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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Projecting the growth of OA: a large scale extrapolation\n\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\n**Heather Piwowar, Jason Priem, Richard Orr** (order tbd) \n_Our Research ([email protected])_\n\nFirst published: September 23, 2019"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Introduction\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The adoption of [open access (OA)](https://en.wikipedia.org/wiki/Open_access) publishing is changing scholarly communication. Predicting the future prevalence of OA is crucial for many stakeholders making decisions now, including:\n\n- libraries deciding which journals to subscribe to and how much they should pay\n\n- institutions and funders deciding what mandates they should adopt, and the implications of existing mandates\n\n- scholarly publishers deciding when to flip their business models to OA\n\n- scholarly societies deciding how best to serve their members.\n\nDespite how useful OA prediction would be, only a few studies have made an attempt to empirically predict open access rates. Lewis (2012) extrapolated the rate at which [gold OA](https://en.wikipedia.org/wiki/Open_access#Gold_OA) would replace subscription-based publishing using a simple log linear extrapolation of gold vs subscription market share. Antelman (2017) used one empirically-derived growth rate for [green OA](https://en.wikipedia.org/wiki/Open_access#Green_OA) and another for all other kinds of OA combined. Both of these studies are based on data collected before 2012, and rely on relatively simple models. Moreover, these studies predict the number of papers that are OA. While this number is important, it is arguably less meaningful than the number of accesses that are OA, since this latter number describes the prevalence of OA as experienced by actual readers.\n\nThis paper aims to address this gap in the literature. In it, we build a detailed model using data extrapolated from large and up-to-date Unpaywall dataset [cite].  We use the model to predict the number of articles that will be OA (including gold, green, hybrid, and bronze OA) over the next five years, and also use data from the Unpaywall browser add-on [cite] to predict the proportion of scholarly article accesses that will lead readers to OA articles over time.\n\nThe method presented here aims to provide a useful model of OA growth, taking the following complexities into account:\n\n- some forms of OA include a delay between when a paper is first published and when it is first freely available\n\n- different forms of open access are being adopted at different rates\n\n- wide-sweeping policy changes, technical improvements, or cultural changes may cause disruptions in the growth rates of OA in the future\n\nFuture work will allow the model to predict the growth of Open Access for individual disciplines, journals, and article selections.\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Data"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The data used in this analysis comes from two sources: (1) the Unpaywall dataset and (2) the access logs of the Unpaywall web browser extension.\n\n### OA status: the Unpaywall dataset of OA availability\n\nPredicting levels of open access publication in the future requires detailed, accurate, timely data.. This study uses the [Unpaywall](https://unpaywall.org/) dataset to provide this data.. Unpaywall is an open source application that  links every research article that has been assigned a Crossref DOI (more than 100 million in total) to the OA URLs where the paper can be read for free. It  is built and maintained by Our Research (formerly Impactstor), a US-based nonprofit organization. Unpaywall gathers data gathered from over 50,000 journals and open-access repositories from all over the world. The full Unpaywall dataset is freely, publicly available (see details: <https://unpaywall.org/user-guides/research>).\n\nThis analysis uses all articles with a Crossref article type of \"journal-article\" published between 1950 and 2018.  [num of articles? 70891275]\n\nOur definitions of OA type (gold, green, hybrid, bronze, closed) are described in Piwowar et al. (2018). To facilitate prediction, for the purpose of this analysis we subdivided bronze OA into immediate and delayed OA. In summary, these definitions are:\n\n- Gold: published in a fully-OA journal\n\n- Hybrid: published in a toll-access journal, with an OA license\n\n- Bronze: published in a toll-access journal, without an OA license\n\n- Immediate Bronze: OA on publication\n\n- Delayed Bronze: OA later\n\n- Green: published in a toll-access journal, with a fulltext copy available in an OA repository\n\n- Closed: everything else\n\n### Relative download patterns: access logs of the Unpaywall web browser extension\n\nPredicting the open access pattern of usage requests requires knowing the relative usage demands of papers based on their age and subject matter. This study has extracted these download patterns from the usage logs of the [Unpaywall browser extension](https://unpaywall.org/products/extension) for Chrome and Firefox.\n\nThis extension is an open-source tool made by the same non-profit as the Unpaywall dataset described above, with the goal of helping people conveniently find free copies of research papers directly from their web browser. The extension has more than 200,000 active users and has received more than 50 million access requests between August 2018 and August 2019. Because readership data is private and potentially sensitive, we are not releasing the Unpaywall usage logs along with the other datasets behind this paper.\n\n[add more about strengths and weaknesses of this data. make a case for why it's representative. add the IP address graphs.]\n\n[add more descriptive stats: num events, num journals, num countries, etc.]"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Methods and Results\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Analysis\n\nThe analysis was writen as an executable python Jupyter notebook using the pandas, scipy, matplotlib, and sqlalchemy libraries. See the Data Availability section below for links to the code."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "*---- delete the text between these lines in the final paper ----*"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Code: Initialization"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "See notebook."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:02:20.531415Z",
"end_time": "2019-09-23T14:02:20.566166Z"
},
"code_folding": [],
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# hidden: code to import libraries, set up database connection, other initialization\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport os\nimport sys\nimport pandas as pd\nimport numpy as np\nimport scipy\nfrom scipy import signal\nfrom matplotlib import pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib import cm\nfrom matplotlib.colors import ListedColormap\nimport seaborn as sns\nfrom sqlalchemy import create_engine\nimport psycopg2\nfrom datetime import timedelta\n\nredshift_engine = create_engine(os.getenv(\"DATABASE_URL_REDSHIFT\"))\n\noa_status_order = [\"green\", \"gold\", \"hybrid\", \"bronze\", \"closed\"]\noa_status_colors = [\"green\", \"gold\", \"orange\", \"brown\", \"grey\"]\noa_color_lookup = pd.DataFrame(data = {\"name\": oa_status_order, \"color\": oa_status_colors, \"order\": range(0, len(oa_status_order))})\nmy_cmap = sns.color_palette(oa_status_colors)\n\ngraph_type_order = [\"green\", \"gold\", \"hybrid\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]\ngraph_type_colors = [\"green\", \"gold\", \"orange\", \"brown\", \"peru\", \"gray\"]\ngraph_type_lookup = pd.DataFrame(data = {\"name\": graph_type_order, \"color\": graph_type_colors, \"order\": range(0, len(graph_type_order))})\nmy_cmap_graph_type = sns.color_palette(graph_type_colors)\n\n# graph style\nsns.set(style=\"ticks\")\n\n# long print, wrap\npd.set_option('display.expand_frame_repr', False)\n",
"execution_count": 135,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Code: Functions"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "See notebook."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:15:43.781101Z",
"end_time": "2019-09-23T14:15:43.944149Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_naive_papers(graph_type, use_graph_type=False, label_for_graph=None, now_delta_years=0, show_graph=True, cumulative=True):\n \n calc_min_year = 1951\n display_min_year = 2010\n now_year = 2019 - now_delta_years\n max_year = 2024\n\n min_y = 0\n max_y = None\n color = graph_type\n if \"bronze\" in graph_type:\n color = \"bronze\"\n \n if graph_type in (\"green\", \"hybrid\"):\n now_year -= 1\n if graph_type in (\"delayed_bronze\", \"closed\"):\n now_year -= 5\n \n if use_graph_type:\n df_this_color = unpaywall_graph_type.loc[(unpaywall_graph_type.graph_type==graph_type)]\n else:\n df_this_color = unpaywall_data_by_year.loc[(unpaywall_data_by_year.oa_status==color)]\n\n totals = pd.DataFrame()\n for i, prediction_year in enumerate(range(calc_min_year, now_year)):\n\n if cumulative:\n df_this_plot = df_this_color.loc[(df_this_color[\"published_year\"] <= prediction_year)]\n else:\n df_this_plot = df_this_color.loc[(df_this_color[\"published_year\"] == prediction_year)]\n y = [a for a in df_this_plot[\"num_articles\"] if not np.isnan(a)]\n prediction_y = sum(y)\n\n totals = totals.append(pd.DataFrame(data={\"prediction_year\": [prediction_year], \n \"num_articles\": [prediction_y]}))\n\n \n x = totals[\"prediction_year\"]\n y = totals[\"num_articles\"]\n# f = scipy.interpolate.interp1d(x, np.log10(y), fill_value=\"extrapolate\", kind=\"linear\")\n f = scipy.interpolate.interp1d(x, y, fill_value=\"extrapolate\", kind=\"linear\")\n xnew = np.arange(now_year-1, max_year+1, 1)\n# ynew = 10 ** f(xnew)\n ynew = f(xnew)\n \n new_data = pd.DataFrame({\"color\":color, \"graph_type\": graph_type, \"x\":np.append(x[:-1], xnew), \"y\":np.append(y[:-1], ynew)})\n\n return new_data\n\n\ndef graph_naive_papers(graph_type, use_graph_type=False, label_for_graph=None, now_delta_years=0, show_graph=True, ax=None, cumulative=True):\n calc_min_year = 1951\n display_min_year = 2010\n now_year = 2019 - now_delta_years\n max_year = 2024\n\n min_y = 0\n max_y = None\n color = graph_type\n if \"bronze\" in graph_type:\n color = \"bronze\"\n \n new_data = get_naive_papers(graph_type, use_graph_type, label_for_graph, now_delta_years, show_graph, cumulative)\n\n # graph! :)\n year_range = range(display_min_year, now_year)\n \n if use_graph_type:\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==graph_type]\n else:\n my_color_lookup = oa_color_lookup.loc[oa_color_lookup[\"name\"]==color]\n\n \n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n if not max_y:\n max_y = 5 * max(new_data[\"y\"])\n\n\n df_actual = new_data.loc[new_data[\"x\"] < now_year]\n x = [int(a) for a in df_actual[\"x\"]]\n y = [int(a) for a in df_actual[\"y\"]]\n df_future = new_data.loc[new_data[\"x\"] >= now_year]\n xnew = [int(a) for a in df_future[\"x\"]]\n ynew = [int(a) for a in df_future[\"y\"]]\n\n ax.plot(x, y, 'o', color=\"black\")\n ax.fill_between(x, y, color=my_color_lookup[\"color\"])\n\n ax.plot(xnew, ynew, 'o', color=\"black\", alpha=0.3)\n ax.fill_between(xnew, ynew, color=my_color_lookup[\"color\"], alpha=0.3)\n if cumulative:\n title = plt.suptitle(\"Extrapolating CUMULATIVE number of papers, by OA type\")\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"CUMULATIVE papers (millions)\")\n ax.set_xlabel(\"year\")\n else:\n title = plt.suptitle(\"Extrapolating papers by year of publication, by OA type\")\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"number of papers (millions)\")\n ax.set_xlabel(\"year of publication\")\n ax.set_xlim(min(year_range), max_year)\n# ax.set_ylim(min_y, max_y)\n title.set_position([.5, 1.05])\n\n# plt.show()\n return new_data",
"execution_count": 169,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:04:02.550187Z",
"end_time": "2019-09-23T14:04:02.602408Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# graph! :)\n\ndef graph_available_papers_at_year_of_availability(graph_type, now_delta_years=0, label_for_graph=None, show_graph=True, ax=None):\n calc_min_year = 1951\n display_min_year = 2010\n now_year = 2018 - now_delta_years\n max_year = 2024\n\n color = graph_type\n if \"bronze\" in graph_type:\n color = \"bronze\"\n\n if graph_type == \"biorxiv\":\n my_color_lookup = {\"color\": \"limegreen\"}\n else:\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==graph_type] \n \n all_papers_per_year = get_papers_by_availability_year_including_future(graph_type, calc_min_year, max_year)\n\n most_recent_year = all_papers_per_year.loc[all_papers_per_year.article_years_from_availability == 0]\n \n x = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year <= now_year][\"prediction_year\"]]\n xnew = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year > now_year][\"prediction_year\"]]\n y = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year <= now_year][\"num_articles\"]]\n ynew = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year > now_year][\"num_articles\"]]\n\n year_range = range(display_min_year, now_year)\n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n max_y = 1.2 * max(ynew)\n\n ax.plot(x, y, 'o', color=\"black\")\n ax.fill_between(x, y, color=my_color_lookup[\"color\"])\n\n ax.plot(xnew, ynew, 'o', color=\"black\", alpha=0.3)\n ax.fill_between(xnew, ynew, color=my_color_lookup[\"color\"], alpha=0.3)\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.2f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"total papers (millions)\")\n\n ax.set_xlim(min(year_range), max_year)\n# ax.set_ylim(0, max_y)\n if not label_for_graph:\n label_for_graph = color\n ax.set_xlabel('year of observation')\n title = plt.suptitle(\"OA status by observation year\")\n title.set_position([.5, 1.05])\n return all_papers_per_year",
"execution_count": 147,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:04:02.767036Z",
"end_time": "2019-09-23T14:04:02.792096Z"
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"trusted": false
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef graph_available_papers_in_observation_year_by_pubdate(graph_type, data, observation_year, ax=None):\n display_min_year = 2010\n max_year = 2024\n\n x = [int(a) for a in data[\"publication_date\"]]\n y = [int(a) for a in data[\"num_articles\"]]\n\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==graph_type]\n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n alpha = 1\n if observation_year > 2018:\n alpha = 0.3\n ax.bar(x, y, color=my_color_lookup[\"color\"], alpha=alpha)\n\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\n ax.set_xlim(display_min_year, max_year)\n max_y = 1.2 * data.num_articles.max()\n ax.set_ylim(0, max_y)\n# ax.set_xlabel('pub year')\n# ax.set_ylabel(\"papers (*10^6)\")\n ax.set_title(\"{}\".format(observation_year)); \n# title = plt.suptitle(\"Availability in {}, by publication date\".format(observation_year))\n# title.set_position([.5, 1.05])\n return \n\n",
"execution_count": 148,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:04:02.983476Z",
"end_time": "2019-09-23T14:04:03.047297Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_papers_by_availability_year_including_future(graph_type, start_year, end_year, exponential=False):\n start_calc_year = 2009\n now_year = 2017\n offset = 0\n \n my_return = pd.DataFrame()\n\n if graph_type == \"closed\":\n now_year -= 5\n elif graph_type == \"green\":\n now_year -= 1\n elif graph_type == \"delayed_bronze\":\n now_year -= 5\n elif graph_type == \"hybrid\":\n now_year -= 1\n elif graph_type == \"biorxiv\":\n now_year += 1\n exponential = True\n \n\n for prediction_year in range(min(start_year, start_calc_year), now_year+1): \n papers_per_year = get_papers_by_availability_year(graph_type, prediction_year, just_this_year=False)\n papers_per_year[\"prediction_year\"] = prediction_year\n my_return = my_return.append(papers_per_year)\n \n if end_year > now_year:\n last_calculated = my_return.loc[my_return[\"prediction_year\"] == now_year]\n all_papers_per_year = my_return.loc[my_return[\"prediction_year\"] <= now_year]\n most_recent_year = all_papers_per_year.loc[all_papers_per_year.article_years_from_availability == 0]\n x = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year <= now_year][\"prediction_year\"]]\n y = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year <= now_year][\"num_articles\"]]\n \n# print zip(x, y)\n \n xnew = np.arange(now_year+1, end_year+1, 1)\n if exponential:\n f = scipy.interpolate.interp1d(x, np.log10(y), fill_value=\"extrapolate\", kind=\"linear\")\n ynew = 10 ** f(xnew)\n else:\n f = scipy.interpolate.interp1d(x, y, fill_value=\"extrapolate\", kind=\"linear\")\n ynew = f(xnew)\n \n for i, prediction_year in enumerate(xnew): \n scale = ynew[i]/y[-1]\n this_frame = last_calculated.copy()\n this_frame[\"num_articles\"] = [int(scale * a) for a in this_frame[\"num_articles\"]]\n this_frame[\"prediction_year\"] = prediction_year\n add_me = pd.DataFrame(this_frame, columns=[\"num_articles\", \n \"article_years_from_availability\", \n \"prediction_year\"])\n my_return = my_return.append(add_me)\n\n return my_return",
"execution_count": 149,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:04:03.179792Z",
"end_time": "2019-09-23T14:04:03.214977Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# graph! :)\n\ndef graph_accesses(graph_type, now_delta_years=0, label_for_graph=None, show_graph=True, ax=None):\n calc_min_year = 1951\n display_min_year = 2010\n now_year = 2018 - now_delta_years\n max_year = 2024\n\n color = graph_type\n\n df_accesses_by_year = get_predicted_accesses(graph_type, display_min_year, max_year)\n\n year_range = range(display_min_year, now_year)\n if graph_type == \"biorxiv\":\n my_color_lookup = {\"color\": \"limegreen\"}\n else:\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==color]\n \n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n \n x = [int(a) for a in df_accesses_by_year[\"observation_year\"]]\n y = [int(a) for a in df_accesses_by_year[\"accesses\"]]\n max_y = 1.2 * max(y)\n\n ax.scatter(x, y, marker='x', s=70, color=my_color_lookup[\"color\"])\n\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"number of accesses (millions)\")\n\n ax.set_xlim(min(year_range), max_year+1)\n# ax.set_ylim(0, max_y)\n if not label_for_graph:\n label_for_graph = color\n ax.set_xlabel('access year')\n title = plt.suptitle(\"Estimated accesses by access year, by OA type\")\n title.set_position([.5, 1.05])\n return df_accesses_by_year",
"execution_count": 150,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:04:03.361780Z",
"end_time": "2019-09-23T14:04:03.518027Z"
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# do calculations\n\ndef get_papers_by_availability_year(graph_type, availability_year, just_this_year=False):\n my_return = pd.DataFrame()\n \n if just_this_year:\n if graph_type == \"closed\":\n rows_published_this_year = unpaywall_data_by_year.loc[unpaywall_data_by_year[\"published_year\"] == availability_year]\n total_this_year = rows_published_this_year.num_articles.sum()\n \n open_this_year = 0\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\"]:\n# for prep_graph_type in [\"hybrid\", \"green\", \"delayed_bronze\"]:\n temp_papers = get_papers_by_availability_year(prep_graph_type, availability_year, just_this_year=False)\n temp_papers = temp_papers.loc[temp_papers.article_years_from_availability == 0]\n num_articles = temp_papers.num_articles.sum()\n# print prep_graph_type, num_articles\n open_this_year += num_articles\n num_closed = total_this_year - open_this_year\n \n my_return = pd.DataFrame({\n \"article_years_from_availability\": [0],\n \"num_articles\": [num_closed]\n })\n else:\n prev_year_history = get_papers_by_availability_year(graph_type, availability_year-1, just_this_year=False)\n prev_year_history[\"article_years_from_availability\"] += 1\n this_year_history = get_papers_by_availability_year(graph_type, availability_year, just_this_year=False)\n df_merged = this_year_history.merge(prev_year_history, on=\"article_years_from_availability\", how=\"left\")\n df_merged = df_merged.fillna(0)\n df_merged[\"num_articles\"] = df_merged[\"num_articles_x\"] - df_merged[\"num_articles_y\"]\n df_merged[\"num_articles\"][df_merged[\"num_articles\"] < 25] = 0\n df_merged = df_merged.loc[df_merged[\"article_years_from_availability\"] <= 10]\n my_return = pd.DataFrame({\n \"article_years_from_availability\": df_merged[\"article_years_from_availability\"],\n \"num_articles\": df_merged[\"num_articles\"]\n })\n\n else:\n \n if graph_type == \"delayed_bronze\":\n my_return = papers_after_cutoffs_after_embargo_delayed_bronze.loc[papers_after_cutoffs_after_embargo_delayed_bronze[\"prediction_year\"]==availability_year]\n my_return[\"article_years_from_availability\"] = my_return[\"article_age_years\"] \n\n elif graph_type == \"green\":\n\n my_green_oa = green_oa_with_dates_by_availability\n\n my_green_oa = my_green_oa.loc[my_green_oa[\"months_old_at_first_deposit\"] >= -24]\n my_green_oa = my_green_oa.loc[my_green_oa[\"months_old_at_first_deposit\"] <= 12*25]\n my_green_oa = my_green_oa.loc[my_green_oa[\"year_of_first_availability\"] <= availability_year]\n\n my_green_oa_pivot = my_green_oa.pivot_table(\n index='published_year', values=['num_articles'], aggfunc=np.sum)\n my_green_oa_pivot.reset_index(inplace=True)\n my_green_oa_pivot = my_green_oa_pivot.sort_values(by=[\"published_year\"], ascending=False)\n my_green_oa_pivot[\"article_years_from_availability\"] = [(availability_year - a) for a in my_green_oa_pivot[\"published_year\"]]\n my_return = pd.DataFrame({\n \"article_years_from_availability\": my_green_oa_pivot[\"article_years_from_availability\"],\n \"num_articles\": my_green_oa_pivot[\"num_articles\"]\n })\n\n elif graph_type == \"closed\":\n my_return = pd.DataFrame()\n for i, year in enumerate(range(availability_year+1, 1990, -1)):\n closed_rows = get_papers_by_availability_year(graph_type, availability_year - i, just_this_year=True)\n closed_rows[\"article_years_from_availability\"] = i\n my_return = my_return.append(closed_rows)\n \n elif graph_type == \"immediate_bronze\":\n temp_papers = unpaywall_data_by_year_with_embargos.loc[(unpaywall_data_by_year_with_embargos.oa_status==\"bronze\") &\n (unpaywall_data_by_year_with_embargos[\"embargo\"].isnull()) &\n (unpaywall_data_by_year_with_embargos.published_year <= availability_year)]\n temp_papers[\"article_years_from_availability\"] = availability_year - temp_papers[\"published_year\"] \n temp_pivot = temp_papers.pivot_table(\n index='article_years_from_availability', values=['num_articles'], aggfunc=np.sum)\n temp_pivot.reset_index(inplace=True)\n my_return = pd.DataFrame({\n \"article_years_from_availability\": temp_pivot[\"article_years_from_availability\"],\n \"num_articles\": temp_pivot.num_articles\n })\n\n elif graph_type == \"biorxiv\": \n my_return = biorxiv_growth.copy()\n my_return = my_return.loc[my_return[\"published_year\"] <= availability_year]\n my_return[\"article_years_from_availability\"] = availability_year - my_return[\"published_year\"]\n else:\n temp_papers = unpaywall_data_by_year.loc[(unpaywall_data_by_year.oa_status==graph_type) &\n (unpaywall_data_by_year.published_year <= availability_year)]\n temp_papers[\"article_years_from_availability\"] = availability_year - temp_papers[\"published_year\"] \n my_return = pd.DataFrame({\n \"article_years_from_availability\": temp_papers[\"article_years_from_availability\"],\n \"num_articles\": temp_papers[\"num_articles\"]\n })\n\n\n if not my_return.empty:\n my_return = pd.DataFrame(my_return, columns=[\"article_years_from_availability\", \"num_articles\"]) \n my_return = my_return.sort_values(by=\"article_years_from_availability\")\n\n return my_return\n\n\n",
"execution_count": 151,
"outputs": []
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"start_time": "2019-09-23T14:04:03.527461Z",
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_predicted_accesses_by_pubdate(graph_type, observation_year):\n\n accesses_per_article = get_accesses_per_article(graph_type)\n \n df_accesses_by_year = pd.DataFrame()\n all_papers_per_year = get_papers_by_availability_year_including_future(graph_type, observation_year, observation_year+1)\n papers_per_year = all_papers_per_year.loc[all_papers_per_year[\"prediction_year\"] == observation_year]\n \n try:\n data_merged_clean = papers_per_year.merge(accesses_per_article, left_on=[\"article_years_from_availability\"], right_on=[\"article_age_years\"])\n data_merged_clean = data_merged_clean.sort_values(\"article_age_years\")\n# print data_merged_clean.head()\n data_merged_clean[\"accesses\"] = data_merged_clean[\"accesses_per_article\"] * data_merged_clean[\"num_articles\"]\n data_merged_clean[\"observation_year\"] = observation_year\n data_merged_clean[\"publication_year\"] = observation_year - data_merged_clean[\"article_age_years\"]\n new_data = pd.DataFrame(data_merged_clean, columns=[\"publication_year\", \"accesses\", \"article_age_years\", \"observation_year\"])\n df_accesses_by_year = df_accesses_by_year.append(new_data)\n except (ValueError, KeyError): # happens when the year is blank\n pass\n \n return df_accesses_by_year",
"execution_count": 152,
"outputs": []
},
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_predicted_accesses(graph_type, now_delta_years=0, label_for_graph=None, show_graph=True):\n# calc_min_year = 1951\n calc_min_year = 1995\n display_min_year = 2010\n now_year = 2020 - now_delta_years\n max_year = 2024\n exponential = False\n\n if graph_type == \"biorxiv\":\n exponential = True\n \n accesses_per_article = get_accesses_per_article(graph_type)\n \n df_accesses_by_year = pd.DataFrame()\n all_papers_per_year = get_papers_by_availability_year_including_future(graph_type, calc_min_year, max_year, exponential)\n for prediction_year in range(calc_min_year, max_year+1): \n# for prediction_year in range(calc_min_year, 2019): \n# for prediction_year in range(2017, 2019): \n papers_per_year = all_papers_per_year.loc[all_papers_per_year[\"prediction_year\"] == prediction_year]\n# print accesses_per_article.head()\n try:\n data_merged_clean = papers_per_year.merge(accesses_per_article, left_on=[\"article_years_from_availability\"], right_on=[\"article_age_years\"])\n data_merged_clean = data_merged_clean.sort_values(\"article_age_years\")\n win = data_merged_clean[\"accesses_per_article\"] \n sig = data_merged_clean[\"num_articles\"]\n accesses_by_access_year = signal.convolve(win, sig, mode='same', method=\"direct\")\n y = max(accesses_by_access_year)\n df_accesses_by_year = df_accesses_by_year.append(pd.DataFrame({\"observation_year\":[prediction_year], \"accesses\": [y]}))\n except (ValueError, KeyError): # happens when the year is blank\n pass\n \n\n return df_accesses_by_year",
"execution_count": 153,
"outputs": []
},
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"start_time": "2019-09-23T14:04:03.861751Z",
