1987)", - "Department of Resources, Energy and Tourism (2007-2013)", - "Department of Science (1972-1975)", - "Department of Science (1975-1978)", - "Department of Science (1984-1987)", - "Department of Science and Consumer Affairs (1975-1975)", - "Department of Science and Technology (1980-1984)", - "Department of Science and the Environment (1978-1980)", - "Department of Secondary Industry (1972-1974)", - "Department of Services and Property (1972-1975)", - "Department of Shipping and Fuel (1948-1950)", - "Department of Shipping and Transport (1951-1972)", - "Department of Social Security (1972-1998)", - "Department of Social Services (1939-1972)", - "Department of Social Services (2013-)", - "Department of Sport, Recreation and Tourism (1983-1987)", - "Department of Supply (1950-1974)", - "Department of Supply and Development (1939-1942)", - "Department of Supply and Development (1948-1950)", - "Department of Supply and Shipping (1942-1948)", - "Department of Sustainability, Environment, Water, Population and Communities (2010-2013)", - "Department of Territories (1951-1968)", - "Department of Territories (1984-1987)", - "Department of Territories and Local Government (1983-1984)", - "Department of Tourism (1991-1996)", - "Department of Tourism and Recreation (1972-1975)", - "Department of Trade (1956-1963)", - "Department of Trade (1983-1987)", - "Department of Trade and Customs (1901-1956)", - "Department of Trade and Industry (1963-1972)", - "Department of Trade and Resources (1977-1983)", - "Department of Transport (1930-1932)", - "Department of Transport (1941-1950)", - "Department of Transport (1972-1982)", - "Department of Transport (1983-1987)", - "Department of Transport (1993-1996)", - "Department of Transport and Communications (1987-1993)", - "Department of Transport and Construction (1982-1983)", - "Department of Transport and Regional Development (1996-1998)", - "Department of Transport and Regional Services (1998-2007)", - "Department of Urban and Regional Development (1972-1975)", - "Department of Veterans' Affairs (1976-)", - "Department of War Organisation of Industry (1941-1945)", - "Department of Workplace Relations and Small Business (1997-1998)", - "Department of Works (1938-1939)", - "Department of Works (1945-1945)", - "Department of Works (1952-1973)", - "Department of Works and Housing (1945-1952)", - "Department of Works and Railways (1916-1932)", - "Department of the Army (1939-1973)", - "Department of the Arts and Administrative Services (1993-1994)", - "Department of the Arts, Sport, the Environment and Territories (1991-1993)", - "Department of the Arts, Sport, the Environment, Tourism and Territories (1987-1991)", - "Department of the Cabinet Office (1968-1971)", - "Department of the Capital Territory (1972-1983)", - "Department of the Environment (1975-1975)", - "Department of the Environment (1997-1998)", - "Department of the Environment (2013-2016)", - "Department of the Environment and Energy (2016-2020)", - "Department of the Environment and Heritage (1998-2007)", - "Department of the Environment and Water Resources (2007-2007)", - "Department of the Environment, Aborigines and the Arts (1971-1972)", - "Department of the Environment, Sport and Territories (1993-1997)", - "Department of the Environment, Water, Heritage and the Arts (2007-2010)", - "Department of the Interior (1932-1939)", - "Department of the Interior (1939-1972)", - "Department of the Media (1972-1975)", - "Department of the Northern Territory (1972-1975)", - "Department of the Northern Territory (1975-1978)", - "Department of the Prime Minister and Cabinet (1971-)", - "Department of the Special Minister of State (1972-1975)", - "Department of the Special Minister of State (1983-1987)", - "Department of the Special Trade Negotiator (1977-1977)", - "Department of the Special Trade Representative (1977-1980)", - "Department of the Treasury (1901-1976)", - "Department of the Treasury (1976-)", - "Department of the Vice-President of the Executive Council (1971-1971)", - "Department of the Vice-President of the Executive Council (1982-1983)", - "Navy Office , Department of the Navy (1939-1973)", - "Navy Office, Department of Defence (1921-1939)", - "Navy Office, Department of the Navy (1915-1921)", - "Postal and Telecommunications Department (1975-1980)", - "Postmaster-General's Department (1901-1975)", - "Prime Minister's Department (1911-1971)", - "Repatriation Department (1917-1974)", - "The Department of Infrastructure, Transport, Regional Development and Local Government (2007-2010)", - "The Department of Jobs and Small Business (2017-2019)" - ], - "index": 37, - "layout": "IPY_MODEL_432d95e1072e4715b151d79c0be7269f", - "style": "IPY_MODEL_e51604d130f244adbe0e094345f6c064" - } - }, - "e51604d130f244adbe0e094345f6c064": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "DescriptionStyleModel", - "state": { - "description_width": "" - } - } - }, - "version_major": 2, - "version_minor": 0 - } + "rocrate": { + "author": [ + { + "mainEntityOfPage": "https://timsherratt.au", + "name": "Sherratt, Tim", + "orcid": "https://orcid.org/0000-0001-7956-4498" + } + ], + "description": "Select an agency from the dropdown list to view its predecessors and successors as a network graph. Each agency is represented as a node whose position and colour is determined by the decade in which the agency was created. The size of the node indicates how long the agency was in existence, while edges between nodes connect agencies to their successors.", + "name": "Visualise the connections of a single Australian government agency" } }, "nbformat": 4, From 7b24dfd8b1ebd8b091ff9a24c76551e1c585831e Mon Sep 17 00:00:00 2001 From: Tim Sherratt Date: Sun, 24 Aug 2025 16:00:36 +1000 Subject: [PATCH 2/5] add notebook --- visualise_all_people_ids.ipynb | 1665 ++++++++++++++++++++++++++++++++ 1 file changed, 1665 insertions(+) create mode 100644 visualise_all_people_ids.ipynb diff --git a/visualise_all_people_ids.ipynb b/visualise_all_people_ids.ipynb new file mode 100644 index 0000000..669dcbc --- /dev/null +++ b/visualise_all_people_ids.ipynb @@ -0,0 +1,1665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9936b7ec-c884-43c0-ad47-ddd541e3f4c4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Visualise the network of Australian people identifiers\n", + "\n", + "Wikidata uses a wide range of external identifiers to help describe and identify people. This notebook explores the range of identifiers applied to Australian people in Wikidata and attempts to visualise their use and connections." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1fcffa31-4360-44bc-a86c-ecd78df98002", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", + "\n", + "import time\n", + "from urllib.error import HTTPError\n", + "\n", + "import altair as alt\n", + "import pandas as pd\n", + "from pyvis.network import Network\n", + "from SPARQLWrapper.sparql_dataframe import get_sparql_dataframe\n", + "from tqdm.auto import tqdm\n", + "from upsetplot import UpSet, from_memberships" + ] + }, + { + "cell_type": "markdown", + "id": "d4a57569-a799-4f33-b586-bf1b88751a1b", + "metadata": {}, + "source": [ + "As a first step we'll find the range of identifiers applied to Australian people in Wikidata. We'll limit this to identifiers from Australian sources, such as Trove, or the Australian Dictionary of Biography. You can view [the results of this query](https://w.wiki/F4kL) using the Wikidata Query Service." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b38f9382-fa4d-426d-b281-7241e972e54c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# This query gets all Australian identifiers applied to people.\n", + "query = \"\"\"\n", + "SELECT DISTINCT ?property ?propertyLabel WHERE {\n", + " ?item wdt:P31 wd:Q5.\n", + " ?item ?propertyclaim [].\n", + " ?property wikibase:propertyType wikibase:ExternalId;\n", + " wdt:P17 wd:Q408;\n", + " wikibase:directClaim ?propertyclaim.\n", + " SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO LANGUAGE],en\". }\n", + "}\n", + "\"\"\"\n", + "\n", + "df_ids = get_sparql_dataframe(\"https://query.wikidata.org/sparql\", query)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cbfd724c-36b6-4a7a-89b4-ca4e60cd44b3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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propertypropertyLabel
0http://www.wikidata.org/entity/P409Libraries Australia ID
1http://www.wikidata.org/entity/P8633OLD Re-Member ID
2http://www.wikidata.org/entity/P9244Women Australia ID
3http://www.wikidata.org/entity/P9245Labour Australia ID
4http://www.wikidata.org/entity/P9246Indigenous Australia ID
.........
73http://www.wikidata.org/entity/P11758WPGA Tour Australasia player ID
74http://www.wikidata.org/entity/P10249Triple J Unearthed artist ID
75http://www.wikidata.org/entity/P11281Biographical Dictionary of the Australian Sena...
76http://www.wikidata.org/entity/P10215Casefile ID
77http://www.wikidata.org/entity/P10233NER portfolio ID
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" + ], + "text/plain": [ + " property \\\n", + "0 http://www.wikidata.org/entity/P409 \n", + "1 http://www.wikidata.org/entity/P8633 \n", + "2 http://www.wikidata.org/entity/P9244 \n", + "3 http://www.wikidata.org/entity/P9245 \n", + "4 http://www.wikidata.org/entity/P9246 \n", + ".. ... \n", + "73 http://www.wikidata.org/entity/P11758 \n", + "74 http://www.wikidata.org/entity/P10249 \n", + "75 http://www.wikidata.org/entity/P11281 \n", + "76 http://www.wikidata.org/entity/P10215 \n", + "77 http://www.wikidata.org/entity/P10233 \n", + "\n", + " propertyLabel \n", + "0 Libraries Australia ID \n", + "1 OLD Re-Member ID \n", + "2 Women Australia ID \n", + "3 Labour Australia ID \n", + "4 Indigenous Australia ID \n", + ".. ... \n", + "73 WPGA Tour Australasia player ID \n", + "74 Triple J Unearthed artist ID \n", + "75 Biographical Dictionary of the Australian Sena... \n", + "76 Casefile ID \n", + "77 NER portfolio ID \n", + "\n", + "[78 rows x 2 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2df23009-5cc1-4d83-bef1-e3057a4b7660", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "df_ids.to_csv(\"australian-identifiers.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "551b9f24-9957-48f0-acc4-5d7dbe8d8ea6", + "metadata": {}, + "source": [ + "Now we want to explore how these different identifiers are connected. For each identifier, we run a query to find all the records of Australian people that use the identifier, then we get details of all the other identifiers used in these records, and count the number of times each pair of identifiers occurs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "777fd0dc-5efd-4a18-9f98-5ffe7f64635b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "# This query finds all the people who have a specific identifier.\n", + "# It then gets a count of any other Australian identifiers attached to these records.\n", + "query_template = \"\"\"\n", + "SELECT DISTINCT ?target_prop ?count WHERE {{\n", + " {{\n", + " SELECT ?propertyclaim (COUNT(*) AS ?count) where {{\n", + " ?item wdt:P31 wd:Q5;\n", + " wdt:{} ?aus_id.\n", + " ?item ?propertyclaim [].\n", + " }} GROUP BY ?propertyclaim\n", + " }}\n", + " ?target_prop wikibase:propertyType wikibase:ExternalId;\n", + " wdt:P17 wd:Q408;\n", + " wikibase:directClaim ?propertyclaim.\n", + "}} ORDER BY DESC (?count)\n", + "\"\"\"\n", + "\n", + "dfs = []\n", + "\n", + "for prop in tqdm(df_ids.to_dict(orient=\"records\")):\n", + " prop_id = prop[\"property\"].split(\"/\")[-1]\n", + " query = query_template.format(prop_id)\n", + " try:\n", + " df = get_sparql_dataframe(\"https://query.wikidata.org/sparql\", query)\n", + " except HTTPError as e:\n", + " print(e.hdrs)\n", + " raise\n", + " df[\"source\"] = prop[\"propertyLabel\"]\n", + " df[\"source_prop\"] = prop[\"property\"]\n", + " dfs.append(df)\n", + " time.sleep(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d4afa78f-3ec7-4269-a700-7f4eea36784b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "df_all = pd.concat(dfs)" + ] + }, + { + "cell_type": "markdown", + "id": "9db3a9c6-6e4b-45bc-a73a-85fec1f3e20a", + "metadata": {}, + "source": [ + "Using the CSV created above, I categorised the identifiers as one of \"arts\", \"politics\", \"sport\", or \"other\" and saved the results to a different CSV file. We can use these groupings to colour the nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a6b2b295-c45d-44ab-9829-96b2a16ef158", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "# Import the CSV file with categories\n", + "df_topics = pd.read_csv(\"australian-identifiers-topics.csv\")\n", + "# Create a df with just the source info for easy merging\n", + "sources = df_all[[\"source_prop\", \"source\"]].drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6596ced7-de02-4c62-83aa-e092006f383f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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source_propsourcetopic_sourcetarget_propsource_targettopic_targetcount
0http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P409Libraries Australia IDother98542
1http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P1315NLA Trove people IDother72907
2http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P9159People Australia IDother2493
3http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P1907Australian Dictionary of Biography IDother2385
4http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P4228Encyclopedia of Australian Science IDother1253
........................
1635http://www.wikidata.org/entity/P10215Casefile IDotherhttp://www.wikidata.org/entity/P9245Labour Australia IDother1
1636http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P10233NER portfolio IDother4
1637http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P1315NLA Trove people IDother1
1638http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P409Libraries Australia IDother1
1639http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P4228Encyclopedia of Australian Science IDother1
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" + ], + "text/plain": [ + " source_prop source \\\n", + "0 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "1 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "2 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "3 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "4 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "... ... ... \n", + "1635 http://www.wikidata.org/entity/P10215 Casefile ID \n", + "1636 http://www.wikidata.org/entity/P10233 NER portfolio ID \n", + "1637 http://www.wikidata.org/entity/P10233 NER portfolio ID \n", + "1638 http://www.wikidata.org/entity/P10233 NER portfolio ID \n", + "1639 http://www.wikidata.org/entity/P10233 NER portfolio ID \n", + "\n", + " topic_source target_prop \\\n", + "0 other http://www.wikidata.org/entity/P409 \n", + "1 other http://www.wikidata.org/entity/P1315 \n", + "2 other http://www.wikidata.org/entity/P9159 \n", + "3 other http://www.wikidata.org/entity/P1907 \n", + "4 other http://www.wikidata.org/entity/P4228 \n", + "... ... ... \n", + "1635 other http://www.wikidata.org/entity/P9245 \n", + "1636 other http://www.wikidata.org/entity/P10233 \n", + "1637 other http://www.wikidata.org/entity/P1315 \n", + "1638 other http://www.wikidata.org/entity/P409 \n", + "1639 other http://www.wikidata.org/entity/P4228 \n", + "\n", + " source_target topic_target count \n", + "0 Libraries Australia ID other 98542 \n", + "1 NLA Trove people ID other 72907 \n", + "2 People Australia ID other 2493 \n", + "3 Australian Dictionary of Biography ID other 2385 \n", + "4 Encyclopedia of Australian Science ID other 1253 \n", + "... ... ... ... \n", + "1635 Labour Australia ID other 1 \n", + "1636 NER portfolio ID other 4 \n", + "1637 NLA Trove people ID other 1 \n", + "1638 Libraries Australia ID other 1 \n", + "1639 Encyclopedia of Australian Science ID other 1 \n", + "\n", + "[1640 rows x 7 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add the topic for source nodes\n", + "df_all = pd.merge(\n", + " df_all, df_topics, how=\"left\", left_on=\"source_prop\", right_on=\"property\"\n", + ")\n", + "# Add the topic for target nodes\n", + "df_all = pd.merge(\n", + " df_all,\n", + " df_topics,\n", + " how=\"left\",\n", + " left_on=\"target_prop\",\n", + " right_on=\"property\",\n", + " suffixes=[\"_source\", \"_target\"],\n", + ")\n", + "# Add the name of target nodes\n", + "df_all = pd.merge(\n", + " df_all,\n", + " sources,\n", + " how=\"left\",\n", + " left_on=\"target_prop\",\n", + " right_on=\"source_prop\",\n", + " suffixes=[None, \"_target\"],\n", + ")\n", + "# Clean up\n", + "df_all = df_all[\n", + " [\n", + " \"source_prop\",\n", + " \"source\",\n", + " \"topic_source\",\n", + " \"target_prop\",\n", + " \"source_target\",\n", + " \"topic_target\",\n", + " \"count\",\n", + " ]\n", + "]\n", + "df_all" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "afaa2d15-62cd-468a-b583-deebae2d3cef", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "df_all.to_csv(\"australian-identifiers-links.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "13d61d7b-6059-41cb-be78-027cb478d858", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Results where the source and target properties are the same tell us the number of times each identifier is used. Here we separate out these results and merge them with the topic data, so we have a row for each identifier that tells us the number of time it is used, and the group it belongs to. We'll use this data to create a list of nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ec88d77-090e-46d5-a948-761a0f1a2a21", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Uncomment for testing or to reuse pre-harvested data\n", + "# df_all = pd.read_csv(\"australian-identifiers-links.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c23dbc0d-a581-4a7b-951b-46584edef15a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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source_propsourcetopic_sourcetarget_propsource_targettopic_targetcount
0http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P409Libraries Australia IDother98542
55http://www.wikidata.org/entity/P8633OLD Re-Member IDpoliticshttp://www.wikidata.org/entity/P8633OLD Re-Member IDpolitics1266
85http://www.wikidata.org/entity/P9244Women Australia IDotherhttp://www.wikidata.org/entity/P9244Women Australia IDother3479
125http://www.wikidata.org/entity/P9245Labour Australia IDotherhttp://www.wikidata.org/entity/P9245Labour Australia IDother918
164http://www.wikidata.org/entity/P9246Indigenous Australia IDotherhttp://www.wikidata.org/entity/P9246Indigenous Australia IDother318
........................
1597http://www.wikidata.org/entity/P11758WPGA Tour Australasia player IDsporthttp://www.wikidata.org/entity/P11758WPGA Tour Australasia player IDsport45
1601http://www.wikidata.org/entity/P10249Triple J Unearthed artist IDartshttp://www.wikidata.org/entity/P10249Triple J Unearthed artist IDarts30
1604http://www.wikidata.org/entity/P11281Biographical Dictionary of the Australian Sena...politicshttp://www.wikidata.org/entity/P11281Biographical Dictionary of the Australian Sena...politics426
1628http://www.wikidata.org/entity/P10215Casefile IDotherhttp://www.wikidata.org/entity/P10215Casefile IDother35
1636http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P10233NER portfolio IDother4
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78 rows × 7 columns

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" + ], + "text/plain": [ + " source_prop \\\n", + "0 http://www.wikidata.org/entity/P409 \n", + "55 http://www.wikidata.org/entity/P8633 \n", + "85 http://www.wikidata.org/entity/P9244 \n", + "125 http://www.wikidata.org/entity/P9245 \n", + "164 http://www.wikidata.org/entity/P9246 \n", + "... ... \n", + "1597 http://www.wikidata.org/entity/P11758 \n", + "1601 http://www.wikidata.org/entity/P10249 \n", + "1604 http://www.wikidata.org/entity/P11281 \n", + "1628 http://www.wikidata.org/entity/P10215 \n", + "1636 http://www.wikidata.org/entity/P10233 \n", + "\n", + " source topic_source \\\n", + "0 Libraries Australia ID other \n", + "55 OLD Re-Member ID politics \n", + "85 Women Australia ID other \n", + "125 Labour Australia ID other \n", + "164 Indigenous Australia ID other \n", + "... ... ... \n", + "1597 WPGA Tour Australasia player ID sport \n", + "1601 Triple J Unearthed artist ID arts \n", + "1604 Biographical Dictionary of the Australian Sena... politics \n", + "1628 Casefile ID other \n", + "1636 NER portfolio ID other \n", + "\n", + " target_prop \\\n", + "0 http://www.wikidata.org/entity/P409 \n", + "55 http://www.wikidata.org/entity/P8633 \n", + "85 http://www.wikidata.org/entity/P9244 \n", + "125 http://www.wikidata.org/entity/P9245 \n", + "164 http://www.wikidata.org/entity/P9246 \n", + "... ... \n", + "1597 http://www.wikidata.org/entity/P11758 \n", + "1601 http://www.wikidata.org/entity/P10249 \n", + "1604 http://www.wikidata.org/entity/P11281 \n", + "1628 http://www.wikidata.org/entity/P10215 \n", + "1636 http://www.wikidata.org/entity/P10233 \n", + "\n", + " source_target topic_target count \n", + "0 Libraries Australia ID other 98542 \n", + "55 OLD Re-Member ID politics 1266 \n", + "85 Women Australia ID other 3479 \n", + "125 Labour Australia ID other 918 \n", + "164 Indigenous Australia ID other 318 \n", + "... ... ... ... \n", + "1597 WPGA Tour Australasia player ID sport 45 \n", + "1601 Triple J Unearthed artist ID arts 30 \n", + "1604 Biographical Dictionary of the Australian Sena... politics 426 \n", + "1628 Casefile ID other 35 \n", + "1636 NER portfolio ID other 4 \n", + "\n", + "[78 rows x 7 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nodes = df_all.loc[df_all[\"source_prop\"] == df_all[\"target_prop\"]]\n", + "nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bc6056b3-9c9e-434e-8554-4f64e34ff62a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(nodes).mark_bar().encode(\n", + " x=alt.X(\"source:N\").sort(\"-y\"), y=\"count:Q\", color=\"topic_source:N\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b3e2d399-6e95-4065-a9ab-6c733ccd5107", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(\n", + " nodes.loc[nodes[\"topic_source\"].isin([\"arts\", \"politics\", \"sport\"])]\n", + ").mark_bar().encode(x=alt.X(\"source:N\").sort(\"-y\"), y=\"count:Q\", color=\"topic_source:N\")" + ] + }, + { + "cell_type": "markdown", + "id": "e15f1750-c254-4ab0-b8fc-66c8a9ec5daf", + "metadata": {}, + "source": [ + "Results where the source and target properties are different tell us the number of times different combinations of identifiers are linked. We'll separate out this data to provide a list of \"edges\" between nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4fd4964e-b469-4036-af7d-00de0adac699", + "metadata": {}, + "outputs": [], + "source": [ + "edges = df_all.loc[df_all[\"source_prop\"] != df_all[\"target_prop\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1b137667-caa6-4665-adf1-8b33714fbcfd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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source_propsourcetopic_sourcetarget_propsource_targettopic_targetcount
1http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P1315NLA Trove people IDother72907
2http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P9159People Australia IDother2493
3http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P1907Australian Dictionary of Biography IDother2385
4http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P4228Encyclopedia of Australian Science IDother1253
5http://www.wikidata.org/entity/P409Libraries Australia IDotherhttp://www.wikidata.org/entity/P2041National Gallery of Victoria artist IDarts1249
........................
1634http://www.wikidata.org/entity/P10215Casefile IDotherhttp://www.wikidata.org/entity/P10012NSW Parliament member IDpolitics1
1635http://www.wikidata.org/entity/P10215Casefile IDotherhttp://www.wikidata.org/entity/P9245Labour Australia IDother1
1637http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P1315NLA Trove people IDother1
1638http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P409Libraries Australia IDother1
1639http://www.wikidata.org/entity/P10233NER portfolio IDotherhttp://www.wikidata.org/entity/P4228Encyclopedia of Australian Science IDother1
\n", + "

