{ "cells": [ { "cell_type": "markdown", "id": "ce57e23d-5b62-4897-9973-d058dd68ebb4", "metadata": {}, "source": [ "Inital data for this came from this filter: https://pasbdc.neoserra.com/conferences?__formid=24&remove=&savename=&sort=START_DATE&sortdir=DESC&expr=&field_1=START_DATE&opt_auto_1=pfy&field_2=F_CENTER_ID&opt_2=-1&field_3=&sortdir=DESC" ] }, { "cell_type": "code", "execution_count": 14, "id": "2ef1bebf-8695-4fdd-853e-75e06bcde5bb", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from pathlib import Path\n", "import sys\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "\n", "notebook_dir = Path().resolve() # Current working directory\n", "project_root = notebook_dir.parent # Goes up to root/\n", "sys.path.insert(0, str(project_root / \"libs\"))\n", "\n", "from pasbdc_data_cleaning import clean_center_name " ] }, { "cell_type": "code", "execution_count": 15, "id": "920f5a8a-298d-4bb2-af2a-b15f55545961", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Primary Training Topic\n", "Business Start-up/Preplanning 298\n", "Marketing/Sales 104\n", "Business Plan 85\n", "Managing a Business 82\n", "Business Financing 67\n", "Social Media 48\n", "Agriculture 48\n", "Other 40\n", "Accounting/Budget 40\n", "International Trade 30\n", "Artificial Intelligence (AI) 25\n", "Internet/Web Training 22\n", "Cybersecurity Assistance 16\n", "Tax Planning 15\n", "Human Resources/Managing Employees 13\n", "Selling to Government 12\n", "Risk Management 11\n", "Government Contracting 8\n", "Legal Issues 8\n", "Government Industrial Base (GIB) Readiness 7\n", "Buy/Sell Business 6\n", "Technology 5\n", "Networking Event 5\n", "Cash Flow Management 5\n", "Prime Vendor Program 5\n", "SBIR/STTR/Other Innovation Programs 4\n", "Customer Relations 4\n", "Subcontracting 3\n", "Procurement Fair 3\n", "Franchising 3\n", "Veterans Outreach Conf. 2\n", "Defense Industrial Base (DIB) Readiness 2\n", "Industrial Base Analysis and Sustainment (IBAS) 2\n", "Mentor-Protégé 1\n", "Defense Production Act (DPA) Title III Support 1\n", "Woman-owned Businesses 1\n", "Small Disadvantaged Businesses 1\n", "Name: count, dtype: int64\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Start DateEvent TitlePrimary Training TopicTraining TopicsCreated ByCenterFunding SourceStatusMaximum AttendeesNumber RegisteredAttendees, Total
09/30/2025 12:00 AMBoost Your Business Visibility: Get Found on G...Marketing/SalesAgriculture, Artificial Intelligence (AI), Bus...rwolf219KutztownCore ServicesClosed300068
19/30/2025 12:00 AMIntroduction to the Business Finance Basic Ser...Accounting/BudgetAccounting/Budget, Business Financing, Busines...rwolf219KutztownCore ServicesClosed0750
29/30/2025 12:00 AMMulti-State Supplier Readiness Webinar P E N ...Managing a BusinessAccounting/Budget, Business Plan, Cash Flow Ma...tcookPittsburghCore ServicesClosed0087
39/30/2025 12:00 AMThe Basics of Business Lending (in-person and ...Business FinancingBusiness FinancingHigginsj5DuquesneCore ServicesClosed006
49/30/2025 12:00 AMUnlocking Business Success: Tools & Resources ...Veterans Outreach Conf.Veterans Outreach Conf.vermaLehighCore ServicesClosed100229
\n", "
" ], "text/plain": [ " Start Date Event Title \\\n", "0 9/30/2025 12:00 AM Boost Your Business Visibility: Get Found on G... \n", "1 9/30/2025 12:00 AM Introduction to the Business Finance Basic Ser... \n", "2 9/30/2025 12:00 AM Multi-State Supplier Readiness Webinar P E N ... \n", "3 9/30/2025 12:00 AM The Basics of Business Lending (in-person and ... \n", "4 9/30/2025 12:00 AM Unlocking Business Success: Tools & Resources ... \n", "\n", " Primary Training Topic Training Topics \\\n", "0 Marketing/Sales Agriculture, Artificial Intelligence (AI), Bus... \n", "1 Accounting/Budget Accounting/Budget, Business Financing, Busines... \n", "2 Managing a Business Accounting/Budget, Business Plan, Cash Flow Ma... \n", "3 Business Financing Business Financing \n", "4 Veterans Outreach Conf. Veterans Outreach Conf. \n", "\n", " Created By Center Funding Source Status Maximum Attendees \\\n", "0 rwolf219 Kutztown Core Services Closed 300 \n", "1 rwolf219 Kutztown Core Services Closed 0 \n", "2 tcook Pittsburgh Core Services Closed 0 \n", "3 Higginsj5 Duquesne Core Services Closed 0 \n", "4 verma Lehigh Core Services Closed 100 \n", "\n", " Number Registered Attendees, Total \n", "0 0 68 \n", "1 75 0 \n", "2 0 87 \n", "3 0 6 \n", "4 22 9 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainings_df = pd.read_csv('trainings.csv')\n", "\n", "clean_center_name(trainings_df)\n", "\n", "\n", "trainings_df.to_csv(\"trainings_cleaned_center_name.csv\")\n", "\n", "print(trainings_df['Primary Training Topic'].value_counts())\n", "trainings_df.head()" ] }, { "cell_type": "code", "execution_count": 16, "id": "0b391bbf-0e2a-41a9-addd-f8aabd18fdb8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "57\n" ] } ], "source": [ "print(trainings_df[trainings_df['Attendees, Total'] == 1].shape[0])" ] }, { "cell_type": "code", "execution_count": 17, "id": "5d4ed5a2-6414-42ac-a44b-f4e809c815f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Primary Training Topic\n", "First Steps 227\n", "Marketing/Sales 104\n", "Business Start-up/Preplanning 99\n", "Managing a Business 82\n", "Business Financing 67\n", "Business Plan 50\n", "Social Media 48\n", "Agriculture 48\n", "Other 40\n", "Accounting/Budget 40\n", "International Trade 30\n", "Artificial Intelligence (AI) 25\n", "Internet/Web Training 22\n", "Cybersecurity Assistance 16\n", "Tax Planning 15\n", "Human Resources/Managing Employees 13\n", "Selling to Government 12\n", "Risk Management 11\n", "Government Contracting 8\n", "Legal Issues 8\n", "Government Industrial Base (GIB) Readiness 7\n", "Next Steps 7\n", "Buy/Sell Business 6\n", "Technology 5\n", "Networking Event 5\n", "Cash Flow Management 5\n", "Prime Vendor Program 5\n", "SBIR/STTR/Other Innovation Programs 4\n", "Customer Relations 4\n", "Franchising 3\n", "Procurement Fair 3\n", "Subcontracting 3\n", "Veterans Outreach Conf. 2\n", "Defense Industrial Base (DIB) Readiness 2\n", "Industrial Base Analysis and Sustainment (IBAS) 2\n", "Mentor-Protégé 1\n", "Defense Production Act (DPA) Title III Support 1\n", "Woman-owned Businesses 1\n", "Small Disadvantaged Businesses 1\n", "Name: count, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def tag_first_steps(series):\n", " if 'first steps' in series['Event Title'].lower() or 'first step' in series['Event Title'].lower():\n", " series['Primary Training Topic'] = 'First Steps'\n", " elif 'next steps' in series['Event Title'].lower() or 'next step' in series['Event Title'].lower() or 'the next step' in series['Event Title'].lower():\n", " series['Primary Training Topic'] = 'Next Steps'\n", "\n", " return series\n", "\n", "trainings_df = trainings_df.apply(tag_first_steps, axis=1)\n", "\n", "first_steps_cols = ['First Steps', 'Next Steps']\n", "\n", "trainings_df['Primary Training Topic'].value_counts()" ] }, { "cell_type": "code", "execution_count": 18, "id": "7b8fa3c5-75dc-4599-88b0-bf24ffe99f77", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Center\n", "Scranton 166\n", "Duquesne 113\n", "Clarion 110\n", "Lehigh 83\n", "Kutztown 79\n", "Temple 73\n", "Widener 71\n", "Wilkes 64\n", "Pittsburgh 63\n", "Lead Office 53\n", "Gannon 49\n", "Bucknell 49\n", "Penn State 42\n", "Shippensburg 9\n", "St. Francis 8\n", "Name: count, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Combine the lead office centers into one center type\n", "\n", "lead_office_centers = ['Pennsylvania SBDC Lead Office', ' Pennsylvania SBDC Lead Office','Southeast Pennsylvania APEX Accelerator', 'Primary Training Topic', 'State Small Business Credit Initiative (SSBCI)']\n", "\n", "def apply_lead_office(series):\n", " if series['Center'] in lead_office_centers:\n", " series['Center'] = \"Lead Office\"\n", "\n", " return series\n", "\n", "trainings_df = trainings_df.apply(apply_lead_office, axis=1)\n", "trainings_df['Center'].value_counts()" ] }, { "cell_type": "code", "execution_count": 23, "id": "98084411-9e4b-4d2c-a440-2970c1a6921e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CenterNumber RegisteredAttendees, Total
0Bucknell371648
1Clarion12431640
2Duquesne12426
3Gannon71594
4Kutztown730918
\n", "
" ], "text/plain": [ " Center Number Registered Attendees, Total\n", "0 Bucknell 371 648\n", "1 Clarion 1243 1640\n", "2 Duquesne 1 2426\n", "3 Gannon 71 594\n", "4 Kutztown 730 918" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_trainings_df = trainings_df.groupby('Center')[['Number Registered', 'Attendees, Total']].sum().reset_index()\n", "all_trainings_attended_df = trainings_df[trainings_df['Attendees, Total'] >= 1].groupby('Center')[['Number Registered', 'Attendees, Total']].sum().reset_index()\n", "\n", "all_no_first_steps_df = trainings_df[~trainings_df['Primary Training Topic'].isin(first_steps_cols)].groupby('Center')[['Number Registered', 'Attendees, Total']].sum().reset_index()\n", "all_no_first_steps_attended_df = trainings_df[~trainings_df['Primary Training Topic'].isin(first_steps_cols) & trainings_df['Attendees, Total'] >= 1].groupby('Center')[['Number Registered', 'Attendees, Total']].sum().reset_index()\n", "\n", "all_trainings_df.head()" ] }, { "cell_type": "code", "execution_count": 20, "id": "51c58b1d-9268-49f4-8081-1fa3638b74df", "metadata": {}, "outputs": [], "source": [ "def make_event_count_chart(df:pd.DataFrame, title:str, filename:str):\n", " fig = px.bar(df, \n", " x='Center', \n", " y='Training Events', \n", " title=title,\n", " text='Training Events',\n", " height=700, \n", " width=1000)\n", "\n", " # Calculate a grand total value\n", " grand_total = df[\"Training Events\"].sum()\n", " fig.add_annotation(xref='paper', yref='paper', \n", " x=0.0, y=1.05,\n", " showarrow=False,\n", " text=f\"{grand_total} total training events\")\n", " \n", " fig.update_traces(showlegend=False, marker_color=\"#71bf44\") \n", " \n", " fig.write_image(filename)\n", " fig.show() " ] }, { "cell_type": "markdown", "id": "306b0f0e-bf26-4303-8058-ea742e66854d", "metadata": {}, "source": [ "Now let's visualize the trainings per center in a few different ways. \n", "\n", "## All trainings per center:" ] }, { "cell_type": "code", "execution_count": 26, "id": "c80653f0-e484-4a7b-8e6d-7f641b98cbf4", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Center=%{x}
Training Events=%{text}", "legendgroup": "", "marker": { "color": "#71bf44", "pattern": { "shape": "" } }, "name": "", "orientation": "v", "showlegend": false, "text": { "bdata": "AAAAAACASEAAAAAAAIBbQAAAAAAAQFxAAAAAAACASEAAAAAAAMBTQAAAAAAAgEpAAAAAAADAVEAAAAAAAABFQAAAAAAAgE9AAAAAAADAZEAAAAAAAAAiQAAAAAAAACBAAAAAAABAUkAAAAAAAMBRQAAAAAAAAFBA", "dtype": "f8" }, "textposition": "auto", "type": "bar", "x": [ "Bucknell", "Clarion", "Duquesne", "Gannon", "Kutztown", "Lead Office", "Lehigh", "Penn State", "Pittsburgh", "Scranton", "Shippensburg", "St. Francis", "Temple", "Widener", "Wilkes" ], "xaxis": "x", "y": { "bdata": "MQBuAHEAMQBPADUAUwAqAD8ApgAJAAgASQBHAEAA", "dtype": "i2" }, "yaxis": "y" } ], "layout": { "annotations": [ { "showarrow": false, "text": "1032 total training events", "x": 0, "xref": "paper", "y": 1.05, "yref": "paper" } ], "barmode": "relative", "height": 700, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermap": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermap" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Count of Trainings Per Center FY25" }, "width": 1000, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Center" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Training Events" } } } }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_event_count_chart(\n", " trainings_df.groupby('Center')\n", " .size()\n", " .reset_index(name='Training Events'), \n", " \"Count of Trainings Per Center FY25\", \"training_events_all.png\")" ] }, { "cell_type": "markdown", "id": "ae03000d-de10-41f7-b44e-2bade616e87b", "metadata": {}, "source": [ "## All training events excluding first steps" ] }, { "cell_type": "code", "execution_count": 28, "id": "3ba51f27-2e58-4723-9afc-3d08ca1c7fad", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Center=%{x}