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_papers_by_availability_year_total(availability_year):\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n# for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\"]:\n temp_papers = get_papers_by_availability_year_including_future(prep_graph_type, availability_year, availability_year+1)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type\n# print \"{:,.0f}\".format(temp_papers.num_articles.max()), \"{:,.0f}\".format(temp_papers.num_articles.sum())\n# print \"\\n\"\n all_data = all_data.append(temp_papers)\n return all_data\n\ndef get_accesses_per_year_total():\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n temp_papers = get_accesses_per_year(prep_graph_type)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type\n# print \"{:,.0f}\".format(temp_papers.num_accesses_per_year.max()), \"{:,.0f}\".format(temp_papers.num_accesses_per_year.sum())\n# print \"\\n\"\n all_data = all_data.append(temp_papers)\n return all_data\n\n\n\ndef get_accesses_per_article_total():\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n temp_papers = get_accesses_per_article(prep_graph_type)\n# print prep_graph_type\n# print \"{:,.0f}\".format(temp_papers.accesses_per_article.max()), \"{:,.0f}\".format(temp_papers.accesses_per_article.sum())\n# print \"\\n\"\n temp_papers[\"graph_type\"] = prep_graph_type\n all_data = all_data.append(temp_papers)\n return all_data\n\n\ndef get_predicted_accesses_total(observation_year):\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n# for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\"]:\n temp_papers = get_predicted_accesses(prep_graph_type, observation_year)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type \n all_data = all_data.append(temp_papers)\n return all_data\n\ndef get_predicted_accesses_by_pubdate_total(observation_year):\n all_data = pd.DataFrame()\n# for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\"]:\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n temp_papers = get_predicted_accesses_by_pubdate(prep_graph_type, observation_year)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type\n all_data = all_data.append(temp_papers)\n return all_data\n",
"execution_count": 154,
"outputs": []
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"start_time": "2019-09-23T14:04:04.088493Z",
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"cell_type": "code",
"source": "def get_accesses_per_year(graph_type):\n if graph_type == \"delayed_bronze\":\n accesses_per_year = data_usage_by_age_years.loc[(data_usage_by_age_years.oa_status==\"bronze\") &\n (data_usage_by_age_years.delayed_or_immediate==\"delayed\")]\n elif graph_type == \"immediate_bronze\":\n accesses_per_year = data_usage_by_age_years.loc[(data_usage_by_age_years.oa_status==\"bronze\") &\n (data_usage_by_age_years[\"delayed_or_immediate\"]==\"immediate\")]\n\n else:\n accesses_per_year = data_usage_by_age_years.loc[(data_usage_by_age_years.oa_status==graph_type)]\n\n accesses_per_year[\"num_accesses_one_month\"] = accesses_per_year[\"num_accesses\"] # this is just for one month\n accesses_per_year[\"num_accesses_per_year\"] = 12.0 * accesses_per_year[\"num_accesses_one_month\"]\n del accesses_per_year[\"num_accesses\"]\n del accesses_per_year[\"delayed_or_immediate\"]\n accesses_per_year = accesses_per_year.sort_values(by=\"article_age_years\")\n accesses_per_year = accesses_per_year.loc[accesses_per_year[\"article_age_years\"] < 15]\n \n return accesses_per_year \n\n\ndef get_accesses_per_article(graph_type):\n if graph_type == \"biorxiv\":\n graph_type = \"green\"\n \n accesses_per_year = get_accesses_per_year(graph_type)\n papers_per_year = get_papers_by_availability_year(graph_type, 2018, just_this_year=False)\n papers_per_year[\"article_age_years\"] = papers_per_year[\"article_years_from_availability\"]\n papers_per_year = papers_per_year.loc[(papers_per_year[\"article_age_years\"] <=15 )]\n\n data_merged_clean = papers_per_year.merge(accesses_per_year, on=[\"article_age_years\"]) \n data_merged_clean[\"accesses_per_article\"] = data_merged_clean[\"num_accesses_per_year\"] / data_merged_clean[\"num_articles\"]\n\n accesses_per_article = pd.DataFrame(data_merged_clean, columns=[\"article_age_years\", \"accesses_per_article\"])\n accesses_per_article = accesses_per_article.sort_values(by=\"article_age_years\")\n\n if graph_type==\"delayed_bronze\":\n # otherwise first one is too high because number articles too low in year 0 for delayed subset\n accesses_per_article.loc[accesses_per_article.article_age_years==0, [\"accesses_per_article\"]] = float(accesses_per_article.loc[accesses_per_article.article_age_years==1].accesses_per_article)\n\n return accesses_per_article",
"execution_count": 155,
"outputs": []
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"start_time": "2019-09-23T14:04:04.500182Z",
"end_time": "2019-09-23T14:04:04.560507Z"
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef plot_totals_and_proportion(df_total_downloads):\n all_data_pivot = df_total_downloads.pivot_table(\n index='x', columns='color', values=['y'], aggfunc=np.sum)\\\n .sort_index(axis=1, level=1)\\\n .swaplevel(0, 1, axis=1)\n all_data_pivot.columns = all_data_pivot.columns.levels[0]\n\n fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 3), sharex=True, sharey=False)\n plt.tight_layout(pad=0, w_pad=2, h_pad=1)\n plt.subplots_adjust(hspace=1)\n\n all_data_pivot_graph = all_data_pivot\n all_data_pivot_graph = all_data_pivot.loc[all_data_pivot.index > 1960]\n # print all_data_pivot_graph\n all_data_pivot_actual = all_data_pivot_graph.loc[all_data_pivot_graph.index < 2019]\n my_plot = all_data_pivot_actual[oa_status_order].plot.area(stacked=True, color=oa_status_colors, linewidth=.1, ax=ax1)\n all_data_pivot_projected = all_data_pivot_graph.loc[all_data_pivot_graph.index >= 2019-1]\n my_plot = all_data_pivot_projected[oa_status_order].plot.area(stacked=True, color=oa_status_colors, linewidth=.1, ax=ax1, alpha=0.3)\n ax1.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\n ax1.set_ylabel(\"CUMULATIVE papers (millions)\") \n ax1.set_xlabel('')\n ax1.set_xlim(2010, 2024)\n ax1.set_ylim(0, 1.2*max(all_data_pivot_projected.sum(1)))\n ax1.set_title(\"Number of papers\");\n handles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:5]), reversed(labels[0:5]), loc='upper left'); # reverse to keep order consistent\n\n df_diff_proportional = all_data_pivot_graph.div(all_data_pivot_graph.sum(1), axis=0)\n all_data_pivot_actual = df_diff_proportional.loc[all_data_pivot_graph.index < 2019]\n my_plot = all_data_pivot_actual[oa_status_order].plot.area(stacked=True, color=oa_status_colors, linewidth=.1, ax=ax2)\n all_data_pivot_projected = df_diff_proportional.loc[all_data_pivot_graph.index >= 2019-1]\n my_plot = all_data_pivot_projected[oa_status_order].plot.area(stacked=True, color=oa_status_colors, linewidth=.1, ax=ax2, alpha=0.3)\n my_plot.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\n ax2.set_xlabel('')\n ax2.set_ylabel('proportion of CUMULATIVE articles')\n ax2.set_title(\"Proportion of papers\");\n ax2.set_xlim(2010, 2024)\n ax2.set_ylim(0, 1) \n handles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:5]), reversed(labels[0:5]), loc='upper left'); # reverse to keep order consistent\n\n plt.tight_layout(pad=.5, w_pad=4, h_pad=2.0) ",
"execution_count": 156,
"outputs": []
},
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"ExecuteTime": {
"start_time": "2019-09-23T16:01:04.940388Z",
"end_time": "2019-09-23T16:01:05.130643Z"
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"cell_type": "code",
"source": "# plot graphs duplicate new\n\ndef get_long_data(graph_type):\n full_range = range(1990, 2020)\n\n totals_bronze = pd.DataFrame()\n for i, prediction_year in enumerate(full_range):\n new_frame = get_papers_by_availability_year(graph_type, prediction_year, just_this_year=True)\n new_frame[\"prediction_year\"] = prediction_year\n new_frame[\"published_year\"] = [int(prediction_year - a) for a in new_frame[\"article_years_from_availability\"]]\n totals_bronze = totals_bronze.append(new_frame)\n\n long_data_for_plot = totals_bronze\n long_data_for_plot = long_data_for_plot.loc[long_data_for_plot[\"article_years_from_availability\"] < 15]\n return long_data_for_plot\n\ndef make_detailed_plots(graph_type):\n num_subplots = 8\n\n long_data_for_plot = get_long_data(graph_type)\n pivot_data_for_plot = long_data_for_plot.pivot_table(\n index='published_year', columns='prediction_year', values=['num_articles'], aggfunc=np.sum)\\\n .sort_index(axis=1, level=1)\\\n .swaplevel(0, 1, axis=1)\n pivot_data_for_plot.columns = pivot_data_for_plot.columns.levels[0]\n pivot_data_for_plot[pivot_data_for_plot < 0] = 0\n # print pivot_data_for_plot\n\n years = [year for year in pivot_data_for_plot.columns if year > 1990]\n\n for historical_graphs in (False, True):\n color_idx = np.linspace(0, 1, len(years))\n fig, axes = plt.subplots(len(years[-num_subplots:]), 1, figsize=(10, 6), sharex=True, sharey=True)\n axes_flatten = axes.flatten()\n axis_index = 0\n max_y_for_this_plot = max(pivot_data_for_plot.max(1))\n for i, prediction_year in zip(color_idx[-num_subplots:], years[-num_subplots:]):\n ax = axes_flatten[axis_index] \n axis_index += 1\n if historical_graphs:\n pivot_data_for_plot[range(2000, prediction_year+1)].plot.area(stacked=True, alpha=0.4, ax=ax, color=[plt.cm.jet(i) for x in range(2000, prediction_year)])\n try:\n pivot_data_for_plot[range(2000, prediction_year)].plot.area(stacked=True, ax=ax, alpha=.9, color=\"lightgray\")\n ax.set_ylim(0, 3*max_y_for_this_plot) \n except TypeError:\n pass \n else:\n pivot_data_for_plot[prediction_year].plot.area(stacked=False, ax=ax, alpha=.4, color=plt.cm.jet(i))\n ax.set_ylim(0, 1.2*max_y_for_this_plot) \n ax.set_xlim(2009, 2018)\n if ax.get_legend():\n ax.get_legend().remove() \n ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n ax.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:.0f}'))\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_visible(False)\n ax.spines['left'].set_visible(False) \n y_label = \"{} made available during {}:\".format(graph_type, prediction_year) \n ax.set_ylabel(y_label, rotation='horizontal', labelpad=150, verticalalignment=\"center\")\n ax.set_yticks([])\n plt.tight_layout()\n plt.show()\n\n fig, ax1 = plt.subplots(1, 1, figsize=(10, 3))\n pivot_data_for_plot[years].plot.area(stacked=True, ax=ax1, alpha=.4, cmap=plt.cm.jet)\n ax1.set_xlim(2000, 2018)\n legend_handles, legend_labels = ax1.get_legend_handles_labels(); ax1.legend(reversed(legend_handles[-8:]), reversed(legend_labels[-8:]), loc='upper left'); # reverse to keep order consistent\n ax1.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n ax1.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:.0f}'))\n ax1.axvline(x=2015, color='black')\n ax1.set_title(\"Total {} OA available in 2019, by year of availability and publication year\".format(graph_type));\n ax1.set_ylabel(\"number of articles\")\n ax1.set_xlabel(\"published year\")\n \n plt.tight_layout()\n plt.show()\n\ndef make_zoom_in_plot(graph_type):\n full_range = range(1990, 2020)\n long_data_for_plot = get_long_data(graph_type)\n color_idx = np.linspace(0, 1, len(full_range))\n\n fig, ax1 = plt.subplots(1, 1, figsize=(4, 4))\n data_for_this_plot = long_data_for_plot\n data_for_this_plot = data_for_this_plot.loc[data_for_this_plot[\"published_year\"]==2015]\n total_sum = data_for_this_plot[\"num_articles\"].sum()\n data_for_this_plot = data_for_this_plot.loc[data_for_this_plot[\"num_articles\"]/total_sum>=0.01]\n# print data_for_this_plot\n # data_for_this_plot = data_for_this_plot.drop(columns=[\"article_age_months\"])\n pivot_df = data_for_this_plot.pivot_table(index='published_year', columns='prediction_year', aggfunc=np.sum)\n pivot_df = pivot_df.div(pivot_df.sum(1), axis=0)\n pivot_df.plot.bar(stacked=True, alpha=.4, ax=ax1, colors=[plt.cm.jet(a) for a in list(color_idx[-len(pivot_df.sum(0)):])])\n ax1.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\n plt.ylabel('proportion of articles')\n plt.title(\"Proportion of {} articles published in 2015\".format(graph_type));\n ax1.set_xlabel(\"\")\n ax1.set_xticks([]) \n legend_handles, legend_labels = ax1.get_legend_handles_labels(); \n cleaned_legend_labels = [a[-5:-1] for a in legend_labels]\n legend_length = len(data_for_this_plot) # just the nonzero ones\n ax1.legend(reversed(legend_handles[-legend_length:]), reversed(cleaned_legend_labels[-legend_length:]), loc='upper left'); # reverse to keep order consistent\n",
"execution_count": 193,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T15:47:47.692441Z",
"end_time": "2019-09-23T15:47:47.924074Z"
},
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Code: SQL"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "See notebook."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:05:54.919954Z",
"end_time": "2019-09-23T14:06:08.205536Z"
},
"code_folding": [],
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for unpaywall_data_by_year_with_embargos and unpaywall_data_by_year\n\nq = \"\"\"\nwith fix_elsevier_delayed_oa_status as (\nselect doi, \ncase when oa_status != 'hybrid' then oa_status \nwhen delayed.embargo is not null and best_license = 'elsevier-specific: oa user license' then 'bronze'\nelse 'hybrid'\nend as oa_status\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n)\nselect date_part('year', published_date::timestamp) as published_year, \nfixed.oa_status,\ndelayed.embargo,\ncount(*) as num_articles\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\njoin fix_elsevier_delayed_oa_status fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l != '0931-7597'\nand published_year > '1950-01-01'::timestamp\nand published_year < '2019-01-01'::timestamp\ngroup by published_year, fixed.oa_status, embargo\norder by published_year asc\n\"\"\"\nunpaywall_data_by_year_with_embargos = pd.read_sql_query(q, redshift_engine)\n\nunpaywall_data_by_year = unpaywall_data_by_year_with_embargos.drop(columns = [\"embargo\"])\nunpaywall_data_by_year = unpaywall_data_by_year.groupby(['published_year', 'oa_status']).sum()\nunpaywall_data_by_year.reset_index(inplace=True)\n",
"execution_count": 159,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:06:08.212313Z",
"end_time": "2019-09-23T14:06:19.097755Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for unpaywall_graph_type\n\nq = \"\"\"\nwith fix_elsevier_delayed_oa_status as (\nselect doi, \ncase when oa_status != 'hybrid' then oa_status \nwhen delayed.embargo is not null and best_license = 'elsevier-specific: oa user license' then 'bronze'\nelse 'hybrid'\nend as oa_status\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n)\nselect date_part('year', published_date::timestamp) as published_year, \nfixed.oa_status,\ncase when fixed.oa_status='bronze' and delayed.embargo is not null then 'delayed_bronze' \n when fixed.oa_status='bronze' and delayed.embargo is null then 'immediate_bronze' \n else fixed.oa_status end\n as graph_type,\ncount(*) as num_articles\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\njoin fix_elsevier_delayed_oa_status fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l != '0931-7597'\nand published_year > '1950-01-01'::timestamp\nand published_year < '2019-01-01'::timestamp\ngroup by published_year, fixed.oa_status, graph_type\norder by published_year asc\n\"\"\"\nunpaywall_graph_type = pd.read_sql_query(q, redshift_engine)\n",
"execution_count": 160,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T18:56:55.873388Z",
"end_time": "2019-09-23T18:57:46.296028Z"
},
"code_folding": [],
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for data_usage_by_age_months_no_color_full_year. maybe don't need this one in the final paper?\n\nq = \"\"\"\nselect datediff('days', published_date::timestamp, received_at_raw::timestamp)/30 as article_age_months, \ncount(u.doi) as num_accesses \nfrom papertrail_unpaywall_extracted extracted \njoin unpaywall u on extracted.doi=u.doi \nwhere genre = 'journal-article' and journal_issn_l != '0931-7597'\nand published_date > '1950-01-01'::timestamp\n-- and published_date < current_date\n-- and received_at_raw > '2019-07-15'\n-- and last_changed_date < '2019-07-01'::timestamp\nand extracted.doi not in ('10.1038/nature21360', '10.1038/nature11723')\ngroup by article_age_months\norder by article_age_months asc\n\n\"\"\"\ndata_usage_by_age_months_no_color_full_year = pd.read_sql_query(q, redshift_engine)",
"execution_count": 202,
"outputs": [
{
"ename": "DatabaseError",
"evalue": "(psycopg2.DatabaseError) SSL SYSCALL error: Operation timed out\n [SQL: \"\\nselect datediff('days', published_date::timestamp, received_at_raw::timestamp)/30 as article_age_months, \\ncount(u.doi) as num_accesses \\nfrom papertrail_unpaywall_extracted extracted \\njoin unpaywall u on extracted.doi=u.doi \\nwhere genre = 'journal-article' and journal_issn_l != '0931-7597'\\nand published_date > '1950-01-01'::timestamp\\n-- and published_date < current_date\\n-- and received_at_raw > '2019-07-15'\\n-- and last_changed_date < '2019-07-01'::timestamp\\nand extracted.doi not in ('10.1038/nature21360', '10.1038/nature11723')\\ngroup by article_age_months\\norder by article_age_months asc\\n\\n\"] (Background on this error at: http://sqlalche.me/e/4xp6)",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mDatabaseError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-202-0b0e361c6465>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \"\"\"\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mdata_usage_by_age_months_no_color_full_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mredshift_engine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_sql_query\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m 312\u001b[0m return pandas_sql.read_query(\n\u001b[1;32m 313\u001b[0m \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m parse_dates=parse_dates, chunksize=chunksize)\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1100\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 988\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 989\u001b[0m \u001b[0;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 992\u001b[0m def read_table(self, table_name, index_col=None, coerce_float=True,\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m 2073\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2074\u001b[0m \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontextual_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclose_with_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2075\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2076\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2077\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, object, *multiparams, **params)\u001b[0m\n\u001b[1;32m 940\u001b[0m \"\"\"\n\u001b[1;32m 941\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 943\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_on_connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_execute_text\u001b[0;34m(self, statement, multiparams, params)\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1103\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1105\u001b[0m )\n\u001b[1;32m 1106\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m 1198\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1199\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1200\u001b[0;31m context)\n\u001b[0m\u001b[1;32m 1201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1202\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m 1411\u001b[0m util.raise_from_cause(\n\u001b[1;32m 1412\u001b[0m \u001b[0msqlalchemy_exception\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1413\u001b[0;31m \u001b[0mexc_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1414\u001b[0m )\n\u001b[1;32m 1415\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/util/compat.pyc\u001b[0m in \u001b[0;36mraise_from_cause\u001b[0;34m(exception, exc_info)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0mexc_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[0mcause\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mexception\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexc_tb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcause\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpy3k\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m 1191\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1192\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1193\u001b[0;31m context)\n\u001b[0m\u001b[1;32m 1194\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1195\u001b[0m self._handle_dbapi_exception(\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sqlalchemy/engine/default.pyc\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDatabaseError\u001b[0m: (psycopg2.DatabaseError) SSL SYSCALL error: Operation timed out\n [SQL: \"\\nselect datediff('days', published_date::timestamp, received_at_raw::timestamp)/30 as article_age_months, \\ncount(u.doi) as num_accesses \\nfrom papertrail_unpaywall_extracted extracted \\njoin unpaywall u on extracted.doi=u.doi \\nwhere genre = 'journal-article' and journal_issn_l != '0931-7597'\\nand published_date > '1950-01-01'::timestamp\\n-- and published_date < current_date\\n-- and received_at_raw > '2019-07-15'\\n-- and last_changed_date < '2019-07-01'::timestamp\\nand extracted.doi not in ('10.1038/nature21360', '10.1038/nature11723')\\ngroup by article_age_months\\norder by article_age_months asc\\n\\n\"] (Background on this error at: http://sqlalche.me/e/4xp6)"
],
"output_type": "error"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:11:10.518533Z",
"end_time": "2019-09-23T06:11:30.036686Z"
},
"code_folding": [],
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for data_usage_by_age_months\nq = \"\"\"\nwith fix_elsevier_delayed_oa_status as (\nselect doi, \ncase when oa_status != 'hybrid' then oa_status \nwhen delayed.embargo is not null and best_license = 'elsevier-specific: oa user license' then 'bronze'\nelse 'hybrid'\nend as oa_status\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n)\nselect datediff('days', published_date::timestamp, received_at_raw::timestamp)/30 as article_age_months, \nfixed.oa_status,\ncount(u.doi) as num_accesses \nfrom papertrail_unpaywall_extracted extracted\njoin unpaywall u on extracted.doi=u.doi \njoin fix_elsevier_delayed_oa_status fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l != '0931-7597'\nand published_date > '1950-01-01'::timestamp\nand published_date < current_date\nand received_at_raw > '2019-07-01'\nand received_at_raw <= '2019-08-01'\n-- and last_changed_date < '2019-07-01'::timestamp\nand extracted.doi != '10.1038/nature21360'\ngroup by article_age_months, fixed.oa_status\norder by article_age_months asc\n\"\"\"\ndata_usage_by_age_months = pd.read_sql_query(q, redshift_engine)\n\n",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:11:30.043557Z",
"end_time": "2019-09-23T06:11:51.055607Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for data_usage_by_age_years\n\nq = \"\"\"\nwith fix_elsevier_delayed_oa_status as (\nselect doi, \ncase when oa_status != 'hybrid' then oa_status \nwhen delayed.embargo is not null and best_license = 'elsevier-specific: oa user license' then 'bronze'\nelse 'hybrid'\nend as oa_status\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n)\nselect datediff('days', published_date::timestamp, received_at_raw::timestamp)/(30*12) as article_age_years, \nfixed.oa_status,\ncase when fixed.oa_status='bronze' and journal_issn_l in (select issn_l from journal_delayed_oa_active) then 'delayed' when fixed.oa_status='bronze' then 'immediate' else null end as delayed_or_immediate,\ncount(u.doi) as num_accesses \nfrom papertrail_unpaywall_extracted extracted \njoin unpaywall u on extracted.doi=u.doi \njoin fix_elsevier_delayed_oa_status fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l != '0931-7597'\nand published_date > '1950-01-01'::timestamp\nand published_date < current_date\nand received_at_raw > '2019-07-01'\nand received_at_raw <= '2019-08-01'\nand extracted.doi != '10.1038/nature21360'\ngroup by article_age_years, fixed.oa_status, delayed_or_immediate\norder by article_age_years asc\n\"\"\"\ndata_usage_by_age_years = pd.read_sql_query(q, redshift_engine)\n",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:11:51.060833Z",
"end_time": "2019-09-23T06:11:55.210541Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\nq = \"\"\"\nwith fix_elsevier_delayed_oa_status as (\nselect doi, \ncase when oa_status != 'hybrid' then oa_status \nwhen delayed.embargo is not null and best_license = 'elsevier-specific: oa user license' then 'bronze'\nelse 'hybrid'\nend as oa_status\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n)\nselect date_part('year', min_record_timestamp) as year_of_first_availability, \ndatediff('days', published_date::timestamp, min_record_timestamp)/30 as months_old_at_first_deposit,\ndate_part('year', published_date::timestamp) as published_year,\ncount(*) as num_articles\nfrom unpaywall u\njoin unpaywall_pmh_record_min_timestamp pmh on u.doi=pmh.doi\njoin fix_elsevier_delayed_oa_status fixed on fixed.doi=u.doi\nwhere fixed.oa_status = 'green'\nand genre = 'journal-article' and journal_issn_l != '0931-7597'\nand year_of_first_availability is not null\ngroup by year_of_first_availability, months_old_at_first_deposit, published_year\n\n\n\"\"\"\ngreen_oa_with_dates_by_availability = pd.read_sql_query(q, redshift_engine)\n",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:11:55.217599Z",
"end_time": "2019-09-23T06:13:40.117841Z"
},
"code_folding": [],
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# queries\n\nmin_prediction_year = 1949\nmax_prediction_year = 2019 + 1\nprediction_year_range = range(min_prediction_year, max_prediction_year)\nunpaywall_data_by_months_cutoffs = pd.DataFrame()\n\nfor i, prediction_year in enumerate(range(min_prediction_year - 1, max_prediction_year)):\n \n q = \"\"\"\n with fix_elsevier_delayed_oa_status as (\n select doi, \n case when oa_status != 'hybrid' then oa_status \n when delayed.embargo is not null and best_license = 'elsevier-specific: oa user license' then 'bronze'\n else 'hybrid'\n end as oa_status\n from unpaywall u\n left join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n )\n select \n datediff('days', published_date::timestamp, '{prediction_year}-01-01'::timestamp)/30 as article_age_months, \n --datediff('days', published_date::timestamp, current_date)/30 as article_age_months_from_now, \n {prediction_year} as prediction_year,\n count(*) as num_articles\n from unpaywall u\n left join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n join fix_elsevier_delayed_oa_status fixed on fixed.doi=u.doi\n where genre = 'journal-article' and journal_issn_l != '0931-7597'\n and fixed.oa_status = 'bronze'\n and delayed.embargo is not null\n and published_date > '1950-01-01'::timestamp\n and published_date <= ADD_MONTHS('{prediction_year}-01-01'::timestamp, -embargo::integer)\n group by prediction_year, article_age_months\n order by prediction_year, article_age_months asc\n \"\"\".format(prediction_year=prediction_year)\n bronze_rows = pd.read_sql_query(q, redshift_engine)\n unpaywall_data_by_months_cutoffs = unpaywall_data_by_months_cutoffs.append(bronze_rows)\n",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:13:40.125568Z",