1562 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " source_prop source \\\n", + "1 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "2 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "3 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "4 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "5 http://www.wikidata.org/entity/P409 Libraries Australia ID \n", + "... ... ... \n", + "1634 http://www.wikidata.org/entity/P10215 Casefile ID \n", + "1635 http://www.wikidata.org/entity/P10215 Casefile ID \n", + "1637 http://www.wikidata.org/entity/P10233 NER portfolio ID \n", + "1638 http://www.wikidata.org/entity/P10233 NER portfolio ID \n", + "1639 http://www.wikidata.org/entity/P10233 NER portfolio ID \n", + "\n", + " topic_source target_prop \\\n", + "1 other http://www.wikidata.org/entity/P1315 \n", + "2 other http://www.wikidata.org/entity/P9159 \n", + "3 other http://www.wikidata.org/entity/P1907 \n", + "4 other http://www.wikidata.org/entity/P4228 \n", + "5 other http://www.wikidata.org/entity/P2041 \n", + "... ... ... \n", + "1634 other http://www.wikidata.org/entity/P10012 \n", + "1635 other http://www.wikidata.org/entity/P9245 \n", + "1637 other http://www.wikidata.org/entity/P1315 \n", + "1638 other http://www.wikidata.org/entity/P409 \n", + "1639 other http://www.wikidata.org/entity/P4228 \n", + "\n", + " source_target topic_target count \n", + "1 NLA Trove people ID other 72907 \n", + "2 People Australia ID other 2493 \n", + "3 Australian Dictionary of Biography ID other 2385 \n", + "4 Encyclopedia of Australian Science ID other 1253 \n", + "5 National Gallery of Victoria artist ID arts 1249 \n", + "... ... ... ... \n", + "1634 NSW Parliament member ID politics 1 \n", + "1635 Labour Australia ID other 1 \n", + "1637 NLA Trove people ID other 1 \n", + "1638 Libraries Australia ID other 1 \n", + "1639 Encyclopedia of Australian Science ID other 1 \n", + "\n", + "[1562 rows x 7 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edges" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "ae33b24e-0668-4c2d-a872-4b764be2f4f9", + "metadata": {}, + "outputs": [], + "source": [ + "def create_graph(nodes, edges, new_range=None, colours=None):\n", + " net = Network(\n", + " notebook=True, width=800, height=600, directed=True, cdn_resources=\"in_line\"\n", + " )\n", + "\n", + " # Do some normalisation of sizes.\n", + " old_max = nodes[\"count\"].max()\n", + " old_min = nodes[\"count\"].min()\n", + " old_range = old_max - old_min\n", + " if not new_range:\n", + " new_range = old_range\n", + "\n", + " # Create nodes, assigning the count to the size property, and using the topic to set a colour\n", + " for node in nodes.itertuples():\n", + " node_id = node.source_prop.split(\"/\")[-1]\n", + " net.add_node(\n", + " node_id,\n", + " label=node_id,\n", + " title=f\"{node.source} ({node.count:,})\",\n", + " size=((node.count * new_range) / old_range) + 20,\n", + " color=colours[node.topic_source] if colours else None,\n", + " )\n", + "\n", + " # Create edges\n", + " for edge in edges.itertuples():\n", + " source_id = edge.source_prop.split(\"/\")[-1]\n", + " target_id = edge.target_prop.split(\"/\")[-1]\n", + " net.add_edge(source_id, target_id, value=int(edge.count))\n", + "\n", + " graph_config = \"\"\"\n", + " var options = {\n", + " \"configure\": {\n", + " \"enabled\": false\n", + " },\n", + " \"nodes\": {\n", + " \"color\": {\n", + " \"background\": \"#7986cb\",\n", + " \"border\": \"#49599a\",\n", + " \"highlight\": {\n", + " \"background\": \"#aab6fe\",\n", + " \"border\": \"#7986cb\"\n", + " }\n", + " },\n", + " \"font\": {\n", + " \"size\": 20\n", + " }\n", + " },\n", + " \"edges\": {\n", + " \"color\": {\n", + " \"color\": \"#b0bec5\",\n", + " \"highlight\": \"#808e95\",\n", + " \"inherit\": false\n", + " }\n", + " },\n", + " \"physics\": {\n", + " \"barnesHut\": {\n", + " \"gravitationalConstant\": -30000,\n", + " \"centralGravity\": 0,\n", + " \"springLength\": 500,\n", + " \"springConstant\": 0.01\n", + " },\n", + " \"minVelocity\": 0.75\n", + " }\n", + " }\n", + " \"\"\"\n", + "\n", + " net.set_options(graph_config)\n", + " return net\n", + "\n", + "\n", + "def filter_nodes(nodes, edges, topics, new_range=None, colours=None):\n", + " select_nodes = nodes.loc[nodes[\"topic_source\"].isin(topics)]\n", + " select_nodes_ids = select_nodes[\"source_prop\"].to_list()\n", + " select_edges = edges.loc[\n", + " (edges[\"source_prop\"].isin(select_nodes_ids))\n", + " & (edges[\"target_prop\"].isin(select_nodes_ids))\n", + " ]\n", + " return create_graph(\n", + " select_nodes, select_edges, new_range=new_range, colours=colours\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "fddd9e34-1da5-4515-9c10-318c5bb6f70d", + "metadata": {}, + "source": [ + "Now we create a network graph using Pyvis, feeding in the node and edges data we've assembled." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "5e7af998-c517-455f-8586-574086cd6b75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "aus_ids.html\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assign each topic group a colour\n", + "colours = {\n", + " \"sport\": \"#1f77b4\",\n", + " \"politics\": \"#ff7f0e\",\n", + " \"arts\": \"#2ca02c\",\n", + " \"other\": \"#d62728\",\n", + "}\n", + "\n", + "net = create_graph(nodes=nodes, edges=edges, new_range=500, colours=colours)\n", + "net.show(\"aus_ids.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "345fcbb7-f408-4977-9a91-0ecb6e747425", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arts_politics_sport_ids.html\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net = filter_nodes(\n", + " nodes, edges, [\"arts\", \"politics\", \"sport\"], new_range=200, colours=colours\n", + ")\n", + "net.show(\"arts_politics_sport_ids.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "73f9adb1-fc88-4dab-a06b-b2f1f60d781e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arts_ids.html\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net = filter_nodes(nodes, edges, [\"arts\"], new_range=200, colours=colours)\n", + "net.show(\"arts_ids.html\")" + ] + }, + { + "cell_type": "markdown", + "id": "c5c4dca7-525e-4659-bad1-a8b678aeefd9", + "metadata": {}, + "source": [ + "## Exploring intersections\n", + "\n", + "The network graphs show how groups of identifiers are connected. We can look more closely at their intersections using an upset chart. " + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "d6aa87d5-1332-4ba8-9233-fdf56afbb7a1", + "metadata": {}, + "outputs": [], + "source": [ + "top_pairs = df_all.copy().loc[\n", + " (df_all[\"source\"] != df_all[\"source_target\"])\n", + " & (df_all[\"count\"] > 20)\n", + " & (df_all[\"topic_source\"].isin([\"arts\"]))\n", + " & (df_all[\"topic_target\"].isin([\"arts\"]))\n", + "]\n", + "top_pairs[\"pair\"] = top_pairs.apply(\n", + " lambda x: \"|\".join(sorted([x[\"source\"], x[\"source_target\"]])), axis=1\n", + ")\n", + "top_pairs.drop_duplicates(subset=[\"pair\"], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "74229d8e-d628-497f-b58c-09c385b93779", + "metadata": {}, + "outputs": [], + "source": [ + "def make_upset(pairs, size=40):\n", + " intersections = from_memberships(\n", + " pairs[[\"source\", \"source_target\"]].values.tolist(), pairs\n", + " )\n", + " chart = UpSet(\n", + " intersections,\n", + " sum_over=\"count\",\n", + " min_degree=2,\n", + " show_counts=True,\n", + " sort_by=\"cardinality\",\n", + " intersection_plot_elements=8,\n", + " totals_plot_elements=4,\n", + " facecolor=\"#002984\",\n", + " element_size=size,\n", + " )\n", + " return chart" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "3de49322-2c34-4151-8d37-e5c234d19725", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "upset_chart = make_upset(top_pairs)\n", + "upset_chart" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "f19c1086-8b71-4b45-9350-f785066ab671", + "metadata": {}, + "outputs": [], + "source": [ + "adb_pairs = df_all.copy().loc[\n", + " (df_all[\"source\"] != df_all[\"source_target\"])\n", + " & (df_all[\"source\"] == \"Australian Dictionary of Biography ID\")\n", + " & (df_all[\"topic_target\"].isin([\"arts\"]))\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "9e0cdb55-391d-48d6-a56b-df1df21df89e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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efUdzLnNLL23t2nU6vj/IkLbcWadkbK0AgCtP7969L7kMYR0AAC/Vtm1bzZ07V/Xr19fx48c1fvx4XX/99dqxY4eSk5Pl7++vyMhIl+fExcUpOTlZkpScnOwS1AvnF867kDFjxmjUqFHOx+np6apevbq6deum8PDwi9b80+5k6Y19pdnMEuvYsYNaNiw6yuByuLNOydhaAQBXJ8I6AABeqkePHs5/N2vWTG3btlWNGjX08ccfKyjIfb22AQEBCggIKDLdz8/vksP2zj2n3mi+vr4lGjZY0rbcychaAQBXJ64GDwBABREZGalrrrlGe/fuVXx8vPLy8pSamuqyzIkTJ5znuMfHxxe5Onzh4+LOgwcAAN6DsA4AQAWRmZmpffv2qUqVKmrdurX8/Py0fPly5/w9e/bo0KFDSkpKkiQlJSVp+/btOnnypHOZZcuWKTw8XI0aNSr3+gEAQMkxDB4AAC/1+OOPq1evXqpRo4aOHTum5557TmazWffee68iIiI0aNAgjRo1StHR0QoPD9fw4cOVlJSkdu3aSZK6deumRo0aqX///poyZYqSk5P1zDPPaNiwYcUOcwcAAN6DsA4AgJc6cuSI7r33XqWkpCg2NlYdO3bUxo0bFRsbK0l69dVX5ePjo759+8pisah79+568803nc83m81avHixhg4dqqSkJIWEhGjAgAGaMGGCpzYJAACUEGEdAAAv9eGHH150fmBgoKZPn67p06dfcJkaNWro66+/Nro0AADgZpyzDgAAAACAlyGsAwAAAADgZQjrAAAAAAB4GcI6AAAAAABehrAOAAAAAICXIawDAAAAAOBlCOsAAAAAAHgZwjoAAAAAAF6GsA4AAAAAgJchrAMAAAAA4GUI6wAAAAAAeBnCOgAAAAAAXoawDgAAAACAlyGsAwAAAADgZQjrAAAAAAB4GcI6AAAGe++999ShQwclJCTo4MGDkqSpU6fqiy++8HBlAACgoiCsAwBgoBkzZmjUqFG69dZblZqaKpvNJkmKjIzU1KlTPVscAACoMAjrAAAY6PXXX9fbb7+tp59+Wmaz2Tm9TZs22r59uwcrAwAAFQlhHQAAA+3fv18tW7YsMj0gIEBZWVkeqAgAAFREhHUAAAxUq1Ytbdu2rcj0JUuWqGHDhuVfEAAAqJB8PV0AAABXklGjRmnYsGHKzc2Vw+HQ5s2b9d///leTJk3Sf/7zH0+XBwAAKgjCOgAABnrooYcUFBSkZ555RtnZ2brvvvuUkJCg1157Tffcc4+nywMAABUEYR0AAIP169dP/fr1U3Z2tjIzM1W5cmVPlwQAACoYzlkHAMBAEyZM0IoVKyRJwcHBzqCelZWlCRMmeLI0AABQgRDWAQAw0Lhx49SjRw+98sorLtMzMzM1fvx4D1UFAAAqGsI6AAAGe/fddzVx4kQ98MADysvL83Q5AACgAiKsAwBgsBtvvFGbNm3Spk2b1LlzZ508edLTJQEAgAqGsA4AgIFMJpMkqU6dOtq4caPCw8PVunVrbdmyxcOVAQCAioSwDgCAgRwOh/Pf4eHh+vrrr3XHHXfo9ttv91xRAACgwuHWbQAAGGjOnDmKiIhwPvbx8dG0adPUsmVLrV692oOVAQCAioSwDgCAgQYMGFDs9AceeEAPPPBAOVcDAAAqKsI6AABlNG3aNA0ZMkSBgYGaNm3aBZczmUwaPnx4OVYGAAAqKsI6AABl9Oqrr6pfv34KDAzUq6++esHlCOsAAKCkCOsAAJTR/v37i/03AADA5eJq8AAAuJHNZtO2bdt09uxZT5cCAAAqEMI6AAAGGjFihGbPni2pIKh36tRJrVq1UvXq1bVy5UrPFgcAACoMwjoAAAb65JNP1Lx5c0nSokWLdODAAf36668aOXKknn76aQ9XBwAAKgrCOgAABjp9+rTi4+MlSV9//bXuuusuXXPNNXrwwQe1ffv2MrU9efJkmUwmjRgxwjktNzdXw4YNU0xMjEJDQ9W3b1+dOHHC5XmHDh1Sz549FRwcrMqVK+uJJ55Qfn5+mWoBAADuRVgHAMBAcXFx2rVrl2w2m5YsWaKuXbtKkrKzs2U2my+73R9++EFvvfWWmjVr5jJ95MiRWrRokRYsWKBVq1bp2LFj6tOnj3O+zWZTz549lZeXp/Xr12vevHmaO3euxo4de9m1AAAA9yOsAwBgoAceeEB33323mjRpIpPJpC5dukiSNm3apAYNGlxWm5mZmerXr5/efvttRUVFOaenpaVp9uzZeuWVV3TTTTepdevWmjNnjtavX6+NGzdKkr799lvt2rVL77//vlq0aKEePXro+eef1/Tp05WXl1f2DQYAAG7BrdsAADDQuHHj1KRJEx0+fFh33XWXAgICJElms1mjR4++rDaHDRumnj17qkuXLnrhhRec07du3Sqr1eo8ICBJDRo0UGJiojZs2KB27dppw4YNatq0qeLi4pzLdO/eXUOHDtXOnTvVsmXLIuuzWCyyWCzOx+np6ZIkq9Uqq9V60VrdObw+Pz//kusvTVvuZGStAIArj5+f3yWXIawDAGCwO++8s8i0AQMGXFZbH374oX788Uf98MMPReYlJyfL399fkZGRLtPj4uKUnJzsXObcoF44v3BecSZNmqTx48cXmf7tt98qODj4ovXuO5pz0fllsXbtOh3fH2RIW+6sUzK2VgDAlad3796XXIawDgCAlzp8+LAeffRRLVu2TIGBgeW23jFjxmjUqFHOx+np6apevbq6deum8PDwiz73p93J0hv73FJXx44d1LJhvCFtubNOydhaAQBXJ8I6AABeauvWrTp58qRatWrlnGaz2bR69Wq98cYbWrp0qfLy8pSamurSu37ixAnnFenj4+O1efNml3YLrxZfuMz5AgICnMP3z+Xn53fJYXu+vu77aeHr61uiYYMlbcudjKwVAHB14gJzAAB4qZtvvlnbt2/Xtm3bnH9t2rRRv379nP/28/PT8uXLnc/Zs2ePDh06pKSkJElSUlKStm/frpMnTzqXWbZsmcLDw9WoUaNy3yYAAFAy9KwDAOClwsLC1KRJE5dpISEhiomJcU4fNGiQRo0apejoaIWHh2v48OFKSkpSu3btJEndunVTo0aN1L9/f02ZMkXJycl65plnNGzYsGJ7zwEAgHcgrAMAYDC73a69e/fq5MmTstvtLvM6depk6LpeffVV+fj4qG/fvrJYLOrevbvefPNN53yz2azFixdr6NChSkpKUkhIiAYMGKAJEyYYWgcAADAWYR0AAANt3LhR9913nw4ePCiHw+Eyz2QyyWazlan9lStXujwODAzU9OnTNX369As+p0aNGvr666/LtF4AAFC+COsAABjo73//u9q0aaOvvvpKVapUkclk8nRJAACgAiKsAwBgoN9//12ffPKJ6tat6+lSAABABcbV4AEAMFDbtm21d+9eT5cBAAAqOHrWAQAw0PDhw/XYY48pOTlZTZs2LXKv7WbNmnmoMgAAUJEQ1gEAMFDfvn0lSQ8++KBzmslkksPhMOQCcwAA4OpAWAcAwED79+/3dAkAAOAKQFgHAMBANWrU8HQJAADgCkBYBwDAYPv27dPUqVO1e/duSVKjRo306KOPqk6dOh6uDAAAVBRcDR4AAAMtXbpUjRo10ubNm9WsWTM1a9ZMmzZtUuPGjbVs2TJPlwcAACoIetYBADDQ6NGjNXLkSE2ePLnI9Keeekpdu3b1UGUAAKAioWcdAAAD7d69W4MGDSoy/cEHH9SuXbs8UBEAAKiICOsAABgoNjZW27ZtKzJ927Ztqly5cvkXBAAAKiSGwQMAYKDBgwdryJAh+uOPP9S+fXtJ0rp16/TSSy9p1KhRHq4OAABUFIR1AAAM9OyzzyosLEwvv/yyxowZI0lKSEjQuHHj9Mgjj3i4OgAAUFEQ1gEAMJDJZNLIkSM1cuRIZWRkSJLCwsI8XBUAAKhoCOsAALgJIR0AAFwuwjoAAGXUqlUrLV++XFFRUWrZsqVMJtMFl/3xxx/LsTIAAFBREdYBACij3r17KyAgwPnvi4V1AACAkiCsAwBQRs8995zz3+PGjfNcIQAA4IrBfdYBADBQ7dq1lZKSUmR6amqqateu7YGKAABARURYBwDAQAcOHJDNZisy3WKx6MiRIx6oCBXdjBkz1KxZM4WHhys8PFxJSUn65ptvnPNnzZqlzp07Kzw8XCaTSampqS7PP3DggAYNGqRatWopKChIderU0XPPPae8vLxy3hIAQGkwDB4AAAN8+eWXzn8vXbpUERERzsc2m03Lly9XrVq1PFEaKrhq1app8uTJqlevnhwOh+bNm6fevXvrp59+UuPGjZWdna1bbrlFt9xyi8aMGVPk+b/++qvsdrveeust1a1bVzt27NDgwYOVlZWlf//73x7YIgBASRDWAQAwwO233y6p4D7rAwYMcJnn5+enmjVr6uWXX/ZAZajoevXq5fL4xRdf1IwZM7Rx40Y1btxYI0aMkCStXLmy2OcXBvlCtWvX1p49ezRjxgxDw/qMGTM0Y8YMHThwQJLUuHFjjR07Vj169JAk5ebm6rHHHtOHH34oi8Wi7t27680331RcXJyzjR9++EGjR4/W1q1bZTKZdN1112nKlClq3ry5YXUCQEXBMHgAAAxgt9tlt9uVmJiokydPOh/b7XZZLBbt2bNH//d//+fpMlHB2Ww2ffjhh8rKylJSUtJlt5OWlqbo6GgDK/tzBMDWrVu1ZcsW3XTTTerdu7d27twpSRo5cqQWLVqkBQsWaNWqVTp27Jj69OnjfH5mZqZuueUWJSYmatOmTVq7dq3CwsLUvXt3Wa1WQ2sFgIqAnnUAAAy0f/9+T5eAK9D27duVlJSk3NxchYaGauHChWrUqNFltbV37169/vrrhg+Bv9gIgGrVqmn27NmaP3++brrpJknSnDlz1LBhQ23cuFHt2rXTr7/+qjNnzmjChAmqXr26pII7LTRr1kwHDx5U3bp1Da0XALwdPesAABjokUce0bRp04pMf+ONN5zDlYHSql+/vrZt26ZNmzZp6NChGjBggHbt2lXqdo4ePapbbrlFd911lwYPHuyGSgucPwJg69atslqt6tKli3OZBg0aKDExURs2bJBUsI0xMTGaPXu28vLylJOTo9mzZ6thw4aqWbOm22oFAG9FWAcAwECffvqpOnToUGR6+/bt9cknn3igIlwJ/P39VbduXbVu3VqTJk1S8+bN9dprr5WqjWPHjunGG29U+/btNWvWLLfUuX37doWGhiogIEB///vfnSMAkpOT5e/vr8jISJfl4+LilJycLEkKCwvTypUr9f777ysoKEihoaFasmSJvvnmG/n6MhgUwNWHsA4AgIFSUlJcrgRfKDw8XKdPn/ZARbgSFV4LoaSOHj2qzp07q3Xr1pozZ458fNzzE7AsIwBycnI0aNAgdejQQRs3btS6devUpEkT9ezZUzk5OYbWOWnSJF177bUKCwtT5cqVdfvtt2vPnj0uy+zbt0933HGHYmNjFR4errvvvlsnTpxwzl+5cqVMJlOxfz/88IOh9QK4OhHWAQAwUN26dbVkyZIi07/55hvVrl3bAxWhohszZoxWr16tAwcOaPv27RozZoxWrlypfv36SZKSk5O1bds27d27V1JB7/a2bdt05swZSX8G9cTERP373//WqVOnlJyc7OzRNtKFRgDEx8crLy+vyD3gT5w4ofj4eEnS/PnzdeDAAc2ZM0fXXnut2rVrp/nz52v//v364osvDK1z1apVGjZsmDZu3Khly5bJarWqW7duysrKkiRlZWWpW7duMplMWrFihdatW6e8vDz16tVLdrtdUsFomePHj7v8PfTQQ6pVq5batGljaL0Ark6MKQIAwECjRo3Sww8/rFOnTjkvpLV8+XK9/PLLmjp1qmeLQ4V08uRJ3X///Tp+/LgiIiLUrFkzLV26VF27dpUkzZw5U+PHj3cu36lTJ0kFF3AbOHCgli1bpr1792rv3r2qVq2aS9sOh8OttReOAGjdurX8/Py0fPly9e3bV5K0Z88eHTp0yHlV++zsbPn4+MhkMjmfX/i4MCAb5fwDanPnzlXlypW1detWderUSevWrdOBAwf0008/KTw8XJI0b948RUVFacWKFerSpYv8/f2dBxokyWq16osvvtDw4cNdtgEALhdhHQAAAz344IOyWCx68cUX9fzzz0uSatasqRkzZuj+++/3cHWoiGbPnn3R+ePGjdO4ceMuOH/gwIEaOHCgsUUVY8yYMerRo4cSExOVkZGh+fPna+XKlVq6dKkiIiI0aNAgjRo1StHR0QoPD9fw4cOVlJSkdu3aSZK6du2qJ554QsOGDdPw4cNlt9s1efJk+fr66sYbb3Rr7WlpaZLkvJ2dxWKRyWRSQECAc5nAwED5+Pho7dq1LhfKK/Tll18qJSVFDzzwgFtrBXD1IKwDAGCwoUOHaujQoTp16pTzQlnAle5SIwBeffVV+fj4qG/fvrJYLOrevbvefPNN5/MbNGigRYsWafz48UpKSpKPj49atmypJUuWqEqVKm6r2263a8SIEerQoYOaNGkiSWrXrp1CQkL01FNPaeLEiXI4HBo9erRsNpuOHz9ebDuzZ89W9+7di4xeAIDLRVgHAMBg+fn5Wrlypfbt26f77rtPUsGVuMPDwwnuuGJdagRAYGCgpk+frunTp19wma5duzrDfXkZNmyYduzYobVr1zqnxcbGasGCBRo6dKimTZsmHx8f3XvvvWrVqlWxF+c7cuSIli5dqo8//rg8SwdwhSOsAwBgoIMHD+qWW27RoUOHZLFY1LVrV4WFhemll16SxWLRzJkzPV0igP95+OGHtXjxYq1evbpIj3i3bt20b98+nT59Wr6+voqMjFR8fHyxF4qcM2eOYmJidNttt5VX6QCuAoR1AAAM9Oijj6pNmzb6+eefFRMT45x+xx13aPDgwR6sDBXZoWNpOp2abWiblSKDlZhQ9DaDVwOHw6Hhw4dr4cKFWrlypWrVqnXBZStVqiRJWrFihU6ePFkkkDscDs2ZM0f333+//Pz83Fo3gKsLYR0AAAOtWbNG69evl7+/v8v0mjVr6ujRox6qChXZoWNpqn/rDOXm2QxtN9DfrD1fD70qA/uwYcM0f/58ffHFFwoLC3Pexi4iIkJBQUGSCnrLGzZsqNjYWG3YsEGPPvqoRo4cqfr167u0tWLFCu3fv18PPfRQuW8HgCsbYR0AAAPZ7XbZbEVD1ZEjRxQWFuaBilDRnU7NNjyoS1Junk2nU7MND+sVYRTAjBkzJEmdO3d2mV54uzup4NZyY8aM0ZkzZ1SzZk09/fTTGjlyZJG2Zs+erfbt26tBgwaG1QcAEmEdAABDdevWTVOnTtWsWbMkSSaTSZmZmXruued06623lqqtGTNmaMaMGTpw4IAkqXHjxho7dqx69OghScrNzdVjjz2mDz/80OXq2nFxcc42Dh06pKFDh+r7779XaGioBgwYoEmTJsnXl58AMF5FGQVQkvvLT548WZMnT77kcvPnzzeiJAAooujlLAEAwGV7+eWXtW7dOjVq1Ei5ubm67777nEPgX3rppVK1Va1aNU2ePFlbt27Vli1bdNNNN6l3797auXOnJGnkyJFatGiRFixYoFWrVunYsWPq06eP8/k2m009e/ZUXl6e1q9fr3nz5mnu3LkaO3asodsMFHL3KAAAuJpwWB0AAANVq1ZNP//8sz766CP9/PPPyszM1KBBg9SvXz/nubAl1atXL5fHL774ombMmKGNGzeqWrVqmj17tubPn6+bbrpJ0p/n2G7cuFHt2rXTt99+q127dum7775TXFycWrRooeeff15PPfWUxo0bV+S8egAA4D0I6wAAGMzX11f9+vVTv379DGvTZrNpwYIFysrKUlJSkrZu3Sqr1aouXbo4l2nQoIESExO1YcMGtWvXThs2bFDTpk1dhsV3795dQ4cO1c6dO9WyZcti12WxWGSxWJyP09PTJUlWq1VWq/Widebn55dlMy/Z9qXWX5q23Kmi1GpknYXtuYvRtQKAJ5Xk7hGEdQAADDRv3jxVqlRJPXv2lCQ9+eSTmjVrlho1aqT//ve/qlGjRqna2759u5KSkpSbm6vQ0FAtXLhQjRo10rZt2+Tv76/IyEiX5ePi4pxXtk5OTnYJ6oXzC+ddyKRJkzR+/Pgi07/99lsFBwdftN59R3NKslmXZe3adTq+v3SjEy7EnXVKFadWI+uUKlatknQqNU/pWcYO2w8PMSs2klErAC6ud+/el1yGsA4AgIEmTpzovNL0hg0b9MYbb2jq1KlavHixRo4cqc8++6xU7dWvX1/btm1TWlqaPvnkEw0YMECrVq1yR+lOY8aM0ahRo5yP09PTVb16dXXr1k3h4eEXfe5Pu5OlN/a5pa6OHTuoZcN4Q9pyZ51SxanVyDqlilXroePpanLb2265GN6OLwcrscrFPysAcCmEdQAADHT48GHVrVtXkvT555/rzjvv1JAhQ9ShQ4cit4kqCX9/f2d7rVu31g8//KDXXntNf/nLX5SXl6fU1FSX3vUTJ04oPr4g0MTHx2vz5s0u7Z04ccI570ICAgIUEBBQZLqfn98lh+258yrzvr6+JRo2WNK23Kmi1GpknYXtuYvRtaZl5rntYnhpmXmG1grg6sTV4AEAMFBoaKhSUlIkFQwb79q1qyQpMDBQOTllHyJst9tlsVjUunVr+fn5afny5c55e/bs0aFDh5SUlCRJSkpK0vbt23Xy5EnnMsuWLVN4eLgaNWpU5loAAID70LMOAICBunbtqoceekgtW7bUb7/95ry3+s6dO1WzZs1StTVmzBj16NFDiYmJysjI0Pz587Vy5UotXbpUERERGjRokEaNGqXo6GiFh4dr+PDhSkpKUrt27SQV3PO9UaNG6t+/v6ZMmaLk5GQ988wzGjZsWLE95wAAwHsQ1gEAMND06dP17LPP6tChQ/r0008VExMjSdq6davuvffeUrV18uRJ3X///Tp+/LgiIiLUrFkzLV261Nlb/+qrr8rHx0d9+/aVxWJR9+7d9eabbzqfbzabtXjxYg0dOlRJSUkKCQnRgAEDNGHCBOM2GAAAuAVhHQAAg+Tn52vatGl66qmnVK1aNZd5xV1d/VJmz5590fmBgYGaPn26pk+ffsFlatSooa+//rrU6wYAAJ7FOesAABjE19dXU6ZMcfs9vAEAwJWPsA4AgIFuvvlmt99aDQAAXPkYBg8AgIF69Oih0aNHa/v27WrdurVCQkJc5t92220eqgwAAFQkhHUAAAz0j3/8Q5L0yiuvFJlnMplksxl/X2cAAHDlIawDAGAgu93u6RIAAMAVgHPWAQBwk9zcXE+XAAAAKijCOgAABrLZbHr++edVtWpVhYaG6o8//pAkPfvss5e8FRsAAEAhwjoAAAZ68cUXNXfuXE2ZMkX+/v7O6U2aNNF//vMfD1YGAAAqEsI6AAAGevfddzVr1iz169dPZrPZOb158+b69ddfPVgZAACoSAjrAAAY6OjRo6pbt26R6Xa7XVar1QMVAQCAioiwDgCAgRo1aqQ1a9YUmf7JJ5+oZcuWHqgIAABURNy6DQAAA40dO1YDBgzQ0aNHZbfb9dlnn2nPnj169913tXjxYk+XBwAAKgh61gEAMFDv3r21aNEifffddwoJCdHYsWO1e/duLVq0SF27dvV0eQAAoIKgZx0AAINdf/31WrZsmafLAAAAFRg96wAAGKh27dpKSUkpMj01NVW1a9f2QEUAAKAiIqwDAGCgAwcOyGazFZlusVh09OhRD1QEAAAqIobBAwBggC+//NL576VLlyoiIsL52Gazafny5apZs6YHKgMAABURYR0AAAPcfvvtkiSTyaQBAwa4zPPz81PNmjX18ssve6AyAABQERHWAQAwgN1ulyTVqlVLP/zwgypVquThigAAQEVGWAcAwED79+/3dAkAAOAKQFgHAMBgy5cv1/Lly3Xy5Elnj3uhd955x0NVAQCAioSwDgCAgcaPH68JEyaoTZs2qlKlikwmk6dLAgAAFRBhHQAAA82cOVNz585V//79PV0KAACowLjPOgAABsrLy1P79u09XQYAAKjgCOsAABjooYce0vz58z1dBgAAqOAYBg8AgIFyc3M1a9Ysfffdd2rWrJn8/Pxc5r/yyiseqgwAAFQkhHUAAAz0yy+/qEWLFpKkHTt2uMzjYnMAAKCkCOsAABjo+++/93QJAADgCsA56wAAAAAAeBl61gEAMECfPn1KtNxnn33m5koAAMCVgLAOAIABIiIiPF0CAAC4ghDWAQAwwJw5czxdAgAAuIJwzjoAAAAAAF6GsA4AAAAAgJchrAMAAAAA4GUI6wAAAAAAeBnCOgAAAAAAXoawDgAAAACAlyGsAwDgpSZNmqRrr71WYWFhqly5sm6//Xbt2bPHZZnc3FwNGzZMMTExCg0NVd++fXXixAmXZQ4dOqSePXsqODhYlStX1hNPPKH8/Pzy3BQAAFBKhHUAALzUqlWrNGzYMG3cuFHLli2T1WpVt27dlJWV5Vxm5MiRWrRokRYsWKBVq1bp2LFj6tOnj3O+zWZTz549lZeXp/Xr12vevHmaO3euxo4d64lNAgAAJeTr6QIAAEDxlixZ4vJ47ty5qly5srZu3apOnTopLS1Ns2fP1vz583XTTTdJkubMmaOGDRtq48aNateunb799lvt2rVL3333neLi4tSiRQs9//zzeuqppzRu3Dj5+/sXWa/FYpHFYnE+Tk9PlyRZrVZZrdaL1uzOHvv8/PxLrr80bblTRanVyDoL23OXq7lWAFcePz+/Sy5DWAcAoIJIS0uTJEVHR0uStm7dKqvVqi5dujiXadCggRITE7Vhwwa1a9dOGzZsUNOmTRUXF+dcpnv37ho6dKh27typli1bFlnPpEmTNH78+CLTv/32WwUHB1+0xn1Hcy5r20pi7dp1Or4/yJC23FmnVHFqNbJOiVoLGV0rgCtP7969L7kMYR0AgArAbrdrxIgR6tChg5o0aSJJSk5Olr+/vyIjI12WjYuLU3JysnOZc4N64fzCecUZM2aMRo0a5Xycnp6u6tWrq1u3bgoPD79onT/tTpbe2FeqbSupjh07qGXDeEPacmedUsWp1cg6JWotZHStAK5OhHUAACqAYcOGaceOHVq7dq3b1xUQEKCAgIAi0/38/C45bM/X130/LXx9fUs0bLCkbblTRanVyDoL23OXq7lWAFcnLjAHAICXe/jhh7V48WJ9//33qlatmnN6fHy88vLylJqa6rL8iRMnFB8f71zm/KvDFz4uXAYAAHgfwjoAAF7K4XDo4Ycf1sKFC7VixQrVqlXLZX7r1q3l5+en5cuXO6ft2bNHhw4dUlJSkiQpKSlJ27dv18mTJ53LLFu2TOHh4WrUqFH5bAgAACg1hsEDAOClhg0bpvnz5+uLL75QWFiY8xzziIgIBQUFKSIiQoMGDdKoUaMUHR2t8PBwDR8+XElJSWrXrp0kqVu3bmrUqJH69++vKVOmKDk5Wc8884yGDRtW7FB3AADgHQjrAAB4qRkzZkiSOnfu7DJ9zpw5GjhwoCTp1VdflY+Pj/r27SuLxaLu3bvrzTffdC5rNpu1ePFiDR06VElJSQoJCdGAAQM0YcKE8toMAABwGQjrAAB4KYfDccllAgMDNX36dE2fPv2Cy9SoUUNff/21kaUBAAA345x1AAAAAAC8DGEdAAAAAAAvQ1gHAAAAAMDLENYBAAAAAPAyhHUAAAAAALwMYR0AAAAAAC9DWAcAAAAAwMsQ1gEAAAAA8DKEdQAAAAAAvAxhHQAAAAAAL0NYBwAAAADAyxDWAQAAAADwMoR1AAAAAAC8DGEdAAAAAAAvQ1gHAAAAAMDLENYBAAAAAPAyhHUAAADAS61evVq9evVSQkKCTCaTPv/8c5f5n332mbp166aYmBiZTCZt27atSBv79u3THXfcodjYWIWHh+vuu+/WiRMnymcDAFw2wjoAAADgpbKystS8eXNNnz79gvM7duyol1566YLzu3XrJpPJpBUrVmjdunXKy8tTr169ZLfb3Vk6gDLy9XQBAAAAAIrXo0cP9ejR44Lz+/fvL0k6cOBAsfPXrVunAwcO6KefflJ4eLgkad68eYqKitKKFSvUpUsXw2sGYAx61gEAAIArlMVikclkUkBAgHNaYGCgfHx8tHbtWg9WBuBSCOsAAADAFapdu3YKCQnRU089pezsbGVlZenxxx+XzWbT8ePHPV0egIsgrAMAAABXqNjYWC1YsECLFi1SaGioIiIilJqaqlatWsnHhygAeDPOWQcAAACuYN26ddO+fft0+vRp+fr6KjIyUvHx8apdu7anSwNwEYR1AAAA4CpQqVIlSdKKFSt08uRJ3XbbbR6uCMDFENYBAAAAL5WZmam9e/c6H+/fv1/btm1TdHS0EhMTdebMGR06dEjHjh2TJO3Zs0eSFB8fr/j4eEnSnDlz1LBhQ8XGxmrDhg169NFHNXLkSNWvX7/8NwhAiRHWAQAAAC+1ZcsW3Xjjjc7Ho0aNkiQNGDBAc+fO1ZdffqkHHnjAOf+ee+6RJD333HMaN26cpIIAP2bMGJ05c0Y1a9bU008/rZEjR5bfRgC4LIR1AAAAwEt17txZDofjgvMHDhyogQMHXrSNyZMna/LkyQZXBsDduAQkAAAAAABehrAOAAAAAICXIawDAAAAAOBlOGcdAAAA8GKHjqXpdGq24e1WigxWYkKE4e2eKyMjQ88++6wWLlyokydPqmXLlnrttdd07bXXSpIcDoeee+45vf3220pNTVWHDh00Y8YM1atXz611ARUBYR0AAADwUoeOpan+rTOUm2czvO1Af7P2fD3UrYH9oYce0o4dO/Tee+8pISFB77//vrp06aJdu3apatWqmjJliqZNm6Z58+apVq1aevbZZ9W9e3ft2rVLgYGBbqsLqAgYBg8AAAB4qdOp2W4J6pKUm2dzS499oZycHH366aeaMmWKOnXqpLp162rcuHGqW7euZsyYIYfDoalTp+qZZ55R79691axZM7377rs6duyYPv/8c7fVBVQUhHUAAAAAhsvPz5fNZivSQx4UFKS1a9dq//79Sk5OVpcuXZzzIiIi1LZtW23YsKG8ywW8DmEdAAAAgOHCwsKUlJSk559/XseOHZPNZtP777+vDRs26Pjx40pOTpYkxcXFuTwvLi7OOQ+4mhHWAQAAALjFe++9J4fDoapVqyogIEDTpk3TvffeKx8fYghwKXxKAAAAALhFnTp1tGrVKmVmZurw4cPavHmzrFarateurfj4eEnSiRMnXJ5z4sQJ5zzgakZYBwDAS61evVq9evVSQkKCTCZTkQsuORwOjR07VlWqVFFQUJC6dOmi33//3WWZM2fOqF+/fgoPD1dkZKQGDRqkzMzMctwKAJBCQkJUpUoVnT17VkuXLlXv3r1Vq1YtxcfHa/ny5c7l0tPTtWnTJiUlJXmwWsA7ENYBAPBSWVlZat68uaZPn17s/MJbHs2cOVObNm1SSEiIunfvrtzcXOcy/fr1086dO7Vs2TItXrxYq1ev1pAhQ8prEwBc5ZYuXaolS5Zo//79WrZsmW688UY1aNBADzzwgEwmk0aMGKEXXnhBX375pbZv3677779fCQkJuv322z1dOuBx3GcdAAAv1aNHD/Xo0aPYeeff8kiS3n33XcXFxenzzz/XPffco927d2vJkiX64Ycf1KZNG0nS66+/rltvvVX//ve/lZCQUGzbFotFFovF+Tg9PV2SZLVaZbVaL1pzfn5+qbezpPLz8y+5/tK05U4VpVYj6yxsz12u1lor0r5anJSUFD377LM6cuSIoqOjdccdd2jChAmSCr5TRo4cqfT0dA0ZMkSpqanq0KGDFi1aJLPZ7Na6AE/z8/O75DKEdQAAKqBL3fLonnvu0YYNGxQZGekM6pLUpUsX+fj4aNOmTbrjjjuKbXvSpEkaP358kenffvutgoODL1rXvqM5l7lFl7Z27Tod3x9kSFvurFOqOLUaWadErYUqyvsvGf+6ni8kJESvvPKKy7R169a5PL7uuut03XXXOR/v3btXe/fudVtNF5KSkqJ3331XP/74oywWi+Lj4/XII4+obt26kqTU1FTNmzdP27ZtU1ZWlho3bqzBgwdf8MAncDGFB9ovhrAOAEAFVJJbHiUnJ6ty5cou8319fRUdHX3R2yKNGTNGo0aNcj5OT09X9erV1a1bN4WHh1+0rp92J0tv7CvVtpRUx44d1LKhMRedcmedUsWp1cg6JWotVFHef8n417WiOnv2rK677jrdcMMNmjhxoipVqqS9e/eqdu3aqlOnjhwOhzp16iQ/Pz999dVXCgsL02uvvabJkyfr559/VkhIiKc3AVcgwjoAAHAREBCggICAItP9/PwuOWzP19d9Py18fX1LNGywpG25U0Wp1cg6C9tzl6u11oq0r1Zkr7zyiqpXr6558+Y5p11zzTXOf//222/atGmTduzYocaNG0uS3nrrLcXHx+uTTz7RQw89VO4148rHBeYAAKiASnLLo/j4eJ08edJlfn5+vs6cOcNtkQDgHF9++aXatGmju+66S5UrV1bLli319ttvO+cXXscjMDDQOc3Hx0cBAQFau3ZtudeLqwM96wAAVEDn3vKoRYsWkv685dHQoUMlSUlJSUpNTdXWrVvVunVrSdKKFStkt9vVtm1bT5UO4Ap26FiaTqdmG9pmpchgJSZEGNrm+f744w/NmDFDo0aN0j//+U/98MMPeuSRR+Tv768BAwaoQYMGSkxM1JgxY/TWW28pJCREr776qo4cOaLjx4+7tTZcvQjrAAB4qczMTJeLLO3fv1/btm1TdHS0EhMTnbc8qlevnmrVqqVnn33W5ZZHDRs21C233KLBgwdr5syZslqtevjhh3XPPfdwQSQAhjt0LE31b52h3Dyboe0G+pu15+uhbg3sdrtdbdq00cSJEyVJLVu21I4dOzRz5kwNGDBAfn5++uyzzzRo0CBFR0fLbDarS5cu6tGjhxwOh9vqwtWNsA4AgJfasmWLbrzxRufjwou+DRgwQHPnztWTTz6prKws5y2POnbsqCVLlrgM0/zggw/08MMP6+abb5aPj4/69u2radOmlfu2ALjynU7NNjyoS1Junk2nU7PdGtarVKmiRo0auUxr2LChPv30U+fj1q1ba9u2bUpLS1NeXp5iY2PVtm1blztuAEYirAMA4KU6d+580R4bk8mkCRMmOO9ZXJzo6GjNnz/fHeUBwBWjQ4cO2rNnj8u03377TTVq1CiybEREwUGD33//XVu2bNHzzz9fLjXi6kNYBwAAAHBVGzlypNq3b6+JEyfq7rvv1ubNmzVr1izNmjXLucyCBQsUGxurxMREbd++XY8++qhuv/12devWzYOV40pGWAcAAABwVbv22mu1cOFCjRkzRhMmTFCtWrU0depU9evXz7nM8ePHNWrUKJ04cUJVqlTR/fffr2effdaDVeNKx63bAAAAAFz1/u///k/bt29Xbm6udu/ercGDB7vMf+SRR3T48GHl5eXp4MGDev755+Xv7++hagtMnjxZJpNJI0aMcE7bt2+f7rjjDsXGxio8PFx33313kdt8omIgrAMAAABABfPDDz/orbfeUrNmzZzTsrKy1K1bN5lMJq1YsULr1q1TXl6eevXqJbvd7sFqcTkI6wAAAABQgWRmZqpfv356++23FRUV5Zy+bt06HThwQHPnzlXTpk3VtGlTzZs3T1u2bNGKFSs8WDEuB2EdAAAAACqQYcOGqWfPnurSpYvLdIvFIpPJpICAAOe0wMBA+fj4aO3ateVdJsqIC8wBAAAAuOocOpam06nZhrZZKTLYrfeDl6QPP/xQP/74o3744Yci89q1a6eQkBA99dRTmjhxohwOh0aPHi2bzabjx4+7tS4Yj7AOAAAA4Kpy6Fia6t86Q7l5NkPbDfQ3a8/XQ90W2A8fPqxHH31Uy5YtU2BgYJH5sbGxWrBggYYOHapp06bJx8dH9957r1q1aiUfHwZVVzSEdQAAAABXldOp2YYHdUnKzbPpdGq228L61q1bdfLkSbVq1co5zWazafXq1XrjjTdksVjUrVs37du3T6dPn5avr68iIyMVHx+v2rVru6UmuA9hHQAAAAAqgJtvvlnbt293mfbAAw+oQYMGeuqpp2Q2m53TK1WqJElasWKFTp48qdtuu61ca0XZEdYBAAAAoAIICwtTkyZNXKaFhIQoJibGOX3OnDlq2LChYmNjtWHDBj366KMaOXKk6tev74mSUQaEdQAAAAC4QuzZs0djxozRmTNnVLNmTT399NMaOXKkp8vCZSCsAwAAAEAFtXLlSpfHkydP1uTJkz1TDAzFJQEBAAAAAPAyhHUAAAAAALwMYR0AAAAAAC/DOesAAAAA4KUOHUvT6dRsw9utFBnstvvBwxiEdQAAAADwQoeOpan+rTOUm2czvO1Af7P2fD2UwO7FGAYPAAAAAF7odGq2W4K6JOXm2dzSYw/jENYBAAAAAPAyhHUAAAAAALwMYR0AAAAAAC9DWAcAAAAAwMsQ1gEAAAAA8DKEdQAAAACA202aNEnXXnutwsLCVLlyZd1+++3as2ePp8vyWoR1AAAAAIDbrVq1SsOGDdPGjRu1bNkyWa1WdevWTVlZWZ4urYjVq1erV69eSkhIkMlk0ueff17uNfiW+xoBAAAAAFedJUuWuDyeO3euKleurK1bt6pTp04eqqp4WVlZat68uR588EH16dPHIzUQ1gEAAAAA5S4tLU2SFB0d7eFKiurRo4d69Ojh0RoYBg8AAAAAKFd2u10jRoxQhw4d1KRJE0+X45XoWQcAAAAAlKthw4Zpx44dWrt2radL8VqEdQAAAABAuXn44Ye1ePFirV69WtWqVfN0OV6LsA4AAAAAcDuHw6Hhw4dr4cKFWrlypWrVquXpkrwaYR0AAAAA4HbDhg3T/Pnz9cUXXygsLEzJycmSpIiICAUFBXm4Ou9DWAcAAAAAuN2MGTMkSZ07d3aZPmfOHA0cOLD8C7qIzMxM7d271/l4//792rZtm6Kjo5WYmFguNRDWAQAAAABu53A4PF1CiW3ZskU33nij8/GoUaMkSQMGDNDcuXPLpQbCOgAAAAAA5+jcubPHDy5wn3UAAAAAALwMYR0AgKvA9OnTVbNmTQUGBqpt27bavHmzp0sCAAAXQVgHAOAK99FHH2nUqFF67rnn9OOPP6p58+bq3r27Tp486enSAADABXDOOgAAV7hXXnlFgwcP1gMPPCBJmjlzpr766iu98847Gj16tIerAwBcKQ4dS9Pp1GzD260UGazEhAhD23RHrUbXSVgHAOAKlpeXp61bt2rMmDHOaT4+PurSpYs2bNhQ7HMsFossFovzcXp6uiTJarXKarVedH35+fkGVH3hti+1/tK05U4VpVYj6yxsz12u1lrZV/9sm/ffeEbWeuh4uprc9rZy82yGtHeuQH+zdnw5WIlVwg1pz121lqZOPz+/Sy5jcnj6EncAAMBtjh07pqpVq2r9+vVKSkpyTn/yySe1atUqbdq0qchzxo0bp/HjxxeZnpaWpvBwY34oAQCAi+OcdQAA4GLMmDFKS0tz/qWmpurkyZMKCwvzdGkAAFw1GAYPAMAVrFKlSjKbzTpx4oTL9BMnTig+Pr7Y5wQEBCggIKA8ygMAABdAzzoAAFcwf39/tW7dWsuXL3dOs9vtWr58ucuweAAA4F3oWQcA4Ao3atQoDRgwQG3atNF1112nqVOnKisry3l1eAAA4H0I6wAAXOH+8pe/6NSpUxo7dqySk5PVokULLVmyRHFxcZ4uDQAAXABXgwcAAAAAwMtwzjoAAAAAAF6GsA4AAAAAgJchrAMAAAAA4GUI6wAAAAAAeBnCOgAAAAAAXoawDgAAAACAlyGsAwAAAADgZQjrAAAAAAB4GcI6AAAAAABehrAOAAAAAICXIawDAAAAAOBlCOsAAAAAAHgZwjoAAAAAAF6GsA4AAAAAgJchrAMAAAAA4GUI66VktVr1xRdfyGq1eroUr8Dr4YrXAQAAAIARCOsAAAAAAHgZwjoAAAAAAF6GsA4AAAAAgJchrAMAAAAA4GUI6wAAAAAAeBnCOgAAAAAAXoawDgAAAACAlyGsAwAAAADgZQjrAAAAAAB4GcI6AAAAAABehrAOAAAAAICXIawDAAAAAOBlCOsAAAAAAHgZwjoAAAAAAF6GsA4AAAAAgJchrAMAAAAA4GUI6wAAAAAAeBnCOgAAAAAAXoawDgAAAACAlyGsAwAAAADgZQjrAAAAAAB4GcI6AAAAAABehrAOAAAAAICXIawDAAAAAOBlCOsAAAAAAHgZwjoAAAAAAF7G19MFAAAAAADgTTKyLEo+laX0LIuCA/0UGx2sSlHB5VoDYR0w0InTmapWJcrTZQCA17LZ7ErPtCjfZpfd7pDZ7KMAf7PCQgI8XVoReXk2ZWRbZLM55HA45Ovro5AgfwUGeN/Pp6zsPOVY8mWz2WUymeRr9lF4aIB8fb1rEKXD4VB6pkV5Vpvsdod8fEzy9zMrPDRAJpPJ0+W5yM//c191OAr21aAAX4UE+3u6tCJyLfnKyslTfn7B+282mxQa7K8Af+/bVzOz8pSbV7Cv+vj8ua+azd61r9rtBfuqNf/PfTXA31dhIf5et69arTZlZOW57KvBgX4KDvLzdGlF5ORalZVjdX5Xmc0mhYcEyM/P7OnSJBV8R63ZeljT52/Rp9/+Kpvd4TK/U5tEDe/XRr1vuqZcava+TzBQgSWfziKsA0Axci35On02W6kZubKf9+NHkvz9zIqJDFJUeJDHA2Z6pkUpqTnKyLIUOz8kyF8xkUGKCPNswLTbHTqbnqOU1BzlWvKLzDedNCkiNEAxkUEeD5h5eTadScvRmbQc5dvsReb7mn0UFRGomIhg+ft79kd7do5Vp89mKy3TIoej6L4aGOCr6IggRYUHejRgOhwOpWUU7KtZOXnFLhMa7K9KUcEeD5g2m11n0gr21Tyrrch8Hx+TIsMCFRMZpKBAzwZMS16+UlJzdDYtVzZ70X3Vz9es6IhARUcEeTxgZmblKSU1R+lZxe+rQQF+iokMUmR4oHx8PPf+OxwOpabnKiU1R9m51iLzj5kyFRZS8L3qyQO3S9fu06OTlmnP/hT5mk1FgrokrfvxsFZvOaTY6GCNG3a9ht7T2q2fLcI6AABwG4fDoWMnM5SSmnPR5fKsNh0/lakTKVlKrBKh8NDy/8GWn2/XgaOpxf6YPFdWTp6ycvIUkOKrWlUjPRIus3OsOnA0tdjgW8jhcCg1I1epGbmKCA1U9SrhHvnBfvpsto6fyiw2TBTKt9l16ky2Tp3JVpXYUMVGh5RjhQUcDocOH09XakbuRZfLteTr2MkMnTidpZpVIzxyICQvz6YDx1KLPUhzrszsPGVm5ykowE+1qkV65EBYRpZFB4+lFXuQrpDd7nAezImOCFLVuDCPHFw4cbrgO+hirPk2nUjJ0skz2aoWF6aoiKByqu5PdrtDB4+lXfCAYqEci1VHTlh1IiVLtapFemRUUK4lX/uPpMqaX/QgTaHCETfpmRaFBvurRkJEuR8Ie3vBT/r7+G+c31P5tuL318IAf+pMtoY9v1Q7957W6093d9t3q3eNNwEAAFeUQ8fSLxnUz2W3O3TgaKpS0y8emIyWn2/X3kNnLhnUz2XJy9feQ2dkybt4YDJaZlae/jhy9qJB/XxpmbnafyT1ooHJHU6cztSxkxkXDernO34qU8mnMt1YVVEOh0P7j6ReMqify2a3648jqZcMTEbLy7Np3+Gzlwzq58qxWLX30BlZi+nVdqe0jFwdOHrxoH6+M2k5OngsrVT7jBGOnki/ZFA/l8Ph0OHkdJ0+m+3Gqoqy2ezad+hsqfY7a76t4Pstp+Tfb0bIybVq36GzFw3q58vMztO+w2dlK8X3W1nNX7xDQ577Wna7Q6Xd7Wb8d6ue+Ndy9xQmwjoAAHCT46cylJZ5eaH7cHK6srKLH9prtMKgVtzw3EvJt9m1/0iq8vPL54elJS//kr2UF5KVk6fDx9PdUFXxzqbllCr8nOvkmSylpJZfCDp8PF2Zl7G/ORwFPZylCc5lYbPZtf/oxXspLyTPatP+o+V3wCY7x6pDx9MvK3SnZ1p07GSGG6oq3qkzWaU6qHiuYyczlFaKgzxldfBYmnIspQ/dhQdC8/LK54CN1WrTgaNpxZ5KcCm5lnwdOFo+B2x27T2lAf9cdNnPd0h6Zd4m/ferncYVdQ7COlAKq1evVq9evZSQkCCTyaTPP//c0yUBgFeyWm06ffbyfvxKBSHoeDn1rqam517Wj99CeVZbuQXLE6ezLuvHb6G0zNxyOQhixPuXfCqrXH6s5+RaS9Wjfj673aHk0+Wzr6ak5pRpJEeuJV9n0y//c1kayacvfurDpaSk5pRLsLTZ7Jd9UKlQeX1XpWdaLuugUqF8m12nzpZtW0vq1NnsyzqoVCgrJ0/pme4ftfL6B1sKEncZ+JhMmvyf9W75viKsA6WQlZWl5s2ba/r06Z4uBajQatasqalTp150mXMPiB04cEAmk0nbtm27ZNulWba0LlX3wIEDdfvttxu+3oooJTWnzD9csnOtyinFsPTLVZaDCoXOpOW6PVjm59uVZsCP18vtQSyNtAxLqYbpF8dmt5fL6RBGvB4ZWXnlMsT8TFrZay2P99+Sl1+mUFkoJc39B8HOphd/0cvSyLPayuV0CCPeOyO291LsdofOppX9s+vufTU906K5n/9S5u8qu8OhX/ac1A/bjxlU2Z8I60Ap9OjRQy+88ILuuOMOT5cCNzp16pSGDh2qxMREBQQEKD4+Xt27d9e6deucy1wstBWGxcqVKysjw3UYX4sWLTRu3Djn4/379+u+++5TQkKCAgMDVa1aNfXu3Vu//vqrJKldu3b6+9//7tLGzJkzZTKZNHfuXJfpAwcO1PXXX3/5G+7FqlevruPHj6tJkyaeLuWiXnvtNZf3pXPnzhoxYoTH6jl//Z07d5bJZJLJZFJAQICqVq2qXr166bPPPjN0vQ6Hw5BQIbn/x1p2jrVMveqFrPk2t/cCnUkr+wEQSUrLtLh92L5R75u733+bzV6mXvVCDoejXILF5Zyqcb5cS77bR1cYdR73mVT3B8uKsq/m5RlzQKDwLhLulJpe/JX0SyszO8+t1wR578vthrXva/bR9P9uNaStcxHWAeA8ffv21U8//aR58+bpt99+05dffqnOnTsrJSWlVO1kZGTo3//+9wXnW61Wde3aVWlpafrss8+0Z88effTRR2ratKlSU1MlSTfeeKNWrlzp8rzvv/9e1atXLzJ95cqVuummm0pVY0VhNpsVHx8vX1/vvolJRESEIiMjPV3GRQ0ePFjHjx/Xvn379Omnn6pRo0a65557NGTIEMPWkZVtLXNPRaG0DPcG4Ms9p77Yttxdq0HtF1552V3y8+0XvJVYaWXnWt3aY52RlWdYGDRi1MNF2zdw/3J3remZxrz/Nrtx+1Jxci35hoW19Avc6s8o6Qb23Lv/e7Vi1PrNmn2GtZVvs+urlXsNa68QYR0AzpGamqo1a9bopZde0o033qgaNWrouuuu05gxY3TbbbeVqq3hw4frlVde0cmTJ4udv3PnTu3bt09vvvmm2rVrpxo1aqhDhw564YUX1K5dO0kFYX3Pnj1KTk52Pm/VqlUaPXq0S1jfv3+/Dh48qBtvvLHYdRU3NDw1NVUmk8nZzsqVK2UymbR8+XK1adNGwcHBat++vfbs2eN8zrhx49SiRQu99dZbql69uoKDg3X33XcrLS3NuUxxvcm33367Bg4c6DItIyND9957r0JCQlS1atWLnl5yfv1nz55Vv379FBsbq6CgINWrV09z5sxxec4ff/yhG2+8UcHBwWrevLk2bNjgMn/t2rW6/vrrFRQUpOrVq+uRRx5RVtaf5/KdPHlSvXr1UlBQkGrVqqUPPvjggvUVOncY/MCBA7Vq1Sq99tprzt7sAwcOSJJ27NihHj16KDQ0VHFxcerfv79Onz7t8hoOHz5cI0aMUFRUlOLi4vT2228rKytLDzzwgMLCwlS3bl198803l6zpfMHBwYqPj1e1atXUrl07vfTSS3rrrbf09ttv67vvvit1e8UxKqhLBT/W3fkD2HaB2/NcDiO3293tG9HrVV5tu/N1NfQ1dfP7b+Tr6u6RFUa+ru6s1ei2K0qtbv+uMrBWd35XnTqTXeqrv19MmhsO2BDWAeAcoaGhCg0N1eeffy6LpWxHc++9917VrVtXEyZMKHZ+bGysfHx89Mknn8hmK77nqEOHDvLz89P3338vSdq1a5dycnI0aNAgpaSkaP/+/ZIKetsDAwOVlJRUppol6emnn9bLL7+sLVu2yNfXVw8++KDL/L179+rjjz/WokWLtGTJEv3000/6xz/+Uer1/Otf/1Lz5s31008/afTo0Xr00Ue1bNmyEj332Wef1a5du/TNN99o9+7dmjFjhipVqlRkOx5//HFt27ZN11xzje69917l5xf0oOzbt0+33HKL+vbtq19++UUfffSR1q5dq4cfftj5/IEDB+rw4cP6/vvv9cknn+jNN9+84IGX4rz22mtKSkpy9mQfP35c1atXV2pqqm666Sa1bNlSW7Zs0ZIlS3TixAndfffdLs+fN2+eKlWqpM2bN2v48OEaOnSo7rrrLrVv314//vijunXrpv79+ys7u+zDTQcMGKCoqCjDhsMb/WPFnaeCG9m2u6+FZuTr6s6hxUa/Du59/41r3N3vv5HvWUXaV936/pf1ymLnt1dBanX7+29gre78rjJ6iH2+zW74a0tYB4Bz+Pr6au7cuZo3b54iIyPVoUMH/fOf/9Qvv/xS6rZMJpMmT56sWbNmad++okOtqlatqmnTpmns2LGKiorSTTfdpOeff15//PGHc5mQkBBdd911Lr3fHTt2VEBAgNq3b+8yPSkpSQEBAZe13ed68cUXdcMNN6hRo0YaPXq01q9fr9zcP4cK5+bm6t1331WLFi3UqVMnvf766/rwww9dev9LokOHDho9erSuueYaDR8+XHfeeadeffXVEj330KFDatmypdq0aaOaNWuqS5cu6tWrl8syjz/+uHr27KlrrrlG48eP18GDB7V3b8EQtUmTJqlfv34aMWKE6tWrp/bt22vatGl69913lZubq99++03ffPON3n77bbVr106tW7fW7NmzlZNT8vP8IiIi5O/v7+zJjo+Pl9ls1htvvKGWLVtq4sSJatCggVq2bKl33nlH33//vX777Tfn85s3b65nnnlG9erV05gxYxQYGKhKlSpp8ODBqlevnsaOHauUlJTL2jfP5+Pjo2uuucbZ819WZrNxPy9MJpN8fEyGtXc+HwN/CZnN7quzoH3jivX1dd9PQB+Tsa+DO19Xs4E7gNvff2o1nJF1ShWnVrMbv1ML2jeyVvd9V8VEBhnaXmiwv+H/vyKsA8B5+vbtq2PHjunLL7/ULbfcopUrV6pVq1ZFLuhWEt27d1fHjh317LPPFjt/2LBhSk5O1gcffKCkpCQtWLBAjRs3dulh7ty5s0so79y5syTphhtucJl+oSHwpdWsWTPnv6tUqSJJLj3KiYmJqlq1qvNxUlKS7Ha7y3D5kjh/FEBSUpJ2795doucOHTpUH374oVq0aKEnn3xS69evL9V2/Pzzz5o7d65zJEVoaKi6d+8uu92u/fv3a/fu3fL19VXr1q2dbTRo0MCQ89F//vlnff/99y7rbtCggSS5HNQ5t36z2ayYmBg1bdrUOS0uLs5lm8rK4XDIZFDICgrwNbQtdwoO9DOsLXfXamT7QQHGbff5/Px85GvQgQWzj4/8/cyGtFWcoMCK8ZpKUnCQgbUauN3ubt+dr2uAv9mwcOXvZzb0gNr5