Training Events=%{text}", "legendgroup": "", "marker": { "color": "#71bf44", "pattern": { "shape": "" } }, "name": "", "orientation": "v", "showlegend": false, "text": { "bdata": "AAAAAACAQ0AAAAAAAABUQAAAAAAAgFNAAAAAAAAAOUAAAAAAAIBOQAAAAAAAgEpAAAAAAACAUEAAAAAAAAA9QAAAAAAAgElAAAAAAAAAY0AAAAAAAAAIQAAAAAAAAPA/AAAAAACASEAAAAAAAMBRQAAAAAAAAERA", "dtype": "f8" }, "textposition": "auto", "type": "bar", "x": [ "Bucknell", "Clarion", "Duquesne", "Gannon", "Kutztown", "Lead Office", "Lehigh", "Penn State", "Pittsburgh", "Scranton", "Shippensburg", "St. Francis", "Temple", "Widener", "Wilkes" ], "xaxis": "x", "y": { "bdata": "JwBQAE4AGQA9ADUAQgAdADMAmAADAAEAMQBHACgA", "dtype": "i2" }, "yaxis": "y" } ], "layout": { "annotations": [ { "showarrow": false, "text": "798 total training events", "x": 0, "xref": "paper", "y": 1.05, "yref": "paper" } ], "barmode": "relative", "height": 700, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermap": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermap" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Count of Trainings Per Center Excluding First Steps FY25" }, "width": 1000, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Center" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Training Events" } } } }, "image/png": "iVBORw0KGgoAAAANSUhEUgAABUgAAAK8CAYAAAA9JlUjAAAgAElEQVR4Xuzdf9BldX3g+W8nGAcMYADlV9kEg/YodOJkWmqsdBVsEVQyltXFurYsi0XczLbskgKkhGwg6eoJZgWKADVMsCeuSyCsdMaiKMosiuUMXUWKFNOVOIXoYkgUHECw6Q0/hCCYZzm3PU/fPn3vfc5zPuec++P76n+ku+/33PN9nc9t7Dfn3rtm6fUfyQ8CBAgQIECAAAECBAgQIECAAAECBAhkKLBGIM3wqtsyAQIECBAgQIAAAQIECBAgQIAAAQIDAYHUIBAgQIAAAQIECBAgQIAAAQIECBAgkK2AQJrtpbdxAgQIECBAgAABAgQIECBAgAABAgQEUjNAgAABAgQIECBAgAABAgQIECBAgEC2AgJptpfexgkQIECAAAECBAgQIECAAAECBAgQEEjNAAECBAgQIECAAAECBAgQIECAAAEC2QoIpNleehsnQIAAAQIECBAgQIAAAQIECBAgQEAgNQMECBAgQIAAAQIECBAgQIAAAQIECGQrIJBme+ltnAABAgQIECBAgAABAgQIECBAgAABgdQMECBAgAABAgQIECBAgAABAgQIECCQrYBAmu2lt3ECBAgQIECAAAECBAgQIECAAAECBARSM0CAAAECBAgQIECAAAECBAgQIECAQLYCAmm2l97GCRAgQIAAAQIECBAgQIAAAQIECBAQSM0AAQIECBAgQIAAAQIECBAgQIAAAQLZCgik2V56GydAgAABAgQIECBAgAABAgQIECBAQCA1AwQIECBAgAABAgQIECBAgAABAgQIZCsgkGZ76W2cAAECBAgQIECAAAECBAgQIECAAAGB1AwQIECAAAECBAgQIECAAAECBAgQIJCtgECa7aW3cQIECBAgQIAAAQIECBAgQIAAAQIEBFIzQIAAAQIECBAgQIAAAQIECBAgQIBAtgICabaX3sYJECBAgAABAgQIECBAgAABAgQIEBBIzQABAgQIECBAgAABAgQIECBAgAABAtkKCKTZXnobJ0CAAAECBAgQIECAAAECBAgQIEBAIDUDBAgQIECAAAECBAgQIECAAAECBAhkKyCQZnvpbZwAAQIECBAgQIAAAQIECBAgQIAAAYHUDBAgQIAAAQIECBAgQIAAAQIECBAgkK2AQJrtpbdxAgQIECBAgAABAgQIECBAgAABAgQEUjNAgAABAgQIECBAgAABAgQIECBAgEC2AgJptpfexgkQIECAAAECBAgQIECAAAECBAgQEEjNAAECBAgQIECAAAECBAgQIECAAAEC2QoIpNleehsnQIAAAQIECBAgQIAAAQIECBAgQEAgNQMECBAgQIAAAQIECBAgQIAAAQIECGQrIJBme+ltnAABAgQIECBAgAABAgQIECBAgAABgdQMECBAgAABAgQIECBAgAABAgQIECCQrYBAmu2lt3ECBAgQIECAAAECBAgQIECAAAECBARSM0CAAAECBAgQIECAAAECBAgQIECAQLYCAmm2l77+xjdv2Za++ch3BwuOOuLwtPPOG+sv9kgCBGoJXHvzHemWHV9J99x+dVp7/NG11tR50Mmnn5/ef9qGdP22C+s83GMIECBAgAABAgQIECBAgEB2AgLphEt+/4MPpS2XXXfAI668+Lx0zqYzZmJYynPs6pwu2XpT+uuH/nbFKDocUSfBnLLuxLRj+9aQ3Rfv+nq66obb0vmbP5g+fcHHVn2s086+KO3e81x6+L5bVr22rwXlOVafb/s1l6aNp67v6zSWn6eIbNUffUS3acW9cf6lQRevt74CafGavnfnrjStWSpfv+OGuHxdd/E6bTpP5bVZ6fVY/Dn4gx/uWfHPy95fwJUnfPyJp9NZ514+9jSKP6eLfRR/To6bk9KkvF6jjjnuz/tyBkedgP8IN+3p8PwECBAgQIAAAQIE8hQQSMdc9/Iv59UQUv7lvo3Q18bIdR1Ii6DQJESWTl2EpFwCaTEfw3frVoNEG/Oz0jHKkFGNocMxpMvQ3DRorbSvlX6/eP1X/VdaE/393ALpSn82zEogLc+jemdv+R+Fhvcxb4F0pf/IUbz+RgXL8t875fryz4PqvxfHrS//XOnyz47o69F6AgQIECBAgAABAgTyEhBIR1zv8i9vk97qWvxFOHonZBuj1mUgLf/Su1LIGLWPLgNpG26zfoxxga6MMm2/DbvpNez6dSCQxid1Wobjznyafzas1qKM1uPuoiz+/P3+k88sv6Ng0QLpuP8YVf3zqfh3xY67//MBd/SPu9YCafx17QgECBAgQIAAAQIECLQrIJBWPMsouNKdNdXLUH0L5qi7bsb95XnUXxbLv8if+i/eNXg7eflj+LzGfQRA3XOvvm16OISOO3bdt+WO+4vx8F1yn/63n1v+bNPiuYsfw3st91wNtKOi8PBxz/vtPxy8NbT8MerOr+G3wQ4f78G/+fbg7cfjnrv89apdcedU8Tmt1eeqvlW77ttHxwXSUcGm+nbVUXc3l/P03//r05Y/NmKlc2lyF+Wot09X7xIrXwe3/bvf3e9tvtXzGfW2/upjVtr78LX9b0/9cPAZn8WPlV4jdfY+fBdt9bqPu9N81FuLy/kedQfpav7MKPY16s+h4rUwvN9Jz7PSNSnnv/o85fyv5FqsrxtIR+19pTme9HqrM0/j/lyv8x8kxn3MyPDaNl8fw5bD513nP2it5t9z1f8os1I0Hj6Xcf8BTyBt9//IORoBAgQIECBAgAABAnEBgbRiWPcv78PLyr9ADoegcb826vPpxgXSasgZ9ZfNJneQlmuG3zo/6tfKv0S3+Rb74bBSja2F/V333L/fnbmjrsekQFqYDQeJUZGl+mvDMXg4LowKSePCwqg7O4sgMxwrVxMl6gbSUW9DHrW2jEN1PxpiNedavhZGfbblqLuxS6tRQbQa2Cbd8Vdn78PXdjVzXCeQDsfy4b1U335cPm7UMYv5LqJ88QVK0UA6ynrUdRz3PEXgr3NNRj3PuD2P+ldU3T9jxwXS4pjj/iPASq+31d5ButqPVJl0B2kbr4/h/U36jyUrBd3Vvr7LP8uu/f1PDv6jRp0QXlyncTF13GeQest9/P/UOQIBAgQIECBAgAABAs0EBNKK22rujimWTgqU1b+Mr+ZusHF/kS8iyzFvOWI5IjYJpOPOoxpOugykK/0FfviyFHv+1fXvWP4W7pXuIB3+BvAycAzH2HGBtHrn1aj917WbdF0K55W+XGpcoBv+9XGfWTnqoxFWG4ZGBfNJf8RM2m/1+rXxOqi79yavj2Kfq/mSpmocrIbx4VA0ae4jgXQ1fw719Tzj5mXSlzQNh7dJd5AWQXn4R93X22pfB8VzrCbmjZvtNl4f1T/Lxj1X8bhfe+8pafjPweq1mPQlTaPuLK9eszohs3yOle5UL8+tfM3VfZdCs//LYxUBAgQIECBAgAABAgRGCwikwUA66YtVqn+BbSMMtRFIx0WC6l/ipxVIR70VdvjOqWkG0nF24+JyMV5N/sI/KpCWoaYMuZPuVqt+udZqw9BqA+lKr4PCofzM3jZeB3X3HgmkxTkPf0nWpH+JrBSQ6nw2ZSRcTvKvXvu+nmelQLrSW8FXE0iHg9+k19tqXwejImx1XyvdsV48vo3XR/XP4/LPg7p3hQ+f92rvIC3Wlnd+1/3zrEnwHPUfF/yfNwIECBAgQIAAAQIECPQhIJCOCaQr/eW9XDbps9Sqb7NvIwxFA+mk6FmNYn0H0nHfmF5nz+MCRJt3kE7ymPR2/OoLuc7dV+PuYBxeu9JdjsN34602DK3mLdPF/sbdZVfuffgusrZeB8OfM1s1LvfeVyAdNhgVkKozPOoP90i4nPTnUJuBdDXP02cgLZ5r3F2Rw6+Z1b4OJv1LuJyt4UC50myPO16d18eoP39Gve7qfJREk0A6KfJW99UkjhbHWM1HW/Txf5A8BwECBAgQIECAAAEC+QgIpJVrvdq/OK50Z9DwZ46u9JfnOn+RrxMLVxrfWb2DNPKxArMYSKvXYTWfZ1gnFBTzVPwo78ycdN2bhKE651A+Z+lfJ/628Tqou/e+Aum07yBdTbjsK8T2HUjrvN6avA4mva6qr5GVPoIj8vqoM8t1w+Rq/z1XGNQNpHXPYZTrav7MWenfc36fAAECBAgQIECAAAECqxEQSEdo1XkrYREkis/BW81n/7URhqqBdNTnTa40ACv9Jb58y2ifd5BOeq46UbiPQFq4jrsTsPr8RTB723FvTRtPXb/f5ajzVuvyeYr/nfQW77rBojhOkzBUHn/SHWl1XgfVeVzN66D6+aXVILvSZ9nWiUrRUDN85/XmD/93gy+xqX7u4qRrVX4mbSRcjrpTutxXm3eQruZ5xv05FP2SplFfEFT39TZunsadaznf436/als8/q8f+tsDXrermcO6fz6POre6z9NVIC0/HmWl1+Uoz8i/b1b6d57fJ0CAAAECBAgQIECAwEoCAukYofIumOpb7UfdBVgG1epn0RXfCj18x9CouDD8Fskmd5BOinbjLv6ot0+P+szJyF9Yx0WQSaFo1N1D5XWYlc8gHbWv4W9KL2egfNxwXFxNlKh7J1XpU70zrZirU//Fu9I5m84YjEGTQFqsG/exB8NvZy6fe9S3dBfHGP6m9uLnqwmkk+6MrLP3usGo+lqp61/aDs9nee2rIa+4BtVwOhzsRr02VvNnxqTXz/C5REJs+edN8b/D8b78M7DOt5t3FUivuuG2tNLrbdI8jfrzctLnfJbzN/zn/qSAvJrXR/XfHaP+fB41/+VzrBQoV/NnUeky6c/u1XwhU7mX6p9ZZVytc5ftSv/Hxu8TIECAAAECBAgQIEBgtQIC6QSxcd+2POozBsu/PJaHG/fNvdXHFQH2wb/5drp35679Ymrdt5sXz1f97L06kaIMO8Pbr/Mt7nUHrEkgLePL8GdLFn/RP++3/zAd85Yjlt9KPs0vaSrOsToXRRzb8J516ZYdX9nvGlavdbG2zucDjotQ4+zrfMt200A6ar7K8xi1lzqvmdUE0upMVF9XK+09EkgnfcZp8Vopvim8uFu0+DEqUBev6eoX6JQhsTQc/v1xAarunxnjXj/FObYZSEc9T/Fn4pbLrqs1310E0uKc6r7ehj+7t843rI+b6XF/zlZnclRArb6Wh/+dUr4+qvM36nOxR32hXZ3A2HYgHWc0vM/qfwCsGjT5sqm6/z7yOAIECBAgQIAAAQIECKwkIJCuJOT3CdQQGPfW2hpLPYTA3As0+aiPud90Rxuo+zEcHT29wxIgQIAAAQIECBAgQCBLAYE0y8tu000FihBU3NE6/PbipncpNj0H6whMU6D68Q3FuYh67V0Rlu1ZOhIBAgQIECBAgAABAgTqCgikdaU8jsDrAtWPMyhRRn3sAjACiygw6mMNvD26vSstkLZn6UgECBAgQIAAAQIECBCoKyCQ1pXyOAIECBAgQIAAAQIECBAgQIAAAQIEFk5AIF24S2pDBAgQIECAAAECBAgQIECAAAECBAjUFRBI60p5HAECBAgQIECAAAECBAgQIECAAAECCycgkC7cJbUhAgQIECBAgAABAgQIECBAgAABAgTqCgikdaU8jgABAgQIECBAgAABAgQIECBAgACBhRMQSBfuktoQAQIECBAgQIAAAQIECBAgQIAAAQJ1BQTSulIeR4AAAQIECBAgQIAAAQIECBAgQIDAwgkIpAt3SW2IAAECBAgQIECAAAECBAgQIECAAIG6AgJpXSmPI0CAAAECBAgQIECAAAECBAgQIEBg4QQE0oW7pDZEgAABAgQIECBAgAABAgQIECBAgEBdAYG0rpTHESBAgAABAgQIECBAgAABAgQIECCwcAIC6cJdUhsiQIAAAQIECBAgQIAAAQIECBAgQKCugEBaV8rjCBAgQIAAAQIECBAgQIAAAQIECBBYOAGBdOEuqQ0RIECAAAECBAgQIECAAAECBAgQIFBXQCCtK+VxBAgQIECAAAECBAgQIECAAAECBAgsnIBAunCX1IYIECBAgAABAgQIECBAgAABAgQIEKgrIJDWlfI4AgQIECBAgAABAgQIECBAgAABAgQWTkAgXbhLakMECBAgQIAAAQIECBAgQIAAAQIECNQVEEjrSnkcAQIECBAgQIAAAQIECBAgQIAAAQILJyCQLtwltSECBAgQIECAAAECBAgQIECAAAECBOoKCKR1pTyOAAECBAgQIECAAAECBAgQIECAAIGFExBIF+6S2hABAgQIECBAgAABAgQIECBAgAABAnUFBNK6Uh5HgAABAgQIECBAgAABAgQIECBAgMDCCQikC3dJbYgAAQIECBAgQIAAAQIECBAgQIAAgboCAmldKY8jQIAAAQIECBAgQIAAAQIECBAgQGDhBATShbukNkSAAAECBAgQIECAAAECBAgQIECAQF0BgbSuVGaPu2TrTYMdX7/twk52fv+DD6Utl12X7rn96rT2+KMnPse1N9+Rvvy1B9LOO2/s5FwclAABAgQIECBAgAABAgQIECBAIF8BgbTFa19ExXt37jrgiOdv/mD69AUfG/z65i3b0jcf+e7gn09Zd2LasX3r8uPLaFj+wvC66kFXExiLtat9/EqB9OTTz09XXnxeOmfTGY0EV3M+ixZIixk47pgjO4vPjS6IRQQIECBAgAABAgQIECBAgACBTAUE0o4vfBESy7ski+j45A+eXY6ip519UfrV9e8YhLLHn3g6nXXu5Wn7NZemjaeuP+DnixZIO2af6cMLpDN9eZwcAQIECBAgQIAAAQIECBAgkJmAQNrhBS/ufHzyB7uX7xQsYmkZQIun/eJdX0+fu/XuwVvHyzsqH77vluUzKgLqJz/+4ZF3aRbHGv5RHnf4DtX3n7Zhv+euPn7t8W8dRNnyx1FHHL78NvZJd5AOP0extrjT9X3/8uTBW+aLf75lx1cGhyz2Uuxh957nlp+jjMVlEC5/XkbDv37ob5cfX/7esFNxoEmPLX6/PPYon1GXuzj+VTfctvxb5TWoXq/qOY9bV17LYYvyWhQzUfoUT1jeRVz99cjduR2OtEMTIECAAAECBAgQIECAAAECBBZOQCDt8JIO3z1aPE01uFWjaBkeizD4+BPPpCs++/mxn7s56i3q1beiF3HyQ2e+b/D2/lGPL36t+FHcsVqeX/m2/tW+xb48/nCULY5ZnNPwxwsUv1Z8rMCoQFp89MDw3bbFY4u7a0cF0nGPLfdRBsbqnbnVy111Gb7Lt2owHLwnratajIvB5ee7VuegePyOu//zsluHI+rQBAgQIECAAAECBAgQIECAAIHsBQTSjkagevdo8TTVt1ZXw1gZOMs7Llf7GaTVO05H3aE66UuRhoNg00A66fjD57NSNCwsdn3jkUFMHXcHaRkYJz12pUBa3efwNalen8L3M7/zW4OgXGfdsMVwHB83B3W+sKqjcXVYAgQIECBAgAABAgQIECBAgEC2AgJpB5e+Gv+Gn6L61vjybe1FjKveMVo8dlwkHXVH6Ki38BdvHS/eMj7uS5Gqb5cv7wBtK5BW3zpe7rdOIC2/ub5OIC0fOxxLC/eVAml1/+W1KmNlGZ3fdtxb97s+k9YVd/8WHzdQDaTlXa2jPoO0+gVfYmkHL0yHJECAAAECBAgQIECAAAECBAiMEBBIOxiLleJi+ZTDd5lWw17xmElf5jMqeK72DtLiPIvP/Cw+A7X40fYdpOVndI76LNGuAmk1pq4USFe6VuV12fCedQOj8uMCJq0bF68nBdLhMax+mVcHI+qQBAgQIECAAAECBAgQIECAAAECPxUQSFsehUl3jw4/VTWilT+vfov9uC/rGRX+Jn0G6ajHVyNfEVh/df07Bp/7uVI4HP5802Jfo6JgNVYOB9muAml1n2WkHf5yrEnXofi9IkwXb+0vfgx/4dPwXZ2j9luuWymQVgNocY7Fj3M2nTH435XsWx5ZhyNAgAABAgQIECBAgAABAgQIZC0gkLZ8+YcjY/XQZTgrf334G+uLX6t+K/pK32Q+/Lbslb7Fvgxv9+7cNXj64vHVb7EvvlH9uGOOrBVIh891+Fvsq28NH/4W++L4P/jhnsEdq10F0mJvw2/rL55z+AudRl3uqnv1i6aK8Fn8KKNpeYxx61YKpMPRtTi/a3//k+mscy9fPrXyYwhaHk2HI0CAAAECBAgQIECAAAECBAgQGCEgkBqLhRao3sW60Ju1OQIECBAgQIAAAQIECBAgQIAAgVULCKSrJrNg1gWG71otztUXHs36FXN+BAgQIECAAAECBAgQIECAAIHpCQik07P3zAQIECBAgAABAgQIECBAgAABAgQITFlAIJ3yBfD0BAgQIECAAAECBAgQIECAAAECBAhMT0AgnZ69ZyZAgAABAgQIECBAgAABAgQIECBAYMoCAumUL4CnJ0CAAAECBAgQIECAAAECBAgQIEBgegJZBdJrb74j7frGI2nH9q0HiBffdn7VDbcNfv2oIw5PO++8cfkxl2y9Kd27c9fg56esO3Hk+uldQs9MgAABAgQIECBAgAABAgQIECBAgEBTgSwC6XD8HBU4i3B6y46vpIfvu2VkOP3crXcvB9PNW7alDe9Zlz59wceamltHgAABAgQIECBAgAABAgQIECBAgMCMCGQRSEvr4k7QJ3/w7AF3gJ58+vnpntuvTmuPP/qAy1INokVsHQ6mM3IdnQYBAgQIECBAgAABAgQIECBAgAABAg0Esg+k9z/4ULris59Pu/c8t8z3/tM2pOu3XTj4+WlnX5Q++fEPp3M2nTH4efH4LZddN/Ju0wb+lhAgQIAAAQIECBAgQIAAAQIECBAgMEWB7ANp+fb74bfXF3eUnr/5g4O30Rf/fOXF5x0QSMfdcTrFa+mpCRAgQIAAAQIECBAgQIAAAQIECBBYpYBAOuIt88Vb8YsfxV2kK91B+uSzL6+S3MMJECBAgAABAgQIECBAgAABAgQItCdw3JEHt3ewDI+UfSAd9Zb54UC60meQCqQZvmpsmQABAgQIECBAgAABAgQIECAwQwICaexiZB9IC77iLtEPnfm+wVvqH3/i6XTWuZen7ddcmjaeuj5Vv5SpGkwF0tgAWk2AAAECBAgQIECAAAECBAgQIBATEEhjflkE0vJzRoepygBa/lrxWaPlj+HPHC1+rbij9N6duwa/fcq6E9OO7VuXHyuQxgbQagIECBAgQIAAAQIECBAgQIAAgZiAQBrzyyKQxogmrxZIu9R1bAIECBAgQIAAAQIECBAgQIAAgZUEBNKVhCb/vkAa80sCaRDQcgIECBAgQIAAAQIECBAgQIAAgZCAQBriSwJpzE8gDfpZToAAAQIECBAgQIAAAQIECBAgEBMQSGN+AmnMTyAN+llOgAABAgQIECBAgAABAgQIECAQExBIY34CacxPIA36WU6AAAECBAgQIECAAAECBAgQIBATEEhjfgJpzE8gDfpZToAAAQIECBAgQIAAAQIECBAgEBMQSGN+AmnMTyAN+llOgAABAgQIECBAgAABAgQIECAQExBIY34CacxPIA36WU6AAAECBAgQIECAAAECBAgQIBATEEhjfgJpzE8gDfpZToAAAQIECBAgQIAAAQIECBAgEBMQSGN+AmnMTyAN+llOgAABAgQIECBAgAABAgQIECAQExBIY34CacxPIA36WU6AAAECBAgQIECAAAECBAgQIBATEEhjfgJpzE8gDfpZToAAAQIECBAgQIAAAQIECBAgEBMQSGN+AmnMTyAN+llOgAABAgQIECBAgAABAgQIECAQExBIY34CacxPIA36WU6AAAECBAgQIECAAAECBAgQIBATEEhjfgJpzE8gDfpZToAAAQIECBAgQIAAAQIECBAgEBMQSGN+AmnMTyAN+llOgAABAgQIECBAgAABAgQIECAQExBIY34CacxPIA36WU6AAAECBAgQIECAAAECBAgQIBATEEhjfgJpzE8gDfpZToAAAQIECBAgQIAAAQIECBAgEBMQSGN+AmnMTyAN+llOgAABAgQIECBAgAABAgQIECAQExBIY34CacxPIA36WU6AAAECBAgQIECAAAECBAgQIBATEEhjfgJpzE8gDfpZToAAAQIECBAgQIAAAQIECBAgEBMQSGN+AmnMTyAN+llOgAABAgQIECBAgAABAgQIECAQExBIY34CacxPIA36WU6AAAECBAgQIJCvwEN7vpa+9Q/3ZQmw/ogz07vffHqWe7dpAgQIEGhfQCCNmQqkMT+BNOhnOQECBAgQIECAQL4C9z/9Z+mvnvnzLAF+422fEkizvPI2TYAAgW4EBNKYq0Aa8xNIg36WEyBAgAABAgQI5CsgkJ6e78W3cwIECBBoVUAgjXEKpDE/gTToZzkBAgQIECBAgEC+AgKpQJrv9Ns5AQIE2hUQSGOeAmnMTyAN+llOgAABAgQIECCQr4BAKpDmO/12ToAAgXYFBNKYp0Aa8xNIg36WEyBAgAABAgQI5CsgkAqk+U6/nRMgQKBdAYE05imQxvwE0qCf5QQIECBAgAABAvkKCKQCab7Tb+cECBBoV0AgjXkKpDE/gTToZzkBAgQIECBAgEC+Al0H0oc+k9IJm5fSYSetWUZ+8mtL6dkH9/28+I2Dj03ppE/sfci3b1xKr7247/er69u6Wr7Fvi1JxyFAgACBQkAgjc2BQBrzE0iDfpYTIECAAAECBAjkK9BVIB2OnKMC6UvfX7McRIf1X9mT0g/+01I64SN7A+nuXUvpqa+uSeuvaP8aCaTtmzoiAQIEchYQSGNXXyCN+QmkQT/LCRAgQIAAAQIE8hXoKpAWokXs/M7No+8gHRdIq1eiPMY7L0jpjUe0e50E0nY9HY0AAQK5CwiksQkQSGN+AmnQz3ICBAgQIECAAIF8BaYVSIffYj/89vrqlXj+0aX02A53kOY7oXZOgACB+REQSGPXSiCN+QmkQT/LCRAgQIAAAU2Uap0AACAASURBVAIE8hWYRiCtahefU3rkqUvpuDP3/1zS4nHFW/UPf/frn+s24veiV80dpFFB6wkQIEBgWEAgjc2DQBrzE0iDfpYTIECAAAECBAjkKzALgbT40qZRb7kv4ughx7/+Fv2ffh5p21dJIG1b1PEIECCQt4BAGrv+AmnMTyAN+llOgAABAgQIECCQr8CsBtKu42hxxQXSfOfezgkQINCFgEAaUxVIY34CadDPcgIECBAgQIAAgXwFphFIH/1CWv4G+/JLmI79wFI6asPet9hPest9m1dKIG1T07EIECBAQCCNzYBAGvMTSIN+lhMgQIAAAQIECOQr0FUgLe4Afe3FfZ8pOvxFTEUgffmpfebDnz+6e9dSeuqrB34W6WHrllp/q71Amu/c2zkBAgS6EBBIY6oCacxPIA36WU6AAAECBAgQIJCvQFeBdB5EBdJ5uErOkQABAvMjIJDGrpVAGvMTSIN+lhMgQIAAAQIECOQrIJCenu/Ft3MCBAgQaFVAII1xCqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15ZhVIr735jrTrG4+kHdu3jlU77eyL0jFvOWK/x1yy9aZ0785dgzWnrDtxv9978tmXY1fAagIECBAgQIAAAQKZCgikAmmmo2/bBAgQaF1AII2RZhFIv3jX19NVN9w2MnAO8xVxtPgxHEiLtZ+79e60884bB7+3ecu2tOE969KnL/jY4OcCaWwArSZAgAABAgQIEMhXQCAVSPOdfjsnQIBAuwICacwzi0BaEhV3gj75g2dH3kFahM9NZ21M/+2pH+53l2k1iFaDqUAaG0CrCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIX/cbjqDVt+EXd5V+8uMfTudsOmMgff+DD6Utl12XHr7vlsHPBdLYAFpNgAABAgQIECCQr4BAKpDmO/12ToAAgXYFBNKYZ/aBtLirtPhx/bYLB/9bDaQnn35+uvLi8w4IpPfcfnVae/zR6YWXXo1dAasJECBAgAABAgQIZCiwtLSUvvrYremBZ3ZkuPuUPrT2U+m9x/x6lnu3aQIECBBoX+DQQ97Q/kEzOmL2gbS4Q3T3nucOuORHHXH44HNHV7qD9IWXX8toXGyVAAECBAgQIECAQDsCg0D6vT/NO5Aee2Y7mI5CgAABAtkLHHrwQdkbRACyD6RVvOodpD6DNDJe1hIgQIAAAQIECBAYL+At9qcbDwIECBAg0IqAt9jHGAXSil81kPoW+9iAWU2AAAECBAgQIEBgnIBAKpB6dRAgQIBAOwICacwxi0BaRM6rbrhtP6nt11yaNp66/gC9aiAtHlB8Tum9O3cNHnvKuhPTju1bl9f5kqbYAFpNgAABAgQIECCQr4BAKpDmO/12ToAAgXYFBNKYZxaBNEY0ebVA2qWuYxMgQIAAAQIECCyygEAqkC7yfNsbAQIE+hQQSGPaAmnMLwmkQUDLCRAgQIAAAQIEshUQSAXSbIffxgkQINCygEAaAxVIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDFPgTTmJ5AG/SwnQIAAAQIECBDIV0AgFUjznX47J0CAQLsCAmnMUyCN+QmkQT/LCRAgQIAAAQIE8hUQSAXSfKffzgkQINCugEAa8xRIY34CadDPcgIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjwF0pifQBr0s5wAAQIECBAgQCBfAYFUIM13+u2cAAEC7QoIpDHPrALptTffkXZ945G0Y/vW/dROO/uitHvPc8u/tv2aS9PGU9cv//ySrTele3fuGvz8lHUn7rf+yWdfjl0BqwkQIECAAAECBAhkKiCQCqSZjr5tEyBAoHUBgTRGmkUg/eJdX09X3XDbyMD5+BNPp+v/w39M12+7cPD75WMfvu+W5Z9/7ta70847bxz8fPOWbWnDe9alT1/wscHPBdLYAFpNgAABAgQIECCQr4BAKpDmO/12ToAAgXYFBNKYZxaBtCQq7gR98gfPHnAH6TBhEUzPOvfydM/tV6e1xx99QBAtAupwMBVIYwNoNQECBAgQIECAQL4CAqlAmu/02zkBAgTaFRBIY54CacXv/gcfSlsuuy6Vd5AWb7//5Mc/nM7ZdMbgkdXfF0hjA2g1AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpxa8Ioh86833Lb6E/+fTz05UXn3dAIC3vMH3hpVdjV8BqAgQIECBAgAABAhkKLC0tpa8+dmt64JkdGe4+pQ+t/VR67zG/nuXebZoAAQIE2hc49JA3tH/QjI4okA5d7CKO/ur6dyx/HmnxWyvdQfrCy69lNC62SoAAAQIECBAgQKAdgUEg/d6f5h1Ijz2zHUxHIUCAAIHsBQ49+KDsDSIAAulP9UbF0eK3ql/K5DNII+NmLQECBAgQIECAAIF9At5if7pxIECAAAECrQh4i32MUSB93a94G/35mz+4/Lb6YdJqEPUt9rGBs5oAAQIECBAgQIBAKSCQCqReDQQIECDQjoBAGnPMIpAWkfOqG27bT2r7NZemjaeuT6N+r3jg+0/bsPxW+0u23pTu3blrsP6UdSemHdu3Lh/LlzTFBtBqAgQIECBAgACBfAUEUoE03+m3cwIECLQrIJDGPLMIpDGiyasF0i51HZsAAQIECBAgQGCRBQRSgXSR59veCBAg0KeAQBrTFkhjfkkgDQJaToAAAQIECBAgkK2AQCqQZjv8Nk6AAIGWBQTSGKhAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjngJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15ZhVIr735jrTrG4+kHdu