"end_time": "2019-09-23T06:15:25.665270Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# queries\n\nmin_prediction_year = 1949\nmax_prediction_year = 2019 + 1\nprediction_year_range = range(min_prediction_year, max_prediction_year)\npapers_after_cutoffs_after_embargo_delayed_bronze = pd.DataFrame()\n\nfor i, prediction_year in enumerate(range(min_prediction_year - 1, max_prediction_year)):\n \n q = \"\"\"\n with fix_elsevier_delayed_oa_status as (\n select doi, \n case when oa_status != 'hybrid' then oa_status \n when delayed.embargo is not null and best_license = 'elsevier-specific: oa user license' then 'bronze'\n else 'hybrid'\n end as oa_status\n from unpaywall u\n left join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n ) \n select \n datediff('days', published_date::timestamp, '{prediction_year}-01-01'::timestamp)/(30*12) as article_age_years, \n {prediction_year} as prediction_year,\n count(*) as num_articles\n from unpaywall u\n left join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n join fix_elsevier_delayed_oa_status fixed on fixed.doi=u.doi\n where genre = 'journal-article' and journal_issn_l != '0931-7597'\n and fixed.oa_status = 'bronze'\n and delayed.embargo is not null\n and published_date > '1950-01-01'::timestamp\n and published_date <= ADD_MONTHS('{prediction_year}-01-01'::timestamp, -embargo::integer)\n \n group by prediction_year, article_age_years\n order by prediction_year, article_age_years asc\n \"\"\".format(prediction_year=prediction_year)\n bronze_rows_by_year = pd.read_sql_query(q, redshift_engine)\n papers_after_cutoffs_after_embargo_delayed_bronze = papers_after_cutoffs_after_embargo_delayed_bronze.append(bronze_rows_by_year)\n",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T12:36:31.738069Z",
"end_time": "2019-09-23T12:36:42.256510Z"
},
"trusted": false
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\nq = \"\"\"select u.year::numeric as published_year, count(distinct u.doi) as num_articles \nfrom unpaywall u\njoin unpaywall u_biorxiv_record on u_biorxiv_record.doi = replace(u.best_url, 'https://doi.org/', '')\nwhere u.doi not like '10.1101/%' and u.best_url like '%10.1101/%'\nand datediff('days', u_biorxiv_record.published_date::timestamp, u.published_date::timestamp)/(30.0) >= 0\nand u.year >= 2013 and u.year < 2019\ngroup by u.year\norder by u.year desc\n\"\"\"\nimport sqlalchemy\nbiorxiv_growth = pd.read_sql_query(sqlalchemy.text(q), redshift_engine)\n",
"execution_count": 67,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "*------------------------------*"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Predicting papers by OA type"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We grouped all articles by OA availability and publication year, then used a linear extrapolation (using the pandas foobar() function) to predict the number of papers that would be published until 2024, in each category. [discuss more why linear for all graphs]  The results are shown in the figure below. [seeing the color legend here would be nice]\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:15:58.662943Z",
"end_time": "2019-09-23T14:16:00.246932Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\nfig, axes = plt.subplots(1, len(oa_status_order), figsize=(12, 3), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\nnaive_data = pd.DataFrame()\nfor i, graph_type in enumerate(oa_status_order):\n new_data = graph_naive_papers(graph_type, use_graph_type=False, now_delta_years=1, cumulative=False, ax=axes_flatten[i])\n naive_data = naive_data.append(new_data)\n \n",
"execution_count": 170,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 5 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The prediction shows continued growth in the absolute number of OA articles, as can be seen in the figure above. Moreover, we also can see a growth in the percentage of all papers that are available as OA, as can be seen in the stacked area chart below..\n\nThe early years of this graph are similar to Figure 2 in Piwowar (2018). There is one noteable difference: some of what was considered Bronze OA (and to a lesser extent hybrid OA) in the earlier paper is classified as Gold OA in the current analysis. This is due to an improvement in Unpaywall's algorithms. Originally, Unpaywall used the Directory of Open Access Journals (DOAJ) as the sole arbiter of whether a journal was \"fully-OA.\" Unpaywall still uses DOAJ in this way, but it now also adds an empirical check for OA journals (if 100% of a journal's articles are OA, it is listed as an OA journal). This results in a more comprehensive and accurate list of fully-OA journals, which in turn moves some articles into Gold from Hybrid and Bronze. .\n\n[cumulative -> total]\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T14:16:02.790335Z",
"end_time": "2019-09-23T14:16:03.437908Z"
},
"trusted": false
},
"cell_type": "code",
"source": "plot_totals_and_proportion(naive_data)",
"execution_count": 171,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 2 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {
"scrolled": true
},
"cell_type": "markdown",
"source": "### Predicting number of papers at their time of availability "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The simple analysis above tells us how OA status varies by publication date. However, it glosses over a detail that become important when we endeavor to make OA prediction useful: *when* do papers become available?  [i think the question is more how much is OA when i look]\n\nFor Gold OA and Hybrid OA the answer is easy: the papers are OA at the time of publication. For Green and Bronze OA the answer is more complicated. Authors often upload their paper to a repository months or years after the official publication date of the paper, typically  because the journal has a policy that authors must wait a certain length of time (the \"embargo period\") before self-archiving. Funder policies that mandate Green OA often allow a delay between publication and availability (notably the National Institutes of Health [NIH] in the USA, accounting for most of the content in the large PubMed Central [PMC] repository). Finally, some journals open up their back catalogs once articles reach a certain age, which has been called \"delayed OA\" [cite lakso] and we consider an important subset of Bronze.\n\nWe explore and model these dynamics below."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Green OA by the time of its availability"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\nWe can explore the delay between the time an article is published and when it becomes available as Green OA by comparing publication dates with dates of repository availability, available from each article's OAI-PMH record (as harvested by Unpaywall).\n\nWe plot the number of Green OA papers made available each year in the graph below, vs their date of publication. The first plot is a histogram of number of papers made available each year (one row for each year). The second plot is the same, but superimposes the articles made available in previous years. This stacked area represents the total cumulative number of Green OA papers that are available in that year -- if you were in that year and wondering what was available as Green OA that's what you'd find.\n\nThe third plot is a larger version of the availability as of 2018, showing the accumulation of availability. It allows us to appreciate that less than half of papers papers published in, say, 2015, were made available the same year -- most of the papers have been made available in subsequent years. The fourth plot is a slice in isolation, for clarity: the Green OA for articles with a Publication Date of 2015.\n"
},
{
"metadata": {
"scrolled": false,
"ExecuteTime": {
"start_time": "2019-09-23T16:01:09.443769Z",
"end_time": "2019-09-23T16:01:17.786500Z"
},
"trusted": false
},
"cell_type": "code",
"source": "make_detailed_plots(\"green\")",
"execution_count": 194,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x432 with 8 Axes>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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wNDdDc1yyFZYDIfqG4CM+2eyxdiZYQTruks+DEEJczyQQCxEDDG+tPSTCHOMb2otpxUXDCGY4FehDtmjm8AeFAs1aonQM3WPv00BHM0KGHaAwFIBmBlAtDkMze3Oj0g2UUW8FW2s4R6AnObKnVoXG+GBd9g18jjjr/TWMkCCPcgSiuHUBYJjBXfdhGH6Ubo2ZJtAxawZbn9YcHEkYOMzwrhoOWQgGad0c0qHX49DDe6PNvnQdDcMcD4350JTVIw04NFBKQ1MaijiUcqGczVEuq25rhIpCNTgswRAdCNJ14LsQ7I2uPwO1p8xeat0Phs8K0vYBMcc8B4ZzOOKtHucW5kWVs5k15tpl9jhrTTwcLnNoiGY9B6YDY7WVFhzLrYWM6b6cXnkhhPiNJBALEQMM3wXsX41AWYFOYWgRv9ZghchLHvurQm/Ms0YgB0Kx9VoFhkzoXrM309DtDkxzOwNDaaiQ8ciBAI1yYGjOS68nauMDz2ZYVfaFQOgYYoXZk2uFMsMMrGBYoVkHw4+BB6jFMPSwCwZlh25QaPjR8KsEvI7mYTfbGYGx1pdYuAqfDH+GkOEoujUcxXptGIB5MyNOHeXQIc6aFzgOgW0ITPuteWagVroPzfCZFwkXfKhaL8qoJ9gLTchxbKRm+0QbgY9K4IBh/10oFXqJEvilE4K/SmJowfk4wWGGZhX6SyZKM68ksLbTNOvZEfJe1o2WYX+PKnAWQ89LtHP0K765uNjpvZSPgGrw4hL3H23hRd6s0cWXXGTIrMvYRojfIDWt1xXblwRiIWKAnvD//K77b+qnai51WbS893u41P1Hq+1Sll/J+n/NvuTnghrxRx8YORFC/GFSr+TODCHEn5rb7TZGjRpluN3uq13KZbne6zcMacO1Qtpwbbje23C9128Y0oZo5AcwhfiTO3v2LPv37+fs2bNXu5TLcr3XD9KGa4W04dpwvbfheq8fpA3RSCAWQgghhBAxTQKxEEIIIYSIaRKIhRBCCCFETJNALIQQQgghYprj1VdfffVqFyGE+H3Fx8fz4IMPEh8ff7VLuSzXe/0gbbhWSBuuDdd7G673+kHaEEkZhiG/miiEEEIIIWKWDJkQQgghhBAxTQKxEEIIIYSIaRKIhRBCCCFETJNALIQQQgghYpoEYiGEEEIIEdMkEAshhBBCiJgmgVgIIYQQQsQ0CcRCCCGEECKmSSAWQgghhBAxTQKxEEIIIYSIaRKIhRBCCCFETJNALIQQQgghYpoEYiGEEEIIEdMkEAshhBBCiJgmgVgIIYQQQsQ0CcRCCCGEECKmSSAWQgghhBAxTQKxEEIIIYSIaRKIhRBCCCFETJNALIQQQgghYpoEYiGEEEIIEdMkEAshhBBCiJgmgVgIIYQQQsQ0CcRCCCGEECKmxWwgnj9/PsuWLbtq77969WpWrFgBQEZGBt9++22T6+fk5LBq1aqoy+655x5Onz592bWUlpYyevToX73dc889x6FDhy77fUPV1dUxY8YMsrOz6d+/PzNmzKCurg6A06dPM378eLKyshgwYABfffVV2LaGYTQ4PufOnePFF19kwIABZGVl2cdaCCGEECJSzAbiq23EiBFMmDDhapfxm6xcuZJ27dpdkX29/fbb+P1+NmzYwMaNG6mvr+edd94BYN68eXTr1o3NmzezePFiJk+ezIULFwA4fPgwY8aMYcuWLWH7y8/Pp02bNvznf/4nhYWFrFmzhq+//vqK1CqEEEKIPxfnxVZYsWIFhYWFJCYm0q1bN7Zv386OHTvIycnhzJkzHD9+nIcffpjJkyezZMkS9u/fj9/vp2PHjsyaNYukpCSqqqqYP38+P/30E16vl/79+/P8889TWVnJ2LFjSU9Pp6ysDLfbzZQpU8jKygqrobKykjFjxtCjRw8OHjyIz+fj5ZdfZu3atRw5coROnTqRl5eHpmksX76cbdu2UV9fz4ULF5g+fTqZmZnU1NTwyiuvUFFRQWpqKg6Hg/vvvx+g0foiHTx4kMWLF+PxePj555956KGHWLRoEXl5edTU1DBnzhwAdu/ezbJly1i/fn2j9Sxbtozq6mp7GwBd11m0aBFlZWWcP38ewzBYuHChXeeXX37JJ598Qk1NDb169WL69Ok4neGncP369axevRpd10lJSWH27NncddddDdqSn5/Ppk2bSElJ4bbbbrPn5+TkkJaWxrPPPttgOiMjg86dO/P999/z0ksv8dprr5Gfn09tbS1vvvkmt9xyCz/88AMej4c5c+bQo0cPTp8+zYwZMzh27BgpKSnceOONpKWlMWnSpLB6unfvzk033YSmmddoHTp04NChQ/h8Pnbu3MncuXPt+bfffjt79uzh0UcfpaCggCeffJK2bduG7e+VV17B7/cD8PPPP+PxeLjhhhvstgNMnjy5wXERQgghROxpsod4z549FBUVUVhYSFFREefPnw9bXldXR0lJCdOmTWPFihU4HA6KiorYuHEjqampLFmyBIBp06YxZMgQe1979+5l8+bNABw/fpzevXtTWFjI1KlTWbx4cdRaKisrycjIoKSkhB49epCbm0teXh4lJSUcOHCAgwcPcuLECfbu3ctHH33Epk2bmDJlCkuXLgVg6dKlJCQkUFpaSn5+Pj/++KO976bqC/XBBx/w4osvsn79ekpKStixYwfl5eUMHTqUzZs34/F4ACgqKmLYsGFN1hNNWVkZp06dYu3atWzevJnBgwezcuVKe/nJkyd57733KC4upqKignXr1oVt/8UXX1BcXExBQQHFxcWMHz++QfAE2LZtG1u3bqW4uJg1a9ZQU1PTaE2R0tLS2LJlC5mZmWHzv/nmG5555hmKi4t56qmneOuttwBYuHAh7dq1Y8uWLeTn5zcY7hDQu3dv7rjjDgBOnDjB+++/T79+/aiurkbXdVq1amWv26ZNG06ePAnAnDlzeOKJJxrsTymF0+lk6tSpDBgwgAceeMDe/+TJkyUMCyGEEMLWZA/xrl276NevHy1atABg1KhRfPbZZ/byQM8lwM6dOzl37hx79+4FwOv10rp1a2pra9m/fz9ut9vumautraWiooLOnTvjcrlIT08HoGPHjpw5cyZqLS6Xi4yMDABuvfVWunTpQlJSEgCpqam43W66du3KG2+8waZNmzh69Kjd0wqwb98+Zs6ciVKKVq1a2YGuqfoie6pff/11du/ezfLlyzly5Ah1dXXU1tbSqVMn2rdvz44dO+jZsyf79u0jNzeXxMTERuuJpkuXLiQnJ7NmzRqOHz/O559/TmJior180KBBNG/eHICBAweya9cuRo4cGXYOjh49yvDhw+15brebM2fOkJKSYs/bt28fmZmZ9vEbMmQIH374YaN1herWrVvU+W3btqVDhw6AeR4//vhjwPwMBV6npqbSr1+/JvdfXl7OCy+8wNNPP03fvn2pqqqKup7D4bikepcsWcK8efN48cUX+de//sWLL754SdsJIYQQInY0GYidTieGYdjTkSEkEM7A/Lp/5syZdrg9f/489fX16LqOYRisWbOGZs2aAeZNUvHx8VRXV+NyueyvyZVSjdbicrnClrtcrgbrfPfdd0ycOJGxY8fSq1cvunfvzrx58+zl0drSVH2RRo0aRfv27enTpw+PP/44ZWVl9j6HDh1KcXExv/zyC5mZmSQmJl60nkg7d+4kNzeXcePG8cgjj3DnnXeycePGBjUHRA6X0HWdQYMGMW3aNHv61KlTJCcnh62nlGr0vEYu83q9YduGnvNQCQkJUfcR+RkKnOtoSkpKmDdvHrNnzyY7OxuA1q1bA2awD7SjqqqKNm3aNLofML/duPvuu2nTpg2JiYn079+frVu3NrmNEEIIIWJTk0Mm0tPT2bp1K+fOnQOgsLCw0XV79+5NQUEBHo8HXdeZPXs2eXl5JCUlcd999/Huu+8CcPbsWUaMGMH27duvYDNM+/fvp1OnTowbN44HHniA7du32+NI+/TpQ2FhIbqu43a77fe/1Prcbjfl5eVMnTqVRx99lKqqKo4dO4au6wBkZmby3XffsW7dOoYNG3bReqL59NNP6du3LyNHjuTee+9l27ZtYeuXlJTg8Xior6+nqKiIv/71r2Hb9+rVi5KSEk6dOgWYv2QxZsyYBu/Tp08fSktLOXv2LLqus2HDBntZy5YtKS8vB8wLgwMHDlzCkW9cenq6/bmprq5m27ZtUS98SktLWbhwIatWrbLDMJiB+uGHH2bt2rUAVFRUcPjwYR588MEm33fLli3861//wjAMPB4PW7ZsoUePHr+pLUIIIYT4c2qyh7hnz54MGzaMv/3tbyQkJJCWlmb3okaaOHEib7zxBoMHD8bv99OhQwdycnIA82vrBQsWkJ2djcfjYcCAAQwcOJDKysor2pgBAwawdetWsrKycLlc9OzZE7fbTU1NDZMmTWLu3Lk8/vjjtGrVirvvvtverrH6QiUnJzNhwgQGDx5MSkoKLVu2pGvXrhw9epSePXsSFxdHVlYWe/fupXPnzhetJ5rhw4czdepUsrOzcTgcdOvWja1bt9qh++abb2bEiBHU1taSmZnJ4MGDw7bv06cPzz33HM888wxKKZKSknjrrbcaBND09HS+//57hgwZQosWLWjfvj3V1dUAjB49mqlTp/LYY49x880388ADD/ymczJjxgxmzZpFdnY2KSkptG3bNqw3OSAvLw/DMJg1a5Y9r2vXrsydO5e5c+cya9YsBgwYgFKKf/zjH/YNco3Jyclh7ty5ZGdno5TikUce4e9//zsgN9UJIYQQIpwyQr/PjvDtt9/y9ddf20Hi3XffpaysjH/+859/WIHi+lZQUEDHjh3p0qULHo+HkSNHMmnSJHtojfiDfP5/QNMAZT4rBygt/GHPV+HLtcA8a7mmBV+HbReyLhpoTus9rfmB91eatT8VPo0Kvk/o68CyP4PQdtpt/JO0TQghrmNN9hDfcccdrFy5knXr1qGU4i9/+QsLFiz4o2oTfwLt2rVjwYIF6LqO1+ulX79+EoavgkP1yWDoKEM3n9FB96AMf3Be4DU6yjDA0AEDZegoDDDMh0I3X1vrBacNc9razlw3uF5guTmNuQ46mrUMzNirMEApFAbKCsJKgbL3Y7ZDGZh1q2C9ShGyTrCWpu5P+GNZdQQuDkJDceTFCaEXKoGLhJALEjTQQi9eHNZ06IWLdX+A5gjZ3rpgCXvPiPdvbL4zHhJaQEIyxLeA+ERwJpiPJu4PEEKIa12TPcRCiD+H8m8OhkwZXFKPa9j/NYT/34RqMCc4P2InEStG28qIWBR9HdWgpkb208gyM/gb4RcG9gWAuSzsgsHwW6HaH3IhEAjjIUHfXt+w96fZFxPB0K/w49A9uAwvTvw4jTocfj+a4UWpwHHSQQ9cIFgXJYGLB0O3mhK8WLG3sS5WgsfSWq5b0yriPNjH0Qh/HXV5yLJADYFpR5wZkp3xEH+D+WiWAvHJkJBkzWsRDNEJLczw7Eowt71mLlSEELHuon+YQwhx/VO6lwZxVfeZvcK6Hwy/FfYighUhm4UNXQh+7W9Yy4yQHk8jovfTCPRwNthp1GpDXgZeG2aMV4Cuowwf6P6wHm47tIaFcCOkbhUyTXAahWENFbH6sc0ASWR7CWtvsIcX+7UROgwErHY31UrzeGv+eutRh0Ovx+H34DB8OPDiRMelDJxOhRMDhwYOZaCa2PfvxtBB94HfCz4v+C6A94L1XAdnT0L1UXO57jMfYZ8nDVzx4Ig3Q3FCCzM8N08xw3PcDVZ4bhFcFt/cCtHNrJ5uIYS48iQQCxEDnN7zGIbfCpFGSLhToDkwlAPDEYeuOTG/To/YgWENWfB7wfCZIdral8LAUJr5TbxygHJiaOb+QkOb2dEYHJ4RNgQjZEhGoHfUCBleYdi9rQYYCkMBSjPr1pwYmhM0lxVmG9YOoAwfSvdaFwCBXuFA46ztlIZSTtAc6A4XaK4Gu7vEKB98e7tX2o8KhETrIkQZhhn1lYaBwudMxKtuAM2JobmiBmoVGF7i95gB2grTDt1jPgwfLuXHoQziNHBoCocyg7T2W3tklWb27DriIA4g5WJbmG3VvWZI9taBr8589l6A+ho4/z9QVW9elOk+0K3PQ+DCxuHZH7FQAAAgAElEQVQye6BdCeBqboXlFGiWDHFWL3RoD3RCsrmuM8HcTnqhhRCXQIZMCBEDKr6J/hcCIxkAfi+a7sXQfWaAM/zBb8ytMaVmj6oDw+HCUNZ1tYrcUWQIDemFBnt8qqE5UZrDDICa67LH+xqGAboPTfeC7scwvFZvcqAX2CpSaWbQtIJ01PqtNphjrc3joKz6g0MjAm1QYIVowwqy6gr0ZJqjIXwov9c6D1aQxjAvElSgN9scK2wEjqFq+N52iNZ9aP46M0D7rZ5o3WP1RPtwYuDSwOmwQrQCpakmLwKuOOs84veC3xMSoi8EX/vqQnqh/cEhIgBo4LSGcriaQXySFZYDvdDNguOeA0Hb2SwYol3NrNBvXmThcJk3iDqk/0iIPzP5Fy5EDNB9nrCxs6E3uoXeEBcIW9g3tFlDBxTWjW4Ayr5ZDr8HQ3lAN8fa6oGb8Kx9GCiUwu7JRTnRQ3s+I0K00v0YuhkAVUgvavgAWHNoQuDmO+yagr28Zs0ulMt8bVg3kRkhN4gpqz2GoduhU9d9aIHwjjKPjQrpSQ8E6CbagAHK5zGHqRh+lG6NR44cjxvSnsA+AscLlHVhoAXbqLnAEY+B2Q67LZrDHtyhAEP3WxchgSEL/ohhCwq/FoffkWCdF5d5bhpWRWDssuavw+GrR4XciKlZz+bNjoHXBhrmPM369kAp0AwdTVNoCjQMlEMzl1vnUQucOmt9pTlRDhfQHKL/0mc4uxfaB756KzhfCPZE11ab80MuzAIXN8ETF3hpWL+Q4ggJw9ZzIGQ7rRAd18ycFxgCEgjZgXHSzviQsN3MGi4SsU/NKb3YQlwDJBALEQu8FzACwU4pIPBrAyG/cBAYAxv6s2eXK+JXKsJuBPN7zBDaYKPQoRBOcMaFhb6mBys0KODSVrF7qx0oNHC40LXQm9qsFcNuaDN7bUMDZvhYYrMn3eyFjgNHILyGjy/+de25SDvChpyAsoatKBUHYeOqIy6AdAP0ejDqsC9+wPxrkyE/DedXLvyu+JA2BNsZnA55vlINi/wchbYz5GZGNGs9pwHx1q+b2ENVCN702OBzEXojovk/4RcuEa8h+A1H4D28gMeACwBWTz6RvzUf8lkJdO9bk+Gfh+C4e/MCVAte6RDo+Y8YRnPRwx06jCg4Jv/iO4g2P8qQpMhNwv5/47cO0fltm19Hbyou010PPHrF9iWBWIgYoDf/X1ftvRuLptfaWK1fU8+1VnukKP2eja7za5ddLdFquhbrFEJcpwwhxJ+a2+02Ro0aZbjd7qtdymW53us3DGnDtULacG243ttwvddvGNKGaOSX1IX4kzt79iz79+/n7NmzV7uUy3K91w/ShmuFtOHacL234XqvH6QN0UggFkIIIYQQMU0CsRBCCCGEiGkSiIUQQgghREyTQCyEEEIIIWKa49VXX331ahchhPh9xcfH8+CDDxIfH3+1S7ks13v9IG24Vkgbrg3Xexuu9/pB2hBJ/nSzEEIIIYSIaTJkQgghhBBCxDQJxEIIIYQQIqZJIBZCCCGEEDFNArEQQgghhIhpEoiFEEIIIURMk0AshBBCCCFimgRiIYQQQggR0yQQCyGEEEKImCaBWAghhBBCxDQJxEIIIYQQIqZJIBZCCCGEEDFNArEQQgghhIhpEoiFEEIIIURMk0AshBBCCCFimgRiIYQQQggR0yQQCyGEEEKImCaBWAghhBBCxDQJxEIIIYQQIqZJIBZCCCGEEDFNArEQQgghhIhpEoiFEEIIIURMk0AshBBCCCFimgRiIYQQQggR0yQQCyGEEEKImBazgXj+/PksW7bsqr3/6tWrWbFiBQAZGRl8++23Ta6fk5PDqlWroi675557OH369GXXUlpayujRo3/1ds899xyHDh267PcNVVdXx4wZM8jOzqZ///7MmDGDuro6AE6fPs348ePJyspiwIABfPXVV2HbGobR5PF54YUXmD9//hWpUwghhBB/PjEbiK+2ESNGMGHChKtdxm+ycuVK2rVrd0X29fbbb+P3+9mwYQMbN26kvr6ed955B4B58+bRrVs3Nm/ezOLFi5k8eTIXLlwA4PDhw4wZM4YtW7Y0WuOBAweuSI1CCCGE+HNyXmyFFStWUFhYSGJiIt26dWP79u3s2LGDnJwczpw5w/Hjx3n44YeZPHkyS5YsYf/+/fj9fjp27MisWbNISkqiqqqK+fPn89NPP+H1eunfvz/PP/88lZWVjB07lvT0dMrKynC73UyZMoWsrKywGiorKxkzZgw9evTg4MGD+Hw+Xn75ZdauXcuRI0fo1KkTeXl5aJrG8uXL2bZtG/X19Vy4cIHp06eTmZlJTU0Nr7zyChUVFaSmpuJwOLj//vsBGq0v0sGDB1m8eDEej4eff/6Zhx56iEWLFpGXl0dNTQ1z5swBYPfu3Sxbtoz169c3Ws+yZcuorq62twHQdZ1FixZRVlbG+fPnMQyDhQsX2nV++eWXfPLJJ9TU1NCrVy+mT5+O0xl+CtevX8/q1avRdZ2UlBRmz57NXXfd1aAt+fn5bNq0iZSUFG677TZ7fk5ODmlpaTz77LMNpjMyMujcuTPff/89L730Eq+99hr5+fnU1tby5ptvcsstt/DDDz/g8XiYM2cOPXr04PTp08yYMYNjx46RkpLCjTfeSFpaGpMmTQqrp3v37tx0001omnmN1qFDBw4dOoTP52Pnzp3MnTvXnn/77bezZ88eHn30UQoKCnjyySdp27ZtgzZ+9tln7Nmzh+HDh3P27Fl7/urVqykvLyc3N7fBNkIIIYSIPU32EO/Zs4eioiIKCwspKiri/PnzYcvr6uooKSlh2rRprFixAofDQVFRERs3biQ1NZUlS5YAMG3aNIYMGWLva+/evWzevBmA48eP07t3bwoLC5k6dSqLFy+OWktlZSUZGRmUlJTQo0cPcnNzycvLo6SkhAMHDnDw4EFOnDjB3r17+eijj9i0aRNTpkxh6dKlACxdupSEhARKS0vJz8/nxx9/tPfdVH2hPvjgA1588UXWr19PSUkJO3bsoLy8nKFDh7J582Y8Hg8ARUVFDBs2rMl6oikrK+PUqVOsXbuWzZs3M3jwYFauXGkvP3nyJO+99x7FxcVUVFSwbt26sO2/+OILiouLKSgooLi4mPHjxzcIngDbtm1j69atFBcXs2bNGmpqahqtKVJaWhpbtmwhMzMzbP4333zDM888Q3FxMU899RRvvfUWAAsXLqRdu3Zs2bKF/Pz8BsMdAnr37s0dd9wBwIkTJ3j//ffp168f1dXV6LpOq1at7HXbtGnDyZMnAZgzZw5PPPFEg/1VVVWRm5vLkiVLcDgcYctGjBghYVgIIYQQtiZ7iHft2kW/fv1o0aIFAKNGjeKzzz6zlwd6LgF27tzJuXPn2Lt3LwBer5fWrVtTW1vL/v37cbvd5OfnA1BbW0tFRQWdO3fG5XKRnp4OQMeOHTlz5kzUWlwuFxkZGQDceuutdOnShaSkJABSU1Nxu9107dqVN954g02bNnH06FG7pxVg3759zJw5E6UUrVq1sgNdU/VF9lS//vrr7N69m+XLl3PkyBHq6uqora2lU6dOtG/fnh07dtCzZ0/27dtHbm4uiYmJjdYTTZcuXUhOTmbNmjUcP36czz//nMTERHv5oEGDaN68OQADBw5k165djBw5MuwcHD16lOHDh9vz3G43Z86cISUlxZ63b98+MjMz7eM3ZMgQPvzww0brCtWtW7eo89u2bUuHDh0A8zx+/PHHgPkZCrxOTU2lX79+Te6/vLycF154gaeffpq+fftSVVUVdb3IkBvK6/Xy0ksvMXPmTFJTUy/aJiGEEELEtiYDsdPpxDAMezoyhATCGZhf98+cOdMOt+fPn6e+vh5d1zEMgzVr1tCsWTPAvEkqPj6e6upqXC6X/TW5UqrRWlwuV9hyl8vVYJ3vvvuOiRMnMnbsWHr16kX37t2ZN2+evTxaW5qqL9KoUaNo3749ffr04fHHH6esrMze59ChQykuLuaXX34hMzOTxMTEi9YTaefOneTm5jJu3DgeeeQR7rzzTjZu3Nig5oDI4RK6rjNo0CCmTZtmT586dYrk5OSw9ZRSjZ7XyGVerzds29BzHiohISHqPiI/Q4FzHU1JSQnz5s1j9uzZZGdnA9C6dWvADPaBdlRVVdGmTZtG91NeXk5lZSWvv/46AP/zP/+D3++nvr5eeoaFEEII0UCTQybS09PZunUr586dA6CwsLDRdXv37k1BQQEejwdd15k9ezZ5eXkkJSVx33338e677wJw9uxZRowYwfbt269gM0z79++nU6dOjBs3jgceeIDt27fj9/sB6NOnD4WFhei6jtvttt//Uutzu92Ul5czdepUHn30Uaqqqjh27Bi6rgOQmZnJd999x7p16xg2bNhF64nm008/pW/fvowcOZJ7772Xbdu2ha1fUlKCx+Ohvr6eoqIi/vrXv4Zt36tXL0pKSjh16hRgjpUdM2ZMg/fp06cPpaWlnD17Fl3X2bBhg72sZcuWlJeXA+aFwW+9IS09Pd3+3FRXV7Nt27aoFz6lpaUsXLiQVatW2WEYzED98MMPs3btWgAqKio4fPgwDz74YKPv2aVLF3bt2sWGDRvYsGEDw4cPJysrS8KwEEIIIaJqsoe4Z8+eDBs2jL/97W8kJCSQlpZm96JGmjhxIm+88QaDBw/G7/fToUMHcnJyAFiyZAkLFiwgOzsbj8fDgAEDGDhwIJWVlVe0MQMGDGDr1q1kZWXhcrno2bMnbrebmpoaJk2axNy5c3n88cdp1aoVd999t71dY/WFSk5OZsKECQwePJiUlBRatmxJ165dOXr0KD179iQuLo6srCz27t1L586dL1pPNMOHD2fq1KlkZ2fjcDjo1q0bW7dutUP3zTffzIgRI6itrSUzM5PBgweHbd+nTx+ee+45nnnmGZRSJCUl8dZbbzUIoOnp6Xz//fcMGTKEFi1a0L59e6qrqwEYPXo0U6dO5bHHHuPmm2/mgQce+E3nZMaMGcyaNYvs7GxSUlJo27ZtWG9yQF5eHoZhMGvWLHte165dmTt3LnPnzmXWrFkMGDAApRT/+Mc/uOGGGy67ppi8qe7E/wtKgaaZz2jma0LmKWVO26+JmL7IMmi4LoDSmlgWuj/RJMMwH4HXGOHzwqaNiG2uwLaBh4ryOYDG54U+/y7rhnx+LraufNaEEI1QRuj32RG+/fZbvv76a/7+978D8O6771JWVsY///nPP6xAcX0rKCigY8eOdOnSBY/Hw8iRI5k0aZI9tEb8Mf6/Tf8HMFCGDoaB0v1h0xg6yghZbj+b31AE1w/ZhvD1lWEA1rMBShkog+B2ynqNGVWUUuZ8DALhRZkbEIw0GmhhkSeEinh5kbDTIAypJiYvJTiphqtd7D10v9leXQd0lN96NoyIZeHnpkGYBWs9Qubr5qLIkBs14FrLA68jQ7O9jm5vHvKChsc+9CRFBNSw46RCzpVquE20QBu6n9Dp0P3Y64QuDz32IdNxzSCpJST9L0hMhsQU85HUEhJbQvMW5jpxzSRACxFDmuwhvuOOO1i5ciXr1q1DKcVf/vIXFixY8EfVJv4E2rVrx4IFC9B1Ha/XS79+/SQMXwXnb+4cnAiEIaVCXgcWRgl0UcNmlOvoBrMavdYmGLiaWN5g0mgkphphT5dew7XICHupDDMcB8KsOY19ARNYFnqRYk5bQZrAa8N+HdxH+AWMeXyt7a06QtdVmBdSTr8Hp+7FhY5T+XHqBg78OAzdOj+BwB4SxsPCN8E22W3VCT+PoT3aIdsH6o42j9Dtouwr8NpXD79Uwk+HwO8zH9YFHwBOF7iaQXwzMyAntrTCcooVoFsF58U3Nx/Ohve0CCGuL032EAsh/hzKyw5ar5r65x4tboaGlshtG9tXtP2oKOtH63GMEpRDd2c0Fop/pcie0tAwCVZAC19PEW2bwP6CvbUqdLuI91Sh72v1mJu9q4Hjo+zrD3v30Q4dwfWj9mwrMAyF2UUfpQc1ZD1CYqxSdhQO2SZ4UWRoDgyt4S+8BNql+Txo3jocvjo0Xz1OnweH34PT8OHEj0spnJrCgYFTs74laLC3q0D3g88DPi94zkN9LdRfMJ89teD1gGGF59ChI64Esyc5IQmSUiCptRmcm1s9z0mt4IZW0Cyk17mJG4uFEFePBGIhYkD5V4GbI63Ap5TZy+j3g+4zh0QY/mBvYkjAC0lvYAUYI3LcMMqaR0iIUua7KYURGEdsjVs2on4V3UQ0igiFSvejdJ9Vu271kOpWz6lVs2FgoKyOcGXXHvZWRsiADRXyFg2+tg/vKW/Q1sjjEDZWOuIYRGueoaP8Xrs9ZjsCodx6CyuMKocTQ3NiOMJ/eeePYuh+lM8Lutc8B34/ytCDg2GUwtA0UA4Mh1ln5PkOXDQo3Y/y1eHw1uHw1ZsPvweH7sVl6DjRcTnAqcABOJSBauI4/u4MwwzFPo/Z01x3Phia6y+A5wLo3mCvc+A/rw6nGZ7jm5thORCUA8G5uTVkI6kVJCRCXHNwxV29dgoRgyQQCxEDvt//X+D3Yfj95rhgw7ACoAKlmWFLaWZ4cTT+9a/SfSi/zwqjVnAL7fFUGobDgdKcVhiKixraDHtsrB78et8IfmVv97Ba42kNDPAHhwkYgYCqHGb40qzwpTVROwb4vWb9hm4FFj3YoxsIsprDCp8uDKcTpV30D3peEsMKvcrvQ9d9aH7zIiRw7OxzoTSzJ9bhBIer0YEiYNj7U1ZbzGOog6EaOR+uPzxQGgB+L5rPg+EPXnwFh+to1kWEw2q3y2x7BGX9p0r5PGYPdCBE++tx+L04dasXWjMDtBNwKNC0q9QLrevg95jhOdDbHHh4LoCvLjhkw/5GQUFcvDlkIyHRDMs3tLbGNqcExzwn3GAN12gGzjhwuMxnp0vGPQtxmSQQCxED/vvggUsOQnZw83kxdLOnK9BzHOwZDQRRqwcwytfo1t7MwOb32qEtON41EEDN/eBwoDtc4HBddoAxDAPl96D8Pgy/z3w/PXgBEOzBNUMnmtXb2mj90XpvQ3rSiRI8NSeG88oET8MwQPeh+b0hFzQ+M/Ba48ANO0RrGJoVopvqjdZDzod1bOwLkbCLgpAQ3cTx+T0Yho7ymecRPfAwb8w0DEDTzAs4pYHdCx3eZvtCx9CDwzi8dWh+L5ruRzN8aLqOZvjRDB0HBhp+HIHeaE0zO+aVgYb5G6Xm4fkdAqffaw7X8NZZ4fl8MDh7aq0e59DwbNE0MwwHHk6XOSwjMLY5Icl8diWYvc7xzUKWWz3RgWBth2oJ1iI2SSAWIgZ899X+kDCq21/LE3rTlX0DV2C8LMH/+AZuwAsMG1AKtMBX5A4MrH3rRrD31149NLRdvOczPID6QdfD67FvBgxsEPLCCqfBOgOvHaCpkF7YwNf6Gkpp9jAApfsaXAQAdogObYOhOeGiwTM4rCPwiw0qtPiI9hhKYQ/iCLRBU9g/GR/4qbyIIIzVw2qPAbZ7jq32+K32BJYHLgqUI6R3vfGecLstYRcDwWdlf0kQcnNb6BgUFZirrFEkWvBcBcZQN2iT2fN/KbHM7oX2hwxXCB2yoDDbqjQ7TNs90hcZ02sH64hjoXQvmn2x50OzH36U7jfDta6joaNhWEM+zB5rB6Bh2CFbu9ShIIZuBmfrnOLzBodv+DzBcdD+evM4eM2hLea/b+uY+HVrCFJksHaCIy48WMc1M4Nzs0SIS4S4hJD5Vpi2Q3Vzc74dqiVYi+uLBGIhYsB3n/+XFUACY15Dxr2GjXMNHftKk4HvkoT9jq055MEIGxYRQoEZB1Ww1sBvJoeG3N9FyM1yYb9wEHJTXcSNeHY7IsYXm2OVldmJGziWYb/X3HD9K9eMxm78C74ODjuJtr5VT+jNeIReYESOmY7yHPr5uWJtimhPg/MTGsQjljf4qbkGbxBsd+i5afCzbZHPoe1WF9n+V7TV/gm9iAuPiPMXrRmNz29kmT0/4iItYn8NWhL5b9f+BibSJXzGQz9qDbeMOnXRjZvS6OpRr7IvZ0e/L7m+CNPhwb9efKVLdGUGxwkhrmlG8+SLr/QHuNjV97V8df5rartq7biE/1hey8c4qlgKANfo+bvuPjNCXA5DCPGn5na7jVGjRhlut/tql3JZrvf6DUPacK2QNlwbrvc2XO/1G4a0IRr5QUQh/uTOnj3L/v37OXv27NUu5bJc7/WDtOFaIW24Nlzvbbje6wdpQzQSiIUQQgghREyTQCyEEEIIIWKaBGIhhBBCCBHTJBALIYQQQoiY5nj11VdfvdpFCCF+X/Hx8Tz44IPEx8df7VIuy/VeP0gbrhXShmvD9d6G671+kDZEkj/MIYQQQgghYpoMmRBCCCGEEDFNArEQQgghhIhpEoiFEEIIIURMk0AshBBCCCFimgRiIYQQQggR0yQQCyGEEEKImCaBWAghhBBCxDQJxEIIIYQQIqZJIBZCCCGEEDFNArEQQgghhIhpEoiFEEIIIURMk0AshBBCCCFimgRiIYQQQggR0yQQCyGEEEKImCaBWAghhBBCxDQJxEIIIYQQIqZJIBZCCCGEEDFNArEQQgghhIhpEoiFEEIIIURMk0AshBBCCCFimgRiIYQQQggR0yQQCyGEEEKImCaBWAghhBBCxDQJxEIIIYQQIqbFbCCeP38+y5Ytu2rvv3r1alasWAFARkYG3377bZPr5+TksGrVqqjL7rnnHk6fPn3ZtZSWljJ69Ohfvd1zzz3HoUOHLvt9Q9XV1TFjxgyys7Pp378/M2bMoK6uDoDTp08zfvx4srKyGDBgAF999VXYtoZhRD0+PXr0YNCgQfZj48aNV6RWIYQQQvy5OK92AbFqxIgRV7uE32zlypVXbF9vv/02fr+fDRs2YBgG06ZN45133mHy5MnMmzePbt268fzzz/Pf//3fTJgwga1bt9KsWTMOHz7MvHnzKCsrIy0tzd7fkSNHSE5OZsOGDVesRiGEEEL8OV00EK9YsYLCwkISExPp1q0b27dvZ8eOHeTk5HDmzBmOHz/Oww8/zOTJk1myZAn79+/H7/fTsWNHZs2aRVJSElVVVcyfP5+ffvoJr9dL//79ef7556msrGTs2LGkp6dTVlaG2+1mypQpZGVlhdVQWVnJmDFj6NGjBwcPHsTn8/Hyyy+zdu1ajhw5QqdOncjLy0PTNJYvX862bduor6/nwoULTJ8+nczMTGpqanjllVeoqKggNTUVh8PB/fffD9BofZEOHjzI4sWL8Xg8/Pzzzzz00EMsWrSIvLw8ampqmDNnDgC7d+9m2bJlrF+/vtF6li1bRnV1tb0NgK7rLFq0iLKyMs6fP49hGCxcuNCu88svv+STTz6hpqaGXr16MX36dJzO8FO4fv16Vq9eja7rpKSkMHv2bO66664GbcnPz2fTpk2kpKRw22232fNzcnJIS0vj2WefbTCdkZFB586d+f7773nppZd47bXXyM/Pp7a2ljfffJNbbrmFH374AY/Hw5w5c+jRowenT59mxowZHDt2jJSUFG688UbS0tKYNGlSWD3du3fnpptuQtPMLy06dOjAoUOH8Pl87Ny5k7lz59rzb7/9dvbs2cOjjz5KQUEBTz75JG3btg3b39dff42maYwePZozZ87w2GOP8W//9m84HA5Wr15NeXk5ubm5jX3shRBCCBFDmhwysWfPHoqKiigsLKSoqIjz58+HLa+rq6OkpIRp06axYsUKHA4HRUVFbNy4kdTUVJYsWQLAtGnTGDJkiL2vvXv3snnzZgCOHz9O7969KSwsZOrUqSxevDhqLZWVlWRkZFBSUkKPHj3Izc0lLy+PkpISDhw4wMGDBzlx4gR79+7lo48+YtOmTUyZMoWlS5cCsHTpUhISEigtLSU/P58ff/zR3ndT9YX64IMPePHFF1m/fj0lJSXs2LGD8vJyhg4dyubNm/F4PAAUFRUxbNiwJuuJpqysjFOnTrF27Vo2b97M4MGDw3phT548yXvvvUdxcTEVFRWsW7cubPsvvviC4uJiCgoKKC4uZvz48Q2CJ8C2bdvYunUrxcXFrFmzhpqamkZripSWlsaWLVvIzMwMm//NN9/wzDPPUFxczFNPPcVbb70FwMKFC2nXrh1btmwhPz+/wXCHgN69e3PHHXcAcOLECd5//3369etHdXU1uq7TqlUre902bdpw8uRJAObMmcMTTzzRYH9+v59evXqxatUqCgoK+K//+i8+/PBDwOydlzAshBBCiIAme4h37dpFv379aNGiBQCjRo3is88+s5cHei4Bdu7cyblz59i7dy8AXq+X1q1bU1tby/79+3G73eTn5wNQW1tLRUUFnTt3xuVykZ6eDkDHjh05c+ZM1FpcLhcZGRkA3HrrrXTp0oWkpCQAUlNTcbvddO3alTfeeINNmzZx9OhRu6cVYN++fcycOROlFK1atbIDXVP1RfZUv/766+zevZvly5dz5MgR6urqqK2tpVOnTrRv354dO3bQs2dP9u3bR25uLomJiY3WE02XLl1ITk5mzZo1HD9+nM8//5zExER7+aBBg2jevDkAAwcOZNeuXYwcOTLsHBw9epThw4fb89xuN2fOnCElJcWet2/fPjIzM+3jN2TIEDssXky3bt2izm/bti0dOnQAzPP48ccfA+ZnKPA6NTWVfv36Nbn/8vJyXnjhBZ5++mn69u1LVVVV1PUcDkeT+xk2bJj9Oi4ujnHjxvHhhx8yduzYJrcTQgghROxpMhA7nU4Mw7CnI0NIIJyB+XX/zJkz7XB7/vx56uvr0XUdwzBYs2YNzZo1A8ybpOLj46mursblctlfkyulGq3F5XKFLXe5XA3W+e6775g4cSJjx46lV69edO/enXnz5tnLo7WlqfoijRo1ivbt29OnTx8ef/xxysrK7H0OHTqU4uJifvnlFzIzM0lMTNKiQ8QAACAASURBVLxoPZF27txJbm4u48aN45FHHuHOO+8MuxEs8vhHDpfQdZ1BgwYxbdo0e/rUqVMkJyeHraeUavS8Ri7zer1h24ae81AJCQlR9xH5GQqc62hKSkqYN28es2fPJjs7G4DWrVsDZrAPtKOqqoo2bdo0uh+A4uJi2rdvT/v27QHz3EceLyHERRiG+dB18xnrta4HXxsGGHr4emHTOvj94PdZ2/rNZ78v+FrXQfeBP2S57jcffh2MkNe639qntY1uWMut99H9wffWfSH7t95TaeBygcNpPpwucDjA4Qq+drrAGbLcnnZZ21rTzrjgNkoznzUHaFr4s8N6LYS4ZjWZENLT05k/fz7jx4/nhhtuoLCwsNF1e/fuTUFBAT179sTpdDJ79myaN2/OwoULue+++3j33XeZOHEiZ8+eZcSIEfz7v/87Xbt2vaKN2b9/P506dWLcuHH4/X7mzZuH3+8HoE+fPhQWFtKjRw/OnTvH9u3bGTRoEElJSY3WN3DgQHvfbreb8vJy/uM//oPk5GS++OILjh07hq7rAGRmZvL666/z008/sWDBgovWE82nn35K3759GTlyJPX19axcuTJs/ZKSEoYNG4ZhGBQVFTF06NCw7Xv16sXs2bMZM2YMqamprF69mg8++IDS0tKw9fr06cOiRYt49tlnSUpKCrvxrGXLlpSXlwPmhcGBAwfo2LHj5ZwOwPwMFRYW8r//9/+murqabdu28eSTTzZYr7S0lIULF7Jq1Sruvfdee77T6eThhx9m7dq1TJgwgYqKCg4fPsyDDz7Y5Pv+/+ydfZAVxb33vz0zZ9/fgUURNS+QABd5AmIAgYtibQRk4QIXwkuMKEJZKFImIAsCK69CpFBebqkQYzRSiFAEpVCkkEKNoK5JWN3c4KOSi8BVyBOWhd2ze15m+vmjp+f0zJlzdoGF3eX8PlWzM9Nv0z09e863f/2bPl9++SX27duHDRs2IBKJYMuWLY7ITknOnLIPlEEnS3Dc1PhEZTUlf2PnfmVLQSaRYs2J8+6TxSlppHgC3MLPObfc6S3uk9ZTjqwbV9JCFWa2qFMFIrc8oo/H0nFvHssj/OSxRxiqZUdtccg9wtIyRZvUtM61uF1vDpcglqLXsveucHWzw5I+H7JPExlEmJ2GiTzMDnP29sYBaCxWjhoPCMGqsVidXX2k1hexeOfaCpzHB2uauI4UwUyKYGYf28LYCIi9FORSUBu28A6k2UJdEeSOaDdEWCAAZOYC+YVAXhGQmQ1kZAFp6Qn+TwmCaApJBfGAAQMwYcIE/PznP0dGRga6du3qWFG9zJw5E6tXr8aYMWNgmia6d++OsrIyAMCaNWuwbNkylJaWIhwOY+TIkRg1ahROnjzZrI0ZOXIk9u3bhxEjRiAQCGDAgAGoqalBbW0tZs2ahfLycgwfPhxFRUX40Y9+5ORLVD+V/Px8zJgxA2PGjEFBQQEKCwvRp08fHD9+HAMGDEBaWhpGjBiBQ4cOoVevXo3Wx4+JEydizpw5KC0tha7r6Nu3L/bt2+eI7s6dO2PSpEkIBoMoKSnBmDFjXPkHDx6M6dOn44EHHgBjDDk5Odi4cWOc5X3IkCH44osvMG7cOOTl5aFbt26orq4GANx7772YM2cO7r77bnTu3Bk//elPL6tP5s+fj4ULF6K0tBQFBQXo1KmTy5osWbt2LTjnWLhwoRPWp08flJeXo7y8HAsXLsTIkSPBGMNvfvMb5ObmJr3uI488gqVLl6K0tBTRaBTDhg1zBhCp+FLd2ZfWAJyDcS50gv3Fz2whwKSo4QCzRYJIx4XeUNPb4QC300Ici10sHDKNTC+lCnfq4uCULUSIc8ylrmExcWmH+wpkeMI5fI55LJvMc7FCQtFogFo3WVYCMeUr+uRdsYUj08Td1DQAGrgmy7TjGBwBxgEhuBgDl8KQMXBoQoQxBs40p1xomjjXNFFtaLbQEuk4AOiaCAfANbsuTtmaIjABMM2+BZr4nNFE76npmcbA5OsqTlZV3Ir2MSlyuR0ny1L6J9ndU0qOS8Bk3zMGDRyMNdFa62f5VgW1ayCjDEBUa7gr3D5uqI8NbkxlYGJabrHuCHXl3LRi4YAQ0emZQEYmkFsA5LcDcguBnHwgNw/ILQLyCkVcZrZIF0hrWvsJIoVgXJ3P9vD555/jr3/9K375y18CAF566SVUVlbi2WefvWoVJNo2W7ZsQY8ePdC7d2+Ew2FMnjwZs2bNclxriKtD1ZG/Jom1RWuCOL/DRgKV4EQFc98o5hKv3rQJK9k0XKK1KcV5LYPu06ZJ6HhB7AhXIH5/0STOd5l3q1VyaffcG82hhRugh+rFFm6AEQ0jYEYQsEykaQwBBugM0JiPwG4NmCYQCQOREBCqB+rrgIagOG6oF+GmCURNAIqVPi1DiOfMbFs822I5Jx/IKQDyCmLiOT1TbORqRqQISZ/073//+9i8eTNef/11MMZw/fXXO+4ABNEUunTpgmXLlsGyLEQiEQwbNozEcEsgp/ITwH0tmsy1c6yh3uOLxjGtxgU7V2cJ0gBuYZsAlwE37trNg7ck3oT7wRoVa40GuNsvrZZ2UqZY0VmcxTzeMq5a+NW0LqurY3D3uhPY9lhNWLO5pikWY1kWc1vmEzQpfrCiJpLlJBtcKc9lzFjsmZmIxXMAph6AmZMObgTEpliNZfuZaUILBaE31MMI10OPhIR4tkykgSOgMRjgMBiSvgPT7Og6oCtW4URwLqzVkTAQDgnRLLfqM8C3/yNcacyIENAyj6YBaXb52bnCNUMK59wCseUVCkt0dq5w2UjPID9pok2T1EJMEMS1QdWRI7g0Mcg8AkVYk4VE4R7DbbLyGxPSXmusomwcV1OPi0VT9UdTmq34CbO4qWpZSCzMZclWprPlrVJdPpSEzjQ7U5UZ08AZswWVd0CS6Fwcc0X6O94b3hzOjWLuCB8