DH1NK9D7Hxzkvve/cd1Yw76rfHxMalArxpC2XNo1vEXgCpaZmalt27Y5z5vdv3+/tm3bpkOHDnm2MBguMDBQXbt21bPPPqv169dr4MCBeu655y6rrcmTJ+ujjz7STz/9VOz8sLAw9erVSy+++KJ+/vlnXX/99XrhhRec82+88Ub99ttvOnr0qFauXKkbbrhB0p9hfd++fTp8+PBFLy7n878j0+cOTbRai78Ctp/fn/9jLAxc9lKcM+bj41NkCOSF1nW5evTooYMHD2rkyJE6duyYbr75Zj3++OMuy1xsOzIzM/W3v/3N+Xnetm2bfv75Z/3++++qU6eOobWeLzMzU7169XJZ97Zt2/T777+rU6dOxdZfuA1lfW8uxGaz6ffff1etWrXK3JYk+fmZFRbib0hblaKCDWnnQiLCAg3puTGZTIqOMLaX5nxG9QIFBfi59QewyWQyrNboyEDDDvwUJyjQz7ADNkb30p0vKjzIkNfC7OOjqPCKsa+GhQTI3999B2vMZh9FhgUa0pa73/+wkADDDlzFRLr3e9Wo18LP17j/lxRn8F0tDTt/32536B/3tr70gqVEWAdKYcuWLWrZsqVatmwpSRo1apRatmypsWPHergyuFujRo1cLj5WGtddd5369Omj0aNHX3JZk8mkBg0auKyrffv28vf315tvvqnc3Fxnb++1116rU6dO6Z133nEOl7+Q2NhYSdLx48ed0y73PuSHDh3SsWN/3kt048aN8vHxUf369Z3rOnc9NptNO3bsKNLOxo0bizxu2LBhieuIjY3VgAED9P7772vq1KmaNWtWiZ/bqlUr7dq1S3Xr1i3y5+/vrwYNGig/P19bt/55G5Y9e/Y4r9JfUv7+/kWuR9CqVSvt3LlTNWvWLLLukJCQUrVvlHnz5uns2bPq27evYW0a8WPN1+yjiLCyn9pxMT4+JkVHlv3HenhIgPzc2AMsSSHB/go0oHfd3aFCkqIjjAmWMRHuDRWSMa9HgL+vQt0YKqSCUxciQsv+eYgMD3DrqSWSFB4aID/fsn8eymNfNeKAoI+P+w/WSca8HiFBxnyPXEyAv69Cg8v+eYiJNOZ75EKaXlNZ7VtUM+S0gPAQf/2lRyMDqnJFWAdKoXPnznI4HEX+Lmd4NLxTSkqKbrrpJr3//vv65ZdftH//fi1YsEBTpkxR7969XZY9evRokd7Rs2fPFtvuiy++qBUrVrgMFd+2bZt69+6tTz75RLt27dLevXs1e/ZsvfPOOy7rCgoKUrt27fT666+rQ4cOMpsLfgD5+/u7TD+/J/ZchW1MnjxZu3fv1qpVq/TMM89c1msUGBioAQMG6Oeff9aaNWv0yCOP6O6771Z8fLwk6aabbtJXX32lr776Sr/++quGDh1abMhdt26dpkyZot9++03Tp0/XggUL9Oijj5aohrFjx+qLL77Q3r17tXPnTi1evLhUQf+pp57S+vXr9fDDDzt7tb/44gvnBebq16+vW265RX/729+0adMmbd26VQ899JCCgkr3Q6lmzZratGmTDhw4oNOnT8tut2vYsGE6c+aM7r33Xv3www/at2+fli5dqgceeOCCFxo0UnZ2tpKTk3XkyBFt3LhRTz31lP7+979r6NChhp1KIRX0ApX1B6G7f6g51xMRXOb1VIpy/w91SYotY7DwNfsoMtyYnsSL8fMzlzlYRoQGurVXtVBkeGCZg2V5vf+Vosq2r5pMJrePVvlzPWV7TQL8fRVuwMGJSwkMKHuwjAoPdOsQ+ELREUFlHgkUG+3+99+I9ZTXAZDhf20jWxkvYmc2mzTozhZuGbFEWAeAc4SGhqpt27Z69dVX1alTJzVp0kTPPvusBg8erDfeeMNl2X//+9/OkRaFf1999VWx7V5zzTV68MEHXS7UVq1aNdWsWVPjx49X27Zt1apVK7322msaP368nn76aZfn33jjjcrIyHCer17ohhtuUEZGRolC1jvvvKP8/Hy1bt1aI0aMcBlqXxp169ZVnz59dOutt6pbt25q1qyZ3nzzTef8Bx98UAMGDND999+vG264QbVr1y62vscee8w5WuWFF17QK6+8ou7du5eoBn9/f40ZM0bNmjVTp06dZDab9eGHH5Z4G5o1a6ZVq1bpt99+0/XXX+8cIZOQkOBcZs6cOUpISNANN9ygPn36aMiQIapcuXKJ1yEVXOTObDarUaNGio2N1aFDh5SQkKB169bJZrOpW7duatq0qUaMGKHIyEjn6Qru9Pbbb6tKlSqqU6eO+vTpo127dumjjz5yeQ+NUjMh8rLPBwwPDVDlmPIZaeDvb1ZilfDLfn5C5TCFGNCLVBJREUGX3bvm42NSzaqRbu9VLVQtPvyyD9gEBviqWnyYwRUVz2QyqWbViMt+XaLCg9w+rLhQcJCfEiqHXvbzq8eHK8Dfvb2qhWKjQxQRenkHhsw+PqpZNcLgii4ssUrEZQ8xDw70U0Ll8tlXzWYf1UiIuOwDNrHRweVyAEQqOGAbd5nf4SaTSTUSItx6IcxCd3VvqF6d613259/XbFKtqpF69u8dDa6sgMnhzpuXXoGsVqu+/vpr3XrrrRftxbpa8Hq42rL9iNo0rebpMgC3GTdunD7//PPLHkKPq0+uJV/7j6TKml/yUQPhoQFKrHL54elypabn6nByeqluO1UlNlSx0eV7+oLD4dCxkxlKSS353QkKw095HVQolJ9v14GjqcrOLfl1K4IC/FSzaoTbTys4X3aOVQeOppbqHNboiCBVjQsrlxEg5zp9NlvHTmaUeHmTyaRqcWGKKoeeynM5HA4dPp6u1IzcSy/8P36+ZtWsGqEgAy/+WBJ5eTbtP5paqtt5hQb7q0ZCRLn0qp8rI8uig8fSSnVbs9joYFWJLZ+DCuc6cTpTJ1JKfhqhj49JiVUiyu2gglTw2e/y4AfatP1YqV5TX7OPYqODte6DAapVLdIttdGzDgAA3CYwwFd1E6MUExl0yfDt72dWQuWwcu39PVdkeKDqVI8q0Y/EkCB/1aoWWe5BXSoIXlXjwlW9BD3XJpNJUeFBqlsjqtyDulRwnnXt6lGKiwm55CgLX7OPKkeHqE5iVLkHdamg17puYnSJLuQWGOCranHhqhYfXu5BXSoYDl+7WlSJhm+HhQSodrXIcg/qUsH+l5gQoapxYZfs0S8c9lw3Marcg7pUMMKmbmKUYqODLznU3M/XrPhKoapVLbLcg7pU8J7WTYxWROilL8AYHOinGgkRHgnqkhRXKVQ1EiIueSFHk8mkiNCSfwcbKTjIT8vn9NMdXQquvXOp76rCW/Q1rFNJWxY86LagLknlMw4GAABctfz8zKoaF64qsWE6m56js2m5yrfZZbc7ZDb7KMDfrJjIIIWFlO8PtOIEB/mpZtVI5eXZdCYtR+lZFtlsBT0tZrNJIUF+iokMdvsFmkoiKiJIURFBys6xKiU1R9m5Vtlsdvn4mGT28VFkeICiwoPKZSjpxfj4mBRXKVSVY0KUlmHRmbQc5Vltstsd8vExyd/PrOiIIEWEBXgk+J7L39+s6lXClVA5VGfScpSWYXHZV4MCfBUTGeSRAx/nCw3xV2iIvyx5+UpJzVFmdp7LvhoW4q+YiOByOe//UmIigxUTGazMrDylpOYoNy/fua8WXkehvM77vhiz2UdVYsMUFxOqtIxcnUnLlTXf5nz/C/bVQIWHen5fDQzwVY2qEbJa//ddlZnnHBViNpsUFOCnSlFBHjnwcb6IsEBFhAUqJ/fc76qCfdXX7KPwUH9FRwR55CBdoaBAPy14tY/W/3RE0/+7VQuW7pbd5pDZbJI133W0zQ1taujhfq3Vq/M1bv9+ZRh8KTHs2xWvh6sjx8+qWpUoT5cBAAAA4DKdTMnSh1/v0qHjaZrx0Y/KzrEqPNRfmz96UPXdcD/1C/H8YWHgChJX6fIv+gIAAADA8yrHhOiR/tdKkj78Zpeyc6wKCwko16Aucc46AAAAAABeh7AOAAAAAICXIawDAAAAAOBlCOsAAAAAAHgZwjoAAAAAAF6GsA4AAAAAgJchrAMAAAAA4GUI6wAAAAAAeBnCOgAAAAAAXoawDgAAAACAlyGsAwAAAADgZQjrAAAAAAB4GV9PF1AWGVmWcl9nfn6+JCkzO0++vvZyX7+34fVwlZ+fz+sAAAAAXCEcDofzv0bmz7CQgEsuQ886AAAAAABehrAOAAAAAICXIawDAAAAAOBlKvQ56wAAAACAiuHE6SztOZCi1PRc+Zp9FBURqGb1KyskyN/TpXklwjpgoBMpmaoaF+npMgDAa1ny8pWWYVG+zS673SGz2Uf+fmZFhQfKx8fk6fJcZGbnKTM7TzabQw6HQ76+PgoO9FN46KUvClSeHA6HUjMsyrXky2azy2Qyydfso/BQfwUF+nm6PBf5+XalZuQqz2qT3e6Qj49J/n5mRYQFyM/X7OnyXORa/txXHY6CfTUwwFcRoQFet6+mZ1qUnWtVfn7B+282mxQa7K/QYO8KQHa7Q6kZubLk2WSz2eXjU7CvRoQFKMDfu2KJNd+m1PRcWfPtzn01wN+syLBAmc3eNTg5O9eqjMw8l301KLBgXzWZPL+vOhwOrd5yWLMW/KSvVu2V3e5wmR8c5Kf+tzXRQ31bqH6tGA9V6cpms7vsq5Jks9uVZ7XJ36/8vqu861MBVHAnU7IJ6wBQjPRMi1JSc5SVYy12/onTmYoMD1R0RJACAzz388Rud+hseo7OpObKYrUVs0SO/P18FBUeqKjwIPn6eu5He57VpjNpOTqblivbeT9+JenU2WwFBfoqOiJIkWGe/dGelZOnM2m5Ss+0yFG0VJ1IyVJYiL9iIoM82sPmcDiUlmnRmbRcZV9gX00+lamoiIJ9tTx/tJ/PZrPrbHquzqTlKM9a9E40p8/mKMDPrOjIQI8HTEtevlJSc5SWYSl2Xz15JlshQX6KjgxSeIi/R/fVjKw8nUnLUWZ2XrH7avLpLEWEBig6MkjBHjwYVnjg40xarnIt+cUuk2zOVFR4kKIjAz12MOzHXcka9MxX2nf4rHzNPkWCuiRl51j1n0+26a2PftKNbWvoPxNuVWx0iAeqlXJyrUpJzVF6pkWFpdr/tyPY7Q79duCMwkL8FR0RpLAQ939XEdYBAIDb2O0OHU5OV0ZW3sWXc0hn0gp+eFaJDVVMZFA5VfgnS16+Dh5LKzb4nCvPateJlGylpOYoMSHCIz/Y0zItOpKcXmyYOFdObr6O5mboTFqOalSJ8MjBheOnMpWSmnPRZRwOKT0zT+mZeYqOCFRC5bByqu5PNptdB4+nXzCkO5ezO3T6bI5SUnNUNS5MkWGB5VThn3JyrTp4LE35tovvABarTcdPZRXsq1UiPHIg7Exajo6fyrzkvpqVY1VWjlWhwX5KrBJR7qMXHA6Hjp7IUGrGxW/N5XBIqRkWpWZYVDk6WJVjyj9UWvNtOnA0TZa84g4o/inf5tCps9lKSc1W9SoR5RIuz7V03R/665NfKD/f/r96Lvzdavvfvrx6yyF1HvC+Fs34i2pXiyyPMp1OncnSiZTsSy6XkZWnjKw8RYQFqFpcmFsPLnnXGA4AAHDFcDgcOnAs7ZJB/XzHT2Xq9NlL/2AykiUvX/uPpF4yqJ8r3+bQgSOpysop3faVVWpGrg4fv3RQP1dObsH22S7yY9kdjp7IuGRQP9+ZtFwdTk53U0XFs9ns+uNI6iWD+rkcDulIcobOpue6sbKisnOt2n8k9ZJB/Vx5Vrv2H0m9YA+su6SkZuvYyUsH9XNlZlu1/2hqsT2w7uJwOHToePolg/r5Tp7J1vFTmW6qqnjWfJv2H0m9ZFA/l90hHTqepvRM4+4Rfimbfjmmfk98rjyrrdjRFBdiszl07FSmev3j43L9/8CJ0yUL6udKy7Do4LE0533Y3YGwDgAA3OLIiYxShZ9zJZ/OUlo5/bC02ewl6qUsjt0hHTqWrrxih8wbLzvXqqMnMi7ruRarTQePl18IPn02+7KDbFqGRSdTsgyu6MIOHU8vVfg517GTGeV2wMaab9OhY2m6nBxrszt08FhauR2wSc+06Pipy3sPc3LzdeRE+e2ryaezSn1QsVBKak6pD0hdLofDoYPH0kt1UPHP50pHktPL5YBNTq5V94xa+L/rfZT++TabQ8dOZugfE5YYX1wxzqbn6tRlHhjIzLa69YANYR0ooZf/PUU3dOqghPhKql2zuu695y79/ttvni7Lq61ZvUrhoYFKTU0t1/VOfPF5dUi6rtzW16TRNZo+/XXn4/DQQC1e9GW5rb80HA6HHnn4H0qsXkXhoYH65ZefL6udgwcPlOn5ZXX+a361KO99uywKLyRXFuUV1goueHb5AcZmd5Tbj/WTKVmX9eO3UHaO9bJDSWnY7Q6dOlO2XrHTZ7PLJVhmZudd8FoKJeFwFFyzpjycSc29rINKhaz59nIbCXCyjO9/emaecnIv/30pqfx8u86kle3ze/pstlt7VwulZVrKFLbtDl12KC2Nz77bo5S0nFL1qJ/PZnNo6do/tP9IqnGFFcPhcJT5/zVn03NlzXfPAVvCOlBCa9eu0ZAhf9PyFav1xaKvZLVadXvvnsrKKr8j/0b7+98eUnhooF55+V8u0xcv+lLhoaU7B+/WW7rqqScfd5nWtl2Sft93QBEREWWu1R2++Hyh/u/W7kqsFq/KlSLVqmVT/WPoEP388zZPl+Y2y5Z9qw8+eE8LFnym3/cdUKNGjV3mf/H5QkWGB+vYsaPFPr9F88YaM/pJVatWvdjnX8jf//aQ7r3nrjLXX2jlqnV64IFBhrVXGkZvi7udf2CjSaNrFB4aqPDQQFWuFKkmja7RgP79tGrl94au14jwasmzlUuP5ZnUsoeX1PRctw/bzbPalJld9vBS1mBSEqkZxV/0rjTs/zs32N2MeD2ycqyy5Lm3x9LhKLj4YVmVx/ufnWs1pAf3TJr7DyycTc8p0wEwqeAgSHo5HAQz4vVIz7Q4zyF3l7c++kk+BpzH7eNj0pyF7u0UyMjKk7WMr4fDYcz/R4pDWAdKaOHni9Tvr/erYaNGatq0mWbOfFuHDx/Wtp9+9HRpZRIYGKipr76ss2fPGt62v7+/4uLiveK2Iecb++zTGjjgr2rarLk+/OgTbf3pF81+Z55q1qylcc8969Ha8vLc9z/8/X/8ofj4eLVtl6S4uHj5+rpebOjWnv+n6OgYzf/g/SLPXbd2jf7Yt0/33z9QZrO52Oe7W+FrUyk2VsHBweW6bpvNJru9fM/3LeRwOJSfb1wQePqZsfp93wFt/ekXvTVrtiIiInRbr1v1rymTDWnfbneUuVe9kLt/rGdm513gqu+lY/vflZndyaiQlZGV5/Zh+0a9b+4OltZ8m2EjDdy9r6ZlWsrUq14oz2p3+7nLZwwaaZKWkevW0RUOh8O4fdXNo2tyLfmXfVrRuRwOGXLQ50J+2p2sbb+ecF5BvSxsdofmfPaLW4fuG/UdU3DQx/gDtoR14DKlpRecSxUVFe3hSsqm8403qXLluCK96+dKSUnRAwP7q3692oqLjVK761prwccfOef//W8Pae3aNZrx5hvOHruDBw8UOwz+i88X6ro2LVUpOlxNGl2j16dNdVlXk0bX6N//ekn/GDpECfGV1KhBXc155z8uy4x99mm1bNFEcbFRatakgZ6fME5Wa8n/B7Z58yZNffVlTZw0RZMmT1H7Dh1VvXqiWrZspSefGqPPFv45hP2PP/bpnr/cqTq1ElUlLkY3dOqg779fXuJ1SdKRI4c1oH8/Va8ap8TqVXTPX+7UwYMHnPMLe2r/NWWyrqlbS61bNtXkSS+q7bWtirTVIek6PT9h3AXXtXbNanW+oaMqRYerXp2aem7sM86Q9/e/PaQnHh+pw4cPKzw0UE0aXVPk+X5+frrn3vv0wQfvFZn33nvz1Oba69SwUaNih8Hv3rVLd915h6pWiVVCfCV173qT/vhjnya++Lzmf/C+vlq8yLl/rFm9SpK0c8cO/d+t3VW5UqRqJCbokYf/oczMP8/9Ku61kYr2Fr/x+mtqd11rxVeOVsP6dTRyxCMu7RTnUs/54P13Vb1qnL7+arGubd1ClaLD9Y+hQ4rdlv797tVjo0Y4n/vUk48rPDRQv+3ZI6ngIEN85WjnvmOxWPTE46NUu2Z1xcZEqFvXG7V16xbn8ws/O99+u1SdOiapUnS4NqxfV2Qb/vhjn5o1aaDHRo0o1Y+E0LAwxcXFq3r1RHXoeL2mvfGmnnxqjF58YYIhp/dk5VjL3KtaKMPNoSIz27iDY0a2VRwjh6+7s1Zrvs2wH9eWPJtbDyxkZlvL3KvqbMvNPasV5f03sn27Q2U6ReFSci35Ze5VLZSVY3Xr6Boj37MMA0boXMj6n44a0qteKC3Toj0HUgxr71wOh8OQ0UpSwQVHc9xwUIGwDlwGu92u0U89rnZJSWrUuGTDgL2V2WzWc+Mm6K2Zb+ro0SPFLmOx5Kply1Za8OlCbdy8VQMfGKQhgx/Uli0/SJJemvKyrmvbTgMHPqjf9x3Q7/sOqFq16kXa+emnHzXg/n7qe+dd2rBpq8b88xm98Px4ffD+uy7Lvf76a2rZsrXWrNukhwb/TSNHPOISIEJDQzVz5tvavOUnvTTlZc2bO0fT35hW4m3+ZMHHCg0N1eAhfyt2/rkjAbKystStW3ctWvyN1qzbpC5duuovd/XV4cOHSrQuq9WqO3r3UmhYqJYsXa5ly75XSEiI+tx+m0sP+qqV3+v333/TF19+pY8XLFT/+wdoz55fXQLczz9v044d2/XX/vcXu65jx47qzr63q1Wr1lq/4Qe9OnWa3p03V1NemiSp4H16+pmxqlq1qn7fd0ArVxUNf5J0//0DtW/vXq1bu8Y5LTMzU198vlD33z/gguu+5ZYuCvAP0KKvlmjVmg3qf/8A2fJteuTRkerT50516drNuX+0bZekrKws3XF7L0VGRmnlqnV6990PtHLlCj3+2AiXts9/bYpj8vHRlH+9rE0//KiZb/1Hq1et1LPP/LPYZUvznOzsbL366r/1xvQZ2vzDT5ryr1eK3ZYOHa/X2jWrnc9bt3aNYmIqac3/pv24dYusVqvatk2SJD37zD/15Refa+as/2jN2o2qXbuO7ri9l86cOeOy/nFjn9G4Cc/rh63b1LhJU5d5O3ZsV/euN+vOu/6il1+ZWuYRLEP/8bAcDoe++mpRmdqRJJuBIxDsDrn1XFAjh4PaDOj1vBgjg4A7eyuNfh3cWauRo2UudlsqIxj5Ohh1MK082ndnrUa3beR33/mM3L/c+ZlKzciV2WzsiMrUdPcctDX+u8r4fZX7rAOX4bGRj2r3rp1aumyFp0sxRK/beqtps+aa+OLzmv7mW0XmJyRU1SOPjnQ+/vvQf2j58mVa+NmnatPmWkVERMjfz09BwcGKi4u/4HreeP013dD5Rj01uiAQ1atXT7/++qtem/qq+v31zwDarVt3Z5AeOepxTX/jda1evUr1rinoCX7yqTHOZWvUqKlHHh2hTz9ZoBEjHyvR9u7d+7tq1qzlMoT7jddf04svTHA+/vW3PxQREaGmTZupadNmzunPjh2nxYu+1NdffaW//X3oJdf16acLZLfb9cb0mc4wNWPm26peNU5r1qzSzTd3lSQFB4fojekz5e//5z1Qb+7SVe+/965at24jSXr/vXfVseP1qlWrdrHr+s+st1S1ajVncLumfn0dP35cz419WqPHPK2IiAiFhoU5h7BfSIOGDXXtdW313nvz1KHj9ZKkhZ99IofDob533l3sc96eNVMR4eGaM+89+fkV3HO6Xr16zvmBQYGy5Flc1jv/g/eUa8nVW2/PVkhIiKTG+tfLU/WXu/powoQXVTku7oKvzfmGDRvu/HeNGjX17NhxGvHocL069cIHcUryHKvVqldeneayDxS3Lddf30lPPfmYTp86JbOvr379dbeefGqM1qxZrUEPDdaaNavVqnVrBQcHKysrS7P/M0sz3npb3bp1lyS9/sYMfd+ovt57d64eHTHK2e7Tz4zVTTd1KVL7po0bdNddffTEE09p+CMjLriNpREdHa3Y2Mo6ePBgmdsqh+sseSV3b3Z5XMDKG7lzq6/Sl9TtjHxdr9b93q3c+JK6Y3RBed7GrywYBg94gcdGjdCSJV9r8ddLVbVqNU+XY5gJE17Q/A/e155ffy0yz2az6aXJE9XuutZKrF5FVeJitPy7ZTpy+HCp1vHbnj1q1y7JZVq7dknat2+vbLY/hzk2OacH0WQyKS4uTqdOnXRO+/STBerapbPq1q6hKnExen7COB0uZS3n+2v/AVq7frOmTntDWVlZzi/czMxMPf3P0WrTqrmqV41TlbgY7dnzq44cKdn6dmzfrj/+2KeE+EqqEhejKnExqlG9inJzc7X/j/3O5Ro3blwkjA4c+KA+/eRj5ebmKi8vTws+/kh/vUDPtiTt2bNH17Vt69LD2i4pSZmZmRccNXEh/fvfry8+X6iMjIJbRL333jzdfkcfhYWFFbv8L7/8oqT2HZxBvST27Nmjpk2a/i+o/6/edkmy2+36/fc/R1IU99qc7/vvl6tXz1tUv15tJcRX0pDBD+rMmRRlZ1/4qrcleY6/v7/L/nghjRo3VlRUtNauXaMN69epWfMWuqXHrc7RCWvXrtH1HTtJkvbv/0NWq9Xls+Dn56fWrdtozx7Xz1/LVkVPhTh85LB639ZTT43+p2FBvZDD4TDkGhO+ZuN+XviY5NbrXpgNrNXs497rc5h9DHxdDWzrfEb3qhm5P53Px8D3zOjtLtp+RdpXjXxd3bivGvw6GPkZLdq2cbX6uHFfjQwLMDxcR4YHGNpeIaM/s+74DiCsAyXkcDj02KgRWrzoSy36aqlq1qzl6ZIM1aHj9bq5S1eNG1f04mqvTX1FM96crhGjHtNXXy/V2vWbdXOXrsqzuuecN9/zAp/JZHIOVdy0aaMeGjRQ3brdoo8/+Uxr1m3S4088JWspaqlTp64OHNjvcp57ZGSk6tSpo4QqCS7LPvPP0Vq06As9N26ClixdrrXrN6tx4yYlvghcVmamWrRspbXrN7v8/bhth+66+y/O5YLPCayFetzaU/7+AVr05Rf65uuvlJ9v1e239ynxdpZFYQ/6ws8+0d69e7Vxwwbdf//ACy4fFBjktlqKe23OdfDgAd19Zx81btJU733wX61as0EvvzJV0oUv1lfS5wQFBZUoKJpMJnXo0FFr1qwuCObXd1KTJk1lybNo186d2rxpozpcf33JNvgcwcFFt71SpUpq3bqNPlnwsdLTjbsPcUpKik6fPqWaNWuWua2gAF8Zla+Dg0p+AMjT7YdQqyTJz9csfz9jfmL6+frIz9d9P1eNfB2CA938/hvYfkX5XJlMUnCg+wYCBwb4GhaCAwN8DT34c76K8vlvVj/O0NMLAvzNqlM9yrD2zmUymRRk0P5l9jEpKMD415WwDpTQqJGP6uOP/qvZ78xVWFioTpxI1okTycrJKZ9765aH8eNf0Ddff6XNmze5TN+4cYN6/t//6Z577lPTps1Uq1Yt7d37u8syfv7+Lr3jxbmmfn1t3LihSNt169aT2WwuUY2bN21U9cREPfHkaLVq1Vp169Yt8fnjhe68625lZmbq7VlFh/yfb+PGDerXr7963dZbjZs0UVxcnA4dKvkw4eYtWmjfvr2KjY1VnTp1XP4udUs7X19f3dfvr3r//Xf1/vvvqm/fuxQUdOFQXL9+fW3etMllGNbGDRsUFhZW6lEgYWFhuv2OPnrvvXn64P15qluvntp36HjB5Rs3aaIN69dd8EJ//n5F94/69etr+47tLrc/3Lhxg3x8fFSvXtGL313Itp9+kt1u18RJL+m669qqXr16On78uOHPudi2SHKet752zWp1vL6TfHx81KFDR7322iuyWCxq1669JKlWrdry9/d3+SxYrVb9+ONWNWjQ8JLrDwoM0sefLFRgQIDuuL2Xc/RDWc148w35+Pio5//dVua2fH19FBFqTE9IdIT7DgRJUniIvyFh0MckRYWX7paXpRUTacxrERzkp8AA954JadT7Fh0R6NaRFQH+voYFl2iD3p8LiQoPlBFZ0NdsMuzzeSFGvf9hIf7y8y3Z74PLYTKZFBVhzOc22qB2LiQkyF8B/sa8Fu78Xu3UprpqVYs05ICt2WzSfT0bK9yN+6tRr0VEWIBbDtYQ1oESmv2fWUpLS9OtPbqpXp2azr/PPl3g6dIM07hJE939l3s0c8Z0l+l16tTV9yuWa9PGDdrz66969JFhOnXypMsyNRJraMuWH3Tw4AGlnD5d7EV7hj8yQqtWfq+XJk/U77//rg8+eE+z3pqhRx4dUeIa69SpqyOHD+uTBR/rjz/2acab07Vo0ZeXfuI52rZtp+GPjNDT/3xKY0Y/qQ3r1+nQoYPavHmT3n13rkwmk3OIaJ06dbXoyy/0yy8/a/v2XzTowQGluiDR3X+5VzExMbr3L3dq/bq1OnBgv9asXqUnHh9VoqHp9w94QKtXrdR3y7696BB4SXpoyN909OgRPf7YSP22Z4++WrxIE198XsMefuSyhrzef/9Abdq4UbNn/0f9+1983UP+NlTp6Rl6YEB//fjjVu3du1f//e8HzgsDJtaooZ07tuv3335TyunTslqtuvsv9yowIFB/H/KQdu3cqdWrVuqJx0fqnnvvc56vXhK169SR1WrVzBlvav/+P/Tf/36gd2b/x/DnFCpuWyTp+k6d9Ouvu7V79y4lJRUE847Xd9LHH32olq1aO4f7h4SEaNBDQ/Ts0//UsmXf6tfduzX84aHKzslW/4uMXjhXSEiIFnz6uXzNZvXtc9slr3x/vsyMDJ04kawjRw5r3do1euThf+hfUyZr7HPjVadOnVK1dSFGBBd/Px+3/kiT/vdj3YCQHR4a4NbhulJBz50RvUDuDhVSQbAs6491k0mKCndvAJaM2VcDA3zd3rPu42NSpAH7alR4yUYMlUVYiL8hoyvcfbDOqHWYfUyKDHP/58qIWkOD/eTv594DIH+7u6UhbdlsDg3q28KQti4kMizAkNEVRh1MPR9hHSih9MzcYv/OvTDaleDpZ8YWCaNPPDlazVu01B2399KtPbqpcuU49fy/Xi7LDH90pMxms65r01K1alYrtre7RYuWmvfuB/r0kwVqd10rTXxhgp5+ZmypXsNbe/6fhj38iB5/bKQ6tm+rTZs2ulxwrqRenDhZs9+Zp19+3qa77+qjls2baED/+2S32/XdilUKDw+XJE2c/JIiIyPV9ebO+stdfXTzzV3VvHmLEq8nODhYS5Z+p2rVq6vffffo2tYtNGzY32XJzVVYWPgln1+3bl21bdtO11xTX9dee91Fl01IqKpPPv1cW7duUfukazXi0eG6f8DAy3p9JCmpfQfVq3eNMtLTde99/S66bExMjBZ/vUSZWZm69ZauuuH6JM2b8458/QqCxcCBD6puvWt0Q6f2qlWzmjZuWK/g4GAt/HyRzp49o843dFD//vep8w036t8vTy1VnU2bNtPEyVM09dWX1e661vr4ow/13PgJhj+nUHHbIkmNGzdRRGSkmjZrrtDQUEkFF56z2Wy6/rwh8OMnvKDbet+uIQ89qOs7ttMff+zTws8XKSqq5EP9QkND9enCL+VwOHRX39tdRihcyosvTFC9OjXVolljDRn8oNLT07Vo8TcaOerxErdxKcGBfmXusYyJDDaomouLjggq0481k0mqFFU+tcaWcT0Bfma396pKBecZlzVYRIYFyteNQ+ALhYeUvccyNrp83v+YyKAy9a6bfUyKjnR/qJTK/pkICvRVaPDFr1diBH8/syLCyvaZiIkMcusQ+EJR4YFlGglkMpXPvnrf/zVWgH/ZTocym026tkkVNatf2bjCimEymVQpqmzfVWEh/grwd89oJZOjAl9iMSPLvfdeLU5+fr5Wr/xOnTp3cbmS9NWK18PVT7uOqWWjhEsvCJSQw+FQi+aNNXjw3/Tw8Ec9XQ5QKvn5du0/kirLZdwnOyo8UFXjir+goTtk5eTpwNG0y7qKdfUq4eUSgAudOpOlEykXvnjihfiaTapdPcqtvWrncjgcOnA07bLukx0c5KdaVSPc3gNcKM9q0x+Hzyr/Mm69VDk6WJVjLn5tDSOlZ1p0ODm91PuqySTVSIgolwBc6PipTKWklv50QX8/H9WuFlUuB2ukgquN/3EkVbmXcZ/s8FB/JVa5+GltRsq15Gv/kdTLOi+8Smyo23qAz/fFit90/1NfXtaF5318TAoL9teKuf1Ur0a04bUV53ByutIyis+WvR/+WKfO5Cg2OkhfvOF6V5zCc+ov52BNWMil/79BzzoAeKnTp07prZkzdPLEiStuBAeuDr6+PqpZLaLU50fHRAaVa1CXCs4HrZEQUaoedpNJqhYfVq5BXZJio0MUX6l04dDfz0e1qkWWW1CXCnqsaiREKCykdOEwNNhPNRPKL6hLBb2rBa9P6X4ax8WUb1CXCk65qB4fXqoedrOPqdyDulQQDkvbaxngX/BelFdQlwrCYa2qEaW+iFtEWMF7UZ4CA3xVs2pEqXrYTabyDeqS1Puma/TK6C4ySSrNJ9lsNik40E+fTutbbkFdkqrFhSmylCMsggJ9VbtapFtHVdAVCgBeqnat6oqJqaTXXp9equHRgDfx8zWrdrVInUnL0Zm0HOVZL3zNh5AgP8VEBrn9PPULCQ32V53EKJ0+m620DMsFe65MJikiNECVooLdfqG2C6kUFaygQF+dPpujzOy8C/ay+ppNigoPUkxkULmGn0I+/wuJBe9/7kV7LgP8zYqJDPrf+e7lF9T/XL+v6lSPUkpqjs6m58qaf+F9NSzEXzGRQeUefguFhwao9v9qTcvI1YU6WX1MUmR4oGIig9w2TPdS4iuFKjjQTympORcdZeHv56Oo8MCC01LcfP2H4pjNPqpV9X/7amruRUcEBQX6KiYyqFzOUy9+/X6qXT1SKak5Sk3PveCIEJOpYF+JiQxy+zUVijOobwtVjg7RkOe+VlaOVSaTLvhdZTabZLM5VC0uXJ9M7aP6tWLKtVaTyaRq8eEKDcnVmbRcZV9kXw3wMys6smBfdfd3FWEdALxUemaup0sADOHjY1KlqGBVigpWRlaeUjNylZ9vl93hkNnHpAB/X0VHBHosTJzL38+shMphiq8UqrPpucrMznOGdrOPSSFBfooKD/RImDhfSJC/QoL8Zc236WxarrJzrbLZHfIxmWQ2mxQeGqCI0ACPBN/zRUcEKToiSNm5Vp1Ny1We1Sa7o6BWfz+zoiICPRImzmc2+6hyTIhio4OVnpWn9EyLy74aFOCnqIjAch2hcCGBAb6qGhem+EohSs3IVWa21WVfDQ32L7iCfDmcS30p4aEBCg8NUJ7VpjNpOcq15Dv31cK7R3jqIN25TCaTYiKDFRMZrMzsPKX+76BN4fvv72dWVHiggrxgX/XzNSu+UqjiYkKUmmFRRpZF+TaHHA6HfM0+CgrwVXSEZw7SnavXjfX0e9uh+njJbs388Ef9uj+l2OVuaJOov93dSt061PLo92tkWKAiwwKVa8nX2f9n777jm6j/P4C/LnsnXbQUCmWj7K24UBEQRUR/goggQ1EEFZUvqIgyFCdOFBdTUKYDcaIIKBsERESEMlq6R/bO5X5/hIamTWfukiDv5+PBQ9tcPvfO3SfXe3/WWVxwe3xgzo8NEDEMMptEd4RK7P8qEvIf0igpOgvMEELIxUqrltV7WHQsiEQMkgzKqA4bbSipRBz1odgNpVJI4yIprw3DBB5vFu0pDg0hFovOJ5ixjqR2MmkgwbwYaFSymI2cqI/yJ1oI/ejISGhUMoy/owvGDeuMvUfycfRkMWa9sw1Wuwd6rRzbVoxGy6aGWIcZQiGXoHFKoK6WN3iIxaKo14nYNwsT8h+SmnRx/AEihBBCCCEkmhiGQZ/O6Rh/R5dgo61aKY27RD2eULJOCCGEEEIIIYTEGUrWCSGEEEIIIYSQOEPJOiGEEEIIIYQQEmcoWSeEEEIIIYQQQuLMRb0avFYd/RU6vd5A+4ZGJYNUGv+rmQqNjkcor1dEx4EQQgghhJAalD9SkmGYmOR09RHLWKlnnRBCCCGEEEIIiTOUrBNCCCGEEEIIIXGGknVCCCGEEEIIISTOULJOCCGEEEIIIYTEGUrWCSGEEEIIIYSQOEPJOiGEEEIIIYQQEmcoWSeEEEIIIYQQQuIMJeuEEEIIIYQQQkicoWSdEEIIIYQQQgiJM5SsE0IIIYQQQgghcYaSdUIIIYQQQgghJM5IYh0AIf8F2XlmlJgc8LMsenZqGutwCCGEEELIJSIn34Kt+86izOyEz+eHQadApzYp6NUpHQzDxDo8EgFK1gmJUHaeGe0GL4LLw0IpF8Fx8OlYh0QIIXHLanfDZHHDx/rh93MQixnIZWIk6VWQycSxDi/I7+dgsrhgtXvA+v0AALFIBLVKigSdAmJx/AxO9HpZlJmdcLp9YFkODANIxCLotXLoNPK4ull3urwoMzvh8QbOv0jEQCYVIUGnhEopjXV4QRzHwWr3wGRxwcf6wXGAWMxAKZcgUa+EVBo/dZVl/TBZXbDZvSF1VauWwaBTQCSKn/Pv8QTqqssTqKsiEQOJWASDTg6tWh7r8ELYHR4YLS54fRfqqlwmRqJeCYU89imU38/hl92nsXDVfnyz9QQ4DhAxDBgGYP0cAKBjmxQ8em8v3HNLB6hVshhHHKirZWYn7E4vfL5AXfWxfhjNThh0iri6Vrk9PpSanPB42WCsLOuH3eGJ6rGMfU0j5CJXYnLA5WFjHQYhhMQtv59DmdmJUpMTbo8v7DbFZQ5o1XIkJyhjetPu9bIoNjpgNLuCiU9FZpsLBSU2GLQKJCeoYnrTbnd4UGJ0wmJ3g+O4Kq+brC5IJWIk6gOxxrKBwXj+/Dtc3rCvl5qcUMqlSE5QxvSmnWX9KDUFYvX6qv5tt9jcKCpzQHe+rsYyAXJ7fCgxOmC0uOD3Vz3/ZpsL+cU2JOgVSDbEtjHManejxOiE1e4O+7rR4oRMKkaSQYkkgypmDQwcd+Fa5XJXvVZZ7UCJ0QG1UoYkQ6CuxkJWthFDHl6DY6dKIREzKP/6+zkOqFAVjp4sxsTnv8MTr/yMZfOH4M4B7WMSr9PlRYnRCZPVFbxWBf/r55BTYAmpq7FsDDNbXSg1OWFzeIK/K4/V7+eQlWOEQi5BkkGJRL1S8GsVJeuEEEIIEYzP58fpcyY43eGTtIqsdjesdjdSElVonKKNQnSh7A4PzuSawybpFZU3PpisLmSk6aDXRv+GvbjMjvxiW63beX0sCkvtMFpcaNHUALksurd+HMchO88Cs81V67ZOtxc5BV6YbW40a6yPesLm8bA4nWuqtkGpHMdxMNtcMNtcSEvWoFGSOkoRXmCxuZGdbw6bpFfE+v2BhN7sQvN0PTTq6DcuFBTbUFRmr3U7j5dFfrEtUFebGKKesLGsH2fzzCFJWnXsTg/sTg+sdg+apmmj2rh08O8C3Dh+FSznGz58bPV1oDyJtzs9uOvxDVj47EA8PLJnNMIMMpqdOFdoDdugWJGP9aO4LFBXWzQ1QKmI7kgbjuOQW2hFmdlZ67Yutw+5hVaYLG5kNtEL2hAaP2O4CCGEEPKfwrJ+ZOUY65SoV1Rc5kBuoUWgqMJzOL04nWuqNVGvyO/ncDbPDLO19kSUT3VN1CvyeFlkZRvhieJIMI7jcCbXXKdEvSKLzY0zuaZab+755PWyyMox1pqoV1ZQYkNhSf3ORaQsNjfO5tWeqFfE+v04nWuCvQ6JKJ/yi611StQrcrl9yMoxBoceR4Pfz+H0OVOdEvWKjBYnsvOid606k2vCgAc+g8XmBltDkl4ZxwX+TZ73I9b9cEzACEMZzU7kFFjq9V32sX6cyjGFHdkgpJx8S50S9YrsTg9O5Zjq9V2sL0rWCSGEECKIs3nmeic/5QJDkR08RxSez+fHmdyG33Bl51uidmNptbvrnaiX87GBhC1aSXB+sa3aYc+1sTk8yCuy8hxR9U7nmsIOe6+LwlJ71Bps3B4fsvPNDTqH5Y0nXm90GmyMZieKyxr2HfZ4WZzNM/McUfXOFViqnaJRG7PNFbUGm/EzN8FkcQXnpNcXwwCjn/o6KtdWh9OLc4UN+w6z/sCILJaNToNNUakdpgZ+h51uL3LyhWuwoWSdkPNefvllMAyDqVOnBn+XlZWFYcOGISUlBTqdDsOHD0dhYWHsgiSEkIuEw+mtdy9VZQ290a+vMrMTvghuCjmOQ4kxOrEWlUa2H7fHB7O1YQl0fZQvJBWJMrMrKr2rFps74saWaNXVEqMjol481h+Ykx8NRREeE7vTE5WRAB4P2+BErVyJ0Slo7yoAHMsqwa97z9Y47L02HAd4vX4s+/JPHiMLr7jMEVHDoNcX+XmpCz6u32abq8EN07WhZJ0QAPv27cOHH36Izp07B39nt9sxYMAAMAyDLVu2YMeOHfB4PBgyZAj89RgmSQi5NOzatQtisRi33HJL2Nc9Hg9effVVdOnSBSqVCsnJybjqqquwdOlSeL2BHp2xY8eCYRg89NBDVd4/efJkMAyDsWPHBn83duxY3H777dXGtHXrVjAMA5PJFPIzwzAQiUTQ6/Xo1q0bpk+fjvz8/AZ/9nD4SAg8XhYWm7CJJcdxvMRqsroE7wVyuX2wOyNPXqKRrJWZI09eyhf7Ehofx8Ph8sLZwJ7ZuvL7ORgtkScvZWan4KMrbHYPL8lLNOpqqTnyhhbW74eJh3NTkw/W/AGJOPK58X6Ow7ur9gvauOD1ssE59ZEoMQp//suf+BApoeoqJevkkmez2TBq1Ch8/PHHSEhICP5+x44dOHPmDJYtW4ZOnTqhU6dOWL58Ofbv348tW7bEMGJCSDxavHgxHnnkEWzfvh15eXkhr3k8HgwcOBAvv/wyJk6ciJ07d2Lv3r2YPHky3n33XRw9ejS4bUZGBlavXg2n88IffpfLhc8++wzNmjXjJdbjx48jLy8P+/btw4wZM/Dzzz+jY8eOOHLkCC/llz9Kig9C36xbbO4GD3+uiK9EqiZ8DV21Oz2CD9vn67wJff49HrbBQ/UrEzqxMFr46b31sX7BR1eU8FRXzTa3oKMrOI5DmSn+r1UOpxeLNxyKqFe9orN5Zvy86zQvZYXDV4OQ2+ODzS7s6Aq+zpvRHP6pDJGiZJ1c8iZPnoxbbrkF/fv3D/m92+0GwzCQyy88QkihUEAkEuH333+PdpiEkDhms9mwZs0aTJo0CbfccguWLVsW8vpbb72F7du345dffsHkyZPRtWtXtGzZEvfccw/27NmDNm3aBLft3r07MjIy8MUXXwR/98UXX6BZs2bo1q0bL/E2atQIaWlpaNu2Le6++27s2LEDKSkpmDRpEi/lO1xe3nru+OhJrrl8/npDIx32X3v5/MUq5HH1ell4eJoX7fWxgi6Kx+c5E7yuXiTnH+AvVo7jBI3V5fbVa1HJmjjdXsF6q7NyjLxeq8RiBgeO8juaqiI+YxXy/HMc1+C1Cipj/X5BGkEpWSeXtNWrV+OPP/7ASy+9VOW1K664Amq1GjNmzIDD4YDdbse0adPAsizvw0UJIRe3tWvXon379mjXrh3uvfdeLFmyJCRZXbVqFfr37x822ZZKpVCrQx/9NH78eCxdujT485IlSzBu3DjB4lcqlXjooYewY8cOFBUVRVxefVYpro3fzwk6ZJfPWPksK3z5/PUwChlrQxe/qr484XpW+Sxb8PPP43EVPtaL47jyXbZQU2H4HrUjYhhBRwLxMay8HN/Xk5CyeT7/fH7ucpSsk0tWTk4OHnvsMaxatQoKRdVn5KakpGDdunX45ptvoNFooNfrYTKZ0L17d4hE9NUhhFywePFi3HvvvQCAQYMGwWw2Y9u2bcHXT5w4gfbt29e5vHvvvRe///47zp49i7Nnz2LHjh3B8oVSHt+ZM2cE3c9/mdCPWo7ms5wJiQSfdZWqfSC55r1MkXAHlsGledKEqKsS/osk5OJw4MABFBUVoXv37sHfsSyL7du3Y+HChXC73RgwYACysrJQUlICiUQCg8GAtLQ0tGzZMoaRE0LiyfHjx7F37158+eWXAACJRIIRI0Zg8eLF6NevHwDUu2c4JSUlOJye4zjccsstSE5O5jv0EOUx8nGTLeZhEaRyIhEjaJIqkfBXtljghlyxmIGXp1GWfJ6jKmXznAQIeVwlYv7KFvKYAvwe12jEytf8ajGP56hq2TzXVYFiTdBV7VSKhN/P8V5mRXweV76vJyFl833+BbhWUbJOLlk33nhjlcWUxo0bh/bt22PGjBkQi8XB35ffJG/ZsgVFRUW47bbbohorISR+LV68GD6fD+np6cHfcRwHuVyOhQsXQq/Xo23btvjnn3/qVe748eMxZcoUAMB7773Ha8zhHDt2DACQmZkZcVlqpQwiEcPL/E2tSl77RhGWz9djt7RqGS/l1FQ+X3MihTyuUqkYCrmEl1jlMglkMnHtGzaQRiUDwzC8TLXQaQSuq2oZbws3atVCxyqH0RL5wl0iEQO1UspDROEp5BJIJWJeFpksv+4JoU3zRCTqFSgz83P+WT+Hq7pl8FJWOFq1jLf1IISsqwzDQKPiJ1aJWASlgv/UmsbykkuWVqtFx44dQ/6p1WokJSWhY8eOAIClS5di9+7dyMrKwsqVK3HXXXfh8ccfR7t27WIcPSEkHvh8PqxYsQILFizAoUOHgv8OHz6M9PR0fP755wCAe+65Bz///DMOHjxYpQyv1wu73V7l94MGDYLH44HX68XAgQMF/RxOpxMfffQRrr32WqSkpERcnkjE8NZrk2RQ8lJOdTRqGeSyyG+wxCIRDAL2VAFAkl7FSzlatVzQBBjg77wJff6lUjF0PCUDQsdq0Cl4GQkgk4oFb1jg61gYtApBe9YZhkGiPv6vVTKZGA+N6M5LLzPDAJe3SsZV3ZvyEFl4iXolLyOilHIpVAI21gD8nTe+PnNllKwTUoPjx4/j9ttvx2WXXYa5c+di5syZeP3112MdFiEkTmzatAlGoxETJkyo0vh35513YvHixQCAqVOn4qqrrsKNN96I9957D4cPH8apU6ewdu1aXHHFFThx4kSVssViMY4dO4a///47ZKQPH4qKilBQUIATJ05g9erVuOqqq1BSUoJFixbxto8kQ+SJpUIugUbg3mqAn5u1BL1C0DmgQOCGnY9eJqGTSgBI0CkjHhLKZ6NPTfg4HhoVP40+NWEYBgk8JJaJeuHPv0ophVIeeZIVjbrKR5IlEYug1wrbADLxru7w87HYJgc8em8vQacXicUiGLSR19XkBOHPv04jh1QS2d9YhmEEq6s0DJ6QCrZu3Rry88svv4yXX345NsEQQuLe4sWL0b9/f+j1+iqv3XnnnXj11Vfx559/onPnzti8eTPefPNNfPjhh5g2bRpUKhUuu+wyPProo8HRPJXpdDpB4m7Xrl1g+J9Gg5YtW2LAgAF44oknkJaWxts+FHIJDFpFRMN2U5PUtW/EgwSdAiVGR4MfNyYWiZDMQ+NEXaQmqWFzeBo8bFulkAreqwoEEu2URBUKSmwNLiPZoBK0V7WcRi2DWilr8COiGIZBo8To1NVkgwpGs6vBq05LJeKoJMAAkJqsxplcU4Pfr9PIoVQI26sKBEZXJOoVET1vOzVZLfgCkM2b6DH0hrb4ZuuJBq9iLhIFhn2PujX83xw+pSSqYLY1/NnjcpkEeh4S/towDIPUJDXOFVoaXEaCTgGpVJjRSpSsE0IIIQ30zTffVPta7969QxIquVyOp556Ck899VS176n8fPbKvvrqq3pt369fv5AYKv8stKZpOni8bIOeY5uWrInKjRoQ6AVq0dSAk2eN9X7kFMMwyGyiF3xYeTmVUoqmqVrkFNT/xlImFSOziYH/oKrRKEkNt4dt0Nxlg1aBtBSNAFGFl9lEj5PZRrg99Z9n36SRNiojQIBAYpnZxIBT54z1ToLEokA9j0YDCBBIthunaJBfXP8GG6VcimaNqzaCCiW9kRYerx9Wu7ve700yKHkZSVQXH8+9Bb1HLEVOvrneC/gxDCBigK/e/b+o1FeFXILm6XqcyTXX+++OVCJGiyYGwUcrlUs0KOH2+hq0folGJUOTVK0AUQXQMHhCCCGECEIkYtAyI6HeQ7fTG2nRKEq96uXkMglaN0+ArB69I2KRCC2bGqBWRSdRK5egV6JZY329evKUcilaN0uERBLdW7+Mxrp69+Qm6pXIaCzMqJLqiMUitMpIgKoePbkMwyAjTYfEKPVUl1MppWjZNKFe89elEjFaNUuAQh7dfrqURDWapGrrVVc1KhlaZkQvUQMuNLrVd+h2o0Q1mqRGr64mJ6jw8+J70CRVB0k9VjIXiRiIxSKsXnAHru+TKVyAlWjVcmQ20dfrXMplErTKSIhaA2i5xinaeo/m0msUaNHUIOwTSwQrmRBCCCGXPJEocBNssblRanJWu+quSMTAoFUgyaCMytDXcOQyCdo0T0SZ2Ykys6vaXlapJDBsNsmginryW86gU0CpkKDU5ITR7Kp2RIBSLkWSQYkEvSJmz2lvkqqDXhMYZmyxu8P2sjEMA61ahmSDKmq91JVJJCK0apYAk8WFUpOz2hEhYpEICfpAXRV6nnp1VEop2mYmoczsRKnJWe1q5jJpYNh7ol4ZtR71ypIMKqiVMpQYHTBZqx8WrVbKkGRQQq+Vx6SuMgyDZul6GGyBulpdLzvDMNBr5EgyKKPeUAcALTMSsG/tONzzv6/w864zkIirf0yeWMyAZTk0TdVi5StDcU3PZlGONpCwt8tMQqnJiTKzs9opHAq5JHCt0imj2lBTUWqyBlq1HKUmJ0xWV7UjArTqwPmPxpQiStYJIYQQIiiGYaDXKqDXKuD2+IJzbv1+DmIxA7lMggSdsKs+15VYLEJKohopiWpY7W5Y7Z7g/FCxOPAYKZ0mNslEZXKZBOmNtEhL1sBkccHp9oJlOTAMA4mEgV6jEHwl5brSqGXQqGXwelkYLS54vCz8fg4iEQOZVCzonM/6CCzipkSCXgmnywuT1QWW5YJ1VSmXwqATfjHBupBIRGiUpEZKogpWuwc2R2hd1aplgj+ira4UcgmapunQOEUDk9UFl9sHlg2cf4k48CSFaPf6V0enkUOnkcNzfgqH13fhWhWoq8qYNdKVS0lUY/PiUfj7ZDEWrf4DS744XKVxiWGAgVe1xJR7emLg1a1iWmelUjHSUjRITVbDbHXD4fKCOR+PSMSgVUZCTBo+wlEpAyvQl9dVt8cXEmv7FslR7fWPj28FIRexZIMKCpkYLk/kz+gkhJD/OrlMEtW5yJHQquVxk+zURCRizg/Fju5w7IaQSsVRn+LQUEqFNGajPOqDYZhgghnvxGJR1OZ3R0omEyM1Ob6vVZe3TsG7zw7E/Mf7Yf9f+fi/qRtQZnYhyaDEH+snoFl69Ob91wXDMDDoFCGPIRSLRXGTqFckkYiQnBCoqxVjjfbwfErWCYlQs3Q9jn83CSUmB/wsJeyEEEIIISR6tGo5ru+TGWhcMrugkEviLlEnDUPJOiE8aJauR7N0Pbze+q94TAghhBBCCCGVxX5yGCGEEEIIIYQQQkJQsk4IIYQQQgghhMQZStYJIYQQQgghhJA4Q8k6IYQQQgghhBASZyhZJ4QQQgghhBBC4gwl64QQQgghhBBCSJyhZJ0QQgghhBBCCIkzlKwTQgghhBBCCCFxhpJ1QgghhBBCCCEkzlCyTgghhBBCCCGExBlJrAOIhNXujvo+fT4fAMDm8EAi8Ud9//GGjkcon89Hx4EQQgghhEQdx3HB/8YiT6oPihXQquW1bkM964QQQgghhBBCSJyhZJ0QQgghhBBCCIkzlKwTQgghhBBCCCFxhpJ1QgghhBBCCCEkzlzUC8wREms5BRaUmpzBnzk/i26Xp8cwIkIIIYQQEqmzeWZ8/cu/KCqzw+70QqeWo0mqFnfc1A7JCapYh0cuEZSsE9JAOQUWdL9zMdweNvg7pVyEwt+fiGFUhBASv/x+DiarCyarG6zPDz/HQSRiIJeJkahXQqOSxTrEIK+PRZnJBavDA78/8JQPkUgEtVKKRL0Ccln83EI5XF6UmZxwun3w+zkwDCAWi6DXyJGgU0Asjo+BlBzHwWL3wGh2wus9f/4ZBlKpCAadAnqNHAzDxDpMAADL+i/UVdYPjgNEIgZKuQSJBiVUCmmsQwzyeFmUmZ2wObwhdVWrkiFBr4BMKo5xhBfYHB4YLS64ztdVEcNALLlQV0Wi2J5/v5/Dz7tO46N1B7F552kwDANxhZhY1o+n3vgVd9zUDg/c1Q29OzWOeZ31+fwwWpyw2D3w+vzB3+UWWpFkUEIhj59rlcvtQ5nZCbvTGxJrUakdiXolJJL4uFYBgNXuQZnZCY+XvRAr64fR4oJBG71rVfycPUIuMqUmZ0iiTgghJDyfz49iowMmiwusn6vyutvDwmLzQC4VI9GgRJJBGYMoA5wuL4rKHLA5POCqhOqHy+1DqckJtVKKlERVTBsYTFYXSoxOuNy+qi96/XC6fCgqtUOnkaNRkjpmSRvHcSguc8BocQVveitye1nYHF4USuxI0CmQnKCKWdLm8bIoLnPAbHUhTFWF28PCZHVDIZcgyaBEgk4R/SDPszk8KDE6YHN4w7waqKvFRge0ahlSElUxbWAoNTlRZq7mvsnLwuH0orDEBoNOgZREFaSS6NfVEqMDdz3+BQ4cLYBYzIDjAnXXX6ki+Fk/vtj8D9b+cAy3Xd8GH88dDGUMjm35+bXY3BWuVecfMQYORosLRosLSoUEyQkq6DW1PyZMKBabGyUmJxzOinX1QqxFZQ4UGx3QaeRISVDFrIGB47hgXfV4K16rLjy6LbfQioJiGxL0CqQkqARvDI2f5gtCCCGE/Oe4PT6cOmdEqckZNlEP2dbLIr/Yhux8c/C5ttFksrpw6pwJVnu4RD2U3enFmVwzSk2O6ARXSX6xDecKrOET9Qr8HGCyupGVbYTd6YlSdBewrB+nzplQVOYIm6hX5PX5UVTmwOlcE3y1bCsEh8uLUzlGGC3hE/WKXG4fcgutyCuyxqSulpqcOJtnriZRD2W1e3D6nAlGiysKkYXiOA45BRbkF9tq7eDwc0CZ2YVTOaZa6zXf8oqsuO6+lTj0TyEAgGVrPqe+869v2nYSt05aG/Xvls0ROKdmq7vWa5XT5UNOvgWFJfboBFdJUakd2fmWSol6VRwHmK1unMoxwmqP/rXK7+dwNs+MghJ7pUS9KtbPocToxKkcEzxeYTvuKFknhBBCiCA8Xhanz5lqvfGpzGLzIDvfIlBU1e3TjXMF1lpvfCvLL7aHrF0SDfnFtnrvk/VzOJtrhtNVe3LHF7+fw+lcM5yu+iVeTpcPp3NNYNnoJewutw9nc83BJKyuyswu5BXZBIqqun06kV9sq1dd5Tggt9AKs80tXGBhnCu0wmyt3z69Pj/O5AqfBJWz2NwY9sh65BVZa03SK/P7ORz4uwD3Pf1N1BqY7E4PzuaZa238rKzY6EBRaXQT9uIyO4rK6teg6eeA7HwzbI7oJewcx9W58asit5fFGYEbFylZJ4QQQoggsvMt9U5+ylntHhSXRefG0uNlca6g4Y0D+cU2OKKUBBstrgY3DgRugi1VhvUKJa+o9p7/6rg9LHKLrDxHFF75jXp9k59yRosLZeboNNg4XV7kFze8ceBcgQVuT3R6rUuMjnon6uV8bKBxKRre+nQf/j1TVu9EvZzfz+GnHaex/qd/eI6sKpb1IzvPUu9GxXJFZYFh89FgtXtQWNqwkUccB+TkW6LWAFJYGlhEsCE8Xj+yI/j7URtK1sklKy8vF/dPGIvmzdLRKNmAK3r3wB9/HAi+XlRYiIcevB9tW7dAakoCht0+BCdPnoxhxNE1/8V5uOrK3sGfH3rwfoy8+64YRvTfQMc1Pq1YvhRDb7tF8P10vLwt3nvv3WpfP3v2DHQaBf7887CgcVSuh88/9yymPfk4r/uwOTwRD2UtNTmjMsS4zOysddhzbaLVu15ijGzYvdcXWDhNaF4fG3EvrsXmiUrvqsnqrnWIfm1KjdE5/4HvRMPfz50fai608rm/kXB7WcETS4+XxeL1hxrcUFNOJGLw4Zo/eIqqesZq1v2oj5IoXasinSLE+jkYLcLH6vdzKIvwmDicXsFGLVGyTi5JRqMRA/pfD6lUig1ffI29+w/ixZdehsFgABD4IzNy5HCcOX0an69Zh9937EGzjGYYOuRm2O0N7+nZs2c3DDoV/u/O2/n5IBWsWrkCGU1SeS+33CuvLsCiDz4WrPxyHS9vC51GAZ1GgbRGibjmqivw5RcbeCtf6GSotvIffexxbNz0vSD7vtjV9dxU3q785/J/6WnJ6N2zG554/LE6NbC5XC68MG8unn56Ji+fIxJNm2bgRNYZXH55h6ju99FHp+Lzz1bi9OlTvJXJR0+jj+UEH7bLcRyMPCQvFptb8F4gh8vLy8Km0UjWjGZXREllxXKExkddDSySJ+ywXZb18/J9MFlcgo+uqLgyeSSEHrGw8dcTvMzlLx8Of/h4IQ9RVY+P4+FwegVfE8BzftHISEXlWlWHNSrqQqgGW0rWySXprTcXoEmTplj0wcfo2bMXMjNb4MYbb0LLlq0AACdPnsS+vXvw5lvvokePnmjTti3efPtdOJ0urF+3psH7/XTFMjz40MPYueN35Ofn8fVx6sXjadjNhF6vDzZmCG3ms8/hRNYZ/LZjD7p374Gx992LPbt3RWXf5Rp6nGqj0WiQlJQUURkNjY1l2eBjff6LNn7zHU5kncGOXXvx/Oy5+Pf4P7jqyl7Y+uuWGt/31VdfQKvV4oor+0a0f6838hsTsViM1NQ0SCTRXQk3KTkZN954ExZ/wk+DnNfH8rZAkNA3ayarO+KeKiDQYyl0L1CkvT/lXG6foMP2OY7jbTEzo0XY0RUut6/ec+qrI3RiabTw0wDCnn+EopD4qqs2h1fQ0RUfrzsY8mi2SIjFDJZ9+ScvZYVjtXvqvf5HdYSuq3yV7/X5BR9dwVesFptbkHU2KFknl6Tvvt2Ebt17YMy996BlZgau7tsHy5YuDr7ucQcuDHLFhcdciEQiyOUy7Nq1s0H7tNls+GLDetx//0QMGHgzVq38NOT1cD3jm77ZCJ3mwmNhjhz5E7fcPADpaclo0jgF1159Jf744wB+274Nkx6aCLPZHOxdnP/iPACBnupXXp6PiQ+MR5PGKXj0kYcBAM/NmoluXTsiNSUBnTu2x7y5s2tMNioP1968+ScMuOl6ZDRJRfNm6bjr/4bh1Kms4OvlvZ0bv/4Kt9w8AKkpCeh7RS/s2bO71mOl0WqRmpqGNm3aYMGbb0OpVOL7778FABz96y/cOnggGiUb0LxZOh6d8jBsttD5e8uXLUHP7l2QkqRHj26d8fFHHwZf69ShPQDg6r59oNMoMHjQTSGf77VXX0bb1i3Qo1snvPzSi+jTq3uV+K66sjfmzZ1d6+cIp/Lw43IvzX8BLZo3RZPGKZj66JSQhHzwoJvw5BNTMWP6NGQ2a4JhQ28FACx8921c0bsH0hol4rJ2rfD41EdDjkV5nfru203o1aMrkhN12LVzBxINGhQWFoTsf8b0aRh40w3Vxq3TKPDJxx/hjmG3oVGyAZ07tsdXX34Rso1Q56auEpOSkJqahhYtWuKWW4dg46bv0bNnL0yZ/BBYtvqbvQ3r1+HmwaFD4A8c2I+hQwYjs1kTNE1vhJsH9sehQwfDHpMRw+9EWqNEvPbqywCA77/7FtddexVSkvTIbNYE99w9POR9TocDD0+aiPS0ZFzevjWWLvkk+FrFUQN+vx/t27bCJx9/FPL+w4cPQa9VIjv7LADAZDJhyuSHgvXn1sEDceRI6A3jGwteQ6sWzZCelozJDz8It7vqzc+gwYOxYf3aao9TfThdPl6SikBZws4F57OHycFT0lcdvpJKvsuqzMf6eelVDZTFCZqs8dloIXRvJZ+xCnn+AX6PhVPA4/rPqVJeGuuAwAryf50o5qWscFxuHs+/4HWVv/KF/F5xHMfbY5j9HOASYD0IStbJJenMmdNY/MlHaNW6Fb78+htMuP8BTP/fk1i1KpBAt23XDhkZGZjz/HMwGo3weDx4843XkZubi4KCglpKD+/LL9ajTdt2aNO2LUbcPRKffrq83r0F948fi/QmTbB12w5s+20XHn9iGqRSKfpccSVefuV16HQ6nMg6gxNZZ/DoYxfmn777zlvo1KkzftuxG9NnPA0g0MP7wQcfY+/+g3jl1QVYvmwp3lv4Tp1jcdjtmDLlMWzdvhPfbPoeIpEIo0aOqNJzO3fO83jkscfx+869aN26DSaMGwOfr+4XM4lEAqlUCo/HC7vdjmG3D4HBkICt23ZgxYpV2Lp1C6Y9OTW4/Zo1n+PFF+bhuefnYN+BQ3h+9ly88MKc4Ln9ddvvAC70wq787MJIiW1bf8WJE//i643fYu26LzF6zH04fvwfHDiwP7jN4cOH8NdfR3Dv6DF1/gy12bb1Vxw/fhzfff8TFi9dgY0bv8bL818I2ebzz1ZCJpNh88+/4q23FwIAGJEIr762AHv2/YEPPvwE27dtxaxnnwl5n8PhwJtvvo6F7y3C3n0H0a17D2S2aIHVn38W3Mbr9WLtmtUYPea+GuN8Yd4cDB16O3bs2oe7ht+NcWNH4/g/gQV1hD43DSESiTDp4SnIzs7GwYPVzyXcvWsnunULbZSxWa0YOepe/LR5C37Zsh2tWrfG/91xO6zW0AWvXpr/AoYMuQ279uzH6DH34Ycfvsc9I4djwICB+H3HHnzz7ffo0bNnyHveffdtdOvWA7/t2IP7H3gQj099FCf+/Tds/P9313CsW7s65Pdr16zGFVdciWbNmgMA7ht9D4qLi7Hhy6+x7bdd6NK1G4bccjPKysoAAF9sWI+X5r+A52fPwbbtO5GamoZPPv6wyv569uiF3NxcnD17pvqDWkd83fwCCD7rWCh89oTw+bnDl89frEKOsmnoQl3VlifgceVzODjfn1vI8vmsS+HL5/O4CherleepC0KOBOLzmPoFr6v8nTOfgOef92uVAMc1Nk+cJyTG/H4/unXvgednB3qfu3Tpir///htLFn+CUaNGQyqVYuVnazDl4YfQPKMxxGIx+l1/A24aMLDBN40rVizDiBEjAQA33TQADz9kwe+/bcc1115X5zLOncvBo1MfR9t27QAArVu3Dr6m1+vAMAxSU9OqvO/aa/vhkUenhvyuPGkHgObNM/HoY1OxYf06TH38yTrFMvT2YSE/v//+h2iR2RT/HDuGyztcmG/76GNTMWjQzQCAZ2bOQu9e3XAqKyv4GWri8Xjw7jtvwWw249rr+mHd2tVwuV348OPFUKvVADrgtQVvYcRdd2Du3BfRKDUV81+chxfnv4zbht4OAMjMbIF//jmGpUsWY9So0UhOTgZwoRe2IpVKjYXvfQCZTBb83Y39b8LKT1egR49AwrXy0xW4+upr0KJFyzodp7qQymR4f9GHUKlUuOzyyzHz2ecw69mn8exzsyESBdpUW7VqjXkvzA953+TJjwT/v3nzTMx6bjamPvYI3nzrQqOL1+vFG2++g06dOgd/N2bMWKz8dAUem/oEgEBPsNvtwrA7/q/GOIcNuwP3jR0PAJj13Gz8+usWfPDB+3jzrXcEPzcN1bZtoJ5lnz2Lnj17VXndZDLBbDajcePGIb+/rt/1IT+/8+77yGiSit9//w033zw4+Pu7ho/AvaMvNHKMGzsGd/7fXZj57HPB31U89gAwYMBAPDDxQQDA409Mw3sL38X27dvQpm3bKvENH3E33n3nLeTkZCMjoxn8fj82rF+H/01/CgCwa+cOHDiwH1mncyCXB0YCvTj/ZWzatBFff/UFxo2/H++/vxCjx4zFmPvGAQCee34Otm79FW5X6E1l2vljkJOdjebNM6vEUh8MP6NKo4LhMVihP3YgVn5uBvn83FXL5rk8fosLLZvHwoWu9/zGKmywDAPeRtcIGatELOJ1rQm5TMxbWZUxfH4TBK+r/O1AxNM0hXB4v1YJUFepZ51cktLS0tC+ffuQ37Vr1x7ncnKCP3fr1h07du1FTm4hTpw8gy+/+gZlZWXIzGxR7/2d+PdfHNi/H3fdFRgOK5FIcMed/4cVK5bVq5zJUx7FI5Mn4bZbb8YbC14LGXZek27dqw7l3rB+HW7q3w+tWzZH49QkzJs7GzkVPn9tTp48iXFjR6Nzx/Zo0jgFHTsEkqKcc6FldOjYKfj/qWmBBKy4uKjGsp+fNRONU5OQmpKAt95cgDlzX8CgQTfj+PHj6NSx0/lkMOCKK66E3+/HiRP/wm634/SpU5gy+SE0Tk0K/nvt1Zdx+lTtC2d16NAhJFEHgLFjx2PD+rVwuVzweDxYt3YN7q2lB7q+OnXsBJVKFfy5d58+sNlsOFfhWHbt1q3K+3799RcMuWUQ2rVpifS0ZEx8YDzKykrhcFxYgVUmk6FjhXMAAKNGjcapU1nYu3cPAGDVqk8x7I47Q45rOL369An5uXfvPvj3eKBnXehz01DljWvV/QF1uQJz1eQKRcjviwoL8ciUSejapQOapjdCk8YpgXNS6TtS+bt15M/D6Fcp0a+s4vkINLClVvud6Ny5C9q1a491awOjDH7/bTuKi4tw+7A7Avs7cgQ2mw2ZzdJDjuvZM2dw+vRpAMC/x/9Bz16hDRW9e4eeSwBQKpUAAIcz8vl7EjF/txdiESPozbpYzF/ZEomwt1ViHo+rkDfAYhG/x0HI48rnMeX7c1fG5/eKz7LC4WseON9lVabXyGvfqB6SDEpey6uI12uVwOdfwmOsQp5/sVjEa8LO5+cOlsl7iYRcBPpccWWVYacnT55ARrNmVbbV6/XnXz+Jg38cwLOznquyTW1WrFgGn8+Htm0uJPocx0Eul+P1BW9Br9eDEYmq9NpXnkP+zMxZuGv43fjxx++x+acfMf/FeVi67FMMuW1ojftXVUrC9uzZjfsnjMUzM2fhxv43QafTY8P6tVj47tt1/kwj7roDGc2a4Z1330fjxunw+/3o07s7vJUWP5NKpcH/L7/hrm345aOPPY5R946BRqNGo0apdb5Rt5+fH/3OwvfRs2fovPC63JBVPk4AcPPgWyCTyfHNxq8hk8ng83lx++131CkePqlUobGdPXsGw//vDky4fyJmPT8HCQmJ2L1rByY//BA8Hk8w+VcqlVWOX0qjRrj55luw6tMVyGyeic0//Yjvvv9J0PgjPTcNdfx8Y0LzzMywrycmJoFhGJhMxpDfP/jg/SgrK8Mrry5As4xmkMnl6H/DdfB4Q+u3utJ5UShrv1GTVPhOAIHvRU3fieEj7sa6tWvwxJP/w7p1a9D/pgHBRQrtdhvS0hrj2zDnz3D+2lVXxvPD5stHOERCrZRCImYa/Iz1inQ830yHK7+Ep8duadWy2jeKgE4t42X+JsMAOrVwx1UiEUGllMLRwOcWV6SQSyCVCNdbqVXJeOsFjkZdNTXwueWVCV5XNXJehoSLRQw0KuFiHXBVC3z+7VFerlUMA/S/MjPyoKqh08hRUNLwJxKFliXs+deq5bysBg8I/73SqmWw2CKfDiGTiqBUSGvfsJ6oZ51ckiZPeRT79u3F66+9gqysLKxduxrLli4ODk0FgC+/2IDftm/D6dOn8O2mb3D7bYNx66234cYb67folc/nw+efrcL8l17Bjp17g/927tqHtMaNsX5dYEGn5OQUWK3WkEfDHQnzCKs2bdpgypRH8fXGbzHktqFYuXIFgMBQ6poW0apo757dyGjWDP+b/hS6d++B1q1bIycnu86fqbS0FCdO/Ivp059Cv+tvQLv27askO5FISk5Gq1atkJqaFpJotmvXDkf+OhJyjHbv3gWRSIQ2bdqiUWoqGjdOx5nTp9GqVauQf+UjIsp7zut6rCQSCe4ZdS9WrlyBlStX4M477wr2QPLlyF9H4KzQm7lv7x5oNBo0bZpR7XsOHTwIv9+P+S+9gt69+6BNmzbIz8+v8z7HjB2HL75Yj6VLF6NFi5Z1Wgl93969oT/v24u27QIjVGJxbmrj9/vxwaL3kJmZiS5duobdRiaToX37y/DP+bn35fbs3oWHJj2MgQMH4bLLL4dcLkNpaUmt++zYoSO2bv2Vj/CD7ho+An//fRQHD/6Br7/6EsOH3x18rUvXrigsLIBEIq5yXJPOJ91t27XH/n37Qsrcty/0XALA338fhVQqxWWXXR5xzAzDwKBT1L5hHSTq+SmnOiqFFAp55H0XEjHDey9dZQl6BS+9QDqNXPBRAHydNyF7KoFAYyEf541hgESDsHVVq5ZBysN5k8vEgibAAJCo5+e8GXQKQUeBPHBXN14SdSDQW33vkE61b9hAMqkYGlXkyaCIAQxaYetqgk7BS4+4WimFXCZs3zJfdTWBp795lVGyTi5JPXr0xKrP12L9urW4ond3vPryS3j5ldeCc8oBoKCgABMfGI+e3btg+v+ewIi778GSZSvqva8fvv8OJpMRo8eMxeUdOoT8Gzp0WHAofM+evaBSqTBn9nM4dSrQgLBq1cpgOU6nE08+MRW/bd+G7Oyz2L1rJ/744wDanU+WmjdrDpvNhq2/bkFpSUnIUOjKWrVqjXM5OVi/bi1OncrCovffwzffbKzzZ0pISEBiYhKWLl2MrKwsbNv6K55+eka9j019DR8xEgq5Ag9NvB9/Hz2K7du24n/THsfdI+9Bo9TASvrPzHwWbyx4DYvefw8nTpzA0b/+wspPlwdHDaSkNIJSqcTPm39CUWEhzGZzrfsdc984bN+2FT9v/qnOQ+BP/Psv/vzzcMi/6lbb93o8mPzwg/jn2DH8+OMPmP/iC5j44EPB+erhtGzVCl6vFx8seh+nT5/C55+vwpLFn1S7fWX9+98ErVaL1159uc6L5X315Rf4dMUynDhxAi++MBcH9u/Dgw9OAhC7c1NRWWkpCgsLcPr0KXz37SbcduvNOHBgPxa+9wHE4up75m7sfxN27wx9ykOrVq2x+vPPcPyff7Bv317cP35snRppnnp6JtavW4sXX5iL4//8g6N//YU333i9Xp+jsubNM9Hniisw5eHAqvaDb7k1+Nr119+I3r374J67h+OXXzbj7Nkz2LN7F+bOfg5//HEAADBp0mSs/HQ5Vn66PHju/jn2d5X97Ny5A337XsVbYxQfN0BKhUSQnorK+EgsE3RVR7HwTSoR89IjKnQDCBAYXhzpkFCxSPgGEABI5KFBQKOSCToCAAg0gvFx7vhKTmqikEugUkb+3RW6rnZtn4pul6VCFOF3VyJm8H8DLxO8cYmPc6fXKgQd0QYEptnotZF/d/n4btZGo5JBLo3su8swgb8BQqBknVyybr55MHbvPYDiUjP2/3EYY8dNCHl90sOTcex4FkqNVhw9dgKznptdZT5zXaxYsQz9rr8hOJy+otuG3o6DfxzAX38dQWJiIj7+ZCl++ukHXNmnJ9avW4unn5kZ3FYsFqOsrBQPTpyA7l074b4x9+KmmwbimZmzAASG9k+Y8ADG3jcaLTKb4q03F1Qb0+BbbsXkKY9i2pOP4+q+fbBnz+6QBedqIxKJsHT5Chw6eBBX9O6Op5+ajhcqLX4mBJVKhS+/+gZGYxn6XXcVRo++B/2uux6vL3gruM19Y8fj3fcWYdXKFbiyTw/cfPNNWLXy0+CCWRKJBK++tgBLl3yCtm1aYOSImhdVAwIL+fXpcwXatm2HXr2qPnYtnHFjR+Pqvn1C/hUVFYbd9rp+16NVq9YYNLA/xt13LwYPvgVPPzOrxvI7deqM+S+/irfeXIArevfA2jWr8fycuXWKDQicw3tGjQbLshh5z6g6veeZmbOwfv069L2iJ1Z/vgpLlq5A+8suAxC7c1PRbUMGo02rTFzZpyeef+5ZtG3XHjt378e11/Wr8X1jxozFTz/9ENI4sPD9D2AymXDN1Vdg4gPj8dCkyUhJSak1hmuuvQ4rPv0M33/3La7q2xu33jIIB/bvr/V9tRk+fCSOHPkTtw65LSSZZhgG67/4Gn2vuhoPP/QgunfthHFjRyM7JxuNGjUCANz5f3dh+oynMevZmbjumiuRk5ONCfdPrLKPDevXBRcQ5INMKo7oxpVhgNSkmtdR4ItBq4iod10qEQl+k16uUaI6oh4rrVoGtVLYXlUgUDcbRXj+GiWpBe1VLadSSCNKLEQM0ChRVfuGPEjUKyGTNvz2XS4TC9YDWFlqkiqikSAJOoXgvaoAMHVMb/gjnAfh54BJd1ddH4hvOo0c6ggaQSRiBilRqqspiaqIGuxUSil0Ak/XKJeaHNm1KiVBJdhoJYYT8nkoArPa+Zm3Ux8+nw/bt/6Ma/v1h0RCU/4v5eNx6J9CXDs69FnpSrkIhb8/EaOIiFA4jkPXLh3wwAMPYsojj8U6HN5MfvhBlJSUYM3aDbVuq9Mo8Nnna3HrkNuiEFn0jbn3HnTp2hVPTpse61Bi4qeffsTMp2dg1579vF7LOY5DToGlQfMBm6Rqo5ZUAIDXx+L0ORM83vqtDC0WMWjR1MDLUPq6sjk8OJtnrvc8a6VCghZNDFFJgMsVlthRbKx+pFd1khOUSEvWCBBReBzH4XSuud7z7BkGyEjTCT6vtiK3x4fT50z1Hr4tlYjQMsMg+AiAikxWF3ILrfWuqxqVFM3T9YKPVik3862teHdVwxtW33lmAMYO61z7hjxgWT9OnTOFfT740ClrUVzmREqiEl8vHB7ymogBMpsaoIrCaKVyTpcXZ3LNYR87V1OscpkYLZsaBB8BUFGpyYH84vBrAtQUq0ErR9M0XYP2qa3D2iHUs04IITUoKS7Ghx8sQlFhIUbdy9+z1WPJbDZj184dWLd2DR58aFKsw4kL816cD7U6eolBvHHY7Xj/g494b3RlGAYZabp6Jd0iBshoXL/38EEqEaNl04R6Jd0yaSD5iWaiDgSGbWY20derh12rlkU9UQcCPVaNU+rXa5WWrI5qog4E6mqLJvp6LbwlFjFonq6PaqIOAHKZBC0zEuo1dFcuE0c9UQcCo1Yy0nSoT7UzaOVRTdQBYN6j12HiXV3r9R7m/L+XHr8+aok6EFhnoWVTQ7162CXiQKNiNBN1AFAqpGjR1FCvtRZUSmnUE3UASDKo0CRVW6/RIMkJygYn6nV1aXWFEkJIPbVskYGkpGS8/e57SEhIiHU4vBg54v9w4MB+jJ/wAG64oX+sw4kLzZtn4qFJD8c6jJgpfxScEBiGQZNULRL1CpSanLDY3AjTyQKpRIREvQIJOqXgi59VRyIRoVWGARa7B2UmJ+zV9LIqFRIk6pUwaOVRTSgqUitlaJuZCJPVhTKTC25v1V42hgkk6Yl6peALitUkyaCCVi1HmdkJo9kVtpdNLAosSpioj87Q53AYhkGzxnrYnR6UmV2w2Nxhe4TlUjESDQoYojD3tzoyqRitmyfAbHOj1OSE0xX+KQFqpRSJBiV0alnM6qpOI0fbzCQYLU4YLa6wo1cYJrDOQaJBGfWEEgjMsX7tfzeibWYSXvp4J0pNTohFTPi6KmbAshyapevx0uP9cGu/NlGPVywWoUVTA6x2D8rMTljt4UcvyWViJOqVSBB4ob6aKOQStGl+/lpldlX7RAuNSnr+WhG7a1WCTgGNSgqj2YUyszPs6BURg/PXKmVUGmopWSeEkBpYbJE/eibefPfD5nq/5794HEh0KRVSNE2TgmX9MNvcYFk//P7Aja9MGlg4LVbJREUME1jUTK+Rw+3xwebwgD1/wyYWM1AppFFZ9K4uxGIRkgwqJBlUsDk8cLl9YFkODBN4TacRftGzupJJxUhL1iA1SQ2zzQ2vl4XfD4hEgFQqhk4tj1kyUZlaGZjX7/NdqKscFzj/Crkkpg0fFTEMA4M20Gjgcvtgd4bWVY1KFrOGj8okEhFSEtVISVTDavfA7QnUVZHowor8sWr4KMcwDCYO74Zxd3TGpq0n8eGaP7DzUG6lbYCBV7XExOHd0K9X85jXWa1aBq1aBo+XhdXuCcYjEonQoqk+KutT1IVIxCBRr0SiXgmHywuH0xtc1E8sEqFtZiJkES7yxhepRIxGSWqkJKpgsXvg9bIhsbZrkRTVuhof32BCLkJJBiXkMnHYOUOEEELCE4tFUVmNmg9ymSRukp3aaFSyuEkia1KeYF4MJFFcODBSCrkk6tMxGqo8wYxXUokYw/q3w7D+7VBUaseV9yw7P19ZhX1rx8Xl9at8QU/x+afIiEVM3CTqlakUUqgU0mDCKxIxcZOoV1TecAsgJNZoNypdHN9qQuJQRpoOf2yYgFLThedjc35K3AkhhBBC/gsaJamDo1MCU3XiL1En/22UrBMSgYw0HTIqLCzh84Wfh0MIIYQQQggh9UGrwRNCCCGEEEIIIXGGknVCCCGEEEIIISTOXNTD4OvyIHm+eb2B9g2NSgapND5Wg40lOh6hvF4RHQdCCCGEkP+I8qdUMAwTk9yjPihWYcQyVupZJ4QQQgghhBBC4gwl64QQQgghhBBCSJyhZJ0QQgghhBBCCIkzlKwTQgghhBBCCCFxhpJ1QgghhBBCCCEkzlCyTgghhBBCCCGExBlK1gkhhBBCCCGEkDhDyTohhBBCCCGEEBJnKFknhBBCCCGEEELiDCXrhBBCCCGEEEJInKFknRBCCCGEEEIIiTOUrBNCCCGEEEIIIXFGEusACLlYZeeZUWJyhPzOz7Lo2alpjCIihJCLC8dxYBgm1mH8p1xMx5RivfRkZRux9MvDyMoxwmRxQaWUIsmgxNAb2mLQ1a0gFlM/Iok/HMfFbN+UrBPSANl5ZrQbvAguDxvye6VcBMfBp2MUFSGExDePh0WZ2QmjxQUf6wfHcRCJGChkEiTqlTDoFBCJ4iMhcji9KDE6YLV7wPr9AACxSAS1KpBcaNXyGEcYwHEczFY3ysxOOFxe+P2BpFIsYqDXypFkUEEhj4/bPZb1B8+/28MGE2CZVIxEvQIJOiUkkvhI1tweH0pNTpit7pC6qpQHzr9eK4+b5N1m96DU5ITNEVpXtWoZkgxKqFWymMbn93P4dtsJvLtqPzbvPA2xmAHH4XxdDcT68bpDaJqqxZRRPTHhzq5ITlDFNGafz49SkwNGiwteb+Bez+tj8e+ZUiTqlUjQKeKmYcHp8qLU5ITF5g6J9fQ50/lrlSxu6qrF5kapyQmH0xsS67kCC5IMSigV0hhHGOD3czBanCgzueD2+uDzBb5XXh+LwhIbEvVKSKXiqMQSH1dvQi4yJSZHlUSdEEJIeG6PD/nFNlhs7iqv+f0cHC4vHC4v8ottSDQokJasidnNpcXmRmGJHU63t8prrN8Pi80Ni80NmVSMRolqJBqUMYgyoKjUjhKjAz7WH/J7juPgYzmUmpwoNTmhVsrQOEUDlTI2N8I+nx/5xTaYrK4qPVQcxwXrR0GJHQatAo1TNDFL2h1OLwpKbLA5PFVe8/s52J0e2J0eSIpESDIo0ShJHbO6ajQ7UVTmgNvjq/Ia6/fDZHXBZHVBIZcgNUkNvVYR9RjtDg/unvYlNm09CbE4cJxY9kId4DgE6++5QiueeXMrXlm8C99/cDf6dGkS9Xi9Xhb5xTaYbe6qvakc4HL7kFdkRUGJDQm6wLUqVkm7ze5BQYkNDlfVaxU4wGp3w2p3QyoRIzlBiZREdfSDPK/U5EBxmQMeb5h7Zw4oMztRZnZCpZAiNVkds8ZQv59DfrEVRosLfn+Y3nQOKCy1o6jMAZ1ajsYpGshkwibt8dEkRAghhJD/JLvDg5NnjWET9cpYvx/FZQ5kZRvBVkpAo6G4zI4zuaawiXplHi+Lc4UW5BVZoxBZKI7jcCbXhIISW5VEPRy704OsHCPMVlcUogvl8bA4mV0Go8VZ61BSjgv0Zp3MLgubgArNYnPj1Dlj2ES9Mh/rR2GpHWdyzeFv6gWWX2xFToGlTsfJ5fbhbJ4ZRaX2KER2gd3hwQ3jVuK77VkAQpP06vg5DharG9fd9ym27TsrdIghXG4fTmYbwzYqVeb3BxrDsnKMwR7iaDKanTidawqfqFfi9QUaILLzzFEfzs1xHHLyLcgttIZP1CtxuLw4k2tGmckZhehC+Xx+ZGUbUWpy1vqd5jgOZpsLJ7PL4KzDOYgEJeuEEEIIEYTT5cXpXFNwaG5dOVxenD5niuqNZanJgfxiW73fV2J0IL84ugl7dp6lTo0fFXEch+x8C6z2+r0vEj6fH6fOGet0k16Rx8viVI4pqkmQze7B2bz6J95WuxvZ+dFNggpLbCguc9S+YSUFJTaUGOv/vobw+zncPe1L7D9aUO9jyvo5eH1+DHl4Lf45VSJQhKE8Hhancozw+upX51xuX+AaF8XGRbPVhZwCS73rnMnqwrmC6F6rcgutMFrql3hzHIdzhRaYLNFrXGTZwLWqLg21FflYP07lmARtXKRknRBCCCG84ziuQclPufJh8dEQGNra8H0VlzmilgSXGB0w2xp2E1t+TqKVWJwrtNQ7US/n9bHIKbDwHFF4fn/guDQ04bbY3FFLgm12Dwoj6CHPK7IK3hMIAN9tP4lNW082+PtfPj1m+utbeI4svLN55jqNUgnH5fYhtzA6SbDXyyI7v+HfC6PFCaM5Or3WJosLZRHsK6fAAk+UppzmFdngcjcs4Wb9fpzNM/Mc0QWUrJNLjtVqxdSpU9G8eXMolUr07dsX+/btC77OcRyee+45NG7cGEqlEv3798eJEydiGDG5WMyePRtdu3YN/jx27FjcfvvtMYtHCGfOnAHDMDh06FCsQ/nPqVx/LnYWm7vBiVo5o7maeYM8KzU5Iu4ZLTFG5wY40qQwsHCS8D1Wbo+v3r3/ldkcngbfQNeH0eKs9+iPykqMtQ/z50Plp9A0qIwo1NWFq/YH56g3FMty2LTtBLIFTISAwHD9+vaoVma2uYOLkAmpzBx5PbtYrlUcx0WU7NeVzxdY3yESLrcPNnvt02cagpJ1csm5//77sXnzZnz66ac4cuQIBgwYgP79+yM3NxcA8Oqrr+Kdd97BBx98gD179kCtVmPgwIFwuaI/148Au3btglgsxi233MJ72cuWLYPBYOC93HJvv/02li1bJlj55TIzM8EwDFavXl3ltQ4dOoBhGN7iyMjIQH5+Pjp27BhRORs2bEC/fv2g1+uh0WjQuXNnzJ07F2VlZbzECQBbt24FwzAwmUy8lckXhmHw1Vdfhfxu2rRp+OWXX+r0/rom9pW3mz17NhiGAcMwkEgkSE5OxrXXXou33noLbje/PcOlPMw5ZP3+eg+hrPc+WD8vyavV7ha8F8hqj7wBBODn3ERrH6U8JKe17yPyWL0+FlaBbtaD+/Dysw+T1SXo6IqsbCN+2nmqTnPUayNiGHy07iAPUVWPj/PPcZzgdTWQvEZ+rXK6vXA4hR1d4Ty/aGik+GicqE1d1tOoC6Guq5Ssk0uK0+nEhg0b8Oqrr+Laa69F69atMXv2bLRu3RqLFi0Cx3F466238Oyzz2Lo0KHo3LkzVqxYgby8vCo31iQ6Fi9ejEceeQTbt29HXl5eTGLweBp2c6TX6wVtDKgoIyMDS5cuDfnd7t27UVBQALWavxVgxWIx0tLSIJE0/GEiM2fOxIgRI9CrVy98//33+Ouvv7BgwQIcPnwYn376KW+x1lVDzy/f+9JoNEhKShI8hg4dOiA/Px/Z2dn49ddfcdddd+Gll15C3759YbXyM5TT42HrtEhXXZSZhG0oNVn5670vNQt7s87XzaDbI1wvEHB+oTgekgoA1a/KzBM7j733QjeC8JW4BBbyE+57teyrw7w9gpH1c/hw7R+8lBW2fNYPc4QjQMrxkUjXxGJz13tOfXWiUVf54GP9MFuFnWLE17Gw2IUZXUHJOrmk+Hw+sCwLhSL08SVKpRK///47Tp8+jYKCAvTv3z/4ml6vR58+fbBr165oh3vJs9lsWLNmDSZNmoRbbrmlSu9wuJ7xr776KuQxOocPH8b1118PrVYLnU6HHj16YP/+/di6dSvGjRsHs9kc7GmcPXs2gEBP9bx58zBmzBjodDpMnDgRADBjxgy0bdsWKpUKLVu2xKxZs+D1Vt9yXHkY/A8//ICrr74aBoMBSUlJuPXWW5GVlRV8vXyI+RdffIHrr78eKpUKXbp0qVPdGzVqFLZt24acnJzg75YsWYJRo0aFJNbhhrGbTCYwDIOtW7cCAIxGI0aNGoWUlBQolUq0adMm2BAQ7v1Hjx7FrbfeCp1OB61Wi2uuuSbkc1W0d+9ezJ8/HwsWLMBrr72Gvn37IjMzEzfddBM2bNiA++67L7jt119/je7du0OhUKBly5aYM2cOfL4LN9UMw+CTTz7BsGHDoFKp0KZNG2zcuDEY5/XXXw8ASEhIAMMwGDt2LACgX79+mDJlCqZOnYrk5GQMHDgQAPDXX3/h5ptvhkajQWpqKkaPHo2SkuoXNyotLcXIkSPRpEkTqFQqdOrUCZ9//nnINuH2lZmZCQAYNmwYGIYJ/ly5F3zr1q3o3bs31Go1DAYDrrrqKpw9exbLli3DnDlzcPjw4WDdrc/ICYlEgrS0NKSnp6NTp0545JFHsG3bNvz111945ZVX6lxOTVw8LrbDZ1lhy+dxmLVb4J51PmMV8rh6vf6Ih5WX8/s53hKUcHitqwIP2Xe5+TsOQsZ6KscE8Ni+UmJ0CjbP3u1heeu59fpYQUcs8Pr9p7oKIHB94WO0EnDhMZR8o2SdXFK0Wi2uvPJKzJs3D3l5eWBZFitXrsSuXbuQn5+PgoICAEBqamrI+1JTU4OvkehZu3Yt2rdvj3bt2uHee+/FkiVL6v1HddSoUWjatCn27duHAwcO4KmnnoJUKkXfvn3x1ltvQafTIT8/H/n5+Zg2bVrwfa+//jq6dOmCgwcPYtasWQAC9WfZsmX4+++/8fbbb+Pjjz/Gm2++WedY7HY7nnjiCezfvx+//PILRCIRhg0bBn+lm9qZM2di2rRpOHToENq2bYuRI0eGJKnhpKamYuDAgVi+fDkAwOFwYM2aNRg/fnyd4ys3a9Ys/P333/j+++9x7NgxLFq0CMnJyWG3zc3NxbXXXgu5XI4tW7bgwIEDGD9+fLXxrlq1ChqNBg8//HDY18sbX3777TeMGTMGjz32GP7++298+OGHWLZsGV588cWQ7efMmYPhw4fjzz//xODBgzFq1CiUlZUhIyMDGzZsAAAcP34c+fn5ePvtt4PvW758OWQyGXbs2IEPPvgAJpMJN9xwA7p164b9+/fjhx9+QGFhIYYPH17tcXK5XOjRowe+/fZb/PXXX5g4cSJGjx6NvXv3hmxXeV/la2QsXboU+fn5IWtmlPP5fLj99ttx3XXX4c8//8SuXbswceJEMAyDESNG4Mknnwz2kOfn52PEiBHVxlkX7du3x80334wvvvgionLK8XnDynGcoD2rfAzVFaKscPg8DkImFXwl6sHyBDyufB5ToddX4PO4CnlMTVY3WJ6PRaRziqtzUdVVHoeD81lWOLzWVZ7PUUjZPF8H+a73ANDwcYyEXKQ+/fRTjB8/Hk2aNIFYLEb37t0xcuRIHDhwINahkUoWL16Me++9FwAwaNAgmM1mbNu2Df369atzGdnZ2fjf//6H9u3bAwDatGkTfE2v14NhGKSlpVV53w033IAnn3wy5HfPPvts8P8zMzMxbdo0rF69GtOnT69TLHfeeWfIz0uWLEFKSgr+/vvvkDng06ZNC87RnzNnDjp06ICTJ08GP0N1xo8fjyeffBIzZ87E+vXr0apVqwYtWJadnY1u3bqhZ8+eABDs+Q3nvffeg16vx+rVqyGVSgEAbdu2rXb7EydOoGXLlsFtqzNnzhw89dRTwZ72li1bYt68eZg+fTqef/754HZjx47FyJEjAQDz58/HO++8g71792LQoEFITEwEADRq1KjKCIw2bdrg1VdfDf78wgsvoFu3bpg/f37wd0uWLEFGRgb+/fffsJ+pSZMmIQ08jzzyCH788UesXbsWvXv3rnZf5QwGQ9i6BwAWiwVmsxm33norWrVqBQC47LLLgq9rNJpgDzlf2rdvj59++omXsvgaAitUeUKVzQgX5vny+dvBxXJMhSivIj6PqdDnn8/jIOQxVSokYBiAz3xQKa/5b0ZDiXg+aYLWVVxEdfVSvVYJcGCpZ51cclq1aoVt27bBZrMhJycHe/fuhdfrRcuWLYM3voWFhSHvKSws5PWmmNTu+PHj2Lt3bzARk0gkGDFiBBYvXlyvcp544gncf//96N+/P15++eVqh2dXVp6oVrRmzRpcddVVSEtLg0ajwbPPPovs7Ow6x3LixAmMHDkSLVu2hE6nCybBlcvo3Llz8P8bN24MACgqKqq1/FtuuQU2mw3bt2/HkiVLGtSrDgCTJk3C6tWr0bVrV0yfPh07d+6sdttDhw7hmmuuqTX5LlfXkRGHDx/G3LlzodFogv8eeOAB5Ofnw+G4MC+44rFSq9XQ6XR1OlY9evSosr9ff/01ZH/ljSPV1RmWZTFv3jx06tQJiYmJ0Gg0+PHHH6ucz8r7qovExESMHTsWAwcOxJAhQ/D2228jPz+/3uXUB8dxvCUuEjF/txd8liV0+VKJmLeywpd/cRxXvssWMlZejymPZYUtn8/vlUS4BCjZoIRYxF+sYjEDnUbOW3kV8XnOGIaJeAX8mkilF8+1is/jKuT3XywW8ZqwC3ENoGSdXLLUajUaN24Mo9GIH3/8EUOHDkWLFi2QlpYWsiKzxWLBnj17cOWVV8Yw2kvP4sWL4fP5kJ6eDolEAolEgkWLFmHDhg0wmwOPcRGJRFWSv8pzyGfPno2jR4/illtuwZYtW3D55Zfjyy+/rHX/lRdl27VrF0aNGoXBgwdj06ZNOHjwIGbOnFmvxcmGDBmCsrIyfPzxx9izZw/27NkDoOqiYxUT3/LkqfJQ+XAkEglGjx6N559/Hnv27MGoUaOqbCM6fwNV8bhVPmY333wzzp49i8cffxx5eXm48cYbQ3qQK1IqlbXGVVHbtm1x6tSpGuf6A4H1CubMmYNDhw4F/x05cgQnTpwIWXOiciMBwzB1OlaVz6/NZsOQIUNC9nfo0CGcOHEC1157bdgyXnvtNbz99tuYMWMGfv31Vxw6dAgDBw6scj4busDf0qVLsWvXLvTt2xdr1qxB27ZtsXv37gaVVRfHjh1DixYteClLrZJBJuXnZtCgU9S+UZyUb9AJk1BcKJ+fWEUi4ZIfIHADrFXzU75GJRM0Cdaq5bwllglC11Utj3WVx7IqG3pD2wY/s7wyiViEIf3aCNa7KpdJeOu112vkvI7UqFq+grfyDVqBr1U81S+GYQStq0DguPJBIZdAIed/0Dol6+SS8+OPP+KHH37A6dOnsXnzZlx//fVo3749xo0bB4ZhMHXqVLzwwgvYuHEjjhw5gjFjxiA9Pf0/97zseObz+bBixQosWLAgJHE6fPgw0tPTg4t4paSkwGq1wm63B98b7vnfbdu2xeOPP46ffvoJd9xxR3CxNJlMBpat28IiO3fuRPPmzTFz5kz07NkTbdq0wdmzZ+v8mUpLS3H8+HE8++yzuPHGG3HZZZfBaDTW+f11NX78eGzbtg1Dhw5FQkJClddTUlIAIKSXNtwxS0lJwX333YeVK1firbfewkcffRR2f507d8Zvv/1Wa/Jd7p577oHNZsP7778f9vXyx6x1794dx48fR+vWrav8E9XxxlomkwFAnc5x9+7dcfToUWRmZlbZX3XJ9o4dOzB06FDce++96NKlC1q2bIl///23TrFJpdI6xdWtWzc8/fTT2LlzJzp27IjPPvss+NnqWnfr4p9//sEPP/xQZapGJBL19WvIqU6SgZ9yqqOQS6BWyiIuRyYV85agVidBp+AlaTFoFRALPGKBr/Mm9PkXiRgk6CO/WWcYBgk6YWPVqGWQyyJPBlQKKZQKYYaVA8DAq1uhWWMdL2X5WD8eGdWLl7Kqc7HUVYlEBD0PjWxikUjwRlC9Vs5Lj7hWLYOUp4bf6sT7+adknVxyzGYzJk+ejPbt22PMmDG4+uqr8eOPPwZ76KZPn45HHnkEEydORK9evWCz2fDDDz9UWUGeCGfTpk0wGo2YMGECOnbsGPLvzjvvDA6F79OnD1QqFZ555hlkZWXhs88+C1kV2+l0YsqUKdi6dSvOnj2LHTt2YN++fcG5v5mZmbDZbPjll19QUlISMry6sjZt2iA7OxurV69GVlYW3nnnnTr10JdLSEhAUlISPvroI5w8eRJbtmzBE0880bADVIPLLrsMJSUlVR7jVk6pVOKKK67Ayy+/jGPHjmHbtm0hc/EB4LnnnsPXX3+NkydP4ujRo9i0aVPIfOmKpkyZAovFgrvvvhv79+/HiRMn8Omnn+L48eNht+/Tpw+mT5+OJ598EtOnT8euXbtw9uxZ/PLLL7jrrruCC+Q999xzWLFiBebMmYOjR4/i2LFjWL16dZVYa9K8eXMwDINNmzahuLgYNput2m0nT56MsrIyjBw5Evv27UNWVhZ+/PFHjBs3rtqkuE2bNti8eTN27tyJY8eO4cEHH6wyhaY6mZmZ+OWXX1BQUBC20eb06dN4+umng8fnp59+wokTJ0Lq7unTp3Ho0CGUlJTU6xnpPp8PBQUFyMvLw5EjR/Duu+/iuuuuQ9euXfG///2vzuXUJlGvjLgXSKPiJzmpDR83WULfqAOBHms+epmSE1Q8RFMznUYe8egKqUQs6AiAcnycO71GLvgweODiqKsiEYNHRvVCpO1KDAO0yjDg+j7N+QmsGgadIuLRFQq5BGpV5I1+teGjETTRwF8PfXUYhuEl1mhcV1VKacSjK0Qi4RrrKFknl5zhw4cjKysLbrcb+fn5WLhwIfR6ffB1hmEwd+5cFBQUwOVy4eeff65xwSzCv8WLF6N///4h56XcnXfeif379+PPP/9EYmIiVq5cie+++y742Kzyx68BgWeCl5aWYsyYMWjbti2GDx+Om2++GXPmzAEA9O3bFw899BBGjBiBlJSUsIuAlbvtttvw+OOPY8qUKejatSt27twZXCW+LkQiEVavXo0DBw6gY8eOePzxx/Haa6/V/aDUQ1JSUo3D05csWQKfz4cePXoER5JUJJPJ8PTTT6Nz58649tprIRaLsXr16mr3tWXLFthsNlx33XXo0aMHPv744xrnsL/yyiv47LPPsGfPHgwcOBAdOnTAE088gc6dOwcXlBs4cCA2bdqEn376Cb169cIVV1yBN998E82b1/2mrUmTJsGF6lJTUzFlypRqt01PT8eOHTvAsiwGDBiATp06YerUqTAYDNX25D/77LPo3r07Bg4ciH79+iEtLa3OI3AWLFiAzZs3IyMjA926davyukqlwj///IM777wTbdu2xcSJEzF58mQ8+OCDAALfg0GDBuH6669HSkpKlUfG1eTo0aNo3LgxmjVrhn79+mHt2rV4+umn8dtvv0Gj0dS5nNpIJCKkJTdsCgAQuPlpnMJfPDXRa+UR9Yor5BIkGYRPgAEgNUkd0XzTJINSkKGa4aQ30kaUFKQ30gieVACBodCRNGBIxCKkJUenribqlRElFmqlTPBeVQAYf0cXGHRKiCPI2DkOmDXpGsHrQKTXGoZhkJ6i5TGi6mnUsoga7GRSMVISGn5dro+URFVEja16jULw0UrlIr3WNE7RCDZVg+H4erjgJcLr9eK7777D4MGD67yg0n/ZpXo8/vg7Hz3+b0mV3yvlIjgOPh2DiAghJD7lFVlRYqx+1Eo4IhGD5un6qN2oAYHHbp3KMcJRz+c5y6RitMpIEHyoZkVOlxenckz1fqSRXqNAs3RdVBLgcmUmJ84VWur9vvRG2qiMAKgoO89c70eEiUUitGhqgEoZvXsgn8+PrBxjvZ/prJRL0TLDIPgUiHL7/8rDNfeugMfnb9Bj7Z64rw8WzOgvQGThFZXaUVBSdQTWTRM+Q1GZA40SVdi8+J6Q1xiGQUaaLioNIOU4jsPpcybYHFXXzKkpVolYhFbNEqIyWqmcx8MiK8cIr6/qCLWaYtWoZGjR1BDVa5XZ6kJ2viXsQrg1xZqapEaqgI111LNOCCGEEMGkN9IiLbnuvRZSiRgtmyZENVEHAg0ELTMS6rXYkFopQ+tmiVFN1AFAqZCidfP63XQnJ6jQvIk+qje/AJBoUKJ5ur7OvU4iEYNmjfVRT9QBoFm6HimJdd+vTCpGq2YJUU3UgcColdbNEqCpx7BrnUaOVs0SopaoA0DPjunYvHgUNEppnVdJL6+eT47tg9f+d6OA0VXVKEmNpql1b8wSi0TIbKKPaqIOBBoIWjQ11GuYuUIuQetmiVFN1AFAJhOjdbOEeo0GSdApo56oA4Beq0CLJoY6z7VnGAZNUrWCJuoAPWedEEIIIQJrlKRGgk6BMrMTZWZX2F4WtVKGJIMSeq2wKyrXRCRi0LyJHk6XCqUmJ0xWV5UeQYZhoNfIkWRQRmWOanXkMgnatUiCxeZGqckJq73qugVikQiJBgWS9CrIZNFtUKhIrw0MZzVanCg1OeFyV+0RlsskSDIokaATfvG7mjRO0SJJrzpfV51hVzXXqGRITlBBq5bFrK6KxSK0zEiAw+lFidEBs81dpUdQJGKg1yiQnKAUdEG5mlzdIwN7147HjAVb8M3WE2AAsGF62SViBj6WQ6uMBMyadDXGDO1ctbAoSDx/DSozB+qqx1v1WqWUS5FkUMLA04KPDcEwDJqm6ZCSGLhWGc2uKiNtGIaBVh24rka78bMiqVSMNpmJsNk9KDU5YbFXrauBRe/kSDKoojZNJxyNWob2LZNhtrpQYnTC6a460komFZ+/Vimjsk4FJeuENECyQQWFTAyXh7/VmAkh5L9MKhUjNVmDRklq2B1e+NjA0FixmIFcJswjbxpKqZCiaZoUjVM0sDu9YM8nbGKxCCqFNCo3aHWl08ih08jh8bBwur1gWQ4ME+h91ahil0xWJhIxSDKokGRQweH0wuNl4fdzEIkYyKTiqPdO10QmEyMtRYPUZDVsDg98Pj84LvC8b4VcEvXeyZqolFI0U+rh8/nhcIXWVbVSGtOGj3LtWiThq4V34VyBBR+vO4gP1vyBorLQqTFDb2iHyff0QL/ezWNeZ8ViEVIS1UhJVMPu8ASPoVgsQpvmiTFr+AhHLpMERy/ZKsXavkVS1Ef91ESjlkGjlsHrZeFweUNivaxVcswaPioLPCFCiQS9Ek6XF24PeyFWiQjtWyZHNZ74udoQchFplq7H8e8mocQU+sfGz+OjlAgh5L+IYRho1LHrka4PsVgUldXI+SCTiWPae14fKqU0rpLz6gR6Ji+O8y+RxH9dbZqmw5xHrsOcR66D2+NDZv+FKCixI72RBuvf5u+xkXxSq2TBJFIkYuIqUa9IJGKg08hDYo2nRL0iqVQMvVQcEmu8JOqVKc8/5jAYawwakihZJ6SBmqXr0Sw9dLXyuj5rmhBCCCHkUiWXSYK9lbHuSScknsV+bAwhhBBCCCGEEEJCULJOCCGEEEIIIYTEGUrWCSGEEEIIIYSQOEPJOiGEEEIIIYQQEmcoWSeEEEIIIYQQQuIMJeuEEEIIIYQQQkicoWSdEEIIIYQQQgiJM5SsE0IIIYQQQgghcYaSdUIIIYQQQgghJM5Qsk4IIYQQQgghhMQZStYJIYQQQgghhJA4I4l1AJGw2t1R36fP5wMA2BweSCT+qO8/3tDxCOXz+eg4EEIIIYTUguO44H9jcU9fVxdLnADFKhShYtWq5bVuQz3rhBBCCCGEEEJInKFknRBCCCGEEEIIiTOUrBNCCCGEEEIIIXGGknVCCCGEEEIIISTOXNQLzBESCzkFFpSanGFf4/wsul2eHuWICCHk4sJxHHysHxwHiBgGYjEDhmFiHVZYfn8gVgAQixiIxfHbz8GyfrB+DgwDiEUiiETxeUw5jgPLcmD9fohForg//6w/UFfj9fyfOFuGJV8cxr6/8lFqcoJhgCS9En27NsW4Ozojs4kh1iFW4fP5Qxbtilccx4XEyXFcXNfVi+GYAoFr1cUQa/m1KpaxUrJOSD3kFFjQ/c7FcHvYsK8r5SIU/v5ElKMihJCLg93pQZnZBYvNjYr3PBIxgwSdEokGBaQScewCPI/jOJhtbpSanHC6fCGvyWViJOqVSNAp4iIZ9vn8MFldKDM74fGGPo1Eq5YhUa+EVi2LUXShXG4fysxOmK1usP4LFUDEAAadAol6JRTy+Lg1tdo9KDM7YXN4QuqqVCJCol6BBJ0SEknsEneO4/Dd9iwsWn0A2/fnQCxmwLIXAj0JI/b/lY+3VuzFjVdm4uGRPdD/yhYxixcAvD4WRrMLRosLXp8/2AjmY/3IzjcjUa+ERhUfddXp8qLU5ITF5g6J859TpefrqgJyWezrKsdxsNg9KDM5YXd6Q2I9caYMiQYFDFpFXDQysez5a5XJBbeXDYn19DkTEg1K6NSyuGgM8XhZlJmdMJpdYCs02PpYP84VWJBoUEKlkEYlltjXMkIuIqUmZ7WJOiGEkPDsTg/yimzVXj99LIdiowMlJge0ahnSU7QxS4RKTQ4UlzngY8P3oLg9LPKLbSgqtSNRr0RqsjrKEQb4/Rzyi20wWV2orrPHavfAavdAJhUhNVkDvab2xwQJweNlkVtohd3pDfu6nwPKzC6UmV1QKaVo0kgTs0TIYnOjoMRWpeGjnNfnR2GpA0VlDug1cqQ30ka90cbn8+PJV3/G0i//hPj8vtkw9bW8QeTXvWfx864zeGx0L8yZcm3U42VZP/KKbVUa6Sqy2Dyw2DyQy8RonKKJWdLudHmRV2yr0khXjvVzKDU5UWpyQqOSokmqNmYNjEaLC0Wldnh94euq28siv9iOwhI7EvRKpCWrY5IIcxyHwlI7ykxO+Ks5/3anF3anF1KJCCmJKiTqldEN8jyvj0VekQ1Wu6fabUxWN0xWNxRyCdIbaQRP2mPfzEIIIYSQ/yyT1YUzueY6NXRyXOCm/dQ5Izze6DeM5hVZkV9srzZRr4j1BxoYzuaZoz40kmX9OJ1rgtFSfaJekcfrR06+BaUmh/DBVeJweXEqx1htol5le6cXp3JMsDurv1kWSqnJiex8S7WJekUcF7hpP3XOBF81yZIQ/H4OE2Z9i2Vf/QkAISMUqlOeyL/96T5MfWlzVOur18fiVI4JZmv1iXpFbg+Ls3lmGC0u4YOrxObw4PQ5U7WJetXtA3XV5a7b9nwqKrUjt9BabaJekZ8L1O0zuWb461Bf+OT3cziTa0aJsfpEvSKvz4+8IhsKS+zCB1eJ2+PDqRxTjYl6RS63D2fOmWCxCfuMeErWCSGEECIIm8OD3EJrnW7SK/J4/TibawbLRi8JKiyxo8xc/wTBavfgXKFVgIjC4zgOZ/MtdU4oKsovtkc1CXJ7fDiba65T40dFrJ9Ddp4lqkmQyepCfrGt3u9zuX04mx+9JGjeot/x5c/H6/2dKrfsqz/x1oq9/AZVDZb1Bxrq6tnwxnGBhjOhk6CKnC4vsvPMdUooK/L6/DibZ4bXF73GxVJTYGRHfdmdXmTnR69xkeM45BRY6txQV1Gx0YESY/QaF70+FmdyzXVq/KjIzwHnCixwuOr/GeuKknXyn7Dj998w/K470LZ1C+g0Cmz6ZmPI6xu//gpDb7sFzZulQ6dR4M8/D1cp49SpLNxz93C0aN4UTRqn4L7Ro1BUWBitj0AIIf8pHMfhXIGlwUlFYAhn/ZOnhnC4vCiO4MbQbHXDZI1OElxc5oCjATe/5fKKrFFLLM4VWuvU8xsO6+ei1gji8/mRG8G+nC4fisqE7wksKLHxkmjP/2gnzFFIhAtL7Q2eOshxQG6hNWqNIDkFlnon6uW8Edaf+nB7fMgvbnhdszm8KDOHXySZb2VmV517qcMpKLFHrcEur8hW70S9nJ8DcvItgjWCULJO/hPsDgc6duyEBW+8Vc3rdlx5ZV/MnftC+Nftdtw+9FYwDINN3/6Anzb/Co/Xg+HD74TfH72eHUII+a8wWd317lGtzGxzR6V3vbonfES7jNpwHBdxzzjHAcYGjCCoL6fL26De/4pcbp+gPVblapr3X+cyLC7BeyyXffkn+NiDx8vis01HeSipeizrhynCusr6I6/vdWG1e+o09aEmNoc3KlN3GjL6p0oZpug0LPLRKBCNhgWvj4XNEdm0G6/PH1HDRE0oWSf/CQMGDMRzz8/BkNuGhn195MhReOrpmeh3/Q1hX9+9eyeyz57Fog8/RoeOHdGhY0d88OEnOPjHAWzb9quQoRMSU3v27IZBp8L/3Xm7IOUvW7oYfa/ohcapSchokoqr+/bBgtdfDb7+0IP3Y+TddwmybwIMHnQTZkyfFvKzTqOATqNAcqIO7dq0xPC77sDGr7/ifd983GRxHAS/Wff5/LwMt3W6fIL3Alnsngb3/lRkjEJiyUdSAQBlUWgE4aOu+lgOJqtwvdVeH4uP1x3iraf5gzV/CFoHTFZXg3uqK4pGssbXPoSO1e/nIm4AAQKjliJNTmtjd3p4WZDZZHEJPrqizBR5Yx0g3PmnZJ0QAB63BwzDQC6/sFKuQqGASCTCrp07YxgZIcL6dMUyPPjQw9i543fk5+fxXvZTM/6HhyY9jN937sVPm3/FY1OfgN0WnaHN8YJl2bgaoTN27HicyDqDw0f+xqerPkf79pdh3NjReHTKw7ztw+X2RdyrWk7oXmA+elXLCX2zbuSpfCF7gYBAUmHmaVqA0KMrbI7Ie1XLCdmw9NuBnIimalTEccDpcyYc+LuAl/LC4auxxu1hBR1d4fWxvH0XhL5WmW3uBk8rqUzoRlC+joWfg+BTjIwWfq6rQo2uoGSdEAC9evWGWq3Gc7NmwuFwwG63Y+YzT4FlWRQWCPfHjJBYstls+GLDetx//0QMGHgzVq38NOT1VStXIKNJasjvNn2zETqNIvjzkSN/4pabByA9LRlNGqfg2quvxB9/HAAAfPfdtxh2x50Yc984tGrVCpddfjnuGj4Cz82eCwCY/+I8fLZqJb7d9E2wt/e37dsAAM/NmoluXTsiNSUBnTu2x7y5s+H1ht6wvfrKS2iZmYH0tGRMmfwQnn/uWVx1Ze+QbZYvW4Ke3bsgJUmPHt064+OPPqzxmAwedBOefGIqnnxiKpqmN0JmsyaYN3d2SA+U2+3GzGeeQrs2LZHWKBHX97smGHfF4/bdt5vQq0dXJCfqkJOTjd+2b0O/665GWqNEZDRJxU39+yE7+2zwfZ98/BE6d7oMSQladO/WCZ9/viokNp1GgeXLluCeu4cjNSUBXbt0wHffbqrx84SjVKmQmpqGJk2aonfvPpg770W8/c5CLFu2BL/++ku9ywunvgtK1cQj8PxqPm+uhB4Gy2f5Qs5b97F+XnpVgUBiycdogurw+ThWr4DnP0+AOdH5RcI1nPJ5LIT8XvFZt1g/J2jDEp/HVOjHEPP5N8DLU2NaOH4/F/F0rYooWSdEIMkpKVj+6Sp8//23aJyahKbpjWA2m9C1azeIRPQ1If9NX36xHm3atkObtm0x4u6R+PTT5fUeFnn/+LFIb9IEW7ftwLbfduHxJ6ZBKg08czS1USr27d0bkpBW9Ohjj+OOO/4P/W8agBNZZ3Ai6wz6XHElAECj0eCDDz7G3v0H8cqrC7B82VK8t/Cd4HvXrPkcr7/2CubOewHbftuFpk0zsPiTj0LKX7Pmc7z4wjw89/wc7DtwCM/PnosXXpiDVatCGyUq+/yzlZBIJPh16+945bXX8d7Cd7B82ZLg69OenIq9e3dj6bIV2Ll7H4YNuwN3DLsNJ0+eDG7jcDjw5puvY+F7i7B330EkJCTinpHDcfXV12Dn7n34+ZdtGDtuQvCZt99s/Bozpj+JRx6Zit17/8D48ffj4YcmYvu2rSGxvfzSixh2x53YuXs/BgwYhPsnjEVZWVktZ6l294waDUNCAjZ+/XXEZQHgddgix/FbXmV89VQB4C1BrbZ8HncQ7rnc/JXN7821X8Dh2nwOBRcyUbPYPRDx/IxsIVdb5/O7IOj3n+fvAZ/Xk8r4/B4IPdqL12uVgLHyXbYQdVXCe4mEXKRuvPEm/HnkGEpLSiCWSGAwGNC6ZXPc+X8tYh0aIYJYsWIZRowYCQC46aYBePghC37/bTuuufa6Opdx7lwOHp36ONq2awcAaN26dfC1p56ZiVH33I2Ol7dD6zZt0Lt3HwwYMAi3D7sDIpEIGo0GCqUCbo8bqalpIeVOn/F08P+bN8/Eo49NxYb16zD18ScBAB9+sAijx4zFvaPvC+zr6ZnYsuVn2G0XVsmd/+I8vDj/Zdw29HYAQGZmC/zzzzEsXbIYo0aNrvYzNWnSFC+/8hoYhkGbtm3x99GjeG/huxg7bgJycrKx8tMV+PufE2jcOB1AoNHh580/YdXK5Xh+9jwAgNfrxRtvvoNOnToDAMrKymA2mzFo0M1o2bIVAKBd+/bBfb7zzpsYNWo0Hpj4IACgTZvHsG/vXrzzzlu49rp+we3uGTUadw0fAQB4fvZcfLDoPRw4sB833TSgxvNUG5FIhNat21TbsFL/8vhNKvguL6RsHhMgAcMMlC9iAJ6SC0GPKd/nn+cktSKGx7LFYuEa99VKKe+NFmqVlNfyKmIY8Da9RNi6ynN5AtZVXq9VAndE8XnOLpbrPyBMrNRlSEglScnJMBgM2Lb1VxQXF2Hw4FtjHRIhvDvx7784sH8/7rprOABAIpHgjjv/DytWLKtXOZOnPIpHJk/CbbfejDcWvIZTp7KCr6WlNcYvW7Zh954DmDRpCnw+Hx568H4Mu31Ira36G9avw039+6F1y+ZonJqEeXNnIycnJ/j6yRP/okfPniHv6dGjV/D/7XY7Tp86hSmTH0Lj1KTgv9defRmnT52qcd+9evcOuYHv3bsPsrJOgmVZHD16FCzLonvXTiHl/v77bzh96nTwPTKZDB07dgr+nJiYiFH3jsaw24dg+F134P33FqKgID/4+vHjx9HnyitD4uhzxZX49/g/Ib+rWKZarYZOp0NxcVGNn6euOI7jLXGRSvi7veCzrHBkUj5jFfNWVjgSHsuXSoWLVSoRg697YIYRtg7wWbZEwDgbJan5LzOR/zLLyfisq4Kef/7iFDGAWCxcYsln/RL6usrv3wDhrlVisQhiHhNsIY4r9ayT/wSbzRaSJJw5ewZ//nkYCQkJyMhohrKyMpw7l4P8/MDN8Yl//wUApKamBnv0Vn66HG3btUdycjL27t2DGdOnYfKUR9GmbdvofyBCBLZixTL4fD60bXNh5AjHcZDL5Xh9wVvQ6/VgRKIqQ0Qrzxt/ZuYs3DX8bvz44/fY/NOPmP/iPCxd9mnIkxku79ABl3fogAcmPohdE3Zg4IAb8ftv20N6jCvas2c37p8wFs/MnIUb+98EnU6PDevXYuG7b9f585UvYvfOwvfRs2foPPZIer/sNhvEYjG2/7YLInHoDYRGc+HGV6lUVkl8F33wMR6aNBk/b/4JX3yxHi/Mm42vNn6L3r371Hn/5VMMyjEMA46HYXwsyyIr6yS6d+8RcVkAoFJIIZeKeZm3mKBT1L5RBPRaBQpL+Vm4y6CT175RJOVr5RE9Y72cWMRAp5bxEFF4IhEDnUYOMw+ro2tUMkF7rLVqGSRihpd5qwatcHW1X69m0KllsPC0GFpqshq9O6XzUlY4Bp2cl++VTCqCWilcXZVJxVAppbx8r/RaBa8jNaqUr5GjoNjGyxQDg8DXVYNWAYst8rrKMIHPLSSDTsHLozeVCgnkMv5Ta+pZJ/8JB/84gKv79sHVfQM3vc88NR1X9+2DF18ILGT1/XebcHXfPrjr/OOpxo0djav79sHiTz4OlnHixAncc/dw9OrRFa+8PB//+98MvDj/5ah/FkKE5vP58PlnqzD/pVewY+fe4L+du/YhrXFjrF+3FgCQnJwCq9UKu/3C0PIjfx6uUl6bNm0wZcqj+Hrjtxhy21CsXLmi2n23a38ZAMDuCNzEyaQysGxoQrd3z25kNGuG/01/Ct2790Dr1q2Rk5Mdsk3rNm3xx4EDIb/748D+4P83Sk1F48bpOHP6NFq1ahXyLzOz5qkt+/ftC/l53749aNWqNcRiMTp36QqWZVFcXFSl3MpD+cPp0qUrnpw2HT//shWXXd4B69auCRyXdu2wZ9eukG337N4VPF5C+2zVpzAZjRh6fsoAHxINyojLYBggQS/sTaVMKoaWh8RVLhMLmlQAgRtgPnqBEvTCJhUAkMjTeUvUR16PasIwDBJ0ke9DxAjbsKRUSDF2WGdezr9IxGDiXd0EHQmQoFPyMrpC6PMf2Ac/5y2Jh2teTcRiEfQ8NAhJJSJBG+sAQKeR8zJqSa+RC1pPgfi/VlHPOvlPuOba62CxVf9oh1H3jsGoe8fUWMacuS9gztwX+A6NkLjzw/ffwWQyYvSYsdDr9SGvDR06DCtWLMOE+x9Az569oFKpMGf2c3ho0sPYv38fVq1aGdzW6XTi2ZlP4/bbh6F5ZibycnPxxx8HMHToMADA4489grTGjXHddf2Q3qQJCgoK8NqrLyM5OSXYm9yseXP88stmnPj3XyQmJkKn16NVq9Y4l5OD9evWonuPHvjxhx/wzTcbQ+J88KFJeHTKw+jWvTv69LkSX2xYh6NH/wpJxJ+Z+Sym/+9J6HR69L9pADxuNw4ePACTyYQpjzxW7fE5dy4HTz81HePH349Dhw7iww8WBRvu2rRpg+Ej7saDEyfgxfmvoHOXLigpKcG2rb+iQ8dOGDTo5rBlnjlzGsuWLsbNg29F48aNceLEv8g6eRIjR44CADz22BO4b8wodO7SFf2uvwE/fP8tNm78Chu/+a6up7XOnA4HCgsL4PP5kJubi03fbMR7C9/BhPsnVjvaoSEMWjkKSyLrBdKqZYIPLQcCN1mRPr4pGkmFSMQgQa9AiTGyXqBoxKpWyiCXiSNadVrOU0NKbRINCpSYHBHNsTboFILOrQWA8Xd2xbsr99e+YS0YBrhvaKfaN4yARCKKeHQFI3ADSDm9Ro5CiT2ileFVSikUcuHTqkS9IuLHriXohG+sAwLXmYISe+0b1lSGwA0gACCXSaBWSmGPYHSFRMzAoBVmBAD1rBNCyCVmxYpl6Hf9DVUSdQC4bejtOPjHAfz11xEkJibi40+W4qeffsCVfXpi/bq1ePqZmcFtxWIxyspK8eDECejetRPuG3MvbrppIJ6ZOQsA0O/6G7Bv316MGT0K3bt2wuhRI6GQK/DNpu+RlJQEIPDM79Zt2uK6a/uiRWZT7N61E4NvuRWTpzyKaU8+jqv79sGePbtDFpwDgBEjRuKJJ/+HZ595GtdefQXOnj2De0aNhlxx4cbuvrHj8e57i7Bq5Qpc2acHbr75Jqxa+SmaN8+s8fjcPXIUXE4nru93NZ58ciomTZqMcePvD76+6IOPcffIUZj5zAz06NYZ99w9HH8cOICMphnVlqlSqvDv8X8xetRIdO/aCY89MhkPTHwQ4ycEyr11yG145dUFeOedN9GnVzcsWfIJ3v/go3ot9ldXy5YtQZtWmejS6XLce8/d+OefY1i2fCXefOud2t9cD2KxCI0baRv8fomYQVqyhseIqqdVyyK60VIrpbz1ztQmJUEFeQTzzVOTVJAJOF+9oiap2gYvuscwQHpqdM6/VCJGagRzwmVSkaDzv8u1bGrAPbd2iGhRLIYBHryrmyBz4CtLS1ZHNIe3cYpG0CkQ5RiGQXojbYNHAohFDNJTolNXlQopkhMansAq5BIkJ6h4jKh6iXollIqGN2AkGZRQKYRbBLGi9EaaiEatBOqPMA0gDMfnMyuizGoX7pET1fH5fNi+9Wdc268/JBIamHCpHY9D/xTi2tHVP/ZJKReh8PcnohgRIaTc0CGD0Sg1FR9/srTBZQwedBM6de6CV159ncfILm0lRke9e1ckYgbN0/VQRulGDQis2ZCdb6l3D7tSIUFmuj4qSUU5j5fFmVwTPPV8/nCSQYnGUUoqyllsbuQUWOrVa80wQNM0neBzVSsrKLHVe9SCTCpCZhND1BpA3B4fhk5Zh92H8+r9mCgRA/Tv2wKrXx8m+NDici63D2dyTWHXBBg6ZS2Ky5xISVTi64XDQ15LTVIhJQoNIBUZLS7kFVmr1NWa4hQxQLN0PTQq4UeAVHSuwAJTmFELNcUql4nRookhauceAHw+P07nmsKOsKkpVr1Wjow0XbTCBADYnR5k51nCPn6vulgZJtCo1NDRSlp17dc46lknhBBy0XE4HFj47ts49vff+Pf4cbz4wlz8+usW3DPq3liHRipJTlChaZoWkjqukqyQS9AyIyGqiToQ6F1r1liH5IS6z7U1aOVo0cQQ1UQdCMyzb9k0AWpl3Y6RiAEap6ijnqgDgbmrLZoa6jx/NZD86qOeqANAWrIm0Jtbxx42tVKKlk0TopaoA4Ehu+vfuhM3XpEJhkGd6mr5NkNvbIeVrwyNarJ24ftctw4dsYhBk1Rt1BN1IDA8vFljfZ1HA8ilYrTMSIh6og4EGrMaJarqPHJFq5ahZdPoJupAYDpEy6YG6DR1O0YiBmiUqIp6og4Epu60aGqAXFa377NUIkJGmk7waUX//a5QQggh/zkMw+CnH3/Aa6+9ArfLhTZt2mLlqtW4/vobYx0aCcOgVUCvkcNkdaPM7ITT5Qt5vXzF38QoDnsMh2ECQ++TDSoYLU6UmV1V5rFKxIEFyRL0iqgmaZVJJCK0aGqAy+1DmdkJk8VVZX0AuUyMRL0SCVGYT10TlUKKtplJsNo9KDM7w45e0KikSNQroYtBkl5RkiFwvExWF0pNzio9giImsOp3kkEZlTnK4WhUMqxZMAwfrz+E9z8/gLN55rAr2pf/rnWzBEy+pyfG3t45JvVAJhWjVUYCHC4vykxOmG3uKr3XSoUEiXolDFp5VOZTV0erlqFtZiIsdg/KTM4q85gZJrBNol4ZkyS9okZJaiQZlDBZXSgzuao8fUMsYmDQKZCoVwiySnldicUiNGush8fLoszshNHsqtJ7LZOKgteqaDd+VqSQS9CmeSJsjgvXqsp1VXV+2pNeE526Ssk6IfWQZFBGvGAOISRySqUSGzd9z3u53/2wmfcySUBg1W0FEnQKeLwsfKwffj8HsYiBTCqO6Q1aZRKJCCmJaqQkquFy+8D6/eA4QCIWQS4TxzSZqEwhlyC9kRZpyRq4PT6wfg4MUx5rfN3madUyaNUy+Hx+eHws/H4OIhEDqUQUlYUE60okYpCoVyJRr4Tb44OPDZz/eKqrEokIk+7ujgeHd8O2fdn4eP1B7DuSF3xcmkjE4LYb2mLiXd1wZdcmcVFnVQopVGlSNGb98HhZiEWB4ygRi9AqIyHG0V3AMAz0Gjn0Gjm8PjZ4vsViEdplJkW9d7omYrEISQYVkgwquNy+YKwSsQjtWiTFtJGuMplUjLRkDRolquH2+ELOf9vMpBhHF0qjkkGjunCtqhhry6aGqMYSX1dxQuJcRpoOf2yYUO3zGDk/JfGEEFIbmVQc017p+ohV72l9iURM1KcONJREIoqrhKcmcpkEse3rr5lIxOD6Ps1xfZ/mAID2t3yAvCIb0pLVWDZ/SIyjC08sFkEpFgUTyXhoSKiOVCIOLugnYpi4rrcKuSQYK8MwcZWoV1R+rboYzn/5tSqWsV4cf4EIiSMZabpq59L4fL6wvyeEEEIIIYSQ+ojf5iFCCCGEEEIIIeQSRck6IYQQQgghhBASZyhZJ4QQQgghhBBC4sxFPWe9Lg+S55vXG2jf0KhkkEovjoVchETHI5TXK6LjQAghhJCYYCosMBaL++T6uFhivVjiBChWocQyVupZJ4QQQgghhBBC4gwl64QQQgghhBBCSJyhZJ0QQgghhBBCCIkzlKwTQgghhBBCCCFxhpJ1QgghhBBCCCEkzlCyTgghhBBCCCGExBlK1gkhhBBCCCGEkDhDyTohhBBCCCGEEBJnKFknhBBCCCGEEELiDCXrhBBCCCGEEEJInKFknRBCCCGEEEIIiTOUrBNCCCGEEEIIIXFGEusACIm17DwzSkwOXsrysyx6dmrKS1mEEPJf5fGw8PpYcBwgEjGQy8QQi+Ov/4DjOLg9LHw+PwBALGagkEvAMEyMI6vK7+fg9vjAshwYBpBIRJDL4vM2z+tl4fX5wfr9EItEkEpEkErFsQ4rxLGsEixafQBf/fIvjBYnPF4/tGoZ2rdIwkMjuuP/Bl4GhTx+ji/HcXC5ffD7ueDPHMfFZV1lWT/cHjYk1njl9bLwn4/Pz3Hw+fyQSOLvWgUgcP65C8fU7+cgEsXf+S+/Vl0M59/n88PjjW1djZ+rDCExkJ1nRrvBi+DysLyUp5SL4Dj4NC9lEULIfwnHcTBZXCg1OeFweUNeYxgGeo0cSQYl1CpZjCK8wOfzo8zsRKnJCa8v9O+DRCxCgl6BJL0KMlnsE0yX24dSkwNGiyt4Q1lOIZcgyaBEgk4ZFzftFpsbpSYnrHZ3lde06sD512nkMYjsgu+3n8TLn+zC9v3ZkIgZ+NgLx7TU5MSuw7nYcfAcHpn/Ex4c3g0zJlyJBL0yZvF6vSxKTU6UmZ3wsX6wbKBhyefz459TpUjUK5CoV8ZFY4jD6UWpydokoSUAANQ5SURBVAmT1QWO40JiPXGmDMkJSui1ipjXVY7jgnXV5vCAPd9Yx/r8OHaqBLrzdVWjjv21imUD16oysyvQWOe7cEyPZZUErlUGZVw03Lk9PpSanDCaXWD9letqCZIMSiTqlXHRcGuze1BqcsJid1epq1nZRiQZlNBr5VFpDIv9mSMkhkpMDt4SdUIIIeGZLC7kFlrB+v1hX+c4DiarCyarC0q5FM3T9TFJhDmOQ36xDaUmZ7U9KD7Wj+IyB4rLHDBoFWiapotJcuHz+ZGdb4bN4al2G5fbh9xCK/KLbUhNUiMlUR3FCC9wOL3IzjfD463+763V7obV7oZMKkZGmi7qjTYcx2H+hzvw7DvbID5/Pism6uXKG0RMFhdeX7IbG376B5sX34PMJoZohguO43CuwBpMfMPx+lgUltpRVOZAol6B9EbamPS0ezwssvPNVRrpKnK6vcgp8CKvyIb0RpqYNYBY7W6cK7BWaaQrx3EczDYXzDYX5DIJmjXWQamQRjnKgMISG4qNjiqNdOVYvx8lRgdKjA7oNHJkpOlikgizrB/nCqww21zVbuPxssgvtqGgxI6UBBXSUjRRjPACl9uH7HwzXG5ftdvYnR7YnR5IikRomqYTvIEx9k0XhBBCCPnPKi6zIzvfXG2iXpnT7cXJ7DI4a7ixFwLHcTibZ0aJ0VHnoY4mqwuncozBXpdo8XhYnMwuqzFRr8jvDzRC5BVZBY6sKovNjawcY42JekUeL4tT50wwW6u/sRfC7IXb8ew72wAAbDXJT2Wsn8OZXBOuHLkMuYUWIcML4fdzOJVjgtFSfaNSRRzHodTkxJlcc9SH8brcPpzMLqsxUa+I9fuRU2BBUald4MiqMpoDx6i6RL0yt8eHrBwjbPa6fQ/5lJNvQWGpvdpEvTKLzY2T2cbglJ5o8fn8yMox1pioV8RxHIrK7MjOMwscWVV2hwdZ2cYaE/WKfKwfZ3JNKDM5BY2LknVCCCGECMJodiK/2Fbv9/lYP06fM8ETxZFP5wqssNiqDs+ujcPlxdm86CVBLOvH6VxTnZPfikqMjqgmQeU96vU9NhzHITvfAnsdGyMi9fm3RzF30e8Neq+P5VBidGDgA59Hpb6WNyrZnfU/Nla7Gzn50WtU8HpZnD5ngq8BjVkFJTbBk6CKrHY3zhVa611X/f7A+ahrgseH/GIrjJb6Hxu3x4fT50x1TvAj5T/fmNWQY2OyuqLaAOb2+HAmt+6NyhWdK7Q06G9HXVGyTuLSSy+9hF69ekGr1aJRo0a4/fbbcfz48ZBtsrKyMGzYMKSkpECn02H48OEoLCwMvr5161YwDBP23759+6L9kQgh5JLCsn7kRtCT62P9DUr0G8Jqdzfo5reczeFBmTk6iUVhqR1uT8MTg4ISW9QaQc4VWhqcGHAch5wC4W/W/X4OM9/6NaIyfCyHoydL8OUvx2vfOEImiyvsnP86v9/qitqohfxiW517qcPJLbJGZdRK+ZSChja4sX4/zkWhrgKA0+VFcVnDF0V2ur0oLotOg12J0VHnERXhlJqccDijM8KqpmladXGuwCJYgy0l6yQubdu2DZMnT8bu3buxefNmeL1eDBgwAHZ74AJjt9sxYMAAMAyDLVu2YMeOHfB4PBgyZAj8579sffv2RX5+fsi/+++/Hy1atEDPnj1j+fFIPZQ3uphMpqjud/bs2ejatWvU9peZmYm33nor+DPDMPjqq6+itv/64DgOEydORGJiIhiGwaFDhxpUzpkzZyJ6f6QqH/NYi/Xx4Fu4Bc/qy2J3w9uAHuT6KuWhB4+PMmrj93MwmiNPtErN/DwBpSZ2hyfi3kaPl40oMa2LX3afxuncyIfcikUM3l0pfEdAifHiqKs+nx/mCHsbOY6LSiOY2eqOqFEBCIywiUbvOh/nrsxc/ToHfOLj3EWjrro9vjpPKaqOj/XDZBGmEYySdRKXfvjhB4wdOxYdOnRAly5dsGzZMmRnZ+PAgQMAgB07duDMmTNYtmwZOnXqhE6dOmH58uXYv38/tmzZAgCQyWRIS0sL/ktKSsLXX3+NcePGxeWjTC5WY8eOBcMwePnll0N+/9VXX9X7OPfr1w9Tp04N+V15o4ter480VEFs2LABN9xwAxISEqBUKtGuXTuMHz8eBw8ejHVogvnhhx+wbNkybNq0Cfn5+ejYsWPI6xs2bIBYLEZubm7Y97dp0wZPPPEEMjIywr6/OmPHjsXtt98eafhB+/btw8SJE3krrz7CfZb6HI+6JvaVtyv/ufyfVqtFhw4dMHnyZJw4caKBnyY8Pm6yonGz7vGwvAxhdLl9gg/bNllcEfX+lCszCX+zztdNttA36wtX7YdEHPk9AevnsOPgOfx1ooiHqMJzOL1wuiPvabQ5PBGNzqiLMnPd5tPXJhrJGl/7KDEK2wjGsn6YeBgV4fXxc82ricXmbtBUncpMVpfg8+zj/VpFyTq5KJjNgVbvxMREAIDb7QbDMJDLL6zAqFAoIBKJ8Pvv4eedbdy4EaWlpRg3bpzwAV9iFAoFXnnlFRiNRt7LLm90iccGlhkzZmDEiBHo2rUrNm7ciOPHj+Ozzz5Dy5Yt8fTTsX2En8cjXNKQlZWFxo0bo2/fvkhLS4NEEvpgkdtuuw1JSUlYvnx5lfdu374dJ0+exIQJEyAWi8O+X2jlxyYlJQUqlSqq+2ZZNjj6p7JoHo+ff/4Z+fn5OHz4MObPn49jx46hS5cu+OWXX3gp3+H08pYMlPHQk1wTPm5+yxkF6lnhu3zW7xf0Zt3v5yLuVS1ntXsEGwrtcHqxaeuJsKu+N4RELMKa7//mpaxwIpmqUaUsgb9XfNVVj5cVtBHM42EbNP8/nJpW5ueD2ermbb654NcqnupX+er7QuIrVoeLv797FVGyTuKe3+/H1KlTcdVVVwV7nK644gqo1WrMmDEDDocDdrsd06ZNA8uyyM/PD1vO4sWLMXDgQDRt2jSa4V8S+vfvj7S0NLz00kvVblNaWoqRI0eiSZMmUKlU6NSpEz7//PPg62PHjsW2bdvw9ttvB3v9zpw5E3YY/IYNG9ChQwfI5XJkZmZiwYIFIfvKzMzE/PnzMX78eGi1WjRr1gwfffRRyDYzZsxA27ZtoVKp0LJlS8yaNQteb917LHbv3o1XX30Vb7zxBt544w1cc801aNasGXr06IFnn30W33//fXDbrKwsDB06FKmpqdBoNOjVqxd+/vnnOu8LAHJycjB8+HAYDAYkJiZi6NChOHPmTMjxu/322/Hiiy8iPT0d7dq1w9y5c8P20nbt2hWzZs2qdl/btm1D7969IZfL0bhxYzz11FPw+XzB/TzyyCPIzs4GwzDIzMys8n6pVIrRo0dj2bJlVV5bsmQJ+vTpgw4dOoTtHT569ChuvfVW6HQ6aLVaXHPNNcjKysLs2bOxfPlyfP3118H6sXXrVgDAkSNHcMMNN0CpVCIpKQkTJ06EzXZhrnO4YwNUHQb/xhtvoFOnTlCr1cjIyMDDDz8cUk44tb1n2bJlMBgM2LhxIy6//HLI5XKMHz8+7GepfDyMRiNGjRqFlJQUKJVKtGnTBkuXLgUAtGjRAgDQrVs3MAyDfv361RhnZUlJSUhLS0PLli0xdOhQ/Pzzz+jTpw8mTJgAlo28NyTSIaVClRUOH70/5bwC9wDxeSz4/NyVeX0sb0kLx3GCHdcSowN8rrXFMIE1BYTC53EQuq5eLN8rPr9Tfj8HlqeGn3B4va566VoFBEYr8DFaqZwQx5WSdRL3Jk+ejL/++gurV68O/i4lJQXr1q3DN998A41GA71eD5PJhO7du0Mkqlqtz507hx9//BETJkyIZuiXDLFYjPnz5+Pdd9/FuXPnwm7jcrnQo0cPfPvtt/jrr78wceJEjB49Gnv37gUAvP3227jyyivxwAMPBNcYyMjIqFLOgQMHMHz4cNx99904cuQIZs+ejVmzZlVJDBcsWICePXvi4MGDePjhhzFp0qSQRQq1Wi2WLVuGv//+G2+//TY+/vhjvPnmm3X+zJ9//jk0Gg0efvjhsK9XHAlgs9kwePBg/PLLLzh48CAGDRqEIUOGIDs7u0778nq9GDhwILRaLX777Tfs2LEDGo0GgwYNCulB/+WXX3D8+HFs3rwZmzZtwvjx43Hs2LGQBRUPHjyIP//8s9oRJrm5uRg8eDB69eqFw4cPY9GiRVi8eDFeeOEFAIHzNHfuXDRt2hT5+fnVLtY4YcIEnDhxAtu3bw85DuvXr6/2e5ibm4trr70WcrkcW7ZswYEDBzB+/Hj4fD5MmzYNw4cPx6BBg4L1o2/fvrDb7Rg4cCASEhKwb98+rFu3Dj///DOmTJkSUnblYxOOSCTCO++8g6NHj2L58uXYsmULpk+fHnbb+rzH4XDglVdewSeffIKjR4/inXfeCftZKps1axb+/vtvfP/99zh27BgWLVqE5ORkAAh+b8p7yL/44osa46yNSCTCY489hrNnzwanG0WC79WGhVy9mM+yhV5l+WKJ9WI5/3aeF6/y+znYHMItiHWxnH8AvPYwC1pXee4Jv1hi5TNBDYfPWC+qa5UAIyuiO/aQkHqaMmUKNm3ahO3bt1fpER8wYACysrJQUlICiUQCg8EQ7CmqbOnSpUhKSsJtt90WrdAvOcOGDUPXrl3x/PPPY/HixVVeb9KkCaZNmxb8+ZFHHsGPP/6ItWvXonfv3tDr9ZDJZFCpVEhLS6t2P2+88QZuvPHGYM9w27Zt8ffff+O1117D2LFjg9sNHjw4mEjPmDEDb775Jn799ddgr+qzzz4b3DYzMxPTpk3D6tWra03Oyv37779o2bJlyJDlN954A88991zw59zcXOj1enTp0gVdunQJ/n7evHn48ssvsXHjxipJZThr1qyB3+/HJ598EmwEWLp0KQwGA7Zu3YoBAwYAANRqNT755BPIZLLgewcOHIilS5eiV69ewfddd911Yb8nAPD+++8jIyMDCxcuBMMwaN++PfLy8jBjxgw899xz0Ov10Gq1wSHb1bn88stxxRVXYMmSJbj22msBAGvXrgXHcbj77rvDvue9996DXq/H6tWrIZVKAQTObzmlUgm32x2y3+XLl8PlcmHFihVQq9UAgIULF2LIkCF45ZVXkJqaWu2xqaziegmZmZl44YUX8NBDD+H999+P6D1erxfvv/9+SB0I91kqy87ORrdu3YILYlYcxZCSkgLgQg85H9q3bw8gMK+9d+/eEZUlEvE3bYVhGF7Lq0zMw3zlckLGGSyfp04msVi4/hpxmEbzSAh1XHWa6q8HDSESMdBr5LVv2EB8Htdo1FW+EiFBv/8811U+ryeViXicDsj3567sYomV7+sgn587WCbvJRLCA47jMGXKFHz55ZfYsmVLcMhnOMnJyTAYDNiyZQuKioqqJOQcx2Hp0qUYM2ZMMAEgwnjllVewfPlyHDt2rMprLMti3rx56NSpExITE6HRaPDjjz/WuXe53LFjx3DVVVeF/O6qq67CiRMnQobvdu7cOfj/DMMgLS0NRUX/z959xzdRv3EA/1z2TpruyS5DpuACZQjIEgERAQFxiwooiCLiQFzgRlBxoOyliIOfC1QE2XuJ7NIC3W2SZq/7/RETmjYdae7Sos/79epLaS7fPHf3zfWe77rLC/+sWbMG3bp1Q1JSElQqFZ577rmwY6novvvuw8GDB/Hxxx/DYrEEehbMZjOmTZuG1q1bQ6fTQaVS4fjx47X+vEOHDuH06dNQq9VQqVRQqVTQ6/Ww2+04c+ZMYLt27dpVSkYffPBBrFq1Cna7HU6nEytXrsR9991X5WcdP34cN9xwQ9DIgG7dusFsNlc5aqIq9913H7766iuUlfke3/X5559jxIgRUKvVIbc/ePAgbrrpprC+p/651v5E3R+v1+sNGkkR6thUtGnTJvTu3RupqalQq9UYN24ciouLYbVWvWhQbd4jkUiC6mNtPfLII1i9ejU6duyIp59+Gtu3bw+7jHD46ysX60NIJdz1BUjEQs7K4rt8qYTfWK+U4yoSCThLsBiGgVjEz+1qXIyC07K9XhapiaGvb1yQSLiLle+6ymX94rOucnn+hQIBr41gdK3inkDAQMThOeMjVkrWSYP02GOPYfny5Vi5ciXUajXy8vKQl5cHm+3y4ipffPEFdu7ciTNnzmD58uUYMWIEpkyZEug59fvtt99w7tw5PPDAA9Hejf+c7t27o1+/fiEXV3vzzTcxb948TJ8+Hb///jsOHjyIfv368bYQWsWEj2GYwMJeO3bswJgxYzBw4EBs2LABBw4cwMyZM8OKpUWLFjh79mzQPHedTofmzZsjNTU1aNtp06Zh/fr1eO2117B161YcPHgQ7dq1q/Xnmc1mdO7cGQcPHgz6OXnyJO66667AduUTVr/BgwdDKpVi/fr1+P777+FyuXDHHXfUej8j4e9BX7t2LU6dOoVt27ZVOxVFLpfzFkuoY1NeVlYWbr31VrRv3x7r1q3Dvn378MEHHwCoerG+2r5HLpfXKQEeMGAAzp8/jylTpuDSpUvo3bt30OgUrvkb2aprHK0tmVQEhYybxlG9VsZJOVWJ0dTt/ISi1/JXh33lc3MsxCIh1Epue5XLEwgY6NTcxKpTy3hLgKQSEUYNvIqzm3Uvy2Ls4No93aIuuKpfDMMgRsPv94qrWGVSERRy/jpaxGIhNByNhtDr+D2mWrWMs17mK+VaJRAw0F0hdVWtlELCQyMIDYMnDdJHH30EAJUWTfriiy8CQ51PnDiBGTNmoKSkBI0bN8bMmTMxZcqUSmUtWrQIXbt2DQzxJPyaM2cOOnbsWKnRZNu2bRgyZAjGjh0LwLdw4MmTJ9GmTZvANhKJpMbFrVq3bo1t27ZVKjszMxNCYe0uktu3b0ejRo0wc+bMwO/Onz9fq/f6jR49GvPnz8eHH36Ixx9/vNptt23bhnvuuQfDhg0D4Eu+yy8OV5Orr74aa9asQUJCAjQaTVhxikQijB8/Hl988QUkEglGjRpVbVLcunVrrFu3DizLBhKYbdu2Qa1Wh704o1qtxogRI/D555/jzJkzyMzMxE033VTl9u3bt8eSJUvgcrlC9q6Hqh+tW7fG4sWLYbFYAgn5tm3bIBAIKtXB6uzbtw9erxdvv/12YN2LtWvXcv6e6vYllPj4eIwfPx7jx4/HTTfdhKeeegpvvfVWYJQAF4vBAb7v4/vvv48mTZqgU6dOnJQZq5PDmhfZ/F2GYXi/qRSJBNCqpBGvCq+USyCT8ntbpVFJIRYJI168Sa+V8f6EjVidnJPH7sXq+D3/j43ujGXfHYm4HJGQwS3dmqJxqi7yoKoglYigUkgifia0RimFmOcRKzEaGfKKzBEPhef7/Ps/g4unI8Rq+X26iEDAIEYri/gRcVKJCCoeG+sAQKnwXQ8jffZ8jEbG+5SNWJ0chaXWiNdZ4KuuUs86aZBYlg35U35O8pw5c5CXlwen04mTJ09i6tSpIW8+Vq5cWSm5I/xp164dxowZg/fffz/o9y1atMDGjRuxfft2HD9+HA8//DDy8/ODtmncuDF27dqFrKwsFBUVhXzE1ZNPPolff/0VL7/8Mk6ePIklS5ZgwYIFYfU4tmjRAtnZ2Vi9ejXOnDmD999/H+vXrw9rP2+44QY8+eSTePLJJzF16lT8+eefOH/+PHbu3IlFixb9M89WEPi8r7/+GgcPHsShQ4dw1113Vfn4rlDGjBmDuLg4DBkyBFu3bsW5c+ewefNmTJ48uVZD0x944AH89ttv+Omnn6odAg8Ajz76KHJycjBp0iT8/fff+Pbbb/Hiiy9i6tSpIRdvrMn999+P7du3Y+HChTV+9sSJE2EymTBq1Cjs3bsXp06dwrJlywLD2Rs3bozDhw/jxIkTKCoqgsvlwpgxYyCTyTB+/HgcPXoUv//+OyZNmoRx48YF5qvXRvPmzeFyuTB//nycPXsWy5Ytw8KFCzl/j1+ofanohRdewLfffovTp0/j2LFj2LBhA1q3bg0ASEhIgFwux08//YT8/PzA4y1rq7i4GHl5eTh79iy+++479OnTB7t378aiRYtq3ehVE51GFnGPJZ+9quXFxUR+kx2NpMLXeBFZL1M0GkAAQC4TRzy6Qi4V89qrCgDXtk9B+5YJiDQfcHtYTLyrCzdBVYOLehaNuioUCiLuvRcKBIjR8B+rWimNePgyX72qFV0p55+rz4nV8f94VbFYCI0ystEVEjF/o5UoWSeEcG727NmVktHnnnsOV199Nfr164eePXsiKSkJQ4cODdpm2rRpEAqFaNOmDeLj40PO6b766quxdu1arF69Gm3btsULL7yA2bNnBzXk1OS2227DlClTMHHiRHTs2BHbt2+v9lFmVXnrrbewcuVKHDhwALfeeitatGiBESNGwOv1YseOHYFe8HfeeQcxMTHo2rUrBg8ejH79+uHqq6+u9ecoFAps2bIFGRkZuP3229G6dWvcf//9sNvtteppb9GiRWB0yXXXXVfttqmpqfjhhx+we/dudOjQARMmTMD9998ftCBfOG688Ua0bNkSJpMJd999d7XbxsbG4rfffoPZbEaPHj3QuXNnfPrpp4Fe9gcffBAtW7ZEly5dEB8fj23btkGhUODnn39GSUkJrrnmGtxxxx3o3bs3FixYEFacHTp0wDvvvIO5c+eibdu2WLFiRbWPIqzre/xC7UtFEokEM2bMQPv27dG9e3cIhcLAUzFEIhHef/99fPzxx0hJScGQIUPC2t8+ffogOTkZ7dq1wzPPPIPWrVvj8OHD6NWrV1jlVIdhGGQka+vcgyuViJCSoOIsnuoo5GIk6KufKlEdnVrG+1BNv4RYJZTyut8UpiWqee9V9UtP1tS5wUYoECAjJbyRRHXBMAzefqqP77lrdSQUMOh9fWP0u7EZh5GFplXLImpsidcreO9V9UuKU9V5tAnDMMhI0fDeq+rXKEVb588Si4RI43GtgvJ818W6f5ZaKY1asq7XyiOaYpAcX/f6E66UBBXEorpdFwWCyP7W1YRhuXy2wn+Ay+XCDz/8gIEDB9JiZbjyj8f+v3LR+Y7POStPLhXAeqDyfG1C6hPLsmjRogUeffRRTJ06tb7DIf8xxjI7snNNYQ0xlEpEaJKqi0pPVXkX800oNoQ3dFurkiEjRcP7sPLyPB4vzl0wwGoPb5pBSoKak1EE4bDaXMi6aIDbU/vRREKBAE3SdLz3qpe3aN1BPPD8/8J+n1DIoFWTWGxfeQ9nc59rwrIscnJNIadu9L1/JQpKrEjQK7Bx0V1Br+m1cqQl8d8AUp7L5cG5i4aQw6GripVhGKQnaaLWAOZntjiRdclQaeh+dcdULBKiSZouakmlX36RGfnFlkq/ry5WlUKCxqm6qDWAAL5FF89fMqLMUnmaQXWxJuiVSIqPTmOtn8PpxtkcQ8hpRlXFKhAwaJSihTrCnvnqUM86IYT8ixUWFmLBggXIy8ur8tnqhPBJq5ahaS1vZhmGgVYlQ/OMmKgn6gCQmqhBSoK6Vr3BQoEAibFKNErlr0elys8WCtA0PQZ6be0Wx5OIhWiUoo16og74Ri00z9BDpahdb65SLkHzRjFRTdQB4P7hHbHijSEQCWu3OjTDAAyArh3TsHXZ3VFL1H2fzSAjRYvEWGWtFhwTCQVIjldFPVEHfEOMm6XHQKeu3ToJMqmvoS7aiToAqJQSNEuPqfX0DbVSiuYZMVFP1AEgMU6F9CRNrXqDBQIGcTEKNEmLbqLu/+zGqVrE6xW1+mzfKAVN1BN1wNdI3DwjptbfZblUjKZpMbwm6gAtMEcIIf9qCQkJiIuLwyeffIKYmJj6Dof8RykVEmQ2joXF6kSxwQaj2RHU0y4SCqDXyhGrk0dtiHZV4mIUiNXJYSxzoNhgg8UWvJiXXCpGrE4OXRQWPqqOQMAgLUmDpDgVSk02FBtscLqCe4Q0Kt+QV75vJmsikQjRND0GDqcbxQYbSo12eMpNlRIIfKuTx+oU9ZL4+N11a1u0y0zAe8t2Y8X3R+F0eVBxQIhIKIDb40WbZnGYPPYa3DO0Q700LAG+hC1er4TBZEexwQabI3ikhUJ2ua5Gu0GpPKFQgIwULVwuD0qMNpQY7ZV6L3VqGWJ1cihr2ajDF7lMjOaN9LDZXSg22CqNXhAKBNDrZIjVKurtvPvFaH3ntsziu65W7L2WSkSI1ckRo4nO2h9VYRgGyfFqJOiVMJT56mrFkRYqhQSxOt+w+fqsq2KxEI1TdXA6PSg2WlFqtAePCmJ8TxCJ1cmj1qBIyTr5T4vTKSCTCGF3crOiMiENDc10Ig2JUiGBUiEBy7Jwu71gWd8w4vq8kQyFYXyPC9JpZPB6WXg8DTdWkUiAeL0S8XolPB4vPB4WDONLkOqzMSEU/3zb5HgVPB4WXi8LgYCBUMjU6w16ee0yE7Do5Vvx1rTeWPLtEXz/+0ls2ZsDt8cLsUiAuwZdhYfuvBo3dExtEDELBAz0Ojn0Ojk8Hi9E/zw3XCQSoHkjfT1HF0wsFiIxToXEOFWlWDNStPUcXTC5TIy0JDFSE9VBcbZpHtcgzrsfwzDQqKTQqKTwetmgWFs2ia3n6IIJhQLE6hSI1Skqnf+m6Q2rM0EiESI5Xo2kON+1KhCrUID05OiOUKFknfynZaRoceKHR1BkiOwxGH5ejh6jRAgh/2YMw9R7D3ptCQQMBIIrI1ahUACOFvPnFcMwEIkaTsITSoxWjifuvhZP3H0t0nq9j4v5ZUiIVWLx67fVd2hVEgoFgUSyISWUoVwpsTIMExRnQ45VIGCuiGMKXFnnXySq3+NKyTr5z8tI0XLWohvq8UuEEEIIIYQQEq6GNZaLEEIIIYQQQgghlKwTQgghhBBCCCENDSXrhBBCCCGEEEJIA0PJOiGEEEIIIYQQ0sBQsk4IIYQQQgghhDQwlKwTQgghhBBCCCENDCXrhBBCCCGEEEJIA0PJOiGEEEIIIYQQ0sBQsk4IIYQQQgghhDQwlKwTQgghhBBCCCENDCXrhBBCCCGEEEJIAyOq7wAiUWZxRP0z3W43AMBsdUIk8kb98xsaOh7B3G43HQdCCCHkX4Rl2cB/6+PeMxwUK/eulDgBipUvfMWqVkpr3IZ61gkhhBBCCCGEkAaGknVCCCGEEEIIIaSBoWSdEEIIIYQQQghpYK7oOeuEEEIIIeTKwrIsLDYXzFYnlHIJVAoxGIap77AIIaTBoWSd/Kfl5JlQbLBxVh7r9aBTmxTOyiOEkH8bf6LmcnvBsiwEAgYyiQgyacO7JfF4vLDYXPB4WbAsC6FQAKVMDJGo4Q1MdLo8sDnc8Hi8YBgGIqGgwSXBx04X4rOvDmLVD3/BanMFfi+XijByQBs8cEdHtG+ZUI8RBmNZFmarC16vb3Epr5eFw+mGVNLw6qrb7YXFHhyrx+OFUNjw6qrd4Ybd6Q6KlWXZBlVX/Sw2Z1CcNrsLcpm4nqOqzOtlYbYGx+p0eSARC+s5sspcbg+s9uDz7/X6/hY0NDa7Cw6XJyjWaGt4VxtCoiQnz4Srhy+Cw+nhrEy5VID8P6dyVh4hhPxbuNwelBrtKDXZ4XJXfmqGXCaCXiuHTi2t95t2m92FEqMdxjI7Kt6bMQygUUmh18qglEvqJ8B/+FYmdqLEaIPZ6qr0ulDAIEYrg14rr9eb9j/35eClD7di1+FLEAoZeDzBB9XmcGP590ew+JvD6HJVMl549Eb0vLZRPUXra/goMdpgMNnh9rDweH311eP14tT5UijlYuh1cmiUknqvqxabEyVGO0xmB1gWQbH+fa4YWpUUep0cinpOMFmWhdHsQInRHmioKR/riXPFiNHIodfJIBbVb4Lp8XhRarKjxGiD0+UNivNMjgEyqQh6rQw6tazeE0yH041igw3GMgc83uC6ejKrBGqlBHqtHGpl/V6rAJS7Vjkr19WzRdBpfNeq+m649XpZGMrsKDHaYXe4AzH6/3syqxgxGhliNPKoNNxSsk7+s4oNNk4TdUIIIaEVllhQUGIFW02nhM3uxkV7GfKLzMhI0dZLcuH1ssjJM6HM4qxyG5YFjGUOGMscUMjFyEjS1EtPu8PpRvYlExyuqv+OebwsikptKCq1Qa+VITleFfXkcsWGo5j48s/wn/qKibqf+5/f7z+eh6GTvsK70/vg3ts7RCnKy/KKzCgqrX7EncXmgsXmgkQsQEaytl6SC4/Hi/O5pqARChWxLGAoc8BQ5oBKIUZGsrZekkub3YXsXFPIRjo/t4dFYakVRQYr4mMUSIhVRjHCy0qMNuQWmqu9VtkdblwqMCO/yIK0JE29JMIsy+JifhkMZdU/RqzM4kSZxQmpRIhGKdp6abRzuT04f8kUSHxD8bJAidGXIGvVUqQlquulIcxsdSIn1wRPNT3oTpcX+cVWFJRYkRSnQqxOzmtMDW9sDCGEEEL+NXILzcgvrj5RL8/tYXHugqHahJkPHo8X5y6G97lWmwtnL5TC5Y5uw6/N7sLZHEO1iXpFJUY7cvJMgecFR8Pan47jkZd+guefYa614R8S+/jrG7Hi+6M8RxgsJ89UY6JentPlxbkLBljtVSfMfHC7vTibY6g2Ua/IbHXh7AUDPJ6qE2Y+mK1OnLtgqDZRL49lgYISKy7ml/EcWWWFJRZcKqg+US/P42WRnWuEoczOb2AVsCyLrIvGGhP18hxOD87mlMLhrDph5oPT5cHZHEO1iXpFxjIHzl00RvVaBQBGswPnLxmrTdTLY9l//r4VWXiNi5J1QgghhPCisMRSp3VBWBbIyTXCFqUkiGVZZOeaYLOHfyPrdHmRddEYtbmMLrcH2TX0/FTFZHYit9DMQ1SVHT5RgAmzfkQknWMTX/kZe47mchdUNXILzTCGkfz4ebwssi8Z4Qyj4SQSXi+LrEvGsBpq/OwON87nRq/Bxu5wIyfXVGkqSW2UmuwoKOY3CSqvxGhDfrE17PexLHAxvwxma/QaFy/kl8ESRkONn9vD4vwlI9y1bDiJlMfjxfmLxlo31JRntbmQk2fiIarQLDYnLuSZat1QU15hqZXT9a8qomSdNDifffoJbriuC1KT45GaHI/eN/fAL7/8HHjdbrdj6pTH0SgjBcmJsRh71ygU5OcHlbFv314MHtQf6amJyEhLwtAht+LIkcPR3hVCCPnPcru9KCgJ/+bXz8sCeTz3WPgZzY463fz6OZy+Oc7RUFBsrdPNr1+J0R6VRpD3l+8BwNbp5tePYYB5S3dzFlNV/PN+68rtYZEfpcSy1GQLq5eyIqvNFVaPbCTyiy11alTyKyy1RmXUitfLRtQ76u9hjQaz1VmnRiU/p8uLIkPdr8vhKCq11alRyc9kdkZthFVuoSWia1V+kZm3BltK1kmDk5qailmzX8EfW3dg85bt6NG9B0aPvAPH//oLADBj+lP46cf/YenSFfjhp43IzcvFmDEjA+83m824fdhtSEtPx2+/b8XPv/wGtUqNYUMGw+WK7lA1UrWtW/6ARiWDwWAAAKxYvhTpqYn1G1Q1Tp44gZt7dUd8rBbdbri2vsOppOLx/Ddr2yYTH3wwP/BvjUqGDd9/V48RkVBKTbaIbn4A39zgaAzbLDFGPow1Gsm6x+OFkYMht1zsb3WKSq34euOJwDz0unJ7WGz44zTviRAXx8NkdkSlx7LEcGXUVafLE3GixbJAKc91FQAMZfaIGhUAX4OdxcZ/YsnFuSs12nkfXcGyLEpNkccajbpqtbsiagADfI3LpSZ+6iol66TBGTBwEPr164/mzZujRYsWeGHWbChVKuzZswtGoxFLly7Ga6+/gR49e6FTp6vx0UefYNfOndi9excA4OTJEygtKcHM515Ai8xMtG7TBs/MmImCgnxkZ2fX897xb9eundBpFLhj+NBav+e1V1+udQJqMpnw8uxZuLZLJyTE6dAoIwU9unfDe+++jdLS0roFfQV47dWXoVQosO/AYXy34ceQ2xQVFmLK45PQplVzxOk1aN60EYYOuRU7d2znNJaB/fti+tPTOC3z8UmPQadRYP3X6zgtF+An3vJOnclC31v68VY+nyo2PHBtwsMPYPSoEUH/1qhk0Khk0OtUaNYkA0MGD8SypYvh9XKbaHCVEPKdWNod7rDm/lbF6fLy3gtkCLE6fV0Yy+y8zl1e9t0RTnuZFn/D38g4r5eFgYObbJYFJ8lJdcxWZ0Q9lX42u5v30RVcJVmlJv4TyyvlWuVyR94AAvimbvA9usJodkTcWAf46jzfoytKOBrCzlfDAiXrpEHzeDz46su1sFosuPba63HwwH64XC707HVzYJvMli2Rnp4eSNZbtMiEXh+LpUsWw+l0wmazYenSxWjZshUaNaq/R8FEy7Kli/HwhEexfdufyM29VO22LMvC7a59a2JJSQl69+qOFcuXYfLjT+C3zVux9c8deOHFWTh06CC+XLsm0vAjwufIiXPnzuL6G7oiI6MRYmNjQ24zdswoHDp8CAs//gz7Dx7B6rVf4aabuqOkpIS3uLhgtVqxbt2XeGLKVCxbtqReYgi3LpaXmJgEqVTKcURVczqju/BZbYQTU5++t+DUmSwc/esE1q3/Fjd174HpT0/DiDuG1fkcVGSxOSMaql0eFz3J1ZfP3U0r37FydYPtZQETjw0L3/52Cl6OEiyvl8W3v57kpKxQyqzOiHtV/XhPgLisq+YrI1aX2xvRFJWaOJzuiHtV/XyPz+OvYcFkdkY8WsnvSrmu+p++wScTR98Fh9PDWV0qj5J10iAdO3oUyYmxiNNrMOWJSVixai1atW6N/IJ8SCQS6HS6oO3jExID89bVajV++PEXrFmzCglxOiQnxmLTxl+wbv23EIn+3U8rNJvN+HrdV3jggYdwS78BWLF8WdDr/qHSv/zyM7rfeAPi9BqsWb0Sc15/FUeOHA70uK1YvjRk+bNnvYALF3Lw+x9bMXbceLRt2w4ZGY3Qu3dffLF4GR586OHAtqtWrUCPm7oiJSkOzZs2wn333o3CgoKw9ud/G77HTd2uR3ysFu3btsLrr70SlExoVDJ89uknGHnncCQl6PHG3NfRoX0bvD/v3aByDh8+BI1KhjNnzoT8HK/Xizmvv4pWmc0Qp9eg2w3XYuPGX4I+58CB/Zg75zVoVDK89urLlcowGAzYvn0bZs9+Bd179ERGRiN06XINnpz2NAYOujWwXU5ONkaNvAPJibFITY7H+HFjgtZcqNgTCgDTn56Ggf37Bl7/88+t+OjDBYHzdf58VmDbgwf2o8dNXZEYH4M+vXvi1Mmab3LXr1+Hlq1aY8rUp7B925+4cCEn6PVQPeOjR43AhIcfCPz7008+RscOVyE+VotmTTIwbszoauMNVRd3bN+Gs2fPYNTIO9CsSQaSE2PRo3s3/P77r9XGX3EY/AvPz0Snjm2RGB+D9m1b4eXZs2psyLl48QLuvWccMtKTkZSgR4+bumLPHt9cWf/IkyWLP0e7q1oiPlYLwHfOJz42AU0apSE1OR63DuwXtDZGTfsysH9fZGdnY8b0pwLHxu/bb9bj2i6dEKfXoG2bTMx//72geNu2ycTcOa/hoQfvQ2pyPCZPerTa/StPKpUiMTEJKSmp6NixE6Y9NR2rVn+Jjb/8XOV3P1xc9KjwUVbo8rnrYeaqgaIqXA6z5rNnvaiU2zmxfC7e5Oaw147vlda5rKt8D9m/Ur5XXMbJslU/mpALXJ4z3q9VXNZVHr9XHo+Xk9FKfnzESsk6aZBaZGbiz+278dvmrbj/gQcx4aEH8Pfx47V6r81mw2OPTcD119+AX3/fgl82/Y42ba7CiOHDYLNFZwGg+rL+66/QIrMlWmRmYuSo0Vi2bEnIVt5ZLzyHWbNfxp59B9Hr5t6YNPkJtG7dBqfOZOHUmSzcPnxEpfd4vV58/fVXGDlyNJKTU0J+fvlnYrpdLsx8/kVs27EbK1evRXZ2NiZMeLDW+7J92594+KH7MeHRx7B77wG89/4CrFyxDG++MSdou9dfewWDB9+GHbv24u7x92DcuPFYviw44Vi+bCm6dbsRzZo1C/lZH364AAvmz8Mrr76OHTv3onefvhh153CcPn0agG+YdevWbTBp8hM4dSYLkx+fUqkMlUoFlUqFDRu+h8MRupXW6/Vi9MgRKC0pwQ8/bcQ33/0PWVnncM/4sbU+LnPfeBvXXnc97rnnvsD5SktLD7w+e/YsvPraHPyxZTtEQiEeffShGstctmQxRo4cDa1Wi759+1Vq5KnJ/v378PRTUzFz5gvYd+Awvv7mO3TtdmOt4i1fF69q2w4WiwW33NIP32/4EVu37UKfPn0xcsRw5OTUfgqLSqXCwoWfYvfeA5j7xttYsvgLfLDg/Sq3N5vNGNC/L3IvXcKaNV9h2449ePyJqUHDws+ePYNvv/0Gy1euwbYdviR+/Li7UFhYiHXrv8UfW3egQ8dOGDxoQGAkRU37snzlGqSmpmLmcy8Ejg0AHDiwH+PvHoPhd4zAjl37MOPZ5/DKyy9VSqTnv/8e2rVrj63bduLp6TNqfXxC6dGzF9q1a4/vvvs2onL8uF5oh8+V1rnqAQYAfpsVwGmvHZ/H1M7xOgN89Fb5cdkRyvci61yeMy7rfSjcHlcev/8c51Z8HleWyyvMlVRXr5DrP8BPrP/ubkZyxZJIJIHEqlOnq7F/3z589OEC3D78DjidThgMhqDe9cKCfCQk+hYn+3LtamSfP49ff/sDAoGvPWrRF0uQkZaE/234HneMuDPq+xMtS5f6ki4A6Nv3Fjw6wYQ/t27BTd17BG0387kXcPPNfQL/ViqVEIlESExMqrLsosJCGAwGNM/MDPp99xtvwKlTvt7b/gMG4ovFvkRv3N33BLZp0qQp3njzbfTs3g1msxkqlarGfZnz+quYMnUaxowZFyhj5vMv4oXnZmLGs88Fthtx50iMHTc+8O8xY8fh1VdmY+/ePejS5Rq4XC58uXYNXnnt9So/a/689/DElCcDdWP2y69iy5Y/8OEH8/HOu/OQmJgEkUgEpVJZ5TESiUT4aOGnmDTpUXy+6FN06NgRN954E4bfcSfatm0HANi8+TccO3YUR479HUhYP/5kEa69phP27duLzp271HhctFotJGIx5ApFyFheeGEWbrypOwBgypNPYcTwobDb7ZDJZJW2BYDTp09jz57dWLHSN4Vh5KjReHbG03h6+oygxpfqXMjJgVKpRP8BA6FWq5GR0QgdOnSsVbwV66Jer0e7du0D/37+hVnY8P13+OF//8PDEx6pVTzlE9dGjRpj8uNPYN1XX+KJKU+G3P7LtatRXFSEzX9sg16vB4BKDTtOpxOffLIIcfHxAIAd27dh3769OHMuJzAE/9XX5mDDhu/w7Tdf4977HkC7du2r3Re9Xg+hUAiVWh10bBbMn4cePXth+jPPAgBatGiBv//+G/Peexdjxt4d2K57956YNPmJWh2T2miR2RLHjh7hpCyhIILndVXAMICAw/Iq4jJWLssKRSBgAI567oRC/mLVKKUoqMNjsKosTyXhrKyKanudqw0+6ynA7TkTCvjtrxMw4KzHktfvP8ffAz6vAQIO6yrDe10VAOBm1AqfdZXrsvm4rlLPOrkieL1eOJwOdOx0NcRiMf7Y/HvgtVMnTyInJwfXXnsdAMBqtUEgEAT9Afb/28tG59mS9eHUyZPYt3cvRvyTcIpEItw+/A4sXbq40radrr6as89dsWoN/ty+G71794XdfnkO1IED+3HniNvRplVzpCTFBYZxX8jJqaqoIEeOHMHcOa8hOTE28DN54qPIy8uF1Xr5JrDiviQnp6Bf/wFYvtQ39/rHH/4Hp9OBYcOGh/wck8mE3NxLuP76G4J+f/31N+Dkib9rFavfkKHDcPLUOaxe+xX69LkFW7duxU3drg/0iJ74+wTS0tKCepZbtW4NnU6HE2F+VlX8DQMAkJTkSwALC6uefrB82WL07tMXsXFxAIBb+vWH0WQK+o7VpNfNvZGenoH2bVvjwQfuxZo1q4LOUXUqnj+z2YyZzz6DLld3QHpqIpITY3HixN+VhuZXZ91XX6Jvn55o3rQRkhNj8fLsWcippt4dPnwY7dt3CCTqoaRnZAQSdcBXP81mMxr/8whJ/8/5rCycO3cuon05eeJEyPp45sxpeDyXb364/B4DAFiWs8RFJuWuL0Aq4bdfgcvypRIhZ2WFcqUc1+YZMZzdtAoFDJqlx3BSVihy2RV0/jk8ZzIpv7FKOayrXO53RVKxEFzlwGKR4J8klR/cHlO+6yp35Ut5rKsCAQOJmJtzxjCAVMx9XaWeddLgzHrxOfTt2w9p6ekwl5nx5ZersXXrFqz/9ntotVrcffc9eHbG04iJiYFao8FT06bi2uuuDyTrN9/cG88/NwNTpzyOCRMehdfrxTvvvAmRSITuFXqY/02WLl0Mt9uNzBZNAr9jWRZSqRRvvf0etFpt4PcKhTLs8uPi46HT6XC6whzo9PQMAIBKrYLRaATgG/47bMhg9O7TB58tWoy4uHjkXMjGsCGD4XTVblEji8WMZ2c+j8G3Da30WvleYmWIfRk//l489OB9eH3um1i+fCluH34HFApFbXc1IjKZDDff3Ac339wH0595FhMfm4DXXn05qEe0OgKBoNKQv3AWzhOJxYH/9ydeVa3y7fF4sHLFCuTn5yFGqwz6/bJlSwILOdYUk1qtxtZtO7F16x/47ddf8eors/H6a69g8x/bKq0vUVHFuvjcs8/gt99/xauvzkHTps0gk8tx99jRtV5AbdeunXjg/nvw7Mzn0btPX2g0Wqz7ai0WzJ9X5XvkcnmN5VasZxaLGUlJyfjfj79U2lb3z3ct0n2piUIZ/ve4OidO/I1GjRtzUpZELIRSLuZkYSi9NvSoEK7EaGS+Z+Ry0Auo19ZclyIRo5HDZI68/kjFQqgU/PVW3zO0PX768ywnZXm8LO4Z1r7mDetIIRNDKhHC4Yy8F5D386+VobDUGvEQcwED6NT8fq/0Wjku2ssiLkchF3PaSFWRUCiARiXlZBGzGA2/x1SjlEAsEnAy31yv4/taJeNkdXyRkIFWxe8CsnqtHHlFlojLUSslEIm4b6yhZJ00OIWFhXj4ofuRl5cHjUaLtm3bYv233weGyr4+900wAgHGjh0Np8OB3r374p33Lt+IZ7ZsiTVfrsOc119Fn949wAgE6NC+A9at/w5JScn1tVu8crvdWLVyBV57fW7QkGIAGD16BL76ci3uf6Dq+eISiSSoxy4UgUCAYcOGY82aVZg+49kq560DvsfnlZQU46XZrwR6kfcf2BfGHgEdOnbEqVMnq5xnXp1b+vWHQqHEos8+waaNv+DHnzdVua1Go0Fycgp27twRGD4OADt37qjVsPSatGrVGhu+/x4A0LJVS1y4cAEXLuQEjsvfx4/DYDCgVavWAIC4uHj89ddfQWUcOXIIYtHlJFxci/NVG7/8/BPM5jL8uX0XhILLLdd//XUMjz7yUGC6SVxcPPLz8gKvezweHP/rWND0CpFIhF69eqNXr954ZsZMpKcmYssfm3HbkKFhxbtz5w6MGTMOg28bAsDXO52dfb7W+7R7106kZ2TgqaefCfyupvnuV7Vti6VLvkBJSUm1vevldejYEfn5eRCJhGjUqHHIbWqzL2KJBN4KxyazZUvs3LmjUlnNm7eAUMhPD8Mfm3/HsWNH8djESZyVqdfJI07WhQKG96RCIGCg4+DGUqUQQyLmt7dKrZRAIhbA6YrsZl2v4/eY9ruxKZLilJzcAMfq5Li1ZwsOoqqaXiuP+FnuYpEAaiV/DSC+z/A1skT6+C6NSsprDzAA6NRS5BWaI15pn+/GOv9nRJqsMwz/jTUMwyBGI0NBSWRTTGRSERQycc0bRkAuE0MuE8Fmj2y9iRiNnNOpKqHo1DLkF1sibgTj6/xTsk4anA8+/Lja12UyGd55dx7eebfqnjJ/z+Z/xU8//gCDoRTj7r4nqAcdAIYMGYalSxdXm6xnNGqE8+ezcPjwIaSmpEKlVod8FNYLs2Zj659b0KvHTZj53PPodHVnKBQKHDt6FLt370Kb1lcBANLT0iGRSPDxwo9w3/0P4Phff+GNuVXPGQ9l+jMzcecdw5CWlo6hQ2+HQCDAkSOH8ddfx/DCiy9V+16hUIgxY8Zi1ovPo1mz5rjuuuur3X7yE1Pw+qsvo0mTpmjfvgOWL1+KI4cP4bNFi2sdb3FxMcaPuwtj7/atkq9SqXBg/3689+47GHSrbzX4Xr1646qr2uKB++7BnDfegtvtxpNTHseNN96Eq6/uDADo3qMn5r33DlauXI5rr70ea1avxPG//kL79h0Cn9UooxH27t2D8+ezoFKqEFPLBLOipUsX45Z+A4LmVQO+ofkznnkaa9esxkMPT0D3Hj3w7Izp+OmnH9G0SVMsmD8vMIoCAH788QdknTuHbjfeCJ1Oh19+/hlerxctWmSGHW+zZs3x/XffYsDAQWAYBq+8/FJYz/9u1qw5LuTk4Ksv1+Lqzp3x808/4ftyK8WHMmLESLz91hu4a9QIzHrpZSQmJeHwoUNISk6usu706tUb1157He4adSdmv/IqmjdvgbzcXPz804+49bYhuPrqzrXal4yMRti27U8Mv2MEpBIpYuPiMGnyE+jZvRvmznkNtw8fgd27d+KTjz+q9poXDofDgfz8PHg8HhQUFGDTxl/wzttvov+AgRh9V+0XO6yJhoPEUqeR8T4PGPDdZPme51z3MmJ10Rm9E6uTI7ew7klwNHpVhUIBHrqzE15ZuC2iBZeEAgb3D+/AeyNIjEaGgmJLRIllrI7/pML/OZEm67E896oCvsRSr5WjMIInA4hFAt57VQFAKZdAJhVFtJChRiXlpVe1Ii5GV0Tj/Ps/50Je3UdXMIxvf/km+qeeRfLoRamEv9FKNGedkH+BpUsXo2evmysl6gBw25ChOLB/H45Ws3DUkCHD0KfPLbh1YD80aZyGr74M/bz02NhY/L75T4y+6y7Me+9d9OpxI264rgtef+0V3H77HXh/wYcAfEPmP1r4Kb5Zvw7XdumEd955C6++OidkmVXp06cv1n61Hr/9tgk9e3RD75u744MP5iMjI6NW7x83/h44nU6MHVfz8PNHHnkMj02cjJnPPoPrr+uMTRt/weq169C8efNax6tSqdDlmmvw4YL5GNCvD66/tjNeefkljL/nXrz19nsAfDcvq9Z8CV1MDAb064MhgweiceMmWLxkedB+Pz19Bl54biZ69fAtyDdq9Jigz5r0+BQIhUJc26UTmjROC2uldL+C/Hz8/NOPGDJkaKXXBAIBbh18G5b9s97BuLvvwei7xuLhh+7HgP590bhJk6BedZ1Wi++/+wa3DuqPazp3xOeLPsXnXyxF6zZtwo73tTlzodPp0Ld3T4wccTt69+4bWKyuNgYOuhWPTZyMaU9OwY1dr8OuXTWvlC6RSPDNtxsQH5+AO4YPxQ3XdcE777xVbS82wzD46utv0bXbjXh0wsO4umM73HvPOGTnZCMhIaHW+zLzuReQff48OrRrgyaN0wAAHTt2wpKlK7Duqy9x/bVX47VXZmPmcy/UeipFTTZt/AUtmjVG2zYtcfvQ27B1yx944823sXrNV5z23DMMg/QkDeqaa8tlIiTFcTvUvyoyqQhJcTUvfFmVuBg5772qfrE6RZ0XXGMYID1Zy3uvKgA8NKJTRHPXhUIGjVK1eOyuyEc41UQgYJCerKnz3GW1UhK1BEilkCBBX/eGoeR4JeQ896r6JcQqoJTX7bMEDP45J/w3gABARrIGojrWValEiJT4ul8/wiEWCZGWpKnz+3VqKe/D9S9/liyikRGpiWreG+r8UhLUdV5zQihgkJFc93NSE4bl83kIPCuzcPMQ+3C43W5s2bwJ3Xv2+dc/s7s2ruTjcfDvfHQfF94jqmoilwqQ/+dUTsskdbN9258YfOsAHP/7dOBJAYSQ6LPYnMi+ZAqr11IhF6NRsiYqSWV5RaXWsIdux+rkSI7Sjbofy7LIzjWF1cPKMEBakiYqPZV+F/JM6H3fShSUWMJ6/rRQ6OuV/fXzu9A4VcdfgBWYzA5cyDNVWr9gyMS1KCyxIV4vx7cLgp8oo1ZKfI1SURgBUl5+kSVkr3V1sSbGKhCvj04DmJ/H40V2rinklJiqYhX+03jC57oKodgdbpy/ZKw0J7y6YyqVCNE4VQuxKDpJpV+pyY5LBWWVetiri1WnliI1UR21BhDAd626VGBGqanyNKOqYmUYIDlexfu0gorcbi+yLhlDjrCoKlaRkEGjFG2dG8DUypqvx9SzTgj5V3E4HLh48QJef+0VDB12OyXqhNQzpVyCpum6WvU8CwUM4mLkaJIand7fiuJiFMhI1tRqQSupWIiUBFXUE3XAN2qhUYoWibEKiGsx9FYpF6NJmi6qiTrgaxzYvGQsWjaOBVDzo6f8CW+z9Bj8sWRsVBN1wDeUuUmarla9wSIhgwS9r75EO1EHgMQ4JVIT1ZDWoudRKhEiPVkT9UQd8E2JaJyqRXyMolaPNVMpfHU12ok64Bth0zRdB61aWuMoCwHjm+veNE0X9UQd8E3d8CWJNV+rxCIBkuOVSEuK3kgFP4ZhkJqoRnK8qlarrstlIjRK0UY9UQd8w+Gbpumg18pqHBHGML5HSjZNj+F9pMqV1RVKCCE1+OrLNXjs0Qlo374DPv50UX2HQwiB7zFhjVK0cLo8KDXaYTTb4XJ7wbK+m16pVIQYjQw6dXTmqFdHo5JCo5LCanehxGCD2eqEx8uCZX2NCQq5GHpt9Ia9Vyder0RcjAImixMlBhvsDjc8XhYMA4iEgsDwbL4ff1ed5HgVNi8Zi683ncDHa/bjwPF8iIQCeFm20nz2Ns3i8Mioq3HHLa2iNlS7IrnMlyw6nG6UGO0wmYNHcfrOvwxalTTqiU9FMRoZYjQymK1OlBhtsFiDe6+1ain0WhmU8vqtqwzDIDFOiYRYBQxlDpQYbXAE9V76Gun0WnnUhj1XRSwSIj1JA7fbi1KTrVKPsFQihF4rh07N/yJ9NVEpJFApJLA73Cg22CqNOFYpLl+r6ruuxurkiNXJYTL7zn/wwnO+hfP0Wlm9fe/9BAIGKQlqJMWpUGqyo9Rkh9MZHGuCXoEYrSxqjTSUrJP/LN8NDDePayENx5ixd3M2t5cQwi2JWIjEOCUSozQXPRIKmRiKpPq9cawNhvE92ijavebhkElFuGvQVbhr0FU4cDwPa386jtxCM/73x2k4nB4o5WJ8/9Gd6Nwmqd6TCj+pRITkeN/ICf9NuVgkRNM0Xf0GFoI/aQMQFGt6BHOb+eBfzdw/Z/pyrIKI1ozgg0gkQLxeiXi9MuiYtmhUtwVd+SSTipCaqAagDoo12iNTasPfGAoEn39f/A2HQMAEGhiA4FgTYqP794uSdfKflZ6kwf5196PYYOOsTNZLiT8hhBBSlU6tk9CpdRIAoNWghbhUYIZWLUWXq/6dj1YlhJBIULJO/tPSkzSctjy73ZE9T5IQQgghhBBCAFpgjhBCCCGEEEIIaXAoWSeEEEIIIYQQQhoYStYJIYQQQgghhJAG5oqes16bB8lzzeXytW+oFBKIxQ1/lVi+0fEI5nIJ6DgQQgghteBf+Z1hmHq5p6utKyVOgGLlw5USJ0Cx8qU+Y6WedUIIIYQQQgghpIGhZJ0QQgghhBBCCGlgKFknhBBCCCGEEEIaGErWCSGEEEIIIYSQBoaSdUIIIYQQQgghpIGhZJ0QQgghhBBCCGlgKFknhBBCCCGEEEIaGErWCSGEEEIIIYSQBoaSdUIIIYQQQgghpIGhZJ0QQgghhBBCCGlgKFknhBBCCCGEEEIaGFF9B0BIfcq+ZESRwcpZeV6PB13apXFWHiGEEFIbZosTOXkmGMrskIiFiNXJ0ShFC4Zh6js0QgghdUTJOvnPyr5kRMuBH8Hu9HBWplwqgPXADM7KI4SQfxuPxwtjmQMutwcsCwiFDKQSEdRKSYNLLB1ON8osTng83kCsKoUEcpm4vkMLOPBXHj5cvQ/LvztS6e9Z84wYTBpzDe4e0g46jayeIgzGsiyMZQ44XR54PF4AgNfLwutlIRA0rPPvdnthNNsDcXq8XpgtTqiUknqOrDKb3QWLzRUUq93hhkza8G71yywO2B3uoPPvdnshEjWsAb9eLwtjmR0e7+VjaiyzQ6OSNrhrldPpQZnVEXT+LVYnlIqGV1etNhcsNmdQrE6nBxKJsJ4jC8ayLExmBxzO4GuVx+OFUBi9utrwvsGEREmRwcppok4IIaRqVpsLxQYbDGV2sCxb6XWxSAi9VoZYnaJeb9r9N2jFBhvMVmfIbRQyMWJ1cug0snq7aT/0dz4efOF/2HM0FyKhAO5/bibLO5NTiifm/ILpb/+KCaM6Y+7Um+vthtjp9KDYaEWJ4XLy4/X66oHH48XxM0WI0coQq5NDKqnf21OzxYligw0miwMsywbi9HpYnL1QGhi5oNfKo3rTXhHLsjCY7Cg22GC1u3wxlov1ZFYxlHIJYnVyaNX1m2C63V6UGG0oMdrgdHmCYvV4vDh+tgg6te/8K+T12xhmd7hRbLCi1GT3NSR5Lh/T85eMEAkF0Gt957++E8wyi+9aZTI7fDGWO/9nckohk4oQq5MjRiOv18Ywr5dFqcmGYoMNdoe7Uqx/nyuCWilFrE4OjUpab3ECgMvlQbHBV1fdnhDXqn/qalyMIiqNYZSsE0IIIYQ3LMviYn4ZSoy2ardzuT3IL7agoMSKjGQNtOro9wS73V6cu2CAzeGqdjur3QVrngv5xRY0SdNFPbn8ZdtZDJ30JZz/NDiHStQBwN8mYnd68P6y3ThwPA/fLhgR9WNbWGJBbqG52m08Xi+KSq0oKrUiKU6FhFhllKK7zOtlkZNrgtFsr3Y7p8uD3EIzCoqtaJSirZeedqfTg3MXDXA43dVuZ7E5YbE5ISsRoUmqDmJx9JNLk9mB7FxjIOEJhWV9yVypyYYYjRxpSep6aVzIKzSjoMRS7TZujxcFJRYUllqRmqCGXiePUnSXeTxenL9krLJB0c/ucONifhnyi3zXqvoYFWR3uHHuggEud/UdZGUWB8osDijlEjRO1dZLQ1ip0YYL+WUhG5T9vF420PAUr1cgOV7Na0wNa7wJIYQQQv41WNbXE1VToh7qPaVhvIcLLpcHp7NLakzUy3O6PDh9vjTQUxQNOw9dxOBH18LhcMNTTfJTkZcF/tyXgyETLyf50ZBXaK4xUa/0niIzcgvLeIooNJZlce6CocZEvTyP14tzFw2BXs1ocTjdOJ1dUmOiXp7d4cbp7FK4XNEdUWgw2XH+UvWJekWlJhuyLhqrTZj4cCHPVGOiXh7LsriQb0JhGO/hgsfjxZmc0hoT9fLc/7zHaqv99Y0LVpsLp7NLakzUy7PYnDidXRoYeh4tRaVW5OSZwqp3hSVW5OSaeIyKknVCCCGE8CS30FznROZCfhnMltrfjEbC62WRddEYGJ4bDo/X1xvvdvN/Y2kyOzBowmp4PF6EkfsEeLwstu7Nwcx5mzmPLZQSgy2s5Ke8whIrijlcALYm2ZdMsNjCr28syyI71wibPTpJkMfzT32rQyLjcvt648NJnCNhsTrDTn78yiwOXMyPXoNNQbElrEbF8nILzTCW1b6RJ1LnLxnr1EDo9foapKLVWOdyeZBVx/rmcLqj2mBjMjtwqaBu9a3UZEN+UXgNkuGgZJ00OB999BHat28PjUYDjUaDG264AT/++GPg9U8++QQ9e/aERqMBwzAwGAxB78/KysL999+PJk2aQC6Xo1mzZnjxxRfhdEbnpo8QQohvmG5Rad2TLZZlkcfjDVB5BpM9rB71ilzuyPa1tpZ/fwSlJntYPeoVeVkWH67ahzILv73BXJy/vEJLVBJLq80VVo96RV4vi/zi6PSuFhtsdWpU8rM73Cg1RWfUSl6RJaJkq8RoC2v0QF15/hnWHolwR4/UlcnsCKtHvSKPN/J9ra2CEkudGpX8LDZn1EatRHr+CkqsvDXYUrJOGpy0tDTMmTMH+/btw969e3HzzTdjyJAhOHbsGADAarWif//+ePbZZ0O+/++//4bX68XHH3+MY8eO4d1338XChQur3J4QQhq6rKwsMAyDgwcP1ncotVZsjDx5tdpdUemxLDZEnryUGG289gKxLIv3l+/hpCyb3YUV3x/lpKyqGMscEd2oA5dX3+YbF+e/zOLkfYg5y7KcxMpFGTWxO9x1GqlQUTRi9S8kFwmny8N7AxjAzfEwlNl5H2Lu9bIwmCI/HtE4/2aLM+JGIZZl6zwyoyaUrJMGZ/DgwRg4cCBatGiBzMxMvPrqq1CpVNi5cycA4IknnsAzzzyD66+/PuT7+/fvjy+++AK33HILmjZtittuuw3Tpk3D119/Hc3dIIRcwXbs2AGhUIhBgwZxXvbixYuh0+kq/b5nz5544oknQr4nPT0dubm