37qf2+BNPp7POvXz517Zfc2naeOr65Z9fsvWmdO/OXYOfn7LuxP3WP/nsy7ErYDUBAgQIECBAgACBTAUEUoE009G3bQIECLQuIJDGSLMIpF+86+vpqhtuGxk4i1887eyL0ic//uF0zqYz0v0PPpS2XHZdevi+WwaPL9Z+7ta70847bxz8fPOWbWnDe9alT1/wscHPBdLYAFpNgAABAgQIECCQr4BAKpDmO/12ToAAgXYFBNKYZxaBtCQq7gR98gfP7ncHaDWIVoNpNYhWg6lAGhtAqwkQIECAAAECBPIVEEgF0nyn384JECDQroBAGvPMPpBWg2fBORxFh+8uLX6vGlQF0tgAWk2AAAECBAgQIJCvgEAqkOY7/XZOgACBdgUE0phn9oG0+FzSL3/tgeW30JeB9LhjjkzXb7swnXz6+enKi88bvP1+OJDec/vVae3xR6cXXno1dgWsJkCAAAECBAgQIJChwNLSUvrqY7emB57ZkeHuU/rQ2k+l9x7z61nu3aYJECBAoH2BQw95Q/sHzeiI2QfS6B2kL7z8WkbjYqsECBAgQIAAAQIE2hEYBNLv/WnegfTYM9vBdBQCBAgQyF7g0IMPyt4gApB9IPUZpJHxsZYAAQIECBAgQIBAcwFvsT+9OZ6VBAgQIEBgSMBb7GPjkH0gLfh8i31siKwmQIAAAQIECBAg0ERAIBVIm8yNNQQIECBwoIBAGpuKLAJp8Tb6q264bT+p7ddcmjaeun7wa48/8XQ669zLl39/+PeKX7xk603p3p27Br9/yroT047tW5cf60uaYgNoNQECBAgQIECAQL4CAqlAmu/02zkBAgTaFRBIY55ZBNIY0eTVAmmXuo5NgAABAgQIECCwyAICqUC6yPNtbwQIEOhTQCCNaQukMb8kkAYBLSdAgAABAgQIEMhWQCAVSLMdfhsnQIBAywICaQxUII35CaRBP8sJECBAgAABAgTyFRBIBdJ8p9/OCRAg0K6AQBrzFEhjfgJp0M9yAgQIECBAgACBfAUEUoE03+m3cwIECLQrIJDGPAXSmJ9AGvSznAABAgQIECBAIF8BgVQgzXf67ZwAAQLtCgikMU+BNOYnkAb9LCdAgAABAgQIEMhXQCAVSPOdfjsnQIBAuwICacxTII35CaRBP8sJECBAgAABAgTyFRBIBdJ8p9/OCRAg0K6AQBrzFEhjfgJp0M9yAgQIECBAgACBfAUEUoE03+m3cwIECLQrIJDGPAXSmJ9AGvSznAABAgQIECBAIF8BgVQgzXf67ZwAAQLtCgikMc/eA+klW29Kf/3Q36add944OPPTzr4o7d7z3OCf77n96rT2+KNjO+p59ZPPvtzzM3o6AgQIECBAgAABAoshIJAKpIsxyXZBgACB6QsIpLFr0HsgLYLoZ37nt9LGU9enL9719fS5W+8exNLin++65/60Y/vW2I56Xi2Q9gzu6QgQIECAAAECBBZGQCAVSBdmmG2EAAECUxYQSGMXoPdAevLp56ft11w6CKTF3aTFj+u3XZjuf/ChtOWy69LD990S21HPqwXSnsE9HQECBAgQIECAwMIICKQC6cIMs40QIEBgygICaewC9B5IN2/ZljadtTGds+mMVMTSKy8+b/DPw3eTxrbU72qBtF9vz0aAAAECBAgQILA4AgKpQLo402wnBAgQmK6AQBrz7z2QPv7E0+mscy8fnPUp605cfkt9EUvff9qGwd2k8/RDIJ2nq+VcCRAgQIAAAQIEZklAIBVIZ2kenQsBAgTmWUAgjV293gPp8Fvsh0/dHaSxC2k1AQIECBAgQIAAgXkTEEgF0nmbWedLgACBWRUQSGNXZmYCqc8gjV1IqwkQIECAAAECBAjMm4BAKpDO28w6XwIECMyqgEAauzIzE0ivvfmO9OWvPTD4Rvt5+uEt9vN0tZwrAQIECBAgQIDALAkIpALpLM2jcyFAgMA8CwiksavXSyAt7w5d6VTLb7df6XGz9PsC6SxdDedCgAABAgQIECAwTwICqUA6T/PqXAkQIDDLAgJp7Or0EkiHT3HcZ5DGtjG91QLp9Ow9MwECBAgQIECAwHwLCKQC6XxPsLMnQIDA7AgIpLFr0XsgjZ3u7K0WSGfvmjgjAgQIECBAgACB+RAQSAXS+ZhUZ0mAAIHZFxBIY9dIII35JYE0CGg5AQIECBAgQIBAtgICqUCa7fDbOAECBFoWEEhjoFMJpKedfVHavee5kWf+8H23xHbU82qBtGdwT0eAAAECBAgQILAwAgKpQLoww2wjBAgQmLKAQBq7AL0H0s1btqXjjjkyXb/twtiZz8hqgXRGLoTTIECAAAECBAgQmDsBgVQgnbuhdcIECBCYUQGBNHZh4sFmogAAIABJREFUeg+kvqQpdsGsJkCAAAECBAgQILAoAgKpQLoos2wfBAgQmLaAQBq7AgJpzM9nkAb9LCdAgAABAgQIEMhXQCAVSPOdfjsnQIBAuwICacyz90BavMV+01kb0zmbzoid+Yys9hb7GbkQToMAAQIECBAgQGDuBARSgXTuhtYJEyBAYEYFBNLYhek9kN7/4EPpis9+Pu2888bYmc/IaoF0Ri6E0yBAgAABAgQIEJg7AYFUIJ27oXXCBAgQmFEBgTR2YXoPpMVnkE764VvsYxfUagIECBAgQIAAAQLzIiCQCqTzMqvOkwABArMuIJDGrlDvgTR2urO32h2ks3dNnBEBAgQIECBAgMB8CAikAul8TKqzJECAwOwLCKSxaySQxvx8SVPQz3ICBAgQIECAAIF8BQRSgTTf6bdzAgQItCsgkMY8pxJIiy9q+uYj3x2c+fZrLk0bT12firfev/+0Den6bRfGdtTzaneQ9gzu6QgQIECAAAECBBZGQCAVSBdmmG2EAAECUxYQSGMXoPdAWsTR4445chBCTzv7ovSZ3/mtQSD94l1fT5+79e65+/ImgTQ2gFYTIECAAAECBAjkKyCQCqT5Tr+dEyBAoF0BgTTm2XsgLe4Uvef2q9Pa44/eL5AW326/5bLrki9pil1QqwkQIECAAAECBAjMi4BAKpDOy6w6TwIECMy6gEAau0K9B9LirtHb/t3vHhBI3UEau5BWEyBAgAABAgQIEJg3AYFUIJ23mXW+BAgQmFUBgTR2ZXoPpNfefEf68tceGLyVvnyL/drj35rOOvfydP7mD6ZPX/Cx2I56Xu0t9j2DezoCBAgQIECAAIGFERBIBdKFGWYbIUCAwJQFBNLYBeg9kBanW76dfvjUr7z4vHTOpjNiu5nCaoF0CuiekgABAgQIECBAYCEEBFKBdCEG2SYIECAwAwICaewiTCWQxk55tlYLpLN1PZwNAQIECBAgQIDA/AgIpALp/EyrMyVAgMBsCwiksesjkMb8kkAaBLScAAECBAgQIEAgWwGBVCDNdvhtnAABAi0LCKQxUIE05ieQBv0sJ0CAAAECBAgQyFdAIBVI851+OydAgEC7AgJpzLP3QHry6een95+2IV2/7cLYmc/IaneQzsiFcBoECBAgQIAAAQJzJyCQCqRzN7ROmAABAjMqIJDGLkzvgbT6BU1HHXH44Bvt5/WHQDqvV855EyBAgAABAgQITFtAIBVIpz2Dnp8AAQKLIiCQxq5k74G0errX3nxHumXHV5Z/+eH7bontqOfVAmnP4J6OAAECBAgQIEBgYQQEUoF0YYbZRggQIDBlAYE0dgGmHkgv2XpTunfnLoE0dh2tJkCAAAECBAgQIDB3AgKpQDp3Q+uECRAgMKMCAmnswvQeSKt3jHqLfewCWk2AAAECBAgQIEBgXgUEUoF0XmfXeRMgQGDWBATS2BXpPZD6kqbYBbOaAAECBAgQIECAwKIICKQC6aLMsn0QIEBg2gICaewK9B5IY6c7e6t9BunsXRNnRIAAAQIECBAgMB8CAqlAOh+T6iwJECAw+wICaewa9RZIiztHt19zadp46vr0+BNPp7POvTzdc/vVae3xRw92UH67vS9pil1QqwkQIECAAAECBAjMi4BAKpDOy6w6TwIECMy6gEAau0ICacwvuYM0CGg5AQIECBAgQIBAtgICqUCa7fDbOAECBFoWEEhjoAJpzE8gDfpZToAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmKdAGvMTSIN+lhMgQIAAAQIECOQrIJAKpPlOv50TIECgXQGBNOYpkMb8BNKgn+UECBAgQIAAAQL5CgikAmm+02/nBAgQaFdAII15CqQxP4E06Gc5AQIECBAgQIBAvgICqUCa7/TbOQECBNoVEEhjnr0G0jqn6lvs6yh5DAECBAgQIECAAIH5FxBIBdL5n2I7IECAwGwICKSx69BbII2d5uyu9i32s3ttnBkBAgQIECBAgMBsCwikAulsT6izI0CAwPwICKSxayWQxvy8xT7oZzkBAgQIECBAgEC+AgKpQJrv9Ns5AQIE2hUQSGOeAmnMTyAN+llOgAABAgQIECCQr4BAKpDmO/12ToAAgXYFBNKYp0D6ut/9Dz6Utlx23bLk+Zs/mD59wceWf37J1pvSvTt3DX5+yroT047tW5d/z1vsYwNoNQECBAgQIECAQL4CAqlAmu/02zkBAgTaFRBIY57ZB9LHn3g6nXXu5Wn7NZemjaeuT9Wff/Gur6fP3Xp32nnnjQPpzVu2pQ3vWbccUAXS2ABaTYAAAQIECBAgkK+AQCqQ5jv9dk6AAIF2BQTSmGf2gbS8e/Th+25Zljzt7IvSJz/+4XTOpjMOCKLVYCqQxgbQagIECBAgQIAAgXwFBFKBNN/pt3MCBAi0KyCQxjyzD6QFX3FX6Dcf+W665/arX7+D9Jl0xWc/v3zH6HAsLR5bDaoCaWwArSZAgAABAgQIEMhXQCAVSPOdfjsnQIBAuwICacyz90B68unnr3jG1c8AXXFB8AHX3nxH+vLXHki79zw3ONLw8xfne+XF5w3uJh0OpEVMXXv80emFl14NPrvl4wR+8pOlrHF+5mfWpDVrsiaweQIECBAgQGCBBZaWltJXH7s1PfDMjgXe5fitfWjtp9J7j/n1LPdu0wQIECDQvsChh7yh/YNmdMTeA2nxhUfHHXPUfl+CVHgXd2p+5nd+a/A5oNXP+ezyehR3hA7fMVo8VxFFy0i60h2kL7z8Wpenl/WxH332m+m+p27N0uCtB789/esTt6SffT2S+kGAAAECBAgQWESBQSD93p/mHUiPPXMRL609ESBAgMAUBA49+KApPOviPGXvgbSIj+UXIg0zFndx7vrGI4NviB8VLbsiH37e8jmKQHvcMUem67dd6DNIu4Kvcdy/f/6/pDsf+4Maj1y8h6x906+kj749z70v3tW0IwIECBAgQGCcgLfYn244CBAgQIBAKwLeYh9jnJlAOvzlR6O+OCm2zfGry+eqfot9+bZ632LflfzKxxVIBdKVp8QjCBAgQIAAgXkWEEgF0nmeX+dOgACBWRIQSGNXo/dAWtyduemsjcuf6VmefhEi77rn/uU7SLdcdl0a/mb52DYnry6e+6obblt+0PBnjha/WHwswL07dw1+/5R1Jw7OsfzhS5q6uzICqUDa3XQ5MgECBAgQIDALAgKpQDoLc+gcCBAgsAgCAmnsKvYeSKt3bJanP/xlSKPe9h7bZnerBdLubAVSgbS76XJkAgQIECBAYBYEBFKBdBbm0DkQIEBgEQQE0thV7D2QFqf7+BNPp7POvXy/Mx/1uaSxrfWzWiDtzlkgFUi7my5HJkCAAAECBGZBQCAVSGdhDp0DAQIEFkFAII1dxakE0tgpz9ZqgbS76yGQNg+kr/7TK+mH//i99JOlH3d3gWb4yG/+uWPToW84aobP0KkRIECAAAEChYBAKpB6JRAgQIBAOwICacxRII35JYE0CDhheV+B9MmvLaVnH1yzfCYnbF5Kh5207+ePfWkpPf/I3p8ffGxKJ32iuz2XR45+i/3Lrz2f/uzvLk3P/fjp7k92Bp/hf3z71em4N71rBs/MKREgQIAAAQLDAgKpQOoVQYAAAQLtCAikMcepBNLTzr4o7d7z3Mgz7+uLmWJs+1YLpG1JHnicPgLp7l1L6amvrknrr9j7/KN+/sO/TOldF+0NpI9+IaVD3raUjjtzX0DtQkAgjakKpDE/qwkQIECAQF8CAqlA2teseR4CBAgsuoBAGrvCvQfS4lvsjzvmyHT9tgtjZz4jqwXS7i5EH4G0uHv0pe+vWb4r9JU9KX3n5pTeeUFKbzziwCBaBNThYNrV7gXSmKxAGvOzmgABAgQI9CUgkAqkfc2a5yFAgMCiCwiksSvceyAtvq1+Xr+QaRS1QBobwEmr+wikxfM/9JmUDvr5pcFdokUwffX1m5tP+MjeO0S/feNSesuvpXTUhr0/f/7RpfTYjn13nHa1e4E0JiuQxvysJkCAAAECfQkIpAJpX7PmeQgQILDoAgJp7AoLpDE/n0Ea9JuFQFq8bf7VF5bSay/ujaDDn0FaxNNjP7B0QCAt7zDtavsCaUxWII35WU2AAAECBPoSEEgF0r5mzfMQIEBg0QUE0tgV7j2QFm+x33TWxnTOpjNiZz4jq91B2t2F6OMO0uodo+UdomUkdQdpd9e3yyMLpF3qOjYBAgQIEGhPQCAVSNubJkciQIBA3gICaez69x5I73/woXTFZz+fdt55Y+zMZ2S1QNrdhegjkI760qXhu0arv+8zSPdd7/LzWkdNQPmlV2VwLh9z5Kndf8FV8VwCaXevS0cmQIAAAQJtCgikAmmb8+RYBAgQyFlAII1d/d4DafEZpJN++Bb72AVdpNV9BNLiDtJnHzzwW+zLt9BXg6hvsZ88YcN35JYBtbwbt/rzLmdVIO1S17EJECBAgEB7AgKpQNreNDkSAQIE8hYQSGPXv/dAGjvd2VvtDtLurkkfgbQ4+8e+tJSef2Tv548WP6qfLzr8+wcfm5a/8b67nac0r59BWtx9W/qN+kKr6kcWdGUokHYl67gECBAgQKBdAYFUIG13ohyNAAEC+QoIpLFrL5DG/HxJU9Bv0vK+AmmHW2h86HkMpNXPcy02X9xx+/JTe6PpK3uW0hN/kdK7LtoXoxsDrbBQIO1K1nEJECBAgEC7AgKpQNruRDkaAQIE8hUQSGPXXiCN+QmkQT+BdLTAPAbS4btHy10V0fS5b6X02ot7o6jPIO3wBePQBAgQIEBgDgUEUoF0DsfWKRMgQGAmBQTS2GXpLZAWnz165cXnpatuuG3iGfsM0tgFXaTV7iD9g8aX8+XXnk9/9neXpud+/HTjY6xmYfExBMWPEz6y7+7Q4i321TtGi4jaRyR1B+lqrp7HEiBAgACB6QkIpALp9KbPMxMgQGCxBATS2PXsLZDGTnN2V/sM0u6ujUA6H4G0/PKl6me3FnePvvT9Nft9Zmvxlvs3HLa0X0jtYoIE0i5UHZMAAQIECLQvIJAKpO1PlSMSIEAgTwGBNHbdBdKYn7fYB/0mLRdI5yOQjrp7tLiu5Zc0Vb/F/tgPLKWjNnT7OaQCaYcvTIcmQIAAAQItCgikAmmL4+RQBAgQyFpAII1d/qkE0tPOvijt3vPcyDP3FvvYBV2k1QLp7AfS3buW0lNfXbP8zfXV+St/v/z1PuJo8VwC6SL9SWAvBAgQILDIAgKpQLrI821vBAgQ6FNAII1p9x5IN2/Zlo475sh0/bYLY2c+I6u9xb67CyGQzn4g7e7qx44skMb8rCZAgAABAn0JCKQCaV+z5nkIECCw6AICaewK9x5Iiy9r2n7NpWnjqetjZz4jqwXS7i6EQCqQNp0ugbSpnHUECBAgQKBfAYFUIO134jwbAQIEFldAII1dW4E05uczSIN+k5YLpAJp0/ESSJvKWUeAAAECBPoVEEgF0n4nzrMRIEBgcQUE0ti17T2QFm+x33TWxnTOpjNiZz4jq91B2t2FEEgF0qbTJZA2lbOOAAECBAj0KyCQCqT9TpxnI0CAwOIKCKSxa9t7IL3/wYfSFZ/9fNp5542xM5+R1QJpdxdCIBVIm06XQNpUzjoCBAgQINCvgEAqkPY7cZ6NAAECiysgkMaube+BtPgM0kk/fIt97IIu0mqBVCBtOs8CaVM56wgQIECAQL8CAqlA2u/EeTYCBAgsroBAGru2vQfS2OnO3mp3kHZ3TQRSgbTpdAmkTeWsI0CAAAEC/QoIpAJpvxPn2QgQILC4AgJp7NoKpDE/X9IU9Ju0XCAVSJuOl0DaVM46AgQIECDQr4BAKpD2O3GejcD0Bb77wt+kl17bM/0TmcIZ/MIbj0/HHfLPp/DMeTylQBq7zr0F0uKt9VdefF666obbJp6xt9jHLugirRZIBdKm8yyQNpWzjgABAgQI9CsgkAqk/U6cZyMwfYE///vfS4//6L9O/0SmcAZnn/B76e2HvXcKz5zHUwqksevcWyCNnebsrvYW++6ujUAqkDadLoG0qZx1BAgQIECgXwGBVCDtd+I8G4HpCwikAmlXUyiQxmQF0pift9gH/SYtF0gF0qbjJZA2lbOOAAECBAj0KyCQCqT9TpxnIzB9AYFUIO1qCgXSmKxAGvMTSIN+AulogbVv+pX00bcLpE3HSyBtKmcdAQIECBDoV0AgFUj7nTjPRmD6AgKpQNrVFAqkMdneA+njTzydzjr38rFn7TNIYxd0kVa7g1QgbTrPAmlTOesIECBAgEC/AgKpQNrvxHk2AtMXEEgF0q6mUCCNyfYeSE87+6L0oTPfl973L09OV3z282nnnTcOdrB5y7a06ayN6ZxNZ8R21PNqn0HaHbhAKpA2nS6BtKmcdQQIECBAoF8BgVQg7XfiPBuB6QsIpAJpV1MokMZkew+kxbfZb7/m0rT2+Lem8377D5cD6f0PPrRfMI1tq7/VAml31gKpQNp0ugTSpnLWESBAgACBfgUEUoG034nzbASmL9BHIP32jUvptRfXHLDZEzYvpcNO2vfrj34hpUPetpSOO/PAx3Yh5Vvsu1Ddd0yBNOY7tUC68dT1qYil5Vvqi0C65bLrln8e21Z/qwXS7qwFUoG06XQJpE3lrCNAgAABAv0KCKQCab8T59kITF+gj0Ba3eXzjy6lJ/4ipXddtDeEPvalpfT8I3v/+chTBdLpT0U7ZyCQxhx7D6TFW+k3vGdd+vQFHxu8rb7852tvviN9+WsPLN9RGttWf6sF0u6sBVKBtOl0CaRN5awjQIAAAQL9CgikAmm/E+fZCExfYBqBtLhT9M2/vJSO2rD/naLFnaaHvzu5g3T6Y9HKGQikMcbeA2n1dIu7SMsf99x+9etvvT86tqOeVwuk3YELpAJp0+kSSJvKWUeAAAECBPoVEEgF0n4nzrMRmL5A34G0evfosIBAOv15aPMMBNKY5tQDaez0p79aIO3uGgikAmnT6RJIm8pZR4AAAQIE+hUQSAXSfifOsxGYvkDfgXTc3aOFhEA6/Xlo8wwE0phm74G0/JKm4jNIF+GHQNrdVRRIBdKm0yWQNpWzjgABAgQI9CsgkAqk/U6cZyMwfYE+A+nuXUvph3+577NHq7sXSKc/D22egUAa0xRIY35JIA0CTlgukAqkTadLIG0qZx0BAgQIEOhXQCAVSPudOM9GYPoCfQbSIoC+5dfSAZ89WioIpNOfhzbPQCCNafYeSIsvZtp01sZ0zqYzYmc+I6sF0u4uhEAqkDadLoG0qZx1BAgQIECgXwGBVCDtd+I8G4HpC/QVSFe6e7SQEEinPw9tnoFAGtPsPZA+/sTT6bzf/sO5+7b6ccwCaWwAJ60WSAXSptMlkDaVs44AAQIECPQrIJAKpP1OnGcjMH2BPgLpK3tS+s7NKR37gQO/ub4QeOxLS+n5R/b/Rvv1V3Rvc/YJv5fefth7u3+iTJ9BII1d+N4CafnZo1suu27iGT983y2xHfW8WiDtDlwgFUibTpdA2lTOOgLzK/DKT15Mz/zjd+d3A8EzP+qNa9PBBx0ePIrlBPoXEEgF0v6nzjMSmK5AH4F0ujsc/+wCabdXRiCN+fYeSBfly5lKdoE0NoCTVgukAmnT6RJIm8pZR2B+BZ778dPpTx75N/O7geCZ/+Y7/n068p+9LXgUywn0LyCQCqT9T51nJDBdAYHUHaRdTaBAGpMVSGN+vqQp6CeQjhZY+6ZfSR99u0DadLwE0qZy1hGYXwGBVCCd3+nN+8wFUoE071eA3ecoIJAKpF3NvUAakxVIY34CadBPIBVIuxghgbQLVcckMNsCAqlAOtsT6uzGCQikAqlXB4HcBARSgbSrmRdIY7K9BtI6p+ozSOso5fEYb7F3B2nTSRdIm8pZR2B+BQRSgXR+pzfvMxdIBdK8XwF2n6OAQCqQdjX3AmlMttdAuv2aS5PPII1dsJxWC6QCadN5F0ibyllHYH4FBFKBdH6nN+8zF0gF0rxfAXafo4BAKpB2NfcCaUxWII35eYt90G/ScoFUIG06XgJpUznrCMyvgEAqkM7v9OZ95gKpQJr3K8DucxQQSAXSruZeII3JCqQxP4E06CeQjhbwJU2xwRJIY35WE5hHgT4D6St7UvrOzfuU3nlBSm88Yt/PH/vSUnr+kTWDXzhs3VI64SN7/7nLH77Fvktdx+5SQCAVSLucL8cmMIsCAqlA2tVcCqQxWYE05ieQBv0EUoG0ixESSLtQdUwCsy3QVyB9/tGl9NiONemEzUvpsJMODJ+PfmGv00mf6NdLIO3X27O1JyCQCqTtTZMjEZgPAYFUIO1qUgXSmGxvgTR2mrO7+slnX57dk5vzM/MWe2+xbzrCAmlTOesIzK9AX4G0CKBv/uWldNSGA+NoEU+f+IuU3nVR93eMVq+UQDq/s5v7mQukAmnurwH7z09AIBVIu5p6gTQmK5DG/NxBGvSbtFwgFUibjpdA2lTOOgLzK9BXIH3oMykd9PNL6bUX90bQ4p/LIPrk15bSc99Ky79X/P6xHxgdU9uWFkjbFnW8vgQEUoG0r1nzPARmRUAgFUi7mkWBNCYrkMb8BNKgn0A6WsBnkMYGSyCN+VlNYB4F+gik5WePDr+9fvgt9cVnj776/Jrlt9ev9Hb8Np0F0jY1HatPAYFUIO1z3jwXgVkQEEgF0q7mUCCNyQqkMT+BNOgnkAqkXYyQQNqFqmMSmG2BPgPp8Jcy7d61lH74l3vfVl8E0uLH8JcyffvGpfSWX0sj35LfpqhA2qamY/UpIJAKpH3Om+ciMAsCAqlA2tUcCqQxWYE05ieQBv0EUoG0ixESSLtQdUwCsy3QRyAtBIq32A/fQTocSIu32L/0/X13kBaPF0hne26c3fQFBFKBdPpT6AwI9CsgkAqkXU2cQBqTFUhjfgJp0E8gFUi7GCGBtAtVxyQw2wJ9BdLq2+iLAHr4u1M67sw1qfoW/CKePvXVNWn9Fd3buYO0e2PP0I2AQCqQdjNZjkpgdgUEUoG0q+kUSGOyAulP/b5419fTVTfcNvjZUUccnnbeeeOy7CVbb0r37tw1+Pkp605MO7ZvXf4932IfG0CBVCDtYoIE0i5UHZPAbAv0FUgLheJzR19+aq/HYeuW9ntLffm5o6XW8NvxuxQUSLvUdewuBQTS07vkdWwCBGZQQCAVSLsaS4E0JiuQvu537c13pFt2fCU9fN8tB2gW4fRzt969HEw3b9mWNrxnXfr0BR8bPFYgjQ3gpNW+xd632DedLoG0qVx83av/9Ep66bV/iB9oTo/wcz9zcDr4oMPm9Ozn+7T7DKSzKCWQzuJVcU51BARSgbTOnHgMgUUSEEgF0q7mWSCNyQqkr/udfPr56Z7br05rjz/6AM1qEK0GU4E0NoAC6WgB32IfmyuBNOYXWf3Cq7vTLd/57fTKP/0ocpi5XXveSX+Ujj74pLk9/3k+cYH036cj/9nb5vkSOvdMBQRSgTTT0bftjAUEUoG0q/EXSGOy2QfS+x98KF3x2c+n3XueW5Z8/2kb0vXbLhz8/LSzL0qf/PiH0zmbzhj8vHj8lsuuW77bVCCNDaBAKpB2MUECaReq9Y4pkAqk9Sal/UcJpAJp+1PliH0ICKQCaR9z5jkIzJKAQCqQdjWPAmlMNvtAWn726PDb64s7Ss/f/MHB2+iLf77y4vMOCKTlHacvvPRq7ApYPVbgmz/8q/Qfv/tvsxQq7iA9/11/mH72Z9Y02v9z//gP6f/8fy9JRTDI8cfH33FN+qVfOCXHrU99z7tf/mH6Dw//r9neQfqb625Iv3j4O6d+HXI8gadffCr98bf+5xy3Ptjz//LP/zgdf9gvZrt/G59PgaWlpfTVx25NDzyzYz43EDzrD639VHrvMb8ePIrlBAjMk8BP/mkp3fLt302P/+i/ztNpt3au/8OJv59Oecu/au14DrS/wKGHvAFJQEAgrXzGaGFZfClT8aO4i3SlO0hfePm1AL+lkwS++cwDeQfSd/8fsUD67YszDqTXpl86QiCdxp8wu196RiB987pp0Gf/nE+/+GT644czDqTvulkgzf5VMH8Ag0D6vT/NO5Aee+b8XThnTIBAY4FBIP3W/553IH3r+xr7WThZ4NCDD0IUEMg+kFbfMl8NpD6DNDBdwaW+pMmXNDUdIW+xbyoXX+ct9t5iH5+iZkfwFntvsW82OVZNW8Bb7E+f9iXw/AQI9CzgLfbeYt/VyHmLfUw2+0Ba8BV3iX7ozPcN3lL/+BNPp7POvTxtv+bStPHU9cm32McGLLJaIBVIm86PQNpULr5OIBVI41PU7AgCqUDabHKsmraAQCqQTnsGPT+BvgUEUoG0q5kTSGOyAulP/YrPGi1/DH/maPFrxVvu7925a/Dbp6w7Me3YvnX5sb6kKTaAk1YLpAJp0+kSSJvKxdf1GUhf2ZPSd27ed87vvCClNx6x/x4e+kxKJ2xeSoed1OzzfFcr4lvsVyvW3uMFUoG0vWlypD4FBFKBtM9581wEZkFAIBVIu5pDgTQmK5DG/JJAGgScsFwgFUibTpdA2lQuvq6vQPr8o0vpsR1rxsbPb9+4lF57cW8UFUjj13UejiCQCqTzMKfO8UABgVQg9bogkJuAQCqQdjXzAmlMViCN+QmkQb9JywVSgbTpeAmkTeXi6/oKpI9+IaU3//JSOmrD+DtDyztMBdL4dZ2HIwikAuk8zKlzFEiHBX7jbZ9K736zQOp1QSA3AYFUIO1q5gXSmKxAGvMTSIN+AulogbVv+pX00bcLpE3HSyBtKhdf11cgLd46f9DP77tLtPjnd120fywVSOPXc56OIJAKpPM0r851n4A7SAVSrwcCuQkIpAJpVzMvkMZkBdKYn0Aa9BNIBdIuRkgg7UK13jH7CKSjwmdxR2nx46RP7DtPgbTeNVuURwmkAuk+9fJlAAAgAElEQVSizHJu+xBIBdLcZt5+CQikAmlXrwKBNCYrkMb8BNKgn0AqkHYxQgJpF6r1jtlnIB3+Uqbdu5bSD/8y7XcXqUBa75otyqMEUoF0UWY5t30IpAJpbjNvvwQEUoG0q1eBQBqTFUhjfgJp0E8gFUi7GCGBtAvVesfsI5AWZ1L9dnqBtN71WeRHCaQC6SLP9yLvTSAVSBd5vu2NwCgBgVQg7eqVIZDGZAXSmJ9AGvQTSAXSLkZIIO1Ctd4x+wqkj31pKb36/Jrlt9QX31p/+LtTOu7MfZ9D6g7SetdsUR4lkAqkizLLue1DIBVIc5t5+yUgkAqkXb0KBNKYrEAa8xNIg34CqUDaxQgJpF2o1jtmX4G0OJvic0dffmrveR22bimd8JF9cbQIpq+9uO/nBx+7/+eT1tvN6h913kl/lI4++KTVL7QiLCCQCqThIXKAqQgIpALpVAbPkxKYooBAKpB2NX4CaUxWII35CaRBP4FUIO1ihATSLlTrHbPPQFrvjPp9lEDar/fwswmkAun0ps8zRwQEUoE0Mj/WEphHAYFUIO1qbgXSmKxAGvMTSIN+AqlA2sUICaRdqNY7pkDqDtJ6k9L+owRSgbT9qXLEPgQEUoG0jznzHARmSUAgFUi7mkeBNCYrkMb8BNKgn0AqkHYxQgJpF6r1jimQCqT1JqX9RwmkAmn7U+WIfQgIpAJpH3PmOQjMkoBAKpB2NY8CaUxWII35CaRBP4FUIO1ihATSLlTrHVMgFUjrTUr7jxJIBdL2p8oR+xAQSAXSPubMcxCYJQGBVCDtah4F0pisQBrzE0iDfgKpQNrFCAmkXajWO6ZAKpDWm5T2HyWQCqTtT5Uj9iEgkAqkfcyZ5yAwSwICqUDa1TwKpDFZgTTmJ5AG/QRSgbSLERJIu1Ctd0yBVCCtNyntP0ogFUjbnypH7ENAIBVI+5gzz0FglgQEUoG0q3kUSGOyAmnMTyAN+gmkAmkXIySQdqFa75gCqUBab1Laf5RAKpC2P1WO2IeAQCqQ9jFnnoPALAkIpAJpV/MokMZkBdKYn0Aa9BNIBdIuRkgg7UK13jEFUoG03qS0/yiBVCBtf6ocsQ8BgVQg7WPOPAeBWRIQSAXSruZRII3JCqQxP4E06CeQCqRdjJBA2oVqvWMKpAJpvUlp/1ECqUDa/lQ5Yh8CAqlA2seceQ4CsyQgkAqkXc2jQBqTFUhjfgJp0E8gFUi7GCGBtAvVescUSAXSepPS/qMEUoG0/alyxD4EBFKBtI858xwEZklAIBVIu5pHgTQmK5DG/ATSoJ9AKpB2MUICaReq9Y4pkMYC6Y9e+//Sa//043rYC/aon11zUPr5g45MaU2zjQmkAmmzybFq2gICqUA67Rn0/AT6FhBIBdKuZk4gjckKpDE/gTToJ5AKpF2MkEDahWq9YwqksUD6rX+4L/0/3/+jetgL9qh/9daPpo1vPff1QNqskAqkAumCvSSy2Y5AKpBmM+w2SuCnAgKpQNrVi0EgjckKpDE/gTToJ5AKpF2MkEDahWq9YwqkAmm9STnwUQJpU7m9637zHQJpTNDqaQkIpALptGbP8xKYloBAKpB2NXsCaUxWII35CaRBP4FUIO1ihATSLlTrHVMgFUjrTYpA2tRp3DqBtG1Rx+tLQCAVSPuaNc9DYFYEBFKBtKtZFEhjsgJpzE8gDfoJpAJpFyMkkHahWu+YAqlAWm9SBNKmTgJp23KON20BgVQgnfYMen4CfQsIpAJpVzMnkMZkBdKYn0Aa9BNIBdIuRkgg7UK13jEFUoG03qQIpE2dBNK25Rxv2gICqUA67Rn0/AT6FhBIBdKuZk4gjckKpDE/gTToJ5AKpF2MkEDahWq9YwqkAmm9SRFImzoJpG3LOd60BQRSgXTaM+j5CfQtIJAKpF3NnEAakxVIY34CadBPIBVIuxghgbQL1XrHFEjnI5A+9JkDr+cJm5fSYSetSU9+bSk9++C+b5I/8tSldNyZzb5Zvt7U7H2UL2lajdaBj/UZpDG/yOpXfvKj9OjzfxU5xFyv/cWf/9X0pjf8QuM9CKQCaePhsZDAnAoIpAJpV6MrkMZkBdKYn0Aa9BNIBdIuRkgg7UK13jEF0vkJpGUQrV7ZR7+Q0kmf2PerRUw99gNL6agN3UZSgbTea2zcowTSmF9k9bP/+P30f/3t/xY5xFyv/Tfr/iQd/nNHN96DQCqQNh4eCwnMqYBAKpB2NboCaUxWII35CaRBP4FUIO1ihATSLlTrHVMgnf9AOiqYHvK27u8iFUjrvcYE0phTF6sFUoG06Vz9xts+ld79ZoG0qZ91BOZVQCAVSLuaXYE0JiuQxvwE0qCfQCqQdjFCAmkXqvWOKZDOTyAdvqLj7iZ9ZU9K37nZHaT1pn+6j3IH6fT8BVKBtOn0CaRN5awjMN8CAqlA2tUEC6QxWYE05ieQBv0EUoG0ixESSLtQrXdMgXQ+Aunw1Sw/c3T9Fftf4/JzSn0Gab3Zn/ajBNLpXQGBVCBtOn0CaVM56wjMt4BAKpB2NcECaUxWII35CaRBP4FUIO1ihATSLlTrHVMgnb9AWlzZIoaOu4v02zcupcPfnTr/oiZvsa/3Ghv3KIE05hdZLZAKpE3nRyBtKmcdgfkWEEgF0q4mWCCNyQqkMT+BNOgnkAqkXYyQQNqFar1jCqSLF0gf+9LS4OKf8BFf0lTvVTCdRwmk03EvnlUgFUibTp9A2lTOumkLvPKTl9I//uSFaZ/G1J7/TW84Mh205qDGzy+QCqSNh2eFhQJpTFYgjfkJpEE/gVQg7WKEBNIuVOsdUyCd/UC6e9fe4Fl+K30RQF96IqV3XbQ3gI76Fvs+3mbvDtJ6r7FxjxJIY36R1X0F0uK1+fJT+5/pqNfmpDvCI/sct9a32DdXFUib21k5XYEnf/Tt9H///eXTPYkpPfvhP3d0+p9+6bp08EGHNT4DgVQgbTw8AmlXdIPjCqRB3ieffTl4BMvHCfz98/8l3fnYH2QJtPZNv5I++vbme3/5tefTn/3dpem5Hz+dpZ9AOr3LLpDOfiB9/tGl9NiO/e8GHf780WqE6SOOFhMrkMZetwJpzC+yus9AesjblsZ+3EXxcRivvbj3tT3uIzMi+xRI29cTSNs3dcR+BARSgbTppJ19wu+ltx8mkDb1W2mdO0hXEpr8+wJpzM8dpEG/ScsFUoG06XgJpE3l4usE0tkPpPGr3M0RBNKYq0Aa84usnpVAWuzhlT0pfedmgTRyPftcK5D2qe252hQQSAXSpvMkkDaVq7dOIK3nNO5RAmnMTyAN+gmkowXcQRobLIE05hdZLZAKpE3nRyBtKrd3nUAa84us7jOQDr/FftTd3QJp5Er2v1Yg7d/cM7YjIJAKpE0nSSBtKldvnUBaz0kgjTmNXe0t9h3Bvn5Yd5C6g7TpdAmkTeXi6wRSgbTpFAmkTeUE0phcfHVfgXT4TMuPyqi+lV4gjV/PPo8gkPap7bnaFBBIBdKm8ySQNpWrt04greckkMacBNKO/CYdViAVSJuOnUDaVC6+TiAVSJtOkUDaVE4gjcnFV08jkBZnXXxecPUzSQXS+PXs8wgCaZ/anqtNAYFUIG06TwJpU7l66wTSek4CacxJIO3ITyAdLeAt9rGBE0hjfpHVAqlA2nR+BNKmcgJpTC6+WiD9k1R8q3PTH/c//Wfpr57586bL53qdQDrXly/rk59GIC2+iO4Nh65JJ31iH/3wF0sefGza7/e6ukC+xT4mK5DG/FZaLZCuJDT5930GaczPZ5AG/QRSgbSLERJIu1Ctd0yBVCCtNykHPkogbSonkMbk4qv7CKTFnaE/+E9L6YSP7P2W+t27ltJTX12T3nlBSm88Yt8e3EEav559HkEg7VPbc7Up0HcgLeJo8WM4kD72paX06vP7gmnxmEOOf/1L6n7652Sb+x0+lkAakxVIY34rrRZIVxISSGNCK6z2GaTd8XqLvbfYN50ugfT/b+/eo+6qynuPzyBgIkg0XEKChEuAGAlekVNaz8AjUvUcdVCtgrVS0KNcBh1IGaD1xqDeoVQZIghWD2I9Ei/UgZ7hBaWmVelBvB1ALhKRIAmBAA23cDPvyXzT+WZmZu215vw9a79rr+zv2z9qePez916fNfft+669typnnyOQEkjVVUQgVeUIpDY5+/R0BFJ/La/78ObXNf38UR8HnnxoY0D1P9N1NNXbF3EEqbqKCKSqHHNdC0xnIPVHiT7juRPu8fude+SOTUHU3yfG94P+D0f3/Ni5xadsuh8chhOB1KZKILX5NU0TSJuECKQ2IQLpUP3qzpxASiBVFx+BVJWzzxFICaTqKiKQqnIEUpucfXq6Aqn9mg7nHAikuiuBVLdjsluB6Qqk8Wctr7xyojaQhi+vO+i9w7UhkNp8CaQ2v6ZpAmmTEIHUJkQgHaofgbRagM8gtS07AqnNzzJNICWQquuHQKrKEUhtcvZpAilHkKqriECqyjHXtcB0BFL/Fnr/E94ynwZSH0+322nTR48QSLteFXmXTyDNc1JPRSBV5TbO8RmkNj8+g9ToRyAlkA5jCRFIh6Gad54EUgJp3krZ8lQEUlWOQGqTs08TSAmk6ioikKpyzHUtMB2BNP3YkLDN2+44MfU2+vSjR+LfDcuII0htsgRSm1/TNIG0Saj+9wRSmx+B1OhHICWQDmMJEUiHoZp3ngRSAmneSiGQqk6D5o7b/9Nu55l7tn22nF+GAIGUQJqxTCpPQiBV5ZjrWmA6Amm6jekRpFW/f2ItX9LU9dpounwCaZOQ7fcEUpsfgdTmRyA1+hFICaTDWEIE0mGo5p0ngZRAmrdSCKSqE4G0bTn7+RFICaTqKiKQqnLMdS0waoE0vL3+gBOde+qc4epwBKnNl0Bq82uaJpA2CdX/nkBq8yOQGv0IpATSYSwhAukwVPPOk0BKIM1bKQRS1YlA2rac/fwIpARSdRURSFU55roWGIVAGqJosBj2lzOFyyGQ2lYfgdTm1zRNIG0SIpDahBqmV967bqjnP85nzrfY8y326vonkKpy9jkCKYFUXUV8Bqkqt3GOt9jb/CzTBFICqbp+CKSqHHNdC3QRSLveZgJpO3uAQNqO46BzIZDafDmC1ObHEaRGv7pxAimBVF1eBFJVzj5HICWQqquIQKrKEUhtcvZpAimBVF1FBFJVjrmuBQik57pZ2+4k74av/Pb9bsXDv5Ln+zxIIB3u3iOQ2nwJpDY/AqnRj0BaLbBgh+e5N+5LIFWXF4FUlbPPEUgJpOoqIpCqcgRSm5x9mkBKIFVXEYFUlWOuawECKYFUXYMEUlUub45Amuc06FQEUpsfgdToRyAlkA5jCRFIh6Gad54EUgJp3krZ8lQEUlWOQGqTs08TSAmk6ioikKpyzHUtQCAlkKprkECqyuXNEUjznAikNqeB03wG6ZBgN5wtb7HnCFJ1dRFIVTn7HIGUQKquIgKpKkcgtcnZpwmkBFJ1FRFIVTnmuhYgkBJI1TVIIFXl8uYIpHlOBFKbE4F0SH51Z0sgJZCqy45AqsrZ5wikBFJ1FRFIVTkCqU3OPk0gJZCqq4hAqsox17UAgZRAqq5BAqkqlzdHIM1zIpDanAikQ/IjkFYL8BmktgVHILX5WaYJpARSdf0QSFU5AqlNzj5NICWQqquIQKrKMde1AIGUQKquQQKpKpc3RyDNcyKQ2pwIpEPyI5ASSIextAikw1DNO08CKYE0b6VseSoCqSpHILXJ2acJpARSdRURSFU55roWIJASSNU1SCBV5fLmCKR5TgRSmxOBdEh+BFIC6TCWFoF0GKp550kgJZDmrRQCqeo0aO64/T/tdp65Z9tny/llCBBICaQZy6TyJARSVY65rgUIpARSdQ0SSFW5vDkCaZ4TgTTT6bDXneJ233WOW3rRmVMTp555vvvesmsn/71k0T6b/Y4vacqEFU7GZ5DyGaTCspkcIZCqcvY5AimBVF1FHEGqym2cI5Da/CzTBFICqbp+CKSqHHNdCxBICaTqGiSQqnJ5cwTSPCcCaYaTj6P+Jw6kX/7GD9xnLr3CLbv8vMnfHXX8We7g5y9yp5949OS/CaQZsOJJCKQEUnHpEEhVuBbmCKQEUnUZEUhVOQKpTc4+TSAlkKqriECqyjHXtQCBlECqrkECqSqXN0cgzXMikDY4+fB55Kte4n6/6h537S9vnjpKNA2iaTAlkNoWYN00gZRAqq4ujiBV5exzBFICqbqKCKSqHIHUJmefJpASSNVVRCBV5ZjrWoBASiBV1yCBVJXLmyOQ5jkRSGuc4gh6zoWXbRZI/VGlJxzzWvemIw+fPIcfXXOdO/6Mc90NP7xk8t8EUtsCJJBWC/At9rZ1RSC1+VmmCaQEUnX9EEhVOQKpTc4+TSAlkKqriECqyjHXtQCBlECqrkECqSqXN0cgzXMikA4Q8J8v6n8+cdbJk/8/DaQHvvRY9753vmWLQPrtL33cLdhjrnvwkSdse4DpgQLX3/Pv7qu3/d1YCvlAeuzij7inbDND2v61j/6H+9xNp7q1j6+W5vs+dMz+Z7uFz1zS983o5fVfs+4ed/ENJ7nH1j/cy+tvvdLHLfqk23v2AfLZ/PSu77tvrfgHeb7Pg4fudpR7xV7HuBkztPu91Q+tchf8+m19JjBd93c8+wK3x057m86DYU3gzgd+5y6+6SRteCuYOuk5n3Nzd5wnbcnExIT77u2XuqvvXirN933o1Qv+xr1495f3fTO4/mMosPz+692lvzljDLfcudnbz3Vve/Yn3OyZz5C2/w/rJ9wlN77HrXj4V9J834fesM8H3JJd/0jejD/8YUKe3RoGt9nQB+qeKj/9adttDZvZ2TbM2PDEZKxXmD9CdM19a7fYAbvMmT35uaNNR5A+uO7Jznbe1n7B19999XgH0ud81BZIb3znGAfSc9zCOQTSLu4j1jxyN4H0GYtk+p+uunLMA+lfuRniH4ZWP7TSXXDDGAfSxRcSSOVbnm1wMpDeeKLtTHo8fdKBPpDOl7ZgMpD+7gvjHUjnHSHZMYRAlwLL7/OB9PQur0Jnlz0ZSBd/0hZIf/234x1IdztU3n+33X+z+8Gdn5Pn+zz4zO3nuf++9wlu1vazBm7G02dt2+dN7Py6j30gTfdAegQpn0Ha3RrlM0j5DFJ19fEWe1XOPsdb7HmLvbqKeIu9Krdxjm+xt/lZpnmLPW+xV9cPb7FX5ZjrWoC32PMWe3UNWt9iv+KhX7mv3PZ+9eJ7PTd35kJ39MKPuu22mTlwO3iLvW0XE0gTvzSQ8i32tgVmmSaQEkjV9UMgVeXscwRSAqm6igikqhyB1CZnnyaQEkjVVUQgVeWY61qAQEogVdcggVSVc45AqtvlThJIGwKp/7X/nNLvLbt28pRLFu0z9Q33/t91X9L05Pon3O0P/cI9+ocHc/fHVnW6XWfu7XabtVDeJgIpgVRdPARSVc4+RyAlkKqriECqyhFIbXL2aQIpgVRdRQRSVY65rgUIpARSdQ0SSFU5Aqkulz9JIM23qjxlXSB9Yv1j7rLl73arH11uvJR+jr9xnw+6BTs+T77yBFICqbp4CKSqnH2OQEogVVcRgVSVI5Da5OzTBFICqbqKCKSqHHNdCxBICaTqGiSQqnIEUl0uf5JAmm9FIC20IpAWgkUn999i/8Z9CaSqIIFUlbPPEUgJpOoqIpCqcgRSm5x9mkBKIFVXEYFUlWOuawECKYFUXYMEUlWOQKrL5U8SSPOtCKSFVgTSQjACqQ6WTBJIW6MsPiMCKYG0eNH85wCBVJUjkNrk7NMEUgKpuooIpKocc10LEEgJpOoaJJCqcgRSXS5/kkCab0UgLbQikBaCEUh1MAJpa3bWMyKQEkjVNUQgVeUIpDY5+zSBlECqriICqSrHXNcCBFICqboG+xhIbzxvwm339Bluv7du2urbvzbhHrh5xuR/mDXPbfY71aZpji9pahKy/55AajTkM0gHAxJI9cXFW+x1Oz/JEaQ2P8s0gZRAqq4fAqkqRyC1ydmnCaQEUnUVEUhVOea6FiCQEkjVNdi3QOrjqP+JA+maayfcPT92bvEpGwPprZ937ml7Trj5R2z897B+CKTDkt10vgRSozGBlEBqXEKV4wRSmyqB1OZnmSaQEkjV9UMgVeUIpDY5+zSBlECqriICqSrHXNcCBFICqboG+xRIffh8xnMn3OP3O/fIHZuOIE2DaBpMVZumOQJpk5D99wRSo+EoBNKVV064e6/Z9NeKnQ/Z/K8XD9w64W5fuun3ex014Xbab7h/3fCsHEGqLy4CqW7nJwmkNj/LNIGUQKquHwKpKkcgtcnZpwmkBFJ1FRFIVTnmuhYgkBJI1TXYl0AaR1DfW+JA6o8q3fVPnNvl4I1NJfSWg96rquTNEUjznCynIpBa9DbMjkIg9Tfe+PMwrvuwc/NeMTF5g33sPuduudC5EEX9XzdWfXeGO+BE5546x7jxDeMEUt2XQKrbEUhtdtZpAimBVF1DBFJVjkBqk7NPE0gJpOoqIpCqcsx1LUAgJZCqa7APgdR/vqj/2evPNwbQNJDGvSUOpMNuLARSddXlzxFI860qTzkKgTS9YnV/7fCnTf/iYSQYOE4g1WUJpLodgdRmZ50mkBJI1TVEIFXlCKQ2Ofs0gZRAqq4iAqkqx1zXAgRSAqm6BvsQSH0vefKhLd9xu+2OE5OfO8oRpOreH/05AqlxH41aIA1HjIYjSNO/dvjNna4PESaQ6ouLQKrbEUhtdtZpAimBVF1DBFJVjkBqk7NPE0gJpOoqIpCqcsx1LUAgJZCqa7APgTTdtrSp8Bmk6t4f/TkCqXEfjVIg9Yd6+5/4M0jD52HEh3sTSI07fRrGCaQ2ZD6D1OZnmSaQEkjV9UMgVeUIpDY5+zSBlECqriICqSrHXNcCBFICqboGt4ZAyrfYq3t/9OcIpMZ9NEqBNGyKP+R79nOcm3/Eps/MiL/EyZ8uHGFq3PzacY4g1XUJpLqdnySQ2vws0wRSAqm6fgikqhyB1CZnnyaQEkjVVUQgVeWY61qAQEogVdfg1hBI/bb7zyl94OaNvWXWvM2/E0a1aZrjM0ibhOy/J5AaDUcxkKYfKpxuoj/SdNgfIOwvk0CqLy4CqW5HILXZWacJpARSdQ0RSFU5AqlNzj5NICWQqquIQKrKMde1AIGUQKquwT4GUnVb254jkLYtuuX5EUiNxqMQSKu+xT5+m328if602+00MfWNbMbNrx0nkOq6BFLdjkBqs7NOE0gJpOoaIpCqcgRSm5x9mkBKIFVXEYFUlWOuawECKYFUXYMEUlXOOQKpbpc7SSDNlRpwulEJpOtWbbqCaRz1UTT8flA4NTJUjhNIdVUCqW5HILXZWacJpARSdQ0RSFU5AqlNzj5NICWQqquIQKrKMde1AIGUQKquQQKpKkcg1eXyJwmk+VaVpxyFQGrchKGNE0h1WgKpbkcgtdlZpwmkBFJ1DRFIVTkCqU3OPk0gJZCqq4hAqsox17UAgZRAqq5BAqkqRyDV5fInCaT5VgTSQisCaSFYdHICqW5HILXZWacJpARSdQ0RSFU5AqlNzj5NICWQqquIQKrKMde1AIGUQKquQQKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUB0sm+Rb71iiLz4hASiAtXjT/OUAgVeUIpDY5+zSBlECqriICqSrHXNcCBFICqboGCaSqHIFUl8ufJJDmWxFIC60IpIVgBFIdjEDamp31jAikBFJ1DRFIVTkCqU3OPk0gJZCqq4hAqsox17UAgZRAqq5BAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2Zmc9IwIpgVRdQwRSVY5AapOzTxNICaTqKiKQqnLMdS1AICWQqmuQQKrKEUh1ufxJAmm+FYG00IpAWghGINXBCKSt2VnPiEBKIFXXEIFUlSOQ2uTs0wRSAqm6igikqhxzXQsQSAmk6hokkKpyBFJdLn+SQJpvRSAttCKQFoIRSHUwAmlrdtYzIpASSNU1RCBV5QikNjn7NIGUQKquIgKpKsdc1wIEUgKpugYJpKocgVSXy58kkOZbEUgLrQikhWAEUh2MQNqanfWMCKQEUnUNEUhVOQKpTc4+TSAlkKqriECqyjHXtQCBlECqrkECqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmZz0jAimBVF1DBFJVjkBqk7NPE0gJpOoqIpCqcsx1LUAgJZCqa5BAqsoRSHW5/EkCab4VgbTQikBaCEYg1cEIpK3ZWc+IQEogVdcQgVSVI5Da5OzTBFICqbqKCKSqHHNdCxBICaTqGiSQqnIEUl0uf5JAmm9FIC20IpAWghFIdTACaWt21jMikBJI1TVEIFXlCKQ2Ofs0gZRAqq4iAqkqx1zXAgRSAqm6BgmkqhyBVJfLnySQ5lsRSAutCKSFYARSHYxA2pqd9YwIpARSdQ0RSFU5AqlNzj5NICWQqquIQKrKMde1AIGUQKquQQKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZnPSMCKYFUXUMEUlWOQGqTs08TSAmk6ioikKpyzHUtQCAlkKprkECqyhFIdbn8SQJpvhWBtNCKQFoIRiDVwQikrdlZz4hASiBV1xCBVJUjkNrk7NMEUgKpuooIpKocc10LEEgJpOoaJJCqcgRSXS5/kkCab0UgLbQikBaCEUh1MAJpa3bWMyKQEkjVNUQgVeUIpDY5+zSBlECqriICqSrHXNcCBFICqboGCaSqHIFUl8ufJJDmWxFIC60IpIVgBFIdjEDamp31jAikBFJ1DRFIVTkCqU3OPk0gJZCqq4hAqsox17UAgZRAqq5BAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2Zmc9IwIpgVRdQwRSVa6dQPrwE/e5JyeesF2Jnk5vO2M7t8N2c+RrTyAlkKqLh0CqyjHXtQCBlECqri830Q4AACAASURBVEECqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmZz0jAimBVF1DBFJVrp1A+vM133RXrfqs7Ur0dPpl897uXrjLa+RrTyAlkKqLh0CqyjHXtQCBlECqrkECqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmZz0jAimBVF1DBFJVjkBqk3OOQGoTfPsiAqkqSCBV5ZjrWoBASiBV1yCBVJUjkOpy+ZME0nwrAmmhFYG0EIxAqoMRSFuzs54RgZRAqq4hAqkqRyC1yRFIrX4EUl2QQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZnPSMCKYFUXUMEUlWOQGqTI5Ba/QikuiCBVLdjslsBAimBVF2BBFJVjkCqy+VPEkjzrQikhVYE0kIwAqkORiBtzc56RgRSAqm6hgikqhyB1CZHILX6EUh1QQKpbsdktwIEUgKpugIJpKocgVSXy58kkOZbEUgLrQikhWAEUh2MQNqanfWMCKQEUnUNEUhVOQKpTY5AavUjkOqCBFLdjsluBQikBFJ1BRJIVTkCqS6XP0kgzbcikBZaEUgLwQikOhiBtDU76xkRSAmk6hoikKpy/Qmka66dcKu+O2NqQ2fNc26/t26+3dd9eNO/dz5kws0/YtPpbUKDp/mSJpssgVT3I5Dqdkx2K0AgJZCqK5BAqsoRSHW5/EkCab4VgbTQikBaCEYg1cEIpK3ZWc+IQEogVdcQgVSV608gvf1rE273l81wT52z8TrfeN6Em/0cNxVB438/dp9zt1zo3LxXTLhdDh5uJCWQ2tYegVT3I5Dqdkx2K0AgJZCqK5BAqsoRSHW5/EkCab4VgbTQikBaCEYg1cEIpK3ZWc+IQEogVdcQgVSV608gTbfQB1P/s9efz3AP3Drhbl86wx303k2nin9v06mfJpDadAmkuh+BVLdjslsBAimBVF2BBFJVjkCqy+VPEkjzrQikhVYE0kIwAqkORiBtzc56RgRSAqm6hgikqlx/A+mtn3fuaXtufBt9VSBdeeWEe+SOGVu8Dd8mteU0gdQmSiDV/Qikuh2T3QoQSAmk6gokkKpyBFJdLn+SQJpvRSAttCKQFoIRSHUwAmlrdtYzIpASSNU1RCBV5foZSMPnkcZHjPrPH43fUk8gta2J6ZomkOrSBFLdjsluBQikBFJ1BRJIVTkCqS6XP0kgzbcikBZaEUgLwQikOhiBtDU76xkRSAmk6hoikKpy/QukIY4ecKKb+jxSvxXhKNJYYqdFE5NvwR/mD0eQ2nQJpLofgVS3Y7JbAQIpgVRdgQRSVY5AqsvlTxJI860IpIVWBNJCMAKpDtZyIF335Fr3+PpHW7s+fTqjbdw27unb7ypfZQIpgVRdPARSVa5fgXRQHK3aev8W/Gc8ly9psq2M4U8TSHVjAqlux2S3AgRSAqm6AgmkqhyBVJfLnySQ5lsRSAutCKSFYARSHazlQHrL2p+4K1Z8rLXr06czWvLMw90rn3WKfJUJpARSdfEQSFW5/gRS/5b5e6/Z/IuYBm21P+3aXzu3+JThHj3qL58jSG1rj0Cq+xFIdTsmuxUgkBJI1RVIIFXlCKS6XP4kgTTfikBaaEUgLQQjkOpgBNLW7AikNsq37EcgVQUJpKpcfwLpjedNuCcf2jJ4hrfah4Dqt2jWPDf0L2cK4gRS29ojkOp+BFLdjsluBQikBFJ1BRJIVTkCqS6XP0kgzbcikBZaEUgLwQikOhiBtDU7AqmNkkCq+xFIdTs/edz+n3Y7z9xTPpOfr/mmu2rVZ+X5Pg8SSG17j0Cq+xFIdTsmuxUgkBJI1RVIIFXlCKS6XP4kgTTfikBaaEUgLQQjkOpgBNLW7AikNkoCqe5HINXtCKQ2OwKpzY9AqvsRSHU7JrsVIJASSNUVSCBV5Qikulz+JIE034pAWmhFIC0EI5DqYATS1uwIpDZKAqnuRyDV7QikNjsCqc2PQKr7EUh1Oya7FSCQEkjVFUggVeUIpLpc/iSBNN+KQFpoRSAtBCOQ6mAE0tbsCKQ2SgKp7kcg1e0IpDY7AqnNj0Cq+xFIdTsmuxUgkBJI1RVIIFXlCKS6XP4kgTTfikBaaEUgLQQjkOpgPQykj93n3C0Xbn7FD3rvpn8/cOuEu33pll9mEp+mNbDojAikNlUCqe5HINXtCKQ2OwKpzY9AqvsRSHU7JrsVIJASSNUVSCBV5Qikulz+JIE034pAWmhFIC0EI5DqYD0MpGuunZi81rscvDGC3v61CffEAzOmvrU5BNJhB9EUnUBqW4YEUt2PQKrbEUhtdgRSmx+BVPcjkOp2THYrQCAlkKorkECqyhFIdbn8SQJpvhWBtNCKQFoIRiDVwXoYSNON9cH0nh87t/iUjcGUQNracpjWMyKQ6twEUt2OQGqzI5Da/Aikuh+BVLdjslsBAimBVF2BBFJVjkCqy+VPEkjzrQikhVYE0kIwAqkOthUE0pVXTrhH7tjyCNJ406bjaFKOILUtQwKp7kcg1e0IpDY7AqnNj0Cq+xFIdTsmuxUgkBJI1RVIIFXlCKS6XP4kgTTfikBaaEUgLQQjkOpgPQ+k4fNI9zpqwu2035afO+o379bPb9zI/d7aGlPlGRFIbb4EUt2PQKrbEUhtdgRSmx+BVPcjkOp2THYrQCAlkKorkECqyhFIdbn8SQLpBqvDXneKW3Pf2im1i84+zb3kkIOm/n3qmee77y27dvLfSxbt45ZedObU71beu26g9hPrH3OXLX+3W/3o8vw9shWdkkCq78wFOzzPvXHfD8pnsO7JB9w/LT/NrX18tXwefR78i30/7ubvsFjehFvW/sRdseJj8nzJYIij814xMfV5pFXz0/WWewJpyd7b8rQEUt2PQKrb+cnj9v+023nmnvKZ/HzNN91Vqz4rz/d5kEBq23sEUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJ8c+kK64c7X7xMVfdZ846+