/be745trxrkEQE6JXD4i9D8yywMwIYEbBbFcAZnGXawRkGboBHkgDawUiigNgkRBYNApEw7Ybg3z+GLimg+u6EM66+4Vu6VqkRULQGoLQw/Uwwg0wImEEzDACloU0jcNgQjxrmtY6rc7SzzwSti3NQaA+CISCwuocbrDFs/0ypHx+jUDM8pybb1udi4DsvJh4zrfFc1aObXmOd5sjiJaGBDFBpABVf65QzoSIYgxCnEXFFxwz5UtVptuP2MnGYh6+LtGkTvszW5QpPqKMAdDcPq7SV1ZTruD7SeSxUqsxZtTZYFlglup/6RGlmu0f650C92hO2WLpyqq2PdZemSJ2zJknTr0X8tgWVYlgpmmLSRPMfuEtZsFFTEwaBqDpsOzVD662uOIAEI1Aiwrhy5362sMkBrtvxYtlXDfAjfhVgSTMjIJFo2CWXHVC+rQDjk+1boBJQWoErq411gO3TLBIGIhG7P8dyx4cMvE8a7oj9rnHV5mBA6apuGvUwwiHYJhhBMwoArAQ0BgC4NDBoGkt186kWCYQiYjBQ4N02bBFdLgeCDWINNGIMmAEEEgHMjKAjGxbMNsW56xcICdPhOXkCfeNnHxhoU7LoBcGiasCCWKCSAGOVhwGi0ZhmRFhxbMst4DRdGH10wxwwwDXE3hTca4IUbnElS3cpCCUy0zpRqOiTczEx4Qsc1Y24KKO4OBcHHPLEsLLyQghQLziK1HdISyYkPVXVlVw119M/cMICMtgM1oxhZiKgJlR8GhUDD7s9nLl+pw1UUyqFln7JS63iIYjxJl9b1pCUHLLAosq7baiSruFuwHXdLsfRR0TWqE5F2JU6UP5rDiDEU0sgcYDaaK9WvJ1y68EzsDBFs8wo0I4iwbb7RXPmWW4X3KTVmcWDUNvCEIPBWFEQkI8WxHopgkdHIbGoDMGHRwaODQmXl9syQFDHI7LRti2NtuW53CDvYXsFVDM2HJ+0uc5kA6kpwurcnae7bJhu2lk58ZEdU6BENIZWba1OoMENHHRkCAmiBTgaMVHtuDVm/RlyS0TLBpxvshjogOAfDFM08Q0sr1MVJOn0S0r5t8qLciaFhNClylgHNFpmeDRiBC88s18wLFOC79XWX8jYf2BmDXaZb21BZ1rBQZ7HdrmtGRy+/6JAU0UmmkC3IwNCvzaYwQUVwlvgdwRp1JEx1YaUUS5FNGaLaKvsmsDN6OKiI7Enj9bLHG1zYZwwUgkgmLWd1uIO4Mgjw+1OtvBNHBdA4MWe9Y10b/NfS/EgCFs/8/Zz5l0SVL6lRtpif/PAKc94nmJQIuGoUWENV8z5RaFbpnQuAXdtKDDgsEYdK0VCGvVbUO6bkj3jVBIWJ/DIWVda7ksqT0YMtKENTk9Iyag8wpj4lluOflCSGdmxwR0K3DdIVoWEsQEkQL8TXGZkK4FTK4/yxUrqWu5J9sap/rwyvVeHVcIBmi2OLEs4WrgrLlrv9Aklwq75Gl0IcY5vKthqGZQ9dDjuiCXCpNr1DpuG3IZMmGtg2VCs6/L5fQ9Z2DccltvbVHkWKOTCU/Vmu5aTkvUP9YeeVM9TfK6ajDbeq20Rb7M5hxrYhk2X7cG2Y/q9L6mNTqocfrEjEK+yBf/vCj+5eoSearbi3OqtgfOEnNO39htEu2xX9ZrRJxx26LKolFYlrjnciYk1n+63e/KzILs04sQf441Xlqo1WXY5L2QzXcs14g9j2D2LIpcBs92p9H0pC4w0ursDGbk8m7qzIpMJ58POXi1nw1n0JSobfJKXP4fhoWgtgW7HhWiWjOj0KwodMuCzi3onENnHIamQQMUcY2mL3N3sUgBHY2IlTUabHcNKaTDIWGFNk3x4qC6djiHWH5OumRIsZxnu2tk5Sji2Q7LzBbiOT2TBPQ1CAligkgBqj75KF5cKS9LOXuXDyzi95eCFAWqGFRfXJN1UnEJWk+dXC9+NTOqtdB1rAg87nMu10lOdF+dvSIO5QFzBTRnQ5K3wRWGWJ84eMW4t49YfFqW4LjZUOrute4CSdqoCnR3lnh8Blhxx37/F+qz6innovD0V6L9xZaZKFuiZ1FtW8LPjqZfPq6/fAe3yfLL6yVpi5qhqbfJqVOSDAnvud8NuJT+uZhn5VqRbM3z2dCz723NUg7QyLJrBEFcI2Rmt9y1r/Ks62XRluqalIsULG2GKzgYajVcg33nGigQRCuFEwRxTVNTU8OnTJnCa2pqWroql0Rbrz/n1IbWArWhddDW29DW6885tcEPcoIhiGuc8+fPo6KiAufPn2/pqlwSbb3+ALWhtUBtaB209Ta09foD1AY/SBATBEEQBEEQKQ0JYoIgCIIgCCKlIUFMEARBEARBpDQkiAmCIAiCIIiURn/yySefbOlKEARxZUlPT0e/fv2Qnp7e0lW5JNp6/QFqQ2uB2tA6aOttaOv1B6gNXuiHOQiCIAiCIIiUhlwmCIIgCIIgiJSGBDFBEARBEASR0pAgJgiCIAiCIFIaEsQEQRAEQRBESkOCmCAIgiAIgkhpSBATBEEQBEEQKQ0JYoIgCIIgCCKlIUFMEARBEARBpDQkiAmCIAiCIIiUhgQxQRAEQRAEkdKQICYIgiAIgiBSGhLEBEEQBEEQREpDgpggCIIgCIJIaUgQEwRBEARBECkNCWKCIAiCIAgipSFBTBAEQRAEQaQ0JIgJgiAIgiCIlIYEMUEQBEEQBJHSkCAmCIIgCIIgUhoSxARBEARBEERKQ4KYIAiCIAiCSGlIEBMEQRAEQRApDQligiAIgiAIIqUhQUwQBEEQBEGkNCkriJcuXYoNGza02PW3bt2KTZs2AQCGDh2Kzz//PGn6srIyvPjii75xP/7xj3H27NlLrsvevXtx7733XnS+6dOn46uvvrrk66o0NDRg/vz5KC0txT333IP58+ejoaEBAHD27Fk8+OCDGDFiBEaOHIm//OUvrrycc9/7s2XLFowZMwbDhw/HnDlzEA6Hm6WuBEEQBEFcW6SsIG5pJk2ahBkzZrR0NS6LzZs3o0uXLs1S1nPPPQfTNPHGG2/gzTffRCgUwgsvvAAAWLJkCfr27Yu33noLTz/9NGbPno36+noAwNdff4377rsPb7/9tqu8ffv24dVXX8VLL72EPXv2IBQK4fe//32z1JUgCIIgiGsLo7EEmzZtwo4dO5CdnY2+ffvi3XffxYEDB1BWVoZz587hxIkTuOOOOzB79mysWbMGFRUVME0TPXr0wMKFC5GTk4PTp09j6dKl+PbbbxGJRHDPPffgoYcewsmTJzF16lQMGTIElZWVqKmpwWOPPYYRI0a46nDy5Encd9996N+/P44cOYJoNIrHH38c27Ztw7Fjx9CzZ0+sXbsWmqbh+eefx/79+xEKhVBfX4958+ahpKQEtbW1eOKJJ3D06FEUFxdD13XceuutAJCwfl6OHDmCp59+GuFwGP/85z9x++23Y+XKlVi7di1qa2uxePFiAMD777+PDRs2YPv27Qnrs2HDBlRXVzt5AMCyLKxcuRKVlZWoq6sD5xzLly936vnnP/8Z77zzDmprazFw4EDMmzcPhuHuwu3bt2Pr1q2wLAsFBQVYtGgRfvjDH8a1Zd26ddi9ezcKCgpw8803O+FlZWXo2rUrpk2bFnc+dOhQ9OrVC1988QV+9atf4amnnsK6desQDAbxzDPP4MYbb8SXX36JcDiMxYsXo3///jh79izmz5+Pb775BgUFBejQoQO6du2KWbNmuepz22234YYbboCmiTFa9+7d8dVXXyEajeLgwYMoLy93wr/3ve/hgw8+wM9+9jNs2bIFY8eORadOnVzl7dq1Cw888AAKCgoACFEdiUQACOt8VVUVVqxYEf/AEwRBEASRciS1EH/wwQfYuXMnduzYgZ07d6Kurs4V39DQgD179mDu3LnYtGkTdF3Hzp078eabb6K4uBhr1qwBAMydOxfjxo1zyjp06BDeeustAMCJEycwaNAg7NixA3PmzMHTTz/tW5eTJ09i6NCh2LNnD/r3748VK1Zg7dq12LNnDz799FMcOXIEp06dwqFDh/Dqq69i9+7deOyxx7B+/XoAwPr165GRkYG9e/di3bp1+Mc//uGUnax+Kq+88goeffRRbN++HXv27MGBAwdQVVWF8ePH46233nKm5Hfu3IkJEyYkrY8flZWVOHPmDLZt24a33noLY8aMwebNm5347777Dr///e+xa9cuHD16FK+//ror/yeffIJdu3Zhy5Yt2LVrFx588ME44QkA+/fvx759+7Br1y689tprqK2tTVgnL127dsXbb7+NkpISV/hnn32GBx54ALt27cJ//ud/YuPGjQCA5cuXo0uXLnj77bexbt26OHcHyaBBg/D9738fAHDq1Cm8/PLLGDZsGKqrq2FZFoqKipy0HTt2xHfffQcAWLx4Mf7jP/4jrrz/+Z//wb/+9S9MmzYNpaWl2LBhA3JzcwEI6zyJYYIgCIIgJEktxO+99x6GDRuGvLw8AMCUKVPw0UcfOfHScgkABw8exIULF3Do0CEAQCQSQbt27RAMBlFRUYGamhqsW7cOABAMBnH06FH06tULgUAAQ4YMAQD06NED586d861LIBDA0KFDAQA33XQTevfujZycHABAcXExampq0KdPH6xevRq7d+/G8ePHHUsrABw+fBgLFiwAYwxFRUWOoEtWP6+letWqVXj//ffx/PPP49ixY2hoaEAwGETPnj3RrVs3HDhwAAMGDMDhw4exYsUKZGdnJ6yPH71790Z+fj5ee+01nDhxAh9//DGys7Od+NGjRyMrKwsAMGrUKLz33nuYPHmyqw+OHz+OiRMnOmE1NTU4d+6cYymV96KkpMS5f+PGjcMf/vCHhPVS6du3r294p06d0L17dwCiH//4xz8CEM+QPC4uLsawYcOSll9VVYVHHnkEv/jFL3DnnXfi9OnTvul0XU9aTjQaxYcffojnnnsOaWlpKCsrwzPPPIMnnngiaT6CIGJwywI4B9S9d+PxYTwaBcwoEI0Cpmnvo0DUBCxThMlz005jesLtdFymj9p7y3Snj0acazjXda6plGlaYs85wFiskfJYDXPOZZwnrSvcLw2LnYP5lO29nl8abzlKHl0HmAboGqDpYJomwnTdCYNmnzMGyHjG7DgttukaGNOUNGqcnccw7HAG6IbYa5pyrLvL1eWxXR/XuQ6m60B6BpCZCZae7vvsEcTVJqkgNgwDnHPn3CtCpDgDxHT/ggULHHFbV1eHUCgEy7LAOcdrr72GzMxMAOIlqfT0dFRXVyMQCDjT5Mz7gaAQCARc8YFAIC7N3/72N8ycORNTp07FwIEDcdttt2HJkiVOvF9bktXPy5QpU9CtWzcMHjwYw4cPR2VlpVPm+PHjsWvXLvzrX/9CSUkJsrOzG62Pl4MHD2LFihW4//77cdddd+EHP/gB3nzzzbg6S7zuEpZlYfTo0Zg7d65zfubMGeTn57vSMcYS9qs3TroZSNQ+V8nIyPAtw/sMyb72Y8+ePViyZAkWLVqE0tJSAEC7du0ACGEv23H69Gl07NgxYTmAEN+q6B81ahT+67/+K2ke4tqBcy6Ejzhxb96wxs6d57eRcjxhXBWSUjRGbWFmKeJOikLLUgSf5Q6Tm6XmtWyxqKSxzJgwdYnHmMjkUUUgKvXgpthgfybCstvAGDhkmwAw0RwwDtFiBucOMYAzIe64poEzDdw+hhY75kxz0kBj4LaY4poGrusiXtPBbbHFdQ1gIh66LtIF0oH0WP5YnBBxTh4pxpgmwjgHM02xRaOAZcbO5WbfR82+Z5plgZlRaBYX6S0LzLKg2ceaxcG4JTaLg5kWGDfFucnFscVFPs6FxlU+F30eYL9A5flCbIACHhu42P0GbtlpLPdzChZ7jp1iLVGy6xmWdVCulaiqzl8Wq6PrOL5tjGlAejqQkQFkZQN5+WCFRUB+IVheHpCXDxQUAvkFQH4BWFY2iWfiipNUEA8ZMgRLly7Fgw8+iNzcXOzYsSNh2kGDBmHLli0YMGAADMPAokWLkJWVheXLl+MnP/kJXnrpJcycORPnz5/HpEmT8PDDD6NPnz7N2piKigr07NkT999/P0zTxJIlS2CaJgBg8ODB2LFjB/r3748LFy7g3XffxejRo5GTk5OwfqNGjXLKrqmpQVVVFX77298iPz8fn3zyCb755htYlvjAKSkpwapVq/Dtt99i2bJljdbHjw8//BB33nknJk+ejFAohM2bN7vS79mzBxMmTADnHDt37sT48eNd+QcOHIhFixbhvvvuQ3FxMbZu3YpXXnkFe/fudaUbPHgwVq5ciWnTpiEnJwdvvPGGE1dYWIiqqioAYmDw6aefokePHpfSHQDEM7Rjxw78+te/RnV1Nfbv34+xY8fGpdu7dy+WL1+OF198EbfccosTbhgG7rjjDmzbtg0zZszA0aNH8fXXX6Nfv35Jr3v33Xfj7bffxoQJE5Ceno79+/e7yk01qpctTBjnHoZyoWoAgHEwrsQy5ctSDk7V7zwGMPnF6fo+VL8kVfmkXtYTpmRxLusVpxa3S5MJrFhR8stcqQhXiuWOOIgl4dLax7kTxW0LHWfMuS9cjukcCx4T8VDDIQSYLFOWJ0WZbW3jUASdbcnjGgOYFIMxIQkp+qSg1OXeEOLQFpTwS+MpBxqLD1etnwltE/FxUhSru+T4WEv9UlkJRB3n9nOmijX72N4zz3lMTCrXY5roW2md1ew905y+ECLcx3rbVDz/A04p9qBFFd/i2LIFugVmyXPLFuBSVJvQo1EEohEEuIUA02CAwwCgX2o9m9KUBANB539einDnf9PbP3IDuBkFGhqAYBCorwOqz4L/7ykgEhZx0aidnYOBucVzfgFYQaG/eC4oBMvMIvFMXBJJBfGAAQMwYcIE/PznP0dGRga6du3qWFG9zJw5E6tXr8aYMWNgmia6d++OsrIyAMCaNWuwbNkylJaWIhwOY+TIkRg1ahROnjzZrI0ZOXIk9u3bhxEjRiAQCGDAgAGoqalBbW0tZs2ahfLycgwfPhxFRUX40Y9+5ORLVD+V/Px8zJgxA2PGjEFBQQEKCwvRp08fHD9+HAMGDEBaWhpGjBiBQ4cOoVevXo3Wx4+JEydizpw5KC0tha7r6Nu3L/bt2+eI7s6dO2PSpEkIBoMoKSnBmDFjXPkHDx6M6dOn44EHHgBjDDk5Odi4cWOc5X3IkCH44osvMG7cOOTl5aFbt26orq4GANx7772YM2cO7r77bnTu3Bk//elPL6tP5s+fj4ULF6K0tBQFBQXo1KmTy5osWbt2LTjnWLgwJtz69OmD8vJylJeXY+HChRg5ciQYY/jNb37j+AMnYvLkyaipqcHYsWNhmib+7d/+zXkeU/Gluv8dPtodEKdJk1irkia52HyJ08d9lScQ1GqGJlz9CuC1hCVJlgxpKbQ8gsERcWKAwQEfAegUErMYqqKL8/j7J0cEZhQwlXROVe1RAVPzirYyZZwUK0y5DxZsMQllQMGUNHJkowhne0BhOSKbgTG52fHObbZr6PosE+Ux76yT5/POO1jhugEEAjFdjHi8YcJibgK2JVha/pllgXNL3B9uh9nHMaHo/HFuhevu2W3nYMKarRtAmjIQc7aYUOeG4W6nfQ0WjUKvD0JrqIfR0AA9FIIeCcMwTaSBI6AxGNyCAQbtIsUzcw2aLo/G/zU4EI2ChcPgoRDQUB8Tz2f/BX7qJBAOg1uJxHMmkJUlxHNhIZBXCJafD+TmCfFsC2gSz4QK49z16eri888/x1//+lf88pe/BAC89NJLqKysxLPPPnvVKki0bbZs2YIePXqgd+/eCIfDmDx5MmbNmuW41hBXh6ojRzwh3Pew6SSw6DYW6BE4jaZLfgEFr9myiY3yzuwm+qaOE+eXzyVLi4u8Fc1SZnPQnMbLy62vZYFFwmDRKJhp2tZNW6bqGmAEAMMAT0uPF9ytAM65qH9YWFRFG+y5EjmbYOjggTQhnl2Zbfkd0sPpiwAAIABJREFUDkOrD0Kvr4ceaoARDsGIRIR4ZkCAAToHdIaLFs9XEyme4RLPdUB9UFih6+uBcBhoTDwXFIIVFMTEc16+Y3V2xHNWFlhaWks3mbhCJLUQf//738fmzZvx+uuvgzGG66+/3nEHIIim0KVLFyxbtgyWZSESiWDYsGEkhlsCe5ZBwD2CQp1H9ob5KQ912lydKmeeLD5for7fqzJQsbr6pmuKCJdFNCK4PZdrFG97mwHu/FXbn0DYq8E+XcVUSzH3HEsLL9TpbdWlIBbGwG0Dr2fA5Joit2yrth2pyZeyhIWTabpTXnwbEW85lVZlMNsLhses5/E3LHagnjt9KU2wiR4OJa30Tw4EwI0AuHwvRe0OzsFCDWCRiBCdtvCU+bmmQTPSYAUCQFpas2r+ZDDGgLR08LR0p6pxaeyNmyZYJCwsrtGI7a7BbX9vDdHcPEQKC4V49r6sbLtEaA0N0BrqhXgON8AIh2FEIzAsC2kAAgB0xqEz7ardA6edjAGBgLD6Ky+h+xFnea4Pxrb/90/wkyeaZnkuKBQ+z4VFYIXtgPbtgaL24jwnVwjnVjiQIpKT1EJMEMS1gbAQJ5hGl7i+yZKIWde0fewFIQ4mpvhdojjRx0tsmlycJhCwiSzF3DeyeeCKi4Kl+K+Cg1leISlFYiyNk95zn8S58Ad10sh2a1K+2DvbP4Ex+0tcCXPuuXOkKntlIONn1fMV9rHrcjDFPdzTR4AzXS/Eo1pfpTTLBCIRMPvFPSmgmS0toDFwQ4emB+yy0pK+UH214JYFFg6LVSvky3ac224L9st+dn0hxY7adm4J4SzFs3ShgP2Cn27ErM6NrJLTUvCoqD8iYUD6NMNuov3CI+zBA/cKPtuFRLU666EG6OEwAqaJADcR0DQEOIeOK+vv3BzEWZ5V8VxfLyzR4TAQEYMMDvtlwcwsIDtbiOYOxUBROyGa27UD2hcLIZ2VTaK5FUKCmCBSgKqKCrcGUox2YtpYLlFlxV7kgSV8Q50MiPkzeq24qs+o9Il0fB/tOMY8PpHM9UIXZ+qXQ5KPJflFanGwqBBezFlVQYovC/FWQvGCFFMrrvjLCm2jikq7Hi4fVPVAnHAmrFRxAlK2XcZpmhCSaju9YtJ+ochPTAIM3LbCapruWDVbQlw5U/bRKCwzChb1uB1oAGd6zN/VMITvrtNQuAZNTFovTfFCmassZrsx6IZYRSIQAIyA6/G7mnAAiESgRcKwolEwMxL7P2GIuSs4Vuf4gQMzo7bVVr5Ux2Pxhg4mRWcrGSx44QAQDkOLRoSIjkZjMwLy/1qX9yAt7rOHQYhNLdQArSEmnHXb8qxbJgIAAozBgBDQGmMt1ueNwaNRIBwCDwaBujqgrlZswTohmCMRsZILIERzdhaQmS3EcvsOQFF74evcrj3QrgNYUbuYaG6F/X+tQoKYIFKAv3/6qVvAmFGh5LjlFqeaHrMAMsUKBrgtYdGo+BKUb8mryzo5a5TqgJHWZNHGgdi6tqYlvjTtJaSYZcFy3sg3FaOoLabt9U65boAbgZgFT9Zf/ZSzTGcQwEzTvUyVTK9pgGaAGfb9MALNYs3hAGBGhRVOrp9rWbEX6xwRLZYa43pjYlKxyNrLh0FdgsvVHy0rKMXUvWi36vcqGqH2o7znRuJn0LJigyGlzYwrAwdNh2YEwAPN138XC7d9lRGRVueo/YKitDrbz2xamlj9Q7bVa3WORtx9KwdfzuoYBphui1D7mWktQsp1D0zFZxv2QFEXnzswdHAjTVjTXQWIP8w0hYAONUBXXhjUI8J1w+AWAroGw+LQmbBAt447IODRKBAKgdcHhViutQVzsA4I258H0tKs6UBWJpCVA7RrD9aug3DHKGoHFLUD2ncQlubsHPFSYCvp67YOCWKCSAGOfvgnR/DKRfKTfYRyAJDTp84PG3gFjL2EmK6D60J4JJxGt39A4WpNo3MzChYRVkfLMm0LpnI9W/TGCWmm1N+5EbINUdsVwHSWwor3SVWEpxFwrWZwOXDTFKIoGhVWSbnigXP/IPpD0+1pbbs9TuXdbXGElu0XGxvUKOmVH1GwZFuu4hevtMSyaMzCBsuMRaqDOF0HAoHkAwfbFYKpq0V4fKmFZwoTYZr9jGsMTNPFLID9v8PtPm7OuyHdFXg0DBaRohGxgYKhx8Sza5ZDvWFKsKkMWK3YzI903XFlls8u08B0cT/FNY2rPpDgpvDxhbJOdMwfnTm+6mK5wYCP37P4wyzLsUBrIa+ANsWSdZoU0BwaLn7ljeaGRyNAKCxE84ULQLBWWJyDQXtQFRb+3+DCyJCZLdwzitoJ94zCIrCCIqB9ByGkpXsGieYmQYKYIFKAv1VUeEJ4bGra9WVpWxZdy0Z5/I1VrwLHB1YDB4Nml8HVNUkBtwXwoqbRPVZop0hFyAgvTWd5LacY9RfIGMCZBg3iRSi5hFRsvVkGizFHrAuhGBE/XgFu+0XIsrQmW29d1nTViulZXs17HHPfUMp01skV9U7UFmf9YQ7xwxFSRJsmwD2CUg5qtJglPPGggMeslPYPTIDHrNtOu5L0k3ObmNJHDADEms2aXX+nTeqayvZxsi92aY2MnwnhMfcV+Ytr8kVAaZW2BaELz1S/+2JiTWDH19b51T4euzd2Pmfgglg/csagKT9IAluIQjeSD1bli2Gm6Yhebs+qxLkJqc+svJf2L9oJIdkUYS0GxNItiXEea6tM73pG7V+/s9fNdizWV0hYc2fGxXZfMWMvDbo+d6Qbi8+qG7DfD9DCtoBuaIAeDgk3jqhYecOwLAQ0sRKBDkDnHKwRw8IVaW8kItwz6uuFaJauGfVB4dMcjbhFc1a2sCQXtlN8mouEpTkv3/Ztl79yaIAZRuwXB+Xno/zfUM990rR10Z10lQmCIK4NYm/Qxz6wuEek+L9wJU+b9kGX+Gdn1Mrw5OcyOD1+vWpRn0v/0G1S/fxoit1ATeO6z3CHq1b2y6ApbbG8AY21I1G8XAe4CddU018szdI/yZbFulz7j3eN40biG+Oi2qvW3eeXWq8UrsHlVbvoRfSTYYifgb7cSwKwGlmlokW4kjbLqCWszqErdwkXV6AtPf/P/2m+wjhBENc0NTU1fMqUKbympqalq3JJtPX6c05taC1QG1oHbb0Nbb3+nFMb/KA1PwjiGuf8+fOoqKjA+fPnW7oql0Rbrz9AbWgtUBtaB229DW29/gC1wQ8SxARBEARBEERKQ4KYIAiCIAiCSGlIEBMEQRAEQRApDQligiAIgiAIIqXRn3zyySdbuhIEQVxZ0tPT0a9fP6Snp7d0VS6Jtl5/gNrQWqA2tA7aehvaev0BaoMX+mEOgiAIgiAIIqUhlwmCIAiCIAgipSFBTBAEQRAEQaQ0JIgJgiAIgiCIlIYEMUEQBEEQBJHSkCAmCIIgCIIgUhoSxARBEARBEERKQ4KYIAiCIAiCSGlIEBMEQRAEQRApDQligiAIgiAIIqUhQUwQBEEQBEGkNCSICYIgCIIgiJSGBDFBEARBEASR0pAgJgiCIAiCIFIaEsQEQRAEQRBESkOCmCAIgiAIgkhpSBATBEEQBEEQKQ0JYoIgCIIgCCKlIUFMEARBEARBpDQkiAmCIAiCIIiUhgQxQRAEQRAEkdKQICYIgiAIgiBSGhLEBEEQBEEQREpDgpggCIIgCIJIaUgQEwRBEARBECkNCWKCIAiCIAgipUlZQbx06VJs2LChxa6/detWbNq0CQAwdOhQfP7550nTl5WV4cUXX/SN+/GPf4yzZ89ecl327t2Le++996LzTZ8+HV999dUlX1eloaEB8+fPR2lpKe655x7Mnz8fDQ0NAICzZ8/iwQcfxIgRIzBy5Ej85S9/ceXlnMfdn0cffRSjR492tltvvRUPPfRQs9SVIAiCIIhrC6OlK5CqTJo0qaWrcNls3ry52cp67rnnYJom3njjDXDOMXfuXLzwwguYPXs2lixZgr59++Khhx7C3//+d8yYMQP79u1DZmYmvv76ayxZsgSVlZXo2rWrU9769eud488++wyzZ89GeXl5s9WXIAiCIIhrh0YF8aZNm7Bjxw5kZ2ejb9++ePfdd3HgwAGUlZXh3LlzOHHiBO644w7Mnj0ba9asQUVFBUzTRI8ePbBw4ULk5OTg9OnTWLp0Kb799ltEIhHcc889eOihh3Dy5ElMnToVQ4YMQWVlJWpqavDYY49hxIgRrjqcPHkS9913H/r3748jR44gGo3i8ccfx7Zt23Ds2DH07NkTa9euhaZpeP7557F//36EQiHU19dj3rx5KCkpQW1tLZ544gkcPXoUxcXF0HUdt956KwAkrJ+XI0eO4Omnn0Y4HMY///lP3H777Vi5ciXWrl2L2tpaLF68GADw/vvvY8OGDdi+fXvC+mzYsAHV1dVOHgCwLAsrV65EZWUl6urqwDnH8uXLnXr++c9/xjvvvIPa2loMHDgQ8+bNg2G4u3D79u3YunUrLMtCQUEBFi1ahB/+8IdxbVm3bh12796NgoIC3HzzzU54WVkZunbtimnTpsWdDx06FL169cIXX3yBX/3qV3jqqaewbt06BINBPPPMM7jxxhvx5ZdfIhwOY/Hixejfvz/Onj2L+fPn45tvvkFBQQE6dOiArl27YtasWa763HbbbbjhhhugaWLSonv37vjqq68QjUZx8OBBR8x2794d3/ve9/DBBx/gZz/7GbZs2YKxY8eiU6dOvs9vOBxGWVkZFixYgOuvv95pOwDMnj3bNw9BEK0fblmAZYGbJmCaceewLHDLAo9ExBaNAtGoiE9YKG80jHvT+OW5nPimpJdts9vsbJyDm6aoo3o/1HM7jRPGeez+ybJlmCcNOHfSccty3WfnWp56cZ96yHvINA3QNIAxMMbEsabFH9vnTNfFMWPuvJoG6HqsTCWPk1ctxxPP1LRquF+8DAsEYLRvD6N9e+j5+dBycsAMsjG2ZZL23gcffICdO3dix44dyM3NxRNPPOGKb2howJ49ewAAGzduhK7r2LlzJxhjWLt2LdasWYMnn3wSc+fOxdSpUzF06FCEQiFMnz4dN910E3r16oUTJ05g0KBBWLRoEd555x2sWrUqThADQhQPHToUK1asQHl5OVasWIE333wTgUAAd911F44cOYKOHTvi0KFDePXVV5GRkYE9e/Zg/fr1KCkpwfr165GRkYG9e/eiuroaY8aMcYRmovp56/HKK6/g0UcfRb9+/VBXV4e77roLVVVVGD9+PMaPH4+ysjKkpaVh586dmDBhAk6dOpWwPn5UVlbizJkz2LZtGzRNw6ZNm7B582annt999x1effVVGIaBadOm4fXXX8fkyZOd/J988gl27dqFLVu2IDMzE3/6058wa9YsvPXWW67r7N+/H/v27cOuXbuQkZGBhx9+ONlj4KJr16549tlnAQBPPfWUE/7ZZ5+hvLwc3bt3x+9+9zts3LgR/fv3x/Lly9GlSxe88MILOHPmDMaOHeuy5EoGDRrkHJ86dQovv/wyli1bhurqaliWhaKiIie+Y8eO+O677wDAGVB89NFHvvXdsWMHiouLXfechDBB+MNVYaWKGq/AsvfcNB3BiWjUEZ48HBbp5LkiSC07LUwzFmeaIr88V8tR89tpeSQi6qMIYZcoVs85B1cEFhizG5tEiDYmSj1pmGffaH7G3Ofe9E2I5+r1ZDI1jDHRb4yJMFmmUjazj7kUgva5k97O7+TzO5YCUx7be6hlSlGp5rPrC85FH8k22ufOBog+9cR5z337L8kAxzuwYd5yZft9ynPus10vLSMDWlYWtMJCIZKLi6EXFcFo1w66FM15edCys4WoJ1olSQXxe++9h2HDhiEvLw8AMGXKFJfwkEINAA4ePIgLFy7g0KFDAIBIJIJ27dohGAyioqICNTU1jmUuGAzi6NGj6NWrFwKBAIYMGQIA6NGjB86dO+dbl0AggKFDhwIAbrrpJvTu3Rs5OTkAgOLiYtTU1KBPnz5YvXo1du/ejePHjzuWVgA4fPgwFixYAMYYioqKHIGUrH5eQbxq1Sq8//77eP7553Hs2DE0NDQgGAyiZ8+e6NatGw4cOIABAwbg8OHDWLFiBbKzsxPWx4/evXsjPz8fr732Gk6cOIGPP/4Y2dnZTvzo0aORlZUFABg1ahTee+89lyA+ePAgjh8/jokTJzphNTU1OHfuHAoKCpyww4cPo6SkxLl/48aNwx/+8IeE9VLp27evb3inTp3QvXt3AKIf//jHPwIQz5A8Li4uxrBhw5KWX1VVhUceeQS/+MUvcOedd+L06dO+6fQmfqi8/PLLWLp0aZPSEtc+3LLsA+7+IvbZ/MK5+sUry7L33vTSIuba2yJSikFYlhB8tiBUhSY8e28aKfikQHWsnzK9Ui5X4rkUo2oeeV3Feue0CYCl3kMpFDRNiAdNA7fFZqI9dF0c67qzcXvP5LGmiX1WlpPH2TQNMAz/cO9eOeZeEdwS+IgypgirRGmZTz6XMJODFCBmxfULV+KYavFVNtbIuQxj6rVluFpXmZexWBmMgVmWI4jlsc4YDM6hc+7MDLYluGWBh8OwGhpg1dXBOn8eVm0tQv/3/6Lhs8/E/1806twDLTMTWnY29MJCGB06wOjQISaaO3SA3r499JwcaFlZwjJNXHWSCmLDMFwfjl4RIsUZIKb7FyxY4Ijburo6hEIhWPYH7GuvvYbMzEwA4iWp9PR0VFdXIxAIOP8MLMmHViAQcMUHAoG4NH/7298wc+ZMTJ06FQMHDsRtt92GJUuWOPF+bUlWPy9TpkxBt27dMHjwYAwfPhyVlZVOmePHj8euXbvwr3/9CyUlJcjOzm60Pl4OHjyIFStW4P7778ddd92FH/zgB3jzzTfj6izxuktYloXRo0dj7ty5zvmZM2eQn5/vSsek5cCnXG9cJBJx5VX7XCUjI8O3DO8zlOyDb8+ePViyZAkWLVqE0tJSAEC7du0ACGEv23H69Gl07NgxYTmS//7v/0Y0GsVPf/rTRtNe65xZtSp2ksBC4o1PFO7K1xSrmtd609R6qGWqU63qdLFqyWQsloYxIfrs8nmSOqgWobgUaphMa3+xu4Sj+tmlWMKkYHQEma7HWSulGARjMSEnj6VYVOJcws8wxOYjBl2iUd28YlGWr9bT2x6f+5IwPtHnuF+4n1XQG+6Xtimo15N5pJhvyqbUi6tlJKqbfO687VMtjXYdHMttonulPmtNbaOK7FN5fWkJ9uZpzPrrsfL6prlMGOdAJALU1wP19dAaGsBCIWjhMHTTRIDzVimemaaBZWRAy8gACgqAG26IS8NNU4jm+npYtbWwLlyAVVuL+tOngXBYzHRIVxJNE2I4Oxt6URECHTtCb9fOZWU22rUTVubMzKR6ibg0kgriIUOGYOnSpXjwwQeRm5uLHTt2JEw7aNAgbNmyBQMGDIBhGFi0aBGysrKwfPly/OQnP8FLL72EmTNn4vz585g0aRIefvhh9OnTp1kbU1FRgZ49e+L++++HaZpYsmQJTNtfbPDgwdixYwf69++PCxcu4N1338Xo0aORk5OTsH6jRo1yyq6pqUFVVRV++9vfIj8/H5988gm++eYbWPZIvKSkBKtWrcK3336LZcuWNVofPz788EPceeedmDx5MkKhEDZv3uxKv2fPHkyYMAGcc+zcuRPjx4935R84cCAWLVqE++67D8XFxdi6dSteeeUV7N2715Vu8ODBWLlyJaZNm4acnBy88cYbTlxhYSGqqqoAiIHBp59+ih49elxKdwAQz9COHTvw61//GtXV1di/fz/Gjh0bl27v3r1Yvnw5XnzxRdxyyy1OuGEYuOOOO7Bt2zbMmDEDR48exddff41+/fo1eu1PPvkE/fv3pw8OAKfvvDNxZFMFRkvn9ZlyTZjucuIvNe3F5mksXSIx6BVYF3Mdv+NklshkaRKlS1aeXz3UND5iNJlQjcvrvV4yES2Flfe5SuJLiuYWY6rF1rtvrO32OdN1MSgKBIC0tKsmGDkQ8ztuYl1d53L2QhaoaUB2Nsy8PCAtTbQJQL1yTVU8s2AQLBRyxLNhmkI0S/EMQGvhz36m62CZmdAyMwHF7U+FR6PgoRCsYBBmbS14XR3M6mpETp4ULkLhsBggcQ6m69Cys6Hl5Ag3jOJiGEVFQji3by+szoWFIo1ipCKaRlJBPGDAAEyYMAE///nPkZGRga5duzpWVC8zZ87E6tWrMWbMGJimie7du6OsrAwAsGbNGixbtgylpaUIh8MYOXIkRo0ahZMnTzZrY0aOHIl9+/ZhxIgRCAQCGDBgAGpqalBbW4tZs2ahvLwcw4cPR1FREX70ox85+RLVTyU/Px8zZszAmDFjUFBQgMLCQvTp0wfHjx/HgAEDkJaWhhEjRuDQoUPo1atXo/XxY+LEiZgzZw5KS0uh6zr69u2Lffv2OaK7c+fOmDRpEoLBIEpKSjBmzBhX/sGDB2P69Ol44IEHwBhDTk4ONm7cGCcIhwwZgi+++ALjxo1DXl4eunXrhurqagDAvffeizlz5uDuu+9G586dL9u6On/+fCxcuBClpaUoKChAp06dXNZkydq1a8E5x8KFC52wPn36oLy8HOXl5Vi4cCFGjhwJxhh+85vfIDc3t9FrHz9+HDf4jNpT8qW6tLTmLe9yhHBLXftS8nkFX1PSJhOEXoEgBYUalyiPt+yLsaJKi6OarjFh7bWqevP6lec9V4Wyn0VU9S/1hifCp1xfsepNI8N0XQjHS/DltKRgVV0RfISsyzXHsuIL8liME7bPI8ydLRBwBKO3X82GBiEabfcatR+Zpom8UjxfhmBkwBWd2rdMEwiHRTvkC5HOxRl4Tg6Qn++I57C3flI8B4Ng9fXx4tmyhHgGWlQ8M8MAMwxo2dkwOnTwTcMjEVi2aLYuXIBVV4fId98h9PXXjo+9fM5YICAEcW6u45phFBXFrMzFxdDy8qDn5ID5zLSnMownma/8/PPP8de//hW//OUvAQAvvfQSKisrnZeqCKIxtmzZgh49eqB3794Ih8OYPHkyZs2a5bjWEFeHzysrxUFjorApAsmPxiyJTSnDL53ftPeV4GKE75WkJQcaqUYkklw42kKLpafHvbzWGrCiUTHtLgWjKrylK4xhiHb4CVfLirVf+rpKq7McONjCmQUCrfIeSOLEs1whA4gNgqQV3WclCMa5yF9fL8RzQwNYOCzEczTqiGZpgW5py7MXzrl48dT2ZzZra2HZ1mYeDMZeSrUHxlp6uhDNhYUIXHcdjOuug1FcjMD118O47jroBQXCNaOVtfNKk1QQ19bWYsGCBTh27BgYY7j++uuxbNmyJvlvEgQAfPzxx1i9ejUsy0IkEsGwYcPillwjrjyfHznib0n0Hvudey1tycRjY76nF8vVtAa3FJdjuW5qOu/LhH7HTY2XLwlK8QSI1QWkT7P0W76YZ0xey28A5NNW7q2fmifR4MyvXdLPWoolP6uxFI7S31OKZ6Ys9yUtrq1w2S0OYWF0fFal+AVi1mfpi56W5v9/a68YIsWmKr6ZJ39rvAcqzSqe6+qg2X7PejSKNM4RQMzi3JrkZNxLgLW1sM6fF24aDQ3gkQiYZYEzBj07G1peHozrrhMiubhYiObrrhOuGrm51+QSc0kFMUEQ1wYuQXyp//KNvRTlLTuRWEkkuBO97OMt82p/ZMlr+qxQ0ai/pE96l9+lz0tLTL2utx5+bglNuR8X49eczH1BFYDJvhDtJdIg1wSW988ui3mEaGuwQDYqHGWdpcuB3z2V7ZbCUelnpua9THeFK4UlxZ5cSs/7zosc8Mh2+OF3D2ycfk9Lu6r+zpdKUvHsGQx5/zeZdNeoq4MWDEILhWBEowgoollvRc+AI5iDQZg1NbBqaoRoDgaFH7Pddi09HVpuLvT27RHo1Em8/NexoyOc9fz8Nuu/TIKYIFKAzz/6yP0SkQrnsS8vxQLE1WlkiRQ0jVmCE72p7j33+kiqvpKJ8Apk+QWs/lCAV5h6RaefkGxMYCaqk7e+XoGvvqwlLWmJhIBsj+wHVUzaZTlWSUWYtISwsCzLEQtyKTc7wt2vqsUtUT3V6XvZj6qQUi2Zss0tuJ6r03a53nI0KivaJCsjpPBM5q6Qliba2Ur9PJ17oKwxDaDp9wCIHzgp1nfXsy7vRStdw9eyLCAUEm1RXoJz/Z96V66SormuLmZptkVzGueOm0ZrEc08EoFVXw/zwgVY586JfW2tEMuRiOg3XYeWkyOWlrv+euGOUVzsuGXohYXiB0xa6UCIBDFBpAD//Ze/CJ9D+QMKXuuN+iWmfpEl+jCWIkCdQlWtYaoPol3WxYg218tL8lhdr1cVvFIsqeIrmegEXC/qeNdqdYlmWVYzWjEdS6TsCykGALd4b6qYlO1Rp/QT9Ycqoq+yuEjYbm8/ep/BRKh9qFpjbVwuHLLNLSAuLLvN0srmes6a4q6gWlzVvlUHp+rzr/wPtBYrtOPv7ON2clGfO431ufr/Hwi0KvcNRzSHQrGBOxDrN9tq7kKK5tpasGAwJpojESGaGWsVopmbJnhDA8xgEFZNjbAwX7ggXDHCYaetjiuGbVHWO3ZEQLpjdOgAPS+vRV/0I0FMEClA1Xvvxa9H28gXpsW54zvJVeuxxCtgGptGV6dRr/A0ukt8KT+CESek1TV75RdponuSyHqbTHgaRrN9KTsDGtkfXhEPuH/EQm6JkH6hXuuc2haPqLzarg2W4r+bcDDkFVPJrO9+gwY//2rFus+8AxTlmWeG0Wz3I85dwc/iKoWTXGe4MZQfc3H1sdrmZMLavq9XcwbC9bnjHXBf7GDR6/vcyl13LMsCGhpiswecx0SzsrSeC87BwmF/SzPEUmIGWm4VDc65s6ycdf688Fu2V8pQXTFYWhr03Fzo7doJ94uOHcUmfZfz86El+B2E5oIEMUGkAJ9XVPhHqEtIeS2LXgFqRDZaAAAgAElEQVTpdVWQX06cO6LBedHKssSvm6lfPn6W3Mam0f2sQcnq47V4S3GnhgG+a806e1tAWJYFJoWEt81e4ZnM2ipFnbcNifyN1TbJeivE/QSu+mtsapvki23yZ4x9/Fqd/E21ysr2JFp6rLF2ef2g1TbJuvi1yTuY07SEX/CumRBlOao41xm1370/ZNJUlPsbJzi998KvzT4/mOKI0MYGq6qbk/qLhfJ/Tr3XfteS26UIa7/PB792ef5ProSwVge/rhU3vAPFi3XdUftTtk31G1afFdlmO82VlJ6WaQors3RXkv2tDsQ9otl5EVARzXooBM00xS8QKoNg+YuEctOUF2RlGOw0Es0Og53/Yu4Dj0bFi34XLsA8d07sL1wAt11QOOdgmiZcMQoKYNhiOVBcDP2665Bz++2XeUdjtJ75BIIgrhzJ1iFu7AuxCV+Y3q98DjT9SyHZmNz7xX6JXMqoP+kVL9aO4F2//TLac0UtGE1tVzNbmy6lTQl/4igQiL/fF8vF9O8l3ovGrpD4J5xsmnttcT/U+9DEdiZrV6NtulQuZ5rd29dJrJBNeSquuIUxEACSvbTm8+xyQLSroAAc4qfYo1eoeleEJP+PtySMuaTrEARxLVNTU8OnTJnCa2pqWroql0Rbrz/n1IbWArWhddDW29DW6885tcGP1vmqH0EQzcb58+dRUVGB8+fPt3RVLom2Xn+A2tBaoDa0Dtp6G9p6/QFqgx8kiAmCIAiCIIiUhgQxQRAEQRAEkdKQICYIgiAIgiBSGhLEBEEQBEEQREqjP/nkk0+2dCUIgriypKeno1+/fkj3/nxoG6Gt1x+gNrQWqA2tg7behrZef4Da4IV+mIMgCIIgCIJIachlgiAIgiAIgkhpSBATBEEQBEEQKQ0JYoIgCIIgCCKlIUFMEARBEARBpDQkiAmCIAiCIIiUhgQxQRAEQRAEkdKQICYIgiAIgiBSGhLEBEEQBEEQREpDgpggCIIgCIJIaUgQEwRBEARBECkNCWKCIAiCIAgipSFBTBAEQRAEQaQ0JIgJgiAIgiCIlIYEMUEQBEEQBJHSkCAmCIIgCIIgUhoSxARBEARBEERKQ4KYIAiCIAiCSGlIEBMEQRAEQRApDQligiAIgiAIIqUhQUwQBEEQBEGkNCSICYIgCIIgiJSGBDFBEARBEASR0pAgJgiCIAiCIFIaEsQEQRAEQRBESmO0dAUIgiAIgiC8cMsCN03XHvJYhlsWrGgUPByGFY2K40gEPBqF5dlzJd6yz7lpOuFqmBWJgEciMO29U24k4lyf6brYDCN2HAiA6To0wwAzDGfvxGta7NjOq+k6oOvQZFmaFsvj2WvecM+1oGkiv6Y1fqzrYIy1dDe3GhjnnLd0JVqCpUuXorCwELNmzWqR62/duhUXLlzAjBkzMHToUKxbtw633HJLwvRlZWXo2rUrpk2bFhf34x//GIcPH0ZRUdEl1WXv3r3YsmUL/vCHP1xUvunTp2PevHno0qXLJV1XpaGhAUuWLEFVVRUsy0KvXr1QXl6OjIwMnD17Fo8//jj+93//F5qmYenSpejTp4+Tl3OO+fPnu+6PaZpYunQpKioqAABDhgzB448/Tv/8BEFcU0hRCM7jjtFInBSAlmkCphm/l4LRszl5lWMo4hSWBUumNU1YlgWowtYWmFYkEhOtipDlkYiTn8u8nLtFsSqSOQfUz3bGAM7jP+8ZAwegMSbSaGKSnGlaLD9jYIzFBKMUkFLMKoLSqYt6f9X6qf3Auain03EcnHMwew97c47tNvj2uZ3fD9kWpmmi/R4xLMOYpkHPzETGDTcgq3NnZHXujMzOnZF5/fUI5OcLkZ5ikIW4hZg0aVJLV+Gy2bx5c7OV9dxzz8E0TbzxxhvgnGPu3Ll44YUXMHv2bCxZsgR9+/bFQw89hL///e+YMWMG9u3bh8zMTHz99ddYsmQJKisr0bVrV6e8N954A//4xz+we/duWJaFiRMnYu/evRg+fHiz1ZkgiMtDFTl+oovbosyKRGBJC2AoJERWOBwTVIoFT6b3hqmWQvX69oEjRLhHtKgb94Sr+Z14H+HjEjpAfDo1Tg2TdQOE0JLn6v2S5XuEGJS9Ktqg7FURyWS95DFj8JoPuB0nEjFnY/Y5swWnPFZFppNOEWZM1+PEJtN1aBkZ0FQrpipGbUsqdB2aei6Fni0G/c6Z1na8RP1EstOfCcIsRZBbnoGDcyyFu2nCDIVQ+8UXOHfkCHg4DM459LQ0BAoLkXH99ci68UZk3XCDEMo33IC0wkLoaWktfWuuGI0K4k2bNmHHjh3Izs5G37598e677+LAgQMoKyvDuXPncOLECdxxxx2YPXs21qxZg4qKCpimiR49emDhwoXIycnB6dOnsXTpUnz77beIRCK455578NBDD+HkyZOYOnUqhgwZgsrKStTU1OCxxx7DiBEjXHU4efIk7rvvPvTv3x9HjhxBNBrF448/jm3btuHYsWPo2bMn1q5dC03T8Pzzz2P//v0IhUKor6/HvHnzUFJSgtraWjzxxBM4evQoiouLoes6br31VgBIWD8vR44cwdNPP41wOIx//vOfuP3227Fy5UqsXbsWtbW1WLx4MQDg/fffx4YNG7B9+/aE9dmwYQOqq6udPABgWRZWrlyJyspK1NXVgXOO5cuXO/X885//jHfeeQe1tbUYOHAg5s2bB8Nwd+H27duxdetWWJaFgoICLFq0CD/84Q/j2rJu3Trs3r0bBQUFuPnmm51wryVaPR86dCh69eqFL774Ar/61a/w1FNPYd26dQgGg3jmmWdw44034ssvv0Q4HMbixYvRv39/nD17FvPnz8c333yDgoICdOjQAV27do2zzN9222244YYbxAccgO7du+Orr75CNBrFwYMHUV5e7oR/73vfwwcffICf/exn2LJlC8aOHYtOnTq5yjNNE/X19QiHw7AsC5FIBOnp6U7bAWD27NnxDzxBtGFcX3h+1kE13iuspJVOOVYtgU64FJvqNLIUrR7haYVCYso5GoUVDjv5XFPW3ilw26roaotEsXjBFlNQBBPsaWTIqWPDAHQd3BZQUlwlRBVwapgq7uKyxFsh48798nqvIQ+TpQNEmxULp0vs2QKQK+fwCER44tSyAMSldcSttww1ryqOvWX6hUsSDCxcG+yBkxJmegciPpZWb3mcc3FvPYOTuPTeeviFA4BlQQuHoVkWAoxB5xwabOtzM6AOKK7GvCY3TZj19QjX1CBcXY36kydx4ehR8X9rWWC6jkB+PjKKi5F1883I7NRJCObOnZFWVAQjK+sq1PLKklQQf/DBB9i5cyd27NiB3NxcPPHEE674hoYG7NmzBwCwceNG6LqOnTt3gjGGtWvXYs2aNXjyyScxd+5cTJ06FUOHDkUoFML06dNx0003oVevXjhx4gQGDRqERYsW4Z133sGqVaviBDEgRPHQoUOxYsUKlJeXY8WKFXjzzTcRCARw11134ciRI+jYsSMOHTqEV199FRkZGdizZw/Wr1+PkpISrF+/HhkZGdi7dy+qq6sxZswYR2gmqp+3Hq+88goeffRR9OvXD3V1dbjrrrtQVVWF8ePHY/z48SgrK0NaWhp27tyJCRMm4NSpUwnr40dlZSXOnDmDbdu2QdM0bNq0CZs3b3bq+d133+HVV1+FYRiYNm0aXn/9dUyePNnJ/8knn2DXrl3YsmULMjMz8ac//QmzZs3CW2+95brO/v37sW/fPuzatQsZGRl4+OGHkz0GLrp27Ypnn30WAPDUU0854Z999hnKy8vRvXt3/O53v8PGjRvRv39/LF++HF26dMELL7yAM2fOYOzYsS5LrmTQoEHO8alTp/Dyyy9j2bJlqK6uhmVZLneQjh074rvvvgMAZ0Dx0UcfucobO3Ys9u7di3//939HNBrFoEGDMHToUAAkhNs6qqVLtZDEWVQuIsxSp2YV8Wj5hLksLgmmceOsMeoUr1cE2pulWEUdf0c5ba0IVVc+77naRjnN7DOF7oTJL3fbEse9wkfxM+SKhQ6MucWnvWe28ITiS4msLEeYwvZ1hOo7qfhMqmW49nY9pGjzE53JRIM6ucx8whrDz1KaLD5RusslaRuTWZwTiTt7z/3ClWPHMizLsafzZT6mPEtOWbKP1Dz+FfcfRCSKU+OlYJQuEkoY94hJdeCippPluCzffnt5LNPKQRiAKICI/CwJBsHr6sCDQaChAVo4DN2yENA0IZobeVZbCqbrMHJyYOTkIOuGG5xwblkwQyFEzp9H5OxZhM+dQ90338AKhYQftabByMlBevv2MaHcuTOybrwR6R06wMjJaTOuikkF8XvvvYdhw4YhLy8PADBlyhSX8JBCDQAOHjyICxcu4NChQwCASCSCdu3aIRgMoqKiAjU1NY5lLhgM4ujRo+jVqxcCgQCGDBkCAOjRowfOnTvnW5dAIOAImptuugm9e/dGTk4OAKC4uBg1NTXo06cPVq9ejd27d+P48eOOpRUADh8+jAULFoAxhqKiIkeUJqufVxCvWrUK77//Pp5//nkcO3YMDQ0NCAaD6NmzJ7p164YDBw5gwIABOHz4MFasWIHs7OyE9fGjd+/eyM/Px2uvvYYTJ07g448/RnZ2thM/evRoZNmjsFGjRuG9995zCeKDBw/i+PHjmDhxohNWU1ODc+fOoaCgwAk7fPgwSkpKnPs3bty4JvsP9+3b1ze8U6dO6N69OwDRj3/84x8BiGdIHhcXF2PYsGFJy6+qqsIjjzyCX/ziF7jzzjtx+vRp33R6I/5NGzduRFFRET788EOEQiHMnDkTv/vd7/DAAw8kzXetUrV0aUIrjOtLW4opJZ13mth7HDcVrEztql+ucdf3SZ9sytnyTveqlh6l3tKiqH7JW/YxY0ykk1+aMszHyuVM/yawiDlWOImdjkOxsimWNykkpZXSJTBVX0UZlpbmhDtCUxGGTjpNE8JSsZY68Z50rjDVb1LSmNWyMUunH96+8j47Sj/F5VPKd/rde21bYHj7MGFdvG26BJqa90rKgETW0Lj/Ie//uo/g9e7j0nnDPGX8//bOPEqKItv/38yspTfobhh23EGWx6DyUGAAERiGRZq2QRRhlB18IMP4BAFtbHYREUWZwyguM46ICCKIOMCADzd4CP60FRWOzwVBkXak96W2jN8fmREdmZVV1UD1Zt/POX26KpfIezOzMr9x40YEMwUppP+We0ZVrfvw347LBdXthuL11hvRJKP7fKLlQzyDFAVKcjLUtDQoHo94RgQA+M2Kql5aaojm8nIoFRVQAgG4GYMLxtBfWh06F4qqwpWYCFdiIhJbtLCsC/l8CJaUwJ+fD39+Ps4dPYpQebmojLuSk+Fp0kQI5AQzqpzYsiXcqal1LoUlqiB2uVyVDyyEi5AkKUSu6zoeeOABIW5LS0vh8/mgmzfJK6+8gsTERADAuXPn4PV6kZ+fD7fbLZrJo/0g3G63Zb3b7Q7b5vPPP8eMGTMwYcIE9O7dG9dffz0WL14s1jv5Es0+O+PGjUPHjh3Rt29fDB06FLm5uaLM0aNHY/v27fjll18waNAgJCcnx7THzoEDB7B8+XJMnDgRAwcOxJVXXok33ngjzGaOPV1C13VkZmZi7ty54nteXh5SU1Mt2wlR4FCufV0gELDsmxShWSQhIcGxDPs9pEb5AezatQuLFy/GwoULkZGRAQBo2rQpAEPYcz/Onj2LFrYfpp1//etfyM7OhsfjgcfjQVZWFvbs2dNgBbHvuuvCF56vuHGKbEQQUE7bOImqsEhNxENbm2DtzbKOzbGx7IqwTomxvkrrLmR7Libs4qQqojHWMaRIGoO1OTYavHRug3gG28RNVFucmn1NP5VolSDbscVyHg3kFRZ7pDNGtM8p99IiGp1EZgThqdjtA6A4XSduPt9H8qHK95E91UBeHi0C69T0zit0/LsUeVcgXWfZPqmyaFlnX34BsFAIoUAA4CM4mNeFPx+EePZ4DJFZRwSj6vVCddAKHMYYmM9npAn5/YZoBgyfGjeG2rSp8EcH4Of7BAKGYDZFM3w+aIEAXDBEm4b4pWZcDJrXC83rhdd8T3P0QADB0lIECgrgy89H4Rdf4NxHH0EPBADGoCUkwJ2aisQ2bZB06aVINPOVE9u0gSctDaqDvqsJogrifv36YcmSJZgyZQoaNWqErVu3Rty2T58+2LhxI3r16gWXy4WFCxciKSkJy5Ytw7XXXosXXngBM2bMQFFREe644w7MnDnTMlJAPDhy5Ai6dOmCiRMnIhQKYfHixQiFQgCAvn37YuvWrejZsyeKi4uxf/9+ZGZmIiUlJaJ9I0aMEGUXFhbi2LFjePbZZ5GamooPP/wQ33//vRG1AjBo0CCsXLkSZ86cwdKlS2Pa48QHH3yA/v37Y+zYsfD5fNiwYYNl+127duG2224DYwzbtm3D6NGjLfv37t0bCxcuxPjx49G8eXNs2rQJL774Inbv3m3Zrm/fvlixYgUmT56MlJQU7NixQ6xLT0/HsWPHABgVg6NHj6Jz584XcjkAGPfQ1q1bcd999yE/Px/79u3DyJEjw7bbvXs3li1bhueee84y2obL5cJNN92EzZs3Y9q0aTh+/Di+/vpr9OjRI+pxO3fujH/+85/o2bMnAoEA3n77bVxzzTUX7Ed9x3PJJdV/EFtELyz6HCdEk3c0ERBJlEQSb3UJm8BxEqFcWIXtF+m7XAmJ5LvD9VMAyzW0CEO+nZzjKx/evtxmn8gNllsI7FFku9iVvuuSfYrDekf/IlSYuDAV0X2Y513Ky7V01Ip3ZIvZUlskse50TvgyIRhdLiNFxe2GWoudnuQWG3HNIlxfPnyZWM7PsaZVCl+H81wl8ex2G5HnWhbPiqJASUiAKgWM7DBdN9IPuGjm501VoaSlQW3eXFxTi2j2+USkGeXlUHw+qKEQXADcQFzzmc8X1e2GJy0NnrQ0JF9+uVjOQiEEy8sRKCiA/9w5lH33HYq++ELkKatuN9yNG4sOfYmtWyPR7NjnbdoUWpTzGA+iCuJevXrhtttuw+23346EhAS0b99eRFHtzJgxA4888giysrIQCoXQqVMnzJ8/HwCwevVqLF26FBkZGfD7/Rg+fDhGjBiB06dPx9WZ4cOHY+/evRg2bBjcbjd69eqFwsJClJSUYNasWcjJycHQoUPRpEkTXH311WK/SPbJpKamYtq0acjKykJaWhrS09PRrVs3nDx5Er169YLH48GwYcNw8OBBdO3aNaY9TowZMwZz5sxBRkYGNE1D9+7dsXfvXiG627ZtizvuuANlZWUYNGgQsrKyLPv37dsXU6dOxaRJk6AoClJSUrBu3bqwB0K/fv1w4sQJjBo1Co0bN0bHjh2Rn58PALjzzjsxZ84cDB48GG3btsUNN9xwUddkwYIFyM7ORkZGBtLS0tC6dWtLNJmzZs0aMMaQnZ0tlnXr1g05OTnIyclBdnY2hg8fDkVRsGrVKjRq1CjmcZctW4YhQ4ZA0zT06tULU6dOBdAwO9XFZXTFqpYRT7HpUBaLsT7itnz7C7UvlpCMEOEMi3bKKSW2/04jDMjlMOlz1OM7HRtWMcw/iShnlJenYq63H5/nbloinvy7VL6lIhItmliVF7gt2snFrOq0vxzh5D5qWlzEkpxzbhG0NlEoooKyGFSUShEfwT8AhiiS/TBFuappEQVjyBRWLBi02CeEvimeVY/HyNuOI5aWHFx8ygiPrMZVPOu6sLMuiGc+/BkiaCvA8EOI5kCg8hy4XEaU2esV11IH4IMpmsvLDdFcVgaUlxudAIPBynxm1KxoVjQN7pQUuFNSkNS2baV/uo5QRQUChYXwFxSgIi8PJV9/bQhlM0/Z3agRvM2bW4XyJZcgRRLcF21ftHGIP/vsM3z88ce46667AAAvvPACcnNzRacqgojFxo0b0blzZ1x33XXw+/0YO3YsZs2aJVJriJrhs9zcyCtjCURzveNWVRGX9m2cHsDVLKJ/NfyafathmK4LgSGEIyoFvOJ2G4KjGoRjvOD5qxbBaK4TI2qYgs/pd8fH/OUdNLlgV8yKg8InfPB661y+px1H8Wyu4+JZcbkiimcwZjTp12HxHA0+qguvDInUDLPDquL1WkZYYYCRz1xSAlZcDFZaCtXngxYKwaMoogNgbcIYg+7zIVhcDH9BAfz5+QgUFSFUUSHylPvZBg24GKL+yq+44gps2LABr776KhRFQatWrUQ6AEFUhXbt2mHp0qVi6LMhQ4aQGK4NnCKKPJoXbZtI3+1UIX+xyvm49iZ5O9FsiRLprLKU5M2v1UhMW6p6vi9EIEdKSXBYJtbxfW0jbIiIszQmLBdRjvbZo+UOy532CWsVsF/nSFF6+Tzxih0fbQMQEWPF7NzlJBwZUBmd45NR8Ag5YBVKNdw5TPV4jM6XMWCMGTO5+f0AH5lEjtbzSKuZduG0v15RIc6BuP7mell415ZYVN1uoIq5p3GJPMv763plRUrToJi5xTVZiVBcLmguFyB1xLejBwLiXoZ8D6SnQ2vZUvxuRVpGeTn04mKwkhKgvBya3w+XosAD1MhoGYqiQEtIgJaQAG+zZmG+BCO0tl/w8RrqTHUE0ZD49OOPK79EE7vn+yKzCzN7SoLtWI6Pm0gCWG4al0U1Fza18eiyNYeLpnCgUmTZRkPgwsE+i5XFW2l8WDlnmEn+Rjy38rmx5e1azpBcVqyOWPb9+EeeU8tzViOM9sI7BkEaFk4WkbLwULiQqgNR2IjCEagcgo777mSvGWW0RJ3lKCP31xSfdRFmTnpiF78ALGM5Kx6P8/W3nwOzH4xFMJrnry5GWu2EiWe5Y5wcdbbfD4yFnUdxH/B87zoWedf9fkMw+3yVYtm81nLnQcYYEAxCLy6GXlwMlJVB9fvh1vUaz1++atq0uJVV+08ggiCqHb2szHgIS8tEhAwwHvLmeLfiJSYLPMCa/2n+F7mnEUS1mCQAEMNiyQLXPnqEXL5l9AiHCCCPToaNx6vrRocuOSqIys5SQozx8iIJa3uqiF1ERhKUsp+Sj4qqVgqBKGKIN+VDmqDC0pQNWIWJ+XKtDWFhmf1Nip6Ke8t8ofK0AyVS0z0QLsLsfpud2apTTCmKIqJ7VfKdN0+bo/EwXbdGGb1ew16b6BH+mjmSkdIVRMS1JiONmhYzp5UjJlox/XCMtEY4BwCM8b8jVZz4M6KWBXRVI8/iPPh8lbMFyve/LWUBqMyddYw081SVGrz+qsdjtDo49NFhoRD0igqjshgIGM9UTYPWrBmUhAQoqlqZv6zrRu5yURFYaSmUigpLKkZdGlZOhgQxQTQAXObLTTy0uXgxozdyvpnlJRYlaifnH4rmN96BhwsYc/KFixFtIhrBBTuf2MLWdC6mfeUvT5cratoFrwBY7OeiRI7Y8jzKGEL2fH0S18F+LSR/oKrGC4pHJSOlg9gm8LBcD6DyethF9EW+aEUz7fn4zXNeua2SeBB+yyI6QvnCZ1vOp7j/eOXKjGTHOxe4qr6LqLOUnyqi/2Y5UdMV5FEI5AoSYBlGTR5zWlxnc9KT6kJxu6G53UCMWcrE/c6vu1kBcHzuRPmdhV1zLqDla85TWKLlC1cDQjhXIWVB5Cnz+54/txxEs67rYHK6CmOWPHchtqtZZCqaBi05GZqDf2LUi4oK4/qa10NJTTVGyTCvJx+Lmfl80IuKwEpKoJid/erC6BgkiAmiARAsKKgcQkpVjQgYj7hFE726brzMpQigLETF0FSSYIz0AooYDbK/zLgA5cLWFDXaeYpRIb7solPOSeUiggv3aAIMCIuGyiJMRIXlSGaE0QB4JBJViERazqEkJuXmaPFiNa+HavoR7XrwDitOglK+vnw2OlWeRe4CRYaiKFCqmPcq7DTvG126dxyj0G63uHcc8z4hXT9ZiNtmLxS2mvtYUjz4b0ieMIXfo9F8rmrU2SldgZdjryjwGQOjlceb7XkliZ87WwtKRGEtVQgvRlie7/1uee4EAuF533LklVfcnVoeeO6zXFG0d56UJwipZgEt8tSjoJspO/Y8X1FhcEhV0UMhS4WjpkVzrOHl9EAAenm5YZ/ZioLERGiNGwu7dAAVjBkd/XgqhtnRj09aUt3jL5MgJogGgBphmDoGVIohmyiyvDTNP3vnJgZA4S8avx9MUaxpFrwsOWLHI7lVbUbnAtTJFodUCkvU2F6onL4AWGZrUwDjYSwNV6UwBj0UCh+ajFcs3O7KaYpjCU9ThIlmZbs/dh9sqRxhLzNZAMu+SCkhCq+EhELGuL3yMGHyeeLiJ1ZU1rSNTz8t7hN5iLFYfsnXiFVOsGHJFZevjxStR0KC0XmPVzoi5DCLioPcgiGfNxgCk882qJqCQ46iVwV+r8vTbot0D/uMivKlk23hAlTySUlIiClCecuJPHW35TesWGdllEW96nZX5oJfqLB2upflVCjZL6myWRXBqaiqIbBibllpH5PSVywVRe67nHoRobXFXkmUW46EwJRaHcR3cx2A8KHy5GdfFRBpC1EQM+TJnTx5JcHrNUSm7TzrwaClVYrfI9wq8RuUfZF9Nn0T6+XUMNtzx5LyZt5nLnPGYztM141Ki89n/I7MFjq1aVOobdpA0TTL+Mt6aakxKoYZXY4nJIgJogGgxIjMWMRIbWOL0tW2XVWTRtGpTQ/4seMd96oLd0uE7O+wfPnqprqOFck/gcdjjHRRTceXidcxYvp0gSjmubjocuJQhhPx9Ftxu8/7uaQAMVNbqpNo/vPWk1g+KQDUpCTANuJE3GAEQfyqKSwsZOPGjWOFhYW1bcoFUd/tZ4x8qCuQD3WD+u5DfbefMfLBiboz3gdBENVCUVERjhw5gqKioto25YKo7/YD5ENdgXyoG9R3H+q7/QD54AQJYoIgCIIgCKJBQ4KYIAiCIAiCaNCQICYIgiAIgiAaNCSICYIgCIIgiPHunAEAACAASURBVAaNtmjRokW1bQRBENWL1+tFjx494D3PiSDqCvXdfoB8qCuQD3WD+u5DfbcfIB/sKIyx6hoWkCAIgiAIgiDqPJQyQRAEQRAEQTRoSBATBEEQBEEQDRoSxARBEARBEESDhgQxQRAEQRAE0aAhQUwQBEEQBEE0aEgQEwRBEARBEA0aEsQEQRAEQRBEg4YEMUEQBEEQBNGgIUFMEARBEARBNGhIEBMEQRAEQRANGhLEBEEQBEEQRIOGBDFBEARBEATRoCFBTBAEQRAEQTRoSBATBEEQBEEQDRoSxARBEARBEESDhgQxQRAEQRAE0aAhQUwQBEEQBEE0aEgQEwRBEARBEA0aEsQEQRAEQRBEg8ZV2wYQBEEQBEEQkWGMgYVCYLpu+dPlZeZnPRSCHgwiFAiABYOVn83leigEPRCA5vEgpVUrJDVtCk9KChS1YcdISRATBEEQBEGgUnjqppCU/4I+H4IVFQj6fND9foQCgbBtGBecwSBCwaClLBYKiXVclOq8jEAAIWlfxpdL2zPGAEkEQ9eh2wQyXw5FCXfOaRljcCcnw9u4MRq1bo1GrVsjpUUL43/r1khq0gTu5GQoTvv+ylAYY6y2jSAIgiAIgogGY8xRqOrBIIIVFQhxwWqK1pD5F/T7jc9+v/FnLrNsW14uthMCVBakwWClIaY4lCUik5cpiiEgFUVEXfl3sQ6AqqpQNA1QVeOzqlZ+1jQo5jL+J5bL6zQNqvRd5eul4yvScRVVFctZKAR/WRl8hYWoKCyEr7AQgdJSEU0GAE9KChLS0pDSqhUat2mDZFMsN2rVCgnp6fAkJ1f3Za8xSBATBEEQBFElLE31UhO95bOuQw8EEPT7wczmej0QqIyoBgKGCJXEaaiiQgjWoCRsQz5fpbj1+8PFsHlsgRzJVBQogBCLsihUNA2qywXV/K+4XNBcLmOZ2238N/806XuYULWLzV8BjDGE/H4ESkuFUK4oKkKwvBwhnw9M16EoCtwpKUhMS0OKGVlu1LIlUtq0McRyWhrciYm17cp5QYKYIAiCIM4Txhhgvj4v6LPxoUqf5f0ZYyJfVA8GLaIzZG+Cl5v0zc8h6TMLhUSeaSgYFGkAFgFrL4MfOxQy7AmFxDJLs30kJMEqxKlNYCqqCsXtFmJVMwWrXZxahKsUOVVtorU6YIw1iDQCO4wxhHw++EtLrZHligrofr8hllUVnpQUJDZpgkamQE5u2RKNWrVCo9atkZCaCldCQm27EgYJYoIgCKLWkaN9cu4kC4VE1FBu8g4FApXf+V8gYIg6/lmORJplMLPpm+l6pUjlQtPM0eSvRS7snLYFbCIXMHI3TZhtWydhDMaM/WJtB0lEm7aEiU5Fqdxear5nPEqqKICqGk36PFpqfrYIU02rbJpXFEuTvOpyAYpiRErl5nlzG1VVocjrpOZ5e3O9iK7aYNL5lMUJk/ziy3Xz/OmmOGXSevFdOia4v2ZqgtN/LqrhlJ4gpTgIeCWA3ztSHq/jZ37t5H3M/5DvM+neA2NQzHtE/K88YcZ1BqCYnxUY6Rji1pC2hbkdv/5qHEU903UEfT74S0qEUPYVFRmRfr/fsFXT4G3UCIlNm6JxmzZIadkSKaZQTmnZEgmpqdA8nrjZdD40WEG8ZMkSpKenY9asWbVy/E2bNqG4uBjTpk3DgAEDsHbtWvz2t7+NuP38+fPRvn17TJ48OWxdhw4dcOjQITRp0uSCbNm9ezc2btyIf/zjH+e139SpUzFv3jy0a9fugo4rU1FRgcWLF+PYsWPQdR1du3ZFTk4OEhIScO7cOdx///348ccfoaoqlixZgm7duol9GWNYsGCB5fwUFBRg0aJF+PLLL5GUlISRI0fizjvvvGg7CeLXhIg2RvqzRf30UEg0XVsEqvmn+/1G8zcXptJ/kcfpsF8oELDmbJqfRSRSxhR+iqIIkSSaxbnok5rHVUmwKS6XIcx4PicvkgtFXr6U5ym+82PJy83/qn1buXxpn7DlDmXKEU37Zx6VVDQNMH0S+/D/8nmQ8lahKNC5yJL+FPM+4NsI4SnZzCS/mOyffAxb+gAcbLIIT36duOCUBLTwWT7PF4GonNhEpxCo9si8Q4WlCgexXFvhQ4T7SKyz3zNy3rF0H8cd07dgSQkChYUIFRcDPh9cug5NUaDF+ZhM1xGsqIC/tBQVBQWoKCiAv7jYEMuBAMAYVE2Dp3FjJP3mN2jctq0hllu2RKM2bZDSvDm8qanQ3O642iVDo0zUEnfccUdtm3DRbNiwIW5lrV+/HqFQCDt27ABjDHPnzsXTTz+N2bNnY/HixejevTvuvvtufPnll5g2bRr27t2LxMREfP3111i8eDFyc3PRvn17Ud7DDz+MpKQkvPXWWwiFQpg5cybatm2L/v37x81mouEgv0CZ/CK1vVzDvtu2tec+imGR7D3P5Uip3FPdFkFlknjkQpLvb/nvUI6c9xn2JzXLW3wIhcIFivmS51E5e66mHBEUEUW5yTsxEVqjRpbmb8XlguJ2i2icoihgcsTR5TKEFBeHiiKElSXiJ4stu+CyC1N5mfQ5TJQ5fY5URqR1tm2USOU7fbcR8bhxJixCLotH+2djB75j5XceiQeMeykUAgIBsZ3wQ4pgMtv5s3RW4/eZJNTDotG8g1k9JkzAy88ZW+Q4aksG35ZXfMzz7ElPh9a6tRDijDGEKioQKChAsLgYrLwcLl2HizFoF5iCoqgq3ElJcCclIblZM6t/uo5AebkRWS4ogK+wED8ePSpagsAYNLcbnkaNkNy8uRFNbtUKjVq1whUDBlzMqbUQUxA/88wz2Lp1K5KTk9G9e3fs378fb7/9NubPn4+CggKcOnUKN910E2bPno3Vq1fjyJEjCIVC6Ny5M7Kzs5GSkoKzZ89iyZIlOHPmDAKBAG6++WbcfffdOH36NCZMmIB+/fohNzcXhYWFuPfeezFs2DCLDadPn8b48ePRs2dPfPLJJwgGg7j//vuxefNmfPPNN+jSpQvWrFkDVVXx17/+Ffv27YPP50N5eTnmzZuHQYMGoaSkBA8++CCOHz+O5s2bQ9M0/Od//icARLTPzieffIJHH30Ufr8fP//8M373u99hxYoVWLNmDUpKSvDQQw8BAN5991089dRT2LJlS0R7nnrqKeTn54t9AEDXdaxYsQK5ubkoLS0FYwzLli0Tdn700UfYs2cPSkpK0Lt3b8ybNw8ul/USbtmyBZs2bYKu60hLS8PChQtx1VVXhfmydu1a7Ny5E2lpabjsssvEcnskWv4+YMAAdO3aFSdOnMB///d/4+GHH8batWtRVlaGxx9/HJdccgm++uor+P1+PPTQQ+jZsyfOnTuHBQsW4Pvvv0daWhqaNWuG9u3bh0Xmr7/+erRp00Y083Tq1An/93//h2AwiAMHDiAnJ0csv/zyy/Hee+/hD3/4AzZu3IiRI0eidevWlvI+//xzLFy4EJqmQdM03HTTTdizZw/69++PtWvXAgBmz57teM//Gik+c8aSfxjrs1MTsqV5z75djH3PZ3v+ArVvw3MW9WAw9licpniT8xstAlIes1MuzykfUhKFdrErv6R0B38tTaSxiPDStkQ0nT7bmnp5NBQul7VXOheaSUlwcaEpRRiF+LSLSVlsSk3kFqFpRiztyyJ9j0sU0NwnLOrnJNIgpRxEifaFreFRP3sZEQuwpizEOp59XxGdlfZRbN/t9vIIrxA5Zlnyd8s5dopkSpUacUz7Z4cIpyWaaV9vj4JW8RyI82D/brt+lqgu39wchsy+rWV7uWzbdxZpncMyXp4CQNE0aB4PNI8HajVEMO0jVlQHjDHRaU4PBgFFgatRI3ibNTNSZUxCfr8QynpZGdRAAG5FgcbYBdunqCo8ycnwJCcjpUULyzo9FEKwvBy+4mL4zMhy0enTxtB3wWDNCeL33nsP27Ztw9atW9GoUSM8+OCDlvUVFRXYtWsXAGDdunXQNA3btm2DoihYs2YNVq9ejUWLFmHu3LmYMGECBgwYAJ/Ph6lTp+LSSy9F165dcerUKfTp0wcLFy7Enj17sHLlyjBBDBiieMCAAVi+fDlycnKwfPlyvPHGG3C73Rg4cCA++eQTtGjRAgcPHsRLL72EhIQE7Nq1C08++SQGDRqEJ598EgkJCdi9ezfy8/ORlZUlhGYk++x2vPjii/jTn/6EHj16oLS0FAMHDsSxY8cwevRojB49GvPnz4fH48G2bdtw22234YcffohojxO5ubnIy8vD5s2boaoqnnnmGWzYsEHY+dNPP+Gll16Cy+XC5MmT8eqrr2Ls2LFi/w8//BDbt2/Hxo0bkZiYiPfffx+zZs3CW2+9ZTnOvn37sHfvXmzfvh0JCQmYOXNmtNvAQvv27fHEE08AMKKwnE8//RQ5OTno1KkTnn/+eaxbtw49e/bEsmXL0K5dOzz99NPIy8vDyJEjLZFcTp8+fcTnH374AX//+9+xdOlS5OfnQ9d1SzpIixYt8NNPPwGAqFD87//+r6W8rl27YseOHejWrRv8fj/27NkDt/mgakhCmPMGT7WRX4bngxS1CSvHVqblxRrts6KEvbjFsaQXtGiO1rTKnEdTVKmqCqaqxjY8WihtK0ScYjSdy9uKcjQNitttaeJVZaEpfYbts6WJWoqEWpqQbdvLkauw5uMo3yPaI586+RxeCLH2kysxUuWFCxdHUcEFDB8NwDZ8lYgCyoLK1twvRJmDUBHn5sI8rh84+B0pImtv8uf/HXOUbcfg51BU4OTryffl+1+AzWIVEH69Igl/+7PHAcuaKkTb5RYNsdweiXYow7K9qhrN99J6BiDg80E3xRr/jUBRjBEsPB64vN7K51QdQlEUEcG1E/L5DLEcDAKMQU1IQGJqKjSvV2zDQiH4CwsRLCqCXlICJRCAG4DKmCWf+XxRNQ2elBR4UlLQqFUryzrLUHhxIKogfueddzBkyBA0btwYADBu3DiL8OBCDQAOHDiA4uJiHDx4EAAQCATQtGlTlJWV4ciRIygsLBSRubKyMhw/fhxdu3aF2+1Gv379AACdO3dGQUGBoy1utxsDzJrApZdeiuuuuw4pKSkAgObNm6OwsBDdunXDI488gp07d+LkyZMi0goAhw4dwgMPPABFUdCkSRMhSqPZZxfEK1euxLvvvou//vWv+Oabb1BRUYGysjJ06dIFHTt2xNtvv41evXrh0KFDWL58OZKTkyPa48R1112H1NRUvPLKKzh16hQOHz6MZGmMv8zMTCSZN+uIESPwzjvvWATxgQMHcPLkSYwZM0YsKywsREFBAdLS0sSyQ4cOYdCgQeL8jRo1qsr5w927d3dc3rp1a3Tq1AmAcR1ff/11AMY9xD83b94cQ4YMiVr+sWPHcM899+CPf/wj+vfvj7Nnzzpup8V4oMyfPx+PPPIIsrKy0KxZM/Tu3Rsff/xx1H1+zbT/85/Dm3cjvDjCIjpypEfGFmWK2rQM28vEYf35EPZCi7lDHOVSpLLs4sMuQPhnJ0Fii2TxqKCIAFoOwwAuLh2uiSUflX/mgtH28q7yWbHfO+a+jqKG2+gUsY1wDkQ5fJtQqNJnWcTY7l1Lzio/tpNd8rmO571QUzjYbIniAuJcKw7nWiyXt5PuMXskXFGUyNHwSNFvWxn2CrP4zUr2q5oG1e2utshqdcPzYnVzODhxP/Mh3Rz80gHo5eUiXYrpurinVU2D5vVC83rr1BBu3CY7eiCAQFkZ9EDAuKdcLnhbtIDrssssz4JgcbGRp1xSAlRUwMUYXIpy0R36VFd8s36jluZyuSw/CrsISZJqErqu44EHHhDitrS0FD6fz2h+ZAyvvPIKEs0x6c6dOwev14v8/Hy43W5Re4j2cnO73Zb1bocfz+eff44ZM2ZgwoQJ6N27N66//nosXrxYrHfyJZp9dsaNG4eOHTuib9++GDp0KHJzc0WZo0ePxvbt2/HLL79g0KBBSE5OjmmPnQMHDmD58uWYOHEiBg4ciCuvvBJvvPFGmM0ce7qEruvIzMzE3Llzxfe8vDykpqZatrM/7ORy7esCPL/LJMmh9ggACdIQKnIZ9nsoWk1x165dWLx4MRYuXIiMjAwAQNOmTQEYwp77cfbsWbSwNavYKSkpwdy5c0VF4JlnnsGll14adZ9fMx6pQuQoCKoSOb7Q6HK8kF6qQphJL3ALThE1c7+YTdjRopHRtrcLlKrA7YnULGy3I0pkvrJIh0qJLBwlESp76CgiJZEp22URvg77ygJVRB2dRJnN56hN1vJxZEFttjDIQplJfts7sgEI61gl9+xn8nrJVqcoq2VZFLst8PMqiXrRWS7abrZrbRlpgBMjmmqpFNnuE/s6S0dAaZ2lo57tuwKIERjOJ1WCAQiaHTB5rjuPUstisa6JZ54XiwjvRcCIYobMme3EbHOKOXqH222IZod3eaiiwhCastBWFHEONK+36gGBakJ1u+G16QvAiBYHy8uNUV34MGxNmkBr08aap1xebs1TZgwuIO4d+qpKVEHcr18/LFmyBFOmTEGjRo2wdevWiNv26dMHGzduRK9eveByubBw4UIkJSVh2bJluPbaa/HCCy9gxowZKCoqwh133IGZM2daRgqIB0eOHEGXLl0wceJEhEIhLF68GCGzh3Lfvn2xdetW9OzZE8XFxdi/fz8yMzORkpIS0b4RI0aIsgsLC3Hs2DE8++yzSE1NxYcffojvv/9e9NodNGgQVq5ciTNnzmDp0qUx7XHigw8+QP/+/TF27Fj4fD5s2LDBsv2uXbtw2223gTGGbdu2YfTo0Zb9e/fujYULF2L8+PFo3rw5Nm3ahBdffBG7d++2bNe3b1+sWLECkydPRkpKCnbs2CHWpaen49ixYwCMisHRo0fRuXPnC7kcAIx7aOvWrbjvvvuQn5+Pffv2YeTIkWHb7d69G8uWLcNzzz1nGW3D5XLhpptuwubNmzFt2jQcP34cX3/9NXr06BH1uK+88orI6/73v/+NLVu24LHHHrtgP+o7ajyGsanKy/5Ciz6PbS2PyliRP2m9UxOo5bvdv+p6KDuJckURQyEJ4Q5YBaNT9I3vbl8WqYLgJN5s4jZsP6dzbMuJFls4DQUmlVWlPF+nKLpdNEda7oTdfgfBKM6fXHEwl4V10lIUa/pKPO8T5pB7Lv+XRLx8rvmQaKrLZQjGOEfOLgbH/gK6bnQE5R1I5Yotj6663YbwtVcEUL/EM78uiDKjm86HEDQFJBfNqssF1euF5vGEpVnwcaN1c1Y5XkHi90KkqG5NoGga3CkpcJut0BzHPOXGjeFt3rzynmUMoUDAEMpFRdDLyqCFQoZQRvTA6cUS9VfTq1cv3Hbbbbj99tuRkJCA9u3biyiqnRkzZogm6lAohE6dOmH+/PkAgNWrV2Pp0qXIyMiA3+/H8OHDMWLECJw+fTquzgwfPhx79+7FsGHD4Ha70atXLxQWFqKkpASzZs1CTk4Ohg4diiZNmuDqq68W+0WyTyY1NRXTpk1DVlYW0tLSkJ6ejm7duuHkyZPo1asXPB4Phg0bhoMHD6Jr164x7XFizJgxmDNnDjIyMqBpGrp37469e/cK0d22bVvccccdKCsrw6BBg5CVlWXZv2/fvpg6dSomTZoERVGQkpKCdevWhd1A/fr1w4kTJzBq1Cg0btwYHTt2RH5+PgDgzjvvxJw5czB48GC0bdsWN9xww0VdkwULFiA7OxsZGRlIS0tD69atLdFkzpo1a8AYQ3Z2tljWrVs35OTkICcnB9nZ2Rg+fDgURcGqVavQqFGjqMedNm0a7r//fgwfPhyMMdxzzz3iujTETnXnLWZt21efFHY+3kWVEaWsqEeR9nMUXbCJxiiRQSchx/+HRUEjRYcjlC8vV+zHchDb/JsSTTRKxGou51Feix9S9F40x0sin+8L5pAzDofotGRDxNefU4QzwjZyZUhR1UqxFAfhaO/gaRew8ljGYZUB8xxZzkcVxHoswRjw+Sqji7LgBCrHDDY7gVV307zodHcB+zLGhOgLScIPsIpnxe0Oy+cFoohn6R6tC+KZX0t3FNEsZuoz0xP4bHGKywUtIQGqw7UMmkMhhgKBSr9hXBPNFNo16XO0POVgRQVC5eXGdQKgJSbCnZZmGZNYDwYRKCpCsLAQodJSqHHOIY46DvFnn32Gjz/+GHfddRcA4IUXXkBubq7oVEUQsdi4cSM6d+6M6667Dn6/H2PHjsWsWbNEag1RM3yamxu+8EJFaFUEZ6yyq3psLr5sTf0xqeWmxJg4+H/eV6MaI/YRI7QycopItOh0HUZEKWXhyCrH+hXCsQ40TzvBdF2IJD0YNAQwIISzGOLOHNIuvABm7MvFJhf1XHirqmjSr61oY1VxFM/GispzYXagdRLPgHE+o4pnOQpfR9I2OIyxMNEMQIhmLvrtfvOJbEQ+s+mnlpBQJ2aTE3nKfr9R2dY0qF6vsO3aiwzayUStHl9xxRXYsGEDXn31VSiKglatWol0AIKoCu3atcPSpUuh6zoCgQCGDBlCYrg2cIhQxhrOybKf9JmhMtpoz0MNQ4ocArZoF/9cBSypA+dLtP2qUGa1yc4oUdgqbXeh5TttyrfnkUt5f1aZJyuGoZPEAreXT7OraBo0t9sYKzhS1Nspoh4pym7b3mk5vxcdt5Ns5MPx8SiUIglH1eNxzOcEUDmeszRGdF2IMiqqagiDKggXPRg0xBIfk9qWcsGHDnOKNAJmpNUmnuvCORA+KIpIE4h19KqI55hpG3wyGin6Chi/A83MDa7JGdcURYErhohlui4mx+HDT/J8Zi0hwaj0SL7yyXS4j7yFQfN44EpMrJGOf5HylPVgEMHy8rgeq8HOVEcQDYnc//f/Kr9wIRyhid2R8xCuspiKmQPqhLwuUuegOhKtY7KfjIWPhyuvM8WYiMKZ6xTAmpfKh1QzNjD+885e0jWziFjJHl7p4IJPTAHLt4Etn5rvHyG/VkaOtIncyAgvRabr1mZ789woPFWAiyhzfOSaaL6vCkI4cvErCX8xzjOPMjrYy6TJUOzCURZadcVfJ3RzZkFm+iHuN9v1d4o4As7ngF93Pr2z6nIZwrGORVrtRBTPiB195dOOM/Neku8Dze2G5vXWKf+ZroscX2Y+c0SKkX24ODMiza8zYFZKPB64EhJqzK/fxrEvWt3JvCcIotoIlJSIF7EFSQzxJlc9GAT4APeSeJPFkgKIsX4dBW60PEjzc9jwYHKHJrnzktShKRLyy1cPhSon+DAjIOJFZBOeQmjCFqWURSYgXuZVjupG8tkcVomLwEiIKKZNTMoROf6i4i/X2nqxcqFgsZevlESk5nZDidR0D0BnDLrPJ8SDSGEwm3EBWMVYNfkshH4MhFDinZtkoQQY+a2mjU526oxBN4fsckxXqMWIq+p2w1OFY0YVi0BlukaEcyByfKXfrzzCB893lSshNX0uqhp55ukrIXkMYun+d5k5u/KzggFiCnQWDIpJReRIbE1ef0VV4U5OjpjLHKyoQIhPtQyzQmDmPssdtxmAQEWFIazNUTIUoHKEjISEOhPUkKEIMUE0AD4zI8Q8aiem85XyJkXHJHNGMS6gIw4iz4xZ48LyD3lZXLRJzegX82CXX7pyMyUfjkqIEDMCpcQSNrpuaT6Wc0g5YVHMaOfjPOEvUHnqZvlaAJIYkKY7jlQxED33ZREtXw9AzBYnNwfX9IvJ4rfpO18u3zdy/mukSKochdYdxBTkiKQpqGpjUgQeKRRTdTukK/Bxa518dcxtBaz3Cj9vqjRFNr9360AkOizfWeogZ4m+x3juOLY88PPBnznmvaPxyG0dQaQs2PN85XvUKc+XV7p4pQGoNdEcCT0QMAQzfzYDlZW6hARrRUAeVs6eipGQcF6/0XhGiEkQE0QD4Mi+feHRUVPsydP+OsFFrz1/UjSh8jKll1A00RapGZ03P8ezGZ0fLyRHL+0RTN4MLL2Uoh1Tjt7yZlBLPiYqX3CKphkzVMUos8r+mBE5R39kEW0TFxGj67xSI5dlRmWNIo17RpVEuUWw1pDQskShzfMvsNkTKRrJsedBWyoOUqqLpZIoH0eaOVCVKhjxOhdCNHJ/5c5RXOia95ZqmwY7os/ytOXSPStXAHlrj2LzU06PqUlhLZ47vOLEr7lDZVHlOeuOBemVZUmVprDUHbP1QvV4ajV6GTHPV87zdvhN20UzH+FF1TQo5gx5tTUcH5/AhPsExipberzesGsnj9sMcxxj1e02ouy2zp2UMkEQxHnhduiUwGEw0wVsERcujORxUQGI/FSRv8r3CQYBnw8hRbEMMSWGBuPpA6pqRO3OpxldmgbVcZIEe86ycK5STHBfReoEXy6nb5i+6OYfH+/VkhZieyHLEddI4kRnDMzns5xLx2G45Fxr2Rcpd9jiCyBmoRPDW/GKD2CIAb+/UgTK11UqV85vrVKlANI9I5dpu28sfvFrJJ9LyS9LOo58jrmP5n0DTTOiSDbRZrEvWkuIWb44Dk/L4QJfElpVzZ3XGQOTW0qk3GHHKa5tNoi0IFNkQ1GMTkvmNYl4HRgTFTNZ8Ir71n5upUoxv8aKy1UlX3XGwHh6Bz+eQ0UiTFjLecdcvFZBWFc1dUWci1DIqDTx9BWpo5w9/zvqM8ch/9lpnGPFnBJeTokCYFmmSPcx38ZxSnIJRVXhSkyEK8IQt0ClwLTMkCeL5oSEsMg4T2MQ+8idYlnlcIji+gmDKm20+Gb3SV5vS31TVNWI/nq9ldOyS4R8PsMfHjVXjJEuXImJYQI4bGrsOEKCmCAaAFUeMqkO5nXVfAM3Ud9RYE43W9uGVDMKAMXjQe0nRNQ+itsdt2HCtChitC6gAHBfwDB4Wh0YRs0Jze2GZpvEI+K2AFDFbc8bRhDEr5rCwkI2btw4VlhYWNumXBD13X7GyIe6AvlQN6jvPtR3+xkjH5ygiiVB/MopKirCkSNHeMBnuAAADj9JREFUUFRUVNumXBD13X6AfKgrkA91g/ruQ323HyAfnCBBTBAEQRAEQTRoSBATBEEQBEEQDRoSxARBEARBEESDhgQxQRAEQRAE0aDRFi1atKi2jSAIonrxer3o0aMHvBcwVE9doL7bD5APdQXyoW5Q332o7/YD5IMdmqmOIAiCIAiCaNBQygRBEARBEATRoCFBTBAEQRAEQTRoSBATRD1l3bp1uPnmm3HzzTdj1apVAICDBw8iIyMDf/jDH/D444+Lbb/88kuMGjUKgwcPxoMPPohgMAgA+PTTTzFq1ChkZGRg+vTp+Pnnn+uk/Zx58+Zh27Zt4vuPP/6IcePGYciQIfiv//ovlJaW1pj9QHx84KxduxZPPfVUtdtsJx4+fPTRRxg1ahQyMzMxfvx4/PDDDzVmPxAfH44ePYqRI0ciIyMDd999NwoLC2vMfiC+99IXX3yBLl26VLvNduLhw/bt29GnTx9kZmYiMzPTcZ+6bH9eXh6mTZuGW265BWPGjMHp06drzH7g4n345ZdfxLnPzMzEgAEDcN1119UrHwDg9OnTGDduHDIzM3HnnXdW7ZkUl/nuCIKoUT744AN2++23M5/Px/x+P7vrrrvYzp07Wb9+/dj333/PAoEAmzRpEjtw4ABjjLGbb76Zffzxx4wxxhYsWMA2btzIdF1n/fr1Y4cOHWKMMbZr1y42ffr0Omn/Tz/9xKZPn866du3KXnvtNVHOtGnT2JtvvskYY2zdunVs1apVNWJ/PH0oKipiCxYsYF27dmVPPvlkjdkfTx/69+/PvvzyS8YYY1u2bGF33313vfPh97//Pfvqq68YY4w9+uij7LHHHqt3PjDGWFlZGbv99tvZ1VdfXWP2x9OHJUuWsJ07d9ao7fG0f/z48ezll19mjDH28ssvs9mzZ9c7HzihUIj98Y9/ZG+88Ua982HOnDls48aNjDHGXnzxRXbffffFPDZFiAmiHtKsWTPMnz8fHo8HbrcbV111Fb777jtcdtlluOSSS+ByuZCRkYHdu3fjhx9+QEVFBa699loAwMiRI7F7927k5+ejoqICPXv2BAD0798f77//Pvx+f52yHwB27tyJgQMHYujQoaKMQCCAI0eOYPDgwRa/aop4+AAA+/fvx+WXX46JEyfWmO3x9MHv92P27Nno2LEjAKBDhw44c+ZMvfIBAN566y20a9cOgUAAZ8+eRePGjeudDwCwcuVKTJgwocZs58TLh88++wzbt2/HiBEjMGfOnBqL1MfD/nPnzuH48eMYM2YMAGDUqFH485//XCP2x8sHmddeew2JiYnIyMiodz7ouo6SkhIAQHl5ORISEmIemwQxQdRD2rdvLwTud999h7feeguKoqBZs2Zim+bNm+Ps2bPIy8uzLG/WrBnOnj2L9PR0JCUl4f333wcA7Nq1C4FAAPn5+XXKfgCYMmUKRo8ebSkjPz8fKSkpcLlcFr9qinj4AAC33HILpk2bBk3TasZwiXj44PF4kJmZCcB4Ca1btw6///3va8iD+F0Ht9uNEydOoF+/fjh8+DBuvvnmmnEA8fNh//79qKiowJAhQ2rGcIl4+dCsWTPMmjULO3bsQKtWrbBkyZJ6Y/+pU6fQunVrrFixAiNGjMCf/vQnuN3uGrE/Xj5wQqEQ1q9fj/vuu6/6DZeIlw+zZ8/G3/72N/Tt2xfPP/88pk6dGvPYJIgJoh7z1VdfYdKkSZg3bx4uvfTSsPWKooA5jKyoKAoURcGTTz6Jp59+GrfccguKi4uRlpZWow/wqtgfiUh+1TQX40NdIR4++P1+zJkzB8FgENOnT68OM6MSDx86dOiAgwcPYsaMGbj33nurw8yoXIwPP//8M9avX4+FCxdWp4kxudjr8Je//AXXXHMNFEXBlClT8O6771aXqY5cjP3BYBBffPEFfve73+GNN97AwIEDMX/+/Oo015F4/Bbee+89XHHFFejQoUN1mBiTi/Vh3rx5WLJkCd577z0sXrwY99xzj+M7Q4YEMUHUUz766CNMmDAB9913H7KystCiRQv8+9//Fuvz8vLQvHnzsOU///wzmjdvDgBwuVz4xz/+ge3btyMrKwu6riMtLa1O2R+JJk2aoKSkBKFQCIDVr5riYn2oC8TDh9LSUkyZMgXBYBDr16+v0UoVcPE++Hw+7Nu3T3wfMWIETpw4Ua0227lYHw4cOICCggLRkQgAMjMzRbNxTXCxPhQXF+Nvf/ub+M4YEy1ANcHF2t+sWTMkJyejf//+AIDhw4fj008/rXa7ZeL1TNq3bx+GDRtWnaZG5GJ9OHfuHL755hvRUjV48GD8/PPPMVs/SRATRD3kzJkzmDlzJlavXi2adq+55hp8++23OHnyJEKhEN58803ceOONaNOmDbxeLz766CMARi/uG2+8EQDwwAMPiAf2888/jyFDhkBVq/+xcD72R8LtdqN79+546623AFj9qgni4UNtEy8f5s6di8suuwxr166Fx+OpCdMF8fDB5XJh8eLFOHbsGADgn//8J7p161Yj9gPx8WH06NHYt28fduzYgR07dgAAduzYgZSUlHrjQ1JSEp599lnk5uYCAF566SUMGjSo3th/6aWXokWLFnjnnXcAAP/zP/+D//iP/6gR+4H4PpM++eQTdO/evbpNDiMePqSnp8Pr9eLo0aMADIGdnJyMJk2aRD12zVW9CIKIG8899xx8Ph9Wrlwplo0ZMwYrV67ErFmz4PP50K9fP5FLuHr1amRnZ6O0tBSdO3fGXXfdBQBYtGgRcnJyUF5ejg4dOmD58uV10v5I5OTkYP78+Vi/fj1atWqFNWvWVLfpgnj5UJvEw4cvvvgC+/fvR7t27XDLLbcAMHL8NmzYUO32A/HxQdM0PP7443jooYcQCoXQokWLGvstAHQvcTRNwxNPPIFFixahoqICl19+uRh2q7qJ1zVYt24dcnJy8OijjyIlJcVSXnUTz/vo1KlTaNmyZXWa60g8fFAUBevWrcPSpUtRUVGB5OTkKg1pSVM3EwRBEARBEA0aSpkgCIIgCIIgGjQkiAmCIAiCIIgGDQligiAIgiAIokFDgpggCIIgCIJo0JAgJgiCIAiCIBo0JIgJgiCIXzUDBgzAZ599Frb88OHDGD58OABg7dq12L59e9RyOnTogHPnzl2ULdOnT8e2bdsuqgyCIOIPjUNMEARBNHhmz55d2yYQBFGLkCAmCIIg6g2HDx/GqlWr0KJFC5w6dQoJCQlYuXIlNmzYgPbt22Py5MkAgPnz51u+v/zyyzh+/Dj8fj8mTpyIW2+91VKuvP2TTz6Jf/3rX3C73UhPT8fDDz8spop96qmnkJubi4KCAkyePBnjxo0DAGzZsgWbNm0S058vXLgQV111Fc6ePYv58+cjLy8PrVu3xi+//BLTx4kTJ2LIkCG4/fbbAQDr169Hfn4+HnjggYjH+fbbb7FkyRKUlZUhLy8PHTt2xBNPPAGv14suXbpg4MCBOH78OFavXo3f/va3cbseBPFrgVImCIIgiHrFF198gUmTJmHnzp0YOXIk5s6dG3Mfr9eL119/Hc8//zwee+wxfPXVV47bnTlzBn//+9/x2muvYdu2bejdu7eY3hwALrnkEmzbtg3r1q3DypUrEQgE8OGHH2L79u3YuHEjtm/fjilTpmDWrFkAgCVLluCaa67Brl27kJ2djW+//TamrePGjcOWLVsAALquY8uWLRgzZkzU47z66qu45ZZbsHnzZuzduxenT5/GgQMHAACBQAD9+/fHnj17SAwTRAQoQkwQBEHUKzp27Iju3bsDAEaNGoUlS5aICG4kxowZAwBo0aIF+vTpg0OHDqFDhw5h27Vo0QIdO3ZEVlYWbrzxRtx4443o1auXWM9zjjt16gS/34+SkhIcOHAAJ0+eFMcAgMLCQhQUFODgwYOYN28eAOCyyy5Djx49YvrXv39/LFu2DMePH8fZs2fRtm1bXHnlldi6dWvE48ydOxcffPABNmzYgO+++w55eXkoKysT2/HzRRCEMySICYIgiHqFpmmW74wxpKengzEmlgUCAcs2qqpatne5nF9/qqripZdewmeffYZDhw5hxYoV6NGjB7KzswFA7KcoiihL13VkZmaKSLWu68jLy0NqaioURbHYFem4dv/GjBmDrVu3Ii8vTwjgaMe59957EQqFMHToUNx00004c+aM5bhJSUkxj0sQDRlKmSAIgiDqFcePH8fx48cBAJs3b0a3bt2Qnp6OY8eOAQDOnTuHo0ePWvZ5/fXXAQA//vgjDh48aIn62ssePnw4rrrqKkyfPh0TJkzAiRMnotrTu3dv7Nq1C3l5eQCATZs2Yfz48QCAvn37YvPmzeLYhw8frpKPo0ePxr59+/D5559j0KBBMY/z/vvvY+bMmRg2bBgURUFubi5CoVCVjkUQBEWICYIgiHrGb37zGzzxxBP44Ycf0KRJE6xatQqapmHOnDkYPHgw2rZtixtuuMGyj8/nQ1ZWFgKBALKzs3HFFVcIYSnTsWNHDB06FKNGjUJSUhISEhJEdDgSffv2xdSpUzFp0iQoioKUlBSsW7cOiqIgJycHCxYswNChQ9GyZUt07NixSj42bdoUXbp0wVVXXQW32x3zOPfeey9mzpyJ1NRUJCYm4vrrr8f3339fxTNKEITC5DYVgiAIgqjDHD58GEuXLsWbb75Z26ZUK+fOncOtt96KjRs3olWrVrVtDkH86qEIMUEQBEHUMGPHjkVpaanjultvvRV/+ctfcPfdd5MYJogagiLEBEEQBEEQRIOGOtURBEEQBEEQDRoSxARBEARBEESDhgQxQRAEQRAE0aAhQUwQBEEQBEE0aEgQEwRBEARBEA0aEsQEQRAEQRBEg+b/A/m/lCm6hxqBAAAAAElFTkSuQmCC\n",
"text/plain": "<Figure size 720x432 with 8 Axes>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x216 with 1 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "[could use clearer separation but figure labels will probably fix this]\n\n[labels should change from \"made available\" to \"available]\n\n[normalize the y-axis differently, so these graph doesn't looks smaller?]\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T16:01:17.792411Z",
"end_time": "2019-09-23T16:01:22.434936Z"
},
"trusted": false
},
"cell_type": "code",
"source": "make_zoom_in_plot(\"green\")",
"execution_count": 195,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 288x288 with 1 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We will use these results in a future section when we predict number of accesses to Green OA content.\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Bronze Delayed OA by the time of its availability"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\nWe approach the availability of Bronze Delayed OA the same way as we did with Green OA above.\n\nWe do not have a record of when Delayed OA papers became available, so we used a list of Delayed OA journals and their embargo periods to estimate the date of availability.\n\nThere was no publicly-available definitive list of Delayed OA journals, so we derived a list empirically based on the Unpaywall database. We cross-checked the list with journal policies as well as Laakso (2013) and these sources:\n\n- <https://www.elsevier.com/about/open-science/open-access/open-archive>\n\n- <http://highwire.stanford.edu/cgi/journalinfo#loc>\n\n- <https://www.ncbi.nlm.nih.gov/pmc/journals/?filter=t3&titles=current&search=journals#csvfile>\n\n- <https://en.wikipedia.org/wiki/Category:Delayed_open_access_journals>\n\nThe list includes 546 journals, with the following embargo lengths:\n\nembargo\tlength (months)|number of journals\n---|---\n6\t|58\n12\t|175\n18\t|137\n24\t|42\n36\t|71\n48\t|63\nTotal\t|546\n\nUsing this list, 2.5 million articles are considered Delayed OA. Their breakdown by availability year and publication year follows. You can see the effect of the 12 and 18 month embargos, which cover more journals and those with higher volume.\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T16:01:22.441122Z",
"end_time": "2019-09-23T16:01:32.729599Z"
},
"trusted": false
},
"cell_type": "code",
"source": "make_detailed_plots(\"delayed_bronze\")",
"execution_count": 196,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x432 with 8 Axes>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x432 with 8 Axes>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x216 with 1 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T16:01:32.735420Z",
"end_time": "2019-09-23T16:01:39.247017Z"
},
"trusted": false
},
"cell_type": "code",
"source": "make_zoom_in_plot(\"delayed_bronze\")",
"execution_count": 197,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 288x288 with 1 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T20:10:53.604913Z",
"end_time": "2019-09-23T20:10:53.610995Z"
}
},
"cell_type": "markdown",
"source": "### Closed OA"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Predicting number of accesses"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now that we have an analysis of OA based on time of observation, rather than simply time of publication, we can extend our examination to include OA as a percentage of usage, rather than just number of articles.\n\nTo do this, we will examine data from the Unpaywall browser extension, as described in the Data section above. This data allows us to make inferences about overall readership trends--which in turn will help us make predictions about how OA will impact global readers of the research literature.\n\nThis will involve the following steps:\n\n1. calculate how often people are to want to access a paper, given its age and OA status\n\n2. calculate the number of papers there are of a given age and OA status in past and future years\n\n3. multiply these together to get the number of accesses that people would make in past and future years\n\n It is well known that readers are more interested in accessing newly-published articles, andindeed this trend can be seen in the Unpaywall usage logs. The figure below summarizes access requests by users of the Unpaywall extension between August 2018 and August 2019, by age of the article they wished to read. As expected, readers are most interested in articles published less than a year ago.\n"
},
{
"metadata": {
"scrolled": true,
"ExecuteTime": {
"start_time": "2019-09-23T06:18:41.248116Z",
"end_time": "2019-09-23T06:18:41.521429Z"
},
"code_folding": [
0
],
"trusted": false
},
"cell_type": "code",
"source": "# hidden: code to query and graph \n%matplotlib inline\n\nmy_plot = data_usage_by_age_months_no_color_full_year.plot.line(x=\"article_age_months\", y=\"num_accesses\")\nmy_plot.set_xlim(-4, 120)\nticks = [round(x, 1) for x in my_plot.get_xticks()/12] # convert months to years\nmy_plot.set_xticklabels(ticks);\nmy_plot.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\nplt.xlabel('article age (years)')\nplt.ylabel('access requests')\nplt.title(\"Access by age of article\");\nmy_plot.get_legend().remove()",
"execution_count": 31,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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3jwFTythve+BSMxtmZk3A+4F78xvdfT7QbmbviEUnAje7eyeha+64wvL4+qb4nrh9Tqxf6CZgppllzGxHYBrwcDzGMWbWambjgYMJ3Zq3Aweb2XgzayUMArmljOsrWzabdD8HpqmkREQqo5wAlnb3BDgUuDOWDe9vJ3e/iRBM/gY8CvzF3a82s5vMbM9YbSZwvpk9FY95YSz/FGE04DxCdnR2LD8H2NfMnox1Pg1gZkeZ2WWxzrXAk8DjwB+Ak9x9rbs/RBj1+DAhkJ7j7gvcfQFwFnAX8BgwO9atmPwgjgZ1IYqIVEw5z4H9w8xuImRUd5vZVYTg0K94/+trRcV/AtbG7XOBvUvsNx84qET5UuCoEqe6BXhPrJMDzog/xfv/APhBifLZwOw+L2YA9CCziEjllZOBfYzw5X5gQffexwZwziWEUYmVNAO4pMLHrJjuyXzTaT3ILCJSIeVkYBe6+0n5N+5+iZldR7hXtNHcfdam7NfPMedW+piVlE1yYTmVhpRmoxcRqZBeA5iZ/RTYGjggDnrIawSmV7thQ0nhcioaxCEiUhl9ZWCXAzsTple6rqC8C7i/mo0aasI9sLSmkhIRqaBeA5i7PwI8Yma3u/vLAGY2Ehjj7i/WqoFDQXiQGT3ILCJSQeUM4tjLzH4cg9cTwFwz+2yV2zWkJImmkhIRqbRyAtiXgZ8RBm3cT3iI+YRqNmqoSZKETCYMo9c9MBGRyigngKXc/QnCrO03u/uKMveTKEnoHsSR5EKXooiIDEw5gSgxsw8BhwG3mdnhhAUqpUzZJInrgYV5g5WFiYgMXDkB7AvAqcBZcfXjs4DPVLVVQ0x+ReZMOvy6dR9MRGTg+n2Q2d3vBQ4xs7b4/h397CJF8supNDSEDEwjEUVEBq6c5VQMuB5oM7O9CbO3f8Ddn65244aKJM6F2JAJGZi6EEVEBq6cLsQfE1YjfjXO3P5jwqhEKVN3BhYDmKaTEhEZuHIC2Bbu/qf8G3e/mLCQpJSpJ4DlB3GoC1FEZKDKCWA5M2smjjw0s4lApqqtGkKSOGQ+P5UUaBCHiEgllBPALgZuBSaY2XeAB2KZlCHbHcDCg8ygACYiUgnljEL8uZk9BxxOmIn+VHe/reotGyKSXAhg6XSKxu5BHOpCFBEZqHLWA8Pd7wHuqXJbhqT8iMOQgeWH0SsDExEZqHKG0a+kxMwb7q6BHGXIzxqVTocVmUEBTESkEsrJwHYueN0E/BuQrU5zhp7CDKyhQQFMRKRSyrkHNr+o6Ltm9iDw/eo0aWgpvAfW04Woe2AiIgO10bPKm9l0YMsqtGVISgpGIWoYvYhI5WzsPbA0YSTiF6vZqKEkP+Iwv5xKYZmIiGy6jb0HlgOWxzXBpAz5LsRMpmcmDk0lJSIycOUEsCnFBWF+38Dd/1zJBg01+QeZ0wUzcWgyXxGRgSsngH0X2At4HOgEdgMWAfmuxV2q1rohoPseWKrwHpi6EEVEBqqcAPYC8F9xXTDMbFfg6+7+gWo2bKjozsAyepBZRKSSyhmFOCMfvADcfS4wtWotGmLyGdj6gzgUwEREBqqcDGyNmX0U+BWQAj4BLK5mo4aSbBIfZM5oGL2ISCWVk4GdDHwO6ADWAMcAp1SzUUPJ+hmYHmQWEamUcmbimAfsamZbAO3uvrr6zRo61ltORXMhiohUTL8ZmJlNNLMbgfuB4WZ2q5lNqn7ThoaeYfSp7h8FMBGRgSt3QcvfA2uBZcBjwGXVbNRQUjiVFEBDJq0uRBGRCigngE1190uBxN073f1LwLZVbteQUZiBATRkUhqFKCJSAeUEsMTMuuuZ2cgy9xN6y8AUwEREBqqcQHQ9cBUw2sw+AdwJ/KaqrRpCegJY+FU3ZFLqQhQRqYB+A5i7fxu4CXgYOBT4GXBulds1ZBR3IWaUgYmIVEQ5y6n80t1PJDzILBtJXYgiItVRThfirmaWqnpLhqik5CAOdSGKiAxUOVNJLQKeNLMHgFX5Qnf/TH87mtnXgA/Ftze6+xeLtu8GXAqMBv4MfNLdu8xsW2AWMAFwYKa7rzKzNsL9uO2BJcCH3H1x0TFTwHnAkUACnOLu98VtXyDMIpIGznT362P58cDZQBNwvrtfVMbvpSzdU0kpAxMRqahyMrD7gWuA+cDrBT99MrNDgH8F3k5YgmUPMyuewX4WcJq7TyPMs5ifoupi4GJ3nw48ApwTy/8bmOPuMwiB70clTn0MMAPYCTgauNLMGsxsL+CE2JZ3AueZ2Vgz2xr4VizbFTjVzHbq7/rKpXtgIiLVUc5UUt/YxGMvAr7g7usAzOwpCp4fM7MpQIu7PxCLrgC+YWaXAe8iBJ98+T3Al4Aj4jaAXwMXmVmju3cWnPcI4Gp3T4BnzGw+sD/wbuB6d28H2s3sbkKWlgLudPelsV3XAsdSNFAlZn9tRdc4ub9fQnEXYqMCmIhIRZTThbhJ3P3J/GszewtwHCGQ5G1FCHJ5iwgBYRywwt27isrX2yd2Na4AxgMLyzjuVoSRlMXluRL19y5xSacDXyt9tb3LFg3iyGgYvYhIRVT9gWQzeyvwJ+AMd3+2YFOpgSFJH+V97VNoY49bzjEBLgC2K/o5oES99Q+UKx7EkdZMHCIiFVC1DAzAzN4BXAec7u5XF21eAEwseD+JkEktAUaZWcbdswXlhfu8bGYNwCg2vB/X23FLld9DCGAHlKi/HndfDiwvur4NL7pIfsRhz4PM6kIUEamEcmajn25mJ5tZysx+b2b/NLN3l7HfNoRJgI8vEbxw9/mEe1HviEUnAjfH+1lzCF2O3eXx9U3xPXH7nKL7X/k6M80sY2Y7AtMIXYc3A8eYWauZjQcOBu4AbgcONrPxZtZKGARyS3/XV67iDCyTVheiiEgllJOB/S9h9o0jCfenPg58B9ivn/3OAJqBHxZkKpcARwFfdfdHgJnApXF+xb8BF8Z6nyKMHjwbeBH4f7H8HOAKM3uSkA3NBDCzo4Cj3P1k4FpgH+DxuM9J7r4WeMjMZhGCWQNwjrsviPufBdxFGEZ/mbs/VMbvpSw9GVjsQmxQBiYiUgnlBLBmd7/KzH4M/Mbd7zazxv52cvfPAp8tLjezFsLSLLj7XEoMmIjZ2UElypcSAmCxW4D3xDo5QvA8o8T+PwB+UKJ8NjC7r+vZVBvcA0un9SCziEgFlDOIY5iZbUkYnn57fN0ygHMuAeYNYP9SZhCyu83OBg8yN6ToVAYmIjJg5XYhzidkX/PM7EXgm5t6Qneftan79nHMuZU+ZqXE+EU6pVGIIiKVVM5s9D8FWuOEvgBvjwtcShmySUIqtf4wet0DExEZuLJGIQIfz49CBB4uZxSiBEmS686+QA8yi4hUSjn3wP6XMOiicBTit6vZqKEkSXLd978gTCWlLkQRkYErJ4A1u/tVhIl5f+PudwP9jkKUIJvkyGQKMzB1IYqIVMJgjEJ8UynuQmxIp0hyPXMkiojIpim3C3E+cK+7zyM8CHxBVVs1hGSTHOl0z6+5oSG8VjeiiMjAaBRilRXfA8vPiahuRBGRgSlnFOII4EIzu8PMxgLfjmVShpCBFXQhNoTXGokoIjIw5XQhXgi8AWwJtBNmgP9ZNRs1lCTFASyjLkQRkUooJ4C93d3PAjrdfQ1hAt3dqtusoSObJOt1IeYDmKaTEhEZmHICWLbofYbSCz5KCRt0IcYh9ZrQV0RkYMoJYH82s+8CLWZ2GPA7wtIjUobiQRz5DEyDOEREBqacAPYlYBXhPti3gMeA/6pmo4aSbPEoRAUwEZGKKGcYfSdwj7vvQ5iN4xF3b696y4aIDQZxpNWFKCJSCeUMo/8W8I34thU4M66ULGUozsDyDzIrAxMRGZhyuhDfT8i8cPeXgQOBf69mo4aSJFd6GH1nlwKYiMhAlBPAGmM3Yt46NAqxbEk21z37BkDbyGEALFupXlgRkYEoZ0Xm+8zsKuByIAd8BHiwqq0aQoozsHGjwzzIry1fO1hNEhEZEsrJwE4DFgPnA9+Prz9bzUYNJcX3wIa3NNIyrIHX3lAGJiIyEOWMQlwN/MHddwUOBR6IM3JIGbLZZL3lVADGtbUoAxMRGSCNQqyyJJcjnSkKYKObFcBERAZIoxCrLFu0oCUoAxMRqQSNQqyy4qmkIASw5as6NJReRGQANAqxyrJJjkxxF2JbC7kcLF3RzpZjWwepZSIi9a3cUYiv0DMK8RU0CrFsSakuRA2lFxEZsH4zsDgK8fM1aMuQFIbRr/93wri2ZkABTERkIPoNYGa2H3AmMAJIEdYD287dt61y24aEMJnv+mXj2kIG9vobCmAiIpuqnC7Ey4C/AKOAq4AVwHXVbNRQUioDa21upLW5gSXKwERENlk5ASzn7t8F7gaeBj4IvKuajRpKkiRZbyqpvC1Gt/C6ZuMQEdlk5QSwlfHf54Cd41pgmeo1aWhJEjYYRg8wvq1FGZiIyACUM4z+QTO7BjgHuNHMpgHZ6jZr6Mj2moE18/zCNwahRSIiQ0M5GdjngPPd/Rng9LjP8VVt1RBS6kFmCBmYHmYWEdl05QyjzwEPxNc3AjdWu1FDSTbJlc7A9DCziMiAlJOByQD0loHlh9LrWTARkU2jAFZlvWVg40brYWYRkYFQAKuy4hWZ8/Qws4jIwJQzCnFAzGwU4UHoI939haJtuwGXAqOBPwOfdPcuM9sWmAVMAByY6e6rzKyN8DD19sAS4EPuvrjomCngPOBIwqz5p7j7fXHbF4BTCIH7THe/PpYfD5wNNBEGrFxUiWtPkhy5HBs8yAzhYebhephZRGSTVTUDM7N9gHuBab1UmQWc5u7TCNNUnRLLLwYudvfpwCOEIfwA/w3McfcZhMD3oxLHPAaYAewEHA1caWYNZrYXcAKwG/BO4DwzG2tmWwPfimW7Aqea2U4DuOxu2SQHsMFUUnlbtOlhZhGRTVXtDOwU4NPAr4o3mNkUoMXdH4hFVwDfMLPLCDN9HF1Qfg/wJeAIemYB+TVwkZkVr1d2BHC1uyfAM2Y2H9gfeDdwfXwQu93M7iZkaSngTndfGtt1LXAscG5Re9uAtqLLmNzXxSe5EMBKZWAQuhGVgYmIbJqqZmDufrK7z+ll81bAooL3iwgBYRywwt27isrX2yduXwGML/O4G1te7HTg+aKf3q4NgGw2PONVvJxK3rjRLby2bC25GOhERKR8gzmIo9S3etJHeV/7DOS45RwT4AJgu6KfA0rU6zlIjEvFC1rm7bhNG8tXdfDi4pUlt4uISO8GM4AtACYWvJ8ELCQMzhhlZpmi8vX2MbMGwgz5r5d53I0tX4+7L3f3Fwp/gJf7usB8BlbqOTCAfd86kVQK/vLEopLbRUSkd4MWwNx9PuFe1Dti0YnAzfF+1hzguMLy+Pqm+J64fU7R/a98nZlmljGzHQkDSB6OxzjGzFrNbDxwMHAHcDtwsJmNN7NWwiCQWypxjfl7YKWG0QOMGdXMjKljuf+JDeKliIj0o+YBzMxuMrM949uZwPlm9hQwHLgwln+KMBpwHqGb7uxYfg6wr5k9Get8Oh7zqDj4A+Ba4EngceAPwEnuvtbdHyKMenyYMDLyHHdf4O4LgLOAu4DHgNmx7oAlSX4QR+kABrDf27bi+YUrWPTa6kqcUkTkTaPqz4EBuPvUgrd/AtbG8rnA3iXqzwcOKlG+FDiqxCluAd4T6+SAM+JP8f4/AH5Qonw2MLvfC9lI3cPoexnEAbDf2yZx+R//zv1PLOLf3r1jpZsgIjJkDUYX4hJgXoWPOQO4pMLHHLDuDKyXQRwAW45tZYfJo/mLuhFFRDZKTTKwQu4+qwrHnFvpY1ZCUkYGBiELm3Xz07z+xlq2GN1Si6aJiNQ9zYVYRdmk7weZ8/Z/21YAPKDRiCIiZVMAq6KeqaT6zsC22XIkUyaO5Ob7X+jO2kREpG8KYFWUlBnAAD548DTmL17JvXMXVLtZIiJDggJYFWWT+CBzH4M48g7YbWumTBzJ7Fuf7n4AWkREeqcAVkXlDuKAkKXNfM90FixZzV2P9jnBh4iIoABWVdkyHmQutO/Ok9hh8mh+/Sens0tZmIhIXxTAqmhj7oEBpFIpTnjPDF5duoZf3fxUNZsmIlL3FMCqaGMzMIA9pk/gvfvdBMmIAAAVUElEQVRP5Xd3/4Mb7v1ntZomIlL3av4g85vJxmZgELKwT3xgF5a+0c7Pfv8EW4xuZr/4nJiIiPRQBlZFm5KB5eufccIeTNtmDN/71aPc+sB8LXopIlJEAayKNiUDy2tuauCrJ+/LzjtswU9++xgXXvMYHZ3ZSjdRRKRuKYBVUblTSfVm1PAmvn7Kfvz7ocbtD7/I6T+8myf+8VolmygiUrcUwKpoIBlYXiY+H3buqfvR2ZXwlZ/exw9mP8ri17V+mIi8uWkQRxWVs6Blud5uE7joi//Cb29/huvuepZ7/voyb582gffsN4W9dppIQ0Z/i4jIm4sCWBV1TyVVgQAGMKwxwwnvncF79pvKbQ/O57YH5/PtKx5m7KhhHLL3FA7afTJbjx8xoIxPRKReKIBVUZIbeBdiKePaWjj+sOkcd8g0HnnqFW55YD6/veMZfnP7M7Q2N7Dj5DZ22XEc++w8iSkTR5IqYyorEZF6owBWRdlsdQJYXiaTZp+dJ7HPzpN4ddka5j6zhGdfWs4zLy1j1i1PM+uWpxk/poWpk0ax1bgRbLfVKPacsSWjRwyrSntERGpJAayK8hlYpboQ+zJhTCuH7jOFQ/eZAsDSFe08PG8xf3tmCQteXcXcZ19jXWeWdDrFzttvwe42gWlTxrDj5DZahuljICL1R99cVVTugpbVMHZUM4ftO5XD9p0KQC6X47kFb/CXxxdy/xOLuOLGed1t290mcPBe27DPWyfS2JCpeVtFRDaFAlgVdXchbgb3oFKpFDtObmPHyW2cePhOvLGqg2dfWs7fn3uNu//6Mt/95Su0DMswbdsx2JSx7Lz9Fuy8wzgaGzS6UUQ2TwpgVdTdhbgZDnEfPWIYe87Ykj1nbMmHD9+Juc8u4cG/L8JfXMa1dz7bPSBkd5vA9KljmTJxJFMmjWLMyObBbrqICKAAVlU9GdggN6QfmdiNuLtNAKC9o4vH//EaDz65mEeeWsy9cxd2150wpgWbMhabMobpU8aw/daj1e0oIoNCAayKNucMrC/NwxrY+60T2futEwFYvrKD+YtX8PzCN/D5y3jqhaXMeWwBAA2ZNDtMHs307qA2lnFtzRq6LyJVpwBWRZV+kHmwtI0cRtvI8ez6lvHdZa+/sRafv4yn5y/D5y/l5r88zx/+/BwQBpDsOLmNCWNb2HJsK9ttNZoZU8fS1KhMTUQqRwGsimL82iwGcVTaFqNb2H+XFvbfJaxV1pVNujO0p19YxguL3uCJ515jbUcXAE2NGXbabixTJ41i8oQRbD1+BFtPGEHbiGHK1kRkkyiAVVE2SUilBmcYfa01ZNK8ZZsxvGWbMRz5zlCWy+VYtbaTp15YytxnlvDEc69x433P09mVdO83vLmBrWIwmzx+BJMnjGTyliPYatwIjYAUkT4pgFVRkuSGZPZVrlQqxcjWJvbeaSJ77xTupyVJjiXL17Lg1VW8vGQlC5esZsGrq/j7c69z96Mvd++bTqeYOLaVbbYcyeQJI7r/nTCmlVEjhtV9t6yIDJwCWBUlSU5ftEXS6RRbjm1ly7Gt7D59wnrb2ju6WLBkFS+9uoqXX1nJS6+u5OVXV/Ho06/Qle1ZkTqVgrYRw9hmy5FsO3Ekk7YYTvOwBpqbMjQ3NTCsKUNzU4bW5kZamxsYNbxJIyVFhiAFsCrKJrk3RfdhpTQPa2CHyW3sMLltvfJsNmHx0jW8/MpKXnujneUrO3j9jbW8+MpK7nj4RdZ29L1SdToFk+JckOPHtNLYkKYhk6axIfw0NWYY0dLIyNZGRrQ2MbK1iZGtjbQMa9D9OZHNmAJYFSkDq4xMJh0GfYwfscG2JMmxcs06OtZlaV/XRUdnlvZ1Wdo7uljT3sWa9k5eX9HOCwtX8MxLy3lo3it0dWVJciVOVKQhk2LU8CZGDR8W/23qfj96RM/7ka1N3Y9KpFPQ2tzI8JZGmpsyCoAiVaQAVkXKwKovnU5t0uz62WxCZzahqyuhozPLqrWdrFrTyco161i1Zh0rVofXK1av441VHaxYvY7nF65gxeoOVq7pLLttw5sbugPa8OZGhrf0vG9tbqClqYHmYQ20DGugZVjoAm1pDu9bhzV2v9YfQiIbUgCropCBaSTd5iiTSYesqQlGEB4LKFc2m7BqbWd3YFu5Zl13Rpdkc6xu72RNeyer27tYvbaT1e2drF7byZr2Lha/viZsX9vJmo4ucmVkggDDWxoZM3IYY0Y2M6wpE7o+G8K/jY09r5sa0jTEbtH8+8aGDMNbGpkwpoUJY1pp1uoDMkTok1xFysCGpkwmzegRwwa8rloulwtdnh1Z1nZ00b4udHuu7ej5WdPexdr2TlasXseylR0sX9XB8pXtrOtK6OxK6OzM0plNWNeZ0NmVXW+wS29SqfBsYjodf1I9/2bSKZoa0yErbGqguTArzGeLTZnurLH7fVMDmUyKTDpNOg2ZdJpMJkVzUwMjWhtpbW5UFikVpwBWRYkCmPQhlQpf8M1NDbSNrMwio0mSozMbA1tXwrquhHWdWVat6eTVZWt4ddkaOtZlSXI5kiRHNsl1v06SHEkO1nWG+4lr27toX5fljVVrwvuOLtZ2ZFnX2fegmd60NjfEe4MN9HVrMJ1KMawpQ0v3iNIQSAvLWpsbGd4cXmcyaRpiwGzIxH/j+3Q6BOVMOh3+jUE2/Bu2N2TCNt2vrD8KYFWUTRL91Sk1lU6nGJbOMKzEtF0zthtbkXNkkxwd63qyxPaOEPCy+YCY5MhmE7qSHO0doRt11drO7n/b13X1ffxsrntQzvJVHd2v29dl6VjXVdYAnE2RTkE6HwjTqe7XmcIgmOl5nY6v8wEw3Uu9fBbb1Jju/qOhsSHNyNYmRrQ0kuSgo7OLrq5czzEyvZyzYFtDQRsK66b7a1PcNhSCtgJYlaxYvY5Xlq5RBiZDTiadis/YNdb83Plu17UdPRliVzYhm82RTcK/XUkSgmk2BNOubBIDa9IdZLuySQy0uVi3Z1v4STbYtv6xio6T5FjXme0+bzaeqyubxAAcMtd8UOnsSroXvB1MhUF7w4C5ftAsGTDTKdIlM97eg2Y6nWLlsiUVab8CWGRmxwNnA03A+e5+0aYcZ/6iFdx43/Pc8chLrOvMcsQ7tqtoO0XezAq7XceMHOzWbLpcLsfaji5Wruns7jJtyKTI5dgwgMbXSangWvA6ie+7ut8X1ttwv2S9bUVBvI9AXVivqzOhqz1Z7/zrnbOgbmF72le9XpHfowIYYGZbA98C9gA6gL+Y2V3uPq+c/V9dtoYnX57P7Q/N5+n5y2hsSHPQ7pN5/4E7MGXiqGo2XUTqUCo1eFns5uCll17ikDv/Z8DHUQALDgHudPelAGZ2LXAscG6+gpm1AW1F+00G+MrF99HYOpatx4/gpKPeyrv32GbAI9RERIaqSt17UwALtgIWFbxfBOxdVOd04Guldj7hvTN45147MWXiyLq/KSoiUi8UwIJSUScpen8BcEVR2WRgzkG7T2byJHUViojUkgJYsAA4oOD9JGBhYQV3Xw4sLywzs+q3TERESlIAC24Hvm5m44HVwDHAqYPbJBER6Ysm6gPcfQFwFnAX8Bgw290fGtxWiYhIX5SBRe4+G5g92O0QEZHyKAMTEZG6pAAmIiJ1SQFMRETqku6BDUwGYPHixYPdDhGRulHwnbnhsgkbQQFsYCYBzJw5c7DbISJSj94CPLepOyuADczDhAegFwH9rfI3GZgT679c5XbVkq6rvui66stQva5tgXuAfw7kIApgA+DuHcC95dQtmLXjZXd/oVptqjVdV33RddWXN8F1rRvIcTSIQ0RE6pICmIiI1CUFMBERqUsKYLWzHPgGRTPaDwG6rvqi66ovuq4+pHK5XGWaIyIiUkPKwEREpC4pgImISF3Sc2BVYGbHA2cDTcD57n5R0fbdgEuB0cCfgU+6e1fNG7qRzGwU8BfgyMJnUuL1XFFQdTywzN13rmkDN5GZnQscC+SAy939h0XbP0Dor88QHl4/1d0H9PxKLZjZncCWQGcs+oS7P1iw/RDgh0ALcI27n137Vm4cM3sf8HVgOHCru3+2YFu9fw5PAL4c397s7mcUbT8AuIDwvfI88BF3X1bbVpan+LuinM+amW0LzAImAA7MdPdVfZ1HGViFmdnWwLeAdwK7Aqea2U5F1WYBp7n7NCAFnFLbVm48M9uH8ND2tOJt7v6Yu+/m7rsB+wPLgE/WuImbxMwOBP4F2AXYEzjNCp6yNLPhwE+AQ939rUAz8NFBaOpGMbMUMB3YNf9/UxS8WoCfA+8HZgB7mdl7B6e15TGz7YFLCG1+G7B7YZvr/HPYClwIHEj43jggfukX+gXwYXd/GzAP+K/atrI8xd8VG/FZuxi42N2nA48A5/R3LgWwyjsEuNPdl7r7auBawl/3AJjZFKDF3R+IRVcAH6x5KzfeKcCngYX91PsycI+7lzVDyWBz93uAd8cMeAKhV2J1wfbVwFR3fyUGswmEL8bNnREyypvNbK6Z/WfR9r2BZ939+Xjts9j8P4cfIPz1/rK7dwLHAQ/2UreuPoeE7D5NyCwb48/aojoz3H2emTUCW7P5fg6Lvyv6/azFa3oX4fsSyvxeVACrvK0IcyPmLSLMZ1bu9s2Su5/s7nP6qmNmbcCphO62uuHunWb2DcJftXcAC0psfy/wIjAOuK32rdxoYwjXcjRwMPBJMzu0YHs9fg53BDJmdquZzQU+RYkv8Xr8HLr7SkLG8TTh8/cCoQuusE6nmb2NMCfiu4Gra9zMspT4rijnszYOWFFwK6Wsz6MCWOWlSpQlG7G9ns0Efu/urw52QzaWu3+NcM9kG0p06br7ze6+BXAD8NMaN2+jufv97n6iu69299eAy4HDC6rU4+ewgdDDcQKwL+Ev+4+UqFd3n0Mz2wX4ODCFsMpFFjijuJ67P+HuWwLfBK6paSM3XTmftU36PCqAVd4CYGLB+0ms3+3W3/Z6djSb6V+FvTGz6fHmP+6+BriecD8sv32smf1rwS5XFW7fXJnZO83s4IKiFD2DOaA+P4eLgdvdfYm7rwV+TwhixerucwgcBtzh7q/GScKvAA7KbzSzZjM7uqD+LOrgcxiV81lbAowys0wfdTagAFZ5twMHm9n4eGP2GOCW/EZ3nw+0m9k7YtGJwM21b2ZlxUEDewD3D3ZbNtL2wKVmNszMmgg3mgvvm6SAWXGEFMCHKHMFgkHWBpwXv/hGEjKV3xVsfxAwM9sxfmkcz+b/ObwBOMzM2mKb3ws8Wlihjj+Hc4FDzGx4vIb3EUa85nUCF5nZHvF9vXwOoYzPWrynOYdwXxPK/F5UAKswd18AnAXcBTwGzHb3h8zsJjPbM1abCZxvZk8RbtpeODitHZiiaxoPrHP39sFs08Zy95uAm4C/Eb4M/+LuV+evzd1fJ9xPuSHed5kGfGnwWlwed78BuJGe6/q5u99vZo+Z2Vbx/+mjwHWEe39P03MDfbMUR1F+j/DFPQ+YD/xiiHwObwN+Tfi/epwwiON/zOwyMzvK3bOEL/efmdljhIFhJw9agzdCX5+1/PXFqp8ijNqeR1j/rN/HOjSVlIiI1CVlYCIiUpcUwEREpC4pgImISF1SABMRkbqkACYiInVJAUykAsxsLzO7JL7e08z6HJJuZleY2QYzLQwmM8uY2Q1mtuUgn3/CYJxf6o8CmEhlvJU4d5u7P+Lux/ZTf3P0BeBud39lME4en3X6HmFWcpF+6TkwkV6YWRo4nzDv3kjCrBwnu/t9ZnYFMBbYAXgAOJSwvtv1wJXAT9x9ZzMbAfwYeAfQRZj+6CzC0hh/d/fvm9kM4EfAFoRZyS9095+XaM+RwFcI60FNAK5093PitjOBk4CVhDXmjnb3qXF2ke8SlunIEB5s/oy7ryg6divwD8IyJUsJD5ueFh+wxcwuje39kZmdRZhhJk2YdPZT7r7QzPYlBKBhhKmA/uTuJ5nZVMIsC08BUwmTC3+FsOTQOuCfwMfyaz/FB1n/n7vPLeO/Sd7ElIGJ9G4fwkza+7n7ToTAdGbB9lZ3f6u7nwR8FZjj7h8rOsa5hDXEZgC7EQLZgfmNZtZAmJXgTHffI247IwYDCuqlCBnSR9x9T0JQ/bKZjTOzwwgzHexFmEZpZMGuZxIC5x7uvithfrn/KXGt/wI84+6vu3uOMGHxyfHcowhTbF1pZicSgtzecd2tm4DL4jE+C3zV3fcBdgKOKpj6aDLwzbgG3lTCPH+7xGv+J+vP63cj8G8l2iiyHq3ILNKLOPXS2cAnzGwHwpfuyoIq5cxFdwjw+dg9liUGLzP7aNw+jZDF/bxgHc0W4O2EzC7flpyF1YiPtLDi9wxCRjicMMv8b919eTz2RYQsB+BIwryIh8bjNwGlZmmfTsjA8q4AvmZm4wnTFt3g7stjFrg38Eg8XgZojft8BDjczL4Sj9cKjABeJwTR/PyET8TfxYNmditwnbs/VHDu5yiYyFakN8rARHphZkcQsgGAPxBWAy5c9qHP5c6jLsLCkvljbmNmWxRszwDLC1ZN3o2QXf2iqC3DCd1/uwN/JazG2xnb01XUrmzR8T9bcOy9KVhgtUAS6wIQg+FvCUuXfJxw7fnjfbfgeHsSskoI3YSHE7ofzyWsW5VvV0d+rad47F0Jy4VkgWvM7HNFbS68BpGSFMBEenco8H/u/lPCzOBHU/AlX6SLMAFrsduBj5hZ2syGEboLDyzY7oTVCU6AEOCAvxO6Agu9BRgFnO3u/xePMSy250bgGDMbHeueRE/QvBX4TzNrivf0LgW+U6KdzxBm5i90EfAZIF2QId0KnBy7FSEEql+Z2RhCMPuSu19PWDF4R0r8vmIWdwdh4uSvA78kBLS87QlBUKRPCmAivbsEONDMHid0fz0HbBcDQbH7gelm9rui8m8QBirMJWRQN8UveADcfR3h/tLJ8Ty3Aee4+31Fx3mcsJzI02b2V+AowszeO7r7nYTAdL+ZPUIYTLIm7vdNwkCLv8X6+XtpxW6P7W8raNtcworHlxTUuyy24wEze5Jw7+qj7r6MEBj/GtvwZeA+QhArdjPwJPD3WHd/4OsF2w9jM58ZXzYPGoUoUufiUiL7u/uF8f3ngX3c/bi+99zgOF8Butz9e/H9DsDdgMXFPqvOzA4CPu3uH6zF+aS+aRCHSP17BviSmZ1K6Dp8kbCG2cb6PvBHM/slcW0mwv2zWgWvDPBFQheoSL+UgYmISF3SPTAREalLCmAiIlKXFMBERKQuKYCJiEhdUgATEZG6pAAmIiJ16f8Dg4FEixeyQN0AAAAASUVORK5CYII=\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T19:00:05.939852Z",
"end_time": "2019-09-23T19:00:49.752447Z"
},
"trusted": false
},
"cell_type": "code",
"source": "my_min = 2016\nmy_max = 2024\nmy_range = range(my_min, my_max)\n\ngraph_type = \"gold\"\nall_predicted_papers = pd.DataFrame()\nfor i, graph_type in enumerate(graph_type_order):\n all_data = get_papers_by_availability_year_including_future(graph_type, my_min, my_max)\n all_data[\"graph_type\"] = graph_type\n all_predicted_papers = all_predicted_papers.append(all_data)\n",
"execution_count": 204,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-22T22:34:31.888046Z",
"end_time": "2019-09-22T22:34:31.901473Z"
},
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T04:32:34.030209Z",
"end_time": "2019-09-23T04:32:39.121254Z"
},
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-21T22:36:40.827219Z",
"end_time": "2019-09-21T22:36:43.198529Z"
}
},
"cell_type": "markdown",
"source": "How can we use this access data to predict the number of accesses that people will be interested in in the future? And how that differs from access demand in the past?\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### How often does someone want to access a paper, given its age and OA status\n\nFirst, we use the access data we have from one specific month: July 2019. We group it by OA access type and plot it by age of the article:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:18:41.527843Z",
"end_time": "2019-09-23T06:18:44.252203Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\ndata = get_accesses_per_year_total() \ndata_now = data.loc[data[\"article_age_years\"] >= 0]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(linewidth=5)\ng.map(plt.plot, \"article_age_years\", \"num_accesses_per_year\", **kws);\n",
"execution_count": 32,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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pEokEp556KnvvvXehwxLJC7UgFBEREREREREZ5PbbbyeVSgGQTCa5/fbbSafTBY5KJD+UIBQRERERERERGaSlpWXAdmNjI21tbQWKRiS/lCAUERERERERERlkxowZQ8qampoKEIlI/kVKEJrZvHwHIiIiIiIiIiIyWdTV1Q0pa2xsLEAkIvkXtQXhirxGISIiIiIiIiIyieRKEKoFoRSrqAnC1WZ2mJmpS7KIiIiIiIiIFL36+vohZUoQSrEqiXjcnsC9QI+ZdQExIO3utXmLTERERERERESkQNSCUKaTqAnCI/IahYiIiIiIiIjIJJKrBaHGIJRiFanLsLuvBhqAxcBOwK7AcXmMS0RERERERESkYNSCUKaTSC0Izexy4GSgElgHLCHocnx5/kITERERERERESmMGTNmEI/HSaVSfWWdnZ10dXVRXl5ewMhExl/USUeOBXYBfg8sB94KtOcrKBERERERERGRQorH49TWDp16Qd2MpRhFTRC+5u5twHPAvu6+Apidv7BERERERERERApLMxnLdBE1QdhtZkcCzwAnmFkdShCKiIiIiIiISBHTOIQyXURNEH4Z+DhwM3AA8Dpwdb6CEhEREREREREptFwJQnUxlmIUaZISd38QeDDcPMTM6t191BphZu8Avg5UA7e6+/lm9jbgfwgmPPm1u18QHruUYNKTOuBu4BPu3mtmiwmSkXMBB85y91YzqweuIZhReRPwbndfb2ZlwM+Ag4EO4Ex3f87MYsB3gZOAFHCuu98X5e8XERERERERkelHXYxluojUgtDM5pvZTWa2yszmAr82s/mjnLMr8GOC2Y/3BQ40s7cDPw/L9gSWhWUQJAE/7e67AzHg3LD8MuAyd98D+DtwYVj+TeAed9+TILF4cVj+GaAtLP8scGVYflp4n3sBpwBXmlmkBKmIiIiIiIiITD/qYizTRdQE2WXADcB5wFbgcYJWestHOOdUghaCawHM7D3AbsDz7v5SWHY1cIaZPQNUhi0VAa4AvmFmPwWOJEjoZcpXEHR5Xh7uA7gWuNTMSsPyfwVw97vNbHbYCnE5cJ27p4BVZrYaOIygtWKfsGXi4J8IFo32AIlMR6ovItGpvohEp/oiEp3qi0h021Nf1MVYpouoCcKd3f1yM/uku/cAXzazp0Y5ZwnB5Ca3AvOBG4GVwGtZx7xGUBkXDFM+G2h2995B5WSfE3ZFbgbmjHCt4coH+yzwtVH+NhEJqL6IRKf6IhKd6otIdKovItFtc33JlSBsbW2lt7eXkhJ1SpTiEfXZnDKzvu7IZlbD6N2TSwha+B0NtAJ/BNpzXZugS/G2lLMd54x0rWzfJ2ipmG0RcE+OY0WmO9UXkehUX0SiU30RiU71RSS6ba4vJSUlzJgxg9bW1gHlzc3NNDQ0jHuAIoUSNUH4e4IJQerM7OPAR4HfjHLOeuB2d98EYGY3AGcAyaxjdgBeBdYRtDIcXL4JqDWzhLsns8rJOmdtOJZgLbA5q/yFiPcxQDj5yoD2wmY2yp8qMj2pvohEp/oiEp3qi0h0qi8i0W1vfamrqxuSIGxqalKCUIpKpElK3P3fgZuBh4Fjgf8FLhrltD8Bx5tZvZklgLcD1wNmZkvCsjOBP7v7aqDTzN4cnvvBsLyHIJP/nuzycP3mcJtw/z3h8X3lZnY40Onua8Lys8wsYWZLgN3Dv0dEREREREREJKdcMxlrHEIpNpFaEJrZp4Er3f2qqBd294fM7DvAvUApcBvwI+A54HdABUHS7vrwlLOAy8Puy48Bl4TlnySYcfgCYA3wvrD8QuAKM1tJ8AvAWWH5D4CfhOVdwAfC8uuBQ4Anw+2PuHtH1L9HRERERERERKYfzWQs00HULsb7Esz8+yfgR+7+SJST3P3nwM8HFd8B7J/j2CeAN+YoX00wjuHg8i3AO3OUdwJn5yhPA18MFxERERERERGRUSlBKNNB1C7GHyPokvsI8CMze9jMPmxmFXmNTkRERERERESkgHIlCNXFWIpNpAQhgLs3E0xM8itgFvApwM3s9DzFJiIiIiIiIiJSULnGIFQLQik2kRKEZvY2M/s1sArYAzjF3Q8C3kL/WIEiIiIiIiIiIkVluC7G6XS6ANGI5EfUFoQ/BO4DdnX3T7j7kwDu/g/g8nwFJyIiIiIiIiJSSBUVFZSXlw8oS6VStLa2FigikfEXaZISd99jhN1Hj08oIiIiIiIiIiKTT319PRs2bBhQ1tjYSE1NTYEiEhlfkccgHIFqg4iIiIiIiIgULc1kLMVuPBKEIiIiIiIiIiJFSzMZS7FTglBEREREREREZARqQSjFTglCEREREREREZER1NfXDylTglCKiRKEIiIiIiIiIiIjUAtCKXaREoRmNm+E3bFxikVEREREREREZNLJ1YKwsbGRdDpdgGhExl/UFoQrRth3xHgEIiIiIiIiIiIyGVVXV5NIJAaUdXd309nZWaCIRMZX1AThajM7zMyGHO/ureMck4iIiIiIiIjIpBGLxdTNWIpaScTj9gTuBXrMrIugW3Ha3WvzFpmIiIiIiIiIyCRRV1fHli1bBpQ1NjYyf/78AkUkMn6iJgjVjVhEREREREREpi21IJRiFqmLsbuvBpYB5wKbgMPCMhERERERERGRojfcRCUixSDqLMZfAf4JeDdQCXzNzC7MZ2AiIiIiIiIiIpNFrhaEzc3NBYhEZPxFnaTkvcCJQJu7bwYOBc7MW1QiIiIiIiIiIpOIWhBKMYuaIOxx967Mhrs3Aj35CUlEREREREREZHLRGIRSzKJOUvKKmS0H0mZWDnwR0BiEIiIiIiIiIjIt1NbWDilra2ujp6eH0tLSAkQkMn6itiA8D/g8sB/QDrwd+FS+ghIRERERERERmUwSiQQ1NTVDytWKUIpBpBaE7v4q8FYzqwIS7t6S37BERERERERERCaX+vp6WloGpkSampqYPXt2gSISGR9RZzGeYWbfAR4AVpjZ18OuxiIiIiIiIiIi00KucQg1UYkUg6hdjH8KLAQ+B/wzsCdwSb6CEhERERERERGZbDRRiRSrqJOUHODultkws78CK/MTkoiIiIiIiIjI5FNfXz+kTAlCKQZRWxBuMLPsDvXVwOt5iEdEREREREREZFJSC0IpVlFbEK4HHjGz3wK9wMkEScNLANz9M3mKT0RERERERERkUtAYhFKsoiYIVzKwS/F1Wevp8QtHRERERERERGRyytXFuLm5mVQqRTwetZOmyOQTKUHo7t8Ybp+ZrQAuGreIREREREREREQmobKyMiorK+no6OgrS6fTtLS05GxdKDJVjEd6u2YcriEiIiIiIiIiMumpm7EUo6hdjLebmX0XmOPu55jZUuByoA64G/iEu/ea2WLgamAu4MBZ7t5qZvXANcCuwCbg3e6+3szKgJ8BBwMdwJnu/pyZxYDvAicBKeBcd78vjOMLwLkESdGvuPvv8/23i4iIiIiIiEhxqa+vZ/369QPKNFGJTHV57SBvZm8Fzskquhr4tLvvDsQIEnYAlwGXufsewN+BC8PybwL3uPueBInFi8PyzwBtYflngSvD8tOAPYG9gFOAK82sxMyWAe8HlgKHA981s4Zx/nNFREREREREpMhpJmMpRnlrQRgm4L4F/Duwv5ntBFS6+4PhIVcA3zCznwJHEiT0MuUrgC8Dy8N9ANcCl5pZaVj+rwDufreZzQ5bIS4HrnP3FLDKzFYDhwHHAL93906g08zuImhl+MsccdcDg0cdXTSGh0KkaKm+iESn+iISneqLSHSqLyLRjVd9URdjKUb57GL8E+CrwI7h9gLgtaz9rxFUxNlAs7v3DiofcE7YFbkZmDPCtUYqfzhHeS6fBb4W6S8UEdUXkehUX0SiU30RiU71RSS6cakvakEoxWg8uhjHBheY2UeBV9z9jpGOIxgncLjy7Tlne+5jsO8DuwxajhjmWJHpTvVFJDrVF5HoVF9EolN9EYluXOpLff3gRohKEMrUF6kFoZlVAfu5+4Nmdj6wP/B1d19D7sr0HmAHM3scaABmAGlgftYxOwCvEkw+UmtmCXdPZpUDrAvPWWtmJUAtsDmr/IVB18qUD76PXOUrcv2t7t4IDGgbbGa5DhWZ9lRfRKJTfRGJTvVFJDrVF5Hoxqu+DNfFOJ1OE4vlap8kMvlFbUH4C+DkcLKP84E1BJOG4O6tgw9292PdfR93X0owVuD/ufuHCMb/e3N42AeBP7t7D3APQVKxrzxcvzncJtx/T3h8X7mZHQ50hsnKm4GzzCxhZkuA3Qm6Fv8ZOM3MqsxsDvBWILt1o4iIiIiIiIjIqKqqqigpGdjeqre3l46OjgJFJDJ2UROEu7r7/wPeAVzh7l8naBm4rc4CvmdmzwLVwCVh+SeBj5nZMwQtEi8Iyy8EDjWzleExnwrLfwCUh+WXAB8Iy68HVgJPAn8EPuLuHe7+N4IZlB8G7gUudPd12xG/iIiIiIiIiExjsVgsZzdjTVQiU1nUSUrKwtvjgS+YWYKg2/Co3P0KgpmJcfcngDfmOGY1cHSO8i3AO3OUdwJn5yhPA18Ml8H7/hv47ygxi4iIiIiIiIgMp66ujtdff31AWVNTEwsWLChQRCJjEzVBeF/Yuq8XuJ+ge+7teYtKRERERERERGSSGm4cQpGpKmoX408DHwOOcPcU8F8EYxGKiIiIiIiIiEwruRKEmslYprJICcJwduH5wJfCGY1rw0ShiIiIiIiIiMi0kmsMQiUIZSqLlCA0s68A/wS8G6gEvmZmF+YzMBERERERERGRyUhdjKXYRO1i/F7gRKDN3TcDhwJn5i0qEREREREREZFJSi0IpdhETRD2uHtXZsPdG4Ge/IQkIiIiIiIiIjJ51dTUEIvFBpR1dHTQ3d1doIhExibqLMavmNlyIG1m5cAXgdX5C0tEREREREREZHKKx+PU1tYOaTXY1NTEnDlzChSVyPaL2oLwPODzwH5AG/D2sExEREREREREZNrROIRSTCK1IHT3V4G3hjMYJ9y9Jb9hiYiIiIiIiIhMXvX19axZs2ZAmcYhlKkq6izGe5jZR4EO4Jdm9qKZHZPf0EREREREREREJie1IJRiErWL8U8IkoMnAXOADwP/nq+gRKSIpdOQ7i10FCIiIiIiImOSK0GoFoQyVUVNEFa4+zXAccBv3P0uoDRvUYlIcdryfVg1I1g2fiVIFoqIiIiIiExB9fX1Q8qUIJSpKmqCsNzM5gHLgdvD9cr8hSUiRafzMdj4OUi3Q7oLtnwbWn5f6KhERERERES2i1oQSjHZli7Gq4F73f0Z4GHg+3mLSkSKT9fKoWVNP534OERERERERMZBrgRhS0sLyWSyANGIjE2kBKG7/wiocvcPhkUHuPvl+QtLRIpO2W5Dy9rugOTWiY9FRERERERkjEpLS6mqqhpQlk6naW5uLlBEItsv8izGwIfNLGZmNwAPaxZjEdkmFcugZOGgwh5ovbEg4YiIiIiIiIyVxiGUYrE9sxjPRrMYi8i2isWh5rSh5S2/m/hYRERERERExkGubsaNjY0FiERkbDSLsYhMnFwJwrZbIdky8bGIiIiIiIiMkSYqkWKhWYxFZOJUvhkS8waWpbug7abCxCMiIiIiIjIG6mIsxUKzGIvIxIkloObUoeUt1098LCIiIiIiImOkFoRSLDSLsYhMrJrTh5a1/hlSbRMfi4iIiIiIyBjkakGoMQhlKoo6i/EM4BIzu8PMGoB/D8tERLZN1VGQmDWwLN0OrbcUJh4REREREZHtNFwLwnQ6XYBoRLZf1C7GlwBNwDygE6gF/jdfQYlIEYuVwIxThpa3ajZjERERERGZWioqKigrKxtQlkwmaWtTDymZWqImCA9w968CPe7eDpwFLM1fWCJS1HLNZtx6I6Q6Jz4WERERERGR7RSLxXK2IlQ3Y5lqoiYIk4O2E0BqnGMRkemi+q0QH/QmmmqFttsKE4+IiIiIiMh20kzGUgyiJgjvNrNvA5VmdjzwB+DO/IUlIkUtVgYzTh5artmMRURERERkitFMxlIMoiYIvwy0EoxD+C3gceBL+QpKRKaBnN2M/wjp7omPRUREREREZDupi7EUg0gJQnfvAVa4+yHAccDf3V2DhYnI9qs+DuLyQP/dAAAgAElEQVSDJkNPNUHbXwsTj4iIiIiIyHZQC0IpBpEShGb2LeAb4WYV8BUzuyBvUYlI8YtXQPVJQ8vVzVhERERERKYQjUEoxSBqF+OTCVoO4u5rgaOA9+YrKBGZJmpOH1rWegOkeyc+FhERERERke2gLsZSDEoiHlcadjPO6CbCLMZm9jXg3eHmTe7+z2b2NuB/gErg1+5+QXjsUuByoA64G/iEu/ea2WLgamAu4MBZ7t5qZvXANcCuwCbg3e6+3szKgJ8BBwMdwJnu/pyZxYDvAieFsZ/r7vdF/PtFJB9mnACxSkh39JclN0P7imCmYxERERERkUmupqaGeDxOKtWfJunq6qKzs5OKiooCRiYSXdQWhPeZ2TVm9hYzOwb4BfDQSCeEicDjgAOApcBBZvY+4OcELRL3BJaZ2dvDU64GPu3uuwMx4Nyw/DLgMnffA/g7cGFY/k3gHnffkyCxeHFY/hmgLSz/LHBlWH5aeJ97AacAV5pZ1ASpiORDvBpmnDi0vOV3Ex+LiIiIiIjIdojFYhqHUKa8qAmyTwMXAd8DeoHbwu2RvAZ8wd27AczsWWB34Hl3fyksuxo4w8yeASrd/cHw3CuAb5jZT4EjCRJ6mfIVBLMqLw/3AVwLXGpmpWH5vwK4+91mNjtshbgcuM7dU8AqM1sNHEbQWrFP2DJx8AACi0b5W0WmpXGpLzWnDU0Itvwe5v0AYokxxScymej9RSQ61ReR6FRfRKLLZ32pq6tj69atA8qampqYN2/eeFxeJO8iJQjdvc3M/ujuXzCzBuBId28f5ZyVmXUz2w14D3AJQeIw4zWCyrhgmPLZQLO79w4qJ/ucsCtyMzBnhGsNVz7YZ4GvjfS3iUifsdeX6uUQK4d0V39ZcgN03A9VR4wtOpHJRe8vItGpvohEp/oiEl3e6kuuiUo0DqFMJXmfxdjM9iZocfhF4B85DkkRdCnelnK245yRrpXt+8AugxZlKURyG3t9SdRC9XFDyzWbsRQfvb+IRKf6IhKd6otIdHmrL7W1tUPK1MVYppKoXYxPJhhLEHdfa2ZHAY8QjAM4LDN7M/A74LPufl143vysQ3YAXgXWDVO+Cag1s4S7J7PKyTpnbTiWYC2wOav8hYj3MYC7NwID0vxmNtKfKTJtjVt9qTkdWm8cWNbye5j7PYhFHSpVZHLT+4tIdKovItGpvohEl8/6kqsF4caNG0mn08RiudoriUwuUb95b/Msxma2I3ADwSzC14XFDwW7bImZJYAzgT+7+2qgM0woAnwwLO8B7iHontxXHq7fHG4T7r8nPL6v3MwOBzrdfU1YfpaZJcxsCcF4iA9H/PtFJJ9mvIMhv1f0roXOvxUkHBERERERkW2Ra5KSF154gTvvvJN0Ol2AiES2TdQWhPeZ2TXAz4A0cDajzGJM0KW4AvifrIz8j4FzCFoVVhAk7TL9CM8CLjezGuAxgvEKAT5JMOPwBcAa4H1h+YXAFWa2kuAXgLPC8h8APwnLu4APhOXXA4cAT4bbH3H3joh/v4jkU2ImVL8N2m4ZWN7yO6g8tDAxiYiIiIiIRDTcZCT33HMPsViMY445ZoIjEtk22zKL8b/RP4vx7fSPSZiTu58PnD/M7v1zHP8E8MYc5auBo3OUbwHemaO8kyCBObg8TZC0/OJIcYtIgdScliNBeD3M+Q6oSb6IiIiIiExi1dXVHHnkkdx9991D9t19993EYjGOPvroiQ9MJKLIsxgDn89zLCIync04BfgEkOwv63kZuh6DigMLFJSIiIiIiEg0Rx99NF1dXTz00NAOlytWrOg7RmQyipQgNLM3AV8BZhDMBpwAdnH3xXmMTUSmk5LZUHU0tN8xsLzleiUIRURERERk0ovFYhx//PEAwyYJY7EYRx111ESHJjKqqJOU/BS4n2Cm4GuAZoJxBEVExk/NaUPLmq8HDeorIiIiIiJTQCZJ+MY3DhlBDYC77rorZzdkkUKLmiBMu/u3gbuA54AzgCPzFZSITFM1pxI0Us7S8zx0PV2QcERERERERLZVLBbjhBNOYNmyZTn333nnndxzzz0THJXIyKImCFvC238A+4QTgSTyE5KITFsl86Hy8KHlLWqwLCIiIiIiU0csFuPtb387Bx98cM79f/3rX7n33nsnOCqR4UVNED5kZr8G/gp80cz+mwEzCYiIjJOa04eWtVw/8XGIiIiIiIiMQSwW48QTTxw2SXjHHXdw3333TXBUIrlFTRB+Dvieu68CPhuedyaAme2Wp9hEZDqqedfQsu6V0PXcxMciIiIiIiIyBpkk4UEHHZRz/+233879998/wVGJDBVpFmN3TwMPhus3ATdl7f41oClGRWR8lC6CikOh88GB5S2/g/KvFiYmERERERGR7RSLxVi+fDnpdJpHH310yP7bbruN7u5uDj/8cEpKIqVpRMZd1BaEI4mNfoiIyDbINZtxy28g3TvxsYiIiIiIiIxRLBbjpJNO4oADDsi5f8WKFVxyySU89NBD9PT0THB0IuOTIEyPwzVERPrlShB2PQkvLYW22yY+HhERERERkTGKxWK84x3vGDZJ2NLSwi233MIll1zCgw8+qEShTKjxSBCKiIyvsl2gPMfIBd0r4ZXjYO3J0P38xMclIiIiIiIyBpkk4dKlS4c9prW1lVtvvZWLL76YBx54QIlCmRBKEIrI5FT/4eH3tf4fvLg3bPxnSDZPXEwiIiIiIiJjFIvFeOc738khhxwy4nFtbW385S9/4eKLL+b++++nu7t7giKU6UgJQhGZnOo/DvXnjXBAD2z5Lry4GzT+DNLJCQtNZMK0r4PGpyCt0TxEREREikksFuOEE07gE5/4BHvttdeIx7a1tXHbbbdx8cUXc9999ylRKHkxHtPjrBqHa4iIDBQrgfk/gLoPwsbzoeOB3MclN8L6j8LWS2HexVB1xMTGKZIvT34dnr4ISMPMA+HN10Lt7oWOSkRERETG0bx58zjjjDPYuHEjd999NytXrhz22Pb2dm6//XbuvfdedtllFxYvXszixYuZP38+8bjaf8nYREoQmtl84BygIbvc3f/Z3d+bh7hERAKVy2DxfdB8LWz6MvSuzX1c12Ow5kioeTfM/Q6U7jSxcYqMp+ZV8PQ3+re3Pgq3HgJH/Bbmv61wcYmIiIhIXsydO5fTTz+dI488knvuuYenn3562GM7Ozt59tlnefbZZwEoKytj0aJFLF68mJ122omFCxdSWlo6UaFLkYjagvD/gLXAP/IYy5TU3tPOH579A/FYnMMXH86OdTsWOiSR4hOLQd2ZUHMybP4ubPk2pDtzH9vyG2i9IUgU1v8TVL4pOF9kKunaNLSspxHuPAEOugR2/+TExyQiIiIieTd37lxOO+20AYnC9CjDzXR3d/Piiy/y4osvAhCPx1mwYEFfC8OFCxcyY8aMiQhfprCoCcIyd39XXiOZgpq7mln646W81PhSX9l+8/Zj+W7LWb7bcg5ddCiJeKKAEYoUmXg1zPl6MIHJxi9Dy3W5j0t3Q/PVwVK+f5AorDsL4npTlCli5gFQsxu0DJqtO52Ev38Kmp6Bg74P8fEYKUREREREJps5c+bwrne9qy9R+NRTT42aKMxIpVKsXbuWtWvXcv/99wNQVVXFvHnzmDt3LnPnzmXevHnMmTOHsrKyfP4ZMoVE/WbxiJnt4+7Dt3Gdhq54/IoByUGAJzc8yZMbnuQ/7v0PGiobOGHJCSzfbTknLDmBhsqGYa4kItukdDEsvBbaPwUbzoeuR4c/tusJ2PAJ2PQlqD0bZv4TlI88CLBIwZVUwVE3wl3LoTVH4/3nL4UWh8N/A2UzJz4+EREREZkQs2fP5tRTT+1LFK5cuZLe3t5tvk57ezsvvfQSL700MIfR0NAwIGk4d+5cGhoaNKbhNBQ1QXgf8LiZvQb0ZArdfde8RDVF9CR7Rty/pWMLv3rqV/zqqV8Rj8U5bMfD+loX7jN3H2Lq9igyNlWHw84PQ9MVsOlfILlh+GNTLdD4w2CpPBJmfhJqToWYfjGTSarW4PiH4J7TYeNdQ/evvx1uPTRIJGryEhFSvb10bd1K5Zw5hQ5FRERk3M2aNYtTTjmFk046iVdffZU1a9awZs0aXnnlFTo7hxl+KYItW7awZcsWnnvuub6yRCLB7NmzmTNnzoBFicPiFjVB+HXgTDQG4QBnLz2b79z/HTa2bRz12FQ6xb1r7uXeNffy/+74f8yrnscBOxzA0nlLWTo/WJY0LFGXZJFtFYsHXY5rToct34HGH0Ny88jndNwdLIl5UP/RYKbk0t00VqFMPuWz4C1/gb+fBy/879D9LavCyUuuh/lvnfj4RCaJV267jQf+5V/obW+nYe+9OfirX2X2/vsXOiwREZFxV1JS0je2IEA6nWbjxo19CcPVq1fT0tIypvtIJpNs2LCBDRsGNsBIJBLMmjWLuXPn9iUNZ8+eTU1NDeXl5WoENcVFTRBucfff5DWSKWh21Wye+eQzXPzQxdy46kYeX/945HM3tG3glhdu4ZYXbukrqy6tZr95+/UlDJfOX8o+c/ehqrQqH+GLFJdELcz5Jsy6AFp+C40/go4HRj4nuQE2fytYSnaC6uPC5a2QULdNmSTipbDsx1C3Nzz6OUinBu7vaYQ7j4eDfwC7/VNhYhQpoN7Ozr7kIMCWlSu57f3vZ4+zz2bf886jpKKiwBGKiIjkTywWY968ecybN49ly5aRTqdpampi9erVrFmzhnXr1rFp0yZSqdToFxtFMplk48aNbNw4tJFUSUkJM2bM6Fuqq6sHbGeWqqoqSktLlUychKImCG8ys/8Cfgd0ZQrdfYSBv6aHWVWzuOiYi7jomItY17yOm5+/mZuev4nbXryN9p72bbpWW08bD6x9gAfW9ic14rE4Nss4eMHBHLLwEA5ZdAj7zduPsoS6RYrkFK+Aug8ES+fjQaKw6WpIj1Ife1dD0+XBQhwqlvUnDCsPgVjphIQvklMsBvYZqNkd7nsP9DQP3J9OwsOfhMaVmrxEpp3OTZv6koMZ6VSKZ3/xC9beeSeHfvObzDnggAJFJyIiMrFisRj19fXU19ezf9iaPplMsnnzZjZu3MiGDRv6knyNjY3jdr+9vb00NjZGumYsFqO8vJyKigrKy8uHrOfaLisrG7KuROP4ivoN4szw9rSssjQwrccgHGxh7ULOPehczj3oXDp7O1nx8gpuev4mbnr+Jl7c+uJ2XTOVTvHs68/y7OvPctWTVwFQnijnwB0O5JCFh3DookM5ZNEh7FS3kyqGyGAVS2H+T2DOd6D5Kth6GXQ/G+HEFHQ+FCyb/w3iNVD1ljBheCyULlF3ZCmMBSfAcQ/CineMPHnJft8MWhyWauZuKX7VixZRt2QJTS+8MGRfy8svc9sHPoB94APs/5nPUFJZWYAIRURECiuRSPRNRLLPPvv0lXd1dfUlC7MThx0dHXmNJ51O09nZOaaxEyFINJaVlQ1IHlZVVQ1owZjdkrG6upqKigrlToYRKUHo7rvkO5BiU1FSwfFLjuf4Jcdz8QkX45udm1YFycJ719xLT2rkCU5G0pXs6m9p+FBQNrd6btDCcOEhvHHhG9ll5i4srFlIZak+CIuQqIOZ50H9p4KxB7deBi2/ByLO/pVqgdY/BgtAvAEqDoSKg8LlQCjdVUlDmRh1e44+ecn624P1GW+A+n37l7p9oWaJWhhKUYnFYhz9ox9xz2c/y5aVK4cekE7jv/wl6+66i0O/+U3mHnTQxAcpIiIyCZWXl7Pjjjuy44479pWl02na2trYtGnTgGUiEofbKp1O09XVRVdXV+RxFxOJxIDkYWVlJeXl5VRWVlJRUTHkNrO+ra0V0+n0lEtERvqGYGafz1Xu7v8zvuEUp1gsxh6z92CP2XvwhcO+QHeym2c2PcPj6x8fsDR1NW33fWxs28iNq27kxlU3DiifWTGThbULWVgTLrULWVCzoG99Yc1C5lTPIR7TTEQyDcRiUHVUsPSuD2Y/br0pHKswGf06qS3QfnuwZMTrw6RhVuKw9A3BJCoi4618FhxzazB5yT8uH/641n8Ey9ob+ssSFVC7V1bScB+o2wuqFinJLVNW9YIFHPerX/HsL37BU5deSqpn6A+xrWvWcPvZZ7P7WWex9PzzKanSGM8iIiKDxWKxvhZ3u+wysK1YJnG4cePGvsRhU1MTra2t9PZGbHxRYMlkkubmZpqbm0c/OEs8Hqe0tJR0Ot23AMOuZ58zeCkrK8tZXllZSVVV1ZClsrJyQpKNUZsQ7Ju1XgYcAdw5/uFMD2WJsr5JSDLS6TSrm1b3JQsfW/8Yj69/nDVNa8Z0X1s7t7K1cytPb3x62GMSsQSzqmYxq3IWs6tm9y3Z27OqZg0or6uoU1JRpraS+TDrK8GSbIL2u6DtL8HSM7Sb2qhSjdD+12DJiNdC+T5QsihYShdBycKs9R00tqFsv0QZvPEnQVfixz4/dPKS4SQ7YeujwZKtpCZonVi3V5BArAuX6p2U6JYpIV5Swt7nnsuiY47hwQsuYPNTTw09KJ1m1dVX8+qKFRzyb//GvGXLJj5QERGRKaq6uprq6mp23nnnAeXpdJru7m5aW1tHXTo6Okgmt6FxxiSRSqXo6uoa/cAc52zreYPFYrEhycOamhr22muvIf+LsYjaxfhD2dtmNhu4atyiEGKxGDvX78zO9Ttzyh6n9JVv6djCI68+wkPrHgqWtQ+xqX3TuN53Mp1kY9tGNrYNnYlo2HiJMbNyJg2VDcysCG6HW+rK66irqOu7rSmrIRFPjOvfIDImiTqoOTlYALpfhLbbgmRh+x2Q2s7Wvalm6Lh/hANikJg3KHG4I5TuFC47B/uVnJHhxGKwx/lQa7knL9kWvS2w+W/Bki1RCbV7hInDPaF6Z6haABU7BLclNWp5KJNK3ZIlHHv11Tz3y1/y5A9+QKq7e8gxra+8wh3nnMNu73sfi487jopZs6iYNYuy2lpicb3mioiIbIvMpCPl5eXMmjVr1ON7e3v7EmednZ0jrnd3d/eVDV6fKq0WxyqdTtPe3k77oEnZHn74YU4//XT23nvvcbmf7RqEyN1fN7OdxyUCGVFDZQPHvuFYjn3DsUDwxHi58eW+ZOGD6x7ksdceoys5toz0tkqTZkvHFrZ0bNmu82eUzehLGNaW1/avl9VSWVpJZUklFSUVfeuVpeH2oPWKkgrKEmUjLkpGyjYr2xXKPg4zPw7pXuh8OGxdeBt0Pgrp8Rp7Iw3J9cHC33MfEiuDksVZScOs5GHJTkFLyHjFOMUjU9aCE+AdLwTdjTf/DbY+CW0vjc+1kx2w9bFgyaWkGioXhMsOWevhdsVcKJ8L5Q1KdsuEiZeUsNeHP8zCo48OWhM+8UTO456/9lqev/bavu1YIkH5zJlUNDRQMXs2FQ0NlDc0BNuzZlE+cybl9fWU19dTVl9PWW0t8YQ+Z0jxSSd76Wp8lc7Nr9C15RU6t6wl2dlCvKSMWEkZ8UR4W1LeX1aSo6y0ItguLc9aryBeWkZM4+GKTFslJSWUlJRQXV09puskk8m+pGF3dzednZ20tbX1tVZsa2sbst2TYxiSqeyRRx6Z2AThoDEIY8AyIHpzMxk3sViMXWbuwi4zd+G9+7wXgO5kN0+sf6KvleGqzatY17yO9a3rSaYnZ9Pd1u5WWrtbWdeyLu/3FY/F+5KF5YlyKksrqSqt6ks2VpaE29nr4W1teW3QSrJyZl9LyUzLyerS6ik36Khsh1gJVL4pWGZ/LUgYdj8HnY8EycLOR6DzMUi3j36t7ZHuDro8j9TtOV4DiblQMje4zV4fcDsH4nUQK1eLr2JUMQf2/pf+7Z4WaFoJjU9B45Ph7VPQvX0/7Ayrtw1ang+WkcTiUD4nWCrm9icOK+Zkrc/t319aq+epjFndrrty7FVX4VddxZOXXEJylC4+6WSSztdfp/P112HVqtHvIBajrKaGsjBpmEkcltfVUV5fT0lVFYmKChLl5ZRUVPStJyoqhm6Ht2rBKBMlnU7T27aFzs1r6Nqyls7Nr9C5JVjv2vpq9KErtlMsXtKXOIxlkoqJEmKJUuKJUmJZS7wkaz1RQixR1p90LK0kXlZBvLSCxKDtvv1hQjIWT0Asrs/wIkUikUhQWVlJZWX0yVmzu0K3t7fT2dlJR0dH36zK2dvZ5ZO1tWJJyfj92LItYxBmRlpMAy8DXxi3KGRMyhJlLFu4jGULl3Ee5/WVJ1NJNrRtYF3zOta1rOu/zV5vXkdLd7TZfqaqVDpFZ28nnb1jm0J9sJJ4yYCk4cyKmcwom0F1WTXVpeFSNvxtVWnVkNaOpfFStX6c7GIlwbiC5ftA3dlBWToJ3asGJg27HgtmP54IqZZg6flHxBNKg27V8dpwqYNEeJsp69s/I1xq+m9jMyAR3sbL8/qnyRiU1sDsQ4MlI52Gjtf6k4ZNT0PTM9D8bJDoy6d0Cjo3BEuUXvvxsoEJwyHJxDlBEjFeFjwP42WQKO/fTmSVq5XKtBZPJNjznHNYePTRPHTBBWx6bJjWsNsjnaa7uZnu5mZa14xt3OiMeGlpkDTMLJkkYo4lXlJCvLSUWElJ33rfbWbJKu87bvB54RLLPjYeD7bjcWKJRLCdfTu4LBYLkvqxWN+6kjCTU3fzRtbd+b80PX8/qe48/cAZQTrVS7Krl2RXnt9/cokniGUvscSgsjjEEuHzOB4kFePxAetBojEeHBNPBC0iw9tYPBEkOwdcM0xQ9pVn7U+UZG2XEEsksq41+DqDY82+71xLuC9zv2rNL9NcWVkZDQ0NNDQ0bNN5PT09JJNJYlnvb5n1wdsZqVSKnp4euru76enpyblk9nV3d9PR0dHXlTh7GW4Mw/Lyco444ojtfCSGivqJ+Urg60ADQQtCgBOB/cYtEhl3iXiCBTULWFCzgGUMPwh3Z28nm9s383r762zuCG4zy+b2zbze0b++qX0TWzu2Fn1SMYreVC+b2jeN+5iQ2eKxeF/SsLyknIqSir4l08U611JZUsnBCw7m9L1Op7I0+q8psp1iCSjfM1jq3h+UpVPQ8yL0rIbetdCzLrjtXQs9a6F3HSQL1RC7B5KvB8uYlWYlESvD1oll4VI+6DZrnbJggpZYSbCQuU0M3M61L5YIy3LdJrKOzWzHB22Ht8T710vmh+tFLhYLxg2sWgALju8vT6egfW2QLGx6Bpqf6V/v2c4xOMcq1R3E1L527NeKxQcmEjNLIldZZru0//kWSwRJxsx6zvLweRov7V9imfWSrPXsfSXDrw8+N/zyOuC++xYlYaKo3Xln3nrllay65hqeuPhikp3j+8PheEn19JDq6aGntbXQoYyPrIQhsVjwRSIeJ55IECstDW4TiSApmbWevR1LJIjH4xDPJGli/evxTLIm1p+wHCGWnMWZa2diDNdzlfddI2uWSrJmrxy8r3LOHHY68UTq3vCGMT2M4+nlP36L1ldyd7ufNlJJ0qkk6dGPLD6x+JBkIxAmDgcm+SGTICXcl6kPYXJ0pKRpJsEaj4ctN/sTr33J1r4EbbBeUlXPzD2Oomr+bgV8gERyy8w0vC0SiQSJRIKKirENCZVMJockDePxOIsWLaKmpmZM184WNUH4Q+DnwKMwtV9HzexM4AKC2Zi/5+6XFjikgqsoqWBh7UIW1i6MfE5PsoetnVv7xiEcaWnqaqK5q5mmziaauppo7S6SD7wTIJVO0ZXsoivZtV1J2WufvpabzrxJv+AXQiwOZUuCZTipLuh9NUwcroOeV4KEYs9q6A1vU2OYdGJC9EBqa7BMZfEGmPsfUP+xQkdSGLE4VC8OlgUn9JdnWhxmEoYtzwfbHa+Gy2tBIm+yS6eC2ZuTkzMhNHaxEZKGsYHHQe592/I+Ub0r7PUlWHzGGGIujHgiwR4f/CCL3vIWXvzDH2hctYrOLVvo3LyZzi1b6G0rQEumYpdO9yfO6P8ikQLoGK8xfSc3v/pqjrvmGuqWjPCZYIKkkj20vvJkocOQQkqnSCdTpJOTcxy2jQ/9mt3e99/MWKy2SCIZiUSCmpqacU0G5hI1Qdjt7v+T10gmgJktBL4FHAR0Afeb2Z3u/kxhI5t6ShOlzK2ey9zqudt8bjKVpKW7pS9hmJ08bOlqoaO3g87eTjp6OgasdyZzlPV20pPqoTvZnXPp6u0iPbVz2mPy5xf+zPNbnmf3WbsXOhTJJV4OZbsEy3CSjdDzcn/isC95+HLQEjG5ifBrloxFagus/zhUHQ1lqi99slsczn/b0P3pdDCmYcer0P4qdL7Wv97xKnSuh85N0LVxbDMsyyjSwfio6QkaG6drM9z3XpixBBoOmJj7HGczFi1iv09/ekh5b0cHXVu39iUMO7dsoSuzvnkzXY2NdDU20t3URFdjIz0t6lEho+tpbeXlm25i//PPL3QoxOIlVMzeic7XXx712