5aNu2LQBg8+bNIUcWVVRxO/+/GYaBQCCAVqtFp06d8PTTTyM3NzeCvQrGsixKjdwkWXzfrFltroh61f3c/zySji9b9+XgxLkScNIewADzlu3htXGBq/NWVMrv+fd4vDBw0CDAVSJdnTKLM6y5v1WxO9y8TzHhagpDqTHyRLomXJ03vs+/08lNg4BvZXZ+G8FKTbbAUx8iYbZGnkjXhKvzRsk6+U/yeDxYvXo1LBYLbrjhhjqXYzQaodfrOYyMEPJvtmjRIkyaNAlbtmzBpUuX6jscCIVCJCUlQSTiZtXxEydO4NKlS9izZw+mT5+OTZs2oW3btjhy5Agn5Vusroh7Vf34TIABRDT8uSI+h2z+su0sRBytjsyywN/nipFfxM9wWLfby0mvKgDYHC5ee6zLLE7OGi2MPA/Z5fK7YOK5F5irWD1eb0TDvmtid7g5SwZNZgevDWBXyrXKVz5354zvvwFcfRecLg8vI8EoWScN0pEjR6BSqSCVSjFhwgSsX78ebdq0qVNZp0+fxvz58/Hwww9zHCUh5N/IbDZjzZo1eOSRRzBo0CAsXrw46PVQPePffPNN0GOGDh06hF69ekGtVkOj0aBz587Yu3cvNm/ejHvvvRdGozHQwz1r1qwaYyo/DD4rKwu9evUCAMTExIBhGNxzzz1h7WNCQgKSkpKQmZmJUaNGYdu2bYiPj8cjjzwSVjlV4SpRB3w363zeAHs83JXN5X5XVGK0gesnWfHVu8ZFj1p5fB5XTusqz0OLuYyV7wURI1lXoVJZfJ5/jo8Dn8f1SrlWAdweB66vJ0Fle7j9+8LHcaVknTRILVu2xMGDB7Fr1y488sgjGD9+PP7666+wy7l48SL69++PESNG4MEHH+QhUkLIv83atWvRqlUrtGzZEmPHjsXnn38e9h/zMWPGIC0tDXv27MG+ffvwzDPPQCwWo2vXrnjvvfeg0WiQm5uL3NxcTJs2Layy09PTsW7dOgC+HvLc3FzMmzcvrDIqksvlmDBhArZt24aCgoKIygIQ9UcuRYLLUPncbZfby3n5kSxUVh2u4+TzuHJZV/mu9hRrw8drXcWVc0y5jJXPaRBXwrWKm/F0hHBMIpGgefPmAIDOnTtjz549mDdvHj7++ONal3Hp0iX06tULXbt2xSeffMJXqISQf5lFixZh7NixAHxrYBiNRvzxxx/o2bNnrcvIzs7GU089hVatWgEAWrRoEXhNq9WCYRgkJSXVKT6hUBiY1pOQkBBy/ntd+GPNyspCQkJCRGUJORquDSAwAoEvQiF3ZXNZVkU6tYzznnWdWsZtgf8QCrgNlM/jKhRwV1f5jBO48mLlqkeUz1gF/9W6yvF+Vy6fu1i5mv4TCtfni4/jSj3r5Irg9XrhcNR+TsnFixfRs2dPdO7cGV988QUEHF40CCH/XidOnMDu3bsxevRoAIBIJMLIkSOxaNGisMqZOnUqHnjgAfTp0wdz5szBmTNn+AiXU/6eMC4SY7lUxFmCrZCJOSmnKnIpd/0WfMbaplkcXBwOLVUrJUiKV3JWXnkikQBikZCbsoQCSMTclBWKQs7dOZNL+a2rCvmVUVcBQC7jLlY+j6tMKuIsYZdKRJw2VFbE6THlsKxQuKyrch7rKsMwkHH0N0Ag4K6soHI5L5GQCM2YMQNbtmxBVlYWjhw5ghkzZmDz5s0YM2YMACAvLw8HDx7E6dOnAfjmtx88eBAlJSUALifqGRkZeOutt1BYWIi8vDzk5eXV2z4RQq4MixYtgtvtRkpKCkQiEUQiET766COsW7cORqMRACAQCCoN8XS5gheVmTVrFo4dO4ZBgwbht99+Q5s2bbB+/fqo7UddHD9+HADQuHHjiMsSi4XQKKURlwMAsTo5J+VURaeRcdILxDAM9Fr+Yr2zf2uoFNzctAqFDB64oyOkEn5u2H3Hgptee71WzuvICplUxFniyndd5epYCAUC6DT8jKrw4+pYqJVSSCT8NdYIBAxiODoWfJ9/tVLKWcNVrE7BSTlVlq/lpnyJWAiNipu/JVXh6rxpVTJeGmsoWScNTkFBAe6++260bNkSvXv3xp49e/Dzzz+jb9++AICFCxeiU6dOgTno3bt3R6dOnfDdd98BADZu3IjTp0/j119/RVpaGpKTkwM/hBBSFbfbjaVLl+Ltt9/GwYMHAz+HDh1CSkoKVq1aBQCIj49HWVkZLJbLK2mHev55ZmYmpkyZgl9++QW33347vvjiCwC+aT4eT2RzhSUSCQBEXI6fzWbDJ598gu7duyM+Pp6TMrm4ARIJBdCq+b1RYxgGel3kN+talRQiEX+3VUqFBPcP78jJsE2Ph8WEkVdzEFXVuEos+WwA8eOirkolIqiUEg6iqZpQKOBk6kKMVsb58O+KNCopJ6Mr+E6AfZ8ReWLJZdJfHS6Oh1Iu4aUHuDyJRAg1Bw220fj+x2jknHwf4mL4iZWSddLgLFq0CFlZWXA4HCgoKMCmTZsCiTrg67FiWbbSj3815HvuuSfk61fSgkeEkOjbsGEDSktLcf/996Nt27ZBP8OHDw8Mhb/uuuugUCjw7LPP4syZM1i5cmXQivE2mw0TJ07E5s2bcf78eWzbtg179uxB69atAfh6rs1mM3799VcUFRXBar38POLCwsKghoKDBw8iPz+/UqyNGjUCwzDYsGEDCgsLYTabw9rXgoIC5OXl4dSpU1i9ejW6deuGoqIifPTRR3U4cqGplJKIh6/GxSh47VX1i9UqIr5Zi4vht6cKAB4Z1TniFaGFQgY3X9cYmY1jOYoqNLFYGHFiqVXJeO1V9dNpZBEnlvFROP+ALyGI5DvBMExUEmCGYSJOXqQSEe+9qoBvdIVKEVlDi14r53UIfNDnRDgSKF4fvboaCaFAEJVkXSCI/DuhlEt4G65PyTohhBACX0Nhnz59oNVqK702fPhw7N27F4cPH4Zer8fy5cvxww8/oF27dli1alXQ49eEQiGKi4tx9913IzMzE3feeScGDBiAl156CQDQtWtXTJgwASNHjkR8fDzeeOONwHtXrlyJTp06Bf18+umnleJJTU3FSy+9hGeeeQaJiYmYOHFiWPvasmVLpKSkoHPnzpgzZw769OmDo0eP1vkRmVVplKKt8+JAWpUMCbH8zKmuSCIRIiO58nmvrdRENadzn6vSskksXprYvc7vFwgYqOQSfPRifw6jqlpqorrODTYyqQhpSWqOIwqNYRg0SdPVOQnSa+XQRyEBBnzzd1MT6n5cMpI1vE1/qCher6xzg41IKECTVB23AVUjI1lb5yHmSrkEyfEqjiMKTSgUoHGqts4NNomxyqg0gAC+YftJcXU7LgzD+P5+8DhaqbykOFWdG2wkYiEapdT970dNaDV4QgghBMD3339f5WvXXntt0OicoUOHYujQoUHb+KfmSCSSwJD5qnz00UeVerE3b95c7Xsqjg56/vnn8fzzz1f7np49ewa9r+K/+SaRCNEsIwbnLhjCekyYViVDRoqGx8gq06ikaJSiRXauKaxjlJKg5n3+Z3nPP3IjLhWU4eO1B8J6n1DIQCYR4YePR/Heq+4nEDBomq7DuQsGWO2umt/wD4VMjMapuqj0VPrJpCI0SdMh66IhrGclx+rkSIkgea4LvU4OFiwuFZhrXVcZhkF6kgZanp4AUJX0ZN/32FBmr/V7xCIhmqTpojKqwk8kEqBZegzOXTTA7nDX+n0qhQSNUuqePNeFUiFBk1Qdzl8yhrXifoJeicQ6Js915W9wzSuq/egvgcCXqPM9raQ8hmHQ+J9jWmap/YLWUokITVJ1vDYqULJOCCGEEN5IJSI0z9CjoMSCUqO92ptLqUSEuBh5VJPf8rRqGZqLhSgotsJkcVSbCKkUEiTolVG9oQR8N5UfvTgAqYlqvLhgCwQCptqh8QIBA6+XRYJeiZ8/HY12mZE9li9cQqEAzTJiUFhiRbHBBpe76kYbsUgIvVaGeL2S9znVoSjk4kBdNZTZq32+s1wqRlyMHDFRGKYbSqxOAZlEhIISa7XJBcMwUCt9dTUaoz9CfX5GihYqgwRFBmu1ibBv4TspEvRKiHl8AkBVxGIhmqX76mqJ0VZto41ELESsTh61qToVqZQSNG8Ug/wiC4zm6q9VSrkE8XpF1HrUK0qIVUImFaGwxAqLzVnldgzDQKuSBraPNoGAQeNULYpKfdeq6hqYhQIB9DoZEvRK3hsVKVknhBBCCK9EIgFSEtRIilPBYLLDUGaHy+2F18sGenz1WnnUE99Q5DIxGqVq4XJ5UGK0ocziDNy0CwUCKBVixOrkURtKHArDMHj+kZtw16C2+Hjtfnyy9gCMZgfEIkGlx7t1ap2Ix8ddixH9WtfLDbA/3oRYJeL1CpjMDpSa7L4b4X9yHOafnjSNSloviU95EokQaUkaJMerUGqyw1jmQOAB9wyDGI0csTp5vSS+FSkVEjRRSOB0elBstMJscV0+pgyDBL0SsTp5vSS+Fel1vqkCFqsTxQYb7E53UKypiWrOFvqKhFAoQFK8ColxShjLHCgx2oLOv0YlhV4rr7fEtzypRISMFC3cbi9KjDaYzI6gYxqr8zV81tf3vjyNSgqNSgq7w41igxVWW/D5T4pTQa+VR23Ye1UYhkG8Xol4vRJlFgdKDHY4XMGxpidpoNPIonatqv+zR0g9idMpIJMIYXdys5oyIYSQ6gkETOCmvaETi4VIjFMhMa6+I6las4wYvDGtN16a2B1fbzyBv88V4d0lu2GxuaBRSvDb4rHofFXDeRIKwzDQqmWBodj+Rd18q/5Hd3h2TYRCAeJiFIiLUUD8TwIhFgkCw7obEolEiOR4NRBf7piKfElnQ6NUSKD8Z25w+VjrazRNVRiGgU4j+2fxwcvnv3EU59HXlkgkQEKsEgmxyqBjmprY8OqqTCoKxFU+1mitTxIOtVIaWNG+fKzRHk1DyTr5z8pI0eLED4+gyGCteeNa8nL0GCVCCCGktuQyMcYMbgsA+GL9YVhsLqhV0gaVqBNCCAkfJevkPy0jRYsMDldwdLlqv4AOIYQQQgghhFSFHt1GCCGEEEIIIYQ0MJSsE0IIIYQQQgghDQwl64QQQgghhBBCSANDyTohhBBCCCGEENLAULJOCCGEEEIIIYQ0MJSsE0IIIYQQQgghDQwl64QQQgghhBBCSANDyTohhBBCCCGEENLAULJOCCGEEEIIIYQ0MJSsE0IIIYQQQgghDQwl64QQQgghhBBCSAMjqu8AIlFmcUT9M91uNwDAbHVCJPJG/fMbGjoewdxuNx0HQggh9YZl2cB/6+M+KRxXSqxXSpwAxcqHKyVOgGLlC1+xqpXSGrehnnVCCCGEEEIIIaSBoWSdEEIIIYQQQghpYChZJ4QQQgghhBBCGpgres46ITl5JhQbbPUdRgDr9aBTm5T6DoMQQggHSk12HDyeD0OZHV4vC61airYt4pEUp6rv0AghhPwHULJOrlg5eSZcPXwRHE5PfYcSIJcKkP/n1PoOgxBCGiy7ww1jmQMutwcsAKGAgVQihE4tg1DYMAb87f8rD599dQBrfzoOpyt40VCBgMGtPZrjoTs74abO6WAYpp6ivMzrZWEos8PucMPt8cXr8XhhtbugkInrObpgLrcHBpMdTpf3cqxeL1xuD8QiYT1HF8xmd8FodsBT7piWGG3QqWUQCOr/vPuxLAuTxQmrzRV0/k1mBzSqmhewiiaPxwtDmQMOZ3BdtTvckEkbVlridPnqavnzX1RqhU4tg0jUMK5VfhabE2UWZ1CspSY7dGppg7hG+bEsC6PZUamumq1OqBSSeo4umNvthaHMDofTE3StcjjdkEqiV1cb1reCkDAUG2wNKlEnhBASmv8GrcRoh9XmCrlNfpEFWrUMeq0M8npKMLNzjRg/43vsO5YHkZCB28NW2sbrZfHDltP47vdTaJ4RgyWvD0a7zIR6iBZwON0oMdp9CYX38mrFAOBlWZzNMUAmFUGvlSFGI6vXm3az1YkSow1lFif+CfFyrF4WJ7NKoFJIEKuT1+tNO8uyMJQ5UGK0wWb3PfHGW+6YXiow/1NXpYjVyaN6016R2+1rPCg12eFyewPx+2PNzjVBIhZAr5UjRlO/jWF2hxvFBhuMZXZ4K55/lsXp7FIo5GLotTJoVfWbYJrMvvNvtroC8fn/m1dkQX6xBRqVFHqtDEp5/dVVr5dFqcmOEuPl++HysV7ML0N+kRk6jQx6rRwScf01hrncHpQY7Cg12QLX1fLnP+uiEVKJ8J9rlbxeG8OsdheKDTaYzI6Q16pT50uhlIsRq5NHpTGMknVCCCGE8MbrZZGdawzc+Fa5Hesbdl5qsiMpTom4GEWUIvQ5crIAtz32JQxldgAImaj7+V87d8GAvvevxOq3h6HntY2iEqefocyOi/llgZvJqtgdblwqMKPEaEfjFG299AheKihDidFe7TYsC5RZfL2DMRoZUhJUUU/Y3G4vzucaA0l6VTxeFiVGX11NSVAjRiOLUoSXWe0uZF8yVltPAcDp8iKvyIJigw2NUrT10ntdbLAht9Bc43ZWmwtWmwulcjsykjVRb1xgWRYX8stgLKv+0VwsCxjLHDCWORAfo0BinDJKEV7mcnuQddFYY6eV28OiqNSGEoMNaUmaehlpUWZxIifXGGikqYrD6UFuoQUlBjsapWrrpXGhoNiCghJrjdtZbC5YbC5oVBKkJ2l4vVY1rDEchBBCCPnX8HpZnLtoqDFRryivyIKCYgtPUVWWnWv0JeomOzw1JD/lebwsbA437py6Hgf/zucxwmClJjsu5NWcqJdnd7hx9kIp3G5vzRtz6EKeqcZEvaJSkx0X8st4iig0j8eLcxcMNSbq5bEscDG/DCXG6K6dY7W7kHXBUGOiXp7L7ds/u6P2+8eFolJrrRL18iw2F85dNMJbU3bHIZZlcf6SscZEvaLCUisuFUS3rrrcHpzNMYQ1utTL+qaPGs3RfZ65yexAdi0S9fIcLg/O5pTC6Yru6Nm8InOtEvXyTGYnsi4aAz3vfKBknRBCCCG8uJBvCiv5Ka+gxBro5ebbvc9ugKHs8lDycLAs4HJ5MGrq+qgkwhabs87JgdPl6zmOlsISCwxhJj9+xjIH8oui12BzPtcERx2Tg9xCM8xWJ8cRheZye5B9Kbzkx8/j9SWk0WqwMZkdyKvjObQ73MjJM3EcUdV85zC8RkW/EqM9aosds/8MGXfV4RyyLHAxzwSbvW77GS67w40LeaawGhX93B4W56PYYFNitKGotG7n0GJz4VJBeA1S4aBknXDutVdfhkYlC/rp3Kl9pe1YlsXtw26DRiXDhu+/C/x+xfKlld7v/yksKIjmrpD/mBXLlyI9NbG+wyB1VP5acv58FjQqGQ4fPlTPUV024eEHMHrUiMC/B/bvi+lPT6vHiPhld7hhMkeWwBSG2ctRFweO52HP0dywetQr8nhZXCo048etZziMLLTCEmudbn79bHY3TFHoXfN62Trf/PoVG6yBBbP4ZLY6q1xLoTZYNjp1FfANKQ+nR70il9uLUlN0EstweykrKrM4o5JYutwelJoiaxgsLLHw2rvqZzQ7IlqvycsCRVFqWCgqtdapUcnP4fJEZSQAy7IRf38NZXa43PyMBKBknfCides2OHUmK/Dzy8bfKm3zwQfzQ87xuH34iKD3njqThd59+uLGG29CfEL9LOJD6teEhx8INNjodSo0a5KBIYMHYtnSxfB6ubuRu334COw/cISz8qpTVFiIcWNGIyMtCanJ8RjYvy9OnTwZVhmdO7VHnF6D/Py8Wm1flwR26JBbodMosG/f3rBiq422bTLxwQfzOS8XANLS0nHqTBbatLkq4rJWrFiGHt27ITE+BilJcRjQrw9+/PGHiMtdvnINnnv+xYjLqU7Fc+7/t/8nJSkO13bphKlTHsfp06c5/WwuhgY7nB7eeyw/++ogRMLI5xsKBQw+Xrufg4iq5nR56tz7V140hm2Xmuo2UqE8L4uojK7g4nhYbC44nPwOMWdZFoYIk0oAESemtWG1uzgZch/uFIq6KDXaI2oAA3w9wdFILLnowTeZHbyPrvB4vJwcj2iMWDBZnHUaqVAeywIlBn7qKiXrhBcikQiJiUmBn9i4uKDXDx8+hAXvz8OHH31c6b1yuTzovUKBEFv+2Ixx4++JUvSkIerT9xacOpOFo3+dwLr13+Km7j0w/elpGHHHMLjd3NwgyeXyqDUIvfDCTBw4sA9rv/waW7ftxIMPTQjr/Tu2b4PdbsOQocOwcsXyGrd3OsNPeHJysrF710489PAjWL50Sdjv54LH46lTg4xQKERiYhJEosgWU5r57DN4YvJEDB9+B7bv3IvfN/+JG27oitEj78DHCz+KqGy9Xg+1Wh1RGXX13fc/4NSZLGzbsRsvzpqNkyf+RrcbrsHm3ys3rNaF18tNUgHwm1gayuxY8+PxiHoq/TxeFlv25uBUVgkHkYXG1bEwW128zwflKla+boD9XG4PyizcNAjxnVgYyhyc1FWny8v76AqujoWxzM7r6AqWZTlrvOC7YcHucNd5WlF5LAveR1eUmiJvAAF8+2zleXRFCUd1tdRk42V0BSXrhBdnzpxGZvMmaN+2Fe6/bzxycrIDr1mtVtx/73i8/c57SExMqrGsVatWQKFQYOjQ2/kMmTRwUqkUiYlJSElJRceOnTDtqelYtfpLbPzlZ6xYvjSwncFgwMTHJqBJozSkJsfj1oH9cOTI4cDrR44cxqABtyAlKQ6pyfHofuMN2L9/H4DQw+DfmPs6mjZOR0pSHCY+NgEvvvAcut1wbeB1/9Dm9+e9ixbNGqNRRgqmTnkcLlf1f1wEAgGuve56XH9DVzRt2gzDbh+OFpmZtT4eS5cuxog7R2LU6DFYtqxyIt22TSbmznkNDz14H1KT4zF50qNod1UrAMCNXa+DRiXDwP59q/2M5cuWol//AXjggYfw1VdrYbMF/0EL1TPe7YZr8dqrLwPw3QS99urLaNOqOeL0GmQ2b4Knpk0F4BsCnp2djRnTnwr08gKXz8EP/9uAazp3RJxeg5ycbOzbtxdDBg9E44xUpKUkYEC/Pjh48ECVsVfsUfZ4PHjs0YfR7qqWSIjT4epO7fDhBwuq3f/du3dh/vvv4eVXXsfkx6egWbNmaNmqFV6YNRuPPjYJz854Ghcu5ATFvWnTRnS5ugOSE2MxbOhg5OXlVll+xWHwbdtk4q035+LRRx5CSlIc2rRqji8+/yzoPRcu5GD8uDFIT01ERnoyRo28A+fPZ1W7H6HoY2ORmJiEJk2aYtCtg/Hdhh/Rpcs1mPjYBHg8kSdxFpsrouGP5Zk5SqRCOXyigPOkddfhi5yWVx5XSSXXZVXkcns4e7Sqw+XhtWHBbHVxklT4yuJ3FAiX5fMdq4Wj8r2s73rCF7vDHXGvqp/V5uJ1jjWX56yMgxE61ZbP4fWFz78BLMtyVr/cHt+Co1yjZJ1wrss11+KjhZ/i62++wzvvzcf58+fR/5beKCvzLYgzY/pTuO766zHo1sG1Km/pksW4Y8RIyOVyPsMmV6AePXuhXbv2+O67bwO/Gz/uLhQWFmLd+m/xx9Yd6NCxEwYPGoCSEl9v1wP33YOU1FRs/mMb/ti6A1OmToNYHPqZzmvWrMJbb87F7JdfwR9bdyAtLR2LPvuk0nZbt/yBc2fP4n8//IyFH3+GlSuWBTUghDJo0GB8+816bNz4S9j7XVZWhm/Wf42RI+/CzTf3hslowvZtf1babv7776Fdu/bYum0nnp4+A7//4dvG36u6fOWaKj+DZVmsWL4UI0fdhcyWLdG0aTN8883XYcX57Tfr8eEH8/He+x/gwKGjWLl6La66yjcsffnKNUhNTcXM514ITHfxs1qtePfdt7Dgg4+we88BxMcnwFxWhtFjxuKXjb/h19+2oFnz5rjj9qGB60pNvF4vUlJSsWTZSuzeewDTn3kWs196AV+v+6rK93z15VqoVCrcd/8DlV6bNNnXIPPtt98ExT1/3rv45LPP8ePPm3AhJwczn32mdgfrH/Pnz0OnTp2xddsuPPDgw5jyxOTA9AiXy4VhQwZDpVbhp59/xcaNv0OpVOL2obfVaeREeQKBAI88OhHZ2dk4cCDyodweDqeneFnwNheUq95/P6GAgYHHHksuEwEupxBVFMn8/9Dl8Rkrd2Vzvd+Vy+cwVp4X7uKyfD5j5bpsLq99Fbm5PP98D4O/Us4/59cq7mOl56wTzt1yS7/A/7dt2w5dulyDtm0ysf7rrxAXF48/tmzGn9t21aqsXbt24sSJv/HJZ5/zFS65wrXIbIljR33zzHds34Z9+/bizLkcSKW+Z4m++tocbNjwHb795mvce98DuHAhB5OfmILMli0BAM2bN6+y7I8XfoRxd9+DsePGAwCemTETv/22CRZz8Oq2Op0Ob73zHoRCITJbtkS/fgOwefNm3HPv/SHL/fv4cdx/33jMfO4FTHrsEbw+5w0Mu304AODAgf3ocVNXnMu6UGn6iN+6r9aiWbPmaN2mDQBg+B0jsHTpYnTtdmPQdt2798SkyU8E/u3vgfX3qlbn999/hdVqQ58+vt73O0eNxrIlizF69Jhq31fehQs5SEhMRK9eN0MsFiM9PQNdulzji0Gvh1AohEqtrhSLy+XCO+++j3btLi9M2aNnr6Bt3p//IdJTE/Hnn1sxYMDAGmMRi8WY+dwLgX83btwEu3ftwvqv1+H24XeEfM/p06fQpElTSCSSSq8lJ6dAo9Hg9KlTQXG/O28+mjZtBgB46OFHMHfOazXGVt4tt/TDgw89DACYMnUaPlgwH1u2/IEWmZlYt+5LeL1eLPhgYWC9j48Wfor01ERs3foHeveufqRETTIzfd+J7PPnA+fp387LQyMAy+ONZTQWsCKEC1xWVar3V57/6jmjYfDkiqTT6dCseQucPXsGf/yxGefOnkV6aiJitErEaJUAgLFjRoUckrt0yRdo374DOnW6OtphkysFywYSlyNHjsBsNqNxRgqSE2MDP+ezsnDu3DkAwGMTJ2PSY4/gtlsH4J2338TZs1Wv3nz61El07tIl6HedO1dOYlq1bgOhUBj4d2JSEooKq35yweuvvYK+ffth6pNPYfXar/DEE5Ow6LNPAQB/HTuKzMyWVSbqALBs2VKMHDU68O+Ro0bjm/VfV+pl7nR13b83y5Yuwe3D7wjM+R5xx53YuXNHtceroqHDbofdZkP7tq0waeIj+P67b2u1voBEIkHbtu2CfleQn49JEx9Bxw5XIS0lAanJ8TCbzbiQk1PreD75eCG633gDmjRKQ3JiLBZ/sQg5F6p/fzh/eBUKRSBRB4CkpCQUVlMPQim/3wzDIDExMVDG0SNHcPbsGaQkxQXqdqP0ZNjtdpw7ey6szwnFv6+hFv4Ml1DA3e2FgOEmplB0ahmn5Xm8LHQabsssj9PjymFZFQk5WLAvuDz+YhUIuIuV6/2uXD53x0HI4X7zXT6f55/r48Dld7Ry2dzFKuC5roqukLrK9XdWJOL+/FPPOuGd2WzGuXNnMWrUXbh9+HCMH39v0OvXX9cZr895EwMGDqz0vvVfr8OLs16OZrjkCnPixN9o1LgxAMBiMSMpKRn/+7Hy0HKdVgsAeHbm8xhx5yj8/POP2PjLz3jt1ZfxxeJlGHzbkDrHUHEYPcMw1Q4xPXr0CO4aMxYA0LFjJ6xZ8xWGDR2M4uIibNr0C8aOu7vK9/59/Dj27N6FfXv34IXnZwZ+7/F4sO6rtUG9+Qqlsk77U1JSgg3ffweXyxU07N/j8WD50iV4YdZsAL6b/YrJbPm5+mlp6dh34Ag2//4bfvvtV0yd8jjmzXsXP/60scqpB4Bvob+KydnDDz+AkpISzH3jbWSkZ0AilaLPzT3gdNVu+PdXX67FczOfwauvzcW1110HlUqN9997B3v37qnyPc2bt8DOHdvhdDor9a7n5l6CyWRC8xYtAr8LVQ/CbWUXVVOXLGYzOna6Gp8tWlzpfXHVNO7U1okTfwNA4PsUCYVMBIbhpndNIa+6rkTqquZxEAoZTocudmzF3+MflQoxHEZu5m8reTyuYpEQUrGwzs8tL08iFkAiFta8YR2pFJVHztSVQsbfMfWXb6zjc+srlcXj+Qd8dTXSRzcCAMMASh6Pq0wqglDAcDLUWiYVcdr4U5FSIQGKuXlEoErOXb0PRSkXczYXnM+6yjAM5DIRJwv3CQUMZBLuU2vqWSecm/nsM/hz6xacP5+FXTt3YMzoOyEUCDFixJ1ITExCm6uuCvoBgPT0dDRu3CSonK/XfQm32x3Ug0hIeX9s/h3Hjh3FkCFDAQAdOnZEfn4eRCIhmjVrFvRTvqe6RYsWmDhxMr797n8YfNsQLK9ifnnzFpnYv29f0O/2c/AIs5SUFGzfvi3w7+tv6IoVq9bgjbmv49zZc3jo4UeqfO/SpYvRrduN2L5jD7Zt3x34mTjpcSytYcV2f8JZ0wJia9esRkpqaqXPeO31uVixYnng/bFx8cjPu/zYOJPJVGmxM7lcjgEDB+HNt97BDz/+gt27duLYsaMAALFEAm8tFzPbtXMHJjzyKPr164/WbdpAKpWguLioVu8FgJ07d+C6667Hgw89jA4dOqJZs2Y4d+5ste8ZfscImM1mfL7os0qvvT/vPYjF4kDdi4YOHTvizJnTiI+Pr1S/tf80RtWV1+vFwo8+QOPGjdGhQ8eIYxUKBdCqpBGXAwB6LX/rlcTrlRhycyYnj24TCBh0bpOEdpn8PVGCq2OhkIshk/LbXxOj5WaEQQyPIxUAQCIWQqXgJhmI1fG7tk6MRsZJL6NIyHD2/awKV3VVo5Ly0lvpxzAMZ3VVz1E5VVHIuPve6nX8xhqjlYGLAVFSiZDTBrVQuKqrOo2Ml8YaStYJ5y5evIj77h2Pzp3aY/zdYxGjj8Wvv/+BuPj4sMpZunQJBt82BDqdjp9AyRXF4XAgPz8Ply5dxMGDB/DWm3MxetQI9B8wEKPv8vVS9+rVG9deex3uGnUnfv11Y6DBaPasF7B//z7YbDY8OfUJbN3yB7Kzz2Pnju3Yv38fWrZsFfIzH57wCJYtXYwVK5bh9OnTeGPu6zh27GjEQ3InPzEVmzb+gqlTHsdfx47h0KGD+HPrVkgkEhQVFeLHH/4X8n0ulwurV63EHSNGVmr0Gj/+XuzdsxvH//qrys+Nj0+AXC7Hpo2/oCA/H0ajMeR2y5YuxtCht1f6jHF334Pi4qLAong9evTA6lUrsX3bnzh29CgmPPRA0HSAFcuXYumSL/DXsWM4d+4sVq9eCblcjvT0DABARkYjbNv2Jy5duojiouoT72bNmmP1qpU48fff2LNnNx64756wFp1s1qw5DhzYj02bNuLUqVN4efaswFMAqnLdddfjkUcn4vnnZmD+++/h7NkzOHniBGa/9CI++nABXn1tLtLS0msdQ6TuHDkasbGxGD3yDmzf9ieyss5h65Y/8NS0qbh48UJYZZUUFyM/Pw/nzp3FD//bgNtuHYB9+/ZiwQcLg85hJPQcJC4SsQAanpOKh0Z04uRxWF4vi4dH8jtlSyYVcdLLxHdSAfgSy0jvWxkGiNHwv7gsFzfrcpkIcp571gUCBlp15N+HGE3l0UtcUykkkHIwIiIadZWL8y8UMJxPqwmFi+OhVkogFvE3WgXwja5RKyNPsvlsrPXTqaWcNNjyVVdpGDzh3OIly8La3mQOvRrvpl83cxAN+bfYtPEXtGjWGCKRCDpdDNq1a4c33nwbd40ZF5h7yTAMvvr6W8x+6UU8OuFhFBUVIjExEV273YiEhAQIhUKUlBTj4YfuR0FBAWJj4zD4tiF4dubzIT9z5MjRyDp3Ds89OwMOhx3Dbh+Ou8aMw74Ie9f79r0F32/4Ea+9+jJu6dsLAoEAPXvejN83/4nvvvsGj0x4EGlpabju+huC3vfD/zagpKQYgwffVqnMlq1aoWXLVli6dDFen/NGyM8ViUR44823MXfOa3j1ldno2rUbfvhpY9A2Bw7sx5Ejh/H+gg8rvV+r1aJHz15YtmQx+vcfgKlPPo2srPO4c8Tt0Gg0mPn8i0E961qtDu+88xaenTEdHo8Hba5qizVr1yE2NhYAMPO5F/DE5Ino0K4NHA5HldcCAFjw4UJMnvQYbrrxeqSmpeHFF2fjuZm1X2n9vvsfwOHDB3Hv+LFgGAbD77gTDzz4EDb+Uv1q/HPfeAtt27bFZ59+gpdnz4JQKESHDh2xavWXGDBwUK0/nwsKhQI//bwJLzw/E2PuGgWzuQzJKSno2aMX1GpNWGXdNnhgoMz09Azc1L0H5s3/EM2aNavhnWHEKxNHPBQyVqfgLJ6q3NAxFa2axOJUdkmdh8MLGF8SNbR37R+/WFfxMQqct4VuaKsNqVjIe68q4BtdodfJUVRa92cYx2hkvPaq+mlUUkglwogeNxcfw39dBXy99waTvc6PRhQKGN57Vf3i9ApczK/dEztCUcjFUPI8XBvwja7QqqURTTGI1cl5HQLvp1PLUFRqhdNVt9XcGQaIi4nO05XiYhQoszjrPB1KLBJAx0HjVE0YhkFcjAJ5RZaaN66CRiWBlIch8ADAsFfwcn1lFv4ej1IVt9uNLZs3oXvPPoGFl/7L6vN4HPw7H93HhdcwwDe5VID8P6fWdxiER0MGD0RCYiI+/eyL+g6FkAbP4/HibI6hTnOX9VoZUhLUPERV2V+nC3HzfStht7vrtEK8UMjgm/kj0OOaDB6iq6yo1FrljeWQiWtRWGJDvF6ObxfcGfSaSMigaXoMr3PAy2NZFucvGWGu4pnO1cWqlIvROFXLew+wn8vtwdkcQ8hnblcXJwAk6BVIiK3bGiF1UWZxIjvXGDIJqi5WhgEap2qjkgD75RWZq2ywqS5WiViApmkxUWmsAXwjY85dNIScu1zT+deqpUhPCq/BNBIOpxtncwwh59nXFGtKgioqvdV+pSZ7lQ021cUqFDBokqbjfbpOeRfyTDBU0WBTXawyqQhN03R1aqxRK2tujKBh8IQQUgWr1YoF8+fh+F9/4eSJE3j1ldn4/fffAovDEUKqJxQK6nTDFRcjj1qiDgBtmsfjq3dvh0wqDGtOsIBhIBQyWPTyoKgl6oCvxyo5PrzkUCL2nYtoJeqAr8eqUYoWGlV4yaFaKUGjlOgl6oBv2G6TNF3YQ7eT4pRRTdQB3/FJT9KENc1AKPCdi2gm6gCQFKcKe9SBTCpCkzRd1BJ1wDfFoHGKNuyFF3VqKdISo3etAgCpxHd8xGEcH4aJfqIO+EbHpCaqw5q/LhYJ0DhVG9VEHQBSE9Vhr5GhkIvRJFXL66gK6homhJAqMAyDX37+CW++ORcOux0tWmRi+YrV6NWrd32HRsgVQyQSoGmaDqUmG0oM9mp72VUKMWJ1Ck7mOobrxs7p2PT5GIx8cj1yck3VrhIvEDDwelnoNFIsnXMbuneJXqLuF6tTQC4To9hgg8nsqHKoqVgkgF4rg14r5/URWFVhGAYZyVoYyuwoNtiqXXVZLhNBr5XzvqhcVSRiIZqm61BitKHUZK9yqDHD+BLmWJ086smvn0YlRbOMGBQbbNUOixcKGOg0MsTq5FFtqCkvMU4JpcJXV8ssVa8QLxELoNfKoddGZ0h5RUKhL0ksNfnqanXTIhRyMWJ18qhMKQlFJhWhWbrv/JeabFWuu8EwgFYlRaxOzvuaClWJ0cggkwhRbLDBWM21SiRkEKORQ6+T8T6nPhSGYZCaqIZKKUGJwVbtFC6pRPhPXZXx3qhIyTohhFRBLpfjuw0/1ncYhFzxBAIGsToFYnUKmK1OGEx2uD1eeFlfMuG/8amvZMKvbYt4HF7/AH7Zdg4fr92P33adD7ld5zZJeHjk1Rhycwve5inWhkImhiJJDLfbi1KTDVa7Gwx8N46+JFkDtVIS1R7qqujUMujUMtjsLpSa7HC5vYFYBQyDZum6eksmyhMKBYjXKxGvV8JkdsBkdgSOH8MwSNArEKOtn2SiIqlEhJQENZLiVCg12WGxOYNiTUlQQafmZ4XqcKkUEqgUEjhdHpQa7bA73UHnv1GKtl4a6SpiGCbQYGC1u1BqtAcd01idrzEp2r2+oYhEAiTGKZEQq4DR7ECZJfj8J8UpoVNHZ92HmshlYqQliZHs8aLUZIfV7gqKNS1JDa1K2iCuVVqVFFqVFA6nG6UmOxxOT1BdbZIW3REq9V/TCKmjWJ084gVhCCGERJf/pr2hEgoFGNC9GQZ0b4azFwzYdfginnrzV5jMTujUUvzw8Si0bRHe0034JhL5Ekz//wOASMj/Svp1IZeJA0m5P1ahUNAgEvWKNCqp79FhwsvHNNpD3mvD1xgmR6xOHhRrtIc814ZELERiXHBdFQoFDSJRr0ghE0MhEwcd0+R4VT1HVRnDMIHGsPKxxkVp0cNwCMvFVT7WaKykHy6pRISkON/5Ll9Xoz2ahpJ1csVKT9Jg/7r7UWyo+0qzXGO91HBACCH/Fk3TdGiapsNLH2yFyeyEQi5ucIk6IYSQfy9K1skVLT1JE9UVOGvidlc9F48QQgghhBBCaqv+JzEQQgghhBBCCCEkCCXrhBBCCCGEEEJIA0PJOiGEEEIIIYQQ0sBc0XPW1cror3LqcvnaN1QKCcTihrdyabTR8QjmcgnoOBBCyL9M+UcM1ce9RzgoVu5dKXECFCsfrpQ4AYqVL/UZK/WsE0IIIYQQQgghDQwl64QQQgghhBBCSANDyTohhBBCCCGEENLAULJOCCGEEEIIIYQ0MJSsE0IIIYQQQgghDQwl64QQQgghhBBCSANDyTohhBBCCCGEENLAULJOCCGEEEIIIYQ0MJSsE0IIIYQQQgghDQwl64QQQgghhBBCSANDyTohhBBCCCGEENLAiOo7AEJqkn3JiCKDtb7DqBWvx4Mu7dLqOwxCCGmwikqt+GnrGRSWWmG1uaBVS9EkTYdbujaFWCys7/AIIYSQBoOSddKgZV8youXAj2B3euo7lFqRSwWwHphR32EQQkiDwrIsdh2+hA9W7sWaH/+Cy+2FUMCAYRh4vF6wLJCgV+Cxu7rggTs6IiVBXd8hw+tlUWqyoczihNvtBQC4PV4Ullig18ohFDacwYkulwfFBhtsDndQrAaTHVq1FAzD1HOEl1ltLpQYbXC6PIFYPR4vLFYnlApJPUd3GcuyMJkdMJgcQcc0r9AMvVYOiaThNCx5PF6UGG2w2FxBsRYbrNCpZQ2qrjqcbpQYbbA7PEGxmswOqJWSBlVXzRYnSk32y3G6vbiYb4JeK4dcJq7n6C5jWRYGkx0mc/C1Kr/IV1cbUiOo2x26rpYYbNBpZBAIGs75tzvcKDZY4XAG11WzxQmVMnrXKkrWSYNWZLBeMYk6IYSQykoMNgx//Cts3pMNkVAAt+efBM3LAmAD2xWUWDH7w62Y/eFWvPDoTXj+kRvr5cbd6fSgsNQCg8kBj9cXK8v64mS9LHILzcgrskCnliFer4BMWn+3UmaLE0UGK8oszssxlos1O9cIUYEAeq0ccTEKiET1k7SxLItSo/2fBgVX0O8BX8PImZxSyKQixOkUiNHK6i1p83i8KCq1osRoh8vtCYqT9bIoKLGgoMQCjUqKOJ0iqjftFdkdbhSWWGE02+H1Vj7/F/PLkFtoRoxGhrgYBaSS+qurJrMDRaVWmK3OwO/Kx5p10QCxSIhYnRyxuvprDGNZFsUGG4oNNjic7uA4y72mkIkRq5MjRiuvlzgBXyNdkcGKEoM95LUqv9iCghIrNEop4vUKKOT118BgtblQVGqF0ewIea26kG/y1VWtDPExinptYDCY7CgqtcJqr3ytYr0szl4ohVQiCtRVvq9VlKwTQgghhBeXCsrQfdwyZF0yAEAgUa+K55+E48UFW3D+khGfzh4U1Z4Ws8WJ85eMgRvfqrCsr9fdUGZHepIGOo0sShFeVlBsQV6Rucbt3B4vCkosMJTZ0SRNF/WEzftPo4HJ7KhxW7vDjQv5JhjNDjRK0Ua9l83hdOPcBQOcrpo7CUxmB0xmBxJjlUiMU0UhumDGMjty8kyBJL0qXq8vwTSYfMe0PhoXcgvLUFhS83RGl9uDvCKzr66m6qKesHk8XmRdNMJic9a4rdXugjXPhTKLE+nJmqg3LtnsLpy7YKjxmsqyLIxmO0wWB1ISVIjVKaIU4WUlBhsuFpQFEt6qeLy+hjKDyY7GqbqoNy6wLIsLeWUoNdlq3NbhdONSQVkgVj4bQhvOuBhCCCGE/GuUWRy45YFVOH/JAI+n+pu0UD7/+hBmvreZ+8CqYLE6ce6iocZEvTyW9SWiBpOdx8gqq22iXp7T5cGZ7FI4ozhajWV9Paa1SdTLK7M4cO6Cocabey45nf8cn1ok6uXlF1uQVxjeuYiUyezA+UvGGhP18jxeL85dNMBsqTkR5dKlgtol6uXZHW6cySkNDD2OBq+XxdkcQ60S9fIMZXacv2TkKarQ7A43zubUnKiXx7K+kRbFUV4DqsRgw4V8U1jfZbfHi7MXSmEr17MdDTm5plol6uVZ7S6cvVAKTxjnIlyUrBNCCCGEcy/M34K/zxbBXYdE3W/OZ9ux/cAFDqMKze329ajVNTnMyTPB7nBzHFVoJrMj7ETdz+3xJWzRSoJzC81Bw57DYbE5cTG/jOOIQmNZFlmXwkt+yisoscBYFp0GG4fTjezcuiWHLMvi/CUjXGE2SNRVicGGotK6JYdOlwdZFw3cBlSNnFxT0BSNcJjMDuTX8TsZLq+XxbkL4TUqlncxvwyWOn4nw2W1uXCxoG7fYa+XRdZFI69JcHkFxb7RR3Vhd7iRk2fiOKLLKFkndXbx4kWMHz8e48aNg0ajQbt27bB3796Q206YMAEMw+C9994L+v3+/fvRt29f6HQ6xMbG4qGHHoLZHN0WalI/Zs2ahY4dOwb+fc8992Do0KH1Fk91srKywDAMDh48WN+hREVDPhcAsHjxYuh0uipf37x5MxiGgcFgiFpMXKj4nbiSWaxOfPbVgcCw9roSCQVYsDL03xUuFRusdb75BXxJULi9h3VVUGyJ6P0OpxvGsvB6uuvC4/Gi2BBeL1VF5Rf34pPJ7Ii4saWgODrnv6jUGlaPekUeb+TnpbYKSiKrq1a7KyojAZxOD4zmyBpbikptEZ2X2io12QJrKdRVQbSuVSWWiBoGXW4PSqMwaomL67fJ7AisccA1StZJnZSWlqJbt24Qi8V4/vnncejQIbz99tuIiYmptO369euxc+dOpKSkBP3+0qVL6NOnD5o3b45du3bhp59+wrFjx3DPPfdEaS/+fXbs2AGhUIhBgwZxXnZNCVKk5s2bh8WLF/NWvl/jxo3BML5VqJVKJa6++mp8+eWX1b4nPT0dubm5aNu2ba0/pyEkXosXLw7sq0AgQFpaGu69914UFBRU+766nAuGYfDNN9/UPVgOde3aFbm5udBqtfUdSpVCHa9p06bh119/rdX7a1u/Km43a9asQJ0QiUSIi4tD9+7d8d5778Hh4C6BW/XDMZitkQ9hdHu8+PKnvyJOUKvDsixKjJHfEBrNdt57gewOd9CiR3UVjWStxGiLuAfft6AX/4kFF8fD5nDBauN32K7vCQWR11Uuzk1NyiyOsKcUhBKNulpsjLyOebzeqEyH4eJ4lFkcvE+Hcbk8KOOgoSUa599gskfUWOtX11EkNaFkndTJ3LlzkZ6ejs8++wyZmZlo0qQJbrnlFjRr1ixou4sXL2LSpElYsWIFxOLghSI2bNgAsViMDz74AC1btsQ111yDhQsXYt26dTh9+nQ0d+dfY9GiRZg0aRK2bNmCS5cu1UsMTmfdLs5arZbXxoDyZs+ejdzcXBw4cADXXHMNRo4cie3bt4fc1ul0QigUIikpCSJR/a7JOWvWrLAbszQaDXJzc3HhwgV8+umn+PHHHzFu3LiQ23o8Hni93qieCz5IJBIkJSU1qEcA+VX3/VCpVIiNjeU9hquuugq5ubnIzs7G77//jhEjRuD1119H165dUVbGzbDjBSv2gqu1wbysb/46X0xmR8Q9VYAvkSox8ntjydXNoMXm5H3YPlc32SVGO6+JpcPprvNQ/Yr4TixKTdz03ro9Xt5HV3B1LEwWB6/D9lmWRYmBmySb7/NvsXL3veWigaI6XDUIOZxu3kdXcHXeDCYHL6MrKFkndfLdd9+hS5cuGDVqFMaPH49rrrkGn376adA2Xq8X48aNw1NPPYWrrrqqUhkOhwMSiQQCweVqKJf7HoHx559/8rsD/0Jmsxlr1qzBI488gkGDBlXqGQ3VM/7NN98EJTSHDh1Cr169oFarodFo0LlzZ+zduxebN2/GvffeC6PRGOiVmzVrFgBfT/XLL7+Mu+++GxqNBg899BAAYPr06cjMzIRCoUDTpk3x/PPPw+Wquteh4tDrn376CTfeeGNgisStt96KM2fOBF73D03/+uuv0atXLygUCnTo0AE7duyo8Vip1WokJSUhMzMTH3zwAeRyOb7//vsq96fiMHj/MOtff/0VXbp0gUKhQNeuXXHixInAsX7ppZdw6NChwPFavHgxWJbFrFmzkJGRAalUipSUFEyePLnGeCPBMAySkpKQkpKCAQMGYPLkydi0aRNsNlugTnz33Xdo06YNpFIpsrOzK52Lnj17YvLkyXj66aeh1+uRlJQUOP/+YwYAw4YNA8MwgX/7e3U///xzZGRkQKVS4dFHH4XH48Ebb7yBpKQkJCQk4NVXXw2K+Z133kG7du2gVCqRnp6ORx99tNrpMYWFhejSpQuGDRsGh8NRaRi8fz9//vlntG7dGiqVCv3790dubm6gDLfbjcmTJwfq2/Tp0zF+/PhqpwMUFxdj9OjRSE1NhUKhQLt27bBq1aqgbXr27ImJEyfiiSeeQFxcHPr161fj8fLbvHkzrr32WiiVSuh0OnTr1g3nz5+vsn7VlkgkCtSJdu3aYdKkSfjjjz9w9OhRzJ07t9blVIVlWRw5VQgu71kOHM/jrrAKLBz2hnJZFt/lh7uQVjhcLg8nvaqAbyisy8XfiAULByNAAmXxeEwBbmPlqoGiKlzFyrIsJ6NJqmJ3uDnpVQV8oyv4HArP6fefw7oUChcjq/z4/F5xWb88Xi8vi+JRsk7q5OzZs/joo4/QvHlzvPjii3j44YcxefJkLFmyJLDN3LlzIRKJqkxGbr75ZuTl5eHNN9+E0+lEaWkpnnnmGQAIuokmtbN27Vq0atUKLVu2xNixY/H555+H3ao5ZswYpKWlYc+ePdi3bx+eeeYZiMVidO3aFe+9916glzY3NxfTpk0LvO+tt95Chw4dcODAATz//PMAfAnx4sWL8ddff2HevHn49NNP8e6779Y6FovFgqlTp2Lv3r349ddfIRAIMGzYMHgr/FGdOXMmpk2bhoMHDyIzMxOjR4+G2137lmeRSASxWBzU4xlqf0KZOXMm3n77bezduxcikQj33XcfAGDkyJF48sknAz2Yubm5GDlyJNatW4d3330XH3/8MU6dOoVvvvkG7dq1q3WsXJDL5fB6vYFjZLVaMXfuXHz22Wc4duwYEhISQr5vyZIlUCqV2LVrF9544w3Mnj0bGzduBADs2bMHAPDFF18gNzc38G8AOHPmDH788Uf89NNPWLVqFRYtWoRBgwbhwoUL+OOPPzB37lw899xz2LVrV+A9AoEA77//Po4dO4YlS5bgt99+w9NPPx0yrpycHNx0001o27YtvvrqK0il0pDbWa1WvPXWW1i2bBm2bNmC7OzsoDo8d+5crFixAl988QW2bdsGk8lU47B+u92Ozp0743//+x+OHj2Khx56COPGjcPu3bsrHTuJRIJt27Zh4cKF1R4vP7fbjaFDh6JHjx44fPgwduzYgYceeggMw1RZvyLRqlUrDBgwAF9//XVE5QBAmcXJ6Q2r/9FTfKnLSvXRKCt0+dwlrXzGGulaBRXVdeG32uAqUQOicP45/l7x6Uo5rlyXzedUGE6P6ZV0/nmMlfPzz0Os9Jx1UiderxddunTBK6+8gh9++AEDBw7E8ePHsXDhQowfPx779u3DvHnzsH///iqHol511VVYsmQJpk6dihkzZkAoFGLy5MlITEwM6m0ntbNo0SKMHTsWANC/f38YjUb88ccf6NmzZ63LyM7OxlNPPYVWrVoBAFq0aBF4TavVBnppK7r55pvx5JNPBv3uueeeC/x/48aNMW3aNKxevbrKpKui4cOHB/37888/R3x8PP7666+guePTpk0LzNF/6aWXcNVVV+H06dOBfaiO0+nE22+/DaPRiJtvvrnK/cnKygr5/ldffRU9evQAADzzzDMYNGgQ7HY75HI5VCpVoAfTLzs7G0lJSejTpw/EYjEyMjJw7bXX1nwwOHLq1CksXLgQXbp0gVqtBgC4XC58+OGH6NChQ7Xvbd++PV588UUAvnqxYMEC/Prrr+jbty/i4+MBADqdrlL98Hq9+Pzzz6FWq9GmTRv06tULJ06cwA8//ACBQICWLVti7ty5+P3333HdddcBAJ544onA+xs3boxXXnkFEyZMwIcffhhU9okTJ9C3b18MGzYM7733XrXD3l0uFxYuXBiYqjNx4kTMnj078Pr8+fMxY8YMDBs2DACwYMEC/PDDD9Uek9TU1KCEf9KkSfj555+xdu3aoPPaokULvPHGG5XeH+p4+ZlMJhiNRtx6662BmFu3bh14PVT9ilSrVq3wyy+/RFyOgIfpByLhlfE3ge+ZFw1xakc08Lnb/9VjyjeGYTibvkCniHu8X6vw3zxpfBzXK+OvH2lwkpOT0aZNm6DftW7dGtnZ2QCArVu3oqCgABkZGRCJRBCJRDh//jyefPLJwJBPALjrrruQl5eHixcvori4GLNmzUJhYSGaNm0azd254p04cQK7d+/G6NGjAfh6i0eOHIlFixaFVc7UqVPxwAMPoE+fPpgzZ07QsPPqdOnSpdLv1qxZg27duiEpKQkqlQrPPfdcoH7UxqlTpzB69Gg0bdoUGo0mUG8qltG+ffvA/ycnJwNAjQuoTZ8+HSqVCgqFAnPnzsWcOXOCFuULtT+hhPvZI0aMgM1mQ9OmTfHggw9i/fr11Y4C2Lp1K1QqVeDntddew4oVK4J+t2LFimpjNBqNgX1t2bIlEhMTg94jkUiC9qM2++rf35qOM+BLtv0NAwCQmJiINm3aBDXIJSYmBpW1adMm9O7dG6mpqVCr1Rg3bhyKi4thtV6eY2ez2XDTTTfh9ttvx7x582q84VYoFEFrapSP32g0Ij8/PyjBFgqF6Ny5c7VlejwevPzyy2jXrh30ej1UKhV+/vnnSnW0pnJC0ev1uOeee9CvXz8MHjwY8+bN433EEcuynCQuSoWY0+RaKGCg18k5K68ikYi7uyshzw3NQiF3sfLZACLkasGCQHlXRqwiEc/nn8NYuaxLIcvnNFYezz/Hx4HPWLm9rl451yqurydBZXN9/nk4rpSskzrp1q1bYH6u38mTJ9GoUSMAwLhx43D48GEcPHgw8JOSkoKnnnoKP//8c6XyEhMToVKpsGbNGshkMvTt2zcq+/FvsWjRIrjdbqSkpAQaRz766COsW7cORqPveawCgaBSK3fFOeSzZs3CsWPHMGjQIPz2229o06YN1q9fX+PnK5XKoH/v2LEDY8aMwcCBA7FhwwYcOHAAM2fODGvxucGDB6OkpASffvopdu3aFRgmXbGM8gsX+hONikPlK3rqqadw8OBBXLhwAaWlpZg+fXq1+1OVcD87PT0dJ06cwIcffgi5XI5HH30U3bt3r3Iuf5cuXYK+QxMmTMBtt90W9Lvbbrut2hjVajUOHjyIo0ePwmKxYMuWLcjMzAy8LpfLa5WgVVwgkmGYGo9zVe+rrqysrCzceuutaN++PdatW4d9+/bhgw8+ABB87qVSKfr06YMNGzbg4sWLdYoj0l6fN998E/PmzcP06dPx+++/4+DBg+jXr1+lOlrb+lTRF198gR07dqBr165Ys2YNMjMzsXPnzohirs7x48fRpEmTiMthGAbdrk7j7AbL62VxU+d0TsoKRa0IPXWiTmUpJZyVxXf5fMYqFgshl4pr3rAWZFIRJBIhJ2WFolJIOOtd5/v8a1Rc1lXuyuKzfIGAgVLOTV0KRSYVQSzipn6pFBIIeEwsuTxnV9K1ist6XxHDMJwdV5FQALmM+0HrlKyTOpkyZQp27tyJOXPmIDc3F6tWrcInn3yCxx57DAAQGxuLtm3bBv2IxWIkJSWhZcuWgXIWLFiA/fv34+TJk/jggw8wceJEvP7661f0StTR5na7sXTpUrz99ttBSdyhQ4eQkpISWPAqPj4eZWVlsFguPwIp1HPDMzMzMWXKFPzyyy+4/fbb8cUXXwDw9cB6PLVbMGj79u1o1KgRZs6ciS5duqBFixY4f/58rfepuLgYJ06cwHPPPYfevXujdevWKC0trfX7axIXF4fmzZvzumJ4VcdLLpdj8ODBeP/997F582bs2LEDR44cCVmGXC5H8+bNAz96vR5qtTrod+V7rUMRCARo3rw5mjZtGljAkQ9isbjW9aM6+/btg9frxdtvv43rr78emZmZIZ9sIBAIsGzZMnTu3Bm9evWK6OkHWq0WiYmJQXPHPR4P9u/fX+37tm3bhiFDhmDs2LHo0KEDmjZtipMnT9bqM2t7vDp16oQZM2Zg+/btaNu2LVauXAkgvO9jbfz999/46aefKk0/qatJY67hbO6eXCbG2MG1f2xiuFRKCaSSyG+whAIBdBoZBxFVLVar4KQcjUoKsZi/BBgAYjkaDcFVOVURi4XQcHSzznesWrWUk95ViVjIawIEcHcsdGoZr73VDMNcMXVVJhVBKY88CWYYBnotv7HqtXJOGi4UMjHkMv4aawDuzpteW7vOj3BRsk7q5JprrsH69euxZs0aTJ48Ga+99hree+89jBkzJqxydu/ejb59+6Jdu3b45JNP8PHHH/O+Ova/zYYNG1BaWor777+/UgPJ8OHDA0Phr7vuOigUCjz77LM4c+YMVq5cGbSCtM1mw8SJE7F582acP38e27Ztw549ewLzZBs3bgyz2Yxff/0VRUVFQUOSK2rRogWys7OxevVqnDlzBu+//36teuj9YmJiEBsbi08++QSnT5/Gb7/9hqlTp9btANWTxo0b49y5czh48CCKiorgcDiwePFiLFq0CEePHsXZs2exfPlyyOXywIiUK1njxo3x66+/Ii8vL6KGlebNm8PlcmH+/Pk4e/Ysli1bhoULF4bcVigUYsWKFejQoUNgwcq6mjRpEl5//XV8++23OHHiBB5//HGUlpZW+4e3RYsW2LhxI7Zv347jx4/j4YcfRn5+fq0+r6bjde7cOcyYMQM7duzA+fPn8csvv+DUqVNB38eK9au23G438vLycOnSJRw5cgTz589Hjx490LFjRzz11FO1Lqc6t/VqgQR95ImlSMjg3tvb894LyMXNWoxWxmuvGgBIJNwkWXwnFQCg08giHhIqEDDQqfltAAG4OR4qBTeNPtXhKsmKxvlXyMWcjK6IRqxcJFliEf8NIAA3x0OrkvI/ZUMogFYV+Xc3Gudfo5JCEmHjJZeNPhVRsk7q7NZbb8WBAwfw5Zdf4siRI3jwwQer3T4rKyto4SgAWLp0KYqLi+FwOHDo0KEqn/9MqrZo0SL06dMHWq220mvDhw/H3r17cfjwYej1eixfvhw//PBD4BFT5R+/JRQKUVxcjLvvvhuZmZm48847MWDAALz00ksAgK5du2LChAkYOXIk4uPjQy6Y5XfbbbdhypQpmDhxIjp27Ijt27dXu6p6RQKBAKtXr8a+ffvQtm1bTJkyBW+++WbtD0oDMHz4cPTv3x+9evVCfHw8Vq1aBZ1Oh08//RTdunVD+/btsWnTJnz//fdRebY2395++21s3LgR6enp6NSpU53L6dChA9555x3MnTsXbdu2xYoVK/D6669Xub1IJMKqVatw1VVX4eabb67VPPpQpk+fjtGjR+Puu+/GDTfcAJVKhX79+kEmq/pm47nnnsPVV1+Nfv36oWfPnkhKSqr2UW/l1XS8FAoF/v77bwwfPhyZmZl46KGH8Nhjj+Hhhx8GELp+1daxY8eQnJyMjIwM9OzZE2vXrsWMGTMCayRwQSwW4vFx10a82I6XBR4dFf6c/3DFaGQR3awJBQLEx3DT612TBL0yosRCKZfw3vgB+BLthNjIjkl8jILXXlU/lVIClaLuPZYMwyAxtm7TXcIVq5NH1LsuEQt571X1S4yL7JhoVTLee1UB31oDkSZaCbGKqCxWqFVLI2oE8X0vo1NXE2IVETVgyqQi3kcr+UX6/dVrZbyNVmJYrpZq/I9wuVyB1c8rzoH8L+L7eOz/Kxed7/ic83L5IpcKYD0wo77DIIREyOv1onXr1rjzzjvx8ssv13c4VyS324vBj67Bxu3n6jwk/pOXBuLBEXVv/AmHw+nGmezSkI8J63v/ShSUWJGgV2DjoruCXhMIGDRNi4GCx3m1FRlMdmTnGkO+Vl2sUokIzTNiopIA+13IM6HEGPrRe9XFGqORIz1ZE40QAfgeuXUmpxR2R+VFP6uLEwDSkzSIiVICDAA2uwtnckpDPn6tuliFAgGaZcRAJo3ew6CKSq24VFAW8rXqYlXIxGiaHsP7aBU/lmVx/pIRJnPlUUo1nf94vQLJ8dVPSeOSy+XBmZxSOF2Vp0JVFyvDMGiUoo3KCAC/MosDWReNIdeIqS5WsUiI5hkxvE/XKS+v0IyCEkvI16qLVa2UonGqlrfGGupZJ4QQ8p93/vx5fPrppzh58iSOHDmCRx55BOfOncNdd1W+MSO1IxIJ8OW7w3FTl4ywHufm3/SNJ2+OWqIO+BLZZhkxYfWwi4SCqCfqgG+IeaMUbViJjEImRrP06CbqAJCWpEF8mFMiYnXRTdQB37DdZukxYc0JZhgGGcnaqCbqgG8dh2bpMWH1sEvEQjRvFN1EHQDiYhRIS9SElcioldKoJurA5UQ2RhPeuUyMVUY1UQd8I5eapYd3LgUCBo1To5uoA75z2SRVF9aUGJlUFPVEHQCS4lVIigtvdJlOLeM1UQfoOeuEEEIIBAIBFi9ejGnTpoFlWbRt2xabNm0KerY5CZ9KKcHPn4zGlLkb8cna/fCyCNkbCPgez+PxstBr5fjg+f4YOaBNyO34JJWIkNk4FqUmG4oNtpC9rMDlocR6rZz3uZ9V0aplkEvFKDZaUWq0hxwRAPiS9FidHDqNrN6eKZ4cr4ZGKUWxwQaj2RGyl41hGGhVUsTq5FBGMCQ9EkKhAE3TdTCWOVBssMFiC/0EE6FAAL1OhlitgteV6qsjl4nRskksSoy+uhqqlxXw1elYnRwxGn4XaquOXieHUiFGUakVBpMDniqeJKJSSBCrk0OjktZLXWUYBunJGug0vrpaZnGGrKsCAQOtSoZYnTzqDXV+YrEQzTP0MJjsKDbYYHOEfqqMSOgb4q/XyqOe/PqplBJkNtajxGhDidEOlzt0XZVLL1+rotlQU15CrBJqpQTFBhsMZfYq/15p/rlWRWNKESXrhBBC/vPS09Oxbdu2+g7jX0kiEeKD5/vjhUduxKJ1h7Bg5V7kFporbXd9h1RMGnsNhvVuWW8JEOC7EY/VKRCrU8BscaLM6gjcOPp6p3RR752qikQiRHK8GklxKhhMdtgcbjDlYm3RSB+VOb+1oVRIoFRI4HZ7UWryJZeBWIUMWjeNq7eGj/IYhoFOI4NOI4Pd4YaxzB50/tOTNPXa8FGeUChAvF6JeL0SZRYHzFZnUKxN02Kg4vkRXbUllYiQmqhBcjwLg8kOuzO4rrZsEsv7An21pVZKoVZK4XJ5UGoKPv8pCep6bfgoTyBgoNfJodfJYbW5YDIHX6sykrXQquun4aMisViIxDgVEmKVMJkdsNpdQbE2z9DXW8NHRXKZGGlJYiTHq2Aos8PhDL5WtWoSF9W/UQ3jW0FIFeJ0CsgkQtid3D2iiBBCSPQlxqnw7MPdMP2BG7D/rzwMeHg1ig02xOrk2LnqHjRvpK/vECtRKSVQKSWBG3OhUNBgEvXyGIZBjFaOGCAwNFooFDSYRL08kciXYALlYhUIGkSiXpFMKoJMqgo6/9Ee8l5b/gSzfKwNJVEvz59gAsF1taEk6uWJxUIkxCqDjmlclBaSDNf/27vvsCiutg3g9+7SOyiCDSWIBWzYTWJ7VewRe+y9gkaNMdEkakwxaoyJxhoTFTX2bowlQVGjoqGIFRF7wYLS25bz/UF2ZQWkzcL65f5d117KztmZZ87O7s5zzpkzVpamsLI01Yu1pCZnKwyZTAZ7WwvYZ7sln0IhN5pEPTuFQo4yDlnvd/bvqpJuTDa+TwZRNm4V7BF1cDyexed9mzBjopHwvsdERP8fKRRyNK5TQXe9pYW5iVEm6kRERKWNyToZPbcK9nCrkPO2ZMZIqcz9miEiIiIiIqLCML4xR0RERERERET/cUzWiYiIiIiIiIwMk3UiIiIiIiIiI8NknYiIiIiIiMjIMFknIiIiIiIiMjJM1omIiIiIiIiMDJN1IiIiIiIiIiPDZJ2IiIiIiIjIyDBZJyIiIiIiIjIyTNaJiIiIiIiIjIxJaQdQHEkpGSW+TZVKBQBITs2EiYmmxLdvbFgf+lQqFeuBiKgAhBC6f0vj97wwGKthvCmxvilxAozVEN6UOAHGaiiGitXW2jzfMuxZJyIiIiIiIjIyTNaJiIiIiIiIjAyTdSIiIiIiIiIjw2SdiIiIiIiIyMi80RPM3YtNRFx8WoluU2jUAICL159AJleU6LaNEetDn9Co4eNVobTDICIyWpdvPMWB4zeQkJQ1SU9CUgZWbQtDb99aKONgWcrRvaRWa/Dn2dsIufAA8UnpAIDE5AzsOnoNXVt7wszUeH7zklMzsfvPKETdikN84stYQy48QJO6FSCTyUo5wpfuxyZi59FrePQ0RRdrUkom7jxMQJUK9qUc3UtCCIReicXhUzcRrz1WkzOwbnckeravATub/CeGKilKlRp/nIhB2JVYvff/jxMx8H3HHQqF8fTNvUhMx84j13Drfrze+x8Z9QR1a5Qr5ej03bj7Anv+itL7rvrpt3/Q27cmXMvalHJ0LwkhcCrsHo6du6v7rkpIzsDmg5fh97/qsLQwLeUIX0rPUGFv0HVcvvFU71g9fu4OWjV2M6rvqidxKdh++BruP07UO1ajbz+HZ1WnEotDJrTT272BLOp/i4xMdYlu09Jcjs1zvNB/zhWkZXDWb9aHPktzOR6fmlraYRARGZVMpRp7g65j1dYwnLv4CAqFDGr1y9MPmQwwUcjRp0MtjO5THw29y5darHHxqdiw7xJWbQvHg8dJMFHIoVLr/76VcbDEyF71MLxHPVR0sS2lSIGoW3FYszMCG/ZdQmqaEqYmcihV+rF6eZTFuH4N0KdjTVhbmpVKnBqNwPHzd7B6Wzj+OBkDmUwGhVymF6tMBrR/2x1j+zZA22ZVIZeXzkl7aroSO49cw8otYbgY/RQmChlUrxyrFmYmGNDVG6N714dXNedSiRMAHj1Nxro9kVizPQJPX6Tm+v6Xd7bBmD4+GNK9NpydrEspUiDi2mP8vD0cW/+4CqVKDRNFzlgberlibL8G8GtbHRbmpdOfqFJpcPjvm1i1NQzHz9+FQi6DWvPy/ZfLZJDJgG5tPDGmjw/eaVCp1BLMhOQMbDl4GSu3hCPm3otcv6tsrc0wzK8uRvSqB4/KjqUSJwDcfhCPX3ddwNpdkUhIzsj1WHWv5ICxfX0woKs3HGwtSiVOIQTOXniA1dvDseev6xAaQKGQ5Yi1RcPKGNvXB51bVoOJSdEbwwoyG/wbnazLvL4u8W0yOdXH+tDHZJ2ISN+t+/HoMWkHbt6Lz3Hi+yptEj+gizeWfOpb4r3Xh07FYOgn+5GRqYYmn9Mjxb/J5I8zfTGke52SCE9HCIEvV5zCd2tDcjR8vEomA4QAyjpaYteS3qhf06UEI81KKAZM24OToffyjVW7vFm9CtiyqAec7Et2pMWl6KfoMXEHHselQC6XQVOAY3XSoEaYO7FViTcubD54Gf5fHoZGI14bJ5CVYJqayPHr113RrY1nCUWYRaXS4MMFf2Lt7sgcDR+v0tZ55fJ22LO0NzyrlFzvJQA8fpaCXpN3IjLqSb7HqnZfOr77FtZ+07XEG8LORNxH3ym7kZiSAQjgdUeAQpFVr3P8W2DykCYl3riw7LdQzPzhGOSy13//a8OytjTDlkV+aNnIrYQizJKWrsToWQex71h0vseq9rfMy6Msdi/tjfLORRtpwVu3ERERUam5dvMZWg/diLsPEwDgtSdqAHQnx5sPXkafybuQkakyeIxamw9eRr+pu5Geqco3UQey9kWtEQj46jAWrQ0pgQizCCHg/+UhfPfvNl+XUGSVz/r3RUI6fEdtxqnQe4YOUScuPg3tRmzC6fD7APKPVbv8/MVH+N/wTXj6PMXgMWqFRD5EuxGb8OxFKgDkmwBrY12y8R+M+vz3fMtL6aff/sHY2X9ApdIUaLsaIZCpUmPQ9L0I3HuxBCLMolSp0X/aHqzbEwkAr01+gJd1/vBJEv43bBMuXn9i8Bi17sUmovXQDbh84ymA/I9V7b4cOX0LncduRVJKpsFj1Dr89010Hb8NSamZEPkk6kDWvggBzP7pJGZ8fwwl2U87e+kJzFh8DELk//0vRNYjNV2J7gHbceB4dAlFCaSkZaLbhG04EHwDQP7HqnZfom7HodWQDbj9IN5gsTFZJyIiIsnFPktG94AdSEzJyPfE51VCAMfP38GEuYdK5MTyWMgdTPjikO5ksbC+WH4Smw5ckj6wXHy58m9s3H+50K9TawQylWr0nbob124+M0Bk+tIzVOg3dTdu3H2R70n6q9QagTsPE9Drg11ITVcaKMKXbtx9gd4f7ER6prrQsQLAjiPX8NmPx6UPLI9tzVxc+G1pk7qJXx/GoVMxkseVc3sCk+cdxZHTNwv9mVKrBZJTM+E3cQfuxyYaJsBsEpIz4BewHY/jUvJN0l+l0QhERj3B4I/3QqUy/AjTsCuxGDR9L1TqgjXUvGr5ljAs2XjeAJHltGJLGBYHniv06zQaAY1aYNjM/QiJfGiAyPSp1RoMm3EA/1yOLXSdqtUCz16konvADjxPMMw8akzWiYiISHJzl5/Ek+eFP/nVEgLYfvgajp6+JXFk+tRqDcZ98UeRkvTsJs87ioTkDGmCysO1m8/w3a9ni/x6jUYgLV2JqfP/lDCq3P266wLOX3pY5Pdfrc5KglZtDZM4spymf/cXktMyi9U7/tNvoYi49ljCqHJKScvExK8Oo7ijmMd/cQhKlWHnfDoZeg8b9l0q8udKrRF4npCGWUtPSBtYLhavC8HNe/GFblTUUmsEgkLuYOuhKxJHpk+IrJE8KpWmWN9Xs5aeMHgjSOyzZMxcfKzIrxfI6t32/9LwDba7/ozC4b9vFvnzr1IL3H2YgIXF+G5+nUIl6/PmzUPjxo1ha2uLcuXKwc/PD1FRUbmWFUKgU6dOkMlk2LNnT65l4uLiUKlS1sQM8fHxuudPnTqFd955B2XKlIGlpSVq1qyJxYsXFyZUIvoP+ebrL/FO8ya6v8eNHYX+7/cpxYj0nTwRDDsbC73vudJYx5vgm6+/hIe7G+xsLHBg/74ir8fYjoH83LlzG3Y2FoiMvFDaoUjieUIath26WuRETUuhkGH19nCJosrd4VM38ehpcoGGvr9OplKNzb8Xvse7MNbsvACFoniZmlojcCrsPqJuxUkUVU5CCKyUIMnWCIFV28KhVhuux/LW/Xj8deZ2sY9VE4UMa3ZESBNUHrYfuoaUNGWxEjUhsi5POHD8hnSB5WL1tvDiH6tqgd1/RRn0coiMTBV+3XWhSCMqspPLZJIc86/zz+VYXIp+Kkmsa3dHShRV7tbvuZjv8Pz8aDQC128/x5mIB5LElJdVW8N0c5AUlVojsH7PRYOMBCpUsh4cHAx/f3+cPXsWR48ehVKphK+vL1JScn6Ifvjhh3wnMBg5ciTq1q2b43lra2sEBATgxIkTuHr1Kj777DN89tlnWL16dWHCJaJiCAk5Cwc7K/Tu5Sf5ujdtDETlioab5Gj+gkVYsfJng61fq7ZXddjZWMDOxgLlyjqgtld1DB08EMHH9VuTmzZrjuiY27C3L9htiTp3bI+Pp08r1jreRFHXruHbeV/jxyU/ITrmNtr7dshRRpvUah9lHG1Rr64XFsyfp9f6XlLHgLF7tdFi3NhRurpzcrCBh7sbunfrjA2B66DRSJcQ/fb75Ryz5xaFWi1w9PQt3Pn3mndDWL29+EkFAEAAq7aGG6wXKDk1Exv3XSx2UglkNYL8stNwDUPB5+/i9oOEYo9WAICHT5Jx9IzhRles3X1BksnhVGqBLQev6G6dJTVtA4gUc4Mp5DKDjlh49DQZB4JvSHKsCg0QuNdwl5js+eu67tZ8xaERAheuPUHYlVgJosrdz9vDYSLBd5VaI/DLzghkKg0zukKl0uDn7eGSzONgYuAG28s3nuLcxUfFbgABsr6jdx25JkFU+gqVrB86dAjDhg2Dt7c36tWrh3Xr1uHu3bsIDQ3VKxcREYFFixbh119/zXNdK1asQHx8PKZNm5ZjmY+PD/r37w9vb29UrVoVgwYNQocOHXDy5MnChEtExbAhcB3GjpuA03+fwqNHhr9mKDeZmUWbsMXe3h4ODg7SBpOHTz+bheiY2wgNj8Sq1b/A3t4e73XrjIULvtWVMTMzg4uLa7FmYJViHVIo6ntSEDdv3QQAdOnaDS4urjA3z3uW1H37DyI65jbCL1zCzE8/x3cL52ND4Drd8pI4BtRqtaQJbklp194X0TG3celKFHbu3osWLVvh4+nT0Kd3D6hU0kzotmZ7hCTrAbJ6gQw1KdbdRwkICrkjTVIBIObeC4P1Au06GoXUdGneH7VaIHDfRYNN4PfrruKPANBSKGT4ZYdhGhZUKg3W7oqU5EQdyJpQbesfhhkKHXYlFldinknSAKLWCJyOeIDoO8+Lv7JcbNwvXXKtEcKgydqaHRGSzeRvopBhnYF6rBOSM7DzyLUiD9V/1fOEdBw8YZjRFUdP38KT56mSrEulFtgbdB1x8dKs71Xr9kRK0gACZN3NYLWEv3269RbnxQkJWS3dTk4vb62QmpqKAQMGYNmyZXB1dc31dVeuXMHcuXMRGBgIuTz/EMLDw3H69Gm0atWqOOESUQElJydj184dGDVqDHw7dMKmjRv0lufWM35g/z7Y2by8L+bFi5Ho0skXFVzLomJ5Z7R8tznCwkJx8kQwxo8bg4SEBF0P3zdffwkgq6d6/rffYMzoEahY3hmTJk4AAMz6/FP41K8NF2dH1K1dE1/OnQOlMu+hRq/2Jh49egS+7dugckUXVHGrgD69e+DmzZcT7Gh7a/ft3YMunXzh4uyIt5s1RkhI/tcf2djawsXFFZUru+Gdd1tgyU/LMf3jGfj6q7mIvn4dQO5D2M+eOY3OHdvDxdkRbpVc4de9K168eIFxY0fh1KmTWLH8J1393LlzO9d17N2zG00a+aCskx1qe1XH0iU/6MVW26s6vls4HxPGj0EF17LwqlkNa39do1cmv7rVXmKwft2vqONdA85l7PHbbxtRxa0CMjL0eyP6v98Ho0cNz7OuLl+6hK6dO6BcWQdUcauASQETkJycrNtOvz49AQD2tpZ6x1JunMqUgYuLK9zcqqBfv/5o1qw5IiIidMtfPQYyMjLw0bSpeKtqZTiXsYdv+zYIDf1Hb50Hfz+A+vW84VzGHl06+WLTpg16da497g/+fgCNG9ZHWSc73Lt3F6Gh/6B7t86o6lYRlSqUQ6cO7RARoX9yaWdjgTU/r0bPHu+hXFkH1K1dE3t278qxX7dv3cr1GExJSUHF8s45XnNg/z64lnNCUlLSa+srO3Nzc7i4uKJChYqoX98H0z76GJu3bMfRI4exaWNggdeTF41G4NaDeEmSCiDrZN1QScWNuy8kX+f124aJ9frtOJgW436+r0pNU+JxnGGGF1+JeSZJAwiQ1bBw7ZZhJsR7npAm6TwDJgo5ou9If0wBwHUDfAZuGCrW288hZbPyo6fJSM8wTMPS9dvPJZvJX6UWuGqgyRvvPkyQZLSSlmGP1TjJGuuArO+A2w8MM7rqakycZA0gGo3AjbvSf06L/K2v0WgwefJkvPPOO6hdu7bu+SlTpuDtt99G9+7dc31dRkYG+vfvj4ULF8LN7fX3z6tUqRLMzc3RqFEj+Pv7Y9SoUUUNl4gKYfeuHfCsXgOe1auj3/v9sWHD+kIP7Rw1YhgqVKyI48F/I/jkGUyZOg2mpqZo2qw5vp3/Hezs7BAdcxvRMbcx6YMputctXfID6tSpi5N/n8X0j2cAAGxsbLBy5c8490845i9YhPXr1mLZT0sKHEtqSgoCAj7A8ROnsf/AH5DL5RjYv1+OXtG5X8zGxA+m4NTpc6hWzRMjhw8pUk/j+AkBEELg99/357o8MvICunXthBo1a+HPoGAcPhKETp26QKNWY/6CRWjStBmGDRuhq59KlSrnWEd4eBiGDhmIXr374ExIKGbM/AxffflFjmRr6dIf4ePTECf/DsGo0WMxZfIkXSMCULC6vXkzBnv37sHG37bi7zPn0KNHL2jUahz8/YCuzNMnT3D40B8YPHhorvuckpKCHn7d4ODgiOPBfyMwcBOOHw/CtA8nAwAmfTAFK1ZmXeqk3e+CCgsLRUREOBo1bpxnmc8/m4l9e/dg5eo1OHnqLN56ywM9/Lrh+fOsH9bbt29h8KD+6Nr1PZw+cx7DR4zCl1/MybGe1NRULF78HX5atgLnzofD2bkckpOS0H/gIBw5GoS/gk7Ao1o19O7plyOB/urLL9C9ux/+PnMeffq+j+HDBiPqmv6QubyOQWtra/Tq1QcbX3l/N24MRHe/HrC1tS1wfeWmVes2qFOnLvbt21us9QBAYkqGZIk6kHWNraFm2Y1PlHbIskIhQ0KyYYZBxydJW68AkCDB8N/c1yttHRgqTqmHrGs0QvJjSis+MQNS38r9hYGG7Mcnpks2WkHLUMdAUoq0632eYKjPv7Trlcmk//7TSkjKgFziUYBSXKqQmxcS/7Ykpyolv42jSVFf6O/vj0uXLuHUqVO65/bt24egoCCEh+c9XGXGjBmoVasWBg0alO82Tp48ieTkZJw9exaffPIJqlWrhv79+xc1ZCIqoMDAdejXL+uz1r69LyaMS8SpkyfQomXBR7fcv38PkyZPQfUaNQAA1apV0y2zt7eDTCaDi0vO0TctW7bGxEmT9Z7TJu0AUKVKVUz6YDJ27tiOyVM+LFAs3f166P29fPkquFethGtXr8LL21v3/KQPJqNjx04AgJmffo4mjX1wMyZGtw8F5eTkBGfncrhz506uy39YvAg+DRpg8Q8vk+JaXl66/5uZmsLSyirX+tH6aemPaNW6DT7+ZCYAwNPTE9euXcOPPyzGwEFDdOV8fTtg9JixAIApU6dh2U9LceJEMDyrVwdQsLrNzMzE6tW/oKyzs+653n36YePGQPTo2QsAsHXrZlSqXDnPY2T7ti1Iz0jHqp9/gbW1NQBvLFz0A/r16Ym5c79GORcX2Ns7AMBr91urfdvWkMvlyMzMhFKpxPDhIzFgQO6/KykpKfhlzWqsWPUzfP+9Dn7pTytwzKsGNgSuwweTp2Ltr7/A07M6vvp6XlZ9Vq+OK1cu47uF8/XWpVQq8f3iJahT5+V8K61at9Ers2TpclSu6IJTp06iU6fOuud79OiJocNGAAA+nzUHx44FYeXK5XrHweuOwSHDhqN929aIjX0EV9fyePrkCY4cPoR9+w/mW18F4Vm9Bi5fKv5wczNThQTR6DM3K/LpymtJHasQgKmJ9PsPAKYmckmuV87OEO8VIH0dGK5OpV2vTGa4OjUzlUPqW7kbLlYFZLKi3QYxL6amhrmBlYmJAiq1dL325mYGqlMDfAYM9vk3VRR7crlXmRno/TeT+P0yUcgk/54u0p4HBATgwIEDOHbsGCpVqqR7PigoCDExMXBwcICJiQlMTLJ+XHv16oXWrVvrymzfvl23vG3btgCAsmXLYvbs2XrbcXd3R506dTB69GhMmTIFc+bMKUq4RFQI0devI/Sff9CnT18AgImJCXr26o3AbNcDF4R/wCRM9B+P97p2wveLFuoNO38dnwYNcjy3c8d2tG/XGtXeqoLyLmXw5dw5uHfvXoFjuXHjBoYPG4y6tWuiYnln1PbOSr7v3ddfh3ftOrr/u/x7Gc/Tp08KvJ3shBB5Xl9+MTISrVq1yXVZQV2PikKzZs31nmvWrDliYm5ArX45aUztbPuU1UDiordPBanbym5ueok6AAwbPgJBf/2Jhw+zrs/dtHEDBg4cnOc+R0VFoU7tOv8m6i/j1Wg0iI6+nutrXmfd+o04dfocTp85j/WBm/D77wcwe9ZnuZa9desmlEqlXn2ZmpqiYcNGiIrK6tmOvn4dDRo21Htdo0Y5e+rNzMz06hQAnjx+jIkB41G/njcqVSiHiuWdkZycjPuv1GPjpk31/m7SpCmuR+n3rL/uGGzUqDFq1fLCb5s2AshqIKnslnX5hSRec8wWhqW5iaQngQqFDE72lpKtLzup16vRCDjav/4SjqJysreUfPI6RzvDxFrGUdp6NVidOkh9XMkMVqeG+AwYLlaLYs+unZ1MBtjb5D2HSXE42Eq73rKSH1NZpH7/1RphsPff0c5C8js4ONoZpl7LOlpJmlzb2ZhLPrdQoZJ1IQQCAgKwe/duBAUFwd3dXW/5J598gsjISEREROgeALB48WKsXbsWALBz505cuHBBt3zNmqzrJ0+ePAl/f/88t63RaHJcH0lE0gsMXAeVSoXqnu5wtLeGo701flmzGvv27tHNUyGTy3OcNL56DfnMTz9HyPlw+HbshODg42jSyAf7CzDE1ipbMgdkzUo/auQw+Pp2xLYdu3Dy7xBM++hjKJUFn+isX5+eePHiBZYsXY6gYycRdCxrskrlK5OlmZqa6v6v/bItygRicXFxePbsKapWrZrrcktLw/zo5MYk2z4BWful3aeC1q21lf57AgD16tVHnTp1sfm3TQgPD8PVq1cwcNBgw+3IKypWqgQPDw/UqFkTPXr2wgT/ACxd8gPS0w0zrE/L0tIyxw/x2LGjEBkZifkLFuHon8dx6vQ5ODmVQWYhjlGt/I7BIUOH6+aQ2LghEIMGDZHsxCAq6hqq5HHMFoZMJkPbZlUlm7RHrRZo17yqJOt6lU8tF9ham0m2PrlchtaNq0i2vuzav+0u2bWVchng5VEWzk5WkqzvVR3f9ZBs0i6FXIbOLT0kWder7G3M0cDLRbJYVWoN2r/tnn/BImjRsDJMFNL1LlpZmKBp3QqSrS+7dhIeqwqFDG2aVIFCwn3PrlMLD8mur5bJAN9335JkXa/ycHNERZfiXe6UnUZjuO/Vtk2rSjqqwqWsNWq6l5Fuhdm0by7d59VEIUPHd6X/rirUke/v74+NGzfit99+g62tLWJjYxEbG4u0tKzx/q6urqhdu7beAwDc3Nx0ib2Hh4fecu3ztWrVQrly5QAAy5Ytw/79+xEdHY3o6Gj88ssv+O677wo0dJ6Iik6lUmHzb5vwzbz5+Pv0Od3j9JnzcC1fHju2bwMAlC3rjKSkJL3bNl7M5f7Qnp6eCAiYhL37fke397rrrrc1NTPT6/19nXMhZ1HZzQ0fTf8EDRo0RLVq1XDv3t0C71NcXByio69j+vRP0LrN/1CjZk3ExxtmUhWtFct/glwuR5eu7+W63Lt2bQQHH8t1GVCw+qleowbOnj2j99zZs2dQrZonFIqC9WoWt26HDB2OTZs2YOOGQLRu879cr63XqlGjBi5euqh3zJw9ewZyuRyentULvM28KBQKqFSqXGerd3d/C2ZmZnr1pVQqERYWipo1awHIGvYeHqZ/K6NXJ6DLS8jZMxg3fgI6dOiIWl5eMDc3Q1xczkmGzp87p//3+XOoXqNmgbah1e/9/rh37y5WLF+Ga9euYsBAaX4Xg48fw+XLl9C9u58k6xvT10eyk3UHW3P4tS3+MZIbSwtTDPOrK8nJuolChq6tqqG8s40EkeXUtG4F1HQvI0kvkEYA4/o1MNjdJYb55bwtb1FpNALDe9STbH2vGtuvgSTXmMpkgHslB7Rq/Pr5mIqqrKMVeravIUkjmEIhw6D36sDGSrqGquw6t/SAs6M0DUFqtcDYvjlH3EllZK/6kk2GaGqiwMCutfMvWARyuQzj+vlI8vmXy2VoVq8CvKo551+4CDyrOqFlo8qSjK6Qy2UY08cHJhJOrpnd+529JLvMSqUWGNWnviTryq5Qe75ixQokJCSgdevWKF++vO6xdetWSYPSaDSYMWMG6tevj0aNGmHZsmWYP38+5s6dK+l2iEjfoT8OIj7+BQYPGQYvb2+9R/fuPXRD4Rs1agwrKyt8MWcWbt6MwbZtW7Dp36G5AJCWloYPp07GyRPBuHv3Ds6eOY2wsFDU+DcxqeJWBcnJyTh+LAhxz54hNTXvW3J4eFTD/Xv3sGP7Nty8GYMVy5dh//59Bd4nR0dHODmVwdq1vyAmJgbBx49hxoyPi1ZBuUhOSsLjx7G4f/8e/j51EpMCJmDhgm8xa/YX8PDIvYV16ofTERYaiimTJ+HSpYu4HhWFNT+vRtyzrASvilsV/PPPedy5cxtxz57l2rs/cdJkBB8/hvnffoPo6Ghs2rQBq1etwKQPJhc49uLWbZ++/fDwwQOsX/drnhPLafXt1x8W5hYYN2YUrly+jBPBx/HRtCl4v/8AlHNxee1rc/M8Lg6PH8fiwYP7OHLkMFYsX4aWLVvBzs4uR1lra2uMHDUGn386E0ePHsG1q1cxMWA8UtNSMXjIMADA8BEjcf16FGZ9/imio6Oxa+cO/LYpqwc7v4TGw6Matmz+DVHXruH8+XMYNWJYrqMn9uzehQ2B6xAdHY2vv5qL0H/OY+zY8YXab0dHR3R7rzs+/2wG/te2HSpWrJT/i16RkZGBx49j8fDhA0REhOO7hfPR//0+6NipM/rncd1/YbVpUgVu5e2KfWKpkMswomc9g12zDgAjetWT5GRdpRYY09dHgohyJ5PJMO79BpL0WFlZmqJPx8I1FBVGRRdbdG5Z/B5LhVyGts2rwr2SgzSB5aJH2xqSDbEe29fHoLfXHN1HmkYwtVpgVC/DNYCYmigwqk99SUYsuJa1hu87hhmtAAB1a5RDI+/yxY7VRCFD3461DDa0HAAGdastyegKjcawDSAAMLZvA0kmGZTJgCHv1cm/YBHZ2ZhjYFfvYn9XyWUy1PF0RkOv/OfcKfS6C1NYCJHrY9iwYa99jZ+fX57LW7duDSGE3v1wJ06ciEuXLiElJQUJCQkICwvD+PHjC3SbNyIqusDAdWjd5n+wt7fPsey97n4IDwvFpUsX4eTkhJ/XrMWRI4fQvGkj7Ni+DTNmfqorq1Ao8Px5HMaOGYkG9etg6JBBaN++A2Z++jkAoGmz5hg5cjSGDR0M96qV8MPiRXnG1LlLV/gHTMK0D6fg3bebIiTkrN6kaPmRy+VYuz4QEeHhaNakAWZ8Mh1fffVNIWrl9b7+ai48Paqifl1vjBk9AomJidh/4A9MmTotz9d4enpiz94DuHQpEm1avYt2bVvh99/3Q/HvPB8TP5gChUKBJo184F61Uq693fXr+2B94Cbs3LEdzZo0wDdfzcWnn83Sm1wuP8WtW3t7e7zX3Q/WNjbo2i33UQRaVlZW2L1nP168eI7Wrd7B4MED0LpVG3y36IcCby+797p1hqdHVdT2qoEPJk6Ar28HrFu/Mc/yX8z9Cu9198OYUSPQ4t1muHkzBrv37IejoyMAoGpVd2zYuBn79u3B280a4Zc1qzHto08A4LX3eweAn5avRHx8PFq82wxjRo/AuPH+cHbO2WMx89PPsWPHdrzdrBG2bN6EX9cGomatWoXe9yFDhiEzMzPfBpK8/Hn0iK7uevq9h5MngrFg4SJs2bqjwKMy8iOXyzBlaJNiJZYyGWBiIscIAyYVAOBR2RFdWnoUqxdIoZChtqczWjTMe3SJFPp2rIUyDpbFilUmA8b19YG1pWF6VbUmDmwETTETS7VGYNKgvO/yIAULcxNMGNCwWA1LCrkMdtbmGNDVO//CxdCkTnk09HYtVmKhkMvwv2ZVUPOtshJGltPwHnVhYaYodhL8weAmBhsCrzVlaJNij64QAhj/vmET4DIOVhj8Xp1izbSuUMhQycUW3dp4ShhZTp1aeKBqRftiHatyuQzvd/KCS9mcl+JJaWxfH8hQvInhNEJgyrCmBmmskwmpZyspQTKvr0t8m5bmcmye44X+c64gLUPayRPeRKwPfZbmcjw+NbW0wyAqMd26dETNWl5Y+N33pR2K5BYu+Ba//vIzrkYVbHLE17GzscBvm7fl26hREJs3b8KMj6fj+o1bMDMzbMJVHEII+H95GJsOXCp00q4939myqAc6tTDM9crZxSelo+3wTbh5P77QvewKhQxOdpYIDhyESq45R3ZILexKLDqM3gylUgNNIStWIZehZSM37Pixp8FmWM9uxZYwfLwoqMivnzuxJSYPaSJhRLlTqzV4f9oeHD19q9BJm/zfRqXfV75vsGvAs3v0NBmth27Ek+cpRTpW3crb49i6gQabtDG7v87eRu8PdkIjRKG/A+QyoHeHWvh5bmeDjlbQ+mLZSSxaF1Lk16+Y3dFgQ+CzS89Qocu4rQi7ElvonmuFXAZLC1MErR1g8MYaAIi+8xxthm1ESqqy8LEqZKjjWQ6Hfn4fVham+b+gmLYduopRn/9e5NcHDGiIb6YUfuJgW+v8R/Wwq5qIiArtxYsX2L9vL06ePIExY8aVdjiS+Hn1KoSG/oNbt25i8+ZNWPLjYsmGhUshNTUVN2/GYPGi7zBixEijTtSBrGHbP85sj/c7F663US6XQSGX49evupZIog4ADrYWOLCiHzyrOBWq11qhkKGckzV+X9m3RBJ1AGjg5YpdS3rB0sKkULHKZEDLRm7YuKB7iSTqQFZP4xz/ot2tYOaYt/HBYMP2qmspFHKsn9cN7ZpXLVTvmkIug7mZCbZ+37NEEnUAKO9sg99X9kN5Z5tC9Voq5DK4V3TAgRV9SyRRB4C2zaoi8Nv3YGoiL3QPu1+7Glg+q2OJJOoA8Pn4dxEwoGH+BbORybIei6a3LZFEHcgaCbLjx15o6F2+UD3sCoUMttZm2LusT4kk6gDgWcUJB1b0g72teaGOVblchno1XLB7aa8SSdSBrFFLSz/1hVxe+B72MX3q46sPWhskLoDJOhERFUGLd5pi/LjRmPvl17p7tr/pYmJuoH+/PmjSyAcL5s9DwMQPdJduGIMfFi9Cowb14OLigqnTppd2OAViaqLAytkd8dWkVrprOfM6addOnOXtURYHVvRFL1/DXVOdm/LONvjzlwHo38U7K7l4zRmb4t8Tuk4tPBAcOKjETn61WjR0Q9DagboEMa8TYe0uWFmaYvLgJtjxY09JZ78viKnDmmL9vG5wK5/VmJFXrNrnK7naYs2XXfDJ6LdLLFEDACsLU2z5rgemDW8GG6usBCGv/FIba6Pa5XH0lwFo26xqCUWZpZqbI4LXD0LXVp6Qy2WvTYTlMhkUChl6d6iJv9YOROUSalTS6tbGEwdXvY+61bMmkc5rgjztPtjbmmP2hBb49auuBrsPeF7b/2ZKGyyZ6asbdp1XY5h2HzwqO2Lr9z0wuo/h5qrIjaOdBQ6s6IsxfX1gYW6S1WiQR1ntsdqyoRuOrR+ExrXLl1ygAOrXdEFw4GC0aVIFMuRdp9qGDzNTBUb0rIuDq/qhjINh7laRl6F+dbHzx16oXsUJQN7HqnYfnB2t8P3H7bDwo7aS3VEiNxwGX0gc9q2P9aGPw+CJiHKXkanCvmPRWLU1DOcuPtJbZmoiR+8OtTC6T3008i7Zk8ncxMWnYsO+S1i5NQwPnyTrLXOyt8DIXvUxvEfdEutNf51rN59hzc4L2LjvIlLTVXrLarqXwfj3G6BPx1oGm/m7oDQagWPn7mD1tnAcOhWjNyxaJsu6Nd2YPj5o19zdoCe+BZGarsSOw9ewYksoLt/Qv7ODpbkJBnT1xqje9eFtoNm0C+PhkySs2xOJNTsi8OxFmt4y17LWGNu3AYZ0rw1nJ8Ne91sQ4Vdj8fP2CGw7dAWZSv1zxoZerhjbrwH82laHhbnhJpQsCJVKg8N/38SqrWE4fl5/zhiFXIZubTwxpo8P3mlQqUQblHKTkJyBLQcvY+WWcMTc07/Tja21GYb51cWIXvXgUdmxlCJ86db9eKzdfQFrd0UiIVn/dtxVK9pjXL8GGNDVGw62hpukryCEEDh74QFWbw/Hnr+u57jcpEXDyhjb1wedWnoUe5RSQYbBv9HJukX9b5GRWbDbP0mFyak+1oc+JutERPmLi09FXHwaUtNVsLMxh0sZK4NPeFYUGo3Ao6fJiE9Kh0Yj4GBnAdey1iU2jLww0jNUiH2WjITkDJiZKOBobwGXMtalnkzkJiE5A0+fpyIpJQM2VmZwdrIq9RP03Agh8PR5Kp4npCFDqYa9jTlcyljDsoSG5haGSqXBo2fJiE9Mh0wmg4Ot+b9D5Y1vEG1quhKPn6UgITkDluYmcLK3MIrGhNw8T0hD3Is0JKdlws7GHOWcrEt8dEpBCCEQ+ywFLxLSoFRr4GCb9V1lyDtpFFWmUo3YZ8mIT8yAiUIGBzsLlHe2McrvqqSUTDx5noLE5AzYWJrBycESZRyku4zk/32yfiXmKeLi0/IvKCGhUePFw0g4VqgLmdz4fqxLGutDn9Co4eNVMtetERERERHRm6kgybrxNbcUQmVXuxK/9kalUuHEQ6BO9XIwMXmjq08SrA99KpUq/0JERERERET5ML6xMURERERERET/cUzWiYiIiIiIiIzMGz1uuSDj/KWmVGa1b9hYmcHU1PgmGClprA99SqWc9UBERERERMXGnnUiIiIiIiIiI8NknYiIiIiIiMjIMFknIiIiIiIiMjJM1omIiIiIiIiMDJN1IiIiIiIiIiPDZJ2IiIiIiIjIyDBZJyIiIiIiIjIyTNaJiIiIiIiIjAyTdSIiIiIiIiIjw2SdiIiIiIiIyMgwWSciIiIiIiIyMkzWiYiIiIiIiIwMk3UiIiIiIiIiI8NknYiIiIiIiMjIMFknIiIiIiIiMjJM1omIiIiIiIiMDJN1IiIiIiIiIiPDZJ2IiIiIiIjIyDBZJyIiIiIiIjIyTNaJiIiIiIiIjAyTdSIiIiIiIiIjY1LaARSVEAJJSUklvl2lUonU1FQkJibC1NS0xLdvbFgf+pRKJUxNTWFrawuZTFba4RARERER0RvqjU3Wk5KSYG9vX9phEOXqyZMncHZ2Lu0wiIiIiIjoDfXGJuu2trZISEgo8e0mJiaicuXKuHfvHuzs7Ep8+8aG9aFPWx9mZmalHQoREREREb3B3thkXSaTlWpyaGdnx+Q0G9aHPg6BJyIiIiKi4uAEc0RERERERERGhsk6ERERERERkZFhsl5I5ubmmD17NszNzUs7FKPA+tDH+iAiIiIiIinIhBCitIMgIiIiIiIiopfYs05ERERERERkZJisExERERERERkZJutERERERERERobJOhEREREREZGRYbJOREREREREZGT+c8n6vHnz0LhxY9ja2qJcuXLw8/NDVFSUXpn09HT4+/ujTJkysLGxQa9evfD48WO9Mnfv3kWXLl1gZWWFcuXK4aOPPoJKpdIrc/z4cTRo0ADm5uaoVq0a1q1bZ+jdK5ITJ06gW7duqFChAmQyGfbs2aO3XAiBWbNmoXz58rC0tES7du0QHR2tV+b58+cYOHAg7Ozs4ODggJEjRyI5OVmvTGRkJFq0aAELCwtUrlwZCxYsMPSulbhly5ahatWqsLCwQNOmTXHu3LnSDomIiIiIiN5A/7lkPTg4GP7+/jh79iyOHj0KpVIJX19fpKSk6MpMmTIF+/fvx/bt2xEcHIyHDx+iZ8+euuVqtRpdunRBZmYmTp8+jfXr12PdunWYNWuWrsytW7fQpUsXtGnTBhEREZg8eTJGjRqFw4cPl+j+FkRKSgrq1auHZcuW5bp8wYIFWLJkCVauXImQkBBYW1ujQ4cOSE9P15UZOHAgLl++jKNHj+LAgQM4ceIExowZo1uemJgIX19fVKlSBaGhoVi4cCHmzJmD1atXG3z/SsrWrVsxdepUzJ49G2FhYahXrx46dOiAJ0+elHZoRERERET0phH/cU+ePBEARHBwsBBCiPj4eGFqaiq2b9+uK3P16lUBQJw5c0YIIcTBgweFXC4XsbGxujIrVqwQdnZ2IiMjQwghxPTp04W3t7fetvr16yc6dOhg6F0qFgBi9+7dur81Go1wdXUVCxcu1D0XHx8vzM3NxebNm4UQQly5ckUAEOfPn9eV+eOPP4RMJhMPHjwQQgixfPly4ejoqKsfIYT4+OOPRY0aNQy8RyWnSZMmwt/fX/e3Wq0WFSpUEPPmzSvFqIiIiIiI6E30n+tZf1VCQgIAwMnJCQAQGhoKpVKJdu3a6crUrFkTbm5uOHPmDADgzJkzqFOnDlxcXHRlOnTogMTERFy+fFlXJvs6tGW063hT3Lp1C7GxsXr7Ym9vj6ZNm+rVh4ODAxo1aqQr065dO8jlcoSEhOjKtGzZEmZmZroyHTp0QFRUFF68eFFCe2M4mZmZCA0N1asnuVyOdu3avXHvORERERERlb7/dLKu0WgwefJkvPPOO6hduzYAIDY2FmZmZnBwcNAr6+LigtjYWF2Z7Im6drl22evKJCYmIi0tzRC7YxDa/cltX7Lva7ly5fSWm5iYwMnJqVB19iZ79uwZ1Gr1a+uJiIiIiIiooExKO4DS5O/vj0uXLuHUqVOlHQoRERERERGRzn+2Zz0gIAAHDhzAsWPHUKlSJd3zrq6uyMzMRHx8vF75x48fw9XVVVfm1dnhtX/nV8bOzg6WlpZS747BaPcnt33Jvq+vTqKmUqnw/PnzQtXZm6xs2bJQKBSvrSciIiIiIqKC+s8l60IIBAQEYPfu3QgKCoK7u7ve8oYNG8LU1BR//fWX7rmoqCjcvXsXzZs3BwA0b94cFy9e1EtQjx49Cjs7O3h5eenKZF+Htox2HW8Kd3d3uLq66u1LYmIiQkJC9OojPj4eoaGhujJBQUHQaDRo2rSprsyJEyegVCp1ZY4ePYoaNWrA0dGxhPbGcMzMzNCwYUO9etJoNPjrr7/euPeciIiIiIiMQGnPcFfSxo8fL+zt7cXx48fFo0ePdI/U1FRdmXHjxgk3NzcRFBQk/vnnH9G8eXPRvHlz3XKVSiVq164tfH19RUREhDh06JBwdnYWM2bM0JW5efOmsLKyEh999JG4evWqWLZsmVAoFOLQoUMlur8FkZSUJMLDw0V4eLgAIL7//nsRHh4u7ty5I4QQ4ttvvxUODg5i7969IjIyUnTv3l24u7uLtLQ03To6duwofHx8REhIiDh16pTw9PQU/fv31y2Pj48XLi4uYvDgweLSpUtiy5YtwsrKSqxatarE99dQtmzZIszNzcW6devElStXxJgxY4SDg4PeXQOIiIiIiIgK4j+XrAPI9bF27VpdmbS0NDFhwgTh6OgorKysRI8ePcSjR4/01nP79m3RqVMnYWlpKcqWLSs+/PBDoVQq9cocO3ZM1K9fX5iZmYm33npLbxvG5NixY7nWydChQ4UQWbdv+/zzz4WLi4swNzcXbdu2FVFRUXrriIuLE/379xc2NjbCzs5ODB8+XCQlJemVuXDhgnj33XeFubm5qFixovj2229LahdLzNKlS4Wbm5swMzMTTZo0EWfPni3tkIiIiIiI6A0kE0KI0unTJyIiIiIiIqLc/OeuWSciIiIiIiIydkzWiYiIiIiIiIwMk3UiIiIiIiIiI8NknYiIiIiIiMjIMFknIiIiIiIiMjJM1omIiIiIiIiMDJN1IiIiIiIiIiPDZJ2IiIiIiIjIyDBZJyIiIiIiIjIyTNaJiIiIiIiIjAyTdSIiIiIiIiIj838wD4o5H8sC+AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "upset_chart = make_upset(adb_pairs)\n", + "upset_chart" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe63eda1-2724-450f-86f1-d1aed1b6c29c", + "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": "3.12.11" + }, + "rocrate": { + "author": [ + { + "mainEntityOfPage": "https://timsherratt.au", + "name": "Sherratt, Tim", + "orcid": "https://orcid.org/0000-0001-7956-4498" + } + ], + "description": "Wikidata uses a wide range of external identifiers to help describe and identify people. This notebook explores the range of identifiers applied to Australian people in Wikidata and attempts to visualise their use and connections.", + "name": "Visualise the network of Australian people identifiers" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ed76b87042916db1290d8be0f4c2680973aa0615 Mon Sep 17 00:00:00 2001 From: Tim Sherratt Date: Sun, 24 Aug 2025 16:00:58 +1000 Subject: [PATCH 3/5] add latest scripts --- scripts/add_nb_metadata.py | 40 +++ scripts/create_previews.py | 34 ++ scripts/extract_metadata.py | 38 +++ scripts/generate_readme.py | 50 +++ scripts/licences.json | 40 +++ scripts/list_imports.py | 41 +++ scripts/test_and_lint.sh | 17 + scripts/update_crate.py | 626 ++++++++++++++++++++++++++++++++++++ scripts/update_version.sh | 14 + 9 files changed, 900 insertions(+) create mode 100644 scripts/add_nb_metadata.py create mode 100644 scripts/create_previews.py create mode 100644 scripts/extract_metadata.py create mode 100644 scripts/generate_readme.py create mode 100644 scripts/licences.json create mode 100644 scripts/list_imports.py create mode 100755 scripts/test_and_lint.sh create mode 100644 scripts/update_crate.py create mode 100755 scripts/update_version.sh diff --git a/scripts/add_nb_metadata.py b/scripts/add_nb_metadata.py new file mode 100644 index 0000000..c1da7cd --- /dev/null +++ b/scripts/add_nb_metadata.py @@ -0,0 +1,40 @@ +import json +from pathlib import Path +from typing import Any, Dict, List, Optional +import nbformat +import re + +DEFAULT_AUTHORS = [{ + "name": "Sherratt, Tim", + "orcid": "https://orcid.org/0000-0001-7956-4498", + "mainEntityOfPage": "https://timsherratt.au" +}] + +def main(): + notebooks = get_notebooks() + for notebook in notebooks: + nb = nbformat.read(notebook, nbformat.NO_CONVERT) + title = extract_notebook_title(nb) + metadata = {"name": title, "author": DEFAULT_AUTHORS} + nb.metadata.rocrate = metadata + # print(nb.metadata) + nbformat.write(nb, notebook, nbformat.NO_CONVERT) + +def extract_notebook_title(nb): + md_cells = [c for c in nb.cells if c["cell_type"] == "markdown"] + for cell in md_cells: + if title := re.search(r"^# (.+)(\n|$)", cell["source"]): + return title.group(1) + +def get_notebooks(): + """ + Returns a list of paths to jupyter notebooks in the current directory + Returns: + Paths of the notebooks found in the directory + """ + files = Path(".").glob("*.ipynb") + is_notebook = lambda file: not file.name.lower().startswith(("draft", "untitled")) + return list(filter(is_notebook, files)) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scripts/create_previews.py b/scripts/create_previews.py new file mode 100644 index 0000000..d545a89 --- /dev/null +++ b/scripts/create_previews.py @@ -0,0 +1,34 @@ +import nbformat +from nbconvert import HTMLExporter +from nbconvert.preprocessors import ExecutePreprocessor +from pathlib import Path +import argparse + +def main(path): + if path: + output = Path(path, "previews") + else: + output = Path("previews") + output.mkdir(exist_ok=True) + + nbs = [n for n in Path(".").glob("*.ipynb") if not n.name.startswith(("index", "draft", "Untitled", "snippets"))] + for nb_path in nbs: + print(nb_path.name) + with nb_path.open() as f: + nb = nbformat.read(f, as_version=4) + #ep = ExecutePreprocessor(skip_cells_with_tag="nbval-skip") + #ep.preprocess(nb, {'metadata': {'path': '.'}}) + html_exporter = HTMLExporter() + + # 3. Process the notebook we loaded earlier + (body, resources) = html_exporter.from_notebook_node(nb) + + Path(output, f"{nb_path.stem}.html").write_text(body) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--path", type=str, help="Path to save", required=False + ) + args = parser.parse_args() + main(args.path) diff --git a/scripts/extract_metadata.py b/scripts/extract_metadata.py new file mode 100644 index 0000000..1de35b4 --- /dev/null +++ b/scripts/extract_metadata.py @@ -0,0 +1,38 @@ +import nbformat +import re + +LISTIFY = ["author", "object", "result"] + + +def listify(value): + if not isinstance(value, list): + return [value] + return value + +def extract_notebook_title(nb): + md_cells = [c for c in nb.cells if c["cell_type"] == "markdown"] + for cell in md_cells: + if title := re.search(r"^# (.+)(\n|$)", cell["source"]): + return title.group(1) + + +def extract_notebook_metadata(notebook, keys): + """Attempts to extract metadata from the notebook. + + Parameters: + notebook: The path to the jupyter notebook + keys: A dictionary of keys to look for in the notebook, and their + corresponding defaults if the key is not found. + + Returns: + A dictionary containing the retrieved metadata for each key. + """ + result = {} + nb = nbformat.read(notebook, nbformat.NO_CONVERT) + metadata = nb.metadata.rocrate + for key, default in keys.items(): + if key in LISTIFY: + result[key] = listify(metadata.get(key, default)) + else: + result[key] = metadata.get(key, default) + return result diff --git a/scripts/generate_readme.py b/scripts/generate_readme.py new file mode 100644 index 0000000..3d5fd37 --- /dev/null +++ b/scripts/generate_readme.py @@ -0,0 +1,50 @@ +from rocrate.rocrate import ROCrate +import json +from pathlib import Path +import re + +def get_create_action(crate, datafile): + actions = crate.get_by_type("CreateAction") + for action in actions: + props = action.properties() + for result in props["result"]: + if result["@id"] == datafile: + return action + +crate = ROCrate("./") +root = crate.get("./").properties() +gw_section = crate.get(root["mainEntityOfPage"]["@id"]) + +md = f"# {root['name']}\n\n" + +if version := root.get("version"): + md += f"CURRENT VERSION: {version}\n\n" + +md += f"{root['description']}\n\n" +md += f"For more information and documentation see the [{gw_section['name']}]({gw_section['url']}) section of the [GLAM Workbench](https://glam-workbench.net)." +md += "\n\n## Notebooks\n" +details = "\n\n## Dataset details" + +for nb in crate.get_by_type(["File", "SoftwareSourceCode"]): + md += f'- [{nb["name"]}]({nb["url"]})\n' + +datasets = [] +for action in crate.get_by_type("CreateAction"): + print(action) + for result in action["result"]: + dataset = crate.get(result["@id"]) + source = crate.get(dataset["isPartOf"]["@id"]) + datasets.append(source) + +if datasets: + md += "\n\n## Associated datasets\n" + for ds in list(set(datasets)): + md += f'- [{ds["name"]}]({ds["url"]})\n' + +md += "\n\n\n\n" + +md += "\n\n----\nCreated by [Tim Sherratt](https://timsherratt.au) for the [GLAM Workbench](https://glam-workbench.net)" + + +md = re.sub(r'', '', md) +Path("README.md").write_text(md) diff --git a/scripts/licences.json b/scripts/licences.json new file mode 100644 index 0000000..5c218d8 --- /dev/null +++ b/scripts/licences.json @@ -0,0 +1,40 @@ +{ + "default": { + "@id": "https://spdx.org/licenses/MIT", + "name": "MIT License", + "@type": "CreativeWork", + "url": "https://spdx.org/licenses/MIT.html" + }, + "mit": { + "@id": "https://spdx.org/licenses/MIT", + "name": "MIT License", + "@type": "CreativeWork", + "url": "https://spdx.org/licenses/MIT.html" + }, + "metadata": { + "@id": "https://creativecommons.org/publicdomain/zero/1.0/", + "name": "CC0 Public Domain Dedication", + "@type": "CreativeWork", + "url": "https://creativecommons.org/publicdomain/zero/1.0/" + }, + "cc0": { + "@id": "https://creativecommons.org/publicdomain/zero/1.0/", + "name": "CC0 Public Domain Dedication", + "@type": "CreativeWork", + "url": "https://creativecommons.org/publicdomain/zero/1.0/" + }, + "no_known_copyright": { + "@id": "http://rightsstatements.org/vocab/NKC/1.0/", + "@type": "CreativeWork", + "description": "The organization that has made the Item available reasonably believes that the Item is not restricted by copyright or related rights, but a conclusive determination could not be made.", + "name": "No Known Copyright", + "url": "http://rightsstatements.org/vocab/NKC/1.0/" + }, + "copyright_not_evaluated": { + "@id": "http://rightsstatements.org/vocab/CNE/1.0/", + "@type": "CreativeWork", + "description": "The copyright and related rights status of this Item has not been evaluated.", + "name": "Copyright Not Evaluated", + "url": "http://rightsstatements.org/vocab/CNE/1.0/" + } +} \ No newline at end of file diff --git a/scripts/list_imports.py b/scripts/list_imports.py new file mode 100644 index 0000000..3de42d0 --- /dev/null +++ b/scripts/list_imports.py @@ -0,0 +1,41 @@ +from pathlib import Path +import json +import re +import importlib.util +import os.path +import sys + +external_imports = ['jupyterlab', 'voila', 'voila-material @ git+https://github.com/GLAM-Workbench/voila-material.git'] + +python_path = os.path.dirname(sys.executable).replace('bin', 'lib') +#print(python_path) + +imports = [] +for nb in Path(__file__).resolve().parent.parent.glob('*.ipynb'): + if not nb.name.startswith('.') and not nb.name.startswith('Untitled'): + nb_json = json.loads(nb.read_bytes()) + for cell in nb_json['cells']: + for line in cell['source']: + if match := re.search(r'^\s*import ([a-zA-Z_]+)(?! from)', line): + imports.append(match.group(1)) + elif match := re.search(r'^\s*from ([a-zA-Z_]+)\.?[a-zA-Z_]* import [a-zA-Z_]+', line): + imports.append(match.group(1)) + +# print(list(set(imports))) + +for imported_mod in list(set(imports)): + try: + module_path = importlib.util.find_spec(imported_mod).origin + except AttributeError: + pass + else: + if module_path: + # print(imported_mod) + # print(module_path) + if 'site-packages' in module_path or python_path in module_path: + external_imports.append(imported_mod) + #print(external_imports) + +with Path(Path(__file__).resolve().parent.parent, 'requirements-tocheck.in').open('w') as req_file: + for mod in external_imports: + req_file.write(mod + '\n') diff --git a/scripts/test_and_lint.sh b/scripts/test_and_lint.sh new file mode 100755 index 0000000..6a1621b --- /dev/null +++ b/scripts/test_and_lint.sh @@ -0,0 +1,17 @@ +#!