RJtS9/4wfuQ5/8orvhh5dM/fszl17hll1+3uS/jzr+LHfw8xe50088evLfBNLBi41Amn9DTE9JINXt/GRfAmluHPXbRCC1rYnpmiaQ6tIEUt3OTxJIdT8CqW7nJwmkuh+BVLdjslsBAimBVF2BBFJVjkCqy+VPjn0gTal8MH3Vm9/lvv2lj7sFe8zdIoj6gBoHUwIpgTT/5pZ/SgJpvlXVKfsQSEPwHPS2ev+t9ru/bIZ76pyNW3jjeRPuaXs4t9efV78F3ya2aZojSG2SBFLdj0Cq2xFIbXYEUpsfgVT3I5Dqdkx2K0AgJZCqK5BAqsoRSHW5/EkCaWL1o2uuc8efce7UEaT+7fcnHPNa96YjD588Zfp7AimBNP/mln9KAmm+VV8DqQ+gD9y8ZewMb7X3X9p07zWbfj9r3vA/f9RbEkhta49AqvsRSHU7AqnNjkBq8yOQ6n4EUt2OyW4FCKQEUnUFEkhVOQKpLpc/SSBNrHwQffURh069hf7Alx7r3vfOt2wRSMMRpg8+8sRA7XVPrHP/68YzxvYzSN+08EPu2Tu/MH81Jqe8/p5/d1+97e/k+T4P+kB67OKPuKdsox0tuPbR/3Cfu+nUsf0M0mP2P9stfOYSeQn88u5/df/8u+n5DFL5Sg5pcMmcw93r9z3NbbONdgFr1t3jLr7hJPfY+oe1M+j51HGLPun2nn2AvBU/vev77lsr/kGe7/Pgobsd5V6x1zFuxgztfm/1Q6vcBb9+W58JTNf9Hc++wO2x097SeUxsmPrR77/hvr/yYmm+70Mvn/8O95JnHem0lefcnQ/8zl1800l9Z5Cv/0nP+Zybu+OGv+IJPxMTE+67t1/qrr57qTDd/5FXL/gb9+LdX97/DWELxk5g+f3Xu0t/c8bYbbff4Nnbz3Vve/Yn3OyZz5C2/w/rJ9wlN77HrXj4V9J834fesM8H3JJd/0jejJvu/bn78vL3yfN9Hpw7c6E7bvHZbtZ2swZuxtOftl2fN7Hz604gjXaBj6MvPGj/qc8j9b9qOoL0wXVPDtyJ6x73gfT08Q6ku7xIXuTX3331eAfS53zUFkhvfOcYB9Jz3MI5hkC62gfSj8prt8+Dk4F0oQ+kWipY88jdBNJnLJKXwE9XXTnmgfSv3Axx7a1+aKW74IYxDqSLL7QF0jv+ebwD6Z5/ZgukN54o3+77PnjSgT6Qzpc2YzKQ/u4L4x1I5x0h2TGEQJcCy+/zgfT0Lq9CZ5c9GUgXf9IWSH/9t+MdSHc7VN5/N6352ZgH0nPcrO1rAumsbWVbBp0jkP7nKqiKo/5X6Zcy8Rmk+TcbvqQp3yo9JW+x1+38ZB8+g9S2hcOb5i32NlveYq/78RZ73c5P8iVNuh9vsdft/CRvsdf9eIu9bsdktwK8xZ632KsrkLfYq3K8xV6Xy58kkG6w8m+jP/aoV069rT7mS4Mo32Kfv7gIpPlWBFLdqmqSQKp7Ekh1Oz9JINX9CKS6HYHUZkcgtfkRSHU/Aqlux2S3AgRSAqm6AgmkqhyBVJfLnxz7QOoD6Ic++cUtxP70sIOn3mp/6pnnu+8tu3byNEsW7eOWXnTm1On5kqbBi41Amn9DJJDqVgTSdu0IpDZPAqnuRyDV7QikNjsCqc2PQKr7EUh1Oya7FSCQEkjVFUggVeUIpLpc/uTYB9J8qupTEkgJpNY1VDXPW+xtqhxBqvsRSHU7P0kg1f0IpLodgdRmRyC1+RFIdT8CqW7HZLcCBFICqboCCaSqHIFUl8ufJJDmW1WekkBKIDUuocpxAqlNlUCq+xFIdTsCqc2OQGrz4zNIdT8CqW7nJwmkuh+BVLdjslsBAimBVF2BBFJVjkCqy+VPEkjzrQikhVa8xb4QLDo5gVS385MEUt2PQKrbEUhtdgRSmx+BVPcjkOp2BFKbHYHU5sd0dwIEUgKpuvoIpKocgVSXy58kkOZbEUgLrQikhWAEUh0smSSQ6pQEUt2OQGqzI5Da/Aikuh+BVLcjkNrsCKQ2P6a7EyCQEkjV1UcgVeUIpLpc/iSBNN+KQFpoRSAtBCOQ6mAE0tbsCKQ2Sj6DVPcjkOp2fpJAqvsRSHU7AqnNjkBq82O6OwECKYFUXX0EUlWOQKrL5U8SSPOtCKSFVgTSQjACqQ5GIG3NjkBqoySQ6n4EUt2OQGqzI5Da/PgMUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdj0Cq2zHZrQCBlECqrkACqSpHINXl8icJpPlWBNJCKwJpIRiBVAcjkLZmRyC1URJIdT8CqW5HILXZEUhtfgRS3Y9Aqtsx2a0AgZRAqq5AAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2PQKrbMdmtAIGUQKquQAKpKkcg1eXyJwmk+VYE0kIrAmkhGIFUByOQtmZHILVREkh1PwKpbkcgtdkRSG1+BFLdr6+B9IjXn7jZRl/59Qt1BCZ7KUAgJZCqC5dAqsoRSHW5/EkCab4VgbTQikBaCEYg1cEIpK3ZEUhtlARS3Y9AqtsRSG12BFKbH4FU9+tjIP3pL25wP/9/N7nj/+r1kxt+0Re+7q76t5+6pf/4MR2Cyd4JEEgJpOqiJZCqcgRSXS5/kkCab0UgLbQikBaCEUh1MAJpa3YEUhslgVT3I5DqdgRSmx2B1OZHINX9+hhI0631wfQ9HzrfcRSpvg76OEkgJZCq65ZAqsoRSHW5/EkCab4VgbTQikBaCEYg1cEIpK3ZEUhtlARS3Y9AqtsRSG12BFKbH4FU99saAukH//5id/1Nv+UIUn0Z9HKSQEogVRcugVSVI5DqcvmTBNJ8KwJpoRWBtBCMQKqDEUhbsyOQ2igJpLofgVS3I5Da7AikNj8Cqe7X50Dq31r/tSu+P7nxHD2qr4G+ThJICaTq2iWQqnIEUl0uf5JAmm9FIC20IpAWghFIdTACaWt2BFIbJYFU9yOQ6nYEUpsdgdTmRyDV/focSMNWX/GdZe5Tn72MSKovg15OEkgJpOrCJZCqcgRSXS5/kkCab0UgLbQikBaCEUh1MAJpa3YEUhslgVT3I5DqdgRSmx2B1OZHINX9toZAeuequ92xJ5/pLjn/LLfHvN10DCZ7JUAgJZCqC5ZAqsoRSHW5/EkCab4VgbTQikBaCEYg1cEIpK3ZEUhtlARS3Y9AqtsRSG12BFKbH4FU9+tjIPVvrX/hc5/tXvyCAyc3PLzVnrfZl60D73bdDb9x55/97rLBETk1gZRAqi5FAqkqRyDV5fInCaT5VgTSQisCaSEYgVQHI5C2ZkcgtVESSHU/AqluRyC12RFIbX4EUt2vj4E0fGt9vNXE0fw1ED6SwE8sWrgXgTSfbmROOXv7ue4vFxJI1R1CIFXlCKS6XP4kgTTfikBaaEUgLQQjkOpgBNLW7AikNkoCqe5HINXtCKQ2OwKpzY9Aqvv1MZDqW8tkLPDBv7/Yrb77PgJpD5cFgdS20wikut/cmQvd0Qs/6rbbZubAM5m/8yz9Aph0BFLjIlh577qB5/DE+sfcZcvf7VY/utx4Kf0cJ5Dq+23BDs9zb9z3g/IZrHvyAfdPy09zax9fLZ9Hnwf/Yt+Pu/k7LJY34Za1P3FXrPiYPN/nQQKpbe8RSHU/AqluRyC12RFIbX4EUt2PQKrb9X2SQNrfPUggte07AqnuRyDV7XInCaS5UgNORyAdDEgg1RcXgVS385MEUt2PQKrb+UkCqe5HINXtCKQ2OwKpzY9AqvsRSHW7vk8SSPu7Bwmktn1HINX9CKS6Xe4kgTRXikBaLEUgLSabGiCQ6nYEUpsdgdTmRyDV/Qikuh2B1GZHILX5EUh1PwKpbtf3SQJpf/cggdS27wikuh+BVLfLnSSQ5koRSIulCKTFZARSnWyzSY4g1SEJpLqdnySQ6n4EUt2OQGqzI5Da/Aikuh+BVLfr+ySBtL97kEBq23cEUt2PQKrb5U4SSHOlCKTFUgTSYjICqU5GIG3JjkBqgySQ6n4EUt2OQGqzI5Da/Aikuh+BVLfr+ySBtL97kEBq23cEUt2PQKrb5U4SSHOlCKTFUgTSYjICqU5GIG3JjkBqgySQ6n4EUt2OQGqzI5Da/Aikup81kK578kH32wev0a9Ajye3mfEUt/eOL3Cztp3dq6244jvL3Kc+e9lm1/kj7zvZvfgFB/ZqO1Y+fKP73799V6+uc1tXlkBqkySQ6n4EUt0ud5JAmitFIC2WIpAWkxFIdTICaUt2BFIbJIFU9yOQ6nYEUpsdgdTmRyDV/ayBdPW6W90Xb/0b/Qr0ePKp2+zgjjvgU27H7Xbp8Vb096oTSM/dEOd3knfgV377frfi4V/J830eJJDqe49AqtvlThJIc6UIpMVSBNJiMgKpTkYgbcmOQGqDJJDqfgRS3Y5AarMjkNr8CKS6H4FUtyOQ6nZtTBJICaTqOiKQqnLOEUh1u9xJAmmuFIG0WIpAWkxGINXJCKQt2RFIbZAEUt2PQKrbEUhtdgRSmx+BVPcjkOp2BFLdro1JAimBVF1HBFJVjkCqy+VPEkjzrSpPufLedQPP4Yn1j7nLlr/brX50ufFS+jlOINX324IdnufeuO8H5TNY9+QD7p+Wn+bWPr5aPo8+D/It9vreI5Dqdn6SQKr7EUh1OwKpzY5AavMjkOp+BFLdjkCq27UxSSAlkKrriECqyhFIdbn8SQJphtWpZ57vvrfs2slTLlm0j1t60ZlTUwTSwYAE0ozFNeAkBFLdzk8SSHU/AqluRyC12RFIbX7H7f9pt/PMPeUz+fmab7qrVn1Wnu/zIIHUtvcIpLofgVS3I5Dqdm1MEkgJpOo6IpCqcgRSXS5/kkDaYPXlb/zAfebSK9yyy8+bPOVRx5/lDn7+Inf6iUdP/ptASiDNv7nln5JAmm9VdUoCqe5HINXtCKQ2OwKpzY9AqvsRSHU7P0kg1f0IpLpdG4H0wcfvces3/N84/sxwM9xO2+8mbzqBlECqLh4CqSpHINXl8icJpA1WaRBNgymBlECaf3PLPyWBNN+KQGqzSqcJpDZP3mKv+xFIdTs/SSDV/Qikuh2B1GZHINX92gik3/n9ee76+3+gX4keT752wbvdAbP/WN4CAimBVF08BFJVjkCqy+VPEkgbrA573SnuhGNe69505OGTp/zRNde54884193ww0sm/00gJZDm39zyT0kgzbcikNqsCKTt+hFIdU8CqW5HILXZEUhtfhxBqvsRSHU7Aqlu5ycJpLrf7O3nur9cSCBVBQmkqhyBVJfLnySQNlgd+NJj3fve+ZYtAum3v/Rxt2CPubXTDz76iPv6LRe4+x+7M3+PbEWnPGz+m9wL9zhE3qJfrvyl+5c7vyDP93lwt1kL3RuefZLbftttpM2456H73D/fer57+In7pfm+D718z2PdQbs/T96Mn/3+Wvevq74kz/d5cJ+dXuRevd+b3bZPmSFtxp1r79qw9s5zT6x/VJrv+9Ar93q7W7zbc+TN+L93XO1+ctdX5Pk+Dy555svc4fu+2m2zjbb2br/v9+7y5ef2mcB03V+zz8luv10WSucxsWHq3353lfvZPd+U9qehLAAAHNZJREFU5vs+9KJdX+P+694v2/CGU+3ntjV3uG/c9g/a8FYw9bqFp7m95jxL2pL16yfcD377rQ1H8V0lzfd96AW7vMK9dJ9Xypvxm3t+6771u0/J830e3G6bme51+53i5s/eXdqMJ/8w4b5165fcbQ/8TJrv+9Ahu73G/cleL5M346bVt7hvr7hQnu/z4A7bPdP92X4nu113nCNtxuNPrndfvekCd/e68fwi5z/e/fXuv+z5EsnOD12/6gZ35e//UZ7v8+Azn7qHe/0BJ7mnz3xanzdjpK87gbRh9zQdQTrSe5crhwACCCCAAAIIIIAAAggggAACCCCAAAK1AgTShgXS9BmkrC8EEEAAAQQQQAABBBBAAAEEEEAAAQQQ6K8AgbRh3zV9i31/dz3XHAEEEEAAAQQQQAABBBBAAAEEEEAAAQQIpBlr4NQzz3ffW3bt5CmXLNrHLb3ozIyp7k5yzoWXuW9debVbdvl5rV4J7+B/PnHWyS4Nx61e0JDOLL7+pRcRvpwr57NnS8+b0yOAAALTJeDvuz/0yS9OfdGgernx46K/X/zxT6+fPF//c/SRL3OXfeMqtzXfX9Y9zpY8XpScVt1XzI2GQNv7Ov2M/GFuZdvXfZjXddzO27/Tbf7uO08+N09/hvV6oC3j6VzDbV3nPp0PvpvvrfRj8/q0L5uua9Nt3T9nW3nXvSPfMJq2czp/H5uuuHO1e9Wb3zX1vLbufnc6ryOXNRwBAulwXLPONbxQjU/cRoBtupPMunIVJ+pDIA13YPHVD1+yNa6BtMrE+9zww0vUpcBcJBBeOMYof3rYwZUvVsYVrirKBbf4S/AG+ZQ8ESk57ajtj7biZd125V6Gf2EV/8SxM+y7+D7En/6is09zLznkINenmJLrURIeSra/5LTDXq9x9A6X1cXjRNVjVnyfWvqiv/T0Fueq637sUa90p5949Ba3i6r7qpLrWnJayzb52S7XqY8aa+5bO7UJu8yZ3foBAE0+XT+u1BkQSJv2Xt7v64yV21rVazx/TcLjZN61sp1Kud62Sxw8nT6niE8Z7iOHddnhfPsUSKuemwx6zPBr6uqf3VB7cBSB1Lkqg0Ffxu2f+xBIh32LHN3zJ5B2uG+q7vzaeDAb10AaPNMnH/4B0R9NawmkHS4T80WHF2yxS7DKiVPmK7AVn4G/rV2y9DtbHCnnn8T89dteNxmL+HGTR5zHRy2mf4ltMip5cVpy2qbLne7fq7Gu5Ho2XUbYN/ELljATIqn/9ze+/aOpIxHCTBcxrWTbq07b5DHo/Nt6nO0yPKXblr548P/++XW/mdYYVfV45a9neBz3/7v0eVLp6S1rKr3+4d9Vj7UE0mZpv99ffcShk4E5/Ph1+fr/cdi0Pr52+bjSZNDldWveg/WnmM7bZt01aTJWrqf62GI17cO84tnGdvUpkIbHuviP094t/gORf/7w3o/9Y9ZjNIF04x/6Yq/w/Cv+A6x/brfyrjVbHOTCEaRt3AL7cx4E0g731aC/Dh38/EWVRxtUvTCIj/gId5rpC7cQcXwg8z/Hn3Gu8y9+fdjxP+nRboM+UmDUjyCteoIT7940kKZ/LU4fhGKjD57xVvf+sz+/WQgLrv4y0gesJuPpXHaDXnCmH5OQPmFJn3TH2xuufwgiTbPpX9LjkOIv5/qbb5siCfvBn2e8D9KjRurOczp8B7mml51ez/j2Fox9hAhHyNStw9SgL2swva+Ljzb0Xul9VhyNll7xL1P3Vf60/ij7I1/1kqm3cwdv/9/9fWe4XwunDR+JEh+9EGJF+oQnDX/xl/Q1rcc21lzTC6r4PqsqYIbrkN6n19120+s96A9J4b/P332XzYxfcND+7hcb1m/48Wv0i596z2ZvRfK/q3qs8v+969txk/mgx8OwZl+4YfvDR/AMWlfp9sdrM6z1usfkNtZWznmkL6DS28cgC+v9WHzdmj6+J328CLeDQY/ng04/rHVX9bgQbjunvuMNU7eLqvs17xA/FoZtq3uM9Lf1sP7ix4e6+9QFe8ydjMzxmgt/QE2PIg7vaupineb+Ia3ueXC4ffrtOOcDJ0z6x/dV4aOo6m7P6f1n/E6vqscVf/5Nt4mc26M/TY5B3WVVfY+Cfzt+WDP+MsJzjmCw+65zptZhGvYHPQY1be8w1nB6meH2MmgNDzJvMh50H9K0D+seW6qu+7Pm7brZ85o02viPTqt6vAnXI943YTYNgvF67eJI7HBdqwJp09pK12x8Ww6vKeLgFU4fO6Yew3ocaFobub/368Q/333TkYdPxr1Pfe5yd9c9900+x/L343HMq3rsTNdufN81aNtz7utznxe18a7YXKvc0/m1F9/n+Rga/yE4No9NmwJpsA5rse7jGUfldphrNo6nI5B2uNebjqqqOrIkflCpCqFHvfa/Of/EO3wGaXqHmf61JL3Bp+cZR8dRDqRNT3DCC1T//8PnNPltDUcl+Ds2/xPHlPjJQ3r+6b6JX1g2GU/3kqsLeXGoqouc6fZWBa/4iXQcV+us/D649pc3T7n7891z/m6TR4akfymNn9jUned0+Ta9kA/XI15n4XqHF6PhATU8WKdxSjUYtTUYrxe/Hz/87v+52dE/TS/mm46Qiddu1Wnj/5beHuLrk+6P9PYR3ycM40iEnBdU/v4r3Ya6NdZ0201vL4O2K17vTfur6XHFz/vHqhV33j35B7t4/U/3Z2TVmdc9HoZoEu736p5Ip+czqo8XdYE0fl7h10z83MByP5auv6ojmNPTVL24bno8jx+fhvn4kRtI/YvbnCNImx4j4xeg8T5puo2mjy3eeBTXqd8mH+wGffZ/3fNg/8eydL/77Qzv7AiR2D8PrLs9+5nSx5Wm20TJ85Qmg7rLqgqkPsLHL+LDfW58MIU3Su8b6x5H667DsNZweplNa7jOvMlYOeKx6fHc74f4D+J1j+Ol69Pfl/vnCvHjefwH3/CaqOpza0vWpnrautcb6X1oGp7S/V71eiPE8qrneycc89qp4Nj1848mvziA+v/tf3zQO+QFiye3YVDMa7otWF7Dljwvatq+Ln4fm/n/7d/t548qDdE5Dqi5gTR97VZnNEq3wy78+3KZBNIO91T615twVcIDZlMgHfRCNtwwfYTwd/7x0XqDzjMEm/Q84yc2W1sgjXd909GUVUfS+Pnw5CL+ffqi358uPWJuOpddG4E0vfMvCaTpbPz5hemDSOxS9QQqHF1dd57TZTvojw/h8gd9zlT6hDX+coX0hUSJwSivwbBe/It5/9fv9Avkml7M1wXS9Hfpv6v+eBKvH/+//VGR/kWy3zf+CA3/5PNPXrxk8mijuqOkw3psa80NekFVtQ3pk6z4OsRrrOm2m173QS8E62636eNKen0HPVaNyu140JdW1T0eDlqzfr00bX9VIE2PHJ/Oz6kLayANpPEfDuss0ttcyf1Y1W0nrKf4d7FHU6xoejwf5rpLH2/jP4ot2GO3xi95SLet5DEydm+6Tx10BFeIByGghHjW9NyxrfvAqvOJj7bxv4+Pnm96Hlz3ZaXxOqi7PfvLLH1cabpNlHrVGdRd1qAjSMNz16bnY+G5a7p2g0l4DKy7DsNaw1XPC9L1kN6n1bnXGTfd51Sdb9VrvHA0Y9Mfff35xdtS8ngz6LlAzmWWrkv19LFn0/Ob9Hqn99/xv6vup+LnSnXPjeLbgrpdbc/FbwkPMe+OlXe7a35x4+Trz0ExL10/6f153WNg0319yfOitj3aOL84Ons//5zNe/jn/f4Anfgt+DmB1L9+iA/0qbKPH5tH6XbYhufWeh4E0g73bNWL4fiOaVBoC38RHxTdwl8a0yeS/t9N0TV9guBnwlFTW1sgjZ3i7fT/O30yVHVofRxH4hdFfj7+q2TV+U3nsmsjkKYxpiSQhr/2ptscgkD8tpr4CL26vzA3ned0+A46grTKO71dhdtw1YuKcPR31bqJT5/uk1Feg2G9+O32n13pf+IjgppezA96QlH1wis9bdWT3vhFk79u/jr5tzH5J53+s+3825j8v+PP2axbj22tt0GBtCoY+ctMv7gmvh7xGovvq+qOaql6YhfO03IE6aDHqlG5HQ8KpHWPh3Vr1pvF33aabn9OIO3i86Hr3ppaZ2G5H8u57YTH6vjjV1KfksfzYa67cD8cb1eIu01v0Rv0XCH3MbLkNloVfPqwTsN9Yc7z4PixNOyPdN+H+9DSx6Cmx5Wm20TOuh90mtSg7rKaAmm8JtOjxOPHAx8O/PPa9GdQ8Ks6gip8jFDd87ySNVz1vKBuDZeYV62z0vvkpiNI4z+Oh+s26Lli3foMrxWrPgM8jlnpfVOXXyYa3/80Pb+pCqTxO02aHk/j167pAQrxR5qEfRD/sbJkzQzrtCGChj/Y+/34lr/+yOQRj/7/hz8CVf1hMP7DYuxU9xiY0x7SbR30EX/DMrGcb4jO/iAy/1zfvxbxdv75v4+kIT77y2gKpGH9pH/Qrnu+NEq3Q4vj1j5LIO1wDw968Kz7i238oFL3l3P/1iL/YJmepimQ1r1tdJQDaXgil36Yf7x70+vvXxTHXzrymUuvmHqgaQqk6V/fco7eK31y1dbSHBRI0wfTdN/HT0qajkIrma3brvgtgnVBKr0+bVmVnM8g16q39PijEsMRG01HkOYG0j6twaqgnn6GZnz7S++nql4IVd2X+f1XeqRPeLLp91H44g+/9vz18z/hYzi6DKRVR1jEazUc+Vq1xppuu+maH3Tbip9cN8WEpiMow2WOwu247kVs3eNhSSCtO6qp6TG55D7Jetq6o63qLJpiUBu3nTh8pOcX9mHu4/kw113dHyTVQJre1sPznNQhPkpl0Dsc6iJzX9bpoCPCYqeqP575/R5/1lzOEaTBq/Rxpek2Yb2txgaWQGo5gjTehpLtrXueV7KGq54XWI4gTfdJbGw5grQqXA667oOeK5Y83qT3F/FR4eF3dfdT1rWZM990BGnd2kofp5oC6aD7i2E+DuQY5J4mXP/4KMVwG4q/TKjpNV3sVLftTc9JSp4X5W7jdJ8ufY4fvw4IH1/gr1NTIPWnCZ9tHYf1OqN4W7u+HU63e58uj0Da4d6qemFWFRLCXybio7D8Z4+kD5j+3+lnkPrNi19YNN3xDToiy/+FZdQDafDJ+Rb79IEkfeLcFEhzPr8lfcvkKAXSKqv4SUS40w5/YU7XZTjSKDzxq5sd9JaXsKb8F1f4z2PzP/GDSt2L6rrznM6bdHpkk7/sqkAanqCG37VxBGmf1mC6flKH9GicsL7qPpty0FGJVZEnfjFS9YQkHKGVfrh63dt6Bx3Vall/JUeceLOrf3bD1EcDDFpjTbfd9PoGn7pvsS8NpIMeq6qOVPCugz5v0GI7aLbOvO7xsOQFa7om/XoLn6vY9Jg8jG0edJ51gbTOoimOlARSfzn+J/7W8nA/G26fcWBJX8T4f6eP5+nph/n4URJIq7zT6+pPk/sYWXVUVnqfVhdIR22d+v0UjvAJazbsu3DfnPs8OKwL///jPySFINV0n1b6uNJ0m8i9XecYWAJpPFtlGf/BNr2c+DGo7joMaw1XPQbXreFB5jnG6e3Sn5e/fP8z6PGq5PE8ff6bPkdqWp9VR1lWfQZpuK5Nf3TNXZ/q6ZoeE+rWVkkgTe/r49cYw3wcUF2q5sLjX/ycLDxHjl9XVr2ujY+0bXreEZ57NT0nKXle1KZDm+cVjqCNn+OH1wHxa/emQBqOAk9fwzQ9XxqV22GbplvbeRFIO9yj4cEzvQrph3bH3zbvv5Uv/TKccIj3oEPc4xe8h77owMa3f4c73nC9wuWNeiD117fqrRrxt7OmT47DW37Sz0ZsCqT+fOK39FV9i/2oBdJ0naV/1Y7t/Pb4F/DxW4DideG94g/6b5pN13oaXsN1S98yPOiLn/zpB53ndN+kq9Zc+qTFH63sf7yr/wkxq+lFVNOTyL6swaoXCsGt6puawzczh9tQ/JaUqm+rD7b+rUbpaeu+xT6slZyjLJv2RRvrbtBjQvwZ0eE+y19efMTcoDXmTxeeDPr/nd52B13v9C1C6eNS/MI5fUJd9eIrvg7x/WXXt+Mm80GPhyWB1BvHnn4fpE+sR+Hxoi6Q+m0YZGG9H0vXYLr20m9bjvdZ1bfYp4/nVacf1rorCaRV91Xpda37ZuvUKX3LbHybS+9TBx0RN2rrNN6GsE7St8BW3bdUvUBN39oY3w6bAlTp40rTbaLk8aLJoDSQxm8rjr/kK34+Ea5f+jwx/riH+DGo6TqExyc/U/fRMCVreNAfKQet4TrzJuOq+5C2A2l8Gelzxab1mT7GVH2L/aD775K12NZpq+5/ctdWTiCNr2caweIjaof1ONCWkz+f9I9C/r+l75oI/y1+J1bV8w7/38Jz4kHb3hRI654LVN3vtmnR1nmlf3QN2xS/wyA1bXoHSHrgUV1L8S0n/HR18FRbllvr+RBIt9Y9y3YhMESBur+MD/FiOWsEEECg9wK5b7/q/YayAb0WYJ32evdVXvm6dz70JW6U7BXWcIlW/0876KOX+r9lbAECCEynAIF0OrW5LAS2EgEC6VayI9kMBBAYukAfjlIZOgIXMPICrNOR30XmK7i1B1LWsHmJ9PoMCKS93n1ceQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAggggAACCCCAAAIIIIAAAtMtQCCdbnEuDwEEEEAAAQQQQAABBBBAAAEEEEAAAQRGRoBAOjK7giuCAAIIIIAAAgiMvsCKO1e7V735XVtc0W9/6eNuwR5zW9mAH11znTv+jHNdm+fZyhXjTBBAAAEEEEAAAQS2SgEC6Va5W9koBBBAAAEEEECgfYFzLrzMXbL0O+5973yLe9ORh09dgA+an/rc5W7pRWe2cqEE0lYYORMEEEAAAQQQQACBTAECaSYUJ0MAAQQQQAABBMZZIBw5msbRQSYHvvTYqV/tMme2W3b5eVP/9r/z5/OZS69wa+5bO/nfjz3qle70E492VUeoLlm0z1R8DZE2nFl8fUJYvejs0yaPQPU/f3rYwe4TZ508zruObUcAAQQQQAABBBBoECCQskQQQAABBBBAAAEEGgVCmLzhh5c0njYE0HCU6alnnu9W3nXvVOQM8TS8hT4Omy855CA36AhSfx2+deXVU7E1xFQfROO5NMg2XmFOgAACCCCAAAIIIDDWAgTSsd79bDwCCCCAAAIIIJAnkEbOQVM+Yq68a81mR22GkBmCaBpQ/Xkd9rpT3AnHvHbyrfuDAqmfSz+X1F8v/+OPEuWt+Xn7klMhgAACCCCAAAIIbC5AIGVFIIAAAggggAACCDQK5AbSo44/y11/822V5xeO9BwUSF99xKGTb7OvCp3hv1WdcXgLPoG0cTdyAgQQQAABBBBAAIEKAQIpywIBBBBAAAEEEECgUSB9e/ugAR9I5+++c+3nfloCad032xNIG3cjJ0AAAQQQQAABBBAgkLIGEEAAAQQQQAABBBSBEB8HfUmTD6j+6M+ckKoEUn+dq+bibSGQKnuWGQQQQAABBBBAAAGOIGUNIIAAAggggAACCGQJ+LfZf2/ZtZPfQB++gCmEy/A29/B5o+m3x/sjS8/5wAluwR5zK0On/wzS8Bb7cB7p5YTLj48i9VH06/9nGZ9BmrUHORECCCCAAAIIIIBAlQCBlHWBAAIIIIAAAgggkC1Q9VmgaQwN0TQ+0xBQw+/S+BkHUn8afyTqJUu/M3kW8Wz838P5p99iX/c2/OwN5YQIIIAAAggggAACYyNAIB2bXc2GIoAAAggggAACCCCAAAIIIIAAAggggEAqQCBlTSCAAAIIIIAAAggggAACCCCAAAIIIIDA2AoQSMd217PhCCCAAAIIIIAAAggggAACCCCAAAIIIEAgZQ0ggAACCCCAAAIIIIAAAggggAACCCCAwNgKEEjHdtez4QgggAACCCCAAAIIIIAAAggggAACCCBAIGUNIIAAAggggAACCCCAAAIIIIAAAggggMDYChBIx3bXs+EIIIAAAggggAACCCCAAAIIIIAAAgggQCBlDSCAAAIIIIAAAggggAACCCCAAAIIIIDA2AoQSMd217PhCCCAAAIIIIAAAggggAACCCCAAAIIIEAgZQ0ggAACCCCAAAIIIIAAAggggAACCCCAwNgKEEjHdtez4QgggAACCCCAAAIIIIAAAggggAACCCBAIGUNIIAAAggggAACCCCAAAIIIIAAAggggMDYChBIx3bXs+EIIIAAAggggAACCCCAAAIIIIAAAgggQCBlDSCAAAIIIIAAAggggAACCCCAAAIIIIDA2AoQSMd217PhCCCAAAIIIIAAAggggAACCCCAAAIIIEAgZQ0ggAACCCCAAAIIIIAAAggggAACCCCAwNgKEEjHdtez4QgggAACCCCAAAIIIIAAAggggAACCCBAIGUNIIAAAggggAACCCCAAAIIIIAAAggggMDYChBIx3bXs+EIIIAAAggggAACCCCAAAIIIIAAAgggQCBlDSCAAAIIIIAAAggggAACCCCAAAIIIIDA2AoQSMd217PhCCCAAAIIIIAAAggggAACCCCAAAIIIEAgZQ0ggAACCCCAAAIIIIAAAggggAACCCCAwNgKEEjHdtez4QgggAACCCCAAAIIIIAAAggggAACCCBAIGUNIIAAAggggAACCCCAAAIIIIAAAggggMDYChBIx3bXs+EIIIAAAggggAACCCCAAAIIIIAAAgggQCBlDSCAAAIIIIAAAggggAACCCCAAAIIIIDA2AoQSMd217PhCCCAAAIIIIAAAggggAACCCCAAAIIIEAgZQ0ggAACCCCAAAIIIIAAAggggAACCCCAwNgKEEjHdtez4QgggAACCCCAAAIIIIAAAggggAACCCBAIGUNIIAAAggggAACCCCAAAIIIIAAAggggMDYChBIx3bXs+EIIIAAAggggAACCCCAAAIIIIAAAgggQCBlDSCAAAIIIIAAAggggAACCCCAAAIIIIDA2AoQSMd217PhCCCAAAIIIIAAAggggAACCCCAAAIIIEAgZQ0ggAACCCCAAAIIIIAAAggggAACCCCAwNgKEEjHdtez4QgggAACCCCAAAIIIIAAAggggAACCCBAIGUNIIAAAggggAACCCCAAAIIIIAAAggggMDYChBIx3bXs+EIIIAAAggggAACCCCAAAIIIIAAAgggQCBlDSCAAAIIIIAAAggggAACCCCAAAIIIIDA2AoQSMd217PhCCCAAAIIIIAAAggggAACCCCAAAIIIPD/AfXIST3dOd1sAAAAAElFTkSuQmCC" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_event_count_chart(\n", " trainings_df[~trainings_df['Primary Training Topic']\n", " .isin(first_steps_cols)]\n", " .groupby('Center')\n", " .size()\n", " .reset_index(name='Training Events'), \n", " \"Count of Trainings Per Center Excluding First Steps FY25\", \"training_events_count_attendees.png\")" ] }, { "cell_type": "markdown", "id": "bc32bdf2-4021-4679-b002-1d48c8d99796", "metadata": {}, "source": [ "## All training events with 1 or more attendees " ] }, { "cell_type": "code", "execution_count": 29, "id": "1856575f-4874-4d16-a6f3-34d62b1345c0", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Center=%{x}