JLqmVQ0LKZ81iIqGnaktGYO6VQv6d5uUr3dpHq7+tb7ypKZ9S5SPd2kejpJ93aR6uki1dsZ3gbb+R7jUKamdLKH15+4SQlCkQKImiB82sz2dfen8hpN/r0N+Ku7bwGw/9/e3cdYVtd3HH/fmdllZ1l02VUoK1BEw09EDdQHfOwSwbbRNhq1NQoJtIKS6h/9w6cKtUCaNJoWaCtR04pYWxqt1hqfUp8JKhW1QGsI3zQNWEEoRnbZsMydmXvu6R/n3Jk7d2fnnmHuw8w971dycx7vPb/zO/O5s/Pd85DSZ4E3Atd0Vkgp7QZ297zv5JG1sAamp6bZvWM3u3f0dvNwZO2sKBZm80uFxccXH2euNXfE+FyrnC7nH2we5JHmIxyYO7B0xuSBuWI46idHP1EzQ7z3lnkZgendMH027Dh79eV5G7JHisuVs19A6+FivHeYPQzZLyF7FNicN9jdFJr/NbQC4UTmpdGAY/YWr93PXXvdrFkWC38BzYeLV2d8vpzuFBObD2OLBc0AAA5ZSURBVBdPT9bmlbfh4e8MrUA4rrzMzM4yMzvLsfv2VVq/vbjIwqFDRxQOO+OtZpNsbo5sfp6s2aRVDrNmk2x+vlg+P7+8Tp+HqGjr2rZr19A+ez15aTQanPY7f8y9X/gz5h/5GVPbdnDMnqIAeMyeU9ixtxzuOZnpHcNrc57n5NliV8GwSZ4tLr3ara7xbJE8a5FnC+RZq1xWFB+zxSbthSbtxTnai82Vr4Vmuc4ceWuhuKS4nVmY3AKmhniLpIn895g0IFUrB6cDP04p/RRY+hd7RGy1sv4+4MGu6QeBF/Ws80fAn46sRRq66alpZqdmB34vvrnFOQ40DywVDA82D3J48TCHFw4fMXxs4bFivGv+3OLcqmc/LmbL8zZ69uMlZ1/C6cefPqA9XpV5GbfGFMw8pXhVkeeQN4tLl9uHioJh+xC0y2HWGX8U2o+VD0BZbViOT1KxsTELO182zC3UOy/TO+DYU4pXP3lePDRlRTGxLCB2FxSzueIS52y+GLY7w555/jE4HMcf5T8uBmNL5GVq2zZ27N3Ljr17B/J5eZ7TXlgoCoidYVk4bDWbtBcWloqK7YUF2q1Wcc/CzrDzarXIWy2yxUXy7nXKV9473Rnvem87y8izjLzdPmK42rLi90t5OXFe36s3VvOk00/n6a997TA3sa687Dwpcdbln6LdmqcxvX0st6JpNBo0ZrYzNbMdGO4lc73yvA3t9lLBcLlw2DPdzsqf53bxns6w3S7nl8vbbfLOe7NW+f7W8n0O2+W8LCvWyzrLWl3rd093zSunyds9845s71KbV9led5s2u5ljj+eEF75+mJvYEr9fpHGoWiC8YqitGJ3Vfvv1/tVwPXBTz7yTgVuH0SBtXbPbiqLjvuOqnWWwXnmek+UZi9ki89k8C9nCikure1+dS6+brSYL2QLPOeE5nHfaeUNpWxfzstU0GkUhbGoWOHFjn5XnkC8sFw3zZjGdz/cMV5s3D2Rdl0X2DJfGs3K4WI6X7yHrWtY97F3eO2wfOZ8Mtp8JT/lA8bCS4TEvVTUasG1X8dq1xiX4VbWzlcXDFQXEo0y354v35Z1Xa3m83Tpyfrv8OV0alq+8tTzeO72e9bu3v+I1huLntifDme+GE84b5lZqmZdGo7H0hOKtLu8tGHbdi3CpuNhViGwvLhbDLFs5v9VaUYTM2+0Vr6V5WVaelXaUAsgaRcu8/Izlwk/5mZ3iT2c75fylglrjyPt4NnqmZ084gRNe8AK2HXvsRrqznyeUl6mZrf9z9kQ0GlMwPUVjun5PuF8qbC4VG8tCY075+6TzwJ2uoj/dGe7ML4qlncLp6kXT5aLqcsG1M941P+/ku830juM47lfPYWbnk4fZDbX8/SJVUelbMSJuGXZDRuQBoPsZ0CcBP+9eISIOAge756WUht8yqUej0WCmMcPM1MymfRKxeam5RqN4KvHUMUDFMxhrzLyM0dQ0TO0Edo67JYNX/qF2ROGwWNi94vL6vcvWe7bX9t3F05WHyLxsfUtP/h13Q2rAvKiqIpczNIb8Hb6ZmRfp6Or2zfAN4KqU0lOBw8AbgJo+slKSJG15jfIJxkyPuyWSJEnawqbG3YBRiogHKC6X/jZwJ3BzRNw+3lZJkiRJkiRJ41O3MwiJiJuBm9f5tmmAhx56aPANksbk/PPPPw24PyIG/ZQJ86KJY16k6syLVJ15kaozL9L6rDcztSsQPkEnAVx44YXjboc0SPcCTwfuG/DnmhdNIvMiVWdepOrMi1SdeZHWZ12ZsUBYzQ8pHm7yIMUjL3t1nnr0CuD+EbZrHNzXyTKM/TIvy9zXyWJehst9nSzmZbjc18liXobLfZ0s48gL1KNvoT77CfXZ18r7ZoGwgoiYB757tOVdTz26PyLuG0WbxsV9VT/mZZn7qn7MyzL3Vf2Yl2Xuq/oxL8vcV/XTLy9Qn76ty35Cvfa1qlo9pESSJEmSJEnSShYIJUmSJEmSpBqzQChJkiRJkiTVmAXCwTgIXF0OJ537qo2qU7+6r9qoOvWr+6qNqlO/uq/aqDr1q/uqQahL39ZlP6Fe+1pJI8/zcbdBkiRJkiRJ0ph4BqEkSZIkSZJUYxYIJUmSJEmSpBqzQChJkiRJkiTV2My4G7DVpZTeAlwJbAeui4gbxtykoUkpfQs4EVgsZ709In4wxiYNXErpScD3gd+OiPtSShcA1wKzwKcj4sqxNnCLMy/mRetTl8yYF/MyCHXJC0x+ZszL8JkX86LqzIt5qQsfUrIBKaWnAd8Fng/MU/ygvTki7h5rw4YgpdQAHgBOjYjWuNszDCmlc4G/BZ4FnAH8HxDAfuBnwJeB6yPiq2Nr5BZmXiaLeRm+umTGvJiXQahLXmDyM2Nehs+8TA7zMnzmZXKYl/68xHhjLgC+FRGPRMRh4LPAG8fcpmFJQA58NaV0V0rpneNu0BBcBrwD+Hk5/SLgvyPi3vIL8h+A3x1X4yaAeZks5mX46pIZ82JeBqEueYHJz4x5GT7zMjnMy/CZl8lhXvqwQLgx+4AHu6YfBE4eU1uG7Xjgm8DrgPOBy1NKrxpvkwYrIi6NiFu7ZtXp+I5CnfrTvEz28R2VuvSpeZncYztKderTic6MeRmJOvWpeZncYzsqdepT8zK5x7YS70G4MY1V5rVH3ooRiIjbgNvKycMppY8Drwa+Pr5WDV1tju+I1KY/zcuSiTy+I1SLPjUvSybu2I5Ybfq0hpmpzbEdodr0qXkBJvTYjlBt+tS8ABN6bKvyDMKNeQD4la7pk1g+XXWipJRenlI6v2tWg+Ubl06q2hzfEalNf5oXYIKP7wjVok/NCzChx3bEatOnNcxMbY7tCNWmT83L5B7bEapNn5qXyT22VXkG4cZ8A7gqpfRU4DDwBuBt423S0OwGrkkpvRTYBlwMXD7eJg3dD4CUUnomcC/wFuDG8TZpSzMvk828DF5dMmNezMsg1CUvUL/MmJfBMy+Ty7wMnnmZXOalh2cQbkBEPABcAXwbuBO4OSJuH2+rhiMivkTxVJ87gB8DN5anIE+siGgClwCfA+4G7qG4Ka2eAPNiXrQ+dcmMeTEvg1CXvED9MmNeBs+8mBdVZ17MS5008jwfdxskSZIkSZIkjYlnEEqSJEmSJEk1ZoFQkiRJkiRJqjELhJIkSZIkSVKNWSCUJEmSJEmSaswCoSRJkiRJklRjFghrLKX0wpTSR8vxF6SU1nykd0rpppTSu0bTOmnzMTNSdeZFqs68SNWZF6k686L1mBl3AzRWZwEnA0TEj4A3jrc50qZnZqTqzItUnXmRqjMvUnXmRZVZIJxAKaUp4DrgxcBxQAO4FLgM2AM8A/h34FXAk1NKnwA+CXw4Ip6TUtoF/A3wMqAF/CtwRc82zgT+CtgLTAN/HRE39mnXGcANwC5gH3An8KaIaKaUXg18EMjK+RcAL4+I+1JKbwX+kOKM118C74yIe9bYzoXAOyLipeX0qeX+nlbu+xHtPlqfRcT3Uko3dfXbl4AvAteW78+BP4+Iz62179rczIyZUXXmxbyoOvNiXlSdeTEvqs68mJdh8BLjyXQuRRhfEhHPpvgieF+5bGdEnBURbwU+ANwaEb/f8/5rgB3AmcDZFF8a+zsLU0ozwGeB90XE88tl70opvbhPuy4DPhkRLwGeCTwdeE1KaS/wKeCiiDgb+DbwtHJb+4GLgVdExDnAh4B/6bOdfwaekVJ6djl9adkH7TXavVafdffbe4GrgWvLz/gD4JV92qPNz8yYGVVnXsyLqjMv5kXVmRfzourMi3kZOAuEEygibgOuBN6eUvoLitOId5WLv1vhIy4APh4RWUQsRMT+iPhO1/IzKCrrN6aU7gRuAWaBc/p87nuBX6SU3gN8hCKcu4BfB+6OiLvK9n8SOFS+5zUUXyzfL7f1IWBPSmnPGvu/APwdcFlKaRq4BPjYWu3u02ewst8+A9yQUvpH4PnA+/vstzY5M2NmVJ15MS+qzryYF1VnXsyLqjMv5mUYvMR4AqWUXkNxSu1fAl8A7gEuKhc/VuEjWhSn0XY+7xTg8a7l08DBsvLfWedE4NE+n/tPFD9znwG+DJxKcVpvqxx2a3dt61NlFb9zKvU+4ECfbX0MuJ3iC+EnUZy2/NyjtbtPn0FXv0XEx1JKXwR+A/gt4KqU0vMiot/+a5MyM4CZUUXmBTAvqsi8AOZFFZkXwLyoIvMCmJeB8wzCyfQq4IsR8RHgh8DrKELXqwVsW2X+N4CLU0pTKaVjKE7R3d+1PIBmSukiWPoy+QlFZX0tvwlcExGfpvgyOrds1/eAM1JKzys/7w3A7nKdrwFvTimdVH7G5cA3+2yHiPhf4DaKewx8pEK7q/YZKaXvU/wPxE3A28q2Ht+vTdrUzIyZUXXmxbyoOvNiXlSdeTEvqs68mJeBs0A4mT4K7E8p/SdFYP6H4tr/3uN9G/CslNLne+ZfDSwAdwF3AF+JiKV7AJSn874WuLTcxteAP4mI7/Vp1/uBz6eUflS28RbgmRHxCPBm4O9TSv9B8aXSAh6PiH+juJHp18ttvQV4fUTkq25hpU9QBP4rFdq9ap+V/3vR6z3ANSmlOyjunXB1RNxXoT3avMxMwcyoCvNSMC+qwrwUzIuqMC8F86IqzEvBvAxQI8+r9Lk0PCmlJ1HcC+CqiHg8pfRrFKcj76v4pbDaZ04BHwZ+GhEfHFxrpfEzM1J15kWqzrxI1ZkXqTrzsjV4D0INTEopAZ8+yuKIiDcdZcGhlNIC8MOU0iKwCPzeWl8UKaV3AxceZfF1wPUU9yN4d9X2S6NmZqTqzItUnXmRqjMvUnXmZbJ5BqEkSZIkSZJUY96DUJIkSZIkSaoxC4SSJEmSJElSjVkglCRJkiRJkmrMAqEkSZIkSZJUYxYIJUmSJEmSpBr7f5iDj0lZmeCPAAAAAElFTkSuQmCC\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We also have the number of papers that were available in July 2019, by age of the article (here on the x axis called \"article years from availability\". [link this back to the earlier section, call it out by name. would be cool even to have these graphs in that section.]\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:18:44.256860Z",
"end_time": "2019-09-23T06:19:25.322755Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\ndata = get_papers_by_availability_year_total(2018) \ndata_now = data.loc[data[\"article_years_from_availability\"] < 15]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\ng.map(plt.bar, \"article_years_from_availability\", \"num_articles\");\n\n\n",
"execution_count": 33,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T19:41:37.890891Z",
"end_time": "2019-09-23T19:41:37.913612Z"
}
},
"cell_type": "markdown",
"source": "Now for each of the OA types individually, we divide these signals by each other. This means that, for each OA type, we divide the number of times someone accessed an article of that was 0 years old (in other words, brand new--in this case published between July 2018 and July 2019) by the number of total articles that were 0 years old -- articles that were published since July 2018. Then we take the next age bucket, 1 year old, and divide the number of accesses to 1 year old articles by the number of articles available in July 2019 that were 1 year old. We do this for all age bins (15 are shown in the graphs).\n\nThe result of these divisions are the signals below: the number of accesses per article, for a given age and OA type.\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:19:25.328798Z",
"end_time": "2019-09-23T06:19:45.508952Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\ndata = get_accesses_per_article_total() \ndata_now = data.loc[data[\"article_age_years\"] >= 0]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(s=50)\ng.map(plt.scatter, \"article_age_years\", \"accesses_per_article\", **kws);\ng.map(plt.plot, \"article_age_years\", \"accesses_per_article\");\n",
"execution_count": 34,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T19:32:03.933970Z",
"end_time": "2019-09-23T19:32:03.948591Z"
}
},
"cell_type": "markdown",
"source": "#### How many papers are there of a given age and OA status\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\nBecause access patterns depend so heavily on recency of publication, to estimate how open access affects access rates we need to take into account the age when articles are made available. We will use the work we did in the \"Predicting number of papers at their time of availability\" section. The figure below shows the number of papers we calculate, then predict, for all OA types.\n\nEach column of the figure is a type of OA (green, gold, hybrid, immediate bronze, delayed bronze, closed).\n\nEach row is a year of availability assessment, from 2016 in the first row to 2023 in the last row.  [talk about what y axis is scaled to?]\n\nEach plot is the number of papers available for that year and that OA type, by year of publication on the x axis.\n\nFuture years are calculated by assuming the new additions in future years will follow a similar pattern to 2018, subject to overall growth. The overall growth is assessed by a linear extrapolation on Year 0.   \n\n[maybe transpose this so that it goes ltr in direction of time, insteaad of top to bottom, which is less common?]  [maybe lose dimming]\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:16:18.004418Z",
"end_time": "2019-09-23T06:16:36.518303Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\nfig, axes = plt.subplots(len(my_range), len(graph_type_order), figsize=(13, 10), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\ni = 0\nfor observation_year in my_range:\n for graph_type in graph_type_order:\n y_max = all_predicted_papers.loc[all_predicted_papers.graph_type == graph_type][\"num_articles\"].max()\n this_data = all_predicted_papers.copy()\n this_data = this_data.loc[this_data.graph_type == graph_type]\n this_data = this_data.loc[this_data.prediction_year == observation_year]\n this_data[\"publication_date\"] = [int(observation_year - a) for a in this_data.article_years_from_availability]\n new_data = graph_available_papers_in_observation_year_by_pubdate(graph_type, this_data, observation_year, ax=axes_flatten[i])\n axes_flatten[i].set_ylim(0, 1.2*y_max)\n i += 1\n",
"execution_count": 29,
"outputs": [
{
"output_type": "display_data",
"data": {
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sZk9lMn8qi9lTWcyelqPQfWgzcw+wp2PZuW2Pv8urT9yWxsLsqSxmT2UyfyqL2VNZzJ5GVeS2PZIkSZIkTZ2yC9pZYBcdx7lPAcdVf9P8Xk7r2KZ1XFU0re+l46q/aX0vHVf9TfN7Oa1jm9ZxVdG0vpeOawxmFnvc302SJEmSpGlX9gytJEmSJEkjsaCVJEmSJFWSBa0kSZIkqZIsaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIlWdBKkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSLGglSZIkSZVkQStJkiRJqiQLWkmSJElSJVnQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEqyoJUkSZIkVZIFrSRJkiSpkixoJUmSJEmVZEErSZIkSaokC1pJkiRJUiVZ0EqSJEmSKsmCVpIkSZJUSRa0kiRJkqRKWlf2AKoiIt4J/CmwCDwP/HFmPhIRHwIupvFe3gbsyszFtu3OAa7LzJObzy8G/re2rl8LbAO2ZeaPJ/LDqFLMnspi9lQm86eymD2VxeyNZmZxcXH4WqtcRATwNeBNmbk/Is4FPgO8D7gWeDNwGPgH4ObM/PuIOAq4EvhPwJOZ+Rs9+v054H5gd2b+xUR+GFWK2VNZzJ7KZP5UFrOnspi90XnIcTGHgEsyc3/z+SPAFuDtwJ7MfC4z54HPAe9srvM7wNHAuwf0+0Hg6bqGS2Nh9lQWs6cymT+VxeypLGZvRB5yXEBmPg48DhARM8AngLuBrTT2krQ8SWM6n8y8C7grIt7aq8+I+O+BPwHetELDVg2YPZXF7KlM5k9lMXsqi9kbnTO0SxARRwN/D7weuITe79/hgt1dBnwxM384puGpxsyeymL2VCbzp7KYPZXF7C2dBW1BEbEdeJBGgN6WmbPAEzT2mrQcR2OvSRHvoHHIgDSQ2VNZzJ7KZP5UFrOnspi90XjIcQER8Trgn2icTL2rremLwM6I+EtgAXgXsLtAf/8djb0uD459sKoVs6eymD2VyfypLGZPZTF7o7OgLeYPge3AeRFxXtvys4DPAw8D62kE7m8K9Pd6YH9mvjTugap2zJ7KYvZUJvOnspg9lcXsjcjb9kiSJEmSKslzaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIllXpRqIjYAJwG7Kf4/ZRUjrU0Lhn+rcw8VPZglsvsVUqtsgfmr2JqlT+zVylmT2WpVfbA/FXMkvNXqKCNiIuAq2hcWeuGzLy5o/1q4D3Az5qLbulcp4/TgAeKjEFT40zg62UPYgzMXvXUJXtg/qqoLvkze9Vj9lSWumQPzF8VFc7f0II2Io4DrgVOAQ4BD0bE3sz8XttqpwEXZuY3lzjQ/QC33347W7ZsWeKmmqQDBw6wY8cOaH5mNWD2KqKG2QPzVxk1zJ/Zqwizp7LUMHtg/ipjlPwVmaE9G7gvM58FiIg7gAuAj7StcyrwwYg4EbgfuCIz5wv0fRhgy5YtbNu2reiYVa66HKZh9qqnLtkD81dFdcmf2ases6ey1CV7YP6qqHD+ihS0x/LqCnk/cHrrSURsAh4FrgAeB3YDHwaubO8kIjYDmzv6NlGSJEmSpJEUKWhneiw70nqQmXPAua3nEXE9cCsdBS1wObBzhDFKkiRJktSlyG179gHtB5tvBZ5qPYmI7RHx7rb2GeClHv3cCJzQ8e/MpQ5YkiRJkiQoNkN7L3BNRBwDPAecD1zW1v4CcF1E7KVxyPH7gS90dpKZs8Bs+7KIGG3UkiRJkqRVb+gMbWbuo3H48F7gO8CezHw4Iu6JiFMz8xngvcCXgKQxQ3v9Co5ZkiRJkqRi96HNzD3Ano5l57Y9vhO4c7xDkyRJkiSpvyLn0EqSJEmSNHUKzdBKkiSp/iLiIuAqYD1wQ2be3Ge9vwb2ZubuCQ5PkrpY0EpSD8O+1EXE1cB7gJ81F93S74uftFTmT2WIiOOAa4FTgEPAgxGxNzO/17bOscBfAGfRuL6KJJXKglaSOhT5UgecBlyYmd8sY4yqL/OnEp0N3JeZzwJExB3ABcBH2tbZAXwR+OnkhydJ3SxoJalbkS91pwIfjIgTgfuBKzJzfuIjVR2NJX8RsRnY3NH3thUbtergWGB/2/P9wOntK2TmxwEi4s39OjF7kibJglaSug38UhcRm4BHgSto3H97N/BhGrc4exW/2GkE48rf5cDOFRyn6memx7IjI/Rj9iRNjAWtJHUb+KUuM+eAl29dFhHXA7fSo6DFL3ZaunHl70YaxW67bcADYxml6mgfcGbb863AUyP0M5HsHVk4xJp1GwovL7pOke2lsiwsLLBuXXcJ12/5arA6f2pJGmzgl7qI2A6cnZm3NhfNAC/16cuiQks1lvxl5iww274sIsY+WNXKvcA1EXEM8BxwPnDZUjspkr1W0biconTNug18+2Nv7Vr+pg99begY+21bdHuNX0TsBP6g+fTLmflnHe0nA7cAr6VxqsX7MnNhsqMs37p169i1a1fX8p07V+++cwtaSeo27EvdC8B1EbGXxiGf7we+0KsjiwqNYGz5k5YiM/dFxJU0rl68HvirzHw4Iu4Brs7MR5b7Go996kKe3rTu5aJxWFG6nIJX1RERZwPnAG8EFoGvRsR5mdn+u+024JLMfCgiPgtcCnx68qMdbjmzqIPWWc2zsIP4jmjqRcRrgAeB38vMxzva3FunsSvypS4i3gt8qdn+deD68kasOjF/KlNm7gH2dCw7t8d675rEeJYzC2sxXCn7gT/JzBcBIuL7wPZWY0QcDxyVmQ81F+0GdjGlBe2wWdRhBW+vbdu316tZ0GqqRcRv0ShY39BnlcrsrVO1DPtSl5l3AndOelxaHcyftHzLKYY1WZn5WOtxRPwK8A7gt9tW6XWxvJ4XWKzCxRg9bHi81pQ9AGmIS2kcTtd1UYo+e+vePrmhSZKkOjqycGhJyzUeEXES8F9o3IrsX9qalnIF7suBH3b8G/t1KxYWeh8Q2G+5Vo4ztJpqmXkJ9D3nsNDeuirsqZMkSdPD2d3Ji4gzaBx5cnlm/l1H8z5gS9vzQVfgHsvFGIcdFuws6/SwoFWVFd1b521TJEmSplRE/BJwF/COzLyvsz0zfxQR8xFxRmZ+A7gY+EqvvopejNGCtT4saFVlRffWedsUSaqZw4cOsXZD7wv7DGqTNJWuADYCn2grPj8D/D6vXGF7B3BLRPwC8CjwyeW8oAVrfVjQqrKK7q3ztimSVD9rN2xgz0kn9Wy76LHHei6XNJ0y8wPAB3o0faZtne8Cp4/j9bz9Tb14UShVTkTcExGnNp/uAG5oXt79aJa5t06SND0OH+p9AZ5+yyVpkJtuuoldu3ZZzNaMn6YqITN/ue1x+60rxra3TpI0XfrNwjoDK0lqcYZWkiSVwhlYSdJyOUMrSRrd4XlYu7H4cq06/S7Q1FruDKwkaTksaKU6ODIPa3oUD/2WS+OydiPs6XEHrYsWJz8WlWJYwWrRKklaSRa0Uh2s2Qj/3KOo+FWLCkkry4JVklSmVXcO7fzCfN/lg9okaVU6POD336A21YbnuUqSplntZmjnF+bZuK77EMvW8o3rNjKzq3sma3FnYyZrUNuwvqUV4yHFKku/Q4rBw4prxPNcJUlVVbmCdrkF63KsZN/SQB5SLGkFeJ6rJKnqpqqgbRWlw4pWi0rVzqCZVmdhtdK8UvGqc/c557B5ZsaCVZJUeVNR0J5w0wksbFp4uSi1aNWq028GFpyF1crzSsWSJKmiVt1FoaTSHOlzAZ1+yyVJkiQNNBUztNKq4HmwKouHFEuSpJqyoB2TQVc69irIq4RXIlaZhhWtHlIsSZJqyIJ2TPqd9wue+7tqOAOrMlm0SpKkVchzaKWl8DxYleVwn4z1Wy5JkrQKOEMrLYWzsFopww4ZdgZWkiSpiwWtBK+c5+p5sCqD57lKkiSNpFBBGxEXAVcB64EbMvPmjvaTgVuA1wL3A+/LzIUxj1Wr0Ipn7wcnwNzCKzOszsCqaSK/9+4+ATYvWLSqi393VRazV8yRhUOsWbeh8HINZ/Y0qqHn0EbEccC1wJuB3wQui4hf71jtNuCPMvMNwAxw6bgHqtXH7KksZk9lMn8qi9krbs26DXz7Y2/t+mcxOxqzp+UoclGos4H7MvPZzHwOuAO4oNUYEccDR2XmQ81Fu4G3j3ugVTe/0PvCLf2WCzB7Ko/ZU5nMn8pi9lQWs6eRFTnk+Fhgf9vz/cDpQ9q3dXYSEZuBzR2LjwdY93xjGE8++eQrA5vrHlqrfVDbctvHvW17+wk3ndDV9sMP/BCAQ4cPsWFt9169fssn7cCBA62Hayf4siuevQM/aX5mm175jPlxj8+xaPu4tx1X3xVW5ezBkPwdbH5mrd8xsz0+w7bfP8tqH/e2S+m7wqqcv0HZO7jYOMy9/e/b7GL3oe+t9kFty20f97ad21dVXbP30+cOA6/+jJ6Z6z5qtGj7uLcdV99VVuXsweD8Pf/888Arn9Pc3FzX9u2f4XLax73tUvquslHyV6Sg7XVz1SNLaG+5HNjZ6wW237sdgLPuPuvlZSdyYtd6rfZBbcttH/e2S+m7IrYCP5jQa6149nZ8aHvzUfvn0OtzLNo+7m3H1XctVDF7MCh/n2vm76YBn/FNQz7jwu1j3nZJfddCFfPXN3ufO9JY/aazBn9Og9qXs22ZfVdQrbK366vPNh7cOeQzWk77NPddLVXMHgzI37333gvA3Xff3Xcgg9qW215m3xVUOH9FCtp9wJkdnT/V0b5lQHvLjTQOD2h3IvCPwFuAJwqMZVK2AQ/Q+LmnaXdHmeNaS+Oz/dYEX3M1Zg/MX6cqZw+qlT+z163K+TN7y2f26p89MH+dqpw9qFb+zF63JeevSEF7L3BNRBwDPAecD1zWaszMH0XEfESckZnfAC4GvtLZSWbOArPtyyKi9fCJzHy86KBXWtu4nnRcrzKpvXQtqy57MBWfc08lj6uS2WuuW5n8mb2+Kpk/s7d8UzAuszcBU/A59+TfXf/ulmUKxrWk/A29KFRm7gOuBPYC3wH2ZObDEXFPRJzaXG0HcENEfB84Gvjk0sYsdTN7KovZU5nMn8pi9lQWs6flKHQf2szcA+zpWHZu2+Pv8uoTt6WxMHsqi9lTmcyfymL2VBazp1EVuW2PJEmSJElTp+yCdhbYRcdx7lPAcdXfNL+X0zq2aR1XFU3re+m46m9a30vHVX/T/F5O69imdVxVNK3vpeMag5nFPvdwkyRJkiRpmpU9QytJkiRJ0kgsaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIlWdBKkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSLGglSZIkSZVkQStJkiRJqiQLWkmSJElSJVnQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEqyoJUkSZIkVZIFrSRJkiSpkixoJUmSJEmVZEErSZIkSaokC1pJkiRJUiVZ0EqSJEmSKsmCVpIkSZJUSRa0kiRJkqRKsqCVJEmSJFXSurIHUBUR8U7gT4FF4HngjzPzkYj4EHAxjffyNmBXZi62bXcOcF1mnty27DxgF3AE+BlwSWb+YGI/jCrF7KksZk9lMn8qi9lTWczeaJyhLSAiAvg48LvNoHwU+HxEnAu8HTgF+A3gbc3nRMRREfFR4O9p23EQEUfRCOJ/bPZ1N/DJCf44qhCzp7KYPZXJ/KksZk9lMXujs6At5hCNvRr7m88fAbbQCNOezHwuM+eBzwHvbK7zO8DRwLs7+loLzACvbT7fBMyv4NhVbWZPZTF7KpP5U1nMnspi9kbkIccFZObjwOMAETEDfILGno6twD+0rfoksK25zV3AXRHx1o6+5iLifcCDEfFTGoE7Y2V/AlWV2VNZzJ7KZP5UFrOnspi90TlDuwQRcTSNKf3XA5fQ+/07PKSPfwtcDfx6Zh4LXAvc2Qyu1JPZU1nMnspk/lQWs6eymL2ls6AtKCK2Aw/SCNDbMnMWeILGXpOW42jsNRnkd4BvtJ2UfTON4+F/cbwjVl2YPZXF7KlM5k9lMXsqi9kbjQVtARHxOuCfgM9n5oWZ+UKz6YvAjog4OiI2AO8C7hrS3beBt0TEv2kGtismAAAgAElEQVQ+/w/ADzPzJyswdFWc2VNZzJ7KZP5UFrOnspi90XkObTF/CGwHzmteArvlLODzwMPAehqB+5tBHWXmfRHxceBrEfEi8CzwP6/IqFUHZk9lMXsqk/lTWcyeymL2RjSzuLg4fC1JkiRJkqaMhxxLkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVUqlXOW5eevo0YD9DbhCs0q2lcQ+sb2XmobIHs1xmr1JqlT0wfxVTq/yZvUoxeypLrbIH5q9ilpy/QgVtRFwEXEXjUtE3ZObNHe1XA+8BftZcdEvnOn2cBjxQZAyaGmcCXy97EGNg9qqnLtkD81dFdcmf2ases6ey1CV7YP6qqHD+hha0EXEccC1wCnAIeDAi9mbm99pWOw24MDO/ucSB7ge4/fbb2bJlyxI31SQdOHCAHTt2QPMzqwGzVxE1zB6Yv8qoYf7MXkWYPZWlhtkD81cZo+SvyAzt2cB9mfksQETcAVwAfKRtnVOBD0bEicD9wBWZOd/eSURsBjZ39L0VYMuWLWzbtq3omFWuuhymcRjMXsXUJXtg/qqoLvkze9Vj9lSWumQPzF8VFc5fkYtCHcurK+T9wMtJiIhNwKPAFcCbaBStH+7Rz+XADzv+OfUvSZIkSRpJkRnamR7LjrQeZOYccG7reURcD9wKXNmxzY3A7o5l27ColSRJkiSNoEhBu4/GSbktW4GnWk8iYjtwdmbe2lw0A7zU2UlmzgKz7csiYqnjlSRJkiQJKFbQ3gtcExHHAM8B5wOXtbW/AFwXEXuBx4H3A18Y8zglSZIkSXqVoefQZuY+GocP7wW+A+zJzIcj4p6IODUznwHeC3wJSBoztNev4JglSZIkSSp2H9rM3APs6Vh2btvjO4E7xzs0SZIkSZL6K3KVY0mSJEmSpk6hGVpJkjQ5EXERcBWwHrghM2/uaL8aeA/ws+aiWzrXkUYxLHtt6/01sDczd09weJLUxYJWknqwoFBZIuI44FrgFOAQ8GBE7M3M77WtdhpwYWZ+s4wxqp6KZC8ijgX+AjiLxvVVJKlUFrSS1MGCQiU7G7gvM58FiIg7gAuAj7StcyrwwYg4EbgfuCIz5yc+UtVNkeztAL4I/LRfJxGxGdjcsXjbeIcqSQ0WtJLUbWwFhV/sNIJjgf1tz/cDp7eeRMQm4FHgChq3y9sNfJjGHQloW8/saakGZg8gMz8OEBFvHtDP5cDOsY9uiY4sHGLNug2Fl49rW0mTZUErSd3GUlA0TcUXO1XKTI9lR1oPMnMOePlOAxFxPXAr3fkze1qqgdlbghtp/F5stw14YIS+RrZm3Qa+/bG3di1/04e+tqLbSmVaWFhg3bruEq/f8jqo508lScszroICpuSLnSplH3Bm2/OtwFOtJxGxHTg7M29tLpoBXurRj9nTUg3MXlGZOQvMti+LiJ7rDpsJHdQOOIuqVWdYwbpu3Tp27drV1b5zZ333b1rQSlK3cRUUS/piJzXdC1wTEccAzwHnA5e1tb8AXBcRe2kcIfB+4AudnZg9jWBY9sZu2EzosPZRZ1EHFb0WxOWIiJ3AHzSffjkz/6yj/WTgFuC1NE71eV9mLkx2lOMxqCgF+s6ktrZbbQXrMBa0ktRtLAWFNIrM3BcRV9K4gux64K8y8+GIuAe4OjMfiYj3Al9qtn8duL68EasuimRvXK9VdtHYr1AGDysuQ0ScDZwDvBFYBL4aEedlZvvf1tuASzLzoYj4LHAp8OnJj3b5hhWlvdra2/VqFrSaehHxGuBB4Pcy8/GOttrsrdP0sKBQ2TJzD7CnY9m5bY/vBO6c9LhUf8Oy17bsXaP0/9inLuTpTessGtVpP/AnmfkiQER8H9jeaoyI44GjMvOh5qLdwC56FLTTcEG81Xgea5l8RzXVIuK3aBSsb+izSm321mm6WFBIkvrxKsjjlZmPtR5HxK8A7wB+u22VXhdr7Fekln5BPA8LniwLWk27S2kczvm3nQ1F99ZNw546SdJkHT50iLUbuguLfsulpfAqyCsjIk4CvkzjVnj/0ta0lCtwT+SCeM7CTg/fbU21zLwE+l7IpOjeutL31EmSJmvthg3sOemkruUXPfZYj7UllS0izqBx5NPlmfl3Hc37gC1tz/tegXtcF8TzasLVYUGrKiu6t85bV0iSJE2piPgl4C7gHZl5X2d7Zv4oIuYj4ozM/AZwMfCVlRyTBWt1WNCqygrtrfPWFZJUP4MOHfawYqlyrgA2Ap9o+472GeD3eeUK2zuAWyLiF4BHgU8u5wU9ZLg+/LRUWWXsrZO0RIfnYe3G4sulgvodUgweVixVTWZ+APhAj6bPtK3zXeD0cbye93OtFwtaVU7H/fDGurdO0pit3Qh7epwdcNHi5MeiyvHCTpLG6aabbmLTpk0WrTVjQatKyMxfbnvcfuuUse2tq60j87Cmx0xYv+WSNEaDilJgYMHqhZ0kScNY0Ep1t2Yj/HOPGbJfdYZM0sobVpRasEqSlmNN2QOYJvML8wOXD2uXpFXncJ/ff/2WS5IkjdGqm6GdX5hn47ruwyxby2d2dc9kLe5szGQNau/X76DXlMbGw4pVFs+RXfU8z1WSVKbaFbTDCtZhReuo+vU7jr6loTysWNIKGVawep6rJKlMU1nQDipKgVIKVkmS6mpY0WrBKkmaVlNZ0A4rSi1Ytep4SLHKMuh+sd5LtjYsWiVJVTVVBa3nmkp9eEixytLvHFnwPNka8DxXSVLVTcVVjk+46QRmds1YzGr1OjLgirCD2iRpBHefcw57TjrJYlaSVHlTNUMrrVr9ZmDBWVitvH6HDntIsSRJmnIWtBMy7OrLWgU8D1bTylvvSJKkirKgnRCvvizPg1VpnIGVJEk1ZUErjYszsCrTsKLVGVhJklRDFrTSuDgDqzJZtEqSpFVoKq5yLFVGvysOeyVirbTDfTLWb7kkSdIq4Ayt1G7YYcPOwmqlDDtk2BlYSZKkLha0UjsLVpXB81wlSZJGUqigjYiLgKuA9cANmXlzR/vJwC3Aa4H7gfdl5sKYx6pVaGLZ88JN6jCR7N19AmxesGhVF//uqixmb/mOLBxizboNhZerwexpVEPPoY2I44BrgTcDvwlcFhG/3rHabcAfZeYbgBng0nEPVKvPRLL3gxMaM7IWs2rj7z2VyfypLGZvPNas28C3P/bWrn8Ws/2ZPS1HkRnas4H7MvNZgIi4A7gA+Ejz+fHAUZn5UHP93cAu4NPtnUTEZmBzR9/HA6x7vjGMJ5988pWBzXUPrdU+qG257ePetmjfhw4fYsPa7l90/ZZP2oEDB1oP107wZVc8ewd+0vxMNr3yGfPjHp9j0fZxb7uSfbdvO8WqnL3muv3zd7D5ubR+x8z2+Jzafv8sq33c246r7ylX5fwNyt7BxcZRAe1/32YXu48UaLUPaltu+7i3XUrf06yu2fvpc4eBV38Oz8x1T7IVbR/3tivZt9kbaCJ/d59//nnglc9ibm6uayDtn9Ny2se97TjHPc1Gyd/MYp8/Bi0R8efA0Zl5VfP5JcDpmXlZ8/m/Az6emW9uPn89cE9z70l7P9cAO4sOTFPrzMz8+iReyOypQ+Wy12y7BvNXB5XLn9mrDbOnslQue822azB/dVA4f0VmaHtcpYQjS2hvuZHG3pR2JwL/CLwFeKLAWCZlG/AAcCYwTbszyhzXWmAr8K0JvuZqzB6Yv05Vzh5UK39mr1uV82f2ls/s1T97YP46VTl7UK38mb1uS85fkYJ2H40fpmUr8FRH+5YB7QBk5iww274sIloPn8jMxwuMZSLaxvWk43qVH0z49VZd9mAqPueeSh5XJbMH1cqf2eurkvkze8s3BeMyexMwBZ9zT/7d9e9uWaZgXEvK39CLQgH3AmdFxDER8fPA+cBXW42Z+SNgPiLOaC66GPjKUgYh9WH2VBazpzKZP5XF7KksZk8jG1rQZuY+4EpgL/AdYE9mPhwR90TEqc3VdgA3RMT3gaOBT67UgLV6mD2VxeypTOZPZTF7KovZ03IUOeSYzNwD7OlYdm7b4+8Cp493aJLZU3nMnspk/lQWs6eymD2NqsghxytplsYlt2eHrThhjqv+pvm9nNaxTeu4qmha30vHVX/T+l46rvqb5vdyWsc2reOqoml9Lx3XGAy9bY8kSZIkSdOo7BlaSZIkSZJGYkErSZIkSaokC1pJkiRJUiVZ0EqSJEmSKsmCVpIkSZJUSRa0kiRJkqRKsqCVJEmSJFWSBa0kSZIkqZIsaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIlWdBKkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSLGglSZIkSZVkQStJkiRJqiQLWkmSJElSJVnQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEqyoJUkSZIkVZIFrSRJkiSpkixoJUmSJEmVtK7sAVRFRLwT+FNgEXge+OPMfCQiPgRcTOO9vA3YlZmLbdudA1yXmSe3Lfsj4D8BLwDfB96fmc9O7IdRpZg9lcXsqUzmT2UxeyqL2RuNM7QFREQAHwd+txmUjwKfj4hzgbcDpwC/Abyt+ZyIOCoiPgr8PW07DiLibcAHgbOafd0D/OUEfxxViNlTWcyeymT+VBazp7KYvdFZ0BZzCLgkM/c3nz8CbKERpj2Z+VxmzgOfA97ZXOd3gKOBd3f0dQpwb2Y+2Xz+eeB/ioj1K/kDqLLMnspi9lQm86eymD2VxeyNyIK2gMx8PDO/DBARM8AngLuBrcB/a1v1SWBbc5u7MvN/BTqn9h8G/n1EHN98/r8A64FfXLmfQFVl9lQWs6cymT+VxeypLGZvdBa0SxARR9OY0n89cAm937/Dg/rIzPuBXcAXIuIR4AiNEL443tGqTsyeymL2VCbzp7KYPZXF7C2dBW1BEbEdeJBGgN6WmbPAEzT2mrQcR2OvyaB+fgH4p8x8U2aeCtzZbKrlSdpaPrOnspg9lcn8qSxmT2Uxe6OxoC0gIl4H/BPw+cy8MDNfaDZ9EdgREUdHxAbgXcBdQ7o7FvhaRLym+fzDwP/dfqUyqcXsqSxmT2UyfyqL2VNZzN7ovG1PMX8IbAfOi4jz2pafReMk64dpHJf+ReBvBnWUmRkR/yfwXyNiDfB1GpfUlnoxeyqL2VOZzJ/KYvZUFrM3opnFxVoW6pIkSZKkmvOQY0mSJElSJVnQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEoqdJXjiLgIuIrGlbVuyMybO9qvBt4D/Ky56JbOdfr0uwE4DdjPkBsEq3RradwD61uZeajswSyX2auUWmUPzF/F1Cp/Zq9SzJ7KUqvsgfmrmCXnb2hBGxHHAdcCpwCHgAcjYm9mfq9ttdOACzPzm0sc8GnAA0vcRuU6k8alv6vO7FVPXbIH5q+K6pI/s1c9Zk9lqUv2wPxVUeH8FZmhPRu4LzOfBYiIO4ALgI+0rXMq8MGIOBG4H7giM+cL9L0f4Pbbb2fLli1FxquSHDhwgB07dkDzM6sBs1cRNcwemL/KqGH+zF5FmD2VpYbZA/NXGaPkr0hBe2xHh/uB01tPImIT8ChwBfA4sBv4MHBleycRsRnY3NH3VoAtW7awbdu2omNWuepymMZhMHsVU5fsgfmrorrkz+xVj9lTWeqSPTB/VVQ4f0UK2pkey460HmTmHHBu63lEXA/cSkdBC1wO7Cw6MEmSJEmSBilyleN9QPvc/FbgqdaTiNgeEe9ua58BXurRz43ACR3/zlzqgCVJkiRJgmIztPcC10TEMcBzwPnAZW3tLwDXRcReGoccvx/4QmcnmTkLzLYvi4jRRi1JkiRJWvWGztBm5j4ahw/vBb4D7MnMhyPinog4NTOfAd4LfAlIGjO016/gmCVJkiRJKnYf2szcA+zpWHZu2+M7gTvHOzRJkiRJkvorcg6tJEmSJElTp9AMrSRJkuovIi4CrgLWAzdk5s191vtrYG9m7p7g8CSpiwWtJPUw7EtdRFwNvAf4WXPRLf2++ElLZf5Uhog4DrgWOAU4BDwYEXsz83tt6xwL/AVwFo3rq0hSqSxoJalDkS91wGnAhZn5zTLGqPoyfyrR2cB9mfksQETcAVwAfKRtnR3AF4GfTn54ktTNglaSuhX5Uncq8MGIOBG4H7giM+cnPlLV0VjyFxGbgc0dfW9bsVGrDo4F9rc93w+c3r5CZn4cICLe3K+TKmTv8Pwch198oWfb2vVHsXbjppG2L7KtpPGyoJWkbgO/1EXEJuBR4Aoa99/eDXyYxi3OXqUKX+w0dcaVv8uBnSs4TtXPTI9lR0boZ2zZW6nC8fCLL/D8j/+lZ9vP/5tfGV7Q9tm+yLZSWebn53nxxRe7lq9fv56NGzeWMKLxsKCVpG4Dv9Rl5hzw8q3LIuJ64FZ6FLRYVGjpxpW/G2kUu+22AQ+MZZSqo33AmW3PtwJPjdDP2LI3qHBstXdqFbuDimGprgYVrS+++CL79+/vatu6dasFrSTVzMAvdRGxHTg7M29tLpoBXurTl0WFlmos+cvMWWC2fVlEjH2wqpV7gWsi4hjgOeB84LKldjKp7A2bJR1WDA/s20OKJy4idgJ/0Hz65cz8s472k4FbgNfSONXifZm5MNlRrrx+BSk0ilJg4CzroKK1rixoJanbsC91LwDXRcReGod8vh/4Qq+OLCo0grHlT1qKzNwXEVfSuHrxeuCvMvPhiLgHuDozHxn3a05r4eghxZMVEWcD5wBvBBaBr0bEeZnZ/rvtNuCSzHwoIj4LXAp8evKjXVn9ClJ4pSit4yzrcljQaupFxGuAB4Hfy8zHO9pWxd46TVaRL3UR8V7gS832rwPXlzdi1Yn5U5kycw+wp2PZuT3We9c4Xs/CUU37gT/JzBcBIuL7wPZWY0QcDxyVmQ81F+0GdlHRgnbQYcFaOgtaTbWI+C0aBesb+qyyKvbWafKGfanLzDuBOyc9Lq0O5m/lvTg3x8LcXNfydZs2sX6ThdRKeWnuWV48uKH0GdiVNK2zztMsMx9rPY6IXwHeAfx22yq9LpbX8wKL03AxxmEXX1qNhwWvJAtaTbtLaRxO97edDXXbWydJmpyFuTme/f73u5a/7td+zYJ2Bb3wk8d5fu1crWdgnXUeXUScBHyZxq3I2t/EpVyBu/SLMdb14kvTyoJWUy0zL4G+5xwW2ls3DXvqJEmT5QysVC0RcQaNI08uz8y/62jeB2xpez7oCtwTuRijhw1PDwtaVVnRvXWl76mT1MOLB2HhYPfyda+B9a+Z/HhUK87AStUREb8E3AW8IzPv62zPzB9FxHxEnJGZ3wAuBr7Sq69JXYzRw4anhwWtqqzo3jpvmzLI4YNwpEdRseY1sNaiQito4SA8+2j38te90YJWklaXK4CNwCfais/PAL/PK1fY3gHcEhG/ADwKfHIlBzTsPFhNDwtaVVbRvXXeNmWIIwdhvkdRsfGNFrSSpla/Q4qhcVixpOrIzA8AH+jR9Jm2db4LnD6u11zOhZssaKeLBe0SHJw/yMEXu2eyXtOcSejV1mp/zUYLg3HpuB/eRPfWSZLGZ9h5roPa+x1SDI3DiiVpEAvW+rCgXYKDLx7k0f3dM1lv3PpGgJ5t7e39imGL3eEy85fbHrffumKse+skjdmg82S16g07z3VQuyQt1dzcHAcPHvTCTTVjQdtm0AzscovOQcWwBa1WlOfIqkyDzpOVJGmCnn76aWZmZrxwU82suoJ2UNFq0ala8hxZSSUadNiwJEnLVbmCdtgs6tD2IYcNS5Kk4oadB+thw5KklVS9gnbILKqzrFqVBh1WLEkryPu9SpLKNJUF7bCrCUvqMOiwYmklDbrok7+za2HYDKwkSWWazoLWw4IlqRoGXfTJgrYyRrk9jjOwkqRpMJUFraQOXqlY0gryPFdJUlVNRUF7YO4AHPSerFJfXqlYZel3SDF4L9kKm3/mGZ5ft87DhiVJlTcVBe1jTz/G/pn9q/rCTSt5D1xJGlm/Q4rBe8lW2Oy//isbZ2c9bFiSVHlTUdBq+NWbVXP9DikGr1SslTfowk6SJElTzIJWmgb9DikGr1SslTfowk6SJElTzIJWmhTvFauyeGsdSZJUUxa00qR4r1iVxVvrSJKkmrKglcbFW+uoTJ4HK0mSViELWmkpBhWt3lpHK2nYYcOeBytJklYhC1ppKTxsWCtlOQWrhw1LkqRVqlBBGxEXAVcB64EbMvPmjvaTgVuA1wL3A+/LzIUxj1Wr0Ipnb+EAvMQrhwV72LCaJvJ7b/4APE+jaLVgVRv/7qosZm/lHZ6f4/CLL3QtX7v+KNZuXL33hTZ7GtWaYStExHHAtcCbgd8ELouIX+9Y7TbgjzLzDcAMcOm4B6rVZyLZO/RYY8a1VcS2ZmA7//W7R6xqaWK/92YfaxSxvWZmtWr5d1dlMXuTcfjFF3j+x//S9a9XkbtamD0tx9CCFjgbuC8zn83M54A7gAtajRFxPHBUZj7UXLQbePu4B7qaHZw/yJMHn+z6d3C+9l+CzZ7KYvZUJvOnspg9lcXsaWRFDjk+Ftjf9nw/cPqQ9m2dnUTEZmBzx+LjAWZ/MgvAgcUDcBAOzB3g2aef7RrIgcUDAP3bhm27kn332XYcfR/gAI89/VhX20n/w0ls2bSFuUNzzL0019W+6ec2sWnDeA5dOXDgQOvh2rF0WMyKZ+/AjxvZY8OBxv8NCwfgUI/PsUg7jH/bley76M9csipnD4bk75lm/uabP+Nsj89h/gBsbP531Pbl9N1v25Xsu7XtFKhy/gZl75mf/QyA+f372biwwPwzzzD7k590DWR+f+Nl+rUN23Yl++637bjGXba6Zu/pnzT+nz/q8CZ+7uAhXpp7lhd6fA5F2oGxb7uSfRf9mctW5ezB4Pz9pPm+Ly4uAvD00093bb+4uMjBgweZm5sbuX05fffbdiX7bm07DUbJX5GvqzM9lh1ZQnvL5cDOXi/wqT//VIFhaEpsBX4wodda8eztuNTsVUgVsweD8ne5+auQKuavb/Yuv/76EYalktQqe+/94P8xwrBUkipmDwbk78///M+XOCyVqHD+ihS0+4AzOzp/qqN9y4D2lhtpHB7Q7kTgH4G3AE8UGMukbAMeoPFzP1nyWNqVOa61ND7bb03wNVdj9sD8dapy9qBa+TN73aqcP7O3fGav/tkD89epytmDauXP7HVbcv6KFLT3AtdExDHAc8D5wGWtxsz8UUTMR8QZmfkN4GLgK52dZOYsMNu+LCJaD5/IzMeLDnqltY3rScf1KpPaS9ey6rIHU/E591TyuCqZvea6lcmf2eurkvkze8s3BeMyexMwBZ9zT/7d9e9uWaZgXEvK39CLQmXmPuBKYC/wHWBPZj4cEfdExKnN1XYAN0TE94GjgU8ubcxSN7Onspg9lcn8qSxmT2Uxe1qOQpd8ycw9wJ6OZee2Pf4urz5xWxoLs6eymD2VyfypLGZPZTF7GlWR2/ZIkiRJkjR1yi5oZ4FddBznPgUcV/1N83s5rWOb1nFV0bS+l46r/qb1vXRc9TfN7+W0jm1ax1VF0/peOq4xmGndk0iSJEmSpCope4ZWkiRJkqSRWNBKkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSLGglSZIkSZVkQStJkiRJqiQLWkmSJElSJVnQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEqyoJUkSZIkVZIFrSRJkiSpkixoJUmSJEmVZEErSZIkSaokC1pJkiRJUiVZ0EqSJEmSKsmCVpIkSZJUSRa0kiRJkqRKsqCVJEmSJFWSBa0kSZIkqZIsaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIlrSt7AFUQEe8E/hRYBJ4H/jgzH4mIDwEX03gfbwN2ZeZiRLwe+DRwDLAe+GxmXt/s67eAm4GjgaeAd2bm/kn/TKoO86eymD2VxeypTOZPZTF7o3GGdoiICODjwO9m5snAR4HPR8S5wNuBU4DfAN7WfA6wG/h/muv/O+C9EfHvI2I9cAfwgcz8tebjz07y51G1mD+VxeypLGZPZTJ/KovZG50F7XCHgEva9mg8AmyhEaQ9mflcZs4DnwPe2Vzns8AegMz8/4B/BY4HTgMOZuY32tY7KyJ+cSI/iarI/KksZk9lMXsqk/lTWczeiDzkeIjMfBx4HCAiZoBPAHcDW4F/aFv1SWBbc5vPtRZGxO8Cvw28B3gL8N/a+n4xIp4BjgN+uoI/hirK/KksZk9lMXsqk/lTWf5/9u4+yLK7vvP7uxkxGsF4UJRVmJHGkpcYvsHeiqXVg7OA4mKZ8rKKy1suCUMYRyaLHkhYL0qsNaWVkBC1KldgBwG1PHhlxDgWU8YlgVkCeBNZU0YgiKBqRG2E6htwWSgaujUjRu1bLU1Pa0adP+696M597u5773no96uqS33P79zTv3v7o+n7Pb/f+R2zt36O0I4pIl4J/Dnwi8C19H/vTnU953dpznO/unW2ZdD7fWrAdgkwfyqO2VNRzJ6KZP5UFLO3do7QjiEiLgC+AjwOvDkzj0fEkzTPmLSdT/OMSfusyr8Frgb2ZOajrX1Oe05EvBz4e8Dhqb8IVZb5U1HMnopi9lQk86eimL31cYR2hIg4B/hr4IuZ+Y7MPN5q+jKwNyJeGRFnAu8C/qLV9nHgvwUu7QgWwP8N/OcR8YbW438OfDszF6f9OlRN5k9FMXsqitlTkcyfimL21m9udXW16D6UWkTcAnwI+E9dTW8BbgD20lwm+8s0l9neDfy49fV3Hft/PDM/FxGXA/+O5hLaPwWuac2Zl3qYPxXF7KkoZk9FMn8qitlbPwtaSZIkSVIlOeVYkiRJklRJFrSSJEmSpEqyoJUkSZIkVVKht+1prdR1GTBPTe+LVCNbaC7//d3MPFF0ZzbK7FVKrbIH5q9iapU/s1cpZk9FqWkoWxgAACAASURBVFX2wPxVzJrzN1ZBGxHvBG6lubLWXZn5ya7224B3A8+2Nt3dvc8AlwEPjdMHlcYVwDeL7sQEmL3qqUv2wPxVUV3yZ/aqx+ypKHXJHpi/Kho7fyML2og4H7gTuAQ4ATwcEQcz8wcdu10GvCMzv73Gjs4DfP7zn2fnzp1rfKpmaWFhgb1790Lrd1YDZq8iapg9MH+VUcP8mb2KMHsqSg2zB+avMtaTv3FGaPcAD2bmMYCIuA+4muZ9ktouBd4fEa8BvgHclJnLYxz7FMDOnTvZvXv3uH1WseoyTcPsVU9dsgfmr4rqkj+zVz1mT0WpS/bA/FXR2Pkbp6A9j9Mr5Hng8vaDiNgOHAJuAp4A9gMfAG7pPEhEnA2c3XVsEyVJkiRJWpdxCtq5PttebH+TmUvAle3HEbEPuIeugha4Ebh9HX2UJEmSJKnHOLftOQx0TjbfBfyk/SAiLoiIf97RPge80Oc4HwP+ftfXFWvtsCRJkiRJMN4I7QPAByPiXOA54Crg+o7248CHI+IgzSnH7wW+1H2QzFwEFju3RcT6ei1JkiRJ2vRGjtBm5mGa04cPAo8CBzLzkYj4WkRcmplHgRuArwBJc4R23xT7LEmSJEnSePehzcwDwIGubVd2fH8/cP9kuyZJkiRJ0mDjXEMrSZIkSVLpjDVCK0mSZici3gncCmwF7srMT3a13wa8G3i2tenu7n2k9RiVvY79/gQ4mJn7Z9g9SephQStJfVhQqCgRcT5wJ3AJcAJ4OCIOZuYPOna7DHhHZn67iD6qnsbJXkScB/wR8Baa66tIUqEsaCWpiwWFCrYHeDAzjwFExH3A1cCHOva5FHh/RLwG+AZwU2Yuz7ynqptxsrcX+DLw09l3b3ZOLS9xauV4z/YtW89iy7btU3uuVKTl5WVWVlZ6tm/dupVt27YV0KPxWNBKUq+JFRQRcTZwdtfm3VPpteriPGC+4/E8cHn7QURsBw4BN9G8Xd5+4AM070hAx35mT2s1NHsAmfkRgIh406CDzCp70ywcT60c5/mnf9iz/RWvfu3ognYDz5WKtLKywvz8fM/2Xbt2WdBKUsVMpKBouRG4fUr9VD3N9dn2YvubzFwCfnangYjYB9xDb/7MntZqaPbWYCbZG1U4Dit428/vp90uldFGRlGrOgI7igWtJPWaVEEB8DGaBW+n3cBDG+6l6uowcEXH413AT9oPIuICYE9m3tPaNAe80Oc4Zk9rNTR7a1CK7A0reIG+bZ3tQ489oliWpmXUKOqworWqI7CjWNBKUq9JFRRk5iKw2LktIibaWdXOA8AHI+Jc4DngKuD6jvbjwIcj4iDNGQLvBb7UfRCzt34rS0ucXFrq2X7G9u1s3V7rKaOjsjeWSWavrIXjqGJZaxcRtwO/3Xr41cz8g672i4C7gVfRvNTnPZl5cra9LL9hRWtdeR9aSer1APCWiDg3Il5B80PdX3a0twuKvx8RcwwoKKT1yMzDNEf7DwKPAgcy85GI+FpEXJqZR4EbgK8ASfOEyr7COlxDJ5eWOPb44z1f/YrcOhmVvSL61C4cu78GTRdWNUXEHuDXgYuBi4BLIuK3una7F/i9zHwdzX/3rpttL2djeXmZRqPR92t52bX/+nGEVqUXETuAh4HfyMwnuto8W6eJy8zDEdH+ULcV+OP2hzrgtsz8XkS0C4qtwDexoNAEZeYB4EDXtis7vr8fuH/W/VL9jcpex7Z3TeLnbcYVgTfjax7DPPD7mbkCEBGPAxe0GyPiQuCszPxOa9N+4A7g0zPu50SsZ1ow1HuUdSMsaFVqEfGrNAvW1w3Y5V7g2sz8TkR8lubZukr+46ZysaAogZUGnGz0bj9jB2zdMfv+qFaGTSvW7GzGFYE342seJTMfa38fEa8F3g68oWOXfos19l05uwwrvI9afGkzTgueJgtald11NKdz/ml3Q93O1pXWqQa82KeoeNkO2GJRoSk62YBjh3q3n3OxBa02rD2tuNs5r399Ab3ZPF5YOsZK48zNPhqpASLil4Gv0rwVXmfVv5YVuAtf4b2uiy+VlQXthDSWGzRW+nzoB3a0Pnj1a9+xdQc7tvnBbJDMvBYGLiYx1tm6Mpypq7QXG7Dcp6jYdrEFraTS2sQLO5Xa8Wee4PktS5t6NFL9RcQbac58ujEz/6yr+TCws+PxsBW4S7HKtmbHgnZCGisNDs33+dAPXLzrYoC+7RfvutiCdv3GPVtX+Jk6SdJsDRuBtaCVyiUifh74C+Dtmflgd3tm/jgiliPijZn5LeAa4Ov9jjWpVbZHTRse1q7ZsqBdg0GjsDsmMPVt2LEteAca92ydZ+okqYJGXec6aNVhr4OVKucmYBvw0Y7i8zPAb9JajBHYC9wdET8HHAI+Mc0OjZo27HWw5WFBuwaDRmHbI7DTOrYFbX/jnq3zXoxSSbnok0YYdZ1rv7bOdknVkJnvA97Xp+kzHft8H7h8Uj9z1AisqsOCtoOjpNXQeesUZny2rpZc9ElFcdEnSVJBXLipPmpX0I4qSoe1O0paXpn5Cx3fd946ZaJn6zYlF32SVBAXbpI0S0tLSzQaDa9zrZn6FbQjitJpThuWJEnjc+EmSbN05MgR5ubmvM61ZipX0DotWFojpxRLKtCohZ0kSdqI6hW0TguW1sYpxSrSsIWfVAujpg2PWthJkqSNqFxBK0mqkGELP6kWnDYsSSpSKQvaad7vVaqlYdOKJWkDXLhJklRm5SxoXbjpNF43rJGGTSuWpsl7ydbCsKLVEVhJUpmVsqDV6bxuWFJpeS/ZSlo+epTnzzjD61wlSZVXioJ2YWkBGo44SgO5UrGKMmgEFlzYqcIWf/Qjti0uOsoqSaq8UhS0jx15jPm5eUccpUFcqVhFGTQCCy7sJEmSCleKglba9AaNwIILO2n6vLWOJEmqKAtaqQwGjcCCCztp+ry1jiRJqqiXFd0BSZIkSZLWwxFaaVa8V6yK4q11JElSTVnQSpMyaiVi7xWraRpWtHprHUmSVFMWtNKkuBKxpmnUKKvXwUqSpE3IglZaC6cNa1o2UrA6yipJkjapsQraiHgncCuwFbgrMz/Z1X4RcDfwKuAbwHsy8+SE+6pNaObZc9qwWmaSveUFeB6nBauHf3dVFLNXvFPLS5xaOd6zfcvWs9iybXsBPZoNs6f1GrnKcUScD9wJvAn4FeD6iPilrt3uBX4vM18HzAHXTbqjGqyx3OCpxlM9X43lAfc1rYiZZO/kArzwVLOQhZcK1u6vQfeIVS3N7N+9xceaRWy/kVltWv7dVVHMXjmcWjnO80//sOerX5FbF2ZPGzHObXv2AA9m5rHMfA64D7i63RgRFwJnZeZ3Wpv2A2+bdEc1WGOlwaH5Qz1fjZXKf0iefvZOPGbBqn78d09FMn8qitlTUcye1m2cKcfnAfMdj+eBy0e07+4+SEScDZzdtflCgMVnFgFYWF2ABiwsLXDsyLGejiysLgAMbhv13Gkee8Bzp3nscV/z0oklll5Y6mnf/vLmtJVBbdvPfGlay8LCQvvbLb2vcGqmnr2Fp5vZ48yF5v8NJxfgRJ/f4zjtMPnnTvPYk3jNM7gKv8rZgxH5O9rK33LrNS72eZ+XF2Bb67/rbd/IsQc9d5rHHvc1z0CV8zcse0effRaA5fl5tp08yfLRoyw+80xPR5bnmz9mUNuo507z2IOeO81jt587C3XN3pFnmv9Pn3VqOy9vnOCFpWMc7/Nej9MOTPy50zz2JF7zyxsn+vZ5kqqcPRiev2da7+vq6ioAR44c6Xn+6uoqjUaDpaWldbdv5NiDnjvNY4/7mmdhPfkb5+PoXJ9tL66hve1G4PZ+P+BTN39qjG6oJHYBfzOjnzX17O29zuxVSBWzB8Pyd6P5q5Aq5m9g9m7ct28d3VJBapW9G97/h+volgpSxezBkPzdfPPNa+yWCjR2/sYpaA8DV3Qd/Cdd7TuHtLd9jOb0gE6vAf4K+DXgyTH6Miu7gYdovu6nCu5LpyL7tYXm7/a7M/yZmzF7YP66VTl7UK38mb1eVc6f2ds4s1f/7IH561bl7EG18mf2eq05f+MUtA8AH4yIc4HngKuA69uNmfnjiFiOiDdm5reAa4Cvdx8kMxeBxc5tEdH+9snMfGLcTk9bR7+esl+nmdVZurZNlz0oxe+5r4L7VcnstfatTP7M3kCVzJ/Z27gS9MvszUAJfs99+XfXv7tFKUG/1pS/kYtCZeZh4BbgIPAocCAzH4mIr0XEpa3d9gJ3RcTjwCuBT6ytz1Ivs6eimD0VyfypKGZPRTF72oixlnTJzAPAga5tV3Z8/31Ov3Bbmgizp6KYPRXJ/KkoZk9FMXtar3Fu2yNJkiRJUukUXdAuAnfQNc+9BOxX/ZX5vSxr38raryoq63tpv+qvrO+l/aq/Mr+XZe1bWftVRWV9L+3XBMy170kkSZIkSVKVFD1CK0mSJEnSuljQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEqyoJUkSZIkVZIFrSRJkiSpkixoJUmSJEmVZEErSZIkSaokC1pJkiRJUiVZ0EqSJEmSKsmCVpIkSZJUSRa0kiRJkqRKsqCVJEmSJFWSBa0kSZIkqZIsaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIlWdBKkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSLGglSZIkSZVkQStJkiRJqqQziu5AFUTE7wD/ClgFngf+ZWZ+LyL+NXANzffxXuCOzFyNiF8EPg2cC2wFPpuZ+7qO+SHgnMz8FzN8Kaog86eimD0VxeypSOZPRTF76+MI7QgREcBHgLdm5kXAvwG+GBFXAm8DLgH+AfDm1mOA/cAXWvv/I+CGiPjHrePtjoj7gJtm+kJUSeZPRTF7KorZU5HMn4pi9tbPgna0E8C1mTnfevw9YCfNIB3IzOcycxn4HPA7rX0+CxwAyMy/A34EXNhqezfwEHDa2RNpAPOnopg9FcXsqUjmT0Uxe+vklOMRMvMJ4AmAiJgDPgr8B2AX8B87dn0K2N16zufaGyPircAbaIaKzLyjtf2D0+67qs/8qShmT0UxeyqS+VNRzN76OUI7poh4JfDnwC8C19L/vTvV9ZzfpTnP/eqOsy3Smpk/FcXsqShmT0UyfyqK2Vs7R2jHEBEXAF8BHgfenJnHI+JJmmdM2s6necakfVbl3wJXA3sy89EZd1k1Yv5UFLOnopg9Fcn8qShmb30saEeIiHOAvwb2t4fuW74M3B4R/x44CbyL5oXZAB+neWH2pZl5dHa9Vd2YPxXF7KkoZk9FMn8qitlbPwva0f4n4ALgtyLitzq2vwX4IvAIzWWyvwz87xHx88C/AH4M/F/NBcsA+HjnPHdpTOZPRTF7KorZU5HMn4pi9tZpbnV1teg+SJIkSZK0Zi4KJUmSJEmqJAtaSZIkSVIlWdBKkiRJkirJglaSJEmSVEmFrnIcEWcClwHzdN0gWKWzheY9sL6bmSeK7sxGmb1KqVX2wPxVTK3yZ/YqxeypKLXKHpi/illz/sYqaCPincCtNJeKviszP9nVfhvwbuDZ1qa7u/cZ4DLgoXH6oNK4Avhm0Z2YALNXPXXJHpi/KqpL/sxe9Zg9FaUu2QPzV0Vj529kQRsR5wN3ApcAJ4CHI+JgZv6gY7fLgHdk5rfX2NF5gM9//vPs3LlzjU/VLC0sLLB3715o/c5qwOxVRA2zB+avMmqYP7NXEWZPRalh9sD8VcZ68jfOCO0e4MHMPAYQEfcBVwMf6tjnUuD9EfEa4BvATZm53HmQiDgbOLvr2LsAdu7cye7du8fts4pVl2kap8DsVUxdsgfmr4rqkj+zVz1mT0WpS/bA/FXR2PkbZ1Go8zi9Qp4HfpaEiNgOHAJuAv4hzaL1A32OcyPwt11fDv1LkiRJktZlnBHauT7bXmx/k5lLwJXtxxGxD7gHuKXrOR8D9ndt241FrSRJkiRpHcYpaA/TvCi3bRfwk/aDiLgA2JOZ97Q2zQEvdB8kMxeBxc5tEbHW/kqSJEmSBIxX0D4AfDAizgWeA64Cru9oPw58OCIOAk8A7wW+NOF+SpIkSZJ0mpHX0GbmYZrThw8CjwIHMvORiPhaRFyamUeBG4CvAElzhHbfFPssSZIkSdJ496HNzAPAga5tV3Z8fz9w/2S7JkmSJEnSYOOscixJkiRJUumMNUIrSZtNRLwTuBXYCtyVmZ/sar8NeDfwbGvT3d37SOtl/lSUUdnr2O9PgIOZuX+G3SuNU8tLnFo53rN9y9az2LJt+9SeK6mXBa0kdYmI84E7gUuAE8DDEXEwM3/QsdtlwDsy89tF9FH1Zf5UlHGyFxHnAX8EvIXm+iqb0qmV4zz/9A97tr/i1a8dXdBu4LlSkZaXl1lZWenZvnXrVrZt21ZAj5osaCWp1x7gwcw8BhAR9wFXAx/q2OdS4P0R8RrgG8BNmbk8856qjsyfijJO9vYCXwZ+OuggEXE2cHbX5t2T7epow0ZCgb5to9odRVWdjSpYV1ZWmJ+f72nftWuXBa0klcx5QOe/2PPA5e0HEbEdOATcRPN2ZfuBD9BcEf40Zflgp0qZSP7MntZhaPYAMvMjABHxpiHHuRG4feK9W6NhI6FA37ZR7Y6iquyGFaVAJQvWUSxoJanXXJ9tL7a/ycwl4GcrvUfEPuAe+hS0lOSDnSplUvkze1OysrTEyaWlnu1nbN/O1u2VLnaGZm8NPkbzREun3cBD6zhW5XiNrIo0rCgFKlmwjmJBK0m9DgNXdDzeBfyk/SAiLgD2ZOY9rU1zwAsDjrWpP9hpXSaVP7M3JSeXljj2+OM92895/eurXtAOzd64MnMRWOzcFhEb61kfZS0cvUZW01TW61iLZEErSb0eAD4YEecCzwFXAdd3tB8HPhwRB2lO+Xwv8KV+B5rVB7tNaaUBJxu928/YAVt3zL4/kzOR/Jk9rcOo7JWKhWO9RMTtwG+3Hn41M/+gq/0i4G7gVTTXDnhPZp6cbS+LV9VpwdNkQTsjjeUGjZXeD147tu5gx7ZKf/DSZnaqAS/2KShetgO2VDfXmXk4Im6huYLnVuCPM/ORiPgacFtmfi8ibgC+0mr/JrCvuB5vUicbcOxQ7/ZzLq50QWv+ilfjKcVDjZO9Wfdp1MJOdVTWkedpiog9wK8DFwOrwF9GxG9lZufJunuBazPzOxHxWeA64NOz7+10DRqBhZeug9XpLGhnpLHS4NB87wevi3ddzI5tOyx4h4iIHcDDwG9k5hNdbZ6tK9KLDVjuU1Bsu7jSBS1AZh4ADnRtu7Lj+/uB+2fdL20O5q9YNZ5SPNKo7HVse9cs+jNqYac62qQjz/PA72fmCkBEPA5c0G6MiAuBszLzO61N+4E76FPQVmFBvGHThgeNwMJL18HqdBa0JTGs4G23d9sMxW5E/CrNgvV1A3bZFGfrJEmTNWwUVrOzGUcj1SszH2t/HxGvBd4OvKFjl34rcA8qUku/IN6ohZu0Nha0EzJohBWaheeGjj1idLfmrqN5fdifdjeMe7auCmfqJEmzNWwUVrOzSUcjNUBE/DLwVZr31u4MxlpW4HZBvE3GgnZCBhWd8NIoq9YuM6+FgQuZjHu2rvRn6iT1Ud9FnzQBg0ZY4aVR1s14HWzZvbB0jJXGmY7AqkdEvJHmpRQ3ZuafdTUfBnZ2PB64AvesFsTbyP1eNVmbrqDdyLWqw56rQox7ts4zdRsxbOEnaZpquuiTJmPQCCu8NMq6Wa+DLbPjzzzB81uWHIHVaSLi54G/AN6emQ92t2fmjyNiOSLemJnfAq4Bvj7rfnbajPd7LavNV9Bu4FrVUc/VzI11ts5bV2zQsIWfJEmSNu4mYBvw0Y7PaJ8BfpOXVtjeC9wdET8HHAI+Mc0Oeb/X6th0Be0wm/xa1cop49k6SdLkuHCTtDlk5vuA9/Vp+kzHPt8HLp9Vn7zfa3XUrqD19jf113U/vJmeraulmt5LVlL1uXCTpElaWlqi0Wh4nWvN1K+gdZS1ljLzFzq+77wX40zP1tVSje8lK6ncho3Aep2rpEk7cuQIc3NzXudaM7UraCVJFeEqxpvesBFYC1pJ0jgsaKW6c0qxyspVjDcFr4OVJE1T5Qpar5GV1sgpxZKmaNS0Ya+DlSRNU/UKWq+R7WGRL6m0hk0rVi04bViSVKTKFbTqZZEvqbSGTStWJbhwkySpzEpZ0A4bcZTUx7DrZCVphGFFqyOwkqQyK2dBO2TEUVIfw66TlabJlYorafnoUZ4/4wyvc5UkVV4pCtqFpQVoeM2nJFWOKxVX0uKPfsS2xUVHWSVJlVeKgvaxI48xPzfvNZ/SIN56R0UZNAILLuwkSZIKV4qCVtII3npHRRk0Agsu7CRJkgpnQSuVwaARWHBhJ0mSJGkAC1qpDAaNwIILO2n6vFesJEmqKAtaaVa8tY6KMmolYu8VK0mSKsqCVpoVb62jaRpWtLoSsSRJqikLWmlSXIlYRXKUVZIkbUJjFbQR8U7gVmArcFdmfrKr/SLgbuBVwDeA92TmyQn3VZtQ6bI3rGh1JeJaKV32Rk0bVq2ULn/aNMxeuZ1aXuLUyvGe7Vu2nsWWbdW+p7TZ03q9bNQOEXE+cCfwJuBXgOsj4pe6drsX+L3MfB0wB1w36Y5q/RrLDZ5qPNXz1VgesKpuScwkeycX4IWnmoUqNP/7wlO9X+32dtHa/TVohWJV0sz+3VtegOefaharK43m991fK61stUdgu78G3SNWleXfXRXF7JXfqZXjPP/0D3u++hW5VWL2tBEjC1pgD/BgZh7LzOeA+4Cr240RcSFwVmZ+p7VpP/C2SXdU69dYaXBo/lDPV2Ol9B+Ep5+9E4+dXpBasKppNv/uLT72UlFqwaqX+HdXRTF7KorZ07qNM+X4PGC+4/E8cPmI9t3dB4mIs4GzuzZfCLD4zCIAC6sL0ICFpQWOHTnW05GF1QWAwW2jnjvNYw947jSPPZHXPOZn5YWFhfa3W8Z7xkRMPXsLTzezx5kLzf8bTi7AiT6/x3HaYfLPneaxZ/GaJ6DK2YMR+Tvayt9y6zUu9nkvlxdgW+u/623fyLEHPXeax57Ea97Wv8trVeX8Dcve0WefBWB5fp5tJ0+yfPQoi88809OR5fnmjxnUNuq50zz2oOdO89jjvuZJqGv2jjzT/H/2rFPbeXnjBC8sHeN4n/dynHZg4s+d5rFn8ZonocrZg+H5e6b13q2urgJw5MiRnuevrq7SaDRYWlpad/tGjj3oudM89iRec6MxmRPw68nfOB855/pse3EN7W03Arf3+wGfuvlTY3RDJbEL+JsZ/aypZ2/vdWavQqqYPRiWvxvNX4VUMX8Ds3fjvn3r6JYKUqvs3fD+P1xHt1SQKmYPhuTv5ptvXmO3VKCx8zdOQXsYuKLr4D/pat85pL3tYzSnB3R6DfBXwK8BT47Rl1nZDTxE83U/VXBfOhXZry00f7ffneHP3IzZA/PXrcrZg2rlz+z1qnL+zN7Gmb36Zw/MX7cqZw+qlT+z12vN+RunoH0A+GBEnAs8B1wFXN9uzMwfR8RyRLwxM78FXAN8vfsgmbkILHZui4j2t09m5hPjdnraOvr1lP06zazO0rVtuuxBKX7PfRXcr0pmr7VvZfJn9gaqZP7M3saVoF9mbwZK8Hvuy7+7/t0tSgn6tab8jVwUKjMPA7cAB4FHgQOZ+UhEfC0iLm3tthe4KyIeB14JfGJtfZZ6mT0VxeypSOZPRTF7KorZ00aMtWxLZh4ADnRtu7Lj++9z+oXb0kSYPRXF7KlI5k9FMXsqitnTeo1z2x5JkiRJkkqn6IJ2EbiDrnnuJWC/6q/M72VZ+1bWflVRWd9L+1V/ZX0v7Vf9lfm9LGvfytqvKirre2m/JmCufU8iSZIkSZKqpOgRWkmSJEmS1sWCVpIkSZJUSRa0kiRJkqRKsqCVJEmSJFWSBa0kSZIkqZIsaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIlWdBKkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSLGglSZIkSZVkQStJkiRJqiQLWkmSJElSJVnQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEqyoJUkSZIkVZIFrSRJkiSpkixoJUmSJEmVZEErSZIkSaokC1pJkiRJUiVZ0EqSJEmSKumMojtQBRHxO8C/AlaB54F/mZnfi4h/DVxD8328F7gjM1cj4heBTwPnAluBz2bmvtax/glwZ+s5LwI3Z+Z/nPVrUnWYPxXF7KkoZk9FMn8qitlbH0doR4iIAD4CvDUzLwL+DfDFiLgSeBtwCfAPgDe3HgPsB77Q2v8fATdExD+OiFcBB4DfbbW9C/hCRPzcDF+SKsT8qShmT0UxeyqS+VNRzN76WdCOdgK4NjPnW4+/B+ykGaQDmflcZi4DnwN+p7XPZ2mGiMz8O+BHwIXAy4H/OTMfa+33A2AO+HuzeCGqJPOnopg9FcXsqUjmT0Uxe+vklOMRMvMJ4AmAiJgDPgr8B2AX0Dls/xSwu/Wcz7U3RsRbgTcA787MZ4AvdDznQ8D/m5l/O71XoCozfyqK2VNRzJ6KZP5UFLO3fha0Y4qIV9Ic1v954K3An/fZ7VTXc34X2Adc3XG2hYg4g2ZI/ynwlil1WTVi/lQUs6eimD0VyfypKGZv7SxoxxARFwBfAR4H3pyZxyPiSZpnTNrOp3nGpH1W5d8CVwN7MvPRjmP9Z8B9NIf9/5vM/OlsXoWqyvypKGZPRTF7KpL5U1HM3vpY0I4QEecAfw3sz8w7Opq+DNweEf8eOEnzYuv9rbaP07ww+9LMPNpxrDOB/xP4PvCezDw59RegSjN/KorZU1HMnopk/lQUs7d+c6urq0X3odQi4haa887/U1fTW4AbgL00l8n+Ms1ltncDP259/V3H/h8HVmguPTLiqgAAIABJREFUtf3/cPpUgf8hM7uPL5k/FcbsqShmT0UyfyqK2Vs/C1pJkiRJUiV52x5JkiRJUiVZ0EqSJEmSKsmCVpIkSZJUSYWuctxagesyYJ6u+ympdLbQXDL8u5l5oujObJTZq5RaZQ/MX8XUKn9mr1LMnopSq+yB+auYNedvrII2It4J3EpzZa27MvOTXe23Ae8Gnm1turt7nwEuAx4apw8qjSuAbxbdiQkwe9VTl+yB+auiuuTP7FWP2VNR6pI9MH9VNHb+Rha0EXE+cCdwCXACeDgiDmbmDzp2uwx4R2Z+e40dnQf4/Oc/z86dO9f4VM3SwsICe/fuhdbvrAbMXkXUMHtg/iqjhvkzexVh9lSUGmYPzF9lrCd/44zQ7gEezMxjABFxH3A1zfsktV0KvD8iXgN8A7gpM5fHOPYpgJ07d7J79+5x+6xi1WWahtmrnrpkD8xfFdUlf2avesyeilKX7IH5q6Kx8zdOQXsep1fI88Dl7QcRsR04BNwEPAHsBz4A3NJ5kIg4Gzi769gmSpIkSZK0LuMUtHN9tr3Y/iYzl4Ar248jYh9wD10FLXAjcPs6+ihJkiRJUo9xbttzGOicbL4L+En7QURcEBH/vKN9Dnihz3E+Bvz9rq8r1tphSZIkSZJgvBHaB4APRsS5wHPAVcD1He3HgQ9HxEGaU47fC3yp+yCZuQgsdm6LiPX1WpIkSZK06Y0coc3MwzSnDx8EHgUOZOYjEfG1iLg0M48CNwBfAZLmCO2+KfZZkiRJkqTx7kObmQeAA13bruz4/n7g/sl2TZIkSZKkwca5hlaSJEmSpNIZa4RWkiRJ9RcR7wRuBbYCd2XmJwfs9yfAwczcP8PuVcKp5SVOrRzv2b5l61ls2bZ96s+XirC8vMzKykrP9q1bt7Jt27ap/mwLWknqY9SHuoi4DXg38Gxr092DPvhJa2X+VISIOB+4E7gEOAE8HBEHM/MHHfucB/wR8Baa66tU0qCiETZeOJ5aOc7zT/+wZ/srXv3a8QraDT5fKsLKygrz8/M923ft2mVBK0mzNs6HOuAy4B2Z+e0i+qj6Mn/Vt7K0xMmlpZ7tZ2zfztbtpS5I9gAPZuYxgIi4D7ga+FDHPnuBLwM/nX33JmdQ0QjNwrG9T7d2sTtsFFWqq2GjsEWyoJWkXuN8qLsUeH9EvAb4BnBTZi7PvKeqo4nkLyLOBs7uOvbuqfVaP3NyaYljjz/es/2c17++7AXteUDnEMs8cHnnDpn5EYCIeNOgg9Qhe6NGSYe1jzy2U4pVkFHTgke1DxuFLZIFraTpOdWAFxu921+2A7bsmH1/xjf0Q11EbAcOATfRvP/2fuADNG9xdpo6fLCrpJUGnOyTvTN2wNZSZw8ml78bgdun2E/Vz1yfbS+u4zilyF5ZR1GdUqyijJoWXOS04Y2woC2JxnKDxkrvh68drQ9eg9p2bCv9BzNtZi82YPlQ7/ZtF5e9oB36oS4zl4Cf3bosIvYB99CnoKUkH+w2nZMNONYne+dcXIWCdlL5+xjNYrfTbuChifRyE6vwlOJRDgNXdDzeBfxkHccpRfY2MooqVVVZpwVPkwVtSTRWGhya7/3wdfGuiwEGtlnQSlMx9ENdRFwA7MnMe1qb5oAXBhyrFB/sVCkTyV9mLgKLndsiYuKd3YwqPKV4lAeAD0bEucBzwFXA9Ws9yKyy59TdeomI24Hfbj38amb+QVf7RcDdwKtoXmrxnsw8OdteFq+q04KnyYJ2RoaNwG60KJ3msaVNatSHuuPAhyPiIM0pn+8FvtTvQBYVWoeJ5U9ai8w8HBG30Fy9eCvwx5n5SER8DbgtM79XbA9Ptxmn7ta1iI+IPcCvAxcDq8BfRsRvZWbnv233Atdm5nci4rPAdcCnZ9/bYlV1WvA0WdDOyLAR2A0XtFM8dhlExA7gYeA3MvOJrjbP1mnixvlQFxE3AF9ptX8T2Fdcj1Un5q94NZ5SPFJmHgAOdG27ss9+75pVn/SSGhfx88DvZ+YKQEQ8DlzQboyIC4GzMvM7rU37gTuoYUE7aAQW6j1teCMsaCdk0CgpvHQdrNYuIn6VZsH6ugG7eLZOUzHqQ11m3g/cP+t+qcOwhZ8qzvwVq8ZTiiunrAs7abIy87H29xHxWuDtwBs6dum3WF7fBRarvhjjoBFYqPe04Y2woO0waurusPZBo6Tw0nWwWpfraE6n+9Puhs10tq60qruKsepg2MJP2vRGjbIOa1d5uLDT5hIRvwx8leatyDp/8WtZgbv0izFuxoWbpsmCtsOoqbujFm7S5GXmtTDwmsOxztZV/UxdqVV3FWNJNTdqlHVYu2anrteEau0i4o00Z57cmJl/1tV8GNjZ8XjYCtyFL8bowk2zZUGrKhv3bF3pz9RJkrQZ1fiaUK1BRPw88BfA2zPzwe72zPxxRCxHxBsz81vANcDX+x2rDIsxunDTbG26gnbU/V5VKeOerSv8TJ0kabIGTRkGpw1LFXQTsA34aEfx+RngN3lphe29wN0R8XPAIeATRXS0zWnD5bH5ClqnDdfGuGfrynCmrtKGXScrSQUZNGUYnDZcZi8sHWOlcaZTinWazHwf8L4+TZ/p2Of7wOWz6pPThqtj0xW0qr6u++GV6mxdLQ27TlaapmGrGDurZlNw4ab6Of7MEzy/ZckpxSrE0tISjUbjZ6Oo6y1YnTZcLha0qoTM/IWO7ztvXTHTs3WSZmjYKsYWtJuCCzdJmqQjR44wNzf3s1FUC9Z6qF1BO+rWO5K6eOsdSQUZdWsdSZJGqV9BO+LWO5K6eOsdSVM0rGgddWsdSZJGqV1Bq16OWksqJa+R3RScNixJmqbKFbQWZ2vnqLWkUvIa2Vpw2rAkqUjVK2gtzqS18RpZSRswqmB12rAkqUiVK2glrZHXyKpIw6YVqzDLR4/y/Bln/Kwo9TpXSVJVlbKgHTatWJJUIcOmFaswiz/6EdsWF39WlHqdqySpqspZ0A6ZViypj2HTiqVpcmEnSZJUoFIUtAtLC9BwYSdp3YZNK5amyYWdJElSgV5WdAcAHjvyGIfmD/WdZixJkiRJUj+lGKGVNIIrFUuSJEk9LGilKnClYhVl0DWy4ErFkiSpcBa0UhkMGoEFF3bS9A1b2GnQNbLgSsWSJKlwFrRSGQwagQUXdtLGjVqJ2FvrSJKkirKglWbFW+uoKK5ELEmSasqCVpqUUQs3eWsdTdOwUVhJkqSaGqugjYh3ArcCW4G7MvOTXe0XAXcDrwK+AbwnM09OuK/ahEqXvWFFqws31crMszdqWrDThjeV0v3bp03D7FXbqeUlTq0c79m+ZetZbNm2vYAejc/sab1G3oc2Is4H7gTeBPwKcH1E/FLXbvcCv5eZrwPmgOsm3VFNR2O5wVONp3q+GsvF3xN4Jtk7uQAvPNUsVKH53xee6v1qt7eL1u6vQQs6qZJm9u/e8gI8/9RLxeqxQ71f7SJ2VLtqw7+7KorZq75TK8d5/ukf9nz1K3LLxOxpI0YWtMAe4MHMPJaZzwH3AVe3GyPiQuCszPxOa9N+4G2T7qimo7HS4ND8oZ6vxkopPiRPP3snHju9ILVgVdNs/t1bfMyiVP34d1dFMXsqitnTuo0z5fg8YL7j8Txw+Yj23d0HiYizgbO7Nl8IsPjMIgALqwvQgIWlBY4dOdbTkYXVBYDBbaOeO81jD3juNI89i9e8dGKJpReWOPr00XbTlr4vcjqmnr2Fp5vZ48yF5v8NJxfgRJ/f4zjtMPnnTvPYRb/mMa/gX1hYaH9buezBiPwdbeVvufUaF/u8V8sLsK313/W2b+TYg547zWPP4jWPqcr5G5a9o88+C8Dy/DzbTp5k+ehRFp95pqcjy/PNHzOobdRzp3nsQc+d5rEn8Zq3nRxvhmRds3fkmeb/l2ed2s7LGyd4YekYx/u8V+O0AxN/7jSPXfRrfnnjRM/2fqqcPRiev2da783q6ioAR44c6Xn+6uoqjUaDpaWldbdv5NiDnjvNY8/iNY9rPfkb5yPlXJ9tL66hve1G4PZ+P+BTN39qjG6oJHYBfzOjnzX17O29zuxVSBWzB8Pyd6P5q5Aq5m9g9m7ct28d3VJBapW9G97/h+volgpSxezBkPzdfPPNa+yWCjR2/sYpaA8DV3Qd/Cdd7TuHtLd9jOb0gE6vAf4K+DXgyTH6Miu7gYdovu6nCu5LpyL7tYXm7/a7M/yZmzF7YP66VTl7UK38mb1eVc6f2ds4s1f/7IH561bl7EG18mf2eq05f+MUtA8AH4yIc4HngKuA69uNmfnjiFiOiDdm5reAa4Cvdx8kMxeBxc5tEdH+9snMfGLcTk9bR7+esl+nmdVZurZNlz0oxe+5r4L7VcnstfatTP7M3kCVzJ/Z27gS9MvszUAJfs99+XfXv7tFKUG/1pS/kYtCZeZh4BbgIPAocCAzH4mIr0XEpa3d9gJ3RcTjwCuBT6ytz1Ivs6eimD0VyfypKGZPRTF72oixlmXJzAPAga5tV3Z8/31Ov3Bbmgizp6KYPRXJ/KkoZk9FMXtar3Fu2yNJkiRJUukUXdAuAnfQNc+9BOxX/ZX5vSxr38raryoq63tpv+qvrO+l/aq/Mr+XZe1bWftVRWV9L+3XBMy17xskSZIkSVKVFD1CK0mSJEnSuljQSpIkSZIqyYJWkiRJklRJFrSSJEmSpEqyoJUkSZIkVZIFrSRJkiSpkixoJUmSJEmVZEErSZIkSaokC1pJkiRJUiVZ0EqSJEmSKsmCVpIkSZJUSRa0kiRJkqRKsqCVJEmSJFWSBa0kSZIkqZIsaCVJkiRJlWRBK0mSJEmqJAtaSZIkSVIlWdBKkiRJkirJglaSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSLGglSZIkSZVkQStJkiRJqqQziu5AFUTE7wD/ClgFngf+ZWZ+LyL+NXANzffxXuCOzFyNiF8EPg2cC2wFPpuZ+1rH+m3g1tahnwFuyMwfzvQFqVLMn4pi9lQUs6cimT8VxeytjyO0I0REAB8B3pqZFwH/BvhiRFwJvA24BPgHwJtbjwH2A19o7f+PgBsi4h9HxKuBzwD/XWb+18AXgX83y9ejajF/KorZU1HMnopk/lQUs7d+FrSjnQCuzcz51uPvATtpBulAZj6XmcvA54Dfae3zWeAAQGb+HfAj4MLMfBp4dWb+fxFxBnAh8NPZvRRVkPlTUcyeimL2VCTzp6KYvXVyyvEImfkE8ARARMwBHwX+A7AL+I8duz4F7G4953PtjRHxVuANwLtbbS9ExKXA/wGcBbx12q9B1WX+VBSzp6KYPRXJ/KkoZm/9HKEdU0S8Evhz4BeBa+n/3p3qes7v0pznfnXH2RYy83uZuRN4O/DViDh7ah1XLZg/FcXsqShmT0UyfyqK2Vs7C9oxRMQFwMM0w/PmzFwEnqR5xqTtfJpnTIiIuYjYB3wI2JOZD7S2nxcR/6T9hMz8S6AB/JczeSGqJPOnopg9FcXsqUjmT0Uxe+tjQTtCRJwD/DXwxcx8R2YebzV9GdgbEa+MiDOBdwF/0Wr7OPDfApdm5qMdh9sGfKG1IhkR8Waa074fn/4rURWZPxXF7KkoZk9FMn8qitlbv7nV1dWi+1BqEXELzbMe/6mr6S3ADcBemstkf5nmMtu7gR+3vv6uY/+PZ+bnIuIq4Daay3EvAv9LZh6a6otQZZk/FcXsqShmT0UyfyqK2Vs/C1pJkiRJUiU55ViSJEmSVEkWtJIkSZKkSrKglSRJkiRVkgWtJEmSJKmSzijyh7eWnr4MmKfrBsEqnS0074H13cw8UXRnNsrsVUqtsgfmr2JqlT+zVylmT0WpVfbA/FXMmvM3VkEbEe8EbqW5VPRdmfnJrvbbgHcDz7Y23d29zwCXAQ+N0weVxhXAN4vuxASYveqpS/bA/FVRXfJn9qrH7KkodckemL8qGjt/IwvaiDgfuBO4BDgBPBwRBzPzBx27XQa8IzO/vcaOzgN8/vOfZ+fOnWt8qmZpYWGBvXv3Qut3VgNmryJqmD0wf5VRw/yZvYoweypKDbMH5q8y1pO/cUZo9wAPZuYxgIi4D7ia5o1/2y4F3h8RrwG+AdyUmcudB4mIs4Gzu469C2Dnzp3s3r173D6rWHWZpnEKzF7F1CV7YP6qqC75M3vVY/ZUlLpkD8xfFY2dv3EWhTqP0yvkeeBnSYiI7cAh4CbgH9IsWj/Q5zg3An/b9eXQvyRJkiRpXcYZoZ3rs+3F9jeZuQRc2X4cEfuAe4Bbup7zMWB/17bdWNRKkiRJktZhnIL2MM2Lctt2AT9pP4iIC4A9mXlPa9Mc8EL3QTJzEVjs3BYRa+2vJEmSJEnAeAXtA8AHI+Jc4DngKuD6jvbjwIcj4iDwBPBe4EsT7qckSZIkSacZeQ1tZh6mOX34IPAocCAzH4mIr0XEpZl5FLgB+AqQNEdo902xz5IkSZIkjXcf2sw8ABzo2nZlx/f3A/dPtmuSJEmSJA02VkErSZIkaeNOLS9xauV4z/YtW89iy7btU3uuVKTl5WVWVlZ6tm/dupVt27Zt6NgWtJKkzWelAScbvdvP2AFbd8y+P10i4p3ArcBW4K7M/GRX+23Au4FnW5vu7t5H5bSytMTJpaWe7Wds387W7cUXJKOy17HfnwAHM3P/DLs3M9MsHE+tHOf5p3/Ys/0Vr37t6IJ2A8+VpmlUwbqyssL8/HxP+65duyxoN4PGcoPGSu8Hrx1bd7Bj246R7VJpnWrAi32KipftgC3FZteCouZONuDYod7t51xceEEbEecDdwKXACeAhyPiYGb+oGO3y4B3ZOa3i+ij1u/k0hLHHn+8Z/s5r3994QXtONmLiPOAPwLeQnN9lVoaVTgOK3jbz+/XZtGpMhtVlA5rn2bBOooFbQU0Vhocmu/94HXxroubBe2Idqm0XmzAcp+iYtvFhRa0FhQq2B7gwcw8BhAR9wFXAx/q2OdS4P0R8RrgG8BNmbk8856qbsbJ3l7gy8BPBx0kIs4Gzu7avHuyXS3WsIIX2NAo6qhiWZqWUUXpsPYiWdBuAo7gSms2sYJiM3yw08SdB3R+YpgHLm8/iIjtwCHgJpq3y9sPfIDmHQno2M/sFWTYtOKSG5o9gMz8CEBEvGnIcW4Ebp947yZoUNEIxReOo4plSaezoC2JYUXnho/tCK60VhMpKFpK/8FOpTPXZ9uL7W8ycwn42Z0GImIfcA+9+TN7BRk2rbjkhmZvDT5G89/FTruBh9ZxrKkYVDSChaPqa5oLMxXJgrYkhhWdkmZuUgUFVOCDnUrnMHBFx+NdwE/aDyLiAmBPZt7T2jQHvNDnOGZPazU0e+PKzEVgsXNbRGysZ+uwGafuugqyynqd6zRZ0M6I036lSplUQVGaD3aqlAeAD0bEucBzwFXA9R3tx4EPR8RBmjME3gt8qfsgZm96yr5S8QaMyl6lbMapu1VeBTkibgd+u/Xwq5n5B13tFwF3A6+ieanPezLz5Gx7WX5lvc51mixo12DUtOB+be12p/1qUyrxKsYjTKSgUIFKflueYTLzcETcQnMF2a3AH2fmIxHxNeC2zPxeRNwAfKXV/k1gX3E93nzKvFLxRoyTvWJ7eDpHI+sjIvYAvw5cDKwCfxkRv5WZnX9b7wWuzczvRMRngeuAT8++t8Wq67ThjbCgXYNR04L7tXW2a30iYgfwMPAbmflEV5tn68qspKsYj2JBUQMlvi3PODLzAHCga9uVHd/fD9w/637VyaiFm2o6AjvSqOx1bHvXrPo0SJVHI9VjHvj9zFwBiIjHgQvajRFxIXBWZn6ntWk/cAd9Ctq6L4hX12nDG2FBq1KLiF+lWbC+bsAunq3TVFhQSPU2auGmOo7ASmWVmY+1v4+I1wJvB97QsUu/xRoHFamVXhBv0AgsNEdh1cuCVmV3Hc3pnH/a3TDu2bq6n6mTJKmuNuPCTptZRPwy8FWat8LrHH5fywrcpV8Qbz0LN0G9r4PdCAvaDi7cVD6ZeS0MXMhk3LN1lT5TV2rVvUZWklQBm3Fhp80qIt5Ic+bTjZn5Z13Nh4GdHY8HrsBdhQXxNuPCTdNkQdvBhZsqZ9yzdaU/U1dZFb1GVlL91XglYql2IuLngb8A3p6ZD3a3Z+aPI2I5It6Ymd8CrgG+Put+jsuFm2bLglZVNtbZuiqcqZO0DsNWMtamV9eViOvGlYrVchOwDfhox2e0zwC/yUsrbO8F7o6InwMOAZ8ooqNtm/F+r2VlQavKqtrZOkkTNmwlY9XeoBFYeGmlYpWfKxULIDPfB7yvT9NnOvb5PnD5pH7mqFHUUe1OGy6PTVfQjrqXrMqv6354pTpbV0vDrpOVpCkaNm140AgsvLRSscrnhaVjrDTOdARWhRs1iuooa3VsvoJ2xL1kVU6Z+Qsd33feOmWiZ+vUx7DrZCVpikbdWkfVc/yZJ3h+y5IjsCrE0tISjUbD29/UzKYraHU6V3aWVFrDrpF1Vk0tuHCTpFk6cuQIc3NzTguumdoVtBZoa+PKzpJKa9g1sha0teDCTZKkjapfQWuBJq2N95KVNEXDRmElSdqo2hW0ktbIe8lK2oBR04a9DlaSNE2VK2idUixJNeE1sqUxahR1vQWr04YlSdNWvYLWKcXS2jilWGXlNbKFWT56lOfPOGPsUVQLVklSWVWuoJW0Rk4pVpGGjcKqMIs/+hHbFhctSiVJlVfKgnbYtGJJUoUMG4WVJEnaoFIUtAtLC9B46TrYYdOKJUmSJEmCkhS0jx15jPm5ea+DldZr2HWy0jS5sJMkSSpQKQpaSRs07DpZaZpc2EmSJBXIglaqAlcqVlEGjcCCCztJkqTCWdBKVeBKxZqmYdOGB43Aggs7SZKkwlnQSmUwaAQWvA5W0+dKxJIkqaIsaKVZGTZteNAILHgdrDbOhZskSVJNWdBKkzLqOlcXbtI0rWfasAs3SdKmcmp5iVMrx3u2b9l6Flu2bS+gR9LGjVXQRsQ7gVuBrcBdmfnJrvaLgLuBVwHfAN6TmScn3FcVoLHcoLHSaN4ruACly956Rlm9zrWSZpK95QV4npcWVxo2ijpqlNVpw7VSun/7tGmYvXo7tXKc55/+Yc/2V7z6tYUXtGZP6/WyUTtExPnAncCbgF8Bro+IX+ra7V7g9zLzdcAccN2kO6piNFYaHJo/xGNHHpv5z55J9k4uwAtPNQtVaP73had6v9rt7aK1+2vQ9a+qpJn9u7f4WLMIPdl4qSDt/moXsaPaVRv+3VVRzJ6KYva0EeOM0O4BHszMYwARcR9wNfCh1uMLgbMy8zut/fcDdwCf7jxIRJwNnN117AsBFp9ZBGBhdQEasLC0wLEjx3o6srDaHCUc2DbqudM89oDnTvPYs3zN7d8RsKXvi5yOqWdv4clvwYmz4cxfhjN2NgvcE32K99Pa+/yez2yNYA9qO4P1PXeaxx7nuUUeu/Wv08LCz2YHVC57rX0H5+9o6/+r5dZrXOzzXiwvwLbWf9fbvpFjD3ruNI9d9Gve9tLDKudvWPaOPvssAMvz82w7eZLlo0dZfOaZno4sz88DDGwb9dxpHnvQc6d57Fm85ra6Zu/IM83/7846tZ2XN07wwtIxjvd5P8ZpByb+3Gkeu+jXPE47VDt7rX0H5u+Z1mtfXV0F4MiRIz0dWV1dpdFosLS0tO72jRx70HOneeyiX3Oj8dLJ+fXkb5yC9jxgvuPxPHD5iPbdfY5zI3B7vx/wqZs/NUY3VBK7gL+Z0c+aevb2Xmf2KqSK2YNh+bvR/FVIFfM3MHs37tu3wS5qhmqVvRve/4cb7KJmqIrZgyH5u/nmmzfQRc3Y2Pkbp6Cd67PtxTW0t32M5tmUTq8B/gr4NeDJMfoyK7uBh4ArgKcK7kunIvu1hWawvjvDn7kZswfmr1uVswfVyp/Z61Xl/Jm9jTN79c8emL9uVc4eVCt/Zq/XmvM3TkF7mOaLadsF/KSrfeeQdgAycxFY7NwWEe1vn8zMJ8boy0x09Osp+3WaWZ2la9t02YNS/J77KrhflcweVCt/Zm+gSubP7G1cCfpl9magBL/nvvy769/dopSgX2vK38hFoYAHgLdExLkR8QrgKuAv242Z+WNgOSLe2Np0DfD1tXRCGsDsqShmT0UyfyqK2VNRzJ7WbWRBm5mHgVuAg8CjwIHMfCQivhYRl7Z22wvcFRGPA68EPjGtDmvzMHsqitlTkcyfimL2VBSzp40Y6z60mXkAONC17cqO77/P6RduSxNh9lQUs6cimT8VxeypKGZP6zXOlONpWqS55PbiqB1nzH7VX5nfy7L2raz9qqKyvpf2q/7K+l7ar/or83tZ1r6VtV9VVNb30n5NwFz7vkGSJEmSJFVJ0SO0kiRJkiStiwWtJEmSJKmSxloUaq0i4nbgt1sPv5qZfxARe4CPAmcBX8jMW7ue8yfAwczc33p8AXAv8F8ACezNzKUS9Osa4H8Dnu44zi2z6ldE/DOac9rngL8F/sfMfHYa71cVlTV7E+yb+SuxsubP7NWf2Ztev8zecGXN3gT7Zv5KrKz5M3uzNfER2tab8uvAxcBFwCUR8d8D9wD/DHg9cFlE/NPW/udFxFeAt3Ud6lPApzLzvwK+B3ygJP26DPhfM/Oi1tdGgzV2vyJiB/Bp+P/bu38XuaoogOPfJQS0MQgWRtRKPYWFlVgo/gM2CqIYIVikChaKQVKksBJJwEZBSxcsJERU8EdllUqxWUHkKBgI6BZiI1YmuhbvTjJq8lB49+15k++n2p25+zjc+TaXfTPDY5n5APA18Eq71KT7tURV25t4Nvsrqmp/trf5bK/fXLY3rmp7E89mf0VV7c/25tfjluNd4KXM/D0zLwHfAvcB32fmhcy8zHCqX71ozwIfAWdXF4iIg8CjwLn20Dv8+0Wefa7mQeBoROxExLsRceuMcx0EjufwXV0wxHV3p/1aoqrtTTJSIHUGAAACbElEQVRbY391Ve3P9jaf7fWby/bGVW1vktka+6uran+2N7PJbznOzG9WP0fEvcDTDF98vLu2bBe4s60/09Y+svb8bcCvbWP/tn6f51qteQ34EngVeJMhxO5zZeYvwIdt7c3ASeANOuzXElVtb8LZVmvsr6Cq/dne5rO9fnPZ3riq7U0422qN/RVUtT/bm1+X99ACRMT9wCfACeASEP9Y8ufIn29d47Gx9XPNRWY+sXat08APc88VEYcYItvJzO2IuOMal5xkv5aoantgfzeCqv3Z3uazvX5z2d64qu2B/d0IqvZne/Pp8inHEfEw8DlwMjO3gR+B29eWHAZ+GrnEz8AtEXHgP66fZa6IOBQRL649tMUQwmxzRcRh4DywAxxrz3fZryWq2t4Us9lffVX7s73NZ3v95rK9cVXbm2I2+6uvan+2N68eHwp1F8NJ/khmvtce/mJ4Ku5pG3AE+Ox612j3dZ9n+Fc4wNGx9XPNBfwGvBwRD7Xfnwc+mGuu9vPHwNnMfCEz96DPfi1R1fammg37K61qf7a3+Wyv31y2N65qe1PNhv2VVrU/25tfj1uOTwA3Aa9HXPkP9tvAc8D77blPufpm4us5DmxHxCngIvDMfs+VmX9ExFPAWzHcT/4dwws511yPM3wy2YGIeLKt/SozjzH9fi1R1fYmmc3+yqvan+1tPtvrN5ftjava3iSz2V95VfuzvZlt7e3t7fcMkiRJkiT9b13eQytJkiRJUm8eaCVJkiRJi+SBVpIkSZK0SB5oJUmSJEmL5IFWkiRJkrRIHmglSZIkSYvkgVaSJEmStEgeaCVJkiRJi/QXESr8UOMipHUAAAAASUVORK5CYII=\n",
"text/plain": "<Figure size 936x720 with 48 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### How many accesses do we predict in past and future years"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\nTo show how we'll estimate accesses, we'll use 2022 as an example. We use the 2022 row from the give above, and the graph it by age of article (rather than year of publication). This flips the direction of the x axis. In this graph to make the next steps more clear we also use a shared y axis across all OA types.\n"
},
{
"metadata": {
"scrolled": true,
"ExecuteTime": {
"start_time": "2019-09-23T19:56:56.263143Z",
"end_time": "2019-09-23T19:57:44.301971Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\nmy_year = 2022\ndata = get_papers_by_availability_year_total(my_year) \ndata_now = data.loc[data[\"article_years_from_availability\"] < 15]\ndata_now[\"publication_year\"] = my_year - data_now[\"article_years_from_availability\"]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(alpha=0.1)\ng.map(plt.bar, \"article_years_from_availability\", \"num_articles\", **kws);\n\n",
"execution_count": 208,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Next, we'll use the signal we calculated in the section \"How often does someone want to access a paper, given its age and OA status\", which shows the number of accesses per article someone made in 2019. An assumption in our model is that this accesses-per-article probability stays the same across time, so we assume that it applies to 2022 as well.\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:20:25.040828Z",
"end_time": "2019-09-23T06:20:45.785473Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\ndata = get_accesses_per_article_total() \ndata_now = data.loc[data[\"article_age_years\"] >= 0]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(s=50)\ng.map(plt.scatter, \"article_age_years\", \"accesses_per_article\", **kws);\ng.map(plt.plot, \"article_age_years\", \"accesses_per_article\");\n",
"execution_count": 36,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now we multiply these two signals together. We multiply them in a similar way that we divided signals in an earlier step -- we take each OA type in turn, and then take each age bin in turn. So the green OA point at 0 years in the graph below comes by multiplying the number of estimated articles in 2022 that are available as green OA and 0 years old by the number of \"accesses-per-article\" we calculated for green OA for articles that are 0 years old. We then do that calculation for every age bin, for every OA type, and get the graph below:\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:20:45.795430Z",
"end_time": "2019-09-23T06:21:31.720737Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\ndata = get_predicted_accesses_by_pubdate_total(my_year) \ndata_now = data.loc[data[\"article_age_years\"] < 15]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(alpha=0.25, linewidth=8)\ng.map(plt.plot, \"article_age_years\", \"accesses\", **kws);\n# kws = dict(alpha=1, linewidth=5, linestyle='dashed')\n# g.map(plt.plot, \"article_age_years\", \"accesses\", **kws);\nfor ax in g.axes[0]:\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"accesses (millions)\")",
"execution_count": 37,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Finally, an estimate of total number of accesses in 2022 comes from summing together the accesses in 2022 across all article ages. In other words, the green X in the graph below at year 2022 is the area under the green curve in the row above -- the sum of all accesses to green OA of age 0, age 1, age 2, age 3, etc.\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:21:31.726082Z",
"end_time": "2019-09-23T06:23:20.151133Z"
},
"trusted": false
},
"cell_type": "code",
"source": "\ndata = get_predicted_accesses_total(my_year) \ndata_now = data.loc[data[\"observation_year\"] >= 2010]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(marker=\"x\", s=70)\ng.map(plt.scatter, \"observation_year\", \"accesses\", **kws);",
"execution_count": 38,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This gives us accesses for each year, 2010 to 2024, by OA type. The following graph is the same as the previous one, but without shared y axis so we can better see the relative trends. \n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T06:23:20.157226Z",
"end_time": "2019-09-23T06:25:15.895918Z"
},
"trusted": false
},
"cell_type": "code",
"source": "# Hi Heather\n\nfig, axes = plt.subplots(1, len(graph_type_order), figsize=(13, 3), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\nprediction_of_accesses = pd.DataFrame()\nfor i, graph_type in enumerate(graph_type_order):\n if True:\n# if graph_type != \"closed\":\n# print graph_type\n new_data = graph_accesses(graph_type, ax=axes_flatten[i])\n new_data[\"graph_type\"] = graph_type\n prediction_of_accesses = prediction_of_accesses.append(new_data)",
"execution_count": 39,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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