/bin/bash +pytest --nbval-lax $1 +if nbqa isort $1; then + echo "ISort: $(tput setab 2)$(tput setaf 7)PASSED$(tput sgr0)" +else + echo "ISort: $(tput setab 1)$(tput setaf 7)FAILED$(tput sgr0)" +fi +if nbqa black $1; then + echo "Black: $(tput setab 2)$(tput setaf 7)PASSED$(tput sgr0)" +else + echo "Black: $(tput setab 1)$(tput setaf 7)FAILED$(tput sgr0)" +fi +if nbqa flake8 $1; then + echo "Flake8: $(tput setab 2)$(tput setaf 7)PASSED$(tput sgr0)" +else + echo "Flake8: $(tput setab 1)$(tput setaf 7)FAILED$(tput sgr0)" +fi diff --git a/scripts/update_crate.py b/scripts/update_crate.py new file mode 100644 index 0000000..faf195e --- /dev/null +++ b/scripts/update_crate.py @@ -0,0 +1,626 @@ +import os +from pathlib import Path +from rocrate.rocrate import ROCrate +from rocrate.model.person import Person +from rocrate.model.data_entity import DataEntity +from rocrate.model.contextentity import ContextEntity +from git import Repo +from git.exc import InvalidGitRepositoryError, GitCommandError +import json +import argparse +import datetime +import nbformat +import sys +import requests +from bs4 import BeautifulSoup +from github import Github +import re +import arrow +import git + +LICENCES = json.loads(Path("scripts", "licences.json").read_text()) +CONTEXT_PROPERTIES = [ + "author", + "action", + "workExample", + "mainEntityOfPage", + "subjectOf", + "isBasedOn", + "distribution", + "isPartOf", + "license" +] + + +def main(crate_path, defaults, version, data_repo): + # Make working directory the parent of the scripts directory + os.chdir(Path(__file__).resolve().parent.parent) + crate_maker = CrateMaker(crate_path, defaults=defaults, version=version, data_repo=data_repo) + # Update the crate + crate_maker.update_crate() + + +def listify(value): + if not isinstance(value, list): + return [value] + return value + + +def delistify(value): + if isinstance(value, list) and (len(value) == 1 or len(set(value)) == 1): + return value[0] + else: + return value + + +class CrateMaker: + + def __init__(self, crate_path="./", defaults=None, version=None, data_repo=None): + # Make working directory the parent of the scripts directory + os.chdir(Path(__file__).resolve().parent.parent) + self.defaults = defaults + self.crate_path = crate_path + self.version = version + self.data_repo = data_repo + + def id_ify(self, elements): + """Wraps elements in a list with @id keys + eg, convert ['a', 'b'] to [{'@id': 'a'}, {'@id': 'b'}] + """ + # If the input is a string, make it a list + # elements = [elements] if isinstance(elements, str) else elements + # Nope - single elements shouldn't be lists, see: https://www.researchobject.org/ro-crate/1.1/appendix/jsonld.html + if isinstance(elements, str): + return {"@id": elements} + elif isinstance(elements, list): + try: + return [{"@id": e.id} for e in elements] + except AttributeError: + return [{"@id": element} for element in elements] + + def creates_data(self, notebook): + """ + Check to see if a notebook creates a file in the specified data repo. + """ + if not self.data_repo: + return True + else: + metadata = self.get_nb_metadata(notebook) + owner, repo = self.get_gh_parts(self.data_repo) + for action in metadata.get("action", []): + for result in action.get("result", []): + if f"{owner}/{repo}" in result.get("url", ""): + return True + return False + + def get_notebooks(self, path="."): + """Returns a list of paths to jupyter notebooks in the given directory + + Parameters: + dir: The path to the directory in which to search. + + Returns: + Paths of the notebooks found in the directory + """ + files = Path(path).glob("*.ipynb") + is_notebook = lambda file: not file.name.lower().startswith( + ("draft", "untitled", "index") + ) and self.creates_data(file) + return list(filter(is_notebook, files)) + + def update_properties(self, entry, updates, exclude=[]): + for key, value in updates.items(): + if key in CONTEXT_PROPERTIES: + self.add_entities(entry, key, listify(value)) + elif not key.startswith("@") and key not in exclude: + entry[key] = value + return entry + + def add_people(self, authors): + """Converts a list of authors to a list of Persons to be embedded within an ROCrate + + Parameters: + crate: The rocrate in which the authors will be created. + authors: + A list of author information. + Expects a dict with at least a 'name' value ('Surname, Givenname') + If there's an 'orcid' this will be used as the id (and converted to a uri if necessary) + Returns: + A list of Persons. + """ + added = [] + # Loop through list of authors + for author_data in authors: + # If there's no orcid, create an id from the name + if not author_data.get("orcid"): + author_id = f"#{author_data['name'].replace(', ', '_')}" + + # If there's an orcid but it's not a url, turn it into one + elif not author_data["orcid"].startswith("http"): + author_id = f"https://orcid.org/{author_data['orcid']}" + + # Otherwise we'll just use the orcid as the id + else: + author_id = author_data["orcid"] + # Check to see if there's already an entry for this person in the crate + author = self.crate.get(author_id) + + # If there's already an entry we'll update the existing properties + if not author: + props = {"name": author_data["name"]} + author = self.crate.add(Person(self.crate, author_id, properties=props)) + added.append(self.update_properties(author, author_data, exclude=["orcid"])) + return added + + def add_update_action(self, version): + """ + Adds an UpdateAction to the crate when the repo version is updated. + """ + # Create an id for the action using the version number + action_id = f"create_version_{version.replace('.', '_')}" + + # Set basic properties for action + properties = { + "@type": "UpdateAction", + "endDate": datetime.datetime.now().strftime("%Y-%m-%d"), + "name": f"Create version {version}", + "actionStatus": {"@id": "http://schema.org/CompletedActionStatus"}, + } + + # Create entity + self.crate.add(ContextEntity(self.crate, action_id, properties=properties)) + + def add_context_entity(self, entity): + """ + Adds a ContextEntity to the crate. + + Parameters: + crate: the current ROCrate + entity: A JSONLD ready dict containing "@id" and "@type" values + """ + return self.crate.add( + ContextEntity(self.crate, entity["@id"], properties=entity) + ) + + def add_page(self, page_data, type="CreativeWork"): + """ + Create a context entity for a HTML page or resource + """ + # If it's a url string, convert to a dict + if isinstance(page_data, str): + page_data = {"url": page_data} + page_id = page_data["url"] + # Check if there's already an entity for this page + page = self.crate.get(page_id) + # If there's not an existing page entity, create one + if not page: + # Default properties, might be overwritten by values from page_data + props = { + "@id": page_id, + "@type": type, + "url": page_data["url"], + } + # Create a new context entity for the page + page = self.add_context_entity(props) + # Update the context entity with additional properties from page data + page = self.update_properties(page, page_data) + # If there's a specific name in an existing record we want to keep it. + # Otherwise add a default name from the page title + default_name = self.get_page_title(page_data["url"]) + if "name" in page_data and page.get("name") == default_name: + page["name"] = page_data["name"] + elif not page.get("name"): + page["name"] = default_name + return page + + def add_pages(self, pages, type="CreativeWork"): + """ + Add related pages + """ + added = [] + for page in pages: + if page: + added.append(self.add_page(page, type=type)) + return added + + def add_licence(self, licences): + added = [] + for licence in licences: + added.append(self.add_context_entity(LICENCES[licence])) + return added + + def add_download(self, downloads): + added = [] + for url in downloads: + download = { + "@id": url, + "@type": "DataDownload", + "name": "Download repository as zip", + "url": url + } + added.append(self.add_context_entity(download)) + return added + + def add_entities(self, record, entity_type, entities): + if entity_type == "author": + added = self.add_people(entities) + elif entity_type == "action": + added = self.add_actions(record, entities) + elif entity_type == "license": + added = self.add_licence(entities) + elif entity_type == "isBasedOn": + added = self.add_pages(entities, type="SoftwareSourceCode") + elif entity_type == "distribution": + added = self.add_download(entities) + else: + added = self.add_pages(entities) + if added and entity_type != "action": + record[entity_type] = delistify(added) + + def get_local_file_stats(self, local_path): + stats = {} + local_file = Path(local_path) + if local_file.is_dir(): + stats["size"] = len(list(local_file.glob("*"))) + file_stats = local_file.stat() + stats["dateModified"] = arrow.get(file_stats.st_mtime).isoformat() + else: + stats["sdDatePublished"] = arrow.utcnow().isoformat() + # Get file stats from local filesystem + file_stats = local_file.stat() + stats["contentSize"] = file_stats.st_size + stats["dateModified"] = arrow.get(file_stats.st_mtime).isoformat() + if local_file.name.endswith((".csv", ".ndjson")): + stats["size"] = 0 + with local_file.open("r") as df: + for line in df: + stats["size"] += 1 + return stats + + def get_web_file_stats(self, url): + stats = {"sdDatePublished": arrow.utcnow().isoformat()} + if "github" in url: + repo = self.get_gh_repo(url) + file_path = url.split(f"/{repo.default_branch}/")[-1] + contents = repo.get_contents(file_path) + stats["contentSize"] = contents.size + stats["dateModified"] = contents.last_modified_datetime.isoformat() + else: + response = requests.head(url) + stats["contentSize"] = response.headers.get("Content-length") + stats["dateModified"] = arrow.get( + response.headers.get("Last-Modified"), "ddd, D MMM YYYY HH:mm:ss ZZZ" + ).isoformat() + return stats + + def get_gh_parts(self, url): + try: + owner, repo = re.search( + r"https*://.*(?:github|githubusercontent).com/(.+?)/([a-zA-Z0-9\-_]+)", url + ).groups() + except AttributeError: + owner = None + repo = None + return owner, repo + + def get_gh_repo(self, url): + owner, repo = self.get_gh_parts(url) + if owner and repo: + g = Github() + return g.get_repo(full_name_or_id=f"{owner}/{repo}") + + def get_gh_path(self, url): + default_branch = self.get_default_gh_branch(url) + return url.split(f"/{default_branch}/")[-1] + + def get_repo_info(self): + # Try to get some info from the local git repo + try: + repo = git.Repo(".") + repo_url = repo.remotes.origin.url.replace( + ".git", "/" + ) + repo_name = repo_url.strip("/").split("/")[-1] + # There is no git repo or no remote set + except (InvalidGitRepositoryError, GitCommandError): + repo_url = "" + repo_name = "example-repo" + return repo_name, repo_url + + def get_repo_link(self, entry): + """ + Files and notebooks are usually part of a code repository. + Also crate can have a codeRepository prop. + If the files have urls then use the url to get repo. + Otherwise use the local git info to get repo url. + """ + repo_url = None + if url := entry.get("url"): + owner, repo = self.get_gh_parts(url) + if owner and repo: + repo_url = f"https://github.com/{owner}/{repo}" + else: + _, repo_url = self.get_repo_info() + return repo_url + + def add_repo_link(self, entry): + if "isPartOf" not in entry: + repo_link = self.get_repo_link(entry) + if repo_link: + entry["isPartOf"] = repo_link + return entry + + def get_default_gh_branch(self, url): + """ + Get the default branch of a GH repository from a url that points to it. + """ + repo = self.get_gh_repo(url) + return repo.default_branch + + def get_gh_file_url(self, file_path): + """ + Construct a url to that points to a notebook file in the code repository. + Note that you could get the url from the GH repo, but there's a possibility + that this script will be run before every notebook has been committed and pushed. + """ + _, repo_url = self.get_repo_info() + default_branch = self.get_default_gh_branch(repo_url) + return f"{repo_url.strip('/')}/blob/{default_branch}/{file_path}" + + + def add_files(self, files): + added = [] + for data_file in files: + local_path = data_file.get("localPath") + url = data_file.get("url") + if url or local_path: + props = {"@type": ["File", "Dataset"]} + data_file = self.add_repo_link(data_file) + if url: + props["name"] = data_file.get("name", os.path.basename(url)) + if local_path: + props.update(self.get_local_file_stats(local_path)) + else: + props.update(self.get_web_file_stats(url)) + fetch_remote = False + file_id = url + if self.data_repo: + # We want the file ids to be relative to the data crate, so use the local path + # or fetch_remote to put them in the right place. + if local_path: + file_id = local_path + else: + fetch_remote = True + file_added = self.crate.add_file( + file_id, properties=props, fetch_remote=fetch_remote + ) + elif local_path: + props["name"] = data_file.get("name", os.path.basename(local_path)) + props.update(self.get_local_file_stats(local_path)) + file_added = self.crate.add_file( + local_path, properties=props, dest_path=local_path + ) + file_added = self.update_properties( + file_added, data_file, exclude=["localPath"] + ) + added.append(file_added) + return added + + def file_in_repo(self, data_file): + """ + Check a data file's url to see if it's part of the data repo specified + by the --data-repo parameter. + """ + owner, repo = self.get_gh_parts(self.data_repo) + if f"{owner}/{repo}" in data_file.get("url", ""): + return True + + def filter_files(self, action_data, file_relation): + files = listify(action_data.get(file_relation, [])) + if self.data_repo: + return list(filter(self.file_in_repo, files)) + else: + return files + + def add_actions(self, notebook, actions): + added = [] + for index, action_data in enumerate(actions): + action_id = ( + f"#{os.path.basename(notebook.id).replace('.ipynb', '')}_run_{index}" + ) + props = { + "@id": action_id, + "@type": "CreateAction", + "instrument": self.id_ify(notebook.id), + "actionStatus": {"@id": "http://schema.org/CompletedActionStatus"}, + "name": f"Run of notebook: {os.path.basename(notebook.id)}", + } + file_dates = [] + for file_relation in ["result", "object"]: + added_files = self.add_files( + self.filter_files(action_data, file_relation) + ) + if added_files: + props[file_relation] = delistify(added_files) + for data_file in added_files: + file_dates.append(data_file.get("dateModified")) + props["endDate"] = sorted(file_dates)[-1] + action = self.add_context_entity(props) + action = self.update_properties( + action, action_data, exclude=["result", "object"] + ) + self.crate.root_dataset.append_to("mentions", action) + added.append(action) + return added + + def add_python_version(self): + # I could also get this from notebook metadata + # Get the version components from the system + major, minor, micro = sys.version_info[0:3] + # Construct url and version name + url = f"https://www.python.org/downloads/release/python-{major}{minor}{micro}/" + version = f"{major}.{minor}.{micro}" + # Define properties of context entity + entity = { + "@id": url, + "version": version, + "name": f"Python {version}", + "url": url, + "@type": ["ComputerLanguage", "SoftwareApplication"], + } + return self.crate.add( + ContextEntity(self.crate, entity["@id"], properties=entity) + ) + + def get_page_title(self, url): + """ + Get title of the page at the supplied url. + """ + response = requests.get(url) + if response.ok: + soup = BeautifulSoup(response.text, features="lxml") + return soup.title.string.strip() + + def get_nb_metadata(self, notebook): + nb = nbformat.read(notebook, nbformat.NO_CONVERT) + return {k: v for k, v in nb.metadata.rocrate.items() if v} + + def add_notebook(self, notebook): + gh_url = self.get_gh_file_url(notebook) + if self.data_repo: + nb_id = gh_url + else: + nb_id = notebook + # Get metadata embedded in notebooks + nb_metadata = self.get_nb_metadata(notebook) + nb_metadata = self.add_repo_link(nb_metadata) + # Default notebook properties + nb_props = { + "@type": ["File", "SoftwareSourceCode"], + "encodingFormat": "application/x-ipynb+json", + "programmingLanguage": self.id_ify(self.add_python_version().id), + "conformsTo": self.id_ify( + "https://purl.archive.org/textcommons/profile#Notebook" + ), + "url": gh_url, + } + # Add notebook to crate + new_nb = self.crate.add_file(nb_id, properties=nb_props) + # Add properties from notebook metadata + new_nb = self.update_properties(new_nb, nb_metadata) + return new_nb + + def get_old_crate_data(self, crate_source="./"): + try: + old_crate = ROCrate(source=crate_source) + old_root = old_crate.get("./") + # Get the old root properties + old_props = old_root.properties() + # Add old properties to new record (except for those that will be populated from notebooks) + root_props = { + k: v + for k, v in old_props.items() + if k in ["name", "description", "mainEntityOfPage"] and not isinstance(v, dict) + } + entities = {k: old_crate.get(v["@id"]) for k, v in old_props.items() + if k in ["mainEntityOfPage", "license"] and isinstance(v, dict)} + # Get version UpdateAction records for inclusion in new crate + versions = old_crate.get_by_type("UpdateAction") + # If there's not an existing crate, try to set some default properties + except (ValueError, FileNotFoundError): + root_props = {} + versions = [] + entities = {} + return root_props, entities, versions + + def prepare_data_crate(self): + _, repo_name = self.get_gh_parts(self.data_repo) + if repo_name: + crate_source = f"./{repo_name}-rocrate" + else: + crate_source = "./data-rocrate" + _, code_repo_url = self.get_repo_info() + root_props, entities, versions = self.get_old_crate_data(crate_source) + if not root_props: + root_props = { + "name": self.defaults.get("name", repo_name), + "description": self.defaults.get("description", ""), + "isBasedOn": self.defaults.get("isBasedOn", code_repo_url), + "distribution": f"{self.data_repo.rstrip('/')}/archive/refs/heads/main.zip", + } + versions = [] + entities = {} + return root_props, crate_source, entities, versions + + def prepare_code_crate(self): + # Load data from an existing crate + root_props, entities, versions = self.get_old_crate_data() + if not root_props: + # Get info from git + repo_name, repo_url = self.get_repo_info() + root_props = { + "name": self.defaults.get("name", repo_name), + "description": self.defaults.get("description", ""), + "codeRepository": self.defaults.get("codeRepository", repo_url), + } + versions = [] + return root_props, "./", entities, versions + + def update_crate(self): + if self.data_repo: + root_props, crate_source, entities, versions = self.prepare_data_crate() + else: + root_props, crate_source, entities, versions = self.prepare_code_crate() + self.crate = ROCrate() + # Add properties to the root + root = self.crate.get("./") + # update_jsonld doesn't seem to work here? + #for p, v in root_props.items(): + # root[p] = v + root = self.update_properties(root, root_props) + # Add version information + for v in versions: + self.crate.add(v) + for k, v in entities.items(): + root[k] = self.id_ify(v.id) + self.crate.add(v) + # Add authors from defaults + self.add_entities(root, "author", self.defaults.get("authors", [])) + # If this is a new version, change version number and add UpdateAction + if self.version: + root["version"] = self.version + self.add_update_action(self.version) + # Add notebooks + for notebook in self.get_notebooks(): + nb = self.add_notebook(notebook) + for author in listify(nb.get("author")): + if author not in root.get("author", []): + root.append_to("author", author) + # Set licence of crate metadata + root["license"] = self.add_context_entity(LICENCES["metadata"]) + # Save crate + self.crate.write(crate_source) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--defaults", + type=str, + help="File containing Crate default values", + required=False, + ) + parser.add_argument( + "--crate-path", type=str, help="Path to crate", default="./" + ) + parser.add_argument( + "--version", type=str, help="New version number", required=False + ) + parser.add_argument("--data-repo", type=str, default="", required=False) + args = parser.parse_args() + if args.defaults: + defaults = json.loads(Path(args.defaults).read_text()) + else: + defaults = {} + + main(defaults=defaults, crate_path=args.crate_path, version=args.version, data_repo=args.data_repo) diff --git a/scripts/update_version.sh b/scripts/update_version.sh new file mode 100755 index 0000000..3824b95 --- /dev/null +++ b/scripts/update_version.sh @@ -0,0 +1,14 @@ +#!/bin/bash +repo_name=$(basename -s .git `git config --get remote.origin.url`) +version_url="https://github.com/GLAM-Workbench/$repo_name/releases/tag/$1" +sed -ri "s|Current version: \[.*\](.*tag\/v.*)|Current version: [$1]($version_url)|" README.md +text=$(sed '/^<\!-- START RUN INFO/,/^<\!-- END RUN INFO/d' README.md | sed -r 's/\[(.*)\]\(.*\.ipynb\)/\1/' | pandoc | sed '1d' | echo $(cat)); +pdate=$(date +"%Y-%m-%d"); +identifier=$(jq -r '.related_identifiers[0].identifier' .zenodo.json | sed "s/\/tree\/v.*/\/tree\/$1/") +jq --arg text "$text" '.description = $text' .zenodo.json \ +| jq --arg version "$1" '.version = $version' \ +| jq --arg identifier $identifier '.related_identifiers[0].identifier = $identifier' \ +| jq --arg pdate "$pdate" '.publication_date = $pdate' > zenodo.json; +rm .zenodo.json; +mv zenodo.json .zenodo.json; +python scripts/update_crate.py --version $1 \ No newline at end of file From 2076c3e517253878dd72baa6525470a0f517a947 Mon Sep 17 00:00:00 2001 From: Tim Sherratt Date: Sun, 24 Aug 2025 16:01:34 +1000 Subject: [PATCH 4/5] add metadata --- ro-crate-metadata.json | 190 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 190 insertions(+) create mode 100644 ro-crate-metadata.json diff --git a/ro-crate-metadata.json b/ro-crate-metadata.json new file mode 100644 index 0000000..fdc77db --- /dev/null +++ b/ro-crate-metadata.json @@ -0,0 +1,190 @@ +{ + "@context": "https://w3id.org/ro/crate/1.1/context", + "@graph": [ + { + "@id": "./", + "@type": "Dataset", + "author": [ + { + "@id": "https://orcid.org/0000-0001-7956-4498" + } + ], + "datePublished": "2025-08-24T05:03:39+00:00", + "description": "", + "hasPart": [ + { + "@id": "agencies-gannt-timeline.ipynb" + }, + { + "@id": "govt-agencies-network.ipynb" + }, + { + "@id": "visualise_all_people_ids.ipynb" + }, + { + "@id": "single-agency-network.ipynb" + } + ], + "license": { + "@id": "https://creativecommons.org/publicdomain/zero/1.0/" + }, + "name": "wikidata" + }, + { + "@id": "ro-crate-metadata.json", + "@type": "CreativeWork", + "about": { + "@id": "./" + }, + "conformsTo": { + "@id": "https://w3id.org/ro/crate/1.1" + } + }, + { + "@id": "https://creativecommons.org/publicdomain/zero/1.0/", + "@type": "CreativeWork", + "name": "CC0 Public Domain Dedication", + "url": "https://creativecommons.org/publicdomain/zero/1.0/" + }, + { + "@id": "https://www.python.org/downloads/release/python-31211/", + "@type": [ + "ComputerLanguage", + "SoftwareApplication" + ], + "name": "Python 3.12.11", + "url": "https://www.python.org/downloads/release/python-31211/", + "version": "3.12.11" + }, + { + "@id": "agencies-gannt-timeline.ipynb", + "@type": [ + "File", + "SoftwareSourceCode" + ], + "author": { + "@id": "https://orcid.org/0000-0001-7956-4498" + }, + "conformsTo": { + "@id": "https://purl.archive.org/textcommons/profile#Notebook" + }, + "description": "This notebook creates a Gannt-style chart showing the creation and dissolution dates of Australian government departments.", + "encodingFormat": "application/x-ipynb+json", + "isPartOf": { + "@id": "https://github.com/GLAM-Workbench/wikidata/" + }, + "name": "Create a Gannt chart of Australian government departments", + "programmingLanguage": { + "@id": "https://www.python.org/downloads/release/python-31211/" + }, + "url": "https://github.com/GLAM-Workbench/wikidata/blob/master/agencies-gannt-timeline.ipynb", + "workExample": { + "@id": "https://glam-workbench.net/wikidata/examples/agencies-gannt-timeline.html" + } + }, + { + "@id": "https://orcid.org/0000-0001-7956-4498", + "@type": "Person", + "mainEntityOfPage": { + "@id": "https://timsherratt.au" + }, + "name": "Sherratt, Tim" + }, + { + "@id": "https://timsherratt.au", + "@type": "CreativeWork", + "name": "timsherratt.au - timsherratt.au", + "url": "https://timsherratt.au" + }, + { + "@id": "https://glam-workbench.net/wikidata/examples/agencies-gannt-timeline.html", + "@type": "CreativeWork", + "name": "View the interactive chart", + "url": "https://glam-workbench.net/wikidata/examples/agencies-gannt-timeline.html" + }, + { + "@id": "https://github.com/GLAM-Workbench/wikidata/", + "@type": "CreativeWork", + "name": "GitHub - GLAM-Workbench/wikidata", + "url": "https://github.com/GLAM-Workbench/wikidata/" + }, + { + "@id": "govt-agencies-network.ipynb", + "@type": [ + "File", + "SoftwareSourceCode" + ], + "author": { + "@id": "https://orcid.org/0000-0001-7956-4498" + }, + "conformsTo": { + "@id": "https://purl.archive.org/textcommons/profile#Notebook" + }, + "description": "This notebook visualises changes in Australian government departments over time, using data from Wikidata. It creates a hierarchically-ordered network graph where each agency is represented as a node whose position and colour is determined by the decade in which the agency was created.", + "encodingFormat": "application/x-ipynb+json", + "isPartOf": { + "@id": "https://github.com/GLAM-Workbench/wikidata/" + }, + "name": "Create a network graph visualisation of Australian government departments", + "programmingLanguage": { + "@id": "https://www.python.org/downloads/release/python-31211/" + }, + "url": "https://github.com/GLAM-Workbench/wikidata/blob/master/govt-agencies-network.ipynb", + "workExample": { + "@id": "https://glam-workbench.net/wikidata/examples/govt-agencies-network.html" + } + }, + { + "@id": "https://glam-workbench.net/wikidata/examples/govt-agencies-network.html", + "@type": "CreativeWork", + "name": "View the interactive graph", + "url": "https://glam-workbench.net/wikidata/examples/govt-agencies-network.html" + }, + { + "@id": "visualise_all_people_ids.ipynb", + "@type": [ + "File", + "SoftwareSourceCode" + ], + "author": { + "@id": "https://orcid.org/0000-0001-7956-4498" + }, + "conformsTo": { + "@id": "https://purl.archive.org/textcommons/profile#Notebook" + }, + "description": "Wikidata uses a wide range of external identifiers to help describe and identify people. This notebook explores the range of identifiers applied to Australian people in Wikidata and attempts to visualise their use and connections.", + "encodingFormat": "application/x-ipynb+json", + "isPartOf": { + "@id": "https://github.com/GLAM-Workbench/wikidata/" + }, + "name": "Visualise the network of Australian people identifiers", + "programmingLanguage": { + "@id": "https://www.python.org/downloads/release/python-31211/" + }, + "url": "https://github.com/GLAM-Workbench/wikidata/blob/master/visualise_all_people_ids.ipynb" + }, + { + "@id": "single-agency-network.ipynb", + "@type": [ + "File", + "SoftwareSourceCode" + ], + "author": { + "@id": "https://orcid.org/0000-0001-7956-4498" + }, + "conformsTo": { + "@id": "https://purl.archive.org/textcommons/profile#Notebook" + }, + "description": "Select an agency from the dropdown list to view its predecessors and successors as a network graph. Each agency is represented as a node whose position and colour is determined by the decade in which the agency was created. The size of the node indicates how long the agency was in existence, while edges between nodes connect agencies to their successors.", + "encodingFormat": "application/x-ipynb+json", + "isPartOf": { + "@id": "https://github.com/GLAM-Workbench/wikidata/" + }, + "name": "Visualise the connections of a single Australian government agency", + "programmingLanguage": { + "@id": "https://www.python.org/downloads/release/python-31211/" + }, + "url": "https://github.com/GLAM-Workbench/wikidata/blob/master/single-agency-network.ipynb" + } + ] +} \ No newline at end of file From 245b9cbbb9e5188b9804fd451b947794881dea8a Mon Sep 17 00:00:00 2001 From: Tim Sherratt Date: Sun, 24 Aug 2025 16:05:22 +1000 Subject: [PATCH 5/5] update runtime --- runtime.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/runtime.txt b/runtime.txt index 3cd3102..0a8325a 100644 --- a/runtime.txt +++ b/runtime.txt @@ -1 +1 @@ -python-3.8 \ No newline at end of file +python-3.12 \ No newline at end of file