Training Events=%{text}", "legendgroup": "", "marker": { "color": "#71bf44", "pattern": { "shape": "" } }, "name": "", "orientation": "v", "showlegend": false, "text": { "bdata": "AAAAAACARkAAAAAAAABZQAAAAAAAgFlAAAAAAAAARUAAAAAAAABOQAAAAAAAADlAAAAAAADAUEAAAAAAAABCQAAAAAAAAE9AAAAAAADAXUAAAAAAAAAiQAAAAAAAACBAAAAAAABAUEAAAAAAAIBRQAAAAAAAgEhA", "dtype": "f8" }, "textposition": "auto", "type": "bar", "x": [ "Bucknell", "Clarion", "Duquesne", "Gannon", "Kutztown", "Lead Office", "Lehigh", "Penn State", "Pittsburgh", "Scranton", "Shippensburg", "St. Francis", "Temple", "Widener", "Wilkes" ], "xaxis": "x", "y": { "bdata": "LWRmKjwZQyQ+dwkIQUYx", "dtype": "i1" }, "yaxis": "y" } ], "layout": { "annotations": [ { "showarrow": false, "text": "859 total training events", "x": 0, "xref": "paper", "y": 1.05, "yref": "paper" } ], "barmode": "relative", "height": 700, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermap": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermap" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Count of Trainings Per Center With At Least 1 Attendee FY25" }, "width": 1000, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Center" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Training Events" } } } }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_event_count_chart(\n", " trainings_df[trainings_df['Attendees, Total'] >= 1]\n", " .groupby('Center')\n", " .size()\n", " .reset_index(name='Training Events'), \n", " \"Count of Trainings Per Center With At Least 1 Attendee FY25\", \"all_attended_trainings.png\")" ] }, { "cell_type": "markdown", "id": "e3f3f9a0-66b7-47e1-b3eb-ba54af46e317", "metadata": {}, "source": [ "# All training events with at least 1 attendee excluding first steps" ] }, { "cell_type": "code", "execution_count": null, "id": "5063daf1-03e7-4404-bc12-52f9d9f6ff14", "metadata": {}, "outputs": [], "source": [ "make_event_count_chart(\n", " trainings_df[\n", " ~trainings_df['Primary Training Topic'].isin(first_steps_cols) &\n", " trainings_df['Attendees, Total'] >= 1\n", " ]\n", " .groupby('Center')\n", " .size()\n", " .reset_index(name='Training Events'), \n", " \"Count of Trainings Per Center With At Least 1 Attendee FY25\", \"all_attended_trainings.png\")" ] }, { "cell_type": "code", "execution_count": null, "id": "7a2ae766-542c-4360-be5b-690129a19900", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f995b431-459f-4d06-8bfd-b3e5249caa32", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "059a6112-f540-417c-a77d-6773ce791cff", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "id": "ca833a98-fb06-4989-9031-84a82a5b0590", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Start DateEvent TitlePrimary Training TopicTraining TopicsCreated ByCenterFunding SourceStatusMaximum AttendeesNumber RegisteredAttendees, Total
09/30/2025 12:00 AMBoost Your Business Visibility: Get Found on G...Marketing/SalesAgriculture, Artificial Intelligence (AI), Bus...rwolf219KutztownCore ServicesClosed300068
19/30/2025 12:00 AMIntroduction to the Business Finance Basic Ser...Accounting/BudgetAccounting/Budget, Business Financing, Busines...rwolf219KutztownCore ServicesClosed0750
29/30/2025 12:00 AMMulti-State Supplier Readiness Webinar P E N ...Managing a BusinessAccounting/Budget, Business Plan, Cash Flow Ma...tcookPittsburghCore ServicesClosed0087
39/30/2025 12:00 AMThe Basics of Business Lending (in-person and ...Business FinancingBusiness FinancingHigginsj5DuquesneCore ServicesClosed006
49/30/2025 12:00 AMUnlocking Business Success: Tools & Resources ...Veterans Outreach Conf.Veterans Outreach Conf.vermaLehighCore ServicesClosed100229
59/30/2025 12:00 AMYour Marketing Help Desk: Social Media, Canva ...Social MediaAccounting/Budget, Artificial Intelligence (AI...rwolf219KutztownCore ServicesClosed100052
69/29/2025 12:00 AMDeveloping an Export Plan Part 2International TradeInternational Tradeavh318LehighPRIMEClosed008
89/29/2025 12:00 AMIn-Person Consulting Opportunity for **Bradfor...Business Start-up/PreplanningBusiness Plan, Business Start-up/Preplanning, ...geezaScrantonCore ServicesClosed500
99/29/2025 12:00 AMQuickBooks Functions & ReportingAccounting/BudgetAccounting/Budget, Business Financing, eCommer...ssharif130LehighSSBCIClosed4005
109/25/2025 12:00 AMEmerging Social Media Trends (webinar)Social MediaSocial MediaHigginsj5DuquesneCore ServicesClosed008
119/25/2025 12:00 AMHow to Access the Capital You Need - virtual s...Business FinancingBusiness Financing, Business Plan, Business St...sym40PittsburghCore ServicesClosed0023
129/25/2025 12:00 AMPitching Your Business Part 1: Creating Your P...Business Start-up/PreplanningBusiness Plan, Business Start-up/Preplanningparth.tyagiTempleCore ServicesClosed500019
139/25/2025 12:00 AMReal Talk Series for Small Business: 8 Questio...Managing a BusinessBusiness Financing, Business Plan, Business St...jbestClarionCore ServicesClosed752914
159/25/2025 12:00 AMValue PropositionMarketing/SalesMarketing/Saleshorne001GannonCore ServicesClosed004
169/24/2025 12:00 AMBRIGHT Fall 2025Business PlanBusiness Plan, Business Start-up/Preplanningparth.tyagiTempleCore ServicesClosed500410
189/24/2025 12:00 AMFrom Hype to Habit: Adopting Human-enabled Gen...Artificial Intelligence (AI)Artificial Intelligence (AI)Higginsj5DuquesneCore ServicesClosed004
199/24/2025 12:00 AMIN PERSON: Business, Bytes, and Brews at Cup O...Marketing/SalesMarketing/Sales, Networking Event, Social MediasstumbriBucknellCore ServicesClosed3008
209/24/2025 12:00 AMIntro to QuickBooks (BLOOM Bootcamp)Accounting/BudgetAccounting/Budget, Artificial Intelligence (AI...ssharif130Lead OfficeSSBCIClosed3008
219/24/2025 12:00 AMPhotography Basics: In The Field (in-person)Marketing/SalesMarketing/SalesHigginsj5DuquesneCore ServicesCanceled600
229/24/2025 12:00 AMQuickBooks Online Version (in-person)Accounting/BudgetAccounting/BudgetHigginsj5DuquesneCore ServicesClosed0010
239/24/2025 12:00 AMTax Talk with Corry - Q & A SessionTax PlanningBusiness Start-up/Preplanning, Legal Issues, M...jbestClarionCore ServicesClosed10009
249/23/2025 12:00 AMAbre Tu Negocio en Pittsburgh - Este programa...Business Start-up/PreplanningBusiness Start-up/Preplanning, Managing a Busi...asb259PittsburghCore ServicesClosed15012
259/23/2025 12:00 AMBusiness Finance BasicsAccounting/BudgetAccounting/Budget, Business FinancingVKNeidererShippensburgOtherClosed2503
269/23/2025 12:00 AMFamily and Medical Leave Act: Understanding th...OtherOtherHigginsj5DuquesneCore ServicesClosed0049
279/23/2025 12:00 AMLV Meet the Buyers ExpoProcurement FairProcurement Fairbds206LehighCore ServicesClosed00108
289/23/2025 12:00 AMManaging Your Business on Google Search and Ma...Managing a BusinessManaging a Business, Marketing/Salesbvp5264Penn StateCore ServicesClosed60012
299/22/2025 12:00 AMDeveloping an Export Plan Part 1International TradeInternational Tradeavh318LehighPRIMEClosed0210
319/22/2025 12:00 AMMastering Excel for Bookkeeping: A Two-Part We...Accounting/BudgetAccounting/Budgettaya.brownTempleCore ServicesClosed500098
339/19/2025 12:00 AMBringing the World to PAInternational TradeInternational Trade, Managing a Businessbds206LehighLEXNETClosed40022
349/19/2025 12:00 AMSecond Step: Developing a Business PlanBusiness Start-up/PreplanningBusiness Plan, Business Start-up/Preplanning, ...tcookPittsburghCore ServicesClosed0031
379/18/2025 12:00 AMFive Fundamentals: How to Successfully Start Y...Business Start-up/PreplanningBusiness Plan, Business Start-up/Preplanning, ...zpiottiWidenerCore ServicesClosed2093
389/18/2025 12:00 AMIntroduction to the Business Finance Basic Ser...Accounting/BudgetAccounting/Budget, Business Financing, Buy/Sel...rwolf219KutztownCore ServicesClosed01412
399/18/2025 12:00 AMMeet the LendersBusiness FinancingBusiness Financinghorne001GannonCore ServicesClosed0012
409/18/2025 12:00 AMOSHA: Ladder & StairwaysRisk ManagementHuman Resources/Managing Employees, Managing a...jbestClarionCore ServicesClosed20071
429/17/2025 12:00 AMIN PERSON | Business Growth | 3rd Wed. @ Start...Managing a BusinessManaging a Business, Networking EventsstumbriBucknellCore ServicesClosed0012
439/17/2025 12:00 AMSo, You Want to Start a Small Business - PAACC...Business Start-up/PreplanningBusiness Start-up/PreplanningHigginsj5DuquesneCore ServicesClosed0023
449/16/2025 12:00 AMBecome an Approved UPMC Supplier of Goods & S...Prime Vendor ProgramManaging a Business, Prime Vendor ProgramtcookPittsburghCore ServicesClosed0496
459/16/2025 12:00 AMCamino al Exito con Kauffman Fall 2025Business PlanBusiness Financing, Business Plan, Business St...ldquinterosalazarWidenerCore ServicesOpen25210
469/16/2025 12:00 AMGo Global: Doing Business with West AfricaInternational TradeInternational Trade, Managing a BusinesszpiottiWidenerCore ServicesClosed1206062
479/16/2025 12:00 AMUnlock The Power of Contracts For Your Busines...Legal IssuesLegal Issuessym40PittsburghCore ServicesClosed0027
\n", "
" ], "text/plain": [ " Start Date Event Title \\\n", "0 9/30/2025 12:00 AM Boost Your Business Visibility: Get Found on G... \n", "1 9/30/2025 12:00 AM Introduction to the Business Finance Basic Ser... \n", "2 9/30/2025 12:00 AM Multi-State Supplier Readiness Webinar P E N ... \n", "3 9/30/2025 12:00 AM The Basics of Business Lending (in-person and ... \n", "4 9/30/2025 12:00 AM Unlocking Business Success: Tools & Resources ... \n", "5 9/30/2025 12:00 AM Your Marketing Help Desk: Social Media, Canva ... \n", "6 9/29/2025 12:00 AM Developing an Export Plan Part 2 \n", "8 9/29/2025 12:00 AM In-Person Consulting Opportunity for **Bradfor... \n", "9 9/29/2025 12:00 AM QuickBooks Functions & Reporting \n", "10 9/25/2025 12:00 AM Emerging Social Media Trends (webinar) \n", "11 9/25/2025 12:00 AM How to Access the Capital You Need - virtual s... \n", "12 9/25/2025 12:00 AM Pitching Your Business Part 1: Creating Your P... \n", "13 9/25/2025 12:00 AM Real Talk Series for Small Business: 8 Questio... \n", "15 9/25/2025 12:00 AM Value Proposition \n", "16 9/24/2025 12:00 AM BRIGHT Fall 2025 \n", "18 9/24/2025 12:00 AM From Hype to Habit: Adopting Human-enabled Gen... \n", "19 9/24/2025 12:00 AM IN PERSON: Business, Bytes, and Brews at Cup O... \n", "20 9/24/2025 12:00 AM Intro to QuickBooks (BLOOM Bootcamp) \n", "21 9/24/2025 12:00 AM Photography Basics: In The Field (in-person) \n", "22 9/24/2025 12:00 AM QuickBooks Online Version (in-person) \n", "23 9/24/2025 12:00 AM Tax Talk with Corry - Q & A Session \n", "24 9/23/2025 12:00 AM Abre Tu Negocio en Pittsburgh - Este programa... \n", "25 9/23/2025 12:00 AM Business Finance Basics \n", "26 9/23/2025 12:00 AM Family and Medical Leave Act: Understanding th... \n", "27 9/23/2025 12:00 AM LV Meet the Buyers Expo \n", "28 9/23/2025 12:00 AM Managing Your Business on Google Search and Ma... \n", "29 9/22/2025 12:00 AM Developing an Export Plan Part 1 \n", "31 9/22/2025 12:00 AM Mastering Excel for Bookkeeping: A Two-Part We... \n", "33 9/19/2025 12:00 AM Bringing the World to PA \n", "34 9/19/2025 12:00 AM Second Step: Developing a Business Plan \n", "37 9/18/2025 12:00 AM Five Fundamentals: How to Successfully Start Y... \n", "38 9/18/2025 12:00 AM Introduction to the Business Finance Basic Ser... \n", "39 9/18/2025 12:00 AM Meet the Lenders \n", "40 9/18/2025 12:00 AM OSHA: Ladder & Stairways \n", "42 9/17/2025 12:00 AM IN PERSON | Business Growth | 3rd Wed. @ Start... \n", "43 9/17/2025 12:00 AM So, You Want to Start a Small Business - PAACC... \n", "44 9/16/2025 12:00 AM Become an Approved UPMC Supplier of Goods & S... \n", "45 9/16/2025 12:00 AM Camino al Exito con Kauffman Fall 2025 \n", "46 9/16/2025 12:00 AM Go Global: Doing Business with West Africa \n", "47 9/16/2025 12:00 AM Unlock The Power of Contracts For Your Busines... \n", "\n", " Primary Training Topic \\\n", "0 Marketing/Sales \n", "1 Accounting/Budget \n", "2 Managing a Business \n", "3 Business Financing \n", "4 Veterans Outreach Conf. \n", "5 Social Media \n", "6 International Trade \n", "8 Business Start-up/Preplanning \n", "9 Accounting/Budget \n", "10 Social Media \n", "11 Business Financing \n", "12 Business Start-up/Preplanning \n", "13 Managing a Business \n", "15 Marketing/Sales \n", "16 Business Plan \n", "18 Artificial Intelligence (AI) \n", "19 Marketing/Sales \n", "20 Accounting/Budget \n", "21 Marketing/Sales \n", "22 Accounting/Budget \n", "23 Tax Planning \n", "24 Business Start-up/Preplanning \n", "25 Accounting/Budget \n", "26 Other \n", "27 Procurement Fair \n", "28 Managing a Business \n", "29 International Trade \n", "31 Accounting/Budget \n", "33 International Trade \n", "34 Business Start-up/Preplanning \n", "37 Business Start-up/Preplanning \n", "38 Accounting/Budget \n", "39 Business Financing \n", "40 Risk Management \n", "42 Managing a Business \n", "43 Business Start-up/Preplanning \n", "44 Prime Vendor Program \n", "45 Business Plan \n", "46 International Trade \n", "47 Legal Issues \n", "\n", " Training Topics Created By \\\n", "0 Agriculture, Artificial Intelligence (AI), Bus... rwolf219 \n", "1 Accounting/Budget, Business Financing, Busines... rwolf219 \n", "2 Accounting/Budget, Business Plan, Cash Flow Ma... tcook \n", "3 Business Financing Higginsj5 \n", "4 Veterans Outreach Conf. verma \n", "5 Accounting/Budget, Artificial Intelligence (AI... rwolf219 \n", "6 International Trade avh318 \n", "8 Business Plan, Business Start-up/Preplanning, ... geeza \n", "9 Accounting/Budget, Business Financing, eCommer... ssharif130 \n", "10 Social Media Higginsj5 \n", "11 Business Financing, Business Plan, Business St... sym40 \n", "12 Business Plan, Business Start-up/Preplanning parth.tyagi \n", "13 Business Financing, Business Plan, Business St... jbest \n", "15 Marketing/Sales horne001 \n", "16 Business Plan, Business Start-up/Preplanning parth.tyagi \n", "18 Artificial Intelligence (AI) Higginsj5 \n", "19 Marketing/Sales, Networking Event, Social Media sstumbri \n", "20 Accounting/Budget, Artificial Intelligence (AI... ssharif130 \n", "21 Marketing/Sales Higginsj5 \n", "22 Accounting/Budget Higginsj5 \n", "23 Business Start-up/Preplanning, Legal Issues, M... jbest \n", "24 Business Start-up/Preplanning, Managing a Busi... asb259 \n", "25 Accounting/Budget, Business Financing VKNeiderer \n", "26 Other Higginsj5 \n", "27 Procurement Fair bds206 \n", "28 Managing a Business, Marketing/Sales bvp5264 \n", "29 International Trade avh318 \n", "31 Accounting/Budget taya.brown \n", "33 International Trade, Managing a Business bds206 \n", "34 Business Plan, Business Start-up/Preplanning, ... tcook \n", "37 Business Plan, Business Start-up/Preplanning, ... zpiotti \n", "38 Accounting/Budget, Business Financing, Buy/Sel... rwolf219 \n", "39 Business Financing horne001 \n", "40 Human Resources/Managing Employees, Managing a... jbest \n", "42 Managing a Business, Networking Event sstumbri \n", "43 Business Start-up/Preplanning Higginsj5 \n", "44 Managing a Business, Prime Vendor Program tcook \n", "45 Business Financing, Business Plan, Business St... ldquinterosalazar \n", "46 International Trade, Managing a Business zpiotti \n", "47 Legal Issues sym40 \n", "\n", " Center Funding Source Status Maximum Attendees \\\n", "0 Kutztown Core Services Closed 300 \n", "1 Kutztown Core Services Closed 0 \n", "2 Pittsburgh Core Services Closed 0 \n", "3 Duquesne Core Services Closed 0 \n", "4 Lehigh Core Services Closed 100 \n", "5 Kutztown Core Services Closed 100 \n", "6 Lehigh PRIME Closed 0 \n", "8 Scranton Core Services Closed 5 \n", "9 Lehigh SSBCI Closed 40 \n", "10 Duquesne Core Services Closed 0 \n", "11 Pittsburgh Core Services Closed 0 \n", "12 Temple Core Services Closed 500 \n", "13 Clarion Core Services Closed 75 \n", "15 Gannon Core Services Closed 0 \n", "16 Temple Core Services Closed 500 \n", "18 Duquesne Core Services Closed 0 \n", "19 Bucknell Core Services Closed 30 \n", "20 Lead Office SSBCI Closed 30 \n", "21 Duquesne Core Services Canceled 6 \n", "22 Duquesne Core Services Closed 0 \n", "23 Clarion Core Services Closed 100 \n", "24 Pittsburgh Core Services Closed 15 \n", "25 Shippensburg Other Closed 25 \n", "26 Duquesne Core Services Closed 0 \n", "27 Lehigh Core Services Closed 0 \n", "28 Penn State Core Services Closed 60 \n", "29 Lehigh PRIME Closed 0 \n", "31 Temple Core Services Closed 500 \n", "33 Lehigh LEXNET Closed 40 \n", "34 Pittsburgh Core Services Closed 0 \n", "37 Widener Core Services Closed 20 \n", "38 Kutztown Core Services Closed 0 \n", "39 Gannon Core Services Closed 0 \n", "40 Clarion Core Services Closed 200 \n", "42 Bucknell Core Services Closed 0 \n", "43 Duquesne Core Services Closed 0 \n", "44 Pittsburgh Core Services Closed 0 \n", "45 Widener Core Services Open 25 \n", "46 Widener Core Services Closed 120 \n", "47 Pittsburgh Core Services Closed 0 \n", "\n", " Number Registered Attendees, Total \n", "0 0 68 \n", "1 75 0 \n", "2 0 87 \n", "3 0 6 \n", "4 22 9 \n", "5 0 52 \n", "6 0 8 \n", "8 0 0 \n", "9 0 5 \n", "10 0 8 \n", "11 0 23 \n", "12 0 19 \n", "13 29 14 \n", "15 0 4 \n", "16 4 10 \n", "18 0 4 \n", "19 0 8 \n", "20 0 8 \n", "21 0 0 \n", "22 0 10 \n", "23 0 9 \n", "24 0 12 \n", "25 0 3 \n", "26 0 49 \n", "27 0 108 \n", "28 0 12 \n", "29 2 10 \n", "31 0 98 \n", "33 0 22 \n", "34 0 31 \n", "37 9 3 \n", "38 14 12 \n", "39 0 12 \n", "40 7 1 \n", "42 0 12 \n", "43 0 23 \n", "44 4 96 \n", "45 21 0 \n", "46 60 62 \n", "47 0 27 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "training_event_counts_all_df = trainings_df[~trainings_df['Primary Training Topic'].isin(first_steps_cols)]#.groupby('Center').size().reset_index(name='Training Events')\n", "training_event_counts_attendees_df = trainings_df[~trainings_df['Primary Training Topic'].isin(first_steps_cols) & trainings_df['Attendees, Total'] >= 1]#.groupby('Center').size().reset_index(name='Training Events')\n", "training_event_counts_all_df.to_csv(\"training_count_nofirst.csv\")\n", "training_event_counts_attendees_df.to_csv(\"training_count_attendees_nofirst.csv\")\n", "\n", "training_event_counts_all_first_df = trainings_df#.groupby('Center').size().reset_index(name='Training Events')\n", "training_event_counts_attendees_first_df = trainings_df[trainings_df['Attendees, Total'] >= 1]#.groupby('Center').size().reset_index(name='Training Events')\n", "\n", "training_event_counts_all_first_df.to_csv(\"training_count_all.csv\") \n", "training_event_counts_attendees_first_df.to_csv(\"training_count_all_attendees.csv\")\n", "\n", "training_01_nofirst_df = trainings_df[(\n", " (trainings_df['Attendees, Total'] != 0) & (trainings_df['Attendees, Total'] != 1)) & \n", " ~trainings_df['Primary Training Topic'].isin(first_steps_cols)\n", " ]#.groupby('Center').size().reset_index(name='Training Events')\n", "\n", "training_01_first_df = trainings_df[(trainings_df['Attendees, Total'] != 0) & (trainings_df['Attendees, Total'] != 1)]#.groupby('Center').size().reset_index(name='Training Events')\n", "\n", "training_01_nofirst_df.to_csv(\"training_no01_nofirst.csv\")\n", "training_01_first_df.to_csv(\"training_no01.csv\")\n", "\n", "training_event_counts_all_df.head(40)" ] }, { "cell_type": "code", "execution_count": 9, "id": "a9e5740a-d7e7-43e9-9970-207fe0a76e34", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CenterNumber RegisteredAttendees, Total
0Bucknell8.11111114.400000
1Clarion12.37000016.400000
2Duquesne0.00980423.784314
3Gannon1.02381014.142857
4Kutztown8.06666715.300000
5Lead Office17.60000030.160000
6Lehigh0.91044811.208955
7Penn State3.41666719.000000
8Pittsburgh7.11290339.016129
9Scranton1.6050426.848739
10Shippensburg0.00000031.555556
11St. Francis0.00000020.250000
12Temple0.07692328.276923
13Widener31.98571434.271429
14Wilkes0.02040815.469388
\n", "
" ], "text/plain": [ " Center Number Registered Attendees, Total\n", "0 Bucknell 8.111111 14.400000\n", "1 Clarion 12.370000 16.400000\n", "2 Duquesne 0.009804 23.784314\n", "3 Gannon 1.023810 14.142857\n", "4 Kutztown 8.066667 15.300000\n", "5 Lead Office 17.600000 30.160000\n", "6 Lehigh 0.910448 11.208955\n", "7 Penn State 3.416667 19.000000\n", "8 Pittsburgh 7.112903 39.016129\n", "9 Scranton 1.605042 6.848739\n", "10 Shippensburg 0.000000 31.555556\n", "11 St. Francis 0.000000 20.250000\n", "12 Temple 0.076923 28.276923\n", "13 Widener 31.985714 34.271429\n", "14 Wilkes 0.020408 15.469388" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_attendees_center_all_df = trainings_df[trainings_df['Attendees, Total'] >= 1].groupby('Center')[['Number Registered', 'Attendees, Total']].mean().reset_index()\n", "mean_attendees_center_all_df.head(40)" ] }, { "cell_type": "markdown", "id": "752b618a-8e92-4d5d-8014-5967d760a6a4", "metadata": {}, "source": [ "# Creating the graphs" ] }, { "cell_type": "code", "execution_count": 11, "id": "139d3093-6783-4d7a-be36-205fb4ee2455", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Value of 'y' is not the name of a column in 'data_frame'. Expected one of ['Start Date', 'Event Title', 'Primary Training Topic', 'Training Topics', 'Created By', 'Center', 'Funding Source', 'Status', 'Maximum Attendees', 'Number Registered', 'Attendees, Total'] but received: Training Events", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmake_event_count_chart\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining_event_counts_attendees_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mNumber of Non-First Steps Training Events With 1 or More Attendees Per Center FY25\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtraining_events_count_attendees.png\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m make_event_count_chart(training_event_counts_all_df, \u001b[33m\"\u001b[39m\u001b[33mNumber of All Non-First Steps Training Events (including non-attended events) Per Center FY25\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mtraining_events_count_all.png\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m make_event_count_chart(training_event_counts_attendees_first_df, \u001b[33m\"\u001b[39m\u001b[33mNumber of Training Events Including First Steps With 1 or More Attendees Per Center FY25\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mtraining_events_count_attendees_first.png\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 2\u001b[39m, in \u001b[36mmake_event_count_chart\u001b[39m\u001b[34m(df, title, filename)\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mmake_event_count_chart\u001b[39m(df:pd.DataFrame, title:\u001b[38;5;28mstr\u001b[39m, filename:\u001b[38;5;28mstr\u001b[39m):\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m fig = \u001b[43mpx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mCenter\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mTraining Events\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mTraining Events\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m700\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 8\u001b[39m \u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[38;5;66;03m# Calculate a grand total value\u001b[39;00m\n\u001b[32m 11\u001b[39m grand_total = df[\u001b[33m\"\u001b[39m\u001b[33mTraining Events\u001b[39m\u001b[33m\"\u001b[39m].sum()\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.13/site-packages/plotly/express/_chart_types.py:381\u001b[39m, in \u001b[36mbar\u001b[39m\u001b[34m(data_frame, x, y, color, pattern_shape, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, hover_name, hover_data, custom_data, text, base, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, color_continuous_scale, pattern_shape_sequence, pattern_shape_map, range_color, color_continuous_midpoint, opacity, orientation, barmode, log_x, log_y, range_x, range_y, text_auto, title, subtitle, template, width, height)\u001b[39m\n\u001b[32m 332\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbar\u001b[39m(\n\u001b[32m 333\u001b[39m data_frame=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 334\u001b[39m x=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 375\u001b[39m height=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 376\u001b[39m ) -> go.Figure:\n\u001b[32m 377\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 378\u001b[39m \u001b[33;03m In a bar plot, each row of `data_frame` is represented as a rectangular\u001b[39;00m\n\u001b[32m 379\u001b[39m \u001b[33;03m mark.\u001b[39;00m\n\u001b[32m 380\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m381\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmake_figure\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 382\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m \u001b[49m\u001b[43mconstructor\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgo\u001b[49m\u001b[43m.\u001b[49m\u001b[43mBar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 384\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrace_patch\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtextposition\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mauto\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 385\u001b[39m \u001b[43m \u001b[49m\u001b[43mlayout_patch\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbarmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbarmode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 386\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.13/site-packages/plotly/express/_core.py:2491\u001b[39m, in \u001b[36mmake_figure\u001b[39m\u001b[34m(args, constructor, trace_patch, layout_patch)\u001b[39m\n\u001b[32m 2488\u001b[39m layout_patch = layout_patch \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[32m 2489\u001b[39m apply_default_cascade(args)\n\u001b[32m-> \u001b[39m\u001b[32m2491\u001b[39m args = \u001b[43mbuild_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstructor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2492\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m constructor \u001b[38;5;129;01min\u001b[39;00m [go.Treemap, go.Sunburst, go.Icicle] \u001b[38;5;129;01mand\u001b[39;00m args[\u001b[33m\"\u001b[39m\u001b[33mpath\u001b[39m\u001b[33m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2493\u001b[39m args = process_dataframe_hierarchy(args)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.13/site-packages/plotly/express/_core.py:1737\u001b[39m, in \u001b[36mbuild_dataframe\u001b[39m\u001b[34m(args, constructor)\u001b[39m\n\u001b[32m 1734\u001b[39m args[\u001b[33m\"\u001b[39m\u001b[33mcolor\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1735\u001b[39m \u001b[38;5;66;03m# now that things have been prepped, we do the systematic rewriting of `args`\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1737\u001b[39m df_output, wide_id_vars = \u001b[43mprocess_args_into_dataframe\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1738\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1739\u001b[39m \u001b[43m \u001b[49m\u001b[43mwide_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1740\u001b[39m \u001b[43m \u001b[49m\u001b[43mvar_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1741\u001b[39m \u001b[43m \u001b[49m\u001b[43mvalue_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1742\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_pd_like\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1743\u001b[39m \u001b[43m \u001b[49m\u001b[43mnative_namespace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1744\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1745\u001b[39m df_output: nw.DataFrame\n\u001b[32m 1746\u001b[39m \u001b[38;5;66;03m# now that `df_output` exists and `args` contains only references, we complete\u001b[39;00m\n\u001b[32m 1747\u001b[39m \u001b[38;5;66;03m# the special-case and wide-mode handling by further rewriting args and/or mutating\u001b[39;00m\n\u001b[32m 1748\u001b[39m \u001b[38;5;66;03m# df_output\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.13/site-packages/plotly/express/_core.py:1338\u001b[39m, in \u001b[36mprocess_args_into_dataframe\u001b[39m\u001b[34m(args, wide_mode, var_name, value_name, is_pd_like, native_namespace)\u001b[39m\n\u001b[32m 1336\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m argument == \u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 1337\u001b[39m err_msg += \u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m To use the index, pass it in directly as `df.index`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1338\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err_msg)\n\u001b[32m 1339\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m length \u001b[38;5;129;01mand\u001b[39;00m (actual_len := \u001b[38;5;28mlen\u001b[39m(df_input)) != length:\n\u001b[32m 1340\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 1341\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mAll arguments should have the same length. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1342\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mThe length of column argument `df[\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m]` is \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[33m, whereas the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 1349\u001b[39m )\n\u001b[32m 1350\u001b[39m )\n", "\u001b[31mValueError\u001b[39m: Value of 'y' is not the name of a column in 'data_frame'. Expected one of ['Start Date', 'Event Title', 'Primary Training Topic', 'Training Topics', 'Created By', 'Center', 'Funding Source', 'Status', 'Maximum Attendees', 'Number Registered', 'Attendees, Total'] but received: Training Events" ] } ], "source": [ "make_event_count_chart(training_event_counts_attendees_df, \"Number of Non-First Steps Training Events With 1 or More Attendees Per Center FY25\", \"training_events_count_attendees.png\")\n", "make_event_count_chart(training_event_counts_all_df, \"Number of All Non-First Steps Training Events (including non-attended events) Per Center FY25\", \"training_events_count_all.png\")\n", "\n", "make_event_count_chart(training_event_counts_attendees_first_df, \"Number of Training Events Including First Steps With 1 or More Attendees Per Center FY25\", \"training_events_count_attendees_first.png\")\n", "make_event_count_chart(training_event_counts_all_first_df, \"Number of All Training Events (including non-attended events and first steps) Per Center FY25\", \"training_events_count_all_first.png\")\n", "\n", "make_event_count_chart(training_01_nofirst_df, \"Number of Non-First Steps Training Events Excluding Those With 0 or 1 Attendees Per Center FY 25\", \"training_events_01_count_nofirst.png\")\n", "make_event_count_chart(training_01_first_df, \"Number of Training Events Excluding Those Excluding 0 or 1 Attendees\", \"training_events_01_count_first.png\")" ] }, { "cell_type": "code", "execution_count": null, "id": "8dd95b4f-c429-4134-a535-741e5c290437", "metadata": {}, "outputs": [], "source": [ "def make_total_attendees_graph(df:pd.DataFrame, title:str, filename:str):\n", " fig = px.bar(\n", " df,\n", " x='Center',\n", " y='Attendees, Total',\n", " text='Attendees, Total',\n", " title=title,\n", " height=700,\n", " width=1000\n", " )\n", "\n", " # Add a attendees grand total\n", " grand_total = df[\"Attendees, Total\"].sum()\n", " fig.add_annotation(xref='paper', yref='paper', \n", " x=0.0, y=1.05,\n", " showarrow=False,\n", " text=f\"{grand_total} total attendees\")\n", "\n", " # Set bar color\n", " fig.update_traces(marker_color='#197f60')\n", " \n", " fig.write_image(filename)\n", " fig.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "2e92cefa-ad3b-4592-be42-dd47ecce12aa", "metadata": {}, "outputs": [], "source": [ "make_total_attendees_graph(trainings_center_all_df, \"Total Event Attendees by Center FY25\", \"trainings_attendees.png\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "279a9c4d-df1b-4707-8e44-3cca0d5a9a13", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "variable=Attendees, Total
Center=%{x}
value=%{y}", "legendgroup": "Attendees, Total", "marker": { "color": "#004649", "pattern": { "shape": "" } }, "name": "Attendees, Total", "offsetgroup": "Attendees, Total", "orientation": "v", "showlegend": true, "textposition": "auto", "texttemplate": "%{y:.}", "type": "bar", "x": [ "Bucknell", "Clarion", "Duquesne", "Gannon", "Kutztown", "Lead Office", "Lehigh", "Penn State", "Pittsburgh", "Scranton", "Shippensburg", "St. Francis", "Temple", "Widener", "Wilkes" ], "xaxis": "x", "y": { "bdata": "AAAAAAAALEAAAAAAAAAwQAAAAAAAADhAAAAAAAAALEAAAAAAAAAuQAAAAAAAAD5AAAAAAAAAJkAAAAAAAAAzQAAAAAAAgENAAAAAAAAAHEAAAAAAAABAQAAAAAAAADRAAAAAAAAAPEAAAAAAAABBQAAAAAAAAC5A", "dtype": "f8" }, "yaxis": "y" }, { "alignmentgroup": "True", "hovertemplate": "variable=Number Registered
Center=%{x}
value=%{y}", "legendgroup": "Number Registered", "marker": { "color": "#73e0c6", "pattern": { "shape": "" } }, "name": "Number Registered", "offsetgroup": "Number Registered", "orientation": "v", "showlegend": true, "textposition": "auto", "texttemplate": "%{y:.}", "type": "bar", "x": [ "Bucknell", "Clarion", "Duquesne", "Gannon", "Kutztown", "Lead Office", "Lehigh", "Penn State", "Pittsburgh", "Scranton", "Shippensburg", "St. Francis", "Temple", "Widener", "Wilkes" ], "xaxis": "x", "y": { "bdata": "AAAAAAAAIEAAAAAAAAAoQAAAAAAAAAAAAAAAAAAA8D8AAAAAAAAgQAAAAAAAADJAAAAAAAAA8D8AAAAAAAAIQAAAAAAAABxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAQAAAAAAAAAAA", "dtype": "f8" }, "yaxis": "y" } ], "layout": { "barmode": "group", "height": 700, "legend": { "title": { "text": "Legend" }, "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermap": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermap" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Mean Training Attendees by Center FY25" }, "width": 1000, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Center" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Mean Value" } } } }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean_attendees_center_all_df['Attendees, Total']= round(mean_attendees_center_all_df['Attendees, Total'], 0)\n", "mean_attendees_center_all_df['Number Registered']= round(mean_attendees_center_all_df['Number Registered'], 0)\n", "\n", "fig = px.bar(\n", " mean_attendees_center_all_df,\n", " x='Center',\n", " y=['Attendees, Total', 'Number Registered'],\n", " barmode='group',\n", " text_auto='.',\n", " height=700,\n", " width=1000,\n", " title='Mean Training Attendees by Center FY25',\n", " color_discrete_sequence=['#004649', '#73e0c6']\n", ")\n", "fig.update_layout(yaxis_title='Mean Value', legend_title_text='Legend')\n", "fig.write_image('trainings_mean_attendees.png')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "537c2c2e-5a3a-4fb2-905d-bb45a33faf67", "metadata": {}, "source": [ "# Creating the word document report\n", "---\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "c24972fb-76cc-47a4-94b3-7efc4eacbf33", "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import sys\n", "from docx import Document\n", "from docx.shared import Inches, Pt, RGBColor\n", "from docx.enum.text import WD_ALIGN_PARAGRAPH\n", "\n", "notebook_dir = Path().resolve() # Current working directory\n", "project_root = notebook_dir.parent # Goes up to root/\n", "sys.path.insert(0, str(project_root / \"libs\"))\n", "\n", "from word_library import WordDocumentBuilder, PageConfig" ] }, { "cell_type": "code", "execution_count": 21, "id": "54bb1233-1f21-4329-92aa-e4c93d633ea5", "metadata": {}, "outputs": [], "source": [ "def trainings_analysis_page_one(builder: WordDocumentBuilder, trainings_count_chart:str=\"training_events_count.png\", attendance_chart:str=\"trainings_attendees.png\", mean_attendees_chart:str=\"trainings_mean_attendees.png\"):\n", " for section in builder.doc.sections:\n", " section.top_margin = Inches(0.5)\n", " section.bottom_margin = Inches(0.5)\n", " section.left_margin = Inches(0.5)\n", " section.right_margin = Inches(0.5)\n", " \n", " heading_paragraph = builder.doc.add_paragraph()\n", "\n", " # TODO: replace this part with using the section numbners of the document builder\n", " heading_run = heading_paragraph.add_run(f\"1.7 Business Trainings Analysis\") \n", " heading_run.font.name = 'Futera'\n", " heading_run.font.size = Pt(12)\n", " heading_run.font.color.rgb = RGBColor(113, 191, 68)\n", " heading_run.bold = True\n", " \n", " picture_table = builder.doc.add_table(rows=2, cols=2)\n", "\n", " row1_cells = picture_table.rows[0].cells\n", " row2_cells = picture_table.rows[1].cells\n", "\n", " # Count chart section\n", " count_chart_paragrah = row1_cells[0].paragraphs[0]\n", " count_chart_run = count_chart_paragrah.add_run()\n", " count_chart_run.add_picture(trainings_count_chart, width=Inches(3.6), height=Inches(2.5))\n", "\n", " \n", " count_chart_note_paragraph = row2_cells[0].paragraphs[0]\n", " # TODO: Change this to use section number and prefix in script\n", " count_note_run = count_chart_note_paragraph.add_run(f\"Figure {\"1.7\"}.{builder.figure_number + 1} shows the count of trainings held by each center.\")\n", " count_note_run.font.name = 'Futera'\n", " count_note_run.font.size = Pt(7)\n", " count_note_run.font.color.rgb = RGBColor(15, 27, 38)\n", " count_note_run.bold = True\n", "\n", " builder.figure_number += 1\n", "\n", " # Attendance Chart Sectino \n", " attendance_chart_paragrah = row1_cells[1].paragraphs[0]\n", " attendance_chart_run = attendance_chart_paragrah.add_run()\n", " attendance_chart_run.add_picture(attendance_chart, width=Inches(3.6), height=Inches(2.5))\n", "\n", " attendance_chart_note_paragraph = row2_cells[1].paragraphs[0]\n", " # TODO: Change this to use section number and prefix in script\n", " attendance_note_run = attendance_chart_note_paragraph.add_run(f\"Figure {\"1.7\"}.{builder.figure_number + 1} shows how many attendees there were for trainings at each center.\")\n", " attendance_note_run.font.name = 'Futera'\n", " attendance_note_run.font.size = Pt(7)\n", " attendance_note_run.font.color.rgb = RGBColor(15, 27, 38)\n", " attendance_note_run.bold = True\n", "\n", " builder.figure_number += 1\n", "\n", " # Mean attendance chart section\n", " builder.doc.add_picture(mean_attendees_chart, width=Inches(5.7), height=Inches(3.5))\n", " last_pg = builder.doc.paragraphs[-1]\n", " last_pg.alignment = WD_ALIGN_PARAGRAPH.CENTER\n", "\n", " mean_note_paragraph = builder.doc.add_paragraph()\n", " \n", " #TODO: Change this to use section numbers and prefix in script\n", " mean_note_run = mean_note_paragraph.add_run(f\"Figure {\"1.7\"}.{builder.figure_number + 1} shows the mean number of registrations for trainings compared to the mean actual number of attendees for trainings per center.\")\n", " mean_note_run.font.name = 'Futera'\n", " mean_note_run.font.size = Pt(7)\n", " mean_note_run.font.color.rgb = RGBColor(15, 27, 38)\n", " mean_note_run.bold = True\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "ae82f582-fd7d-4b75-b7e0-5e449d7469c2", "metadata": {}, "outputs": [], "source": [ "pages = [\n", " PageConfig(trainings_analysis_page_one, add_page_break=False),\n", "]\n", "\n", "builder = WordDocumentBuilder()\n", "\n", "doc = builder.create_document(\n", " pages,\n", " \"section1_7.docx\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "021ecaff-74a9-45d6-b63c-3c5760da2ba3", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }